As environmental niche modeling increases in popularity and utility, it is important to develop a common language and strategy for creating biologically relevant and management pertinent models. Contrary to what we find in ornithological literature, species distribution models are currently underrepresented in small mammal studies. While these modeling techniques are still emerging in the small mammal community, we seek to synthesize common strategies for building and discussing models to facilitate their integration into small mammal conservation. Using a set of eligibility criteria, we searched databases and selected peer-reviewed articles that created small mammal distribution models from environmental variables. We identified the families that were covered by this suite of articles and then analyzed trends in modeling tools, model parameters, and raster properties such as extent and resolution. During the development of small mammal climate models, and as the future projections become more accurate and available, it is important to unite the community in language and strategy. We seek to explain the current trends in modeling parameters, sources, and properties to provide a solid basis for continued research in small mammal management.
Based on our criteria, this analysis included 59 peer-reviewed articles. Chiroptera was the most commonly modeled order, perhaps bridging the gap between avian climate modeling and small mammals. According to our preliminary analysis, Maxent (maximum entropy) and GARP (Genetic Algorithm for Rule-Set Prediction) were the two most commonly used modeling tools. Many studies modeled multiple species, but few projected habitat models into the future based on climate change predictions. As anticipated, AIC (Akaike’s Information Criterion) and AUC (Area Under the Curve) dominated the model selection strategies. With further summary information and statistical analysis of climatic niche modeling of small mammals we can build strong support for the basics of these models, enabling us to incorporate more complex drivers of species ranges such as biotic interactions or local adaptation.