Regional distribution models with lack of proximate predictors: Africanized honeybees expanding north
Article first published online: 9 NOV 2013
Published 2013. This article is a U.S. Government work and is in the public domain in the USA.
Diversity and Distributions
Volume 20, Issue 2, pages 193–201, February 2014
How to Cite
Jarnevich, C. S., Esaias, W. E., Ma, P. L. A., Morisette, J. T., Nickeson, J. E., Stohlgren, T. J., Holcombe, T. R., Nightingale, J. M., Wolfe, R. E., Tan, B. (2014), Regional distribution models with lack of proximate predictors: Africanized honeybees expanding north. Diversity and Distributions, 20: 193–201. doi: 10.1111/ddi.12143
- Issue published online: 7 JAN 2014
- Article first published online: 9 NOV 2013
- NASA Applied Sciences Program
- Africanized honeybee;
- Apis mellifera ;
- habitat suitability;
- species distribution modelling;
- vegetation phenology
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.
United States of America.
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.
Variable importance did differ between the two regions for which we modelled AHB. Phenology metrics were important, especially in the south-east.
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.