Gulf Menhaden (Brevoortia patronus): modeling the ...Grant D. Adams February 22, 2016 4 Prospectus...

Gulf Menhaden (Brevoortia patronus): modeling the variation in individual condition,

individual growth, and commercial fishery fleet dynamics correlated to spatial-temporal and

environmental drivers.

A Master’s Thesis Prospectus by:

Grant D. Adams

The University of Southern Mississippi

Department of Coastal Sciences

Ocean Springs, MS

February 22, 2016

Advisor:

Dr. Robert T. Leaf

Grant D. Adams February 22, 2016

1

Master’s Thesis Project

Gulf Menhaden (Brevoortia patronus): modeling the variation in individual condition, individual

growth, and commercial fishery fleet dynamics correlated to spatial-temporal and environmental

drivers.

Project Summary

Understanding the spatial-temporal trends and environmental drivers of fishes and fish

harvesters is critical for the management and sustainability of marine fisheries (Haynie et al. 2013,

Large et al. 2013, Joo et al. 2014, Peterson et al. 2014). Gulf Menhaden, Brevoortia patronus, is a

small clupeid fish distributed along shelf and estuarine waters in the northern Gulf of Mexico

(NGOM). Gulf Menhaden supports the second largest fishery by weight in the United States. Gulf

Menhaden is also the most abundant forage fish in the NGOM, representing an important trophic

link between primary production and higher trophic levels (Ahrenholz 1991, Robinson et al. 2015).

However, research into the spatial-temporal trends and environmental processes correlated with

Gulf Menhaden and the commercial Gulf Menhaden fishery is limited. In some cases, such as the

effect of spring Mississippi and Atchafalaya river discharge on Gulf Menhaden populations, results

have varied (Govoni 1997, Vaughan et al. 2011, Sanchez-Rubio & Perry 2015). With this in mind

I plan to examine the spatial, temporal, and environmental dynamics related to the individual

growth and condition of Gulf Menhaden and the foraging distribution and behavior of the

commercial Gulf Menhaden fleet in the Northern Gulf of Mexico. Research will be split into three

chapters;

Chapter 1) Temporal and environmentally driven fluctuations of Gulf Menhaden condition.

Chapter 2) Intra and inter-annual growth of Gulf Menhaden: using a hierarchical Bayesian

approach.


2

Chapter 3) The effect of environmental variation on the movement dynamics of the Gulf

Menhaden fishery.

Literature Cited

Ahrenholz DW (1991) Population biology and life history of the North American menhadens,

Brevoortia spp. Mar Fish Rev 53:3–19

Govoni JJ (1997) The association of the population recruitment of gulf menhaden, Brevoortia

patronus, with Mississippi River discharge. J Mar Syst 12:101–108

Haynie AC, Pfeiffer L, Rochet M-J (2013) Climatic and economic drivers of the Bering Sea

walleye pollock (Theragra chalcogramma) fishery: implications for the future. Can J Fish

Aquat Sci 70:841–853

Joo R, Bertrand A, Bouchon M, Chaigneau A, Demarcq H, Tam J, Simier M, Gutiérrez D,

Gutiérrez M, Segura M, Fablet R, Bertrand S (2014) Ecosystem scenarios shape fishermen

spatial behavior. The case of the Peruvian anchovy fishery in the Northern Humboldt Current

system. Prog Oceanogr 128:60–73

Large SI, Fay G, Friedland KD, Link JS (2013) Defining trends and thresholds in responses of

ecological indicators to fishing and environmental pressures. ICES J Mar Sci 70:755–767

Peterson W, Fisher J, Peterson J, Morgan C, Burke B, Fresh K (2014) Applied fisheries

oceanography: ecosystem indicators of ocean conditions inform fisheries management in the

California Current. Oceanography 27:80–89

Robinson KL, Ruzicka JJ, Hernandez FJ, Graham WM, Decker MB, Brodeur RD, Sutor M (2015)

Evaluating energy flows through jellyfish and gulf menhaden (Brevoortia patronus) and the

effects of fishing on the northern Gulf of Mexico ecosystem. ICES J Mar Sci


3

Sanchez-Rubio G, Perry H (2015) Climate-related meteorological and hydrological regimes and

their influence on recruitment of Gulf menhaden (Brevoortia patronus) in the northern Gulf

of Mexico. Fish Bull 113:391–406

Vaughan DS, Govoni JJ, Shertzer KW (2011) Relationship between Gulf Menhaden recruitment

and Mississippi River flow: model development and potential application for management.

Mar Coast Fish 3:344–352


4

Prospectus Chapter 1: 1

Environmentally-driven fluctuations in condition factor of adult Gulf Menhaden 2

(Brevoortia patronus) in the Northern Gulf of Mexico. 3

4

INTRODUCTION 5

In the Northern Gulf of Mexico (NGOM), multiple environmental dynamics influence the 6

magnitude of recruitment (Vaughan et al. 2011), abundance (Sanchez-Rubio & Perry 2015), and 7

growth and survival (Deegan 1990) of the most abundant forage fish, Gulf Menhaden, 8

Brevoortia patronus. Primary Productivity, dissolved oxygen (Langseth et al., 2014), sea surface 9

temperature (SST; SEDAR 2013), climate oscillations (Sanchez-Rubio & Perry 2015), and river 10

flow (Govoni 1997, Vaughan et al. 2011) have been identified as potential drivers in these biotic 11

processes. For example, the Mississippi River plume front is thought to influence Gulf 12

Menhaden recruitment because it effects nearshore larval transport (Govoni et al. 1989, Grimes 13

& Finucane 1991, Hitchcock et al. 1997). Northerly and easterly winds promote upwelling and 14

CO2 uptake in the NGOM, which leads to increased primary production of the Mississippi River 15

plume front (Huang et al. 2013). Deegan (1986) indicated that mean length and lipid content of 16

Gulf Menhaden seem to be high when spring water temperatures are high. While correlation 17

between growth and temperature has not been evaluated in Gulf Menhaden, lower number of 18

cumulative growing degree-days (i.e. number of days above a specific SST) has been correlated 19

to reduced growth of Atlantic Menhaden, B. tyrannus (Humphrey et al. 2014). Interannual 20

climate regimes such as the El Niño Southern Oscillation (ENSO) are also positively correlated 21

to the recruitment of Gulf Menhaden (Sanchez-Rubio & Perry 2015). However, during warm 22


5

ENSO events, SST is also lower in the Gulf of Mexico (Lau & Nath 2001) while northerly winds 23

are enhanced, highlighting the complexity of environmental drivers of Gulf Menhaden stocks. 24

The effect of river discharge, often correlated with oceanographic processes such as 25

primary production, has also been shown to influence population dynamics of Gulf Menhaden 26

(Govoni 1997, Vaughan et al. 2011, Sanchez-Rubio & Perry 2015). However, results have varied 27

on the effect of river discharge on Gulf Menhaden population dynamics. For example, some 28

studies found that large amounts of spring Mississippi and Atchafalaya river discharge have been 29

negatively correlated to the recruitment of larvae (age-0.5; Govoni 1997; Vaughan et al. 2011) 30

and seem to limit the growth and survival of juveniles (age-1; Deegan 1990). Alternatively, 31

Sanchez-Rubio and Perry (2015) reported that recruitment was positively associated with cold, 32

wet spawning and recruitment seasons (i.e. winter and spring) and thus a positive relationship 33

between river discharge and recruitment of larval and juvenile Gulf Menhaden. A possible 34

mechanism for improved recruitment success may be that increases in spring river flow can 35

result in increased primary production (Lohrenz et al. 1997, 2008), which promotes the growth 36

of Gulf Menhaden (Govoni 1997, Zhang et al. 2014). However, this relationship may be age 37

dependent because increased river discharge may push larvae further offshore, prolonging 38

shoreward transport to estuarine nurseries and exposure to predation. 39

Weight-at-length condition indices can be used to identify seasonal trends in energy 40

storage strategies (Cubillos & Claramunt 2009) and the effects of interdecadal climate regimes 41

and environmental conditions (Ventresca 1995, Ballón et al. 2008, Thorson 2015) on fish 42

species. Condition indices are a common tool for evaluating the “fatness” of fish (Blackwell et al 43

2000). In theory, heavier individuals of the same length or age are more likely to have greater 44

energy reserves, which is positively correlated to reproductive investment and survival (Glazier 45


6

2000, Rideout et al. 2005, Jørgensen et al. 2006). Given the correlation between reproduction 46

and individual condition, information on individual condition can also explain the highly 47

variable relationship between spawning stock biomass and recruitment (Thorson 2015). 48

However, individual condition is influenced by resource availability (energy intake and 49

expenditure), oceanographic conditions, and seasonal reproductive dynamics (Ventresca 1995, 50

Cubillos & Claramunt 2009). Decreases in individual condition can have large consequences for 51

the individual survival, recruitment, and population abundance of fishes (Lambert & Dutil 1997, 52

Scott et al. 2006). Despite the utility of understanding of fish condition and its relevance to 53

population dynamics, to date little is known on the seasonal and environmental trends of Gulf 54

Menhaden condition. 55

While the majority of research on the spatial-temporal variation and environmental 56

drivers of Gulf Menhaden stocks has focused on larvae and juveniles, the commercial Gulf 57

Menhaden reduction fishery located in the NGOM rarely catch fish younger than age-1 (SEDAR 58

2013). Menhaden reach maturity between age-1 and age-2 (SEDAR 2013), therefore the 59

individual condition of adult Gulf Menhaden may impact the recruitment and abundance of Gulf 60

Menhaden. To address gaps in our understanding of environmental influences on Gulf Menhaden 61

stocks, I plan to examine the relationship between the condition of adult (ages 1-6) Gulf 62

Menhaden and environmental variables (i.e. river discharge, chlorophyll, SST, and ENSO). This 63

study has three objectives 1) understanding the effect of spring Mississippi River discharge and 64

2) examining spatial-temporal dynamics and the effect of interannual climate variation in the 65

form of ENSO and 3) determining the spatially explicit effect of wind speed and direction, SST, 66

and chlorophyll. 67

68


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MATERIALS & METHODS 69

Gulf Menhaden length and age data will be provided by NOAA’s National Marine 70

Fisheries Service (NMFS). Between 1964 and 2011 NMFS recorded the length, weight, age, and 71

fishing grounds to a 10- x 10-min grid resolution of Gulf Menhaden sampled from the 72

commercial reduction fishery throughout the fishing season (April to October). The study area 73

will include all fishing grounds across the northern Gulf of Mexico, ranging from 28.5 to 31° N 74

and 95 to 84° W (Fig. 1). Spatial location (latitude and longitude) will be calculated from the 75

centroid of each 10- x 10-min grid cell in which samples were taken. Spurious data will be 76

identified and removed if they are from outside the boundary between land and ocean as 77

identified by NOAA’s Global Self-consistent, Hierarchical, High-resolution Shoreline at full 78

resolution , are older than ten years, weigh less than one gram, and are greater than 500 mm in 79

length. In addition, because fishing by the commercial reduction fishery as reported by Langseth 80

et al. (2014) does not exceed depths of 100 m, samples taken from grid cells with average depths 81

greater than 100 m identified from GEBCO’s gridded bathymetric data set at a 30 arc-second 82

interval (IOC et al. 2003) will be removed. 83

84

Figure 1. Study area and locations of Gulf Menhaden samples (in red) in the northern Gulf of 85

Mexico, 1964–2011. 10- x 10-min grid cells are marked in grey. 86

87


8

88

I will select specimens of Gulf Menhaden greater than 137.2 mm for analysis because 89

Gulf Menhaden reach maturity (L50) at this length (N Brown-Peterson, unpublished data). 90

Specimens outside of the 99.99% prediction interval of the log-transformed weight-at-length 91

relationship were deemed spurious and removed from further analysis. Relative condition factor 92

(𝐾𝑛) will be calculated for each fish; 93

Eq. 1 𝐾𝑛 =𝑊

�̂�× 100 94

where 𝑊 is weight (g) and �̂� is the theoretical individual weight derived from �̂� = 𝑎𝐿𝑏. 95

96

Table 1. Periods and sources of environmental data used in analysis of the effect environmental 97

factors on the condition of Gulf Menhaden in the northern Gulf of Mexico. 98

Variables Units Years Sources

Monthly Multivariate ENSO Index 1964-2011 1ESRL

Daily Mississippi River Discharge ft3/s 1964-2011 2USACE

Monthly zonal and meridional wind 2003-2011 3ESRL PSD

Monthly blended SST at 9 km2 °C 2003-2011 4GSFC

Monthly blended Chlorophyll-a at 9

km2

Mg/ml 2003-2011 4GSFC

1ESRL: Earth Systems Research Laboratory (http://www.esrl.noaa.gov/).

2USACE: US Army Corps of Engineers. River discharge measured at Tarbert Landing, MS.

(http://rivergages.mvr.usace.army.mil/).

http://www.esrl.noaa.gov/

http://rivergages.mvr.usace.army.mil/


9

3NOAA/OAR/ESRL PSD: Earth Systems Research Laboratory Physical Sciences Division.

NCEP Reanalysis data (Kalnay et al. 1996) (http://www.esrl.noaa.gov/psd/).

4GSFC: Goddard Space Flight Center. MODIS Aqua Sensor. (http://modis.gsfc.nasa.gov/)

99

Time series of climatological and oceanographic variables used to identify the response 100

of adult Gulf Menhaden to local environmental conditions are presented in Table 1. Total 101

monthly river discharge of the a Mississippi River will be calculated from daily values following 102

Govoni (1997) and Vaughan et al. (2011). The ENSO index (MEI) will be included with a 103

temporal lag of one month to account for the delayed biological response following Sanchez-104

Rubio et al. (2011). 105

To examine the variable effect of spring river discharge on adult Gulf Menhaden 106

condition throughout the year, I will use a two-level hierarchical linear model approach. In 107

hierarchical linear models, variance at one level is taken into account when calculating variance 108

components of other levels (Gallagher et al. 2015). Level-2 coefficients are also estimated to 109

explain variability in group means rather than individual level-1 responses. I will model relative 110

condition of Gulf Menhaden (C) of fish i in month j with spring river discharge as a fixed effect 111

and month as a random effect; 112

Eq. 2 𝐾𝑛𝑖 = 𝛼𝑗[𝑖] + 𝛽𝑗[𝑖]𝑥𝑖 + 𝜖𝑖 113

where 𝛼𝑗 are the level-1 intercepts, and 𝛽𝑗 are the level-1 regression coefficients (i.e. effect of 114

spring river discharge), and 𝜖𝑖 is the random error term. The level-2 hierarchical structure allows 115

intra-annual variation (i.e. months) to influence level-1 coefficients; 116

Eq. 3 𝛼𝑗 = 𝛾0𝑎 + 𝛾1

𝑎𝑢𝑗 + 𝜎𝑗𝑎 117

Eq. 4 𝛽𝑗 = 𝛾0𝛽

+ 𝛾1𝛽

𝑢𝑗 + 𝜎𝑗𝛽

118

http://www.esrl.noaa.gov/psd/

http://modis.gsfc.nasa.gov/


10

where 𝛾 are level-2 regression coefficients, and 𝑢𝑗 is month, and the random error is 𝜎. Given 119

this structure, models will be fit with randomly varying intercepts and slopes and covariance 120

between intercept and slopes. Performance of models will be evaluated using Akaike information 121

criterion (AIC). 122

I will use Generalized Additive Models (GAMs; Hastie and Tibshirani 1986) to examine 123

the temporal trends and the effect of MEI on Gulf Menhaden condition. By separating MEI from 124

River discharge we will avoid issues with collinearity. GAMs were selected because they are 125

able to detect non-linear trends that may be present in nature and I expect condition to vary in 126

coordination with the annual reproductive cycle. In order to explore the effects of spatial-127

temporal and climatic predictors on relative condition I will fit GAMs following the “double 128

penalty” shrinkage approach (Marra and Wood 2011). Shrinkage smoothing parameter selection 129

can remove a component from the model entirely, reducing error inherent in stepwise selection 130

procedures. In addition, I will fit models using Restricted Maximum Likelihood (REML) using 131

cubic regression splines assuming a Gaussian distribution to improve model stability; 132

Eq. 5 𝐾𝑛 ~𝑠1(𝑚𝑜𝑛𝑡ℎ) + 𝑠2(𝑦𝑒𝑎𝑟) + 𝑠3(𝑀𝐸𝐼) + 𝑠4(𝑙𝑎𝑡𝑖𝑡𝑢𝑑𝑒) + 𝑠5(𝑙𝑜𝑛𝑔𝑖𝑡𝑢𝑑𝑒) + 𝜀 133

where s1-5 are the smooth functions and 𝜀 is the remaining error. 134

To examine the effect of oceanographic variables on relative condition of Gulf Menhaden 135

in the NGOM and if this relationship is spatially dependent (e.g. in relation to the Mississippi 136

River bloom) I will create spatially-dependent GAMs. In the spatially-dependent model 137

coefficients of a function smoothly change in relation to geographic position (latitude and 138

longitude). These models are will be able to identify locations where Gulf Menhaden condition is 139

expected to increase or decrease in relation to changes in predictors. I will construct spatially-140

dependent GAMs (e.g. geographically weighted regression) with four predictors; SST, CHL, and 141


11

zonal and meridional wind vectors. Models will be fit by Restricted Maximum Likelihood using 142

cubic regression splines with an added shrinkage component assuming a Gaussian distribution: 143

Eq. 6 𝐾𝑛(∅,𝜆) = 𝑠1(∅, 𝜆) × 𝑆𝑆𝑇 + 𝑠2(∅, 𝜆) × 𝐶𝐻𝐿 + 𝑠3(∅, 𝜆) × 𝑍𝑜𝑛𝑎𝑙 𝑊𝑖𝑛𝑑 +144

𝑠4(∅, 𝜆) × 𝑀𝑒𝑟𝑖𝑑𝑖𝑜𝑛𝑎𝑙 𝑊𝑖𝑛𝑑 + 𝜀 145

where 𝐾𝑛(∅,𝜆)is Gulf Menhaden condition at latitude ∅ and longitude 𝜆, CHL is the chlorophyll-146

a, s1–4 are nonparametric smoothing functions, and 𝜀 is the random error. 147

148

EXPECTED RESULTS 149

Results of modelling will inform on: 1) the effect of spring Mississippi River discharge 150

and 2) spatial-temporal dynamics and the effect of ENSO and 3) the spatially explicit effect of 151

wind speed and direction, SST, and chlorophyll on adult Gulf Menhaden condition. Hierarchical 152

linear modelling will identify possible correlations between spring Mississippi River discharge 153

and relative condition. I expect a positive correlation between river discharge and condition, 154

however, by allowing a hierarchical structure I will be able to examine how the correlation 155

between river discharge varies by month and among years. GAM models will identify temporal 156

variations in condition on inter-annual and intra-annual scales, informing on the seasonal energy-157

storage strategy of Gulf Menhaden. I hypothesize that condition will be greatest during the 158

summer when SST and primary productivity are greatest. Furthermore, GAM models will inform 159

on possible non-linear effects of ENSO on individual condition, increasing our understanding of 160

climate driven population dynamics of Gulf Menhaden. Spatially-dependent GAM modeling will 161

give insight into the effects of wind, SST, and chlorophyll on condition. Allowing parameters to 162

vary spatially will test if environmental variables affect the condition of Gulf Menhaden at the 163

plume front differently than in surrounding waters. I hypothesize that, given the relative 164


12

abundance of Gulf Menhaden and primary productivity in the plume front, wind and chlorophyll 165

will have greater positive correlation with condition. 166

167

LITERATURE CITED 168

Ballón M, Wosnitza-Mendo C, Guevara-Carrasco R, Bertrand A (2008) The impact of 169

overfishing and El Niño on the condition factor and reproductive success of Peruvian hake, 170

Merluccius gayi peruanus. Prog Oceanogr 79:300–307 171

Cubillos LA, Claramunt G (2009) Length-structured analysis of the reproductive season of 172

anchovy and common sardine off central southern Chile. Mar Biol 156:1673–1680 173

Deegan LA (1986) Changes in body composition and morphology of young-of-the-year gulf 174

menhaden, Brevoortia patronus Goode, in Fourleague Bay, Louisiana. J Fish Biol 29:403–175

415 176

Deegan LA (1990) Effects of estuarine environmental conditions on population dynamics of 177

young-of-the-year gulf menhaden. Mar Ecol Prog Ser 68:195–205 178

Gallagher BK, Hice LA, McElroy AE, Cerrato RM, Frisk MG (2015) Factors influencing daily 179

growth in young-of-the-year winter flounder along an urban gradient revealed using 180

hierarchical linear models. Mar Coast Fish 7:200–219 181

Glazier DS (2000) Is fatter fitter? Body storage and reproduction in ten populations of the 182

freshwater amphipod Gammarus minus. Oecologia 122:335–345 183

Govoni JJ (1997) The association of the population recruitment of gulf menhaden, Brevoortia 184

patronus, with Mississippi River discharge. J Mar Syst 12:101–108 185

Govoni JJ, Hoss DE, Colby DR (1989) The spatial distribution of larval fishes about the 186

Mississippi River plume. Limnol Oceanogr 34:178–187 187


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Grimes CB, Finucane JH (1991) Spatial distribution and abundance of larval and juvenile fish, 188

chlorophyll and macrozooplankton around the Mississippi River discharge plume, and the 189

role of the plume in fish recruitment. Mar Ecol Prog Ser 75:109–119 190

Hastie T, Tibshirani R (1986) Generalized Additive Models. Stat Sci 1:297–318 191

Hitchcock GL, Wiseman WJ, Boicourt WC, Mariano AJ, Walker N, Nelsen TA, Ryan E (1997) 192

Property fields in an effluent plume of the Mississippi River. J Mar Syst 12:109–126 193

Huang W-J, Cai W-J, Castelao RM, Wang Y, Lohrenz SE (2013) Effects of a wind-driven cross-194

shelf large river plume on biological production and CO 2 uptake on the Gulf of Mexico 195

during spring. Limnol Oceanogr 58:1727–1735 196

Humphrey J, Wilberg MJ, Houde ED, Fabrizio MC (2014) Effects of temperature on age-0 197

Atlantic Menhaden growth in Chesapeake Bay. Trans Am Fish Soc 143:1255–1265 198

Jørgensen C, Ernande B, Fiksen Ø, Dieckmann U (2006) The logic of skipped spawning in fish. 199

Can J Fish Aquat Sci 63:200–211 200

Kalnay E, Kanamitsu M, Kistler R, Collins W, Deaven D, Gandin L, Iredell M, Saha S, White G, 201

Woollen J, Zhu Y, Chelliah M, Ebisuzaki W, Higgins W, Janowiak JE, Mo KC, Ropelewski 202

C, Wang J, Leetmaa A, Reynolds RW, Jenne R, Joseph D (1996) The NCEP/NCAR 40-203

year reanalysis project. Bull Am Meteorol Soc 77:437–471 204

Lambert Y, Dutil JD (1997) Condition and energy reserves of Atlantic cod (Gadus morhua) 205

during the collapse of the northern Gulf of St. Lawrence stock. Can J Fish Aquat Sci 206

54:2388–2400 207

Langseth BJ, Purcell KM, Craig JK, Schueller AM, Smith JW, Shertzer KW, Creekmore S, Rose 208

KA, Fennel K (2014) Effect of changes in dissolved oxygen concentrations on the spatial 209

dynamics of the Gulf Menhaden fishery in the northern Gulf of Mexico. Mar Coast Fish 210


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6:223–234 211

Lau N-C, Nath MJ (2001) Impact of ENSO on SST variability in the North Pacific and North 212

Atlantic: seasonal dependence and role of extratropical sea–air coupling. J Clim 14:2846–213

2866 214

Lohrenz SE, Fahnenstiel GL, Redalje DG, Lang GA, Chen XG, Dagg MJ (1997) Variations in 215

primary production of northern Gulf of Mexico continental shelf waters linked to nutrient 216

inputs from the Mississippi River. Mar Ecol Ser 155:45–54 217

Lohrenz SE, Redalje DG, Cai W-J, Acker J, Dagg M (2008) A retrospective analysis of nutrients 218

and phytoplankton productivity in the Mississippi River plume. Cont Shelf Res 28:1466–219

1475 220

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Stat Data Anal 55:2372–2387 222

Rideout RM, Rose G a., Burton MPM (2005) Skipped spawning in female iteroparous fishes. 223

Fish Fish 6:50–72 224

Sanchez-Rubio G, Perry H (2015) Climate-related meteorological and hydrological regimes and 225

their influence on recruitment of Gulf menhaden (Brevoortia patronus) in the northern Gulf 226

of Mexico. Fish Bull 113:391–406 227

Sanchez-Rubio G, Perry HM, Biesiot PM, Johnson DR, Lipcius RN (2011) Oceanic-atmospheric 228

modes of variability and their influence on riverine input to coastal Louisiana and 229

Mississippi. J Hydrol 396:72–81 230

Scott BE, Marteinsdottir G, Begg GA, Wright PJ, Kjesbu OS (2006) Effects of population 231

size/age structure, condition and temporal dynamics of spawning on reproductive output in 232

Atlantic cod (Gadus morhua). Ecol Modell 191:383–415 233


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SEDAR (2013) SEDAR 32A - Gulf of Mexico menhaden Stock Assessment Report. North 234

Charleston SC 235

Thorson J (2015) Spatio-temporal variation in fish condition is not consistently explained by 236

density, temperature, or season for California Current groundfishes. Mar Ecol Prog Ser 237

526:101–112 238

Vaughan DS, Govoni JJ, Shertzer KW (2011) Relationship between Gulf Menhaden recruitment 239

and Mississippi River flow: model development and potential application for management. 240

Mar Coast Fish 3:344–352 241

Ventresca DA (1995) El Niño effects on the somatic and reproductive condition of Blue 242

Rockfish (Sebastes mystinus). CalCOFI 36:167–174 243

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Boicourt WC, Ludsin SA (2014) Effects of hypoxia on habitat quality of pelagic 245

planktivorous fishes in the northern Gulf of Mexico. Mar Ecol Prog Ser 505:209–226 246

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248

249

250

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Intra and inter-annual growth of Gulf Menhaden, Bravoortia patronus, in the northern Gulf 253

of Mexico: using a hierarchical Bayesian approach 254

255

Introduction 256

Variation in the individual growth of fishes occurs on intra and inter-annual scales (Xu et 257

al. 2013, Van Beveren et al. 2014). Temporal variations in individual growth can have implications 258

for population dynamics because the survival and abundance of fish populations is in part 259

dependent on growth (Rice et al. 1993, Campana 1996, Sirois & Dodson 2000). According to the 260

growth-mortality hypothesis, fish that grow faster are better able to avoid predation, reduce larval 261

stage duration, and out compete smaller individuals for food resources (Takasuka et al. 2004). 262

Campana (1996) found that both otolith increment width and mean length-at-age of juvenile 263

Atlantic Cod (Gadus morhua) were positively correlated with survival. In the case of Japanese 264

Anchovy (Engraulis japonicas), Takasuka et al. (2003) found that predation mortality was greatest 265

for fish that had slower growth rates even for fish of the same size, indicating a direct link between 266

survival and growth. Therefore, understanding the variability in individual growth can improve 267

our understanding of the mechanisms that affect fish populations. 268

Temporal variation in fish growth can be examined by measuring calcified structures 269

(Pilling et al. 2007, Stocks et al. 2011) and corresponding length-at-age models (Midway et al. 270

2015). Calcified structures such as otoliths and scales are composed of periodically deposited 271

increments (annuli) consisting of opaque and translucent zones. The number of translucent zones 272

is used to estimate age and the width of opaque zones (i.e. increment width) can be used to estimate 273

individual growth history of fishes as a proxy for somatic growth (Fossen et al. 1999). Increment 274


17

width has been shown to have a strong linear relationship with fish length (Thresher et al. 2007), 275

however, this relationship may be species-specific (Beckman & Wilson 1995). Age estimates from 276

annuli are used with length data to estimate the mean individual growth curve of a population (i.e. 277

length-at-age models). von Bertanlanffy growth functions (VBGFs) are two or three parameter 278

asymptotic models that are used to estimate the mean asymptotic length and growth rate coefficient 279

(Campana 2001). However, the use of VBGF growth parameters do not allow an understanding of 280

inter-annual variation in individual growth (Pilling et al. 2002). Thus, because individual 281

variability in growth can influence the population parameters (Sainsbury 1980), incorporating 282

knowledge of individual variability in growth within a population may improve our understanding 283

of population dynamics. 284

Temporal variation in growth often occurs at inter-annual scales and can provide insights 285

into the underlying environmental conditions that favor the growth of fish. For example, in the 286

northern Gulf of Mexico, variation of annual VBGF parameter estimates for the forage fish Gulf 287

Menhaden (Brevoortia patronus) between 1954 and 2011 has been reported (Fig.1; SEDAR 2013). 288

However, comprehensive examination of individual growth variability is lacking for this species. 289

Temporal variation in the growth rates of other forage fish have been found to be correlated to sea 290

surface temperature (SST) and food availability (Cubillos et al. 2001, Brunel & Dickey-Collas 291

2010, Takahashi et al. 2012). In an analysis of VBGF parameter estimates, Brunel and Dickey-292

Collas (2010) noted inter-annual variability of mean asymptotic weight of Clupea harengus was 293

negatively correlated to SST. Alternatively, Takahashi et al. (2012) found that standardized 294

increment width of Engraulis mordax had a negative relationship with SST and a positive 295

relationship with food availability related to increased upwelling in the California Current between 296

2005 and 2006. 297


18

298

299

Figure 1. von Bertanlanffy Growth Functions parameter estimates for Gulf Menhaden in the 300

northern Gulf of Mexico, 1964-2011. Solid line represents the asymptotic length (L∞) mm fork 301

length (FL) and dotted line represents growth rate coefficient (k, y-1). 302

303

Most fishes in non-tropical regions also have seasonal variation in the increment width of 304

calcified structures (Beckman & Wilson 1995). Examination of this variation can also give insight 305

into the life history of fishes and how growth evolved to ambient and dynamic environmental 306

conditions (Amara et al. 1994). In Gulf Menhaden, preliminary analysis of increment growth 307

indicates faster growth in the spring (Fig.2). Conversely, Coulson et al. (2014) found that the 308

increment growth of opaque zones in flathead otoliths (Leviprora inops and Platycephalus 309

laevigatus) was greatest during mid-summer to early-autumn months. Seasonal variations in 310

individual growth of forage fishes may also have implications to higher level ecosystem dynamics 311

(i.e. predator and prey abundance) because growth can impact energy storage strategies (Cubillos 312

et al. 2001). 313


19

314

Figure 2. Scale length unit of age-1 Gulf Menhaden in the northern Gulf of Mexico, 1964-2011. 315

316

Intra-annual variation in growth may also vary on inter-annual scales, which can be 317

examined through the use of hierarchical structured models (Takahashi et al. 2012). Gallagher et 318

al. (2015) suggests that optimal growth conditions are temporally and spatially variable. Takahashi 319

et al. (2012) found inter-annual variation in seasonal growth of juvenile Northern Anchovy 320

(Engraulis mordax), possibly related to climate driven shifts in upwelling. Increment data of 321

calcified structures is inherently hierarchical through the repeated sampling of many individuals 322

spanning multiple overlapping seasons, years, cohorts, and populations (Morrongiello & Thresher 323

2014). Thus, hierarchical structured models can be used to understand temporal variation, through 324

the nested effects of inter-annual variation on intra-annual variation. Using a hierarchical structure 325

in the analysis of individual growth can also remove the assumption that all age-groups in a year 326

have experienced the same growth environment over their past lifespan, leading to increased model 327


20

performance (He & Bence 2007). However, the integration of hierarchical structures is rarely used 328

when examining individual growth in fishes. 329

Despite the importance of individual growth variability in fisheries assessments little is 330

known about the temporal variation of individual growth of Gulf Menhaden. The Gulf Menhaden 331

fishery is the second largest fishery by weight in the United States and the species represents an 332

important trophic link between primary production and higher trophic levels (Ahrenholz 1991). 333

Thus, information regarding variation in individual growth can provide more accurate length-at-334

age information for stock assessment and understanding the role of Gulf Menhaden in the northern 335

Gulf of Mexico. This study will address pertinent gaps regarding our understanding of the temporal 336

variability of Gulf Menhaden growth using scales provided by NOAA’s National Marine Fisheries 337

Service (NMFS) to estimate individual growth. I plan to examine 1) the seasonal trends in growth, 338

2) overall inter-annual variation in growth, and 3) examine hierarchical temporal dynamics in 339

growth. To account for the hierarchical effects of inter-annual variation on seasonal growth we 340

plan to use a hierarchical linear model within the Bayesian framework. 341

342

Materials and Methods 343

Fishery-dependent survey data from the commercial reduction fishery in the Gulf of 344

Mexico from 1964 to 2011 were provided by NOAA’s National Marine Fisheries Service (NMFS). 345

Age determination was done using scales taken along the flank below the insertion of the dorsal 346

fin and in the vicinity of the lateral line following NMFS protocol. Validation of scales was 347

conducted by scale-otolith comparisons by the NOAA Beaufort Lab and a relatively low level of 348

ageing error and similar ageing error matrices was found (Nancy Brown-Peterson, unpublished). 349


21

All fish were assumed to have a 1 January birthdate because spawning occurs in the winter. Fish 350

specific scale growth (G) was determined to reduce error from variation within the population; 351

𝐺 =𝐿𝑇 − 𝐿𝑌𝑖

𝐿𝑇 352

where 𝐿𝑌𝑖 is the width of the scale to the last circuli and 𝐿𝑇 is the width of the entire scale. 353

To evaluate temporal patterns in Gulf Menhaden growth I plan to use a hierarchical Bayesian 354

model where G is the dependent variable. Independent variables will be Julian day nested in year 355

and nested in size class. 356

357


Using hierarchical Bayesian models and scale measurements we will be able to determine 359

temporal variations in the individual growth of Gulf Menhaden. This includes seasonal or intra- 360

and inter-annual variation in individual growth. The hierarchical structure of the models will allow 361

insight into intra-annual variation on inter-annual scales (i.e. shifts in the timing of seasonal growth 362

among years). Results will provide insight into the seasonal growth patterns of Gulf Menhaden in 363

addition to the possible conditions that determine variability in individual growth and its effect on 364

Gulf Menhaden populations. In addition to being the first analysis of increment growth using 365

hierarchical structure in the Bayesian framework, the methods used through this project will 366

provide a framework for analyzing variation in individual growth of fishes on multiple temporal 367

scales. 368

369

370

371

372


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Ahrenholz DW (1991) Population biology and Life history of the North American menhadens, 374

Brevoortia spp. Mar Fish Rev 53:3–19 375

Amara R, Desaunay Y, Lagardere F (1994) Seasonal variation in growth of larval Sole solea (L.) 376

and consequences on the success of larval immigration. Netherlands J Sea Res 32:287–298 377

Beckman D, Wilson C (1995) Seasonal timing of opaque zone formation in fish otoliths. In: Secor 378

D, Dean J, Campana SE (eds) Recent Developments in fish otolith research. Belle W. Baruch, 379

Columbia, OH, p 27–44 380

Beveren E Van, Bonhommeau S, Fromentin J-M, Bigot J-L, Bourdeix J-H, Brosset P, Roos D, 381

Saraux C (2014) Rapid changes in growth, condition, size and age of small pelagic fish in the 382

Mediterranean. Mar Biol 161:1809–1822 383

Brunel T, Dickey-Collas M (2010) Effects of temperature and population density on von 384

Bertalanffy growth parameters in Atlantic herring: A macro-ecological analysis. Mar Ecol 385

Prog Ser 405:15–28 386

Campana SE (1996) Year-class strength and growth rate in young Atlantic cod Gadus morhua. 387

Mar Ecol Prog Ser 135:21–26 388

Campana S (2001) Accuracy, precision and quality control in age determination, including a 389

review of the use and abuse of age validation methods. J Fish Biol 59:197–242 390

Coulson PG, Black BA, Potter IC, Hall NG (2014) Sclerochronological studies reveal that patterns 391

of otolith growth of adults of two co-occurring species of Platycephalidae are synchronised 392

by water temperature variations. Mar Biol 161:383–393 393

Cubillos LA, Arcos DF, Bucarey DA, Canales MT (2001) Seasonal growth of small pelagic fish 394

off Talcahuano, Chile (37°S, 73°W): a consequence of their reproductive strategy to seasonal 395


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upwelling? Aquat Living Resour 14:115–124 396

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(Hippoglossoides platessoides) in the Barents Sea. ICES J Mar Sci:689–696 398

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growth in young-of-the-year winter flounder along an urban gradient revealed using 400

hierarchical linear models. Mar Coast Fish 7:200–219 401

He JX, Bence JR (2007) Modeling annual growth variation using a hierarchical bayesian approach 402

and the von Bertalanffy growth function, with application to lake trout in southern Lake 403

Huron. Trans Am Fish Soc 136:318–330 404

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and temporal variability in growth of southern flounder (Paralichthys lethostigma). Fish Res 406

167:323–332 407

Morrongiello JR, Thresher RE (2014) A statistical framework to explore ontogenetic growth 408

variation among individuals and populations: a marine fish example. Ecol Monogr 85:93–409

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growth variability in fish, and the correlation between von Bertalanffy growth parameters. 412

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Gadus morhua L.: has climate change affected otolith annulus formation and growth? J Fish 415

Biol 70:584–599 416

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Growth rate variation and larval survival: inferences from an individual-based size-dependent 418


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predation model. Can J Fish Aquat Sci 50:133–142 419

Sainsbury KJ (1980) Effect of individual variability on the von Bertalanffy growth equation. Can 420

J Fish Aquat Sci 37:241–247 421


Charleston SC 423

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fish, the rainbow smelt Osmerus mordax. Mar Ecol Prog Ser 203:233–245 425

Stocks J, Stewart J, Gray C a., West RJ (2011) Using otolith increment widths to infer spatial, 426

temporal and gender variation in the growth of sand whiting Sillago ciliata. Fish Manag Ecol 427

18:121–131 428

Takahashi M, Checkley DM, Litz MNC, Brodeur RD, Peterson WT (2012) Responses in growth 429

rate of larval northern anchovy (Engraulis mordax) to anomalous upwelling in the northern 430

California Current. Fish Oceanogr 21:393–404 431

Takasuka A, Aoki I, Mitani I (2003) Evidence of growth-selective predation on larval Japanese 432

anchovy Engraulis japonicus in Sagami Bay. Mar Ecol Prog Ser 252:223–238 433

Takasuka A, Aoki I, Mitani I (2004) Three synergistic growth-related mechanisms in the short-434

term survival of larval Japanese anchovy Engraulis japonicus in Sagami Bay. Mar Ecol Prog 435

Ser 270:217–228 436

Thresher RE, Koslow JA, Morison AK, Smith DC (2007) Depth-mediated reversal of the effects 437

of climate change on long-term growth rates of exploited marine fish. Proc Natl Acad Sci U 438

S A 104:7461–7465 439

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interannual variation and spatial distribution of Peruvian anchovy (Engraulis ringens) 441


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population dynamics from 1991 to 2007: A three-dimensional modeling study. Ecol Modell 442

264:64–82 443

444

445

446


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The effect of environmental variation on the movement dynamics of the Gulf Menhaden 448

fishery in the northern Gulf of Mexico. 449

450

INTRODUCTION 451

Marine environments consist of dynamic environmental processes that can influence the 452

foraging distribution and behavior of predators. Environmental drivers of foraging include sea 453

surface temperature (SST), primary production, dissolved oxygen concentration, and depth 454

(Cuevas et al. 2013, Joo et al. 2014, Langseth et al. 2014, Ramírez et al. 2014). For example, 455

Ramírez et al. (2014) noted the importance of the abundance of intra and inter-specific 456

competition, high primary productivity, and the presence of marine fronts in determining the 457

foraging distribution of the Magellanic penguins (Spheniscus magellanicus). Joo et al. (2014) 458

found that spatial fishing behavior of the commercial Anchovy fishery in the Humboldt Current is 459

indirectly linked to SST, chlorophyll concentration, and dissolved oxygen through their effects on 460

anchovy (Engraulis ringens) abundance and distribution. In the southern Gulf of Mexico, wind 461

direction and speed have been found to influence the probability of fishing for spotted eagle rays 462

(Aetobatus narinari) in certain locations because fishermen prefer more turbid waters (Cuevas et 463

al. 2013). Environmentally driven variation in the foraging distribution and patterns of predators 464

may have consequences for foraging success and fisheries management because the catchability 465

of fishes are spatially and temporally variant (Ziegler et al. 2003, Jiao et al. 2006). 466

Recent work has studied the foraging patterns of fishing vessels through the fitting of 467

probability distribution functions (PDFs) to distance cruising, searching, and fishing data (Bertrand 468

et al. 2007, Marchal et al. 2007). Examination of foraging patterns can inform on the maximization 469


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of catch-per-unit energy (i.e. optimal foraging) because despite all available knowledge, 470

uncertainty on prey location is a major driver of fishing spatial behavior (Joo et al. 2014). Possible 471

PDF families that have been used to describe foraging behavior include the power-law (i.e. Lévy 472

walks), exponential, and Poisson distributions (Edwards et al. 2012). However, care should be 473

taken when choosing the appropriate PDF to model foraging behavior (Edwards 2011, Edwards et 474

al. 2012). If fitted correctly, PDF parameter estimates can be comparable within PDF families. 475

PDF parameter estimates of foraging patterns provides a novel tool to examine variation in 476

foraging behavior due to environmental dynamics because the distance spent cruising, searching, 477

and fishing can be influenced by environmental processes. 478

Environmental variation in the northern Gulf of Mexico (NGOM) is characterized by 479

variation in multiple processes including spatial-temporal distribution of hypoxic conditions, wind 480

direction and speed, sea surface temperature (SST), and primary production (Enfield & Mayer 481

1997, Sanchez-Rubio et al. 2011, Huang et al. 2015). These processes often covary and are 482

impacted by variations in climate regimes such as the El Niño Southern Oscillation (ENSO; Lau 483

& Nath 2001, Sanchez-Rubio et al. 2011). For example, during positive ENSO anomalies (i.e. El 484

Niño) SST in the NGOM is lower and spring river discharge is greater (Lau & Nath 2001, Sanchez-485

Rubio et al. 2011). River discharge from the Mississippi and Atchafalaya rivers is greatest during 486

the spring and increased discharge along with enhanced northerly and easterly winds can favor 487

offshore transport and enhanced primary production of the Mississippi and Atchafalaya River 488

plume (Huang et al. 2015). In turn the distribution and extent of hypoxic conditions is driven by 489

nutrient loading and discharge from the Mississippi and Atchafalaya Rivers, primary production, 490

and temperature (Rabalais et al. 2002). However, the effect of these processes on the fishery 491

dynamics and fisherman behavior in the NGOM is relatively unknown. 492


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The NGOM is home to the commercial Gulf Menhaden (Bravoortia patronus) reduction 493

fishery, the second largest fishery by weight in the United States. The fishery is open from the 494

from the third Monday in April through November 1 each year (SEDAR 2013). Fishing for Gulf 495

Menhaden occurs mostly inshore in near coastal waters that are most vulnerable to hypoxia and 496

variation in oceanographic conditions (SEDAR 2013, Langseth et al. 2014). Recent work by 497

Langseth et al. (2014) has demonstrated that the probability of fishing and fishing effort of the 498

Gulf Menhaden fishery in the Louisiana Bight declines with dissolved oxygen, describing a link 499

between variation in fishing activity to oceanographic conditions. The relationship between 500

dissolved oxygen concentrations and fishing activity was shown to be spatially dependent. 501

However, variation in fishing activity has not been examined for other environmental drivers that 502

are known to influence foraging distribution and behavior of fishing vessels in other systems 503

(Cuevas et al. 2013, Joo et al. 2014) and the distribution and abundance of Gulf Menhaden (Deegan 504

1990, Vaughan et al. 2011). 505

In this study, Captain’s Daily Fishing Reports (CDFRs) from the commercial Gulf 506

Menhaden reduction fishery will be used to examine correlations between environmental variation 507

and fishing activity. Research will focus on two areas; 1) the environmental drivers of spatially 508

dependent CPUE, fishing effort, and probability of fishing and 2) variations in foraging patterns 509

of the commercial Gulf Menhaden reduction fishery correlated with environmental variation. 510

Information on how fishing activity is affected by previous environmental conditions can provide 511

insight into future fishing when such conditions are present. Environmental drivers examined will 512

include river discharge, SST, primary production, and wind direction and speed. Given the 513

relationship between the oceanographic drivers included and ENSO, analysis will use a 514


29

hierarchical structure to examine how variation in ENSO is related to variation in the effects of 515

oceanographic drivers on fishing behavior. 516

517

MATERIALS AND METHODS 518

CDFR data for years 2006-2009 and 2011 were provided by the commercial Gulf 519

Menhaden fishery. Total monthly river discharge (ft3/s) of the a Mississippi River at Tarbert 520

Landing, MS will be calculated from daily values provided by the Army Corps of Engineers 521

following Govoni (1997) and Vaughan et al. (2011). Monthly ENSO anomalies will be described 522

using the Multivariate ENSO Index (MEI) and daily wind vector and speed data from NOAA’s 523

Earth System Research Laboratory (http://www.esrl.noaa.gov/). Monthly SST and chlorophyll-a 524

data at a 9 km2 resolution will be obtained from the MODIS Aqua Sensor distributed by NASA’s 525

Goddard Space Flight Center (http://modis.gsfc.nasa.gov/). All environmental data, excluding 526

river discharge, will be lagged one month to account for the delayed biological response following 527

Sanchez-Rubio et al. (2011). 528

To examine the variation in foraging patterns of the Gulf Menhaden reduction fishery 529

CDFR data will be binned by month and year. A suite of PDFs including exponential, power-law, 530

Poisson, and normal distributions will be fit to individual vessel foraging distance data using 531

likelihood based methods following those recommended by Edwards et al. (2012). PDF fits 532

between families will then be compared using Akaike information criterion (AIC) and AIC weights 533

and the best fit PDF will be selected for further analysis (Edwards et al. 2012). PDFs will then be 534

fit to the binned CDFR data by month for each individual vessel to estimate PDF parameters. 535

To examine how variation in foraging patterns is correlated to environmental variation 536

PDF parameter estimates analyzed using hierarchical regression with the following covariates; 537

http://www.esrl.noaa.gov/

http://modis.gsfc.nasa.gov/


30

SST, river discharge, MEI, chlorophyll concentration, and wind direction and speed data. A three-538

level hierarchy will be constructed with factors influencing individual boats nested in months 539

nested in year. Environmental drivers at different temporal scales will be included as covariates at 540

different levels. 541

Using CDFR data a spatially-dependent model will be created to determine how the 542

probability of fishing, CPUE, and effort of commercial Gulf Menhaden vessels are influenced by 543

variation in SST, chlorophyll concentration, river discharge, MEI, and wind direction and speed. 544

Potential models being considered for analysis include spatially-dependent generalized additive 545

models and Maximum Entropy (MaxEnt) modelling. 546

547


Results of this study will provide information on how foraging patterns of commercial Gulf 549

Menhaden reduction fishery vessels are influenced by environmental processes in the NGOM. 550

Specifically how variation in the distance travelled cruising, searching, and fishing is correlated to 551

variation in wind direction and speed, SST, chlorophyll concentrations, and river discharge. The 552

hierarchical structure will also inform on the effect of ENSO on parameter estimates for each 553

environmental driver and variation between months and years. The spatially-dependent model will 554

inform on how the probability of fishing, CPUE, and effort is distributed and how the distribution 555

is influence by environmental variation, specifically SST, chlorophyll concentration, wind 556

direction and speed, and ENSO anomalies. The analysis can be used to improve management of 557

the Gulf Menhaden fishery and will be of interest to the commercial Gulf Menhaden fishery. 558

559

560


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Bertrand S, Bertrand A, Guevara-Carrasco R, Gerlotto F (2007) Scale-invariant movements of 562

fishermen: the same foraging strategy as natural predators. Ecol Appl 17:331–337 563

Cuevas E, Pérez J, Méndez I (2013) Efecto de factores ambientales y la asignación del esfuerzo 564

pesquero sobre la captura de la raya Aetobatus narinari (Rajiformes: Myliobatidae) en el sur 565

del Golfo de México. Rev Biol Trop 61:1341–1349 566

Deegan LA (1990) Effects of estuarine environmental conditions on population dynamics of 567

young-of-the-year gulf menhaden. Mar Ecol Prog Ser 68:195–205 568

Edwards AM (2011) Overturning conclusions of Levy flight movement patterns by fishing boats 569

and foraging animals. Ecology 92:1247–1257 570

Edwards AM, Freeman MP, Breed GA, Jonsen ID (2012) Incorrect likelihood methods were used 571

to infer scaling laws of marine predator search behaviour. PLoS One 7:e45174 572

Enfield DB, Mayer DA (1997) Tropical Atlantic sea surface temperature variability and its relation 573

to El Niño-Southern Oscillation. J Geophys Res 102:929 574

Govoni JJ (1997) The association of the population recruitment of gulf menhaden, Brevoortia 575

patronus, with Mississippi River discharge. J Mar Syst 12:101–108 576

Huang S, Wang Y, Zheng X, Wang W, Cao X (2015) Comparative analysis of three methods of 577

making scale specimens for small fish. Environ Biol Fishes 98:697–703 578

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in the fisheries of Lake Erie using a Bayesian error-in-variable approach. ICES J Mar Sci 580

63:1695–1704 581

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spatial behavior. The case of the Peruvian anchovy fishery in the Northern Humboldt Current 584

system. Prog Oceanogr 128:60–73 585

Langseth BJ, Purcell KM, Craig JK, Schueller AM, Smith JW, Shertzer KW, Creekmore S, Rose 586

KA, Fennel K (2014) Effect of changes in dissolved oxygen concentrations on the spatial 587

dynamics of the Gulf Menhaden fishery in the northern Gulf of Mexico. Mar Coast Fish 588

6:223–234 589

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North Pacific and North Atlantic: seasonal dependence and role of extratropical sea–air 591

coupling. J Clim 14:2846–2866. J Clim 14:2846–2866 592

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density: examples from some North Sea fisheries. Fish Res 83:33–43 594

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anthropogenic factors affecting the feeding ecology of a top marine predator, the Magellanic 598

penguin. Ecosphere 5:art38 599

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modes of variability and their influence on riverine input to coastal Louisiana and Mississippi. 601

J Hydrol 396:72–81 602


Charleston SC 604

Vaughan DS, Govoni JJ, Shertzer KW (2011) Relationship between Gulf Menhaden recruitment 605

and Mississippi River flow: model development and potential application for management. 606


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Mar Coast Fish 3:344–352 607

Ziegler PE, Frusher SD, Johnson CR (2003) Space - Time variation in catchability of southern 608

rock lobster Jasus edwardsii in Tasmania explained by environmental, physiological and 609

density-dependent processes. Fish Res 61:107–123 610

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