Estimating spatial distribution of a species is traditionally achieved using global regression models with the assumption of spatial stationarity of relationships between species and environmental variables. However, species abundance and environmental variables are often spatially correlated and the strength of environmental effects may exhibit spatial non-stationarity on the species distribution. We applied local models, such as season-, sex-, and size-specific geographically weighted regression (GWR) models, on American lobster to explore non-stationary environmental effects on the presence and density of lobsters in the inshore Gulf of Maine (GOM). This species and its fishery have undergone a dramatic increase in abundance over the past two decades. Model results showed that the strength of the estimated relationships in the western GOM were different with the relationships in the eastern GOM during 2000–2014. Bottom water temperature had a more significant positive impact on the increase of lobsters in the eastern GOM, while the influence of temperature was less significant in the west and the more distinguishable drivers of distribution needed to be identified. The estimation of locally varied relationships can further improve regionally informed management plans. The modeling approach can be widely applied to many other species or study areas.
As most exploited fisheries lack a coherent time series of biomass index, development of data-limited stock assessment methods such as stock reduction analysis (SRA), is critical for fishery stock assessment due to their modest data requirements for estimating stock status and overfishing catch limits. In this study, we propose that sporadic time series of biomass indices, if available, may be fully utilized to inform priors of recent relative biomass (BT/B1) for data-limited stocks. We evaluated the performance of SRA incorporating this index-based prior by comparing two other common SRA priors (a deterministic prior set at 40% of the unfished biomass and a catch-based prior) with estimates from the likelihood-based assessments of 91 fish stocks from the RAM Legacy database. We extended our analysis by evaluating performance based on life history attributes and two depletion levels with BT/BMSY equaling 1 as the breakpoint. Results suggest index-based priors enhance accuracy for fish stocks at both depletion levels. We demonstrate that performance of SRA can be affected by three factors: the reliability of priors for BT/B1, recent depletion level, and life history.
24Habitat use and distribution is a critical aspect in the management and conservation of a species, shortcomings of conventional HSIs: 1) the abundance indices from survey catch data typically 58 incorporated in these models do not account for changes in catchability over a time series; and 2) 59 the commonly used abundance indices, and therefore HSIs, are unable to incorporate surveys 60 from multiple gear types which sample different segments of the population and likely cover 61 different types of habitat. These issues need to be addressed in order to produce an unbiased 62 evaluation of spatio-temporal changes in habitat quality for a species over its distributional 63 range. Conventional HSIs use available data from sampled locations, hereinafter referred to as sample-
78based HSIs, which are often restricted to the locations of occurrence and typically processed to 79 assume that the samples are representative (i.e., the species is effectively sampled) and are 80 comparable through time (i.e., no changes in sampling distribution and efficiency). Therefore, 81 the sample-based HSIs might not be appropriate in at least the following two situations: 1) the 82 survey misses a significant portion or type of the species' habitat; and 2) sampling efficiency 83 (i.e., catchability) changes over space and/or through time due to density-dependent processes.
84Density-dependent habitat selection is a likely process for species in decline (MacCall 1990).
85When a species population is high, individuals move into previously marginal habitat because 86 high quality habitat is saturated; thus, the overall suitability of all occupied habitat declines on 87 average (MacCall 1990 Cusk (Brosme brosme) in the Gulf of Maine is one species where assessment is difficult using 104 conventional HSIs. It is a data-limited species, with low abundance and low catchability. central GOM to better sample species that primarily reside in complex habitat (Hoey et al. 2013).
146Six survey strata were selected for the LLS from ten offshore and four inshore strata from the The study area was divided into 5,710 cells (0.05º x 0.05º) for predicting grid-based densities, The second stage of the model approximates positive catches (c):
199The probability density function Gamma (c, x, y) is evaluated at c given a gamma distribution, knots that are generated based on the proportional density of survey data over the defined 208 domain (i.e., the 0.05º x 0.05º grid; Thorson et al. 2015). The spatial (ω) and spato-temporal (ε) 209 random effects were used in both spring and fall density estimates.
211Encounter probability p and positive catch rates λ are approximated using linear predictors
212( Thorson et al. 2015):
215where and ߣ are the expected probabilities of an occupied habitat and positive catches given 216 occupied habitat for sample i at a given location; ݀ ் () is the average reference density
217(encounters/positive catch rates) in year ܶ () ; ܳ is catchability for each survey; w i is the area 218 swept for sample i;...
Reduced abundance and contracted spatial distribution of Atlantic cod, Gadus morhua, in the Gulf of Maine (GOM) may indicate large spatio‐temporal variation in their habitat quality. Season‐specific Habitat Suitability Index (HSI) models were developed to quantify such variation in the offshore GOM management area. Data used were non‐zero cod catch rates with calibrations from the Northeast Fisheries Science Center (NEFSC) spring and fall bottom trawl surveys over the period 1982–2013 and key physical environmental variables including depth, bottom temperature, bottom salinity and sediment types. Significant declines were found in the average HSI across the study area in the springs of early 2000s and 2010s. These low average HSI values coincide with reduced age‐1 recruitment of GOM cod stock after the mid‐1990s. Moreover, the western coastal areas of the GOM generally exhibited higher average HSI values than the eastern coastal areas, whereas the offshore areas always had the lowest average HSI. Relatively higher cod survey catch rates in the western GOM may imply positive influences of environmental controls on the distribution of GOM cod.
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