Using genetic data to improve species distribution models

Abstract

Tsetse flies (Diptera, Glossinidae) transmit human and animal trypanosomoses in Africa, respectively a neglected human disease (sleeping sickness) and the most important constraint to cattle production in infested countries (nagana). We recently developed a methodology to map landscape friction (i.e. resistance to movement) for tsetse in West Africa. The goal was to identify natural barriers to tsetse dispersal, and potentially isolated tsetse populations for targeting elimination programmes. Most species distribution models neglect landscape functional connectivity whereas environmental factors affecting suitability or abundance are not necessarily the same as those influencing gene flows. Geographic distributions of a given species can be seen as the intersection between biotic (B), abiotic (A) and movement (M) factors (BAM diagram).Here we show that the suitable habitat for Glossina palpalis gambiensis as modelled by Maxent can be corrected by landscape functional connectivity (M) extracted from our friction analysis. This procedure did not degrade the specificity of the distribution model (P=0.751) whereas the predicted distribution area was reduced. The added value of this approach is that it reveals unconnected habitat patches.The approach we developed on tsetse to inform landscape connectivity (M) is reproducible and does not rely on expert knowledge. It can be applied to any species: we call for a generalization of the use of M to improve distribution models.