Physiology in ecological niche modeling: using zebra mussel's upper thermal tolerance to refine model predictions through Bayesian analysis. Issue 2 (7th November 2019)

• Record Type: Journal Article
• Authors: Feng, Xiao; Liang, Ye; Gallardo, Belinda; Papeş, Monica
• Is Part Of: Ecography. Volume 43:Issue 2(2020)
• Page Start: 270
• Page End: 282
• Publication Date: 2019-11-07
• Subjects: Bayesian analysis -- climate change -- ecological niche model -- Grinnellian niche -- invasive species -- physiological tolerance; Ecology -- Periodicals; Biodiversity -- Periodicals; 574.5
• DOI: 10.1111/ecog.04627
• Languages: English
• ISSNs: 0906-7590

Abstract

Abstract : Climate change and human‐mediated dispersal are increasingly influencing species' geographic distributions. Ecological niche models (ENMs) are widely used in forecasting species' distributions, but are weak in extrapolation to novel environments because they rely on available distributional data and do not incorporate mechanistic information, such as species' physiological response to abiotic conditions. To improve accuracy of ENMs, we incorporated physiological knowledge through Bayesian analysis. In a case study of the zebra mussel Dreissena polymorpha, we used native and global occurrences to obtain native and global models representing narrower and broader understanding of zebra mussel' response to temperature. We also obtained thermal limit and survival information for zebra mussel from peer‐reviewed literature and used the two types of information separately and jointly to calibrate native models. We showed that, compared to global models, native models predicted lower relative probability of presence along zebra mussel's upper thermal limit, suggesting the shortcoming of native models in predicting zebra mussel's response to warm temperature. We also found that native models showed improved prediction of relative probability of presence when thermal limit was used alone, and best approximated global models when both thermal limit and survival data were used. Our result suggests that integration of physiological knowledge enhances extrapolation of ENM in novel environments. Our modeling framework can be generalized for other species or other physiological limits and may incorporate evolutionary information (e.g. evolved thermal tolerance), thus has the potential to improve predictions of species' invasive potential and distributional response to climate change.