Ignoring seasonal changes in the ecological niche of non-migratory species may lead to biases in potential distribution models: lessons from bats

Ignoring seasonal changes in the ecological niche of non-migratory species may lead to biases in... Phenology is a key feature in the description of species niches to capture seasonality in resource use and climate requirements. Species distribution models (SDMs) are widespread tools to evaluate a species’ potential distribution and identify its large-scale habitat preferences. Despite its chief importance, data phenology is often neglected in SDM development. Non-migratory bats of temperate regions are good model species to test the effect of data seasonality on SDM outputs because of their different roosting preferences between hibernation and reproduction. We hypothesized that (1) the output of SDMs developed for six non-migratory European bat species will differ between hibernation and reproduction; (2) models built from datasets encompassing both ecological stages will perform better than seasonal models. We employed a dataset of 470 independent occurrences of bat hibernacula and 400 independent records of nursery roosts of selected species and for each species we developed separate winter, summer and mixed (i.e. generated from both winter and summer occurrences) models. Seasonal and mixed potential ranges differed from each other and the direction of this difference was species-specific. Mixed models outperformed seasonal models in representing species niches. Our work highlights the importance of considering data seasonality in the development of SDMs for bats as well as many other organisms, including non-migratory species, otherwise the analysis will lead to significant biases whose consequences for conservation planning and landscape management may be detrimental. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png Biodiversity and Conservation Springer Journals

Ignoring seasonal changes in the ecological niche of non-migratory species may lead to biases in potential distribution models: lessons from bats

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Publisher
Springer Netherlands
Copyright
Copyright © 2018 by Springer Science+Business Media B.V., part of Springer Nature
Subject
Life Sciences; Biodiversity; Ecology; Conservation Biology/Ecology; Climate Change/Climate Change Impacts
ISSN
0960-3115
eISSN
1572-9710
D.O.I.
10.1007/s10531-018-1545-7
Publisher site
See Article on Publisher Site

Abstract

Phenology is a key feature in the description of species niches to capture seasonality in resource use and climate requirements. Species distribution models (SDMs) are widespread tools to evaluate a species’ potential distribution and identify its large-scale habitat preferences. Despite its chief importance, data phenology is often neglected in SDM development. Non-migratory bats of temperate regions are good model species to test the effect of data seasonality on SDM outputs because of their different roosting preferences between hibernation and reproduction. We hypothesized that (1) the output of SDMs developed for six non-migratory European bat species will differ between hibernation and reproduction; (2) models built from datasets encompassing both ecological stages will perform better than seasonal models. We employed a dataset of 470 independent occurrences of bat hibernacula and 400 independent records of nursery roosts of selected species and for each species we developed separate winter, summer and mixed (i.e. generated from both winter and summer occurrences) models. Seasonal and mixed potential ranges differed from each other and the direction of this difference was species-specific. Mixed models outperformed seasonal models in representing species niches. Our work highlights the importance of considering data seasonality in the development of SDMs for bats as well as many other organisms, including non-migratory species, otherwise the analysis will lead to significant biases whose consequences for conservation planning and landscape management may be detrimental.

Journal

Biodiversity and ConservationSpringer Journals

Published: Apr 13, 2018

References

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