OCS Study

BOEM 2019-037

U.S. Department of the Interior

Bureau of Ocean Energy Management

Gulf of Mexico OCS Region

Abundance and Distribution of

Commercially Important Estuarine

Dependent Species Populations

within the Gulf of Mexico
OCS Study

BOEM 2019-037

Published by

U.S. Department of the Interior

Bureau of Ocean Energy Management

Gulf of Mexico OCS Region

New Orleans, LA

June 2019

Abundance and Distribution of 

Commercially Important Estuarine 

Dependent Species Populations 

within the Gulf of Mexico 

Authors

Martin T. O’Connell

Mark S. Peterson

Sean P. Powers

Ann M. Uzee-O’Connell

J. Read Hendon

E. John Anderson

Prepared under BOEM Contract

M12AC00002

by

Pontchartrain Institute of Environmental Sciences

University of New Orleans

2000 Lakeshore Drive

New Orleans, LA 70148
DISCLAIMER

Study collaboration and funding were provided by the US Department of the Interior, Bureau of Ocean 

Energy Management (BOEM) Environmental Studies Program, Washington, DC, under Agreement 

Number M12AC00002. 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 2019-037. The report is also available at the National Technical Reports Library at 

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

CITATION

O'Connell, M.T., Peterson, M.S., Powers, S.P., Uzee-O'Connell, A.M., Hendon, J.R., and Anderson, E.J. 

Abundance and distribution of commercially important estuarine dependent species populations 

within the Gulf of Mexico. New Orleans (LA): US Department of the Interior, Bureau of Ocean 

Energy Management. OCS Study BOEM 2019-037. Agreement No. M12AC00002.

ABOUT THE COVER

Personnel of the University of New Orleans prepare to measure aquatic environmental conditions in Lake 

Borgne, Louisiana, before sampling for fishes, crabs, and shrimp.
i

Contents

List of Figures................................................................................................................................................ ii

List of Tables.................................................................................................................................................iii

List of Abbreviations and Acronyms............................................................................................................. iv

1 Background and Objectives ..................................................................................................................6

2 Materials and Methods..........................................................................................................................8

2.1 Study Location...............................................................................................................................8

2.2 Data Collection..............................................................................................................................9

2.3 Data Analysis ..............................................................................................................................10

2.3.1 Biotic Spatial Analyses........................................................................................................10

2.3.2 Environmental Spatial Analyses..........................................................................................11

2.3.3 Biotic Temporal Analyses....................................................................................................11

2.3.4 Environmental Temporal Analyses .....................................................................................12

3 Results ................................................................................................................................................13

3.1 Biotic Spatial Analyses................................................................................................................13

3.2 Environmental Spatial Analyses..................................................................................................13

3.3 Biotic Temporal Analyses............................................................................................................18

3.4 Environmental Temporal Analyses .............................................................................................23

4 Discussion...........................................................................................................................................25

5 References..........................................................................................................................................31

Appendix A: SIMPER Results.....................................................................................................................41

Appendix B: INDVAL Results......................................................................................................................47

Appendix C: Environmental ANOVA Results..............................................................................................54
ii

List of Figures

Figure 1. Map of sample collection sites.......................................................................................................8

Figure 2. PCA for Spring Trawl Environmental Variables...........................................................................15

Figure 3. PCA for Fall Trawl Environmental Variables. ..............................................................................16

Figure 4. LINKTREE for Spring Trawl Data. ...............................................................................................17

Figure 5. LINKTREE for Fall Trawl Data.....................................................................................................18
iii

List of Tables

Table 1. Number of nekton collections made over a three-decade period (1986–2015) in Louisiana, 

Mississippi, and Alabama............................................................................................................................10

Table 2. Combinations of environmental variables that best explained community patterns in spring trawl 

samples taken over three decades (1986–2015) from Louisiana, Mississippi, and Alabama....................14

Table 3. Combinations of environmental variables that best explained community patterns in fall trawl 

samples taken over three decades (1986–2015) from Louisiana, Mississippi, and Alabama....................15

Table 4. Summary of coastal species trajectory responses of three decades (1986–2015) in Louisiana, 

Mississippi, and Alabama............................................................................................................................19

Table 5. List of 13 coastal species that exhibited significant declines in more than one state-season-gear 

scenario over three decades (1986–2015) in Louisiana, Mississippi, and Alabama..................................21

Table 6. Changes in three environmental variables (temperature, salinity, dissolved oxygen) over a threedecade period (1986–2015) in Louisiana, Mississippi, and Alabama.........................................................24

Table A-1a. Spring, Species, Louisiana, Mississippi ..................................................................................41

Table A-1b. Spring, Species, Louisiana, Alabama .....................................................................................42

Table A-1c. Spring, Species, Mississippi, Alabama....................................................................................43

Table A-1d. Fall, Species, Louisiana, Mississippi.......................................................................................44

Table A-1e. Fall, Species, Louisiana, Alabama ..........................................................................................45

Table A-1f. Fall, Species, Mississippi, Alabama .........................................................................................46

Table B.1.a. Seine Spring ...........................................................................................................................47

B.1.b. Seine Fall..........................................................................................................................................48

Table B.1.c. Trawl Spring Louisiana ...........................................................................................................49

Table B.1.d. Trawl Spring Mississippi.........................................................................................................50

Table B.1.e. Trawl Spring Alabama ............................................................................................................51

Table B.1.f. Trawl Fall Louisiana.................................................................................................................52

Table B.1.g. Trawl Fall Mississippi..............................................................................................................53

Table C-1. Results of ANOVA comparison of environmental variables among decades (86–95, 96–2005, 

2006–2015) using seasonal variables. .......................................................................................................54
iv

List of Abbreviations and Acronyms

ANOSIM analysis of similarity

ANOVA analysis of variance

BIOENV PRIMER routine that matches biotic to 

environmental patterns

BOEM Bureau of Ocean Energy Management

CPUE corresponding catch-per-unit-effort

DOI US Department of the Interior

Gulf Gulf of Mexico

PCA principle components analysis

ppm parts per million 

SIMPER similarity percentages
v

ACKNOWLEDGMENTS

This work was funded by the Bureau of Ocean Energy Management through a cooperative agreement 

award (#M12AC00002). We would like to thank the Louisiana Department of Wildlife and Fisheries, the 

Gulf Coast Research Laboratory (Mississippi), and the Alabama Marine Resources Division for providing 

portions of the historical data. We are appreciative of Arie Roth Kaller, Brad J. Blythe, Michael Rasser, 

Kathleen Craig, and Paula Barksdale for assistance with managing the cooperative agreement. We also 

thank Christopher S. Schieble, Arnaud Kerisit, Iain Kelly, Maiadah Bader, Patrick Smith, Angela 

Williamson, Geoff Udoff, Damon Morse, Meghan Gahm, and other students and personnel for their 

efforts in the field and laboratory collecting data. 
6

1 Background and Objectives

Estuaries are some of the most productive ecosystems in the world and they provide vital resources to 

many coastal organisms (Hastings 2009). Coastal areas, though, are becoming more vulnerable to 

increased urbanization, climate change impacts, and other anthropogenic alterations (Duarte 1995; 

GESAMP 1990; Lowe and Peterson 2014; NRC 1994; Roessig et al. 2004; Sand-Jensen and Borum 

1991). As these potential impacts threaten important estuarine-based fisheries, there is a need to 

accurately monitor the response of these coastal communities to environmental changes (Chevillot et al. 

2016). However, measuring how estuarine organisms react to disturbances can be problematic for two 

reasons. First, estuarine organisms are typically resilient to events, such as hurricanes, having evolved to 

withstand violent changes in these coastal habitats (O’Connell et al. 2014; Piazza and La Peyre 2009; van 

Vrancken and O’Connell 2010). Second, environmental conditions in estuaries can be extremely variable 

from year to year, making it difficult to relate abiotic changes to observed biotic changes (e.g., changes in 

a community’s species composition).

One method to overcome these two sources of ecological “noise” in estuaries is to analyze data sets that 

encompass large temporal and spatial scales (Valesini et al. 2014). Such an approach has allowed for 

identifying long-term changes in abundances of individual species that would not have been detected in 

short-term data sets (O’Connell et al. 2004). When analyzing estuarine data over a large spatial scale, it is 

necessary to discern if adjacent regions contain similar communities and habitats due to shared 

organisms, the migratory nature of many estuarine organisms, and shared similarities in environmental 

conditions (Lewis et al. 2011). It is possible that, due to differences in land configuration, habitat metrics, 

riverine influences, and anthropogenic effects, estuarine communities and habitats can vary spatially 

across adjacent coastal regions (Bennett 1989; Lewis et al. 2011). Determining the scale at which the 

aquatic community of an adjacent estuary changes naturally is essential for accurately determining 

possible disturbance-driven environmental changes (Valesini et al. 2014). For example, if a hurricane 

strikes Estuary A while adjacent Estuary B is not affected, post-storm sampling may reveal a difference in 

species composition between the two systems (i.e., one system may have species that the other does not).

Without adequate baseline data from both estuaries, it will be impossible to determine if the observed 

differences in species composition due to the hurricane or due to natural changes in communities over 

geographic distance.

The Gulf of Mexico (Gulf) contributes 25% of the recreational and 45% of the commercial fish harvest to 

the United States. (Lewis et al. 2011; USEPA 1994, 1999). In particular, the northern Gulf has some of 

the most productive estuarine-based fisheries in the US. For example, Louisiana estuarine-based fisheries 

are far more productive than other nearby fisheries due to their close proximity to the Mississippi River 

which provides extensive freshwater nutrients (Chesney et al. 2000). Unfortunately, this region has been 

beset by numerous natural and anthropogenic disasters over the last two decades, including hurricanes 

Katrina and Rita in 2005 and the Deepwater Horizon oil spill disaster in 2010. Other ongoing 

environmental issues include river diversions (e.g., openings of the Bonnet Carre Spillway into Lake 

Pontchartrain), wetland loss, coastal development, eutrophication, and climate change (Lin and 

Mendelssohn 2012; Rabalais et al. 2002a, 2002b; Rakocinski et al. 1997; Turner 1997). If the estuarinebased fisheries of the northern Gulf are to remain productive, a better understanding is needed of how the 

aquatic ecosystem has responded to these threats in order to protect it.

The purpose of our research was to assess the status of the local aquatic organisms, habitats, and 

environmental conditions of three adjacent coastal areas in the northern Gulf. Specifically, we tested for 

changes in populations of nekton species, habitats, and environmental conditions over time. To do this we 

analyzed fishery-independent data from trawl and beach seine collections completed over three decades 

(1986–2015). Our first goal was to determine if these three areas differed in regard to aquatic community 

species composition and environmental conditions (including static habitat conditions). We defined the 

aquatic community as consisting of all finfish species, combined with three ecologically and 
7

economically important crustacean species: the blue crab (Callinectes sapidus), brown shrimp 

(Farfantepenaeus aztecus), and white shrimp (Litopenaeus setiferus). Our second goal was to determine if 

individual species and environmental conditions changed from 1986–1995 to 1996–2005 to 2006–2015. 

Out of concern for the ecosystems and the fisheries they support, we focused our analyses on detecting 

which species experienced the most significant declines across this time frame. 
8

2 Materials and Methods

2.1 Study Location

We collected and analyzed fishery independent data from three adjacent coastal areas: southeastern 

Louisiana, Mississippi Sound, and coastal Alabama (herein referred to as the states Louisiana, 

Mississippi, and Alabama). This region represents a large portion of the coastal habitats of the northern 

Gulf and extends about 200 km along the coast from west to east (Figure 1). The Louisiana collection 

sites were all within Lake Borgne, an embayment down-estuary from Lake Pontchartrain with a wide 

opening to the east into Mississippi Sound (Hastings 2009). Lake Borgne has a surface area of 694 km2

and a typical maximum depth of 2–3 m (Barrett 1970). The majority of Lake Borgne’s shoreline is 

brackish marsh with a smaller amount of salt marsh to the east and south (Hastings 2009). Along this 

shoreline, shell beaches are more common than sand beaches (Hastings 2009). In Mississippi, collection 

sites were either located along the main shoreline or near offshore islands in Mississippi Sound (Figure 

1). The surface area of Mississippi Sound is 4,792 km2 and its mean depth is 4 m (USEPA 1999; USFWS 

1982). The shoreline in Mississippi Sound is dominated by sand beaches (both natural and anthropogenic) 

and artificial armoring such as bulkheads and seawalls, though some small areas of brackish and salt 

marsh remain (Mississippi Office of Geology 1994; Morton et al. 2004; Partyka and Peterson 2008). One 

of the four Alabama collection sites was in eastern Mississippi Sound while the remaining three occurred 

within Mobile Bay (Figure 1). Mobile Bay has a surface area of 1,070 km2 and a mean depth of 3 m 

(Handley et al. 2011). The shoreline of lower Mobile Bay is dominated by brackish and salt marshes 

while more freshwater marshes and swamps occur farther north (Smith et al. 2013).

Figure 1. Map of sample collection sites.

Trawl (circles) and beach seine (triangles) sites for samples collected from Louisiana, Mississippi, and Alabama in 

spring (April, May, and June) and fall (September, October, November). Recent samples were collected from 2012 to 

2015 while historical samples were collected at the same or nearby sites from 1986 to 2012. Due to limited available 

shallow-water habitat, samples were collected from only three beach seine sites in Mississippi.
9

2.2 Data Collection

To complete our analyses, we used two sets of fishery independent data: data we collected ourselves from 

2012 to 2015 and historical data that had been collected at the same or nearby sites from 1986 to 2012.

For our sampling, we used two gear types: 4.9 m otter trawls for deep offshore habitats and 15.25 m 

beach seines for shallow nearshore habitats. For historical and current samples, a trawl consisting of 16–

19 mm bar mesh body with a 4.8–6.3 mm bar mesh tail was used. Trawls were pulled for 10 minutes and 

the speed kept at a constant 1.83 m/s when possible. Beach seines (15.25 m X 1.83 m bag seine with 

0.48–0.63 cm mesh) were pulled such that 50 m X 15.25 m of habitat was sampled at each site. All 

finfishes and the three focus species of crustaceans (C. sapidus, F. aztecus, and L. setiferus) were either 

counted, identified, and released in the field or anesthetized with sodium bicarbonate (UNO-IACUC 

Protocol # 09-016; USM-IACUC Protocol # 16031001), fixed with 10% formalin, and processed back in 

the laboratory to calculate their corresponding catch-per-unit-effort (CPUE) for each collection, herein 

referred to as abundances. For each collection, salinity, dissolved oxygen (ppm), and surface water 

temperature (o

C) were measured. Additionally, static values for percentage substrate were calculated for 

all sites based either on dbSEABED data or on field descriptions and classifications taken during 

sampling (Jenkins 2010). These substrate data were only collected for those samples used for the spatial 

analyses. Three monthly collections were made during both spring (April–June) and fall (September–

November) from 2012 to 2015. In Louisiana and Alabama, four trawl sites and four beach seine sites 

were sampled each month while in Mississippi four trawl sites and only three beach seine sites were

sampled due to a lack of appropriate available habitats (Figure 1).

Historical data were provided by the Louisiana Department of Wildlife and Fisheries, the Gulf Coast 

Research Laboratory (Mississippi), and the Alabama Marine Resources Division. When these historical 

data were combined with the data we collected, the total was 6270 collections: 3198 spring collections 

(2383 trawls and 815 beach seine hauls) and 3072 fall collections (2258 trawls and 814 beach seine hauls; 

Table 1).
10

Table 1. Number of nekton collections made over a three-decade period (1986–2015) in Louisiana, 

Mississippi, and Alabama. 

Samples were taken over two seasons (spring and fall) each year and two gear types (trawl and beach seine) were 

used. Data were divided into three decades (1986–1995, 1996–2005, and 2006–2015) for analytical comparisons 

over time.

Decade Trawl Beach Seine

Spring

Louisiana 86–95 128 17

96–05 154 48

06–15 113 120

Mississippi 86–95 121 59

96–05 120 61

06–15 235 123

Alabama 86–95 502 70

96–05 492 109

06–15 518 208

Fall

Louisiana 86–95 73 10

96–05 107 36

06–15 147 156

Mississippi 86–95 122 60

96–05 120 60

06–15 207 117

Alabama 86–95 460 67

96–05 491 103

06–15 531 205

2.3 Data Analysis

2.3.1 Biotic Spatial Analyses

For the biotic spatial analyses, we conducted analysis of similarity (ANOSIM) to compare species 

abundance among the three states using historical and current data and PRIMER v6 software (Clarke and 

Warwick 2001). This multivariate method allows for comparing multiple communities and establishes if 

statistically significant differences exist among them. The data were divided into three decades (1986–

1995, 1996–2005, 2006–2015) then further divided by season (spring and fall), and then by gear type 

(trawl and beach seine). The abundance data, dispersion weighted by site to downweight the contributions 

of clusters of species that exhibited pronounced sample-to-sample variability, were used to construct a 

Bray-Curtis resemblance matrix that defined similarities among samples (Valesini et al. 2014). A oneway ANOSIM, with collection sites nested within each state, was performed to determine composition 

differences among states. If differences among states were detected, then for all subsequent analyses 

states were assessed as individual and separate communities. If communities were not different among 

states, then data from similar states were pooled for all subsequent analyses. For those ANOSIM tests that 

were significantly different, we used pairwise PRIMER similarity percentages (SIMPER) to determine 

which species contributed most to the observed differences in species composition among states. Within 
11

each SIMPER comparison, we focused on those species which contributed the most to the difference, not 

including those species which did not contribute to the top 50% of cumulative total difference.

2.3.2 Environmental Spatial Analyses

We used a combination of the PRIMER BIOENV routine and principle components analyses (PCA) to 

test for differences in environmental conditions, habitat, and geographic location between areas that were 

found to have different communities from the ANOSIM analyses (Clarke and Gorley 2006) only when 

biotic analyses showed significant differences in species composition. For the BIOENV routine and PCA,

all variables were log transformed (except for temperature, latitude, and longitude) and normalized. For 

the PCA, all variables were averaged by site, then sample scores for collection sites were overlaid on the 

axes to indicate where different communities occurred along the axes. By using the BIOENV routine and 

PCA simultaneously, we could determine which environmental conditions and habitats best explained the 

differences in the communities. Specifically, we wanted to determine which combination of 

environmental variables (temperature, salinity, and dissolved oxygen), habitat (percentage substrate of 

gravel, sand, mud, clay, rock, or shells), and geographic location (latitude and longitude) best explained 

the patterns and differences between the communities. BIOENV and PCA allow for identifying those 

variables that are important in driving changes among communities; the LINKTREE routine statistically 

tests the relative strength of each environmental variable in dividing one community from another (Clarke 

et al. 2008). A linkage tree is developed based on differences in the community data but the binary 

divisions are constrained to threshold values of each environmental variable. The resulting linkage tree 

divides the communities based on environmental thresholds indicating which environmental variables and 

the levels at which they best separate the different communities (based on a calculation of “R”) while not 

including community comparisons that have, “...no demonstrated multivariate structure in the biotic 

composition that needs explaining” (Clarke et al. 2008). In other words, all branches represent significant 

(p < 0.05) group separations. The y-axis of the linkage tree is an absolute measure of group differences 

represented by the term B% which is a measure of how well each group on a particular branch separates 

relative to the maximum separation shown.

2.3.3 Biotic Temporal Analyses

To conduct the biotic temporal analyses, we used an approach developed to assess temporal changes in 

coastal aquatic communities by combining data on species abundance and frequency of occurrence over 

different time periods (Sirot et al. 2015). This method was modified from the “indicator value method” 

that had been originally developed to compare different populations spatially and show the relationships 

between species and habitats (De Caceres and Jansen 2016; Dufrene and Legendre 1997). From our data, 

we chose the 25 most common species (based on percent of total specimens) for each state-season-gear 

scenario (e.g., Louisiana-spring-trawl, etc.). When the total list was developed to include all species from 

all state-season-gear scenarios, 56 species were identified and analyzed. For each of the 56 species, an 

index (IndValip) was calculated for each of the three decades included in the study. This index was 

calculated based on a species’ specificity (its relative abundance within the state in a specific decade) and 

its fidelity (its frequency of occurrence within the state in a specific decade; Sirot et al. 2015). We then 

used R software to test for differences in IndValip for each species across the three decades using 

permutation tests (De Caceres and Jansen 2016; The R Foundation for Statistical Computing 2016). The 

resulting data produced five possible “trajectories” for each species across the three decades: constant, 

hump-shape, u-shape, increasing, or decreasing (Sirot et al. 2015). A constant trajectory indicated no 

significant change in the species over the three decades. Hump-shape and u-shape trajectories occurred 

when IndValip was significantly higher or lower in the second (or middle) decade compared to the first 

and third decade. Increasing and decreasing trajectories were those where IndValip for the third (or final) 

decade was significantly higher or lower than the first decade, respectively (Sirot et al. 2015). Though it 

would be interesting to examine the reasons why some species either exhibited no change or an increase 
12

over the three decades in question, out of concern for the ecosystems and the fisheries they support we 

focused on those species that declined in multiple state-season-gear scenarios over the study period.

2.3.4 Environmental Temporal Analyses

For the environmental temporal analyses, we used analysis of variance (ANOVA) with equal variances 

not assumed for the post-hoc comparisons (Tamhane’s T2) to test for differences among the three decades 

for each environmental variable (temperature, salinity, and dissolved oxygen). Separate tests for each of 

the state-season-gear scenarios were conducted using the IBM SPSS® Statistics 23 package.
13

3 Results

3.1 Biotic Spatial Analyses

When species composition was compared among trawl samples for the biotic spatial analyses, there were 

significant differences among the communities in all three states for both spring (ANOSIM, R = 0.543, p 

= 0.001) and fall (ANOSIM, R = 0.722, p = 0.001) sampling periods (Appendix B). In spring trawl 

samples, Louisiana differed from Mississippi in having fewer of the following species: bay anchovy

(Anchoa mitchilli), F. aztecus, spot (Leiostomus xanthurus), sand seatrout (Cynoscion arenarius), Atlantic 

cutlassfish (Trichiurus lepturus), C. sapidus, and least puffer (Sphoeroides parvus). Individual species 

will be listed within sentences from most influential on community change to least influential based on 

the results of the SIMPER routine. A similar pattern between these two states occurred in fall trawl 

samples with Mississippi again having more A. mitchilli, L. xanthurus, and C. arenarius. Mississippi also 

had more L. setiferus, Atlantic bumper (Chloroscombrus chrysurus), C. sapidus, and harvestfish (Peprilus 

paru); Louisiana had more S. parvus and spotted whiff (Citharichthys macrops).

Mississippi spring trawl samples differed from Alabama in having more A. mitchilli, L. xanthurus, C. 

arenarius, T. lepturus, and Atlantic croaker (Micropogonias undulatus). Alabama spring trawl samples 

had more C. sapidus, hardhead catfish (Ariopsis felis), and inshore lizardfish (Synodus foetens). In fall 

trawl samples, A. mitchilli, L. xanthurus, and C. arenarius were again more numerous in Mississippi, as 

were L. setiferus, C. chrysurus, and Peprilus paru. Alabama fall trawl samples had more S. foetens, C. 

sapidus, F. aztecus, M. undulatus, and fringed flounder (Etropus crossotus).

The two non-adjacent states, Louisiana and Alabama, also exhibited differences in trawl samples from 

both seasons. In spring trawl samples, Alabama had more A. mitchilli, F. aztecus, C. sapidus, L. 

xanthurus, A. felis, C. arenarius, S. parvus, and S. foetens while Louisiana had more Atlantic stingray 

(Dasyatis sabina) and C. macrops. Alabama again had more A. mitchilli, S. foetens, C. sapidus, F. 

aztecus, C. arenarius, and A. felis along with more L. setiferus and M. undulatus in fall trawl samples;

Louisiana had more S. parvus, C. macrops, D. sabina, and Southern kingfish (Menticirrhus americanus).

Unlike trawl samples, beach seine samples exhibited no significant differences in species composition 

among the states for either the spring (ANOSIM, R < -0.06, p = 0.61) or fall (ANOSIM, R < 0.167, p = 

0.14) samples. Because of these results, all subsequent analyses on beach seine data were combined 

across all states and no environmental spatial analyses were necessary.

3.2 Environmental Spatial Analyses

When abiotic conditions were compared among trawl samples for the environmental spatial analyses, 

separation of different state spring communities was best described by a combination of temperature, 

salinity, dissolved oxygen, percentage sand, and mud (BIOENV, r = 0.148; Table 2). The six best 

subsequent combinations of descriptor variables all contained temperature and salinity followed by 

various combinations of less important substrate variables (BIOENV, r = 0.143–0.146; Table 2). For fall 

trawl communities, the states were best separated by a combination of temperature, latitude, percentage 

mud, and shells (BIOENV, r = 0.109; Table 3). The seven best subsequent combinations of descriptor 

variables all contained temperature with various combinations of less important substrate variables and 

latitude (BIOENV, r = 0.096–0.105; Table 3). The PCA for each of the two seasons further explained 

how abiotic conditions differed among the states and the results were similar for spring and fall (Figures 

2, 3). Louisiana sites were dominated by a high percentage of shells, occurred at lower latitudes, and 

exhibited intermediate salinities (Figures 2, 3). Mississippi sites were characterized by a high percentage 

of mud and lower salinities (Figures 2, 3). Alabama sites had higher salinities in general and were 

characterized by higher percentages of sand, gravel, and rock (Figures 2, 3).
14

The LINKTREE analyses for both spring and fall trawl samples further explained whether environmental 

variables were associated with particular states. For the spring samples, the greatest significant (p < 0.05) 

separation of groups involved Louisiana differing from Mississippi and Alabama (B% = 86) based on 

latitudes < 30.1 ̊, longitudes less than -89.3 ̊, and substrates that were >11% shell (Figure 4). Subsequent 

significant separations resulted in combinations of Mississippi and Alabama sites with some sites having 

higher latitudes and lower salinities and others having lower latitudes and higher salinities (B% = 66; 

Figure 4). For the fall samples, the greatest significant separation was between a single Mississippi site 

(MS1) and all other sites (B% = 94). Compared to the other sites, MS1 contained an average salinity < 

7.22 and a substrate of 100% mud (Figure 5). Of the remaining sites, Louisiana separated from 

Mississippi and Alabama in having lower latitudes and longitudes and more shell substrate (B% = 64; 

Figure 5). The final separation involved the remaining Mississippi sites with lower longitudes, higher 

percentage mud, and less percentage sand differing from all Alabama sites (B% = 46; Figure 5).

Table 2. Combinations of environmental variables that best explained community patterns in 

spring trawl samples taken over three decades (1986–2015) from Louisiana, 

Mississippi, and Alabama

Correlation values are generated by the BIOENV procedure (PRIMER 6) and are listed in decreasing order.

Number of 

Variables Correlation Selections

5 0.148 temperature, salinity, dissolved oxygen, sand, mud

4 0.146 temperature, salinity, sand, mud

5 0.146 temperature, salinity, sand, mud, clay

3 0.144 temperature, salinity, mud

4 0.144 temperature, salinity, mud, clay

4 0.143 temperature, salinity, dissolved oxygen, mud

5 0.143 temperature, salinity, dissolved oxygen, mud, clay

4 0.137 salinity, dissolved oxygen, sand, mud

5 0.137 salinity, dissolved oxygen, sand, mud, clay

3 0.132 salinity, dissolved oxygen, mud
15

Table 3. Combinations of environmental variables that best explained community patterns in fall 

trawl samples taken over three decades (1986–2015) from Louisiana, Mississippi, and 

Alabama

Correlation values are generated by the BIOENV procedure (PRIMER 6) and are listed in decreasing order.

Number of 

Variables Correlation Selections

4 0.109 temperature, latitude, mud, shells

5 0.105 temperature, latitude, sand, mud, shells

3 0.101 temperature, mud, shells

5 0.100 temperature, latitude, longitude, mud, shells

3 0.099 temperature, latitude, mud

4 0.098 temperature, sand, mud, shells

4 0.096 temperature, latitude, sand, mud

4 0.096 temperature, salinity, mud, shells

3 0.096 latitude, mud, shells

4 0.095 temperature, latitude, sand, shells

Figure 2. PCA for Spring Trawl Environmental Variables.

Principle components analysis diagram showing spring trawl sites from Louisiana (LA, circles), Mississippi (MS, 

squares), and Alabama (AL, triangles) as they compare with averaged environmental variables (N=11) taken at each 

site from 1986 to 2015.
16

Figure 3. PCA for Fall Trawl Environmental Variables.

Principle components analysis diagram showing fall trawl sites from Louisiana (LA, circles), Mississippi (MS, 

squares), and Alabama (AL, triangles) as they compare with averaged environmental variables (N=10) taken at each 

site from 1986 to 2015.
17

Figure 4. LINKTREE for Spring Trawl Data.

Linkage tree constructed using a Bray Curtis similarity matrix of spring trawl community data and associated 

environmental data from sites in Louisiana (LA), Mississippi (MS), and Alabama (AL) for the period of 1986 to 2015. 

The LINKTREE routine statistically tests the relative strength of each environmental variable in dividing groups of 

communities from each other. The linkage tree was constructed based on differences in the community data but the 

binary divisions are constrained to threshold values of each environmental variable. The resulting linkage tree divides 

the communities based on environmental thresholds indicating which environmental variables and the levels at which 

they best separate the different communities (based on a calculation of “R”). All branches represent significant (p < 

0.05) group separations. The y-axis of the linkage tree is an absolute measure of group differences represented by 

the term B% which is a measure of how well each group on a particular branch separates relative to the maximum 

separation shown. Values for environmental thresholds are given at each branching node.
18

Figure 5. LINKTREE for Fall Trawl Data.

Linkage tree constructed using a Bray Curtis similarity matrix of fall trawl community data and associated 

environmental data from sites in Louisiana (LA), Mississippi (MS), and Alabama (AL) for the period of 1986 to 2015. 

The LINKTREE routine statistically tests the relative strength of each environmental variable in dividing groups of 

communities from each other. The linkage tree was constructed based on differences in the community data but the 

binary divisions are constrained to threshold values of each environmental variable. The resulting linkage tree divides 

the communities based on environmental thresholds, indicating which environmental variables and the levels at which 

they best separate the different communities (based on a calculation of “R”). All branches represent significant (p < 

0.05) group separations. The y-axis of the linkage tree is an absolute measure of group differences represented by 

the term B% which is a measure of how well each group on a particular branch separates relative to the maximum 

separation shown. Values for environmental thresholds are given at each branching node.

3.3 Biotic Temporal Analyses

For the results of the biotic temporal analyses, several species experienced significant trends in 

occurrence and specificity over the three decades. In general, for all state-season-gear scenarios, all five 

possible trajectories were represented by various species, but the most commonly observed ones were the 

constant and decreasing trajectories (Appendix C). Of the 56 species we analyzed, 32 exhibited 

significant decline in at least one state-season-gear scenario over the study period (Table 4). Of these 32 

species, 13 exhibited significant declines in more than one state-season-gear scenario over the study 

period (Table 5). The species with the most declines were C. sapidus and S. parvus with four significant 

declines out of a possible eight state-season-gear scenarios (Table 5). Bay whiff (Citharichthys 

spilopterus), E. crossotus, blackcheek tonguefish (Symphurus plagiusa), and A. felis exhibited three 

significant declines, (Table 5), and seven other species showed two significant declines: F. aztecus,

tidewater silverside (Menidia peninsulae), longnose killifish (Fundulus similis), Atlantic threadfin 

(Polydactylus octonemus), blue catfish (Ictalurus furcatus), sand seatrout (C. arenarius), and Southern 

flounder (Paralichthys lethostigma); Table 5). The remaining 19 species exhibited just one significant 

decline in one state-season-gear scenario over the study period (Table 4; Appendix C).
19

Table 4. Summary of coastal species trajectory responses of three decades (1986–2015) in 

Louisiana, Mississippi, and Alabama

Samples were taken over two seasons (spring and fall) each year and two gear types (trawl and beach seine) were used. Species 

are listed phylogenetically. Values in parentheses indicate the number of times the species experienced that trajectory. Bolded text 

indicates those species that exhibited more than one decline.

Trajectories

Monotonous Non-monotonous

Increasing Decreasing Constant Hump-shape U-shape

Increasing Decreasing Constant Hump-shape U-shape

A. lyolepis (1) C. sapidus (4) C. sapidus (4) A. lyolepis (1) D. petenense (1)

B. marinus (2) F. aztecus (2) F. aztecus (6) N. candidus (2) A. felis (1)

M. martinica (1) L. setiferus (1) L. setiferus (7) L. parva (2) C. macrops (2)

M. salmoides (1) A. hepsetus (1) D. sabina (2) M. gulosus (1) T. maculatus (2)

C. chrysurus (1) A. lyolepis (1) A. hepsetus (7) P. paru (1)

S. setapinnis (2) B. patronus (1) A. lyolepis (1)

E. argenteus (1) D. petenense (1) A. mitchilli (8)

L. rhomboides (1) H. jaguana (1) B. patronus (6)

C. arenarius (1) I. furcatus (2) D. petenense (2)

B. chrysoura (2) A. felis (3) H. jaguana (3)

L. fasciatus (1) M. curema (1) A. felis (4)

S. lanceolatus (1) M. peninsulae (2) B. marinus (1)

T. lepturus (1) F. similis (2) S. foetens (3)

P. burti (1) P. tribulus (1) M. cephalus (2)

E. argenteus (1) M. curema (1)

L. rhomboides (1) M. beryllina (3)

P. octonemus (2) M. martinica (1)

B. chrysoura (1) S. marina (1)

C. arenarius (2) F. grandis (1)

C. nebulosus (1) P. tribulus (1)

C. nothus (1) C. chrysurus (4)

L. xanthurus (1) O. saurus (1)

M. americanus (1) L. rhomboides (3)

M. undulatus (1) P. octonemus (1)

S. ocellatus (1) B. chrysoura (4)
20

Trajectories

Monotonous Non-monotonous

Increasing Decreasing Constant Hump-shape U-shape

C. boleosoma (1) C. arenarius (5)

E. crossotus (3) C. nebulosus (1)

C. spilopterus (3) C. nothus (1)

P. lethostigma (2) L. xanthurus (6)

T. maculatus (1) M. americanus (4)

S. plagiusa (3) M. undulatus (7)

S. parvus (4) S. lanceolatus (1)

G. dormitator (1)

C. boleosoma (1)

C. faber (1)

T. lepturus (3)

P. burti (3)

P. paru (1)

T. maculatus (2)

S. plagiusa (1)

S. parvus (2)
21

Table 5. List of 13 coastal species that exhibited significant declines in more than one state-season-gear scenario over three decades 

(1986–2015) in Louisiana, Mississippi, and Alabama

Samples were taken over two seasons (spring and fall) each year and two gear types (trawl and beach seine) were used. Species are listed from most to least total scenario declines. 

For each species, it is noted whether the decline occurred in the second (1996–2005) or third (2006–2015) decade. For beach seine samples, there were no significant differences in 

species composition found among the three states, so all states were combined for the analyses. 

*One of three ecologically and commercially important crustacean species included in the analyzed community. 

**Member of the Order Pleuronectiformes (flatfishes).

Species

and/or 

Scenario

LA 

spring

trawl

LA 

fall 

trawl

MS 

spring 

trawl

MS 

fall 

trawl

AL 

spring 

trawl

AL

fall 

trawl

LA/MS/AL

spring 

seine

LA/MS/AL

fall 

seine

Callinectes 

sapidus*

no decline decline in 

third decade

decline in 

third decade

decline in 

third decade

decline in third 

decade no decline no decline no decline

Sphoeroides 

parvus

no decline no decline decline in 

third decade

decline in 

second decade

not present in 

samples

decline in 

third decade

decline in 

second decade

not present in 

samples

Citharichthys 

spilopterus**

not present in 

samples

not present in 

samples

decline in 

third decade

not present in 

samples

decline in third 

decade

decline in 

third decade

not present

in samples

not present in 

samples

Etropus 

crossotus**

not present in 

samples

not present in 

samples

not present in 

samples

decline in 

second decade

decline in 

second decade

decline in 

third decade

not present

in samples

not present

in samples

Symphurus 

plagiusa**

not present in 

samples

not present in 

samples

decline in 

third decade

decline in 

second decade no decline decline in third decade

not present in 

samples

not present in 

samples

Ariopsis 

felis

no decline no decline decline in 

second decade

decline in 

second decade no decline no decline decline in second decade no decline

Farfantepenaeus 

aztecus*

no decline no decline

decline in 

third decade

decline in 

third decade no decline no decline no decline no decline

Paralichthys 

lethostigma**

decline in 

third decade

not present in 

samples

decline in 

third decade

not present in 

samples

not present in 

samples

not present in 

samples

not present in 

samples

not present in 

samples

Menidia 

peninsulae

not present in 

samples

not present in 

samples

not present in 

samples

not present in 

samples

not present in 

samples

not present in 

samples

decline in 

second decade

decline in 

second decade

Fundulus 

similis

not present in 

samples

not present in 

samples

not present in 

samples

not present in 

samples

not present in 

samples

not present in 

samples

decline in 

third decade

decline in 

third decade

Ictalurus 

furcatus

decline in 

second decade

not present in 

samples

decline in 

third decade

not present in 

samples

not present in 

samples

not present in 

samples

not present in 

samples

not present in 

samples
22

Species

and/or 

Scenario

LA 

spring

trawl

LA 

fall 

trawl

MS 

spring 

trawl

MS 

fall 

trawl

AL 

spring 

trawl

AL

fall 

trawl

LA/MS/AL

spring 

seine

LA/MS/AL

fall 

seine

Cynoscion 

arenarius

no decline no decline decline in 

third decade

decline in 

third decade no decline no decline no decline no decline

Polydactylus 

octonemus

no decline not present in 

samples

decline in 

second decade

not present in 

samples

decline in 

second decade

not present in 

samples

not present in 

samples

not present in 

samples
23

3.4 Environmental Temporal Analyses

For the results of the environmental temporal analyses, in Louisiana there were no significant differences 

in temperature among the three decades either in spring (ANOVA, range of pair-wise inter-state 

comparisons: p = 0.15–0.88) or fall (ANOVA, p = 0.14–0.98) trawl samples (Table 6; Appendix D).

Salinity in Louisiana, however, increased over time during both seasons: spring salinity was significantly 

lower in the first decade compared to the second and third decades (ANOVA, p < 0.001) and fall salinity 

increased significantly each decade from the first to the second decade (ANOVA, p < 0.01) and from the 

second to the third decade (ANOVA, p = 0.001–0.002; Table 6, Appendix D). Spring dissolved oxygen 

was greater in the second decade compared to the third (ANOVA, p < 0.001), yet in fall samples 

dissolved oxygen was less in the second decade compared to the third (ANOVA, p < 0.001; Table 6, 

Appendix D). Note that for these Louisiana trawl analyses, no dissolved oxygen data for the first decade 

were available.

In Mississippi trawl samples, spring temperatures were significantly lower in the first decade compared to 

the second and third decades (ANOVA, p = 0.001–0.018) but were not significantly different over time in 

fall samples (ANOVA, p = 0.56–1.00; Table 6, Appendix D). Salinity exhibited a “non-trend” result in 

spring samples with the third decade having higher salinity than the second decade (ANOVA, p < 0.031) 

but not being significantly different from the first decade. In fall trawl samples, salinity increased from 

the first and second decades to the third decade (ANOVA, p < 0.001; Table 6, Appendix D). There were 

no significant differences in dissolved oxygen among the three decades for spring trawl samples 

(ANOVA, p = 0.43–0.92). Fall samples exhibited a “non-trend” result with the first decade having 

significantly lower dissolved oxygen than the second decade (ANOVA, p = 0.03) but not being 

significantly different than the third decade (ANOVA, p = 0.52).

Temperature in Alabama spring trawl samples increased significantly each decade from the first to the 

second decade (ANOVA, p < 0.001) and from the second to the third decade (ANOVA, p < 0.001; Table 

6, Appendix D). In fall samples, the first decade was significantly less warm than either the second or

third decades (ANOVA, p < 0.001; Table 6, Appendix D). There were no significant changes in salinity 

over time either in spring (ANOVA, p = 0.14–0.70) or in fall (ANOVA, p = 0.06–1.00) Alabama 

samples. Dissolved oxygen appeared to be lowest in the second decade for both spring and fall trawl 

samples. In spring, the first decade had significantly higher dissolved oxygen than the second decade 

(ANOVA, p < 0.004) yet did not significantly differ from the third decade (ANOVA, p = 0.423; Table 6, 

Appendix D). In fall, both the first decade (ANOVA, p < 0.01) and the third decade (ANOVA, p < .001) 

exhibited significantly higher dissolved oxygen than the second decade.

Environmental temporal analyses on data collected during the beach seine samples which were combined 

across all three states showed that temperature was significantly lower in the first decade versus the 

second and third decades (ANOVA, p = 0.002–0.003) for spring samples. This was not the case for fall 

seine samples where temperatures in the second decade were significantly warmer than either the first or 

third decade (ANOVA, p < 0.001–0.005; Table 6, Appendix D). Salinity was significantly higher in the 

first decade compared to the two later decades in spring beach seine samples (ANOVA, p < 0.001) yet in

fall samples, all decades were significantly different (ANOVA, p < 0.001–.0.004); the first decade had the 

highest salinity and the second decade had the lowest. For spring samples, the first decade exhibited 

significantly higher dissolved oxygen than the third decade (ANOVA, p < 0.001) but was not 

significantly different from the second decade (ANOVA, p = 0.30; Table 6, Appendix D). Dissolved 

oxygen in these last two decades was also not significantly different from each other (ANOVA, p < 0.10).

For fall samples, decades one (ANOVA, p < 0.001) and two (ANOVA, p < 0.003) had significantly 

higher dissolved oxygen than decade three (Table 6; Appendix D).
24

Table 6. Changes in three environmental variables (temperature, salinity, dissolved oxygen) over a three-decade period (1986–2015) in 

Louisiana, Mississippi, and Alabama 

Samples were taken over two seasons (spring and fall) each year; two gear types (trawl and beach seine) were used. For beach seine samples, there were no significant differences in 

species composition found among the three states so all states were combined for the environmental analyses. For comparison purposes, the study period was divided into three 

decades (D1 = 1986–1995, D2 = 1996–2005, D3 = 2006–2015) and differences among the decades were tested using ANOVA (equal variances not assumed, critical p = 0.05). See 

Appendix C for ANOVA post-hoc test result values. Decades not included either lacked environmental data or not enough samples were taken. *For these comparisons, differences 

were measured among the decades but the seconddecade was significantly different than the first and third suggesting a non-monotonous (“humped”) trend in the data.

Variable and/or Scenario

LA 

spring 

trawl

LA 

fall 

trawl

MS 

spring 

trawl

MS 

fall 

trawl

AL 

spring 

trawl

AL

fall

trawl

LA/MS/AL

spring

seine

LA/MS/AL

spring

seine

temperature no change no change D1<D2=D3 no change D1<D2<D3 D1<D2=D3 D1<D2=D3 D1=D3<D2*

Salinity D1<D2=D3 D1< D2<D3 D2<D3 D1=D2<D3 no change no change D1>D2=D3 D1>D3>D2*

dissolved oxygen D2>D3 D2<D3 no change D1<D2 D1>D2 D1=D3>D2* D1=D2>D3 D1=D2>D3
25

4 Discussion

Our analysis of the long-term trawl data indicates that although the estuaries of southeastern Louisiana, 

Mississippi Sound, and coastal Alabama are adjacent to each other, the aquatic communities in these 

habitats have marked differences in species composition based on abundances. One counter-intuitive 

result that seemed to drive some of these inter-state differences in species composition was that the three 

crustacean species (C. sapidus, F. aztecus, and L. setiferus) were consistently more common in 

collections from Mississippi and Alabama, not Louisiana. This contradicts the well-established fact that 

Louisiana produces more shrimp and crab landings than any other Gulf state (Chesney et al. 2000; 

O’Connell et al. 2005). One reason for this finding is that metapopulation dynamics may cause 

differences in populations of species among areas in Louisiana, with some areas not included in this study 

(e.g., Breton Sound) being more productive than our study sites. Additionally, the relatively widespread 

nature of habitats in Louisiana (compared to the other two states) may result in organisms being more 

dispersed than in Mississippi and Alabama where preferred habitats are more spatially concentrated.

Another likely explanation is that fewer crabs and shrimp were collected in Louisiana because fishing 

pressure is high or much higher there than in the other two states or because fishing pressure sources can 

differ among the three states (Caillouet et al. 2008; Lindstedt 2005; Nance, 1993; Saari et al. 2014). For 

commercial fishermen, it is more efficient to target the higher densities of these organisms in Louisiana 

(compared to other states), especially during the spring and fall seasons when these fishery-independent 

data were collected. Previously, we saw evidence of the extent of this fishing pressure in Louisiana after 

hurricanes Katrina and Rita caused extensive damage to commercial fishing boats and infrastructure 

(Buck 2005). With the fishing pressure mostly removed, the numbers of many benthic species actually 

increased after the storms, despite many other environmental impacts being present (O’Connell et al. 

2014). Similar post-2015 hurricane increases in estuarine fishes were observed in Mississippi (Schaefer et 

al. 2016) and were also attributed to the reduction of commercial fishing due to extensive storm-related 

damage to the industry (Odom 2015; Pappas 2006). Northern Gulf of Mexico shrimp populations also 

appeared to be more influenced by an overall reduction in fishing pressure rather than other potential 

impacts such as wetlands loss, hypoxia, or climate change: as fishing pressure reduced by 75% over two 

decades, populations experienced an 80% rebound (Kemp et al. 2016). Other evidence that might explain 

these results comes from another project where we collected fishery-dependent data with a commercial 

fisherman deploying out of Bayou La Batre, Alabama (Udoff 2016). A typical multi-day trip to collect 

shrimp consisted of pulling 28 two-hour trawls, five of which were in Alabama waters and 23 in 

Louisiana waters (Udoff 2016). If this scenario is representative of most commercial fishermen targeting 

shrimp in this region, it is easy to understand how the less productive estuaries (Mississippi and Alabama) 

could generate more shrimp and blue crabs during fishery-independent sampling than a much more 

heavily fished Louisiana estuary (Chesney et al. 2000). Intense fishing pressure in Louisiana may also 

explain why A. mitchilli was collected more in Mississippi and Alabama during both spring and fall.

Although not targeted commercially, the small, filter-feeding A. mitchilli is normally one of the most 

numerous species in all three of these estuaries and is one of the most commonly occurring finfish species 

in shrimp trawl bycatch (Eustis 2011; Thorpe and Sessions 2006). One could predict that without 

commercial shrimp harvesting, A. mitchilli numbers would be higher in Louisiana. In fact, post-2015 

hurricane trawl collections in Lake Pontchartrain of A. mitchilli (when shrimping boats and infrastructure 

were mostly destroyed) were almost two times higher than pre-hurricane trawl collections (O’Connell et 

al. 2014).
26

Beyond differences in fishing pressure among the three states, there were obvious habitat differences that 

also drove differences in species composition for trawl collections. As with other estuarine communities, 

temperature and salinity were the major drivers determining which species were or were not present 

(Allen 1982; Anderson et al. 2017; Blackburn 1976; Ditty and et al. 1991; Harrison and Whitfield 2006b; 

Marshall and Elliott 1998; Nash 1988; Valesini et al. 2014). Temperature was a major driver in both 

spring and fall trawl samples. In most estuaries, changes in temperature can trigger the movement of 

aquatic organisms for the purposes of spawning, feeding, or moving to warmer water (Able and Fahay 

2010; Anderson et al. 2017; Methven et al. 2001; Szedlmayer and Able 1993). Movement triggered by 

temperature change may be on a small temporal scale (e.g., daily) or on a large temporal scale (e.g., 

seasonal), both of which can drive changes in species composition. For example, as a weather front in fall 

moves from west to east over the northern Gulf, the drop in temperature in Louisiana might cue some 

estuarine aquatic species to egress and find warmer, deeper water (Heupel et al. 2003; May et al. 1976).

The same trigger may not reach Mississippi or Alabama for another 24 – 48 h (or ever), meaning the same 

species may remain in these estuaries longer. On a seasonal scale, some species may occur in one part of 

the northern Gulf of Mexico in the spring and then migrate to another whether for spawning, feeding, or 

some other purpose. We found that S. parvus was more common in Mississippi and Alabama than 

Louisiana in spring trawls, yet appeared to migrate west over the summer and occur most frequently in 

Louisiana fall trawls. Though little is known about the migratory patterns of S. parvus in the Gulf of 

Mexico, its sister species, the Northern puffer (S. maculatus), is known to make “annual coastal 

migrations” that are strongly influenced by temperature (Able and Fahay 2010). Because all species 

respond differently to both small and large temporal changes in temperature, we can expect differences in 

species composition across these adjacent estuaries.

Differences in salinity drove changes in species composition mostly in spring trawl samples and had little 

to no influence in the fall. In the northern Gulf of Mexico, hurricanes may sporadically produce 

downpours in the fall and freshen estuarine waters, but most major rainfall events take place in spring and 

occur more consistently on an annual basis than hurricanes (Hastings 2009). For many estuarine species, 

the presence of freshwater in the estuary serves as a cue either for spawning by adults (Armor and 

Herrgesell 1985; Fraser 1997; Gibson et al. 2003; Kimmerer 2002) or settlement by incoming larvae and 

juveniles (Baptista et al. 2010; Kimmerer 2002; Peebles 2004; Strydom 2003). If there are differences in 

salinity among estuaries in the spring, one would also expect differences in which species are present.

Mississippi trawl sites had the lowest salinities while salinities at Alabama trawl sites were the highest.

One might have predicted that the Louisiana trawl sites would exhibit the lowest salinity due to their 

proximity to the Mississippi River, yet their salinities were intermediate between the other two states.

These sites experienced an ‘on and off’ situation regarding salinity, based on whether the Mississippi 

River was flooding or not. That is, in low water years, the sites may remain brackish for most of the year 

while during Mississippi River flooding they may become entirely inundated with fresh water. The 

salinity also decreases at these sites whenever the Bonnet Carre Spillway is opened and Mississippi River 

water enters Lake Pontchartrain: this occurred three times during the study period (Chao et al. 2012). The 

variability in salinity in Louisiana may not only explain why species composition differs from Mississippi 

and Alabama but may also contribute to the highly variable distribution of settling larvae and juveniles in 

the marshes northwest of Lake Borgne (W. Stein unpublished data).

Louisiana was further separated from Mississippi and Alabama by the position of its collection sites 

(latitude and, to a lesser extent, longitude) and the presence of shell material in the substrate. When 

comparing estuaries such as these three, it has been argued that different scales (whether local, estuarywide, or regional) need to be considered when assessing differences in species composition (Idelberger et 

al. 2016; Teather et al. 2012; Valesini et al. 2014). Though all of the Louisiana collection sites are south 

of the sites in the other two states (i.e., lower latitude), the relatively small size of our study area (~ 200 

km west to east) suggests the change in species IndVal is not a region-level change in habitats as was 
27

measured in southwestern Australia across a study area twice this size (Valesini et al., 2014). The 

differences we measured in Louisiana may have been more influenced by estuary-wide difference in the 

geography or shape of the estuary. The Mississippi and Alabama sites have mostly open access and 

connectivity to the Gulf, but the Louisiana sites connect to the Gulf only via a single opening to the east 

through Mississippi Sound (Figure 1; sample site map). Such a geographical difference can alter spatial 

differences in estuarine communities by restricting some species while protecting others (Harrison and 

Whitfield 2006a; Valesini et al., 2014). On the local scale, the higher occurrence of shell substrate at 

Louisiana sites may have had more of an influence on species composition than the geography of the 

estuary. In most estuaries, shell substrate provides increased habitat complexity, spawning material, and 

prey sources which leads to higher diversity and biomass of fishes and invertebrates (Lehnert and Allen 

2002; O’Connell et al. 2005; Szedlmayer and Able 1996). The main two sources of shell substrate in 

southeastern Louisiana are Rangia clams (Rangia cuneata), which occur more in the upper estuary 

(optimal salinity = 0–10), and Eastern oysters (Crassostrea virginica), which occur in the lower estuary 

(optimal salinity = 10–20; (O’Connell et al. 2005). Both of these filter-feeding species thrive in shallow 

habitats regularly inundated with Mississippi River water containing both high concentrations of nutrients 

and calcium for shell-production (Hastings 2009). Though Mississippi and Alabama estuaries also receive 

riverine input of nutrients and calcium, it is far less than what the largest river on the continent can 

provide.

In yet another counter-intuitive result, the long-term beach seine data differed from the long-term trawl 

data in that there were no significant differences in species composition in nearshore habitats among the 

three states. Though trawling typically captures adult organisms (both fishes and crustaceans) that have 

actively occupied the targeted benthic habitat to feed, reproduce, or migrate through, beach seine 

sampling typically captures a combination of estuarine resident species and juvenile estuarine-dependent 

species that occur in shallow water. Estuarine resident species have evolved resilience to abrupt changes 

in environmental conditions such as temperature and salinity, with most being small-bodied organisms 

that complete their life cycles within a relatively small area of shallow water (Able and Fahay 2010; 

Elliott and Quintino 2007). The threat of predation from larger organisms in deeper habitats means that 

depth is the primary environmental factor determining where estuarine resident species occur. For 

example, the rainwater killifish (Lucania parva) occurs in estuaries across the northern Gulf and has been 

collected from a wide range of salinities (Peterson and Ross 1991). It completes its life cycle entirely 

within the estuary and has evolved adaptations to remain in shallow habitats (e.g., adhesive eggs for 

attachment to vegetation; Able and Fahay 2010). As long as shallow estuarine habitats occur along the 

northern Gulf, it should be expected that L. parva along with other estuarine resident species such as the 

Gulf killifish (Fundulus grandis), sheepshead minnow (Cyprinodon variegatus), and sailfin molly 

(Poecilia latipinna) will be present in the nearshore community. The presence of this “base” of common 

estuarine resident species helps to partly explain the lack of difference in species composition in seine 

collections among the three states. The second group that occurs in these habitats consists of juvenile 

estuarine-dependent species. After transforming from larvae in offshore habitats, these species settle in 

shallow estuarine habitats and use them either as nursery habitats or as corridors to migrate to other 

nursery habitats further up the estuary (Able and Fahay 2010). In the northern Gulf, this group contains 

the three ecologically and economically important crustacean species (C. sapidus, F. aztecus, and L. 

setiferus) and finfish species from the Family Sciaenidae such as L. xanthurus, C. arenarius, and M. 

undulatus. Though these species have evolved adaptations to help them safely locate nearshore habitats,

such as detecting chemical settlement cues (Deary et al. 2016; Diaz et al. 1999; Forward et al. 2003; 

Welch et al. 1997) or using selective tidal stream transport to move farther up the estuary (Forward and 

Tankersley 2001; Holt and Holt 2000; Ogburn et al. 2013), their initial distribution along the coast as 

larvae (and to a lesser extent as younger juveniles) is determined mostly by somewhat arbitrary abiotic 

factors, such as wind direction, ocean currents, and storms (Eggleston et al. 2010; Matlock 1987; Miller et 

al. 1984; Ogburn et al. 2012; Pattillo et al. 1997). For example, a F. aztecus larva in the early spring 
28

might be displaced east from Louisiana due to a Mississippi River flood while a C. sapidus larva in the 

fall might be displaced west from Alabama due to a hurricane. The haphazard nature of how these species 

come to occur in nearshore habitats likely contributes to the lack of difference in species composition 

among the three states: a base of similar resident species are seasonally overrun by waves of estuarine 

dependent larvae and juveniles from common and numerous source populations.

The most disturbing result of our analysis of species’ changes over time was the decline in C. sapidus in 

trawl samples across all three states (Table 5). This species supports an enormous fishery (NMFS 1988; 

Zimmerman et al. 2002) and is a key trophic link in the estuarine ecosystem, being an abundant 

detritivore that, in turn, is a main prey item for other important estuarine species such as red drum 

(Sciaenops ocellatus; Scharf and Schlicht 2000). Like most other estuarine dependent species in the 

northern Gulf, C. sapidus is resilient to harsh environmental conditions, yet it differs somewhat in that 

adults lack the advanced swimming mobility of most adult finfishes and even shrimp (Hench et al. 2004).

In Lake Pontchartrain after the 2005 hurricane season, small invertebrates and small, less mobile resident 

finfish species appeared to be impacted most by hurricanes Katrina and Rita (O’Connell et al. 2014; 

Poirrier et al. 2008). The implication was that the reduced swimming ability of these organisms made 

them less able to avoid hurricane impacts (e.g., storm surge) than larger, more mobile organisms 

(O’Connell et al. 2014; Poirrier et al. 2008). If C. sapidus is declining more than other organisms, then it 

could involve its inability to effectively escape whatever problems are present. Habitat loss or disturbance 

is typically the leading cause of declines in organisms (Lowe and Peterson 2014; Wilcove et al. 1998; 

Wilson 1989). For C. sapidus, both juvenile habitat (nearshore marshes) and adult habitat (deeper, benthic 

habitat) are at risk in the northern Gulf. Nearshore marshes are being significantly impacted throughout 

the region, especially by human development in Mississippi (Lowe and Peterson 2014). Offshore benthic 

habitats have been subjected to more than a half-century of intense shrimp trawling, a fishing method that 

has been compared to “clear cutting” in regard to the level of habitat destruction it causes (Kaiser et al. 

2000; Watling and Norse 1998). Both habitats have been under anthropogenic pressure, but C. sapidus

did not decline in nearshore habitats over the thirty year study period suggesting the issue involves 

deeper, benthic habitats. Beyond the possible negative effects of trawling (Guillory 2001; Steele et al. 

2002), other local natural and anthropogenic events may have contributed to the decline in C. sapidus.

Recently, a seasonal hypoxic zone has been identified in Chandeleur Sound, an area just east of the 

Louisiana sites and just south of the Mississippi Sites (Henkel et al. 2012).

Because no consistent historical dissolved oxygen data exist for this area, it is unknown whether this 

hypoxic zone has always been present or whether it is a relatively recent occurrence (Henkel et al. 2012).

Around the world, hypoxic zones have decimated populations of aquatic organisms, especially those 

associated with benthic habitats (Diaz and Rosenberg 2008; Steckbauer et al. 2011). The main cause of 

hypoxia in coastal ecosystems is the often the presence of increased nutrients derived from riverine input 

(Hagy et al. 2004; Rabalais et al. 2002b). One possible source of excess nutrients in Chandeleur Sound 

was the Bonnet Carre Spillway (Spillway) which was opened three times (1997, 2008, and 2011) during 

the study period (Chao et al. 2012). The Spillway is opened periodically when high water in the 

Mississippi River threatens infrastructure in the Greater New Orleans Metropolitan Area and nutrient-rich 

river water is diverted in to Lake Pontchartrain and is eventually transported farther down the estuary into 

Chandeleur Sound. The possible relationship between this seasonal hypoxia and Spillway openings 

requires further research that includes whether either is affecting local populations of aquatic organisms.

Another anthropogenic event that may have impacted the northern Gulf during the study period was the 

Deepwater Horizon oil spill disaster in 2010 (Hale et al. 2016; Jones et al. 2015). Record amounts of oil 

and dispersant were released into the aquatic ecosystem and benthic organisms appeared to be the most 

affected (Murawski et al. 2014, 2016). Most of the damage associated with the oil and dispersant occurred 

in Louisiana, while fewer incidents were recorded as these substances dispersed east into Mississippi, 

Alabama, and beyond (Jones et al. 2015; Moody et al. 2013; Peterson et al. 2017; Rozas et al. 2014; 

Schaefer et al. 2016; Stanfill 2017). In our results, though, there was no clear evidence that organisms at 
29

the Louisiana sites were affected more than those at sites in Mississippi and Alabama.

The decline of other species also suggests that offshore benthic habitats in the northern Gulf have been 

negatively affected by one or more disturbances during the study period (Table 5). Along with C. sapidus, 

twelve other species experienced multiple declines over thirty years. Four of these twelve species were 

flatfishes (Order Pleuronectiformes): C. spilopterus, E. crossotus, S. plagiusa, and P. lethostigma. This 

group of highly specialized fishes have evolved to lie-in-wait on (or buried in) the substrate and ambush 

passing prey fishes and invertebrates (Nelson 2006). It could be argued that no other order of fishes is as 

reliant on the presence of adequate benthic habitat as the flatfishes. Their dominance in this list of 

declining species (4 of 13 species) further indicates possible problems with benthic habitats (Madrid-Vera 

et al. 2010; Stanfill 2017; Steele et al. 2002). Like C. sapidus, the only other species with four out of eight 

declines was S. parvus, a small member of the Family Tetraodontidae or puffers (Nelson 2006). This 

species is commonly collected with F. aztecus in shrimp trawls dragged over benthic habitats (Shipp 

1988) and, similar to other puffer species, likely deposit their eggs in depressions made in the substrate 

(Able and Fahay 2010). Disturbance to the substrate could impair their ability to reproduce. Two catfish 

species, A. felis and I. furcatus, also experienced declines. Both species are benthic rover scavengers that 

rely mostly on food items that occur on or below the substrate. A. felis is a true “sea catfish”, but I. 

furcatus is a nominally freshwater species that occurs commonly in estuarine habitats (Fabrizio et al. 

2017; O'Connell unpublished data; Ross 2001). Declines in any catfish species could be caused by loss of 

benthic prey items or direct mortality by bottom-oriented gear such as trawls and crab traps (Fabrizio et 

al. 2017). The second of our three economically important crustacean species to be identified as declining 

is F. aztecus, which is targeted by the commercial shrimping industry. Though this fishing pressure may 

be the cause of long-term declines, the fact that F. aztecus declined only in Mississippi and not in 

Louisiana or Alabama suggests other factors may be involved. The decline of P. octonemus in spring 

trawl samples in Mississippi and Alabama is another instance where a benthic or demersal species 

appears to have been negatively affected. This species has evolved specialized detached pectoral fin rays 

which are used to detect prey items in the substrate and obviously relies on organisms in this benthic 

habitat (Hoese and Moore 1998; Shipp 1988). The only drum species (Family Sciaenidae) that 

experienced declines was C. arenarius. Though this water column predator is the least demersallyoriented of these declining species (Shipp 1988), diet analyses reveal that the vast majority of its prey 

items are taken from benthic zones (Pelaez-Rodriguez et al. 2005). All of these declining species have at 

least one connection to the benthic habitat, whether they live in the substrate or feed on those prey items 

that live there.

The remaining two declining species, M. peninsulae and F. similis, are estuarine dependent fin fishes that 

did not occur in any trawl samples. These shallow water species declined in both spring and fall seine 

sampling during the study period. The decline of these two species is not likely caused by the same factor 

or factors driving the decline in the benthic species discussed above. Both species require shallow water 

with M. peninsulae using intertidal submersed aquatic vegetation as a spawning substratum on which it 

attaches its eggs (Middaugh and Hemmer 1987). As human coastal development increases across the 

northern Gulf, estuarine marshes and shallow water habitats are being reduced or compromised (Lowe 

and Peterson 2014). As these habitats are lost, species like M. peninsulae and F. similis will have fewer 

areas in which to feed and spawn. It should also be noted that many of the benthic species discussed 

above who are experiencing declines due to a possible disturbance of benthic habitat may also be 

impacted by loss of these shallow nearshore habitats. This could be especially problematic for estuarine 

dependent species such as C. sapidus, F. aztecus, and C. arenarius whose larvae and juveniles use these 

areas as nursery habitats.

Environmental changes in the northern Gulf should be of concern, especially in Mississippi and Alabama 

where abiotic measurements during trawl sampling revealed significant increases in temperature over the 

study period (Table 6). As global temperatures rise, many fisheries will be affected as species move to 
30

find optimal temperatures (Fodrie et al. 2010; Roessig et al. 2004). Indeed, there is evidence this 

phenomenon is already occurring (Murawski 1993; Southward et al. 1995). The ability of some fishery 

species to move either north or south in response to changing temperatures may allow them to survive 

climate change, albeit in another area beyond their original distribution. In the northern Gulf, however, as 

in similarly aligned freshwater habitats, the predominantly east-west oriented coastline will likely 

preclude any ability for local fishery species to “escape” rising temperatures (Ficke et al. 2007; Lejeusne 

et al. 2010), putting these fisheries at risk. In Louisiana, though, it is encouraging that there were no 

changes in temperature over the study period, suggesting that the cooler water of the Mississippi River 

may be creating a temperature refuge for estuarine habitats and species. How long this potential refuge 

may last is questionable especially in light of the increasing salinity observed in Louisiana over the study 

period. As land loss increases in this region, saltwater intrusion will move farther inshore destroying 

marshes and eliminating estuarine habitats (Boesch et al. 1994; Morton 2008). Environmental data from 

seine sampling were less conclusive than those from the trawl sampling, perhaps a reflection of the 

physically sheltered nature of these shallow water habitats.

In summary, our long-term trawl data revealed that the species composition of the benthic estuarine 

communities differed among these three states, while seine data showed similarities across the region.

Future attempts to assess the health of these ecosystems and fisheries need to bear in mind that although 

these estuaries are adjacent to each other, different environmental factors (including proximity to the 

Mississippi River) help determine local species composition. Our study also showed that over thirty years 

some benthic offshore species, including commercially important crustacean species, have declined in the 

northern Gulf. We cannot here determine if the declines are due to one cause or multiple causes, but 

during the study period there have been multiple potential negative impacts, such as habitat destruction, 

climate change, and both natural and anthropogenic disasters. The long-term changes in temperature and 

salinity we measured should also be cause for concern about the future of these ecosystems and fisheries.

Many of these estuarine species have evolved to be resilient to such environmental variation and will 

likely show little change in the near future; we have shown that in another estuary a combination of

multiple impacts have led to long term declines in key species (O’Connell et al. 2004). With these 

declines in commercially important fishery species and changes in environmental conditions, the northern 

Gulf faces multiple challenges in maintaining its estuarine-based fisheries in the future.
31

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41

Appendix A: SIMPER Results

Tables A-1a through A-1f: Mean abundances, contribution percentages, and cumulative percentages for 

species that contributed significantly to differences in benthic communities among Louisiana, Mississippi, 

and Alabama from SIMPER analyses.

Bold numbers indicate higher values. Results are by season (spring and fall).

Table A-1a. Spring, Species, Louisiana, Mississippi

Spring

Mean 

abundance

per 

collection

Mean 

abundance

per 

collection 

Percent 

contribution 

to assemblage 

change

Cumulative 

percent 

contribution to 

assemblage 

change

Species Louisiana Mississippi

Anchoa mitchilli 0.11 0.47 17.55 17.55

Farfantepenaeus aztecus 0.09 0.16 6.88 24.43

Leiostomus xanthurus 0.01 0.21 5.4 29.83

Cynoscion arenarius 0.03 0.14 4.17 34

Ariopsis felis 0.06 0.06 4.07 38.06

Trichiurus lepturus 0.01 0.16 4.06 42.12

Callinectes sapidus 0.07 0.09 4.02 46.14

Sphoeroides parvus 0.05 0.07 3.51 49.65
42

Table A-1b. Spring, Species, Louisiana, Alabama

Spring

Mean 

abundance

per 

collection

Mean 

abundance

per 

collection 

Percent 

contribution 

to 

assemblage 

change

Cumulative 

percent 

contribution to 

assemblage 

change

Louisiana Alabama

Anchoa mitchilli 0.11 0.14 8.7 8.7

Farfantepenaeus aztecus 0.09 0.16 7.66 16.36

Callinectes sapidus 0.07 0.15 5.9 22.26

Leiostomus xanthurus 0.01 0.18 5.33 27.58

Ariopsis felis 0.06 0.07 4.65 32.23

Dasyatis sabina 0.07 0.06 4.41 36.64

Cynoscion arenarius 0.03 0.09 3.63 40.28

Sphoeroides parvus 0.05 0.07 3.62 43.9

Synodus foetens 0.01 0.09 3.16 47.06

Citharichthys macrops 0.08 0.01 2.67 49.73
43

Table A-1c. Spring, Species, Mississippi, Alabama

Spring

Mean 

abundance

per collection

Mean 

abundance

per collection 

Percent 

contribution 

to assemblage 

change

Cumulative 

percent 

contribution to 

assemblage 

change

Mississippi Alabama

Anchoa mitchilli 0.47 0.14 13.4 13.4

Leiostomus xanthurus 0.21 0.18 6.79 20.19

Farfantepenaeus aztecus 0.16 0.16 5.52 25.72

Callinectes sapidus 0.09 0.15 4.28 30

Cynoscion arenarius 0.14 0.09 4.07 34.07

Trichiurus lepturus 0.16 0.02 3.52 37.59

Ariopsis felis 0.06 0.07 3.01 40.6

Sphoeroides parvus 0.07 0.07 2.73 43.34

Micropogonias undulatus 0.1 0.06 2.63 45.97

Synodus foetens 0.03 0.09 2.61 48.57
44

Table A-1d. Fall, Species, Louisiana, Mississippi

Fall

Mean 

abundance 

per collection

Mean 

abundance 

per collection

Percent 

contribution 

to assemblage 

change

Cumulative 

percent 

contribution to 

assemblage 

change

Louisiana Mississippi

Anchoa mitchilli 0.08 0.61 13.74 13.74

Cynoscion arenarius 0.03 0.36 5.82 19.56

Leiostomus xanthurus 0.01 0.42 5.78 25.33

Litopenaeus setiferus 0.03 0.27 4.27 29.6

Chloroscombrus chrysurus 0.01 0.12 3.74 33.35

Callinectes sapidus 0.06 0.08 3.62 36.97

Peprilus paru 0.02 0.1 3.46 40.43

Sphoeroides parvus 0.09 0.05 3.35 43.79

Citharichthys macrops 0.15 0 3.2 46.99
45

Table A-1e. Fall, Species, Louisiana, Alabama

Fall

Mean 

abundance 

per 

collection

Mean 

abundance 

per 

collection

Percent 

contribution to 

assemblage 

change

Cumulative 

percent 

contribution to 

assemblage 

change

Louisiana Alabama

Anchoa mitchilli 0.08 0.14 6.88 6.88

Synodus foetens 0.04 0.17 5.37 12.24

Callinectes sapidus 0.06 0.1 4.75 16.99

Farfantepenaeus aztecus 0.06 0.11 4.57 21.56

Cynoscion arenarius 0.03 0.15 4.16 25.72

Sphoeroides parvus 0.09 0.05 4.15 29.87

Citharichthys macrops 0.15 0.01 3.77 33.64

Dasyatis sabina 0.07 0.05 3.48 37.12

Ariopsis felis 0.05 0.06 3.3 40.42

Menticirrhus americanus 0.06 0.05 3.3 43.72

Litopenaeus setiferus 0.03 0.08 2.93 46.65

Micropogonias undulatus 0.01 0.09 2.92 49.57
46

Table A-1f. Fall, Species, Mississippi, Alabama

Fall

Mean 

abundance 

per collection

Mean 

abundance 

per collection

Percent 

contribution to 

assemblage 

change

Cumulative percent 

contribution to 

assemblage change

Mississippi Alabama

Anchoa mitchilli 0.61 0.14 11.81 11.81

Cynoscion arenarius 0.36 0.15 6.03 17.84

Leiostomus xanthurus 0.42 0.07 5.6 23.45

Litopenaeus setiferus 0.27 0.08 4.16 27.6

Synodus foetens 0.05 0.17 4.04 31.65

Chloroscombrus chrysurus 0.12 0.02 3.16 34.81

Callinectes sapidus 0.08 0.1 3.08 37.88

Peprilus paru 0.1 0.04 2.97 40.85

Farfantepenaeus aztecus 0.09 0.11 2.8 43.65

Micropogonias undulatus 0.07 0.09 2.76 46.41

Etropus crossotus 0.04 0.1 2.65 49.06
47

Appendix B: INDVAL Results

Tables B-1a–B-1f: Results of IndVal for the top 25 species (including our three invertebrates) for each 

gear and season that looked for differences in value over time. 

IndVal (index based on specifity [relative abundance] and fidelity [frequency of occurrence within 

ecosystem]). Data are from spring and fall collections taken 1986–2015 in Louisiana, Mississippi, and 

Alabama. Results were overall for seine and by state for the trawl. The five trajectories (constant, 

increasing, decreasing, hump shaped [96–05 value higher than 86–95 and 06–15], or u-shaped [96–05 

value lower than 86–95 and 06–15]) assignations are based on differences in this “IndVal” for each of the 

species among the three decades.

Table B.1.a. Seine Spring

Seine Spring Decade

Species 1 2 3 index Stat p-value Trajectory

B. patronus 1 1 1 7 0.7848254 NA Constant

A. mitchilli 1 1 1 7 0.8348248 NA Constant

M. martinica 0 1 1 6 0.5112132 0.004 Increasing

M. beryllina 1 1 1 7 0.7745967 NA Constant

L. xanthurus 1 1 1 7 0.6783234 NA Constant

F. aztecus 1 1 1 7 0.6701341 NA Constant

M. cephalus 1 1 1 7 0.6655410 NA Constant

M. curema 1 1 1 7 0.3978470 NA Constant

L. rhomboides 1 1 1 7 0.5494001 NA Constant

L. parva 0 1 0 2 0.3268244 0.001 Hump

C. arenarius 1 1 1 7 0.5148113 NA Constant

C. sapidus 1 1 1 7 0.6343926 NA Constant

M. undulatus 1 1 1 7 0.5219125 NA Constant

B. chrysoura 1 1 1 7 0.3485289 NA Constant

A. hepsetus 1 1 0 4 0.3967837 0.009 Decreasing

A. felis 1 0 0 1 0.4468214 0.001 Decreasing

F. similis 1 1 0 4 0.4637776 0.001 Decreasing

M. peninsulae 1 0 0 1 0.5005040 0.001 Decreasing

S. parvus 1 0 0 1 0.3466440 0.001 Decreasing

S. marina 1 1 1 7 0.4633831 NA Constant

N. candidus 0 1 0 2 0.2295294 0.002 Hump

M. americanus 1 0 0 1 0.3015093 0.007 Decreasing

L. setiferus 1 1 1 7 0.2888522 NA Constant

C. boleosoma 0 0 1 3 0.2145876 0.062 Constant

M. salmoides 0 1 1 6 0.2220976 0.022 Increasing
48

B.1.b. Seine Fall 

Seine Fall Decade

Species 1 2 3 index Stat p-value Trajectory

A. mitchilli 1 1 1 7 0.8227419 NA Constant

M. beryllina 1 1 1 7 0.7821724 NA Constant

L. setiferus 1 1 1 7 0.7614805 NA Constant

M. undulatus 1 1 1 7 0.4529456 NA Constant

C. sapidus 1 1 1 7 0.7114369 NA Constant

M. martinica 1 1 1 7 0.4636677 NA Constant

B. patronus 1 1 1 7 0.3154498 NA Constant

A. felis 1 1 1 7 0.4474882 NA Constant

L. parva 0 1 0 2 0.3074208 0.003 Hump

F. aztecus 1 1 1 7 0.5651640 NA Constant

F. similis 1 1 0 4 0.5420429 0.001 Decreasing

H. jaguana 1 1 1 7 0.3676073 NA Constant

M. peninsulae 1 0 0 1 0.4405573 0.001 Decreasing

C. arenarius 0 1 1 6 0.4421152 0.001 Increasing

F. grandis 1 1 1 7 0.2953364 NA Constant

M. cephalus 1 1 1 7 0.5292431 NA Constant

M. americanus 1 1 1 7 0.5018394 NA Constant

N. candididus 0 1 0 2 0.2715849 0.001 Hump

M. gulosus 0 1 0 2 0.3369188 0.001 Hump

A. hepsetus 1 1 1 7 0.3807403 NA Constant

S. ocellatus 1 1 0 4 0.3791823 0.003 Decreasing

C. nebulosus 1 1 0 4 0.4609174 0.001 Decreasing

M. curema 1 1 0 4 0.3913635 0.001 Decreasing

O. saurus 1 1 1 7 0.4405714 NA Constant

E. argenteus 1 1 0 4 0.4052842 0.001 Decreasing
49

Table B.1.c. Trawl Spring Louisiana

Trawl Spring 

LA Decade

Species 1 2 3 

index Stat p-value Trajectory

A. mitchilli 1 1 1 7 0.91402573 NA Constant

M. undulatus 1 1 1 7 0.90427956 NA Constant

F. aztecus 1 1 1 7 0.84193869 NA Constant

C. arenarius 1 1 1 7 0.64435229 NA Constant

L. xanthurus 1 1 1 7 0.71156807 NA Constant

B. patronus 1 1 1 7 0.63644583 NA Constant

C. sapidus 1 1 1 7 0.63045091 NA Constant

A. felis 1 0 1 5 0.62201299 0.001 U-shaped

A. hepsetus 0 1 1 6 0.29833700 0.170 Constant

L. setiferus 1 1 1 7 0.43864002 NA Constant

S. parvus 1 1 1 7 0.36974161 NA Constant

P. octonemus 1 0 0 1 0.15309311 0.060 Constant

L. rhomboides 1 1 1 7 0.32994054 NA Constant

C. macrops 1 0 1 5 0.36748296 0.002 U-shaped

T. maculatus 1 0 1 5 0.32615354 0.019 U-shaped

D. sabina 1 1 1 7 0.36974161 NA Constant

P. lethostigma 1 1 0 4 0.30420359 0.009 Decreasing

B. chrysoura 0 1 1 6 0.23301218 0.166 Constant

T. lepturus 0 0 1 3 0.39898510 0.001 Increasing

I. furcatus 1 0 0 1 0.25130558 0.004 Decreasing

M. beryllina 0 1 0 2 0.12507854 0.549 Constant

C. chrysurus 0 1 1 6 0.14990634 0.213 Constant

P. burti 0 1 1 6 0.22065615 0.025 Increasing

G. dormitator 1 0 0 1 0.08838835 0.599 Constant

M. americanus 0 1 1 6 0.20185550 0.104 Constant
50

Table B.1.d. Trawl Spring Mississippi

Trawl Spring 

MS Decade

Species 1 2 3 index Stat p-value Trajectory

A. mitchilli 1 1 1 7 0.9404540 NA Constant

L. xanthurus 1 1 1 7 0.8043997 NA Constant

M. undulatus 1 1 0 4 0.8857386 0.001 Decreasing

F. aztecus 1 1 0 4 0.7787182 0.001 Decreasing

B. patronus 1 1 0 4 0.5807243 0.001 Decreasing

C. arenarius 1 1 0 4 0.8051801 0.001 Decreasing

A. hepsetus 1 1 1 7 0.4225771 NA Constant

P. octonemus 1 0 0 1 0.3520894 0.001 Decreasing

A. lyolepis 0 1 1 6 0.2661042 0.045 Increasing

T. lepturus 1 1 1 7 0.5285941 NA Constant

C. sapidus 1 1 0 4 0.7689437 0.001 Decreasing

P. burti 1 1 1 7 0.5797710 NA Constant

L. setiferus 1 0 0 1 0.4964568 0.001 Decreasing

A. felis 1 0 0 1 0.6084392 0.001 Decreasing

T. maculatus 1 1 0 4 0.5404659 0.001 Decreasing

C. spilopterus 1 1 0 4 0.5609493 0.001 Decreasing

D. petenense 1 1 1 7 0.4099600 NA Constant

S. parvus 1 1 0 4 0.4032613 0.002 Decreasing

C. chrysurus 1 1 1 7 0.2862390 NA Constant

B. chrysoura 1 1 1 7 0.3609046 NA Constant

S. plagiusa 1 1 0 4 0.4295127 0.001 Decreasing

H. jaguana 1 1 1 7 0.2291746 NA Constant

I. furcatus 1 1 0 4 0.3127008 0.001 Decreasing

P. lethostigma 1 1 0 4 0.4032867 0.001 Decreasing

C. nothus 1 1 0 4 0.2808044 0.056 Constant
51

Table B.1.e. Trawl Spring Alabama

Trawl Spring 

AL Decade

Species 1 2 3 index Stat p-value Trajectory

A. mitchilli 1 1 1 7 0.8934654 NA Constant

L. xanthurus 1 1 1 7 0.8329364 NA Constant

M. undulatus 1 1 1 7 0.8886408 NA Constant

F. aztecus 1 1 1 7 0.7951573 NA Constant

B. patronus 1 1 1 7 0.4919995 NA Constant

C. arenarius 1 1 1 7 0.7413523 NA Constant

L. rhomboides 0 1 1 6 0.4298703 0.001 Increasing

C. sapidus 1 1 0 4 0.7489610 0.001 Decreasing

P. octonemus 1 0 0 1 0.4066186 0.001 Decreasing

A. hepsetus 1 1 1 7 0.3459899 NA Constant

A. lyolepis 0 1 0 2 0.2227894 0.002 Hump

L. setiferus 1 1 1 7 0.5509732 NA Constant

A. felis 1 1 1 7 0.5527708 NA Constant

S. plagiusa 1 1 1 7 0.4280199 NA Constant

C. spilopterus 1 1 0 4 0.5654692 0.001 Decreasing

P. burti 1 1 1 7 0.3992387 NA Constant

C. boleosoma 1 1 0 4 0.3381977 0.001 Decreasing

T. maculatus 1 1 1 7 0.4564355 NA Constant

B. chrysoura 0 1 1 6 0.3694453 0.001 Increasing

P. tribulus 1 1 0 4 0.4286536 0.001 Decreasing

D. petenense 1 0 1 5 0.2893918 0.042 U-shaped

S. foetens 1 1 1 7 0.4130798 NA Constant

T. lepturus 1 1 1 7 0.4066250 NA Constant

E. crossotus 1 0 0 1 0.4343127 0.001 Decreasing

H. jaguana 1 1 0 4 0.1850462 0.012 Decreasing
52

Table B.1.f. Trawl Fall Louisiana

Trawl Fall 

LA Decade

Species 1 2 3 index Stat p-value Trajectory

A. mitchilli 1 1 1 7 0.9036115 NA Constant

A. felis 1 1 1 7 0.6795393 NA Constant

L. setiferus 1 1 1 7 0.7682539 NA Constant

F. aztecus 1 1 1 7 0.6057825 NA Constant

C. chrysurus 0 1 1 6 0.5476203 0.002 Increasing

M. undulatus 1 1 1 7 0.6032532 NA Constant

S. parvus 1 1 1 7 0.6032532 NA Constant

C. arenarius 1 1 1 7 0.5773503 NA Constant

A. hepsetus 1 1 1 7 0.4025910 NA Constant

C. sapidus 1 1 0 4 0.5659790 0.001 Decreasing

L. xanthurus 1 1 1 7 0.4175068 NA Constant

L. rhomboides 1 1 0 4 0.4958514 0.001 Decreasing

M. americanus 1 1 1 7 0.4424010 NA Constant

B. marinus 1 1 1 7 0.3668195 NA Constant

B. chrysoura 1 1 0 4 0.3090736 0.044 Decreasing

C. nebulosus 1 1 1 7 0.2926208 NA Constant

C. macrops 1 0 1 5 0.4079422 0.001 U-shaped

S. setapinnis 0 0 1 3 0.3369546 0.001 Increasing

D. sabina 1 1 1 7 0.3709645 NA Constant

C. faber 1 1 1 7 0.3078981 NA Constant

P. tribulus 0 1 1 6 0.2864358 0.166 Constant

B. patronus 1 1 0 4 0.2525855 0.063 Constant

S. foetens 1 1 1 7 0.2473097 NA Constant

T. maculatus 1 0 1 5 0.2900162 0.009 U-shaped

P. paru 0 1 0 2 0.2979588 0.002 Hump
53

Table B.1.g. Trawl Fall Mississippi

Trawl Fall 

MS Decade

Species 1 2 3 index Stat p-value Trajectory

A. mitchilli 1 1 1 7 0.9211690 NA Constant

L. setiferus 1 1 1 7 0.7203686 NA Constant

L. xanthurus 1 1 0 4 0.6661343 0.001 Decreasing

C. chrysurus 1 1 1 7 0.6171275 NA Constant

A. lyolepis 1 1 1 7 0.2947197 NA Constant

C. arenarius 1 1 0 4 0.8159557 0.001 Decreasing

M. undulatus 1 1 1 7 0.6967951 NA Constant

A. felis 1 0 0 1 0.6775617 0.001 Decreasing

A. hepsetus 1 1 1 7 0.5276329 NA Constant

F. aztecus 1 1 0 4 0.6183887 0.001 Decreasing

B. patronus 1 1 1 7 0.4789558 NA Constant

L. fasciatus 0 0 1 3 0.2082158 0.037 Increasing

B. marinus 0 1 1 6 0.4308345 0.001 Increasing

B. chrysoura 1 1 1 7 0.4647963 NA Constant

D. petenense 1 1 0 4 0.4585481 0.010 Decreasing

S. lanceolatus 0 1 1 6 0.2753949 0.095 Constant

C. sapidus 1 1 0 4 0.6075634 0.001 Decreasing

S. plagiusa 1 0 0 1 0.5914940 0.001 Decreasing

P. burti 1 1 1 7 0.4114183 NA Constant

S. setapinnis 0 1 1 6 0.4055192 0.001 Increasing

T. lepturus 1 1 1 7 0.3370248 NA Constant

E. crossotus 1 0 0 1 0.4908527 0.001 Decreasing

H. jaguana 1 1 1 7 0.3745822 NA Constant

S. parvus 1 0 0 1 0.4408155 0.001 Decreasing

P. paru 1 1 1 7 0.3775433 NA Constant
54

Appendix C: Environmental ANOVA Results 

Table C-1. Results of ANOVA comparison of environmental variables among decades (86–95, 96–2005, 2006–2015) using seasonal 

variables. 

The contrast p-values are from post-hoc tests (Tamhane’s T2; equal variances not assumed) of predicted values from the ANOVA between: 1) one and two, 2) one and three, and 3) 

two and three.

Gear Season Variable State One Two Three Contrast

Mean (S.E.) Mean (S.E.) Mean (S.E.) Effect 1 2 3

Seine Spring Temperature 24.36(0.33) 25.92(0.27) 25.85(0.19) 0.001 0.003 0.002 0.995

Salinity 11.04(0.51) 7.47(0.42) 6.54(0.29) 0.001 0.001 0.001 0.235

DO 7.52(0.15) 7.25(0.12) 6.96(0.080 0.002 0.298 0.001 0.107

Fall Temperature 23.02(0.44) 25.15(0.37) 23.85(0.24) 0.001 0.001 0.294 0.005

Salinity 18.30(0.62) 13.34(0.52) 15.44(0.33) 0.001 0.001 0.001 0.004

DO 7.59(0.17) 7.48(0.14) 6.96(0.09) 0.001 0.933 0.001 0.003

Trawl Spring Temperature LA 24.89(0.34) 25.20(0.30) 25.81(0.35) 0.155

Salinity 3.51(0.37) 6.36(0.33) 6.43(0.39) 0.001 0.001 0.001 0.999

DO 7.55(0.15) 6.941(0.09) 0.001

Temperature MS 23.63(0.36) 25.104(0.36) 25.78(0.26) 0.001 0.018 0.001 0.282

Salinity 11.33(0.77) 11.26(0.77) 13.63(0.55) 0.011 1.000 0.060 0.031

DO 7.11(0.11) 6.88(0.11) 7.04(0.08) 0.275

Temperature AL 22.12(0.19) 24.54(0.19) 25.48(0.19) 0.001 0.001 0.001 0.001

Salinity 16.51(0.47) 17.14(0.47) 15.83(0.46) 0.135

DO 6.72(0.08) 6.35(0.08) 6.56(0.08) 0.006 0.004 0.423 0.189
55

Gear Season Variable State One Two Three Contrast

Mean (S.E.) Mean (S.E.) Mean (S.E.) Effect 1 2 3

Fall Temperature LA 23.80(0.55) 24.05(0.45) 22.93(0.37) 0.128

Salinity 9.038(0.68) 11.17(0.54) 15.00(0.46) 0.001 0.002 0.001 0.001

DO 6.65(0.17) 7.28(0.08) 0.001

Temperature MS 22.82(0.43) 23.49(0.44) 23.50(0.33) 0.416

Salinity 16.82(0.85) 15.72(0.85) 20.73(0.64) 0.001 0.772 0.001 0.001

DO 7.03(0.13) 7.55(0.13) 7.22(0.10) 0.018 0.029 0.517 0.197

Temperature AL 21.62(0.23) 23.36(0.23) 23.53(0.22) 0.001 0.001 0.001 0.925

Salinity 21.56(0.36) 22.72(0.35) 21.66(0.34) 0.035 0.056 0.996 0.105

DO 6.76(0.09) 6.21(0.09) 6.78(0.08) 0.001 0.001 0.999 0.001
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