Plateau: a new method for ecologically plausible climate envelopes for species distribution modelling

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Authors Mark J. Brewer, Robert B. O'Hara, Barbara J. Anderson, Ralf Ohlemüller
Journal/Conference Name Ecology and Evolution
Paper Category , ,
Paper Abstract Ecologists often wish to describe mathematical relationships between response variables and climate covariates in spatial models of species distribution; these relationships are commonly termed climate envelopes. There are many situations when the functional form of the envelopes should be either unimodal or monotonic, but current practice tends towards the use of either low-degree single-variable spline curves fitted as part of a Generalised Additive Model (GAM) or piecewise linear forms in software such as maxent. We argue that such curves are often inappropriate, as they (i) can easily produce relationships which are ecologically implausible and (ii) frequently ignore interactions between multiple climate variables in a general regression context. We propose an novel alternative parametric form for climate envelopes that appeals to ecological plausibility and can encompass realistic features of species' presence/climate relationships on several variables simultaneously. The proposed plateau climate envelope function is applied via a spatial Bayesian species distribution model to data on two European tree species to demonstrate the approach. For Fagus sylvatica, a complete climate envelope is estimable, but for Quercus coccifera, only a partial climate envelope can be estimated as the geographical extent of the data set does not cover the full environmental niche for the species. We show that such an approach is practical, produces climate envelopes with an ecologically meaningful form and furthermore allows the inclusion of information external to the data set being analysed. We discuss the use of this new plateau climate envelope function in the context of ecological niche modelling and argue that in some instances ecological realism should be regarded as more important than the use of formal model comparison statistics.
Date of publication 2016
Code Programming Language R
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