Estimating the temporal scale of lagged responses in species abundance and occurrence

Abstract

AbstractUnderstanding drivers of species distributions is a central theme in ecology, and species are known to respond to drivers at multiple spatiotemporal scales. Yet, little attention has been paid to the temporal dimensions of predictors of species abundance or occurrence. Studies considering lagged responses typically contrast few points in time, rather than integrating multiple temporal scales. Here, I transfer an approach developed for the integration of predictors across spatial scales into the temporal realm. In this approach, a weighted mean predictor is used in a generalized linear model for abundance or occurrence; weights are estimated for each temporal scale based on a parametric function, and the parameters defining the function are estimated within the model. In a simulation study using a half‐normal declining weight function, I show that the scale parameter σ (measuring temporal scale of response) and effect strength of the predictor, , can be estimated accurately in abundance models with and without imperfect detection for a range of sample sizes and input values of σ and . Accuracy for both parameters was lower in occurrence‐based models. Estimates of σ improved in accuracy with increasing , and precision of both estimates tended to be lower in models with imperfect detection. A higher resolution time series of predictor values improved estimates of both parameters, and lower detection probability most strongly increased bias in in occurrence models. The approach can be viewed as a more parsimonious but less flexible case of memory models or distributed lag models. It is easily incorporated into standard methods used to model animal abundance and occurrence and therefore hopefully will open up avenues for future research into lagged responses in wildlife distribution to environmental conditions.