Advantages of combining generalized linear models and occupancy models to find indicators of habitat selection: Small mammals in agroecosystems as a case study

Abstract

Models of habitat variables can be used to find indicators for a quantitative prediction of the likeliness of species occurrence or abundance. Methodological bias due to variable detectability can be critical to properly determine habitat use and, thus, for understanding species ecology, distribution, and requirements for survival. In spite of recent advances in dealing with imperfect detection through detailed modeling, this approach requires large amounts of data and usually leads to larger standard errors in parameter estimates. In this work, we explore the advantages of combining generalized linear models (GLMs) and occupancy models (OMs) for the detection of variables that may be used as indicators of habitat suitability for rodent species. As a case study, we analyzed live trapping data of three rodent species that inhabit agroecosystems at micro- and macrohabitat scales. Both methods provided complementary information: while OMs revealed that some habitat features believed to be selected by studied species actually affected detectability, some effects could only be detected by GLMs. Moreover, for some covariates apparently affecting habitat selection at both scales, comparing results between scales allowed us to determine for which it was actually relevant rather than a reflection of the other. Therefore, we advise applying complementary modeling approaches at multiple scales for habitat selection studies. A variety of outcomes and their implications are thoroughly discussed and may guide other researchers facing similar situations.