MARINE ECOLOGY PROGRESS SERIESMar Ecol Prog Ser

Vol. 420: 145–156, 2010doi: 10.3354/meps08849

Published December 16

INTRODUCTION

The American lobster, Homarus americanus, is dis-tributed throughout the continental shelf of the North-west Atlantic from Newfoundland, Canada to offshoreNorth Carolina, USA (Lawton & Lavalli 1995). Al-though lobster are mainly found in waters shallowerthan 50 m (Pringle & Burke 1993), they also inhabitwaters up to 700 m deep (Cooper & Uzmann 1971). H.americanus is the most abundant large mobile benthicinvertebrate in the region (Steneck & Wilson 2001)and supports one of the most valuable commercialfisheries in the US, with landings of over 45 000 tworth over $386 million in 2006 (http://www.st.nmfs.noaa.gov/st1/commercial/landings/annual_landings.html). The majority of commercial catch in the US isfrom inshore waters of the Gulf of Maine (GOM;ASMFC 2009).

Although lobster density varies considerably alongthe coast in the GOM (Steneck & Wilson 2001), tempo-

ral patterns are similar geographically (Steneck & Wil-son 2001, Incze et al. 2006). Temporal consistency inthe distribution of lobster at large spatial scales sug-gests that the distribution of lobster may be regulatedby environmental variables. Furthermore, recent find-ings on the dispersal potential of lobsters in the GOMsuggest that a significant amount of lobster recruit-ment is derived from local sources (Xue et al. 2008,Incze et al. 2010), which implies that local habitat fea-tures and fine-scale oceanic processes also influencethe spatial distribution of lobster in the GOM.

The spatial distribution of the American lobstervaries seasonally. In the GOM, lobsters migrate intoinshore waters in the spring and then into deep watersin late fall (Chen et al. 2006). In Great Bay, New Hamp-shire (Watson et al. 1999) and in Canadian estuaries(Munro & Therriault 1983), most movement is intoestuaries during the spring and towards the coast forthe rest of the year, suggesting control by environmen-tal variables such as temperature and salinity.

© Inter-Research 2010 · www.int-res.com*Email: jui-han.chang@umit.maine.edu

Estimating spatial distribution of American lobsterHomarus americanus using habitat variables

Jui-Han Chang1,*, Yong Chen1, Daniel Holland2, Jonathan Grabowski2

1School of Marine Sciences, University of Maine, Orono, Maine 04469, USA2Gulf of Maine Research Institute, 350 Commercial Street, Portland, Maine 04101, USA

ABSTRACT: The spatial distribution of the American lobster Homarus americanus is influenced bymany factors, which are often difficult to quantify. We implemented a modeling approach for quanti-fying season-, size-, and sex-specific lobster spatial distribution in the Gulf of Maine with respect toenvironmental and spatial variables including bottom temperature, bottom salinity, latitude, longi-tude, depth, distance offshore, and 2 substratum features. Lobster distribution was strongly associ-ated with temperature and depth, and differed seasonally by sex. In offshore waters in the fall,females were dominant at higher latitudes and males at lower latitudes. This segregation was notapparent in the spring although females were still dominant at higher latitudes in offshore waters.Juveniles and adults were also distributed differently; juveniles were more abundant at the lower lat-itudes in inshore waters, while adults were more widespread along the entire coast. These patternsare consistent with the ecology of the American lobster. This study provides a tool to evaluatechanges in lobster spatial distribution with respect to changes in key habitat and other environmen-tal variables, and consequently could be of value for the management of the American lobster.

KEY WORDS: American lobster · Homarus americanus · Spatial distribution · Gulf of Maine · 2-stage GAM

Resale or republication not permitted without written consent of the publisher

Mar Ecol Prog Ser 420: 145–156, 2010

Spatial distribution of lobster can also differ greatlyby sex and size (Karnofsky et al. 1989, Campbell 1990).Small lobsters are more likely to reside in inshorewaters, whereas large lobsters are more common inoffshore waters (Cooper et al. 1975, Briggs 1985).These differences in spatial distribution are likely dueto sex- and size-specific responses of lobster to theenvironment (Jury et al. 1994, Factor 1995). Althoughseason-, size-, and sex-specific patterns in the spatialdistribution of the American lobster are prevalent,these have yet to be modeled quantitatively based onhabitat and environmental variables. Such models maybe useful in characterizing climate-induced range con-traction and in defining appropriate managementactions.

Statistical regression models have been widely usedfor quantifying and predicting habitat use of fish spe-cies as a function of environmental variables (East-wood et al. 2001, Kupschus 2003, Jensen et al. 2005,Austin 2007). These analyses can be used to evaluatespatial variability of biological production along envi-ronmental gradients, to manage populations that willbe impacted by habitat alterations due to coastaldevelopment and climate change, and to designateand protect essential fish habitat (Austin 2007).

The general additive model (GAM; Hastie & Tibshi-rani 1990) is the most general form of regression mod-els (Guisan et al. 2002). GAMs are more suitable forecological studies than other types of regression modelbecause of their flexibility and capacity to explore spe-cies responses to environmental gradients both lin-early and nonlinearly (Lehmann et al. 2002, Ray et al.2002). An extension of GAMs in spatial modeling is the2-stage GAM in which presence/absence data anddensity of presence observations are used sequentiallyin deriving density estimates (Barry & Welsh 2002,Jensen et al. 2005). The coupled model has advantagesin preserving the information of zero observations,which are common in fisheries (Maunder & Punt 2004).

In this study, we develop season-, size-, and sex-specific models that predict the spatial distribution ofAmerican lobster along the coast of the GOM by quanti-fying lobster responses to environmental conditions us-ing a 2-stage GAM approach. We then validate the per-formance of the 2-stage GAMs and evaluate whetherlobster responses differ by season, sex and size.

The Gulf of Maine Ocean Observing System(GoMOOS) circulation nowcast/forecast system is anoperational numerical prediction system for the GOM(Xue et al. 2005). The model produces daily and short-term forecasts of physical properties such as tempera-ture and salinity in 3 dimensions. Using the data pro-duced by this system and the habitat modelsdeveloped in this study, we can predict the spatial dis-tribution of lobsters in the GOM. We used the 2006

GoMOOS circulation nowcast/forecast system physi-cal output to predict season-, size- and sex-specificspatial distribution. Variation in spatial distribution ofthe American lobster by season, sex and size is evalu-ated and potential ecological and management impli-cations are discussed.

MATERIALS AND METHODS

Maine-New Hampshire Inshore Trawl Survey data.The Maine-New Hampshire Inshore Trawl Survey is abiannual fishery-independent survey operated by theMaine Department of Marine Resources (DMR) eachfall and spring since fall of 2000. Fig. 1 shows data fromsurveys carried out in spring and fall in 2006. The sur-vey follows a stratified random design with 4 depthstrata (9–37 m, 37–64 m, 64–100 m, and >100 m with12 km offshore limit) and 5 longitudinal regions basedon oceanographic, geological, and biological charac-teristics. The survey total area is 16 002 km2 and tar-gets 115 stations (137 km2 per station) per season. Thetrawl is designed in such a way as to effectively samplecomplex bottom in the nearshore areas of the GOMwithout targeting any specific near-bottom species(Chen et al. 2006). Target tow duration is 20 min at2.5 knots to cover a mean distance of 1509 m with an

146

Fig. 1. Homarus americanus. The density (per tow record)and size frequency for spring and fall surveys in 2006. CL =

carapace length

Chang et al.: Lobster distribution in Gulf of Maine

average swept area of ~15 853 m2 per tow. All the lob-sters captured are counted and sex, carapace length(CL), weight, and presence of eggs, v-notches, anddamage is measured. In addition to recording thestart/end times and coordinates of each tow, a Sea-BirdElectronicsTM 19plus SEACAT profiler is attached tothe net to collect start/end depth, bottom salinity, andbottom temperature. In this study, a total of 1712 towrecords that sampled 96 596 lobsters between fall 2000and fall 2007 were used. The size of individual lobstersranged between 19 and 186 mm and between 16 and201 mm CL for the spring and fall surveys, respec-tively.

Spatial and environmental variables. Our GAMmodels explored the influence of bottom temperature,bottom salinity, 2 sediment-related and 4 spatial vari-ables (latitude, longitude, depth and distance offshore)on the distribution of the American lobster. These vari-ables were selected because they are known to influ-ence the distribution of American lobster. For instance,water temperature has a strong effect on survival rate,reproduction, growth and maturation (Aiken & Waddy1986). Fluctuations of salinity can induce physiologicalchanges that may be extremely stressful to lobster(Jury et al. 1994). Wahle & Steneck (1991) suggestedthat local lobster density might be correlated withshelter availability. D. Lincoln & K. L. Lavalli (unpubl.)overlaid the 2 lobster spatial distribution maps pub-lished by Grosslein & Azarovitz (1982) for Nova Scotiato Cape Hatteras and Backus (1987) for Georges Bankon substrate maps and found lobsters more frequentlyon the edges between 2 different substrates. Lobsterdensity and size composition also vary with waterdepth (Wahle & Steneck 1991, Wilson 1998, Palma etal. 1999, Chen et al. 2006) and distance offshore, due toseasonal movements (Munro & Therriault 1983, Wat-son et al. 1999).

Temperature and salinity were obtained directly fromthe survey. Sediment-related variables were obtainedfrom the map of sediment grain-size distribution for thecontinental margin of the eastern US (resolution: 0.00001decimal degrees; 1.11 m). Sediment types include bed-rock, gravel, gravel-sand, sand, clay-silt/sand, sand-clay/silt, clay, sand-silt/clay, and sand/silt/clay (Poppe etal. 2005). The sediment type and the shortest distancefrom a sampling station to a boundary between differentsediments (distance to sediment boundary) were as-signed to each sampling station using ArcGIS 9.3.Distance offshore was calculated using ArcGIS as theshortest distance to the mainland.

Model development. Standardized lobster densities(per min towing; 792 m–2) were divided by sexand 2 size classes, juveniles (≤60 mm CL) and adults(>60 mm CL) (minimum size at maturity for GOMstock; ASMFC 2009). Seasonal movement starts to

change as individuals become functionally mature(Factor 1995). To depict such behavioral differences,we used minimum size at maturity to distinguish juve-nile and adult. The spring and fall survey data wereanalyzed separately. This approach resulted in 8groups (2 size classes × 2 sexes × 2 seasons) that weremodeled independently. The 2007 survey data, themost recent available, were excluded in the modeldevelopment because they were used to validatemodel performance.

We used a 2-stage GAM (Barry & Welsh 2002,Jensen et al. 2005). GAM1 uses a logic link functionwith a binomial error distribution to estimate the prob-ability of presence of lobsters (p), and GAM2 uses anidentity link function with a Gaussian error distributionto estimate the log-transformed lobster density (y). Thecomprehensive log-transformed lobster density (D)was then estimated by multiplying the results gener-ated from GAM1 and GAM2:

GAM1: logit (p) = s(S) + s(T) + s(De) + s(DO) + s(DS)+ s(La) + s(Lo) + Se + ε (1)

GAM2: ln(y) = s(S) + s(T) + s(De) + s(DO) + s(DS)+ s(La) + s(Lo) + Se + ε (2)

ln(D) = p ln(y) (3)

where S: salinity; T: temperature (°C); De: depth(meter); DO: distance offshore (km); DS: distance tosediment boundary (km); La: latitude (degree); Lo: lon-gitude (degree); Se: sediment type (categorical vari-able); and s: spline smoother.

The GAMs were estimated using the R 2.9.0 mgcvpackage with a penalized regression method in whichdegrees of freedom for each term was estimated in themodel based on the tradeoff between model fitting andmodel smoothness. Chi-square and F tests were per-formed for the statistical significance of each term inboth GAM1 and GAM2, and only the significant terms(p < 0.05) were retained in the model. A preliminaryanalysis was conducted to evaluate the performance ofvariables in both single and interaction terms. Thisprocess starts by evaluating one variable and its inter-actions with all other variables, and then selecting themost significant interaction term. The selected interac-tion term was then included in the model with all othervariables as main effects. We compared the perfor-mance of this model with the model containing all thevariables but without any interaction terms. The inter-action term was retained in the final model only if itwas significant and increased the explanatory powerof the model by at least 5% relative to the model with-out interaction terms.

Model validation. The estimated GAMs were usedto predict lobster density for each station sampled in

147

Mar Ecol Prog Ser 420: 145–156, 2010

2007 (the data withheld in the estimation of theGAMs). The predicted densities (D’) were then re-gressed against the observed densities (D) using thefollowing simple linear regression model:

ln(D) = a + b ln(D’) (4)

The coefficient a indicates systematic bias in pre-dicted densities. A value of coefficient b not signifi-cantly different from 1 indicates that observed andpredicted densities have similar spatial patterns. A 2-tail t-test was performed to test the hypotheses H0: a = 0 and H0: b = 1 respectively for each modelinggroup. The overall relationship between the predictedand observed densities was also evaluated usingadjusted R2.

To evaluate whether season, size, and sex influencelobster responses to their environment, we used themodels developed for different season, sex, and sizeto predict lobster densities of other season, sex, andsize class. For example, the models developed basedon the spring-juvenile-male, fall-juvenile-female, andspring-adult-female data were used to predict thespring-juvenile-female data. The model-data mismatchimpacts (MDMI) of sex, season, and size on the pre-diction of lobster densities were quantified using thefollowing formula:

(5)

where R2m is the sample size adjusted coefficient of

determination using predictions from the mismatchmodel, and R2 is the adjusted coefficient of determina-tion using predictions from the model developed basedon the correct sex, season, and size. A negative MDMIindicated that the model-data mismatch model pre-dicted lobster density less effectively than the correctmodel.

Model prediction and mapping. To predict season-,size-, and sex-specific lobster distributions along thecoastal waters of the GOM, a set of environmental andspatial variables that cover the whole study area werecollected and added to the 8 modeling groups ofGAMs. Monthly averages of point estimates of bottomtemperature and bottom salinity in 2006 were obtainedfrom the GoMOOS circulation nowcast/forecast sys-tem (Xue et al. 2005). The monthly data of April toJune and October to December for each location wereaveraged to represent the temperature and salinityin spring and fall, respectively. Ordinary Kriging(ArcGIS) was applied to spatial interpolation of theseasonal temperature and salinity using the Gaussiansemivariogram model to create continuous rastermaps. A cell size of 15 850 m2, which is the size of theswept area per tow, was chosen for the 2 raster maps toensure a consistent spatial scale between lobster andenvironmental data. Depth was obtained from a GOM

bathymetric raster map, which was derived from theUnited States Geological Survey, Coastal and MarineGeology Program (Roworth & Signell 1998). The 3 lay-ers were overlaid, and variables, including distanceoffshore, distance to sediment boundary and sedimenttype, were assigned to each cell. Lobster densitieswere estimated where environmental and spatial con-ditions were within the predictable ranges. Finally, wecalculated ratios of female density to total density andadult density to total density for each cell in spring andfall, respectively to delineate differences in spatial dis-tributions between the sexes and size classes in eachseason.

RESULTS

Model performance

The deviance of probability of presence and densityof lobster explained by the various GAM1s andGAM2s varied from 53% to 72% for the 8 modelinggroups except the spring-adult-female GAM1, whichonly explained 34% of the deviance (Table 1). All thepredictor variables were significant and included in atleast 4 of the final models. The significant predictorvariables varied among different modeling groups forall GAM1s and GAM2s for the spring. In contrast, allthe GAM2s for the fall included temperature, depth,distance offshore, and the latitude-longitude interac-tion term. Response curves for the predictor variableswere similar, while differences were still detectedamong different modeling groups. We included a par-tial set of response graphs for the purposes of illustra-tion and comparison (Figs. 2 to 4).

Environmental variables

Temperature was the most commonly included en-vironmental variable in both GAM1s and GAM2s(Table 1). Probability of presence and density of lobsterincreased with temperature in all the models (Fig. 2).

Both probability of presence and density of lobsterdeclined slightly with an increase of salinity over 31.Seasonal differences in the response of lobster to lowsalinity (below 31) were detected by GAM1s, for whichthe predicted probability of presence was higher in thespring than in the fall (Fig. 2).

Effects of distance to sediment boundary were notsignificant in any of the GAM2s (Table 1). However,probability of presence of lobster (GAM1) decreasedlinearly with an increase of the distance. Effects of thedistance on the probability of presence were more sig-nificant for juveniles than adults (Fig. 2).

MDMI = R RR

m2 2

2

–

148

Chang et al.: Lobster distribution in Gulf of Maine

Effects of sediment type on the probability of pres-ence were only significant for juveniles (Table 1). Theprobability of presence of juveniles was significantlylower in the sand, sand/silt/clay, and sand-silt/claysubstrates compared with other sediment types. Sedi-ment type only significantly affected lobster density inthe spring. Highest lobster densities in spring were inthe clay-silt/sand substrate for juveniles and in clay-silt/sand and gravel-sand substrates for adults (Fig. 3).

Spatial variables

Depth had significant non-linear effects in GAM1sand GAM2s for all modeling groups (Table 1). Theprobability of presence peaked at depths ranging be-tween 50 and 75 m. Density was high at depths rangingfrom 30 to 70 m, and decreased with increasing depthfrom 70 to 100 m. Beyond 100 m, few lobsters werecaught in the survey, resulting in wide confidence in-tervals and variation in response curves (Fig. 4).

Effects of distance offshore were significant in mostmodels (Table 1). The probability of lobster presencedeclined for distances ranging from 0 to15 km. Lobsterdensity decreased linearly with distance offshore inthe spring. In the fall, density decreased and then lev-eled off for juveniles beyond the distance of 7.5 km;however, adults increased slightly beyond 20 km. Ingeneral, few lobsters were caught in the surveybeyond 15 and 20 km offshore, and response curvesvaried widely (Fig. 4).

Effects of longitude and latitude were significanteither separately or accompanied in GAM1s andGAM2s for all modeling groups (Table 1), which sug-gested that lobster distribution may also be influencedby other environmental factors that were not includedin this study (e.g. food availability).

Model validation

The eight 2-stage GAMs explained an average of66% of the variation in lobster density from the 2007trawl survey (Table 2). The lobster density models hadadjusted R2 values ranging from 0.76 to 0.81 for fall andfrom 0.47 to 0.60 for spring. The spring-juvenile den-sity had the poorest fit among all modeling groups. Theintercepts (coefficient a in Eq. 4) for the 8 modelinggroups were all positive and significantly differentfrom 0 except for fall-juvenile models. The slope coef-ficients (b in Eq. 4) were not significantly different from1 for all the adult lobster models, whereas all thejuvenile lobster models had slopes significantly largerthan 1. These results suggest that the GAMs predictedlobster density relatively well, but with a slight bias

149

Mod

elS

easo

nS

ize

Sex

ST

De

DO

DS

Lo

La

Se

Inte

ract

ion

Dev

ian

ce

Sam

ple

clas

sg

rg

r-sa

sasa

-cl/

sisa

/si/

clsa

-si/

clva

riab

lep

-val

ue

(%)

size

GA

M1

Sp

rin

gJu

ven

ile

Fem

ale

–<

0.00

1<

0.00

1<

0.00

1–

–<

0.00

1–

––

––

0.02

6D

S/L

o<

0.00

159

.157

4M

ale

0.00

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0.00

1<

0.00

10.

008

0.01

8–

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026

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0.00

159

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ltF

emal

e0.

041

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0.00

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0.00

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<0.

001

<0.

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34.5

574

Mal

e–

<0.

001

0.01

3<

0.00

1–

––

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La/

Lo

<0.

001

53.6

574

Fal

lJu

ven

ile

Fem

ale

–0.

021

<0.

001

<0.

001

0.01

4–

<0.

001

––

––

––

S/L

o<

0.00

157

.048

Mal

e0.

031

<0.

001

0.01

3–

0.02

3–

––

–0.

010

––

–L

a/L

o<

0.00

160

.148

1A

du

ltF

emal

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<0.

001

<0.

001

0.00

10.

019

<0.

001

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001

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––

––

––

56.2

481

Mal

e–

<0.

001

<0.

001

––

––

––

––

––

La/

Lo

<0.

001

60.7

481

GA

M2

Sp

rin

gJu

ven

ile

Fem

ale

–<

0.00

1<

0.00

1–

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0.00

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0.00

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0.04

5<

0.00

1<

0.00

10.

023

<0.

001

––

61.0

329

Mal

e–

<0.

001

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001

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001

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0.00

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001

0.02

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0.00

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027

0.04

6<

0.00

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–61

.331

9A

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001

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0.01

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0.00

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0.00

1L

a/L

o<

0.00

172

.349

2M

ale

0.01

5<

0.00

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0.00

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0.00

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001

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001

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0.01

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007

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0.00

1–

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.348

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all

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nil

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emal

e–

<0.

001

<0.

001

0.01

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<0.

001

65.8

284

Mal

e–

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001

<0.

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277

Ad

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70.6

413

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001

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169

.639

0

Tab

le 1

. Hom

aru

s am

eric

anu

s.S

um

mar

y re

sult

s of

th

e ‘G

AM

1 =

Pro

bab

ilit

y of

pre

sen

ce m

odel

’ an

d ‘G

AM

2 =

Den

sity

mod

el’ f

or t

he

8 m

odel

ing

gro

up

s. p

-val

ues

are

pre

-se

nte

d f

or a

ll t

he

sig

nif

ican

t (p

< 0

.05)

sin

gle

an

d in

tera

ctio

n t

erm

s (e

.g. D

S/L

ore

fers

to

inte

ract

ion

bet

wee

n D

San

d L

o). P

erce

nt

of d

evia

nce

exp

lain

ed a

nd

sam

ple

siz

e ar

ep

rese

nte

d f

or e

ach

mod

elin

g g

rou

p.

S:

sali

nit

y; T

: te

mp

erat

ure

(°C

); D

e:

dep

th (

m);

DO

: d

ista

nce

off

shor

e (k

m);

DS

: d

ista

nce

to

sed

imen

t b

oun

dar

y (k

m);

Lo

: lo

ng

itu

de

(deg

ree)

; La:

lati

tud

e (d

egre

e); S

e: s

edim

ent

typ

e (g

r: g

rave

l;sa

: san

d; c

l: c

lay;

si:

sil

t)

Mar Ecol Prog Ser 420: 145–156, 2010

toward underprediction for all groups. The degree ofunderprediction increased with density for juvenilesbut not for adults, suggesting that predicted densitywas more accurate for adults than for juveniles.

Validation analysis of MDMI (Fig. 5) suggested thatthe standardized deviation of R2 values decreased themost when model and data were mismatched by theseasons for each modeling group. Sex generally hadthe least influence among the 3 factors (i.e. season, sex,and size class); however, all the male groups had alower R2 when predicted using the female models.

Model prediction

The season-, size-, and sex-specific spatial distribu-tions of American lobster were estimated for 2006(Fig. 6). In general, lobster density was predicted to be

higher in inshore waters. The models predicted seasonaldifferences in lobster distribution by sex (Fig. 7). Inoffshore waters, significant sexual segregation occurredin the fall with females dominating the higher latitudesand males more abundant at lower latitudes. This se-gregation was not apparent in the spring, althoughfemales still dominated in offshore waters at higherlatitudes. Males were predicted to be more abundantalong the coast in the fall, and females in the spring.Although predicted lobster densities were low in Pen-obscot Bay, a difference in sex ratio was predicted in theestuary. Relatively more females remained in the bayin fall, whereas more males did so in the spring.

The models suggested that juveniles and adults aredistributed differently. Juveniles were predicted to bemore abundant in the inshore waters at lower latitudes(around Muscongus Bay and Casco Bay), while adultswere more widespread along the entire coast (Fig. 7).

150

6 8 10 12

–4

–2

0

2

4

Temperature

Eff

ect

on lo

bst

er d

ensi

ty

FLf2

3 4 5 6 7 8

–4

–2

0

2

4

Temperature

SPf2

28 30 32 34

–15

–5

5

15

Salinity

FLm1

27 29 31 33

–15

–5

5

15

Salinity

SPm1

0 2 4 6 8 10 12 14

–15

–5

5

15

Distance to sediment boundary

Eff

ect

on lo

bst

er p

rese

nce

FLf1Fig. 2. Homarus americanus. Estimatedsmooth of 3 environmental variables (tem-perature, salinity, and distance to sedimentboundary) on the probability of presenceand density of lobster. SP = spring, FL = fall,f = female, m = male, 1 = juvenile, 2 = adult.y-axes are the partial effect of the variable.Dashed lines are the 2 standard-error con-fidence intervals. Rugplots along the x-axes are the number of samples collected

according to the variable

–4

–2

0

2

4 SPm1

–1.5

–1.0

–0.5

0.0

Par

tial f

or s

edim

ent SPf2

–1.5

–0.5

0.5

0.0

–1.0

SedimentSedimentSediment1 2 3 4 5 6 71 2 3 4 5 6 71 2 3 4 5 6 7

SPm2

Fig. 3. Homarus americanus. Partial effect of sediment type on the probability of presence of male juveniles and density of adultsin the spring. Sediment types: 1 = cl-st/sd, 2 = gr, 3 = gr-sd, 4 = sd, 5 = sd-cl/st, 6 = sd/st/cl, 7 = sd-st/cl. See Fig. 2 and Table 1 for

further explanation

Chang et al.: Lobster distribution in Gulf of Maine

DISCUSSION

We developed a modeling approach for predictingseason-, size-, and sex-specific distribution of lobstersas a function of environmental and spatial variables.Validation tests of the model suggested reasonablepredictive ability, and the results were consistent withthe ecology of American lobster. The study provides ameans to evaluate changes in lobster spatial distribu-

tion with respect to environmental variables, whichmay be important for the management of Americanlobster in the face of shifting environmental conditionsoccurring with global climate change.

Environmental and spatial variables including salin-ity, temperature, depth, distance offshore, distance tosediment boundary, sediment type, latitude and longi-tude influenced the spatial distribution of Americanlobster. Although several covariates such as depth and

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Fig. 4 Homarus americanus. Estimated smooth of 2 spatial variables (depthand distance offshore) on the probability of presence and density of

lobster. See Fig. 2 for explanation

Spring FallJuvenile Adult Juvenile Adult

Female Male Female Male Female Male Female Male

Intercept (a) estimates 0.119 0.126 0.160 0.176 0.072 0.060 0.093 0.133p-value (H0: a = 0) 0.012 0.007 0.001 0.001 0.058 0.077 0.037 0.002

Slope (b) estimates 1.655 1.563 1.047 1.098 1.304 1.341 1.059 0.997p-value (H0: b = 1) <0.001 0.001 0.578 0.269 <0.001 <0.001 0.353 0.957

Adjusted R2 0.517 0.471 0.596 0.597 0.777 0.812 0.764 0.789

Table 2. Homarus americanus. Summary results of the regression analysis of the observed density on the predicted density for 2007.The coefficient estimates and adjusted R2 values are presented for each modeling group. Significant p-values (p < 0.05) in bold

Mar Ecol Prog Ser 420: 145–156, 2010

distance offshore were correlated, different combina-tions of attributes were important in predicting lobsterdistribution, and each covariate had a uniquely shapedresponse curve. Season-, sex-, and size-specific lobsterresponses to these covariates were found in bothGAM1s and GAM2s, leading to distinct differences inspatial distributions for different groups.

Temperature was the most important environmentalvariable correlated with lobster density; as tempera-ture increased, so did lobster density. Whereas higherwater temperatures favor growth, since molting fre-quency increases with temperature (Aiken 1977), moltincrement decreases with temperature (ASMFC 2009).Warmer water temperatures can cause maturation atsmaller sizes (Aiken & Waddy 1986, Little & Watson2003), but also oocyte resorption (Aiken & Waddy1986). Lobsters are reportedly able to sense changes in

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Fig. 5. Homarus americanus. Model-data mismatch impacts(MDMI) of sex, season, and size on the prediction of

lobster densities. For abbreviations see Fig. 2

Fig. 6. Homarus americanus. Predicted season-, size-, and sex-specific lobster density (per 792 m2) distribution for 2006

Chang et al.: Lobster distribution in Gulf of Maine

water temperature and regulate their behavior accord-ingly (Crossin et al. 1998). Adult lobsters appear to fol-low isotherms and stay in relatively warm waters whenwater temperature does not exceed the thermalthreshold (Ennis 1984). This is consistent with our pre-dictions that adults concentrated in areas where watertemperatures were greater than 5°C in spring and 8°Cin fall, which were the common bottom water temper-atures in the inshore waters along the entire coastalGOM.

Behavioral thermoregulation was more obvious formale than female adults as higher abundances ofmales occurred in warm inshore waters. Temperaturestrongly affects embryonic development and onset ofhatching in American lobster (Aiken & Waddy 1986).Migrations of ovigerous lobsters apparently maximizethe degree-days of exposure for embryos (Lawton &Lavalli 1995), although migratory (Cooper & Uzmann1971, Campbell 1986) and non-migratory (Cooper &Uzmann 1980, Lawton & Lavalli 1995) ovigerous lob-sters have been detected in mark-recapture studies.Cowan et al. (2007) suggested that tagged ovigerouslobsters attempted to occupy water with consistentrather than high temperatures. Moreover, a low watertemperature that is below 8°C or even 5°C may be re-quired for ovarian development, which will affect theonset of the next brood (Waddy & Aiken 1992). Adultfemales must balance the tradeoff between optimizinggrowth and promoting reproduction instead of simplyresiding in areas of high water temperatures to opti-mize growth. In our analyses, female adults werewidely distributed along the coast rather than concen-trated in warmer areas. Moreover, an adult-female-dominated sex ratio occurred in areas where bottomwater temperatures were below 6°C, although densitywas generally low in those areas.

In our analyses, lobster density increased with watertemperature up to 14°C. Where temperatures areabove 19°C, lobsters will avoid the habitats (Crossin etal. 1998) and a thermal threshold exists at 20.5°C (Pow-ers et al. 2004). Such high water temperatures raiserespiration rates or promote the release of harmfulchemicals from sediments, both of which can be stress-ful or lethal for lobsters (Robohm & Draxler 2003). Con-sequently, the relationship between lobster densityand water temperature will be dome-shaped, with apeak somewhere between 14°C and 19°C.

The American lobster has limited osmoregulatorycapacity to adapt to variation in salinity (Jury etal. 1994). Extreme spring rainfall can lower bottomsalinity dramatically and cause severe mortality(Thomas & White 1969). Lobster tolerate salinitiesranging between 15 and 32 (Harding 1992), and typi-cally prefer higher salinities of 28 to 32 (Jury et al.1994). Salinity had little effect on lobster density inour analysis, probably because most of our sampleswere at 30 to 34, at which there is little variation inpreference by American lobster (Jury et al. 1994).These results indicate that temperature is more im-portant than salinity in driving the onshore-offshorelobster distribution in the GOM, such that lobsterdensity is greater inshore where water temperature ishigher.

Male lobsters are more abundant than females in theestuary (Munro & Therriault 1983, Jury et al. 1994)because females are more sensitive to decreases insalinity (Jury et al. 1994). This is consistent with ourprediction of the skewed sex ratio with males generallydominant in the coastal waters and females dominantin offshore waters. Although more males are predictedto reside along the coast, an adult-female-dominatedsex ratio was predicted for Penobscot Bay estuary in

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Fig. 7. Homarus americanus. Ratiosof female density to total density,and adult density to total density in

spring and fall, 2006

Mar Ecol Prog Ser 420: 145–156, 2010

the fall (Fig. 7). A similar difference was discovered inthe study at Great Bay estuary (Jury et al. 1994), whereit results from migrations of berried females into theestuary to hatch eggs. Estuarine habitats are charac-terized by large fluctuations in salinity, which canstress pelagic lobster larvae because they are sensitiveto low salinities (Aiken & Waddy 1986, Charmantier &Aiken 1987). This might explain why berried femalesreside in the upper bay where salinity is relativelystable.

Lobsters in the early benthic phase (EBP), whichranges from 5 to 40 mm CL, are the most shelter-restricted of all stages in the life history (Wahle & Ste-neck 1992). The EBP lobsters prefer shelter-providingsubstrates such as cobble (Wahle & Steneck 1991),rocks on sand (Hudon 1987), and peat reefs (Able et al.1988) because they provide refugia from predation andphysical disturbance (Cobb et al. 1983, Wahle & Ste-neck 1991). As juvenile lobsters grow out of EBP, thepreference for specific substrates diminishes (Hudon1987, Wahle & Steneck 1991). This is consistent withour results where sediment type affected the probabil-ity of presences of juveniles but not adults. Similarly,the variable distance to sediment boundary, which wasused to represent the complexity of the habitat, had astronger effect on juveniles. Unlike for adults, whichfollowed temperature and salinity gradients and wereconsequently abundant along the entire coast, ourmodels predicted that juveniles should be dominant ininshore areas at lower latitudes where substrates aremost complex in the GOM.

In spring, lobsters remain in the relatively warmerinshore waters (Ennis 1984, Jury et al. 1994, Watson etal. 1999), which promotes competition for limited shel-ter (Steneck 2006). Therefore, it is reasonable that lob-sters were only affected by sediment type in springwhen they were more concentrated in the warmerinshore waters.

Statistical modeling is a cost effective method to mapspecies distributions in unsampled areas (Brotons et al.2004), and consequently can be useful in predictingdistributions. However, the models make a number ofassumptions that can limit the applicability of the pre-dictions. A major assumption of our models was thatbehavioral responses to environmental gradients didnot vary between years. To test this assumption, we re-estimated the models for each modeling group usingthe data from fall 2000 to fall 2005 (excluding the 2006data), and used the 2007 data to validate the models.The 2007 data were still well predicted without 2006data. The predictive power was similar but slightly bet-ter for the models using 2000 to 2006 data. Therefore,we conclude that the models constructed using histori-cal data are somewhat robust to temporal change.However, if there are future substantial changes in

ocean conditions that are outside the observed ranges,these models may not be suitable.

We used the coupled, but independently estimated,GAMs to estimate lobster presence and density in thisstudy. An assumption behind this approach is that zerorecords indicate absence of lobster in the surveyedhabitat rather than just low abundance. Variability oftrawl survey estimates can be high for schooling fishspecies with highly patchy distributions, and zerorecords might occur when animals were present butmissed by the trawl. However, lobsters do not formaggregations because they are solitary and territorial(Ennis 1984, Steneck & Wilson 2001). Therefore, trawlsurvey densities are considered to be a reliable indica-tor of lobster presence, and have been used in indexinglobster abundance (Chen et al. 2006, ASMFC 2009).Moreover, Incze et al. (2010) examined lobster densities(>90 mm CL) from trawl surveys and visual lobstercounts from remotely operated vehicle (ROV) surveysand concluded that, although ROV data were ~30%higher than trawl survey data, the difference was notstatistically significant. Therefore, we believe that it isappropriate to use the coupled GAM in this study.

The value of GAM1 was evaluated by using onlyGAM2 to predict the 2007 data, which decreased R2

values for all groups. We also re-estimated the modelsusing a single GAM with a quasi-Poisson error distrib-ution, which performed slightly better for some juve-nile groups but worse for all adult groups. To maintainconsistency and retain both presence/absence anddensity predictions, we believe the coupled GAM usedin this study is preferable.

Our study utilized trawl survey data, which mightnot reflect true lobster density. High lobster densitiesoccur in habitats with boulder (Cooper & Uzmann1980) and rocky substrates (Steneck 2006). However,those habitats generally have poor trawl capture effi-ciency (Steneck & Wilson 2001), and are not sampledby the Maine DMR trawl survey. Our data showed thatthe highest mean lobster captures were in the clay-silt/sand areas followed by gravel area, but our sedi-ment maps (Poppe et al. 2005) did not categorize theboulder substrate. Moreover, we cannot quantify theeffect of the absence of survey data from boulder andcobble habitats on our results. The lack of trawl sur-veys from certain habitats likely resulted in the modelunderestimating the importance of the sediment-related variables, and might affect the explanatorypower of the model. Although we cannot ignore thisweakness of trawl survey data, the hot spots (e.g. west-ern coastal areas of Penobscot Bay) and cold spots (e.g.northeast corner of Penobscot Bay) predicted by ourmodels coincide with those from quadrat surveys (Ste-neck & Wilson 2001), suggesting that our models arerobust to trawl survey bias.

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Chang et al.: Lobster distribution in Gulf of Maine

Acknowledgements. We thank K. Stepanek, S. Sherman andC. Wilson of Maine Department of Marine Resources for pro-viding the trawl survey information,; A. Thomas, H. Xue, R.Wahle and J. Wilson for valuable comments and discussion;and D. Brzezinski for editing the MS. We thank the detailedand constructive comments from Editor R. Lipcius and the 4anonymous reviewers. Financial support for this study wasprovided partially by the NSF Award no. 0709527 and NASAGrant no. NNX08AT92G.

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Editorial responsibility: Romuald Lipcius,Gloucester Point, Virginia, USA

Submitted: October 26, 2010; Accepted: September 27, 2010Proofs received from author(s): November 30, 2010