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Spatiotemporal variability of suitable habitat for American lobster (Homarus Americanus) in Long Island Sound.

ABSTRACT A Habitat Suitability Index (HSI) model was developed using four environmental variables (bottom temperature, bottom salinity, depth, and bottom substrate type) and 29 years of spring and fall lobster surveys for evaluating the spatiotemporal variability of suitable lobster habitat in Long Island Sound (LIS). The suitability indices calculated for the four environmental variables were combined to form a composite HSI using an arithmetic mean model and geometric mean model. A cross-validation study was conducted to evaluate the predictive performance of the HSI models. Annual geographic information system maps of estimated HSI values were produced using Kriging interpolation for adult and juveniles in spring and fall from 1978 to 2012. The overall spatial distribution of suitable habitat for lobster was mainly concentrated in the western-central part of LIS during spring (April-June), but showed clustering patterns throughout LIS during fall (September-October). An examination of the temporal change in annual median HSI values identified possible time blocks when habitat conditions were extremely poor and revealed a statistically significant decreasing trend in availability of suitable habitat for juveniles during spring from 1978 to 2012. Spatiotemporal variability in availability of suitable habitat may imply changes in carrying capacity of LIS for the American lobster.

KEY WORDS: lobster, habitat, suitability index, habitat suitability index model, Homarus americanus, Long Island Sound

INTRODUCTION

The American lobster (Homarus americanus) is a benthic crustacean distributed throughout coastal Northwest Atlantic waters, most commonly from Newfoundland, Canada through North Carolina (Thunberg 2007). The species can be found in waters ranging from the intertidal zone to depths of up to 700 m, but tend to be most abundant in coastal waters shallower than 50 m (Lawton & Lavalli 1995, Van der Meeren et al. 2010). Coarse rocky substrates (cobbles and boulders) are the most common habitat, but lobsters can also be found on several other substrates including mud, sand base with rock, shell, eelgrass, or algae (Lawton & Lavalli 1995).

The American lobster is an ectothermic species with a specific preferred thermal range for optimum physiological functionality (Reynolds & Casterlin 1979). Water temperature has a significant impact on the physiology of juvenile and adult lobsters, especially in nonoptimal dissolved oxygen and salinity conditions (Mercaldo-Allen & Kuropat 1994). Lobsters have been found in waters of temperatures ranging from 0 to 25[degrees]C and with salinities ranging from 15-32 ppt, but lobsters prefer a thermal range between 12 and 18[degrees]C (Crossin et al. 1998) and salinities of 20-32 ppt (Harding 1992, Jury et al. 1994, ASMFC 2009). Lobsters use more energy for respiration in warmer water conditions leaving less energy for feeding, growth, immune response, and reproduction (Qadri et al. 2007). As water temperatures rise above 20[degrees]C, lobster show various physiological stress responses such as increased respiration rates and decrease in immunocompetence (Dove et al. 2005, Fogarty et al. 2007).

The spatial distribution of lobster is known to vary by sex, size, and season (Chang et al. 2010). American lobsters typically remain within a home range of about 5-15 [km.sup.2] (Lawton & Lavalli 1995). Large adults are more likely to be found in deeper, cooler waters, but migrate to shallow coastal waters seasonally to reproduce. Large mature lobsters in the Gulf of Maine (GOM) move inshore and into estuaries in spring (Watson et al. 1999) and often remain close to shore in the summer, then move back offshore in late fall to escape winter turbulence (Chen et al. 2006a). Small juvenile lobsters are more likely to be found inshore at depths of less than 10 m and do not make seasonal migrations offshore in winter (Cooper et al. 1975). These differences

in spatial distribution suggest size-specific responses to environmental variables such as bottom temperature and salinity (Jury et al. 1994, Factor 1995).

The American lobster fishery in the northeastern United States has experienced significant expansion in both effort and landings over the last 60 years (Berger 2014). Lobster landings were generally around 25 million pounds until the early 1950s, increasing to roughly 150 million pounds in 2012 (Berger 2014). Approximately 94% of total U.S. landings came from the GOM (Berger 2014). The 2009 benchmark stock assessment showed record high stock abundance and recruitment in the GOM and Georges Bank (GBK); however, the Southern New England (SNE) lobster stock was found to be in poor condition because of prolonged low abundance and persistently poor recruitment (ASMFC 2009). Changes in water temperature, salinity, and dissolved oxygen coupled with continued high fishing mortality have been identified as principal causes of low recruitment and poor stock condition (ASMFC 2009).

The lobster population in Long Island Sound (LIS) is a part of the Southern New England lobster stock. With ex-vessel values over $40 million, the LIS lobster fishery accounted for over 90% of the value of commercial landings in the region and remained the third largest lobster fishery in the United States until 1998 (Shields 2013). The LIS lobster stock has experienced a substantial decrease in abundance over the last 20 years because of deteriorating habitat and heavy exploitation (ASMFC 2009). Triggered by the major mortality event, possibly caused by stress from warm temperatures, pollutants and decreasing oxygen concentrations, landings declined by 89% in 1999 with cumulative landings from 1999 to 2010 only reaching 0.42 million pounds (CTDEP 2010). Epizootic shell disease has also become an increasing threat to the stability of the LIS lobster fishery (Bell et al. 2012, Castro et al. 2012). As climate change continues to alter Northwest Atlantic coastal ecosystems (Mills et al. 2013), the poorly adapted LIS lobster stock is under increasing stress caused by changes in suitable habitat availability. To illustrate the extent that habitat deterioration has influenced the American lobster's decline in LIS, it is necessary to quantify changes in suitable lobster habitat over time.

Habitat suitability index (HSI) models are widely used in wildlife management to describe the relations between species abundance and ecological variables (Franklin 2010, Chang et al. 2012, Morrison et al. 2012). An empirical HSI model is derived from observations of the species in the field, and reflects the impacts of multiple habitat variables given the input data (e.g., abundance index or relative biomass) (Dettki et al. 2003, Ahmadi-Nedushan et al. 2006, Tian et al. 2009, Chen et al. 2010). HSI models standardize habitat suitability a target species on a scale from 0 to 1, representing "least suitable" to "most suitable" habitat qualities respectively (Schamberger et al. 1982). Oftentimes evaluation of habitat suitability is based on a limited number of habitat variables that influence organism abundance and distribution. Therefore, HSI implies relative habitat quality rather than actual population levels (Jian et al. 2013). Habitat suitability index modeling results in combination with GIS provide an effective means of evaluating spatiotemporal variability in habitat conditions of a target species and produce habitat maps that can be used by managers and policymakers to make informed decisions (Terrell 1984, Bovee & Zuboy 1988). In fisheries management, the HSI model is often used to characterize fish habitat preference, availability, and quality (Morris & Ball 2006). For the lobster fishery, the HSI model can evaluate variability of suitable lobster habitat considering all key environmental variables for different life history stages.

The objective of this study is to develop an HSI model for evaluating the spatiotemporal variability of suitable habitats for LIS juvenile and adult lobsters in spring (April-June) and fall (September-October) from 1978 to 2012. The model is used to describe how the proportion and spatial trend of suitable habitat have changed over time. Finally, HSI model results were used to identify possible periods when lobster habitat conditions were extremely poor in LIS to determine whether habitat conditions have influenced the decline of lobsters in this region.

MATERIALS AND METHODS

Study Area

The LIS is an estuary 181.9 km long and 33.8 km wide at its widest and covers approximately 3419 [km.sup.2] area (Fig. 1). The depth of LIS varies from 4.6 to 60.4 m, averaging 22.6 m. Salinity ranges from 23 ppt at the western end to 35 ppt at the eastern end (Gottschall 2013).

Fishery Data

Fisheries-independent data tend to provide a better representation of species distribution and abundance than fisheries-dependent data as they are normally collected using standardized gear and sampling methods with a clearly defined spatiotemporal scale (Tian et al. 2009). Bottom trawl survey data collected from LIS by the Connecticut Department of Energy and Environmental Protection (CTDEP) from 1984 to 2012 were used to develop the HSI models in this study (Fig. 2).

The LIS trawl survey is a semiannual fishery-independent survey operated by the CTDEP. The survey encompasses an area from longitude 72[degrees] 03' (New London, CT) to longitude 73[degrees] 39' (Greenwich, CT), and includes both Connecticut and New York state waters from 5 to 46 m in depth over mud, sand, and transitional (mud/sand) substrate types. The survey is based on a stratified-random sampling design, and the survey area is divided into 1.85 x 3.7 [km.sup.2] sites assigned to 12 strata classified by depth (0-9 m, 9.1-18.2 m, 18.3-27.3 m, and 27.4 + m) and bottom substrate type (Gottschall & Pacileo 2013).

The survey was conducted in the spring, from April through June, and during the fall, from September through October, with 40 sites sampled monthly for a total of 200 sites annually. It was done during daylight hours with a 14 m otter trawl with a 51 mm codend sampling gear to reduce the sampling bias associated with diurnal variability in catchability (Sissenwine & Bowman 1978). Target tow duration was 30 min at 3.5 knots to cover a mean distance of 3241 m at each site (CTDEP 2013). At each site, tow date, tow location (latitude and longitude), tow duration, environmental variables (e.g., bottom temperature, bottom salinity, and depth), and biological information of the catch [e.g., carapace length (CL), weight, cull condition, and shell disease presence] were recorded (Gottschall 2013).

No information with regard to measure of area swept was available before 2012 (CTDEP 2012, 2013). The size specifications for the trawl net and associated gear remain unchanged as far as since 1992 (Reid et al. 1999). The standardized survey design allows for temporal comparisons of lobster catch and distribution. In this study, a total of 5353 tows that sampled 156,202 lobsters between fall 1984 and spring 2012 were analyzed. Lobster CL ranged between 16.1 and 112 mm and between 16 and 117 mm for the spring and fall surveys, respectively.

Environmental Data

The Finite-Volume Community Ocean Model (FVCOM) was used to produce bottom temperature and bottom salinity estimates by depth, time, and location in LIS from 1978 to 2012. This regional coastal ocean circulation model was developed by University of Massachusetts Dartmouth-Woods Hole Oceanographic Institution joint efforts and is suited for forecasting and hindcasting the ecosystem dynamics for areas characterized by complex coastlines and intertidal zones (Chen et al. 2006b). In addition, data on distribution of surficial substrate (resolution: 0.00001 decimal degrees or 1.11 m) throughout LIS was obtained from the U.S. Geological Survey (Poppe & Seekins 2000). Bottom substrate types in LIS include gravel (pebbles defined as 2.00-64.00 mm, cobbles defined as 64-256 mm, boulder defined as above 256 mm), gravel-sand (0.62-2.00 mm), sand-clay (0.001-0.004 mm), silt (0.004-0.062 mm)/sand, sand-clay/silt, sand-silt/clay, and sand/silt/clay (Poppe et al. 2000). Bathymetry data were obtained from the U.S. Coastal Relief Model--Northeast Atlantic by the NGDC-NOAA (National Geophysical Data Center 1999).

Habitat Suitability Index Mode!

HSI is a numerical index based on suitability indices (SI) that can quantify the habitat conditions from 0 (least suitable habitat) to 1 (most suitable habitat) for key habitat variables. The SI can be calibrated from presence/absence data, presence only data, or using expert knowledge (Franklin 2010). Development of HSI model requires: (1) selection of habitat variables to include in the model, (2) development of SI for each habitat variable, and (3) combination of those SI via a mathematical equation to produce a composite HSI (Schamberger et al. 1982). Based on the literature on American lobster ecology and behavior (ASMFC 2009, Chang et al. 2010), the following four environmental variables were chosen for their potential influence on American lobster habitat: bottom temperature ([degrees]C), bottom salinity (ppt), depth (m), and bottom substrate type.

Data Analysis and Processing

Bottom trawl survey data for American lobster in LIS from 1982 to 2012 were used in this study. To depict behavioral difference throughout lobster life stage, the dataset was divided into two size classes, juveniles ([less than or equal to] 0 mm CL) and adults (>60 mm CL), as 60 mm represents the minimum size at maturity defined by Atlantic States Marine Fisheries Commission (ASMFC 2009). The spring and fall survey data were analyzed separately. This approach resulted in four groups of lobster (2 size classes x 2 seasons). Each lobster group was modeled independently.

The abundance index derived from LIS bottom trawl survey was considered a good indicator of lobster abundance in developing SI and HSI models in this study (Chang et al. 2010). The nominal abundance index, calculated as a survey catch per unit of sampling effort (CPUE) at sampling station i, in season j, and year y, was calculated as

[CPUE.sub.ijy] = ([Count.sub.ijy] / [Tow duration.sub.ijy]) * 20 (1)

where count is the total number of either adult or juvenile lobsters caught. Tow duration is towing time duration measured in minutes, which usually varied from 20 to 30 min but was standardized to 20 min at each sampling station.

The relationship between lobster CPUE and habitat variables from 1984 to 2012 was identified. For each habitat variable, a SI based on species abundance (CPUE) was first developed. The SI were estimated using a common approach known as the histogram method (Vinagre et al. 2006, Chen et al. 2010). The three continuous habitat variables (bottom temperature, bottom salinity, and depth) were delineated into 10 classes using Fisher's natural breaks classification method (Bivand 2013), whereas the categorical habitat variable bottom substrate was classified into seven substrate types (Poppe et al. 2000). For class k of habitat variable i in each lobster group, the average CPUE over all the sampling stations falling within the class was calculated as [CPUE.sub.i,k]. The SI value of class k for habitat variable i, [SI.sub.i,k], was then calculated on a scale of 0.0-1.0 using the following formula (Chang et al. 2012):

[SI.sub.i,k] = [CPUE.sub.i,k] - [CPUE.sub.i,min] / [CPUE.sub.i,max] - [CPUE.sub.i,min] (2)

where [CPUE.sub.i,min] and [CPUE.sub.i,max] are the minimum and maximum values of the average CPUEs of all the classes for habitat variable d. Thus, the SI for the most suitable class should have a value of 1, whereas the SI for the least suitable class should have a value of 0. An SI value was assigned to every class of the habitat variables in the form of a linear transfer function to qualitatively analyze the relationships between the habitat variable and lobster abundance. As a result, a total of 16 SI were calculated (i.e., four SI corresponding to the four environmental variables for four lobster groups including two seasons and two stages).

For the purpose of sensitivity analysis, the SI curves were first drawn by mean function, and then redrawn by trimmed mean function to remove any missing values and 5% of the highest and lowest scores (Tukey 1977, Crawley 2007). The suitable ranges were identified as area under both SI curves. The SI values derived from each habitat variable were then combined to form composite HSIs also scaled 0-1 and proportional to habitat quality. The following two empirical HSI models were developed in this study (Cooperrider et al. 1986) (Fig. 3):

1. Arithmetic mean model (AMM):

His = [n.summation over (i=1)] [SI.sub.i] / n (3)

2. Geometric mean model (GMM):

[MATHEMATICAL EXPRESSION NOT REPRODUCIBLE IN ASCII] (4)

where [SI.sub.i] is a value of SI associated with the ith habitat variable and n is the number of habitat variables included in the HSI model.

Model Validation

A cross-validation approach was applied for evaluating the predictive performance of the HSI models. Models were developed independently for each lobster group (e.g., spring-adult, spring-juvenile, fall-adult, and fall-juvenile) using a randomly selected subset of data representing 80% of all the data, referred to as training data. The remaining 20% of the data, referred to as testing data, were set aside for the cross-validation to assess the predicting ability of models developed from training data (Smith 1994, Zuur et al. 2007). The predicted HSI values were compared against the observed HSI values. Linear regression analysis was performed on predicted versus observed HSI values, and the regression intercept, slope, [R.sup.2] value, and the Akaike information criterion (AIC) score were used to evaluate the predictive performance of the HSI model. An unbiased prediction should have an intercept parameter not significantly different from 0, a slope not significantly different from 1, and a high [R.sup.2]. In all, 100 rounds of cross validation were conducted using random selection in each round to obtain 100 sets of regression parameters. This validation process was conducted for both AMM and GMM HSI models to determine which model performed better.

Mapping HSI Values

The predicted HSI values were assigned to every FVCOM grid in LIS, and this procedure was conducted for every year that was available in the FVCOM dataset between 1978 and 2012. The ordinary Kriging method using the exponential semivariogram function was applied to create continuous HSI maps. The area with the highest class of HSI (e.g., 0.6-1.0) was designated as good habitat and correspondingly the area with the lowest HSI (e.g., 0-0.2) as poor habitat. The spatial distribution of median HSI values for a total of 35 years was mapped to observe the overall spatial trend in suitable habitat distribution for each group of lobsters. The median HSI maps were then compared with spatial trends in CPUE from the survey to verify the model. Finally, a median HSI value for each year was calculated with a fitted linear regression model to analyze whether there was any statistically significant trend in suitable habitat. The following R packages were used to implement this analysis; sp (Pebesma et al. 2014), maptools (Lewin-Koh et al. 2014), rgdal (Bivand et al. 2014), gstat (Pebesma & Graeler 2014), maps (Becker et al. 2014), and fields (Nychka et al. 2014).

RESULTS

Suitability Indices

The highest SI for bottom temperature differed by season and lobster size. The suitable thermal range (i.e., bottom temperature with SI > 0.8) for spring-adult lobsters was found to be 11.1-12.4[degrees]C (Fig. 4A), whereas the suitable thermal range for spring-juvenile lobsters was 8.45-9.55[degrees]C (Fig. 4B). In spring, the suitable depth range for adult and juvenile lobsters was similar at 31.9-37.2 m (Fig. 4C) and 31.9-37.3 m (Fig. 4D), respectively. The suitable salinity range for spring-adult lobsters was 21-23.7 ppt (Fig. 4E), whereas the spring-juvenile lobsters had a suitable salinity range of 21.9-24.4 ppt (Fig. 4F).

The suitable thermal range for fall-adults was 17.9-19.2[degrees]C (Fig. 5A), and 15.6-16.6[degrees]C as well as 17.4-18.4[degrees]C for fall-juveniles (Fig. 5B). The suitable depth range for fall-adult lobsters was between 14.8-17.9 m and 31.9-37.3 m (Fig. 5C), whereas the suitable depth range for fall-juvenile lobsters was 15-17.9 m (Fig. 5D). Higher suitable salinity ranges were observed in fall for both size groups: 27.6-28.1 ppt for adults (Fig. 5E) and 26.6-27.4 ppt and 29.4-30.4 ppt for juveniles (Fig. 5F).

Sand/silt/clay was found to be the substrate type with the highest SI values for adult lobsters in both seasons (Figs. 4G and 5G). For the spring-juvenile group, sand-silt/clay showed the highest SI values, whereas gravel appears to be the most suitable substrate type for fall-juveniles (Figs. 4H and 5H).

Model Validation and Selection

The GMM model showed intercepts ([alpha]) closer to 0, whereas the AMM model showed slopes ([beta]) closer to 1 in the regression between predicted and observed HSI values in cross validation; however, the AMM-HSI models showed higher [R.sup.2] values in all four groups. When compared with an ideal model without prediction bias (i.e., [alpha] = 0, [beta] = 1, and [R.sup.2] = 1), predictive performance for the spring-adult lobster was found to be the best ([alpha] = 0.145, [beta] = 0.732, [R.sup.2] = 0.611), and predictive performance for the spring-juvenile lobster was the poorest ([alpha] = 0.212, [beta] = 0.595, median [R.sup.2] = 0.456). The AMM model also predicted HSI values better, because AIC values were smaller in all modeling groups (Table 1). Finally, because of the nature of geometric mean algorithm, GMM-HSI model yielded a "0" HSI value when the model included an SI value of 0. Thus, the AMM can better distinguish subtle differences in areas of low SI values, and was determined to be more appropriate than the GMM for estimating lobster HSI in LIS.

Spatial and Temporal Variability in HSI Values

Using the AMM-HSI model, the spatial distribution of estimated HSI values in LIS was mapped for each lobster group. A visual examination of HSI maps revealed that the suitable habitats (i.e., HSI > 0.6) are concentrated in western-central LIS in spring for both size groups of lobster, but showed clustering patterns throughout LIS in fall (Fig. 6). The season- and size-specific distribution of suitable habitats predicted by the AMM-HSI model generally coincides with high and low lobster catches on the bottom trawl survey (Fig. 6). Temporal variation in proportion of suitable habitat was observed in all four groups over the 35-year time series (Fig. 7). There were no statistically significant temporal trends in suitable habitat change for adult lobsters in spring (P = 0.317) and fall (P = 0.609). For juvenile lobsters, a significant declining trend in proportion of suitable habitat was found in spring ([beta] = -0.003, P = 0.016), and a significant increasing trend was found in fall (P = 0. 002, P = 0.015) (Fig. 8).

In the HSI model, depth and substrate type are static components, whereas bottom temperature and bottom salinity are dynamic components; however, no relationships were observed between temporal trends in temperature, salinity, and availability of suitable habitat (Fig. 9). Possible periods of extremely poor habitat conditions (such as a year when both seasons had an HSI value above 0.6 in less than 10% of the studied area) were identified. For adult lobsters, poor habitat conditions were observed in 1979 to 1980, 1983, 1985, 1988 to 1989, 1994 to 1999, 2004 to 2005, 2007, and 2009. For juvenile lobsters, poor habitat conditions occurred in 1980, 1983, 1985, 1997, 1999, 2002, 2004 to 2005, and 201l (Fig. 10).

DISCUSSION

This study developed a modeling approach to analyze the spatiotemporal variability of suitable habitat as a function of bottom temperature, bottom salinity, depth, and substrate for lobster in LIS.

The SI results for bottom temperature, bottom salinity, and depth were consistent with past observations of preferential lobster habitat. The SI for bottom temperature identified different suitable thermal ranges between spring and fall for both size classes of lobsters. Adult lobsters showed a slightly higher and broader suitable thermal range compared with juveniles. In spring, the suitable thermal range for adults appeared to be considerably warmer than that of juveniles, whereas the difference in thermal range between the two size classes was less in fall. Overall, the suitable thermal range for lobsters in fall appears to be greater than that in spring. Water temperatures above the thermal threshold were avoided in both seasons (Crossin et al. 1998). Finally, two separate suitable thermal ranges were identified for juveniles in fall. These distinctive suitable thermal ranges between two seasons and two life history stages may be because of differences in behavioral thermoregulation as lobsters mature. It is generally accepted that lobster behavior is strongly regulated by water temperature (Crossin et al. 1998), and that the relationship between lobster density and bottom temperature is dome shaped with a peak somewhere between 14 and 19[degrees]C (Chang et al. 2010). In this study, the suitable thermal range (SI > 0.8) varied from 8.45 to 18.4[degrees]C. This is consistent with a previous study in the GOM, where lobster concentrations observed in areas with water temperature greater than 5[degrees]C in spring and 8[degrees]C in fall (Chang et al. 2010).

The SI for depth showed differences in suitable depth ranges across all groups. In fall, the suitable depth range for adult lobsters was between 14.8-17.9 m and 31.9-37.3 m, which might reflect a skewed spatial distribution by sex caused by migrations of ovigerous lobsters and sex-specific responses to different salinity ranges (ASMFC 2009, Chang et al. 2010). Contrary to previous findings suggesting that small juveniles are more likely to remain inshore at depths of less than 10 m (Cooper et al. 1975), both adult and juvenile lobsters in spring showed a deeper suitable depth range when compared with depths in fall. These findings agree with the in situ observations of seasonal lobster movements in Bonavista Bay, Newfoundland (Ennis 1984) and suggest possible seasonal shift in suitable depth ranges for both life stages of lobster.

Model results indicate a suitable salinity range of 21-30.4 ppt, which is consistent with lobster salinity tolerance observed in past studies of 20-32 ppt with occasional tolerance as low as 15 ppt (Harding 1992, Jury et al. 1994, ASMFC 2009). The bottom salinity SI identified relatively constant suitable salinity ranges between adults and juveniles; however, higher suitable salinity ranges were observed in fall for both age groups, which may indicate different salinity tolerance of lobsters under different thermal regimes (Mercaldo-Allen & Kuropat 1994, Ennis 1995). There were two separate suitable salinity ranges identified for fall juveniles. This may indicate differential distribution of male and female juveniles in the fall. Because females are more sensitive to low salinities, males are generally more dominant in inshore waters and females dominant in offshore waters (Jury et al. 1994, Chang et al. 2010).

This study identified sand/silt/clay as the most suitable bottom substrate type for adult lobsters in both spring and fall. Sand-silt/clay was the substrate with the highest SI for juveniles in spring, but was the least suitable substrate for juveniles in fall. Gravel yielded the highest SI for fall juveniles. These findings are relatively inconsistent with the previously documented postsettled lobster habitat preferences of shelter-providing rocky and boulder substrates (Barshaw & Bryant-Rich 1988, Wahle & Steneck 1991). Several factors have been identified as the potential source of this inconsistency. First, the U.S.G.S. substrate data used in the HSI models did not differentiate boulder or cobble substrate, because the category of gravel includes grain size greater than 2 mm in diameter (Poppe et al. 2000). This overgeneralization of bottom substrate type may have affected SI values derived from the histogram method and may have resulted in underrepresentation of bottom substrate as a habitat variable in the HSI model. Second, the CTDEP bottom trawl survey may have shown biased lobster density as rocky substrate could sometimes interrupt a tow (CTDEP 2013) and boulder, and rocky substrates are generally associated with lower trawl capture efficiency (Steneck & Wilson 2001). Whereas the lack of trawl survey data with certain key substrates cannot be quantified or ignored, these data-driven biases can be potentially corrected by the use of expert knowledge as quantitative assessment criteria (Store & Kangas 2001, Vincenzi et al. 2006, Vincenzi et al. 2007). Furthermore, although shelter-providing rocky/cobble/boulder substrates are generally considered to be preferred habitat by both newly settled and older lobsters (Cooper & Uzmann 1980, Steneck 2006), preference for specific substrates diminishes as lobsters grow out of the early benthic phase (Hudon 1987, Wahle & Steneck 1991). This inconsistency in substrate preference between lobster life stages was also observed in the GOM, where substrate type affected the probability of juveniles, but not adult, presence (Chang et al. 2010). Similarly, mud base (particle size < 0.06 mm) with burrows is known to be a preferred substrate by adult lobsters in inshore and in estuaries where lobsters can create shelters by excavating soft substrate (Lawton & Lavalli 1995). This literature supports the result that sand/silt/clay is the most suitable substrate type for adults in both spring and fall. Overall, despite insufficient resolution of the substrate data and trawl survey bias, agreement of the seasonal size-specific suitable lobster habitat predictions by the HSI model and patterns in survey catch suggest the robustness of modeling results.

Whereas cross-validation of the AMM-HSI model suggested reasonable predictive performance, the Sis derived from the spline smooth regression method can be used to deal with possible nonlinear relationships between covariate and response variables in a semiparametric manner for further analysis (Maunder & Punt 2004, Chang et al. 2012).

The type and number of habitat variables to be included in the calibration of an HSI model is critical to the successful identification of suitable habitats (Tian et al. 2009). Distribution and abundance of lobster can be influenced by many other environmental variables such as availability of prey, presence of predators, thermal fronts, latitude and longitude, time of the day, light levels, and dissolved oxygen concentration (Wahle & Steneck 1992, Mercaldo-Allen & Kuropat 1994, Crossin et al. 1998, Chang et al. 2010). Consequently, more habitat variables may need to be incorporated and evaluated in future analyses. Whereas these variables are likely to be correlated, application of dimension reduction technique such as principal component analysis can be incorporated to develop more comprehensive HSI model (Daskalov 1999).

Furthermore, although equal weight was assigned to each habitat variable for the empirical HSI model in this study (Vayghan et al. 2013), the relative importance of different habitat variables in regulating lobster spatiotemporal distribution is likely to be variable, which could significantly influence the predictive performance of HSI models (Gong et al. 2012). For the existing HSI models to better predict spatiotemporal distribution of suitable lobster habitat, the impact of differential weighing of habitat variables should be carefully analyzed based on relative contribution to the spatial distribution of lobsters (Chang et al. 2010). The selection and weighting of habitat variables in an empirical HSI model should be further studied to improve the model's hindcasting or forecasting ability. This will be important in promoting the use of HSI models in fishery management and could be particularly useful when considering shifts in the marine environment because of climate change.

Most stock assessments neglect to incorporate habitat information into the assessment models, but habitat data are important to many aspects of the stock assessment process. The SI and HSI modeling results for juvenile lobsters have implications for lobster recruitment, whereas results for adult lobsters have implications for spawning stock biomass. For example, periods of low habitat suitability, such as years when both seasons had an HSI value above 0.6 in less than 10% of the studied area (Fig. 10), partially overlap with periods of estimated low recruitment abundance (2003-2007) and low spawning stock abundance (2004-2007) from the 2009 benchmark assessment (ASMFC 2009). Further analysis may (1) reveal statistically significant correlations between habitat suitability and recruitment or spawning stock abundance, and (2) link availability of suitable habitat to carrying capacity of LIS for American lobsters. Also, change in habitat availability could potentially be related to the recent collapse of LIS lobster stock.

Application of HSI models can improve lobster stock assessment by allowing us to (1) hindcast and forecast periods of distinct lobster productivity and recruitment dynamics in LIS, and (2) define and compare different modeling time periods with respect to these processes. Traditional stock assessment models focus on the context of commercial fishing, where natural mortality is relegated to a single, typically time-invariant parameter that is often not related to lobster ecology. The recent management shift toward ecosystem-based fisheries management requires scientists and managers to develop useful, quantitative measures to illustrate the history of stock fluctuations in an ecological context. Incorporating habitat availability modeling into stock assessments will aid in effective implementation of ecosystem-based management.

ACKNOWLEDGMENTS

We thank Chongliang Zhang, Samuel Truesdell, Bai Li, Jie Cao, Katherine Thompson, and Max Ritchie of University of Maine for their generous support, ideas, and perspectives. We would also like to thank Kurt Gottschal from the Connecticut Department of Energy and Environmental Protection for providing lobster survey data. Financial support for this study was provided by NSF IGERT program, NSF Coastal SEES program, and Maine Sea Grant College Program. This work uses the FVCOM model-predicted database created by Dr. Chen's research team at the Marine Ecosystem Dynamics Modeling Laboratory, University of Massachusetts Dartmouth.

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KISEI TANAKA * AND YONG CHEN

School of Marine Sciences, University of Maine, 5741 Libby Hall, Orono, ME 04469

* Corresponding author. E-mail: kisei.tanaka@maine.edu

DOI: 10.2983/035.034.0238

TABLE 1.
Summary of regression analyses from 100 runs of cross validations.

                                   Intercept ([alpha])

Model   Life stage   Season   Mean    Median     (95% CI)

AMM     Adult        Spring   0.145   0.143    0.042   0.266
        Juvenile     Spring   0.212   0.212    0.082   0.368
        Adult        Fall     0.161   0.168    0.007   0.271
        Juvenile     Fall     0.179   0.183    0.075   0.291
GMM     Adult        Spring   0.128   0.124    0.012   0.280
        Juvenile     Spring   0.136   0.136    0.036   0.284
        Adult        Fall     0.161   0.166    0.001   0.305
        Juvenile     Fall     0.152   0.162    0.052   0.247

                             Slope ([beta])

Model   Life stage   Mean    Median     (95% CI)

AMM     Adult        0.732   0.732    0.529   0.922
        Juvenile     0.594   0.595    0.397   0.797
        Adult        0.682   0.676    0.493   0.949
        Juvenile     0.683   0.681    0.465   0.867
GMM     Adult        0.750   0.746    0.484   1.123
        Juvenile     0.680   0.669    0.343   1.194
        Adult        0.628   0.615    0.341   0.954
        Juvenile     0.679   0.673    0.470   0.910

                        [R.sup.2]         AIC

Model   Life stage   Mean    Median   Mean   Median

AMM     Adult        0.594   0.611    -533    -539
        Juvenile     0.458   0.456    -357    -351
        Adult        0.495   0.492    -274    -273
        Juvenile     0.549   0.550    -199    -194
GMM     Adult        0.536   0.541    -306    -298
        Juvenile     0.441   0.448    -194    -181
        Adult        0.384   0.358     -94     -84
        Juvenile     0.507   0.512    -148    -144

The table shows model parameters for the linear regression between
the predicted and observed HSI values and AIC for the two HSI models
AMM and GMM.
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