Are niche‐based species distribution models transferable in space?

Abstract

based on the area under the curve of a receiver-operating characteristic plot (ROC plot); TI, transferability index. ABSTRACTAim To assess the geographical transferability of niche-based species distribution models fitted with two modelling techniques.Location Two distinct geographical study areas in Switzerland and Austria, in the subalpine and alpine belts.Methods Generalized linear and generalized additive models (GLM and GAM) with a binomial probability distribution and a logit link were fitted for 54 plant species, based on topoclimatic predictor variables. These models were then evaluated quantitatively and used for spatially explicit predictions within (internal evaluation and prediction) and between (external evaluation and prediction) the two regions. Comparisons of evaluations and spatial predictions between regions and models were conducted in order to test if species and methods meet the criteria of full transferability. By full transferability, we mean that: (1) the internal evaluation of models fitted in region A and B must be similar;(2) a model fitted in region A must at least retain a comparable external evaluation when projected into region B, and vice-versa; and (3) internal and external spatial predictions have to match within both regions.Results The measures of model fit are, on average, 24% higher for GAMs than for GLMs in both regions. However, the differences between internal and external evaluations (AUC coefficient) are also higher for GAMs than for GLMs (a difference of 30% for models fitted in Switzerland and 54% for models fitted in Austria). Transferability, as measured with the AUC evaluation, fails for 68% of the species in Switzerland and 55% in Austria for GLMs (respectively for 67% and 53% of the species for GAMs). For both GAMs and GLMs, the agreement between internal and external predictions is rather weak on average (Kulczynski's coefficient in the range 0.3-0.4), but varies widely among individual species. The dominant pattern is an asymmetrical transferability between the two study regions (a mean decrease of 20% for the AUC coefficient when the models are transferred from Switzerland and 13% when they are transferred from Austria). Main conclusionsThe large inter-specific variability observed among the 54 study species underlines the need to consider more than a few species to test properly the transferability of species distribution models. The pronounced asymmetry in transferability between the two study regions may be due to peculiarities of these regions, such as differences in the ranges of environmental predictors or the varied impact of land-use history, or to species-specific reasons like differential phenotypic plasticity, existence of ecotypes or varied dependence on biotic interactions that are not properly incorporated into niche-based models. The lower variation between internal and external evaluation of GLMs compared to GAMs further suggests that overfitting may reduce transferability.

Predicting the future of forests in the Mediterranean under climate change, with niche- and process-based models: CO2 matters!

“…Despite their broad use, uncertainties about nichebased model predictions remain high (Hampe, 2004;Heikkinen et al, 2006;Randin et al, 2006). To date, the main drawback of niche-based models is their inability to consider important relationships such as biotic interactions, mortality, or growth (Davis et al, 1998;Hampe, 2004) and their reliance on observed distributions, which are the results of long-term historical factors (e.g., postglacial recolonization and human management), and environmental stochasticity, among other factors.…”

Incorporating uncertainty in predictive species distribution modelling

“…Such a measure must identify how well predictions fit observed data (dealing appropriately with the uncertainty in the observations themselves) while penalizing for model complexity, and should remain unaffected by both autocorrelation in the observed pattern [51] and prevalence [50]. Ideally, models should be tested against independent data, such as in an introduced range [91,92], through use of historic [93] or palaeontological [47] datasets to retrodict distribution, and through the use of simulated data where the ability of the model to recover known processes is a measure of performance.…”

Strengths and Weaknesses of Quantitative Climate Reconstructions Based on Late-Quaternary Biological Proxies

“…There is currently much debate about the basic assumptions underlying this approach (e.g. Pearson & Dawson 2003;Hampe 2004;Pearson & Dawson 2004;Heikkinen et al 2006;Dormann 2007a;Beale et al 2008;Jiménez-Valverde et al 2008;Thuiller et al 2008;Araújo et al 2009;Aspinall et al 2009;Beale et al 2009;Duncan et al 2009;Morin & Thuiller 2009;Peterson et al 2009;Willis & Bhagwat 2009;Chapman 2010;Mouton et al 2010;Real et al 2010;Willis et al 2010b), namely that present-day distributions are controlled by climate (Araújo & Luoto 2007;Beale et al 2009;Blach-Overgaard et al 2010;Chapman 2010), that the distributions are in equilibrium with climate today (Svenning & Skov 2004;Araújo & Pearson 2005;Svenning & Skov 2005de Marco et al 2008;Svenning et al 2008;Normand et al 2009), that the distributional data and the climate data are reliable (Rolland 2003;Kitricos & Leriche 2010) and, in the case of montane and alpine biota, that the modern climate data are from the same altitudes as where the species being modelled actually grow (Dahl 1951(Dahl , 1998Randin et al 2006;Lundquist & Cayan 2007;Pape et al 2009;…”