AFSC PROCESSED REPORT 2024-01

Alaska Fisheries Science Center

Resource Ecology and Fisheries Management Division

Age and Growth ProgramProceedings of the
Fourth Research Workshop
on the Rapid Estimationof Fish Age Using Fourier
Transform Near Infrared
Spectroscopy

JANUARY 2024

U.S. Department of Commerce | National Oceanic and Atmospheric Administration | National Marine Fisheries Service

ooper J. T Watson1 and M. W. Callahan2

AFSC Processed Report

This document should be cited as follows:
Matta, M. E., and Helser, T. E. (editors). 2024. Proceedings of the Fourth Research Workshop on the
Rapid Estimation of Fish Age Using Fourier Transform Near Infrared Spectroscopy. AFSC Processed
Rep. 2024-01, 524 p. Alaska Fish. Sci.
- Cent., NOAA, Natl. Mar. Fish. Serv., 7600 Sand Point Way NE,
Seattle WA 98115.

This document is available online at: https://repository.library.noaa.gov/

Reference in this document to trade names does not imply endorsementby the National Marine Fisheries Service, NOAA.

–

Proceedings of the Fourth Research Workshop on the Rapid Estimation ofFish Age Using Fourier Transform Near Infrared Spectroscopy

Mary Elizabeth Matta and Thomas E. Helser (editors)

Resource Ecology and Fisheries Management Division
Alaska Fisheries Science Center
NOAA, National Marine Fisheries Service
7600 Sand Point Way NE
Seattle, WA 98115

January 2024

PREFACE

Increasing demand for fish age data to support stock assessments of the Nation’s fisheries
has necessitated investigation into alternative technologies to produce these data more
efficiently. Among the advanced technologies the National Marine Fisheries Service (NOAA
Fisheries) has explored in recent years, Fourier transform near infrared (FT-NIR) spectroscopyof otoliths represents a transformative, machine-based approach for rapidly estimating fish ages.
This method is significantly faster and more reproducible than the traditional method of age
estimation, which relies on a human reader’s visual interpretation of growth patterns in hard
structures. In contrast, FT-NIR spectroscopy functions at the molecular level, measuring
absorbance associated with vibrations of covalent bonds in the NIR wavenumber range.
Predictive analytics or deep learning tools are then used to establish a predictive relationshipbetween otolith NIR absorbance and fish age.
In 2019, NOAA Fisheries funded a 5-year Strategic Initiative (SI) in support of researchand development toward system deployment of FT-NIR spectroscopy for fish age determination.
To date, the Alaska Fisheries Science Center (AFSC) has hosted four FT-NIR spectroscopy
workshops, inviting staff from the regional NOAA age determination laboratories and experts inthe field from around the world to share their research. These proceedings summarize
presentations made at the fourth SI research workshop entitled, “Rapid Estimation of Fish Age
Using Fourier Transform Near Infrared Spectroscopy”, held at the AFSC in Seattle, WA,
April 3-7, 2023. Presentations given at the workshop are included in this document as extended
abstracts, and in many cases, full research papers. The studies presented herein reflect thetremendous progress made since the start of the SI towards application development andiii

implementation of FT-NIR spectroscopy for fish age determination and integration into the stock
assessment process. Case studies on the application of FT-NIR spectroscopy for predicting fish
age and other biological characteristics such as reproductive status and body condition arepresented for a number of fish taxa and regions of the United States. In several studies, deep
machine learning was used to predict age and advance the ability to quantify uncertainty notavailable to classical chemometric methods. Several extended abstracts highlight research
towards developing a flexible simulation framework that can be applied to explore uncertainty inmodel-based age predictions for a range of scenarios and species. In another, the authors describe
the envisioned full integration of FT-NIR spectroscopy into the production ageing process,providing a framework for system deployment in the laboratory. Finally, three case studies arepresented that evaluate the sensitivity of stock assessment model outputs to the substitution ofage data products generated from the FT-NIR spectroscopy approach. These are just a few of theworks in this document, which represents the most comprehensive collection yet of theapplication of FT-NIR spectroscopy in fisheries science.

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CONTENTS

PREFACE ...................................................................................................................................... iii
CONTENTS .................................................................................................................................... v
1.

NOAA Fisheries Fourier Transform Near Infrared Spectroscopy Fish Ageing Strategic
Initiative: Past, Present, and Future(T. Helser) ................................................................................................................................. 1

2.

Preliminary Analysis of Fourier Transform Near Infrared (FT-NIR) Spectroscopy andConvolutional Neural Network (CNN) Models to Predict Ages of Yellowfin Sole(T. TenBrink, I. Benson, B. Hsieh, M. Matta, T. Helser) ..................................................................... 93.

A Preliminary Analysis and Review of FT-NIR Spectroscopy and DNA CpG Site
Methylation for Fish Age Prediction(L. Lam, T. Helser, K. Nichols, B. Mayne, I. Benson, B. Hsieh, O. Berry, S. Jarman, K. McNeel) ............ 27

4.

Applying FT-NIRS Predictive Ageing to Different Genetic Stocks of White Grunt(Haemulon plumierii) Within the U.S. South Atlantic(J. Clark, A. Ostrowski, J. Potts, B. Barnett, S. Garner, W. Rogers) .................................................... 49

5.

A Novel Approach for Determining the Reproductive Status of Walleye Pollock(Gadus chalcogrammus) Using Raman Spectroscopy(S. Neidetcher, M. Arrington, T. Helser, E. Goldstein, I. Benson) ....................................................... 61

6.

Fourier Transform Near Infrared Spectroscopy Ageing of Finfish and Shark Species
in the Northwest Atlantic(A. Rubin, M. Passerotti, E. Robillard) .......................................................................................... 83

7.

Exploration of Fourier Transform Near Infrared Spectroscopy for the Shortbelly Rockfish(Sebastes jordani), an Ecologically Important Forage Fish, off the West Coast Region(J. Choi, M. Monk).................................................................................................................... 93

8.

Investigating the Use of FT-NIR Spectroscopy to Age Gag Grouper(Mycteroperca microlepis), a Protogynous Hermaphroditic Species(B. Barnett, I. Benson, T. Helser, S. Lowerre-Barbieri, H. Menendez) ................................................. 99

9.

Exploring the Use of FT-NIR Spectroscopy for Ageing Sablefish (Anoplopoma fimbria)
and Pacific Hake (Merluccius productus) off the U.S. West Coast(J. Wallace) ........................................................................................................................... 117

v

10.

Developing Spectroscopy Approaches to Measure Life History Characteristics of Fish
Throughout Ontogeny(E. Goldstein, C. Waters, H. Fulton-Bennett, M. Matta, T. Helser, J. Vollenweider, C. Hinds, K. McNeel,
C. Kastelle, S. Neidetcher, D. Oxman, F. Mueter) ......................................................................... 127

11.

Rapid Daily Age Estimation of Juvenile Walleye Pollock in the Gulf of Alaska using
FT-NIR Spectroscopy(M. Matta, E. Goldstein, I. Benson, H. Fulton-Bennett, C. Waters, B. Hsieh, T. Helser) ........................ 141

12.

Developing NIR Sampling Methodology for Modeling Species Discrimination in
Live Catfish for Aquaculture(C. Vance, A. Poudel, L. Chen, P. Allen, A. Kouba) ...................................................................... 151

13.

Fourier Transform Near Infrared (FT-NIR) Spectroscopy Discriminates Archived
Otoliths of Newly Detected Cryptic Species, Etelis carbunculus and Etelis boweni(K. Dahl, J. O’Malley, B. Barnett, W. Kline, J. Widdrington) .......................................................... 161

14.

Trials and Tribulations of Using FT-NIR Spectroscopy on Coastal Pelagic Species:
Method Development for Scanning Pacific Sardine Otoliths(E. Saas, B. Schwartzkopf, E. Dorval, D. Porzio) .......................................................................... 185

15.

Benefits and Challenges of Using FT-NIR Spectroscopy for Age Estimation at theNortheast Fisheries Science Center(E. Robillard, M. Passerotti, A. Rubin) ........................................................................................ 205

16.

Deep Learning Coupled with Fourier Transform Near Infrared Spectroscopy of Otoliths
Improves Age Prediction for Long-Lived Fish
(I. Benson, B. Barnett, T. Helser) ............................................................................................... 215

17.

Automatic Fish Age Prediction Using Deep Machine Learning – Combining Otolith
Images, NIR Spectra, and Metadata Features(A. Zheng, Y. Li, A. Vardanyan, S. Arsov, J. Hwang, I. Benson, T. Helser, J. Short, K. Bayer, C. Kastelle,
B. Barnett, A. Rezek, F. Wallace, R. Hill, W. Kline, L. Thorton, N. Evou, R. Allman, N. Willett) .......... 239

18.

Calibration and Variation of FT-NIR Otolith Spectra Among NIR Spectrometers andSpecies(A. Ostrowski, B. Barnett, J. Clark, J. Potts, A. Rubin, M. Passerotti, M. Monk, J. Choi, B. Schwartzkopf,
E. Saas, E. Dorval) .................................................................................................................. 257

19.

A Simulation-Based Approach to Evaluate Best Practices for Estimating Fish Age Using
FT-NIR Spectroscopy(M. Arrington, J. Healy, T. Helser, E. Goldstein, I. Benson, B. Hsieh, A. Punt) ................................... 269

20.

Database Design and Considerations with FT-NIR Spectra Data Collection andManagement(J. Short) ............................................................................................................................... 287
vi

21.

Envisioning the Future of Production Fish Ageing: End-to-end Integration of the FT-NIR
Spectroscopy Age Estimation Enterprise at the Alaska Fisheries Science Center(T. Helser, I. Benson, M. Matta, E. Goldstein, B. Hsieh, M. Arrington, J. Short) ................................. 297

22.

A Simulation Framework to Examine the Effect of Ageing Error on FT-NIR
Model-Based Age Predictions(M. Arrington, T. Helser, I. Benson, M. Matta, E. Goldstein, A. Punt) ............................................... 325

23.

Fourier Transform Near Infrared Spectroscopy Data and Application Within the
Eastern Bering Sea Pollock Assessment Model(J. Ianelli) .............................................................................................................................. 353

24.

Sensitivity of Pacific Cod Stock Assessment to Alternative Age Composition Data
Derived from Fourier Transform Near Infrared Spectroscopy of Otoliths(T. Helser, M. Siskey, I. Benson, S. Barbeaux) ............................................................................. 363

25.

Sensitivity of Gray Snapper (Lutjanus griseus) Stock Assessment Models to Age Inputs
Estimated with Near Infrared Spectroscopy(S. Garner, B. Barnett, D. Chamberlin, F. Forrestal, T. Helser, I. Benson, W. Kline, W. Patterson) ......... 399

26.

Quality Control and Assurance of Reference Age Data at the Alaska Fisheries
Science Center(M. Matta, J. Brogan, J. Short, T. Helser)..................................................................................... 413

27.

Quality Assurance and Quality Control of Fourier Transform Near Infrared
Spectroscopy Data for Age Prediction(E. Goldstein, B. Hsieh, M. Arrington, T. Helser) .......................................................................... 427

DISCUSSION TOPICS AND RECOMMENDATIONS ........................................................... 447
ACKNOWLEDGMENTS .......................................................................................................... 461
CITATIONS ............................................................................................................................... 463
APPENDIX A. Supplemental Tables and Figures ..................................................................... 499
APPENDIX B. Workshop Agenda ............................................................................................. 519
APPENDIX C. Workshop Participants....................................................................................... 523

vii

1. NOAA Fisheries Fourier Transform Near Infrared Spectroscopy Fish Ageing
Strategic Initiative: Past, Present, and FutureThomas E. Helser

Chair, NOAA Fisheries FT-NIR Spectroscopy SIDT
Program Manager
AFSC Age & Growth Program
Alaska Fisheries Science Center
NOAA, National Marine Fisheries Service
7600 Sand Point Way NE
Seattle, WA USA

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2

INTRODUCTION

The National Oceanic and Atmospheric Administration (NOAA) National Marine
Fisheries Service (Fisheries) has undertaken a nationally coordinated research-and-development(R&D) effort across seven biological laboratories to investigate the use of Fourier transform nearinfrared (FT-NIR) spectroscopy of otoliths for fish age estimation (Helser et al. 2019a). NOAA
Fisheries funded a 5-year strategic initiative (SI) entitled “A revolutionary approach for
improving age determination efficiency in fish using FT-NIR spectroscopy” that began withfunding in Fiscal Year (FY) 2020 with the overarching goal of improving precision andefficiency within the NOAA Fisheries fish ageing enterprise. To achieve this objective, the FTNIR Strategic Initiative Development Team (SIDT), comprised of NOAA scientists from allseven biological laboratories, convened a planning workshop entitled, “Rapid Estimation of FishAge Using Fourier Transform Near-Infrared Spectroscopy” at the Alaska Fisheries Science
Center in Seattle, WA, April 11-12, 2019 (Helser et al. 2019a). This collaborative effort involved
the participation of national and international scientists and industry experts in FT-NIR
spectroscopy and resulted in a framework and “roadmap” toward operationalizing the FT-NIR
spectroscopy ageing technology across NOAA Fisheries science centers.
Among the advanced technologies NOAA Fisheries has been exploring in recent years(https://www.fisheries.noaa.gov/insight/advanced-technologies), FT-NIR spectroscopy
represents a transformative, machine-based approach for rapidly estimating fish ages for over
200,000 hard structures, such as otoliths, that annually support fish stock assessments and fishery
management plans. Multiples of that number of ageing requests are made, and in many cases,
otoliths are collected. Nevertheless, the capacity to fulfill this demand remains unmet. The

3

central principle governing fish age determination based on the growth patterns visible in otoliths
has changed little over the last century. While ageing fish using scales can be documented as far
back as the early 1700s, Johannes Reibisch was the first to describe a protocol for ageing fishusing otoliths in 1899 (Jackson 2007). Microscopic counting of the annual growth zones inotoliths was the foundation of the ageing method then and is still the basis of age estimation
today. With the exception of advancements in microscopy and otolith preprocessing techniques,
the standard practice today remains essentially unchanged. The entire process of generating ages
can be expensive, time consuming, and labor intensive, especially for species with long lifespansor complex otolith processing methods. Age data have been referred to as one of the mostexpensive sources of data collected for stock assessments.
FT-NIR spectroscopy ageing of fish is fundamentally different from traditional
microscopic ageing based on visual ring counts. It functions at the molecular level, measuring
absorbance associated with vibrations of covalent bonds (O-H, C=O, C-H, C-N, and N-H) in the
NIR wavenumber range (4,000 to 12,500 cm−1). Predictive analytics or deep learning is
employed, enabling the establishment of a mathematical relationship between otolith NIR
absorbance and fish age. The overarching challenge of the SI was to transfer a mature, yet
continuously evolving technology from other fields like medicine, agriculture, andpharmaceutical industries to unique conditions and problems presented in fishery science. It is
my belief that the SIDT has risen to those challenges, both conceptually and technologically,
although some challenges were completely unexpected.
Over the past several years, our R&D indicates that machine-based ageing using FT-NIR
spectroscopy of otoliths holds substantial promise of a transformative impact on the NOAA
Fisheries fish ageing enterprise. The irrefutable proof lays in the science underpinning the use of4

FT-NIR spectroscopy for age prediction as demonstrated by a number of published studies
across fish taxa and regions in the United States. (Helser et al. 2019a, Helser et al. 2019b, Healyet al. 2021, Arrington et al. 2022, Passerotti et al. 2020a, Passerotti et al. 2022b, Benson et al.
2023), with others in Australia (Wedding et al. 2014, Rigby et al. 2016, Wright et al. 2021), andmore are yet to be published (including a number of studies described in these proceedings).
Taken together, we are between the “Proof of Concept Development” and “Application
Development” stages, a pathway describing the nine technical readiness levels (TRL) for
technologies in machine learning systems described by Lavin et al. (2022).
Time horizons of attaining operational readiness vary depending on domain, but thegeneral thinking is that five years or more are required to advance innovative technologies todeployment with a TRL-9 (Lavin et al. 2022). An article in Forbes Magazine under Leadership
Strategy (https://www.forbes.com/sites/chuckswoboda/2020/06/15/leading-innovation-is-messyso-get-over-it/?sh=2b9f47ae7434) says that “innovation is messy and success requires embracing
the unexpected.” We certainly did not expect the global SARS-CoV-2 pandemic that began on
March 11, 2020 (https://www.who.int/europe/emergencies/situations/covid-19); the very firstyear of SI Funding. Needless to say, the pandemic had a profound impact on our SI R&D. A“significant delay” was the consensus of the SIDT, which was communicated to the NOAA
Fisheries Science Board and resulted in extending SI funding for an additional year (FY2025).
Despite the 2+ year setback from the pandemic, substantial progress of the SI has beenmade. However, that progress has not been shared equally among the science centers. The
reasons for varying progress among the ageing laboratories, in my estimation, stem from several
reasons, although others may be in play. Ageing laboratories have different levels of staff andresources available to dedicate towards the FT-NIR spectroscopy SI, while at the same time5continue to provide production ages for annual stock assessments. The AFSC and Panama City
ageing laboratories invested in FT-NIR spectroscopy instruments prior to the SI funding andwere able to spearhead proof-of-concept development and initiate otolith scanning sooner thanother laboratories. Moreover, during the SARS-CoV-2 pandemic, Bruker FT-NIR spectroscopy
instruments could not be fielded and set up at other laboratories because infection mitigation
procedures differed in various regions of the country, resulting in varying access to laboratory
facilities. If the impression that one gets, from reading these proceedings, is that AFSC andPanama City have made greater advancements in SI R&D, it is not for lack of trying from SIDT
members at other ageing laboratories. The central message to the reader from my perspective isthat good progress toward operationalization has been made, although much work is yet to bedone across all science centers. Within the pages of this research proceedings document, thereader will hopefully comprehend the depth and breadth of our R&D of FT-NIR spectroscopy
and its application to fish ageing and beyond.
Our April 2023 FT-NIR spectroscopy fish ageing workshop featured three main themesthat formed the backbone of the “roadmap” towards operational readiness: (1) application
development, (2) application implementation, and (3) stock assessment integration (Helser et al.
2019a). Application development focused on the broad-scale applicability of the technology toachieve stated goals for age prediction from otolith spectra in real-world scenarios with robust,
verifiable data. In application implementation, the goals were to investigate the delivery of new
data generated by predictive models, define and evaluate model performance, establish protocols
for data processing and quality control, and standardize operating procedures and best practices
to guide decisions related to sample size, precision, and performance measures. The stock
assessment integration theme sought to evaluate the sensitivity of population model output of6

critical management outcomes and benchmarks from incorporating FT-NIR spectroscopy data
products. Taken together, each element of the hierarchy builds upon the previous tier with“discovery switch-backs” helping to refine and improve the technical knowledge of the system.
Within the pages of this document, “Proceedings of the Fourth Research Workshop on
the Rapid Estimation of Fish Age Using Fourier Transform Near Infrared Spectroscopy”, we
present the accumulated knowledge of the FT-NIR spectroscopy SIDT R&D efforts across thethree themes. Presentations given at the workshop are presented in this document as extended
abstracts, and in many cases, full research papers likely to appear in the scientific literature. Casestudies within the application development theme are presented describing the successful
application of FT-NIR spectroscopy for predicting fish age across a number of fish taxa andregions of the U.S. In several of those applications, deep machine learning, using convolutional
neural networks (CNN), was employed to improve age prediction and advance the ability toquantify uncertainty not available to classical chemometric methods. Within the application
implementation theme, several extended abstracts highlight research toward developing aflexible simulation framework that can be applied to explore uncertainty on model-based age
predictions for a range of scenarios and species. In another abstract, the authors describe the endto-end integration of the FT-NIR spectroscopy production ageing process as it would beenvisioned for system deployment in the laboratory. Finally, within the stock assessment
integration theme, three case studies are presented which evaluate the sensitivity of stock
assessment model (e.g., Stock Synthesis) outputs and critical management quantities andbenchmarks to the substitution of age data products generated from the FT-NIR spectroscopy
approach.

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On behalf of the SIDT and other scientific contributors, I am proud to present thecollected works and accomplishments made on the strategic initiative, particularly underchallenging circumstances. The abstracts mentioned represent just a few of the works of ourefforts to explore the application of FT-NIR spectroscopy in fisheries and ecological science,
several of which I consider cutting edge. I believe the goals of the SI are achievable with good
probability of success, but more work is needed to get us over the finish line.

82. Preliminary Analysis of Fourier Transform Near Infrared (FT-NIR) Spectroscopy andConvolutional Neural Network (CNN) Models to Predict Ages of Yellowfin Sole

Todd T. TenBrink, Irina M. Benson, Brenna C. Hsieh, Mary E. Matta,
and Thomas E. Helser
Alaska Fisheries Science Center
NOAA, National Marine Fisheries Service
7600 Sand Point Way NE
Seattle, WA USA

9

10

ABSTRACTWe present a preliminary analysis of two alternative methods to the traditional
microscopic approach to predict ages for yellowfin sole (Limanda aspera). Yellowfin sole is themost abundant flatfish (Family Pleuronectidae) along the eastern Bering Sea continental shelfand is the target of the largest flatfish fishery in the world. For this study, we used microscopebased reference ages from fish collected from 2012 to 2017 (age range: 1 to 43 years; n = 4,808)
to test the utility of alternative age determination methods, Fourier transform near infrared (FTNIR) spectroscopy using partial least squares regression (PLS) models and convolutional neuralnetworks (CNN). The performance of the PLS models for predicting yellowfin sole ages resulted
in r2 values of 0.90, with root mean square errors (RSME) of approximately 2.2 years. The CNN
handled the non-linearity of the data slightly better than PLS models, with r2 values of 0.92
(RMSE = 1.97 years) and 0.91 (RMSE = 2.03 years) for the training and test datasets,
respectively. The CNN’s age predictions were comparable with the traditional microscope-based
age estimates up to approximately 25 years and were most impacted by absorbance at specificwavelength ranges and by otolith weight.

11

12

INTRODUCTION

Effective fisheries management is the key to sustainability and long-term success of
exploited fish populations (Melnychuk et al. 2021). Age composition data used in age-structured
stock assessment models is a critical component of fish stock assessments. These ages areincorporated into assessments to inform key model parameters such as natural mortality,
recruitment, and growth, and support the calculation of biological reference points, which arebenchmarks used to evaluate the status of a stock. With an effort of broadening assessments offish stocks to a more holistic approach that includes ecosystem management, climate effects arealso being recognized for single-species assessments (e.g., Townsend et al. 2019). These effects
may have negative consequences on the age and size structure of populations (Ohlberger et al.
2022). Impacts to stocks from both fisheries and climate change have resulted in an increased
demand in both the quality and quantity of age composition data. This demand has led to aneffort to pursue complementary or alternative methods to the traditional microscope approach ofage estimation. Traditional ageing involves the use of collecting otoliths (ear stones), calcium
carbonate structures located behind the brain in bony fishes, and enumerating annual bands or
growth zones to estimate an age, often after some process of otolith preparation. In some species,
it can be difficult to interpret these growth zones, and preparation methods do not always provide
clarity of the age reading surface. The traditional approach is time consuming, potentiallyexpensive, and requires experienced age readers.
Methods for automating the ageing process from otolith samples have been investigated.
The use of machine-based technologies to streamline, increase the efficiency, and improve
repeatability of age determination in fisheries science has recently been attempted. The approach13

of using Fourier transform near infrared (FT-NIR) spectroscopy has been employed as a method
to generate ages from otoliths of marine fish (e.g., Robins et al. 2015, Helser et al. 2019b,
Passerotti et al. 2020b). FT-NIR spectroscopy is used in a wide variety of industries including
pharmaceutical, chemical, petrochemical, and agricultural manufacturing. It has applications
ranging from raw material identification to quality control monitoring and determination of
product composition. FT-NIR spectroscopy itself is a vibrational spectroscopy technique basedon the interaction of electromagnetic energy of a specific frequency range with the covalentbonds in the near infrared region. The bonds associated with different chemical functional groups(C–H, N–H, and O–H) absorb energy at unique and characteristic frequencies, and the relative
amount of each functional group present is proportional to the amount of energy absorbed.
Quantitative analysis of FT-NIR spectra is often performed using partial least squares regression(PLS) models, a classic linear method applied to spectral data and that one that works well on
this type of data as it reduces the high dimensionality of an x-matrix of spectra down to its
principal components (Norgaard et al. 2000). In addition to FT-NIR spectroscopy, the use ofmachine learning algorithms in the form of artificial neural networks has also garnered attention
in fisheries science (e.g., Chen and Wang, 1999; Dempsey et al. 2020). Artificial neural networks
are computational structures mimicking cerebral neural networks consisting of layers orcomputational units (Cook 2020). Algorithms of convolutional neural networks (CNN) were
found to be useful for FT-NIR spectral data (Cui and Fearn 2018). Furthermore, recent studies
have shown that CNN have been successful in the age prediction of fish using otolith images(Ordoñez et al. 2020). Benson et al. (2023) developed a multimodal convolutional neuralnetwork (MMCNN) capable of predicting fish ages using FT-NIR spectra and corresponding
biological and geospatial data. Here, we present a study on the utility of FT-NIR spectroscopy

14

coupled with CNN models to predict ages from otoliths of yellowfin sole (Limanda aspera) witha comparison to reference ages from traditional methods.

METHODS

Species Description

Yellowfin sole is a medium-sized and moderately long-lived flatfish (Family
Pleuronectidae) inhabiting eastern North Pacific waters from British Columbia, Canada, to the
Chukchi Sea. In Alaska, it is the most abundant flatfish along the eastern Bering Sea continental
shelf and is the target of the largest flatfish fishery in the world with annual catches exceeding
125,000 metric tons (Spies et al. 2022). Yellowfin sole is managed as a single stock in the Bering
Sea-Aleutian Islands (BSAI) management area (Spies et al. 2022). Yellowfin sole is assessedannually through age-structured modeling. Each year, approximately 1,300 otoliths fromyellowfin sole are aged by the Age and Growth Program of the Alaska Fisheries Science Center.
This species has an age range from 1 to 43 years from a historic collection of approximately
54,000 ages (https://apps-afsc.fisheries.noaa.gov/refm/age/stats/max_age.htm). Ages areincorporated into the assessment of the yellowfin stock to support calculations of management
reference points, such as acceptable biological catch (ABC) and overfishing level (OFL).

15

Reference Data and Spectral Acquisition

Otoliths from yellowfin sole specimens were collected by the Alaska Fisheries Science
Center during the summer on bottom trawl research surveys of the eastern Bering Sea from 2012
to 2017 (Lauth et al. 2019). Reference ages from these specimens (n = 4,808) were determinedby traditional methods following protocols in Matta and Kimura (2012). Otoliths were scannedand spectra acquired using a Bruker Tango-R spectrometer. Scanning of each otolith occurred
after blotting dry glycerol-thymol preservative and recording otolith weights (nearest 0.001 g).
Orientation and placement of each otolith was consistent for every sample following initial
testing of different presentation methods. The effects of otolith side (blind and eyed) andaccessories (apertures and stamps) applied to the Tango-R spectrometer scan (aperture) window
were evaluated. It was determined that either otolith side placed within a chrome ring andcovered with a flat bottom chrome stamp provided the best prediction results (Fig. 2.1). Spectra
were collected on the Tango-R spectrometer primarily between 11,500 and 4,000 cm-1. Samples
were analyzed at a resolution of 16 cm-1 with 64 replicate scans.

Data Splitting and Preprocessing

Preprocessing of raw spectral data was a fundamental process subsequent to classification
model building and involved several pretreatment methods to remove significant noise and otherfactors. Preprocessing was conducted using the chemometric software OPUS™ (version 7.8,
Bruker Optics). Spectral data were preprocessed with a first derivative Savitsky-Golay transform(17 point smooth). To evaluate the extent of spectral variability across collection years and detect16outliers, we used principal component analysis (PCA) and Hotelling’s T2 (Rodionova and
Pomerantsev 2020). Data splitting was conducted using the Kennard-Stone (KS) algorithm(Kennard and Stone 1969). This procedure maximized the Euclidean distance based on theimportance of the principal components, measuring the most representative samples. The
training and test datasets were split 80% to 20%, respectively. The training set was used to train
the model, and included the full range of reference ages and spectral variation. The test set
consisted of unobserved samples not included in the training set.

Modeling Approaches

For this study, we used two modeling approaches to predict ages of yellowfin sole.
Spectral data and reference ages were fit using PLS models. Additionally, CNN was considered,as it has the potential to handle nonlinearity in data better than the traditional chemometrics
approach (i.e., PLS; Benson et al. 2023). For the PLS models, Solo 8.7 software (Eigenvector
Research, Inc., Manson, WA, USA) was used to select wavenumbers and generate the regression
models. Wavenumber selection was completed using the interval PLS regression algorithm(Norgaard et al. 2000) with interval size 10 and automatic selection of the interval number. To
implement the CNN algorithms, we employed Python 3.7.0 using TensorFlow v. 2.5.0 with
implementation of the Keras API (i.e., application programming interface; Chollet 2015). To
build the CNN model, input features of biological data (fish length) and geospatial data (gear
depth, gear bottom temperature, latitude) during the study period (2012-2017) were introduced
along with the spectral data. To interpret the predictions, the Python SHAP (SHapley Additive
exPlanations) library was used to understand the importance of the variables in the trained model

17

for predictions (Lundberg and Lee 2017). Model predictive performances were compared using
the coefficient of determination (r²) and root mean square error (RMSE; Helser et al. 2019b).
To compare the accuracy between traditional microscope-based ages and those ages
predicted by our modeling approaches, we reported the frequency of relative bias over all ages
and mean bias by age class. Traditional bias was calculated as a difference between two trained
age readers’ microscope-based age estimates for the same specimen. Relative bias (B) was
calculated as a difference between age predicted by the models (rounded to the nearest integer;
BMODEL = AgeMODEL – AgeTraditional) and traditional microscope-based age estimation (BTraditional =AgeReader1 – AgeReader 2; Helser et al. 2019b).

RESULTS AND DISCUSSION

For the PLS models, r2 values for both the training and test datasets were 0.90 (Table
2.1). The RMSE for the training and test datasets suggest the PLS models predicted ages within
2.2 years, based on an age range up to 43 years (Table 2.1). The non-linearity of the data, whichmight be the result of the measured spectral data associated with the otoliths, indicated that CNN
might be a more robust method to deal with this relationship (Fig. 2.2). The CNN performed
slightly better than PLS (Table 2.1; Fig. 2.3). CNN appears to be the better method for predicting
yellowfin sole ages than PLS. The optimal CNN model resulted in a coefficient of variation (r2)of 0.92 and 0.91 for the training and test datasets, respectively. The RMSEs associated with thesedatasets were approximately 2.0 years. Variable importance from the CNN model suggested thatabsorbance associated with 6,000 to 4,000 cm−1 wavenumbers had the highest influence onmodel predictions followed by otolith weight (Fig. 2.4). Otolith weight was highly correlated18with fish age, resulting in greater influence on the model than fish length. Given the results of
CNN, the comparison between the traditional age estimates suggested that CNN had similar
precision up to ages of 25 years (Fig. 2.5).
FT-NIR spectroscopy of otoliths coupled with machine learning techniques such as CNN
can predict fish age with comparable precision to traditional age-based methods. Precision in thiscase was comparable ±1 years up to approximately 25 years, which represented >90% of theages associated with the reference age dataset. Further use of CNN should be explored. Benson
et al. (2023) demonstrated that integrating the entire range of wavenumbers of FT-NIR spectra
and corresponding biological and geospatial data for use with CNN can predict ages of walleye
pollock (Gadus chalcogrammus) comparable to classical PLS and yield good or slightly better
precision than traditional microscope-based ages.
Yellowfin sole is currently considered a healthy stock and not in danger of being
overfished; however, current environmental conditions in the eastern Bering Sea (e.g., elevatedbottom temperatures observed cyclically) might impact fish growth and condition potentiallyaltering the size and age structure of the population (Spies et al. 2022). In the stock assessment ofyellowfin sole, the growth of males and females are modeled separately and inform assessment
model parameters, such as natural mortality and recruitment. Furthermore, the use of annual
observed population mean weight-at-age from the trawl survey allows for time-varying growth in
the age-structured assessment model, given the observed correlation between bottom temperatureand growth. Future analysis will need to account for these variables observed for yellowfin sole.
If CNN are to be explored further, models separated by sex would be recommended.

19

Table 2.1. -- Modeling prediction results for yellowfin sole (Limanda aspera) age using thecoefficient of determination (r²) and root mean square error (RMSE) from partial
least squares regression (PLS) and convolutional neural networks (CNN). Training
and test numbers are based on a data-split of 80-20%.
Model

DatasetAge range

N

r2

RMSE

PLS

Training

1 - 43

3,851

0.901

2.251

Test

2 - 34

957

0.903

2.156

Training

1 - 43

3,851

0.924

1.971

Test

2 - 34

957

0.914

2.027

CNN

20

Figure 2.1. -- Yellowfin sole (Limanda aspera) otolith sample presentation on the scanningwindow of the Tango-R (Bruker) spectrometer. A chrome ring is inserted inside
the cover mount with the otolith placed inside the chrome ring directly on thewindow. A chrome stamp is then placed over the sample prior to instrument
scanning and spectral acquisition.

21

Figure 2.2. -- Model results from partial least squares regression (PLS) of the training and test
datasets for predicting ages of yellowfin sole (Limanda aspera).

22

Figure 2.3. -- Model results from convolutional neural networks (CNN) of the training and test
datasets for predicting ages of yellowfin sole (Limanda aspera).

23

Figure 2.4. -- The relative importance of convolutional neural network (CNN) input features forpredicting ages of yellowfin sole (Limanda aspera). Wavenumbers are aggregatedby 1,000 cm−1. The y-axis indicates the feature name in order of importance from
top to bottom. The x-axis indicates the average of the absolute Shapley value ofeach feature.

24

Figure 2.5. -- Frequency of mean bias by age class between the convolutional neural network(CNN) based on FT-NIR spectral data and the traditional microscope-based ages
for yellowfin sole (Limanda aspera). The error bars show one standard error
interval around the mean. The solid line represents a 100% agreement line, while
the dashed line represents ±1 year.

25

26

3. A Preliminary Analysis and Review of FT-NIR Spectroscopy and DNA CpG Site
Methylation for Fish Age Prediction

Laurel S. Lam1,2, Thomas E. Helser3, Krista M. Nichols2, Benjamin T. Mayne4,
Irina M. Benson3, Brenna C. Hsieh3, Oliver F. Berry4, Simon N. Jarman5,
and Kevin W. McNeel6

1

Pacific States Marine Fisheries Commission
205 SE Spokane Street, Suite 100
Portland, OR USA
Northwest Fisheries Science Center
NOAA, National Marine Fisheries Service2725 Montlake Boulevard E
Seattle, WA USA
2

Alaska Fisheries Science Center
NOAA, National Marine Fisheries Service
7600 Sand Point Way NE
Seattle, WA USA
3

4

The Commonwealth Scientific and Industrial Research Organisation (CSIRO)
54 Fairway
Crawley, Western Australia AUSTRALIA
School of Biological Sciences
The University of Western Australia
54 Fairway
Crawley, Western Australia, AUSTRALIA
5

Age Determination Unit
Alaska Department of Fish and Game
1255 W 8th Street
Juneau, AK USA
6

27

28

ABSTRACT

Production ageing of groundfishes for stock assessments relies on validated methods thathave high accuracy and precision, and are scalable to large sample sizes. Traditional methods are
the most reliable but increasingly inefficient in the face of growing stock assessment demands.
Lingcod (Ophiodon elongatus) have traditionally been aged using fin rays, a process that is bothtime intensive and costly in terms of materials, space, and personnel, yet has the highest
accuracy, readability, and minimal between-reader bias compared to other age structures.
However, emerging technologies that are cheaper, faster, and comparable in accuracy andprecision are showing promise. Here, we assessed the performance of two alternative, modelbased methods of predicting lingcod age, DNA methylation and Fourier transform near infrared(FT-NIR) spectroscopy. Average coefficient of variation (ACV), percent agreement, and bias
from the two alternative methods were compared to traditional methods of counting annuli using
fin rays and otoliths (break and burn). Results demonstrated that ages estimated by DNA
methylation and FT-NIR spectroscopy have slightly higher ACV but are in close agreement(greater than 95% agreement within 2 years) with fin ray and otolith-determined ages. Between
the two alternative methods, the FT-NIR spectroscopy model performed slightly better than the
DNA methylation model. Considering the potential for scalability, increased efficiency, andreduced cost, these methods have significant potential to be used in production ageing forlingcod stock assessments.

29

30

INTRODUCTION

Lingcod (Ophiodon elongatus) are a demersal top predator found along the U.S. West
Coast, and play an important role ecologically in nearshore and shelf-slope habitats, andeconomically in commercial and recreational fisheries. The coastwide lingcod stock is federally
managed as part of the Pacific Coast Groundfish Fishery Management Plan (FMP), where stock
assessors use age-structured models to estimate population abundance, stock productivity, andfuture harvest goals. Fishery-independent and -dependent lingcod age data throughout their range
are vital in age-structured stock assessments models and are provided by federal and stateagencies dedicated to the production ageing of groundfish for management purposes.
Lingcod are traditionally aged using hardened and cross-sectioned fin rays from thesecond dorsal fin (Beamish and Chilton 1977), a method that has been validated using knownage fish and consistently has the highest accuracy, readability, and minimal between-reader bias
compared to the use of other hard structures (e.g. otoliths, scales, vertebrae) (McFarlane andKing 2001, Claiborne et al. 2014). In spite of this, the fin ray method is also the most timeintensive and costly in terms of materials, space, and personnel due to the extensive preparation
process needed before the fin ray can be aged under a microscope. This preparation processincludes drying the fin, hardening the fin using cyanoacrylate glue, sectioning the fin into equal
lengths, and mounting the sections onto slides (Beamish and Chilton 1977). During production
ageing for an assessment year, upwards of 1,000 lingcod fin rays are required and can take a
dedicated age team between 3-6 months, depending on the number of personnel and lab space
available (N. Atkins, PSMFC, pers. comm.). In comparison, ageing the same number of otoliths(break and burn) takes 1-1.5 months, though with varying accuracy and precision depending on

31

geographic location (K. McNeel, ADFG, unpublished report). In recent years, alternative, modelbased methods of ageing fish using DNA methylation and Fourier transform near infrared
spectroscopy (FT-NIR) spectroscopy have been explored across a variety of fish taxa withpromising results. DNA methylation and FT-NIR spectroscopy have broad applications inmedicine, pharmaceuticals, agriculture, and food production; however, their applications to thefield of fisheries and resource management is relatively new with respect to production ageing
for stock assessment purposes. Here, we examined the performance of these two model-based
ageing methods on a commercially harvested, temperate marine fish from the North Pacific
Ocean.
DNA methylation is a key epigenetic mechanism associated with aging and refers to theaddition of a methyl group to cytosine residues within cytosine-phosphate-guanine (CpG) sites.
Generally, there is an age-dependent hypermethylation of specific CpG sites, also known as“clock sites”. Because DNA methylation changes are clock type (i.e., they follow a certain rate
and affect certain loci), methylation of specific loci can be isolated to create a model, or genetic
clock, that can be used to age certain species (Bird 1993, Jung and Pfeifer 2015). DNA
methylation can be readily measured using DNA sequencing, making it an attractive option to be
used for wild populations where large sample sizes are often needed (Heather and Chain 2016).
Methods in obtaining DNA samples (via skin, tissue, or blood) can also be non-lethal, aiding inconservation efforts for protected species (Mayne et al. 2021). Moreover, biomarkers for age
have been shown to be transferable across distantly related taxa (e.g., mammals, reptiles, birds,
and fish), thereby increasing efficiency for identifying, isolating, and sequencing optimal CpG
sites and eliminating the need to sequence entire genomes (De Paoli-Iseppi et al. 2017, Mayne
et al. 2020, Lu et al. 2023).

32

Near infrared spectroscopy, widely used in agriculture, pharmaceutical and petrochemicalindustries, and most recently in terrestrial ecology (Vance et al. 2014) and fishery science(Helser et al. 2019b), functions by exciting covalent bonds (O-H, C=O, C-H, C-N, and N-H) at
the molecular level using NIR light and measuring absorbance in the wavenumber range 4,000 to
12,500 cm−1. The light interaction with the sample results in measurable vibrational frequencies
represented by spectral signatures associated with molecular combinations and overtones thatmake up compounds in the sample (Conzen 2014, Siesler et al. 2002). Otoliths are composed ofalternating layers of calcium carbonate and otolin, an organic protein structure that comprises 45% of an otolith and accumulates as fish age (Campana 1999). In otoliths, these spectral
signatures derived from their molecular composition are a proxy for fish age. The use of FT-NIR
spectroscopy of otoliths to estimate fish age has been reported for a number of species (Wedding
et al. 2014, Helser et al. 2019b, Passerotti et al. 2020b). This approach is based on generating a
predictive model between the spectra of the ageing structure and fish age, which, once calibrated
and validated, is able to produce age estimates at many times the rate compared to traditional
methods (Helser et al. 2019b).
The objective of this study was to 1) develop an epigenetic clock for lingcod using
known age-associated CpG sites in zebrafish (Danio rerio), 2) develop a predictive model
between otolith spectra from FT-NIR spectroscopy and reference ages to age lingcod, and 3)
compare the performance of these two alternative methods of predicting lingcod age (DNAmethylation and FT-NIR spectroscopy) with two traditional methods (fin rays and otoliths) using
metrics commonly used in age structure comparisons studies: percent agreement, average percent
error, and average coefficient of variation. Our goal was to investigate the application of FT-NIR

33

spectroscopy and DNA methylation as alternative methods of production ageing lingcod forstock assessment purposes.

METHODS

Sampling Methods

Lingcod fin rays and otoliths from California were collected between 2015 and 2017 for a
total of 735 paired samples. Otoliths were initially scanned by the Alaska Fisheries Science
Center (AFSC) using FT-NIR spectroscopy, then sent to be aged microscopically by the Alaska
Department of Fish and Game (ADFG) Age Determination Unit (ADU) in Juneau, Alaska.
Lingcod gill tissue was collected for DNA extraction (n = 206) from fish caught off southeast
Alaska to the U.S.-Mexico border (excluding Canadian waters). Of the 206 individuals withgenetic material, 70 also had otoliths, collected making for a maximum of 70 paired samples.
Two hundred and two individuals out of the 206 fish in the genetic subset had paired fin rays
collected.

Traditional Method: Fin Ray

Prior to age estimation, fin rays were dried, hardened using cyanoacrylate glue, cut into 2
mm sections using a high-speed saw, and mounted onto microscope slides (Beamish and Chilton
1977), a process that can take up to seven days for a batch of 100 fin rays. Ages were determinedby counting the number of annuli under a compound microscope (40× magnification) using34

transmitted light. Early annuli can be difficult to discern or become resorbed, but can be verified
using known mean radii measurements for the first, second, and third annuli (Cass and Beamish
1983, McFarlane and King 2001, Laidig et al. 2001). These methods of fin ray preparation andage estimation follow Beamish and Chilton (1977) and the Committee of Age Reading Experts
manual (CARE 2006), and are currently used by the Washington Department of Fish andWildlife, Oregon Department of Fish and Wildlife, and Northwest Fisheries Science Center.

Traditional Method: Otolith (Break and Burn)

The ADFG ADU has used sagittal otoliths to estimate lingcod age since 1995. Using thestandard break and burn protocol (Williams and Bedford 1974), otoliths were broken through thecore to produce two halves. The anterior half of the left otolith is preferred, and the final age
estimate is determined from the ventral side of the cross-section. Annual growth bands arecounted using reflected light on a stereoscopic microscope with standard 0.63-6.0X
magnification with a lower powered eyepiece (16X). Location of the first annulus is verified
using a size criterion derived from measurements of otoliths from young-of-the-year and oneyear-old lingcod (unpublished ADFG manuscript obtained from K. McNeel, Age Determination
Unit, Juneau, Alaska).

Alternative Method: DNA Methylation

Using lingcod DNA sequenced from gill tissue and known age-associated CpG sites fromzebrafish (Danio rerio), we used a genome pairwise alignment method to identify conserved age-35

associated CpG sites in the lingcod genome. These sites were targeted for primer design in themultiplex polymerase chain reaction (PCR) assay (Mayne et al. 2020). Multiplex PCR was
designed for primers to target and isolate conserved age-associated CpG sites using PrimerSuite(Lu et al. 2017) and to measure DNA methylation at these sites. CpG sites were sequenced using
designed primers and a model was fitted to predict age from DNA methylation. To generate themodel to predict age from DNA methylation, samples were randomly assigned to either atraining or testing data set with a 70/30 split, respectively. Age in years was log transformed
prior to fitting the data with an elastic net linear regression model (Friedman et al. 2010). The
glmnet function in the glmnet R package was used to apply the elastic net regression model andwas set to a 10-fold cross validation and the α-parameter was set to 0.5, similar to other studies(Horvath 2013, Mayne et al. 2020). The testing data set was used as a validation of the modeland to test for accuracy. The performance of the model was measured using Pearson correlations
and mean absolute errors (MAE). A similar Pearson correlation and MAE between training andtesting datasets indicates similar performance, a lack of overfitting, and high reproducibility(Mayne et al. 2020). We also determined the performance of the model within age ranges todetermine if the model was biased to specific age classes.

Alternative Method: FT-NIR Spectroscopy

For spectroscopic analysis, unaltered whole otoliths were removed from their vials andgently cleaned with Kimwipes to remove contaminants before placement on the 22 mm sample
window of a Bruker MPA II FT-NIR spectrometer. Otoliths were covered with a gold stamp andanalyzed using diffuse reflectance on an integrated sphere and spectra collected at 16 cm−1
36

resolution at 8 wavenumber intervals with 64 co-scans, scanner velocity of 7.5 kHz andmeasuring absorbance values from 12,500 to 4,000 cm−1. Spectral data were pre-processed using
1st derivative and a Savitsky-Golay (2nd order polynomial, 17 points) smoothing filter. Spectral
variability across collection years and detection of outliers was evaluated using principal
component analysis (PCA) and measures such as Hotelling’s T2 and Q-statistic. Helser et al.
(2019b) reported that scan time to acquire otolith spectra ranges between 40-60 seconds. Spectral
regions that exhibited the highest correlation with changes in the reference values (age) wereisolated using PCA. We used the chemometric software Solo 8.7 (Eigenvector Research, Inc.,
Manson, WA, USA) for exploratory analysis using PCA, the subsequent data split into training
and test data sets for data processing and model generation, and to select the optimal model andused partial least squares (PLS) regression (Chen and Wang 2001) to develop a quantitative NIR
model from the reference values (fin ray ages and otolith ages, respectively) to predict age. To
cross validate the model, each calibration sample is temporarily removed from the dataset, then a
PLS model is created from the remaining samples and the sample that was temporarily removed
is predicted as an unknown. The difference between the reference value and the predicted value
is determined, then the sample is returned to the dataset. This is repeated for each sample. Model
performance and robustness was determined using the coefficient of determination (r2), whichassesses predictive accuracy of the model, and the root mean square error of cross validation(RMSECV), an indicator of model accuracy (Helser et al. 2019b).

37

Ageing Error

Ageing error was quantified and compared between traditional and alternative methods(fin rays vs. DNA methylation, fin rays vs. FT-NIR spectroscopy, otoliths vs. DNA methylation,
otoliths vs. FT-NIR spectroscopy). Ageing bias was examined graphically using age bias plots
and statistically using symmetry tests to identify systematic biases, such as over- or underageing, and where these biases may be occurring (e.g., in older ages or younger ages) (Campana
et al. 1995). Precision, or repeatability, was quantified using percent agreement, average percent
error (APE), and average coefficient of variation (ACV) (Campana 2001). APE and ACV are
functionally interchangeable when there are only two age readers, but ACV is preferred when
there are more than two readers as ACV is based on the standard deviation divided by the
number of times the fish was aged. It is considered more accurate than APE as variance is abetter estimator of precision than the absolute difference between reads and tends to be moreunbiased and consistent (Chang 1982). While the median ageing error across studies is an ACVof 7.6 (or a 5.5 APE), there is no acceptable standard value of precision as readability is
influenced by the species and the structure being aged (Campana 2001).

RESULTS
Epigenetic Clock Model Performance

We found 11 CpG sites out of the initial 29 zebrafish CpG sites were needed as theminimum number of predictors with the highest performance for use in the lingcod epigenetic
model. Two separate models were created based on the reference dataset used to calibrate the38model, one using fin ray ages as reference (n = 202) and one using otolith ages as the reference(n = 68). A high correlation was found in the training data set between the fin ray reference ages
and predicted ages (Fig. 3.1A, Pearson correlation = 0.93, p < 2.20 × 10-16). The high correlation
was also maintained in the testing data set (Fig. 3.1B, Pearson correlation = 0.92, p < 2.20 ×
10-16). This suggests a lack of overfitting as the training and testing data set had similar
performance. This was further demonstrated with no statistical difference found between theabsolute error rates between the training (median = 1.06 years) and the testing (median =1.14 years) data sets (Fig. 3.1, p = 0.868, t-test, two-tailed). No difference in absolute error rates
was found between sexes in both the training (p = 0.1806, t-test, two-tailed) and testing data sets(p = 0.544, t-test, two-tailed). The otolith-referenced epigenetic clock similarly had highcorrelation in the training data (Fig. 3.2A, Pearson correlation = 0.79, p < 2.10 × 10-11) and thetesting data set (Fig. 3.2B, Pearson correlation = 0.83, p < 1.30 × 10-5). There was no statisticaldifference found between the absolute error rates between the training (median = 1.34 years) andthe testing (median = 1.14 years) data sets (Fig. 3.2, p = 0.455, t-test, two-tailed).

FT-NIR Spectroscopy Model Performance

Optimal models from PLS regression ranged from 7 to 9 latent variables with the mostinformative region for age prediction being within the 7,000 to 4,000 cm-1 wavenumber region.
Similar to the epigenetic models above, two models were generated based on the reference data
set used. The FT-NIR spectroscopy age prediction model using the fin ray ages as reference (n =735) yielded high performance, with 93% of the variability in estimated ages explained by themodel where 68% of estimated ages should fall within 0.82 years of expected age (r2 = 0.93,

39

RMSECV = 0.82 years). The model using otolith ages as reference (n = 70) was comparable inpredictive accuracy, with 90% of the variability in estimated ages explained by the model and
68% of estimated ages falling within 1.2 years of expected age (r2 = 0.90, RMSECV = 1.2
years).

Comparison of Relative Accuracy/Precision Between Traditional and Alternative Methods

Bias plots, percent agreement, and ACV were determined for each ageing method pair:fin rays and epigenetics (fin ray reference); fin rays and FT-NIR spectroscopy (fin ray reference);
otoliths and epigenetics (otolith reference); and otoliths and FT-NIR spectroscopy (otolithreference). Between the epigenetic method and FT-NIR spectroscopy (using fin ray reference),
the FT-NIR spectroscopy predictive model had higher percent agreement (50% agreement,
92.4% agreement within one year, 95% agreement within 2 years) and slightly lower error
compared to the epigenetic predictive model (37.1% agreement, 85.6% agreement within one
year, 98.8% agreement within 2 years) (Table 3.1). The fin ray referenced FT-NIR spectroscopy
model showed less bias and no evidence of systematic under- or over-ageing (Fig. 3.3) comparedto the epigenetic model.
Between the epigenetic method and FT-NIR spectroscopy using the otolith reference, the
FT-NIR spectroscopy predictive model similarly had higher percent agreement (35.7%
agreement, 80.0% agreement within one year, 95.7% agreement within two years) and lower
error compared to the epigenetic predictive model (20.6% agreement, 58.8% agreement within
one year, 86.85 agreement within two years) (Table 3.1). Both the epigenetic and FT-NIR
spectroscopy models appeared to be slightly biased towards over-ageing rather than under-40

ageing; however the FT-NIR spectroscopy model outperformed the epigenetics model overall(Fig. 3.3).

DISCUSSION AND CONCLUSIONS

Understanding the applications, benefits, and limitations of alternative, model-based
methods in the production ageing of groundfish is important for the future of stock assessments
and fisheries management agencies. While thousands of ageing structures across multiple
groundfish species are collected in fisheries-independent and -dependent surveys, only a fraction
are regularly aged due to resource constraints, such as time and personnel. In this study, wedemonstrated that DNA methylation and FT-NIR spectroscopy appear to be promisingalternative methods of ageing lingcod with accuracy and precision comparable to traditionallyused methods and thus have the potential to greatly increase ageing throughput and efficiency
during time sensitive assessment years.
There are many benefits to using DNA methylation for ageing, namely that epigenetic
clocks are not influenced by temperature as somatic growth tends to be (Anastasiadi and Piferrer2019), and methods are continually refined, speeding up the initial model-building process acrossdiverse taxa (Mayne et al. 2020). Here, an epigenetic clock was developed for lingcod, further
illustrating that conserved CpG sites between evolutionary divergent species (e.g., zebrafish) still
maintain age-association. Both fin ray and otolith reference datasets were able to generate highperforming predictive models given the available reference data. The model using otoliths had
higher absolute error, possibly due to smaller sample size compared to the fin ray referenced
model. While both models could have benefitted from more thorough sampling of older age

41

classes, as model performance was still quite high, increasing sample sizes might not result in a
dramatic increase in r2. This highlights the limitations of utilizing machine-learning basedmodels. While epigenetic ageing has the advantages of being non-lethal and high throughput, themodel cannot be extrapolated to ages outside of its calibrated range (Mayne et al. 2021). This is
the case with other machine learning based models where performance is often reduced outsideof calibration data. Unlike otoliths where annual growth rings may be consistent throughout thelife of the fish, epigenetic ageing is limited to the original model’s training data. Additionally,
the model’s performance decreased with increasing age as younger ages are typically moreavailable than older ones, especially in fished populations (Beamish et al. 2006).
The use of FT-NIR spectroscopy in ageing fish for stock assessments has been
investigated in temperate and tropical species (e.g., walleye pollock, red snapper, barramundi)
for purposes of production ageing and has yielded promising results (Helser et al. 2019b,
Passerotti et al. 2020b, Wright et al. 2021). FT-NIR spectroscopy is advantageous for being rapid
and non-destructive to the ageing structure and is highly repeatable with as good or better
precision to that of traditional methods. Moreover, recent developments illustrate FT-NIR
spectroscopy coupled with machine learning can improve model prediction (Benson et al. 2023).
Both FT-NIR spectroscopy models created here for lingcod were comparable in performance,
though the fin ray calibrated model had higher accuracy and lower bias. Overall, both FT-NIR
spectroscopy models were higher in precision than the epigenetics models in terms of ACV andpercent agreement. However, as FT-NIR spectroscopy is also a secondary method based on
predictive analytic models, it is similarly limited due to the availability and accuracy of thereference data used to calibrate the model. Also, both alternative methods may require predictivemodel recalibration to account for unseen variability or when factors affecting biological42

processes or the underlying biochemical mechanisms change. In such cases, reference data using
traditional microscopic methods will continually need to be collected (though at a much smaller
percentage) to monitor changes in precision or drift from non-stationary processes, such as
fishing pressure, changing ocean conditions, and spatio-temporal variability (Heino et al. 2015,
Lam et al. 2021, Fennie et al. 2023).
Between the two alternative methods investigated here, DNA methylation and FT-NIR
spectroscopy, FT-NIR spectroscopy may at present be a more efficient approach for highthroughput, production ageing requirements for groundfish stocks that are non-threatened andwhere otoliths are regularly collected from survey and fishery sources, though this can change inthe future as epigenetic ageing methods improve in efficiency or as stock statuses change. The
FT-NIR spectroscopy model using fin ray reference data had the highest percent agreement andlowest error among all comparisons and would be the most promising alternative method for use
along the U.S. West Coast; however, traditional, microscopic ageing will still serve as an
indispensable primary method to monitor consistency and precision in the reference data should
predictive model recalibration be necessary.

43

Table 3.1. -- Precision between traditional age determination methods (fin ray and otolith
examination) and alternative age determination methods (epigenetics and Fouriertransform near infrared spectroscopy, FT-NIRS) for lingcod (Ophiodon elongatus)
was calculated using average coefficient of variation (ACV), average percent error(APE) and percent agreement with exact agreement (% Agreement). While themedian ageing error across studies is an ACV of 7.6 (or a 5.5 APE), there is noacceptable standard value of precision as readability is influenced by the species
and aged structure (Campana 2001). Percentage of age estimates with agreement
within one year (± 1 yr) and within two years (± 2 yr) are also shown.
n

ACV

APE

% Agreement± 1 yr± 2 yrFin ray vs. Epigenetics

202

9.0

6.4

37.1

85.6

95.0

Fin ray vs. FT-NIRS

735

8.8

6.2

50.0

92.4

98.8

Otolith vs. Epigenetics

68

12.8

9.0

20.6

58.8

86.8

Otolith vs. FT-NIRS

70

8.3

5.9

35.7

80.0

95.7

44

Figure 3.1. -- Performance of the epigenetic model using lingcod (Ophiodon elongates) fin ray reference ages was measured bycomparing Pearson correlations and mean absolute errors between the A) training data set and B) testing data set. The
1:1 age agreement line is shown as a black diagonal line on Figure 3.1A and 3.1B. Significant p-value (p < 0.05)
indicates that fin ray ages and predicted ages from the epigenetic model are correlated. Similar Pearson correlation andmean absolute error between training and testing datasets indicates similar performance between training and testingdata sets.

45

Figure 3.2. -- Performance of the epigenetic model using lingcod (Ophiodon elongates) otolith reference ages was measured bycomparing Pearson correlations and mean absolute errors between the A) training data set and B) testing data set. The
1:1 age agreement line is shown as a black diagonal line on Figure 3.1A and 3.1B. Significant p-value (p < 0.05)
indicates that fin ray ages and predicted ages from the epigenetic model are correlated. Similar Pearson correlation andmean absolute error between training and testing datasets indicates similar performance between training and testingdata sets.

46

Figure 3.3. -- Age agreement plots between traditional lingcod (Ophiodon elongates) age
determination methods (fin ray and otolith examination) and alternative age
determination methods (epigenetics and FT-NIRS) to identify systematic under- orover-ageing and symmetry. The 1:1 age agreement line is shown as a blue dasheddiagonal line. Plotted numbers along the line represent the frequency of paired
samples that had 100% age agreement. Numbers above and below the 1:1
agreement line indicate the frequency of samples where age estimates differed. In asymmetric age-agreement table, disagreements in age would be expected to fallrandomly on either side of the 1:1 line.

47

48

4. Applying FT-NIRS Predictive Ageing to Different Genetic Stocks of White Grunt(Haemulon plumierii) Within the U.S. South Atlantic

Jamie M. Clark 1, 2, Andy D. Ostrowski 1, Jennifer C. Potts 1, Beverly K. Barnett 3,
Steven B. Garner 2, 3, and Walter D. Rogers 1, 2Southeast Fisheries Science Center
NOAA, National Marine Fisheries Service
101 Pivers Island Road
Beaufort, NC USA
1

2

Cooperative Institute for Marine and Atmospheric Studies
University of Miami
4600 Rickenbacker Causeway
Miami, FL USA
Southeast Fisheries Science Center
NOAA, National Marine Fisheries Service
3500 Delwood Beach Road
Panama City, FL USA
3

49

50

INTRODUCTION

Age data are an essential component of modern fisheries stock assessments because they
are utilized to estimate growth, longevity, maturation, mortality, track cohorts, and characterize
landings compositions to understand population dynamics of state and federally-managed
fisheries. The Southeast Fisheries Science Center collectively receives up to 85,000 otolithsamples annually and is responsible for providing age data to three different management
councils in multiple regions. Otoliths (inner ear stones) are the primary fish hard part collected toestimate fish age, but traditional ageing methods are time-consuming because samples require
processing, sectioning, and visually counting the incremental annuli using microscopes. Thus,
the process of production ageing requires a large investment of time and staff resources toprovide data for stock assessment. Fourier transform-near infrared (FT-NIR) spectroscopy has
recently been applied to fish ageing with the potential to dramatically increase ageing efficiency
without any loss in productivity. NOAA Fisheries staff are continuing to evaluate the feasibilityof FT-NIR spectroscopy for accurately predicting fish ages compared to traditional ageing
methods.
FT-NIR spectroscopy has been utilized in the pharmaceutical, chemical, and agriculturalindustries for decades, but has only recently been applied to fish ageing (Robins et al. 2015;
Helser et al. 2019b). FT-NIR spectroscopy is a non-destructive method that transmits nearinfrared light through a sample, thereby exciting its chemical bonds. Absorbance measurements
are collected from the scanned object to form a spectral signature, which quantifies the presence
and concentration of organic bonds and is visualized as a continuous spectrum across therelevant wavelength (cm-1) range (i.e., the spectral signature). Peaks in spectral absorbance relate51

to the chemical composition of the sample in a consistent way. Thus, when spectral data arepaired with a representative sample of observed ages, a predictive model can be built to predictage from spectral data alone without the need for sectioning and ageing thousands of otoliths.
However, this technique has only been applied to a handful of species and requires further
investigation. Accuracy, potential bias, and the frequency of model updating must be evaluatedbefore age estimates predicted with FT-NIR spectroscopy can be used at a production scale forstock assessment.
We examined white grunt (Haemulon plumierii) as a candidate species for FT-NIR
spectroscopy because it has relatively high precision among traditional age estimates, adequate
sample availability, and an upcoming stock assessment scheduled in 2027. White grunt is a reefassociated coastal fish found in the western Atlantic from the Chesapeake Bay south through the
Gulf of Mexico and the Caribbean to the coast of Brazil. Previous studies have identified four
genetically distinct stocks of white grunt: (1) the Carolinas, (2) Southeast Florida and the Florida
Keys, (3) the eastern Gulf of Mexico, and (4) the Caribbean Sea (O’Donnell et al. 2019). Agedata indicate that fish from Southeast Florida grow much slower and attain smaller sizes thanfish collected from North Carolina and South Carolina (Potts & Manooch 2001). Fish from theGulf coast of Florida reach comparable size-at-age to fish from the Atlantic coast of Florida, yetboth are smaller at age than fish from the Carolinas (Murie and Parkyn 2005). White grunt
supports important U.S. commercial and recreational fisheries (Potts 2000), but there has neverbeen a stock assessment using age-based methods. Additionally, previously planned assessments
have been delayed for various reasons, one being uncertainty in stock definition (O’Donnell et al.
2019). The application of FT-NIR spectroscopy to ageing white grunts could provide critical age

52

data to support the upcoming assessment and efficiently provide age data in the future, given the
26,033 otolith samples currently awaiting processing at the SEFSC Beaufort laboratory.
The objectives of this study were to examine the efficacy of FT-NIR spectroscopy for 1)
predicting age of white grunt and 2) predicting age for genetically distinct stocks of white gruntin the U.S. South Atlantic (i.e., the Carolinas and South Florida). Our specific goals were to (1)develop standardized practices for FT-NIR spectroscopy across various NOAA labs; (2) assess
the overall efficacy of FT-NIR spectroscopy (i.e., accuracy and precision) by comparing ages
predicted with FT-NIR spectroscopy models to observed ages for white grunt; and 3) determine
if single or separate age prediction models are required for estimating ages of white grunt stocks
in the Carolinas and South Florida.

METHODS

In total, 1,930 Atlantic white grunt otoliths (left otoliths were mostly used; right otolith
was used if left was missing or damaged) were weighed, scanned with a TANGO-R spectrometer(Bruker), sectioned on a Hillquist high-speed saw, and aged under a Nikon stereo-microscopewith transmitted light. Fish were assigned to each genetic stock based on landing location
because the majority of samples were fishery-dependent and fishing location was not known.
Assigning fish to a genetic stock by landing location is the most accurate metric without a known
fishing location. However, misassignment of genetic stock is possible since fish from the
opposite stock could be landed in the opposite region. Fish landed in NC, SC, and GA wereassigned to the Carolinas stock (n = 863). Fish landed between Cape Canaveral and Key West,FL, were assigned to the South Florida stock (n = 1,065). A subset of otoliths from each stock53

was randomly selected (~10 samples per age class; roughly 10% of the entire sample size) andcomprised the calibration set used to build the FT-NIR spectroscopy predictive model. The
remaining set of otoliths comprised the validation set and was used to compare accuracy andprecision between “true ages” and ages estimated with FT-NIR spectroscopy. Age estimatesprovided by the primary reader were compared with age estimates from an expert reader tocompare accuracy and precision of the observed ages, but consensus ages were not estimated if
ages differed between readers. The primary reader’s age estimates were considered the “trueages” when compared to ages predicted with FT-NIR spectroscopy. Two partial least squares(PLS) regression models, one for each genetic stock, were fitted to paired observed versus
predicted ages in R (R Core Team 2021). The best calibration model was then applied to eachstock’s validation set with unknown ages and compared to the true ages to assess model
performance. Comparison metrics included r2, root mean square error of cross-validation(RMSECV), root mean square error of prediction (RMSEP), residual percentage difference(RPD), and average percent error (APE).

RESULTS AND DISCUSSION

Morphometric data in this study agreed with previous work indicating that white grunt
from the Carolinas grew to a larger size and older age than South Florida white grunt (Fig. 4.1).
Our data also indicated that otolith weight was greater for white grunts from the Carolinas thanthose from South Florida, which was not previously known (Fig. 4.2). In addition, there weredifferences in the quality of age estimates between the two stocks. Reader agreement amongobserved ages for white grunt from the Carolinas was higher and error was much lower (APE =54

2.8%, Fig. 4.3A) than fish from South Florida (APE = 5.8%, Fig. 4.3C). When compared withtraditional age estimates, ages predicted with FT-NIR spectroscopy models were less accurate
and were positively biased at the youngest age class and negatively biased at older ages for bothstocks. Results from comparisons of calibration versus validation age data sets indicated a better
correlation between spectral signatures and predicted age for the Carolinas stock (r2 = 0.91, Fig.
4.3B) than the South Florida stock (r2 = 0.85; Fig. 4.3D). For the Carolinas model, RMSEP was
1.5, which indicates the model-predicted age had a mean difference of ±1.5 years from theobserved age; the RPD value of 3.3 indicates a good model fit (RPD > 3). For the South Florida
model, RMSEP was 1.3, but the RPD was below the accepted performance threshold (RPD =2.6). Predicted ages for fish from the South Florida stock had higher APE when compared to thetrue ages (Fig. 4.3C-D), which was likely due to higher ageing error in the traditional ages used
to build the predictive models, greater variation in size-at-age, and fewer age classes among
South Florida white grunt. Fish from the South Florida stock experience less seasonality intemperature regimes than fish from the Carolinas stock, which likely affects otolith banding
clarity causing decreased precision in traditional age estimates. We recommend including moreyoung (age-0 and 1) and old fish (>age-12), if available, into each calibration model to reduce
ageing bias. We also recommend using a second age reader to maximize ageing precision
through consensus ages for the true ages used to build the predictive models. Overall, weobserved a moderately good correlation between (1) true ages and spectral signatures and (2) true
ages and model-predicted age, but additional age data and reducing imprecision in input data will
greatly increase our ability to more accurately predict fish age with FT-NIR spectroscopy.
We recommend that significant improvement in the precision between predicted andobserved age estimates is needed before ages predicted with FT-NIR spectroscopy are of55

sufficient quality for use in production ageing for stock assessment. Reduced precision in age
estimates will increase uncertainty around stock assessment model parameter estimates andaffect assessment outputs. Ageing bias at the youngest and older ages will also negatively impact
assessment model outputs due to cohort smoothing and imprecision, respectively. However,ageing bias at older age classes may have little effect on assessment model outputs if bias occurs
at ages comprising the plus group of the assessment model. Ageing bias can be somewhat
corrected in stock assessment models by specifying expected ages and age-specific error in theageing error matrix, but increased imprecision around age estimates can erroneously alterparameter estimates affected by size-at-age when fitting to multiple data sources in complex
assessment models. Additional research is needed to improve the precision of predicted ages forfish from the South Florida stock. We also recommend further analyses be done to understandthe influence of sexually dimorphic growth on age estimates, which was not explored in thisstudy but is known to affect size-at-age estimates for this species (Potts 2000). Overall, genetic
differences between stocks that affect size-at-age, growth, or maximum age are important toconsider when building age prediction models. FT-NIR spectroscopy shows great promise as amethod to produce indirect age estimates at high volume with great efficiency and acceptable
precision but requires additional research before applying it to the production ageing of whitegrunt.

56

Figure 4.1. -- Boxplots of length (fork length mm)-at-age (yr) for the two different genetic stocks(Carolinas, red and South Florida, blue) of white grunt (Haemulon plumierii) in the
U.S. South Atlantic. Horizontal lines within the boxes indicate median values, box
ends indicate the 25th and 75th quartiles, whisker ends indicate 1.5*interquartile
range, and points indicate outlying values.

57

Figure 4.2. -- Boxplots of otolith weight (g)-at-age (yr) for the two different genetic stocks(Carolinas, red and South Florida, blue) of white grunt (Haemulon plumierii) in the
U.S. South Atlantic. Horizontal lines indicate median values, box ends indicate the25th and 75th quartiles, whisker ends indicate 1.5*interquartile range, and points
indicate outlying values.

58

Figure 4.3. -- Comparison plots of traditional age estimates (primary vs. expert reader age (Panels A and C) and age estimates
predicted with FT-NIR spectroscopy (Panels B and D) for each white grunt (Haemulon plumierii) stock. Sample sizes
and average percent error (APE) are shown for each panel. Red and blue points indicate mean values for the Carolinas
and South Florida stocks, respectively, while gray-scale points indicate individual age estimates with a transparency of
1/10 (e.g., 10 observations of the same age difference would produce a fully darkened point). Vertical lines indicate the
95% confidence interval of the mean. The histogram above each panel indicates the sample count for the expert orobserved ages by age class; the histogram to the right of each panel indicates the sample count for the difference in age
estimates between the primary and expert reader or observed and predicted age.
59

60

5. A Novel Approach for Determining the Reproductive Status of Walleye Pollock(Gadus chalcogrammus) Using Raman Spectroscopy

Sandi K. Neidetcher1, Morgan B. Arrington2, Thomas E. Helser1, Esther D. Goldstein1,
and Irina M. Benson1
Alaska Fisheries Science Center
NOAA, National Marine Fisheries Service
7600 Sand Point Way NE
Seattle, WA USA
1

2

Cooperative Institute for Climate, Ocean, and Ecosystem Studies (CICOES)
University of Washington
3737 Brooklyn Ave NE
Seattle, WA USA

61

62

INTRODUCTION

Knowledge of the reproductive status of fishes is vital to the sound management of fishstocks. Spawning stock biomass (SSB) provides an estimate of the proportion of the stock
contributing to reproduction and is used to determine stock status and abundance to set catch
quotas. These estimates are obtained through the construction of maturation schedules (spawning
ogives) based on field-based observations of ovarian development and provide estimates of the
proportion of reproductively mature females in a stock or population. In addition to providing
estimates of spawning potential, knowledge of reproductive strategy and the geography andphenology of spawning can greatly benefit our understanding of interactions between a species
and its environment (e.g., identification of early life stage distribution and dispersal to nurseries
where young fish develop). In recent decades, climate-driven warming trends have been shown
to impact fish distribution, phenology, and growth rates (Hollowed et al. 2013). In addition,
reproductive patterns in fish, such as gonad maturation and ovulation, are often tied totemperature thresholds (Alix et al. 2020). Understanding the impacts of climate change on the
reproductive biology of fish will increase the need for data and for tools with accuracy andefficiency in these collections.
Histological analysis, considered the most accurate method of determining oocyte staging(West 1990), involves the microscopic examination of ovary tissues that have been fixed,
stained, and mounted on a slide. This process allows for the identification of microscopic
structures and conditions that occur as an ovary develops. However, histological processing iseffort intensive and incurs costly processing fees from specialized laboratories. To conduct
histological analysis, ovaries are often collected in the field and preserved in formalin, a63hazardous chemical. The cost associated with histological processing, the challenges of working
with formalin in the field, and the time required for processing and slide analysis are often
limiting factors to this approach.
Vibrational spectroscopy is an emerging technology in ecological research that may
provide an alternative approach for determining maturity and reproductive status of fishes.
Several spectroscopy techniques have been applied to ovarian tissue. Examples include the useof Fourier transform near infrared (FT-NIR) spectroscopy (10,000 to 4,000 cm-1) to differentiate
non-spawning from spawning-capable fish (TenBrink et al. 2022) and Raman spectroscopy todiscriminate egg quality and viability by tracking macromolecule fluctuations during specificdevelopment stages post-fertilization (Ishigaki et al. 2016).
Ovary tissue, like most biological tissues, is composed primarily of various proteins,
lipids, carbohydrates, and phosphates with levels that vary throughout development. As theovary matures, changes observed through histological analysis include structural development
and material accumulations, which reflect changes in the molecular compositions. Raman
spectroscopy has unique characteristics that may make it an optimal spectroscopy method for
detecting changes in the levels and configuration of the molecular components throughreproductive maturation. In Raman spectroscopy, a laser is used to irradiate a material across a
range of wavenumbers, and highly sensitive lenses are used to collect the light that is scattered
back. Scattered light is analyzed and provides a spectrum “fingerprint” with patterns or bands
unique to the molecular composition and configuration of the sample under interrogation. Thiscan be useful when unknown molecular constituents in biological tissues need to be identified orquantified. Raman spectroscopy is also less sensitive to water than other spectroscopy techniques
and therefore may be more useful for biological tissue samples, as they contain large amounts of64

water (Butler et al. 2016). The use of Raman spectroscopy as a tool to access ovary maturation
would allow for more rapid, and in-situ, evaluations while reducing the use of hazardous
formalin and expensive histological processing.
In this study, we evaluate the potential for Raman spectroscopy to provide information on
the maturity and reproductive status of walleye pollock (Gadus chalcogrammus), referred to as“pollock” hereafter. Pollock is a semi-pelagic, schooling fish widely distributed in the North
Pacific Ocean and supports highly lucrative commercial fisheries in the Bering Sea and the Gulf
of Alaska. Reproductive information used in pollock stock assessments is currently based on
macroscopic evaluation of their gonads (Williams 2007). Visual maturity estimates are collected
at the point of capture and used to estimate maturation status and spawning phenology. Because
pollock are a multi-batch spawning species, ovaries may contain oocytes at varying levels of
maturation and are often difficult to stage macroscopically. Differentiating between immature,developing, and spent fish is particularly difficult for this species. In addition, recent research has
identified shifts in the phenology of spawning (Rogers and Dougherty 2019) and in distribution
on the spawning grounds for pollock (Eisner et al. 2020). Improved monitoring of this species
would be beneficial to evaluate changes in reproduction that may impact stock assessment
estimates and management.
We hypothesize that 1) mature pollock ovaries with histologically visible yolk
development (hereinafter referred to as “yolked” ovaries) can be differentiated from pollock
ovaries with little to no visible yolk development (hereinafter referred to as “non-yolked”
ovaries) based on the Raman spectra of their oocytes; 2) within non-yolked ovaries, mature but
not developing and mature but recently spent ovaries can be differentiated from immature or
early developing ovaries based on the Raman spectra of their oocytes. Further, we explore the65ability for this tool to differentiate among progressive reproductive stages in yolked ovaries forpotential application in spawning phenology studies.

METHODS

Ovary specimens were collected aboard the NOAA ship Oscar Dyson during the
Midwater Assessment and Conservation Engineering (MACE) Program of the Alaska Fisheries
Science Center’s acoustic-trawl pollock stock assessment surveys. Samples were collected in the
Gulf of Alaska (GOA) during February and March 2017 (Fig. 5.1; McCarthy et al. 2018).
Ovaries were removed from the fish, weighed, and placed in a mesh bag and preserved in 10%
formalin. Additional collections of 1 and 2 year old pollock were included in this study due tothe rare capture of immature pollock during the prespawning cruises. For these collections,young-of-the-year pollock were beach seined and reared through the first two years of life at an
aquaculture facility at Little Port Walter, NOAA. In preparation for histological analysis andRaman spectra collection, a cross-section of tissue from each ovary was processed and stained byhistology laboratories. We collected one subsample of tissue (<1 g) from a location adjacent to
the histology tissue sample for spectral data acquisition.
Immature (IMM) fish are those which have not aged to first maturation. Developing stage(DEV) fish show increased oocyte sizes and the development of cortical alveoli and may include
very early signs of yolk development. Vitellogenic stages (VIT) include increasing yolk
concentrations as well as the migration of the nucleus. The prespawning stage (PSWN) includes
a brief period in the annual cycle when yolk droplets, along with the nucleus break down, or
coalesce. Hydration, or the rapid increase in size due the uptake of water, marks the early66

spawning stage (SWN), which further progresses with ovulation as oocytes are released from thefollicles and an increase of empty or post-ovulatory follicles (POFs) is observed. The Partial
Spent stage (PSNT) accounts for the batch spawning strategy of pollock and where vitellogenic
stages occur with POFs. Spent stage (SNT) ovaries are composed mostly of POFs and possibly a
few atretic yolk stages that failed to ovulate. Mature but not developing (MND) ovaries wereidentified as having spawned previously, but which show no signs of development in the capture
season. Ovary stage assignments were made using the most predominant oocyte structures
present or by the presence of combinations described above. In addition, each specimen was
identified with either the presence or absence of visible yolk development regardless of otherstaging identification.
We acquired Raman spectral data from ovary tissue using a Raman Process BallProbewith a 780 nm laser and a wavenumber range of 50-3300 cm-1 (MarqMetrix). The sphericalsapphire lens on the fiber optic probe was positioned to touch the sample during eachmeasurement. The laser power on the samples was 400 mW with an exposure time of 1000 ms.
Ten repeat measurements were collected and averaged to create one representative spectrum per
sample. All fluorescent lights were turned off during Raman spectra collection.

Spectra Collection and Processing

Prior to data analysis, we subtracted a blank spectrum (measured with >2 ft between theprobe and closest object) from each sample spectrum to eliminate peaks due to the sapphire in
the fiber optic probe (Raml et al. 2011). Spectra were truncated into the fingerprint region (2001800 cm-1) and high frequency region (2600-3250 cm-1). We then preprocessed raw spectral data67

using extended multiplicative signal correction (EMSC) to normalize spectra (Martens and Stark
1991) and performed polynomial subtraction using the modified polyfit method to remove
fluorescent background (Lieber and Mahadevan-Jansen 2003). For the fingerprint region, weparameterized EMSC and modified polyfit with seventh-order polynomials. For the highfrequency region, we parameterized EMSC with a fifth-order polynomial and modified polyfit
with a third-order polynomial. Savitzky-Golay smoothing was applied to both regions to reduce
unwanted noise in spectra (Delwiche and Reeves 2010). We parameterized Savitzky-Golay
smoothing with a second-order polynomial and 25 points.

Statistical Analyses

We used principal component analysis (PCA) as an exploratory analysis for datavisualization and to identify spectral outliers. Detection and elimination of outliers was
performed based on robust calculation of outlier limits (Pomerantsev and Rodionova 2014).
Values that fell outside a significance level of 0.01 were considered outliers and removed fromthe data set (n = 2).
We used a two-step analysis to classify samples as biologically mature or immature fromthe Raman spectra of their ovaries. First, we used a partial least-square (PLS) regression analysis
followed by a linear discriminant analysis (LDA) to classify yolked and non-yolked samples(Boulesteix 2004). A PLS regression is a multivariate method commonly used in chemometricanalysis for dimensionality reduction and to linearize spectral data (Wold et al. 1884, Boulesteix2004). In PLS, the independent (here, spectral data) and dependent (here, yolk presence as
determined by histological analysis) data matrices are decomposed into a set of scores and68

loadings (hereinafter referred to as latent variables). The latent variables are then used as
predictors in LDA. Linear discriminant analysis performs well on a smaller number of
uncorrelated variables (Dudoit et al. 2002). In LDA, the independent variables (here, latentvariables from PLS) are assumed to have normally distributed probability density functions withrespect to the dependent variable. Class membership is determined from the class with maximum
posterior probability. All samples classified as yolked in the PLS-LDA were consideredbiologically mature. A second PLS-LDA was applied to classify the non-yolked samples as
either biologically mature (if histology indicated it was MND or SNT) or immature (if histology
indicated it was IMM or DEV).
To evaluate the ability to further predict the spawning progression of walleye pollock
ovaries with visual yolk development from their Raman spectra, we used a PLS-LDA to classify
yolked specimens to their histologically determined reproductive stage (VIT, PSWN, SWN,
PSNT). Visible yolk at barely discernible levels can sometimes be seen in DEV ovaries and insmall amounts of residual, yolked oocytes in SNT ovaries. For these stages, yolk occurs at
minute levels compared to the yolk contained in VIT, PSWN, SWN, or PSNT ovaries, and werepredicted as non-yolked. Here, yolked samples with histologically determined stages of DEV or
SNT were removed from this analysis due to insufficient sample sizes (n = 1).
Variable importance in projection (VIP) scores were calculated for each wavenumber to
identify the spectral regions most important for classification in each model (Goldstein et al.
2021). The calculation of VIP includes the covariance between independent and dependent
variables and reflects how much a variable contributes to describing both independent anddependent data (Anderson and Bro 2010). A VIP score of less than one indicates a less importantvariable.

69

Cross-validation was used to determine the optimal number of latent variables(Boulesteix 2004) and the predictive ability of the three models on new data. For all categories,
we report specificities, or the accuracy of rejecting a sample from an incorrect category,sensitivities, or the accuracy of assigning a sample to the correct category, and overall model
accuracy and area under the curve (AUC). Data analysis was conducted in R statisticalcomputing software version 4.1.0 (R Core Team 2021).

RESULTS AND DISCUSSION

We summarize PLS-LDA classification results and whether they support the ability toclassify fish as biologically mature or immature from the Raman spectra of their ovaries, andfurther, to classify yolked specimens to their histologically determined reproductive stage (VIT,
PSWN, SWN, PSNT). In total, 226 specimens were included in this analysis (Table 5.1).

Maturity Status

The Raman spectra of biologically mature ovaries with yolk development weredifferentiable from the spectra of ovaries with little to no yolk development (Fig. 5.3, Table 5.2).
Spectral data showed clear separation between yolked and non-yolked samples along latentvariable (LV) 1 and LV2 from PLS used for dimensionality reduction (Fig. 5.4). The optimal
number of latent variables from PLS used for LDA was 8, which explained 96.34% of variancein the spectral data matrix and 88.72% of variance in the response data (here, yolked vs. nonyolked). All but two samples were correctly classified in the PLS-LDA (99.0% accuracy; AUC

70

of 0.99) (Table 5.3). Wavenumber regions 450-558, 776-818, 838-851, 1192-1214, 1296-1426,
1474-1510, 1582-1628, 1751-1778, and 2794-2831 cm-1 contributed substantially to variation inspectra related to the response variable (VIP > 1) (Fig. 5.3, Table 5.2). All samples classified as
yolked were considered mature. Ovaries with no yolk development can signify a biologicallyimmature fish, but may also be a mature fish under certain circumstances. The differentiation
between maturity statuses of non-yolked ovaries is historically difficult to determine using
existing rapid methods (visual maturity).
The Raman spectra of mature but non-yolked ovaries (MND or SNT) were differentiablefrom the spectra of non-yolked immature ovaries (IMM or DEV (Fig. 5.3). There was separation
between non-yolked immature and mature ovaries along LV1 and LV2 (Fig. 5.4). The optimal
number of LVs from PLS used for LDA was 5, which explained 95.03% of variance in the
spectral data matrix and 71.09% of variance in the response data (here, mature vs. immature). Of
samples classified as non-yolked, 98.0% of mature samples were correctly classified by PLSLDA and 88% of immature samples (AUC = 0.98) (Table 5.3). Wavenumber regions 437-553,
666-755, 770-784, 873-924, 1062-1130, 1242-1299, and 1455-1465 cm-1 contributed mostsubstantially to variation in spectra related to the response variable (VIP > 1) (Fig. 5.3, Table
5.2). Overall from the two-step analysis, 43 out of 50 (86.0 %) total biologically immaturesamples were corrected classified and 175 out of 176 (99.4 %) total biologically mature samples.

Yolked Stage Progression

There was less differentiation in Raman spectra among yolked samples by histologicallydetermined reproductive stage. Progression in stages with yolk present was visible along LV171and LV5, especially for SWN and PSNT samples, but with overlap among stages (Fig. 5.4). The
optimal number of LVs from PLS was 9, which explained 94.90% of variance in the spectral
data matrix but only 42.63% of variance in the response data. The highest classification accuracy
was for PSNT samples (86.0%; AUC = 0.92) followed by SWN samples (85.0% accuracy; AUC= 0.93) and VIT samples (85.1% accuracy; AUC = 0.95) (Table 5.3). The PSWN samples had
the lowest classification accuracy (78.9% accuracy; AUC = 0.90). Wavenumber regions 324339, 422-528, 1573-1587, 2873-2877 cm-1 contributed most substantially to variation in spectra
related to the response variable (VIP > 1) (Fig. 5.3, Table 5.2).
The use of Raman spectroscopy to identify molecular changes in biological tissue iscomplex, as each molecular component contributes to information captured in the spectra, andthe specific functional groups present in the biological tissue are not always known (Talari et al.
2015). The chemical composition of pollock ovaries has not yet been fully investigated. In
addition, our application of this technology to identify shifts in molecular composition duringovary maturation is also complex, as the individual components of the ovary change over thecourse of maturation. However, patterns in the spectral data and associated molecularassignments in published literature provide evidence supporting our model results. Gorbatenko
and Lazhentsev (2016) analyzed the biological composition of pollock ovaries and showed that
lipids, proteins, and carbohydrates varied within a broad range of maturation. These authors
observed the lowest values of these molecular components in post-spawning individuals, while
the highest lipid and protein content of the gonads was recorded from females at the advanced
yolk stages.
Our results suggest that spectroscopy can provide an effective tool in differentiating 1)
yolked from non-yolked stages and 2) immature fish from mature fish, an important metric for72

fisheries management. Differentiating yolked stages would also benefit researchers in tracking
shifts in the capture location, timing, and periodicity of spawning marked by the presence of laterstage yolked oocytes and partially spent ovaries.

73

Table 5.1. -- Length distribution (in cm) by ovary stage (IMM = immature, DEV = developing,
VIT = vitellogenesis, PSWN = prespawning, SWN = spawning, PSNT = partiallyspent, SNT = spent, and MND = mature, no development) for walleye pollock(Gadus chalcogrammus) collected from the Gulf of Alaska.
IMM
Length
15
20
25
30
35
40
45
50
55
60
Total

2
9
6
21
4

DEV

1
7

VIT

5
26
9
1

42

8

41

PSWN

SWN

PSNT

SNT

MND

5
19
4
1

1
18
8
2
3

3
18
4
2

7
10
3
2
2

8
11
4

29

32

27

24

23

74

Total
2
9
6
21
4
30
109
32
7
6
226

Table 5.2. -- Variable importance in projection (VIP) wavenumber (cm-1) ranges for each model
identifying the spectral regions most important for classification (VIP > 1).
Yolked vs. non-yolked

Non-yolked:
Immature vs. Mature

Yolked:Stage Progression

227-229280-300
330-409324-339374-407409-428435-558437-553495-528613-623657-760
751-770776-854770-784; 787-824

795-863873-924996-1016
1087-1113

1062-1130; 1144-1175

1026-1173

1192-1214

1183-1216

1189-1212

1296-1426

1242-1299

1397-1428

1474-1510

1426-1512

1476-1483

1523-1539
1555-1560; 1592-1628
1680-1728; 1749-1778

1532-1642
1606-1692

1677-1778

2634-2659

2637-2655

2674-2679

2665-2679

2616-2624
2651-2654

2690-2695
2794-2831

2700-2703; 2715-2724; 2777-2781; 2800-2802

2869-2909

2902-2931

2861-2865; 2872-2913

2926-3004

2947-3000

2957-2964

3013-3022
3067-3132

3072-3081

3147-3190

3064-3092
3104-3109; 3128-3155
3190-3200

3214-3217

3211-3221
3231-3232
3236-3238

75

Table 5.3. -- Results of classification of walleye pollock (Gadus chalcogrammus) reproductive
state based on Raman spectra of ovaries. The ratio of correctly predicted samples
relative to total sample size, the sensitivity (true positive rate), the specificity (truenegative rate), the balanced accuracy, and area under the curve (AUC) relative tothe no information rate of the model predictions from cross validation. Results
shown as proportions of total samples.

Analyses

Class

Ratio

Sensitivity

Specificity

Balanced
Accuracy
(%)

AUC

No
informationrate

1. Yolk
presence

non-yolked
yolk present

95:96
129:130

0.99

0.99

99.1

0.99

0.58

2. Nonyolked:
Maturity
statusimmature
mature

43:49
46:47

0.88

0.98

92.8

0.98

0.51

3. Yolked:
Stage
Progression

vitellogenic
prespawning
spawning
partial spent

33:41
20:29
23:32
21:26

0.80
0.69
0.72
0.81

0.90
0.89
0.98
0.91

85.1
78.9
85.0
86.0

0.95
0.90
0.93
0.92

0.32
0.32
0.32
0.32

76

Figure 5.1. -- Transect line locations for the Alaska Fisheries Science Center’s (AFSC) acoustictrawl walleye pollock (Gadus chalcogrammus) stock assessment surveys.

77

78

Figure 5.2. -- Images reflecting oocyte structures and components used to assign ovary
maturation stages in walleye pollock (Gadus chalcogrammus). Immature oocytes
are characterized by small, tightly grouped previtellogenic oocytes; they enter thematuration cycle and begin to develop with cortical alveoli and early vitellogenesis(or yolk accumulation; as yolk continues to accumulate, the oocyte increases
exponentially in size and is considered spawning-capable. Oocytes continue toincrease in size during vitellogenesis, followed by coalescence and hydration
during the pre-spawning stage. Evidence of spawning includes the presence of
hydrated oocytes and post-ovulatory follicles (POFs; the structural support ofoocytes during development). After spawning, the remaining follicle structures andresidual oocytes that remain are resorbed during the spent stage. A resting or
reproductively inactive stage (non-spawning; is often identified as the length oftime between spent and developing where the ovary appears inactive or with signsof late resorption, and early perinuclear development.

79

Figure 5.3. -- Raman spectra of walleye pollock (Gadus chalcogrammus) ovaries preprocessed with extended multiplicative scattercorrection, polynomial subtraction, and Savitzky-Golay smoothing. The left column shows all spectra, and the rightcolumn shows average spectra and standard error for a) yolked vs. non-yolked samples, b) the maturity status of samples
predicted as non-yolked, and c) the finer-scale stage progression for samples predicted as yolked (VIT = vitellogenesis,
PSWN = prespawning, SWN = spawning, and PSNT = partial spent). Wavenumbers identified as important for
projection (VIP score > 1) with respect to the response variables are shown highlighted in the white spaces. All otherwavenumbers are grayed out (VIP scores < 1).
80

Figure 5.4. -- Scores on latent variables with 95% confidence ellipses shown from partial leastsquares dimensionality reduction for a) yolked vs. non-yolked samples b) the
maturity status of samples predicted as non-yolked and c) the spawning phenologyof samples predicted as yolked (VIT = vitellogenesis, PSWN = prespawning, SWN= spawning, and PSNT = partial spent) based on Raman spectra of walleye pollock(Gadus chalcogrammus) ovaries. The latent variables with the most differentiation
by response variable shown for each analysis.

81

82

6. Fourier Transform Near Infrared Spectroscopy Ageing of Finfish and Shark Species
in the Northwest Atlantic

Alexander M. Rubin1, 2, Michelle S. Passerotti2, and Eric M. Robillard3

IBSS Corporation
1110 Bonifant Street
Silver Spring, MD USA
1

Northeast Fisheries Science Center
NOAA, National Marine Fisheries Service
28 Tarzwell Drive
Narragansett, RI USA
2

Northeast Fisheries Science Center
NOAA, National Marine Fisheries Service
166 Water Street
Woods Hole, MA USA
3

83

84

INTRODUCTION

Fourier transform near infrared (FT-NIR) spectroscopy is an emerging method toestimate age of marine fishes. In this method, hard structures traditionally used for ageing fish(e.g., otoliths and vertebrae) are irradiated with light from the near infrared (NIR) spectrum.
Organic molecules comprising these structures absorb NIR light at different wavenumbers
depending on their chemical, and to a lesser extent, physical, composition. Throughout thelifespan, the physical and chemical characteristics of these structures change, and these changes
are reflected in NIR absorbance spectra. FT-NIR spectroscopy has been previously shown to bean effective method at predicting ages of individuals through the use of partial least squares(PLS) regression models relating NIR absorbance spectra to fish age, with models trained on
known-age samples of otoliths or vertebrae (Arrington et al. 2022, Healy et al. 2021, Passerotti
et al. 2020b, Rigby et al. 2016). This method has been applied to shorter-lived teleost species(Healy et al. 2021, Passerotti et al. 2020b) and smaller elasmobranch species (Arrington et al.
2022), but few studies have used it to age long-lived teleosts and no studies to date have used it
to age large-bodied sharks.
We assessed the efficacy of FT-NIR spectroscopy to predict the age of two hard-to-age
species in the northwest Atlantic: Acadian redfish (Sebastes fasciatus) and blue shark (Prionace
glauca) using otoliths and vertebrae, respectively. We also investigated the optimal sample
presentation for blue shark vertebrae given their large size. Finally, we evaluated the efficiency
of FT-NIR spectroscopy scanning with respect to processing times associated with traditional
ageing of both species.

85

METHODS

SamplesAcadian redfish sagittal otoliths were collected by the NOAA Fisheries Northeast
Fisheries Science Center (NEFSC) bottom trawl survey from 2016-2019 and were stored dry
after collection. A total of 1,891 otoliths were selected for FT-NIR spectroscopy analysis. All
otoliths were cleaned by wiping with 70% ethanol using a lab tissue, and blotted dry before
scanning.
Blue shark vertebrae were collected by the NEFSC Apex Predators Program from 19942018. A total of 184 whole vertebrae and 188 sectioned vertebrae were selected for FT-NIR
spectroscopy analysis. All samples were cleaned of bulk tissue with a scalpel and cleaned of any
residual tissues by immersion in a bleach solution and rinsing with water. Afterward, vertebrae
were stored in 70% ethanol, and were blotted dry before scanning. Vertebrae were not allowed to
fully dry, so as not to jeopardize their use in future traditional ageing.
Traditional ages were generated by expert age readers for otolith and vertebra samples
using secondary structures from the same fish (for Acadian redfish, the sectioned paired otolith,
and for blue shark, a second vertebra from the same shark).

Spectroscopy

All samples were scanned on a Bruker TANGO-R near infrared spectrometer (Bruker,
Billerica, MA). Otoliths were covered with a rubber-rimmed gold reflective stamp while
scanning to reduce stray light. Vertebrae were scanned without the stamp due to size limitations.
86All absorbance spectra were collected at a resolution of 64 scans every 16 wavenumbers.
Acadian redfish were scanned sulcus side down with rostrum oriented horizontally on the sample
window. Blue shark vertebrae were scanned in two different sample presentations: whole centra
and half centra. Whole vertebrae were scanned with the focus centered over the sample window
and the neural arches facing forward, and half centra were sectioned frontally through the focus
and scanned with the cut side facing down, with half of the resulting “bowtie” centered over the
sample window (Fig. 6.1). The models from the two vertebra presentations were compared todetermine the optimal presentation for age predictions.

Data Analysis

Traditional ages were paired with FT-NIR spectra, and PLS regression models weredeveloped for training and test sets of samples using OPUS software (Bruker Scientific).
Training sets comprised a subset of randomly selected samples from the original sample
collections (n = 500 for Acadian redfish, n = 88 whole centra and 94 half centra for blue shark),with samples selected proportionally to the overall age distribution of each sample set. Sample
sizes for Acadian redfish models were based on preliminary work that found age prediction did
not meaningfully improve with calibration set sample sizes above 500 (Rubin, unpublished data).
Test sets comprised the remaining samples for each species. Acadian redfish spectra werepreprocessed using a Savitsky-Golay first derivative transform with 17-point smoothing, andblue shark spectra were preprocessed using a standard normal variate transformation. The
models were assessed using the coefficient of determination (r2), root mean square error of crossvalidation (RMSECV) and of prediction (RMSEP), residual prediction deviation (RPD), model
bias, and slope.
87

RESULTS

FT-NIR spectroscopy models predicted age to within 1.8 years and 1.7 years for Acadian
redfish training and test sets, respectively (Table 6.1). FT-NIR spectroscopy predicted ages
showed strong correlation with traditional ages (r2 = 0.937), although regression fit was worse atolder ages (Fig. 6.2). Average scanning time for otoliths was 1.1 minutes per sample, with anadditional 0.75 minutes required for cleaning otoliths prior to scanning.
Blue shark FT-NIR spectroscopy models predicted age to within a similar prediction
range (1.5-1.8 years) as otoliths but with poorer correlation of predicted and traditional age (r2 =0.790 for whole centra, 0.812 for half centra, Fig. 6.2). Models trained on absorbance spectra ofhalf centra predicted ages better than whole centra models (RMSECV = 1.82 years for wholecentra and 1.68 years for half centra; RMSEP = 1.56 years for whole centra and 1.5 years for half
centra, Table 6.1). Average scanning time for whole centra was also 1.1 minutes, but half centra
required sectioning, which took an average of 5-10 minutes per vertebra, making the use of half
centra for FT-NIR spectroscopy substantially slower.

DISCUSSION

Preliminary findings indicate that FT-NIR spectroscopy may be useful for predicting age
in Acadian redfish and blue shark using rapid scans of otoliths and vertebrae, respectively.
However, while otoliths required little preparation for scanning, blue shark vertebrae often
exceeded the physical size of the spectrometer scanning window and required sectioning tofacilitate better prediction results. Additional work to find optimal sample presentation for large
shark vertebrae is needed. Prediction results for sharks were not as promising as those for88

Acadian redfish otoliths, which may also stem from high subjectivity in vertebral ages and thelack of age validation for blue shark.
The primary advantage of using FT-NIR spectroscopy is its processing speed. Whilescanning time was short across both types of ageing structures, otolith cleaning added an averageof 0.75 minutes per sample and vertebra sectioning added 5-10 minutes per sample. These
additional processing steps reduce the efficiency gains of FT-NIR spectroscopy for ageing,
although overall times are still slightly better than traditional ageing processes. Further model
development and improvement in sample presentation for shark vertebrae are needed to fully vet
FT-NIR spectroscopy as a potential replacement for traditional ageing.

89

Table 6.1. -- Fourier transform near infrared spectroscopy calibration and validation model results for Acadian redfish (Sebastes
fasciatus) and blue shark (Prionace glauca). RMSECV = root mean square error of the cross validation, RMSEP = root
mean square error of the prediction, and RPD = residual prediction deviation.
Calibration
Data

n

r2

Acadian redfish

500

93.86

1.81

Blue shark whole centra

88

74.6

Blue shark half centra

94

78.21

Validation
n

r2

RMSEP

RPD

4.04 -0.00775 0.942

1391

93.72

1.73

3.99

0.00384 0.966

1.82

1.98 -0.0318 0.768

96

79.03

1.56

2.18

-0.008

0.805

1.68

2.14

94

81.15

1.5

2.32

-0.202

0.902

RMSECV RPD

Bias

0.01

90

Slope

0.797

Bias

Slope

Figure 6.1. -- (A) Whole and (B) half blue shark (Prionace glauca) vertebral centra centeredover the spectrometer scanning window.

91

Figure 6.2. -- Cross validation models for (A) Acadian redfish (Sebastes fasciatus), (B) blue shark (Prionace glauca) whole centrum
scans, and (C) blue shark half centrum scans, and test set validation models for (D) Acadian redfish, (E) blue shark wholecentrum scans, and (F) blue shark half centrum scans. The green line represents a 1:1 relationship and the blue line is theregression line between traditional and Fourier transform near infrared spectroscopy (FT-NIRS) predicted age for eachmodel. Gray shading represents the 95% confidence interval for the regression model.

927. Exploration of Fourier Transform Near Infrared Spectroscopy for the Shortbelly
Rockfish (Sebastes jordani), an Ecologically Important Forage Fish, off the West Coast
RegionJessica J. M. Choi¹,² and Melissa H. Monk¹

Southwest Fisheries Science Center
NOAA, National Marine Fisheries Service
110 McAllister Way
Santa Cruz, CA USA
1Fisheries Collaborative Program
Institute for Marine Ecosystems and Climate
University of California Santa Cruz
110 McAllister Way
Santa Cruz, CA USA
2

93

94

INTRODUCTION

Shortbelly rockfish are an ecologically important forage fish off the West Coast region of
North America. The species' lifespan can be up to 22 years, and the maximum length is 35 cm(13.7 in) (Pearson et al. 1991). This species of rockfish can be found from La Perouse Bank,
Vancouver Island, BC, to San Benito Island, Baja California. In the water column, adults can befound at depths from 91 to 491 m (300 to 1620 ft) but occur most commonly at 150 to 200 m(495 to 665 ft) (Love et al. 2002). They are generally a pelagic midwater schooling species.
Shortbelly rockfish mature early; 50% of females are mature by age 3 and 100% by age 4(Wyllie-Echeverria 1987). This species of rockfish is important to its ecosystems as it is a forage
fish, often prey for salmon and other rockfishes.
Shortbelly rockfish are caught in significant numbers in regional surveys, including theWest Coast Groundfish Bottom Trawl Survey (WCGBTS), the primary goal of which is toestimate abundance to support stock assessments of chilipepper and several other species ofslope and shelf rockfish species along the coast (widow, chilipepper, bocaccio, and several
others). The trawl survey is also used to estimate young-of-year ages (in days) and to evaluate
regional and interannual variability in growth and recruitment.
The shortbelly rockfish stock was last assessed in 2007 and has further data to update
models. The previous 2007 assessment was modeled using Stock Synthesis 2, which takes age
and size into account. The model treats cohorts as a collection of fish that has a mean andvariance of size-at-age (Field et al. 2007).
This study was initiated to explore the feasibility of using Fourier transform near infrared(FT-NIR) spectroscopy for age determination of shortbelly rockfish. Methods of using shortbelly
otoliths for the FT-NIR spectroscopy Tango machine were decided upon due to having an
95

abundance of already-aged shortbelly otoliths. Shortbelly rockfish were chosen as the focal
species of this study because they are fairly short-lived for a Sebastes species. Shorter-lived
species have been shown to have better age prediction results from otolith spectra. Samples camefrom a region-wide distribution along the West Coast from a single survey source. This study
will further analyze changes in growth by latitude, as well as changes in growth by sex. Samples
came from the same source with the same age readers, resulting in less aging bias.
In addition, we explored the potential for relationships between growth and latitudinal
indices. We also explored the suitability of how little an otolith could weigh to produce spectralscans of optimal and reproducible results.

METHODS AND RESULTS

The shortbelly rockfish otolith samples sourced from WCGBTS covered the entire West
Coast region and were collected from 2011-2013 (n = 466). The available metadata included
individual fish weight, length, sex, capture date, location, and depth. Shortbelly rockfish otoliths
were aged at the Southwest Fisheries Science Center in Santa Cruz, CA, by Don Pearson.
This study’s shortbelly rockfish samples were divided into North, Central, and Southregions from Alaska to California in an effort to represent growth along the West Coast region.
The study followed a standardized protocol that was specific to this species and included otolithpreservation methods as well as species-specific accessory use when using the TANGO FT-NIR
machine. We found that there is in fact a weight limitation of how ‘small’ an otolith could weighfor this study. A Teflon disc with a 4-mm aperture was used to scan shortbelly rockfish otoliths.
We also evaluated time efficiency of FT-NIR scanning as an aging method relative to
traditional methods for shortbelly rockfish. For the traditional ‘break and burn’ method, one
96

otolith takes 5 to 10 minutes to prepare and age, resulting in 20 otoliths aged in ~1 hour. Using
the Tango, one otolith scan takes ~45 seconds, resulting in 20 otoliths aged in ~15 minutes. If
calibrated for the species, this method can effectively increase the number of otoliths aged.
Concluding the initial timeframe of the study, the stages of modeling are still exploratory.
Goals are to see which models produce the best analysis results given the data we have.

FUTURE DIRECTIONS

Future research objectives include the following:•

Include sex as a predictor of age to determine if it is a significant factor.

•

Many Sebastes species exhibit dimorphic growth. Evaluate whether separate PLS
models are needed for each sex.

•

Quantify ageing error between traditional ages and FT-NIR spectroscopy.

•

Continue exploring model selection and covariates.

•

Explore cross-validation methods.

•

Explore patterns in growth by latitude, which may have implications for species
management.

•

Determine whether there is increased error in age estimates by latitude. Rockfish arebecoming more difficult to age in the southern region of the California Current, where
seasonality is weaker, and intraseasonal and interannual variability is stronger incomparison to latitudinal regions moving farther north.

97

98

8. Investigating the Use of FT-NIR Spectroscopy to Age Gag Grouper (Mycteroperca
microlepis), a Protogynous Hermaphroditic Species

Beverly K. Barnett1, Irina M. Benson2, Thomas E. Helser2, Susan K. Lowerre-Barbieri3,and Hayden S. Menendez4

Southeast Fisheries Science Center
NOAA, National Marine Fisheries Service
3500 Delwood Beach Road
Panama City, FL USA
1

Alaska Fisheries Science Center
NOAA, National Marine Fisheries Service
7600 Sand Point Way NE
Seattle, WA USA
2

3

School of Forest, Fisheries, and Geomatics Sciences
University of Florida
100 8th Avenue SE
St. Petersburg, FL USA

Fish and Wildlife Research Institute
Florida Fish and Wildlife Conservation Commission
100 8th Avenue SE
St. Petersburg, FL USA
4

99

100

ABSTRACTFish age is a critical data input for estimating longevity, growth, maturity functions, andmortality in age-structured stock assessment models. However, providing these age estimates is alabor-intensive process and can often lead to delays in providing age data for an assessment.
Fourier transform near infrared (FT-NIR) spectroscopy may provide a more rapid and objective
method for providing age estimates from fish otoliths and perhaps move fish ageing closer toproviding real-time data for stock assessments. In this study, FT-NIR spectroscopy was applied
to otoliths from gag, Mycteroperca microlepis, collected from the U.S. Gulf of Mexico (GOM).
Gag is a protogynous hermaphroditic species that supports important commercial andrecreational fisheries in the GOM. Objectives of this study were to: 1) investigate FT-NIR
spectroscopy to estimate fish age from otoliths of gag, and 2) explore potential patterns in FTNIR spectra from a small set of otoliths for which histological sex had been assigned toinvestigate if FT-NIR spectra would differ among males and females. Whole otoliths were
scanned with an FT-NIR spectrometer and traditional age estimates assigned from whole orsectioned otoliths. The spectra and corresponding age data were subset into calibration and test
sets, and partial least squares regressions (PLS) fit to each of the two sets. Three types of ages
(annulus count, calendar age, and fractional age) were input into separate PLS predictive models
to investigate which age type would provide the best performing model. PLS models that usedfractional ages slightly outperformed the other two age types for both the calibration and test sets
(calibration n = 1,230: r2 = 0.89, RMSECV = 0.888, test n = 809: r2 = 0.90, RMSEP = 0.947).
Results from principal component analysis of FT-NIR spectra by sex (n = 30) showed someseparation between females and males; however, this was only a small set of data, so increasing
sample size will be the next step to see if this pattern continues to hold. Additional research and101

development are required before this technology can be fully implemented in production ageing
for fisheries management, but results from ongoing case studies show promise.

102

INTRODUCTION

Fish age is a critical data input for estimating longevity, growth, maturity functions, andmortality in age-structured stock assessment models. However, these ages are often subject topoor reader precision, which can lead to ageing error being propagated through stock assessment
models (Beamish and McFarlane 1983; Campana 2001). Furthermore, conventional methods(e.g., microscopic interpretation) for providing these age estimates is a labor-intensive and timeconsuming process that can often lead to delays in providing age data for an assessment.
Oftentimes, due to the time-consuming process of preparing age structures and estimating age,subsampling strategies are needed to provide a subset of age estimates for stock assessment. To
meet the need for more stock assessments or to provide more real-time age data to support the
Fishery Management Councils, more efficient alternatives to the conventional methods forproviding age estimates are needed.
In recent years, Fourier transform near infrared (FT-NIR) spectroscopy has been applied
to predict fish age from otoliths (Wedding et al. 2014, Helser et al. 2019b, Passerotti et al.
2020b) and skate vertebrae (Arrington et al. 2022), to classify fish species from different
ecosystems (Benson et al. 2020), to assess fish condition (Goldstein et al. 2021), and to predictspawning status (TenBrink et al. 2022). While relatively new to applications in fish ecology, FTNIR spectroscopy is a well-established method that has been used across a wide range ofindustries (e.g., agricultural, pharmaceutical, dairy products, etc.) to ensure quality
control/quality assurance. It is a rapid, non-destructive process that requires little to no sample
preparation. Applications of FT-NIR spectroscopy, whether in industry or fisheries science, rely
on developing a calibration model that relates a feature of interest (e.g., analyte concentration,
fish age, etc.) to an NIR spectrum. The calibration model is then applied to another set of data103

(i.e., test) to determine how well the calibration model will predict unknown data. For predicting
fish age, predictive calibration models rely on age estimates assigned by either an individual
reader or from consensus ages to provide the reference age. Thus far, FT-NIR spectroscopy has
been applied to predict ages for red snapper, Lutjanus campechanus (Passerotti et al. 2020b) andgray snapper, Lutjanus griseus (Barnett, unpublished data) from the Gulf of Mexico (GOM), andNIR-predicted ages from each species has shown promising results. Therefore, to further
investigate FT-NIR’s applicability for predicting fish age, otoliths from other species need to bescanned and predictive models built to determine the feasibility of applying this technology to
predict ages for other species.
Gag, Mycteroperca microlepis, are a moderately long-lived species (maximum observed
age 33 years; SEDAR 2021) that support economically important commercial and recreational
fisheries in the GOM. As juveniles, gag generally settle into seagrass beds (Switzer et al. 2012)
and then make an ontogenetic shift offshore to deeper reefs (Bullock and Smith 1991, Carruthers
et al. 2015). However, gag are predominantly spatially segregated during most of the year until
adult females and males come together to spawn (Lowerre-Barbieri et al. 2020). Gag are a
protogynous hermaphrodite (i.e., born as female but change to male), and most commerciallylanded fish are not sexed during dockside sampling since the fish are usually gutted at sea. Gag
are federally managed and highly regulated in the GOM (GMFMC 2013), and their stock status
is currently listed as overfished and undergoing overfishing (SEDAR 2021). Most otoliths fromgag are aged whole, given that their annuli are more easily identified from whole otoliths rather
than sectioned otoliths. Only otoliths from older fish and those with annuli that are difficult tointerpret are typically sectioned. Therefore, it was of interest to predict fish age using FT-NIR
spectra measured from whole otoliths of gag.

104

The objectives of this study were to 1) investigate FT-NIR spectroscopy to estimate fish
age from otoliths of gag, and 2) explore potential patterns in FT-NIR spectra from a small set of
otoliths for which histological sex had been assigned to investigate if FT-NIR spectra would
differ among males and females.

METHODS

Sample Collection
Sagittal otoliths from gag were collected from the GOM for years 2017-2019 by fisherydependent and fishery-independent sources. Otoliths were stored dry in coin envelopes andarchived at the National Marine Fisheries Service, Panama City Laboratory (PCL). An additional
set of gag otoliths, for which sex was histologically assigned, collected from 2018-2019 by
Florida Fish and Wildlife Research Institute (FWRI) were sent to PCL to investigate if sex could
be identified based on FT-NIR spectra. Whole otoliths were weighed to the nearest 0.1 mg. Most
otoliths were aged whole, and remaining otoliths from older fish and those with annuli that weredifficult to interpret were sectioned using a Hillquist Thin Section Machine (Hillquist Inc.).
Calendar ages were based on annulus counts and marginal edge code assignment. For all fishwith a wide translucent zone (i.e., translucent zone > 2/3 of new growth after the last observed
opaque zone) and landed between 1 January and 30 June, annulus count was advanced by one
year. Calendar age for all other fish was set equal to the annulus count. Fractional (i.e.,
biological) age was calculated using calendar age, date of peak spawning (1 March), and date ofcapture using the following equation:105

AF = AC + ((DC – DS)/365), where
AF = fractional age (years)
AC = calendar age (years)
DC = date of capture
DS = date of peak spawning

FT-NIR Spectroscopic Measurements

Otoliths were scanned using a Bruker Multi Purpose Analyzer II (MPA II) FT-NIR
spectrometer. OPUS software (version 8.5, Bruker Optics, Ettlingen, Germany) was used toacquire diffuse reflectance measurements of all otoliths. Otoliths were removed from coinenvelopes, placed on the integrating sphere in a concave up position, and covered with a goldcoated reflector stamp (see Benson et al. (2020) for more details). Spectral absorbances for all
otoliths were acquired between 11,500 and 4,000 cm-1, averaged across 64 scans, and with a scan
resolution of 16 cm-1. Spectral data for the ageing component of this study were preprocessedwith first derivative, second order polynomial, and a 17-point Savitzky-Golay smoothing
function. Otoliths received from FWRI were preprocessed using first derivative plus meancentering.

Data Analysis

For the ageing part of this study, a total of 2,060 samples were selected; however, 21
samples had to be excluded from further analysis due to adhering tissue remaining on the otolithsurface. A linear regression was fit to fractional age and left otolith weight. The spectra and106

corresponding age data were divided into calibration and test sets using OPUS software. The
calibration set consisted of 60% of the samples (n = 1,230), and the test set consisted of 40% (n =809). Partial least squares regressions (PLS) were fit to each of the two sets. Calibration models,
which used a cross validation method (i.e., “leave-one-out”), were evaluated using the root meansquare error of cross validation (RMSECV) to determine the predictive power on new data, andthe test models were evaluated using the root mean square error of prediction (RMSEP) as ameasure of prediction error. Three types of ages (annulus count, calendar age, or fractional age)
were input into separate PLS predictive models to investigate which age type would provide thebest performing model. Percent agreement for the two methods, traditional observed ages and
FT-NIR predicted ages, was investigated using calendar ages. However, to compare between
these two methods, FT-NIR predicted ages, which are output as continuous numbers, wererounded to an integer age. FT-NIR predicted ages with a decimal of 0.5 or higher were rounded
up to the next integer (e.g., 3.5 rounded to 4), and those with a decimal age <0.5 were rounded
down (e.g., 3.4 rounded to 3). Principal component analysis (PCA) using Solo+MIA 9.1(Eigenvector Research, Inc., Manson, WA USA) chemometrics software was used to investigate
patterns in the spectra from otoliths (n = 30) sent by FWRI.

RESULTS AND DISCUSSION

Calendar ages for the PCL samples ranged from 2 to 26 years, and fork lengths ranged
from 320 to 1,226 mm (Fig. 8.1A). The linear regression showed a high correlation (r2 = 0.86, n= 1,333) between fractional age and left otolith weight (Fig. 8.1B). PLS models that usedfractional ages slightly outperformed the other two age types (annulus count, Figs. 8.2A and
8.2B, and calendar ages, Figs. 8.2C and 8.2D) for both the calibration and test sets (calibration, n107

= 1,230: r2 = 0.89, RMSECV = 0.888, test n = 809: r2 = 0.90, RMSEP = 0.947; Figs. 8.2E and8.2F). Most otoliths from gag are aged whole given that their annuli are more easily identified
compared to sectioned otoliths from gag. Percent agreement analysis showed that the traditional
reader age had a higher (>70%) agreement for 0 difference in calendar age than the FT-NIRpredicted age (~50%; Fig. 8.3). However, it is possible that percent agreement of the FT-NIRpredicted age could be affected by the age rounding process, given that some ages are rounded
either up or down. To explore this further, a set of traditional reader ages using 3-year-old fish as
an example (n = 57) were plotted against capture date represented as day of year (Fig. 8.4).
Results from this exploratory analysis showed that rounding to the nearest integer caused these3-year-old fish to be placed in four age classes: age-2 (n = 1); age-3 (n = 35); age-4 (n = 17), andage-5 (n = 4) (Fig. 8.4A). Exploring this rounding scenario further, this same set of 3-year-old
fish (n = 57) with decimals <0.99 were rounded down to the next lower integer (e.g., 3.99
rounded to 3), which placed slightly more (n = 38) of the 3-year-olds in their respective 3-year
age class (Fig. 8.4B). Since most stock assessments rely on calendar ages to assess recruitment
and year class strength, a better method and understanding is needed for converting the FT-NIRpredicted ages (i.e., continuous numbers) to an integer age.
There were 14 females, 14 males, and two transitional gag included in the FWRI data set.
A plot of absorbance by wavenumbers showed otoliths from males having higher absorbancethan females (Fig. 8.5A). Spectra from otoliths of the two transitional fish were intermixed with
spectra from the otoliths of females (Fig. 8.5A). Results from the PCA of FT-NIR spectra by sex
showed some separation between histologically-assigned males and females (Fig. 8.5B).
However, this was only a small sample set of 30 samples, so increased sample size will be thenext step to see if this pattern continues to hold. One potential factor that may be driving thisseparation is otolith weight, since otoliths from males tended to be heavier compared to females
108and transitional gag. Passerotti et al. (2020b) found that for most otoliths in their study, FT-NIR
spectra predicted otolith weight within a milligram or less, but they went on to conclude that
other factors may also be influential in driving this relationship. It is possible that the proteins
incorporated into an otolith could provide markers for development and physiological change
during a fish’s life (Thomas et al. 2019). Indeed, otolith proteomics data suggests otoliths mayarchive sex-specific proteins and hormones (Oliver Thomas, personal communication). Since
FT-NIR spectroscopy is often used to identify protein content from a sample (Ingle et al. 2016),
it may be useful in differentiating sexes from otoliths based on sex-specific proteins incorporated
into the organic matrix of an otolith (Rideout et al. 2023). A recent proteomics study on otoliths
from Atlantic cod, Gadus morhua, revealed at least two proteins (SPATA6 protein in males andVitellogenin-2-like in females) that are sex-related biomarkers (Rideout et al. 2023). If thespectral patterns observed for females and males in the current study should continue to hold as
more otoliths are scanned from gag that have a histological sex assigned, it may be possible thatsex could be assigned from otoliths, particularly for commercially-landed fish that are gutted at
sea and no field sex can be assigned by the dockside samplers. Sex ratios for gag are difficult toassign given that females and males exhibit spatial segregation until coming together to spawn(Lowerre-Barbieri et al. 2020). For a hermaphroditic species such as gag, it could provide amethod to better understand the percentages of males and females that are being caught in thefishery.
Overall, FT-NIR predicted ages for gag collected from the GOM tended to be in good
agreement with the traditional reader ages, which provide promising results, but more otoliths
need to be scanned, particularly for younger and older ages. Since stock assessments generally
rely on calendar ages to assess recruitment and year class strength, additional research andperhaps stock assessment simulations are needed to find a best practice for converting the FT109

NIR-predicted ages, which are continuous numbers, to an integer age. Most otolith spectra
showed an increase in absorbance in specific wavenumber regions as age increased. It is possiblethat this increased absorbance may be due to proteins, which are incorporated into the otolith
matrix (Thomas et al. 2019). To investigate this further, research on otolith proteomics for red
snapper, Lutjanus campechanus, and walleye pollock, Gadus chalcogrammus, is currently
underway. These two species were chosen given that their genomes have been sequenced. The
goal for the proteomics research is to try and learn more about what FT-NIR spectroscopy
measures from an otolith that is so closely related to age. Having a better understanding of what
FT-NIR spectroscopy is measuring from an otolith will perhaps provide guidance for building
better predictive models and provide knowledge about the uncertainty or error around an FT-NIR
predicted age. Additional research and development are required before this technology can be
fully implemented in production ageing for fisheries management, but results from ongoing casestudies, such as those presented here for gag, show promise.

110

Figure 8.1. -- Scatterplots of A) fork length versus fractional age, and B) fractional age versus
left otolith weight for Gulf of Mexico gag, Mycteroperca microlepis.

111

Figure 8.2. -- Results from partial least squares (PLS) regression for A) calibration and B) test
sets using annuli count as reference age, C) calibration and D) test sets using
calendar age as reference age, and E) calibration and F) test sets using fractional
age as reference age for Gulf of Mexico gag, Mycteroperca microlepis. R2 =coefficient of determination; RMSECV = root mean square error of crossvalidation; RMSEP = root mean square error of prediction.
112

Figure 8.3. -- Percent agreement between two age readers and Fourier transform near infrared(FT-NIR) predicted age (gray bars) and reference age (black bars) for Gulf of
Mexico gag, Mycteroperca microlepis.

113

Figure 8.4. -- Exploratory analysis for a set of 3-year old gag (Mycteroperca microlepis) showing
the potential impact of rounding Fourier transform near infrared (FT-NIR)
predicted ages A) up (decimal age ≥ 0.5) or down (decimal age < 0.5), and B)
rounding down (decimal age ≤ 0.99) to the next lower integer.

114

Figure 8.5. -- A) Fourier transform near infrared (FT-NIR) spectra, and B) principal component
analysis of FT-NIR spectra from otoliths of gag (Mycteroperca microlepis; n = 30)
colored by histologically-assigned sex.

115

116

9. Exploring the Use of FT-NIR Spectroscopy for Ageing Sablefish (Anoplopoma fimbria)
and Pacific Hake (Merluccius productus) off the U.S. West CoastJohn R. Wallace

Northwest Fisheries Science Center
NOAA, National Marine Fisheries Service2725 Montlake Boulevard E
Seattle, WA USA

117

118

INTRODUCTION

Otolith scanning using Fourier transform near infrared (FT-NIR) spectroscopy was used
to explore the chronological age of sablefish (Anoplopoma fimbria) (n = 1,357) and Pacific hake
(Merluccius productus) (n = 2,337) caught off the West Coast of the contiguous U.S. in 2019.
This non-destructive spectroscopic scanning procedure was directly compared to the slower
traditional method of ageing (TMA). The TMA for both sablefish and Pacific hake is thestandard ‘break and burn’ method for otoliths. Using FT-NIR spectroscopy for ageing otoliths
was first espoused by Wedding et al. (2014) and further advocated by Helser et al. (2019b).

METHODS

Near infrared spectrometry produces spectra that show the intensity of the response of themolecular structure to different wavelengths on the infrared scale (Fig. 9.1). To start, an iterative
partial least squares (iPLS) model was used to define ranges of spectra with the mostinformation. Partial least squares (PLS) models applied to those informative iPLS ranges gavelack-luster results. Hence neural net models using the same data were researched, deployed in R,
and directly compared to the PLS models.
Keras software (https://keras.io) that sits on top of Google’s TensorFlow software(https://www.tensorflow.org) is an industry standard for neural net models and was selected forthis task. A complete port of Keras is available as an R package (https://tensorflow.rstudio.com;
RStudio is not needed). On the penultimate validation step, the main data was split into thetraining set and the test set, using a 2/3, 1/3 split. On the last validation step, a k-fold model with
10 levels was employed. For the k-folding, one tenth of the data was left untouched for testing119the 2/3, 1/3 model which was run on the remaining 90% of the data. This was repeated for each
one tenth of data until each otolith had a predicted age using a model that did not contain theotoliths for which the age was being predicted. Starting with different pseudo random numbers,
the entire k-folding process was repeated 20 times. Medians across predicted ages for eachotolith were then taken as each complete k-fold model was added sequentially, for a total of 20
medians. Across all otoliths, various summary statistics that measure how close the median of
the predicted ages were to the respective TMA were calculated. Along with the original order,
five different randomized orders of the additions over the 20 complete k-folds were looked at togauge the rate of improvement as the number of randomized k-folds added to the medianincreased.

RESULTS AND DISCUSSION

For sablefish, the original run order and five other randomized order additions of 20
complete k-fold models are shown in Figure 9.2. In the original run order, the 12th model
addition had the best stats. The median over those 12 complete k-fold neural net models versus
the TMA is shown in Figure 9.3. The root mean square error (RMSE) was 3.527 years and thesum of absolute differences (SAD) was 2,789.
For the relatively short-lived Pacific hake, the original run order and five otherrandomized order additions of 20 complete k-fold models are shown in Figure 9.4. In the original
run order, the 20th model addition had the best stats. The median over those 20 complete k-foldneural net models versus the TMA is shown in Figure 9.5. The RMSE was 0.773 years and the
SAD was 882.

120

In the Figures 9.3 and 9.5, note that the SAD equals the sum of the correctly matchedotoliths (zeros, in red), plus 1 for each estimated age off by one year from the TMA (yellow),
plus 2 for each estimated age off by two years (green) from the TMA, and so on. The SAD iswell suited to rounded age data and works well to compare runs within a dataset. Note thatstandardized SAD and RMSE are closely correlated, as seen in Figures 9.2 and 9.4.
In the future not only will medians be calculated, but also corresponding lower (0.025)
and upper (0.975) quantiles based on the 20 random replicates will be calculated, reported, andplotted as error bars in a credible interval type figure. The quantiles will reflect the NN models
precision based on the 20 random replicates, not the accuracy to a TMA age.

121

Figure 9.1. -- Raw spectra of 2019 Pacific hake (Merluccius productus) otoliths scanned on a Bruker MPA spectrometer. The x-axis
represents wavenumber and the y-axis represents absorbance.

122Figure 9.2. -- Randomized additions of 20 complete k-fold models for sablefish (Anoplopoma
fimbria). Correlation values are shown in black, r2 values are shown in red, thestandardized root mean square error values are shown in green, and thestandardized sum of absolute differences is shown in purple. In the original runorder, the 12th model addition had the best statistics.

123

Figure 9.3. -- Median over the first 12 complete k-fold models for sablefish (Anoplopoma fimbria) in the original order. Using the first
12 models gave the best statistics (see Figure 9.2).

124

Figure 9.4. -- Randomized additions of 20 complete k-fold models for Pacific hake (Mercluccius
productus). Correlation values are shown in black, r2 values are shown in red, thestandardized root mean square error values are shown in green, and thestandardized sum of absolute differences is shown in purple. In the original runorder, the 20th model addition had the best statistics.

125Figure 9.5. -- Median over all 20 complete k-fold models for Pacific hake (Mercluccius productus) in the original order. Using all 20
models gave the best statistics (see Figure 9.4).
12610. Developing Spectroscopy Approaches to Measure Life History Characteristics
of Fish Throughout Ontogeny

Esther D. Goldstein1, Charles D. Waters2, Heather K. Fulton-Bennett2, Mary E. Matta1,
Thomas E. Helser1, Johanna J. Vollenweider2, Christine M. Hinds3, Kevin W. McNeel3,
Craig R. Kastelle1, Sandi K. Neidetcher1, Dion S. Oxman3, and Franz J. Mueter4

Alaska Fisheries Science Center
NOAA, National Marine Fisheries Service
7600 Sand Point Way NE
Seattle, WA USA
1

Alaska Fisheries Science Center
NOAA, National Marine Fisheries Service
17109 Point Lena Loop Road
Juneau, AK USA
2

Mark, Tag and Age Laboratory
Alaska Department of Fish & Game
10107 Bentwood Place
Juneau, AK USA
3

4

College of Fisheries and Ocean Sciences
University of Alaska Fairbanks
17101 Point Lena Loop Road
Juneau, AK USA

127

128

INTRODUCTION

Changing environmental conditions necessitate adaptive management strategies that
require timely and comprehensive collection of life history information to be effective (Holsman
et al. 2019, Shotwell et al. 2022). Despite links with population productivity and recruitment,
information on daily age, body condition, and reproductive biology is often limited in scope orinconsistently incorporated into management because data collection is laborious. The
discrepancy between data requirements and collection limitations justify the exploration of newtechnologies to increase timely availability of data. Fourier transform near infrared (FT-NIR)
spectroscopy and Raman spectroscopy are commonly used for food quality (Herrero 2008,
Ferreira et al. 2013) and medical applications (Choo‐Smith et al. 2002) to measure properties
linked with molecular composition including lipid content and purity, but are rarely used for
ecological or fisheries applications despite similarities in data needs.
Utilizing spectroscopy approaches to collect data for fisheries management applications
requires the successful development of calibration models for key biological indices. Calibration
models correlate spectra from targeted tissues or hard parts with known or well-quantified
reference data such as age, body condition, or reproductive status. Prior to implementation, thepredictive skill of calibration models must be tested using samples that were not included in thecalibration. Furthermore, comprehensive calibration models for life history metrics requirereference data that encompass ontogenetic shifts and energy allocation throughout ontogeny(Martin et al. 2017) to capture the entire data domain.
This work focused on two commercially and ecologically important fish species, walleye
pollock (Gadus chalcogrammus) and Pacific cod (Gadus macrocephalus), to test applications of129

FT-NIR and Raman spectroscopy approaches for biological data collection. Walleye pollock andPacific cod both responded negatively to a recent marine heatwave in the Gulf of Alaska (GOA)
due to increased energetic demands in warmer waters (Barbeaux et al. 2020, Rogers et al. 2021),
highlighting the need for rapid and comprehensive data collection to monitor their populations ina changing environment. We selected key energetic, life history, and demographic characteristics
that are time-consuming to collect using traditional approaches: daily and annual age, body
condition, and reproductive maturity. We reared fish at the National Oceanic and Atmospheric
Administration (NOAA), Alaska Fisheries Science Center’s (AFSC’s) Little Port Walter
Research Station (LPW) starting from the first year of life over the course of several years tofacilitate flexible and frequent sampling throughout ontogeny. We also obtained wild-caught
specimens from the GOA to test and refine our calibration models. These comprehensive
reference datasets will provide foundational information for the development of operational
calibration models to assess life history parameters associated with two commercially important
Alaska species and a framework for expanding spectroscopy applications to other taxa andecosystems.

METHODS

Age-0 fish were collected in the summers of 2020-2022 in southeast Alaska and reared at
LPW (Fig. 10.1) in indoor large round ponds and outdoor net pens under ambient conditions toemulate the natural environment. Data collection is ongoing, but sampling followed alongitudinal project design. Sampling frequency was weekly for young fish and reduced tobiweekly or monthly as fish increased in age. Fish were sacrificed by submersion in tricaine

130

methanesulfonate (MS222), weighed (g), and measured (fork length, mm). Target biologicaltissues for reference data and spectroscopy scans were collected and preserved for later
processing (Table 10.1). Additional wild-caught samples for model testing were obtained
opportunistically from field surveys in the GOA conducted by NOAA AFSC’s Resource
Assessment and Conservation Engineering Division and Auke Bay Laboratory (Fig. 10.1).
Reference materials were scanned using a Bruker TANGO R or MPA II FT-NIR
spectrometer (Bruker Optics, Ettlingen, Germany) with diffuse reflectance and an integrating
sphere with a reciprocal wavelength range from 11,500 and 4,000 cm-1 to obtain FT-NIR spectral
data. Raman data were collected with a MarqMetrix All-In-One high-performance Raman
spectrometer with 785 nm wavelength excitation and a 12.7 mm diameter sapphire probe.
Sample presentation differed among material types and included a modified aperture for FT-NIR
scans of small otoliths (Matta et al. this volume-b) and a quartz vial configuration suitable forplasma and tissue samples (Fig. 10.2; Goldstein et al. 2021).

RESULTS AND DISCUSSION

Three cohorts of walleye pollock and two cohorts of Pacific cod were reared at LPW.
Growth trajectories were similar among the three reared cohorts, but sizes of wild specimensfrom the same annual cohort differed regionally and differed from growth trajectories of rearedfish (Fig. 10.3). Such differences may be attributed to hatch dates or environmental conditions
but highlight the need to test calibration models built with reared fish against diverse specimen
sources.

131Spectra and reference data have been collected from a subset of samples, and preliminary
analyses indicate notable differences in tissue spectral patterns and tissue-specific protocols fordata acquisition. Spectroscopy data acquisition from blood plasma was limited by the volume
available from an individual fish. This prohibited Raman data collection for some specimens bylimiting submersion of the probe. Plasma volume was less of a constraint for the sample window
configuration of the FT-NIR spectrometer (Fig. 10.2). Collection of reference data is still being
conducted, but FT-NIR spectral data from female pollock plasma indicate spectral separation
among fish of different body sizes (Fig. 10.4), suggesting value in pursuing hormone analyses,
since body size of juvenile fish is inherently linked with growth and development.
Preliminary exploration of FT-NIR scans focusing on body condition from liver andmuscle showed variation in spectral patterns among tissue types. Spectra were more complex(i.e., peaks and valleys were evident) for liver than for muscle, suggesting greater material
complexity (Fig. 10.5). Variability in liver spectra also coincided with informative wavenumber
regions for determining lipid content from homogenized Pacific cod tissues (Goldstein et al.
2021), suggesting that liver may be an appropriate tissue for building calibration models for body
condition. FT-NIR absorbance spectra from liver and muscle showed only weak visual patterns
related to Fulton’s K, a coarse condition index based on length and weight relationships(Wuenschel et al. 2018; Fig.10.5). Correlations between Fulton’s K and focal biochemical
analyses are positive but weak for juvenile Pacific cod (Goldstein et al. 2021), suggesting that
decoupling between Fulton’s K and spectra does not necessarily negate potential correlations
between spectra and energy density or lipid content. Additionally, comparisons of FT-NIR
triplicate liver scans from different regions of the same liver suggested that for some specimens,
there was greater variability among liver scan regions within individuals than among specimens.

132

This finding suggests that repeated scans or homogenization may be necessary to obtainrepresentative liver scans (Fig. 10.6).
Most of the work to date has focused on reproduction and age, which have both yielded
promising calibration model results. Daily age and otolith FT-NIR spectroscopy results arepresented in Matta et al. (this volume-b). Raman results from ovary scans and histologicalreference data that used a subset of fish reared at LPW indicate Raman could be usedsuccessfully to differentiate among various ovary maturity stages (Neidetcher et al. this volume).
Preliminary analyses for the suite of metrics examined here reinforce importantconsiderations for calibration model development of fish life history indices. Variation in scans
and spectral data repeatability indicate the importance of standardizing and optimizing material
preparation and presentation. Variation in body size among reared fish and their free-ranging
counterparts emphasizes the importance of curating comprehensive calibration and test datasets
prior to model implementation. Following these considerations, finalized and vetted calibration
models will likely provide new routes to incorporate, develop, and update indices for ecosystembased management and stock assessment.

133

Table 10.1. -- Description of data types and biological tissue collections from reared walleye
pollock (Gadus chalcogrammus) and Pacific cod (G. macrocephalus). R denotesthat Raman spectra were obtained for the scan material and F refers to Fouriertransform near infrared spectral data collection.
Life history metric

Reference andspectroscopy
scan material
ovary (R, F)

Reference data collection

Preservation

Structures from histological slides

formalin

Maturity: hormones

blood plasma(R, F)

frozenAge: daily

otolith (F)Enzyme-linked immunosorbent assay(ELISA) from plasma extracted fromblood
Otolith microstructure analysis

Age: annual

otolith (F)Otolith analysis

dry

Body condition: liver

liver (R, F)

frozenBody condition:musclemuscle (R, F)

Proximate composition analysis forpercent composition of lipid, protein, andwater
Bomb calorimetry for energy density

Maturity: ovary

134

dry

frozenFigure 10.1. -- Location of Little Port Walter Research Station (LPW) and catch locations for asubset of wild-caught walleye pollock (Gadus chalcogrammus) and Pacific cod(G. macrocephalus) specimens from 2019-2022 for comparison with fish rearedat LPW. The vertical dotted line represents the designation of eastern Gulf of
Alaska (GOA) compared to central and western GOA for this study.

135

Figure 10.2. -- Soft tissue (ovary, muscle, liver) and plasma sample presentation for (a) FT-NIR
spectroscopy and (b) Raman data acquisition. Photo credit: Delsa Anderl, AFSC
(retired).

136

Figure 10.3. -- Growth trajectories and sampling dates of (a) walleye pollock Gadus chalcogrammus and (b) Pacific cod G.
macrocephalus cohorts reared at Little Port Walter Research Station (LPW) and wild-caught fish that have beencollected in the eastern Gulf of Alaska (EGOA) and the central (CGOA) and western GOA (WGOA; see Fig. 10.1).
Wild-caught specimens were aged annually and correspond with annual cohorts that are being reared at LPW.

137

Figure 10.4. -- Spectra from Fourier transform near infrared spectrometer (FT-NIRS) scans ofplasma from female walleye pollock (Gadus chalcogrammus) from a subset offish. Color scale is fish fork length, since reference hormone data collection is inprogress. Data were pre-processed with a first derivative Savitzky-Golay filter(polynomial order = 2, window size = 15).

138

Figure 10.5. -- Fourier transform near infrared spectra (FT-NIRS) from (a, b) liver, and (c, d)muscle scans from reared walleye pollock (Gadus chalcogrammus) and Pacificcod (G. macrocephalus). Color scale is Fulton’s condition index (Fulton’s K; 100* length/weight3) as a coarse metric of body condition in the absence of reference
biochemical data for the preliminary analyses. Note that the color scale changes
for each panel, and some specimens differ between liver and muscle scans due tolimitations in liver size that constrained FT-NIRS data collection. Data were preprocessed with a first derivative Savitzky-Golay filter (polynomial order = 2,
window size = 19).

139

Figure 10.6. -- Triplicate Fourier transform near infrared spectral scans of liver tissue from fiveindividual walleye pollock (Gadus chalcogrammus) specimens (panels) takenfrom the anterior, middle, and posterior regions of the liver. Data were preprocessed with a first derivative Savitzky-Golay filter (polynomial order = 7,
window size = 19) and subsequently with Standard Normal Variate preprocessing in an effort to correct for any additional issues that may create
differences in spectra among specimens, such as light scatter (Fearn 2008).

140

11. Rapid Daily Age Estimation of Juvenile Walleye Pollock in the Gulf of Alaska
using FT-NIR Spectroscopy

Mary Elizabeth Matta1, Esther D. Goldstein1, Irina M. Benson1, Heather K. Fulton-Bennett2,
Charles D. Waters2, Brenna C. Hsieh1, and Thomas E. Helser1

Alaska Fisheries Science Center
NOAA, National Marine Fisheries Service
7600 Sand Point Way NE
Seattle, WA USA
1

Alaska Fisheries Science Center
NOAA, National Marine Fisheries Service
17109 Point Lena Loop Road
Juneau, AK USA
2

141

142

INTRODUCTION

Estimates of daily age can provide valuable insight and context for understanding earlylife history and recruitment dynamics of marine fishes. Specifically, daily ages may be used todetermine the phenology of hatching and spawning and to estimate growth, which in turn can
influence mortality and overwinter survival and ultimately, the recruitment success of a
population (Rogers and Dougherty 2019, Yoklavich and Bailey 1990). However, obtaining daily
age estimates is laborious, destructive, and requires specialized equipment and expertise in
growth pattern interpretation, limiting its feasibility in terms of broader, production-scale
applications. One innovative method that has shown promise to rapidly and non-destructively
produce estimates of daily age and growth in juvenile fish is Fourier transform near infrared (FTNIR) spectroscopy. A recent study on young-of-year (age-0) red snapper (Lutjanus
campechanus) used FT-NIR spectroscopy to successfully predict fish age within 6 days of
traditional, microscopic age estimates, with both methods resulting in comparable estimates ofgrowth (Passerotti et al. 2020a). Here, we applied a similar approach to predict daily ages ofjuvenile walleye pollock (Gadus chalcogrammus), a commercially important species in Alaska
waters.

METHODS

Young-of-year walleye pollock were caught in summer and fall 2020 and raised at
NOAA’s Little Port Walter Research Station in Southeast Alaska. Fish were sacrificed
sequentially as a part of a larger longitudinal study to evaluate changes in growth and143

development from the juvenile stage through maturation. Sampling was conducted at shortintervals (weekly) at the beginning of the study and longer intervals (biweekly to monthly)
thereafter.
A subset of young-of-year otoliths was aged using traditional, microscopic examination.
Only fish sacrificed prior to October 1 were aged, due to compression in the daily rings thattypically occurs near the otolith edge in fall as growth starts to slow, which can complicate
pattern interpretation. Otoliths were thin-sectioned to a thickness of about 10 microns and read atleast twice by two different analysts at 400-1000X magnification. Ages estimatedmicroscopically were used to derive the median date of hatching for the 2020 cohort. This hatch
date was applied to the remaining unaged fish to determine estimates of daily age. Only otoliths
that had a Chang’s coefficient of variation (CV; Chang 1982) less than 10% (Jones 2013,
Passerotti et al. 2020a) were retained to predict the median hatch date for the rest of the sample.
All otoliths were scanned on a Bruker MPA-II FT-NIR spectrometer to produce aspectrogram for each otolith (Fig. 11.1). Due to the small size of the otoliths, we tested several
different aperture and stamp combinations to optimize the signal-to-noise ratio (Passerotti et al.
2020a) of spectra from small otoliths in a related species, Pacific cod (Gadus macrocephalus).
The best results in terms of predicting fish length from otolith spectra were obtained using
Teflon discs with drilled 2-mm and 5-mm diameter apertures and a gold-coated reflector stamp
covering the otolith. We opted for a 5-mm aperture diameter to accommodate the largest otoliths
in our sample (Fig. 11.2).
Otolith spectral data were split randomly into 75% train and 25% test data sets and used
to build partial least squares regression (PLS) predictive models between spectral data and fish
daily age. Spectral regions dominated by noise were excluded (>7500 cm-1) and data were144

preprocessed using a first derivative Savitzky-Golay filter (17-point smooth, polynomial order =3) (Fig. 11.1). An initial calibration model was constructed, including all the wavenumbers>7500 cm-1. Wavenumber variables for inclusion in the final model were then selected using a
combination of variable importance in projection (VIP) and jack-knifed coefficient p-values (α =0.05) (Farrés et al. 2015).

RESULTS AND DISCUSSION

In total, we aged 58 otoliths using traditional methods. The overall precision (% CV) ofage estimates was 7.92%. Acceptable levels of precision (<10% CV) were achieved for 36 of theotoliths; these otoliths were used to estimate the hatch date distribution of the sample. Estimatedhatch dates ranged from April 16 to May 31, with a median date of May 2.
Preliminary results demonstrated satisfactory predictive capability of PLS models
between otolith spectra and daily ages (Fig. 11.3). The root mean square error (RMSE) and r2 of
the cross validation (leave-one-out) data set were 9.34 days and 0.93, respectively. The RMSE
and r2 of the test data set were 12.09 days and 0.87, respectively. The percent RMSE, a measurethat can be used to compare the error relative to the maximum observed age in a study, was very
low at 4.02%, and comparable to results for juvenile red snapper and adult walleye pollock(Helser et al. 2019b; Passerotti et al. 2020a).
Ultimately, FT-NIR spectroscopy appears to be an effective method of estimating daily
ages of juvenile walleye pollock. Future work will focus on increasing sample size, refining
hatch date estimates of unaged fish, and incorporation of data from wild-caught fish and other145

cohorts to test the model’s ability to predict ages of fish from different areas, years, and over a
wider range of ages.

146

Figure 11.1. -- Raw (top) and preprocessed (bottom) spectrograms of age-0 walleye pollock(Gadus chalcogrammus) otoliths.

147

Figure 11.2. -- Accessories used to scan otoliths from age-0 walleye pollock (Gadus
chalcogrammus). Left: Teflon disc with a drilled 5-mm diameter aperture to
improve the signal-to-noise ratio. Right: Gold reflector stamp used to cover theotoliths.

148

Figure 11.3. -- Partial least squares regression showing relationship between human-estimatedage and model-predicted age of age-0 walleye pollock (Gadus chalcogrammus).
Black symbols indicate cross-validation dataset and pink symbols indicate test
dataset. Closed circles represent fish with ages that were estimated using
traditional microscopic methods; open circles represent fish with ages based on
the median hatch date of the sample and time of sacrifice.

149

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12. Developing NIR Sampling Methodology for Modeling Species Discriminationin Live Catfish for Aquaculture

Carrie K. Vance, Ashmita Poudel, Li-Dunn Chen, Peter J. Allen, and Andrew J. Kouba

Mississippi State University
75 B.S. Hood Drive
Mississippi State, MS USA

151

152

INTRODUCTION

Near infrared (NIR) spectroscopy of otoliths for age determination and speciesclassification in fish such as Pacific cod (Gadus macrocephalus), red snapper (Lutjanus
campechanus), walleye pollock (G. chalcogrammus) and saddletail snapper (L. malabaricus)
occurs post-mortem (Helser et al. 2019b, Benson et al. 2020). In other post-mortem studies, NIR
spectroscopy has been used to assess the quality and nutritional composition of fish filets,classify fresh or frozen tissue, and predict microbial contamination or adulteration of market
products such as fish meal (Liu et al. 2013). NIR spectroscopy has not been used to examine
physiology and health of living fish for pre-harvest management decisions, as the prospect ofusing NIR spectroscopy for aquatic species in vivo is often dismissed due to fears of water signal
interference. Yet, we have successfully used NIR spectroscopy to address physiology and healthquestions in live amphibians, terrestrial and aquatic, specifically for species discrimination, sex
determination in monomorphic species or juvenile animals, reproductive status in female adults,
and to detect subclinical levels of fungal disease (Vance et al. 2016a, Chen et al. 2022, Chen etal. 2023). Similar information may be obtainable for live fish in real time with NIR spectroscopy,
particularly if coupled with machine learning algorithms. Here we develop methodology for NIR
spectral collection of species-specific information from live catfish as a proof of principle in NIR
spectroscopy for in vivo physiological assessment.
To examine if biochemical information for physiological traits of interest could becollected from live fish, we analyzed the discriminatory capacity of NIR spectroscopy for speciesclassification of adult blue catfish (Ictalurus furcatus) and channel catfish (Ictalurus punctatus)
catfish with intent to transfer the model predictions to discriminate fingerling fish for153

aquaculture. We needed to develop a method for collecting NIR spectra from live adult catfish
that could give us physiological information despite the presence of a thick mucus layer on theskin and handling in an aquatic environment. To this end, we first compared the NIR spectra
prediction results for catfish that were or were not anesthetized with tricaine methanesulfonate(MS-222) to see if spectra from calm fish were more informative than those of stressed fish, andif there was a masking effect of the MS-222 anesthetic in the NIR spectrum. Second, we
compared the spectral collection method for three important regions of the live fish. Spectra fromthe head could contribute to species differentiation, sexual dimorphism at breeding, andinfectious disease such as Edwardsiella ictaluri bacterium, which causes enteric septicaemia.
Spectra from the lateral line may also differentiate species, sexual maturity, and channel catfish
virus disease, which is a disease with 100% mortality in fingerlings. Spectra from the vent may
provide information on species, sexual maturity, and even translate to measuring gamete
development. Finally, we tested six model algorithms (PCA-LDA, GLMnet, KMM, RF,
XGBoost, and SVM) to see if predictive capacity for our trait of interest was dependent on themodeling approach.

METHODS

Adult blue and channel catfish (n = 20 per species) were grown in the aquaculture facility
at Mississippi State University. Near infrared spectra were collected from fish that were notanesthetized and then again when they were anesthetized with MS-222. NIR spectra werecollected from the head, lateral line, and vent (three spectra per site), using a portable ADS
FieldSpec 3 + Indigo-Pro w/fiber optic source and a low-intensity contact probe with a diameter

154

quartz window (Fig. 12.1A). Each spectrum was collected over the range 350-2500 nm and was
an average of 100 scans with a 34 μs integration time, totaling about 12 seconds of direct contact
with the live fish. Spectra were then transformed with SNV + detrend and 1st derivative (GAP
25; segment 19), and SG smoothing (order 1, 12 symmetric kernel points) (Fig. 12.1B). Spectrafrom 32 fish (16/species) were used to generate the calibration and internal validation sets (5-foldcross-validation) and spectra from the remaining 8 fish (4/species) were the external validation
set in the discriminative models for each collection site.
Six conventional machine learning modeling algorithms for discrimination of species
were examined and compared using Tukey’s honestly significant difference test (p < 0.05) andanalysis of variance: 1) principal component analysis-linear discriminant analysis (PCA-LDA),
which is a combined unsupervised-supervised dimensionality-reducing technique fordiscriminating observations amongst discrete classes, 2) generalized linear model with elastic net
regression (GLMnet), which is a supervised linear regression method, 3) k-nearest neighbors(KNN), a supervised machine learning algorithm that predicts the class of an observation basedon the majority-class of k-nearest neighbors, found best for fish species, 4) random forest (RF)search, a supervised algorithm that applies an ensemble approach when placing predictions basedon repetitive splitting of features (branches) at random to split nodes into decision trees, 5)extreme gradient boosting (XGBoost), a supervised algorithm that employs an ensemble of trees
for prediction, and 6) support vector machine (SVM), which is a linear supervised algorithm thatplots all observations on a hyperplane comprised of all predictor features (Chen et al. 2023).

155

RESULTSClassification of species using NIR spectroscopy across all six algorithms provided
informative models with 77.3-97.5% accuracy (Table 12.1). Species discrimination was obtainedwith NIR spectra collected from the head, lateral line, and vent region of catfish with external
validation average predictions for anesthetized fish with >90% accuracy, and for nonanaesthetized fish between 75% and 90% accuracy for all algorithms tested. Models built with
spectra from anesthetized fish exhibited improved species classification in the head and ventregions, and MS-222 did not appear to interfere with species distinguishing biochemistry (Table
12.1). Species classification did not significantly differ by scanning region, although spectrafrom the lateral line were more informative than spectra from the head or vent regions in nonanesthetized fish, while head and vent spectra were most informative in anesthetized fish. The
use of anesthesia may lead to decreased stress response and reduce noise, allowing for increased
signal detection for predicting species designation, yet alternative means to collect spectra
without stress may provide more rapid data collection and yield similar results.

CONCLUSIONS

Catfish species can be classified via NIR spectroscopy collected from the epidermis ofliving individuals with water and mucus layers. Spectral collection is improved when animals arecalm or when collecting spectra from easily accessible regions (lateral line and head vs. vent),
although developing alternatives to administering anesthesia would benefit field studies.
Furthermore, spectral collection from different regions contributes information on different

156physiologically based traits such as disease and reproduction, and all regions provide
discriminatory information that can be tailored to the trait of interest. Finally, use of acomputationally simple model (e.g., PCA-LDA) can provide sufficient information for basic
physiological analysis in live fish, despite the presence of water.
Fingerlings from different catfish species have significant differences in not only themetabolic processes that affect their growth rates, nutrient composition, and marketability, butalso in disease susceptibility and transmission. Currently, channel, hybrid, and blue fingerlings
can only be discriminated through polymerase chain reaction amplification of genetic material as
no distinguishing visual characteristics are evident in their physiology. Similar to our work inamphibians, NIR spectroscopy may also be able to detect pathogenic disease presence and load
at early stages to enable isolation or treatment prior to spread or population outbreak, which canwipe out an entire crop. NIR spectroscopy could provide a spectral fingerprint based on thebiochemistry of each species in real time, allowing investment decisions without termination.

157

Table 12.1. -- Discrimination of live channel and blue catfish by near infrared spectroscopy, withaggregated accuracies from five repeats of 5-fold cross validation from the external
dataset of 8 fish left out of the prediction models.

No Anesthesia

Anesthesia

Model

Head

Lateral Line

Vent

Head

Lateral Line

Vent

Random Forest

80.3 ± 11.7%

84.4 ± 13.5%

81.0 ± 9.2%

94.2 ± 7.7%

90.0 ± 7.7%

95.8 ± 2.6%

XGBoost78.9 ± 10.2%

84.5 ± 13.7%

83.6 ± 8.5%

94.2 ± 5.7%

93.9 ± 9.4%

96.7 ± 3.1%

GLMnet78.0 ± 10.7%

82.1 ± 11.7%

81.5 ± 13.4%

97.5 ± 5.0%

95.8 ± 6.5%

97.5 ± 2.0%

SVM

81.7 ± 11.6%

87.8 ± 15.5%

79.3 ± 60.8%

97.5 ± 5.0%

95.0 ± 4.1%

95.8 ± 4.6%

LDA

78.3 ± 11.6%

77.2 ± 15.5%

78.1 ± 10.8%

95.0 ± 4.9%

93.3 ± 5.7%

95.8 ± 4.6%

KNN

77.3 ± 10.9%

83.3 ± 8.6%

73.7 ± 8.3%

95.8 ± 6.5%

92.5 ± 7.2%

95.0 ± 4.1%

158

Figure 12.1. -- Average raw and processed spectra collected from two species of live catfish (n =20 fish /species) at three positions on the body. Aquaphotomics performed at thewater signal highlighted by the blue box in the transformed spectra has potential
for additional classification power.

159

160

13. Fourier Transform Near Infrared (FT-NIR) Spectroscopy Discriminates Archived
Otoliths of Newly Detected Cryptic Species, Etelis carbunculus and Etelis boweni

Kristen A. Dahl1*, Joseph M. O’Malley1, Beverly K. Barnett2, William Kline2,
and Joseph B. Widdrington3

Pacific Islands Fisheries Science Center
NOAA, National Marine Fisheries Service
1845 Wasp Boulevard, Building 176
Honolulu, HI USA
1Southeast Fisheries Science Center
NOAA, National Marine Fisheries Service
3500 Delwood Beach Road
Panama City, FL USA
2Southern Seas Ecology Laboratories
School of Biological Sciences
University of Adelaide
North Terrace, South Australia AUSTRALIA
3

161

162

INTRODUCTION

Etelis carbunculus Cuvier (Cuvier and Valenciennes 1828) is an important component of
the bottomfish management unit species (BMUS) fisheries in the Pacific Territories of theUnited States (Langseth et al. 2019). There has long been speculation about whether E.
carbunculus actually comprises two species due to its extensively wide range from Seychelles(western Indian Ocean) to Hawai’i, and geographic variation in certain morphological characters,such as maximum body size and otolith growth and morphology (Anderson 1981, Smith andKostlan 1991, Wakefield et al. 2014, Williams et al. 2015). Historically, E. carbunculus was
divided into E. carbunculus (Seychelles holotype 1828) and E. marshi (Hawaii holotype), but E.
marshi was later designated as a junior synonym (i.e., a previously published name) of E.
carbunculus, and not a unique species (Anderson 1981). Later, phenotypic differences in otolithmorphology of E. carbunculus reported from different regions ignited a new debate around thepossibility of a cryptic species pair (Smith and Kostlan 1991, Smith 1992). Recent genetic andmorphological studies have provided conclusive evidence that Etelis carbunculus comprises two
cryptic species, E. carbunculus, the pygmy ruby snapper, and E. boweni, the newly described
giant ruby snapper (Loeun et al. 2014, Andrews et al. 2016, 2021). Although exceptionallysimilar in their morphology, the two species differ in coloration of the dorsal caudal fin tip andthe shape of the opercular spine (Andrews et al. 2016).
Now that taxonomic revision of the species complex has been verified, life history
research has revealed fundamental differences in the biology between E. carbunculus and E.
boweni, including maximum body size, growth rate, and length at age (Williams et al. 2017,
Wakefield et al. 2020). Due to their overlapping distributions across the Indo-Pacific, both163

species have been previously misreported as a single species (E. carbunculus) in fisheries catch
data and in biological studies (Williams et al. 2013, Wakefield et al. 2020). This complicates life
history research, because the otolith collections that were thought to be solely E. carbunculus are
now known to be contaminated with an unknown number of E. boweni otoliths. Collections of
biological samples on these species for which misidentification is a potential issue go backdecades, and fisheries age-structured assessments that incorporate life history data from thecryptic species alongside E. carbunculus may suffer from model misspecification. This is also
true for data poor assessment approaches that rely heavily on otolith-derived growth estimates.
Given the inherent vulnerability of Eteline snappers to overexploitation (Williams et al. 2013), it
is imperative that methods be developed to recognize speciation within archived otolithcollections to move forward with sustainable management.
Fourier transform near infrared (FT-NIR) spectroscopy is an emerging technology infisheries and conservation biology (Wedding et al. 2014, Vance et al. 2016b, Helser et al. 2019b)
which may offer an alternative to both morphometric and shape analyses. The technique relies onlight from the near infrared region (12,800-4,000 cm-1 wavenumbers) of the electromagnetic
spectrum and absorbance patterns that reflect a sample’s organic chemical composition. These
spectra can then be compared among samples using multivariate statistical analysis todiscriminate known variables (e.g., age, stock, or species) of interest (Murray and Williams
1987). FT-NIR spectroscopy has recently gained attention with its ability to predict fish age fromotoliths (Wedding et al. 2014, Helser et al. 2019a, 2019b, Passerotti et al. 2020b), as well as thepotential to discriminate geographical differences expressed in otolith chemistry (Wedding et al.
2014, Benson et al. 2020). FT-NIR spectroscopy, if effective in distinguishing cryptic Etelis
species, would provide a time efficient alternative to manual morphometrics using calipers or164

image analysis, especially of species with fragile otoliths prone to breaking, and could be used todifferentiate future cryptic species from archived collections. Furthermore, because FT-NIR
spectroscopy scanning does not damage the sample (i.e., otoliths are scanned whole), otoliths
may later be used for fish ageing following spectral scans and the determination of species.
Herein, we examine the utility of FT-NIR spectroscopy to distinguish between archived
otoliths of cryptic species E. carbunculus and E. boweni. We assessed the efficacy of FT-NIR
spectroscopy using voucher otoliths from the Southwest (SW) Pacific Ocean that werepreviously identified to species using phenotypic and genetic differences. Following calibrationand validation of voucher (i.e., known) data, we applied optimal models to predict and classify
archived Etelis sp. otoliths previously collected around Guam.

METHODS

Samples

Voucher otoliths from E. carbunculus and E. boweni were located at the PacificCommunity Marine Specimen Bank archive based in New Caledonia (Smith et al. 2017) (Fig.
13.1). Fish were collected between July 2012 and April 2014 from research surveys andcommercial fishers from seamounts and island reef slopes in the Exclusive Economic Zones(EEZs) of New Caledonia, Fiji, Tonga, and Vanuatu (range: -15.1°-24.4°S, 164.9°E-174.3°W) in
the SW Pacific Ocean. Samples (n = 93) were chosen by fork length (FL) to capture the full size
range of each species, as well as to maximize the overlap in their size distributions. Samples
were selected to also minimize spatial variability, selecting samples as evenly from as few

165regions as possible (i.e., similar numbers of each species for each EEZ). Voucher Etelis samples
were distinguished phenotypically at the time of capture based on the presence (E. boweni) or
absence (E. carbunculus) of a black margin on the upper lobe of the caudal fin and a rounded (E.
boweni) or acutely sharp (E. carbunculus) opercular spine (Andrews et al. 2016). Fork length
was measured to the nearest 1 mm, wet weight was recorded to the nearest 0.1 g, and the sagittal
otoliths were removed, rinsed, and stored dry.
Presumed E. carbunculus otoliths were collected from the waters around Guam (13.4°N,
144.7°E) at the Guam Fishermen’s Cooperative from 2009-2019 (n = 91) and were archived at
the Pacific Islands Fisheries Science Center in Honolulu, Hawai’i. Currently, these samples
comprise a potentially mixed collection of cryptic species, E. carbunculus and E. boweni. As
above, fork length was measured to the nearest 1 mm, wet weight was recorded to the nearest 0.1
g, and the sagittal otoliths were removed, rinsed, and stored dry.

Spectral Measurements and Pre-processing

All otoliths were scanned using diffuse reflectance on a Bruker Multi Purpose Analyzer
II Fourier transform near infrared spectrometer with a 22-mm diameter sample window andOPUS software (version 7.8; Bruker Scientific, Billerica, MA). The spectrum acquisition was
performed from 10,000 cm-1 to 4,000 cm-1 (64 scans, resolution 16 cm-1). Scans were averaged to
produce a single representative spectrum for each sample.
Following acquisition, spectral data were uploaded into Solo 8.9.2 (2021), a chemometricsoftware that uses a MatLab framework (Eigenvector Research Inc., Manson, WA) for dataprocessing and model generation. Selected spectral regions were in the 7,500 to 4,000 cm-1

166

range. Raw spectra were plotted to detect any spectral outliers in need of rescanning. Spectral
data were pre-processed with 13-point Savitzky-Golay smoothing (2nd order polynomial), 13point Savitzky-Golay first derivative transformation, and mean-centering, which produced thebest separation among groups.

Principal Component Analysis
Exploratory data analysis based on principal component analysis (PCA) was performed
to visualize trends in the voucher data and to evaluate the discriminatory possibility of the FTNIR spectra between species. The Guam Etelis sp. samples were also explored using PCA, withsamples ≥60 cm FL assigned to E. boweni based on known disparities in maximum size between
species (Andrews et al. 2016). PCA on the pre-processed data matrix was obtained using two or
three principal components, and cross-validation was performed using venetian blinds with ten
splits and algorithm-SVD (singular value decomposition). Species groups were visualized using
a scores plot. Hotelling’s T2 and Q residuals were inspected to detect any spectral outliers inneed of rescanning.

Multivariate Classification

Partial least squares discriminant analysis (PLS-DA) was used to evaluate spectral
variability from voucher E. carbunculus and E. boweni based on a known class membership (i.e.,
species) of the samples. To obtain the discrimination model, each Etelis species was defined as aseparate class. PLS-DA models on voucher data were calibrated and validated using two
approaches. First, the entire dataset was used to calibrate a discrimination model, and cross167

validation (CV) of the model was executed using 10 sub-validation sets of 8 samples each(venetian blinds cross-validation). For the second approach, the total sample set was partitioned
into calibration, assembled from 70% of samples, and validation datasets, using the remaining
30% of samples. The split was done randomly using the Kennard-Stone method (Kennard andStone 1969). The calibration dataset was used to generate the classification model (venetianblinds CV), and the validation dataset was used to evaluate predictive (i.e., classification)
performance after assigning class membership to one of the two species.
In each case, automatic variable selection was used to find subsets of variables (i.e.,wavenumber regions) that improve predictions (compared to all 949 variables). Goodness of fit
for each model was based on the cross-validation (CV) error, r2 (coefficient of determination),
RMSECV (root mean square error of cross validation), and specificity (true negative rate) andsensitivity (true positive rate) of the model. Class error was calculated for each model as theaverage of the false positive and false negative rates and was viewed as a confusion matrix.
Lastly, PLS-DA models built on voucher samples were used to predict class (i.e., species)
membership of the Guam Etelis sp. samples. The PLS-DA classification method builds a modelon the calibration dataset (e.g., voucher) and, by default, assumes the two classes have equal
prior probability, regardless of the relative number of samples of each class in the calibration
dataset. However, if there is known information on the prior probability of each class in the
population, it is possible to incorporate these priors into the model's classification so that themodel would be more accurate for datasets randomly sampled from that population. The
assumption that classes were evenly distributed in Guam samples was not met based on evidenceof far more E. carbunculus (~80%) compared to E. boweni (~20%) in the dataset, thus priorprobabilities of the PLS-DA model were modified using “priorprob”. Species predictions of168

these samples were compared to quadratic discriminant analysis results from otolithmorphometrics.

RESULTS

Despite outward similarities in phenotype, E. carbunculus reaches a much smaller
maximum size compared to E. boweni. Fork length of voucher samples ranged from 25.0 to 58.0
cm for E. carbunculus (n = 38) and from 29.0 to 85.0 cm for E. boweni (n = 45) (Pacific Islands
Fisheries Science Center 2023) (Fig. 13.2, Table 13.1). Mean (±SE) FL was 42.26 (±1.49) cm forE. carbunculus and 53.47 (±2.43) cm for E. boweni (Fig. 13.2, Table 13.1). Out of all 83 voucher
samples, only 22% (n = 18) had whole, unchipped otoliths for morphometric measurements. The
remaining otoliths (n = 65) had material missing from the right otoliths due to chipped orcompletely broken rostrums. Fork length of Guam samples ranged from 18.1 to 89.0 cm (Fig.
13.2, Table 13.1). Out of all 91 Guam samples, only 20% (n = 19) had otoliths pairs in wholecondition for morphometric measurements (i.e., no rostrum breaks), whereas 24 samples had awhole right (n = 15) or left (n = 9) otolith. The remaining 48 samples (>50%) had material
missing from both right and left otoliths from chips or breaks.
Spectral data showed distinct absorbance patterns (i.e., peaks and valleys at certain
wavenumbers) based on the organic chemical composition of the otoliths (Fig. 13.3). Twelve
samples in the voucher dataset and one sample in the Guam dataset were rescanned due toabnormally high peaks apparent in spectral absorbance, and the best spectra were selected foranalysis. Distinct peaks among all spectra were observed at approximately 6,800 cm-1, 5,160
cm-1, and 4,300 cm-1 (Fig. 13.3). When voucher spectral data was pre-processed, species169separation became apparent in several wavenumber regions, with the largest differences
observed around 7,100 cm-1, 5,300 cm-1, 4,400 cm-1, and 4,000 cm-1 (Fig. 13.4).
The PCA on voucher data included all 949 wavenumber regions as variables, and showedsignificant clustering between species (Fig. 13.5), thus demonstrating the possibility ofclassifying Etelis sp. samples based solely on differences in their chemical compositions that are
visible in the NIR region. Most of the variation in the multivariate morphometric data (99.4%)
was described by PC1 (96.96%) and PC2 (2.48%) (Fig. 13.5). The two species clusters wereclearly separated along PC1 and PC2; however there was some overlap present.
A total of 46 E. boweni and 39 E. carbunculus voucher sample spectra were used tocalibrate PLS-DA models. Variable selection for each model selected 480 (fully cross-validated)and 623 (test split) wavenumber regions (out of 949) as the most important for discrimination
between spectra. The fully cross-validated PLS-DA model was computed with four latentvariables (LVs) and performed nearly perfectly for species classification, with a class error rateof 2.4%. The model correctly classified 83 voucher samples into E. boweni (n = 45) and E.
carbunculus (n = 38) classes (Fig. 13.6A). Only one sample of each species was misclassified incross-validation (Table 13.2). Both specificity (0.974) and sensitivity (0.978) were very high.
Comparable results were observed when voucher samples were split into calibration(70%, n = 60) and external validation (30%, n = 25) datasets (Fig. 13.6B). Class error of thecalibration CV (venetian blinds) was 5.0%, and only one E. boweni and two E. carbunculus weremisclassified (Table 13.2). The PLS-DA model also performed very well for true predictions on
the external validation dataset and correctly predicted the species class of 24 voucher samples
without information on the true species. Only one E. carbunculus was misclassified as E. boweni

170

(Table 13.2, Fig. 13.6B). The class error for the validation set was slightly lower than thecalibration at 4.0%.
The fully cross-validated model (lowest class error rate) was used to test predictions on
the archived Guam Etelis sp. samples. Prior probabilities were modified so that the model would
not assume an equal probability of observing either species, as E. boweni appear to be rare in
Guam (an assumption which was corroborated by morphometric results herein). The model
predicted that 8% (7 of 91) of archived otoliths belonged to the newly identified E. boweni. Two
Guam samples over 60 cm FL (GECC-1133, GECC-1317) were classified as E. boweni frompredictions made on voucher-calibrated PLS-DA model, which we assume to be a correct species
assignment (Fig. 13.7). Three Guam samples over 60 cm FL (GECC-0318, GVDP-0370, andGVDP-0503) were classified as E. carbunculus which we assume to be false, based on maximum
reported FL of the species (Fig. 13.7). However, sample GVDP-0370 stood out as an outlier inthe overall plot of Hotelling’s T2 and Q residuals, so prediction on this sample is unresolved.
Five additional Guam samples were classified as E. boweni: GECC-0069 (50.4 cm), GECC-0591(46.8 cm), GECC-0711 (39.9 cm), GECC-1494 (30.4 cm), and GVDP-0381 (35.7 cm) (Fig.
13.7).

DISCUSSION

This study explored the use of FT-NIR spectroscopy as a potential new method todistinguish the cryptic species pair. Differentiation of the cryptic species pair Etelis carbunculus
and Etelis boweni was shown to be possible and highly reliable through examination of archived
otoliths. FT-NIR spectral absorbance patterns of archived voucher otoliths from the SW Pacific171were distinct between species. Classification models applied using NIR spectral data were able topredict species with a high degree of accuracy, despite a relatively large spatial area of voucher
specimen collection (±10° latitude and longitude) and regardless of whether otoliths were whole(i.e., unbroken). In addition, FT-NIR spectroscopy identified members of the newly described E.
boweni species in the archived collection of E. carbunculus otoliths captured around Guam,which confirmed that the species’ distributions overlap in this region. The identification of bothE. carbunculus and E. boweni in the archived catch from Guam has important implications forfisheries management; therefore, it is imperative that the corresponding otolith collections are
examined to ensure that the otoliths are assigned to the correct species.
FT-NIR spectroscopy was able to identify and separate the two cryptic Etelis species inthis study with extremely high classification accuracy. Importantly, the model performed bestwhen making predictions for voucher data, indicating spatial (or some other) variability existed
within both Etelis species from these distant sampling locales. Further, while exploring thespectral data, we observed PCA clustering of fish based on region within voucher samples. Thus,
FT-NIR spectroscopy may be a useful tool to also discriminate spatial differences (e.g., stock
discrimination) in these species, and conceivably others. Robins et al. (2015) also reported
evidence of spatial discrimination using this method. The best solution to avoid such geographic
issues is to include samples from all locations in the calibration stage, in which case, the fullscope of otolith microchemistry variation that may be encountered in the test dataset is capturedby the model (Robins et al. 2015). Had our calibration models included some spectra from fish
collected in Guam (for which species identification could be confirmed), it would haveundoubtedly improved predictions.

172

Species predictions from best models suggested that archived otoliths from Guam belong
primarily to E. carbunculus (92%: 84 of 91). Seven archived otoliths were predicted to belong to
the newly identified E. boweni. There was a general lack of overlap in body sizes between thetwo Etelis species identified in Guam. Based on FT-NIR spectroscopy, samples classified as E.
boweni included two of the largest individuals (>60 cm FL) and two smaller individuals (30.4
and 35.7 cm FL). Given what we know about maximum body sizes in E. carbunculus (<60 cm
FL), it appears more spectra from these populations of fish are needed to provide reliablepredictions of these cryptic species.
Predictive models for FT-NIR spectroscopy approaches are only as good as thecalibration data provided to train models on. An important assumption of these analyses is thateach voucher specimen was correctly identified to species. Voucher specimens were collected
after recognition of the new cryptic species and the identification of the distinguishing external
characteristics. However, the presence of the dark margin on the upper lobe of E. boweni caudal
fin is not always easy to see and the ‘sharpness’ of the opercular spine is relative between thetwo species and not always obvious. While we are not calling into question the ability of theresearchers to correctly identify the fish used in this study, the possibility of a misidentification
cannot be discounted.
In summary, differentiation of voucher Etelis carbunculus and Etelis boweni specimens
was shown to be possible and highly reliable through examination of FT-NIR spectroscopy.
Beyond this cryptic species pair, FT-NIR spectroscopy is likely to provide the same level ofaccuracy regardless of species and be applicable to archived otoliths when other cryptic species
are identified subsequent to collection. Case-specific, otolith morphometrics (i.e., shape analysis)
may be the preferred method to determine species classification if otolith length is not a173

distinguishing feature between the two species, otolith rostrums are not susceptible to breaking
during extraction and transportation, and the archived sample size is not overwhelming;
conversely, FT-NIR spectroscopy may provide greater accuracy and time efficiency.

174

Table 13.1. -- Descriptive statistics for fish fork length, fish weight, and seven otolith morphometric variables for voucher Etelis
carbunculus and Etelis boweni from the SW Pacific and for Etelis sp. from Guam. Sample sizes (n) differ by variable
depending on whether whole (i.e., unbroken) otoliths (*) were used, or whether field data existed for the sample.
Morphometric abbreviations are noted in parentheses.

Metrics
Fork length, cm
Fish weight, kg
Otolith length, mm (L)Otolith width, mm (W)Otolith thickness, mm (T)SW Pacific voucher samples
Etelis carbunculus
Etelis boweni
Range
Mean
SE
n
Range
Mean
SE
25.0-58.0
42.26 1.49 38
29.0-85.0 53.47
2.43
0.28-3.70
1.59 0.17 38
0.40-11.10
3.79
0.47
10.6-14.4
11.80 0.48
8 *11.6-17.4 15.35
0.67
4.69-8.85
6.25 0.16 38
3.88-7.61
5.84
0.14
1.17-2.65
1.78 0.06 38
0.99-2.16
1.37
0.04

n
45
43
10
45
45

Otolith area, mm2 (A)Otolith perimeter, mm (P)Otolith sulcus groove width, mm (S)Otolith excisura major length, mm (E)39.0-72.5
30.2-48.7
0.78-1.28
6.84-9.63

10
10
45
40

49.32
36.35
1.04
8.26

4.50
2.40
0.02
0.12

8
8
38
36

*
*

40.0-72.4
33.6-72.4
0.59-1.17
7.18-13.93

175

60.72
43.74
0.85
10.25

3.70
2.10
0.02
0.29

*

*
*

Guam samples
Etelis sp.
Range
Mean
SE
18.1-89.0 34.77
1.44
0.10-13.32
1.11
0.21
7.7-20.6 10.89
0.38
3.50-8.20
5.13
0.11
0.88-3.04
1.33
0.04

n
91
90
43
91
91

19.2-85.5
20.7-60.2
0.58-1.39
5.30-12.20

43
43
91
91

38.76
31.09
0.90
7.67

2.24
1.23
0.02
0.15

*

*
*

Table 13.2. -- Confusion tables for cross-validation and external validation of partial leastsquares discriminant analysis models on spectral data obtained via Fouriertransform near infrared spectroscopy fit using voucher Etelis species samples fromthe SW Pacific. Values in bold are the number of correct species assignments
based on known species of the voucher dataset.
Actual Class
Data set

ModelnFull cross-validation

Calibration

85

Split-Test

Calibration

60

Validation

25

Predicted as

E. boweni

E. carbunculus

E. boweni
E. carbunculus
E. boweni
E. carbunculus

45
1
32
2

1
38
1
25

E. boweni

121

E. carbunculus

0

12

176

Figure 13.1. -- Proximal view of right sagittal otoliths from Etelis carbunculus (50 cm FL, left),and Etelis boweni (67 cm FL, right). Scale bar (in red) is 5 mm.

177

Figure 13.2. -- Length frequency distribution of voucher Etelis carbunculus (green, n = 39) and
Etelis boweni (red, n = 46) specimens from the SW Pacific, and Etelis sp. (n = 91)
previously collected from the Guam bottomfish fishery, which were assumed to
be all E. carbunculus but are now known to be a mix of E. carbunculus and E.
boweni.

178

Figure 13.3. -- Raw (top) and average (bottom) Fourier transform near infrared spectra (10,0004,000 cm-1) of voucher otoliths by Etelis species (n = 85). Absorbance is on the yaxis for each plot.

179

Figure 13.4. -- Raw (top) and average (bottom) Fourier transform near infrared spectra (10,0004,000 cm-1) of voucher Etelis carbunculus (green) and Etelis boweni (red) otoliths
by species (n = 85), pre-processed by 13-point Savitzky-Golay smoothing (2ndorder polynomial), 13-point Savitzky-Golay first derivative transformation, andmean-centering.

180

Figure 13.5. -- Scores plot of PC1 and PC2 from principal component analysis of Fouriertransform near infrared spectral data from voucher otolith samples colored by
Etelis species.

181

Figure 13.6. -- Partial least squares discriminant analysis model validation results showing Etelis
species classification predictions by calibration models built using voucher
samples (n = 85). Models were developed and validated on, A) all data (fullrandom cross-validation) or B) data split into calibration (70%) and validation(30%) datasets. For the bottom panel, samples 1-60 represent the calibration (i.e.,
training) dataset and samples 61-85 represent the validation dataset. For each plot,
class 1 = true Etelis boweni and class 2 = true Etelis carbunculus.

182

Figure 13.7. -- Partial least squares discriminant analysis model testing results showing themodel trained on voucher Fourier transform near infrared spectra used to predict
the species of Guam Etelis sp. samples. For A) samples 1-85 represent thecalibration (i.e., training) dataset and samples 86-176 represent the Guam (i.e.,
unknown) dataset. The discrimination line (red dash) indicates the point at whichsamples above are predicted as Etelis boweni, and below are predicted as Etelis
carbunculus. Guam data are displayed by a size range cut-off, given that only
Etelis boweni are expected to grow larger than 60 cm FL. B) Model predictions
for E. boweni are shown.
183

184

14. Trials and Tribulations of Using FT-NIR Spectroscopy on Coastal Pelagic Species:
Method Development for Scanning Pacific Sardine Otoliths

Emma M. Saas1, Brittany D. Schwartzkopf2, Emmanis Dorval3, and Dianna L. Porzio4

1

Saltwater Inc. (under contract with Southwest Fisheries Science Center)
733 N Street
Anchorage, AK USA
Southwest Fisheries Science Center
NOAA, National Marine Fisheries Service
8901 La Jolla Shores Drive
La Jolla, CA USA
2

3

Lynker (under contract with Southwest Fisheries Science Center)
202 Church Street SE, Suite 536
Leesburg, VA USA
Marine Region
California Department of Fish and Wildlife
2451 Signal Street
AltaSea Berth 59
San Pedro, CA USA
4

185

186

INTRODUCTION

One innovative approach that is being evaluated to rapidly age fish is Fourier transformnear infrared (FT-NIR) spectroscopy. FT-NIR spectroscopy is a non-destructive analytical toolthat collects information about a sample’s light absorption within the near infrared
electromagnetic spectrum (800-2,500 nm). The technology itself has been well established in thedairy and agricultural industry for decades (Rasco et al. 1991, Sorvaniemi et al. 1993); however,its use in fisheries research is still relatively novel. The research and development for
implementation of FT-NIR spectroscopy at the six National Oceanic and Atmospheric (NOAA)
Fisheries Science Centers is part of a NOAA strategic initiative (Helser et al. 2019a). The
initiative funds research to develop methodology for respective species of interest at each centerand potentially makes FT-NIR spectroscopy ready for practical use on a regular basis.
The application of FT-NIR spectroscopy has proved to be a useful tool for fisheriesscientists despite the limited understanding of what is being measured within an otolith. FT-NIR
spectroscopy has been used to scan the otoliths and derive ages of individual fish of several
species, including walleye pollock (Gadus chalcogrammus) (Helser et al. 2019b), red snapper(Lutjanus campechanus) (Passerotti et al. 2020a, b), saddletail snapper (Lutjanus malabaricus)(Wedding et al. 2014, Robins et al. 2015), Pacific cod (Gadus macrocephalus) (Healy et al.
2021), and barramundi (Lates calcarifer) (Wright et al. 2021). The methods for analyzing
otoliths of the aforementioned species include scanning the otoliths convex side down at aconsistent orientation but vary slightly in the accessories used. Helser et al. (2019b) and
Passerotti et al. (2020a, b) used a gold-coated reflector (i.e., stamp) over the otolith, though thelatter found the additional use of a Teflon disc improved the prediction models due to the small

187

size of otoliths. Wedding et al. (2014) placed their samples in quartz glass vials, and Robins et al.
(2015) used no accessories at all. The use of scanning accessories likely needs to be examinedfor each new species to be aged using FT-NIR spectroscopy.
The Southwest Fisheries Science Center in La Jolla, California, is tasked with assessing
coastal pelagic species (CPS), which include performing stock assessments. Pacific sardine(Sardinops sagax) are an important CPS whose populations are assessed annually, and thus ages
are generated frequently. Pacific sardine range from southeast Alaska down to Baja California,
can grow up to about 41 cm standard length (SL), though individuals caught in the fishery are
most frequently under 30 cm SL, and are short lived with a maximum reported age of 15 years
old, though most individuals collected from trawl surveys and port sampling are less than 5 years
old (Eschmeyer et al. 1983, Kuriyama et al. 2020).
The use of FT-NIR spectroscopy technology to estimate age must be evaluated on a
species-to-species basis, as some otolith morphology and increment deposition patterns might bea better fit for this type of analysis than others. Pacific sardine were chosen to be evaluated dueto their importance in CPS stock assessments, management, and the California Current
Ecosystem. Pacific sardine present a particular challenge to FT-NIR spectroscopy due to theirsmall size and short lifespan; their otoliths are extremely small (<6 mm, Javor et al. 2011)
compared to long-lived species whose otoliths grow to much larger sizes. In this study, wedeveloped a methodology that best captures the spectral information from Pacific sardine
otoliths. Otolith condition, placement, and spectrometer accessories were assessed for optimal
spectral output. Otoliths were scanned using 10 accessories, such as a chrome ring and a Teflon
disc, to investigate what tools were needed to accommodate the small otolith size.

188

METHODS

Pacific sardine were collected along the west coast from CPS spring and summer trawl
surveys conducted by the NOAA’s Southwest Fisheries Science Center (NOAA-SWFSC) from2004 to 2021, and port sampling conducted by California Department of Fish and Wildlife(CDFW), Washington Department of Fish and Wildlife (WDFW), and Oregon Department ofFish and Wildlife (ODFW) from 2004 to 2016 (see Dorval et al. 2022 for detailed sampling
methods). Samples collected for Pacific sardine range from Vancouver Island, British Columbia,
Canada (49.65°N, 125.44°W) to around San Diego, California (33.25°N, -117.56°W). All Pacificsardine samples that were analyzed were considered to be the northern stock, using the most upto-date population structure assessment (Zwolinski and Demer 2023). After sample collection,
sagittal otoliths were extracted, cleaned, dried, and stored in labeled plastic tubes. A set ofPacific sardine otoliths from an age validation study were included as ‘known age’ samples (K.
C. James et al., SWFSC, in review). Juvenile fish were collected in 2014 and 2015, werechemically marked with oxytetracycline (OTC), and maintained in captivity for up to a year.
Sagittal otoliths were extracted after mortality and stored dried in black, labeled plastic tubes.

Traditional Ageing

Pacific sardine otoliths were traditionally aged using the methods described by Yaremko(1996), which include submerging whole otoliths in distilled water with a black background. The
number of annuli on the distal side of the otolith was counted using a dissecting microscope at
25x magnification with reflected light. Only otoliths whose ages were agreed upon by all three189

age readers were chosen for the reference sets to create calibration models for each species. The
NOAA-SWFSC and CDFW data sets only comprised otoliths whose ages were previously
included in stock assessments. To increase the sample size for older age classes of Pacificsardine, we included otoliths collected off Oregon and Washington and aged by WDFW, as older
Pacific sardine are generally found in the Pacific Northwest. Among all sources, we aimed tohave at least 25 samples per age class for the reference set, but this was hard to achieve for older
ages that were not frequently caught (Table 14.1).

Scanning Specifications

For all experiments and model predictions described below, sagittal otoliths were scanned
using near infrared spectroscopy on a Bruker TANGO-R FT-NIR single-channel spectrometer(Bruker Optics, Ettlingen, Germany). Spectral data were collected using the OPUS softwarev8.5(SP1). Scanning specifications for the TANGO were set for 64 scans (averaged into a final
raw absorbance value), with a frequency of 16 cm-1 at 8 wavenumber intervals. All spectral data
were plotted as absorbance values by wavenumbers ranging from 11,528 to 3,952 cm-1. All
otoliths were placed convex side down and were weighed to the nearest 0.0001g after being
scanned. Primary scanning and analysis were performed at the NOAA-SWFSC in La Jolla,
California, with some additional scans for experiments performed at the NOAA-SWFSC in
Santa Cruz, California.

190

Experimental Scans

A series of experimental scans determined the best placement, otolith condition, andaccessories for the otoliths in this study. Unless otherwise stated, all experimental scans weredone only on the otoliths collected from the NOAA-SWFSC trawl survey, hereafter referred toas the Pacific sardine trawl set (Table 14.1). Otoliths were scanned using a chrome ring and alarge gold stamp for the initial tests to determine best placement and otolith condition.
The first sets of experimental scans were completed to determine the best orientation andposition on the scanning window. Results were judged by visually assessing the raw spectra in
OPUS to see if it met the criteria for a ‘usable’ scan. A usable scan of an otolith has minimal
noise and a near-flat ~11,528 to 7,192 (or >8,000) wavenumber range, as well as a scan that falls
below 2 AU (absorbance unit) on the y-axis (Jason Erikson, Bruker, pers. comm). We firstscanned three pairs of Pacific sardine otoliths in four different orientations in the center of thewindow: 0°, 90°, 180°, 270° (Fig. 14.1a). We then scanned those same three Pacific sardine
otoliths in nine different positions on the window with the 0° orientation (Fig. 14.1b). Otoliths
were placed in the 0° orientation in the center of the window (position 5; Fig. 14.1b) for all
subsequent experimental scans.
Otoliths are sometimes broken or slightly dirty (Fig. 14.2). Breakage and excess tissue
aren’t uncommon, so we tested these conditions to determine if those otoliths provided usablescans. Left and right otoliths, where one was broken and the other was whole, were scanned from42 individuals from the Pacific sardine trawl sample. Age predictions from a calibration modelfrom only broken otoliths, and only whole otoliths were then compared. This same analysis was191then done for left and right otoliths collected from 40 individual fish where one otolith was dirty
and the other one was clean.
To determine the accessory that produced the best scan, we tried several combinations ofaccessories to determine what tools, if any, were needed to give the most reliable, precise, andrepeatable results. The nine combinations of scanning accessories that were tested on five Pacificsardine otoliths included 1) no accessory, 2) a small chrome stamp, 3) a small gold stamp, 4) alarge gold stamp, 5) a chrome ring, 6) a chrome ring plus a flat chrome stamp, 7) a chrome ringplus a small gold stamp, 8) a chrome ring plus a large gold stamp, 9) and a Teflon ring (Fig.
14.3). The flat chrome stamp (Fig. 14.3b), rather than the chrome stamp (Fig. 14.3a), was used in
combination with the chrome ring to prevent light from escaping through any gaps. Accessories
that resulted in scans that visually lacked the above criteria for a ‘usable’ scan were excluded
from any future testing.
To test for fine-scale variability between large gold stamps of the same manufacturer andmodel, two otoliths were scanned by one individual on Santa Cruz SWFSC’s TANGO with thechrome ring and the La Jolla SWFSC’s large gold stamp, then without changing any othervariables, scanned again with the Santa Cruz SWFSC’s large gold stamp. The resulting raw
spectra were judged qualitatively.

RESULTSFor both rotation and otolith placement, consistency was most important. Raw spectra
appeared different among orientations (Fig. 14.4). The position on the TANGO window wasvery important, and the resulting raw spectra differed greatly among some positions (Fig. 14.5).

192

Having the otolith in position seven, eight, or nine (bottom left, bottom center, bottom right,
respectively) produced the worst scans, with almost no prominent peaks, whereas position five(center) appeared to produce the best scans with very prominent peaks (Fig. 14.5).
Raw spectra from broken and whole otoliths of the same individual showed somedifferences (Fig. 14.6), whereas raw spectra from dirty and clean otoliths of the same individual
showed little difference (Fig. 14.7). It is important to note that otoliths that were dirty were notcompletely covered in tissue, but rather had random spots of tissue occurring at different
locations on the surface of the otolith.
The choice of accessory greatly influences the shape and absorbance values observed for
raw spectra for the Pacific sardine otoliths (Fig. 14.8). The accessories that were completely
ruled out for further use due to a lack of a near-flat 11,528 to 8,000 wavenumber range(recommended by Jason Erikson, Bruker, pers. comm.) included the small chrome stamp andsmall gold stamp, with or without the chrome ring, the large gold stamp without a chrome ring,and Teflon disc (Fig. 14.9). The three accessories set-ups that were chosen for future evaluationon the trawl set for Pacific sardine were 1) no accessory, 2) chrome ring only, and 3) chrome ringand large gold stamp.
There appeared to be a difference in absorbance values between the raw spectra from thesame otoliths scanned with La Jolla’s gold stamp and Santa Cruz’s gold stamp, where La Jolla’s
gold stamp produced higher absorbance values (Fig. 14.10).

193

CONCLUSIONS

Overall, consistency is the key to obtaining reliable FT-NIR spectroscopy scans for small
CPS. Otoliths should be scanned in a manner that is easily reproduced from scan-to-scan andtechnician-to-technician. Left and right otoliths that were both clean and whole showed nosignificant difference, which is consistent with Robins et al.’s (2015) findings for red snapper
otoliths. Whole, clean, and dry otoliths must be in the same orientation and position on thewindow for every scan. Researchers have a higher risk for error when placing small otoliths on
the scanning window than larger otoliths, so extra care needs to be taken. Small amounts of dried
tissue on an otolith seems to be acceptable, though clean otoliths are always preferred if
available. Markings to indicate correct otolith placement, without compromising the integrity of
the TANGO’s quartz scanning window, are helpful. Minimizing the number of accessories
means fewer changing variables over time, such as scratches and replacement parts, yet using noaccessory yields unusable scans for the smallest otoliths. We used our best judgment to decidethat the gold stamp potentially added unnecessary variability for Pacific sardine and we
concluded using only the chrome ring is the best accessory, but this may not be true for otherspecies with larger otoliths. In conclusion, the method developed in this study may not be theideal solution for all species with small otoliths, but hopefully provides a starting point for futurespecies-specific method development.

194

Table 14.1. -- Sample sizes per age class for the reference set from all data sources for Pacificsardine (n = 537) that were used to create a calibration model. CDFW = California
Department of Fish and Wildlife, WDFW = Washington Department of Fish andWildlife, OTC = oxytetracycline, SWFSC = Southwest Fisheries Science Center.
Age (years)
Source

0

1

2

3

4

5

6

7

8

9

CDFW - Fishery
WDFW - Fishery
SWFSC – OTC
SWFSC - Trawl

9
0
92
6

13
0
131
15

19
0
3
20

19
0
0
18

9
0
0
18

9
0
0
17

4
14
0
21

3
22
0
25

2
10
0
28

0
10
0
0

195

Figure 14.1. -- a) Otoliths were scanned in four orientations, 0°, 90°, 180°, and 270°. b) Otoliths
were scanned in nine different positions on the scanning window. The otoliths
shown here are from Pacific sardine (Sardinops sagax) viewed as if you weredirectly above the scanning window looking down.

196

Figure 14.2. -- Examples of a) whole and broken and b) dirty (left) and clean (right) sagittal
otolith pairs that were used to examine scan difference between otoliths ofdifferent condition. The otoliths pictured in a) were submerged in water.

Figure 14.3. -- The accessories used to test scanning methods, sometimes in conjunction with
one another, were a) small chrome stamp, b) flat chrome stamp, c) small gold
stamp, d) large gold stamp, e) chrome ring taped to the TANGO, and f) Teflon
disc taped to the TANGO.

197

Figure 14.4. -- Raw spectra of three Pacific sardine (Sardinops sagax) otoliths in four different
orientations. See Figure 14.1a for what each orientation represents.

Figure 14.5. -- Raw spectra of three Pacific sardine (Sardinops sagax) otoliths in nine different
positions on the TANGO window. See Figure 14.1b for what each window
position represents.

198

Figure 14.6. -- Raw spectra of Pacific sardine (Sardinops sagax) otoliths pairs from 42
individuals with one otolith being broken (red) and the other being whole (black)from each individual.

199

Figure 14.7. -- Raw spectra of Pacific sardine (Sardinops sagax) otoliths pairs from 40
individuals with one otolith being dirty (red) and the other being clean (black)from each individual.

200

Figure 14.8. -- Raw spectra of the nine different accessory combinations for Pacific sardine(Sardinops sagax). This figure is meant to showcase the large differences inabsorbance values as the spectral shape of certain accessories is masked due to thedifferences in y-axis values.

201

Figure 14.9. -- Raw spectra of five Pacific sardine (Sardinops sagax) otoliths for the ninedifferent accessory combinations. This figure is meant to showcase the different
shapes of the raw spectra, so each accessory has a different y-axis.

202

Figure 14.10. -- Raw spectra of two Pacific sardine (Sardinops sagax) scanned on Santa Cruz
SWFSC’s TANGO with the chrome ring and the La Jolla SWFSC’s large gold
stamp (blue), then without changing any other variables, scanned again with the
Santa Cruz SWFSC’s large gold stamp (red).

203

204

15. Benefits and Challenges of Using FT-NIR Spectroscopy for Age Estimation at theNortheast Fisheries Science Center

Eric M. Robillard1, Michelle S. Passerotti2, and Alexander M. Rubin2,3

Northeast Fisheries Science Center
NOAA, National Marine Fisheries Service
166 Water Street
Woods Hole, MA USA
1

Northeast Fisheries Science Center
NOAA, National Marine Fisheries Service
28 Tarzwell Drive
Narragansett, RI USA
2

IBSS Corporation
1110 Bonifant Street
Silver Spring, MD USA
3

205

206

INTRODUCTION

Fourier transform near infrared (FT-NIR) spectroscopy is an emerging method toestimate age of marine fishes. FT-NIR spectroscopy has been previously shown to be aneffective method at predicting ages of individuals using partial least squares (PLS) regression
models trained on known-age samples (Arrington et al. 2022, Healy et al. 2021, Passerotti et al.
2020b, Rigby et al. 2016). However, the efficiency and cost-savings of FT-NIR spectroscopy
relative to traditional ageing techniques is species- and laboratory-specific and has yet to beevaluated directly within the Northeast Fisheries Science Center (NEFSC) Age and Growth
Laboratory.
Within the framework of high-volume production ageing, we compare the feasibility of
estimating ages using FT-NIR spectroscopy for Acadian redfish Sebastes fasciatus to that of
traditional ageing methods that require processing of otoliths and visual counts of annual growthrings under a microscope. Production ageing of otoliths at the NEFSC uses a unique highthroughput processing setup that employs the use of barcoding, data entry applications and a
Labcut 250B saw (Extec, Enfield, CT) to maximize efficiency. This setup allows multiple
otoliths to be processed simultaneously and represents a different perspective on efficiency thathas not been directly compared to FT-NIR spectroscopy ageing methods to date. Herein, we use
three metrics to compare these methods: efficiency, cost, and ageing precision.

207

METHODS

Processing

Processing otoliths for ageing with the Labcut 250B saw consists of four steps: 1) otoliths
are mounted on polystyrene resin in trays, 2) once the resin is dry, the blocks with otoliths are
removed from the trays, 3) the blocks are cut using the Labcut 250B saw, and 4) the sections aremounted on clear plastic slides (NEFSC in review). Each block holds on average 75 samples,
which can be cut simultaneously with the saw.
For FT-NIR spectroscopy, all samples were scanned on a Bruker TANGO-R nearinfrared spectrometer (Bruker, Billerica, MA) per the methods outlined in Rubin et al. (thisvolume). All otoliths were cleaned with 70% ethanol and blotted dry using laboratory tissue
before scanning. Otoliths then were scanned sulcus side down with the rostrum oriented
horizontally on the sample window while covered with a rubber-rimmed gold reflective stamp
while scanning to reduce stray light. All absorbance spectra were collected at a resolution of 64
scans every 16 wavenumbers.

Efficiency

To estimate efficiency of the traditional Labcut 250B method, processing times wererecorded for 4,000 otoliths across all steps including embedding, sectioning, and mounting
sections on slides and averaged to calculate the number of otoliths that could be processed perhour (Table 15.1). For FT-NIR spectroscopy, efficiency was estimated using scans of Acadian208

redfish otoliths from the fall 2015 (n = 984) and fall 2016 (n = 925) Northeast Trawl Survey.
Processing time for FT-NIR spectroscopy included cleaning the otolith, placing it on the sample
window, initiating and completing a scan, and placing the otolith back in the envelope. Times
were averaged per otolith (Rubin et al. this volume).

Cost

Costs were estimated using metrics of initial and long-term cost. Initial cost for eachmethod includes the purchase of two Labcut 250B saws and two Tango-R spectrometers (one asa backup, which would be necessary to ensure no stoppage of work should one malfunction) andall supplies needed for one year of processing. The long-term cost includes supplies needed to
produce about 60,000 samples and maintenance costs.

Precision

All otolith samples were aged by an expert age reader using a Leica MZ6 microscope
under reflected light at 25X to 50X magnification. To test for precision, a random subsample of
345 otoliths was re-aged by the same reader with no knowledge of prior ages. Percent agreement(PA) and coefficient of variation (CV) were calculated and symmetry tests conducted (Campana
et al. 1995, Chang 1982, Evans and Hoenig 1998).
FT-NIR spectroscopy age prediction models were created per the methods outlined in
Rubin et al. (this volume). The models were assessed for accuracy using the coefficient ofdetermination (r2), root mean square error (RMSE) of cross validation and of prediction, residual

209

prediction deviation (RPD), model bias, and slope. To facilitate direct comparison of precisionand bias metrics between FT-NIR spectroscopy and traditional ages (PA, CV, and the EvansHoenig symmetry test), raw FT-NIR spectroscopy (i.e., fractional) ages were rounded to thenearest integer using a standard rounding routine, meaning any fractional age ending in ≥ 0.5
was rounded up, and anything ending in ≤ 0.4 was rounded down.

RESULTS

Efficiency

Using the Labcut 250B method, it took 56 minutes to process 70 otoliths per hour to thepoint of being ready to age. Using FT-NIR spectroscopy, it took 130 minutes to clean and scan
70 samples (Table 15.1).

Cost

For the Labcut 250B saw, initial cost estimates are around $38,000, while for FT-NIR
spectroscopy, the initial cost is around $150,000. For the long term, the Labcut 250B saw costs
about $3,000, while the FT-NIR spectroscopy long-term cost ranges from $6,000 to $17,000
depending on software needed for data analysis and whether or not a service contract is needed
for maintenance and repairs (Table 15.1).

210Precision

Ageing precision for traditional ageing of Acadian redfish otoliths produced a 69.6%
agreement and a CV of 1.81% between reads. FT-NIR spectroscopy ages produced a 29.5%
agreement and a CV of 15.88% relative to traditional ages. The Evans-Hoenig test of symmetry
for traditional age estimates did not find any bias across ages (ꭓ2 = 1.14, d.f. = 4, p = 0.889). FTNIR spectroscopy age estimates were not biased overall (ꭓ2 = 7.84, d.f. = 8, p = 0.480) but did
however show bias for some individual age classes up to age 5 (Fig. 15.1).

DISCUSSION

Our results show that, with minimal investments in sample processing technology fortraditional ageing, the traditional method utilized by the NEFSC for otolith processing is twice as
fast as the FT-NIR spectroscopy method. We did not include in our analysis the time required for
producing ages because the process for producing FT-NIR spectroscopy ages requires modelbuilding and data optimization, which is a significant time investment on a much different scalethat is difficult to compare to traditional ageing. On average, 15 samples can be aged per hourusing traditional methods, while model building and data optimization to produce the FT-NIR
spectroscopy model for Acadian redfish took several weeks and is still considered preliminary.
This time estimate could be reduced for future FT-NIR spectroscopy models, but at this time it isstill less efficient than producing a traditional age estimate.
FT-NIR spectroscopy is three times more expensive in the short-term and five times more
expensive in the long-term when compared to the normal operating costs of the Labcut 250B

211

processing method. In terms of precision, FT-NIR spectroscopy predicted ages had lower
agreement and higher variation relative to traditional ages than was demonstrated by traditional
inter-reader variability. Given the lower efficiency in processing time, increased cost, and lower
precision of FT-NIR spectroscopy age prediction relative to traditional methods, we caution
potential adopters of FT-NIR spectroscopy methodology to do a cost analysis of the two methods
before implementing this new technology. While some species require less efficient otolith
processing methods (e.g., those cut individually with low-speed saws), roughly 80% of the work
done at the NEFSC utilizes the high-throughput Labcut 250B saw. Unique methodologies
specific to each laboratory will define whether FT-NIR spectroscopy conveys time and costsavings in the long term.

212

Table 15.1. -- Comparison of cost and efficiency of Traditional (Labcut 250B saw) ageing
method versus Fourier transform near infrared spectroscopy (FT-NIRS). Initialcost includes the purchase of two Labcut 250B saws and two Tango-R
spectrometers and supplies needed for one year of processing. The long-term cost
includes supplies needed to produce about 60,000 samples and maintenance costs.
Efficiency is the amount of time it takes to process 70 samples.

Traditional
Short-Term Cost

Equipment$36,000$130,000

Supplies$2,300$1,000Total$38,300$131,000

Supplies$2,300$1,000$500$5,600Service contract0$9,000Software

0500$2,800$16,100

Mount/Clean

42

70

Section/Scan

14

60

Total

56

130

Replacement parts
Long-Term Cost

Total
Efficiency

FT-NIRS213

Figure 15.1. -- Mean bias (black circles) ± standard deviation for Acadian redfish Sebastes fasciatus. A) Fourier transform nearinfrared spectroscopy (FT-NIRS) and B) traditional ages by age class for all test sets combined, as predicted by the All
Regions Combined Truncated Calibration Model.

21416. Deep Learning Coupled with Fourier Transform Near Infrared Spectroscopy of
Otoliths Improves Age Prediction for Long-Lived Fish

Irina M. Benson1, Beverly K. Barnett2, and Thomas E. Helser1

Alaska Fisheries Science Center
NOAA, National Marine Fisheries Service
7600 Sand Point Way NE
Seattle, WA USA
1Southeast Fisheries Science Center
NOAA, National Marine Fisheries Service
3500 Delwood Beach Road
Panama City, FL USA
2

215

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ABSTRACTFish age estimation is crucial for effective stock management, but traditional methods arelabor-intensive and subject to poor repeatability for longer-lived species. This study investigatedan alternative approach utilizing Fourier transform near infrared (FT-NIR) spectroscopy ofotoliths coupled with multimodal convolutional neural networks (MMCNN). Multiple previous
studies applied partial least squares regression (PLS) to otolith spectroscopic data for age
prediction. One recent study applied the FT-NIR/MMCNN approach to shorter-lived walleye
pollock (Gadus chalcogrammus). In this study, we extended the approach to two longer-lived
economically-valuable species: northern rockfish (NRF, Sebastes polyspinis) from the Gulf of
Alaska (GOA) and red snapper (RS, Lutjanus campechanus) from the U.S. Gulf of Mexico(GOM). We trained MMCNN to integrate spectral data with biological and geospatial data.
MMCNN performed well, yielding high r2 values (0.92 for NRF and 0.91 for RS) and low
RMSE values (3.4 years for NRF and 1.0 years for RS) for training data. The models
demonstrated high accuracy for a holdout data set yielding high r2 values (0.92 for NRF and 0.89
for RS) and low RMSE values (3.4 years for NRF and 1.1 years for RS) for test data. The BlandAltman plots confirmed the agreement with low systemic bias between MMCNN predictions andtraditional microscope-based ages. The SHAP (SHapley Additive exPlanations) featureimportance analysis revealed the significant influence of otolith spectra, otolith weight, fish
length, and geospatial data on age predictions. This study demonstrated the potential of the FTNIR/MMCNN approach for efficient age estimation in long-lived fish species, contributing toenhanced fisheries management and conservation efforts.

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218

INTRODUCTION

Fish age estimation is a vital aspect of fisheries management, providing crucial data forunderstanding fish populations and informing sustainable harvesting practices. Traditional age
estimation methods, particularly those based on microscopic examination of otoliths, have
proven to be labor-intensive and prone to reader variability, which can hinder accurate andtimely assessments. To overcome these limitations, researchers have explored innovativetechniques like Fourier transform near infrared (FT-NIR) spectroscopy for more efficient andreliable age predictions. A few recent studies implemented a traditional chemometric approachof using partial least squares regression (PLS) to build a model using otolith FT-NIR
spectroscopic data of various fish species (Wedding et al. 2014, Helser et al. 2019b, Healy et al.
2021, Passerotti et al. 2020b). A more recent study of eastern Bering Sea (EBS) walleye pollock(Gadus chalcogrammus) otoliths used FT-NIR spectroscopy coupled with multimodal
convolutional neural networks (MMCNN) to rapidly predict fish age with comparable
performance to traditional microscope-based methods (Benson et al. 2023). Benson et al. (2023)
suggested the MMCNN approach provides improved age predictions compared to traditional
chemometric models such as PLS. While previous studies have demonstrated the success of thisapproach for certain fish species, its applicability to economically valuable long-lived species,
such as northern rockfish (Sebastes polyspinis) from the Gulf of Alaska (GOA) and red snapper(Lutjanus campechanus) from the U.S. Gulf of Mexico (GOM), remains to be explored.
Northern rockfish (NRF) are commercially important rockfishes that live 80+ years and are ofparticular interest due to their high abundance in the GOA and EBS waters (Kastelle et al.,2016). Red snapper (RS) are an ecologically and economically important reef fish that live 50+219

years and are estimated to be rebuilding in GOM waters (SEDAR 2018). By extending theapplication of FT-NIR spectroscopy coupled with MMCNN to these species, our study aims toshed light on the potential advantages and limitations of this method, offering valuable insights
into its effectiveness and potential contributions to fisheries management and conservation
efforts.

METHODS

Data Collection

Otoliths from NRF were collected during biennial bottom trawl surveys conducted by the
Alaska Fisheries Science Center (https://www.fisheries.noaa.gov/alaska/ecosystems/alaska-fishresearch-surveys) in the GOA in the years 2013, 2015, 2017, and 2019. The data set comprised
1,460 otoliths, along with corresponding fish length in addition to geographic coordinates, depth,
and temperature data (Table 16.1A, Fig. 16.1A-B). The Age and Growth Program at the Alaska
Fisheries Science Center collected otolith weights and fish ages using traditional microscopebased methods, following established protocols described in Matta et al. (2012) and Helser et al.
(2019b). For RS, otoliths (n = 6,053) were collected from GOM fishery dependent and fisheryindependent sources between the years 2017 and 2019. The Biology and Life History Branch at
Panama City weighed, sectioned, and aged RS otoliths using traditional, microscope-based
methods. The data included ages, substock information assigned as East GOM or West GOM
using the Mississippi River as the separating line (SEDAR 2018), fork length, and otolith weight(Table 16.1B, Fig. 16.2A-B).

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Spectroscopic Measurements

Diffuse reflectance measurements from all otoliths were obtained using Bruker Optics
Tango R or MPA II spectrometers. The OPUS software version 7.5 (Bruker Optics, Ettlingen,Germany) was used for spectral acquisition. Before scanning, NRF otoliths were removed fromglycerin-thymol solution and patted dry with a laboratory tissue. At the time of otolith collection,
all RS otoliths were stored dry in coin envelopes; therefore, no preparation was needed prior toscanning. Each otolith was placed in a concave up position on the spectrometer's integrated
sphere and covered with a gold-coated reflector stamp (see Benson et al. (2020) for a detailed
description and pictures of scanning setup). Final absorbance spectra were acquired between
11,500 and 4,000 cm-1, averaging 64 scans at a resolution of 16 cm-1. Spectral data for eachspecies were preprocessed with first derivative, second polynomial, 17-point Savitzky-Golay
smoothing (Figs. 16.1C-F, 16.2C-D).

Data Analysis

The data set for each species was divided into training and test groups using the "onion"
method in the chemometrics software Solo 8.7 (Eigenvector Research, Inc., Manson, WA, USA).
The training group was used to train a neural network, while the test group was kept aside toevaluate a model on unseen data. For NRF, 90% of the samples (n = 1,418) were assigned to thetraining set, and the remaining 10% (n = 142) were designated as the test set. The 90/10 split was
selected to maximize the amount of data in the training set due to the total low sample size.
Integer age was assigned as the target variable for NRF. For RS, specimens with higher-221confidence ages (n = 1,406) were placed in the training set, and the remaining data wererandomly split, with 74% assigned to the same training set. The specimen with the oldest
microscope-based age was also included in the training set. The final training (n = 4,845) and test(n = 1,208) sets for red snapper comprised an 80/20 data split, with fractional age as the target
variable.
The MMCNN model, as described in Benson et al. (2023), was applied to both species(Fig. 16.3). The model consisted of two primary branches, one associated with spectral data andthe other with biological and geospatial data. The spectral data branch included a convolutional
layer and the output was flattened and concatenated with the biological/geospatial data branch. A
fully connected dense layer activated by a rectified linear unit (ReLU) introduced nonlinearity to
the model and dropout was used for regularization to prevent overfitting. The final fully
connected dense layer had a single output node with a linear activation function. Python 3.7.0(Van Rossum and Drake 2009) with TensorFlow v. 2.5.0 (Abadi et al. 2016) and Keras API(Chollet 2015) implementation were used to train the models. The hyperband optimization (HB)
was applied to improve the models’ generalizing abilities (Li et al. 2017).

Performance Evaluation

The performance and robustness of the models were assessed using coefficients ofdetermination (r2) for training and test data. Root mean square error (RMSE), which represents
the average magnitude of the errors between predicted and traditional ages, was calculated forboth data sets to measure the accuracy of the predictions. Bland-Altman (BA) plots were
employed to compare MMCNN predictions with traditional microscope-based ages, enabling the222

identification of any systematic differences or biases between the two methods (Bland andAltman 1999). The plot allowed examination of the level of agreement in terms of themeasurement variability. It consisted of a scatter plot where the x-axis represented the average ofthe two measurements being compared and the y-axis showed the difference between the two
measurements for each data point. Each data point represented a single measurement. A center
line, which represented the overall bias between the two measurements, was drawn at the meandifference between the two methods. Two additional lines, which represented the range within
which approximately 99% of the differences between the two measurements were expected tolie, calculated as the mean difference ±2.58 times the standard deviation of the differences. Anydata points that fell outside the limits of agreement suggested discrepancies between the twoageing methods for those particular measurements.
Comparison of accuracy between ages generated by the traditional and FT-NIR age
estimation method was done by plotting the frequency of the mean bias by age. Traditional bias
was calculated as a difference between two trained age readers’ microscope-based age estimates
for the same specimen. FT-NIR bias was calculated as a difference between age predicted by the
MMCNN models and traditional microscope-based age estimation (Helser et al. 2019b).
Fractional numbers were rounded to the nearest integer. We examined precision for bothtraditional methods and FT-NIR method by calculating percent agreement (PA), the percent
agreement within one year (PA ± 1), coefficient of variation (CV), and average percent error(APE) (Campana 2001, Ogle 2017b, Ogle 2018). The metrics for each species were calculatedon the subset of specimens that had two traditional ages in addition to the age predicted by the
MMCNN model.

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Model Interpretability

To interpret the MMCNN predictions, SHAP (SHapley Additive exPlanations) was used
to compute a contribution of each feature to the model’s output (Shapley 1953, Lundberg and
Lee 2017). The SHAP values, based on cooperative game theory, provided a fair and consistentmeasure of feature importance. The most influential features driving the model's predictions
were identified and visualized using SHAP feature importance plots. Each bar in the plot
represented a feature with its length corresponding to the magnitude of its effect on the model's
output. The plot allowed us to identify the most influential features driving the model'spredictions. The spectral wavenumber contributions were aggregated by thousands and allfeatures were ordered according to their importance.

RESULTS AND DISCUSSION

The application of FT-NIR spectroscopy combined with MMCNN proved to be a
promising approach for accurate and efficient fish age estimation. The MMCNN models
exhibited strong performance, demonstrating a high degree of correlation between FT-NIR ages
and traditional microscope-based ages for both NRF and RS. The scatter plots of the MMCNN
age predictions showcased a close alignment with the reference microscopic ages, indicating
accuracy and reliability of the models for both species (Fig. 16.4). For NRF, the MMCNN model
achieved an r2 of 0.92 and an RMSE of 3.4 years for both the training and test data sets. At least
67% of the estimated NRF ages were predicted within 3.4 years of traditional microscopic ages,
while 95% of the estimated ages were within 6.8 years of the reference ages. Similarly, the RS

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MMCNN model demonstrated strong predictive capabilities, with an r2 of 0.91 and an RMSE of
1.0 year for the training data set, and an r2 of 0.89 and an RMSE of 1.1 years for the test data set.
At least 67% of the estimated RS ages were accurately predicted within 1.1 years of traditional
microscopic ages, and 95% of the estimated ages fell within 2.2 years of the reference ages. A
higher mean age and maximum age for NRF (max age = 58 years, mean age = 25 years; Table
16.1A) compared to RS (max age = 50 years, mean age = 6 years; Table 16.1B) could potentiallyexplain higher RMSE for NRF. In addition, the majority of the RS specimens were aged 20 years
old or less (Fig. 16.2A-B) with just a few older specimens being included in the training data set
and not available for the test set (Fig. 16.4C-D). For NRF, the training and test data set included
both young and old fish, which led to a wider spread of ages in the dataset (Figs. 16.1A-B,
16.4A-B). Predicting ages accurately for both young and old fish could have been morechallenging, resulting in higher RMSE. Another factor that could have contributed to the NRF
having higher RMSE is a smaller sample size that could lead to less representative data fortraining the model. Another factor that could contribute is inherent biological variability, leading
to age-related growth variations even within the same age group. The growth patterns of RS
otoliths in this study led to a linear relationship of otolith weight with age (Fig. 16.2B), while anon-linear growth pattern of NRF where growth slows with age led to a non-linear relationshipof otolith weight with age (Fig. 16.1B), which can make age prediction more challenging andcontribute to higher RMSE.
The performance metrics of the MMCNN models were comparable to previous studies
that utilized FT-NIR spectroscopy and PLS for longer-lived fish age estimation. For instance,
Passerotti et al. (2020b) reported an r2 of 0.92 and an RMSE of prediction of 0.99 to 1.02 years
for red snapper. Wedding et al. (2014) achieved an r2 of 0.94 and an RMSE of prediction of 1.54

225

years for shorter-lived saddletail snapper (Lutjanus malabaricus). Our results were also
consistent with Benson et al. (2023), who applied MMCNN to FT-NIR spectral, biological, andgeospatial data of shorter-lived walleye pollock. Benson et al. (2023) reported an r2 of 0.92 andan RMSE of prediction of 0.91 years for the test set.
The BA plots provided additional evidence of the agreement between MMCNN-predictedages and traditional microscope-based ages (Figs. 16.5A and 16.6A). Low systematic bias for
either species was indicated by the center line not deviating significantly from zero (0.47 years
for NRF and -0.06 years for RS). The narrow limits of agreement for RS (+2.86/-2.97 years)
indicated a better level of agreement compared to NRF (+9.06/-8.13 years). The wider limits ofagreement indicated greater variability and less agreement between the two ageing methods for
NRF. The presence of multiple data points falling outside of the limits of agreement in the RS
plot might be attributed to the large range of RS otolith sizes. Passerotti et al. (2020b) noted that
RS otoliths’ FT-NIR spectra might be affected by otolith size and otolith thickness in addition tophysical and chemical changes underlying otolith growth. The mean bias by age was equivalent
between the traditional and FT-NIR methods up to around 40 years of age for NRF (Fig. 16.5B)
and up to around 18 years of age for RS (Fig. 16.6B).
For the NRF model predictions, the PA of 13.9% and PA ± 1 year of 41.9% were lower
than the PA of 28.7% and PA ± 1 year of 64.9% for the traditional microscopic-based betweenreader agreement (Table 16.2A). Between-reader APE and CV were higher for the FT-NIR
method, with an APE of 5.4% and CV of 7.6% in comparison to an APE of 3.3% and CV of
4.7% for the traditional method (Table 16.2A). The typical inter-reader precision for NRF is
usually higher (PA = 44.4%, APE = 2.9%, CV = 4.1%; n = 2852; Goetz et al. 2012) than in ourstudy, while the mean age is usually lower (~16 years) than in our study (25 years; Table 16.1A).

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Since the FT-NIR method relies on traditional age data, difficulties with generating ages by the
traditional method most likely affected accuracy and precision of the FT-NIR method. For the
RS model predictions, the PA of 52.1% and PA ± 1 year of 88.79% were lower than the PA of
68.7% and PA ± 1 year of 93.6% for the traditional microscopic-based between-reader
agreement (Table 16.2B). Between-reader APE and CV were higher for the FT-NIR method,with an APE of 5.5% and CV of 7.8% in comparison to an APE of 3.3% and CV of 4.7% for the
traditional method (Table 16.2B). Our results were slightly better than the previous study thatutilized a PLS model for GOM RS spectral data and reported a PA of 41-50%, PA ± 1 year of
87.1%, and APE of 7.3-13.2% (Passerotti et al. 2020b). The FT-NIR method’s performance
metrics for either species could also be affected by the fact that we had to round fractional ages.
Further studies are needed to establish the best protocol for establishing rounding convention for
the FT-NIR ages.
The SHAP feature importance plots provided valuable insights into the significant
factors influencing the MMCNN predictions (Fig. 16.7). Otolith spectra in the 4,000 cm-1
wavenumber region emerged as the most influential feature for both species, confirming thesignificance of spectral information in age estimation. Additionally, otolith spectra in the 7,0005,000 cm-1 wavenumber region played a key role in age predictions, particularly for RS. Otolithweight emerged as the most influential among non-spectral features for both species,
highlighting the impact of otolith size on age determination. Specifically, for the NRF model,
otolith weight and spectra in the 5,000 cm-1 region exhibited considerable influence. For the RS
model, spectra in the 5,000 cm-1 and 7,000 cm-1 regions held greater importance than otolithweight. Fish length, along with latitude for NRF and substock for RS, were also influential
features, aligning with existing knowledge of these factors' influence on fish growth and age.

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These results are comparable to the recent study using the MMCNN on walleye pollock datawith otolith spectra in the 7,000-4,000 cm−1 wavenumber region having the highest impact on
predicting fish age (Benson et al. 2023). Otolith weights were not available in that study;
however, fish length and latitude were the most important features among biological andgeospatial data ingested by the model. While the MMCNN model demonstrated excellent
performance, enhancing its interpretability remains an area of improvement. Exploringvisualization techniques or attention mechanisms could provide deeper insights into the model'sdecision-making process.
Despite the promising results, several limitations and future considerations should be
acknowledged. The relatively small sample size for NRF could potentially impact the model's
generalizability, calling for further exploration with larger and more diverse datasets. Updating
the models with known-age fish or external validation against alternative age estimation methods
would strengthen the model's reliability. Temporal validation across different time periods could
also account for potential variations in fish growth rates and age estimation. Extending the study
to include data from different geographic regions and a wider range of fish species would
provide a more comprehensive assessment of the FT-NIR/MMCNN approach under varying
environmental conditions and life histories. Addressing the discussed limitations and considering
future model updating methods will further advance the FT-NIR/MMCNN method as a valuabletool.
Our results demonstrated the ability of the FT-NIR/MMCNN approach to closely alignits age predictions with traditional microscope-based ages for both species, indicating its
accuracy and reliability. The FT-NIR spectroscopy of otoliths coupled with MMCNN
demonstrated robust and accurate fish age predictions for long-lived fish species. The study's

228

findings contribute valuable insights into the potential use of this method for diverse fish species,
supporting sustainable fisheries practices and effective population monitoring. The model's
performance metrics, agreement with traditional methods, and identification of influential
features underscore the efficacy of the FT-NIR spectroscopy of otoliths coupled with MMCNN
as a valuable tool for enhancing fish age estimation accuracy and efficiency.

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Table 16.1. -- Summary of available data for (A) northern rockfish Sebastes polyspinis and (B)red snapper Lutjanus campechanus.
(A)
Min
Max*
Mean
SD
MedianAge (years)
3
58
25
12
22

Total length (mm)
140
480
391
42
400

Otolith weight (g)
0.02
0.44
0.22
0.07
0.23

Depth (m)
57
255
116
30
110

* The oldest northern rockﬁsh aged by the Age and Growth Program between 1990 and 2021 was 88 years and 410
mm (Goetz et al. 2012).

(B)
Min
Max
Mean
SD
MedianFractional age (years)
0.70
49.64
5.9
3.48
4.95

Fork length (mm)
215
892
513
114
493

Otolith weight (g)
0.14
5.56
0.87
0.46
0.73

*The maximum calendar age used in the most recent assessment was 57 years (SEDAR 2022).

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Table 16.2. -- Precision statistics for the (A) northern rockfish Sebastes polyspinis (n = 296) and(B) red snapper Lutjanus campechanus (n = 1,090) collections. Specimens that
weren’t assigned a second traditional age have been excluded. PA = percent
agreement, PA ± 1 yr = percent agreement within 1 year, APE = average percent
error, and CV = coefficient of variation.
(A)
MethodTraditional
FT-NIR

PA
28.7%
13.9%

PA ± 1 yr
64.9%
41.9%

APE
3.3%
5.4%

CV
4.7%
7.6%

PA
68.7%
52.1%

PA ± 1 yr
93.6%
88.79%

APE
3.3%
5.5%

CV
4.7%
7.8%

(B)
MethodTraditional
FT-NIR

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Figure 16.1. -- Northern rockfish Sebastes polyspinis (NRF) (A) fish length-for-age and (B)otolith weight-for-age scatter plots with blue dots indicating training data set andorange dots indicating test set. (C) Raw and (D) first-derivative-transformed
otolith Fourier transform near infrared (FT-NIR) spectra for NRF training set, (E)raw and (F) first-derivative-transformed otolith Fourier transform near infrared(FT-NIR) spectra for NRF test set.

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Figure 16.2. -- Red snapper Lutjanus campechanus (RS) (A) fish length-for-age and (B) otolithweight-for-age scatter plots, and (C) raw and (D) first-derivative-transformed
otolith Fourier transform near infrared (FT-NIR) spectra for RS combined data
set.

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Figure 16.3. -- Schematic diagram of the structure of the multimodal convolutional neuralnetwork (MMCNN).

234

Figure 16.4. -- Scatterplots showing MMCNN fish age predictions versus traditional age
estimates for northern rockfish Sebastes polyspinis (A) training and (B) test sets
and red snapper Lutjanus campechanus (C) training and (D) test sets. The dashedline is 1:1 agreement and the solid line is linear regression fit. Point density isillustrated by overlapping semi-transparent data points.

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Figure 16.5. -- (A) Bland-Altman plot showing difference in traditional microscope-based age
estimates and age estimates predicted by the model versus the mean of thereference and predicted age estimates for northern rockfish Sebastes polyspinis(NRF). Point density is illustrated by overlapping semi-transparent data points.
(B) Frequency of mean bias by age class for NRF. The error bars show one
standard error intervals around the mean. The solid line represents a 100%agreement line. The dashed lines represent ±1 year.

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Figure 16.6. -- (A) Bland-Altman plot showing difference in traditional microscope-based age
estimates and age estimates predicted by the model versus the mean of thereference and predicted age estimates for red snapper Lutjanus campechanus(RS). Point density is illustrated by overlapping semi-transparent data points. (B)Frequency of mean bias by age classes for RS. The error bars show one standard
error intervals around the mean. The solid line represents a 100% agreement line.
The dashed lines represent ±1 year.

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Figure 16.7. -- The relative importance of the input features for the (A) northern rockfish
Sebastes polyspinis and (B) red snapper Lutjanus campechanus models.
Wavenumbers are aggregated by 1,000 cm−1. The y-axis indicates the featurename in order of importance from top to bottom. The x-axis indicates the averageof the absolute Shapley value of each feature.

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17. Automatic Fish Age Prediction Using Deep Machine Learning – Combining Otolith
Images, NIR Spectra, and Metadata Features

Aotian Zheng1, Yudong Li1, Ara Vardanyan1, Stefan Arsov1, Jenq-Neng Hwang1,
Irina M. Benson2, Thomas E. Helser2, Jonathan A. Short2, Kathrin A. Bayer2, Craig R. Kastelle2,
Beverly K. Barnett3, Amanda R. Rezek3, Farron R. Wallace3, Ronald L. Hill3, William Kline3,
Laura A. Thornton3, Nancy Evou3, Robert J. Allman3, and Naeem Willett3

University of Washington
1410 NE Campus Parkway
Seattle, WA USA
1

Alaska Fisheries Science Center
NOAA, National Marine Fisheries Service
7600 Sand Point Way NE
Seattle, WA USA
2Southeast Fisheries Science Center
NOAA, National Marine Fisheries Service
75 Virginia Beach Drive
Miami, FL USA
3

239

240

ABSTRACTFish ages, which are critical for managing stocks, are used to estimate growth rates,
mortality, age at maturity, and population trends. Unfortunately, traditional otolith age estimation
methods are labor-intensive and subject to poor repeatability. While Fourier transform nearinfrared (FT-NIR) spectroscopy of otoliths has recently emerged as a highly efficient fish age
estimation method, our project investigates another possible data modality for rapid and effective
fish age estimation, red-green-blue (RGB) imagery of whole fish otoliths. Our focus is on how
effective the three different data modalities (otolith images, FT-NIR spectra, and associatedbiological and geospatial data) are for deep learning-based fish age prediction, both individually
and when combined. We use data collected from three different species in our model training andevaluation: walleye pollock, red snapper, and Atlantic menhaden.

241

242

INTRODUCTION

Traditional fish ageing methods generally involve manual counting of growth patterns oneither otoliths or scales by trained experts. These methods are not only time consuming and labor
intensive, but they also contain some degree of uncertainty, as even expert readers can often
arrive at different ages for the same otolith sample. Recent research has explored using Fouriertransform near infrared (FT-NIR) spectroscopy (Helser et al. 2019b) for rapid and effective
ageing of otoliths. Besides FT-NIR spectroscopy, focus has also been put on using otolith redgreen-blue (RGB) imagery in combination with deep learning (Moen et al. 2018, Politikos et al.
2021) for ageing as well. We analyze the effectiveness of the three data modalities: FT-NIR
spectra, otolith image, and associated metadata (biological and geospatial), in fish age prediction
and the effectiveness of combining all three modalities in one neural network model. Most of ourexperiments are conducted on walleye pollock (Gadus chalcogrammus) data, but we also show
results on red snapper (Lutjanus campechanus) and Atlantic menhaden (Brevoortia tyrannus).

METHODSWe designed our model to utilize all three available data modalities by having threedifferent input branches, one for each modality. The corresponding embedding features for eachdata modality were then concatenated together before being fed to a final, unified prediction
layer. Following Politikos et al. (2021), we used an inceptionV3 (Szegedy et al. 2016)
convolutional neural network (CNN) architecture for the image branch, while both the spectral
data and metadata used a simple multilayer perceptron (MLP). Since we formulated ageing as a243classification task, we trained our network using cross-entropy loss. Rather than training with a
one-hot age label, we instead used a soft label to indicate the uncertainty in our ages obtained
from human experts. Our methodology is illustrated in Figure 17.1.

Data

We evaluated our method on data from three different species: walleye pollock, redsnapper, and Atlantic menhaden. Details of the three datasets can be seen in Figures 17.2, 17.3,
and 17.4. For walleye pollock metadata, we used fish length, otolith weight, latitude, longitude,sex, gear depth, bottom depth, and catch month. The samples in the walleye pollock test set werealso aged by a second reader so we were able to compare our model versus reader accuracy to areader versus reader agreement. For red snapper metadata, we used fish length, otolith weight,
sex, and catch location. For Atlantic menhaden metadata, we used fish weight, fish length, andcatch month. Note that for Atlantic menhaden, we did not have spectra data, only images andmetadata.

RESULTS AND DISCUSSION

We used classification accuracy to evaluate our model performance compared to a given
reader age. Due to the difficulty associated with age determination, even for trained humanreaders, we used top-2 accuracy for walleye pollock and red snapper ageing (Figs. 17.5, 17.6). In
top-2 accuracy, a prediction is considered correct if the groundtruthed age is one of the two ages244

with the highest scores based on our model output. Regular top-1 accuracy was used for Atlantic
menhaden ageing (Fig. 17.7).
Top-2 accuracy for walleye pollock ageing was very high at low ages but gradually
dropped as age increases (Fig. 17.5). This trend can likely be attributed to two factors. First, thedifficulty of determining an exact age from an otolith increases as the fish gets older, even forexpert human readers. Second, older ages (>10 years) are what we call minority classes, meaning
they are rarer than younger ages and have significantly less training data. This lack of training
data also affects the model’s ability to accurately age older otoliths. One interesting thing to noteis that for our walleye pollock data, there was an anomalous dip in accuracy at ages 11 and 12,
which was not explained by the two factors we identified.

Comparison to Humans

To evaluate whether our deep learning model can replace humans in otolith ageing, we
compared results with expert human readers on the walleye pollock test set (Fig. 17.8). We foundthat humans achieved a 93.5% reader agreement with a 1-year margin, meaning that 93.5% of
the time, two expert human readers estimated ages that were within 1 year of each other. Ourmodel achieved a slightly lower accuracy of 88.1% with a 1 year margin, meaning that for 88.1%of the samples, our model’s top age prediction was within 1 year of the human expert-provided
ground truth age. While our current deep learning model still fell a bit short of human experts, it
was only at higher ages (>10) and can potentially be remedied through long-tail training
methods.

245

Ablation Study

We conducted an ablation study on the walleye pollock dataset to determine how
effective each data modality was individually for age prediction and how well they
complemented each other when combined together (Table 17.1). To better understand thecontribution of each data modality to age discrimination, we removed one or more input
branches from our model in each experiment of our ablation study. We found that when only
using one data modality, image was the most informative, providing a top-2 accuracy of 76.4%
compared to 73.0% of metadata and 70.6% of spectra data. From combining data modalities
together, we found that each data modality offered unique information for age prediction, as
models using multiple modalities always produced better results than only using a single input
modality. Our best result was achieved from using all three data modalities, giving us an 81.1%
top-2 classification accuracy.

246

Table 17.1. -- Ablation study of model performance with different combinations of input
modalities.
Input Data
Image

Meta

MethodSpectral

Top 1

Top 2

CNN

50.6%

76.4%

MLP

42.0%

73.0%

x

MLP

43.2%

70.6%

x

MLP

48.0%

75.3%

CNN

51.5%

78.1%

x

CNN

50.3%

77.8%

x

CNN

52.8%

81.1%

Reader vs Reader

67.0%

x
x
x
x

x

x
x

x

Test Accuracy

247

Figure 17.1. -- Our model architecture for combining otolith imagery, Fourier transform near infrared spectra, and associated metadata for age classification.

248

Figure 17.2. -- Age distribution of walleye pollock (Gadus chalcogrammus) data. Data were split roughly into 70% training, 10%
validation, and 20% test sets. Example walleye pollock otolith images are on the right.

249

Figure 17.3. -- Age distribution of red snapper (Lutjanus campechanus) data. Data were roughly split into 70% training, 10%
validation, and 20% test sets. Example red snapper otolith images are on the right.

250

Figure 17.4. -- Age distribution of Atlantic menhaden (Brevoortia tyrannus) data. Data were roughly split into 70% training, 10%
validation, and 20% test sets. Example Atlantic menhaden scale images are on the left.

251

Figure 17.5. -- Walleye pollock (Gadus chalcogrammus) classification accuracy on test set using
our best model combining all three data modalities. Top-2 accuracy was used in
the evaluation, with overall accuracy being 81.1%.

252

Figure 17.6 -- Red snapper (Lutjanus campechanus) classification accuracy on test set using ourbest model combining all three data modalities. Top-2 accuracy was used in theevaluation, with overall accuracy being 70.1%. Note: Groundtruth labels for red
snapper data are being reevaluated. Results in this figure may not be reliable.

253

Figure 17.7. -- Atlantic menhaden (Brevoortia tyrannus) classification accuracy on test set using
our best model combining image and metadata. Top-1 accuracy was used in theevaluation, with overall accuracy being 87.3%.

254

Figure 17.8. -- Comparison of model performance versus human reader for walleye pollock (Gadus chalcogrammus). Model
prediction versus ground truth age is shown on the left, while reader versus reader age is shown on the right.

255

256

18. Calibration and Variation of FT-NIR Otolith Spectra AmongNIR Spectrometers and Species

Andy D. Ostrowski1, Beverly K. Barnett2, Jamie M. Clark1,3, Jennifer C. Potts1,
Alexander M. Rubin4,5, Michelle S. Passerotti4, Melissa H. Monk6, Jessica J. M. Choi6,7,
Brittany D. Schwartzkopf8, Emma M. Saas8,9, and Emmanis Dorval8,10Southeast Fisheries Science Center
NOAA, National Marine Fisheries Service
101 Pivers Island Road
Beaufort, NC USA
1Southeast Fisheries Science Center
NOAA, National Marine Fisheries Service
3500 Delwood Beach Road
Panama City, FL USA
2

3

Cooperative Institute for Marine and Atmospheric Studies
University of Miami
4600 Rickenbacker Causeway
Miami, FL USA
Northeast Fisheries Science Center
NOAA, National Marine Fisheries Service
28 Tarzwell Drive
Narragansett, RI USA
4

IBSS Corporation
1110 Bonifant Street
Silver Spring, MD USA
5Southwest Fisheries Science Center
NOAA, National Marine Fisheries Service
110 McAllister Way
Santa Cruz, CA USA
6257

Fisheries Collaborative Program
Institute for Marine Ecosystems and Climate
University of California Santa Cruz
110 McAllister Way
Santa Cruz, CA USA
7Southwest Fisheries Science Center
NOAA, National Marine Fisheries Service
8901 La Jolla Shores Drive
La Jolla, CA USA
8

Saltwater Inc.
733 N Street
Anchorage, AK USA
9

Lynker202 Church Street SE, Suite 536
Leesburg, VA USA
10258

INTRODUCTION

Traditional ageing techniques have long been standardized using reference collections
between age readers and laboratories to ensure consistency in the ages produced. This allows
confidence when combining multiple datasets for analysis in stock assessments. Fouriertransform near infrared (FT-NIR) spectroscopy is currently being researched as a potential
technology to replace traditional processing and ageing techniques. Using spectroscopy, a beamof light is transmitted through a sample, in this case a whole otolith, and the amount of lightabsorbed is correlated to traditional age estimates yielding a predictive model to estimate fish age
from absorbance data alone. As with traditional ages, FT-NIR spectroscopy ages must also be
standardized across spectrometers to ensure consistency across multiple machines, laboratories,
and users. To date, this has not been researched, and with the increase of machines acrossmultiple NOAA laboratories throughout the country, it is a unique opportunity to investigate
scanning consistency and to quantify ageing precision for FT-NIR spectroscopy ages acrossspectrometers and laboratories.

METHODS

Otoliths from Beaufort’s laboratory collection for gag (Mycteroperca microlepis, n =116)
and vermilion snapper (Rhomboplites aurorubens, n = 154) from the U.S. South Atlantic werechosen as calibration samples to be scanned using either Bruker Tango (Beaufort, Narragansett,Santa Cruz, and La Jolla laboratories) or Bruker MPA II (Panama City [PC] laboratory)
spectrometers (Bruker Corp., Billerica, MA). All laboratories followed the same scanning259

protocol that the samples were originally scanned with in Seattle 2019 on PC’s MPA II. This
included the use of a standard gold stamp with a rubber gasket to reduce stray light and identicalscanning protocols (16 cm-1 resolution, 64 sample scan time and background scan time, andwavelength from 11,550 to 3,950 cm-1). Once all the samples were scanned by each laboratory,
spectra were analyzed using Bruker’s OPUS software to compare spectral data and resulting
calibration models. Next, we analyzed the new spectral scans and input them into an establishedcalibration model created on PC’s MPA II while meeting in Seattle during 2019. The same
preprocessing techniques were used and models were reran, then compared using model rank,
the coefficient of determination (r2), root mean square error, bias, and residual prediction
deviation (Table 18.1). Lastly, the best predictive model was chosen by species, and the otherlaboratory location spectra were used as a test set to that model.

RESULTS AND DISCUSSION

When comparing the raw spectral files between laboratories and machine types, twothings were clear: raw spectra differed between spectrometer types (TANGO vs. MPA II) anddiffered according to use of the gold stamp (Figs. 18.1 and 18.2). Both of these factors caused
problems for standardizing spectra and calibration models across laboratories. The MPA II
spectrometer had lower raw absorbance values than the Tangos, while Tango raw spectra
overlapped across all laboratories (Figs. 18.1 and 18.2). This seemed to be due to different
preamplifier gain settings within the two spectrometer models, where the amplitude shift inspectra was corrected for in the model build of the Tango but was not in that of the MPA II.
Regarding the effect of stamp use on spectral data, one laboratory (Santa Cruz) scanned half of260

the sample sets with a standard gold stamp and the other half without. When no stamp was used,higher absorbance amplitudes were observed than were with a standard stamp, regardless of thespecies (Figs. 18.1 and 18.2). Although preprocessing can remove many artifacts of ambient
scanning conditions, these differences may nonetheless affect model results and should be
avoided if possible.
These results are preliminary and sample sizes were small; hence, it is difficult todefinitively state conclusions without further investigations and statistical testing. Further, theacceptable range of variability between instruments needs further investigation. Third, since thelaboratories involved in this project are new to optimizing models, we investigated creating newmodels from each location’s scans without any preprocessing designated. Instead, each model
was run, had outliers removed, reran as needed, optimized, and the best model was chosen.
Model output was compared between laboratory locations and the best tested model was graphed(Figs. 18.3 and 18.4). Regarding differences in model building between users, preprocessing
choices varied by user and led to differences in overall model results between laboratories,
indicating that preprocessing techniques need to be standardized by species. For example,
Narragansett and Beaufort laboratory users optimized Narragansett spectra without any
preprocessing designated, whereas other users did not. While some variation in raw spectra wasobserved across laboratories, each calibration model generally identified the same samples asspectral outliers during model testing (i.e, youngest ages for gag and chipped otoliths samples forvermilion snappers).
This was the first attempt to evaluate cross-machine and cross-user precision in spectral
data collection and model building. Initial results indicate that scanning between machine types
when utilizing the same scanning protocol provides consistent spectral data; however, it’s261

important to establish calibration sets similar to reference collections that are used to maintainconsistency in traditional ageing. Another important takeaway from this study is that consistentscanning methods need to be established between machines prior to the start of scanning toensure that results can be combined. Finally, model building should be well thought out by allusers for individual species, and decisions should be made jointly on which parameters andpreprocessing should be utilized.

Future discussions should include but not be limited to the following questions:

1. How should spectra be analyzed if scanning methods are changed? Is there a correction
factor?
2. What is the threshold of acceptable variability between models?
3. How do we handle new spectral scans while utilizing existing models?
4. What is the minimum number of samples needed to create a model (calibration set vs. testset)?
5. How does the physical size of the sample affect models (i.e, some otoliths are muchsmaller or larger than the aperture window)?
6. How can we handle user variability in model optimization? How do we ensure we areusing the same criteria for removing outlier samples?
7. Should we work with other data providers to create the same preprocessing protocol priorto scanning?
8. Should spectra be combined across data providers to create a single model?

262

Table 18.1. -- Model comparison output by location using established preprocessing protocols
from previous scans (using the Panama City laboratory’s spectrometer in Seattle)
for vermilion snapper Rhomboplites aurorubens (top) and gag Mycteroperca
microlepis (bottom). Preprocessing methods are outlined for each species. PC =Panama City, LJ = La Jolla, BFT = Beaufort, NAR = Narragansett, SC = Santa
Cruz, RMSECV = root mean square error of the cross validation, RPD = residual
prediction deviation.
Vermilion snapper
Preprocessing method: Multiplicative Scattering Correction
Regions of interest: 7456-4944, 4504-4000
PC Seattle

LJ

BFT

NAR

PC

SC-allSC-Gold

SC-No gold

Rank

3

2

6

3

4

3

3

2

R2

80.22

79.55

76.39

73.93

81.24

77.14

61.50

74.07

RMSECV

1.06

1.05

1.12

1.18

1.00

1.10

1.03

1.21

Bias

0.002860.00053-0.34200

0.00769

0.008160.01130

0.02440

RPD

2.25

2.21

2.06

1.96

2.31

2.09

1.61

1.96

Gag grouper
Preprocessing method: 1st Derivative + Vector Normalization
Regions of interest: 7456-6400, 5504-4944, 4776-4000
PC Seattle

LJ

BFT

NAR

PC

SC-allSC-Gold

SC-No gold

Rank

5

4

4

5

6

4

2

2

R2

83.06

76.49

80.49

73.49

78.49

65.54

63.16

56.30

RMSECV

0.746

0.879

0.801

0.933

0.848

1.060

1.110

1.050

Bias

0.006890.01760

0.00747

0.008210.03450-0.00347

0.03750-0.05110

RPD

2.43

2.06

2.26

1.94

2.16

1.70

1.65

1.51

263

Figure 18.1. -- Raw spectral data of vermilion snapper (Rhomboplites aurorubens) otoliths scanned at each location’s laboratory using
two types of spectrometer: Tango (Santa Cruz, Beaufort, Narragansett, and La Jolla laboratories) and MPA II (Panama
City [PC] laboratory). Note: Santa Cruz measured half of the samples without a gold stamp (upper level yellow
spectra), while the other half were measured with a gold stamp (which line up with the rest of the Tango scans).

264

Figure 18.2. -- Raw spectral data of gag grouper (Mycteroperca microlepis) otoliths scanned at each location’s laboratory using two
types of spectrometer: Tango (Santa Cruz, Beaufort, Narragansett, and La Jolla laboratories) and MPA II (Panama City[PC] laboratory). Note: Santa Cruz measured half of the samples without a gold stamp (upper level yellow spectra),while the other half were measured with a gold stamp (which line up with the rest of the Tango scans).

265

Figure 18.3. -- Vermilion snapper (Rhomboplites aurorubens) best model comparison output
using other location scans as test sets. The best predictive model was from thePanama City (PC) laboratory, and the best test set was also from PC scans from
Seattle in 2019. Model output is displayed under the graph.

266

Figure 18.4. -- Gag grouper (Mycteroperca microlepis) best model comparison output using
other location scans as test sets. The best predictive model was from Beaufort andthe best test set was from La Jolla. Model output is displayed under the graph.

267

268

19. A Simulation-Based Approach to Evaluate Best Practices for Estimating Fish Age
Using FT-NIR Spectroscopy

Morgan B. Arrington1,3, Jordan T. Healy2, Thomas E. Helser3, Esther D. Goldstein3,
Irina M. Benson3, Brenna C. Hsieh3, and André E. Punt2

1

Cooperative Institute for Climate, Ocean, and Ecosystem Studies (CICOES)
University of Washington
3737 Brooklyn Ave NE
Seattle, WA USA
2School of Aquatic and Fishery Sciences
University of Washington
1122 NE Boat Street
Seattle, WA USA

Alaska Fisheries Science Center
NOAA, National Marine Fisheries Service
7600 Sand Point Way NE
Seattle, WA USA
3

269

270

ABSTRACT

Fourier transform near infrared (FT-NIR) spectroscopy applied to otoliths has been used
to predict the age of fish with comparable precision and improved efficiency relative to
traditional, microscope-based methods. This approach requires the development of predictive
calibration models that relate otolith spectra with fish age. However, before FT-NIR
spectroscopy can be operationalized for production fish ageing and fisheries management,
factors that may impact predictive skill of calibration models while maintaining efficiency must
be investigated. The influences of calibration data set sample size (the number of otoliths that
require traditional ageing) and sample selection methods on model predictive skill have not beenthoroughly evaluated. Using eastern Bering Sea (EBS) walleye pollock as a case study, we
conducted a simulation study of model predictive skill across 39 scenarios differing in selection
methods and sample sizes. Specifically, we resampled with replacement from a subset of walleye
pollock otoliths collected between the years of 2014-2021 (n = 2,055) and compared threeselection methods: 1) random selection from samples where two age readers agreed on age basedon the assumption that these ages were the most “accurate”; 2) random selection from the full
subset of samples; and 3) selection using the Kennard-Stone algorithm to sample from the fulldomain of spectral variation. We evaluated each selection method at a range of sample sizes,
each with 200 iterations, to determine the approach that maximizes efficiency but best represents
the larger data set. Partial least squares regression calibration models were fitted to each data set
and predictive skill on future, unseen data was evaluated by applying each model to a hold-out
data set (n = 7,272) and estimating the root mean square error (RMSE) between predicted age
and reference age for each scenario. We also evaluated two model validation approaches: cross-271

validation and external validation, and their relative ability to accurately estimate model
performance on new data (represented by model performance on the hold-out data set). For EBS
walleye pollock collected between 2014 and 2021, calibration sample selection using the
Kennard-Stone algorithm resulted in the most consistent model predictive skill at sample sizes
greater than 500 (~7% of the hold-out data set), with negligible gains over 900 (~12% of thehold-out data set). The random selection approach was nearly comparable in performance toKennard-Stone. Our results did not support any benefit of restricting calibration samples to thosefor which two readers agreed on fish age and also suggest that model cross-validation is a good
indicator of future performance on unseen data when the Kennard-Stone selection approach isutilized but tended to overpredict performance when the random selection approach was
employed. External validation tended to over-predict future predictive performance on unseen
data for both selection approaches, especially when the external validation data set was small.
Future work could explore the optimal sample size for an external validation data set that would
better quantify model performance on new data. This case study outlines calibration model
optimization techniques and approaches that can be universally applied to new species and data
sets.

272

INTRODUCTION

Information on the age of fish is an essential component of fisheries research and hence
management decision-making. Accurate age data allow for a more robust understanding of
population dynamics and contribute to our ability to conserve and manage species effectively(Campana 2001, Lai and Gunderson 1987). Fish age data are essential when applying agestructured methods of stock assessment, as they inform estimates of the size of population
cohorts as well as important life-history parameters related to growth, mortality, maturity, andlongevity.
At the Alaska Fisheries Science Center alone, over 30,000 age estimates from over 22
species of fish are generated annually for inclusion in stock assessment (Lambert et al.
unpublished report, Helser et al. this volume-a), representing an enormous time and monetaryinvestment for processing and traditional age estimation. Fourier transform near infrared (FTNIR) spectroscopy has been shown to be an effective method for predicting the age of numerous
fish taxa with improved efficiency relative to traditional methods (Rigby et al. 2014, Wedding etal. 2014, Rigby et al. 2016, Helser et al. 2019a, Helser et al. 2019b, Arrington et al. 2022, Healyet al. 2021, Passerotti et al. 2020a, Passerotti et al. 2020b, Wright et al. 2021, Passerotti et al.
2022, Benson et al. 2023).
The eastern Bering Sea (EBS) walleye pollock (Gadus chalcogrammus) fishery is one of
the largest fisheries in the Alaska region (1.2 million t, on average, since the late 1970s) andaccounts for roughly a third of all production age estimates. The FT-NIR spectroscopy approachhas predicted the age of EBS walleye pollock with comparable precision to traditional methods
and improved efficiency (Helser et al. 2019b, Benson et al. 2023). Helser et al. (2019b) found273

that 75% of FT-NIR age estimates for EBS walleye pollock collected during 2016 and 2017 frompartial least squares regression (PLS) models were equivalent to traditional production integer
ages. In contrast, the traditional age reading method results in only 68% agreement between
reader and tester ages. Benson et al. (2023) found that for EBS walleye pollock collected
between 2014 and 2018, 81% of FT-NIR age estimates from multimodal convolutional neuralnetwork models (MMCNN) were the same as traditional production ages, in contrast to only
73% agreement between reader and tester in the traditional method. Both PLS and MMCNN
resulted in predicted ages that had less bias for most age classes than age estimates compared
between two age readers in traditional methods.
However, despite promising proof-of-concept studies, the FT-NIR method of fish ageing
has yet to be formally used for life history studies or for production ages included in stock
assessments. Best practices still need to be developed to understand methodological impacts on
the predictive skill of FT-NIR models when applied to future data. Here, we develop asimulation framework to explore the influence of calibration data set sample size and selection
method on PLS model consistency and predictive accuracy. We define consistency as the range
in predictive performance of calibration models on future data. We define predictive accuracy as
how well a calibration model predicts future data. Both consistency and accuracy are evaluatedbased on root mean square error (RMSE) between model predictions and traditionally estimatedage. We address the following three questions:•

Question 1: What are the best methods for selecting a calibration data set to optimize
performance on new data?

274

•

Question 2: What is the minimum sample size required for a consistent calibration
data set?

•

Question 3: What are the best methods for validating a calibration model to determine
how it will perform on new data?

METHODS

Walleye pollock otoliths included in this study were collected on fishery-independent
EBS bottom trawl surveys conducted by the Alaska Fisheries Science Center between the years
2014 and 2021. Otoliths were stored in glycerin-thymol solution after extraction at sea.
Traditional age estimates for all samples included in this study (n = 9,327) were determined
microscopically by counting pairs of annual bands corresponding to the age of the fish using one
paired sagittal otolith (Matta and Kimura 2012). Two age readers independently read a randomly
selected 20% subset of specimens to evaluate the precision and bias of traditional methods.
Traditional age reading methods for walleye pollock are destructive, so the other otolith wasused for spectroscopic evaluation.

Spectroscopy

Spectral absorbance data were collected on either a Bruker TANGO-R FT-NIR
spectrometer (Bruker Optics, Ettlingen, Germany) or a Bruker MPA II FT-NIR spectrometerwith an integrating sphere. Whole, unaltered otoliths were removed from their vials and excess
glycerin-thymol was blotted off using Kimwipes (Kimtech Science). The otolith was then placed275

on the sampling window distal side up in a 0° orientation and covered with a gold transflectancecap. Absorbance data were acquired at 16 cm-1 resolution with 64 averaged scans between thewavenumbers 11,500 and 4,000 cm-1.

Analysis
We conducted data analysis in R (version 4.3.0, R Foundation for Statistical Computing,
Vienna, Austria, see https://www.Rproject.org), with chemometric package mdatools (version
0.14.0, Kucheryavskiy (2020a), https://github.com/svkucheryavski/mdatools). Raw spectral data
were pre-processed with mean centering and Savitzky-Golay smoothing (first derivative, second
order polynomial, 17-points; Savitzky and Golay 1964), to remove the offset between
instruments while preserving the chemical signal of interest (Benson et al. 2023, Luthria et al.
2011).
We removed outliers based on orthogonal distances (Q) and score distances (Hotelling’sT2). This was done by calculating critical limits using a data-driven approach assuming a joint
distance that follows a chi-squared distribution (Rodionova and Pomeranstev 2020). Values thatfell outside a significance level of 0.01 were considered outliers and removed from the data set(n = 100). We split the remaining data into two data sets. One data set (hereinafter referred to as
the double-read data set) had all specimens that were read by two age readers (n = 2,055), andthe other data set had just a single read age (hereinafter referred to as the hold-out data set) (n =7,272) (Fig. 19.1).
We then evaluated three methods for selecting calibration models from the double-readdata set at a range of sample sizes: 1) the “agree ages” approach – we used just the spectra fromotoliths where both the reader and tester agreed on the age (n = 1,386) – this was based on the276assumption that these otoliths had the least error in reference ages, 2) the “random selection”
approach - we randomly selected a subset of data for the calibration model from the full doubleread data set; and 3) the “Kennard-Stone algorithm” approach - we used the Kennard-Stone
algorithm (Ferreira et al. 2021, Kennard and Stone 1969) to select representative samples thatencompassed the full range of spectral variation in the double-read data set. When selecting a
subset of data for a calibration sample, there are many possible combinations depending on the
total sample size. Some combinations may, based on random chance, be better or worse at
predicting future samples than others. By utilizing a simulation-based approach with n = 200
repetitions, we were able to simulate sampling from a larger population and generate a
distribution of possible results.

Agree ages approachTo evaluate the agree ages approach, we resampled with replacement from the specimens
where the reader and tester agreed on age (1,386 samples out of the 2,055 samples in the doubleread data set) at sample sizes from n = 100 to the maximum possible, n = 1,386, at intervals of100. We resampled 200 data sets per calibration sample size. Each had a paired external
validation set that included any samples from the double-read data set not included in eachcalibration data set.

Random selection approachTo evaluate the random selection approach for selecting calibration samples, weresampled with replacement from the full double-read data set (n = 2,055) at sample sizes from n= 100 to n = 1,386 to be equivalent to the agree ages approach. This was to eliminate variation in277

model performance due to different calibration sample sizes, and resulted in 200 simulated data
sets per calibration sample size, each with a paired external validation data set that included any
samples from the double-read data set not included in each calibration data set (Fig. 19.1).
Kennard-stone algorithm approachTo evaluate the Kennard-Stone algorithm for selecting calibration samples, we resampledwith replacement from the full double-read data set (n = 2,055) to generate 200 simulated data
sets. We then applied the Kennard-Stone algorithm to each simulated data set to selectcalibration sets with sample sizes from n = 100 to n = 1,386 to be equivalent to the agree ages
and random selection approaches. This resulted in 200 simulated calibration sets per calibration
sample size, each with a paired external validation set that included any samples from the
double-read data set not included in each calibration data set (Fig. 19.1).
Evaluating model performance
To evaluate model performance in each scenario, we then fitted PLS models (Wold et al.
1984, Helser et al. 2019b) to each calibration set selected via each method at each sample size.
To evaluate and compare model performance on new data, we applied each model to predict ages
for the hold-out data set (n = 7,272) (Fig. 19.1). Model predictive skill was summarized by
calculating the root mean square error (RMSE) of predicted ages relative to reference ages foreach iteration in each scenario. This resulted in 200 RMSE values per method, per sample size.
In most proof-of-concept studies, a hold-out data set with such a large number of samples(n = 7,272 for EBS walleye pollock) with traditionally estimated reference ages is not typical. To
simulate model evaluation methods more typical in a proof-of-concept study, we evaluated theaccuracy of external validation and cross-validation methods for estimating future performance278

on new data (represented by model performance on the hold-out data set) (Fig. 19.1). To evaluate
external validation methods, we applied the same PLS models to each paired external validation
set and calculated the RMSE of the predicted ages versus traditionally estimated reference ages.
This resulted in 200 RMSE values per method, per sample size. PLS models were also tested
through cross-validation by fitting models using each data set iteratively. In each iteration, 20
samples were systematically left out and parameter estimates were used to estimate the age of theleft-out samples. The mean error of all predictions versus reference ages was calculated as the
RMSE of cross-validation. This resulted in 200 RMSE values per method, per sample size.

RESULTS AND DISCUSSION

The results of this simulation suggest that model predictive accuracy (represented by
RMSE) can vary depending on what samples are included in the model calibration set, theselection approach utilized, and the sample size. Additionally, it is important to give model
validation approaches careful consideration as they can over-estimate model predictive
performance on new data.
When all calibration sets were used to predict fish age for the hold-out data set (n =7,272) to represent predictive skill on new data, the best and most consistent model performance
was achieved using the Kennard-Stone algorithm at the highest sample size tested (n = 1,386)(Fig. 19.2). The most variability in model performance was for sample sizes less than 500,
suggesting that more traditional ages are required to reliably select a representative sample of the
population. However, there were negligible gains over 900 samples, suggesting that this may be
a good target number for traditionally estimated reference ages for EBS walleye pollock.

279

Although there was near full overlap among the Kennard-Stone and the other two
selection methods’ RMSE values, the random and agree ages approaches had larger ranges in
RMSE that encompassed models with poorer predictive performance than the Kennard-Stoneapproach (Fig. 19.2). Of the three methods, the agree ages approach resulted in a range thatencompassed the poorest models at calibration sample sizes over 500 (Fig. 19.2). These results
do not support a benefit to restricting calibration samples to those with agreement between the
primary age reader and the test reader. Though random selection resulted in some models withslightly poorer performance than the Kennard-Stone algorithm, they had nearly comparable
RMSE values and both methods may be useful in different contexts (Fig. 19.2). When selecting a
subset of samples for a spectroscopy study, random selection of samples for traditional ageing
may be most appropriate if spectra are not yet collected, since the Kennard-Stone algorithm
requires information about spectral variation. However, in a scenario where spectra are available
that do not yet have reference ages, the Kennard-Stone selection method can be used to optimize
the samples to process for traditional age estimation.
Finally, model validation techniques are often used to estimate the future performance ofa model on new data in a proof-of-concept study where data are limited. We found that crossvalidation over-estimated predictive skill for the random selection approach, showing better
RMSE than when the same calibration models were applied to the hold-out data set (Fig. 19.3a).
However, cross-validation adequately represented the predictive skill for the Kennard-Stone
selection approach and resulted in a conservative estimate of model predictive skill, except for
the highest two sample sizes (Fig. 19.3b). External validation (using paired validation data sets)
tended to overestimate predictive skill for the random approach at sample sizes greater than 300(Fig. 19.4a). It also tended to overestimate predictive skill for the Kennard-Stone selection

280

approach at sample sizes greater than 200 (Fig. 19.4b). In a data-limited scenario, if using the
Kennard-Stone algorithm for selecting calibration samples, our recommendation would be to use
cross-validation to estimate predictive performance on future, unseen data. If using random
selection, future work could explore the adequate sample size of an external validation data set
that would result in better estimates of performance on new data. Anecdotally for these results,
when ~1,700-1,900 samples are included in the external validation relative to 100-300 samples
in the calibration, the external validation better estimated “actual” performance as represented by
predictive skill on the hold-out data set (n = 7,272) (Fig. 19.4a,b). However, calibration models
fitted on just 100-300 samples were also highly variable and inconsistent in their predictive
performance on future, unseen data (Fig. 19.2). To more accurately estimate the future predictive
skill of calibration models fitted on an optimal number of 500-900 samples, a larger paired
external validation data set of spectra with traditional ages would likely be required than wasavailable in our double-read data set (n = 2,055) (Fig. 19.1).
This simulation utilized EBS walleye pollock for a case study, yet the simulation
framework developed in the study is flexible and can be applied in other contexts. Findings fromthis study may be generalizable across species. Recommendations to use the Kennard-Stoneapproach or random selection approach for calibration sample selection are likely broadly
applicable. Similarly, our results highlight important considerations for validation methods that
accurately estimate a model’s future performance on new data. We recommend building fromthis framework to explore best practices on an application-specific basis since optimal calibration
sample sizes may vary by species and modeling approach (e.g., MMCNN). A better
understanding of these best practices, such as the impact of calibration sample size and selection281

methods on PLS model consistency and predictive accuracy for any candidate species, isimportant for the successful use of FT-NIR spectroscopy for production fish ageing.

282

Figure 19.1. -- Schematic of study design. The full data set including all traditionally aged
eastern Bering Sea walleye pollock Gadus chalcogrammus (n = 9,327) from 2014
to 2021 was split into a “double-read data set” with all samples that had a reader
and tester age (n = 2,055), and a “hold-out” data set with all other samples that
had just one traditionally estimated age (n = 7,272). The double-read data set wasused for comparing calibration data set sample size and selection methods so thatthe agree ages selection approach, which required that two age readers agreed onage, could be compared to random selection and the Kennard-Stone algorithm.
The hold-out data set was used to represent model performance on future, unseen
data. The hold-out data set was kept unchanged so that calibration models could
be compared against a comparable benchmark. Two typical validation approaches- external validation and cross-validation - were compared in their ability toestimate future model performance on new, unseen data (represented by hold-out
data set). Above, “cal” represents calibration and “val” validation.

283

Figure 19.2. -- Violin plots (kernel density) comparing root mean square errors among the threecalibration selection approaches to predict ages for the hold-out data set (n =7,272). This is shown for calibration sample sizes 100-1,386 at intervals of 100.
Each has 200 iterations.

284

Figure 19.3. -- Violin plots (kernel density) comparing estimated model predictive skill (rootmean square error) from model cross-validation versus actual performance on
future, unseen data represented by the hold-out data set for a) the random
selection approach, and b) the Kennard-Stone algorithm selection approach.

285

Figure 19.4. -- Violin plots (kernel density) comparing estimated model predictive skill (rootmean square error) from external validation (paired validation sets) versus actual
performance on future, unseen data represented by the hold-out data set for a) the
random selection approach, and b) the Kennard-Stone algorithm selection
approach.

286

20. Database Design and Considerations with FT-NIR Spectra Data Collection andManagement

Jonathan A. Short

Alaska Fisheries Science Center
NOAA, National Marine Fisheries Service
7600 Sand Point Way NE
Seattle, WA USA

287

288

INTRODUCTION

Fourier transform near infrared (FT-NIR) spectra have been recently discovered to be
informative in age estimation of many species of fishes (e.g., Helser et al. 2019a, Robins et al.
2015, Wedding et al. 2014). To take full advantage of this new data stream, a well-designed andscalable database is useful for managing the data collection process and for creating a searchable
clearinghouse of spectral data. The Alaska Fisheries Science Center (AFSC) Age and Growth
Program (AGP) uses a relational SQL Server database called AGE3 to house specimen and age
data and it has been expanded to include FT-NIR data. As of this writing, the AGE3 databasehouses specimen data for over two million fish, over one million traditional ages, and over
100,000 FT-NIR spectra.
Fish specimens come to the AGP from survey and fishery collections that have alreadybeen through a quality control process and have unique fish identifiers. Survey collections have aunique combination of vessel code, cruise number, species, and specimen number. Fisherycollections are given unique barcodes. With either combination of identifiers, we can relate tocatch data, such as the date of collection and latitude and longitude.

DATABASE DESIGN AND ARCHITECTURE

The AGE3 database is a relational database that has data objects that reflect physicalobjects. For example, the “Fish” data object or table contains attributes directly relating to fish
specimens, such as fork length, sex, and body weight (Fig. 20.1). Another data object called“Age structures” is related to Fish via a primary key-foreign key relationship and contains

289

attributes related to the structure such as structure type (otolith, vertebra, scales, etc.), structurelabel information, and structure storage location. FT-NIR data are collected on parts of fish andnot the entire fish, so the “NIR scans” object is related to the Age structures table. In this way wecan spectrometrically interrogate many aspects of the fish without duplicating attributes such as
fish length and weight and while maintaining data integrity. The AGE3 database stores the paths
to the spectral files on a network share and metadata describing the conditions of the structure
scanned. The database also stores information relating to instrument settings and quality control
notes and changes.
The database consists of a SQL Server 2016 back end housed in the AFSC server space.
It is accessible with Windows Authentication, so users do not need to go through an additional
login procedure after logging in to their computer workstation. The database front ends are acustom Python/Qt interface, several Microsoft Access front ends, and may be queried directlywith code and an ODBC connection. The database structure and user access are limited to set
processes so it is not possible for users to inadvertently change data that are outside theirpurview.

SPECTRUM COLLECTION

An important aspect of the collection process is creating unique spectrum file names.
This is accomplished in the AGE3 database by beginning with fish specimen data already in the
database and tracking the number of times a fish structure has been scanned. Data sessions are
created and the structures to be scanned are added to it by a data manager. Scripts in the databaseinterface check the number of times a structure has been scanned to ensure data integrity. Once290the session is set up and samples are available, then it is ready for scanning. The data manager
writes a description of the sample selection process and the treatment involved with each data
session (Fig. 20.2).
Once a session is ready, a scan form is opened by the NIR operator (Fig. 20.3). The scan
form is pre-filled with specimen numbers or barcodes of the samples ready to be scanned. The
NIR operator records sample conditions such as broken, crystallized, structure side, percent
affected, sample weight and any other comments pertaining to the sample, and then pastes the
database-generated spectrum file name into the name field in the Bruker Opus software. Opus
scans the structure and generates the spectrum file in a temporary network location to which all
AGP users have read/write access. The AGE3 scan form locks completed records one by one as
the operator clicks the save button and checks that the spectrum file is generated in the expected
file location.
Once all spectra in a session are collected, the NIR operator checks their data and marks
the session as complete. This automatically generates an email notification to the data manager
that a session is ready for QA/QC. After quality control, the data manager executes a script thatmoves all spectrum files in a session to a permanent archival location with read-only access.

DATA CLEARINGHOUSE

Archived NIR data can be retrieved by querying all available data in the AGE3 database.
Data analysts can search with many criteria, since all related metadata are stored in the database(Fig. 20.4). A Microsoft Access front end has common criteria preset into a data selection form.
This can build a query that can be further modified to the specific needs of the analyst. A script291

in the front end copies all selected spectra and related metadata from the archive library to thedestination of choice.

292

Figure 20.1. -- A simplified table structure of the Alaska Fisheries Science Center AGE3
database showing entities and attributes, primary keys and foreign keys forcollection data, fish data, and near infrared (NIR) spectra.

293

Figure 20.2. -- The AGE3 data session management form. This form displays metadata relating to near infrared (NIR) spectra in theactive collection process and gives NIR operators access to in-process data sessions.
294

Figure 20.3. -- The near infrared (NIR) scan form for a fishery collection in AGE3. NIR operators enter otolith characteristics during
the scanning process and this form creates the Scan Name, which is copied to Opus to create the spectrum file name.

295Figure 20.4. -- The spectra file export form is a clearinghouse of spectral data. Analysts can filter data sessions by species, status,
project, year, type, and region, then select or exclude specimens based on otolith characteristics. With the script in thisform, spectra and metadata will be copied from the archived library to the analyst’s chosen file destination.

296

21. Envisioning the Future of Production Fish Ageing: End-to-end Integration of the FTNIR Spectroscopy Age Estimation Enterprise at the Alaska Fisheries Science Center

Thomas E. Helser1, Irina M. Benson1, Mary Elizabeth Matta1, Esther D. Goldstein1,
Brenna C. Hsieh1, Morgan B. Arrington1,2, and Jonathan A. Short1

Alaska Fisheries Science Center
NOAA, National Marine Fisheries Service
7600 Sand Point Way NE
Seattle, WA USA
1

2

Cooperative Institute for Climate, Ocean, and Ecosystem Studies (CICOES)
University of Washington
3737 Brooklyn Ave NE
Seattle, WA USA

297

298

ABSTRACT

Fourier transform near infrared (FT-NIR) spectroscopy of otoliths is emerging as a highly
efficient fish age estimation alternative to the labor intensive, time-consuming traditional
microscope-based approach (TMA). Within the National Oceanic and Atmospheric
Administration (NOAA) National Marine Fisheries Service (Fisheries), seven biologicallaboratories are engaged in production ageing that provides critical data for stock assessments insupport of managing the nation’s fishery resources. Research by Alaska Fisheries Science Center(AFSC) scientists demonstrated the potential utility of FT-NIR spectroscopy for ageing eastern
Bering Sea (EBS) walleye pollock, which led to NOAA Fisheries funding a 5-year strategic
initiative in 2020 entitled “A revolutionary approach for improving age determination efficiency
in fish using Fourier transform near infrared (FT-NIR) spectroscopy.” The roadmap toward
operationalizing the FT-NIR spectroscopy ageing technology across science centers wasenvisioned as encompassing three major related tasks: (1) application development, (2)application implementation, and (3) stock assessment integration. Within this framework,
examples of successful fish ageing applications of FT-NIR spectroscopy have been documented
for numerous species and regions over the last several years by the Strategic Initiative
Development Team (SIDT). In a few regions, headway has also been made in application
implementation through simulation studies for best practices and even stock assessment
integration by evaluating stock assessment model sensitivity to data products generated using
FT-NIR spectroscopy. However, before such innovative methods attain a technical readinesslevel (TRL) for transition, we must begin to focus on adaptation of FT-NIR spectroscopy
methods into existing TMA operations that result in a smooth delivery of data streams with299

essential quality control processes that generate consistent and reliable data products. This
chapter describes in detail the end-to-end integration of FT-NIR spectroscopy methods for age
estimation envisioned for the production ageing process at the AFSC. In this case study, we
employ a predictive model for EBS walleye pollock, leveraging over 9,000 observations
spanning from 2014 to 2018, to investigate the envisioned process for generating future age
predictions.

300

INTRODUCTION

The central principle governing fish age determination based on the growth patterns
visible in otoliths has changed little over the last century. While ageing fish using scales can be
documented as far back as the early 1700s, Johannes Reibisch was the first to describe a protocol
for ageing fish using otoliths in 1899 (Jackson 2005). Microscopic counting of the annual growthzones in otoliths was the foundation of the ageing method then and is still the basis of age
estimation today. With the exception of advancements in microscopy and otolith preprocessing
techniques, the standard practice today remains the visual examination and enumeration ofannual banding patterns. Ageing laboratories worldwide have improved age estimation methods
through various otolith processing techniques to enhance the microstructure for visualinterpretation. These techniques involve clearing whole otoliths in various solutions or sectioning
them and then applying burning or staining processes to improve contrast between growth zones.
Another common practice is “double reading”, wherein the same otolith is evaluated by a second(or even third) analyst to estimate precision or repeatability of age estimates. Hence, the age
reading process aligns with the scientific method and allows for the application of statisticalmethods to estimate age reading precision. McBride (2015), Campana (2001), and others havediscussed statistical techniques that can be used to assess age reading precision. Generating ages
for stock assessments follows the scientific method provided that ages are validated, age
determination criteria are consistently applied, and independent double readings are conducted.
Campana (2001) provides a thorough treatise of age validation methods along with measures ofstatistical precision. The entire process of generating ages can be expensive, time consuming,301

and labor intensive, especially for species with long lifespans or complex otolith processing
methods.
In recent decades, efforts have been made to increase the efficiency and improve
repeatability of age determination with machine-based technologies. These technologies have
utilized otolith morphometrics (Pilling et al. 2007, Fablet et al. 2009, Mahe et al. 2016), image
analysis (Nasreddine et al. 2013), or both (Troadec and Benzinou 2002, Fablet and Le Josse
2005), with variable levels of success. When a large number of age estimates is needed for stock
assessments, the goal is to increase efficiency without hindering age accuracy or precision(Fablet and Le Josse 2005). Although a number of alternative approaches have been explored foruse in age estimation, none have been widely adopted as a large-scale production tool for age
determination. Following the lead of several promising studies in Australia (Wedding et al. 2014,
Robins et al. 2015), scientists at the AFSC have shown that Fourier transform near infrared (FTNIR) spectroscopy applied to walleye pollock (Gadus chalcogrammus) otoliths (Helser et al.
2019b) holds promise of high-throughput fish ageing. FT-NIR spectroscopy is used in a wide
variety of industries including pharmaceutical, chemical, petrochemical, agricultural, and dairy
manufacturing. FT-NIR spectroscopy is a vibrational spectroscopy technique that functions by
exciting covalent bonds (O-H, C=O, C-H, C-N, and N-H) at the molecular level using NIR lightand measuring absorbance in the wavenumber range 4,000 to 12,500 cm−1. The light’sinteraction with the sample results in measurable vibrational frequencies represented by spectral
signatures associated with molecular combinations and overtones that make up compounds in thesample (Conzen 2014, Siesler et al. 2002). In otoliths, these spectral signatures correlate wellwith age, enabling the establishment of a mathematical relationship between absorbance and fish
age.
302

With the promise for improving efficiency and repeatability of generating age data forstock assessments, the National Oceanic and Atmospheric Administration (NOAA) National
Marine Fisheries Service (Fisheries) has undertaken a nationally coordinated research-anddevelopment effort across seven biological laboratories to integrate FT-NIR spectroscopy
technology into the fish age determination enterprise (Helser et al. 2019a). NOAA Fisheries
funded a 5-year strategic initiative entitled “A revolutionary approach for improving age
determination efficiency in fish using FT-NIR spectroscopy” in 2020. With the participation ofnational and international scientists and industry experts in FT-NIR spectroscopy, a framework
toward operationalizing the FT-NIR spectroscopy ageing technology across fisheries science
centers emerged from a three-day planning workshop in April 2019 (Helser et al. 2019a). The
Strategic Initiative Development Team (SIDT) envisioned a “roadmap” consisting of three
related and overlapping tasks: (1) application development, (2) application implementation, and(3) stock assessment integration (Helser et al. 2019a). Application development focused on the
exploration of the technology to generate reliable NIR spectra (good signal-to-noise) fromotoliths and determine whether a strong significant mathematical relationship between otolith
spectra and fish ages of different species and in different regions of the country could beestablished. In application implementation, the goals were to investigate the delivery of new data
generated by predictive models, define and evaluate model performance, establish protocols fordata processing and quality control, and develop a manual of best practices to guide decisions
related to sample size, sampling distributions, ageing precision, and performance measures.
While not the end point, the stock assessment integration objective sought to evaluate thesensitivity of population model output of critical management outcomes and benchmarks fromincorporating FT-NIR spectroscopy data. Taken together, each element of the hierarchy builds

303

upon the previous tier with “discovery switch-backs” helping to refine and improve the technicalknowledge of the system.
Collectively, the NOAA Fisheries ageing laboratories have made reasonably good
progress on application development, despite significant delays and setbacks due to the 20202022 global pandemic. The science underpinning the use of FT-NIR spectroscopy for age
prediction is demonstrated by a number of published studies across fish taxa and regions in the
U.S. (Helser et al. 2019a, Helser et al. 2019b, Healy et al. 2021, Arrington et al. 2022, Passerotti
et al. 2020b, Passerotti et al. 2022, Benson et al. 2023), with others in Australia (Wedding et al.
2014, Rigby et al. 2016, Wright et al. 2021), and more are yet to be published (including a
number of studies described in these proceedings). Although work is ongoing or still needed
across all science centers, these efforts hold great promise for improving efficiency andrepeatability of generating age data for stock assessments using FT-NIR spectroscopy. At the
AFSC, simulation studies are underway to evaluate best practices, and two case studies foreastern Bering Sea (EBS) walleye pollock and Pacific cod (G. macrocephalus) demonstrate thesensitivity of the stock assessment models to using FT-NIR spectroscopy-generated age
compositions compared to the traditional method of ageing (TMA). The fourth element, which
was not identified in the planning document, relates to the technical readiness level (TRL) andfull transition of the new method into existing operations that result in a smooth end-to-end
pipeline with essential quality control processes that generate consistent and reliable data. This
chapter describes the end-to-end integration of FT-NIR spectroscopy methods for age estimation
envisioned within the production ageing framework at the AFSC’s Age and Growth Program(AGP).

304

THE UNIFIED SYSTEM

The AFSC conducts production ageing on over 22 species of fish in Alaskan waters,
generating on average between 30,000 and 40,000 ages per year for inclusion in stock
assessments 1. Technical readiness and transition of FT-NIR spectroscopy-based age estimation
may occur a few species at a time and may not be applicable for all species. In the FT-NIR
spectroscopy method, ages are generated from a mathematical relationship between otolith
spectra and associated TMA ages using statistical methods such as principal component
regression (PCR) or partial least squares regression (PLS) to predict age using a small set ofintermediate linear latent variables (Chen and Wang 2001). For species in which the TMA
precision is very low, FT-NIR spectroscopy may simply not be possible without propagatingexcess error into the model age predictions. The impact of ageing imprecision and bias on FTNIR-based age predictions is currently being investigated using simulation analysis (SeeArrington et al. this volume-b). Walleye pollock and Pacific cod account for approximately 50%of the AGP annual production demand. FT-NIR spectroscopy models for EBS walleye pollock
and Pacific cod, which have well developed age-structured stock assessment models andreasonable TMA ageing precision, have shown promising results (Helser et al. 2019b, Healy et
al. 2021, Benson et al. 2023) and most likely will represent the first stocks to reach a TRL fortransition. Moreover, we have advanced from using typical chemometric modeling techniquessuch as PCR or PLS to deep machine learning, specifically multimodal convolutional neuralnetworks (MMCNN), to improve age prediction for these species (Benson et al. 2023). Deep

1

G. Lambert et al., unpublished report. Importance of age data collection for stock assessments: A U.S. national
perspective. Report from the Otolith Sample Size Working Group. NOAA Fisheries.

305machine learning provides a number of advantages over classical linear methods by dealing withsystem nonlinearity, automatic feature extraction from spectra and metadata, and error
quantification when used within a Bayesian framework. Regardless of which analytical tool isused to predict ages, what is clear is that both TMA and FT-NIR spectroscopy age estimation
approaches have to operate in parallel in a unified process. The current TMA production processneeds to adapt FT-NIR spectroscopy for species that have reached a sufficient TRL for transition
because both, to a greater or lesser extent, rely on reference ages (TMA) to evaluate proper
system functioning. For FT-NIR spectroscopy, reference ages are needed for evaluating
predictive model performance and for model updating, if necessary. Model updating is anotherarea of research that is being investigated by scientists at the AFSC. Model updating or
recalibration may be required in situations where there is unseen variability in the spectral orreference data or when the system response function changes due to instrumentation (Wise andRoginski 2015).
Below we describe an augmented process for integrating the FT-NIR spectroscopy
ageing method into the existing AGP workflow system. This chapter uses EBS walleye pollock
as an illustrative example and draws upon a number of other chapters in this volume to illustrate
an emerging vision of transitioning this species into an end-to-end parallel process within anexisting fish ageing enterprise. The scenario presented in this paper is based on an alreadyvalidated age prediction model for walleye pollock using over 9,000 otolith spectra and TMAages from 2014-2018 EBS bottom trawl surveys. This MMCNN model was published by Benson
et al. (2023) and will not be described in detail here. Rather, our intent is to predict new unseen
ages from this base model and otolith scans collected during the 2019 and 2021 EBS bottom306

trawl surveys and highlight the process flow, data pipelines as well as data quality control
measures.
We illustrate a simplified schematic of the process (Fig. 21.1) through which the 2019and 2021 pollock sample otoliths and data streams flow. The large rectangle on the left side of
the schematic, identified as microscopic ageing in which the goal is to maintain consistency inthe reference data, represents the established TMA process in the AGP (more details of the
AGP’s database design, and how FT-NIR data will be incorporated into the workflow, can befound in Short this volume). The TMA process is discussed in Section 1 below, with further
details found in Matta et al. (this volume-a). The entirely new parallel process of FT-NIR
spectroscopy age prediction is represented by diamonds and the workflow represented by arrows
on the right side of Figure 21.1. The associated QC tools applied at each stage to evaluate thereliability of spectral data collection are described in Section 2 below, with further details in
Goldstein et al. (this volume-a). Following the solid arrows, the data stream enters the FT-NIR
spectroscopy-based model performance evaluation component of the process. This is describedin Section 3 below. Under acceptable performance (solid arrow), age predictions move to final
reporting, library storage, and sharing with end users. The dotted arrows illustrate the alternative
flow when new variability in the data does not conform to expected model performance andthere is a need for model updating. Some model updating strategies are presented and discussed
in Section 4 below.

1. TMA Age Estimation and Data Quality Control
The traditional age determination process within the AGP, including quality control (QC)
procedures, is well established (Kimura and Anderl 2005, Matta and Kimura 2012, Matta et al.

307this volume-a). Walleye pollock age determination is based on mixed methods, primarily
consisting of surface ageing (generally younger fish with clearer annuli) and sectioning-andtoasting (Matta and Kimura 2012). TMA QC metrics used by the AGP are illustrated for the2014 to 2018 walleye pollock age data from which the MMCNN model was developed (Fig.
21.2). Total collection size was over 9,000 analyzed otoliths, with slightly more than 2,100
double reads to give a percent agreement (±0 years) of about 67%, average percent error (APE)of 3.05%, and Chang’s coefficient of variation (CV) of 4.32%. Relative bias between age readers
was low (not significant based on tests of symmetry) and a robust regression of reader on tester
age gave r2 = 0.9 and RMSE = 1.0 years. This indicates that 67% or 95% of ages from the readage were within 1 or 2 years of the test age, respectively.
When applying the published age determination criteria (Matta and Kimura 2012) for
walleye pollock and conducting the usual 20% random subsample for re-analysis, QC metrics ofprecision for double-read TMA data from the 2019 and 2021 EBS BTS showed similar results,
except that the samples consisted of fewer older fish than compared to the 2014-2018 sample(Matta et al. this volume-a). TMA double reads in 2021 showed a slightly higher CV than
expected, with a slight relative bias. It should be noted that while existing TMA procedures use a
20% random subsample, efforts are underway to evaluate whether a lower percentage of double
reads, such as 10%, could be used to evaluate reference age consistency in the parallel FT-NIR
spectroscopy-based ageing process. Crude estimates of walleye pollock TMA ageing efficiency
derived from empirical data found that approximately 35 ages could be produced per person per
day1, although these are likely underestimates of productivity. Enhancements in the AGP AGE3
database will allow better tracking of time-flow statistics for future evaluation. With better timeflow statistics for both the TMA and FT-NIR spectroscopy ageing processes, we hope to conduct308

a thorough cost-benefit analysis to evaluate new technology utilization, efficiency, and its effecton program resources.

2. FT-NIR Spectral Data Acquisition and Quality Control
Quality of spectral data collection from otoliths begins with a well-designed relational
database that has a user-friendly interface and an architecture that collects and stores uniquespectral files, executes QA/QC tools to evaluate spectral reliability, and archives spectra to apermanent location. The AGP AGE3 database has been enhanced to accommodate the newspectral data stream and details can be found in Short (this volume). This section will describe
the flow of spectral data acquisition and the QC measures that have been implemented to ensure
reliability, with additional details described in Goldstein et al. (this volume-a). As shown in
Figure 21.1, key personnel at this stage of the process include instrument operators (who are also
production agers) and a lab manager who is responsible for quality checking each scan session
and warehousing the data in a permanent library. The operator shares responsibility for thequality of spectral data because standardized sample presentation methods for each species havebeen developed and documented in a user manual.
The AGP FT-NIR spectroscopy laboratory currently runs two Bruker MPA II FT-NIR
instruments (illustrated in Fig. 21.1). The production ageing needs of survey (RACE) andobserver (FMA) and special collections are entered in a web-based application (AGPS) and areprioritized on the basis of demand and capacity of AGP resources and stock assessment
priorities. Similarly to the age reading sheets created during the TMA ageing process, a data
session for otolith scanning is created by the database manager to identify specimens to bescanned during that session. From a typical annual RACE EBS bottom trawl survey collection309

for walleye pollock, there may be approximately 1,500 to 1,600 otoliths, so the data manager
will create six scan sessions with approximately 280 otoliths (two boxes) each. An operator will
select a session, physically locate the associated otolith boxes, open the session form on the
AGE3 interface, and begin the scanning process (Short this volume). The scan session interface
is pre-populated with specimen number or barcodes as well as metadata associated with theparticular biological structure (otolith, etc.), including vessel, region, fish length, and other fields
related to the specimen. Several QC tools have been built into the complete data pipeline
process. The first, real-time and integral to the AGE3 process, is that the operator can compare
the recorded fish length with that predicted from a large-sample PLS model that relates otolith
spectra to fish length after each scan. Large departures between observed and predicted fish
length can aid in identifying scan anomalies, including misplacement on the instrument sample
window, incorrect sample identification number, or other mistakes that might otherwise go
undetected. Additional QC tools have been developed to monitor scan data reliability, includingi) generating absorbance profiles for each otolith, ii) generating absorbance profiles at the end of
the session to check for anomalies, and iii) running a script that displays PCA plots, Hotelling’s
T2, and Q-residuals to identify gross outliers (Fig. 21.3). In Figure 21.3, multi-panel plots
associated with QC tools of the scanning process are illustrated for EBS walleye pollock otolithscans using data from 2021, with details described in Goldstein et al. (this volume-a). Wise andRoginski (2015) provide a thorough discussion of these diagnostic tools for spectral data. Once
final data checking is complete, the lab manager executes a script to move all spectral files to apermanent location. While we are still evaluating the time-flow statistics for the complete scan
process, spectral data acquisition yields an efficiency of about 40-60 scans per hour per
instrument, but this can vary by operator and species.

310

3. FT-NIR Spectroscopy Model Development and Performance Evaluation
The next step in the process is where the data analyst employs a predictive model that has
been trained and tested (sometimes called calibrated and validated) to predict fish ages fromfuture otolith spectra. The process for developing an FT-NIR spectroscopy predictive model ishighlighted in numerous chapters in this volume; however, the specific methodological approach
using MMCNN pertaining to this discussion can be found in Benson et al. (2023). The data
analyst examines the quality of the spectral data yet again, often looking for outliers that mayexert undue leverage. Codes related to the condition of the otolith (whether broken, cracked,
crystalized, etc.) during the scanning process can sometimes be identified as the reason for
extreme predictions. As previously mentioned, our scenario is based on a model alreadydeveloped by Benson et al. (2023) using MMCNN from otolith spectral data, reference TMA
ages, and meta-data from 2014-2018. Results from their analysis yielded r2 values of 0.92 and0.91 and RMSE values of 0.83 and 0.91 years for training and test data, respectively (Fig. 21.4).
Good model performance can be seen showing minimal differences between predicted ages and
TMA ages, with comparable precision as illustrated in agreement plots between the two
methods. Age predictions for 2019 and 2021 are shown in Figure 21.5, including somediagnostic plots that illustrate relative differences in FT-NIR and TMA age predictions.
Quantities of relative bias for TMA ages were calculated as (read age – test age) and for FT-NIR
as (NIR age – final age). For most ages, the relative bias between methods is consistently near
the zero-reference line for the expected predictions of the base model. The model performancemeasures using FT-NIR spectroscopy are on par with those expected from independent doubleread data from the TMA approach (r2 = 0.90 and RMSE = 1.0). Interpretation of this311

performance indicates that 67% or 95% of ages from the FT-NIR spectroscopy age are within 1
or 2 years of the final TMA age, respectively.
In order to evaluate the performance of this MMCNN model for pollock age predictions
from future samples, in particular for otolith scans collected in 2019 and 2021, we proposedusing Bayesian uncertainty quantification or Bayesian dropout for deep machine learning (Galand Ghahramani 2016, Gal et al. 2022, Herlau et al. 2022). In concept, it operates bystochastically omitting neurons within the network and can be interpreted as a form of Bayesianmodel averaging (Herlau et al. 2022). This approach has emerged as a powerful tool within themachine learning field to provide optimal inference under model misspecification. Specifically,
we developed 95% marginal posterior predictive internals about the base MMCNN model as thecriteria to evaluate future model performance. Figure 21.4 (bottom panel) illustrates the Bayesian
dropout approximation for 95% marginal posterior intervals along with the predicted walleye
pollock ages from the same 2014-2018 data set. The jagged nature of the posterior interval along
the traditional age domain axis is what would be expected from averaging all posterior predictive
densities for each data point. Of course, posterior predictive intervals would appear muchsmoother with age under infinite domain intervals but instead are constrained by integers. For
evaluation of model performance, we simply calculated the probability that the observations (agepredictions) from future samples in 2019 and 2021 were outside the predictive interval at the 5%
level, which would be expected from random chance, or the prob (2.5% < â > 97.5%) ≤ 5%. The
calculated probability based on the criteria suggests that predicted ages for both 2019 and 2021
are less than 5% (Fig. 21.6). Under this scenario, the model is performing as expected from thenull hypothesis and no action to consider modeling updating would be needed. We have notexplored other predictive model performance procedures, but consistent with our current

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MMCNN modeling approach, the Bayesian dropout technique is a natural extension. Moreover,the flexibility of MMCNN models may provide a natural means of updating existing predictivemodels under unseen variability and poor model performance, which is discussed in the next
section.

4. Model Updating Strategies
When introducing an innovative technology into an existing process control system, theultimate goals are data stream reliability and efficiency. In this context, we have described ascenario where the TRL of FT-NIR spectroscopy fish ageing has advanced to a point thatwarrants a serious consideration of its integration within the existing TMA production ageing
enterprise, along with potential modification for optimal coadaptation. Within this system, any
action or intervention in the process creates an associated cost to efficiency, aimed atmaintaining data reliability. As discussed in the previous section, model performance forpredicting 2019 and 2021 walleye pollock ages was deemed satisfactory, obviating the need formodel updating. However, should model updating be required, we will outline general steps that
could be taken, along with the reasons for such actions. Additionally, we will address
outstanding questions that can guide future research to establish best practices. Barring the needfor recalibration due to instrument standardization issues, which could be addressed using moreprescriptive measures like transfer models (Wise and Roginski 2015), the central questions
revolve around the nature of the dataset used for model updating. In essence, determining thenumber of samples needed and the selection process for expanding the calibration space toencompass new variability within the system become crucial. Although there are currently no313

definitive answers for applications in fish age estimation, ongoing simulation studies that
describe the general framework are currently underway (Arrington et al. this volume-a).
Considering the schematic in Figure 21.1, the dotted arrows indicate situations wheremodeling updating becomes necessary due to new unseen data variability. In such cases, bothotolith spectra and associated TMA (reference) ages will be needed to augment the calibration
data set. While otolith spectra will routinely and automatically be collected within the outlined
framework, reference ages associated introduces an additional cost. Several key considerations
are discussed below:

1. TMA methods prescribe a 20% subsample from a collection for double reading. Can thisbe reduced to 10% to maintain consistency and measure precision in the TMA and be
applied to FT-NIR model updating when needed?
2. Sample size requirements for the model updating dataset may be affected by the inherent
ageing precision level of the species. For instance, if model predictions are poor andageing precision is low, how do we disentangle prediction error from new spectral
variation versus poor reference age estimates?
3. The sample selection method could necessarily be intertwined with sample size (10% up
to 20%). Methods that select samples based on maximizing spectral variability, such as
Kennard-Stone, may require fewer samples. However, this approach may not represent a“random” or “representative” subsample of the population.
4.

The sample size and sample selection method of the updating dataset may also be
dictated by the predictive algorithm or model used to calibrate otolith spectra to fish age.

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In deep machine learning, techniques called “transfer learning” can be used to fine-tune
the original model based on a new dataset (Alzubaidi et al. 2021).

CONCLUSIONSIn this paper, and volume as a whole, we have described an innovative machine-based
technology that could perhaps transform the fish ageing enterprise within NOAA Fisheries.
Taken together, FT-NIR spectroscopy coupled with MMCNN represents a machine learning(ML) system. Deployment of ML systems must follow a well-defined, principled process that
embodies data processing, training, testing, and model deployment within the production processthat it is to replace, or adapt into. Lavin et al. (2022) described the framework for TRL formachine learning systems that is far more detailed than provided here. Nevertheless, several key
ideas for a ML TRL framework relevant to our work include: 1) ML system generality, 2)
nonlinearity, 3) ML-specific failure modes, and 4) probabilistic ML systems.
Robust research and development that leads to broad scale applicability of technologicalsystem development for the domain of interest satisfies the proof of concept stage in the ML
system lifecycle framework. FT-NIR spectroscopy in other domains, as mentioned earlier, is awell-developed technology that obviates the need for the earliest stages of life cycle
development, such as greenfield research or proof of principle development. In fact, early
research in Australia (Robins et al. 2015) and the United States (Helser et al. 2019a) have alreadyshown promising results that to some extent satisfy the proof of concept development stage forapplications in fish ageing. According to Lavin et al. (2022), the proof of concept stage is theentry point in the ML system R&D framework of most organizations, and this is what our SI

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framework refers to more broadly as application development. Within these proceedings, wehave shown broad-scale applicability of the technology and ML system in real-world scenarioswith robust, verifiable data. In general, sufficient confidence in the technology must be achieved
with real-world data of the case-use within the application context to move forward in TRL.
Most ML system workflows use prescriptive compartmentalized, linear processes
through the various stages of ML lifecycle development. Instead, a robust workflow should bethought of as nonlinear, allowing “discovery switchbacks” that iterate over previous, more basic,
developmental stages as knowledge of the technology improves. This has been our experiencemoving from FT-NIR spectroscopy application development to application implementation andback again. We found “out of the box” PLS models to be successful in modeling otolith spectra
to TMA ages for a variety of species within different regions, yet some systemic issues in model
performance were rectified by revisiting basic model concepts in deep machine learning. In
another instance, engineering modifications were needed to augment FT-NIR spectrometer
sample presentation to achieve improved performance (signal-to-noise) for a number of species
and life stages where otolith size was too small for the sample window diameter. These are just a
few of the many discovery switchbacks from our experience.
Prior to technology deployment, failure modes must carefully be addressed both in the
productization of the system and ML-specific algorithms for future predictions. Adhering tostrict QC standards will be key to deploying the ML system in the context of the larger process.
Here we have shown tools and metrics to measure the quality of data streams from bothreference and spectral data, even before being ingested downstream into the ML system.
Moreover, ML-specific failure modes need to be addressed as early on as possible prior to
deployment and on a continuing basis once deployment occurs. Failure points may include316

nonlinearity in predictions from otolith spectra and reference data, unseen variability in reference
data or spectra from biological/environmental interactions, or domain shifts that might mimic
population changes. The key is to have a tool embedded in the system to monitor systemic ordynamic changes and continually evaluate model performance and intervene when needed tomake corrections or updates.
The probabilistic ML system should quantify the uncertainties of components andpropagate them through the system. We are implementing simulation studies to quantify thebroadest axis of uncertainty associated with the various elements within the system. Probabilistic
ML methods, rooted in Bayesian probability theory, provide a principled approach to
representing and manipulating uncertainty about models and predictions. As discussed, we have
implemented the Bayesian dropout method for the EBS walleye pollock case study, which
addresses uncertainty around model structure. However, methods to account and quantify
uncertainty in data, system fault points, and unseen future variability is still an active researcharea. Component uncertainties must be assembled in a principled way to yield a meaningfulmeasure of overall system uncertainty, based on safe decisions and best practices.
Reaching operational readiness of a system comprising innovative technologicalhardware and machine learning algorithms has been discussed as we have envisioned FT-NIR
spectroscopy age prediction at the AFSC. The simple framework we started with did not follow
the detailed steps that are found in other application domains such as engineering and aerospace,but in concept follows the R&D procession needed in application development, data training,data testing, quality control monitoring, and system integration, if not using different
terminology. Nevertheless, the key ideas and objectives are the same for robust technologies and317

ML systems that strive to integrate or co-adapt innovations into existing systems of data
pipelines, software, hardware, database architecture, and human interactions.

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Figure 21.1. -- Stylized schematic illustrating the proposed adaptation of Fourier transform nearinfrared (FT-NIR) spectroscopy fish ageing process at the Alaska Fisheries
Science Center’s Age and Growth Laboratory.

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Figure 21.2. -- Selected tables and graphs from the Alaska Fisheries Science Center Age and
Growth Laboratory’s traditional method of ageing data quality control report.

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Figure 21.3. -- Principal component analysis extreme values and outliers for the 2021 case study
using pre-processed data showing thresholds based on (a,b) Hotelling T2 (Tsquared) and Q residuals. (c, d) Extreme and outlier spectra identified by partial
least squares models with fish length prediction based on Hotelling T2, Q
residuals, and Y-distance (fish body length observations compared to predictions.
See Goldstein et al. (this volume-a) for details.

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Figure 21.4. -- Results from multimodal convolutional neural network model applied to 20142018 eastern Bering Sea shelf bottom trawl survey walleye pollock (Gadus
chalcogrammus) otolith Fourier transform near infrared (FT-NIR) spectra andassociated biological and geospatial data. Over 9,000 walleye pollock otoliths
were analyzed using FT-NIR spectrometers (Bruker Tango-R and Multipurpose
Analyzer II) to generate this base model.

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Figure 21.5. -- Results of applying the multimodal convolutional neural network model (BaseModel) to walleye pollock (Gadus chalcogrammus) otolith spectra and associatedmetadata collected during the 2019 and 2021 eastern Bering Sea shelf surveys.

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Figure 21.6. -- Age prediction of walleye pollock (Gadus chalcogrammus) collected during the2019 and 2021 eastern Bering Sea shelf surveys. Predictions are from themultimodal convolutional neural network (MMCNN) Base Model using otolith
Fourier transform near infrared (FT-NIR) spectra and other biological andgeospatial data and plotted against traditional age estimates. Shadowed region is
the MMCNN “Base Model” 95% credible interval from the Bayesian dropout
method, to which the 2019 and 2021 predictions are compared to evaluate
predictive model performance.

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22. A Simulation Framework to Examine the Effect of Ageing Error on
FT-NIR Model-Based Age Predictions

Morgan B. Arrington1,3, Thomas E. Helser3, Irina M. Benson3, Mary Elizabeth Matta3,
Esther D. Goldstein3, and André E. Punt2

1

Cooperative Institute for Climate, Ocean, and Ecosystem Studies (CICOES)
University of Washington
3737 Brooklyn Ave NE
Seattle, WA USA
2School of Aquatic and Fishery Sciences
University of Washington
1122 NE Boat Street
Seattle, WA USA

Alaska Fisheries Science Center
NOAA, National Marine Fisheries Service
7600 Sand Point Way NE
Seattle, WA USA
3

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INTRODUCTION

Fourier transform near infrared (FT-NIR) spectroscopy is an emerging technology in thefield of fish ageing. It has shown promise for estimating fish ages from biological structures
including otoliths, vertebrae, and spines from a variety of species more rapidly than traditional,
microscope-based methods (Rigby et al. 2014, Wedding et al. 2014, Robins et al. 2015, Rigby
et al. 2016; Helser et al. 2019b, Passerotti et al. 2020a, Passerotti et al. 2020b, Arrington et al.
2022, Healy et al. 2021, Benson et al. 2023). Fish ages, an important data type used to better
understand fish ecology and population dynamics, are a critical component of integrated stock
assessments to inform fisheries management (Maunder and Punt 2013, Methot and Wetzel
2013). Age estimation using FT-NIR spectroscopy may allow for efficiencies in time andmonetary expenditure, improved spatial and temporal data collection for some species, andimproved reproducibility relative to traditional methods.
The ability to estimate fish age using FT-NIR spectroscopy depends upon calibrating a
predictive model between fish age and the near infrared (NIR) spectrum of an ageing structure(e.g., otolith). Each NIR spectrum captures a unique absorbance signature across a range ofwavenumbers when an otolith is irradiated with NIR light. A spectrum is determined by the typeand quantity of organic bonds present in a material and can also be affected by the material’s
structure (Siesler et al. 2008). The molecular or structural basis for NIR otolith spectra
correlating with fish age is still not fully understood. Passerotti et al. (2022) found that it is likely
a complex combination of physical and chemical properties in otoliths that vary with age that aredetectable by NIR light, including otolith size and thickness, structure, and the main constituentsof calcium carbonate and protein.

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There are currently two sources of uncertainty in predictive models when utilizing the
FT-NIR spectroscopy approach for age prediction: spectral variability at age and observation
error in reference values. Variability in otolith spectra-at-age is ubiquitous among species. Thismay be, in part, because the composition and structure of otoliths are influenced by numerous
other biological and environmental factors in addition to age (Chang and Geffen 2013). Indeed,otolith spectra have also been used to discriminate among species and ecosystems (Benson et al.
2020). Other factors such as storage medium and storage time may also contribute variability tospectra (Robins et al. 2015, B. Hsieh, AFSC, pers. comm.). Additionally, variability in spectraat-age may be driven by some otolith spectra getting misassigned to inaccurate age classes.
When the FT-NIR method is used for age prediction, traditional estimates of animal age are usedas the response variable (henceforth referred to as the reference value) for model calibration,
since the true age of fishes is often not known. Traditional age estimation is done by an analyst
counting growth increments on a biological structure. There is often ambiguity in theinterpretation of species’ ageing structures that results in inaccurate age estimates, henceforthreferred to as observation error.
Statistical models assume variance between the model and the data (here, the modeled
spectra-at-age and observed spectra-at-age). However, they do not often account formeasurement error (here, observation error in the age estimate) (Buonaccorsi 1996). There areestablished methods to account for observation error when age estimates are used as a regressor(Punt et al. 2008, Cope and Punt 2007), but to our knowledge, no methods yet exist to accountfor observation error when age is used as a reference value, as is the case in the FT-NIR
spectroscopy approach.

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The FT-NIR ageing method shows promise for age prediction, but the application of theapproach is still limited by our lack of understanding of how sources of uncertainty in predictivemodels impact age predictions. Specifically, it is unknown whether variability in spectra-at-age
may lead to greater prediction uncertainty (Helser et al. 2019b), or whether prediction error isdue to observation error in reference ages. This information is important for understanding where
to focus efforts to improve the predictive accuracy of this technology to achieve technicalreadiness (Helser et al. this volume-a). Here, we present a preliminary study to investigate these
two sources of uncertainty influencing model-based age predictions for eastern Bering Sea (EBS)
Pacific cod (Gadus macrocephalus). A simulation approach was used to disentangle observation
error from spectral variability to address the following questions:

1. How does observation error in the reference age impact model predictions?
2. How does spectral variability at age impact model predictions?

METHODSWe characterized the impact of two sources of uncertainty on age predictions from partial
least squares regression (PLS) models fitted between Pacific cod age and otolith spectra:
observation error in the reference value and spectral variability at age. A simulation approach
was used in R version 4.3.0 (R Core Team 2023). The data simulated in this study were based on
Pacific cod collected on EBS summer bottom trawl fishery-independent surveys conducted by
the Alaska Fisheries Science Center between the years 2010 and 2019.

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Broadly, in this approach, we simulated datasets with “true ages” (henceforth referred toas “known ages”) and paired “age estimates” for four different spectral variability scenarios: low,
medium, high, and empirical. Each dataset had a known age vector (n x 1), an estimated age
vector (n x 1), and a matrix of spectra (n x p), where n = sample size and p = number ofchannels/ wavenumbers.

Y i x 1 = known age
Y*i x 1 = estimated age
X i x p = spectrap = number of channels
i = number of individuals

We simulated 100 datasets for each spectral variability scenario (400 datasets total). A
model was fitted between spectra and known age, and spectra and estimated age for each of thesedatasets. Predictive accuracy was compared among models to evaluate the effect of observation
error in the reference values. More details on each step of this approach are included below.

Age Data Simulation

The known age vector, Y, was generated using a uniform distribution to represent
theoretical fish with integer age a of 1 through 14 years. Each age class had 100 individuals (n =1,400). This was a simplifying assumption used for this preliminary study, but future work330

should explore using more biologically realistic age distributions. We simulated an age estimate
for each known age “fish” by first modeling empirical ageing error for EBS Pacific cod using themethod described in Punt et al. (2008). This method allows the estimation of an ageing error
matrix that estimates the relationship between traditionally estimated fish age and known age.
The empirical age estimates used to estimate the ageing error matrix were determined
microscopically by at least two analysts counting pairs of annual bands corresponding to the ageof the fish using sagittal otoliths (Matta and Kimura 2012). In the resulting ageing error matrix,
each known age, a, has a unique probability distribution Fa of being estimated age 0 to 14 years.
This was used to simulate an estimated age Yi* by resampling with replacement from set{0,1,2…14} for each individual Yi based on the Fa. The resulting matrix had a vector of known
ages (Yi) and a vector of age estimates (Y*i) that were based on the modeled relationshipbetween known age and estimated age for Pacific cod (Fig. 22.1).

Spectral Data Simulation

Spectra were then simulated to allow us to introduce different levels of simulated
variability with respect to age. Spectra were simulated based on empirical Pacific cod otolith
spectra to retain empirical shapes and profiles. Empirical spectra were available from a subset of
the EBS Pacific cod otoliths (collection years 2013-2018, n = 10,255). See Benson et al. (2020)
for more information on spectral data acquisition methods. We simulated spectra based on theprinciple that a spectral matrix X i x p can be decomposed via principal component analysis (PCA)
into a score matrix Ti x a, and loading matrix PTa x p, and a residual error matrix E i x p where i isthe number of individuals, a is the number of components, and p is the number of wavenumber

331channels (Beattie and Esmonde-White 2021) (Equation 1). These separate matrices can then also
be reconstructed into spectra by multiplying the score vector by the transposed PC loading
matrix (Fig. 22.2). The more PCs included in the reconstruction, the smaller the residual error
and the closer the spectra will be to the original (Beattie and Esmonde-White 2021).

(1)

X i x p = Ti x a * PTa x p + E i x p

Ti x p = score matrix
Pp x a = loadings matrix
p = number of wavenumber channels
a = number of components
i = number of individualsFirst, we decomposed empirical Pacific cod otolith spectra into scores and loadings using
PCA (Equation 1). Spectra had been preprocessed using Savitzky-Golay smoothing based onstandard methods at the Alaska Fisheries Science Center to remove baseline shifts and prepare
data for modeling (Helser et al. 2019b, Arrington et al. 2022, Healy et al. 2021, Benson et al.
2023). We standardized the scores from each principal component (PC) 1-5 to have unit variance(Fig. 22.3). The first 5 PCs were selected because they explained a prior threshold variance ofgreater than 99.95%. We then averaged scores among specimens of the same age to obtain an
average score by age for each PC 1-5. Variability was introduced around each average agespecific score to simulate the four variability scenarios. In each scenario, we included 100
random draws per age and per PC to match the simulated age data set. Our four scenarios were:332

the “real world” scenario with scores sampled from the real, empirical distributions; a low
variability scenario that was informed by observations of the low end of standard deviation (SD)in the score distributions by age; a medium variability scenario; and a high variability scenario,
also informed by the higher end of SD in score distributions by age. For the low, medium, andhigh variation scenarios, we randomly sampled from normal distributions with a mean equal tothe mean score by age and a SD of 0.01 for low variability, 0.05 for medium variability, and 0.10
for high variability to simulate scores (Fig. 22.4). Principal components are uncorrelated, so we
sampled from individual distributions. However, sampling from normal distributions was asimplifying assumption for this preliminary approach. True distributions were sometimes closer
to bimodal (Fig. 22.3), and future work could explore using different distributions to simulate
variability in scores. The scores with simulated variability were then multiplied by the originaltransposed loadings matrices and back-transformed to spectra (Equation 1, Fig. 22.5). Thisapproach allowed us to avoid making any assumptions about the empirical shape of spectra thatis captured and retained by using empirical loadings matrices. We then bootstrapped this stepwith 100 datasets per scenario, each with 100 individuals per known age 1 to 14 years (n =1,400). Due to small sample sizes of empirical spectra over age 10 that may not have been
representative, we truncated our simulated dataset at a known age of 10 years (some simulated
estimated ages exceeded this).

Evaluation of Uncertainty

Finally, we fitted PLS models between simulated spectra and known age and between
simulated spectra and estimated age for each spectral variability scenario. This allowed us to333

evaluate the effect of observation error in reference age on model predictions at different levelsof spectral variability. Predictions were rounded to the nearest integer and evaluated relative toknown fish age using root mean square error (RMSE), the coefficient of determination (r2), andage-bias plots (Campana et al. 1995) for all scenarios. Future work could explore additional
methods and metrics to quantify and compare predictive accuracy.

RESULTS AND DISCUSSION

FT-NIR spectroscopy has shown great potential for predicting the age of fish fromotoliths with improved efficiency, repeatability, and near-similar accuracy to traditional methods
for many species including EBS Pacific cod. However, to date, age estimates have been relied
upon for model calibration and evaluation, since true fish ages were unknown. The effect thatobservation error in reference ages may have on model predictions could not be evaluated. This
study is the first to evaluate the impact of ageing error on FT-NIR spectroscopy-based age
predictions from fish otoliths. The preliminary results of this study suggest that uncertainty inmodel-based age predictions is driven by spectral variability at age for EBS Pacific cod more sothan by observation error in traditional ages, but that bias in reference ages may still have an
impact. At lower levels of spectral variability, we observed a greater impact of ageing bias on themodels’ predictive skill. At higher levels of spectral variability, we observed a reduced impact ofageing bias on the models’ predictive skill, including in the “real world”, empirical scenario. At
higher levels of spectral variability, nonlinearity emerged with a tendency for models to
overpredict young ages and underpredict older ages.

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Spectral variability at age was the greater driver of uncertainty in model-based age
predictions in this simulation. At the lowest level of simulated spectral variability, predicted ages
were within just one year of the known age regardless of which reference value (known age or
estimated age) was used except for age-10 fish (Figs. 22.6, 22.7, 22.8, Table 22.1). At empiricallevels of spectral variability, there was greater uncertainty in model-based age predictions
regardless of whether known ages or estimated ages were used as the models’ reference values(Figs. 22.6, 22.7, 22.8, Table 22.1). This suggests that the variability at age in empirical spectra
cannot be attributed to ageing error alone. It is still unknown whether variability in spectra-at-age
is largely due to heterogeneity in the molecular or structural composition of otoliths at age oraffected by other factors such as otolith storage. Effects of storage time on otolith spectra havebeen observed for saddletail snapper (Lutjanus malabaricus) and barramundi (Lates calcarifer)(Robins et al. 2015), which may contribute to spectral variability at age, but more work is needed
to explore this for Pacific cod. Storage media may also contribute to storage effects, and whilesnapper and barramundi otoliths were stored dry, Pacific cod otoliths included in this study werestored in glycerin thymol.
Bias in traditionally estimated reference ages also impacted age predictions in this study.
The age reading error model best supported by the Akaike information criterion was a curvilinear
coefficient of variation (CV) and a curvilinear bias between estimated age and known age.
Pacific cod are among the most difficult Alaska groundfish species to age and commonly havestrongly defined “checks” in early life that make growth patterns hard to interpret (Matta andKimura 2012). The modeled bias for Pacific cod included in this study was most prominent foryounger (under age 2 years) and older (over age 6 years) fish, which was reflected in thesimulated age estimates that tended to be overestimates of known age (Fig. 22.1). At lower levels335

of spectral variability, we observed a greater impact of ageing bias on the models’ predictive
skill with larger differences in the RMSE, r2, and bias plots between age estimate models andknown age models (Figs. 22.6, 22.7, 22.8, Table 22.1). At higher levels of spectral variation, we
observed a reduced impact of ageing bias on the model’s predictive skill, including in the “realworld”, empirical scenario (Figs. 22.6, 22.7, 22.8, Table 22.1). We also observed slightly
improved r2 values for predictions from models fitted to age estimates compared to models fitted
to known ages (Fig. 22.7, Table 22.1). This was an unexpected result but was likely due to thepositive bias in age estimates counteracting nonlinearity in model predictions at older ages.
At higher levels of spectral variability, we observed increased nonlinearity with atendency for models to overpredict young ages and underpredict older ages (Figs. 22.6, 22.8).
Nonlinearity was also observed in the case study applying FT-NIR spectroscopy to age Pacificcod, with a tendency to underpredict the age of older fish (Healy et al. 2021). Since the true agesof fish were not known in that study, predictions were compared only relative to age estimates. It
was unknown whether the cause of the nonlinearity was age estimation error in reference values,
nonlinearity inherent in the spectra, or a combination.
The presence of nonlinearity in predictions for both known age models and age estimate
models in this simulation study supports that the nonlinearity in predictions is driven, at least inpart, by the spectra as opposed to solely by observation error in traditionally estimated reference
ages. The increase in nonlinearity with spectral variability also suggests that when more spectral
variability at age is present, the effect of the nonlinearity on predictions may be exacerbated.
This is to be expected given that there would likely be more overlap between spectra ofsubsequent age classes. This can be somewhat visualized in the distributions of scores-at-age forthe four spectral variability scenarios (Fig. 22.4).

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Several possible causes for nonlinearity in spectra with fish age have been suggested.
There is a decline in otolith accretion with age for many fish species (Helser et al. 2012, Matta etal. 2020), and therefore the relationship between fish age and analyte concentration in otoliths
may not stay consistent. There may also be a nonlinear relationship between spectra and theanalyte being measured. This can be caused by a decline in light attenuation with sample
thickness (Padalkar and Pleshko 2015, Passerotti et al. 2020b) and violations to the BeerLambert Law, where light is either scattered from the sample or absorbed by the sample (as canbe the case with thick samples) instead of being transmitted (Miller et al. 1993, Lui et al. 2018,
Benson et al. 2023).
An alternative modeling approach may help to resolve nonlinearity in EBS Pacific codage predictions. Linear multivariate methods, such as the PLS models used in this study, canoften deal with nonlinearity through wavenumber selection and dimensionality reduction (Miller
at al. 1996). However, they are not always successful and other nonlinear modeling approaches
may be better suited (Miller et al. 1993, Benson et al. 2023). Only spectra with the lowest
variability at age were able to produce linear age predictions in this study using PLS models.
Benson et al. (2023) found that a multimodal convolutional neural network model outperformed
PLS models for predicting the age of walleye pollock (Gadus chalcogrammus), especially for
older fish. This approach may be successful at resolving nonlinearity in predictions for Pacificcod.
It is important to note that model predictive accuracy is reduced in the empirical scenario
in the present study compared to Healy et al. (2021); however, this is likely due to differing goals
between studies. The goal of Healy et al. (2021) was to optimize age predictions, while the goalof the present study was to evaluate the impact of error in reference ages. Models in this present337

study were not optimized for wavenumber region or number of latent variables. Instead,predictive accuracy was compared among scenarios to evaluate them relative to one another.
When RMSE and r2 are calculated relative to the age estimate instead of known age, they arefurther worsened (Table 22.2). This indicates that in real applications of this technology, model
predictions may actually be closer to true fish ages than the age estimates being used to evaluatethem.
We make several assumptions in this study that may impact our findings. We assumed auniform distribution of known ages 1 to 10 to build our models, but future work should explore
the effect of a more realistic age structure, which is an important consideration whencharacterizing ageing error (Nesslage et al. 2022). Since bias in age estimates relative to known
age was highest for older fish and there are typically fewer older fish in real-world populations,
we would expect that our RMSE values may reflect a greater impact of ageing error on model
predictions than in real-world scenarios. Additionally, though we did our best to disentangle two
confounded sources of error, we used empirical spectra that we averaged based on traditional age
estimates (since true fish age is unknown) as the foundation of our simulation. This approachassumes an even distribution of ageing error around true age, which it would appear is notentirely true for Pacific cod (Fig. 22.1). However, the results of the low spectral variability
scenario indicate that linear age predictions with good predictive accuracy are possible using oursimulation approach. Finally, we added random variability to average PC scores by age for thelow, medium, and high scenarios by drawing from normal distributions. This was a simplifying
assumption. However, when compared to the empirical scenario for which variability was drawn
from empirical distributions, the low, medium, and high scenarios seem reasonable (Fig. 22.4).
The largest difference between the empirical and non-empirical scenarios is that the empirical338

scenario had varying levels of spectral variability at age, while the non-empirical scenarios hadconstant variability across the full age range (Figs. 22.4, 22.6, 22.8). It is still unknown why
spectral variability changes with age but may reflect the individual growth variability in fish
populations that can potentially increase with age (Quinn and Deriso 1999, Ogle et al. 2017).
The results of this study address two sources of uncertainty in PLS models fitted between
Pacific cod otolith spectra and fish age. This is the first study to separately evaluate the effects ofthese two sources of uncertainty on PLS model predictions for otolith spectra, and preliminary
results suggest that ageing error in reference values may not impact model predictions to thesame degree as spectral variability and nonlinearity with age for EBS Pacific cod. Some of thefindings of this study may be applicable to other species, such as the impact of bias on modelbased age predictions. But, ageing error and otolith spectra can vary by region (Robins et al.
2015, Benson et al. 2020, Passerotti et al. 2020b) and species (Benson et al. 2020), and werecommend exploring the effect of ageing error on a region- and species-specific basis. This
simulation framework is flexible and can be applied to explore the effects of age estimation error
and spectral variability on model-based age predictions for a range of scenarios and species. Thisis important for understanding the utility of the FT-NIR spectroscopy approach and where tofocus efforts to improve predictive accuracy to achieve technical readiness.

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ACKNOWLEDGMENTS

We thank Brenna Hsieh, Jonathan Short, and the AFSC Age and Growth Program forspectral data collection, traditional age estimation, quality control and assurance, and support
with data access, and without whom this study would not have been possible. We thank the
AFSC Resource Assessment and Conservation Engineering division for survey data collection.
We also thank Matthew Siskey for assistance in getting started with the “Puntilizer” code, andare grateful for feedback from attendees at the FT-NIR Spectroscopy 2023 Workshop that
improved this abstract.

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Table 22.1. -- The root mean square error (RMSE) and r2 values between model predictions andknown age. Each spectral variability scenario is represented by 100 iterations ofmodels fitted to age estimate reference values.
Spectralvariability

Model reference
age type

RMSE (years)
known ager2
known age

empirical

known age

1.39-1.54

0.62-0.70

age estimate

1.43-1.58

0.66-0.73

known age

0.20-0.27

0.99-0.99

age estimate

0.50-0.56

0.97-0.97

known age

0.94-1.06

0.84-0.88

age estimate

1.01-1.14

0.85-0.89

known age

1.56-1.74

0.45-0.59

age estimate

1.61-1.77

0.52-0.64

low
mediumhigh343

Table 22.2. -- The root mean square error (RMSE) and r2 values between model predictions andestimated age. Each spectral variability scenario is represented by 100 iterationsof models fitted to age estimate reference values. This represents the status quo
method of model calibration and evaluation when true fish age is unknown.
Spectralvariability

Model reference
age type

RMSE (years)
age estimater2
age estimate

empirical

age estimate

1.62-1.78

0.56-0.66

low

age estimate

0.58-0.64

0.96-0.97

mediumage estimate

1.12-1.28

0.81-0.86

highage estimate

1.80-1.98

0.40-0.54

344

Figure 22.1. -- Simulated known age and corresponding age estimates (in gray) relative to themodeled relationship between known age and estimated age for eastern Bering
Sea Pacific cod (Gadus macrocephalus) collected from 2010-2019 (solid red line)with 95% confidence intervals (dashed red lines). The size of the gray pointreflects the number of simulated observations. The one-to-one line is shown inblack.

345

Figure 22.2. -- One empirical, raw spectrum (solid line), and the same spectrum recomposedusing principal component (PC)1 scores multiplied by the transposed PC1 loading(dotted line).

346

Figure 22.3. -- Kernel density plots showing empirical variability in standardized scores for principal components (PCs) 1 through 5
by empirically estimated age. This is the foundation for calculating the average score by age for each PC 1 to 5 tosimulate spectra.
347

Figure 22.4. -- Simulated scores for just principal component (PC) 1 for empirical or “real world” variability, low variability (standard
deviation, SD = 0.01), medium variability (SD = 0.05), and high variability (SD = 0.10). Scores were simulated for PCs
1 to 5.

348

Figure 22.5. -- Simulated spectra for four spectral variation scenarios: empirical, low, medium, and high. Example of one data set per
scenario; each had 100 resampled data sets.

349

Figure 22.6. -- Predicted ages from models fitted between simulated spectra and known age(green) or age estimate (pink) plotted relative to known age. Predictions areshown as dots that scale with count and the average prediction with standard error
is shown as a ribbon. This illustrates the impact of ageing error in the reference
value on model predictions at four different levels of spectral variation: empirical,
low, medium, and high.

350Figure 22.7. -- The root mean square error (RMSE) and r2 values for model predictions relative
to known age. Kernel density plot comparisons between RMSE and r2 for age
estimate models and known age models are shown for the four spectral variability
scenarios. Ranges in values are based on 100 iterations per reference age type per
scenario.

351

Figure 22.8. -- Prediction bias relative to known age compared among levels of spectral
variation: empirical, low, medium, and high for models fitted to age estimate(pink) and known age (green).

352

23. Fourier Transform Near Infrared Spectroscopy Data and Application Within the
Eastern Bering Sea Pollock Assessment ModelJames N. Ianelli

Alaska Fisheries Science Center
NOAA, National Marine Fisheries Service
7600 Sand Point Way NE
Seattle, WA USA

353

354

ABSTRACT

Adopting age determination error within stock assessments has been a feature applied andavailable in stock assessments since the early 1980s (Fournier and Archibald 1982). Findings
from applying preliminary walleye pollock (Gadus chalcogrammus) age data using Fouriertransform near infrared (FT-NIR) spectroscopy method are summarized below.

1. Modification of assessment model a step forward: The study concludes that themodification of the assessment model used in the research represents a positive step
forward in accurately evaluating the fishery or population under investigation. The
specific modifications made to the model may be highlighted, along with their impact
on improving the accuracy or reliability of the assessment results.

2. Flexibility to use data from any source/year/gear type: The study emphasizes theimportance of flexibility in utilizing data from various sources, years, and gear types.
By incorporating data from diverse sources, the assessment becomes morecomprehensive and robust. This flexibility allows for a broader understanding of thefishery or population dynamics and facilitates better-informed management decisions.

3. More detail and random effects application: The study suggests that incorporating
more detail and applying random effects in the assessment model can lead to moreaccurate and nuanced results. By accounting for additional variables or factors that355

influence the fishery or population, the assessment becomes more comprehensive andbetter represents the complex dynamics at play.

4. Preliminary results seem promising: The preliminary results obtained from theassessment are described as promising. This indicates that the initial findings suggest
positive outcomes or trends regarding the health and sustainability of the fishery or
population. However, further analysis and validation may be necessary to confirm thereliability of these results.

5. Age error "shape" relatively limited, application needs more careful evaluation: The
study finds that the shape of age errors, which refers to the pattern or distribution oferrors associated with age determination, is relatively limited. However, the summary
cautions that the application of age error estimation requires more careful evaluation.
This implies that while the estimation method shows some promise, its practicalapplication needs to be thoroughly examined to ensure its accuracy and suitability for
future assessments.

356

FT-NIR DATA AND APPLICATION COMPARISONS

For the data made available from 2014-2018 using the FT-NIR determined ages, we seethat both the proportions-at-age and body mass-at-age have differences (Figs. 23.1 and 23.2).
Interpretation of these patterns suggest that there may be some important age-specific patterns
between the traditional age determination methods and the FT-NIR age determination approach.
Some standard assessment model diagnostics (root mean square error and effective sample size)
suggest that the model fit to the traditional age data is better than the fit to the alternative FT-NIR
age data (Table 23.1). Preliminary application of the new data compared with the conventionalage determination approach does have some impact on the assessment results (Fig. 23.3).
However, more work is needed to estimate the observation-error matrices used in stock
assessments. Specifically, this should entail a careful study of conventional reader/tester age
estimates with those from FT-NIR estimates.

357

Table 23.1. -- Indicative eastern Bering sea walleye pollock (Gadus chalcogrammus) model
diagnostics using the “conventional” age-determination error matrix and datacompared with the Fourier transform near infrared (FT-NIR) matrix and data, fromvarious sources (BTS = bottom trawl survey, ATS = acoustic trawl survey, AVO =acoustic vessels of opportunity). Lower values of root mean square error (RMSE)
and higher values of effective sample size “effective N” indicate better fits.
Component
RMSE BTSRMSE ATS
RMSE AVO
Effective N Fishery
Effective N BTS
Effective N ATS

Conventional
0.16
0.21
0.22
1,022
213
253

358

FT-NIR
0.16
0.20
0.21
899
210
256

Figure 23.1. -- Comparison of the data in terms of proportions-at-age over time for the Fouriertransform near infrared spectroscopy (NIRS) and traditional age determination
data. Fishery data is shown on the left and survey on the right.

359

Figure 23.2. -- Comparison of the data in terms of body mass-at-age over time for the Fouriertransform near infrared spectroscopy (NIRS) and traditional age-determination
data. Fishery data is shown on the left and survey on the right. Rows represent age
classes.

360

Figure 23.3. -- Preliminary indication of comparisons of a stock assessment model run with theconventional Alaska Fisheries Science Center Age and Growth Program’s
conversion matrix (AGP_age_error) with that of the Fourier transform nearinfrared estimate (NIR_age_error). Spawning biomass estimates are shown on the
top panel and recruitment on bottom.

361

362

24. Sensitivity of Pacific Cod Stock Assessment to Alternative Age Composition Data
Derived from Fourier Transform Near Infrared Spectroscopy of Otoliths

Thomas E. Helser1, Matthew R. Siskey2, Irina M. Benson1, and Steven J. Barbeaux1
Alaska Fisheries Science Center
NOAA, National Marine Fisheries Service
7600 Sand Point Way NE
Seattle, WA USA
1

2

Washington Department of Fish and Wildlife
1111 Washington Street SE
Olympia, WA USA

363

364

ABSTRACT

Fourier transform near infrared (FT-NIR) spectroscopy of otoliths is emerging as a highly
efficient fish age estimation alternative to the labor intensive, time consuming traditional
microscope-based approach (TMA). However, before such innovative methods can be
operationalized in the production fish ageing and management process, FT-NIR spectroscopy
generated data must be vetted within the stock assessment model. Using southeastern Bering Sea
Pacific cod as a case study, we present the first investigation of the stock assessment model
sensitivity to alternative age data predicted from FT-NIR spectroscopy and neural networks(NIR-NN). Specifically, we employed Markov Chain Monte Carlo simulation of the Pacific codstock assessment model to evaluate the sensitivity of key internal parameters and externallyderived management quantities by substituting a 6-year span of survey age compositions (20132018) that were based on more than 8,000 generated NIR-NN ages. Our results showed thatassessment model outcomes were highly insensitive to the substitution of TMA age compositions
for age compositions from the NIR-NN ageing approach, such that the probability of observing
differences for any parameter or derived management quantity was no more than 4%.

365

366

INTRODUCTION

The eastern Bering Sea (EBS) Pacific cod (Gadus macrocephalus) fishery is one of thelargest fish stocks managed in the Alaska region, bringing in a yearly average of 185,896 metrictons from 1991 through 2018 (Thompson 2018). Pacific cod stocks are primarily managed withan age-structured, length-at-age-structured, or stage-structured stock assessment model(Thompson 2018). The annual EBS shelf bottom trawl surveys provide age compositions thatalong with abundance trends represent crucial data inputs for the assessment, shaping essential
model parameters estimated internally, such as natural mortality, recruitment, and growth. These
quantities then offer insights into population abundance, productivity, and female spawning
biomass level relative to fishery management benchmarks. Subsequently, this information serves
as the scientific advice that guides resource managers in establishing harvest goals (Hilborn andWalters 1992, Maunder and Punt 2013). Integrated stock assessments, like those used for Pacificcod (Methot and Wetzel 2013, Thompson 2018), heavily rely on age and length data (Maunderand Punt 2013, Ono 2015). However, traditional ageing methods, which involve some form of
otolith preparation (such as sectioning and roasting) followed by microscopic evaluation ofannual growth rings, can be subjective, prone to error, and labor intensive. Campana (2001) has
suggested that age data obtained through traditional methods are considered one of the mostexpensive data sources for stock assessments.
Ageing error is one of the major sources of measurement errors on composition data mostoften used in age-structured assessments (Deriso et al. 1985). Ageing error can lead to profounduncertainty on estimates of growth, weight- or maturity-at-age, natural mortality, andproductivity (i.e., fewer older fish means higher natural mortality and higher productivity) that367

will in turn affect management reference points (Reeves 2003, De Pontual et al. 2006, Bertignac
and De Pontual 2007). Two properties of ageing error are precision and accuracy (Kimura andLyons 1991, Beamish and McFarlane 1995). Imprecision occurs when age reading errors occur
at random between independent trials of age assignment (e.g., by two age readers). Accuracy, or
bias, occurs when there is a systematic difference between the age assigned and the true age. In
practice, both types of error occur during the age determination process, but generally precision
is most often routinely quantified for use in stock assessments by production ageing laboratories.
Recent research has explored a novel approach of using Fourier transform near infrared(FT-NIR) spectroscopy of otoliths as a rapid, nondestructive, and more efficient method ofgenerating fish ages (Helser et al. 2019a). Near infrared (NIR) spectroscopy has been widelyused in agriculture, pharmaceutical, and petrochemical industries, and most recently in terrestrial
ecology (Vance et al. 2014) and fishery science (Helser et al. 2019a, Helser et al. 2019b). It
functions by exciting covalent bonds (O-H, C=O, C-H, C-N, and N-H) at the molecular level
using NIR light and measuring absorbance in the wavenumber range 4,000 to 12,500 cm−1. The
light interaction with the sample results in measurable vibrational frequencies represented byspectral signatures associated with molecular overtones and combinations that make up
compounds in the sample (Conzen 2014, Siesler et al. 2002). In otoliths, these spectral signatures
are a good proxy for fish age. The use of FT-NIR spectroscopy of otoliths to estimate fish age
have been reported for a number of species (Wedding et al. 2014, Helser et al. 2019b, Passerotti
et al. 2022), including EBS Pacific cod in particular (Healy et al. 2021). Healy et al. (2021)
found the Pacific cod multiyear model yielded high precision metrics (r2 = 0.869, root meansquare error RMSE = 0.614 years, percent agreement PA = 63%, coefficient of variation CV =7.412%), which were highly comparable to precision metrics from the traditional microscopic
368

approach (r2 = 0.763-0.869, RMSE = 0.639-0.737 years, PA = 63%-70%, CV = 5.671-6.698%).
Moreover, recent work by Benson et al. (2023) has combined FT-NIR spectroscopy of otoliths
and neural networks (NIR-NN) for fish ageing that directly ingests the whole range of collected
spectral information and corresponding geospatial and biological data that improve predictive
skill.
With the promise for improving efficiency and repeatability of generating age data forstock assessments, the National Oceanic and Atmospheric Administration, National Marine
Fisheries Service has undertaken a nationally coordinated R&D effort across seven biologicallaboratories with the goal of operationalizing this new technology into the fish age determinationenterprise (Helser et al. 2019a). These efforts hold great promise for improving efficiency andrepeatability of generating age data for stock assessments; however, no study to date has
evaluated the impact of these new data products on model outputs used for management systems.
Here, we present a first case study that uses more than 8,000 otolith spectra and biological data
across 6 years to generate ages using the NIR-NN model, which are then incorporated as age
composition data in the EBS Pacific cod stock assessment. Specifically, this evaluation was
performed as a sensitivity analysis by replacing ages generated by the traditional microscopebased approach (TMA) with NIR-NN predictions. We evaluate a number of scenarios in which
age compositions from each ageing method are incorporated into the assessment model withdifferent assumptions about ageing error using Markov Chain Monte Carlo simulation andcompare key assessment model internally-estimated parameters and derived management
quantities.

369

METHODS

Pacific cod otoliths were taken from recent collections for this analysis. Pacific cod
otoliths and corresponding biological data (fish length and sex) were collected during 2013-2018
EBS bottom trawl surveys (BTS) conducted by Alaska Fisheries Science Center (AFSC)
Groundfish Assessment Program (https://www.fisheries.noaa.gov/alaska/sciencedata/groundfish-assessment-program-bottom-trawl-surveys). The BTS, which covers thesoutheast EBS continental shelf, occurs annually and employs a 20 ✕ 20 nautical mile fixed griddesign at 0-50 m (inner shelf), 50-100 m (middle shelf), and 100-200 m (outer shelf) depths. The
survey area averages 493,000 km2 over approximately 300-356 hauls annually (Thompson 2018).
Otoliths are taken from fish subsampled within each haul; fish are dissected on deck and ages arelater determined in the laboratory. BTS estimates of abundance and corresponding age andlength compositions are generated using a depth-area stratified design-based approach (Lauth etal. 2019).
In statistical analysis involving otolith spectra, interest is in predicting the target variable(often referred to and used interchangeably as reference data), which in our case is the TMA age.
For the reader not familiar with chemometrics, in simple terms, it involves reducing thedimensionality of otolith spectra (the X data matrix) while at the same time maximizing thecorrelation with reference values (Y data vector) through a small set of intermediate linear latentvariables (Chen and Wang 2001). Standard classical methods of generating this relationshipinvolve techniques such as principal component regression or partial least squares (PLS)
regression. Our approach, however, employs deep machine learning, but the reference datapreparation and spectral data acquisition are essentially the same, described below.
370

Reference Age Data

Pacific cod ages for all samples (n = 8,261) were estimated by analysts enumerating
annual growth rings of otoliths, following the ageing guidelines and procedures of the AFSC’s
Age and Growth Manual (Matta and Kimura 2012). This was done for only one of the paired
sagittal otoliths belonging to a given fish, leaving the other otolith for spectroscopic analysis.
Age determination is performed using a dissecting microscope under reflected light, which ispreceded by preparation techniques such as cutting to expose the otolith core and roasting toenhance annual bands. The AFSC has historically employed a random 20% double read between
two independent analysts (referred to as a reader and tester) to monitor precision andrepeatability of the TMA process (Matta and Kimura 2012). The metrics of precision are
expressed as percent agreement, average percent error, and the coefficient of variation (CV)(Matta and Kimura 2012). Since several of our assessment model scenarios included TMA age
reading error, this was quantified through double-read data from the 1993-2019 EBS BTS.

Otolith Spectral Acquisition and Data

For spectroscopic analysis, whole otoliths were removed from their vials and gently
blotted dry with Kimwipes to remove excess glycerol-thymol (storage medium) before
placement on the sample window of a Bruker Tango-R or MPA II FT-NIR spectrometer.
Otoliths were covered with a gold stamp and analyzed using diffuse reflectance on an integrated
sphere, and spectra were collected at step sizes of 8 cm−1 for MPA II and 8.3 cm−1 for TANGO
R, with 64 co-scans, measuring absorbance values from 12,500 to 4,000 cm−1. Helser et al.
371

(2019b) reported that scan time to acquire otolith spectra ranges between 40 and 60 seconds. The
entire paired data set of TMA age, otolith spectra, and associated biological data was split intotraining (80%) and testing (20%) sets using the “onion” algorithm (Gallagher et al. 2020). The“onion” algorithm was applied to each year in the split and then recombined to capture annual
variability in the spectra in both train and test data sets. Spectral data were pre-processed using
1st derivative and a Savitsky-Golay (2nd order polynomial, 17 points) smoothing filter. Spectral
variability across collection years and detection of outliers was evaluated using principal
component analysis (PCA) and measures such as Hotelling’s T2 and Q-statistic (Mujica et al.
2011, Rodionova and Pomerantsev 2020). A more comprehensive description of sample
presentation, spectral data preprocessing and outlier detection can be found in Benson et al.
(2020, 2023).

Multimodal Convolutional Neural Network Model

The proposed multimodal convolutional neural network (MMCNN) was built by
incorporating otolith spectra and biological datasets as multimodal input within different
branches. Benson et al. (2023) provide a more detailed description of the MMCNN model,
although the general model architecture is described here. The model is trained to extract
information out of the entire spectral data range before integrating it with information extracted
from additional data blocks that includes corresponding biological data. Biological information
routinely collected at sea include fish length and fish sex, both of which were proposed in themodel since they bear an allometric relationship with fish somatic growth and otolith accretion
rates.

372

We employed Python 3.7.0 using TensorFlow and Keras to train and test a neuralnetwork model. The model consisted of two primary branches and 14 layers. The first branch
was associated with spectral data input (layer 1) and represented 5 layers with the convolutional
layer (layer 2) activated by the rectified Linear Unit (ReLU) activation function for nonlinear
activation. The convolutional neural network is capable of extracting the abstract features of FTNIR spectra and selecting most featured wavenumbers (Chen and Wang 2019). Output was
flattened (layer 3) before passing to the fully connected dense layer (layer 4) and regularizedthrough dropout (layer 5). For this model, all fully connected dense layers were activated withthe exponential linear unit (eLU) activation function to capture the non-linearity in the data. The
dropout layers were used for regularization to prevent overfitting. A number of nodes for fully
connected dense layers and a dropout rate for dropout layers were tuned using the Hyperband
optimization tuning algorithm.
The second branch was associated with corresponding biological data that have an effecton fish growth and provide information that improves the ageing model performance. Fish sex
was preprocessed with one-hot encoding, which represented categorical variables as numericalvalues in a machine learning model. Fish length was scaled with a zero mean and unit variance.
The concatenated preprocessed biological data input layer (layer 6) was processed with dense
layer (layer 7) and regularized with dropout layer (layer 8) and then concatenated to the second
branch (layer 9). Within the combined, two fully connected dense layers (layers 10 and 12) wereregularized through dropout layers (layers 11 and 13). Then the output of the previous layers was
fed to the final fully connected dense layer (layer 14) with a single output node of predicted ages
and linear activation function.

373

Model performance was evaluated through plotting NIR-NN predicted ages on TMAages and calculating r2 and RMSE of test data. The SHapley Additive exPlanations (SHAP)
method was used to interpret the importance of features for predictions (Lundberg and Lee2017). NIR-NN model performance was also evaluated with the TMA approach using a modified
Bland-Altman plot (Ogle 2017a, 2018) and also a measure of relative bias with and without
respect to age using:∑𝑛𝑛𝑖𝑖=1(𝐴𝐴1 − 𝐴𝐴2) 𝑖𝑖
𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎 𝑏𝑏𝑏𝑏𝑏𝑏𝑏𝑏 =.
𝑛𝑛
In the above equation, A1 and A2 represent TMA age estimates of the reader and tester,
respectively. For the NIR-NN model predictions, the same equation was used for the difference
between the NIR-NN model predicted age and the average of the A1 and A2 TMA estimates.

Stock Assessment Model

The EBS Pacific cod stock assessment is an age-structured model based on Stock
Synthesis V3.30.16.02 (Methot and Wetzel 2013), which is an integrated statistical catch-at-age
modeling framework coded using Automatic Differentiation Model Builder (ADMB; Fournier et
al. 2012). The base model specification used in this analysis most closely resembles Model 16.6of the 2020 stock assessment report (Thompson et al. 2018). Data inputs included: 1) BTS swept
area biomass 1982-2019, 2) BTS age compositions from 1994-2019, 3) BTS size compositions
1982-2019, 4) fishery catches 1977-2020, and 5) fishery size compositions 1977-2020. The basemodel included BTS shelf age compositions, as the goal of this analysis was to evaluate the374

sensitivity of model parameter estimation (e.g., natural mortality, recruitment, and growth) and
derived-quantity outputs most dependent on those data to changes to this data source (e.g.,
missing data, alternate age data from the same specimens generated via FT-NIR spectroscopy,
and alternate ageing error estimates via FT-NIR spectroscopy ageing). Model parameters
estimated internally include: 1) survey catchability, q, 2) natural mortality rate, M, 3) natural log
of initial recruitment, lnR0 and time-varying recruitment deviations, 4) growth parameters (LMin,
LMax, von Bertalanffy k, Richards k, and variability for size at minimum and maximum age), 5)
fishery and survey selectivity parameters, and 6) Dirichlet-multinomial log(theta) parameters(Table 24.1).
One difference between our base model (i.e., the TMA comps/TMA error model) and themodel employed for stock assessment (i.e., the original model) was that the otolith ages used forgenerating age compositions in this study included a subset of the original specimens. A single
whole sagittal otolith is needed for instrument scanning to acquire spectra, and in some cases
both otoliths have already been processed for TMA age estimates. All age compositions used inthis analysis were calculated using a design-based estimator rather than model-based estimation(e.g., via VAST). Another difference between our base model and the original model was thatageing error was only estimated for the years 1977-2007, while the years 2008-2019 used anageing error vector calculated externally to the model via the nwfscAgeingError package (Punt etal. 2008) which estimates age imprecision and age reading bias-at-age.
For the ageing error vector estimated within Stock Synthesis (years 1977-2007), there areseven parameters that are used in estimation of ageing error-at-age. The second and third ageing
error parameters are attributed to the bias at the starting age (age-0) and bias at the maximum age(age-20) in the form of an additive offset from unbiased age. These two parameters were375

estimated within the time-varying module of Stock Synthesis in order to incorporate the block
structure that was specified in the original assessment model (i.e., estimated for the 1977-2007
block and not estimated for 2008-2019). Two ageing error vectors were calculated externally to
Stock Synthesis using the Punt et al. (2008) method. The first ageing error vector, which isrepresentative exclusively of the TMA method, was calculated using 8,998 double reads from
1993-2020 (i.e., reader age vs. tester age). This error vector was referred to as TMA error. The
second vector, which represented error arising from both the TMA and the NIR methods,
consisted of triplicate age outcomes for the same otolith. Here the error vector was calculatedusing 1,946 double reads from 2013-2018 (reader and tester) and the FT-NIR spectroscopy
predicted age (from NIR-NN). This vector is referred to as TMA-FTNIR error. These ageing error
vectors were used as inputs for the ageing error module within SS for the second time block of
2008-2019, thus ageing error parameters were not estimated for these years. A fourth and final
vector of error was calculated from a sample of 600 otoliths that were predicted with the NIRCNN model from otoliths collected from 2016 and 2017 EBS BTS that were scanned on two
different instruments and by two different operators. This was not used in SS but rather simply
used to compare measurement error arising from spectral data collection.
Model scenarios developed for this analysis included age compositions generated fromthe TMA and NIR-NN approaches and two alternative error vector assumptions (Table 24.1).
The default or base scenario used the 2013-2018 TMA age compositions and the TMA ageing
error (identified as TMA comps/TMA ageing error). An alternative scenario employed the same
specification and inputs as the base model scenario but used 2013-2018 age compositions
generated from the NIR-NN approach and assumed the same TMA age reading error. This
scenario is identified as Mixed comps/TMA ageing error. Another considers the same model376

specification and inputs as the previous scenario using NIR-NN age compositions but here
augments ageing error to account for both the TMA ages (e.g., reader and tester) and thedifference between TMA and NIR-NN ages (involving triplicate outcomes). This scenario was
identified as Mixed comps/Mixed ageing error. A final scenario was used to examine the effector sensitivity of the choice of the 2013-2018 time period over which the TMA versus NIR-NN
age composition substitutions were made (Missing comps/TMA error model). Here, the basemodel was simply run by removing age composition data for years 2013-2018.
We employed Markov Chain Monte Carlo (MCMC) simulation to quantify and compare
sensitivity of assessment outcomes to the use of NIR-NN versus TMA data sources for age
composition and ageing error. We specified non-informative uniform priors on all parameters
except for M, which was empirically derived, approximating a log-normal density (Thompson etal. 2020). We performed MCMC on the four different scenarios (Table 24.1) to compare modeloutcomes between ageing methods and assumed ageing errors. Marginal posterior densities(MPD) of the key parameters from 900 MCMC iterations (1,000,000 with 1/1,000 thinning and a
100-iteration burn interval) were compared to evaluate stock assessment model sensitivity.

RESULTS

Ageing error of Pacific cod using the TMA method for the 2013-2018 period (percent
agreement ± 0 years = 65.3%; CV = 6.96%) was typical of historic data throughout the timeseries. Pacific cod is considered a moderately difficult species to age given the relatively low
mean age of the sample (~3.9 years) and relatively short lifespan of the stock (90% of ages were
below 8 years old). There was no indication of relative bias between TMA double read data377

based on the Evan-Hoenig test of symmetry (p > 0.05). Robust regression of age estimates fromthe two analysts resulted in an r2 of 0.86 and an RMSE of 0.52, indicating that age estimates of asecond age reader are expected to be within 0.52 years of the first reader 67% of the time. Allerror types estimated from the model resulted in a best fit model (AICc) which was represented
by a non-linear standard deviation (SD) of the observed error with “true age” (Fig. 24.1). The
TMA ageing method showed the greatest magnitude error (TMA error; Fig. 24.1) while themixed error was intermediate in magnitude to TMA ageing error or measurement error (FT-NIR
spectroscopy). Not surprising was that the FT-NIR spectroscopy error was the smallest,suggesting that repeatability of age prediction is high even when otolith NIR scanning is
performed by different operators and on different instruments.
Spectral data collection from Pacific cod otoliths showed consistently good signal tonoise, with absorbance increasing within the 7,000 to 4,000 cm-1 wavenumber regions (Fig.
24.2A). Two distinct groups of raw spectra (Fig. 24.2A) can be seen as a baseline shift that is
associated with otolith scanning using two different FT-NIR spectrometers (Tango-R vs. MPA
II). Instrument differences in baseline absorbance are not unusual and otolith spectra were
harmonized by preprocessing techniques employed prior to modeling (Fig. 24.2B, 24.2D).
Preprocessed spectra plotted in bivariate space indicate that the vast majority of spectral
variability is accounted for by the 1st and 2nd principal components. Further, the ‘onion’algorithm extracts roughly the same extent of spectral variability for both the train and test data
sets (Fig. 24.2C).
The NIR-NN was trained to extract information out of spectral data, including allwavenumbers, before integrating the output with the scaled data block that includes
corresponding biological data. The data types were ordered according to their importance for378

prediction in the NIR-NN model using the mean of absolute Shapley values (Fig. 24.3). The
collective contributions of otolith spectra in the range of 7,000-4,000 cm-1 wavenumbers had thehighest impact on predicting fish age, followed by fish length and sex. The NIR-NN model
results show good performance resulting in a high degree of correlation between NIR-NN
predicted and TMA estimated ages (Fig. 24.3). The NIR-NN model using training data resulted
in good performance with r2 = 0.93 and RMSE = 0.52 years. Likewise, on the test data set(which represents new unseen variability by the model, n = 1,652) the NIR-NN model yielded
similarly good performance, with r2 = 0.92 and only a slight increase in the RMSE (0.56 years).
Predicting Pacific cod age using otolith spectra and associated biological data is expected
to result in age predictions within 0.6 years of the TMA age 67% of the time and within 1.2 years
98% of the time. Precision associated with NIR-NN predictions (RMSE = 0.56 years) was
clearly consistent with double reads of the TMA age determination approach, which had an
RMSE of 0.52 years. Comparing NIR-NN model age predictions with TMA estimated ages,
Bland-Altman plots showed a high level of consistency of ages from the different methods over
the range of ages, where the majority of deviations fell within 1.96 SD (Fig. 24.3D). Age
predictions were highly consistent along a 1:1 line, although there was a very slight underprediction for fish greater than 10 years. Very few older Pacific cod ages were observed, andtherefore predictions may be improved by inclusion of older age fish in the training model.
A measure of relative bias or differences between ages estimated using TMA and NIRNN predictions also showed a very high degree of consistency between ageing methods, except
for a departure after 9 years of age (Fig. 24.4A). Since so few ages beyond age 9 were available,
there was insufficient information to evaluate relative differences between ageing methods forolder fish. Overall, up to 90% of the observations resulted in zero bias between NIR-NN

379

predicted ages and TMA estimated ages, compared to roughly 70% from TMA age estimates(Fig. 24.4B). Nearly all estimated or predicted ages were within 1 year of age for both methods(Fig. 24.4B).
Survey age compositions generated from the design-based estimator using data derived
from TMA and NIR-NN ageing methods were comparable, with only slight variations inproportions-at-age (Fig. 24.5). The largest differences in proportions-at-age were for age-3 in2018 (TMA = 0.213, NIR-NN = 0.166), age-4 in 2017 (TMA = 0.264, NIR-NN = 0.307), andage-4 in 2014 (TMA = 0.139, NIR-NN = 0.092). Similarly, model fits to the age composition
data had little variation across scenarios, with the largest differences in expected proportion-atage being for age-5 in 2018 (TMA comps/TMA error = 0.236, Mixed comps/Mixed error = 0.249,
Mixed comps/TMA error = 0.235). Only very small differences between age composition
likelihood were observed, suggesting model fits among these scenarios were equally good (orpoor). Recent assessments have reported a relatively strong 2013 year-class, seen as age-1 fish in
the 2014 age compositions. Age compositions for both ageing methods exhibit the movement of
this year class through the age compositions in subsequent years (Fig. 24.5).
Marginal posterior densities generated from MCMC of key internal and externallyderived management parameters among the different scenarios show little difference in thehighest posterior density (HPD) and kernel densities (Figs. 24.6 and 24.7). Table 24.2 provides
comparisons of key model parameters over all sensitivity scenarios. The two primary scenarios
in which NIR-NN age compositions are substituted for TMA age compositions (Mixedcomps/TMA error and Mixed comps/Mixed error) resulted in nearly identical median values andkernel densities for M and lnR0 (Fig. 24.6), but did shift slightly to higher values when NIR
ageing error was assumed. The only estimated parameter with notable shifts in median value

380

across 1,000 MCMC iterations was Q, which shifted from the most negative values in the TMA
comps/TMA error scenario to more positive values in other scenarios (Table 24.2). In terms of
uncertainty, Lmax displayed the highest degree of change in standard deviation despite this change
being relatively small (TMA comps/TMA error = 3.206, Missing comps/TMA error = 3.765;
Table 24.2). The years chosen (2013-2018) for data substitution did produce an effect on thesensitivity analysis (Missing comps/TMA error), such that kernel densities of key parameters
shifted to lower median values. The relative change in kernel densities from the Missing
comps/TMA error scenario was in fact greater than kernel density shifts across scenarios thatused different age data products (TMA comps/TMA error, Mixed comps/TMA error, Mixedcomps/Mixed error; Fig. 24.6).
Similar shifts in median values of the MPDs for derived management quantities such as
relative spawning biomass (SSB/SSBunfished) and unfished female spawning biomass wereobserved, but in all cases the sensitivity of this change resulted in less and a maximum of a 4%
probability (Fig. 24.7). Differences in the probability of relative depletion between the NIR-NN
and TMA age data scenarios and with either ageing error assumption were exceedingly low. All
model run scenarios resulted in decreased median derived management quantity values andstandard deviations in comparison to the base model scenario (TMA comps/TMA error; Table
24.2, Fig. 24.7). The Missing comps/TMA error scenario resulted in the largest reductions in the
median value of derived management quantities (1.2% reduction for median of SSB0 to 4.8%
reduction for median of overfishing level OFL) as well as the standard deviation (3.2% reduction
for SD of SSB0 to 14.3% reduction for SD of 2020 recruitment). The Mixed comps/Mixed error
scenario resulted in the smallest reductions in the median value of derived quantities (0.2%
reduction for median of 2020 recruitment to 0.6% reduction for median of OFL) as well as381

standard deviation (2.2% reduction for SD of OFL to 7.0% reduction for SD of 2020
recruitment).

DISCUSSION

Fish age determination based on growth patterns visible in otoliths has changed little overthe last century. While ageing fish using scales can be documented as far back as the early
1700s, Johannes Reibisch was the first to describe a protocol for ageing fish using otoliths in1899 (Jackson 2007). Microscopic counting of the annual growth zones in otoliths was thefoundation of the ageing method then and is still the basis of age estimation today. FT-NIR
spectroscopy of otoliths to predict fish age is an emerging technology that may have atransformative impact on traditional microscopic age determination. Studies suggest that FT-NIR
spectroscopy of otoliths may produce efficiency gains by as much as 10x and substantially
improve repeatability compared to traditional microscopic methods (Helser et al. 2019, Benson
et al. 2023). Crude estimates of TMA age production rates suggest a throughput of 31 ages perperson per day for Pacific cod 2. In comparison, age predictions from FT-NIR spectroscopy withan already calibrated model can produce up to 35-50 ages per hour. Research is underway tobetter quantify time-flow statistics for cost-benefit analysis for both methods.
Fish otoliths largely comprise the same composition of molecular constituents dominatedby CaCO3 as aragonite and lesser amounts of organic material (Campana 1999), so it is not
surprising that other studies applying FT-NIR spectroscopy found wavenumbers in the 7,000 to2

Lambert et al. 2017. Importance of age data collection for stock assessments: a U.S. national perspective. NOAA
Fisheries, Alaska Fisheries Science Center, Unpublished report.

382

4,000 cm-1 range most informative for fish otoliths (Wedding et al. 2014, Helser et al. 2019b,
Benson et al. 2020, Passerotti et al. 2020b). Many promising studies that have analyzed FT-NIR
spectroscopy of otoliths using partial least squares for age prediction have been reported forteleost fish (Wedding et al. 2014, Helser et al. 2019a, Helser et al. 2019b, Wright et al. 2021,
Passerotti et al. 2020b, Healy et al. 2021) as well as chondrichthyan species (Rigby et al. 2016,
Arrington et al. 2022). For many of them, model performance has been quite good withcoefficients of determination in the high 80% and even low 90%, and with precision on par with
TMA precision estimates. Our study, and that of Benson et al. (2023), have coupled FT-NIR
spectroscopy of fish otoliths and a deep machine learning approach that allowed the network to
extract important spectral features automatically by utilizing a convolutional layer before
connecting its output with additional biological or geospatial layers. Using NIR-NN for EBS
walleye pollock G. chalcogrammus, Benson et al. (2023) reported superior performance, relative
to classical partial least squares, that achieved not only better predictive skill but also rectified
nonlinearity at older ages. While we did not compare our EBS Pacific cod NIR-NN model to the
PLS methods, our model performance achieved similarly good results, with r2 in low 90% and as
good or slightly better precision compared to TMA.
Ageing error associated with TMA is well known in fish ageing, and statistical methods
for quantifying it have been well documented (Beamish and Fournier 1981, Kimura and Lyons
1991, Campana 2001). The impact of ageing error on the performance of stock assessments has
also been well documented in the literature, resulting in inappropriate yield projections andoverfishing (Lai and Gunderson 1987), poorly defined stock-recruitment relationships (Richards
et al. 1992), and in some cases overly optimistic yield projections (Reeves 2003). Punt et al.
(2008) articulated the justification and necessity of including ageing error in stock assessment

383

models, widely used today (Methot and Wetzel 2013). Hence, alternative ageing methods used togenerate age composition data, such as FT-NIR spectroscopy, must also seriously consider such
sources of error and how to quantify them. Among the ageing error assumptions considered inthis study, the Mixed error assumption is probably the most realistic for age compositions
generated from the NIR-NN approach, although we examined an alternative of using TMA error.
Statistically, the TMA approach gives rise to an estimate of age, whereas the NIR-NN approachgives rise to a prediction. The question arises of how best to quantify and propagate uncertainty
from a predictive model that is trained on data that have inherent error among outcomes. Since
the standard practice is to incorporate the error specific to the age composition data within thestock assessment model, it seems logical that error associated with both TMA ageing and FTNIR spectroscopy predictions (no matter what modeling technique is used) should be used, as
was done with the Mixed comps/Mixed error scenario. It should be noted that ageing bias for
Pacific cod has yet to be quantified and analyzed within the assessment model. If ageing bias
exists in TMA, it will also be the case for NIR-NN age predictions.
Finally, several considerations of this study are warranted. First, the time span of NIR
data substitution was somewhat narrow relative to the entire range of years over which age
composition data are used in the assessment. The over 8,000 ages generated from NIR otolithscanning from 2013-2018 was a practical consideration. Had resources been available tosubstitute NIR age composition data over the entire time series, might the results have beendifferent, particularly during periods of more dynamic change in the population and fishery?
Maybe not, because the EBS Pacific cod population experienced a rather dramatic change overthe 2013-2018 period explored in this analysis (Thompson et al. 2020). Cod recruitment reached
an all-time high in 2013, which was followed by a population size increase to a near record high384

in 2014 and then to a near record low in 2018 (Thompson et al. 2020). The Missing comps
scenario, which attempted to measure the “effect size” of the time period, showed the mostsubstantive changes in model outputs, but yet the model results among the other scenarios inwhich NIR-NN age compositions were substituted for TMA resulted in minimal differences.
Our sensitivity analysis showed only minimal differences in model outputs when using
FT-NIR spectroscopy generated age compositions. Under the B40% minimum depletion thresholdlevel, the model suggests the stock in 2020 was at about 52% of unfished female biomass andthis depiction of stock status did not change under any sensitivity scenario. Key estimatedparameters and externally derived management quantities were largely unchanged among thedifferent scenarios and assumed ageing error structures based on the stock assessment model
specification used for this sensitivity analysis. However, compared to some assessment models,the Pacific cod model only uses age data to a somewhat limited extent. Fishery age compositions
are currently not used in the assessment. Time varying growth, which may be a biologicallyrealistic feature of the population, is not specified. If future Pacific cod stock assessments evolve
to incorporate more age data for other fleets (fisheries) or add complexity of their use, such as
conditional age-at-length compositions, further sensitivity analyses of the use of FT-NIR
spectroscopy generated age data such as this may be warranted.

385

386

ACKNOWLEDGEMENTS

We express sincere appreciation to NOAA/AFSC Age and Growth Program staff who
scanned and aged Pacific cod otoliths. We also thank the NOAA Fisheries Science Board forfunding this work under the NOAA Fisheries Strategic Initiative.

387

388

Table 24.1. -- Pacific cod Gadus macrocephalus Stock Synthesis (SS) model specifications andscenario descriptions used in the sensitivity analysis. TMA = traditional method ofageing; FTNIRS = Fourier transform near infrared spectroscopy.
SS Model Parameter
Lmin
Lmax
Von Bert K
Richards
SD_young_Fem_GP_1
SD_old_Fem_GP_1
Ln_R0
M
Q
Size selectivity fishery params
Size selectivity survey paramsln(DM_theta)

Description
Length at minimum age
Length at maximum age
Von Bertalanffy growth coefficient
Richards growth coefficientvariability for size at age <= minimum age
variability for size at age >= maximum age
log of virgin recruitment level
Natural mortality
Catchability of survey
5 of 6 double normal selectivity parameters
2 of 6 double normal selectivity parameters
3 log theta parameters

Sensitivity Scenario NameTMA comps/TMA ageing error

Description• TMA age compositions for all years• Estimate ageing error 1977-2007• TMA ageing error (2-way; read & testages) for all years 2008-2019

Missing comps/TMA ageing error

•
•
•
•

Mixed comps/TMA ageing error

•
•
•
•

Mixed comps/Mixed ageing error

•
•
•
•

TMA age compositions for all years
2013-2018 missing
Estimate ageing error 1977-2007
TMA ageing error (2-way; read & testages) for all years 2008-2019
FTNIRS age compositions for 2013-2018
TMA age compositions for other years
Estimate ageing error 1977-2007
TMA ageing error (2-way; read & testages) for all years 2008-2019
FTNIRS age compositions for 2013-2018
TMA age compositions for other years
Estimate ageing error 1977-2007
Mixed ageing error (3-way; read, test,
FTNIRS) for all years 2008-2019389

Table 24.2. -- Summary statistics for estimated parameters and derived management quantities
across 1,000 Markov Chain Monte Carlo (MCMC) runs. TMA = traditional
method of ageing. See Table 24.1 for descriptions of model parameters and text for
management quantities.
Parameter
Lmin

Lmax

Von Bert K

MRichards K

Q

LnR0

Management quantity
OFL

SSB0

SSB2020

Recruitment

Scenario
TMA comps/TMA error
Missing comps/TMA error
Mixed comps/TMA error
Mixed comps/Mixed error
TMA comps/TMA error
Missing comps/TMA error
Mixed comps/TMA error
Mixed comps/Mixed error
TMA comps/TMA error
Missing comps/TMA error
Mixed comps/TMA error
Mixed comps/Mixed error
TMA comps/TMA error
Missing comps/TMA error
Mixed comps/TMA error
Mixed comps/Mixed error
TMA comps/TMA error
Missing comps/TMA error
Mixed comps/TMA error
Mixed comps/Mixed error
TMA comps/TMA error
Missing comps/TMA error
Mixed comps/TMA error
Mixed comps/Mixed error
TMA comps/TMA error
Missing comps/TMA error
Mixed comps/TMA error
Mixed comps/Mixed error
Scenario
TMA comps/TMA error
Missing comps/TMA error
Mixed comps/TMA error
Mixed comps/Mixed error
TMA comps/TMA error
Missing comps/TMA error
Mixed comps/TMA error
Mixed comps/Mixed error
TMA comps/TMA error
Missing comps/TMA error
Mixed comps/TMA error
Mixed comps/Mixed error
TMA comps/TMA error
Missing comps/TMA error
Mixed comps/TMA error
Mixed comps/Mixed error

390

Median14.6648
14.6454
14.6699
14.6817111.027112.186111.813111.391
0.111400.10779
0.109280.11069
0.363640.35787
0.362480.36356
1.52657
1.54333
1.53427
1.53252-0.19012-0.14926-0.17404-0.18258
13.3122
13.2487
13.2955
13.3096
Median182,363
173,475
178,285
181,243
1,274,570
1,258,560
1,269,815
1,271,135
647,083
627,444
637,056
644,436
476,590447,298
468,742475,393SD
0.393410.398610.41049
0.41323
3.206223.76545
3.693123.552160.009280.010320.010160.01005
0.01259
0.01204
0.012150.012070.04497
0.04837
0.048020.04826
0.079190.071480.074360.07533
0.11836
0.10959
0.11218
0.11187SD
22,138
19,77120,80821,18333,65532,59033,63932,91558,20552,09754,07454,938
58,10049,79653,41954,000CV
0.026810.027220.027960.02815
0.02883
0.03349
0.03289
0.03179
0.083140.09556
0.093130.090890.034640.033640.03351
0.033190.02943
0.03135
0.031240.03151-0.41557-0.47303-0.42821-0.40899
0.008890.00827
0.008440.00840CV
0.120216
0.113215
0.115564
0.115999
0.026394
0.025894
0.026521
0.025868
0.089261
0.082814
0.084511
0.084847
0.120768
0.110445
0.113134
0.112762

Figure 24.1. -- Ageing error vectors showing standard deviation (SD) of estimated age relative to“true age” from traditional method of ageing (TMA) and Fourier transform nearinfrared spectroscopy (FTNIRS) data inputs. TMA ages are represented bydifferences between reader age and tester age, FT-NIRS is the difference between
multiple FTNIRS predicted ages from multiple instruments and analysts, and
TMA-FTNIRS is the difference between tester age, reader age and FTNIRS
predicted ages.

391

Figure 24.2. -- Pacific cod Gadus macrocephalus otolith Fourier transform near infrared spectra(measured as absorbance) from over 8,000 samples showing (A) raw spectra,
preprocessed spectra (1st derivative, Savitsky-Golay smoothing) for (B) train and(D) test data sets, and (C) principal component analysis (PCA) of otolith spectra
separated into training and testing data sets using the onion algorithm.

392

Figure 24.3. -- Age predictions from multimodal convolutional neural network (MMCNN)
model for Pacific cod Gadus macrocephalus from otolith spectra and biological
data as inputs. Model results are illustrated as the relationship between model
predictions and traditional age for A) training set with r2 = 0.93 and RMSE = 0.52
years and B) test data sets with r2 = 0.92 and RMSE = 0.56 years. C) MMCNN
model importance plot, and D) Bland-Altman plot of difference between methods.

393

Figure 24.4. -- Relative bias or differences between estimated traditional (TMA) ages andFourier transform near infrared (FT-NIR) spectroscopy predicted ages A) withrespect to age and B) without respect to age. Relative mean bias was expressed as
the difference between reader and tester age for the TMA method, and difference
between multimodal convolutional neural network predicted age and final age for
the FT-NIR method.

394

Figure 24.5. -- Pacific cod Gadus macrocephalus eastern Bering Sea shelf bottom trawl survey
age compositions (expressed as proportions at age by year) generated using adesign-based estimators and Stock Synthesis (SS) model fits from three scenarios
based on traditional (TMA) and Fourier transform near infrared spectroscopy (FTNIRS) ageing methods and associated ageing errors. Age compositions are shownas bars and SS model fits to age comps are shown as lines.

395

Figure 24.6. -- Marginal posterior kernel densities of internally estimated Pacific cod Gadus
macrocephalus Stock Synthesis model parameters (Lmax, M, Q, and LnR0) from
1,000 Markov Chain Monte Carlo simulations based on four different model
scenarios comparing traditional and Fourier transform near infrared spectroscopy
age composition and assumed ageing errors.

396

Figure 24.7. -- Marginal posterior kernel densities of externally derived management quantities(overfishing limit OFL, recruitment, relative spawning stock biomass(SSB/SSBunfished), and unfished female SSB) in the Pacific cod Gadus
macrocephalus Stock Synthesis model from 1,000 MCMC simulations based onfour different model scenarios comparing traditional and Fourier transform nearinfrared spectroscopy age composition and assumed ageing errors.

397

398

25. Sensitivity of Gray Snapper (Lutjanus griseus) Stock Assessment Models to Age Inputs
Estimated with Near Infrared Spectroscopy

Steven B. Garner1,2, Beverly K. Barnett2, Derek W. Chamberlin3, Francesca C. Forrestal4,
Thomas E. Helser5, Irina M. Benson5, William Kline2, and William F. Patterson III3

1

Cooperative Institute for Marine and Atmospheric Studies
University of Miami
4600 Rickenbacker Causeway
Miami, FL USA
Southeast Fisheries Science Center
NOAA, National Marine Fisheries Service
3500 Delwood Beach Road
Panama City, FL USA
2

3

Department of Fisheries and Aquatic Sciences|
University of Florida
7922 NW 71st Street
Gainesville, FL USA
Southeast Fisheries Science Center
NOAA, National Marine Fisheries Service
75 Virginia Beach Drive
Key Biscayne, FL USA
4

Alaska Fisheries Science Center
NOAA, National Marine Fisheries Service
7600 Sand Point Way NE
Seattle, WA USA
5

399

400

INTRODUCTION

Modern, age-structured stock assessment models require age data of sufficient quality(i.e., high precision and accuracy), quantity (i.e., sample size), and representativeness (i.e.,
composition). Standard protocols for developing age estimates from fish ageing structures(primarily otoliths) require substantial sample processing procedures that can take several
minutes per sample over multiple days. Thus, the number of samples available for input into
stock assessments is dependent upon the processing power of ageing laboratories, while thequality of age estimates is dependent upon the ageing expertise of fishery scientists and speciesspecific otolith section clarity. Fourier transform near infrared (FT-NIR) spectroscopy has thepotential to greatly increase sample processing efficiency in production ageing laboratories. Usedin the food and drug industry for decades, FT-NIR spectroscopy has only recently been applied
to ageing fish (e.g., Robins et al. 2015, Helser et al. 2019b, Passerotti et al. 2020b), but withgreat promise. With FT-NIR spectroscopy, all otoliths of interest are irradiated with near infrared
light to collect spectral absorbance data. A subset of the scanned otoliths are sectioned and aged
with traditional methods. The observed ages are paired with their spectral absorbance data, and amodel is developed to predict age for the remainder of otoliths from only the spectral data.
Considering that a spectral scan requires approximately one minute to complete, there isenormous potential for increasing production ageing efficiency with this approach. However, theeffects of utilizing age estimates generated from FT-NIR spectroscopy on stock assessment
outputs need to be explored. Our objective was to evaluate the sensitivity of the Gulf of Mexico
gray snapper (Lutjanus griseus) stock assessment to FT-NIR spectroscopy-predicted ages by401

comparing assessment model outputs under different simulation scenarios in the Stock Synthesis(SS) framework.

METHODS

Sample Selection, Model Building, and Simulations

Gray snapper otoliths were selected from the archive at the National Marine FisheriesService Panama City Laboratory from the years 2016-2020, with a target sample size of 300 per
year. Each otolith was placed distal side up on a quartz sampling window of a Bruker MultiPurpose Analyzer II FT-NIR spectrometer to collect spectral scans of diffuse reflectance (Robins
et al. 2015, Helser et al. 2019b). Samples were irradiated with near infrared light at 16 cm-1
resolution with absorbance measured in wavenumbers (cm-1). Following scanning, otoliths were
processed using standard protocols and sectioned on a Hillquist high-speed saw to produce athin-section (~0.5 mm) for traditional ageing. Only whole, clean, unbroken, left otoliths were
sampled to maintain consistency among FT-NIR scans. Observed ages were input along withpaired spectral data into chemometric software (OPUS, v8.5, Bruker Optics) to develop
predictive models using partial least squares regression (Chen and Wang 2001). Individual
samples were selected from the calibration set iteratively and cross-validated to develop the best
possible predictive model based on root mean square error (RMSE) and the ratio of prediction
deviance (RPD). The predictive model built from the calibration set was then applied to thevalidation set (i.e., the remaining 70% of scanned otoliths) to predict age from spectral scans402

without including the observed age estimate. Ages predicted with FT-NIR spectroscopy werethen compared to their observed age estimate to compare accuracy and precision.

Simulation Set 1

The FT-NIR spectroscopy-predicted age estimates from the calibration and validation
sets were substituted for their corresponding observed ages in the dataset used to assess graysnapper in the most recent stock assessment model (Southeast Data Assessment and Review 75).
Ages without an FT-NIR spectroscopy-predicted age were unchanged and retained in the data.
The assessment model was rerun to estimate a new base model and then run in the R package
ss3sim (Anderson et al. 2014) with n = 10,000 iterations. The ss3sim function fits relevant
distributions to the data in the operating (base) model from which to resample the data to thespecified number of iterations and reruns the model estimating procedure (estimation model).
The SS output of interest was compared to the base model and summarized across all iterations
as relative error. Model outputs including spawning stock biomass (SSB), fraction unfished,
B/BMSY (ratio of observed biomass to biomass that would provide maximum sustainable yield,
MSY), F/FMSY (ratio of observed fishing mortality to the fishing mortality rate that would resultin MSY), depletion, SSB at MSY, and unfished biomass and were compared between the baseassessment model and the model with substituted FT-NIR spectroscopy-predicted ages. For thebase model, ageing error was estimated by comparing observed ages from the primary reader toan expert reader and assuming no bias in the expert reader’s age estimates. For the model with
FT-NIR spectroscopy-predicted ages substituted for observed ages, ageing error was estimatedby comparing the FT-NIR spectroscopy-predicted ages to the expert reader’s age estimates. Age403

data for 2016-2020 contained both observed ages and FT-NIR spectroscopy-predicted ages; thus,
applying FT-NIR spectroscopy ageing error was a conservative approach given that observed
ages had less imprecision than FT-NIR spectroscopy-predicted ages.

Simulation Set 2

Due to the low number of FT-NIR spectroscopy-predicted ages (~3-5%) relative to thenumber of observed ages in each year from 2016-2020, a second set of simulations was
conducted with resampled FT-NIR spectroscopy ages. The FT-NIR spectroscopy-predicted ages
were resampled (random with replacement) so that year-specific sample sizes of FT-NIR
spectroscopy-predicted ages were equal to the number of observed ages in each year (Table
25.1). For example, FT-NIR spectroscopy-predicted ages for 2016 (n = 481) were resampled toequal the number of observed ages from 2016 (n = 3,643). Samples from the calibration andvalidation set were included in the resampling pool to maximize the age range and sample size
comprising the resampled dataset. Each FT-NIR spectroscopy-predicted age was resampled withall of its corresponding data (e.g., observed age). The observed ages that corresponded to theresampled FT-NIR spectroscopy-predicted ages were used to estimate a new base model. The
FT-NIR spectroscopy-predicted ages were then substituted for all the observed ages, and the SS
model was rerun to get new parameter estimates. The model was then run through the simulation
framework for comparison to the base model output from the simulation framework. The modelwith resampled observed ages (primary vs. expert age comparisons) and the model with theresampled, fully substituted FT-NIR spectroscopy-predicted ages (FT-NIR spectroscopy vs.
expert age comparisons) was specified with the same ageing error matrices used in simulation set

404

1. As with the first set of simulations, only data from 2016-2020 were altered, while data fromyears prior to 2016 remained unchanged. Individual SS model outputs as well as simulation
outputs were compared with relative error. Output was not compared between simulation sets 1
and 2, only within each simulation set.

RESULTS

In total, 1,419 gray snapper otoliths were available for FT-NIR spectroscopy scanningwith n = 420 samples comprising the calibration set and n = 996 samples comprising thevalidation set (Table 25.1). Observed ages ranged from 2 to 29 yr, while FT-NIR spectroscopypredicted ages ranged from 0 to 26 yr. The prediction model tended to overestimate age for fish
<10 yr and increasingly underestimate age fish >15 yr. The relationship between the observed
ages and FT-NIR spectroscopy-predicted ages in the calibration set had an r2 of 0.92, an RMSEof the cross validation of 1.5 yr, and an RPD of 3.5. The validation set had an r2 of 0.897, an
RMSE of prediction of 1.6 yr, an RPD of 3.1, an average percent error (APE) of 7.6, and an
average coefficient of variation (ACV) of 10.7 (Fig. 25.1).
Outputs between assessment models with primary reader ages versus FT-NIR
spectroscopy-predicted ages were very similar in single model runs (Fig. 25.2), indicating thatutilizing FT-NIR spectroscopy-predicted ages had a negligible effect on reference point
estimates of biomass or fishing mortality. For example, relative error (RE) in SSB ranged from0.002 to 0.007 across all years. Greater differences in reference points were observed between
single models that used the resampled datasets, but RE was still low (RE ranged from -0.072 to
0.093 in SSB across all years; Fig. 25.2). The RE in B/BMSY (≤10%) was higher than in F/FMSY405

(<5%), but median estimates were very similar between models with FT-NIR spectroscopypredicted ages either partially (e.g., Fig. 25.3A vs. 25.3B) or fully substituted (e.g., Fig. 25.3E vs.
25.3F) for observed ages. Similarly, summary data indicated RE in median estimates ofdepletion, SSB at MSY, and unfished biomass were very similar within and between simulation
sets when FT-NIR spectroscopy-predicted ages were substituted, partially or fully, for observed
ages (Fig. 25.4). The RE in depletion estimates was similarly positively biased in summary data
for sets 1 and 2; RE in median estimates of SSB at MSY and unfished biomass were unbiased inboth simulation sets (Fig. 25.4).

DISCUSSION

FT-NIR spectroscopy-predicted ages were generally similar to observed ages but withgreater imprecision and relatively low bias in the younger and older age classes. FT-NIR
spectroscopy-predicted ages were overestimated for ages 2-5 yr and underestimated for ages >15
yr. The APE for FT-NIR spectroscopy-predicted ages versus expert reader ages was nearly threetimes higher than error between primary and expert readers. Imprecision of FT-NIR
spectroscopy estimates for young ages, which had extremely high between-reader agreement, has
a disproportionate impact on APE. Assessment output can be sensitive to imprecision in younger
age estimates due to cohort smoothing of infrequent strong year-classes. However, bias in older
ages has minimal impact on assessment outputs because older fish represent relatively few
individuals in the landings or population, and most are contained in the plus group in age
composition data for this species (≥21 yr). While, greater imprecision in FT-NIR spectroscopypredicted ages is a concern, the assessment model was insensitive to this source of error,

406

especially when the FT-NIR spectroscopy-predicted ages comprised a low proportion of age
samples. Results of simulation set 2 indicated low sample sizes of FT-NIR spectroscopy ages did
minimize their impact on assessment outputs, but relative error was still low (<10%) when FTNIR spectroscopy-predicted ages were fully substituted for their corresponding observed age forthe last five years of the dataset (2016-2020). Generally, greater imprecision in age estimates will
increase uncertainty in and possibly shift assessment outputs, but the stock assessment model
appears robust to the increased imprecision associated with FT-NIR spectroscopy-predicted ages,
at least on the scale of imprecision examined with gray snapper. Scanning additional otoliths
would allow us to fully evaluate effects of utilizing FT-NIR spectroscopy-predicted ages versus
observed ages by better refining imprecision between traditional and FT-NIR spectroscopy
methods. Furthermore, scanning a larger number of younger and older individuals may reduce orremove the bias observed for these age classes at the sample sizes used in this study.

407

Table 25.1. -- Number of primary reader versus Fourier transform near infrared (FT-NIR)
spectroscopy-predicted ages per year from 2016 to 2020 in each dataset. The
simulation 1 dataset contains the FT-NIR spectroscopy-predicted ages (n = 999)
from the validation set of scanned otoliths, while the simulation 2 dataset contains
the FT-NIR spectroscopy-predicted ages from the calibration and validation sets
resampled to match the year-specific sample sizes aged by the primary reader.
Ages from prior to 2016 were unchanged in both datasets. FT-NIR spectroscopypredicted ages were resampled with all corresponding data so that each had apaired observed age for re-estimating the base model that utilized resampled thedata.

Simulation 1 dataset

Year

2016

2017

2018

2019

2020

Observed ages

n

3,643

2,773

3,795

4,109

1,861

NIRS-predicted ages

n

347

215

136

220

78

Observed ages

n

3,643

2,773

3,795

4,109

1,861

NIRS-predicted ages

n

3,643

2,773

3,795

4,109

1,861

Simulation 2 dataset

408

Figure 25.1. -- Observed (expert reader) age (yr) versus Fourier transform near infrared
spectroscopy (NIRS) predicted age (yr) for the A) calibration and B) validation
sets of gray snapper (Lutjanus griseus) otoliths. Panels C and D indicate agespecific means (with 95% CIs) for the calibration and validation sets,
respectively. NIRS-predicted ages were rounded to the nearest integer age.
Sample sizes are shown at the top left of each panel.

409

Figure 25.2. -- Single model output for A) spawning stock biomass (SSB, tons) and B) fraction
unfished by year from each gray snapper (Lutjanus griseus) stock assessment
model used in simulation sets 1 or 2. Models include the 1) primary reader ages
(Base), 2) Fourier transform near infrared spectroscopy, FT-NIRS (FT-NIRS
substituted ages), 3) resampled primary reader ages (Res. base), or 4) fully
substituted, resampled FT-NIRS-predicted ages (Res. FT-NIRS). Models 1 and 2
should not be compared with models 3 and 4 as they utilize different datasets but
are shown together for simplicity. Color-corresponding shaded regions indicate
model uncertainty (95% CI) in the estimate of SSB or fraction unfished.

410

Figure 25.3. -- Relative error in reference points (B/BMSY or F/FMSY) from simulation set 1 (observed vs. Fourier transform nearinfrared spectroscopy (FT-NIRS)-predicted ages; Panels A-D) or simulation set 2 (resampled observed vs resampled
FT-NIRS-predicted ages; Panels E-H). Solid black lines show the median values while the dashed lines indicate the
95% quantiles for each set of simulation runs (n = 10,000 iterations).

411

Figure 25.4. -- Boxplots of relative error in median values for model outputs (depletion,
spawning stock biomass SSB at maximum sustainable yield MSY, or unfished biomass) fromgray snapper (Lutjanus griseus) stock assessment models in A) simulation set 1 (primary reader
ages vs. substituted Fourier transform near infrared spectroscopy (FT-NIRS)-predicted ages) andB) simulation set 2 (resampled primary reader vs fully substituted, resampled FT-NIRS-predictedages). Box ends indicate the 25th and 75th quantiles while whiskers indicate the 5th and 95th
quantiles. The solid black line on each box indicates the median value while red dashed lines
indicate the 0 value of relative error.

412

26. Quality Control and Assurance of Reference Age Data
at the Alaska Fisheries Science Center

Mary Elizabeth Matta, John D. Brogan, Jonathan A. Short, and Thomas E. Helser

Alaska Fisheries Science Center
NOAA, National Marine Fisheries Service
7600 Sand Point Way NE
Seattle, WA USA

413

414

INTRODUCTION

Fish age data are a critical component of fisheries stock assessment models. Every year,
the Age and Growth Program at the Alaska Fisheries Science Center (AGP-AFSC) is tasked with
providing upwards of 30,000 age estimates to support stock assessments of commercially
important groundfish species in the federal waters of Alaska. Historically, the AGP-AFSC has
met these requirements through microscopic examination and visual interpretation of otolith
growth patterns, but we are in the process of implementing the application of Fourier transformnear infrared (FT-NIR) spectroscopy to rapidly predict age from otolith spectral scans for someof our key stocks. Models that predict age from FT-NIR spectra of fish otoliths are reliant on
high-quality reference age data. The AGP-AFSC has long had in place a framework to maintainquality control and assurance of fish age data (Kimura and Anderl 2005), a process that willcontinue following adoption of the FT-NIR spectroscopy method to check model performanceand preserve consistency in reference information over time. Here, using bottom-trawl survey
collections of walleye pollock (Gadus chalcogrammus) otoliths from the eastern Bering Sea asan example, we describe how traditional (microscopic) age estimates are generated and vetted by
the AGP-AFSC prior to being included in predictive models of age from FT-NIR spectra (Helser
et al. this volume-a).

METHODS

Otoliths were collected from walleye pollock during the 2019 and 2021 AFSC Resource
Assessment and Conservation Engineering (RACE) summer bottom trawl surveys in the eastern

415

Bering Sea and preserved in glycerin-thymol solution until age determination in the laboratory.
Otoliths were aged from the otolith surface or using the break-and-burn technique, wherein
otoliths are sectioned transversely through the core using a Buehler Isomet low-speed saw andheated using an alcohol burner or toaster oven to enhance contrast between growth zones (Matta
and Kimura 2012). Otolith annuli were enumerated and recorded as integer ages in the AGPAFSC database, AGE3.
Otoliths were assigned an initial age estimate (termed the “Read Age”) by age readersthat had prior experience and had demonstrated proficiency in ageing walleye pollock. A random
test subsample of 20% was aged independently by a second experienced age reader (“Test Age”)
to evaluate precision and bias between readers (Kimura and Anderl 2005). Because survey
otolith collections are rather large, they were split into sub-collections among several readertester pairs in each year. The precision statistics percent agreement (PA), average percent error(APE; Beamish and Fournier 1981), and Chang’s coefficient of variation (CV; Chang 1982)
were calculated. Bias was evaluated statistically using Bowker’s test of symmetry (Bowker 1948,
Kimura and Anderl 2005) and visually using contingency tables and age bias plots (Campana
1995). Discrepancies between readers and testers were discussed and resolved. In cases where
systematic differences were detected, specimens were re-aged by the reader and reevaluated by
the tester as a data check. Any ages that were reconciled to an age that differed from the original
Read Age were indicated as a separate field (“Update Age”) in AGE3. Upon completion of age
reading, all ages were finalized. In cases where there was an Update Age, it became the “FinalAge”; otherwise, the Read Age was accepted as the Final Age.
Prior to releasing age data to end users, length-at-age observations were graphicallyexamined for potential outliers. Otoliths identified as outliers were reexamined by the reader to416

determine if there were any obvious age reading errors and compared to otoliths from fish ofsimilar body length to ascertain instances of collection recording error at sea; corrections weremade as necessary, and fast- or slow-growing fish were noted in AGE3. One last inspection wasmade on each age collection by the production team lead to ensure that Final Age estimates weresatisfactory. The team lead examined test results to ensure that the level of precision was
acceptable and that any biases were addressed, and also confirmed that dominant year classes
were consistent with previously aged collections. Following the team lead’s assessment, Final
Age data were provided to the RACE survey team and stock assessment scientists to perform
age-length expansions and incorporate age information into the statistical age-structured modelfor the Bering Sea walleye pollock stock (Ianelli et al. 2022).

RESULTSThe 2019 and 2021 Bering Sea walleye pollock RACE bottom trawl otolith collections
were split into smaller sub-collections of ten and seven parts, respectively. Sub-collections
consisted of 78-280 otolith pairs and comprised a variety of reader-tester combinations. Readers’experience level in walleye pollock age determination varied from <1 year to over 30 years, andall testers had at least 2 years of experience.
The overall precision between paired readings was higher in 2019 (PA = 65%, APE =3.19%, CV = 4.51%) than in 2021 (PA = 56%, APE = 4.34%, CV = 6.14) (Table 26.1). This was
likely attributable to several factors: the average age of the 2021 survey collection was older(6.44 years for the full collection, compared to 5.87 years in 2019), the growth patterns in thisyear were judged to be more difficult to interpret due to checks and unusual amounts of growth417

on the otolith edge given the time of capture, and one of the age readers in 2021 was relatively
new at walleye pollock age determination. During the age determination process, a bias was
detected between the new reader and her testers on the two age sub-collections she was tasked
with reading. Other experienced age readers were consulted to provide advisory ages, and it wasagreed that the tester ages were likely more reliable. The bias appeared to be limited to
specimens approximately 6 years and older; consequently, the reader re-aged all untested
specimens that were originally aged as ≥6 years old, and the testers aged an additional 20-50% of
these re-aged specimens as a quality control check. Ages were reconciled to Final Age estimates
based on these results. The effects of the testing and reconciliation process are evident looking at
the pooled test sample results for 2019 and 2021 (Fig. 26.1). Figure 26.1a shows paired Read andTest Age estimates, with a clear bias occurring after age-8 for the 2021 collection that was also
confirmed with Bowker’s test of symmetry (Table 26.1). Figure 26.1b shows paired Test and
Final Age estimates, where there is no evidence of systematic bias (Bowker’s χ2 = 22.25, df = 17,
p = 0.18).
Despite the difficulty associated with visual age interpretation for these samples, overall
precision between reader and tester in both years was considered satisfactory based oncomparison to precision values from previous historical collections (Fig. 26.2). Furthermore, age
readers successfully identified the dominant 2013 year class (Ianelli et al. 2022) in bothcollection years (Fig. 26.3), providing corroborative support for the age estimates (Kimura et al.
2006). Estimated length-at-age was similar in both 2019 and 2021 (Fig. 26.4). In total, 20
observations were identified by age readers as outliers and were deemed to represent fast- or
slow-growing fish. Only two observations were identified as probable collector errors at sea
based on the size of the otolith relative to the recorded fish length.

418

DISCUSSION

This example illustrates the power of the random test subsample as an early detector of
problems with age interpretation prior to providing data to end users. Further, splitting otolithcollections into sub-collections among multiple readers and testers can reduce overall bias whileproviding data to end users in a timelier manner. As the AGP-AFSC workforce ages andemployee onboarding and retention is becoming increasingly hampered by budgetary constraints,
these tools are perhaps our best assets in terms of maintaining high quality age data. Continuing
the test subsample protocol will also ensure consistency in reference age data and can quantify
prediction error following implementation of the FT-NIR spectroscopy method (Helser et al. thisvolume-a).
The AGP-AFSC is currently investigating making slight improvements to how weevaluate quality and check traditional age data for errors. We will continue to calculate PA, APE,
and CV and to visually assess bias, but we may discontinue Bowker’s test of symmetry in favorof McNemar’s and Evans and Hoenig’s tests of symmetry, which have recently been shown tooutperform Bowker’s test under certain scenarios (McBride 2015, Nesslage et al. 2022). We arealso investigating ways to more objectively detect age-length outliers.
In addition to the quality control procedures outlined in this case study, our program also
employs several forms of quality assurance for assessed Alaska stocks. Prior to producing age
estimates, new age readers are trained extensively using a combination of physical referencecollections, annotated image libraries, and written and visually recorded species-specific age
determination manuals. All new age readers are initially tested at a rate of 100% until they
become proficient at ageing a given species. The AGP-AFSC is also an active participant in the419Committee of Age Reading Experts (CARE), a coalition of international, state, and federal
agencies from the U.S. West Coast and Canada. The CARE group conducts biennial meetingsand performs regular blind structure exchanges to standardize and maintain consistency in age
determination methodology across laboratories. Finally, the AGP-AFSC has completed a numberof age verification and validation studies to assess and quantify accuracy of our age estimates forassessed stocks, including for walleye pollock (Kastelle and Kimura 2006, Kimura et al. 2006),
the subject of the largest commercial fishery in Alaska (Ianelli et al. 2022).
Though implementation of the FT-NIR spectroscopy method will result in smaller
numbers of traditional microscopic age estimates, we anticipate that the general workflow for
maintaining quality control of the reference age estimates will remain much the same. The AGPAFSC will continue to traditionally age and test subsample a certain percentage of otolithcollections to evaluate precision and bias in reference age data and to check FT-NIR
spectroscopy model prediction performance. In this way, we can ensure consistency of age
estimates as we transition to this new technology.

420

Table 26.1. -- Paired otolith age reading results for walleye pollock Gadus chalcogrammus fromthe 2019 and 2021 Bering Sea bottom trawl survey. Year is year of collection, n is
the number of paired readings, 𝑇𝑇 is the average tester age, PA is percent
agreement, APE is the mean average percent error, CV is the mean Chang’s
coefficient of variation, and χ2, df, and p are the test statistic, degrees of freedom,and p value for Bowker’s test of symmetry.
Year

n

2019
2021

307
359

𝑇𝑇
5.86
6.57

PA (%)APE (%)CV (%)

χ2(df)

p

64.82
56.27

3.19
4.34

4.51
6.14

23.69 (17)50.09 (28)

0.128
0.006

421

Figure 26.1. -- Comparisons of Read and Test Age (a) and Final and Test Age estimates (b) for
walleye pollock Gadus chalcogrammus otoliths collected from the eastern Bering
Sea bottom trawl survey in 2019 and 2021. The black solid diagonal lines
represent a 1:1 relationship. The red dashed lines represent fitted generalized
additive models. The size of the points represents the number of observations.

422

Figure 26.2. -- Comparison of reader agreement metrics between the 2019 (purple), 2021
(yellow), and historical collections of walleye pollock Gadus chalcogrammus(gray points). Average Test Age is shown versus a) percent agreement and b)
mean values of Chang’s coefficient of variation (CV). The dashed lines and grayareas indicate 95% and 99% confidence ellipses, respectively. Blue lines indicate
fitted generalized additive models (GAMs). Points outside the ellipse but above
the GAM in the case of percent agreement or below the GAM in the case of CV
would still be desirable outcomes because they indicate better precision than
expected for this species.

423

Figure 26.3. -- Finalized age distributions of walleye pollock Gadus chalcogrammus otoliths
collected from the 2019 and 2021 bottom trawl surveys in the eastern Bering Sea.
Dashed red lines represent the mean age values in each year.

424Figure 26.4. -- Observations of length at age fitted with von Bertalanffy growth functions formale (top panel) and female (bottom panel) walleye pollock Gadus
chalcogrammus from the 2019 (purple) and 2021 (yellow) eastern Bering Sea
bottom trawl survey.

425

426

27. Quality Assurance and Quality Control of Fourier Transform Near Infrared
Spectroscopy Data for Age Prediction

Esther D. Goldstein1, Brenna C. Hsieh1, Morgan B. Arrington1,2, and Thomas E. Helser1

Alaska Fisheries Science Center
NOAA, National Marine Fisheries Service
7600 Sand Point Way NE
Seattle, WA USA
1

2

Cooperative Institute for Climate, Ocean, and Ecosystem Studies (CICOES)
University of Washington
3737 Brooklyn Ave NE
Seattle, WA USA

427

428

INTRODUCTION

Implementation of Fourier transform near infrared (FT-NIR) spectroscopy to obtain fish
ages for stock assessment requires rigorous quality assurance and quality control (QA/QC)
practices to ensure that new data sources maintain the caliber of traditional age data. Unlike
traditional ageing approaches with established QA/QC protocols (Bland and Altman 1986,
Kimura and Lyons 1991, Matta and Kimura 2012, McBride 2015, Ogle et al. 2021), spectral data
for fish age prediction lack well-defined best practices. Additionally, fish ages derived from FTNIR spectroscopy require spectral acquisition from otoliths and subsequent calibration model
development (Helser et al. 2019b), eliciting the need for QA/QC at multiple junctures andincreasing the potential to propagate error related to data quality.
Quality assurance and quality control of spectral data must address several potential issues:(1) Spectral quality related to the specific sample such as crystallization, broken otoliths, or
tissue remnants obscuring the otolith.
(2) Spectral quality related to instrument issues such as instrument drift, maintenancerequirements, or malfunction.
(3) User errors such as sample presentation on the scan window or data entry errors.
(4) Differentiation of unusual spectra that expand the data domain and may be importantfor inclusion in the calibration model for age prediction (Wise and Roginski 2015).

Issues 1 and 2 can primarily be recognized through visualization and analysis of spectra
alone to identify unusual patterns and outliers (Pomerantsev and Rodionova 2014). Issues 3 and429

4 may require exploration of spectra in relation to other data such as age or body length torecognize mismatches between spectra and specimen information (Rodionova and Pomerantsev
2020), an approach that is often applied to traditional age data (see Matta et al. this volume-a).
With the goal of developing QA/QC tools to address the four issues outlined above, we
present a first step toward standardized QA/QC practices and reporting for spectral dataacquisition prior to calibration model development. We also offer suggestions for next steps to
obtain data from spectral outliers and considerations for unusual (henceforth, “extreme”) spectrathat do not qualify as outliers. The proposed best practices build from user experience throughout
the National Marine Fisheries Service-funded strategic initiative, particularly at the Alaska
Fisheries Science Center (AFSC), and borrow insights from the spectroscopy discipline as well
as QA/QC of traditional age data.

METHODS

We present a case study using walleye pollock (Gadus chalcogrammus) collected during
fishery-independent surveys conducted by the AFSC in the Bering Sea from 2014-2021 todemonstrate FT-NIR spectra QA/QC. Fish were measured (fork length) at sea and traditional age
data were collected following standard protocols (Matta and Kimura 2012). Spectra werecollected from otoliths using a Bruker MPA II FT-NIR spectrometer (Bruker Optics, Ettlingen,Germany) with a wavenumber range between 11,500 to 4,000 cm−1 and a 22-mm diameter
sample window. An a priori decision was made to focus only on fisheries independent data andspectral data collected from a single spectrometer. While datasets obtained from multiple
spectrometers and data sources can be combined for analysis, fishery-independent and dependent
430

data may encompass different data domains (e.g., fish size or age), and spectra collected fromdifferent instruments may require additional data processing (Oliveri et al. 2013). Since the goalof QA/QC is to identify poor quality data with scrutiny, we opted to exclude these extraneousand potentially confounding factors from the work flow during this initial development. Future
work could evaluate the inclusion of spectra from different instruments and from fisherydependent surveys. Data were collected with 64 scans per sample, a resolution of 16 cm−1, and ascanner velocity of 7.5 kHz. Detailed scan methods and sample presentation are presented in
Benson et al. (2020). Wavenumbers >7,500 cm−1 are primarily noise and were excluded from theanalyses. For analyses with pre-processed data, a Savitsky-Golay smooth (first-derivative, 17point, 2nd order polynomial; Benson et al. 2020) was used.
QA/QC practices (see Fig. 27.1 for schematic) were developed using multivariate
analyses that are frequently applied to spectral data due to data multicollinearity: principal
component analysis (PCA) and partial least squares regression (PLS) (Carrascal et al. 2009,
Haenlein and Kaplan 2004, Kvalheim 2010). PCA is an unconstrained ordination technique that
reduces dimensionality in the spectral dataset, whereas PLS decomposes both X (spectra) and Y(response variable) matrices while constraining the components to maximize covariance between
X and Y. PCA and PLS provide complementary information about spectral data for QA/QC.
PCA potentially addresses issues 1 and 2 (outlined above) by providing a view of spectral
characteristics. PLS potentially addresses issue 3 and 4 (outlined above) by offering additional
information about whether new spectra relate to a meaningful characteristic (response variable)
in an expected way. For example, traditional age data may not be available for newly acquired
spectral data, but relationships between spectra and fish length, which is presumed to increase431

with age, should follow a consistent trend among all data sets and may provide a QA/QC checkin the absence of age data.
Both PCA and PLS QA/QC are context dependent such that outlier spectra may only be
identifiable when they are compared with other data. This is particularly true for potential
instrument issues that may affect a large portion of a dataset or the majority of recently scanned
data. Therefore, we developed QA/QC practices that compare “new” data with previously
collected spectra. To emulate a typical data collection process, we selected 2019 and 2021 as
focal years that represent “new” data. We then built PCA and PLS models from data excluding
each focal year in turn, using the larger dataset from 2016-2021. For PCAs, meaningfulcomponents were selected based on the Broken Stick Model (Legendre and Legendre 2012), or if
only the first principal component met the criteria, then the first two were retained in the PCA.
When FT-NIR spectroscopy is operationalized, “new” spectral data lack paired age datafrom traditional methods. Therefore, we built our base QA/QC PLS models (on all but “focal”
years) to relate spectra with fish length. Optimal components for PLS models were selected bycomparing root mean square error of cross validation among models. If the optimal number ofcomponents exceeded 10, then components were further reduced by selecting only those that
explained greater than 0.5% of the variability in fish length. This further reduction incomponents reduced model overfitting (modeling noise in the spectral data), but alternative
approaches for selecting components should be explored in future studies. We retained allwavenumber variables >7500 cm−1 in the final models rather than incorporating variableselection (Farrés et al. 2015) because the focus of this analysis was not to optimize length
prediction, but rather to provide a benchmark for assessing “new”, incoming spectral data
quality. Outliers were also removed from the base PLS model dataset following the iterative432

procedures using the “robust” approach outlined in (Rodionova and Pomerantsev 2020). This
was to ensure that PLS models for comparison of “new” data were representative of lengthspectra relationships. These same outliers were excluded from the PCA dataset so that the bothbaseline PCA and PLS models contained equivalent data. We then utilized the respective PCA
and PLS models that excluded our focal years to predict (or project in the case of PCA) our new
spectral data and determine outliers (Fig. 1).
PCA and PLS were paired with multivariate distance metrics (Hotelling’s T2 and Q
residuals) and comparisons of observed versus predicted values (henceforth, Y-distance) toquantitatively determine extreme and outlier data points in “new” scan data (Legendre andLegendre 2012, Wise and Roginski 2015). Hotelling’s T2 is a multivariate extension of aStudent’s t-test and provides information about how far a data point is from the center scores of a
PCA or PLS model. Q residuals are the magnitude of variation (distance) between the actual data
and the model predictions (PCA or PLS) in projected space. Y-distance applies to only PLS since
PCA is not a predictive modeling approach. Extreme and outlier spectra were defined using
outlier analyses, following Pomerantsev and Rodionova (2014) for PCA and Rodionova and
Pomerantsev (2020) for PLS as implemented in R package mdatools (Kucheryavskiy 2020b)
using statistical moments (mean and standard deviation) and a 5% significance threshold for
extreme spectra and 1% threshold for outliers based on a chi-square distribution. Analyses wereconducted using R statistical software (R Core Team 2023) using packages “prospectr” (Stevens
and Ramirez-Lopez 2022), “mdatools” (Kucheryavskiy 2020a), and “opusreadr2” (Baumann etal. 2023). Standardized QA/QC reporting for record keeping and reproducibility were developed
using Quarto implemented via Posit (https://docs.posit.co/).

433

RESULTS AND DISCUSSION

Outlier detection case studies for Bering Sea walleye pollock from 2019 and 2021
highlight the utility of employing multiple tools to discern spectral data quality. We present a
detailed description and example of interpretation of the QA/QC process from the 2021 dataset.
We do not discuss the 2019 dataset in detail, but an example QA/QC draft report using the 2019
dataset is included in Appendix A of these proceedings.

Dataset Metadata
QA/QC reporting begins with a summary of metadata for the “new” scans. This includes
the total number of scans, the number of scan sessions, and the number of scans per session (seeAppendix A Tables A1 and A2 for examples). At the AFSC, scan sessions typically refer to agroup of samples that were scanned by a single individual. For implementation of the QA/QC
procedure, each scan session could be checked upon completion and/or QA/QC procedures canbe implemented for a full collection year once all sessions are complete. This decision may
depend on scanning timelines or collection sizes for different species. For the 2021 dataset, there
were a total of 1,500 scans and 7 scan sessions that range in sample size from 133-277 otoliths,
along with a small proportion of otoliths that were unscannable or had other identified problems
such as damage, crystallization, or dried tissue on the otolith.

434

QA/QC Using PCA – 2021 Collection Year
PCA plots of unprocessed spectra provide an overview of whether “new” scan data
ordinate differently than previous scans. Separation by length quantile for PCAs minimizes thenumber of obscured data points and allows for a visual representation of whether length classes
differ among “new” and “old” scans. For the 2021 data, PCA data clouds from unprocessed data
differed slightly from other years, but pre-processing addressed such shifts in the spectra (Fig.
27.2). Further, PC scores visualized by scan date suggest that there were no issues with thespectrometer or user errors associated with a particular time period (only showing pre-processedplots for demonstration; Fig. 27.3). While pre-processing spectra is typically done prior tomodeling and data analysis (Rinnan et al. 2009), PCAs with unprocessed and pre-processed data
maximize the ability to identify scan data quality issues. For example, stray light correction
miscalculations can be more easily identified using unprocessed spectra (M. Arrington, pers.
comm.).
Quantitative designations of spectral outliers were performed only using pre-processed
data, under the assumption that these data exclude any correctable instrument-based issues(Rinnan et al. 2009). Outlier designation from PCA used Hotelling’s T2 and Q residuals toprovide a definitive cut-off for inclusion in analyses (Fig. 27.4). While the 5% threshold for
extreme values and 1% threshold for outliers present a decision step for the analyst, thisapproach provides a repeatable tool and threshold to apply across all datasets. Of 1,500 total
specimens scanned from the 2021 dataset, 30 were identified as outliers from PCA and a greaternumber were identified as extremes (Fig. 27.4). Many of these spectra differed visually fromothers (Fig. 27.4b), but plots of PC scores indicate that thresholds based on PC1 and PC2 scores435

would not be sufficient to identify outliers to the same degree as Hotelling’s T2 and Q residuals(Fig. 27.5).

QA/QC Using PLS – 2021 Collection Year
Fish lengths from “new” scan data were predicted from PLS models that excluded each
respective focal year. Outlier identification using Y-distance and full X-distance, that combined
Hotelling’s T2 and Q residuals into a single metric (Rodionova and Pomerantsev 2020),
identified 52 outliers. Many outlier spectra diverged from the 1:1 observed and predicted line,
following expectations for outliers detected by Y-distance (Fig. 27.6). For the 2021 case study,
these outliers were often larger fish compared to most previous observations, indicating thatthese larger fish might be important for expanding the data domain for future outlier detection,and potentially for age prediction (Fig. 27.6). However, other outliers did not diverge from the
1:1 line, suggesting that X-distance metrics are also necessary to identify extreme and outlier
spectra. While these extreme and outlier values may not impact length prediction, the impacts on
final age models require additional exploration. Outliers detected from PLS exceeded those from
PCA alone, but also excluded some outliers that were identified by PCA based on comparisonsof individual specimen identifiers (see Appendix A Table A5 for example). This highlights thebenefit of a combined PCA and PLS approach to QA/QC as both provide different information
about outliers based on spectra (PCA) and through variation in relationships with a meaningful
biological characteristic (PLS).

436

Final QA/QC Step with Combined PCA and PLS – 2021 Collection year
Outliers identified from PCA and PLS were used to generate a series of diagnostic plots
and generate a list of outlier spectra for further inspection by a data manager. Plots of preprocessed spectra showing “new” scan data served as a visual check for a data analyst andindicated that many of the spectra identified as outliers using the two methods in conjunction had
some visually discernible spectral irregularities (i.e., shifted peak, additional peaks, or unusual
spectral shape). The outliers detected by PLS also encompassed spectra with both larger andsmaller first derivative pre-processed peak heights (differences in slope in raw data) compared tomost spectra (Fig. 27.7).
Other standardized QA/QC output includes file names for outlier spectra as well as
details about potential issues with the otolith (broken, crystallized, or other problems) to aid indetermination of next steps for a particular specimen (see Appendix A Table A5 for example).
For the 2021 case study, 24 of the 61 total outliers were identified as being broken, crystallized,
or other. This number far exceeds the proportion of the 1,500 total scans with these issues or thatwere unscannable. This report tool would then signal the QA/QC analyst that these otoliths
should be aged using traditional methods. In addition to outliers, a large proportion of spectra
were identified as “extreme” from both PCA and PLS approaches. These data points will require
additional exploration to determine their impact on age prediction models. However, one
potential approach is to consider “extreme” spectra as values that may increase the spectral data
domain across additional ages or lengths, thus representing data points to include in calibration
model updates (see Helser et al. this volume-a). The next course of action for the remaining
outliers may depend on the workflow and focal species. A suggested workflow outlined in
Figure 27.1 is: (1) Check the metadata for each sample to determine if comments were included437

in data collection that may explain the unusual spectra. For example, a fish may be collectedfrom an unusual geographic region, the fish may be smaller or larger in size than historiccollections, or there may be a data entry error. (2) Rescan the otolith if there is no indication ofother potential issues. (3) Use traditional ageing methods to obtain an age.

Considerations

The QA/QC process will be ongoing with new data collection, and documentation isessential for record keeping purposes and reproducibility. One option to ensure good record
keeping and documentation is to utilize markdown documentation and code in R statisticalsoftware or Python to produce standardized reports and to maintain records of datasets used foranalysis. In practice, analysts may not be well versed in a particular programming language.
Therefore, interactive output such as html documents, user-friendly interfaces, or specialized
functions or packages may facilitate the use of QA/QC procedures and documentation (examples
shown in Appendix A are exported as pdf documents derived from html with interactive plots).
PCA and PLS models built with large datasets may also be constrained by computing power. Our
recommendation is to build these models and store them (e.g., R object or workspace) to load for
QA/QC of “new” data. An additional consideration is to update these baseline models annuallywith “new” scan data.
As FT-NIR spectroscopy approaches for fish age prediction continue to develop andimplementation proceeds across agencies, these QA/QC practices will likely be refined.
Ultimately, these tools can develop into standardized best practices across agencies with apotential goal to implement programmatic packages for universal use. In light of this goal, we

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document a first step toward standardization at the AFSC and highlight considerations for
implementation.

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Figure 27.1. -- Schematic of the quality assurance/quality control (QA/QC) process conducted by a data manager and final actions toaddress outlier spectra in order of operation. The schematic uses 2021 walleye pollock Gadus chalcogrammus otolithdata as an example. For a description of Issues 1-4, see the Introduction.

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Figure 27.2. -- Principal component analysis (PCA) of a) unprocessed and b) pre-processed spectra by length quartile for the 2021
case study where data from 2021 were projected using the PCA built with all other data.

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Figure 27.3. -- a) Principal component axis-1 (PC1) and b) PC2 of pre-processed spectra by scandate for the 2021 case study where data from 2021 were projected using the PCA
built with all other data.

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Figure 27.4. -- Principal component analysis (PCA) extreme values and outliers for the 2021 case study using pre-processed data
showing thresholds based on a) Hotelling T2 (T-squared) and Q residuals and b) plots of new spectra based on regular,
extreme, and outlier categories. Extreme = 5% significance threshold, outliers = 1% threshold, and regular did notexceed either threshold. Black dots in (a) are observations from the previous scan data used to build the PCA.

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Figure 27.5. -- Outlier spectra (red points) principal component analysis (PCA) scores in thecontext of all other scan data for the 2021 case study where data from 2021 were
projected using the PCA built with all other data.

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Figure 27.6. -- Extreme and outlier spectra identified by partial least squares regression (PLS) models showing a) outlier thresholds
and b) length predictions. “New” data from 2021 were predicted using a PLS model built with all other years. In (a)“new” spectral data are shown in the color scale and previous data are black points.

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Figure 27.7. -- Pre-processed spectra from “new” scan data (2021) showing outliers identified byprincipal component analysis (PCA) and partial least squares regression (PLS)models.

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DISCUSSION TOPICS AND RECOMMENDATIONSThe Fourier Transform Near Infrared (FT-NIR) Spectroscopy Workshop held in Seattle
April 3-7, 2023 at the Alaska Fisheries Science Center (AFSC) included research presentationsorganized into three main sections: Application and Development, Spectroscopy Implementation,and Stock Assessment Integration, with open discussions during the last two days of theworkshop pertaining to these and other relevant FT-NIR spectroscopy topics. During thepresentation Q&A sessions, as well as the open discussion sessions, several salient themes wereidentified, some of which were also recurring topics of discussion during the first in-person FTNIR Spectroscopy Fish Age Determination Workshop held in Seattle in 2019 (Helser et al.
2019a). The major themes outlined in the 2019 workshop were: 1) Instrument Settings andOptimization, 2) Sample Presentation – Preparation and Storage, 3) Calibration Models –Statistical Approach to Quantitative Analysis, 4) Communication to Assessment Reviewers, theFishing Industry, and Policy Makers, and 5) Questions or Concerns about Technology. In the2023 workshop, these themes of discussion remained largely the same, but with a greater
emphasis on establishing best practices in scanning methodology, model development, andprediction evaluation, identifying barriers and building pathways towards implementation andstock assessment integration, and communicating with stakeholders. Below we describe the key
points raised during the 2023 workshop discussions, with recommendations for further
development.

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ISSUE #1: SCANNING METHODOLOGY AND SAMPLE PRESENTATION

Since the 2019 workshop, most of the laboratories on the FT-NIR Spectroscopy Strategic
Initiative Development Team (SIDT) have experimented with various sample presentation
methods, tissue standardization methods, and scanning accessories to reduce excess lightscattering and optimize spectral absorbance profiles of otoliths and other tissues. This has been
particularly important for small otoliths, which generally have low signal-to-noise ratios when
scanned directly on the instrument window alone. Using various transflectance stamps inconjunction with steel rings and Teflon discs to reduce the aperture size and focus the light on
the sample has yielded promising results (Passerotti et al. 2020a, TenBrink et al. this volume,
Matta et al. this volume-b). The choice of accessories is likely to be dependent on the applicationand may be species- or life stage-specific, requiring thorough investigation.
The discussion shifted focus towards exploring methods to optimize scanning procedures
and improve the efficiency of the scanning process. Variations in scanning practices among
different SIDT laboratories were noted. Thomas Helser noted the extensive effort by the AFSC
laboratory to refine sample presentation methods and establish best practices for scanning,
including wearing gloves when handling otoliths, using plastic forceps, and standardizing theorientation and position of specimens on the sample window. While there are certain centerspecific steps that may affect efficiency (e.g., weighing, imaging, wiping away preservation
media), there are commonalities that can be shared among centers through a best-practices
scanning manual that Brenna Hsieh (AFSC) is developing, allowing standardization of448

procedures. Development of standard operating procedures could also be expanded to othertissues (e.g., gonads, liver, muscle) and applications (e.g., hormone analysis, disease detection),where appropriate.

ISSUE #2: CALIBRATION MODEL DEVELOPMENT

Discussions pertaining to calibration model development revolved around two main
topics: ensuring representative sampling of traditional microscopic ages (TMA) and employing
the most appropriate model type to achieve unbiased predictions. Because FT-NIR spectroscopy
is a secondary method in which age predictions from otolith spectra are “calibrated” against
TMA reference age estimates, choice of those reference data is an important issue. It wasstressed during the workshop that calibration models used to predict future data need to be
representative of any new observations. Research may be needed to evaluate otolith spectral
variability, both temporally and or spatially, before a base model is accepted for future
predictions. The sample size must be large enough and the age distribution must be adequately
covered in each calibration data set. The simulation work of Arrington et al. (this volume-a)suggests diminishing returns for calibration data sets when sample sizes exceed 900 for eastern
Bering Sea walleye pollock (Gadus chalcogrammus) collections between 2014 and 2021. They
recommend evaluating optimal sample sizes based on a species-specific basis due to varying
levels of ageing error among species. To date, the AFSC has not employed a pre-specified
method or sampling distribution for selecting samples for model calibration. This is because
different sample selection approaches will depend on a number of factors such as data quantity(TMA ages and associated otolith spectra), data variability, and the model employed. It can also449

be said that the model employed for training or calibration will be dependent upon data quantity.
In cases of data limitations or exploratory calibration model development, a boxcar distribution
may be a reasonable approach, as it represents ages and associated otolith spectra equally over
the age range. Arrington et al. (this volume-a) suggest algorithms that maximize variability inotolith spectra in principal components space, particularly in cases where spectral variability
increases with age, are preferable for calibration model sample selection. Their results indicatethat the Kennard-Stone algorithm performs the best. However, in instances where this algorithm
is not suitable, random selection is a viable alternative for sample selection. Morgan Arrington(University of Washington) noted during the discussions that she is happy to share her code withthe rest of the SIDT.
Standard chemometric approaches using partial least squares (PLS) regressions are themodels most typically used by all the laboratories to predict age and other metrics. These models
are easy to implement directly in the Bruker OPUS software, increasing their appeal. However,
several SIDT members are exploring modeling alternatives that may generate more accurate
predictions. High-performance computing alternatives, such as cloud computing, graphics
processing units, and virtual machines, were identified as potential needs when employing moresophisticated deep learning tools and larger quantities of data. Wallace (this volume) at NWFSC
and Benson et al. (2023, this volume) at AFSC have employed deep learning methods asalternatives to PLS regressions for developing calibration models. Wallace (this volume) has
found the keras R package (https://keras.io) that sits on top of Google’s TensorFlow software(https://www.tensorflow.org) to be useful for neural network modeling for age predictions ofsablefish (Anoplopoma fimbria) and Pacific hake (Mercluccius productus). When asked about
the number of otoliths required to construct a deep learning model, Irina Benson (AFSC)

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referenced a subject matter expert who recommended a minimum of 1,000 samples, although it
is generally recognized that having more data is beneficial for improved model training. The
ongoing deep machine learning research being conducted by the University of Washington’sInformation Processing Laboratory, under the guidance of Professor Jenq-Neng Hwang,
integrates FT-NIR spectra, images, and metadata. This work suggests potential advantages ofusing separate models for older and younger fish (Zheng et al. this volume). The rapid expansionof deep learning and cloud computing in spectral data analysis across multiple industries
highlights the SIDT’s future focus on leveraging these tools as a central point of the strategic
initiative. The SIDT suggested that any new proprietary or open source software forchemometric analysis should be vetted for compatibility and reliability and incorporated into a“tool box” for FT-NIR spectroscopy fish age modeling in NOAA Fisheries. Also notable is that
the SI could greatly benefit from personnel with expertise in data science and deep machinelearning to enhance and improve the development of tools.

ISSUE #3: AGE PREDICTION AND MODEL EVALUATION

Considerable time was dedicated to discussing the assessment of FT-NIR spectroscopy’s
reliability in predicting fish age, as well as determining the optimal timing for model updates. To
reach this objective, maintaining some level of traditional age determination to verify model
predictions will be essential, although the exact amount is still uncertain. Since FT-NIR
spectroscopy is a secondary method reliant on TMA for model training and updating, TMA willcontinue to be necessary. This includes species that have transitioned into the FT-NIR process
and those that are still completely dependent on TMA for stock assessments. Papers on TMA

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ageing error have demonstrated that characteristics such as the coefficient of variation (CV), thenumber of samples, and age class distribution can have significant effects on bias detection(McBride 2015, Nesslage et al. 2022). Arrington et al.’s (this volume-b) simulation work using
Pacific cod (Gadus macrocephalus) as a case study demonstrated that bias may have more of an
impact on model prediction than precision, though these impacts also depend on the level ofspectral variation. Extending Arrington et al.’s work to other species with different error
structures would be very informative towards understanding the interaction between ageing error
and spectral variation. Here again, the SIDT noted that it would be helpful to bring in a data
scientist who understands predictive analytics to evaluate different model-updating scenarios. A
team member highlighted the longstanding acceptance of TMA among stakeholders, recognizing
the challenges of communicating the complicated methods and earning acceptance of the new
approach. The SIDT might benefit from further discussion on collaborating with a dedicatedcommunications expert to effectively convey scientific concepts to the public.
The next topic of discussion focused on how to best compare FT-NIR spectroscopy
decimal age predictions with TMA ages, which are typically in integer form. The predictions arecontinuous random data, but TMA data are multinomial. There are several different options for
how to deal with this problem: the decimal ages can be rounded (in different ways) to integer
ages, or the reference TMA ages can be converted to decimal ages if marginal increment
information is available (Barnett et al. this volume). It was noted that the AFSC tested thesemethods on walleye pollock, but it did not have much effect on the overall outcome, most likely
because predictions were within 0.6 years of TMA ages. Treating the response, TMA age, as acategorical outcome would produce an integer age. Linear methods and deep learning methods
can be both used to produce integer ages with probabilities, though this may be problematic for452

long-lived stocks that have many age classes Such a method using otolith images and NIR
spectra using convolutional neural networks was presented at the workshop (Zheng et al. thisvolume).
An SIDT member involved in stock assessment raised concerns regarding the alignment
between TMA and FT-NIR spectroscopy ages. Our current analysis suggests a potential
tendency of underfitting at older age groups. For some species, we have also observed overfittingat the youngest ages, although both underfitting and overfitting across various age classes are
possible. This pattern may be attributable to inherent observation uncertainty stemming from
TMA errors or natural variation in otolith spectra. Tom Helser (AFSC) noted that models derived
from otolith spectra collected among different analysts are more precise than TMA double-reads
and that variability in agreement between TMA readers can reduce agreement between TMA and
FT-NIR spectroscopy. Additionally, spectra collected from species with a broad range of otolith
sizes might challenge the proportionality assumption of the Beer-Lambert law concerning
analyte concentration and absorbance, potentially causing non-linearity. To address thesechallenges, exploring different modeling approaches such as leveraging deep learning models,
which are capable of handling non-linearity using specialized activation functions, may offer
promising solutions. Benson et al. (this volume) demonstrated the potential of suchmethodologies. Furthermore, it is important to consider the extent of disagreement between
TMA and FT-NIR spectroscopy ages. Predictions within 1 year may have little impact on stock
assessment outcomes, especially in cases where ageing imprecision is high (CV > 5%) or thedeviation is near or beyond the plus group. Our focus remains on striving for the most accurate
predictive model using the best goodness-of-fit tools available. However, it is essential toevaluate impacts on stock assessments, as outlined in the next section.

453

Additionally, a mixed method approach could be established whereby FT-NIR
spectroscopy is used to predict age for the majority of specimens, while ages of the remaining
group of fish, often comprising the oldest and least numerous in the sample, are estimated by
TMA. Such an approach could still result in reasonably good efficiency of age estimate
production for the vast majority of otoliths. It is not uncommon to see mixed methods employedwithin the TMA determination process itself. Typically, ages are derived from otolith surfaces ofthe youngest fish or those with the clearest growth patterns, while otoliths from older fish are
sectioned and sometimes burned or stained. Issues related to stock assessment models are worthyof further consideration. If mixed methods are employed to generate ages for age compositions,
it may be necessary to construct separate ageing error vectors to account for the uncertainty
arising from the different methods. A simulation using the R package ss3sim (https://cran.rproject.org/web/packages/ss3sim/) would be a good framework to investigate these effects.

ISSUE #4: STOCK ASSESSMENT INTEGRATION

Overall, FT-NIR spectroscopy age prediction shows promise, as was demonstrated by anumber of case studies in these proceedings. Yet, a better understanding of the sensitivity of
population dynamics models to changes in the input data type to evaluate stock assessment
outcomes is needed. It will be necessary to evaluate model sensitivity on a case-by-case basis
because assessments can vary greatly in model structure, parameter specification, data quantity,and data use. Nevertheless, some general criteria for acceptance of substituting FT-NIR
spectroscopy age data could be defined and universally applied. For instance, Ianelli (thisvolume) used changes in log likelihoods to evaluate goodness-of-fit from substitution of age

454

compositions based on the different ageing methods. However, depending on the gradient of thelikelihood space, small differences in likelihoods may not be that informative. Garner et al. (thisvolume) employed the R package ss3sim to simulate age data using an operating model and an
estimation model under ageing error assumptions based on the two ageing methods. Their choicefor using that approach was based on limited data for which there were both TMA and FT-NIR
spectroscopy ages for direct substitution of age compositions. Finally, Helser et al. (this volumeb) used MCMC simulation in Stock Synthesis (V3.30.16.02) to evaluate the probability in the
difference of the marginal posterior density of model parameters and management quantities
when substituting a 6-year time series of age compositions from the two ageing methods. While
there may not be a universal solution, criteria and benchmarks to evaluate assessment model
outputs could be defined and used to provide the evidence needed to accept FT-NIR
spectroscopy age data as a credible substitute for TMA.
Another question is how to best blend different data types (i.e., TMA and FT-NIR
spectroscopy age data) in a stock assessment. Historical age estimates are largely generated
through TMA, so combining them with FT-NIR spectroscopy age predictions for more recent
years may require a transition matrix necessary to estimate the probabilistic difference between
them. Alternatively, most modern statistical age-structured assessment models can incorporate an
evolution of data type properties, such as age composition and ageing error, within a time series
through blocking structure mitigating a need to make modifications to the past data. In such acase, a clean transition to age composition data generated from FT-NIR spectroscopy ageing canbe made if such data result in no discernable assessment model outcomes as discussed above.
Different rounding scenarios for the continuous FT-NIR spectroscopy age predictions
could also be explored in more detail. Several workshop participants presented simulations455

integrating both data types and different ageing error scenarios into assessments. These casestudies demonstrated varying effects on recruitment, spawning stock biomass, and natural
mortality, all of which impact biological reference points (Garner et al. this volume, Ianelli thisvolume, Helser et al. this volume-b). Notably, these simulations all focused on relatively shortlived stocks (walleye pollock, Pacific cod, and gray snapper). Considering species with diverse
life histories could prove beneficial, as longevity is expected to impact these estimates.
A full evaluation of stock assessment model sensitivity may be challenging, particularly
in cases when intact otoliths are not available for FT-NIR scanning. For example, typically, only
one of each otolith pair is collected in fishery samples of gray snapper. Given the frequently
destructive nature of TMA methods, a second otolith is not usually available for scanninghistorical samples. Because the SEFSC laboratory has not evaluated whether there aredifferences between left and right otolith spectral data, predictive models were based on only left
otoliths, further reducing sample size. It was noted that the AFSC laboratory has not detectedsignificant differences between scans of right and left otoliths for a number of species including
flatfish, which have asymmetrical otoliths, suggesting that the otolith side may be
inconsequential. To address situations where sample sizes are relatively small, an SIDT member
suggested performing an analysis outside the stock assessment whereby bootstrapped age
compositions are calculated from each dataset and the uncertainty is summarized as a weighting
factor.
In general, the initial work evaluating sensitivity of stock assessment outcomes fromdifferent ageing methods shows promise, but there is still more work to do to improvepredictions, particularly given the concerns about lack of fit. Deep learning models have largely
been able to rectify non-linearity in TMA reference data and FT-NIR spectroscopy age

456

predictions. However, how many ages and how many years of age composition data are needed
for a sensitivity analysis in stock assessment models remain pertinent questions. Again, theseissues may need to be evaluated on a case-by-case basis because models vary considerably in thequantity of age data used, age-related model specifications (e.g., time-varying growth), and thecompeting influence of other data types. Clearly, SIDT members need to work closely with stock
assessment scientists to define the most critical assessment model outcomes, the criteria used totest the different ageing methods, and analytical platforms for the sensitivity analysis.

ISSUE #5: PATHWAYS AND BARRIERS TOWARDS IMPLEMENTATION

The development of Standard Operating Procedures (SOPs) for all the steps associatedwith generating FT-NIR spectroscopy age predictions is critical for creation of a data pipeline
and providing a framework for data quality assurance and quality control (QA/QC). These SOPs
would include standardized QA/QC procedures to 1) maintain consistency during the scanning
process itself, 2) evaluate spectral data, 3) determine the percentage of otoliths that need to beaged using TMA methods, 4) evaluate model predictions, and 5) determine when model updatingis necessary. Staff at the AFSC are actively working towards creating protocols to standardize
both the scanning process (Hsieh et al. in prep.) and data/model evaluation procedures (Goldstein
et al. this volume-a). These SOPs will be disseminated to other laboratories participating in the
SI as either NOAA publications (e.g., NOAA Tech Memos or Processed Reports) or in peerreviewed journals.
Having an establish database infrastructure will also facilitate more rapid adoption andimplementation of FT-NIR spectroscopy to age fish. Jon Short (AFSC) has built a relational457

database (AGE3) using SQL Server and a Python front end for all of AFSC’s age and related
data (Short this volume). Not every other laboratory in the SI has a database like AGE3, but there
was great interest in patterning new database structures based on AGE3. It was noted that
replicating AGE3 exactly is not feasible due to its reliance on various data sources, making a
direct “plug and play” into the workflows of other laboratories unlikely. However, Jon is willingto share AGE3’s general database table structure and suggested that other laboratories start with
Microsoft ACCESS to build new tables copying AGE3’s structure. Code sharing and furthering
open communication among database managers from different laboratories would greatly
facilitate the process. Discussion also centered on funding for expertise in developing anenterprise-level database within NOAA Fisheries that can harmonize the different data types,
standardize data storage and archiving, and develop data discovery and analytic tools for quality
control and modeling applications for age prediction.
Achieving buy-in from stock assessment scientists, the fisheries management councils,
and stakeholders could pose a potential challenge in the adoption and transition of the FT-NIR
spectroscopy ageing method. Active collaboration between SIDT members and assessment
scientists is imperative to test effects of integrating FT-NIR spectroscopy age data into
population models and on biological reference points. The SIDT will continue to work directlywith assessment scientists to determine criteria and benchmarks that must be met before the new
method is adopted. Additional workshops and meetings with the council bodies, such as the
Scientific and Statistical Committee, are envisioned as is critical review of the methodology by
the Center for Independent Experts.
Resource and staffing limitations were a major concern brought up by member of the
SIDT during the discussion. Many SIDT laboratories have limited personnel and resources to458

dedicate to FT-NIR spectroscopy SI research and development while maintaining their core
responsibility of meeting annual production ageing demands. Potential avenues ofcommunication to alleviate these concerns are discussed in detail in the next section.

ISSUE #6: COMMUNICATION WITH DATA END USERS, STAKEHOLDERS,
AND NOAA LEADERSHIP

Modern stock assessments of commercial stocks often rely heavily on fish age data.
While the FT-NIR spectroscopy method shows great promise for producing age data more
efficiently, gaining full support from data end users and stakeholders will require continued
serious investments in research and development of FT-NIR spectroscopy age estimation
methods. Improving communication among the SIDT, the NOAA Science Board, stock
assessment community, and stakeholders will be key. At a few laboratories, efforts are alreadyunderway where SIDT members are working side by side with assessment teams tocommunicate and evaluate assessment models with regard to the new technology. However,more outreach is needed to communicate differences between ageing methods, develop atransition plan which defines the data pipeline with quality control processes and data controls,
and finalize data products that will enable users to better understand, and therefore accept, FTNIR spectroscopy age data. Direct conversations with the Science Board are also necessary tounderstand their expectations regarding the accomplishments at the conclusion of the SI,
expected at the end of FY2025. Several SIDT laboratories, particularly the smaller laboratories
that have fewer dedicated personnel, are resource-limited and unable to fully perform theresearch and development needed to implement the technology. These laboratories voiced the459

concern that the timeline to operational readiness may not be feasible. Additional support fromindividual science centers would be helpful to offset the disruption caused by this technologicalinnovation. Sharing resources among the laboratories and bringing in outside experts to assist
with development and integration were discussed as possible solutions towards improving
efficiency and reducing redundancy. Dedicated funds for staff training could also assist in FTNIR spectroscopy integration but would require cutbacks related to other outputs and tasks. Openscience tools (e.g., Google groups, GitHub, NOAA Virtual Lab, Confluence pages) werementioned as ways to improve communication within the SIDT. The monthly SIDT meetings
have provided valuable opportunities for SIDT members to discuss progress, setbacks, andtroubleshooting. Since the April 2023 workshop, the SIDT meetings have refocused on topicalthemes each month, resulting in rich, in-depth discussions that have helped advance and share
the SIDT’s collective knowledge. The SIDT has concluded that sharing documentation of bestpractices as they develop among members would be helpful in proceeding towards
implementation. We have used NOAA Virtual Lab as a platform for knowledge transfer among
SIDT members and will continue to do so until a potentially better platform is found.
In short, great progress has been made since the Strategic Initiative began, and the
SIDT’s continued efforts have forged a path towards implementing and integrating FT-NIR
spectroscopy data collections into fisheries stock assessments and communicating research
products.

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ACKNOWLEDGMENTS

We thank the staff of the AFSC Age and Growth Program for their help in coordinating
the fourth workshop of the NOAA Strategic Initiative on Rapid Estimation of Fish Age Using
Fourier Transform Near Infrared Spectroscopy, particularly Andrew Chin and Irina Benson. We
also thank members of the Strategic Initiative Development Team, many of whom were also
workshop attendees, for their participation in monthly progress meetings to discuss advancing
the technology towards implementation. We are grateful to workshop participants for sharing
their research and lessons learned, helping us towards our goal of operational readiness. Many
thanks to Morgan Arrington, Esther Goldstein, and Todd TenBrink, who took notes during theworkshop sessions that facilitated writing the Discussion Topics and Recommendations section(additional thanks to them and to Irina Benson and Brenna Hsieh for providing helpful
suggestions that greatly improved that section of this document). Thanks also to Rebecca Whiteof the AFSC Communications Team, who designed the beautiful graphics for the workshop
agenda. Finally, we thank the leadership of the Alaska Fisheries Science Center for theircontinued support and the NOAA Fisheries Science Board for funding this Strategic Initiativeand making this workshop possible.

461

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APPENDIX A. Supplemental Tables and Figures

Supplemental information to “Quality Assurance and Quality Control of Fourier Transform Near
Infrared Spectroscopy Data for Age Prediction”

Esther D. Goldstein (Esther.Goldstein@noaa.gov)1, Brenna C. Hsieh1, Morgan B. Arrington1,2,
and Thomas E. Helser1

Alaska Fisheries Science Center
NOAA, National Marine Fisheries Service
7600 Sand Point Way NE
Seattle, WA USA
1

2

Cooperative Institute for Climate, Ocean, and Ecosystem Studies (CICOES)
University of Washington
3737 Brooklyn Ave NE
Seattle, WA USA

499

500

Quality assurance and quality control (QA/QC) procedures utilize both principal
component analysis (PCA) and partial least-squares regression (PLS) to visually identify unusual
patterns in spectra and quantitatively determine outliers from PCA and PLS. This approach relies
on a comparison of new scan data with previous scans to ensure that data remain comparable
across surveys and years.

501

1. Scan session information

Table A1. -- New scan data information.
Year

Total scans all sessions

2019

1,478

Table A2. -- New scan data summary information by scan session.
Scan session

Year

Scans per session

NIR_162201901201A

2019

138

NIR_162201901201B

2019

261

NIR_162201901201C

2019

140

NIR_162201901201D

2019

140

NIR_162201901201E

2019

85

NIR_94201901201A

2019

124

NIR_94201901201B

2019

236

NIR_94201901201C

2019

138

NIR_94201901201D

2019

138

NIR_94201901201E

2019

78

502

Table A3. -- New scan data summary of potential otolith issues.

Totalbrokencrystallized

other problem

unscannable

0

0

0

0

0

0

0

7

0

0

49

0

0

0

29

29

0

31

0

0

0

14

0

14

0

4

4

0

0

3

3

3

11

0

0

0

13

0

0

13

2

0

2

0

1

0

1

1

1

1

0

1

28

53

88

68

56

106

176

136

503

2. PCA

2.1 Unprocessed spectra visual inspection

Figure A1. -- PCA of unprocessed spectra with new spectra (2019) projected using PCA from theother collection years. Data are divided into fish fork length quartiles (Q1-Q4) tobetter discern patterns in data “clouds”.

504

Figure A2. -- PC1 (a) and PC2 (b) of unprocessed spectra by scan date to visually assess drift
over time related to instrumentation or scan procedures. New spectra (2019) were
projected using PCA from the other collection years.

505

2.2 Pre-processed spectra visual inspection

Figure A3. -- PCA of pre-processed spectra with new spectra (2019) projected using PCA fromother scan years. Data are divided into fish fork length quartiles (Q1-Q4) to better
discern patterns in data “clouds”.

506

Figure A4. -- PC1 (a) and PC2 (b) of pre-processed spectra by scan date to visually assess drift
over time related to instrumentation or scan procedures. New spectra (2019) were
projected using PCA from the other collection years.

507

2.3 PCA outliers

Figure A5. -- PCA outliers using pre-processed data. PCA from other collection years data wasused to set the thresholds for new data (2019) using the approach outlined in
Pomerantsev and Rodionova (2014) using statistical moments. Extreme = 5%
significance threshold and outliers= 1%.

508

Figure A6. -- Pre-processed new data (2019) showing normal (gray), extreme (orange), andoutlier (red) spectra from PCA-based outlier designations shown in Fig. A5.

509

Figure A7. -- Outliers (red) identified from PCA of pre-processed spectra plotted in PCA biplot
space. Data are divided into fish length quartiles (Q1-Q4) to better discern patterns
in data “clouds”.

510

3. PLS

Figure A8. -- Outlier, extreme, and all other (regular) data identified from PLS. Pre-processed
spectra were used to build a PLS model from 2014, 2015, and 2021 collection
years (black dots) and fish lengths were predicted from spectra from the 2019
dataset to determine outlier and extreme data points. Normalized full X-distance is
a combination of Hotelling T-squared and Q-residuals, and normalized Y-distanceis a comparison of observed and predicted values (Rodionova and Pomerantsev
2020). Critical limit extreme = 0.05 and critical limit outlier = 0.01.

511

Figure A9. -- Observed and predicted fish lengths from the PLS. Red points show outliers fromnew data (2019) based on Y-distance and full X-distance.

5124. PCA and PLS outliers combined

Figure A10. -- Pre-processed spectra from 2019 showing PCA-based outliers in red and PLSbased outliers in dark red. Spectra that were not identified as outliers are in gray.

Table A4. -- Summary of outlier spectra from PCA and PLS using pre-processed data.
Total outliers

PCA outliers

PLS outliers

42

27

42

513

Table A5. -- List of file names for spectra identified as outliers from PCA and PLS, andinformation about potential issues with otoliths that may have impacted scanquality.
File name

PCA

PLS

WALLEYE_POLLOCK_94201901201_186_OA1.0

outlier

outlier

WALLEYE_POLLOCK_94201901201_212_OA1.0

outlier

outlier

WALLEYE_POLLOCK_94201901201_218_OA1.0

outlier

outlier

WALLEYE_POLLOCK_94201901201_248_OA1.0

outlier

outlier

WALLEYE_POLLOCK_94201901201_280_OA1.0

outlier

outlier

WALLEYE_POLLOCK_94201901201_435_OA1.0

outlier

outlier

WALLEYE_POLLOCK_94201901201_442_OA1.0

outlier

outlier

WALLEYE_POLLOCK_94201901201_566_OA1.0

outlier

outlier

WALLEYE_POLLOCK_94201901201_589_OA1.0

outlier

outlier

WALLEYE_POLLOCK_94201901201_605_OA1.0

outlier

outlier

WALLEYE_POLLOCK_94201901201_609_OA1.0

outlier

outlierX

WALLEYE_POLLOCK_94201901201_620_OA1.0

outlier

outlierX

WALLEYE_POLLOCK_94201901201_632_OA1.0

outlier

outlierX

WALLEYE_POLLOCK_94201901201_728_OA1.0

outlier

outlier

WALLEYE_POLLOCK_94201901201_776_OA1.0

outlier

outlier

WALLEYE_POLLOCK_162201901201_8_OA1.0

outlier

outlierX

WALLEYE_POLLOCK_162201901201_55_OA1.0

outlier

outlierX

WALLEYE_POLLOCK_162201901201_135_OA1.0

outlier

outlier

WALLEYE_POLLOCK_162201901201_136_OA1.0

outlier

outlier

WALLEYE_POLLOCK_162201901201_196_OA1.0

outlier

outlier

WALLEYE_POLLOCK_162201901201_324_OA1.0

outlier

outlier

WALLEYE_POLLOCK_162201901201_334_OA1.0

outlier

outlier

WALLEYE_POLLOCK_162201901201_339_OA1.0

outlier

outlier

WALLEYE_POLLOCK_162201901201_464_OA1.0

outlier

outlierX

WALLEYE_POLLOCK_162201901201_504_OA1.0

outlier

outlierX

WALLEYE_POLLOCK_162201901201_596_OA1.0

outlier

outlierX

WALLEYE_POLLOCK_162201901201_764_OA1.0

outlier

outlier

WALLEYE_POLLOCK_94201901201_246_OA1.0

outlier

514

brokencrystallized

otherproblem

X

X

X

X
X

X

X

X

File name

PCA

PLS

brokencrystallized

WALLEYE_POLLOCK_94201901201_338_OA1.0

outlier

WALLEYE_POLLOCK_94201901201_347_OA1.0

outlier

WALLEYE_POLLOCK_94201901201_409_OA1.0

outlier

WALLEYE_POLLOCK_94201901201_410_OA1.0

outlier

WALLEYE_POLLOCK_94201901201_421_OA1.0

outlierX

WALLEYE_POLLOCK_94201901201_497_OA1.0

outlierX

WALLEYE_POLLOCK_94201901201_705_OA1.0

outlier

WALLEYE_POLLOCK_94201901201_755_OA1.0

outlier

WALLEYE_POLLOCK_162201901201_163_OA1.0

outlier

WALLEYE_POLLOCK_162201901201_459_OA1.0

outlier

WALLEYE_POLLOCK_162201901201_485_OA1.0

outlier

WALLEYE_POLLOCK_162201901201_561_OA1.0

outlierX

WALLEYE_POLLOCK_162201901201_640_OA1.0

outlierX

WALLEYE_POLLOCK_162201901201_737_OA1.0

outlier

X

515

other
problem

X
X
X

X

516

CITATIONS

Rodionova, O. Y., and A. L. Pomerantsev. 2020. Detection of outliers in projection-basedmodeling. Anal. Chem. 92:2656-2664. https://doi.org/10.1021/acs.analchem.9b04611
Pomerantsev, A. L., and O. Y. Rodionova. 2014. Concept and role of extreme objects in
PCA/SIMCA. J. Chemom. 28(5):429-438. https://doi.org/10.1002/cem.2506

517

518

APPENDIX B. Workshop Agenda

WORKSHOP: Rapid Estimationof Fish Age Using Fouriertransform Near Infrared
Spectroscopy (FT-NIRS)

Virtual attendance via Webex will also be available

April 3-7, 2023 NOAA Western Regional Campus, Alaska Fisheries Science Center
7600 Sand Point Way, NE, Building 9 Auditorium, Seattle, WA 98115
519

Monday, 3 April
1:00 PM – 1:30 PM WELCOME AND INTRODUCTIONS
1:30 PM – 2:30 PM NOAA Fisheries FT-NIRS Fish Ageing Strategic Initiative: Past, present & future(Thomas Helser, AFSC — Seattle Lab)
2:30 PM – 4:30 PM Session 1: AFSC Spectroscopy Lab Tour & demonstration(Bruker Applications Scientist - Jason Erikson). AFSC, Building 4 Room 1111
5:00 PM Informal social: Burke Gilman Brewing Company, 3626 NE 45th Street, Suite 102

Tuesday, 4 April
Presentations FT-NIRS APPLICATION DEVELOPMENT
9:00 AM – 9:30 AM FT-NIRS age prediction of eastern Bering Sea yellowfin sole: a case study of amoderately long-lived flatfish (Todd TenBrink, AFSC – Seattle Lab)
9:30 AM – 10:00 AM A preliminary analysis and review of FT-NIR spectroscopy and DNA CpG sitemethylation for fish age prediction (Laurel Lam, NWFSC – Seattle Lab)
10:00 AM – 10:30 AM Applying FT-NIRS predictive ageing to different genetic stocks of white grunt within the
U.S. south Atlantic (Jamie Clark, SEFSC – Beaufort Lab)Break
11:00 AM – 11:30 AM A novel approach for determining the spawning phenology of walleye pollock using
Raman spectroscopy (Sandi Neidetcher, AFSC – Seattle Lab)
11:30 AM – 12:00 PM FT-NIRS ageing of finfish and shark species in the northwest Atlantic (Alex Rubin,
NEFSC – Narragansett Lab)
12:00 PM – 12:30 PM Exploration of FT-NIRS for shortbelly rockfish (Sebastes jordani): an ecologicallyimportant forage fish off the coast of California (Jessica Choi, SWFSC – Santa Cruz Lab)
Lunch
2:00 PM – 2:30 PM Investigating the use of FT-NIR spectroscopy to age gag (Mycteroperca microlepis), a
protogynous hermaphroditic species (Beverly Barnett, SEFSC – Panama City Lab)
2:30 PM – 3:00 PM Exploring the use of FT-NIRS for ageing sablefish (Anoplomona fimbria) and Pacifichake (Merluccius Productus) of the U.S. west coast (John Wallace, NWFSC – Seattle)

520

3:00 PM – 3:30 PM Developing spectroscopy approaches to measure life history characteristics of fish
throughout ontogeny (Esther Goldstein, AFSC – Seattle, Lab)Break
4:00 PM – 4:30 PM Rapid daily age estimation of juvenile walleye Pollock in the Gulf of Alaska using FT-NIR
spectroscopy (Beth Matta, AFSC – Seattle Lab)
4:30 PM – 5:00 PM Fish otolith proteomics and its relationship and impact on the use of FT-NIR
spectroscopy for fish age prediction (Oliver Thomas, University of Melbourne)
Close

Wednesday, 5 April
Presentations cont.
9:00 AM – 9:30 AM Developing NIR sampling methodology for modeling species discrimination in live
catfish for aquaculture (Carrie Vance – Mississippi State University)
9:30 AM – 10:00 AM Fourier transform near infrared spectroscopy discriminates archived otoliths of newly
detected cryptic species, Etelis carbunculus and Etelis boweni (Kristen Dahl, PIFSC)

FT-NIRS IMPLEMENTATION
10:00 AM – 10:30 AM Trials and tribulations of using FT-NIRS on Pacific Sardine: Method development forscanning Pacific Sardine otoliths (Emma Saas, SWFSC – La Jolla Lab)
10:30 AM – 11:00 AM Benefits and challenges of using FT-NIRS for production age estimation at theNortheast Fisheries Science Center (Eric Robillard, NEFSC – Woods Hole Lab)Break
11:30 AM – 12:00 PM FT-NIR spectroscopy of otoliths coupled with deep machine learning to improve age
prediction (Irina Benson, AFSC – Seattle Lab)
12:00 PM – 12:30 PM Automatic fish age prediction using deep machine learning: combining otolith image,
NIR spectra and metadata features (Aotian Zheng – University of Washington)
12:30 PM – 1:00 PM Calibration and variation of FT-NIRS otolith spectra among NIR spectrometers andspecies (Andy Ostrowski, SEFSC – Beaufort Lab)
Lunch
2:00 PM – 2:30 PM A simulation study exploring best practices for model development andupdating (Morgan Arrington, CICOES-AFSC)
2:30 PM – 3:00 PM Database architecture and management envisioned for the FT-NIRS paradigm at Alaska
Fisheries Science Center (Jon Short, AFSC – Seattle Lab)
3:00 PM – 3:30 PM Group discussion: Likelihood of operational success, timeline for operational readinessand communication to stake holders (follow up on Friday, April 7 at 9:00 AM).

521

3:30 PM – 5:30 PM Session 2: AFSC Spectroscopy Lab Tour & demonstration (Bruker Applications Scientist– Jason Erikson). AFSC, Building 4 Room 1111
6:00 PM – 10:00 PM Organized social mixer and casual dinner – Elliot Bay Public House & Brewery
12537 Lake City Way NE., Seattle WA 98125

Thursday, 6 April
Presentations cont. FT-NIRS STOCK ASSESSMENT INTEGRATION
9:00 AM – 10:00 AM Envisioning the future of production fish ageing: end-to-end integration of the FT-NIRS
age estimation enterprise (Thomas Helser, AFSC – Seattle Lab)
10:00 AM – 10:30 AM A simulation framework to examine the effect of ageing error on model-based
age predictions (Morgan Arrington, CICOES-AFSC)
Break
11:00 AM – 11:30 PM Integration of FT-NIRS age data products into the eastern Bering Sea walleye pollock
and Pacific cod stock assessments (Thomas Helser, AFSC – Seattle Lab)
Lunch
1:00 PM –1:30 PM Efficacy of FT-NIRS predicted ages for use in the Gulf of Mexico gray snapper stock
assessment (Steve Garner, SEFSC – Panama City Lab)
1:30 PM – 5:00 PM Open discussion with national stock assessment forum

Friday, 7 April
FT-NIRS STRATEGIC INITIATIVE BUSINESS
9:00 AM – 10:30 AM Wrap up - open discussion
Break
10:30 AM – 12:00 PM FT-NIR strategic initiative business and budget meeting
Meeting Adjourn522

APPENDIX C. Workshop Participants
NameAlex Rubin
Amanda Rezek
Anderson, Alena
Andrew Chin
Andrew Claiborne
Andy Ostrowski
Audrey Ty
Austin Anderson
Beth Matta
Beverly Barnett
Bill Kline
Brenna Groom
Brittany Schwartzkopf
Carrie Kim Vance
Cathy Mattson
Charlie Piston
Chelsea Rothkop
Cheryl Barnes
Chris Hinds
Chris Johnston
Christina Jump
Christopher Gburski
Claire Stuart
Cooke, Chelsea
Craig Kastelle
Denise Parker
Emma Saas
Emmanis Dorval
Eric Robillard
Esther Goldstein
Han Ju Kim
Heather Moncrief-Cox
Hwan Sung Ji
Irina Benson
James Hale
Jamie Clark
Jason Erikson
Jennifer Potts
Jenny Topping
Jessica Choi
Jessica Horn
Jim Ianelli
Joan Forsberg
John Brogan
John Wallace
Jon Short

EmailAlexander.Rubin@noaa.gov
amanda.rezek@noaa.gov
aqa5@msstate.edu
andrew.chin@noaa.gov
Andrew.Claiborne@dfw.wa.gov
andy.ostrowski@noaa.gov
Audrey.Ty@dfo-mpo.gc.ca
Austin.Anderson@dfw.wa.gov
beth.matta@noaa.gov
beverly.barnett@noaa.gov
bill.kline@noaa.gov
brenna.groom@noaa.gov
brittany.schwartzkopf@noaa.gov
ckv7@msstate.edu
cathy.mattson@alaska.gov
charlie.piston@noaa.gov
Chelsea.Rothkop@dfo-mpo.gc.ca
cheryl.barnes@oregonstate.edu
chris.hinds@alaska.gov
chris.johnston@iphc.int
Christina.Jump@dfw.wa.gov
christopher.gburski@noaa.gov
claire.stuart@humboldt.edu
Chelsea.Cooke@dfo-mpo.gc.ca
craig.kastelle@noaa.gov
dparker@psmfc.org
emma.saas@noaa.gov
Emmanis.dorval@noaa.gov
eric.robillard@noaa.gov
Esther.Goldstein@noaa.gov
hj0528@korea.kr
heather.moncrief-cox@noaa.gov
ninise9@nfrdi.go.kr
irina.benson@noaa.gov
jhale@psmfc.org
jamie.clark@noaa.gov
Jason.Erickson@bruker.com
jennifer.potts@noaa.gov
Jennifer.Topping@dfw.wa.gov
jessica.choi@noaa.gov
jessica.horn@alaska.gov
jim.ianelli@noaa.gov
joan.forsberg@iphc.int
john.brogan@noaa.gov
john.wallace@noaa.gov
jon.short@noaa.gov

Organization
NOAA, NEFSC, Apex Predators Program
NOAA, SEFSC, Biology & Life History
Mississippi State University
NOAA, AFSC, Age and Growth Program
WDFW
NOAA, SEFSC, FATES, Life History
DFO/PBSWDFW
NOAA, AFSC, Age and Growth Program
NOAA, SEFSC, FATES, Biology & Life History
CIMAS, NOAA, SEFSC, Biology & Life History
NOAA, AFSC, Age and Growth Program
NOAA, SWFSC, Life History
Mississippi State University
ADF&G-Juneau
NOAA, AFSC, Age and Growth Program
DFO/PBSODFW-MRP
ADF&G-Juneau
IPHC
WDFW
NOAA, AFSC, Age and Growth Program
NOAA, SWFSC, Habitat and Groundfish Ecology
DFO/PBSNOAA, AFSC, Age and Growth Program
NWFSC/PSMFC; Cooperative Ageing Lab
Saltwater Inc. and NOAA SWFSCNOAA, SWFSC, Life History Program
NOAA, NEFSC, Age and Growth Program
NOAA, AFSC, Age and Growth Program
Fisheries Resources Research Center
CIMAS, NOAA, SEFSC, Biology & Life History
Fisheries Resources Research Center
NOAA, AFSC, Age and Growth Program
NWFSC/PSMFC; Cooperative Ageing Lab
NOAA, SEFSC, FATES, Biology & Life History
Bruker rep
NOAA, SEFSC, FATES, Biology & Life History
WDFW
FCP/UCSC, NOAA-SWFSC, Groundfish
ADF&G-Kodiak
AFSC/SSMAIPHCNOAA, AFSC, Age and Growth Program
NWFSC
NOAA, AFSC, Age and Growth Program

523

Home office location
Narragansett, RI
Beaufort, NC
Starkville, MS
Seattle, WA
Olympia, WA
Beaufort, NC
Nanaimo, BC, CA
Olympia, WA
Seattle, WA
Panama City, FL
Panama City, FL
Seattle, WA
La Jolla, CA
Starkville, MS
Juneau, AK
Seattle, WA
Nanaimo, BC, CA
Newport, OR
Juneau, AK
Seattle, WA
Olympia, WA
Seattle, WA
Santa Cruz, CA
Nanaimo, BC, CA
Seattle, WA
Newport, OR
La Jolla, CA
La Jolla, CA
Woods Hole, MA
Seattle, WA
Tongyoung, KR
Panama City, FL
Tongyoung, KR
Seattle, WA
Newport, OR
Beaufort, NC
Madison, WI
Beaufort, NC
Olympia, WA
Santa Cruz, CA
Kodiak, AK
Seattle, WA
Seattle, WA
Seattle, WA
Seattle, WA
Seattle, WANameJosh Dore
Julie Pearce
Kali Stone
Kathrin Bayer
Kathryn Berry
Kelsey MagraneKevin McNeel
Kouba, Andy
Kristen Dahl
Leif Rasmuson
Li-Dunn Chen
Liz Ortiz
Marian Ford
Mark PlumbMark Terwilliger
McAree, Danielle
Melissa Monk
Meredith Emery Boeck
Merrie Schultz
Michelle Passerotti
Morgan Arrington
Naeem Willett
Nikki Paige
Oliver Thomas
Patrick McDonald
Poudel, Ashmita
Robert Tobin
Sandi Neidetcher
Seung Hwan Lee
Sonya Elmejjati
Steve Barbeaux
Steve Garner
Tera Winters
Thomas Helser
Tien-Shui Tsou
Todd TenBrink
Tracey Loewen
Tracy McCulloch
Tyler Johnson
Walt Rogers

Emailjosh.dore@alaska.gov
julie.pearce@noaa.gov
kali.stone@noaa.gov
kathrin.bayer@noaa.gov
Kathryn.Berry@dfo-mpo.gc.ca
kelsey.magrane@iphc.int
kevin.mcneel@alaska.gov
a.kouba@msstate.edu
kristen.dahl@noaa.gov
leif.k.rasmuson@odfw.oregon.gov
lc1817@msstate.edu
lortiz@psmfc.org
marian.ford@alaska.gov
mark.plumb@alaska.gov
mark.r.terwilliger@odfw.oregon.gov
dmm787@msstate.edu
melissa.monk@noaa.gov
memeryboeck@psmfc.org
Merrie.Schultz@dfw.wa.gov
michelle.passerotti@noaa.gov
morgan.arrington@noaa.gov
naeem.willett@noaa.gov
npaige@psmfc.org
oliverrbthomas@gmail.com
pmcdonald@psmfc.org
ap2751@msstate.edu
robert.tobin@iphc.int
sandi.neidetcher@noaa.gov
hwan2915@korea.kr
sonya.elmejjati@alaska.gov
steve.barbeaux@noaa.gov
steven.garner@noaa.gov
tera.winters@noaa.gov
thomas.helser@noaa.gov
Tien-Shui.Tsou@dfw.wa.gov
todd.tenbrink@noaa.gov
Tracey.Loewen@dfo-mpo.gc.ca
Tracy.McCulloch@noaa.gov
tjohnson@psmfc.org
Walt.rogers@noaa.gov

Organization
ADF&G-Juneau
NOAA, AFSC, Age and Growth Program
NOAA, AFSC, Age and Growth Program
NOAA, AFSC, Age and Growth Program
DFO/PBSIPHC
ADF&G-Juneau
Mississippi State University
NOAA, PIFSC, Life History Program
ODFW-MRP
Mississippi State University
NWFSC/PSMFC; Cooperative Ageing Lab
ADF&G-Homer
ADF&G-Juneau
ODFW-MRP
Mississippi State University
NOAA, SWFSC, Habitat and Groundfish Ecology
NWFSC/PSMFC; Cooperative Ageing Lab
WDFWNOAA, NEFSC, Apex Predators Program
UW CICOES-AFSC, Age and Growth Program
CIMAS, NOAA, SEFSC, Biology & Life History
NWFSC/PSMFC; Cooperative Ageing Lab
The University of Melbourne
NWFSC/PSMFC; Cooperative Ageing Lab
Mississippi State University
IPHC
NOAA,AFSC, Age and Growth ProgramWest sea Fisheries Resources Research Division
ADFG
AFSC/SSMA
CIMAS, NOAA, FATES, Life History
CIMAS, NOAA, SEFSC, Biology & Life History
NOAA, AFSC, AGP
WDFW
NOAA, AFSC, Age and Growth Program
DFO/Freshwater Institute
NOAA, SEFSC, FATES, Biology & Life History
NWFSC/PSMFC; Cooperative Ageing Lab
NOAA, SEFSC, FATES, Biology & Life History

524

Home office location
Juneau, AK
Seattle, WA
Seattle, WA
Seattle, WA
Nanaimo, BC
Seattle, WA
Juneau, AK
Starkville MS
Honolulu, HI
Newport, OR
Starkville MS
Newport, OR
Homer, AK
Juneau, AK
Newport, OR
Starkville, MS
Santa Cruz, CA
Newport, OR
Olympia, WA
Narragansett, RI
Seattle, WA
Panama City, FL
Newport, OR
Melbourne, VIC, AU
Newport, OR
Starkville MS
Seattle, WA
Seattle, WA
Incheon, KR
Kodiak, AK
Seattle, WA
Panama City, FL
Panama City, FL
Seattle, WA
Olympia, WA
Seattle, WA
Winnipeg, MB, CA
Beaufort, NC
Newport, OR
Beaufort, NC

U.S. Secretary of Commerce

Gina M. Raimondo

Under Secretary of Commerce for
Oceans and Atmosphere

Dr. Richard W. Spinrad

Assistant Administrator,
National Marine Fisheries Service.

Janet Coit

January 2024
www.fisheries.noaa.gov
OFFICIAL BUSINESSNational Marine
Fisheries Service

Alaska Fisheries Science Center
7600 Sand Point Way N.E.
Seattle, WA 98115-6349