Genes to the Niche! Five reasons why genetic information can improve predictive niche models and their underlying theory
Jan O. Engler (1), Niko Balkenhol (2), Catherine H. Graham (3)
1 Zoological Research Museum Koenig, Adenauerallee 160, D‐53113 Bonn, Germany, j.engler.zfmk@uni‐bonn.de, @engler_j
2 Department of Wildlife Sciences, University of Göttingen, Büsgenweg 3, D‐37077 Göttingen, Germany, niko.balkenhol@forst.uni‐goettingen.de
3 Department of Ecology and Evolution, Stony Brook University, NY 11789, USA, catherine.graham@stonybrook.edu
The technological revolution in the past 25 years now allows the analysis of species occurrence information in completely novel ways. Correlative environmental niche models (ENM) that link occurrence information to a set of environmental variables appeared as a central tool in this regard, and they are frequently used to address questions related to global change. Despite their popularity, ENMs often suffer from a lack of biological realism and other methodological challenges. To this end, researchers have begun to integrate genetic information into ENMs. However, there is currently no conceptual framework that integrates population genetic information into the theoretical assumptions made for ENMs. Here, we highlight five major reasons why the conceptual integration of genetic information in ENMs can improve model predictions and refine underlying theory. Specifically, genetic data can elucidate how environmental change alters functional connectivity, spatial genetic structure, hybridization, density-dependent priority effects, and source-sink dynamics. Thus, linking genetic and distribution data can lead to a better understanding why species respond to environmental change in a certain way, and improve our ability to forecast these responses. We discuss these points in the context of modeling challenges in the era of the Anthropocene, where habitat fragmentation, biotic invasions, and climate change are major human-driven threads to global biodiversity. Our overview shows that integrating different kind of genetic information into ENMs permits a more holistic view of niche theory and points to shortcomings associated with how niche theory is currently being implemented in ENMs.