NOAA Technical Memorandum NMFS-PIFSC-53

June 2016

Design and Implementation of a Bottomfish Fisheryindependent Survey in the Main Hawaiian Islands

Benjamin L. Richards, Steven G. Smith, Jerald S. Ault, 

Gerard T. DiNardo, Donald Kobayashi, Reka Domokos, 

Jeff Anderson, Jeremy Taylor, William Misa, Louise Giuseffi, 

Audrey Rollo, Daniel Merritt, Jeffrey C. Drazen, 

M. Elizabeth Clarke, and Clayward Tam

Pacific Islands Fisheries Science Center

National Marine Fisheries Service

National Oceanic and Atmospheric Administration

U.S. Department of Commerce

doi:10.7289/V5RR1W87
About this document

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A NOAA Technical Memorandum NMFS issued by the PIFSC may be cited using the 

following format:

Richards, Benjamin L., S. G. Smith, J. S. Ault, G. T. DiNardo, D. Kobayashi, 

R. Domokos, J. Anderson, J. Taylor, W. Misa, L. Giuseffi, A. Rollo, D. Merritt,

J. C. Drazen, M. E. Clarke, and C. Tam.

2016. Design and Implementation of a Bottomfish Fishery-independent 

Survey in the Main Hawaiian Islands. U.S. Dep. Commer., NOAA Tech. 

Memo., NOAA-TM-NMFS-PIFSC-53, 54p. 

__________________________

For further information, direct inquiries to

Director, Science Operations Division

Pacific Islands Fisheries Science Center

National Marine Fisheries Service

National Oceanic and Atmospheric Administration

U.S. Department of Commerce

1845 Wasp Boulevard

Honolulu, Hawaii 96818-5007

Phone: 808-725-5331

Fax: 808-725-5532

___________________________________________________________
Pacific Islands Fisheries Science Center

National Marine Fisheries Service

National Oceanic and Atmospheric Administration

U.S. Department of Commerce

Design and Implementation of a Bottomfish Fisheryindependent Survey in the Main Hawaiian Islands

Benjamin L. Richards1, Steven G. Smith2, Jerald S. Ault2, 

Gerard T. DiNardo1, Donald Kobayashi1, Reka Domokos1, 

Jeff Anderson5, Jeremy Taylor5, William Misa5, Louise Giuseffi1, 

Audrey Rollo5, Daniel Merritt5, Jeffrey C. Drazen3, 

M. Elizabeth Clarke4, and Clayward Tam6

1Pacific Islands Fisheries Science Center

NOAA National Marine Fisheries Service

1845 Wasp Boulevard

Building 176

Honolulu, Hawaii 96818

2University of Miami, Rosenstiel School of Marine and Atmospheric Science,

4600 Rickenbacker Causeway, Miami, FL, 33149 USA

3Department of Oceanography, University of Hawaii at Manoa,

1000 Pope Road, Marine Sciences Building, Honolulu, HI, 96822, USA

4NOAA National Marine Fisheries Service, Northwest Fisheries Science Center,

2725 Montlake Blvd E., Seattle, WA, 98112, USA

5

Joint Institute for Marine and Atmospheric Research, University of Hawaii at Manoa,

1000 Pope Road, Marine Sciences Building 312, Honolulu, HI, 96822, USA

6Pacific Islands Fisheries Group,

150 Hamakua Dr. PBN #430, Kailua, HI, 96734, USA

NOAA Technical Memorandum NMFS-PIFSC-53

June 2016

doi:10.7289/V5RR1W87

iii

Executive Summary

Commercial and recreational fishing are important to the economy and culture of Hawaii. The 

deep-slope bottomfish commercial fishery preferentially targets seven high value “Deep-7” 

species (i.e, six snappers and one grouper, hereafter referred to as bottomfish). The NOAA 

Pacific Islands Fisheries Science Center (PIFSC) Stock Assessment Program is responsible for 

regularly conducting assessments of bottomfish. The stock assessment process requires reliable 

time-series of catches, fishing effort and life history demographics to estimate stock abundance 

trends and evaluate sustainability benchmarks. Current bottomfish stock assessments that rely 

solely on fishery-dependent abundance indices may be biased (Ault et al., 2014). A series of 

workshops convened in 2001-2005 evaluated existing bottomfish stock assessment 

methodologies in the Pacific Islands Region (PIR; Mace et al., 2001; Ralston et al., 2004). Mace 

et al. (2001) noted that the greatest impediment to credible bottomfish stock assessments was the 

lack of accurate and precise input data. To improve abundance estimates, Ralston et al. (2004) 

recommended development of a fishery-independent survey using available “advanced 

technologies”. A 2005 workshop on Ecosystem Science and Management Planning reiterated the 

pressing need for a design and implementation of population-level fishery-independent survey to 

obtain size-structured abundance data for bottomfish, and to aggressively pursue development of 

length-based assessment models.

This goal of this technical report was to improve stock assessments through optimal design and 

implementation of a bottomfish fishery-independent survey for the Deep-7 bottomfish complex 

in the main Hawaiian Islands (MHI) with three primary objectives:

1. Evaluate the most effective survey gears for obtaining species-specific spatial sizestructured abundance metrics of the Hawaiian bottomfish stock;

2. Conduct quantitative gears intercalibration studies to determine the relative fishing power 

of the multiple gears to be used in the survey;

3. Detail the required methodologies for efficient conduct of a multi-gear fisheryindependent survey of the MHI bottomfish stocks.

Six pilot survey missions were conducted in the MHI over the 2011–2015 timeframe. Two 

primary operation goals drove the experimental designs: (1) gear efficacy comparisons; and, 2) 

pilot sampling in an effort to determine the appropriate operational survey design.

The primary survey gears evaluated for the fishery-independent sampling survey were: (1) hook 

and line fishing; (2) stationary stereo-video camera platforms (BotCam); autonomous underwater 

vehicles using stereo-video cameras; and (4) active acoustics. Gear efficacy assessments were 

based on size-structured catches, relative fishing power and operational costs. A two-stage 

stratified random sampling design was chosen for the survey. Sampling gears were randomly 

allocated to primary sampling units (PSUs) stratified mapped at 500-m resolution by “habitat” 

classes that were combinations of the factors: substrate composition, bottom slope, and depth. 

Habitat classes were defined as: hardbottom, high slope (HBH); hardbottom, low slope (HBL); 
iv

and, softbottom, no slope (SBA). Each of these habitat classes were further divided into three 

depth ranges: 75–200; 200–300; and, 300–400 m. At least three of the four gear types were 

deployed on each mission, but due to logistical and contracting issues the full suite of four 

sampling gears was only deployed on two missions.

To compare the efficiency of each sampling gear in a comparative manner, it was necessary to 

first define a standard sampling method and sampling unit. For research fishing, a standard 

sample was defined as 30 minutes of active fishing effort within a PSU by one vessel using two 

bait types on separate rods with each line comprised of four 3/0 hooks. A standard BotCam 

sample was defined as a 15-minute deployment, which was reduced from an initial 45-minute 

drop. Reducing soak time greatly reduced analysis time and resulted in a 37% cost savings per 

sample. Due to limited resources for BotCam video analysis, PIFSC prioritized the analysis these 

data through a partnership with the University of Hawaii at Manoa. For the AUV, within each 

PSU three parallel 200 m transects separated by 25 m were run. For active acoustics 2-6 passes 

using a Simard EK60 were made within a particular PSU. With the possible exception of active 

acoustics, all gears appeared to detect bottomfish species, but to greatly differing degrees. 

Research fishing resulted in little bycatch of non-target species, BotCam and AUV mostly 

contained bottomfish, though some additional species were detected. Active acoustics were 

largely unreliable in discriminating bottomfish species. These gear performance analyses 

suggested that use of research fishing and BotCam were the most reliable to be used in the 

eventual MHI fishery-independent bottomfish survey. 

For several primary bottomfish species, research fishing and BotCam indicated similar 

occurrence and relative abundance (CPUE) patterns. Mean occurrence and CPUE were generally 

higher in HBH and HBL habitats, as compared to SBA habitats, but greater in high slope as 

compared to low slope hardbottom habitats. CPUE variance was proportional to the mean. While 

there were minor variations between research fishing and BotCam, both methods indicated that 

Ehu and Onaga had higher CPUE at depths greater than 175 m, while the highest Opakapaka 

CPUE occurred at depths less than 175 m. Bottomfish exhibited a clear preference for certain 

baits. Onaga and Ehu CPUE were greatest when fish was used; whereas, Opakapaka CPUE was 

highest for squid. Mean occurrence was typically greater for research fishing, while mean fish 

seen (or caught for fishing) was greater higher for BotCam. Thus, overall relative fishing power 

was higher for BotCam as to research fishing. Research fishing cost per sample was about half of 

that for a 15-min BotCam sample.

We believe that most efficient design of the operation survey would stratify PSUs by depth and 

habitat. This stratification would include two depth intervals: 75–200 m; and, 200–400 m; and 

three habitat strata: hardbottom–high slope, hardbottom–low slope, and softbottom areas. These 

recommended stratifications effectively partition the variance of CPUE and would produce the 

most precise mean stock abundance estimates for the lowest cost. 

Our results to date suggest that a multi-gear fishery-independent survey of Hawaii bottomfish is 

feasible and cost-effective. Research fishing and BotCam appear to be effective complimentary 

gears in sampling bottomfish assemblages and show striking similarity in species-specific length 

compositions as well as spatial distribution patterns across depths and habitats. However, these 

gears cannot be deployed over the full range of the bottomfish stock complex. Research fishing 
v

can be employed across all depth-habitat strata, but, because it is an extractive technology, 

cannot be used within marine protected areas. On the other hand, BotCam can be employed in all 

geographic areas, but cannot sample in depths greater than 300 meters due to ambient light 

restrictions. Thus both gear types should be used in the operational fishery-independent survey. 

Preliminary results suggest that an operational fishery-independent survey for MHI bottomfish 

could be accomplished using only cooperative research vessels. Due to the greater distance 

between sampling locations in an operational MHI survey, we suggest the use at least four 

cooperative research vessels within each of four MHI zones during a fall and possibly a spring 

survey sampling window of 30–60 days.
vi
vii

CONTENTS

Executive Summary....................................................................................................................iii-v

Introduction......................................................................................................................................1

Methods............................................................................................................................................2

Phase 1: Sampling Methods Development ................................................................................6

Phase 2: Gear Calibration Experiments...................................................................................11

Results............................................................................................................................................13

Phase 1: Sampling Methods Development ..............................................................................13

Phase 2: Gear Calibration Experiments...................................................................................23

Discussion......................................................................................................................................24

Efficacy of research fishing and botcam gears for bottomfish survey...........................................24

Research Fishing......................................................................................................................24

Botcam.....................................................................................................................................25

Catch Composition...................................................................................................................28

Fishing Power ..........................................................................................................................28

Transition to Operations ..........................................................................................................29

Refinements to Sampling Methods..........................................................................................29

Gear Development ...................................................................................................................31

Survey Domain and Sample Size Projections for the Main Hawaiian Islands........................32

Relative Costs of BotCam and Research Fishing Gears..........................................................36

Seasonality ...............................................................................................................................36

Data for Stock Assessment ......................................................................................................38

Acknowledgments..........................................................................................................................39

References......................................................................................................................................41

Appendix A—SeaBED AUV ........................................................................................................48

Appendix B—EK60 Active Acoustics ..........................................................................................51
viii
1

INTRODUCTION

Commercial fishing for deep-slope bottomfish has been an important component of the 

Hawaiian economy and culture for over a century (Haight et al., 1993). The bottomfish fishery 

preferentially targets the “Deep-7” (i.e., six snappers and one grouper species), primarily using 

hook and line gears (Brodziak et al., 2011; Fig. 1). In 2012, there were 458 State of Hawaii 

Commercial Marine License (CML) holders that reported landings of bottomfish in the main 

Hawaiian Islands (MHI). The commercial fleet uses vessels ranging from 4.5 to 21 m in length 

(WPRFMC, 2010), and 2012 commercial harvests totaled 2315,000 lbs with an ex-vessel value 

of $1.6 million USD (Brodziak et al., 2014).

Figure 1.--The main Hawaiian Islands “Deep-7” bottomfish complex: (A) Onaga (Etelis 

coruscans), (B) Ehu (Etelis carbunculus), (C) Kalekale (Pristipomoides sieboldii), (D) 

Opakapaka (Pristipomoides filamentosus), (E) Gindai (Pristipomoides zonatus), (F) Hapu’upu’u 

(Hyporthodus quernus), and (G) Lehi (Aphareus rutilans). Artwork by Les Hata (Hawaii 

DAR/DLNR).

The NOAA Pacific Islands Fisheries Science Center’s Fisheries Research and Management 

Division is responsible for conducting stock assessments of bottomfish every three years. These 

assessments are used by the Western Pacific Regional Fishery Management Council (WPRFMC) 

to determine sustainability status of the resource and for setting annual catch limits for fishery 

(i.e., combined commercial, recreational and subsistence users). Once an annual catch limit is 

reached (non-commercial fishing accounts for a very small portion of the reported catch), the 

fishery is closed. 

The stock assessment process requires reliable time-series of catches, fishing effort and life 

history demographics to estimate stock abundance trends and evaluate sustainability 
2

benchmarks. Past assessments have relied on commercial catch and effort data that has been of 

varying quality. Data derived from commercial catch reports can be biased due to imposed size 

and catch limits, variable gear types, and market forces (Maunder and Punt, 2004). To date, 

Hawaii Deep-7 bottomfish stocks have been assessed as a holistic complex, creating substantial 

uncertainty in species-specific abundances and population dynamics. 

The commercial fleet utilizes a variety of fishing methods to capture and sample the stock, many 

of which can yield CPUE statistics, which may not be proportional to the true abundance of the 

stock (Maunder and Punt, 2004). Additionally, while current assessments have taken into 

account short and long-term temporal effects (yearly and seasonal) as well as the effects of 

geography (reporting grids specified by the Hawaii Division of Aquatic Resources (HDAR)) and 

fisher skill when standardizing CPUE (Brodziak et al., 2014), these assessments have utilized 

only fishery-dependent data and have treated the Deep-7 species as a homogenous stock 

complex, rather than as individual species.

A series of workshops convened in 2001–2005 evaluated existing bottomfish stock assessment 

methodologies in the Pacific Islands Region (PIR; Mace et al., 2001; Ralston et al., 2004). Mace 

et al. (2001) noted that the greatest impediment to credible bottomfish stock assessments was the 

lack of accurate and precise input data. To improve abundance estimates, Ralston et al. (2004) 

recommended development of a fishery-independent survey using available “advanced 

technologies”. A 2005 workshop on Ecosystem Science and Management Planning reiterated the 

pressing need for the design and implementation of a population-level, fishery-independent 

survey to obtain size-structured abundance data for bottomfish, and to aggressively pursue 

development of length-based assessment models.

The goal of this technical report is to improve stock assessments through optimal design and 

implementation of a fishery-independent survey for the Deep-7 bottomfish complex in the main 

Hawaiian Islands (MHI) with three primary objectives:

1. Evaluate the most effective survey gears for obtaining species-specific spatial sizestructured abundance metrics of the Hawaiian bottomfish stock;

2. Conduct quantitative gears inter-calibration studies to determine the relative fishing 

power of the multiple gears to be used in the survey;

3. Detail the required methodologies for efficient conduct of a multi-gear fisheryindependent survey of the MHI bottomfish stocks.

METHODS

This study considered several fishery-independent sampling methods: cooperative-research hook 

and line fishing (research fishing); stationary stereo-video camera platform (BotCam); 

autonomous underwater vehicle with stereo-video cameras; and, active acoustics (Simrad EK60). 

Gears were chosen based on their current use within and applicability to the fishery (research 
3

fishing), their ability to provide species-level size-structured abundance estimates (research 

fishing, camera platforms), or their ability to sample both day and night covering a large portion 

of the water column over a large spatial area (research fishing, EK60). EK60 active acoustics 

and the SeaBED AUV were determined to not yet be ready for operational use and they will not 

be discussed further. Some preliminary details on these gears are shown in Appendices 1 and 2.

Field operations have been carried out from 2011 to 2014 in three phases (Table 1). Phase 1 

concentrated on sampling methods development and standardization. Phase 2 focused on 

calibration of the gears developed during Phase 1. Phase 3 further refined sampling methods and 

conducted a pilot study for transition to an operational multi-gear fishery-independent survey. 

Overall, six survey missions of 10 - 15 days each were completed (Table 1) utilizing three 

distinct sampling designs, one for each experimental phase (Figs. 2–4, respectively).

Table 1.--Survey missions with experimental design for each mission and survey gears fielded 

during each mission. PSU reflects the number of primary sampling units surveyed by each gear 

during each mission. The BotCam and EK60 gears were each fielded by a single vessel while 

research fisher operations were conducted by 3 vessels, allowing for a greater number of PSUs to 

be sampled using that gear.

Study Area and Sample Units

Field surveys were conducted during the spring and fall in a study area between the islands of 

Maui, Lanai, and Molokai (inset, Fig. 2). That area–along with the Penguin Bank area–is 

generally regarded as a center of the MHI deep-slope bottomfish fishery (Parke, 2007). The 

survey domain was gridded at 500 m and each 500 × 500 m grid cell constituted a primary 

sampling unit (PSU). The size of the PSU was chosen to accommodate all four gears. This PSU 

size ensured that research fishing operations could easily be conducted while remaining within 
4

the PSU especially at times of high wind or current, and was also sufficiently large to allow for 

individual transects across the cell by both the EK60 and AUV. For BotCam, while the 

deployment position of a camera lander can easily be determined prior to deployment, the system 

can drift horizontally with the current during its descent to the sea floor. From previous 

experience (Dr. Jeffrey Drazen, pers. comm.) it was reasonable to expect that a camera lander 

would remain within 50 m of the target center point of the 500 × 500 m PSU during its descent, 

even during times of high current, and also not attract fish from adjacent grid cells during 

sampling period of up to 45 minutes.

Figure 2.--Survey domain for the quantitative comparison among advanced fishery-independent 

sampling methods showing Phase 1 sampling. The survey area was gridded at 500 m and 

classified by substrate, composition (hard, soft), benthic slope (high, low), and depth (target 

range (75–400 m). In 2011–2012, sampling was primarily constrained to PSUs within each of 2 

survey boxes (B,D), each of which contained a wide range of depth and habitat types.
5

Figure 3.--Survey domain for the quantitative comparison among advanced fishery-independent 

sampling methods showing Phase 2 sampling. PSUs which had shown high bottomfish 

occurrence in Phase 1 were intensively resampled to develop gear-specific calibration factors.

Figure 4.--Survey domain for the quantitative comparison among advanced fishery-independent 

sampling methods showing Phase 3 sampling. PSUs (500 × 500 m) were stratified by substrate 

composition (hard, soft), benthic slope (high, low), and depth (75–200 m; 200–300 m; 300–400 

m). Gears were allocated in a stratified-random design to PSUs across the full study area within 

the Maui Nui region.
6

Depth and habitat characteristics of each PSU were determined from 20-m-resolution multibeam 

bathymetric and backscatter data. The depth of each PSU was estimated as the average depth of 

the 20-m pixels within each PSU. High slope for a 20m pixel was defined as slope greater than 

20°. Hard or soft substrate for a 20-m pixel was determined from backscatter data. Slope and 

substrate pixels were used to designate habitat type for a PSU of 500 × 500 m following a 

hierarchical classification scheme (Table 2). 

Table 2.--Hierarchical habitat classification scheme for each 500 m2primary sampling unit 

(PSU) based on 20 m per pixel resolution multi-beam products. HBH = Hardbottom, high slope; 

HB_L = Hardbottom, low slope; SB_H: Softbottom, high slope; SB_L = Softbottom, low slope; 

UN_U = Unclassified. If any if any HB_H pixels were present within the PSU, the PSU was 

classified as HB_H. If no HB_H pixels were present but there was presence of either HB_L or 

SB_H, the classification was based on proportion of pixels within the PSU of HB_L and SB_H. 

The PSU was classified as SB_L if only this type was present. 

Criterion Habitat Designation

Area of HB_H > 0 HB_H

HB_H=0, HB _L > SB_H HB_L

HB_H=0, SB_H > HB_L SB_H

HB_H=0, HB_L=0, SB_H=0, SB_L>0 SB_L

HB_H=0, HB_L=0, SB_H=0, SB_L=0 UN_U

Phase 1: Sampling Methods Development

Research Fishing

The Hawaii bottomfish fishery is generally conducted by fishermen on small vessels (20–40 ft) 

using manual or hydraulic handline fishing gear (Fig. 5). Research fishing was conducted by the 

Pacific Islands Fisheries Group (PIFG) operating within a sampling design developed in 

cooperation with scientists from PIFSC. To ensure that the data collected in this study were 

comparable with fishery-dependent data, each vessel conducted fishing operations using a 

standard protocol developed by PIFSC researchers, vessel captains, and observers, which was 

designed, to the extent possible, to mimic a commonly used commercial fishing method. Local 

commercial bottomfishing vessels, captains, and fishing observers were hired to ensure that this 

fishing method remained similar to one style of fishing widely used by the local fishing 

community.

Each day within a survey mission (Table 1), each of three PIFG vessels was randomly assigned 

to PSUs within the given survey area. A standard sampling unit for research fishing was defined 

as 30 minutes of active fishing effort within a PSU by one vessel with each of two bait types on 

separate lines. One line was baited with squid while the other was baited with anchovy or a 

similar fish (e.g. aku, kawakawa, opelu). Hooks were standardized to the size equivalent of a #10 

Mustad circle hook, Maruto BKN #22, Eagle Claw EC-10 or similar. Each vessel used a small 
7

palu or chum bag that was filled with ~1 kg of a 50/50 mixture of ground squid and the 

aforementioned fish bait. The operative assumption was that, with standardization of 

methodology among the participating vessels, differences in catch rates (CPUE) from one area to 

another reflect differences in the resident fish assemblage rather than differences in fishing 

method among vessels.

Figure 5.--A cooperative research fishing vessel contracted by the Pacific Islands Fisheries 

Group for this study.

Prior to fishing a PSU, vessels were allowed to use their onboard acoustic fishfinders to search 

for aggregations within the PSU. This is typical of how many bottom fishermen locate fish 

during normal fishing operations. If fish aggregations were not found using the fishfinder, 

fishermen would look for favorable bottom topography. The standard survey period was defined 

as the interval from time the lines entered the water to the time they left the bottom upon 

retrieval 30 minutes later. Haul-back time did not count towards the 30-minute survey time. For 

each PSU, observers recorded the date, vessel, survey start/end time/position, the number of lines 

being fished, and the type of bait and palu being used. For each fish caught, the observer on the 

vessel recorded the species name and fork length. Logsheet data were transcribed into 

spreadsheet format and analyzed to determine how various factors affected the CPUE and for 

comparison against other gears.

In-situ stationary stereo-video camera platforms are increasingly being used by marine scientists 

to non-extractively generate high-resolution, species-specific, size-structured abundance 

estimates (Cappo et al., 2006). BotCam (Fig. 6) (Merritt, 2005; Merritt, 2011) is a fully 

autonomous stationary stereo-video camera system that has been effectively used to collect 

fishery-independent species-specific size-structured abundance data on bottomfish in the main 

Hawaiian Islands (Moore et al., 2013; Sackett et al., 2014). The system consists of two low-light 

video cameras (Monochrome Navigator, Remote Ocean Systems, San Diego, CA) with an 80° 

diagonal field of view that can image targets in ambient light to a depth of 300 meters in 
8

BotCam

Figure 6.--A BotCam stationary stereo-video camera system.

Hawaiian waters. Video data are recorded to a digital video recorder (XM-DVR, DataToys, 

Mequon, WI). BotCam also employs a temperature and pressure recorder (SBE39TP, Seabird 

Electronics Inc., Bellevue, WA), a custom-built battery pack (The Sexton Company, Salem, 

OR), and syntactic foam blocks or trawl floats for positive buoyancy (Flotation Technologies, 

Biddeford, ME). The aluminum frame (1.2 m wide × 0.5 m deep × 0.45 m tall) is designed to 

protect the cameras and maintain fixed camera positions and also allows for the attachment of 

oceanographic instruments such as current meters, temperature and depth recorders, and 

hydrophones. An extension arm is used to carry both a stereo-video synchronizing (SVS) device 

and bait canister. The SVS, a grid of light emitting diodes (LED) that flash in rapid succession, 

allows the video stream from the two cameras to be synchronized to within 1/30th of a second 

(one video frame) for accurate stereo-video measurements. The LEDs flash at 30 Hz for 1 second 

every minute; no reaction to the lights has been observed with any of the target species. The bait 

used in this study is a frozen 1 kg standard 1:1 mixture of fish (anchovies or sardines) and squid, 

ground to approximately 1 cm grain size, the same as that used in research fishing operations.

The system is moored to the bottom using an anchor weight (45–75 kg steel anchor chain links) 

attached to an acoustic release or shear pin and anchor line. BotCam is designed to float 

approximately three meters above the seafloor and to record video by pointing horizontally 
9

down-current with a downward angle of 15° and a diagonal field of view of 80°. This 

configuration was chosen as the target species are known to school in the water column several 

meters above the bottom and higher and are known to favor steep and rocky slopes (Ralston and 

Polovina, 1982; Haight et al., 1993). 

Within each survey mission (Table 1), BotCam units were randomly allocated to PSUs. During 

the first three missions, a single BotCam unit was allocated to each PSU and soak time was 45 

minutes. Starting with the fourth mission (SE1302), soak time was reduced to 15 minutes based 

on analysis of data from the first 3 missions (discussed later in this document) and 2–3 replicate 

BotCam units were assigned to each PSU. Resulting stereo-video sequences were analyzed using 

the MaxN method to estimate size-structured abundance for each bottomfish species by a 

standard pool of analysts using stereo-photogrammetric software (EventMeasure, vers. 3.33 

[SeaGIS Pty Ltd, Victoria, Australia]) (Fig. 7). MaxN is a commonly used (Ellis and DeMartini, 

1995; Priede et al., 1996; Willis et al., 2000; Cappo et al., 2003; Gledhill et al., 2005; Merritt et 

al., 2011; Misa et al., 2013) conservative estimator of abundance that ensures that individuals are 

not recounted as they repeatedly leave and re-enter the frame. MaxN has also been found to 

correlate well with the traditional CPUE parameter used in fishing surveys for bottomfish (Ellis 

and DeMartini, 1995) and other species (Willis et al., 2000; Stoner et al., 2008). For the first 

three missions, MaxN was calculated for both the full 45-minute soak time as well for just the 

first 15 minutes. From SE1302 on, MaxN was calculated for the 15-minute soak time. This 

allowed us to compare soak times of 45 and 15 minutes and also provided a standard 15-minute

MaxN data set from all missions.

The visual area sampled by BotCam varies with water quality, surface wave action, and degree 

of ambient light. Based on estimates by Moore et al. (2013), BotCam visual sampling area within 

the 100–300 m depth range in the main Hawaiian Islands ranged from 4 to 416 m2given 

minimum and maximum view distances of 2–10 m from the cameras. In their study, measured 

fish averaged 1.9 ± 0. 98 m from the cameras, with a maximum distance of 8.2 m. 

Lengths of fish were taken at the time of MaxN or when the most measurable fish were present 

in the field of view. For each fish, the mean of 5 replicate measurements was computed to 

estimate fork length (FL). Measurements were accepted only if the root mean-square (RMS) 

error was < 10 mm and the RMS-to-FL ratio was < 10%. In addition, the substrate type was 

classified visually as to slope, bottom hardness, and general substrate type (rock outcrop, 

boulders, sand, etc.).

To evaluate optimal BotCam soak-time for this project, data from Misa et al. (in review) were 

used in addition to data from experiments SE1102, SE1107, and SE1208. Misa et al. (in review) 

used 1504 BotCam deployments with a 45-minute soak time in the main Hawaiian Islands and 

calculated MaxN values for time blocks that increased from 5 to 45 minutes (e.g. 0–5 minutes, 

0–10 minutes, 0–15 minutes, etc). Misa et al. were able to determine cutoff periods during which 

MaxN occurred for the majority of deployments.
10

Figure 7.--A screen grab example from the SeaGIS EventMeasureTM desktop software package 

showing measurement of individual fishes.

Study Design

During Phase 1, sampling was constrained to PSUs within delineated survey boxes (subsets of 

the overall study area), each of which contained a wide range of depths and habitat types (Fig. 2). 

This facilitated co-sampling of PSUs by the various sampling gears within the limited 10–15 day 

time period of a given mission. A general space blocking scheme according to habitat class and 

depth was employed for missions SE1102, SE1107, and SE1208. Target PSUs were randomly 

selected within each space block for sampling by all gears.

Gear Comparisons

Data from Phase 1 missions were used to compare the research fishing and BotCam gears with 

respect to species catch and length compositions, and species abundance patterns by habitat and 

depth. Evaluation of the species composition of fishes captured by research fishing or observed 

by BotCam examined the percentage of Deep-7 bottomfish species vs. other species groups. 

Statistical analysis of differences in species length frequency between research fishing and 

BotCam were carried out using Kolmogorov-Smirnov tests. 

Species abundance patterns were evaluated to understand the potential utility of using depth and 

habitat as stratification variables for a fishery-independent survey of bottomfish. The goal of 

stratification is to delineate the sample survey frame (i.e., the grid cell map) into sub-regions or 

strata with low, moderate, and high variance of CPUE. Since mean and variance of CPUE are 

usually highly correlated for reef-fishes (Smith et al., 2011), analyzing differences in mean 
11

CPUE provides insights into differences in variance of CPUE among potential strata. Several 

arrangements of habitat and depth strata were evaluated using standard nonparametric tests 

(Mann-Whitney, Kruskal-Wallis, Conover) for differences in means and variances of CPUE for 

research fishing and BotCam gears. Nonparametric tests were used due to the highly skewed, 

non-normal distributions of CPUE observations for Deep-7 species for both gears. 

Phase 2: Gear Calibration Experiments

Relative Fishing Power

Standardization of abundance indices between the research fishing and BotCam gears was 

carried out following the 'fishing power’ method, which is based on fundamental principles of 

population dynamics theory (Beverton and Holt, 1957; Ricker, 1975). This approach was 

originally conceived by Gulland (1956) and Beverton and Holt (1956), and then formalized 

statistically by Robson (1966). Catch C in number of animals is related to average population 

abundance N̅in a specified time interval by

C = FN̅= qfN̅ (1)

where F is the instantaneous rate of fishing mortality, defined as the product of nominal fishing 

effort f and catchability coefficient q, the fraction of the stock removed per unit of nominal 

fishing effort. Catch-per-unit-effort (CPUE), a relative index of abundance, is given by

C/f = qN̅ (2)

Catchability q may differ substantially among gears or sampling methods, but is often very 

difficult to estimate in practice; in addition, nominal effort f may be measured in different units 

for different gears, e.g., angler-hours, trap soak-hours, trip-days, etc.

The fishing power method estimates the relative catchability among different gears operating on 

the same unit stock. For two gears, CPUEs for gear 1 and gear 2 can be used to 

express the fishing power λ, or relative catchability, of one gear in terms of the other,

(3)

In this case gear 2 is designated as the 'standard' gear, and the fishing power λ can be multiplied 

by the effort of gear 1 to express the CPUE of gear 1 in terms of CPUE of the standard gear, 

(4)
12

These principles also apply to abundance indices from fishery-independent surveys, whether they 

employ common capture gears such as trawls, traps, or seines, or whether they utilize nonextractive methods such as diver visual transects measuring fish density, the number of 

individuals observed (i.e., C) per unit area searched (i.e., f).

Fishing power models usually ascribe variation in CPUE to two main factors: (1) the times and 

locations of sampling effort, and (2) the type of sampling gears (or vessels) employed. Following 

Robson (1966), CPUE for time-location i and gear j can be estimated by a two-way analysis of 

variance (ANOVA) model of the form

CPUEij = α + bi + gj + εij (5)

where α is a constant, bi is a time-location coefficient, gj is a gear coefficient, and εij is an 

additive error term. The main assumption is that, for a given species or species life stage, the 

capture efficiency gj is constant for each gear j. Our principal focus is obtaining accurate and 

precise estimates of gear parameters gj in Equation 5. The coefficients bi are included in 

Equation 5 to control for temporal and spatial variation in CPUE. The model-predicted CPUE for 

gear j is estimated by 

CPUEj = α + gj (6)

Following Equation 3, fishing power for gear j is estimated as the ratio of the model-predicted 

CPUE for gear j to the model-predicted CPUE of a standard gear (i.e., j = S),

λj = CPUEj / CPUEs (7)

In this formulation, any gear can be selected as the standard. 

A generalized linear model for estimating the parameters of Equation 5 for time-locations i = 

1,2,..., h and gears j = 1,2,..., k is

y = α + b1X1

(b) + ... + bh-1Xh-1(b) + g1X1(g) + ... + gk-1Xk-1(g) + ε (8)

where the parameters to be estimated are intercept α, time-location coefficients bi’s, and gear 

coefficients gj’s (Kutner et al., 2004; Fox, 2008). The independent X variables are discrete 

categorical or “dummy” variables, Xi

(b) for time-locations and Xi(g) for gear types. Dummy 

variables are coded as for a standard two-way analysis of variance (ANOVA) model (cf., Kutner 

et al., 2004). 

Bottomfish sampling data for research fishing and BotCam gears data contain a substantial

proportion of zero observations resulting in highly skewed, non-normal distributions for CPUE. 

Observations were modeled following the ‘delta’ approach in which all observations follow a 

distribution with a discrete probability of obtaining a non-zero value, e.g., binomial, and in 

which positive value observations follow some continuous distribution, e.g., normal or gamma 
13

(Pennington, 1983; Stefansson, 1996). Parameter estimation of Equation 8 was carried out in two 

separate stages. The first stage applied the model of Equation 8 to presence-absence data using 

logistic regression, and the second stage applied Equation 8 to non-zero CPUE observations 

using generalized linear regression. Substituting the model-predicted occurrence p from the 

logistic regression for CPUE in Equations 6 and 7 yields the fishing power for occurence, λ(p). 

Likewise, substituting the model-predicted catch when present μ from the generalized linear 

regression for CPUE in Equations 6 and 7 yields the fishing power for catch when present, λ(μ). 

Multiplying the occurrence fishing power λ(p) and the catch when present fishing power λ(μ) 

yields the overall fishing power λ(CPUE). Separating the gear calibration components in this 

manner allows the flexibility to standardize bottomfish survey data for subsequent analyses using 

presence-absence observations, non-zero CPUE observations, or combined zero and non-zero 

CPUE observations.

Study Design

Data from missions SE1302 and SE1306 (Table 1, Fig. 3) were used for analysis of fishing 

power. Catch data for BotCam for this analysis were defined as the species-specific Max N 

values for a 15-minute sample. For these experiments, an individual targeted PSU was 

considered as a space block, with samples inside each PSU treated as replicates. PSUs which 

had consistently shown high biomass during Phase 1 missions were selected for intensive repeat 

sampling. This experimental design was chosen to maximize bottomfish encounter rates by each 

gear, generating paired samples necessary for developing gear-specific fishing power and 

calibration factors (Fig. 3). Each mission was designated as a time block. Space-time blocks 

that had at least one positive catch for each gear were included in the analysis for a given target 

species. 

RESULTS

Phase 1: Sampling Methods Development

Standard Fishing Sample

The 'standard fishing sample' for the Research Fishing gear was refined over the course of Phase 

1 sampling missions (Table 1). The general method was to fish within a target grid cell (500 ×

500 m primary sample unit) for a specified amount of time. The fishing sample entails a series 

of ‘drifts’ within a target cell for a total fishing time of 30 minutes. For the initial experiment 

SE1102, 57.4% of the individual drifts stayed completely within the target cell without extending 

into adjacent cells. Based on this finding, the participating scientists and fishers collaborated on 

methods and navigational aids to improve the ability of fishing vessels to stay within the 

boundaries of a target cell during a fishing sample. For the next Research Fishing experiment, 

SE1208, 89.7% of the individual drifts remained fully within the target cell. Fishers and 

scientists also worked together between missions SE1102 and SE1208 to standardize the number 

of lines, the type and number of hooks per line, bait type per line, and the definition of 'active 

fishing time' for a 30-minute sample within a PSU.
14

BotCam Soak Time

In a companion study, Misa et al. (in review) found that the likelihood of recording species 

MaxN increased with longer bottom time. Species-specific MaxN values were recorded within 

15 minutes after camera touchdown in 50% of deployments where a given bottomfish was 

present and within the first 30 minutes in 80% of deployments (Fig. 8).

Figure 8.--Cumulative proportion of BotCam deployments where MaxN occurred as shown by 5-

minute time bins from camera touchdown (minute 0) up to 40 minutes recorded from 1,504 

BotCam deployments conducted during bottomfish surveys in the main Hawaiian Islands (figure 

from Misa et al [in review])). d = no. of BotCam deployments where a species was present.

For Phase 1 mission data, time-to-first sighting occurred within the first 15 minutes in about 75-

85% of the samples where a given Deep-7 species was observed (Table 3, Fig. 9). The one 

exception was lehi, for which the time-to-first sighting was less than 15 minutes in 48 percent of 

the samples. The distribution of time-to-MaxN for a 45-minute period was shifted towards longer 

times compared to the time-to-first sighting (Fig 9).

Table 3.--Percentage of samples where time-to-first sighting occurred within the first 15 minutes. 

Sample size n is the number of BotCam samples in which a given species was observed. (table 

from Misa et al. [in review]). 
15

(A) Lehi (B) Ehu

(C) Onaga (D) Opakapaka

Figure 9.--Frequency distribution of time-to-first sighting (top panel) and time-to-maxN (bottom 

panel) for Deep-7 species: (A) Lehi, (B) Ehu, (C) Onaga, and (D) Opakapaka. Units for x-axis is 

minutes and for y-axis is number of samples.

Species and Length Composition

Data from experiments SE1102 and SE1208 (Table 1, Fig. 2) were used to analyze the species 

composition of fish captured/observed for research fishing and BotCam gears. Research fishing 

yielded 309 fish caught and BotCam yielded 1621 fish encounters (Table 4). The majority of 

individual fish captured/observed by the two gears were Deep-7 bottomfish species. About 84% 

of the fishes captured by the hook-line gear were either Deep-7 species or other species of 

bottomfish (snappers, groupers, jacks), suggesting that this gear is very targeted to Deep-7

species with a low bycatch of non-bottomfish species. Deep-7 and other bottomfish species 

accounted for about 73% of the fishes observed for the BotCam gear. The hook-line gear 

0

1

2

3

4

0.5 5.5 10.5 15.5 20.5 25.5 30.5 35.5 40.5 45.5

15min

45min

0

1

2

3

4

0.5 5.5 10.5 15.5 20.5 25.5 30.5 35.5 40.5 45.5 0 5 10 15 20 25 30 35 40 45

0 5 10 15 20 25 30 35 40 45

Time to First Sighting

n=23

Time to Max N

0

1

2

3

4

5

6

0.5 5.5 10.5 15.5 20.5 25.5 30.5 35.5 40.5 45.5

15min

45min

0

1

2

3

4

5

6

7

8

9

0.5 5.5 10.5 15.5 20.5 25.5 30.5 35.5 40.5 45.5 0 5 10 15 20 25 30 35 40 45

0 5 10 15 20 25 30 35 40 45

Time to First Sighting

n=34

Time to Max N

0

1

2

3

4

0.5 5.5 10.5 15.5 20.5 25.5 30.5 35.5 40.5 45.5

15min

45min

0

1

2

3

4

0.5 5.5 10.5 15.5 20.5 25.5 30.5 35.5 40.5 45.5 0 5 10 15 20 25 30 35 40 45

0 5 10 15 20 25 30 35 40 45

Time to First Sighting

n=13

Time to Max N

0

1

2

3

4

5

6

7

8

9

10

11

12

13

0.5 5.5 10.5 15.5 20.5 25.5 30.5 35.5 40.5 45.5

15min

45min

0

1

2

3

4

5

6

7

8

9

10

11

12

13

14

15

16

17

18

0.5 5.5 10.5 15.5 20.5 25.5 30.5 35.5 40.5 45.5 0 5 10 15 20 25 30 35 40 45

0 5 10 15 20 25 30 35 40 45

Time to First Sighting

n=68

Time to Max N
16

captured a higher proportion of sharks compared to BotCam, and BotCam observed a wider 

range of the fish community compared to Research Fishing. 

Table 4.--Species composition observed within research fishing catch and BotCam observational 

data. Both gears were highly selective for Deep-7 bottomfish, which made up 76% and 62% of 

the catch, respectively.

Length data from Phase 1 experiments were compared for research fishing and BotCam. In 

general there was good correspondence in the length frequencies between the two gears for a 

given species, especially for cases with relatively high sample sizes (n > 20) for both gears (Fig. 

10, Table 5). While differences in length-frequency distribution for kalekale and opakapaka were 

significant, owing to high sample sizes, the magnitude of these differences was minimal. 

BotCam did not commonly observe target species at sizes smaller than those captured by 

research fishing.

Table 5.--Size structure statistics for Deep-7 species by method. Minimum length, mean length, 

maximum length and SE for each species are reported in cm. Significant differences between 

methods (based on Kolmogorov–Smirnov tests) were found only for kalekale and opakapaka.
17

Figure 10.--Comparison of length frequencies derived from research fishing and BotCam for 

four of the Deep-7 species: Kalekale (Pristipomoides sieboldii), Opakapaka (Pristipomoides 

filamentosus), Ehu (Etelis carbunculus), and Onaga (Etelis coruscans). Sample size for 

Hapu’upu’u, Gindai, and Lehi was less than 10 individuals and insufficient for plotting. The 

vertical dotted line indicates the mean of the distribution.
18

Stratification Variables: Patterns by Habitat and Depth

Both habitat type and depth have been proposed as stratification variables for a full-scale fisheryindependent survey of bottomfishes in the Hawaiian Archipelago. Data from experiments 

SE1102, SE1107, and SE1208, which had broad habitat and depth coverage, were used to 

evaluate habitat type and depth as potential stratification variables. Based on an initial analysis 

of depth and habitat type for bottomfish species, several arrangements of habitat type and depth 

were evaluated for stratification. Analysis of 3 habitat strata (hard-bottom-high slope, hardbottom-low slope, soft-bottom) for 4 Deep-7 species are shown in Figure 11. In general, mean 

CPUE was higher for hard-bottom compared to soft-bottom habitats for all 4 species, and there 

were considerable differences in the variance (i.e., standard deviation) among habitat types. 

Depth was divided into two strata, 75–200 m and 200–300 m, for analysis. All 4 species 

exhibited strong depth preferences, with higher mean CPUE and variance for Opakapaka and 

Kalekale in depths less than 200 m and for Onaga and Ehu in depths greater than 200 m (Fig. 

12). The 3 habitat strata and 2 depth strata were combined into 6 habitat-depth strata. The 

results of Fig. 13 show that this 6-strata scheme partitioned both the mean and variance of CPUE 

into different levels by combinations of habitat and depth for all 4 species; consequently, this 

combination stratification scheme appears to be more effective compared to stratifying by either 
19

habitat type or depth alone. For BotCam, the same patterns of mean CPUE by depth and habitat 

were observed for 15-minute and 45-minute soak times for the 4 species. 

0.0

0.5

1.0

1.5

2.0

HB_H HB_L SB_A

CPUE (+SD)

Habitat Class

0.0

0.5

1.0

1.5

HB_H HB_L SB_A

CPUE (+SD)

Habitat Class

0.0

1.0

2.0

3.0

4.0

5.0

HB_H HB_L SB_A

CPUE (+SD)

Habitat Class

0.0

5.0

10.0

15.0

20.0

HB_H HB_L SB_A

CPUE (+SD)

Habitat Class

0.0

0.5

1.0

1.5

HB_H HB_L SB_A

CPUE (+SD)

Habitat Class

0.0

0.2

0.4

0.6

0.8

HB_H HB_L SB_A

CPUE (+SD)

Habitat Class

0.0

0.2

0.4

0.6

0.8

HB_H HB_L SB_A

CPUE (+SD)

Habitat Class

0.0

0.5

1.0

1.5

HB_H HB_L SB_A

CPUE (+SD)

Habitat Class

Research Fishing BotCam

(A) Ehu

(B) Onaga

(C) Opakapaka

(D) Kalekale

K-W: *

Conover: ***

K-W: **

Conover: ***

K-W: ns

Conover: ***

K-W: *

Conover: ***

K-W: ns

Conover: ***

K-W: **

Conover: ***

K-W: **

Conover: ***

K-W: ***

Conover: ***
20

Figure 11.--Mean CPUE and associated standard deviation by habitat class for Research Fishing 

(left panels) and BotCam (right panels) gears for (A) Ehu, (B) Onaga, (C) Opakapaka, and (D) 

Kalekale. Non-parametric tests: Kruskal-Wallis (K-W) one-way ANOVA for differences in 

means; Conover one-way squared ranks test for differences in means and variances.
21

Figure 12.--Mean CPUE and associated standard deviation by depth range for Research Fishing 

(left panels) and BotCam (right panels) gears for (A) Ehu, (B) Onaga, (C) Opakapaka, and (D) 

Kalekale. M-W: non-parametric Mann-Whitney U-test for differences in means.

0.0

0.5

1.0

1.5

75-200 m 200-300 m

CPUE (+SD)

Depth

0.0

1.0

2.0

3.0

75-200 m 200-300 m

CPUE (+SD)

Depth

0.0

0.2

0.4

0.6

0.8

75-200 m 200-300 m

CPUE (+SD)

Depth

0.0

0.5

1.0

1.5

2.0

75-200 m 200-300 m

CPUE (+SD)

Depth

0.0

0.2

0.4

0.6

75-200 m 200-300 m

CPUE (+SD)

Depth

0.0

2.0

4.0

6.0

75-200 m 200-300 m

CPUE (+SD)

Depth

0.0

0.5

1.0

1.5

75-200 m 200-300 m

CPUE (+SD)

Depth

0.0

5.0

10.0

15.0

20.0

75-200 m 200-300 m

CPUE (+SD)

Depth

Research Fishing BotCam

(A) Ehu

(B) Onaga

(C) Opakapaka

(D) Kalekale

M-W: *

Conover: ***

M-W: ***

Conover: ***

M-W: ***

Conover: ***

M-W: ***

Conover: ***

M-W: ns

Conover: ***

M-W: ***

Conover: ***

M-W: ***

Conover: ***

M-W: ns

Conover: ***
22

Figure 13.--Mean CPUE and associated standard deviation by habitat and depth for Research 

Fishing (left panels) and BotCam (right panels) gears for (A) Ehu, (B) Onaga, (C) Opakapaka, 

and (D) Kalekale. 

0.0

0.5

1.0

1.5

2.0

HB_H HB_L SB_A

CPUE (+SD)

Habitat Class

75-200 m

200-300 m

0

1

2

3

4

HB_H HB_L SB_A

CPUE (+SD)

Habitat Class

75-200 m

200-300 m

0.0

0.5

1.0

1.5

HB_H HB_L SB_A

CPUE (+SD)

Habitat Class

75-200 m

200-300 m

0.0

0.5

1.0

1.5

2.0

HB_H HB_L SB_A

CPUE (+SD)

Habitat Class

75-200 m

200-300 m

0.0

0.5

1.0

HB_H HB_L SB_A

CPUE (+SD)

Habitat Class

75-200 m

200-300 m

0

2

4

6

HB_H HB_L SB_A

CPUE (+SD)

Habitat Class

75-200 m

200-300 m

0.0

0.5

1.0

1.5

HB_H HB_L SB_A

CPUE (+SD)

Habitat Class

75-200 m

200-300 m

0

5

10

15

20

25

HB_H HB_L SB_A

CPUE (+SD)

Habitat Class

75-200 m

200-300 m

Research Fishing BotCam

(A) Ehu

(B) Onaga

(C) Opakapaka

(D) Kalekale

K-W: **

Conover: ***

K-W: ***

Conover: ***

K-W: ***

Conover: ***

K-W: ***

Conover: ***

K-W: **

Conover: ***

K-W: ***

Conover: ***

K-W: ***

Conover: ***

K-W: ***

Conover: ***
23

Phase 2: Gear Calibration Experiments

Sufficient data for fishing power estimation was available for 4 Deep-7 species: ehu, opakapaka, 

onaga, and kalekale (Tables 6, 7, 8). Model-fitting diagnostics for the positive catch GLM 

suggested that error residuals were best modeled with either a gamma or normal probability 

density function for a given species (Table 6).

Table 6.--Number of space-time blocks and observations for the logistic and positive catch 

fishing power models for three species. Also shown are the selected response variable and error 

pdf for the positive catch GLM.

Logistic GLM Positive Catch GLM

Selected Positive Catch 

Model

Species Location-Time Blocks n

Location-Time 

Blocks n

Response 

Variable

Error 

pdf

Ehu 6 98 6 42 μ gamma

Onaga 7 126 8 70 μ gamma

Opakapaka 13 258 13 129 log(μ) normal

Kalekale 14 238 14 93 log(μ) normal

Table 7.--Sample sizes for the logistic (p) and positive catch (μ) GLM models by gear and 

species.

Research Fishing BotCam

Species n (p) n (μ) n (p) n (μ)

Ehu 56 23 42 19

Onaga 70 40 56 30

Opakapaka 144 69 114 60

Kalekale 136 49 102 44

For the combined SE1302 and SE1306 experiments, mean occurrence was higher for research 

fishing for onaga, and higher for ehu, opakapaka, and kalekale for BotCam. Mean catch when 

present was higher for BotCam for all four species. Overall, the relative fishing power for CPUE 

was higher for BotCam compared to research fishing in all cases (Table 8).
24

Table 8.--Model-predicted mean occurrence (p), catch when present (μ), and CPUE by species 

and gear, and the associated fishing power for each metric. Note that research fishing was 

designated as the standard gear for this analysis.

GLM Predicted Mean Relative Fishing Power

Species Gear p μ CPUE λ(p) λ(μ) λ(CPUE)

Ehu

Research 

Fishing 0.376 1.957 0.735 1.000 1.000 1.000

BotCam 0.438 2.722 1.191 1.164 1.391 1.620

Onaga

Research 

Fishing 0.569 2.634 1.500 1.000 1.000 1.000

BotCam 0.513 7.406 3.800 0.901 2.811 2.533

Opakapaka

Research 

Fishing 0.452 1.774 0.801 1.000 1.000 1.000

BotCam 0.534 4.340 2.316 1.181 2.447 2.890

Kalekale

Research 

Fishing 0.383 1.891 0.724 1.000 1.000 1.000

BotCam 0.451 11.771 5.307 1.178 6.225 7.333

DISCUSSION

Efficacy of Research Fishing and Botcam Gears for Bottomfish Surveys

Commercial and recreational bottomfishing remain important components of the Hawaiian 

economy and culture. To enhance sustainability, the NOAA National Marine Fisheries Service, 

Pacific Islands Fisheries Science Center conducts an assessment of the Deep-7 stock every 3 

years. To date, while assessments have taken into account short and long-term temporal effects 

(yearly and seasonal) as well as the effects of geography (HDAR reporting grid) and some 

differences among fishers when standardizing CPUE, these assessments have utilized only 

fishery-dependent data and have treated the Deep-7 species as a stock complex, rather than as 

individual species. Use of the types of advanced sampling technologies detailed in this study is 

aimed at improving species-specific, size-structured abundance estimates and, hence, the 

resulting assessments. Fishery-independent data can include data on portions of the stock before 

they recruit to the fishery, while the use of advanced technologies allows for increased precision 

and accuracy with respect to species-specific size-structured abundance. The inclusion of data 

from multiple gears helps to guard against bias associated with any particular gear.

Research Fishing

The hook and line methods employed by the Research Fishers most closely match the methods 

employed by the commercial and recreational fishery. Hence, data from research fishing is most 

directly comparable in kind to the catch data used in previous assessments. Cooperation with 

local fishers also helps to increase stakeholder buy-in while making use of local knowledge and 

skill. Research fishing operations can be conducted both day and night and hooks can be 
25

positioned to catch fish throughout the water column. Research fishing also provides a high 

degree of accuracy and precision with respect to species identification and size. However, these 

advantages hinge on the expertise of the observer recording the information. In early iterations of 

this study, observers were inconsistent in their species naming and size measuring conventions, 

using local common names instead of scientific names and varying in their units of measure. 

There was also inconsistency in the way length measurements were taken, with some observers 

measuring total length while others measured fork length. Some used a measuring board while 

others used a flexible tape draped over the body of the fish. Such inconsistencies increased the 

variability in early research fishing data and necessitated many hours of data review, translation, 

and additional training. The variability between vessel captains and observers is one of the main 

challenges associated with data derived from research fishing. Differences in skill level, vessel 

type, and fishing style (e.g. drifting, power drifting, anchoring) can all lead to differences in 

CPUE and composition of the catch. Rigorous training resulted in improved data collection and 

recording procedures by the end of this study. Our results indicate that both bait types should be 

retained to comprise a standard fishing sample for surveys targeting the Deep-7 complex, but 

that catch should continue to be recorded separately for each bait type to allow continued 

refinement of the analysis of bait preference.

Less easily overcome, are challenges associated with gear saturation, behavior, and sampling 

logistics. In this study, each vessel fished a total of 2 lines with 4 hooks on each line. While 

differences between low and high densities of fish can easily be detected, once the gear becomes 

saturated (i.e., all the hooks have fish), it can be difficult or impossible to distinguish between 

high and very high densities. Similarly, research fishing is subject to false negatives. In several 

cases BotCam revealed fish in an area where target species were not biting and were thus not 

captured by fishing operations. This can be due to any number of factors including moon phase, 

time of day, water temperature, and type of bait being used, among many others. Properly 

quantifying false negatives remains a challenge for fishing operations. Research fishing 

operations are also typically fielded using small contract vessels (18–40 feet) that also take part 

in the fishery. This may be challenging to implement in an operational fishery-independent 

survey that covers the range of the stock as many of these vessels do not have the range to reach 

the outer boundaries of the stock. These less heavily fished areas have the potential to greatly 

influence an assessment. Research fishing is necessarily an extractive technology. While this is 

beneficial in ensuring correct species identification and size and allows for the collection of 

specimens for biosampling, it cannot be used in areas where extraction is prohibited (i.e., the 

State’s bottomfish restricted fishing areas (BRFAs), MPAs, etc.) or if some future catch quota 

for a target or bycatch species is reached. Sampling within protected areas will continue to be 

important as, if they are effective (Sackett et al., 2014), they have the potential to influence the 

assessment.

BotCam

BotCam represents an effective, non-extractive means for collecting fishery-independent, 

species-specific, size-structured abundance estimates. Stationary stereo-video camera platforms, 

like the BotCam, have been used by researchers at the PIFSC and University of Hawaii—Manoa 

since 1995 (Ellis and DeMartini, 1995) and have been used to non-destructively sample 

bottomfish assemblages associated with the Hawaii BRFAs (Moore et al., 2013; Misa et al.,
26

2013; Sackett et al., 2014). Stereo-video camera systems provide a high degree of accuracy and 

precision with regard to species-specific, size-structured abundance estimates, can easily be 

deployed and recovered by a team of 2 to 4 minimally trained field staff, and provide 

standardized data collection regardless of the personnel deploying the system or location of 

deployment. Their small size and weight means that they can be deployed by hand off a variety 

of vessels, including cooperative research vessels.

Our results indicate that MaxN statistics from a 15-minute soak time likely characterize the 

abundance of Deep-7 species in the immediate vicinity of the BotCam gear, whereas the higher 

abundance metrics associated with a 45-minute soak time are likely due to the inclusion of fishes 

attracted from farther away. The 15-minute soak time likely samples Deep-7 fishes in a more 

restricted space compared to the 45-minute soak time, thus guarding against drawing fishes from 

adjacent PSUs and disparate habitat strata into the count. As MaxN was recorded within 15 

minutes for 50% of deployments, time to first sighting occurred within the first 15 minutes for 

nearly 85% of deployments, and there was no significant difference in abundance metrics by 

habitat between the 2 soak times, we have adopted the 15-min soak time for BotCam, with the 

associated 37% reduction in cost per sample.

Species-specific length-frequency distributions were similar between BotCam and research 

fishing, suggesting that both gears were effectively sampling the same assemblages. BotCam 

units were easily deployed throughout the survey area and species were easily categorized and 

measured in the resulting video data. Interestingly, BotCam did not encounter the small, juvenile 

fishes that would typically be missed by hook-based fishing gears due to size-selectivity. As a 

video-based method not dependent on fish taking a hook, we expect false negative to be less of 

an issue for BotCam than for research fishing. Hence, the lack of small, juvenile fishes of Deep-7 

species in the BotCam gear further suggests that this life stage does not reside in the same 

habitats and/or depths as the larger, adult life stages for these species (Moffitt and Parrish, 1996; 

Parrish et al., 1997). Misa et al., (2013) did observe small juveniles of opakapaka in hardbottom 

and softbottom low slope habitats but in shallower water than the adults (40–100 m). Juvenile 

onaga were only observed in the Pailolo channel despite sampling 6 sites throughout the main 

Hawaiian Islands. This could have important implications for stock assessment as the status of 

juvenile populations would not be captured in the survey design.

Unlike research fishing, the BotCam relies on ambient light and therefore, in the MHI, can only

sample to a depth of 300 m during daylight hours. Essential Fish Habitat (EFH) for Deep-7 

bottomfish extends to 400 m, rendering a significant portion of the habitat beyond the range of 

the BotCam. While the majority of non-commercial and likely the majority of commercial 

bottomfishing operations occur during the day, a significant portion of commercial fishing 

occurs at night, especially for opakapaka and onaga (Nichols and Nakagawa, pers. Comm.). 

These limitations should be considered as data from day-time, fishery-independent surveys are 

incorporated into stock assessment models. The use of imaging sonar, such as DidsonTM or 

BlueViewTM, may enable enumeration and sizing of fish targets in zero-light. Species 

identification may prove more difficult, however, as the detailed morphology of the target is not 

visible in the EK60 images. BotCam is also typically deployed as a single unit at given height 

above the seafloor. While the field of view of the cameras is able to capture fish over a wide 

area, fishes existing at significantly shallower depths than that at which the BotCam is deployed, 
27

will not be captured, resulting in false negatives. This limitation could be alleviated by deploying 

multiple BotCam units at varying depths along a single downline, or by using EK60 to target 

BotCam deployments to depths where bottomfish are apparent.

Stereo-video methods like BotCam commonly use the MaxN method for estimating abundance. 

While MaxN was developed as a conservative estimator to avoid double-counting individual 

fishes, estimated abundance can be biased upward or downward based on aspects of the 

assemblage (Schobernd et al., 2014). For species that form patchily distributed schools, MaxN 

may overestimate abundance as the density of a school seen in a single frame is applied to the 

overall sample. For species that do not school or exist at low densities, abundance may be 

underestimated. For example, multiple distinct individuals seen singly in different frames would 

be recorded as a single individual. The MaxN method also has the potential to bias size 

estimates. As size measurements are made at or near time of MaxN, mean size for the species 

may be downwardly biased as higher numbers of small individuals can fit within the finite field 

of view of the camera. For species where smaller size classes are more gregarious, size structure 

would, likewise be downwardly biased. This is a potential explanation for the single, large 

kalekale and opakapaka that were captured during research fishing operations, but which exceed 

the BotCam length-frequency distributions for those species.

The use of bait to attract target fishes to the field of view of the camera also has the potential to 

distort the sampling volume, with fishes being drawn from beyond the field of view of the 

camera. While less of an issue for estimates of relative abundance, uncertainty in the sampling 

volume presents a significant issue when attempting to generate estimates of absolute abundance. 

However, studies have found that variance of abundance estimates within habitats is reduced 

when bait is used compared to unbaited camera deployments (Harvey et al., 2007). The reduction 

in soak time from 45 to 15 minutes should significantly reduce bait-based bias, however, 

alternative sampling and video processing methods that could reduce this bias should continue to 

be investigated. 

Data processing time remains an obstacle to the increased use of BotCam and other video-based 

methods. Translating the raw video footage into the species-specific size-structured abundance 

data required for use by stock assessors requires highly trained and skilled video analysts. The 

training period for a new analyst can easily take 6 months, and can vary among analysts, the 

number of species in question, and the complexity of the environment. Depending on the 

abundance and species diversity in a given sample, 15 minutes of stereo-video footage can take 

from 30 minutes to 12 hours to process, with an average processing time of 2.5 hours. In an 

operational context, stock assessment scientists would prefer to have numeric data within a few 

months of the end of a survey and it is unlikely that human analysis will be able to keep pace 

with data collection. Automated image analysis and video processing is an active area of 

research (Shortis et al., 2013) and NMFS has recently established a Strategic Initiative for 

Automated Image Analysis (NMFS, 2014). Development of automated methods for processing 

optical data streams are likely necessary before these data streams can regularly be used for 

routine assessments. 

Research fishing and BotCam both produce relative metrics (CPUE and MaxN) of abundance. 

However, relative metrics are not typically used in stock assessment until a significant time 
28

series has been developed. Before that time, such measures may have greater utility in the 

estimation of absolute abundance. This presents significant challenges, first of which is the issue 

of sample area, which can be affected by the duration of the survey, use of bait and by the 

behavioral interaction between the fish and the gear. Stereo-camera systems are typically 

deployed in a baited configuration. While bait is useful to attract the fish to the area immediately 

in front of the cameras, where they can be identified and measured, bait can also attract 

individuals from outside the immediate area, which is an issue for research fishing as well. The 

reduction in deployment time from 45 to 15 minutes should reduce this issue, but it is likely that 

the area of attraction still varies to some extent based on current speed, current direction, and 

benthic topography. While the identical bait mixture is used by both BotCam and research 

fishing, the use of bait may still produce varying individual and interacting behaviors among 

target species.

Catch Composition

Research fishing and BotCam were both selective for Deep-7 species with minimal bycatch, 

suggesting these gears are appropriate to include in an operational fishery-independent 

bottomfish survey. In general, there was good correspondence in species-specific length 

composition estimates between the 2 gears, especially for species with high sample sizes. The 

significant differences in length frequency distributions between the 2 gears for kalekale and 

opakapaka were likely due to the high samples sizes as the magnitude of the difference was 

minimal. Gindai and hapu’upu’u were rarely captured by either gear, suggesting that other 

methods will need to be devised to survey these species. While BotCam sample size for lehi was 

reasonable, few were captured by research fishing, suggesting that fishery-dependent methods 

may not be appropriate for sampling this species. BotCam did not record fish at sizes 

significantly smaller than those recorded by research fishing. A higher sensitivity of BotCam to 

smaller individuals was hypothesized due to hook-based size selectivity inherent to research 

fishing. The fact that smaller individuals were not observed in BotCam data suggest that 

juveniles may be utilizing different habitats or geographic regions than larger individuals, as 

other authors have reported (Moffitt and Parrish, 1996; Parrish et al., 1997). The apparent 

species-level differences in sensitivity of each gear type also suggest that surveying and 

assessing bottomfish as individual species may be more appropriate than treating bottomfish as a 

homogenous complex. Bottomfish catch in the MHI is dominated by opakapaka and onaga 

(Brodziak et al., 2014), and grouping the remaining species with these two likely does not 

accurately reflect their true abundance or how they respond to fishing pressure.

Fishing Power

The two-stage GLM approach enabled estimation of relative fishing power of research fishing 

and BotCam gears despite a high frequency of zero CPUE observations. The strategy of 

experiments SE1302 and SE1306, which entailed repeated intensive sampling by both gears in 

small space blocks (500-m grid cells) over a short time frame (10-12 days), yielded the most 

reliable data for estimating fishing power factors because time-space variation of sampling for 

bottomfish species was tightly controlled, thus enabling much clearer discrimination of 

differences in catchability between the 2 gears. 
29

For the 4 species analyzed, model-predicted estimates of CPUE were higher compared to 

research fishing. This is likely due to the different 'catchability' or 'viewing' properties of the 2

gears. BotCam samples provide a localized estimate of the density of fish for target species, 

approaching the 'true' abundance within the viewing area of the camera. In contrast, research 

fishing only captures some fraction of the total fish available within the grid cell, those that will 

take the bait. Accordingly, the model-predicted estimates of CPUE were higher for the BotCam 

gear compared to research fishing.

Transition to Operations

The primary goal of this study is to evaluate and identify an effective and efficient suite of 

fishery-independent gears for sampling the main Hawaiian Islands Deep-7 bottomfish 

assemblage for use in an operational multi-gear, fishery-independent survey. The ultimate 

purpose is to improve the data products supporting stock assessments.

Refinements to Sampling Methods

Analysis of bait preference by species for Research Fishing was conducted utilizing fishing 

samples that deployed both bait types from experiments SE1208, SE1302, and SE1306. These 

samples were filtered to only include habitat-depth grid cells in which at least one Deep-7 

species was either captured by Research Fishing or observed by BotCam. This ensured that the 

paired-bait samples occurred within Deep-7 habitats. Four Deep-7 species were analyzed, and 

the paired-bait samples were further filtered by species to exclude depths where a given species 

does not occur, i.e., deeper depths for Opakapaka and Kalekale and shallower depths for Onaga 

and Ehu. These filtering procedures had the effect of excluding fishing samples with zero or 

close to zero probability of occurrence from the analyses for the target species. A paired-sample 

t-test was used to analyze the difference in CPUE between bait types as the statistical 

observation, and evaluate whether this difference was significantly different from zero. A 

positive mean difference indicated preference for fish bait and a negative mean difference 

indicated preference for squid bait. The results are shown in Table 9. Mean CPUE was 

significantly higher for Onaga for fish bait and for Opakapaka for squid bait. Preference for 

either fish or squid bait was not observed for Ehu or Kalekale. These findings suggest that both 

squid and fish bait should be retained in operational research fishing surveys.

Table 9.--Analysis of bait preference for four Deep-7 species. Sample size n is the number of 

paired fish-squid fishing samples used in the analysis.

For BotCam, the change from a 45-minute to 15-minute soak time may have resulted in an 
30

effective sample area that was smaller than the PSU; thus, more than one camera deployment 

may be required to adequately characterize each 500 × 500 m cell. This issue was investigated 

using data from SE1302. In this experiment, a small set of PSUs of the most favorable habitat 

type for Deep-7 species, HB_H, was sampled repeatedly by both fishing and BotCam gears. 

These data were analyzed with respect to a two-stage survey design (Cochran, 1977; Smith et al.,

2011). Each BotCam drop within a given PSU was considered a second-stage unit (SSU). The 

optimum number of SSUs to sample within a given PSU, m*, was estimated from

(9)

where su is the sample standard deviation,

(10)

and s

2

1 is the sample variance among PSUs, s

2

2 is the sample variance among SSUs, and M is the 

total number of SSUs in a given PSU. For this analysis, there were n = 14 PSUs sampled with a 

total of nm = 218 drops for an average m of about 15 to 16 SSUs per PSU. Since the area of an 

SSU is unknown, a set of plausible SSU cell sizes was evaluated ranging from 50 × 50 m to 

150×150 m. Estimates of m* for 5 Deep-7 species were mostly around m* = 3 SSUs per PSU 

for each species (Table 10). The target m was rounded to the next highest integer (Cochran,

1977). Results suggest that there is some heterogeneity of variance of CPUE among BotCam 

drops within a PSU, and that conducting 3 drops per PSU would be optimal for controlling this 

variance.

Table 10.--Analysis of m*, the optimum number of BotCam drops per 500 × 500 m grid cell, for 

five Deep-7 species. Standard deviation of CPUE was estimated from eq. (2); M is the total 

possible SSUs within a PSU (500-m grid cell).
31

Gear Development

Both research fishing and BotCam are able to provide usably accurate and precise speciesspecific size-structured abundance data in a reasonable period of time. Both gears have relatively 

low equipment costs and can be deployed by small field teams using small to medium-sized 

cooperative research vessels. Based on the evaluation thus far, these gears appear adequate for 

operational use in an assessment survey.

While AUVs and EK60 show promise, significant challenges remain with their operational use 

for Hawaii bottomfish surveys. The SeaBED AUV can provide important information across 

habitat gradients or focused on particular areas of interest, however, high equipment costs, large 

field team requirements and low sample numbers, make it difficult to deploy as a survey tool 

across the operational survey domain. With technological advancement, cost reductions, and the 

ability to operate multiple AUVs simultaneously and independently from large research vessels, 

their applicability to operational surveys will likely increase.

Resources should also be devoted to the development of robust methods for accurate and precise 

analysis of stereo-video data, whether from stationary or mobile camera systems. For stationary 

camera systems, the MaxN method is well supported in the literature ((Ellis and DeMartini, 

1995; Priede et al., 1996; Willis et al., 2000; Cappo et al., 2003; Gledhill et al., 2005; Merritt et 

al., 2011; Misa et al., 2013)). However, recent papers by (Schobernd et al., 2014) suggest that 

alternative methods may better reflect the assemblage. Additional research should be devoted to 

development of robust methods to estimate size-structured abundance from stereo-video data. 

Finally, timely analysis of stereo-video data from an operational survey could quickly exceed the 

capabilities of human analysts. Automated tools to extract estimates of species-specific, sizestructured abundance from stereo-video data are sorely needed.

EK60 has the unique ability to identify targets simultaneously throughout the water column 

during both day and night operations. However, the inability to distinguish among Deep-7 

species and between bottomfish and non-bottomfish severely hampers the utility of these data. 

Additional research and development should be devoted to parsing EK60 data into accurate and 

robust species-level estimates of abundance and biomass. Further validation of EK60 data 

through simultaneous EK60 and optical data collection will improve our ability for species- or 

taxa-specific estimates of size-structured abundance. Development of broadband acoustic 

methods could also provide significant improvements. Until such time as EK60 data can be 

attributed to species or taxonomic group, they will be of limited utility. Currently at PIFSC, the 

EK60 system is operated from the NOAA Ship Oscar Elton Sette. Availability of this high-cost 

asset is increasingly limited. Further, this single asset cannot be simultaneously deployed in each 

survey zone. Portable EK60 systems exist and, if they can be operated from cooperative research 

or small contract research vessels, would be of greater utility. As with stereo-video data, 

significant resources will need to be devoted to analysis of the large volume of data from an 

operational survey.
32

Survey Domain and Sample Size Projections for the Main Hawaiian Islands

The Hawaii Department of Aquatic Resources (HDAR) divides the waters of the MHI into 82 

nearshore and coastal commercial bottomfish reporting grids (Fig. 14). Bottomfish EFH (100–

400-m depth range) within these reporting grids encompasses the logical range for the stock and 

therefore the spatial domain for an operational fishery-independent survey (Fig. 14). This area 

comprises 31,083 PSUs that, where habitat maps exist, can be stratified by both depth and 

substrate composition. Our results suggest that an effective scheme for depth 

stratification/blocking would be to divide depth into 2 intervals, the first interval < 200 m and the 

second > 200 m, resulting in the stratification given in Table 11.

Figure 14.--The main Hawaiian Islands sampling domain. An operational fishery-independent 

survey would cover the Hawaii Department of Aquatic Resources (HDAR) commercial fishery 

reporting grids and would be constrained to the 100–400-m depth range utilized by Deep-7 

bottomfish species. The area (red color) is gridded at 500-m resolution and, where possible, 

stratified by depth, substrate composition, and slope. To facilitate sampling logistics, the area is 

divided into 4 zones. Each survey gear would be simultaneously allocated to PSUs within each 

zone, utilizing cooperative research vessels. It is estimated that sampling could be accomplished 

using 4 vessels in each zone.
33

Table 11.--Number of MHI primary sampling units (PSU) by survey zone, depth, and habitat 

strata.

The 2011 stock assessment of the main Hawaiian Islands Deep-7 bottomfish complex (Brodziak 

et al., 2011) highlights 22 of these reporting areas as having an estimated average relative 

abundance higher than that of the Penguin Bank reference area (area 331) (Fig. 15). These areas 

span the MHI with the exception of the islands of Oahu and Kauai. To efficiently survey the 

stock across its range, we propose dividing the area into four zones (Niihau-Kauai, Oahu, 

Molokai-Lanai-Maui-Kahoolawe, and Big Island) (Fig. 16) and to stratify the area by depth, 

substrate composition, and slope, as outlined above.

Figure 15.--A map of the 82 nearshore and coastal HDAR commercial bottomfish reporting areas 

highlighting (red) the 22 areas where the 2011 stock assessment of the main Hawaiian Islands 

Deep-7 bottomfish complex estimates an average relative abundance higher than that of the 

Penguin Bank reference area (area 331). Areas of highest estimated average relative abundance 

are situated around the islands of Niihau and Kaula Rock as well as in the Maui Nui region.
34

The first section of this report is based on surveys conducted in Zone 3, which comprises the 

center of the MHI commercial bottomfish fishery and where comprehensive high-resolution 

bathymetric and substrate composition maps are available. Before a stratified-random sampling 

design for the full MHI can be completed, comprehensive habitat maps with standard 

classifications and data ranges must be developed. While bathymetric and therefore slope data 

exist for most areas, useful substrate composition data do not (Fig. 16). These data have been 

collected for much of zones 1, 2, and 4, but have been collected using a wide variety of sensors 

and using a variety of scale ranges, such that similar numeric values from disparate areas do not 

signify similar levels of substrate composition (Chris Kelley, pers. comm.). The lack of a usable 

synthesis product across all zones is a major impediment to the development of a properly 

stratified operational fishery-independent survey for MHI bottomfish.

Figure 16.--A map of the 4 proposed research zones for an operational multi-gear fisheryindependent survey of Deep-7 Bottomfish in the main Hawaiian Islands. Bathymetric GIS layers 

(blue gradient) exist for the full sampling domain. Substrate composition (red, pink, tan, and 

yellow in the above map) is currently available only for zone 3. Incorporation of existing 

substrate composition information into an expanded synthesis layer that covers the full sampling 

domain is necessary for a properly stratified experimental design for a domain-wide operational 

survey.
35

Cooperative research or contract vessels are the most efficient means for deploying research 

fishing and BotCam operations. Data for ehu, which prefers fish bait and deeper water (> 200 

m), and opakapaka, which prefers squid bait and shallower water < 200 m) were used to generate 

power curves to determine initial sampling effort for an operational survey. The required sample 

size n*, i.e., number of primary sample units, to achieve a specified variance of CPUE in a future 

survey, was estimated by

(11)

where wh is the stratum weighting factor, i.e., the proportion of total sample units N in stratum h, 

sh is the stratum sample standard deviation, and V is the desired variance of CPUE. The desired 

variance was estimated using a target CV for mean CPUE for the stratified domain (Ault et al.,

1999). Values of wh were determined from the GIS sampling grid for the main Hawaiian Islands, 

and estimates of sh were obtained from research studies conducted in the Maui Nui region.

The projected number of PSUs necessary to obtain a 15% CV of mean CPUE was 245 for ehu 

and 504 for opakapaka (Fig. 17) . The trio of research fishing vessels used in field operations todate typically sample 18 PSUs per day. BotCam has typically completed 6 PSUs per day (3 

replicates in each PSU). The combined total of around 24 PSUs per day, suggests that a 285 

target PSUs would require approximately 10 days of sampling, 504 PSUs would require 21 days. 

Taking into account the greater spacing of PSUs in an MHI operational survey and the associated 

increased in transit time between sites, we suggest the use of 4 or more cooperative research 

vessels and a sampling window of 21 days.

Figure 17.--Sampling power curve for ehu and opakapaka based on strata-specific variance 

estimates from the Maui Nui region and extrapolation of Maui Nui habitat relative abundance to 

other MHI zones. Approximately 245 and 504 samples are required to achieve 15% CV for ehu 

and opakapaka, respectively.
36

These initial recommendations are based on extrapolations of habitat-class prevalence from the 

Maui Nui area species-specific variance structure within each habitat class observed in Maui Nui 

to the rest of the MHI. To make the estimates more robust, synthesized substrate composition 

maps must be produced for the entire survey domain and samples must be collected in each of 

the strata within zones 1, 2, and 4, to ensure that variance estimates within strata are consistent 

across the MHI or, if not, that appropriate zone-and-strata-specific variance estimates can be 

used to guide sampling. The multibeam backscatter data needed to construct a synthesized 

substrate composition map for the MHI have already been collected and the resources necessary 

to create the synthesis product are available (Chris Kelley, pers. comm.). At this point, all that is 

required is funding to convert the existing data products to a range scale common across the 

MHI. This work could be completed in approximately 3 months.

Mission SE1402 conducted a pilot survey for full MHI-wide sampling. Gears were allocated in a 

stratified-random design to PSUs across the full Maui-Nui study area (Table 1, Fig. 4) to allow 

for the development of initial overall abundance indicators based on strata-specific estimates.

Relative Costs of BotCam and Research Fishing Gears

We compared costs to field each survey gear based on a cost per sample calculation. Cost per 

sample was defined as the total funding allocation required to mobilize, field, and demobilize the 

gear as well as costs to annotate or transcribe the data to the point where it was ready for 

analysis, divided by the number of samples that were collected during a standard 10-day field 

mission. This includes all equipment, vessel and personnel costs. For BotCam, we calculated 

cost per sample based on the number of deployments that could be conducted using both 45 and 

15-min soak times. Relative costs of BotCam soak-time options were analyzed by comparing 

the number of samples per day that had been collected using a 45-min soak time during survey 

missions SE1102, SE1107, and SE1208 to the number of samples collected per day using a 15-

min soak time during missions SE1302, SE1306 and SE1402. In our evaluation we also included 

the cost in human hours to process the video data from 45-min vs. 15-min deployments 

(calculate MaxN and measure individuals).

Based on in-the-field trials, reducing soak time for BotCam from 45 minutes to 15 minutes 

resulted in a 47% increase in the number of deployments that could be accomplished each day 

and a 37% reduction in cost per sample. Cost per sample from a 15-min BotCam soak time was 

46% higher compared to that from 30-min research fishing surveys.

Seasonality

Little is known regarding the spawning behavior and meso-scale movement patterns of 

bottomfish in the main Hawaiian Islands (Western Pacific Regional Fishery Management 

Council, 2012). Several studies, mainly from the 1980s, have investigated life history and 

spawning seasonality of deepslope bottomfish species in Hawaii (Table 12). Many of the Deep-7 

species exhibit similar life history traits (Andrews et al., 2014). For the majority of species, 

spawning appears to occur in the summer, with the exception of hapu’upu’u, which appears to 

spawn in the early spring (Andrews et al., 2014). While Andrews et al. cite a Western Pacific 

Regional Fishery Management Council report (2012) and state that opakapaka appear to have a 
37

slightly longer (10-month) spawning period compared with the 6-month period common to 

onaga and hapu’upu’u, others believe these spawning periods to be overstated (DeMartini, 2014, 

pers. comm.). Uchimaya and Tagami (1984) provide a detailed discussion of bottomfish 

spawning in the Northwestern Hawaiian Islands. Spawning season was determined by the 

seasonal distribution of mean gonadal somatic indices (GSI). Using this method, these authors 

determined spawning seasons for opakapaka peaked in August with ripe ovaries collected from 

June through December. Onaga spawning peaked in August and September. Ehu spawned from 

May through October, with a peak from July to September. Kalekale peaked in August and 

September, with ripe ovaries collected from June through September. Gindai with ripe ovaries 

were collected only in August. Hapu’upu’u were anomalous, with individuals with ripe ovaries 

being collected in January and February. Everson (1989) also used GSI to determine that female 

onaga began maturing in June but that fully ripe ovaries were not found until July, with a 

spawning peak in October. GSI values dropped sharply in November and the incidence of 

completely or partially spawned individuals increased abruptly.

Unfortunately, literature on seasonal movement patterns appears sparse, with the majority of 

movement studies focusing on movement with respect to marine protected areas or between 

islands. Weng (2013) used acoustic telemetry to track the movement of opakapaka, onaga, and 

ehu on the eastern side of Ni’ihau and documented occasional movement of individuals across 

the boundaries of bottomfish restricted fishing areas. Importantly, movements beyond the limits 

of the detection network could not be recorded, so it is not known how far individuals may have 

ranged outside the small study area. Preliminary tag-recapture results from Okomoto (1993) 

indicate limited movement of opakapaka, primarily within 3 miles of the release site. Two 

conventional tagging studies described by Weng (2013) and O’Malley (2015), which suffer from 

limited recapture rates, show that the majority of medium size individuals were recovered near 

the tagging locations, with some inter-island movement. Opakapaka, in particular, did not exhibit 

regular large-scale horizontal movement, with only two fish recaptured more than 30 km from 

location of release (O’Malley, 2015). Overall, of the 81 opakapaka that were recaptured, the 

majority (53%) moved less than 1 km, 33% moved 1-5 km, 5% moved 5-10 km, 4% moved 10-

20 km, and 6% moved > 20 km. A single opakapaka traveled from Penguin Bank off Molokai to 

Makapu’u Ledge, Oahu. However, the results of these studies should be taken with caution due 

to the paucity and limited size range of recaptures. Due to paucity of recaptures, analyses of 

movement seasonality could not be completed.

Haight et al. (1993) discuss one anecdotal observation of opakapaka spawning behavior in the 

Northwestern Hawaiian Islands in mid-April: “A commercial fisherman using a chromoscope 

depth sounder observed an opakapaka aggregation at about 150 m become very dense during the 

mid-morning hours. Catch rates decreased at this time, and egg masses were observed adhering 

to the fishing gear. By mid-afternoon the fish school became less compacted and catch rates 

increased. Opakapaka caught during this time were in spawning condition, some fe-males 

released eggs and males released milt. Free-floating eggs were noted covering a large surface 

area around the vessel.” 
38

Table 12.--Spawning months for Hawaii Deep-7 Bottomfish species.1

1Kikkawa 1980, 2Ralston 1981, 3Kikkawa 1984, 4Uchimaya and Tagami 1984, 5Everson 1984, 6Everson et al. 1989, 

7Haight et al. 1993, 8DeMartini et al. in press.

The peak of the spawning season for the majority of the Deep-7 species appears to occur in 

August and September, with few exhibiting any spawning behavior from January through March. 

In order to provide a representative estimate of the spatio-temporal distribution of the stock, 

including spawning, recruitment and migration, operational survey efforts should be timed to 

avoid periods when individuals are in transition, e.g. moving to or from spawning areas. 

Biannual surveys, which may be more practicable, but which may miss some of the more 

detailed seasonality in the stock, should therefore be conducted in the January–March period to 

capture the non-spawning period and again in August or September to capture the spawning 

period. The limited information currently available suggests that Deep-7 bottomfish do not 

exhibit regular large-scale horizontal movement patterns and there is little, if any, information 

available on whether seasonal and/or spawning-related movements occur. In the absence of 

compelling information, specific timing of a fishery-independent survey may not be able to 

consider biological movement patterns. If, however, the anecdotal report in Haigh et al. (1993), 

that species aggregate to spawn and that catch rate briefly decrease during actual spawning 

events, is correct and common, timing of survey effort could be important. Abundance estimates 

would be inflated by a high number of aggregations in the survey effort and conversely deflated 

by low numbers. Abundance estimates from fishing surveys would be deflated if catch rates 

decrease during spawning. More research on spawning seasonality, movement, and behavior is 

needed to provide better clarity in these areas, however, it may be advisable to avoid the August 

peak spawning period.

Data for Stock Assessment

The proposed multi-gear fishery-independent survey will provide essential information for the 

assessment of insular fishery resources in the main Hawaiian Islands, especially the Deep-7 

bottomfish complex. There are 3 primary types of information that will be collected. First, the 

survey will provide information on the life history parameters of bottomfishes and other fishery 

resources through the direct collection of samples from the research fishing. Fish will be sampled 

for length, weight, age, maturity stage, and fecundity using a probability-based sampling scheme 

to characterize the life history parameters of botttomfishes. This information will be analyzed by 

the PIFSC as well as collaborating universities. These analyses will provide a reliable time series 

of information on bottomfish which will be fill a severe data gap in life history parameters that 

has been noted as an impediment to conducting structured stock assessments in the region 

(PIFSC, 2014) and measuring the potential productivity of fishery resources.

 

1 BOLD CAPS denotes peak spawning months.
39

The primary information produced by the proposed, multi-gear fishery-independent survey will 

be size-structured indices of abundance for bottomfishes and other fishery resources. The survey 

biomass indices produced from the expansion of stratified density estimates to the survey domain 

will give an index of absolute biomass of the bottomfish complex. This index will be used as a 

measure of abundance for individual species and the bottomfish complex as data to be predicted 

in either an integrated size-structured stock assessment model for individual species (Methot and 

Wentzel, 2013) or as a biomass dynamics-based stock assessment model for the Deep-7 complex 

(Brodziak et al., 2011). In particular, the survey biomass index will provide a measure of the 

scale of exploitable biomass, or biomass selected by the standardized fishing gear. This index 

will be assumed to be measured with observation error in the modeling process and will be 

formally treated as an input data point for fitting assessment model parameters. Prior 

distributions will be also developed for the survey catchability coefficients of individual species 

and the multi-species complex.

The proposed survey will also provide quantitative information on the state of the insular 

ecosystem of the main Hawaiian Islands, including biotic and abiotic processes. Survey results 

will be interpolated to produce maps of the spatial distribution of fishery resources. These maps 

can be used for marine spatial planning, including evaluation of the effectiveness of bottomfish 

restricted fishing areas as well as impacts of coastal development and offshore energy projects.

With appropriate synchronous sampling, the survey could also provide fine-scale time series of 

data to describe regional physical oceanography and productivity, including sea surface 

temperatures and plankton densities. This data could be shared with other research efforts to 

foster the implementation of an ecosystem-based approach to managing ocean resources in the 

main Hawaiian Islands (Brodziak and Link, 2002).

ACKNOWLEDGEMENTS

The authors would like to greatly acknowledge the efforts of the officers and crew of the NOAA 

Ship Oscar Elton Sette, which served as a base of operations for these studies. Clay Tam, Layne 

Nakagawa, Eddie Ebisui, Kevin Awa, Scott Eguchi, Gary Dill, Bob Moffitt, Brealand Tam, and 

Basil and Sally Oshiro spent countless hours on the water supporting research fishing operations. 

Christopher Demarke, Jason Friedman, Matt Waterhouse, Bo Alexander, and Jamie Barlow were 

tireless in their deployment and recovery of the BotCam systems. Captains Ryan Wagner and 

Allan Armstrong kept them safe and on station while piloting the RV Huki Pono during BotCam 

deployments. John Rooney, Erica Fruh, Curt Whitmire, and Clay Kunz spent countless hours 

keeping the SeaBED AUV alive and well throughout the duration. The NOAA Pacific Islands 

Fisheries Science Center, the Joint Institute for Marine and Atmospheric Research at the 

University of Hawaii at Manoa, the NOAA Fisheries Office of Science and Technology, the 

NOAA Fisheries Advanced Sampling Technology Working Group provided funding and support 

for this project. The use of trade, firm, or corporation names in this publication is for the 

convenience of the reader and does not constitute an official endorsement or approval of any 

product or service to the exclusion of others that may be suitable.
40
41

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48

APPENDIX A— SeaBED AUV

The SeaBED Autonomous Underwater Vehicle (AUV) (Fig. 13) was designed and built by 

Woods Hole Oceanographic Institution (WHOI) as an autonomous, stable platform for slowspeed near-bottom surveys (Singh et al., 2004; Armstrong et al., 2006; Rivero-Calle et al., 2008;

Williams et al., 2010). The capabilities of SeaBED have continued to increase such that it is now 

being used for species and habitat specific sampling of insular bottomfish and coral reef fish 

assemblages in the Pacific Islands Region as well as for routine sampling of groundfish stocks 

and their habitat along the U.S. west coast (Tolimieri et al., 2008; Clarke et al., 2009). SeaBED is 

approximately 2 m long and weighs nearly 200 kg. Unlike other more traditional AUVs, 

SeaBED is designed to autonomously follow the benthic terrain at a close, fixed altitude 

(typically 3–10 m) above the sea floor, providing a stable low-speed (0.5 kn) platform for the 

collection of high resolution optical data. The primary imaging system used in this study is a pair 

of forward-facing ultra low light Monochrome Navigator (Remote Ocean Systems, San Diego, 

CA) cameras, identical to that used on BotCam.

Figure 13.--The SeaBED AUV being recovered after a mission (left). Note the forward-facing 

stereo-video camera system mounted to the forward strut and indicated by the red arrows. The 

SeaBED AUV during operations along the south coast of Oahu, HI (right).

For this study, SeaBed generally completed 2 surveys per day, completing two 200-m transect

lines within each of 2, adjacent PSUs during each survey. To date, the SeaBed has completed 37 

surveys of which 26 recorded usable video data. However, analysis of the resultant stereo-video 

data has not yet been completed due to a lack of sufficient human video analysts and the need to 

develop robust methods for analyzing this transect style of stereo-video data that accurately 
49

reflects bottomfish abundance. An initial investigation of a portion of the video data indicates 

that Deep-7 and other bottomfish species are observed. Cross-referencing the video time stamp 

of each fish observation with the time-stamped coordinate data from the AUV can be used to 

create maps of species distribution across the survey domain. To date, this has been done as a 

proof of concept using 3 AUV surveys conducted south of Maui in 2011 during mission SE1107 

(Fig. 14).

Figure 14.--A map showing fish observations from forward-looking stereo-video data collected 

during SeaBED AUV surveys. High resolution time and position for the AUV can be correlated 

with the time an individual fish was observed in the video data to calculate the position of each 

fish seen. Green circles denote a fish sighting while the diameter represents the number of 

individuals observed. While genus and species level observations are possible, do to a greater 

range from the camera, this level of classification is more difficult than with the BotCam system.

However, at this time, resources are not available to complete analysis of the full AUV video 

data set. Before analysis can continue, robust methods must be developed to ensure that 

abundance estimates derived from the AUV data are precise and accurate. Steps must be taken to 

ensure that fish following along with the AUV, such as Seriola dumerili as observed in our 

surveys, are not over-represented and further studies should be carried out to quantify behavioral 

interactions (i.e. avoidance or attraction) between the AUV and resident fish assemblages.

The stereo-video camera system on the SeaBED AUV is identical to that used in the BotCam and 

should have the same accuracy and precision when it comes to identifying and sizing Deep-7 

individuals. SeaBED also has a high degree of positional precision and accuracy and can also be 
50

used to survey across habitat gradients or to make repeated surveys over a feature of particular 

interest. While the SeaBED AUV performed well, resources were not available to analyze the 

video data streams and robust analysis methods for this style of transect video for bottomfish 

species have yet to be identified. While the MaxN method is appropriate for point surveys and 

ensures that individuals are not counted repeatedly, MaxN is likely not appropriate for transect 

surveys. Measures need to be adopted to minimize replicate counting as some species—

specifically Seriola dumerilii—have been seen following or leading the AUV during surveys. 

While the forward motion of the AUV is slow and the AUV is not baited, is it still a large object 

moving through the water column and may attract or repel fishes. Behavioral reactions likely 

vary by species and need to be quantified. A study is currently underway in the Gulf of Mexico 

to quantify behavioral interactions between target species and survey gears including this AUV. 

The results of that study will have direct implications for our work.

While the SeaBED AUV can vary its depth during a survey, like research fishing and BotCam, it 

can sample only a single depth at a time. Hence, it is possible to miss fish that happen to be at a 

different depth, resulting in false negatives. SeaBED also has a high equipment cost and requires 

a team of 4 to 6 personnel to operate. This reduces the likelihood that multiple units will be 

fielded simultaneously. While the AUV can cover a large swath of sea floor during a single 

survey, the deployment, recovery, and servicing time required for each survey means that only 1

or 2 surveys covering 2–4 independent PSUs are possible per day.
51

APPENDIX B— EK60 Active Acoustics

EK60 data were collected using a Kongsberg Maritime AS Simrad EK60 echosounder system 

(Horten, Norway) on board the NOAA Ship Oscar Elton Sette. The Simrad split-beam, narrowband transducers with 7̊beam angles are mounted on a “transducer pod” underneath the hull of 

the ship and operate at 38, 70, and 120 kHz frequencies, facing vertically down. The depth of all 

transducers were approximately 5 m below surface, although the 70 kHz is positioned about 14 

cm deeper than the 38 kHz transducer as the surface of the pod is slightly sloped aft relative to 

the horizontal. The exact depth of the transducers, however, depends on a variety of factors, such 

as ballast configuration, roll, and pitch, thus continuously changed throughout the surveys. 

Before each survey, the EK60 system was calibrated using a 38.1-mm diameter tungsten carbide 

sphere with 6% cobalt by the standard method (Foote et al., 1987). During EK60 data collection, 

all other sounders and activities that interfered with the bioacoustics signals were secured (e.g., 

fathometers, acoustic Doppler current profiler, needlegunning). Survey speed was approximately 

4 kn.

Initially, data were collected using uniform 1,024 μs pulse-widths with 2, 1, and 0.5 kW power 

for the 38, 70, and 120 kHz channels, respectively. These settings were selected to provide 

maximum penetration achievable for the three frequencies and still stay within the recommended 

limits for the channels. However, during a software upgrade in 2013 from ER60-V2.2.1 to ER60-

V2.4.2, the power settings originally selected were not available anymore and were changed to 

the new maximum settings of 2, 0.75, and 0.25 kW for the 38, 70, and 120 kHz frequencies, 

respectively. For all data collection, ping rate was set to maximum, resulting in approximately a 

pulse emitted at every 0.65 seconds. As initial data analyses proved that data from the 70 kHz 

frequency could be utilized best for separation of fish with gas-bladder from other organisms 

based on their acoustic properties, and this channel provided data down to over 700 m in depth, 

pulse-widths were shortened to 512 μs to double the vertical resolution during data acquisition. 

With these settings, the depth ranges of the 38, 70, and 120 kHz frequencies were approximately 

> 1,500 m, 600 m, and 250 m while the vertical resolution were kept at about 38 cm, depending 

on small variability in the speed of sound.

To obtain an equation representing the relationship of acoustic target strength (TS) to FL, 

simultaneous EK60 and optical observations (using baited BotCam, AUV, and ROV) and 

simultaneous EK60 and fishing operations were conducted. Length and species information 

obtained from the optical observations were compared to the TS readings and other acoustic 

descriptors such as aggregation density, size, shape, movement patterns, depth, distance from 

bottom, and bottom roughness. However, data from the AUV and ROV were not available at the 

time of data processing and analyzes, thus only video data from the BotCam could be used. The

intermediate goal was to identify the Deep-7 as a group and separate them acoustically from all 

other species. Unfortunately, only one TS-FL pair could be obtained from BoCam with an 

additional 5 from fishing, so the general physoclist equation was used as first approximation 

(Foote, 1987).
52

Over the course of this study, three types of EK60 surveys were conducted. In 2011 and 2012, 

parallel transects were executed across all PSUs within a survey box (approx. 65 km total) (Fig. 

2.a). These surveys were repeated at least every other day and night over the course of the survey 

mission to assess variability in the fish community. In 2013, when sampling methodology 

changed from stratification within survey boxes (SE1302, Fig. 2.a, Table 1), single surveys were 

conducted across 3-5 adjacent PSUs. During mission SE1306 (Table 1), six EK60 surveys (3 

north-south, 3 east-west) were conducted within each PSU. Single-pass surveys were designed to 

achieve maximum coverage across the survey area and to quickly quantify the acoustic field 

within each PSU. After the first survey year, nighttime surveys were discontinued as daytime 

surveys detected significantly higher biomass and as all other gears were restricted to daytime 

operation. High variability between surveys necessitated the implementation of multi-pass 

surveys to determine the scale of temporal variability and determine the optimal number of 

repeat passes needed to adequately survey each PSU.

Initial data processing of the EK60 signals was performed using Myriax EchoviewTM software 

(Hobart, Tasmania). While all three frequencies were used in separating fish from smaller 

organisms, 38 kHz data and 70 kHz data were used for quantitative backscatter processing and 

analyses. During the earlier surveys, unavailability of an appropriately calibrated 70 kHz 

transducer required the use of the 38 kHz as the signal to noise ratio at 120 kHz was too low at 

250–400 m depths, the deepest regions of bottomfish habitat. Prior to processing, noisy pings 

and bubble dropouts were removed and echograms were visually inspected to ensure high quality 

of data. The analysis domain was limited to 10 m below surface to the seafloor. However, EK60 

cannot detect organisms effectively in close proximity of the bottom in rugose environments, 

creating an acoustics “deadzone”. The depth of the deadzone depends on instrument settings, 

environment, and bottom topography with high rugosity. Seafloor depth was detected using 

Echoview’s “best bottom candidate” line pick algorithm and manually adjusted to exclude all 

data due to bottom topography. Data were thresholded to only contain larger fish (approximately 

> 25 cm FL) , then regions with EK60 backscatter interpreted as bottomfish were classified as 

“bottomfish schools” and “bottomfish tracks” using Echoview’s School Detection and Fish 

Tracking algorithms. Regions were selected using parameters developed from the simultaneous 

EK60 and complementary observations to specify parameters for these algorithms (Domokos, in 

prep). Regions of presumed bottomfish schools were identified from echograms of volume 

backscattering strength (Sv) thresholded to - 62 dB (dB re 1 m-1) and put through a 3 × 3 pixel 

median then a 3×3 pixel dilution filter to help better define boundaries of fish schools. These 

regions were masked out of single target echograms to reduce erroneous single targets due to 

densely aggregated fish. Individual fish from loose aggregations or “strays” were identified from 

these single target echograms, thresholded to -42 dB (dB re 1 m2).

TS associated with bottomfish were also exported from Echoview to estimate mean FL and 

weight of fish in the PSU using the general pysoclist TS-FL relation and mean Hawaii snapper 

values given by Froese and Pauly (2009). Once these values were obtained, they were used to 

estimate bottomfish densities within Echoview. EK60 backscatter from regions of presumed 

bottomfish, in the form of Sv, were integrated over 50 m horizontal bins over the water column 

and the resulting area backscattering coefficients (sa in m2m-2) exported from Echoview. All 

subsequent data processing and analyses were carried out using MathWorks Matlab software 

(Natick, Massachusetts).
53

Bottomfish abundance was estimated by averaging Sa from each bin to arrive at a single value 

for an SSU. Values from bins affected by bottom topography were weighed to compensate for 

the reduction in area. Mean number of fish per m2 within a grid cell was estimated by 

Sa

σbs

(Simmonds and MacLennan, 2005) where Sais the mean sa from the ESUs within the cell 

and σbsis the mean backscattering cross section (in units of m2) from the ESUs. To obtain an 

estimate of σbs, mean TS from all bottomfish tracks were averaged in the linear domain and used 

in the equation σbs = 10TS/10(Simmonds and MacLennan, 2005). The mean number of fish per 

m2for each PSU were multiplied by the area of PSUs (500 m2) to obtain the estimated 

bottomfish abundance for that cell. Abundance estimates for m2from the Sa values were 

converted to weight using the mean lengths from TS and by the method described above (Froese 

and Pauly, 2009).

The hull mounted EK60 acoustic system was able to quickly provide a large amount of data on 

fish targets throughout the majority of the water column, over large spatial areas both day and 

night. However, presently the EK60 has low selectivity and the difficulty in distinguishing 

bottomfish from non-bottomfish species and in separating Deep-7 species from one another limit 

the utility of these data. More EK60 and simultaneous observations, especially from AUV 

cameras that seemed to be most cost-effective way to obtain information, will be necessary to 

improve current EK60 descriptors. Our current understanding of the echograms with focus on 

Deep-7 species is illustrated in Figure 15. While the information was obtained from simultaneous 

EK60 and other methods, images are selected to best illustrate the types of specific aggregations 

and loosely swimming fish echoes and are not necessarily from simultaneous observations.
54

Figure 15.--Examples of bottomfish echograms at different depth ranges. Top left: near-surface 

and mid-water aggregations of Decapterus spp. in the evening; Top middle: fish aggregations at 

around 10-m depth, likely small Pristipomoides filamentosus, and/or Lutjanus kasmira, tightly 

aggregated on the bottom, with possibly some Mulloidichthys vanicolensis and/or 

Pristipomoides sieboldii intermixed. Above this aggregation are loosely spaced larger predatory 

P. filamentosus, Aphareus rutilans, and/or Seriola dumerili, and further up about 20 m off the 

bottom a tight aggregation of fish, possible Decapterus spp.; Top right: loose aggregations of 

Etelis carbunculus or maybe E. coruscans near the bottom, with a tight aggregation to their right, 

likely Antigonia spp.; Bottom: thick aggregations on the bottom are P. sieboldii, small P. 

filamentosus, L. kasmira, and possibly Naso unicornis, while loosely aggregated individuals 

extending over 50 m off the bottom are likely P. filamentosus, E. carbunculus, and/or S. 

dumerili. In this figure, species determination is based on 

The use of EK60 for estimating biomass of demersal and semi-demersal fishes in other nontrawlable habitats is well established (Stanley et. al., 2000; Jones et. al., 2012; Ressler et. al., 

2009; Rooper et. al., 2010; Rooper et. al., 2012). The EK60 has advantages over other gears as it

is able to sample the majority of the water column simultaneously, avoiding the issue of the fish 

and the gear being in the same space horizontally, but in different locations vertically. EK60 can 

also sample the 100–400 m depth range utilized by Deep-7 bottomfish both day and night, 

without the artificial light that would currently be needed by the BotCam and AUV, and which 

would likely result in unknown behavioral interactions between the sampling gear and their 

targets. Surveying at 4 knots, EK60 methods can also cover a large spatial area in a short period 

of time, covering a larger portion of the geographic range of the stock than is possible with other 

methods in the same unit time. EK60 surveys can also provide simultaneous data on the 

distribution of fish as well as interactions between fish and their forage and can provide data at 

scales appropriate for effective ecosystem based management (Godø et al., 2014).
55

However, as currently implemented, the process of calibrating the EK60 transducers is time

consuming. The current 9- to 36-hour calibration is untenable in an operational context. In 

addition, choppy waters due to commonly strong winds in the MHI can cause bubble dropout 

and cavitation noise, significantly reducing data quality. This is exacerbated by the position a of 

the EK60 transducers on the NOAA Ship Oscar Elton Sette, causing bubble sweep-down across 

the transducer faces.

The problem of estimating size distribution and species composition of acoustically observed 

targets has yet to be solved (McClatchie et al., 2000). This is a significant problem in the MHI 

region, where the Deep-7 assemblage often forms mixed schools both in terms of species and 

size class. The use of simultaneous EK60 data collection with video observations and fishing 

was used to develop EK60 descriptors for species or groups of species. However, due to limited 

resources, these data were not available at the time of data analyses, limiting the capability of 

EK60 to separate Deep-7 from other fish. This type of ground-truthing also does not adequately 

account for species observed acoustically, but not in optical or fishing surveys (false negative) 

and more robust methods for ground-truth should be developed. Our current inability to resolve 

species makes it difficult to quantitatively compare EK60 data to the highly selective speciesspecific, size-structured data produced by the other gear types. A distribution-specific (horizontal 

and vertical) species-size allocation derived from temporally similar optical and fishing methods 

for each PSU could be applied to EK60 data to scale the acoustically derived abundances to 

obtain more species specific information.

EK60 methods are also not able to sample the acoustic “deadzone” that exists for a certain 

distance above the bottom (Godø and Wespestad, 1993; Ona and Mitson, 1996). The height of 

this deadzone is determined at least in part by seafloor complexity, which is high in Hawaii and 

which is the preferred habitat of many of the Deep-7 species (Kelley et al., 2006; Misa et al., 

2013). High spatial variability in seafloor complexity would require complex spatially varying 

models for this deadzone, which currently do not exist. Adjusting for bias due to the deadzone 

would require information on the proportion of species, individuals, and size classes resident 

within the deadzone compared to areas outside the deadzone. This information is also not 

currently available. In BotCam video footage, many the Deep-7 species are seen in close 

proximity (0-3 m) to the bottom and it is likely that hapu’upu’u, gindai, and ehu rarely leave the 

acoustic deadzone. The significance of the deadzone and the species-specific variation in its 

significance makes it more difficult to proportionally allocate the EK60 data by species using 

data from the other gear types.

At present, EK60 methods remain promising, especially if current efforts to include broadband 

acoustics move forward. Benoit-Bird et al. (2003) and Au and Benoit-Bird (2003) show that 

Deep-7 species do have species-specific signatures in broadband acoustic returns due to 

differences in gas bladder characteristics. Hence, the use of broadband acoustics should improve 

our ability to discriminate Deep-7 species from one another. Presently, EK60 may best be used 

to map the 3-dimensional spatiotemporal distribution of fish in the MHI, which can then be used 

to target deployment of more selective survey gears.
56

Availability of NOAA Technical Memorandum NMFS 

Copies of this and other documents in the NOAA Technical Memorandum NMFS series issued 

by the Pacific Islands Fisheries Science Center are available online at the PIFSC Web site 

http://www.pifsc.noaa.gov in PDF format. In addition, this series and a wide range of other 

NOAA documents are available in various formats from the National Technical Information 

Service, 5285 Port Royal Road, Springfield, VA 22161, U.S.A. [Tel: (703)-605-6000]; URL: 

http://www.ntis.gov. A fee may be charged. 

Recent issues of NOAA Technical Memorandum NMFS–PIFSC are listed below: 

NOAA-TM-NMFS-PIFSC-50 Injury determinations for marine mammals observed interacting

with Hawaii and American Samoa longline fisheries during 

2009-2013. 

A. L. BRADFORD and K. A. Forney

(March 2016)

51 Stock assessment updates of the bottomfish management unit 

species of American Samoa, the Commonwealth of the Northern 

Mariana Islands, and Guam in 2015 using Data from 2013.

A. YAU, M. NADON, B. RICHARDS, J. BRODZIAK, and E.

FLETCHER

(March 2016)

52 Status Review Report: Orange Clownfish (Amphiprion percula)

K. MAISON and K. GRAHAM

(April 2016)