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

Abstract

AbstractEstimating 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.