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.
Climate change is continuing to influence spatial shifts of many marine species by causing changes to their respective habitats. Habitat suitability as a function of changing environmental parameters is a common method of mapping these changes in habitat over time. The types of models used for this process (e.g. bioclimate models) can be used for projecting habitat if appropriate forecasted environmental data are used. However, the input data for this process must be carefully selected as less reliable results can incite mis-management. Thus, a knowledge of the organism and its environment must be known a priori. This paper demonstrates that these assumptions about a species’ life history and the environment are critical when applying certain types of bioclimate models that utilize habitat suitability indices. Inappropriate assumptions can lead to model results that are not representative of environmental and biological realities. Using American lobster (Homarus americanus) of the Gulf of Maine as a case study, it is shown that the choice of extrapolation data, spatial scale, environmental parameters, and appropriate subsetting of the population based on life history are all key factors in determining appropriate biological realism necessary for robust bioclimate model results.
Changes in bottom-up forcing are fundamental drivers of fish population dynamics. Recent literature has highlighted the need to incorporate the role of dynamic environmental conditions in stock assessments as a key step towards adaptive fishery management. Combining a bioclimate envelope model and a population dynamic model, we propose a model-based approach that can incorporate ecosystem products into single-species stock assessments. The framework was applied to a commercially important American lobster (Homarus americanus) stock in the Northwest Atlantic. The bioclimate envelope model was used to hindcast temporal variability in a lobster recruitment habitat suitability index (HSI) using bottom temperature and salinity. The climate-driven HSI was used to inform the lobster recruitment dynamics within the size-structured population dynamics model. The performance of the assessment model with an environment-explicit recruitment function is evaluated by comparing relevant assessment outputs such as recruitment, annual fishing mortality, and magnitude of retrospective biases. The environmentally-informed assessment model estimated (i) higher recruitment and lower fishing mortality and (ii) reduced retrospective patterns. This analysis indicates that climate-driven changes in lobster habitat suitability contributed to increased lobster recruitment and present potential improvement to population assessment. Our approach is extendable to other stocks that are impacted by similar environmental variability.
American lobster (Homarus americanus) supports one of the most valuable regional fisheries in the United States, with its abundance and distribution profoundly influenced by environmental conditions. To explain how lobster distribution has changed over time and assess the role of environmental variables on these changes, we used random forest classification and regression tree models to estimate occupancy and biomass in two seasonal periods. The occupancy models were fit to static and dynamic variables, which yielded model fits with AUC scores of 0.80 and 0.78 for spring and fall, respectively. Biomass models were fit with the same data and resulted in models explaining 61% and 63% of the spring and fall biomass variance, respectively. Significant variables scored in the formation of the regression trees were secondary productivity (i.e., zooplankton), bathymetry characteristics, and temperature. American lobster suitable habitat has changed regionally; habitat has increased in the Gulf of Maine and declined in Southern New England. There is also evidence of declining habitat along the inshore margin of the Gulf of Maine, which has been accompanied by a shift in occupancy probability offshore. Habitat suitability results from the random forest models provide insights on the structure and function of lobster habitat and context to understand recent population trends.
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