Estimating spatial non-stationary environmental effects on the distribution of species: a case study from American lobster in the Gulf of Maine

Bai, Li; Cao, Jie; Guan, Lisha; Mazur, Mackenzie; Chen, Yong; Wahle, Richard A.

doi:10.1093/icesjms/fsy024

Cited by 17 publications

(16 citation statements)

References 30 publications

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“…Li et al. (2018) modeled lobster spatial distribution allowing relationships to vary among space and found that the relationships between lobster and environmental variables were different in the western and eastern GOM. Similarly, the relationship between bathymetry variables and lobster may have changed overtime with fluctuations in the abundance of predators and therefore the need for sheltered habitat.…”

Section: Discussionmentioning

confidence: 99%

“…Li et al. (2018) found a significant relationship between temperature and lobster habitat in the eastern GOM, but not in the western GOM, with the operating hypothesis that predators may have a greater effect on lobsters than temperature in the western GOM. Predation risk on lobsters is lower in the GOM than in SNE, which may affect lobster habitat (Wahle, Brown, & Hovel, 2013).…”

Section: Discussionmentioning

confidence: 99%

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Dynamic changes in American lobster suitable habitat distribution on the Northeast U.S. Shelf linked to oceanographic conditions

Mazur

Friedland

McManus

et al. 2020

Fisheries Oceanography

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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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Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

Dynamic changes in American lobster suitable habitat distribution on the Northeast U.S. Shelf linked to oceanographic conditions

Mazur

Friedland

McManus

et al. 2020

Fisheries Oceanography

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“…(Brunsdon et al, 1996;Fotheringham et al, 2002). Past literature has demonstrated evidence of spatial nonstationarity in the GOM region (Li et al, 2018;Staples et al, 2019). Accounting for nonstationarity in SDMs allows for the incorporation of spatial and/or temporal dependencies that cannot be explained by environmental variables alone (Bakka et al, 2016).…”

Section: Introductionmentioning

confidence: 99%

Examining Scale Dependent Environmental Effects on American Lobster (Homarus americanus) Spatial Distribution in a Changing Gulf of Maine

Behan

Chen

2021

Front. Mar. Sci.

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The Gulf of Maine (GOM) is a highly complex environment and previous studies have suggested the need to account for spatial nonstationarity in species distribution models (SDMs) for the American lobster (Homarus americanus). To explore impacts of spatial nonstationarity on species distribution, we compared models with the following three assumptions : (1) large-scale and stationary relationships between species distributions and environmental variables; (2) meso-scale models where estimated relationships differ between eastern and western GOM, and (3) finer-scale models where estimated relationships vary across eastern, central, and western regions of the GOM. The spatial scales used in these models were largely determined by the GOM coastal currents. Lobster data were sourced from the Maine-New Hampshire Inshore Bottom Trawl Survey from years 2000–2019. We considered spatial and environmental variables including latitude and longitude, bottom temperature, bottom salinity, distance from shore, and sediment grain size in the study. We forecasted distributions for the period 2028–2055 using each of these models under the Representative Concentration Pathway (RCP) 8.5 “business as usual” climate warming scenario. We found that the model with the third assumption (i.e., finest scale) performed best. This suggests that accounting for spatial nonstationarity in the GOM leads to improved distribution estimates. Large-scale models revealed a tendency to estimate global relationships that better represented a specific location within the study area, rather than estimating relationships appropriate across all spatial areas. Forecasted distributions revealed that the largest scale models tended to comparatively overestimate most season × sex × size group lobster abundances in western GOM, underestimate in the western portion of central GOM, and overestimate in the eastern portion of central GOM, with slightly less consistent and patchy trends amongst groups in eastern GOM. The differences between model estimates were greatest between the largest and finest scale models, suggesting that fine-scale models may be useful for capturing effects of unique dependencies that may operate at localized scales. We demonstrate how estimates of season-, sex-, and size- specific American lobster spatial distribution would vary based on the spatial scale assumption of nonstationarity in the GOM. This information may help develop appropriate local adaptation measures in a region that is susceptible to climate change.

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“…Exploring environmental effects on species distribution at a single large spatial scale may mask the intrinsic relationships between them at finer scales. Accounting for spatial nonstationarity can improve our understanding of the interactive process between species distribution and environmental variables at various spatial scales (Windle et al, 2010, 2012; Sadorus et al, 2014; Liu et al, 2017; Li et al, 2018; Bi et al, 2019).…”

Section: Introductionmentioning

confidence: 99%

Exploring spatial nonstationary environmental effects on Yellow Perch distribution in Lake Erie

Liu

Jiao

et al. 2019

PeerJ

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Background Global regression models under an implicit assumption of spatial stationarity were commonly applied to estimate the environmental effects on aquatic species distribution. However, the relationships between species distribution and environmental variables may change among spatial locations, especially at large spatial scales with complicated habitat. Local regression models are appropriate supplementary tools to explore species-environment relationships at finer scales. Method We applied geographically weighted regression (GWR) models on Yellow Perch in Lake Erie to estimate spatially-varying environmental effects on the presence probabilities of this species. Outputs from GWR were compared with those from generalized additive models (GAMs) in exploring the Yellow Perch distribution. Local regression coefficients from the GWR were mapped to visualize spatially-varying species-environment relationships. K-means cluster analyses based on the t-values of GWR local regression coefficients were used to characterize the distinct zones of ecological relationships. Results Geographically weighted regression resulted in a significant improvement over the GAM in goodness-of-fit and accuracy of model prediction. Results from the GWR revealed the magnitude and direction of environmental effects on Yellow Perch distribution changed among spatial locations. Consistent species-environment relationships were found in the west and east basins for adults. The different kinds of species-environment relationships found in the central management unit (MU) implied the variation of relationships at a scale finer than the MU. Conclusions This study draws attention to the importance of accounting for spatial nonstationarity in exploring species-environment relationships. The GWR results can provide support for identification of unique stocks and potential refinement of the current jurisdictional MU structure toward more ecologically relevant MUs for the sustainable management of Yellow Perch in Lake Erie.

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Estimating spatial non-stationary environmental effects on the distribution of species: a case study from American lobster in the Gulf of Maine

Cited by 17 publications

References 30 publications

Dynamic changes in American lobster suitable habitat distribution on the Northeast U.S. Shelf linked to oceanographic conditions

Dynamic changes in American lobster suitable habitat distribution on the Northeast U.S. Shelf linked to oceanographic conditions

Examining Scale Dependent Environmental Effects on American Lobster (Homarus americanus) Spatial Distribution in a Changing Gulf of Maine

Exploring spatial nonstationary environmental effects on Yellow Perch distribution in Lake Erie

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