Regional distribution models with lack of proximate predictors: Africanized honeybees expanding north
Product Type:Journal Article
Author(s):Jarnevich, C.S., W.E. Esaias, P.L.A. Ma, J.T. Morisette, J.E. Nickeson, T.J. Stohlgren, T.R. Holcombe, J.M. Nightingale, R.E. Wolfe, and B. Tan
Suggested Citation:Jarnevich, C.S., W.E. Esaias, P.L.A. Ma, J.T. Morisette, J.E. Nickeson, T.J. Stohlgren, T.R. Holcombe, J.M. Nightingale, R.E. Wolfe, and B. Tan. 2014. Regional distribution models with lack of proximate predictors: Africanized honeybees expanding north. Diversity and Distributions. 20(2): 193-201.
Aim Species distribution models have often been hampered by poor local species data, reliance on coarse-scale climate predictors and the assumption that species–environment relationships, even with non-proximate predictors, are consistent across geographical space. Yet locally accurate maps of invasive species, such as the Africanized honeybee (AHB) in North America, are needed to support conservation efforts. Current AHB range maps are relatively coarse and are inconsistent with observed data. Our aim was to improve distribution maps using more proximate predictors (phenology) and using regional models rather than one across the entire range of interest to explore potential differences in drivers.
Location United States of America.
Methods We provide a generalized framework for regional and local species distribution modelling with our more nuanced and spatially detailed forecast of potential AHB spread using multiple habitat modelling techniques and newly derived remotely sensed phenology layers.
Results Variable importance did differ between the two regions for which we modelled AHB. Phenology metrics were important, especially in the south-east.
Main conclusions Results demonstrate that incorporating a combination of both climate drivers and vegetation phenology information into models can be important for predicting the suitable habitat range of these pollinators. Regional models may provide evidence of differing drivers of distributions geographically. This framework may improve many local and regional species distribution modelling efforts.