OCS Study

BOEM 2020-002

NOAA NCCOS 270

US Department of the Interior

Bureau of Ocean Energy Management

Headquarters (Sterling, VA)

Regional Essential Fish Habitat 

Geospatial Assessment and Framework 

for Offshore Sand Features

Volume 3: Predicting the Distribution of Select Fish 

Species of the Gulf of Mexico, South Atlantic, and

Greater Atlantic
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OCS Study

BOEM 2020-002

NOAA NCCOS 270

US Department of the Interior

Bureau of Ocean Energy Management

Headquarters (Sterling, VA)

Regional Essential Fish Habitat 

Geospatial Assessment and Framework 

for Offshore Sand Features

Volume 3: Predicting the Distribution of Select Fish 

Species of the Gulf of Mexico, South Atlantic, and 

Greater Atlantic

January 2020

Authors:

Bradley A. Pickens1,2

J. Christopher Taylor1

Prepared under IA M17PG00028

By

1

NOAA, National Ocean Service, National Centers for Coastal Ocean Science, Biogeography Branch, 

Beaufort Laboratory, Beaufort, North Carolina, USA 28516

and

2

CSS-Inc., 10301 Democracy Lane, Suite 300, Fairfax, Virginia 22030, USA; Under NOAA/NCCOS 

Contract #GS-00F-217CA
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DISCLAIMER

This study was funded, in part, by the US Department of the Interior, Bureau of Ocean Energy 

Management (BOEM), Environmental Studies Program, Washington, DC, through Interagency 

Agreement Number IA M17PG00028 with the National Oceanic and Atmospheric Administration

(NOAA). This report has been technically reviewed by BOEM, and it has been approved for publication. 

The views and conclusions contained in this document are those of the authors and should not be 

interpreted as representing the opinions or policies of the US Government, nor does mention of trade 

names or commercial products constitute endorsement or recommendation for use.

REPORT AVAILABILITY

To download a PDF file of this report, go to the US Department of the Interior, Bureau of Ocean Energy 

Management Data and Information Systems webpage (http://www.boem.gov/Environmental-StudiesEnvData/), click on the link for the Environmental Studies Program Information System (ESPIS), and 

search on 2020-002. The report is also available at the National Technical Reports Library at 

https://ntrl.ntis.gov/NTRL/.

CITATION

Pickens BA, Taylor JC. 2020. Volume 3: Predicting the Distribution of Select Fish Species of the Gulf of 

Mexico, South Atlantic, and Greater Atlantic. In: Pickens, BA, Taylor JC (eds.). Regional Essential Fish 

Habitat Geospatial Assessment and Framework for Offshore Sand Features. Sterling (VA): US 

Department of the Interior, Bureau of Ocean Energy Management. OCS Study BOEM 2020-002, NOAA 

NCCOS Technical Memorandum 270. https://doi.org/10.25923/akzd-8556 67 pp.

ACKNOWLEDGMENTS

NOAA and BOEM funded this work. Matthew Poti of the NOAA Biogeography Branch shared models of 

sediment grain size, probability of hard bottom, and provided a synthesis of hard bottom points that were 

valuable in our analyses. Dan Obenour and Rohith Matli of North Carolina State University provided data 

on probability of hypoxia in the Gulf of Mexico. We are especially thankful for the data collection efforts, 

data synthesis, ideas, feedback, and interpretation provided by several biologists that were key to our 

study. William 'Trey' Driggers (Southeast Fisheries Science Center, Mississippi Laboratory) was 

invaluable regarding shark species in the Gulf of Mexico and South Atlantic. Matthew Campbell 

(Southeast Fisheries Science Center, Mississippi Laboratory) was instrumental for work with snappers in 

the Gulf of Mexico. Nathan Bacheler (Southeast Fisheries Science Center, Beaufort Laboratory) was 

instrumental to our efforts in the South Atlantic. Bryan Frazier of the South Carolina Department of 

Natural Resources provided guidance and expertise on SEAMAP bottom longline surveys. For the 

Greater Atlantic region, Kevin Friedland (National Marine Fisheries Service, Narragansett, RI) 

generously shared the modeling efforts and products he has led on demersal species. Our work would not 

have been possible without these valuable contributions. We thank the BOEM staff as well as Arliss 

Winship and Matthew Poti (CSS-Inc. and Affiliates of the NOAA Biogeography Branch) for their review 

of an earlier draft of the report. B.A. Pickens was supported by CSS-Inc. under NOAA/NCCOS Contract 

#GS-00F-217CA.
i

Table of Contents

List of Figures................................................................................................................................................iii

List of Tables................................................................................................................................................ iv

List of Abbreviations and Acronyms............................................................................................................. vi

Abstract .......................................................................................................................................................viii

1 Predicting the Marine Distribution of Three Penaeid Shrimp Species in the Northern Gulf of Mexico.1

1.1 Introduction.................................................................................................................................... 1

1.2 Methods......................................................................................................................................... 2

1.2.1 Study Area............................................................................................................................. 2

1.2.2 Biological Data ...................................................................................................................... 3

1.2.3 Geographic Information System (GIS) Methods and Environmental Data ...........................3

1.2.4 Statistical Analysis................................................................................................................. 6

1.3 Results .......................................................................................................................................... 7

1.4 Discussion...................................................................................................................................13

1.4.1 Brown Shrimp......................................................................................................................13

1.4.2 Pink Shrimp.........................................................................................................................13

1.4.3 White Shrimp.......................................................................................................................14

1.4.4 Conclusions and Implications for Dredging.........................................................................14

2 Predicting the Marine Distribution of Snappers and Sharks in the Northern Gulf of Mexico..............15

2.1 Introduction..................................................................................................................................15

2.2 Methods.......................................................................................................................................16

2.2.1 Study Area...........................................................................................................................16

2.2.2 Biological Data ....................................................................................................................16

2.2.3 Fish Age Classification........................................................................................................17

2.2.4 GIS Methods and Environmental Data................................................................................18

2.2.5 Statistical Analysis...............................................................................................................21

2.3 Results ........................................................................................................................................22

2.4 Discussion...................................................................................................................................36

2.4.1 Red Snapper .......................................................................................................................37

2.4.2 Lane Snapper......................................................................................................................37

2.4.3 Blacktip, Spinner, and Atlantic Sharpnose Shark ...............................................................37

2.4.4 Conclusions and Implications for Dredging.........................................................................38

3 Predicting the Marine Distribution of Red Snapper, Black Seabass, and Shark Species in the South 

Atlantic.........................................................................................................................................................39

3.1 Introduction..................................................................................................................................39
ii

3.2 Methods.......................................................................................................................................40

3.2.1 Study Area...........................................................................................................................40

3.2.2 Biological Data ....................................................................................................................40

3.2.3 GIS and Environmental Data Sources ................................................................................41

3.2.4 Statistical Analysis...............................................................................................................43

3.3 Results ........................................................................................................................................45

3.3.1 Red Snapper .......................................................................................................................45

3.3.2 Black Sea Bass ...................................................................................................................45

3.3.3 Blacknose Shark .................................................................................................................45

3.3.4 Sandbar Shark ....................................................................................................................45

3.3.5 Tiger Shark..........................................................................................................................46

3.4 Discussion...................................................................................................................................54

3.4.1 Red Snapper and Black Sea Bass......................................................................................54

3.4.2 Blacknose, Sandbar, and Tiger Shark ................................................................................54

3.4.3 Conclusions and Implications for Dredging.........................................................................55

4 Predicting the Marine Distribution of Demersal Species in the Greater Atlantic.................................56

4.1 Introduction..................................................................................................................................56

4.2 Methods.......................................................................................................................................56

4.3 Summary of Model Outputs ........................................................................................................57

5 Literature Cited....................................................................................................................................58

Appendix A: Common and Scientific Names Cited in the Text...................................................................67
iii

List of Figures

Figure 1.1. The study area with training and validation zones depicted with trawl surveys overlaid............6

Figure 1.2. Relative importance of variables from the a) brown shrimp, b) pink shrimp, and c) white shrimp 

SDMs for the nGoM. ..................................................................................................................................... 8

Figure 1.3. Predicted brown shrimp CPUE in the a) summer and b) fall seasons. ....................................10

Figure 1.4. Predicted white shrimp CPUE in the a) summer and b) fall seasons.......................................11

Figure 1.5. Predicted pink shrimp probability of presence in the summer and fall combined. ...................12

Figure 2.1. Relative importance of variables in models of a) red snapper age-0, b) red snapper age-1, c) 

lane snapper age-0, d) lane snapper age-1................................................................................................25

Figure 2.2. Relative importance of variables in models of a) Atlantic sharpnose shark, b) blacktip shark, c) 

spinner shark...............................................................................................................................................26

Figure 2.3. Predicted red snapper age-0 CPUE in the a) summer and b) fall seasons. ............................29

Figure 2.4. Predicted red snapper age-1 probability of presence in the summer and fall seasons 

combined.....................................................................................................................................................30

Figure 2.5. Predicted lane snapper age-0 probability of presence in the a) summer and b) fall seasons. 31

Figure 2.6. Predicted lane snapper age-1 probability of presence in the summer and fall seasons 

combined.....................................................................................................................................................32

Figure 2.7. Predicted blacktip shark probability of presence in the spring–fall seasons. ...........................33

Figure 2.8. Predicted spinner shark probability of presence in the spring–fall seasons.............................34

Figure 2.9. Predicted Atlantic sharpnose shark CPUE in the spring–fall seasons. ....................................35

Figure 3.1. The study area with training and validation zones depicted with surveys overlaid. SERFS trap 

surveys were often at video survey locations. ............................................................................................44

Figure 3.2. Relative importance of variables in models of a) blacknose shark, b) sandbar shark, c) tiger 

shark, d) red snapper, and e) black sea bass.............................................................................................48

Figure 3.3. Predicted red snapper probability of presence in spring–fall seasons. ....................................49

Figure 3.4. Predicted black sea bass probability of presence in spring–fall seasons.................................50

Figure 3.5. Predicted blacknose shark probability of presence in spring–fall seasons. .............................51

Figure 3.6. Predicted sandbar shark probability of presence in spring–fall seasons..................................52

Figure 3.7. Predicted tiger shark probability of presence in spring–fall seasons........................................53
iv

List of Tables

Table 1-1. Predictor variables developed to predict the distribution of brown, pink, and white shrimp in the 

nGoM............................................................................................................................................................. 4

Table 1-2. BRT specifications and measures of accuracy for the shrimp species distribution models........8

Table 1-3. Confusion matrix from the validation data of the pink shrimp model at the optimal threshold to 

distinguish presence/absence, which was with a probability of 0.38............................................................9

Table 2-1. Sources of fisheries-independent survey data spanning 2003–2015........................................17

Table 2-2. Environmental variables developed to predict snappers and sharks in the nGoM. ..................20

Table 2-3. Biological predictor variables developed to predict the distribution of snappers and sharks. ...21

Table 2-4. Frequency of select snapper and shark species in the trawl and bottom longline surveys from 

2003–2017. .................................................................................................................................................22

Table 2-5. BRT specifications and percent deviance explained for marine SDMs of snappers and sharks.

....................................................................................................................................................................23

Table 2-6. Confusion matrix from the validation data of the red snapper age-1 model at the optimal 

threshold to distinguish presence/absence, which was with a probability of 0.31......................................24

Table 2-7. Confusion matrix from the validation data of the lane snapper age-0 model at the optimal 

threshold to distinguish presence/absence, which was with a probability of 0.37......................................24

Table 2-8. Confusion matrix from the validation data of the lane snapper age-1 model at the optimal 

threshold to distinguish presence/absence, which was with a probability of 0.44......................................24

Table 2-9. Confusion matrix from the validation data of the blacktip shark model at the optimal threshold 

to distinguish presence/absence, which was with a probability of 0.47......................................................24

Table 2-10. Confusion matrix from the validation data of the spinner shark model at the optimal threshold 

to distinguish presence/absence, which was with a probability of 0.33......................................................24

Table 3-1. Sources of fisheries-independent survey data. Sample sizes are provided for waters within our 

study area....................................................................................................................................................41

Table 3-2. Environmental variables developed to predict fish species in the South Atlantic......................42

Table 3-3. BRT specifications and AUC statistics for marine species distribution models of snappers and 

sharks..........................................................................................................................................................46

Table 3-4. Confusion matrix from the validation data of the red snapper model at the optimal threshold to 

distinguish presence/absence, which was with a probability of 0.41..........................................................47

Table 3-5. Confusion matrix from the validation data of the black sea bass model at the optimal threshold 

to distinguish presence/absence, which was with a probability of 0.58......................................................47

Table 3-6. Confusion matrix from the validation data of the blacknose shark model at the optimal 

threshold to distinguish presence/absence, which was with a probability of 0.15......................................47

Table 3-7. Confusion matrix from the validation data of the sandbar shark model at the optimal threshold 

to distinguish presence/absence, which was with a probability of 0.33......................................................47
v

Table 3-8. Confusion matrix from the validation data of the tiger shark model at the optimal threshold to 

distinguish presence/absence, which was with a probability of 0.42..........................................................47

Table 4-1. Federally managed species and the accuracy of models provided by the NEFSC. .................57
vi

List of Abbreviations and Acronyms

AUC area under the curve statistic

BL bottom longline

BOEM Bureau of Ocean Energy Management

BPI bathymetric position index

BRT boosted regression trees

CPUE catch per unit effort

CRM Coastal Relief Model

CSSP Congressional Supplemental Sampling Program

CCMA Center for Coastal Monitoring and Assessment

CV coefficient of variation

DOI Department of the Interior

EFH Essential Fish Habitat

FL fork length

ft foot/feet

GAM generalized additive model

GHRSST Group for High Resolution Sea Surface Temperature

GIS Geographic Information System

IE interaction effect

IUCN International Union for Conservation of Nature

kg kilogram(s)

km kilometer(s)

km2 kilometers squared

L liter(s)

m meter(s)

min minute(s)

MLD mixed layer depth

mg milligram(s)

mm millimeter(s)

MODIS Moderate Resolution Imaging Spectroradiometer

MUR Multiscale Ultrahigh Resolution

MSLABS Southeast Fisheries Science Center, NMFS, Mississippi Laboratory

n sample size

nat TL natural total length

NCCOS National Centers for Coastal Ocean Science

NCODA Navy Coupled Ocean Data Assimilation

NEFSC Northeast Fisheries Science Center

nGoM northern Gulf of Mexico

NMFS National Marine Fisheries Service

NOAA National Oceanic and Atmospheric Administration

NOS National Ocean Service

nm nautical mile(s)

NWI National Wetlands Inventory

OCS Outer Continental Shelf

OCSLA Outer Continental Shelf Lands Act

ppt part(s) per thousand

psu practical salinity unit(s)

Rs Spearman rank correlation 

s second(s)
vii

SDM species distribution model

SEAMAP Southeast Area Monitoring and Assessment Program

SERFS Southeast Reef Fish Survey

SL standard length

SLA Submerged Lands Act

SST sea surface temperature

TL total length

°C degree(s) celsius
viii

Abstract

Species distribution models (SDMs) are a state-of-the-art statistical modeling approach that quantifies the 

relationships between species and spatially explicit environmental data. SDMs work by extending the 

identified species-habitat relationships to the entire distribution of species under consideration. These 

predictive modeling results are ideal to inform management decisions. In this volume, we used a variety 

of fisheries-independent data sources in the Gulf of Mexico and South Atlantic to produce SDMs for 

select marine fish and shrimp species. Environmental data on habitats included oceanographic conditions, 

geomorphology, geography, prey, and the nearby ecosystems of wetlands and estuaries. For the Greater 

Atlantic, we summarize SDMs developed by the Northeast Fisheries Science Center that combined trawl 

surveys with data on oceanographic conditions, substrate, and zooplankton. Together, these maps and 

quantified habitat relationships (or lack thereof) add to the information synthesized in Volume 1: Fish 

Habitat Associations and the Potential Effects of Dredging on the Atlantic and Gulf of Mexico Outer 

Continental Shelf. The analyses evaluated the best habitat predictors of marine species and depicted the 

distribution of select marine fish and shrimp species. Species' relationships with geomorphology 

characteristics were limited and of minor importance compared to other habitat predictors. None of the 

Gulf of Mexico species examined were related to bottom currents, slope, or the heterogeneity of depth. Of 

minor importance in the models, white shrimp had a higher catch per unit effort (CPUE) farther away 

from shoals, and pink shrimp were positively related to sand grain sizes. Red snapper age-0 had a higher 

CPUE in close proximity to shoals and where the bathymetric position index predominately showed a hill 

topography. In the South Atlantic, none of the five species examined were associated with 

geomorphology characteristics. Overall, species' distributions were primarily related to oceanographic 

conditions, nearby wetlands and estuaries, and prey species. When applicable, geomorphology predictors 

only had minor influence on species distribution.
1

1 Predicting the Marine Distribution of Three Penaeid Shrimp 

Species in the Northern Gulf of Mexico

1.1 Introduction

In the northern Gulf of Mexico (nGoM), USA, the Penaeid species of brown shrimp (Penaeus aztecus), 

pink shrimp (Farfantepenaeus duorarum), and white shrimp (Litopenaeus setiferus) have high economic 

value. From 2006 to 2015, annual commercial catches of shrimp in the region had an economic value 

ranging from $327.6 to $585.8 million and catches have ranged from 178.9 to 289.0 million pounds 

(National Marine Fisheries Service 2017). As a specific example of the Penaeid species, 2016 landings 

included brown shrimp ($157 million), white shrimp ($206 million), and pink shrimp ($24.4 million) 

(NOAA NMFS Office of Science and Technology 2019). In addition to their economic importance, 

shrimp are key components of the ecosystem as a common prey for both benthic and pelagic fish 

(Tarnecki et al. 2016). In particular, brown shrimp have been documented as an integral part of the nGoM 

food web, as they are described as prey of small pelagic fish, small demersal fish, flatfish, king mackerel, 

Spanish mackerel, benthic feeding sharks, several snapper and grouper species, black drum, red drum, 

and others (Tarnecki et al. 2016).

Brown, pink, and white shrimp are estuarine-dependent in their early life stages, which is when they are 

relatively well studied. Within estuaries, juvenile white and brown shrimp are most abundant at open 

water-marsh edges, and they respond positively to the amount of time the marsh is flooded (Minello and 

Rozas 2002; Rozas and Minello 2015; Rozas et al. 2007). As adults, these three Penaeids are demersal 

and utilize the marine environment. Of the three Penaeids, brown shrimp are the best studied in the 

nGoM. Of particular importance, research has concentrated on the effects of hypoxia on brown shrimp

(Craig 2012; Craig and Crowder 2005; Craig et al. 2005). These studies show that brown shrimp 

aggregate at the edge of hypoxic zones and may move farther inshore or offshore to avoid hypoxic waters.

Since 2007, the development of species distribution models (SDMs) have begun to proliferate for marine 

species (Melo-Merino et al. 2020). SDMs use statistical species-habitat relationships to predict the spatial 

distribution of species (Guisan and Zimmermann 2000). Montero et al. (2016) developed an SDM for 

brown shrimp and found brown shrimp relative abundance was positively related to mud, shallow depths 

(< 100 m), lower salinities (< 20 ppt), higher bottom temperatures (> 20°C), latitude/longitude, and were 

uncommon in hypoxic conditions. For pink shrimp, Drexler and Ainsworth (2013) developed an SDM

and tested five predictor variables. Their results were initially modeled at a 10-km scale and were 

aggregated into broad polygons that improved model accuracy (Drexler and Ainsworth 2013). Their

model showed pink shrimp were less abundant in waters with mud sediments compared to other grain 

sizes. Drexler and Ainsworth (2013) also showed pink shrimp abundance declined most in waters > 120 

m in depth and with waters < 15°C. They found a negative relationship with chlorophyll and a positive 

relationship with dissolved oxygen of ≥ 5 ml -L. Concerning adult white shrimp, their habitat use and 

marine distribution remains poorly documented.

Recent advances in modeling the distribution of brown and pink shrimp have greatly improved our 

knowledge of the marine distribution of Penaeids. However, even with these species, much remains 

unknown about their relationships to geomorphology, ocean currents, and the nearby ecosystems on

which each species depends. Turner (1977) found a strong positive correlation between coarse measures 

of inshore brown shrimp landings and emergent wetland area in the nGoM. Diop et al. (2007) found 

landings of white shrimp over time were positively, but weakly, correlated with late juvenile abundances. 

Yet, the distribution of Penaeids in the offshore, marine ecosystem has not yet been related to nearby 

wetlands and estuaries. Overall, testing the relationships of shrimp species' distribution with a 
2

comprehensive suite of habitat variables has the potential to inform management, strengthen 

environmental impact assessments, and refine our knowledge of important waters for each species.

The need to fill these knowledge gaps is exemplified by marine resource extraction activities like 

dredging. The demand for offshore marine sand is increasing in the United States (Drucker et al. 2004), 

and sand is commonly used for beach renourishment, barrier island restoration, and wetland restoration. 

Throughout this report, we use the term "sand" to broadly characterize sediment resources, and we 

recognize that sediment dredging may include a variety of grain sizes depending on the application. Sand 

shoals are often preferred resources because of the quantity of sand per unit area, and the dredging of 

Outer Continental Shelf (OCS) sand shoals is likely to increase in the future as demand increases due to

renourishment cycles for beaches, emergency repairs of beaches after storms, and the projected effects of 

sea-level rise (Nairn et al. 2004). In particular, the nGoM coast benefits greatly from barrier islands that 

reduce storm surge (Grzegorzewski et al. 2011), and these islands require regular sediment replenishment. 

The Bureau of Ocean Energy Management (BOEM), as part of the US Department of the Interior, is 

responsible for the management and development of mineral resources on the OCS, including sediment

resources. As demand for OCS sand increases, BOEM faces complex multi-user interactions, including 

issues of resource allocation, cumulative impacts from repeated use, fisheries use and potential conflicts, 

protection of archaeological sites, oil and gas infrastructure, potential renewable energy infrastructure, 

and impacts on Essential Fish Habitat (EFH) (Michel et al. 2013).

As part of our project, we have identified shoal locations and developed a classification scheme based on 

expert opinion and shoal characteristics of interest (Volume 2). The classification includes characteristics 

hypothesized to be related to fish such as bottom current direction and velocity, slope, depth, sediment 

grain size, rugosity (i.e., depth heterogeneity), and shell presence. In addition to the effect of shoals 

themselves, these characteristics are largely untested in relation to fish and shrimp species. Here, our 

objectives were to:

1) Test for habitat relationships of brown, pink, and white shrimp with a broad suite of 

environmental factors, including geomorphology, oceanographic characteristics, and nearby

ecosystems

2) Model the spatial distribution of white, brown, and pink shrimp with multiscale predictors

1.2 Methods

1.2.1 Study Area

The study area spanned the extent of the nGoM from Texas to Florida, USA. The landward boundary 

began with federally managed waters (3 nm from the shoreline of Louisiana, Mississippi, and Alabama; 9 

nm from the shoreline of Texas and Florida) through the 50-m depth contour. More specifically, we 

defined the landward boundary of the study area by the 1953 Outer Continental Shelf Lands Act 

(OCSLA), which distinguishes Federal- and state-managed waters. The oceanic boundary of the study 

area was defined by a 50-m contour line from National Oceanic and Atmospheric Administration’s 

(NOAA’s) Coastal Relief Model (CRM) (NOAA National Centers for Environmental Information 2010). 

Only waters ≤ 50 m in depth were included in the study because our focus was on the potential impact of 

sediment dredging and the logistics of dredging limit potential areas to these shallow depths. The study 

area had a total surface area covered of 162,985 km2

. The benthic substrate of the area consists of 

unconsolidated sediments ranging from mud to gravel with natural patches of hard bottom reefs.
3

1.2.2 Biological Data

We obtained offshore shrimp data from fishery-independent trawl surveys of the Southeast Area 

Monitoring and Assessment Program (SEAMAP) in the nGoM. With the exception of minor changes in 

sample selection procedures in 2010 and thereafter, surveys have used similar gear, protocols, and a 

random stratified sampling design based on depth and shrimp statistical area since 1992 (Craig et al. 

2005; Gulf States Marine Fisheries Commission 2017). From these data, we used the years of 2003–2017 

because we wanted to depict current conditions as best as possible, and this timeframe still provided us 

with an adequate sample size. Trawl surveys from the SEAMAP program span the entire nGoM and are 

conducted in the summer (June–August) and fall (October–December) seasons. Trawl surveys targeted 

shrimp and groundfish using a 12.8-m net in the central and eastern nGoM and with a 6.1-m net near 

Texas. Surveys were conducted at all hours of the day. Complete counts of shrimp were conducted in 

summer surveys. In the fall, complete counts were conducted for samples < 22.7 kg. For larger catches, 

totals counts were estimated by extrapolating from a subset of the catch. We used the centroid of trawl 

survey tows to represent survey locations and to calculate environmental variables. We removed 

extremely long or short tow survey lengths, and subsequently, trawls ranged from 11–52 min and 1.0–5.2 

km. We calculated the relative abundance of each shrimp species by calculating catch per unit effort 

(CPUE) as shrimp km-1 of survey. Because initial relative abundance models showed below-average 

results for pink shrimp, we used presence/absence data for modeling this species. Prior to analyses, we 

used Generalized Additive Models (GAMs) (knots=3) to explore the effects of trawl length and duration 

on the presence/absence or CPUE of species. All tests showed <1% of the deviance was explained by 

these effects except for the effect of length on white shrimp CPUE, which explained 3% of the deviance. 

Given these minimal effects, we proceeded with using CPUE and presence/absence data. We used 

summer (June–August) and fall (October–December) trawl surveys for analyses of shrimp.

Pink shrimp ranged throughout the nGoM, and we used the entire study area for the analysis. We 

restricted the analyses of white shrimp and brown shrimp to their primary geographic ranges because the 

inclusion of hundreds, or thousands, of absence points outside a species’ range may skew the results 

towards predictor variables characterizing the species’ geographic range rather than their fine-scale

distribution and habitat associations. Additionally, our goal was to add to existing knowledge of 

distributions to aid in management applications. For white shrimp, we excluded trawl surveys east of a 

longitude of W 87.9°. Only 1 of 1,653 trawl surveys east of this latitude recorded a white shrimp, and this 

longitude was > 200 km from that presence location. For brown shrimp, we excluded trawl surveys east 

of a longitude of W 84.5° because only 6 of 1,203 locations (8 of 570,733 individuals) were found east of 

this longitude. The next nearest brown shrimp in a trawl survey east of this longitude was > 100 km away.

1.2.3 Geographic Information System (GIS) Methods and Environmental Data

We converted all fish survey and environmental GIS data to the North America Albertson Equal Area 

Conic map projection. To calculate environmental variables with focal statistics (e.g., mean depth within 

a 3-km radius), we initially included a 5-km buffer of the study area. We removed this buffer after final 

maps were developed. Because we anticipated hierarchical relationships of shrimp with predictor 

variables (e.g., broad oceanographic factors and fine-scale substrate factors and depth), we predicted all 

SDMs to a 90-m resolution raster. The 90-m resolution was the same as several, but not all environmental 

variables. For data initially at a resolution > 90 m, we conducted a bilinear resampling and then aligned 

the data to the other 90-m datasets.

We developed predictor variables to depict oceanographic conditions, substrate, geography, and nearby

ecosystems (Table 1-1). For bathymetry, and variables derived from bathymetry, we used the CRM

(NOAA National Centers for Environmental Information) for offshore waters of Texas, Louisiana, 

Mississippi, and Alabama. Offshore of Florida, we observed large errors in depth values that spanned tens 

of kilometers and were usually observed as rectangular boxes with little variability. Therefore, we used 
4

sounding data developed into a 50-m raster grid by the US Geological Survey (Robbins et al. 2007). To 

be consistent with the regional analysis, we resampled to a 90-m resolution using bilinear interpolation. 

To correspond to the approximate length of trawl surveys, we used ArcGIS focal statistics to calculate the 

CV (coefficient of variation) of depth, mean slope, mean sediment grain size, proportion of area with 

shoal, and proportion of area with a positive bathymetric position index (BPI) within a 3-km radius.

Table 1-1. Predictor variables developed to predict the distribution of brown, pink, and white 

shrimp in the nGoM. 

Oceanographic predictors were obtained from aggregations of monthly means spanning 2003–2017 unless otherwise 

noted.

Variable type Predictor variable (units) Resolution Data source

Substrate CV of depth 90 m CRM + modifications

Substrate Distance to shoal (km)

90 m Pickens and Taylor, NOAA Biogeography 

Branch, identification of shoals

Substrate Proportion of area with shoal 

90 m Pickens and Taylor, NOAA Biogeography 

Branch, identification of shoals

Substrate Mean sediment grain size (mm)

370 m Chris Jenkins, University of Colorado, 

interpolation of usSEABED data

Substrate Proportion of area with BPI ≥1 90 m CRM + modifications

Substrate Slope (degrees) 90 m CRM + modifications

Oceanographic Mean depth (m) 90 m CRM + modifications

Oceanographic Bottom temperature (°C) 4.4 km HYCOM + NCODA

Oceanographic Chlorophyll-a (mg m-3) 5.5 km Aqua MODIS satellite, 8-day composites

Oceanographic Bottom salinity (psu) 4.4 km HYCOM + NCODA

Oceanographic Bottom current, U- and Vdirectional velocity (m s-1) 9.3 km HYCOM + NCODA

Oceanographic

Mixed layer thickness (m) (depth 

where temperature change from 

surface is 0.2°C)

4.4 km HYCOM + NCODA

Oceanographic Hypoxia (mean probability of 

hypoxia) North Carolina State University

Geography East or West of W 88o longitude 90 m

Geography Distance to shoreline (km) 90 m Submerged Lands Act

Nearby 

ecosystems Nearby wetlands (km2) 90 m National Wetlands Inventory 

Nearby 

ecosystems Nearby estuaries (km2) 90 m National Wetlands Inventory

* HYCOM + NCODA = HYbrid Coordinate Ocean Model + Navy Coupled Ocean Data Assimilation; MODIS = 

Moderate Resolution Imaging Spectroradiometer

We used the ArcGIS Benthic Terrain Modeler (Wright et al. 2012) to calculate the slope and BPI. The 

BPI is an index that represents underwater hill and valley topography, with values ≥ 1 indicating a cell is 

higher than surrounding cells and a BPI ≤ -1 indicating a cell is lower than surrounding cells. We used the 

BPI to calculate the proportion of area as a topographic high. For mean sediment grain size, we used a 

data interpolation of usSeabed from NOAA/National Ocean Service (NOS) National Centers for Coastal 

Ocean Science (NCCOS) (Kinlan et al. 2013). Aspect, BPI classified as a valley, and sediment grain size 
5

classes were initially explored but did not provide further information beyond slope, sediment grain size, 

and shoal features that were already depicted.

We obtained oceanographic predictor variables by using Duke University’s Marine Geospatial Ecology

Toolbox (Roberts et al. 2010) within ArcGIS to summarize monthly means. Variables included bottom 

water temperature, chlorophyll-a (chlorophyll), bottom salinity, mixed layer thickness, and bottom 

current velocity for U and V directions (Table 1-1). All measures were averaged monthly over the period 

extending from January 2003 through December 31, 2017. Monthly measures were then averaged by 

seasons: spring = March 1–May 31, summer = June 1–Aug 31, fall = Sept 1–Nov 30, and winter = Dec 1–

Feb 29. We used only remote sensing data to characterize oceanographic conditions because these 

measures are consistent across the geography and variables represented a relatively long-term 

characterization of the water column (i.e., monthly averages rather than a single day of a specific survey). 

Therefore, oceanographic measures represented spatial tendencies and related ecological processes rather 

than instantaneous conditions. Data on bottom temperature, bottom salinity, and bottom currents (U- and 

V- directions) were obtained from the HYCOM + NCODA Gulf of Mexico 1/25 degree analysis 

(GLMI0.04) (Chassignet et al. 2009). The Marine Geospatial Ecology Toolbox derived values calculated 

each day from the following datasets that have identical spatial extents and resolutions:

• 1 January 2003–30 April 2009: HYCOM dataset expt_20.1

• 1 May 2009–1 April 2014: HYCOM dataset expt_31.0

• 2 April 2014 and later: HYCOM dataset expt_32.5

The v-directional raster characterized the north (+) and south (-) currents and the u-directional raster 

characterized the east (+) and west (-) currents.

To quantify hypoxia ( ≤ 2 mg L-1 dissolved oxygen), we used the results of Matli et al. (2018), who 

modeled the extent and probability of hypoxic conditions for each year based on three separate data 

collection efforts. To obtain a relative index of long-term conditions, we used the mean probability of 

hypoxia for July and August from 2003–2017 as a predictor variable. As the data were initially points, we 

created an interpolated raster dataset by using ordinary, spherical kriging with calculations including eight 

adjacent points. Because the dataset ended abruptly at an area of high hypoxia probability, we used the 

ArcGIS “Expand” tool to extrapolate all values up to 35 km.

Geographic and nearby ecosystem predictor variables considered included distance to shoreline, nearby 

wetland area, nearby estuarine area, and a longitudinal threshold that depicted whether the location was 

east (1) or west (0) of the W 88o longitude near Mobile Bay, Alabama, USA. Waters west of this 

longitude are dominated by mud sediments, large river influences, lower salinities, and higher chlorophyll 

concentrations that extend farther offshore compared to eastward waters. Waters east of this longitude are 

dominated by sandy substrates and higher salinities. The abundance of brown shrimp (Montero et al. 

2016) and juvenile red snapper (Dance and Rooker 2019) have been shown to differ at this longitude 

threshold. To determine if coastal wetlands contributed to the distribution of shrimp in the marine 

environment, we used National Wetlands Inventory (NWI) data (U.S. Fish and Wildlife Service 2018)

and its classification of “estuarine and marine wetland”. To quantify nearby wetland area (km2

), we first 

calculated the farthest distance from a wetland in the study area. Using the resulting 160 km distance, we 

then used ArcGIS focal statistics to sum all wetland cells (90 m resolution) within 160 km of a cell in the 

marine environment. The metric was converted to wetlands km2

. Unfortunately, we could not use NWI 

data to define estuaries because of large areas of missing data. Therefore, we defined nearby estuary area 

from a map of EFH from red drum Sciaenops ocellatus (NOAA NMFS n.d.) that characterized all 

estuaries in the nGoM. As the layer included some nearshore waters, we removed all waters that were 

seaward of the shoreline position. Similar to nearby wetlands, estuary cells within 160 km of each cell 

were summarized for the study area. Distance to shoreline was calculated from the boundaries of the 

Submerged Lands Act (Bureau of Ocean Energy Management 2010; 2012), which depicts a distance of 3
6

or 9 nm from shoreline, depending on the state. We used the buffer tool to re-create the approximate 

shoreline boundaries and then calculated the Euclidean distance from the shoreline to each cell in the 

study area. 

1.2.4 Statistical Analysis

We examined predictor variables for multicollinearity, and we removed highly correlated variables (r > 

0.80) prior to analyses. In an initial analysis of pink shrimp, we found a strong negative relationship with 

nearby estuaries. This was likely a result of pink shrimp being common on the Florida shelf, which 

coincides with low estuarine areas. Because this relationship was likely because of other correlates on the 

Florida shelf (e.g., sand sediments, higher salinity), we did not include nearby estuaries as a predictor of 

pink shrimp. In addition to environmental predictors, we used season (summer or fall trawl survey) and 

start time of surveys as predictors. The time at which surveys are conducted can affect the detectability of 

species and has previously been documented as affecting brown shrimp catch (Craig and Crowder 2005). 

Regarding seasonality, changes in summer and fall distributions are likely because of species’ natural 

history. We did not use year as an explanatory factor because the primary objective of our research was to 

determine long-term value of waters and substrates of the nGoM. Therefore, we assume years of high or 

low abundance are representative of long-term shrimp distribution.

Our trawl survey dataset had a large number of locations, which led to some being in close proximity to 

others. Individual surveys are likely to be independent over the 15 years of data collection, but we wanted 

to ensure our models were robust to specific survey locations. Fourcade et al. (2018) found that a purely 

random split of training and validation data for SDM assessments led to a high validation accuracy 

assessment for models derived from fake predictors. They found a “checkerboard” approach of 

aggregated training or validation locations was helpful, and a “block” approach was best at distinguishing 

models as being poor when they were truly poor (Fourcade et al. 2018). Similarly, we aggregated training 

and validation data with alternating bands along a longitudinal gradient (Figure 1.1). The use of a 

longitudinal gradient maintained a depth gradient in each block. More specifically, we reclassified a raster 

of longitude into 70 equal interval divisions, which resulted in 23-km bands across the study area. We 

then alternated the delineation of training (two bands) and validation (one band) datasets to achieve the 

desired ratio of training and validation data.

Figure 1.1. The study area with training and validation zones depicted with trawl surveys overlaid.
7

We used boosted regression trees (BRT) to model species-habitat relationships with the training data. In 

comparative studies of SDM statistical methods, BRTs and similar techniques have outperformed 

generalized linear models (GAMs), Maxent, and other techniques (Couce et al. 2012; Rooper et al. 2017; 

Smolinski and Radtke 2017). Classification and regression decision trees are the basis of BRT. Decision 

tree analyses are ideal for quantifying nonlinear relationships and complex interactions, which are both 

inherent in ecological data (De'ath and Fabricius 2000). The predictive power of decision trees are 

enhanced by boosting, which sequentially adds trees that improve the model; the results are then derived 

from an ensemble of hundreds of trees (De'Ath 2007; Elith et al. 2008). We followed the general 

procedures outlined by Elith et al. (2008) to develop BRTs. We iteratively assessed tree complexities of 

1–5 and used learning rates that resulted in > 1,000 trees. The tree complexity represents the level of 

interaction allowed to occur (e.g., 1 = no interaction effects, 2 = interaction between two variables), and 

the number of trees are iterations. For brown and white shrimp, we used a Poisson log-linear model using 

CPUE as the dependent variable. For pink shrimp, we used a binomial model to predict probability of 

presence. We followed the BRT model “simplification” procedure described by Elith et al. (2008). In this 

procedure, the weakest predictor is dropped sequentially, predictors are ranked in order of importance, 

and the change in model deviance with each drop is assessed. The inflection point where the model’s 

deviance sharply increases after a drop defines which variables remain in the model with the goal of 

having a parsimonious model without losing predictive power. To be consistent, we defined an inflection 

point as an increase of > 2% of the deviance explained when dropping a single variable and ≥ 3% for 

multiple dropped variables. 

To assess accuracy of relative abundance models, we report the percent deviance explained (similar to an 

r

2 for Poisson regression) from the cross-validation and validation datasets. A Spearman rank correlation 

(Rs) was also calculated between observations and predictions of the validation dataset. For the pink 

shrimp presence/absence model, we used a receiver operator characteristic, area under the curve statistic 

(AUC). The AUC has been commonly used to test predictive ability of SDMs (Guisan and Zimmermann 

2000) and is independent of thresholds. Measures of the AUC range from 0.0 to 1.0 and were interpreted 

as suggested by Manel et al. (2001) and Swets (1988) as follows: < 0.50 = no discriminatory power; 0.50–

0.69 = poor power; 0.70–0.89 = good power; and 0.90–1.0 = excellent discriminatory power. In addition, 

we report the confusion matrix for the presence/absence model at the probability of presence threshold 

determined by the maximum Kappa that optimally discriminates presence and absence. We report relative 

importance of each variable in the model as suggested by Elith (2008). The relative importance of all 

variables in the model sums to 100%. Likewise, we assessed the strength of interaction, and these results 

are on a continuous scale with zero showing no interaction effect. We report interaction effect (IE)

measures with a score of > 10 because these were most straightforward to interpret. 

We used the statistical program R (R Core Team 2018) and the package “dismo” (Hijmans et al. 2017) to 

implement BRT. To predict models to the extent of the study area, or geographic range of species, we 

used the R packages “rgdal” (Bivand et al. 2019) and “raster” (Hijmans 2019). We assumed the effect of 

time of survey represented a detectability effect rather than a change in distribution. Therefore, we 

applied the models during each species' peak time of detectability: 10:30 for brown shrimp, 02:00 for 

white shrimp, and 06:00 for pink shrimp. 

1.3 Results

Brown and white shrimp models explained a substantial amount of variation in CPUE, and the pink 

shrimp model was very good at predicting presence/absence (Table 1-2). Brown shrimp were present on 

76.3% of trawl surveys with a total of 570,725 individuals. Mixed layer depth (MLD) in the spring and 

summer were most influential in the model with a deeper mixing of water related to a higher brown 

shrimp CPUE (Figure 1.2 and Figure 1.3). Brown shrimp CPUE was lowest from 11:00 through 24:00, 

and the season effect showed CPUE was greatest during summer. We found a slight positive relationship
8

of brown shrimp with fall bottom temperature, and nearby wetlands had a positive effect on CPUE. There 

was a strong interaction between spring MLD and nearby wetlands (IE = 41), which showed the highest 

predicted CPUE where high nearby wetland area was combined with a relatively deep spring MLD. 

Table 1-2. BRT specifications and measures of accuracy for the shrimp species distribution 

models.

Species n

Tree 

complexity

Learning 

rate

# of 

trees

Crossvalidation Validation

Validation 

Spearman 

correlation 

Brown 

shrimp 4,417 4 0.02 1,850

Deviance 

explained = 

45%

Deviance 

explained= 

37% 0.64

White 

shrimp 3,967 5 0.01 1,200

Deviance 

explained = 

41% 

Deviance 

explained = 

30% 0.55

Pink shrimp 5,620 3 0.01 2,000 AUC = 0.84 AUC = 0.85 NA

Figure 1.2. Relative importance of variables from the a) brown shrimp, b) pink shrimp, and c) white 

shrimp SDMs for the nGoM.

MLD = mixed layer depth, Temp = bottom water temperature, Grain size = sediment grain size, Dist = distance 
9

White shrimp were present on 39.1% of trawl surveys, with a total of 44,455 individuals. White shrimp 

were most abundant with salinities of 22–30 psu, high chlorophyll concentrations, within 30 km of the 

shoreline, and with greater amounts of nearby estuaries and wetlands (Figure 1.2 and Figure 1.4). They 

had a higher CPUE during the fall season, with higher summer bottom temperatures, and with depths of 

approximately 15–30 m; they had a lower CPUE near midnight. White shrimp were slightly more 

abundant farther away from shoals. There was an interaction between salinity and nearby wetland area (IE

= 17) that showed CPUE was greatest where high salinity coincided with a high nearby wetland area. The 

lowest predicted CPUE was at lower salinities with few nearby wetlands. An interaction between 

chlorophyll and season (IE = 13) showed chlorophyll had a greater influence on CPUE during the fall.

Pink shrimp were present on 22.6% of trawl surveys with a total of 36,015 individuals. Surveys from 

11:00 to 23:00 had an extremely low probability of presence (Figure 1.2 and Figure 1.4). Pink shrimp 

presence was associated with salinities spanning 31–36 psu and substrate with sand grain sizes (less likely 

with silt or granule gravel grain sizes) (see Wentworth 1922). The relationship with nearby wetland area 

showed substantial variability, but pink shrimp were less likely when wetland area was extremely low and 

when wetland area was very high. Pink shrimp had the highest likelihood of presence with depths of 18–

30 m, although considerable uncertainty existed at shallow depths. Survey time interacted with both 

salinity (IE = 65) and wetlands (IE = 65). This showed that these habitat variables had the greatest effect 

during times when pink shrimp were catchable. The pink shrimp model discriminated absence much 

better than presence (Table 1-3), and the overall accuracy represented by the AUC score was very good. 

The spatial models of the three species showed distinct patterns (Figure 1.3 to Figure 1.5). 

Table 1-3. Confusion matrix from the validation data of the pink shrimp model at the optimal 

threshold to distinguish presence/absence, which was with a probability of 0.38.

Observed 

absence

Observed 

presence

User's 

accuracy (% 

correct)

Predicted 

absence 1,323 180 88%

Predicted 

presence

132 241 65%
10

Figure 1.3. Predicted brown shrimp CPUE in the a) summer and b) fall seasons.

The study area is indicated by the dashed line, and CPUE represents the predicted number of shrimp per km of trawl survey. 
11

Figure 1.4. Predicted white shrimp CPUE in the a) summer and b) fall seasons.

The study area is indicated by the dashed line, and CPUE represents the predicted number of shrimp per km of trawl survey. 
12

Figure 1.5. Predicted pink shrimp probability of presence in the summer and fall combined.

The study area is indicated by the dashed line, and probability of presence represents the probability of capture given a trawl survey. 
13

1.4 Discussion

Few studies have linked marine and terrestrial ecosystems despite important linkages such as between 

coastal estuaries and offshore marine habitats (Beger et al. 2010). By analyzing three Penaeid species, we 

found a consistent pattern that the distributions of these species in the offshore, marine environment were

positively related to the amount of nearby coastal wetlands and estuaries. In fact, brown and white shrimp 

both had interaction effects that included nearby wetlands and an oceanographic factor (MLD and 

salinity, respectively). These interactions directly demonstrate the linkages between these two 

ecosystems. Wetland loss and the loss of open water-marsh edges have been independently identified as 

contributors of shrimp declines in estuarine (Rozas et al. 2007) and marine environments over time (Diop 

et al. 2007). However, the relationships we quantified further suggest that wetland loss is likely to lead to 

changes in shrimp distribution in the marine environment.

None of the three Penaeid species' distributions were related to bottom currents or geomorphology 

metrics of slope, BPI, or CV of depth. Of the geomorphology variables, only distance to shoal and 

sediment grain size were selected as predictors, and the importance of these variables were relatively 

minor. Predicted white shrimp CPUE was less in close proximity to shoals and pink shrimp were 

positively associated with sand grain sizes. Similar to findings of Drexler and Ainsworth (2013) and a 

qualitative assessment by Mulholland (1984), we found pink shrimp occurred with sand substrates and 

had a lower probability of occurrence with mud, silt, or gravel substrates. In contrast to Montero et al. 

(2016), we did not find brown shrimp were associated with mud and silt sediments. However, we 

excluded trawl data offshore of Florida because it appeared to be outside of the geographic range of the 

species. If we had included the hundreds of absence locations in those eastern nGoM waters with sand 

substrates, then the results may have been similar to Montero et al. (2016). By analyzing the three Penaeid 

species separately, we observed the spatial distinctions among species. Brown shrimp had the highest 

CPUE in the western nGoM, while pink shrimp were most common east of the W 88o longitude near 

Mobile Bay, Alabama. White shrimp mostly occurred near the shoreline of the central nGoM.

1.4.1 Brown Shrimp

A greater CPUE of brown shrimp in summer coincides with their peak spawning season in the nGoM 

(Gulf of Mexico Fishery Management Council 1981). Notably, there was no direct relationship with the 

high frequency hypoxic zone. Nonetheless, the predicted CPUE of brown shrimp was extremely low in 

the hypoxic zone. This is likely because brown shrimp had a higher CPUE with a deeper MLD, and these 

waters tended to occur outside of the high frequency hypoxic zone. The association with fall bottom 

temperature is also associated with farther offshore habitats. Craig et al. (2005) found brown shrimp move 

both farther inshore and offshore when hypoxia is severe (i.e., leaving the intermediate depth zone), and

this spatial pattern is observed in our model of brown shrimp distribution. We note that our data describes

the long-term average brown shrimp distribution rather than annual events that other studies have 

considered. Harvest strategies for shrimp also do differ in Louisiana and Texas. In Louisiana, shrimp 

trawlers work in the most shallow waters (< 20 m depth) near estuaries earlier in the year. For Texas, 

harvest does not occur until later and shrimp trawlers use areas > 20 m in depth (Craig et al. 2005).

1.4.2 Pink Shrimp

The time of day for surveys was influential for pink shrimp and brown shrimp, as CPUE was far higher 

from approximately midnight to 11:00 for these species. In a lab study, pink shrimp buried themselves in 

response to daylight and were active in dark conditions (Hughes 1968). Hughes (1968) also found 

individuals synchronized emergence within a 20–30 minute timeframe. Studies of other Penaeids

worldwide have also documented this phenomenon (Wassenberg and Hill 1994). Our pink shrimp model
14

showed the association with salinity resulted in a distribution skewed towards Florida, although pink 

shrimp were also predicted to be in other parts of the central nGoM where sand sediments were present. 

The interaction effects among habitat variables and the time of surveys suggests that surveys at 

ineffective times are likely to complicate the quantification of species-habitat relationships. The confusion 

matrix for pink shrimp showed absence can be more accurately predicted compared to presence. This 

result is expected given that species are detected imperfectly in surveys (see MacKenzie et al. 2002 for 

details on the topic), and our results depict the predicted catch in a trawl survey. In essence, a species may 

be present even though a single survey did not detect that species. Applications of these SDMs should 

consider the results as relative rather than absolute numbers. Waters with a greater probability of 

occurrence or relative abundance (CPUE) of species should be interpreted in the context of waters with 

lower probability of occurrence or relative abundance. 

1.4.3 White Shrimp

White shrimp CPUE was much higher in the coastal zone where salinity was < 30 psu. White shrimp 

selected waters closer to the shoreline, with a greater chlorophyll-a concentration, and depths of 12–30 m.

White shrimp also had a positive association with summer bottom temperatures that were > 28°C. 

Research on the temporal dynamics of white shrimp in Louisiana coastal waters showed greater water 

temperatures related to higher abundances of early juveniles, and to a lesser extent, more adult white 

shrimp. A greater CPUE of white shrimp in the fall coincides with their peak spawning season in the 

nGoM (Gulf of Mexico Fishery Management Council 1981).

1.4.4 Conclusions and Implications for Dredging

In summary, we have identified habitat associations and modeled the distribution of three federally 

managed Penaeid shrimp species. Nearby wetlands and oceanographic characteristics, such as bottom 

temperature, salinity, MLD, and chlorophyll drove the majority of the spatial patterns for these species. 

Bottom currents, slope, and depth heterogeneity were not associated with species' distributions. In the 

cases where sediment grain size and distance to shoal were associated with species, the variables 

explained only a small portion of the variance compared to other variables. We do caution that fisheryindependent trawl surveys typically sample ~3 km tows, and microhabitats within these areas were not 

assessed by our study. Shoals themselves are poorly sampled because of their depth, and, therefore, 

samples near shoals were assumed to represent those areas. Importantly, the spatial models do show 

strong spatial patterns that will help inform the identification of important waters for Penaeid species, and 

their predators, in the nGoM. Broad spatial patterns typical of oceanographic predictors as well as habitat 

associations with grain size and distance to shoal will help guide impact assessments at a local level, such 

as at individual dredge sites.
15

2 Predicting the Marine Distribution of Snappers and Sharks in the 

Northern Gulf of Mexico

2.1 Introduction

SDMs and similar methods of spatial modeling have proliferated in the last two decades as GIS

technologies, remote sensing, availability of spatial data, and computation capacity have rapidly 

improved. Recent distribution models within marine ecosystems have been applied to ecosystem-based 

management (Gruss et al. 2018), marine spatial planning (Hattab et al. 2013), scenario assessments 

(Delevaux et al. 2018), stock assessments (Saul et al. 2013), and climate change (Morley et al. 2018; Su et 

al. 2013). SDMs can illuminate habitat relationships as well as map the spatial patterns of species' 

distributions. In turn, mapping is critical for the identification of EFH (Moore et al. 2016; Pennino et al. 

2016), defined as those waters and substrates required for a species to spawn, breed, feed, or grow to 

maturity (US Sustainable Fisheries Act, 1996, Public Law 104-297). EFH is inclusive of prey species, 

although we are not aware of EFH mapping that explicitly includes the distribution of prey species.

A decade after Robinson et al. (2011) declared that marine ecosystems provide a prime opportunity to test 

the influence of prey on species' distributions, few studies have explored this possibility (see Volume 2). 

The nGoM presented an opportunity to test predator-prey relationships with data on a variety of trophic 

levels. Food webs in the nGoM have been summarized by Tarnecki et al. (2016), including common 

linkages among Penaeid shrimp, menhaden, squid, and other small demersal and pelagic fish. In 

particular, the abundance of Gulf menhaden has been shown to have major effects on fisheries (Geers et 

al. 2016; Robinson et al. 2015). Estuaries, subaquatic vegetation, oyster reefs, and coastal wetlands in the 

nGoM also contribute towards offshore marine productivity by providing habitat for estuarine-dependent 

life stages and producing common prey species of the marine environment (e.g., menhaden, shrimp, 

crabs) (Spies et al. 2016). Much like the effect of prey distribution, the effect of such nearby ecosystems 

has rarely been considered in SDMs.

In addition to these knowledge gaps, we previously have documented biases towards testing particular 

habitat variables for particular fish guilds (Volume 1). More specifically, there has been a trend to test 

numerous substrate predictor variables—but few oceanographic variables—with reef fish, such as 

snapper. Conversely, the trend for sharks is to test more oceanographic variables, but few substrate 

variables. Therefore, testing a comprehensive suite of habitat variables has the potential to test these 

biases and lead to an improved understanding of EFH.

We selected snapper and shark species to study based on their breadth of designated EFH, overlap of a 

species' EFH with our study area (Federal waters, ≤ 50 m), socio-economic importance, data availability, 

and potential vulnerability to sand dredging (i.e., demersal species with an affinity to soft sediments).

Below, we outline characteristics of each of the selected species:

Sharks:

• Blacktip shark is a large coastal shark that prey on teleost fishes (Cortés 1999) and is listed as 

globally “vulnerable” by the International Union for Conservation of Nature (IUCN) (Burgess 

and Branstetter 2000). In the nGoM, juvenile blacktip shark diet is dominated by Gulf menhaden 

(Bethea et al. 2004). Blacktip shark is the second most valuable shark to commercial fisheries of 

the southeastern US (Castro 1996).

• Spinner shark is listed as globally “near threatened” by the IUCN (Burgess 2009) and is a 

common target by commercial fisheries. Spinner shark primarily prey on teleost fishes (Cortés 

1999), particularly Gulf menhaden (Bethea et al. 2004).
16

• Atlantic sharpnose shark is a relatively small, demersal shark that feeds on crustaceans and teleost 

fishes (Cortés 1999). In the nGoM, Atlantic sharpnose are regularly caught in recreational and 

commercial fisheries.

Snappers:

• Red snapper juveniles are demersal before inhabiting natural and artificial reefs as adults 

(Gallaway et al. 2009). In 2016, commercial landings totaled $26.5 million in the nGoM (NOAA 

NMFS Office of Science and Technology 2019); the species is also important for recreational 

fisheries.

• Lane snapper is a subtropical, reef-associated species that has a demersal juvenile life stage that 

inhabits shallow waters with sand/mud bottoms, including shoal habitats (Mikulas and Rooker 

2008; Wells et al. 2009). Commercial landings of lane snapper are modest, primarily in Florida,

where landings totaled $86,219 in 2016 (NOAA NMFS Office of Science and Technology 2019).

They are a regular recreational catch offshore of Florida as well.

The objectives of our study were the following:

1) Test for habitat relationships of snapper and coastal shark species with a broad suite of 

environmental factors, including prey, nearby ecosystems, geomorphology, and oceanographic 

characteristics.

2) Model the spatial distribution of snapper and shark species.

2.2 Methods

2.2.1 Study Area

The study area is described in Section 1.2.1. 

2.2.2 Biological Data

Fish data were derived from fishery-independent surveys of SEAMAP and the National Marine Fisheries 

Service (NMFS) Mississippi Laboratories, which included regular surveys as well as a 1-year survey from 

the Congressional Supplemental Sampling Program (CSSP) (Table 2-1). From these data, we only used 

data from 2003 and later because we wanted to depict current conditions as best as possible, and this 

timeframe still provided us with an adequate sample size.

Trawl surveys from the SEAMAP program span the entire nGoM and are conducted in the summer 

(June–August) and fall (October–December). With the exception of minor changes in sample selection 

procedures in 2010 and thereafter, surveys have consistently used similar gear and protocols, and a 

random stratified sampling design based on depth and shrimp statistical area (Craig et al. 2005; Gulf 

States Marine Fisheries Commission 2017). Trawl surveys targeted shrimp and groundfish using a 12.8-m 

net in the central and eastern nGoM and a 6.1-m net near Texas. Trawls were conducted consistently 

during day and night hours. For processing, complete counts were conducted for samples < 22.7 kg, and a 

sample was taken and the count extrapolated for most species if the sample was larger. Snapper in 

summer surveys were counted in their entirety. In the fall, only a portion of the biological catch was 

measured for large samples (> 22.7 kg). For those surveys, the full trawl catch was projected based on a 

measured sample (e.g., when only half catch was sampled, the number of individuals for each species was 

doubled). We assumed that this proportionate extrapolation applied to each age group. 
17

Table 2-1. Sources of fisheries-independent survey data spanning 2003–2015. 

Sample sizes are provided for waters within our study area.

Data source

Acronym 

for 

dataset Dates

Gear used for 

survey (n = 

sample size) Species modeled

Southeast Fisheries 

Science Center, NMFS, 

Mississippi Laboratory

(MSLABS)

MSLABSBL2003-2017;

8 April–16 Nov

Bottom longline;

n = 1,014

Atlantic sharpnose 

shark, blacktip shark, 

and spinner shark

Congressional 

Supplemental Sampling 

Program (CSSP)

CSSP-BL 2011;

7 April–25 Oct

Bottom longline; 

n = 498

Atlantic sharpnose

shark, blacktip shark, 

and spinner shark

SEAMAP-trawl SEAMAP 

trawl

2003-2017;

30 May–19 Dec

Trawl;

n = 5,620

Red snapper (age 0 & 

age 1), lane snapper 

(age 0)

We removed extremely long or short trawl survey lengths, and subsequently, trawls ranged from 11–52 

min and 1.0–5.2 km. We transformed trawl survey counts to CPUE, calculated as fish km-1 of survey.

Prior to analyses, we used GAMs (knots=3) to explore the effects of trawl length and duration on the 

presence/absence or CPUE of species. All tests showed <2% of the deviance was explained by these 

effects except for a negative association with red snapper age-0 (2.7%) and a positive association with 

lane snapper age-1 (4.5%). Given these mixed effects, we proceeded with using CPUE and 

presence/absence data. We used summer (June–August) and fall (October–December) trawl surveys.

Surveys from the CSSP-Bottom Longline (CSSP-BL) and MSLABS-BL programs used the same 

sampling methodology. The methods are described in detail by Driggers III et al. (2012) and are outlined 

here. All bottom longline surveys used a 15/0 circle hook baited with Atlantic mackerel. The MSLABSBL and CSSP-BL surveys were randomly placed throughout the nGoM. For BL surveys, gear soak times 

were targeted to be 1 hour, as defined by the time elapsed between completion of deployment and 

initiation of retrieval. We used the centroid of trawl tows and bottom longline surveys to depict fish 

survey locations. For bottom longline surveys, we removed extreme survey efforts of ≥134 min, and 

survey effort ranged from 37–107 min. We also removed surveys with low hook counts (<80 hooks); the 

remaining surveys had 86–104 hooks per survey. Fish from bottom longline surveys were measured as 

count 100 hooks-1 hr-1

. We used GAMs (knots=3) to explore the effects of survey length (km) and 

duration (min) on each species. All tests showed < 2% of the deviance were explained by these factors.

2.2.3 Fish Age Classification

The age of red and lane snapper were distinguished for the analysis because these species undergo

ontogenetic shifts between early juvenile stages and the adult stage, when they inhabit reefs. The vast 

majority of sharks were juveniles and adults, both of which occur in similar habitats. Therefore, we did 

not distinguish age for sharks, but simply had the goal to estimate the proportion of each life stage

present. During trawl surveys, fish lengths were measured as total length (TL), fork length (FL), or 

standard length (SL). We converted all lengths to TL for age classification. For lane snapper, we used the 

conversion equations from the species’ stock assessment (SEDAR 2016). We categorized age classes 

based on a Bermuda study of lane snapper because the study measured numerous juveniles (Luckhurst et 

al. 2000), which were not well described elsewhere. Luckhurst et al. (2000) found a minimum length of 

185 mm FL (199 mm TL) for age-1 individuals. Based on this finding, we defined lane snapper age-0 
18

(< 199 mm TL) and age-1 or greater (≥ 199 mm TL). Given the dramatic reduction in growth after age 

one (Luckhurst et al. 2000), we did not distinguish further age groups.

For red snapper length conversions, we used equations from the Gulf of Mexico red snapper stock 

assessment report (SEDAR 2018). Red snapper of 19–50 mm TL are distinguished as post-settlement 

juveniles (Gallaway et al. 2009), and their prey items differ from older age classes that begin to prey on 

fish (Szedlmayer and Lee 2004). Because only 0.01% of red snapper captured by trawl were <50 mm TL, 

we discarded this age class from the analysis. Red snapper < 172 mm TL have mostly been found in open 

habitats, whereas the majority of those ≥ 172 mm inhabit reef habitats (Szedlmayer and Lee 2004). This 

threshold is further supported by Powers et al. (2018), who aged red snapper captured in trawl surveys 

and found age-0 red snapper = 30–170 mm, age-1 = 175–297 mm, and age-2 = 320–360 mm TL. 

Therefore, we categorized red snapper into the following juvenile age classes (range of catch 18–893 mm 

TL):

Age-0 = 51–172 mm TL (n = 25,528)

Age-1 = 173–300 mm TL (n = 5,670)

The majority of sharks were measured by natural total length (nat TL). When only FL was measured, we 

converted it to nat TL using equations dervived from available MSLABS-BL data. For Atlantic 

sharpnose, the conversion equation was: nat TL mm = 57.308 + 1.118 (FL mm); r2

= 0.952. For blacktip 

shark, the equation was nat TL mm = 44.129 + 1.162 (FL mm); r2

= 0.954. For spinner shark, the equation 

was TL mm = 25.811 + 1.170 (FL mm); r2 = 0.972). To further classify the age classes of sharks 

surveyed, we investigated the literature on length-age class associations. For Atlantic sharpnose shark, we 

followed the classifications used by Drymon et al. (2012) and Hoffmayer and Parsons (2003), who used 

the following age classes: Young-of-year = 330–590 mm nat TL, Juvenile = 600–840 mm nat TL, Adults 

≥ 850 mm nat TL. Castro (1996) reported blacktip shark neonates ranged from 530–660 mm nat TL 

offshore of Florida, and we used 660 mm nat TL as the maximum length to classify young-of-year 

blacktip shark. To distinguish juveniles from adults, we classified adults as those ≥ 1,407 mm nat TL. 

This is the median length reported for female blacktip sharks at maturity (Carlson et al. 2006). Carlson et 

al. (2006) showed male median length at maturity was slightly lower (1,246 mm TL); therefore, our 

estimate is likely to be slightly biased towards juveniles rather than adults. However, not all sharks were 

sexed, so detailed evaluation could not be conducted. With an approximation based on Carlson's study 

(Carlson and Baremore 2005), we defined those spinner shark < 70 cm as young-of-year, 70–116 cm as 

juvenile, and those > 116 cm as adults.

Of the sexed blacktip sharks, 54% were females and 46% were males. Blacktip shark lengths ranged 656–

2,466 mm nat TL with a median of 1,350 mm nat TL in MSLABS-BL data and 1,252 mm nat TL in 

CSSP surveys. Of blacktip sharks with an age classification, 0.002% (3 individuals) were classified as 

young-of-year, 65% were juvenile, and 35% were adults. Of the sexed Atlantic sharpnose sharks, 49% 

were males and 51% were females. Atlantic sharpnose shark length ranged 340–1,223 mm nat TL with a 

median of 920 mm nat TL in MSLABS-BL surveys and 895 mm nat TL in CSSP surveys. Of Atlantic 

sharpnose sharks with an age classification, 2% were young-of-year, 27% were juvenile, and 71% were 

adults. Of sexed spinner shark, 49% were female and 51% were male. Spinner shark age classification 

showed 3% as young-of-year, 43% as juvenile, and 54% as adults.

2.2.4 GIS Methods and Environmental Data

All GIS analysis procedures and data collection methods were followed as described in Section 1.2.3 with 

the exceptions described below. Overall, we developed predictor variables to depict oceanographic 

conditions, geomorphology, nearby ecosystems, geography, and prey species (Tables 2-2, 2-3). Natural 

reef locations were mapped during SEAMAP reef fish video surveys and were acquired through several 

mechanisms, including available charting, historical knowledge from fishermen, and bathymetric 

mapping (i.e., side-scan sonar and multi-beam sonar) (pers. communication, Matthew Campbell and 
19

Brandi Noble, NOAA National Marine Fisheries). The point density of and distance to artificial 

structures, including artificial reefs as well as oil and gas platforms, were calculated for the study area.

Sea surface temperature (SST) variables were obtained as predictors using the same methods as used for

other oceanographic data (Section 1.2.3). We used SST data processed from GHRSST (Group for High 

Resolution Sea Surface Temperature) version 4.1 of the Multiscale Ultrahigh Resolution Level 4 analysis, 

which obtains high resolution data via a blend of satellite measures (JPL MUR MEaSUREs Project 

2015).

Prey species were first identified from the literature. Then, we developed predictor variables for prey that 

were either readily sampled by the SEAMAP trawl surveys or were available from models developed 

during our project (Table 2-3). We derived data on menhaden (primarily Gulf menhaden), croaker, spot 

croaker, mantis shrimp, and squid from SEAMAP trawl data. The SEAMAP survey locations were 

interpolated to create a continuous surface. We synthesized data on prey species from 2003–2017 and 

conducted ordinary kriging with a spherical semivariogram model. Eight points were used for analysis 

within a maximum distance of 10 km. The ArcGIS expand tool was used to further extrapolate prey 

distributions when trawl surveys were > 10 km from a location in the study area. 
20

Table 2-2. Environmental variables developed to predict snappers and sharks in the nGoM. 

Oceanographic predictors were obtained from aggregations of monthly means spanning 2003–2017.

Variable type Variable (units)

Radius of 

analyses 

(km)

Resolution of 

data Data source

Substrate CV of depth 3 90 m CRM + modifications

Substrate

Density of oil platforms & 

artificial reefs (structures 

km-2)

5 90 m BOEM, Marine Cadastre

Substrate Distance to oil platforms & 

artificial reefs (km) NA 90 m BOEM, Marine Cadastre

Substrate Distance to natural reef 

(km) NA 90 m

Matthew Campbell, NOAA National 

Marine Fisheries

Substrate Distance to shoal (km) NA 90 m

Pickens and Taylor, NOAA 

Biogeography Branch, identification 

of shoals

Substrate Proportion of area with 

shoal 3 90 m

Pickens and Taylor, NOAA 

Biogeography Branch, identification 

of shoals

Substrate Mean sediment grain size 

(mm) 3 370 m

Chris Jenkins, University of 

Colorado, interpolation of 

usSEABED data

Substrate Proportion of area with BPI

≥ 1 3 90 m CRM + modifications

Substrate Slope (degrees) 3 90 m CRM + modifications

Oceanographic Mean depth (m) 3 90 m CRM + modifications

Oceanographic Sea surface temperature 

(°C) NA 1.2 km

GHRSST blend of satellite 

measures, MUR-JPL_L4-GLOBv4.0, NASA Jet Propulsion Lab

Oceanographic Bottom temperature (°C) NA 4.4 km HYCOM + NCODA

Oceanographic Chlorophyll-a (mg m-3) NA 5.5 km Aqua MODIS satellite, 8-day 

it Oceanographic Bottom salinity (psu) NA 4.4 km HYCOM + NCODA

Oceanographic

Mixed layer thickness (m) 

(depth where temperature 

change from surface is 

0.2°C)

NA 4.4 km HYCOM + NCODA

Oceanographic Bottom current velocity—U 

& V directions(m s-1) NA 9.3 km HYCOM + NCODA

Oceanographic Hypoxia (mean probability 

of hypoxia) NA 90 m North Carolina State University

Geography East or west of longitude 

W 88o NA 90 m -

Geography Distance to shoreline (km) NA 90 m -

Nearby

ecosystems Nearby wetlands NA 90 m NWI

Nearby 

ecosystems Nearby estuaries NA 90 m NWI

* HYCOM + NCODA = HYbrid Coordinate Ocean Model + Navy Coupled Ocean Data Assimilation; GHRSST = 

Group for High Resolution SST Level 4 analysis, Multiscale Ultrahigh Resolution (MUR) based on nighttime; MODIS 

= Moderate Resolution Imaging Spectroradiometer; CRM = Coastal Relief Model; NWI = National Wetlands Inventory
21

Table 2-3. Biological predictor variables developed to predict the distribution of snappers and 

sharks.

2.2.5 Statistical Analysis

We examined predictor variables for multicollinearity, and we removed highly correlated variables (r > 

0.80) prior to analyses. In addition to environmental predictors, we used season (summer or fall) for trawl 

surveys of snappers, day-of-year for shark species, and start time of surveys as predictors. The time at 

which surveys are conducted can affect the detectability of species and has previously been documented 

as affecting shark catch (Driggers III et al. 2012). Seasonality of trawls represented changes in summer 

and fall distributions because of species’ natural history. We did not use year as an explanatory factor 

because the primary objective of our research was to determine long-term value of waters and substrates 

of the nGoM. Therefore, we assume years of high or low abundance are representative of long-term fish 

distribution. 

Because improving our understanding of species-habitat relationships was one of our objectives, the 

variables in shark and snapper models differed by a few variables. We excluded variables with no 

hypothesized relationship to particular fish species to help in minimizing issues with moderately 

correlated variables. Chlorophyll-a, nearby wetlands, and nearby estuaries were tested only for sharks. 

SST variables were only tested for blacktip shark and spinner shark, as the other species have demersal 

habits. We only used density of artificial structures, distance to artificial structures, and distance to natural 

reefs to test with snappers. To summarize predictor variable results, we calculated the frequency of each 

variable type as depicted in Table 2-2.

Training (70%) and validation (30%) data were selected by the same process as used with the statistical 

analyses of Penaeid shrimp (Section 1.2.4). The BRT analysis procedures outlined in Section 1.2.4. were

used to quantify species-habitat relationships, relative importance of predictors, and interaction effects for 

snappers and sharks. We also assessed the accuracy of models using the same procedures. We assumed 

the effect of survey time represented a detectability effect for blacktip shark and applied the model at the 

Species Prey species predictor 

variables

Justification for prey 

inclusion

Red snapper 

age-0

Brown shrimp (fall), pink shrimp, 

mantis shrimp (Squilla spp.), squid 

(Loligo spp.)

(Bradley and Bryan 1975; 

Szedlmayer and Lee 2004; 

Wells et al. 2008a)

Red snapper 

age-1

Searobin (Prionotus spp.), 

lizardfish (Synodus spp.), squid (Szedlmayer and Lee 2004; 

Wells et al. 2008a)

Lane snapper 

age-0

Brown shrimp (fall) & pink shrimp (Franks and VanderKooy 

2000)

Lane snapper 

age-1 None identified NA

Atlantic 

sharpnose shark

Menhaden (Brevoortia spp.), 

croaker (Micropogonias undulatus), 

pink shrimp, brown shrimp 

(summer)

(Bethea et al. 2004; 

Drymon et al. 2012; 

Harrington et al. 2016)

Blacktip shark Menhaden, croaker (Barry et al. 2008; Bethea 

et al. 2004)

Spinner shark

Menhaden (Bethea et al. 2004; Cortés 

1999)
22

peak time of 02:00. For Atlantic sharpnose shark, day-of-year was a factor in the model, and we predicted 

at the peak time of year in the model (9 April). 

2.3 Results

The frequency of species ranged from red snapper age-1 on 18.7% of trawl surveys to Atlantic sharpnose 

shark on 58.6% of bottom longline surveys (Table 2-4). The relative abundance models for red snapper 

age-0 and Atlantic sharpnose shark explained 41–43% of the deviance in the validation data with an Rs of 

0.59–0.60 in their respective models (Table 2-5). All species modeled with presence/absence data had an 

AUC value of ≥ 0.80 in the validation data, indicating the models were very good at discriminating 

presence and absence (Table 2-4). The confusion matrices showed that absence was consistently 

predicted more accurately than presence (Table 2-6 to Table 2-10).

Across all snapper and shark models, 45 variables were selected across the following variable types:

oceanographic (22 variables selected), prey (6), substrate (6), geographic (4), temporal variables (4), and 

nearby ecosystems (3). Of the 22 oceanographic predictors, the most common variables were MLD (6), 

bottom temperature (5), and salinity (5). Of the substrate variables, three were related to artificial or 

natural reefs; sediment grain size, BPI, and distance to shoal were each selected one time. Variable 

importance varied considerably among variables, though sediment grain size, BPI, and distance to shoal 

were of relatively minor importance (Figures 2-1, 2-2).

Table 2-4. Frequency of select snapper and shark species in the trawl and bottom longline

surveys from 2003–2017.

Common name Number of fisheryindepedent surveys (n) Type of modeling

Percent of 

surveys with 

presence

Total 

count of 

species

Red snapper 

(age 0) 5,620 Count 36 23,076

Red snapper

(age 1) 5,620 Presence/absence 18.7 4,753

Lane snapper

(age 0) 5,620 Presence/absence 25.7 9,784

Lane snapper

(age 1) 5,620 Presence/absence 20.3 1,143

Atlantic 

sharpnose shark 1,506 Count 58.6 8,765

Blacktip shark 1,506 Presence/absence 28.2 1,831

Spinner shark 1,506 Presence/absence 12.6 872
23

Table 2-5. BRT specifications and percent deviance explained for marine SDMs of snappers and 

sharks.

Species

Tree 

complexity

Learning 

rate

# of 

trees Cross-validation Validation

Validation 

Spearman 

correlation

Red 

snapper 

(age 0)

5 0.01 1,400 Deviance

explained = 50%

Deviance

explained = 41% 0.59

Red 

snapper 

(age 1)

2 0.01 1,550 AUC = 0.83 AUC = 0.80 NA

Lane 

snapper 

(age 0)

3 0.01 1,950 AUC = 0.84 AUC = 0.83 NA

Lane 

snapper 

(age 1)

2 0.02 2,550 AUC = 0.91 AUC = 0.89 NA

Atlantic 

sharpnose 

shark

5 0.005 1,900 Deviance

explained = 45%

Deviance

explained = 43% 0.60

Blacktip 

shark 1 0.02 1,250 AUC = 0.84 AUC = 0.80 NA

Spinner 

shark 2 0.005 1,400 AUC = 0.90 AUC = 0.87 NA
24

Table 2-6. Confusion matrix from the validation data of the red snapper age-1 model at the optimal 

threshold to distinguish presence/absence, which was with a probability of 0.31.

Observed 

absence

Observed 

presence

User's accuracy 

(% correct)

Predicted 

absence 1,323 174 88%

Predicted 

presence

172 207 55%

Table 2-7. Confusion matrix from the validation data of the lane snapper age-0 model at the 

optimal threshold to distinguish presence/absence, which was with a probability of 0.37.

Observed 

absence

Observed 

presence

User's accuracy 

(% correct)

Predicted 

absence 1,185 200 86%

Predicted 

presence

201 290 59%

Table 2-8. Confusion matrix from the validation data of the lane snapper age-1 model at the 

optimal threshold to distinguish presence/absence, which was with a probability of 0.44.

Observed 

absence

Observed 

presence

User's accuracy 

(% correct)

Predicted 

absence 1,379 154 90%

Predicted 

presence

104 238 70%

Table 2-9. Confusion matrix from the validation data of the blacktip shark model at the optimal 

threshold to distinguish presence/absence, which was with a probability of 0.47.

Observed 

absence

Observed 

presence

User's accuracy 

(% correct)

Predicted 

absence 323 61 84%

Predicted 

presence

34 67 66%

Table 2-10. Confusion matrix from the validation data of the spinner shark model at the optimal 

threshold to distinguish presence/absence, which was with a probability of 0.33.

Observed 

absence

Observed 

presence

User's accuracy 

(% correct)

Predicted 

absence 333 76 81%

Predicted 

presence

24 52 68%
25

Figure 2.1. Relative importance of variables in models of a) red snapper age-0, b) red snapper 

age-1, c) lane snapper age-0, d) lane snapper age-1.
26

Figure 2.2. Relative importance of variables in models of a) Atlantic sharpnose shark, b) blacktip 

shark, c) spinner shark.
27

2.3.1.1 Red Snapper Age-0

As expected, red snapper age-0 (Figure 2.3) were more abundant in the fall as individuals become large 

enough to be captured by trawl sampling. MLD in the spring and fall had strong, positive relationships

with red snapper. These young juveniles were positively associated with prey species of brown shrimp, 

mantis shrimp, and squid. In regard to geomorphology, red snapper age-0 had a higher CPUE within the 

first few km of shoals and with a high proportion of area with a positive BPI. An interaction between BPI 

and distance to shoal (IE = 49) showed red snapper had a high CPUE within a few km of a shoal 

regardless of BPI. At greater distances from shoals, the BPI had a linear, positive effect when the BPI had 

a positive value positive for > 40% of the surrounding area. MLD in summer primarily affected red 

snapper during the fall season (IE = 31), and CPUE was highest when a deeper MLD in spring was 

coincided with a higher brown shrimp CPUE (IE = 19).

2.3.1.2 Red Snapper Age-1

Red snapper age-1 (Figure 2.4) shifted farther offshore compared to age-0, but still had a positive 

association with spring MLD. Individuals moved closer to artificial structures (often within 25 km) and 

natural reefs (often within 75 km). In regard to interactions, red snapper age-1 moved farther offshore 

when artificial structures were within 20–40 km (IE = 44). Interpreting these distance from mapped 

variables, we observed that this meant red snapper age-1 moved farther offshore near Alabama, 

Mississippi, Louisiana, and Texas (where oil platforms and other artificial reefs were common), but 

individuals did not move farther offshore in Florida shelf waters. Distance to shoreline interacted with 

MLD spring (IE = 32) and showed MLD spring had the strongest effect farther from the shoreline. Red 

snapper age-1 had the highest probability of presence at greater depths up to a 50 m depth.

2.3.1.3 Lane Snapper Age-0

As expected, lane snapper age-0 (Figure 2.5) were more abundant in the fall as individuals become large 

enough to be captured by trawl sampling. Lane snapper age-0 had a higher probability of presence with 

higher fall and spring bottom temperatures. They had a higher probability of presence farther from the 

shoreline, with more shallow MLD, and with salinities in the range of 20-34 psu compared to salinities 

> 34 psu. An interaction between salinity and spring bottom temperature (IE = 61) showed low 

probability of occurrence at higher salinities (> 32 psu) except where spring bottom temperatures were 

> 22°C. Probability of occurrence was highest where a shallow MLD in summer coincided with high fall 

bottom temperatures (IE = 61). 

2.3.1.4 Lane Snapper Age-1

Lane snapper age-1 (Figure 2.6) had a greater probability of presence in waters < 40 m in depth and with 

cooler spring bottom temperatures. They were less common with mud and silt substrates (particularly, 

< 0.03 mm grain size) and when grain sizes became larger than granule gravel (see Wentworth 1922). No 

interactions were observed.

2.3.1.5 Blacktip Shark

Blacktip shark (Figure 2.7) were positively related to chlorophyll and nearby wetlands; they had the 

highest probability of occurrence with low salinity waters spanning 27–34 psu. Blacktip shark were 

positively related to croaker prey, but did not directly select for their primary prey of menhaden. The 

species had a higher probability of occurrence where MLD in summer was relatively deep, and they were 

more likely to be caught from 00:00–03:00. Interactions were minimal. 
28

2.3.1.6 Spinner Shark

Spinner shark (Figure 2.8) had the highest probability of occurrence with salinities of ≤ 30 psu, and they 

were positively related to chlorophyll and nearby estuaries. Spinner shark had a positive relationship with 

hypoxia, as probability of occurrence was highest in waters with 20–40% probability of hypoxia. We 

examined the hypoxia map, and the 20-40% range corresponded well with the edge of highest probability 

hypoxic zone. Spinner shark had a moderate interaction between hypoxia and nearby estuaries (IE = 38),

showing that spinner shark selected waters with 20–40% hypoxia probability that coincide with a

relatively high amount of nearby estuaries. 

2.3.1.7 Atlantic Sharpnose Shark

Atlantic sharpnose shark CPUE (Figure 2.9) was positively related to the prey species of brown shrimp 

and croaker as well as the amount of nearby wetlands. Atlantic sharpnose shark CPUE was highest in the 

spring, farther from the shoreline, and at greater depths up to our 50-m maximum in the study area. 

Bottom salinity showed a dichotomy where salinities of 27.5–30 psu and 33.5–36 psu had the highest 

CPUE, but intermediate salinities had fewer Atlantic sharpnose shark. Interaction effects were minimal.
29

Figure 2.3. Predicted red snapper age-0 CPUE in the a) summer and b) fall seasons.

The study area is indicated by the dashed line, and CPUE represents the predicted number of red snapper per km of trawl survey. 
30

Figure 2.4. Predicted red snapper age-1 probability of presence in the summer and fall seasons combined.

The study area is indicated by the dashed line, and CPUE represents the predicted number of red snapper per km of trawl survey. 
31

Figure 2.5. Predicted lane snapper age-0 probability of presence in the a) summer and b) fall seasons.

The study area is indicated by the dashed line, and CPUE represents the predicted number of lane snapper per km of trawl survey.
32

Figure 2.6. Predicted lane snapper age-1 probability of presence in the summer and fall seasons combined.

The study area is indicated by the dashed line, and probability of presence represents the probability of capture given a trawl survey. 
33

Figure 2.7. Predicted blacktip shark probability of presence in the spring–fall seasons.

The study area is indicated by the dashed line, and probability of presence represents the probability of capture given a bottom longline survey. 
34

Figure 2.8. Predicted spinner shark probability of presence in the spring–fall seasons.

The study area is indicated by the dashed line, and probability of presence represents the probability of capture given a bottom longline survey. 
35

Figure 2.9. Predicted Atlantic sharpnose shark CPUE in the spring–fall seasons.

The study area is indicated by the dashed line, and CPUE represents the predicted number of sharks hooks-100 hr-1 of bottom longlne survey. 
36

2.4 Discussion

By testing a comprehensive set of predictors, we discovered new species-habitat relationships with MLD, 

the area of nearby wetlands and estuaries, prey species, and substrate characteristics. Across all species 

considered, oceanographic variables were the most commonly selected predictors, and they often scored 

high in variable importance. Although distance to artificial or natural reefs were important predictors in 

the red snapper age-1 model, the other geomorphology predictors had low variable importance. 

Geomorphology predictors only appeared to refine fish distributions where oceanographic characteristics 

were already suitable for fish. For example, red snapper age-0 had a higher CPUE in close proximity to 

shoals and where the BPI predominately showed a hill topography. Yet, their spatial distribution was 

highly skewed towards the western GoM, where MLD was deeper and brown shrimp prey were most

common. This inference of variable influence across spatial scales is supported by Mannocci et al. (2017),

who shows that oceanographic features operate at intermediate scales of 1–100 km and factors such as 

prey operate at a scale of 10 m–1 km. We further suggest that geomorphology also affects species at this 

local scale. We acknowledge that prey species may be also be correlated with geomorphology, 

oceanography, and nearby ecosystems characteristics. SDMs are correlative models, but the specieshabitat relationships we quantified here provide for refined distributions of species, reveal patterns of 

important habitat factors, and provide a basis for future hypothesis testing concerning EFH. As with the

shrimp results (above), no species analyzed here was associated with benthic characteristics of bottom 

current velocity, slope, or depth heterogeneity.

We discovered MLD was an influential predictor of red snapper age-0 and age-1, lane snapper age-0, 

blacktip shark, and Atlantic sharpnose shark. MLD was also a predictor of brown shrimp CPUE (Section 

1.3.), which were related to red snapper age-0 and Atlantic sharpnose shark. Measures of MLD have 

rarely been included as predictor of marine fish SDMs (Volume 1). In the nGoM, MLD appears to be 

influenced by the Loop Current, eddies, and wind stress. The Loop Current enters the Gulf of Mexico 

through the Yucatan Channel, goes northward in the basin, and then exits via the Straits of Florida. 

Importantly, the Loop Current produces large spin-off eddies that often take a westward path originating

near the Mississippi Delta (Johnson et al. 2017). To a lesser extent, wind influences water vertical 

structure and contributes to the vorticity of eddies (Ohlmann et al. 2001). The end result is an exchange of 

shelf waters and deeper waters (Johnson et al. 2017; Ohlmann et al. 2001), and upwelling or downwelling

can contribute towards biological productivity (Spies et al. 2016). Therefore, the relationships with MLD 

and shrimp, snapper, and sharks may be the result of enhanced productivity in these waters.

We found prey species and nearby ecosystems were common predictors of species. In our review of 

spatial distribution models, prey were only considered in 4% of studies (Volume 1). Of these, none were 

studies of sharks or reef fish. Because SDMs are correlative models, we do not have direct evidence of 

drivers of species' distributions. However, the statistical models suggest that prey species can be better 

predictors than other environmental attributes. There are two probable reasons for these results: 1) fishprey relationships represent a causal relationship, or 2) prey species represent a combination of 

environmental conditions, which are better correlated to species compared to single predictor variables.

Future studies could further test predator-prey relationships to determine their applicability to EFH and 

ecosystem-based management. The productivity of nearby ecosystems, such as wetlands and estuaries, 

are usually ignored in marine fish research. Only a few studies have linked offshore marine fish or shrimp 

distribution to the proximity of mangroves (Barbier and Strand 1998) or estuaries (Beger and Possingham 

2008). Nonetheless, interactions among coastal and marine ecosystems have been cited as important areas 

for conservation (Pickens et al. 2017), and trophic energy exchanges are thought to be important among 

these ecosystems (Zuercher and Galloway 2019).
37

As with pink shrimp (Section 1.3), the confusion matrices showed predictions of absences were 

consistently more accurate than predicted presences. Given that detectability is imperfect during surveys 

(MacKenzie et al. 2002), the SDMs presented here should be considered as relative rather than absolute 

numbers. Waters with a greater probability of occurrence or relative abundance (CPUE) should be 

interpreted in comparison to waters with lower probability of occurrence or relative abundance. 

2.4.1 Red Snapper

Research on juvenile red snapper in the nGoM has focused on geomorphology, including the 

chronological selection of sand, shell, low-relief shell, and high-relief shell (Lingo and Szedlmayer 2006; 

Szedlmayer and Howe 1997; Wells et al. 2008b). Our study adds to these known associations with 

substrate complexity by quantifying topography at a landscape scale in the form of high relief (i.e., a 

positive BPI) and the proximity of sand shoals. Observational evidence has suggested a role between 

juvenile red snapper and sediment grain size (Powers et al. 2018; Szedlmayer and Mudrak 2014);

however, we did not find evidence of such an association at the scale of our study area. Spatial patterns of 

red snapper age-0 were primarily determined by MLD and the predicted CPUE of brown shrimp, with 

minor contributions from other prey species and substrate complexity. A distinct ontogenetic shift 

occurred from age-0 to age-1 red snapper. Age-1 red snapper were still associated with spring MLD, but 

they moved to greater depths, farther offshore, and closer to natural and artificial structures. Our spatial 

models have similar patterns as Dance and Rooker's (2019) results, though we validated our model and 

took a more mechanistic approach, rather than testing variables like latitude and longitude. 

Switzer et al. (2015) found the abundance of juvenile red snapper near Louisiana were reduced during 

years with severe hypoxia, and juveniles moved to deeper, cooler waters during those years. Although 

hypoxia was not selected as a variable for red snapper age-0 in our study, the model showed a low 

predicted CPUE in waters with a high frequency of hypoxia. The brown shrimp prey species is well 

known to avoid hypoxic waters (Craig 2012; Craig et al. 2005), and the correlation between brown shrimp 

and red snapper age-0 may have indirectly resulted in the low predicted CPUE in waters prone to 

hypoxia. Furthermore, the correlations between brown shrimp and predatory species provides evidence 

that hypoxia could affect fish farther up the food chain. We caution that trawl surveys were near shoals, 

but shoals themselves are usually too shallow to survey. Therefore, our habitat models do not account for 

fine-scale variation directly at the shoal. Dubois et al. (2009) does present evidence that Ship Shoal 

offshore of Louisiana is a hypoxia refuge for benthic invertebrates.

2.4.2 Lane Snapper

We found both age groups of lane snapper were strongly associated with oceanographic variables, 

particularly bottom temperature and salinity. They shifted from using salinities of ≤ 34 psu at age-0 to 

waters with > 34 psu at age-1. Given their subtropical / tropical range, the relationship with bottom 

temperature was not surprising. However, we expected more relationships with geomorphology. Juvenile 

lane snapper had been associated with sand shoals offshore of Louisiana (Brooks et al. 2005) and Texas 

(Mikulas and Rooker 2008; Wells et al. 2009). Our findings of juvenile lane snapper age-1 did show an 

association sandy sediments, but an association with shoals was lacking. 

2.4.3 Blacktip, Spinner, and Atlantic Sharpnose Shark

Spinner and blacktip shark were not correlated with their main prey, menhaden, but bottom trawl surveys 

may not sample menhaden very well. Both shark species were associated with factors that indirectly 

describe menhaden habitat. Gulf menhaden utilize estuary and nearshore waters of moderate salinity,

where they prey directly on phytoplankton and zooplankton (Olsen et al. 2014). These prey are likely 

correlated with chlorophyll-a measures. Gulf menhaden use estuaries and open water-marsh edges 

(Costanza et al. 1989; Rozas and Minello 2015; Rozas et al. 2007); therefore, a shark association with 
38

these habitats is expected. Drymon et al. (2013) found the distribution of blacktip shark was best 

explained by crustacean biomass, but noted these sharks eat small fish. Here, we infer that wetland 

productivity likely leads to greater blacktip shark CPUE. Spinner shark have previously been found in 

waters with relatively low dissolved oxygen (Drymon et al. 2013). We found a positive relationship

between spinner shark and hypoxia with a peak probability of occurrence with a 25-40% chance of 

hypoxia. These results suggest that spinner shark may feed on prey that either aggregate at the edge of 

hypoxic zones (e.g., Craig 2012) or aggregate towards the surface (e.g., Hazen et al. 2009). In lab 

experiments, Atlantic menhaden (B. tyrannus) have been shown to avoid waters with low dissolved 

oxygen (Wannamaker and Rice 2000), but the specific behavior of menhaden due to hypoxia in the 

nGoM needs further study.

For Atlantic sharpnose shark, we found habitat relationships that differed from studies that found positive 

correlations with chlorophyll concentration (Drymon et al. 2013) and depths < 30 m (Drymon et al. 

2010). Similar to red snapper, the association with brown shrimp and greater depths meant that low 

CPUEs of Atlantic sharpnose shark were predicted for frequently hypoxic waters. Harrington et al. (2016)

found juvenile Atlantic sharpnose shark fed heavily on Penaeid shrimp offshore of Texas, and other 

studies in the nGoM have shown they feed on a wide range of species (Bethea et al. 2006; Drymon et al. 

2012). The lack of a relationship with chlorophyll in our results may be because salinity, croaker, and 

nearby wetlands better explained Atlantic sharpnose shark distribution in nearshore waters where 

chlorophyll is high.

2.4.4 Conclusions and Implications for Dredging

In our wide-ranging study, we can conclude that not all shoals have equal value to fish. This is supported 

by the fact that Rutecki et al. (2014) reported lane snapper and red snapper in the GoM were either 

frequent, common, or rare, depending on the shoal. Oceanographic factors, prey species, and nearby 

wetlands and estuaries all play key roles in determining species' distributions. Evidence shows 

geomorphology only plays a minor or localized role in determining fish species' distributions. We caution 

that our analyses were based on fish surveys that typically span 3 km in length and microhabitat selection 

within this range may have been missed. Yet, our models showed a high predictive ability and were able 

to quantify suspected relationships between red snapper and substrate complexity. SDMs can illuminate 

habitat relationships as well as map the spatial patterns of species' distributions. We have demonstrated 

that modeling the distribution of species can be accomplished at a relatively fine scale. These spatial 

patterns can be applied to make management decisions and apply a strategic, regional perspective on 

natural resource use in the nGoM.
39

3 Predicting the Marine Distribution of Red Snapper, Black Seabass,

and Shark Species in the South Atlantic

3.1 Introduction

The US Southeast Atlantic OCS (hereby South Atlantic) supports a diverse assemblage of fishes that 

provide high economic value to commercial and recreational fisheries. Fisheries economic impact in 

recent years has exceeded $4.4 billion annually and supports more than 77,000 jobs (National Marine

Fisheries Service 2017). Geographically identified along the coastlines of the states of North Carolina, 

South Carolina, Georgia, and Florida, the region is bounded by prominent geographic features of Cape 

Hatteras and Cape Canaveral and oceanographically by the Gulf Stream current to the east. The region is 

home to species that use an array of seafloor and oceanographic habitats dominated by unconsolidated 

sediments forming sheets, ridges, and shoals, with interspersed emergent rocky reefs and other hard 

structures like artificial reefs and shipwrecks. Managed fish species are grouped into complexes based 

upon their general habitat preference (e.g., “snapper-grouper complex” that use reef and rocky habitats), 

migratory behaviors (“coastal pelagics” transient predators that seasonally migrate along the coastline), 

and migratory top predators (sharks).

Fishery-independent surveys have assessed managed species to derive indices of abundance that 

contribute to stock assessments for managing the populations. However, details on habitat selection and 

drivers of the broader distribution patterns remain poorly understood for the vast majority of species, 

including members of the snapper-grouper complex that are surveyed around rocky reefs throughout the 

region. For the snapper-grouper complex, there is evidence that some species are likely to be observed 

over soft sediments, though those sediment habitats may be adjacent to structured habitats (Bacheler et al. 

2014). Studies of shark distributions are rare in the South Atlantic and mostly focus on seasonality, size 

and ages in coastal waters and estuaries (Thorpe et al. 2004; Ulrich et al. 2007).

Improvements in species distribution information and a greater understanding of relationships of fish to 

seafloor characteristics are needed to guide planning and permitting for ocean activities that may impact 

fish and their habitats. The demand for offshore marine sands is increasing in the United States (Drucker 

et al. 2004), and sand is commonly used for beach renourishment, barrier island restoration, and wetland 

restoration. Sand shoals are often preferred sand resources because of the quantity of sand per unit area, 

and the dredging of OCS sand shoals is likely to increase in the future as demand increases due to 

renourishment cycles for beaches, emergency repairs of beaches after storms, and the projected effects of 

sea-level rise (Nairn et al. 2004). BOEM is part of the US Department of the Interior and is responsible 

for the management and development of mineral resources on the OCS, including sediment resources. As 

demand for OCS sand increases, BOEM faces complex multi-user interactions, including issues of 

resource allocation, cumulative impacts from repeated use, fisheries use and potential conflicts, protection 

of archaeological sites, oil and gas infrastructure, potential renewable energy infrastructure, and impacts 

on EFH (Michel et al. 2013).

For this paper, we focus on two primary objectives:

1) Test for habitat relationships of red snapper, black seabass, and selected shark species with a 

broad suite of environmental factors, including geomorphology, oceanographic characteristics,

and nearby ecosystems

2) Model and predict the spatial distribution of five species of hard bottom fish and sharks to 

identify overlap with sand shoals that may be targeted for dredging activities
40

3.2 Methods

3.2.1 Study Area

The study area depicted the South Atlantic region of the United States spanning from Cape Hatteras, 

North Carolina through the South Atlantic Bight. The landward boundary of the study area was defined 

by OCSLA, which distinguishes Federal- and state-managed waters. The oceanic boundary of the study 

area was defined by a 50-m contour line from NOAA’s CRM (NOAA National Centers for 

Environmental Information). This particular area was the focus because sand dredging operations 

authorized by the BOEM for beach and barrier island restoration does not exceed 50 m. The study area 

had a total surface area of 84,924 km2

. The benthic substrate of the area consists of fine to coarse sand 

sediments with patchy areas of hard bottom reefs. The most prominent feature of the South Atlantic is the 

Gulf Stream current, which creates a cross-shelf mixing of waters and strong water stratification (Castelao 

2011). Surface and bottom water intrusions from the Gulf Stream are most frequent in the summer 

months, are influenced by wind stress, interact with salinity, and are more frequent at the extreme 

northern and southern waters of the South Atlantic Bight (Castelao 2011).

3.2.2 Biological Data

Fish data were derived from fishery-independent surveys of SEAMAP, the Southeast Reef Fish Survey 

(SERFS) and the NOAA Southeast Fisheries Science Center, Mississippi Laboratories (Table 3-1). From 

these data, we only used data from 2004 and later because we wanted to depict current conditions as best 

as possible, and this timeframe still provided us with an adequate sample size. The vast majority of 

surveys were conducted from spring through fall, and, therefore, our SDMs do not represent snapper or 

shark distributions in the winter.

Surveys from the MSLABS-BL program used the methodology outlined in detail by Driggers III et al. 

(2012), with details briefly outlined here. All bottom longline surveys used a 15/0 circle hook baited with 

Atlantic mackerel. Surveys were randomly placed throughout the study area. Gear soak times were 

targeted to be 1 hr, as defined by the time elapsed between completion of deployment and initiation of 

retrieval. We used the centroid of bottom longline surveys to depict fish survey locations. For MSLABSBL data, we removed extreme survey lengths. More specifically, we removed one survey that was 25 km 

in length and one that was 0.08 km. The remaining surveys ranged from 0.50–3.7 km. Additional bottom 

longline data were obtained from the SEAMAP program; however, the survey methodology differed, so 

these data were only used as presence data for validation purposes. Of the SEAMAP data, we only used 

surveys offshore of Florida, Georgia, and South Carolina for analysis because waters in our study area 

were not sampled offshore of North Carolina. The SEAMAP bottom longline methods offshore of Florida 

and Georgia included using either a 15/0 or a 12/0 circle hook baited with squid. For South Carolina, 

SEAMAP used a 15/0 circle hook and had a soak time target of 30 min, but they used Atlantic mackerel 

and striped mullet as bait.

The SERFS program surveys hard bottom locations in the South Atlantic with Chevron traps, and, since 

2010, video attached to Chevron traps (Bacheler et al. 2014). The determination of sampling locations and 

details of both methods are provided by Bacheler et al. (2014), with a brief outline given here. Chevron 

traps were baited with 24 menhaden, and traps were deployed in groups of six, with each > 200 m from 

each other. The soak time for traps was targeted for 90 min. Since 2010, video cameras have been 

mounted over the mouth of traps with a view of approximately 60o from the trap. Video frames were read 

to identify fish at 30 second intervals over 20 min, which resulted in 41 frames read per sampling effort. 

For SERFS video surveys, we removed any video surveys that had < 41 frames read. For SERFS trap 

surveys, we removed traps with a duration of < 10 min and those > 150 min, because they had a much 

lower catch rate in preliminary models.
41

Table 3-1. Sources of fisheries-independent survey data. Sample sizes are provided for waters 

within our study area.

Data source

Acronym 

for dataset Dates

Gear used for 

survey (n = # of 

surveys) Species modeled

Southeast FisheryIndependent Survey SEFIS video 2010–2017;

21 Apr–27 Oct

Video;

n = 6,264

Red snapper

Validation only for 

sandbar shark, tiger 

shark

Southeast Area 

Monitoring and 

Assessment: reef fish 

traps

SEAMAP 

traps 2010–2017;

23 Apr–26 Oct

Chevron traps;

n = 5,210 Black sea bass

Southeast Fisheries 

Science Center, NMFS, 

Mississippi Laboratory

MSLABS-BL 2004–2017;

21 Apr–25 Sep

Bottom longline;

n = 386

Blacknose shark, 

sandbar shark, tiger 

shark

Southeast Area 

Monitoring and 

Assessment: bottom 

longline

SEAMAP 

longline

2006–2016;

22 Apr–29 Dec Bottom longline

Validation only for 

blacknose shark, 

tiger shark

3.2.3 GIS and Environmental Data Sources

All GIS analysis procedures and data collection methods were followed as described in Section 1.2.3,

with the following modifications and additions. We developed predictor variables to depict oceanographic 

conditions, geomorphology, nearby ecosystems, and geography (Table 3-2). We used a bathymetry 

dataset derived as part of the South Atlantic Marine Bight Assessment (Conley et al. 2017). Despite these 

data being the best available for the South Atlantic, a few notable errors persisted. We updated a 1,600 

km2 and a 400 km2 region offshore of northeastern North Carolina with sounding points acquired in 2016 

(NOAA National Centers for Environmental Information 2016). To be consistent, we used the same 

kriging methods as the South Atlantic Marine Bight Assessment (Conley et al. 2017). To describe aspect, 

we calculated the aspect cosine and sine. We calculated depth, CV depth, slope, sediment grain size, 

proportion of area with shoal, slope, aspect, and the BPI within a 3-km radius using ArcGIS focal 

statistics.

Hard bottom habitats were depicted by three datasets that were combined. A predictive model (developed 

by Matthew Poti, NOAA Biogeography Branch) depicting probability of hard bottom occurrence was 

converted into a presence/absence model by representing areas with ≥ 36.3% probability of occurrence as 

hard bottom presence locations. This threshold was derived as the maximum AUC value, which is the 

threshold that corresponds with the model’s optimal discrimination ability. We then added polygon data 

from South Atlantic Marine Bight Assessment mapping of hard bottom features. With this dataset, we 

removed the classifications of “possible” and “potential” hard bottom or hard bottom slope; these low 

confidence areas depicted seabed features > 2 km from known hard bottom locations and had substantial 

uncertainty. All “probable” hard bottom locations (≤ 2 km from known hard bottom), “high confidence”

(≤ 1 km from known hard bottom), and very confident (mapped) locations were added to the spatial 

model. The probable and high confidence locations often depicted polygons where hard bottoms were 

known to be present along the periphery. The third dataset was the original synthesis of hard bottom point 

locations that were used as input into NOAA’s predictive model. We removed point locations already 

classified as hard bottom (from above) and those within 90 m of hard bottom. These adjacent points often 

surrounded areas depicted as hard bottom. These restrictions resulted in 687 additional points. Because 

these were often isolated points, we calculated a 1 km buffer around these locations to add to the hard 

bottom model.
42

Table 3-2. Environmental variables developed to predict fish species in the South Atlantic. 

Oceanographic predictors were from aggregations of monthly means spanning 2003-2017.

Variable type Variable Resolution 

of data Source data

Substrate Mean depth (m) 90 m CRM plus modifications

Substrate CV of depth 90 m CRM plus modifications

Substrate Density of artificial reefs 

(reefs km-1) 90 m BOEM, Marine Cadastre

Substrate Distance to artificial reef (km) 90 m

Substrate Distance to shoal (km) 90 m

Pickens and Taylor, NOAA 

Biogeography Branch, 

identification of shoals

Substrate Proportion of area with shoal 90 m

Pickens and Taylor, NOAA 

Biogeography Branch, 

identification of shoals

Substrate Mean sediment grain size 

(mm) 90 m

The Nature Conservancy, 

South Atlantic Marine Bight 

Assessment, substrate data

Substrate Proportion hills (with BPI ≥1) 90 m CRM plus modifications

Substrate Slope (o) 90m CRM plus modifications

Substrate Proportion hard bottom 90 m

Matt Poti, NOAA Biogeography 

Branch + The Nature 

Conservancy- SAMBA

Substrate Distance from hard bottom 

(km) 90 m

Matt Poti, NOAA Biogeography 

Branch + The Nature 

Conservancy- SAMBA

Substrate Aspect (cosine) 90 m CRM plus modifications

Substrate Aspect (sine) 90 m CRM plus modifications

Oceanographic Bottom temperature (°C) 9.8 km HYCOM + NCODA

Oceanographic Chlorophyll-a (mg m-3) 5.1 km Aqua MODIS satellite, 8-day 

composites

Oceanographic

Surface current direction- U

(eastward water velocity, 

m s-1)

8.9 km HYCOM + NCODA

Oceanographic

Surface current direction- V

(northward water velocity, 

m s-1)

8.9 km HYCOM + NCODA

Geography Distance to shoreline 90 m -

Nearby 

ecosystems Nearby wetlands (km2) 90 m NWI

Nearby 

ecosystems Nearby estuaries (km2) 90 m NWI

* HYCOM + NCODA = HYbrid Coordinate Ocean Model + Navy Coupled Ocean Data Assimilation; MODIS = 

Moderate Resolution Imaging Spectroradiometer. CRM = Coastal Relief Model; NWI = National Wetlands Inventory

Salinity data were initially explored, but were excluded because there was minimal variation in our study 

area. To define the Gulf Stream, and subsequently the distance to the Gulf Stream, we examined summer 

U- and V- current velocity (m s-1

) for the summer season. The mean current velocity was highest during 

this season. We visually examined the data in 0.5 m s-1 increments and classified waters averaging 

≥ 0.5 m s-1 for either the U or V direction as part of the Gulf Stream. This classification created a unified 

Gulf Stream current in our study area. We used Euclidean distance to calculate distance to the Gulf 

Stream.

To determine if coastal wetlands contributed to the distribution of fish in the marine environment, we 

used the classification of “estuarine and marine wetland” to depict estuarine wetlands and “estuarine and 

marine deepwater” to depict estuaries from the NWI dataset (U. S. Fish and Wildlife Service 2018). First, 
43

we calculated the maximum distance to wetlands within our study area, which was approximately 130 km 

in the South Atlantic. Then, we summed the number of 90-m cells depicting wetlands, and estuaries, 

within a radius of 130 km of each cell. The metric was converted to wetland km-2 and estuaries km-2

within each 130-km cell. Distance to shoreline was calculated by back-transforming boundaries of the 

Submerged Lands Act (SLA). The boundaries of the SLA represent a distance of 3 nm from the shoreline 

of South Atlantic states. We used the buffer tool to re-create the approximate shoreline boundaries (3–

9 nm from SLA), and then calculated the Euclidean distance from the shoreline to the entirety of the study 

area.

3.2.4 Statistical Analysis

We investigated predictor variables for multicollinearity and removed highly correlated variables (r > 

0.80) prior to analyses. Of note, we removed summer bottom temperature because of a strong correlation 

with depth (r = -0.83). In regard to temporal predictors, we used day-of-year and start time as variables in 

all models. The time at which surveys are conducted can affect the detectability of species and has 

previously been documented affecting shark catch (Driggers III et al. 2012). We did not use year as an 

explanatory factor because our primary objective was to determine long-term use of waters and substrates 

of the South Atlantic. Therefore, we assume years of high or low abundance are representative of longterm fish distribution.

Because improving our understanding of species-habitat relationships was one of our objectives, the 

variables in species models differed by a few variables. We excluded variables with no hypothesized 

relationship to species to help minimize issues with moderately correlated variables. We used chlorophyll 

as predictor of species except for red snapper. Red snapper are usually farther offshore than a relationship 

with chlorophyll would suggest. In the Gulf of Mexico (section 2), red snapper were found to be related to 

shrimp distribution, so we did include nearby wetlands in the South Atlantic models of red snapper. In 

models of demersal species (red snapper, black sea bass, blacknose shark), we did not test SST. Given the 

nGoM results (Sections 1 and 2), we did not test bottom current velocity. Instead, we tested surface 

current velocities because of the observed association between surface currents and MLD in the Gulf of 

Mexico. Data on MLD was not available for the South Atlantic.

For reasons described in Section 1.2.4, we aggregated training (70%) and validation (30%) data with 

alternating bands along a latitudinal gradient (Error! Reference source not found.). The use of a latitudinal 

gradient maintained a depth gradient in each block. More specifically, we reclassified latitude into 70 

equal interval divisions, which resulted in 15-km bands across the study area. We then alternated the 

delineation of training (two bands) and validation (one band) datasets to achieve the desired ratio of 

training and validation data. The BRT analysis procedures outlined in Section 1.2.4 were used to quantify 

species-habitat relationships, the relative importance of predictors, and interaction effects for snappers 

and sharks. We also assessed the accuracy of models using the same procedures as described from 

presence/absence modeling. For blacknose shark, the threshold obtained from the maximum Kappa 

statistic provided an extremely small sample size of predicted presences. Therefore, we used the 

presence/absence threshold obtained from minimum distance on the receiver operating characteristic plot.

We assumed the effect of time of survey represented a detectability effect of tiger shark. Therefore, we 

applied the model at their peak time of detectability, which was 23:00.
44

Figure 3.1. The study area with training and validation zones depicted with surveys overlaid. 

SERFS trap surveys were often at video survey locations.
45

3.3 Results

The validation results showed AUC statistics ranged from 0.73–0.89, which indicated a good or very 

good ability of models to discriminate present and absent locations of species (Table 3-3). The confusion 

matrices (Table 3-4 to Table 3-8) showed absences were better predicated than presences for blacknose 

shark and red snapper. Tiger and sandbar shark models had similar abilities to discriminate presence and 

absence. For the most common species, black sea bass, presence was predicted better than absences. No 

geomorphology variables were selected by species, and variable importance measures showed a mixed of 

oceanographic condition, nearby estuaries and wetlands, and depth all contributed to the SDMs (Figure 

3.2).

3.3.1 Red Snapper

Red snapper (Figure 3.3) were present on 24% of reef video surveys. Red snapper were positively related 

the amount of nearby wetlands and had the highest probability of occurrence with a depth range of 25–

38 m. Red snapper were more likely to occur within approximately 45 km of the Gulf Stream and with 

westward surface currents. Interaction effects were particularly strong. Red snapper were more likely to 

occur with a combination of a high amount of nearby wetlands and a greater depth (IE = 212). A 

combination of westward currents and a depth range of approximately 25–35 m also had a strong positive 

effect on red snapper occurrence (IE = 82).

3.3.2 Black Sea Bass

Black sea bass (Figure 3.4) were present on 66% of reef trap surveys. Black sea bass had a strong, 

positive relationship with chlorophyll and primarily a positive relationship with nearby estuaries. 

However, they had a negative relationship with very high amounts of nearby estuaries (> 7,000 km2

), 

which only occurred offshore of North Carolina. Black sea bass had a particularly low probability of 

occurrence > 75 km from the shoreline (Figure 3.4). They had a greater likelihood of occurrence either 

near the Gulf Stream (< 25 km) or much farther away from the Gulf Stream (> 85 km). Strong 

interactions occurred among variables. Distance to shoreline had a minimal effect where nearby estuaries 

were > 7,000 km2 (near North Carolina) (IE = 126). For waters with > 7,000 km2 of nearby estuaries, the 

Gulf Stream had a greater effect on black sea bass probability of occurrence (IE = 116).

3.3.3 Blacknose Shark

Blacknose shark (Figure 3.5) were present on 10% of MSLABS-BL bottom longline surveys. An 

additional 56 blacknose shark presence locations from SEAMAP bottom longline surveys were used for 

validation. The blacknose shark had positive relationships with chlorophyll and the amount of nearby 

estuaries. They had a higher probability of occurrence with a higher velocity of U-direction surface 

currents, particularly westward currents; however, eastward currents also had a positive effect. 

Interactions were not present.

3.3.4 Sandbar Shark

Sandbar shark (Figure 3.6) were present on 25% of MSLABS-BL surveys. An additional 50 sandbar 

shark presence locations from SERFS video were used for validation. For sandbar shark, probability of 

presence was highest with water depths of 42–50 m and probability declined steadily at more shallow 

depths. Sandbar shark were related to higher fall bottom temperatures with a sharp increase in their 

predicted occurrence with temperatures > 24.5°C. Interactions were not present.
46

3.3.5 Tiger Shark

Tiger shark (Figure 3.7) were present on 42% of MSLABS-BL surveys. An additional 37 tiger shark 

presence locations from SERFS video surveys and 16 SEAMAP bottom longline survey presence 

locations were used for validation. Tiger shark presence was associated with a greater amount of nearby 

wetlands and with greater water depths. Peak tiger shark occurrence was predicted with depths of 25–

50 m. Survey time also affected the presence of tiger sharks in surveys, as 11:00–23:00 was predicted to 

have the highest probability of occurrence. There was an interaction with nearby wetlands and survey 

time (IE = 10). This interaction showed that depth did not have as much of an effect during low 

probability survey times. 

Table 3-3. BRT specifications and AUC statistics for marine species distribution models of 

snappers and sharks.

Species

Tree 

complexity

Learning 

rate

# of 

trees

Crossvalidatio

n AUC

Validation 

AUC

Red snapper 5 0.02 1,850 0.86 0.79

Black sea bass 4 0.03 1,150 0.86 0.81

Blacknose shark 5 0.001 4,300 0.85 0.88

Sandbar shark 1 0.03 4,950 0.83 0.73

Tiger shark 2 0.002 1,700 0.73 0.73
47

Table 3-4. Confusion matrix from the validation data of the red snapper model at the optimal 

threshold to distinguish presence/absence, which was with a probability of 0.41.

Observed 

absence

Observed 

presence

User's accuracy 

(% correct)

Predicted 

absence 1,501 331 82%

Predicted 

presence

135 168 55%

Table 3-5. Confusion matrix from the validation data of the black sea bass model at the optimal 

threshold to distinguish presence/absence, which was with a probability of 0.58.

Observed 

absence

Observed 

presence

User's accuracy 

(% correct)

Predicted 

absence 274 153 64%

Predicted 

presence

239 1,157 83%

Table 3-6. Confusion matrix from the validation data of the blacknose shark model at the optimal 

threshold to distinguish presence/absence, which was with a probability of 0.15.

Observed 

absence

Observed 

presence

User's accuracy 

(% correct)

Predicted 

absence 101 7 94%

Predicted 

presence

24 61 72%

Table 3-7. Confusion matrix from the validation data of the sandbar shark model at the optimal 

threshold to distinguish presence/absence, which was with a probability of 0.33.

Observed 

absence

Observed 

presence

User's accuracy 

(% correct)

Predicted 

absence 73 34 68%

Predicted 

presence

21 59 74%

Table 3-8. Confusion matrix from the validation data of the tiger shark model at the optimal 

threshold to distinguish presence/absence, which was with a probability of 0.42.

Observed 

absence

Observed 

presence

User's accuracy 

(% correct)

Predicted 

absence 52 25 68%

Predicted 

presence

34 79 70%
48

Figure 3.2. Relative importance of variables in models of a) blacknose shark, b) sandbar shark, c) 

tiger shark, d) red snapper, and e) black sea bass.
49

Figure 3.3. Predicted red snapper probability of presence in spring–fall seasons.

The study area is indicated by the dashed line, and probability of presence represents the probability of capture given 

a video survey at a hard bottom location.
50

Figure 3.4. Predicted black sea bass probability of presence in spring–fall seasons.

The study area is indicated by the dashed line, and probability of presence represents the probability of capture given 

a trap survey at a hard bottom location. 
51

Figure 3.5. Predicted blacknose shark probability of presence in spring–fall seasons.

The study area is indicated by the dashed line, and probability of presence represents the probability of capture given 

a bottom longline survey. 
52

Figure 3.6. Predicted sandbar shark probability of presence in spring–fall seasons.

The study area is indicated by the dashed line, and probability of presence represents the probability of capture given 

a bottom longline survey. 
53

Figure 3.7. Predicted tiger shark probability of presence in spring–fall seasons.

The study area is indicated by the dashed line, and probability of presence represents the probability of capture given 

a bottom longline survey. 
54

3.4 Discussion

Overall, we discovered new species-habitat relationships that incorporated the nearby ecosystems of 

wetlands and estuaries along with the most prominent oceanographic feature, the Gulf Stream. 

Concerning geomorphology, our results with the five South Atlantic fish species analyzed here are 

consistent with the results from analyses of nGoM shrimp, snapper, and sharks (Sections 1 and 2). 

Oceanographic conditions and nearby ecosystems are the dominant correlates with fish distribution, and 

the value of shoals as fish habitat is highly variable based on those factors (rather than geomorphology 

characteristics). In the South Atlantic, none of the five species were associated with the benthic 

characteristics of slope, aspect, BPI, depth heterogeneity, distance to shoal, or proportion of area as shoal.

3.4.1 Red Snapper and Black Sea Bass

We discovered the proximity to the Gulf Stream was an influential predictor of red snapper and black sea 

bass. The Gulf Stream is a prominent feature of the South Atlantic, and it creates a cross-shelf mixing of 

waters as well as strong water stratification (Castelao 2011). Surface and bottom water intrusions are most 

frequent in the summer months, are influenced by wind stress, interact with salinity, and are more 

frequent at the extreme northern and southern waters of the South Atlantic Bight (Castelao 2011). These 

characteristics of Gulf Stream water intermixing with shelf waters is consistent with the red snapper 

habitat relationships we observed here. Red snapper presence was most likely within approximately 40 

km of the Gulf Stream and with a relatively strong western surface current. In the nGoM, we observed a 

positive relationship with red snapper and a deeper MLD. In the Gulf of Mexico, surface currents and the 

Loop Current were correlated with deeper MLD (unpublished data). Data sources for MLD were lacking 

in the South Atlantic, but the surrogates of distance to Gulf Stream and surface current velocity 

demonstrate the importance of this process. Black sea bass had a greater probability of presence both 

within 20 km of the Gulf Stream and much farther from the Gulf Stream (> 80 km). The association with 

the latter probably characterizes their affinity towards high chlorophyll concentrations and being closer to 

the shoreline. Red snapper were associated with more nearby wetlands, which is consistent with their 

association with shrimp prey species in the nGoM, whereas the association black sea bass and estuaries 

may be a result of their use of estuaries as juveniles (Mercer and Moran 1989).

3.4.2 Blacknose, Sandbar, and Tiger Shark

Studies conducted in the shallow waters offshore of North Carolina (Thorpe et al. 2004) and South 

Carolina (Ulrich et al. 2007) found blacknose shark were common in waters of 3-15 m depths. Our results

support these observations, however, we identified more refined habitat characteristics that contributed to 

their presence within shallow waters. In the nGoM, Drymon et al. (2013) quantified habitat associations

of blacknose shark and found depth (or temperature as a correlate) supported a depth preference of 10–

30 m. Here, a positive association with chlorophyll meant that blacknose shark were in a close proximity 

to the shoreline. The positive association with the amount of nearby estuaries and a relatively high 

velocity of U-direction surface currents resulted in a predicted distribution near many inlets and sounds in 

the South Atlantic. This finding builds on a previous study that observed blacknose shark near inlets in 

South Carolina waters (Ulrich et al. 2007). These inlets may be particularly important habitats for 

blacknose shark.

Our model of sandbar shark showed they primarily used waters ≥ 35 m in depth. In support of this result, 

observations of sandbar shark in the Gulf of Mexico showed they are rarely encountered at shallow depths

but have been recorded in depths of > 30 m (Drymon et al. 2010). We emphasize that we did not analyze 

data from nearshore, state-managed waters where juveniles may be found (e.g., Ulrich et al. 2007). 

Seasonal differences in sandbar shark depth use also appear to exist. Conrath and Musick (2008) reported 

that 80% of summer sandbar shark locations were in depths of < 12 m (range of 0–24 m) in the water 

column, but winter observations ranged 0–172 m. Total depth of the water column itself was not reported 
55

in this study. In our study, depth was negatively related to bottom temperature. Therefore, the selection 

for depths up to 50 m is also indicative of sharks using relatively cooler water temperatures in the spring 

and summer. Additionally, we found an association between sandbar shark and fall bottom temperature, 

which were the lowest temperatures of the year. Sandbar shark are known to leave waters that get too 

cold. For example, Conrath and Musick (2008) tracked sharks offshore of eastern Virginia during the 

summer, and all seven individuals wintered offshore of North Carolina. The authors suggest that this is 

because of North Carolina's proximity to the warm Gulf Stream.

For tiger shark, we found a greater probability of presence with higher amounts of nearby wetlands.

Worldwide, tiger sharks are known to use nearshore habitats (e.g., seagrasses) and to prefer shallow 

waters; presumably, these preferences characterize waters with a high prey density (Heithaus et al. 2006). 

Similarly, coastal wetlands of the South Atlantic likely are productive waters for tiger shark prey species.

3.4.3 Conclusions and Implications for Dredging

In our wide-ranging study, we can conclude that not all shoals have equal value to fish in the US 

Southeast Atlantic. Oceanographic factors, such as distance to Gulf Stream, and surface currents play a 

substantial role, as well as the amount of nearby wetlands and estuaries. Evidence shows geomorphology 

only plays a minor role in the distribution of hard bottom fish and sharks. We caution that our analyses 

were based on fish surveys that typically span 3 km in length, and microhabitat selection within this range 

may have been missed. Yet, our models showed a high predictive ability. For example, three of five of 

our models had an AUC of ≥ 0.79. We have demonstrated that modeling the distribution of species can be 

accomplished at a relatively fine scale. These predicted spatial patterns can be used in decision support 

tools to identify potentially sensitive habitats, and relative hotspots for species, and allow for a strategic, 

regional perspective on natural resource use in the South Atlantic. 
56

4 Predicting the Marine Distribution of Demersal Species in the 

Greater Atlantic

4.1 Introduction

SDMs are state-of-the-art statistical models that predict the distribution of species based on specieshabitat relationships (Guisan and Zimmermann 2000; Robinson et al. 2011). Extending survey samples 

across the complex habitat mosaics allow for determination of relative hotspots for species and can 

identify particularly sensitive areas to avoid when planning offshore uses that potentially impact benthic 

habitats. The Northeast Fisheries Science Center (NEFSC) has recently developed SDMs for the Greater 

Atlantic (personal communication and unpublished data, Kevin Friedland, NEFSC). They had the 

objective of modeling the spatial distribution of North Atlantic species for the spring and fall seasons. 

Their work also examined the broad importance of predictor variables categorized as the following: 

physical (e.g., depth, temperature), primary production, secondary production (i.e., zooplankton), and 

benthic habitat complexity (i.e., substrate measures). The NEFSC provided us with a subset of species 

models derived from the last decade of fishery-independent trawl data, while their broader work covers 

over 30 years of fishery and oceanographic survey data.

The multispecies spatial modeling effort led by Kevin Friedland (NEFSC) is in the preparation phase for a 

peer-reviewed manuscript; therefore, we only provide a brief overview here. Currently, there is a 

manuscript concerning American lobster that is in review, and this manuscript will describe more detailed

methodology for the species described here and included in the ShoalMATE tool (see Volume 4). Here, 

we provide a brief overview of the modeling data sources, methodology, and results.

4.2 Methods

The NEFSC used the following components to build SDMs for the Greater Atlantic:

• Fishery-independent trawl survey data from 2009–2018 were used for statistical modeling. The 

NEFSC has a longstanding trawl survey program, and the latest decade of data were used for 

modeling. All species with an adequate sample size from trawl surveys were modeled.

• Predictor variables sampled during trawl surveys included remote sensing data on oceanographic 

conditions, substrate descriptors, zooplankton abundance, and in situ measures of depth, water 

temperature, and salinity. Oceanographic predictor variables, such as chlorophyll concentration, 

were summarized on a monthly basis, and each month was tested as a predictor.

• The NEFSC used Random Forest (Cutler et al. 2007) for statistical models. Similar to BRT 

(Sections 1, 2, and 3), Random Forest is a machine-learning method that uses an ensemble of 

regression trees to make predictions. Random Forest has been demonstrated as an effective 

predictive modeling method (Rooper et al. 2017). Model variables were reduced using the model 

selection criteria of Murphy et al. (2010). Individual models often had > 20 predictor variables.

Further details on the modeling methodology will be provided by Friedland et al. (2020).

• The predictive models quantify the probability of occurrence for each species by season (spring 

and fall), and the accuracy of models were tested with a cross-validation procedure. With this

method, species were assessed with a receiver operating characteristic, AUC. The AUC has been 

commonly used to test the predictive ability of SDMs (Guisan and Zimmermann 2000), and the 
57

analysis is independent of thresholds. Measures of the AUC range from 0.0 to 1.0 and were 

interpreted as suggested by Manel et al. (2001) and Swets (1988) as follows: < 0.50 = no 

discriminatory power; 0.50–0.69 = poor power; 0.70–0.89 = good power; and 0.90–1.0 = 

excellent discriminatory power.

4.3 Summary of Model Outputs

Thirty-four federally managed species were modeled for the spring and fall seasons, and these 68 models 

all had > 70% accuracy based on the AUC statistics for each season (Table 4-1). Overall, the models 

showed inshore-offshore movements of species were common, so distributions were often strikingly 

different between seasons. Importantly, these models do not depict summer and winter seasonal 

distributions of species.

A few inferences can be drawn from all the species modeled by the NEFSC (EFH species and other 

species) with the longer timeframe of modeling results. Among the categories of predictors, benthic 

substrate variables were selected least often, and measures of variable importance showed these substrate 

variables had less importance in models compared to other categories of predictor variables. Secondary 

production, primary production, and physical variables had varying importance depending on season and 

type of species (e.g., benthivore vs. planktivore).

Table 4-1. Federally managed species and the accuracy of models provided by the NEFSC.

Common name

Fall 

AUC

Spring 

AUC Common name

Fall 

AUC

Spring 

AUC

Acadian redfish 0.94 0.92 Scup 0.91 0.9

American plaice 0.94 0.91 Sea scallop 0.84 0.84

Atlantic cod 0.89 0.84 Silver hake 0.82 0.81

Atlantic herring 0.9 0.76 Smooth skate 0.88 0.89

Black sea bass 0.86 0.86 Spiny dogfish 0.8 0.79

Bluefish 0.86 0.88 Summer flounder 0.9 0.84

Butterfish 0.77 0.86 White hake 0.88 0.87

Clearnose skate 0.92 0.92 Windowpane 0.85 0.83

Goosefish 0.77 0.76 Winter flounder 0.87 0.88

Haddock 0.84 0.85 Winter skate 0.88 0.81

Little skate 0.86 0.83 Witch flounder 0.9 0.84

Longfin squid 0.85 0.89 Yellowtail flounder 0.88 0.87

Northern shortfin squid 0.81 0.88 Atlantic mackerel 0.77 0.77

Offshore hake 0.93 0.91 Barndoor skate 0.88 0.86

Red hake 0.82 0.8 Ocean pout 0.82 0.79

Rosette skate 0.93 0.93 Pollock 0.86 0.82

Scup 0.91 0.9 Atlantic angel shark 0.91 0.92
58

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Appendix A: Common and Scientific Names Cited in the Text

Common Name Scientific Name Common Name Scientific Name 

Acadian redfish Sebastes fasciatus Lizardfish Synodus spp.

American plaice Hippoglossoides 

platessoides Longfin squid Loligo pealeii

Atlantic cod Gadus morhua Mantis shrimp Squilla spp.

Atlantic herring Clupea harengus Menhaden Brevoortia spp.

Atlantic mackerel Scomber scombrus Northern shortfin 

squid Illex illecebrosus

Black sea bass Centropristis striata Ocean pout Macrozoarces americanus

Bluefish Pomatomus saltatrix Offshore hake Merluccius albidus

Butterfish Peprilus triacanthus Pollock Pollachius virens

Croaker Micropogonias undulatus Red drum Sciaenops ocellatus

Flounder, summer Paralichthys dentatus Searobin Prionotus spp.

Flounder, windowpane Scophthalmus aquosus Sea scallop Placopecten magellanicus

Flounder, winter Pseudopleuronectes 

americanus Scup Stenotomus chrysops

Flounder, witch Glyptocephalus 

cynoglossus Shark, Atlantic angel Squatina dumeril

Flounder, yellowtail Limanda ferruginea Shark, Atlantic 

sharpnose Rhizoprionodon terraenovae

Goosefish Lophius americanus Shark, blacknose Carcharhinus acronotus

Haddock Melanogrammus 

aeglefinus Shark, blacktip Carcharhinus limbatus

Hake, red Urophycis chuss Shark, sandbar Carcharhinus plumbeus

Hake, silver Merluccius bilinearis Shark, spinner Carcharhinus brevipinna

Hake, white Urophycis tenuis Shark, spiny dogfish Squalus acanthias

Shark, tiger Galeocerdo cuvier Skate, little Leucoraja erinacea

Shrimp, brown Farfantepenaeus aztecus Skate, clearnose Raja eglanteria

Shrimp, pink Farfantepenaeus 

duorarum Skate, smooth Malacoraja senta

Shrimp, white Litopenaeus setiferus Snapper, lane Lutjanus synagris

Skate, barndoor Dipturus laevis Snapper, red Lutjanus campechanus
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