The importance of biotic interactions for modelling species distributions under climate change

Abstract

Aim There is a debate as to whether biotic interactions exert a dominant role in governing species distributions at macroecological scales. The prevailing idea is that climate is the key limiting factor; thus models that use present-day climate-species range relationships are expected to provide reasonable means to quantify the impacts of climate change on species distributions. However, there is little empirical evidence that biotic interactions would not constrain species distributions at macroecological scales. We examine this idea, for the first time, and provide tests for two null hypotheses: ( H 0 1) -biotic interactions do not exert a significant role in explaining current distributions of a particular species of butterfly (clouded Apollo, Parnassius mnemosyne ) in Europe; and ( H 0 2) -biotic interactions do not exert a significant role in predictions of altered species' ranges under climate change.Location Europe.Methods Generalized additive modelling (GAM) was used to investigate relationships between species and climate; species and host plants; and species and climate + host plants. Because models are sensitive to the variable selection strategies utilised, four alternative approaches were used: AIC (Akaike's Information Criterion), BIC (Bayesian Information Criterion), BRUTO (Adaptive Backfitting), and CROSS (Cross Selection). ResultsIn spite of the variation in the variables selected with different methods, both hypotheses ( H 0 1 and H 0 2) were falsified, providing support for the proposition that biotic interactions significantly affect both the explanatory and predictive power of bioclimatic envelope models at macro scales. Main conclusionsOur results contradict the widely held view that the effects of biotic interactions on individual species distributions are not discernible at macroecological scales. Results are contingent on the species, type of interaction and methods considered, but they call for more stringent evidence in support of the idea that purely climate-based modelling would be sufficient to quantify the impacts of climate change on species distributions.

Detecting the potential sympatric range and niche divergence between Asian endemic ungulates of Procapra

“…If sufficient niche dimensions are examined, regions of potential presence that lack species records may highlight instances where either historical causes or biotic interactions have played a role in restricting species' realized distributions (Anderson et al 2002). In particular, we can use SDMs to assess biogeographical relationships between ecologically interacting species, such as potential competitors (Acevedo et al 2010;Anderson et al 2002), predators and their prey (Real et al 2009), and hosts and their parasites (Araújo and Luoto 2007). For related species occurring along well-sampled environmental gradients, SDMs can be helpful in exploring the roles of competitive interactions and/or environmental characteristics in limiting and shaping their distributions and in predicting ranges where their coexistence can be expected, as well as in answering questions about niche evolution (Acevedo et al 2010;Costa and Schlupp 2010;Martínez-Freiría et al 2008;Warren et al 2008).…”

Shading and litter mediate the effects of soil fertility on the performance of an understorey herb

“…Grace et al, 2011). Where biotic conditions significantly interact with and mediate the effects of abiotic variables, models constructed with abiotic variables alone may lack the mechanism for the observed distribution pattern and may overestimate the capacity of changing climates to affect future distributions (Araujo and Luoto, 2007). Models built without biotic conditions could lead to erroneous predictions if the existing correlations between abiotic and biotic conditions (i.e.…”

“…We quantified the predictive power of each model by comparing the rate of true positives versus false positives with Wilcoxon signed-rank tests (e.g. Araujo and Luoto, 2007). This test indicates whether model predictions for true and false positives are significantly different, indicating that models are able to distinguish conditions for presences and absences.…”

“…The performance and distribution of plant species with respect to climate (Araujo and Luoto, 2007;Meier et al, 2010) and soil fertility (Elmendorf and Moore, 2008) can also be strongly affected by biotic conditions such as the degree of mycorrhizal symbiosis (Ruotsalainen et al, 2004), competition or facilitation by neighbours (Choler et al, 2001;Freestone, 2006) and overstorey cover (Meier et al, 2010). However, biotic conditions are often omitted in species distribution models (Elith and Leathwick, 2009) due to scarcity of relevant data, close correlations between biotic and abiotic variables, and uncertainty regarding the relative importance of biotic conditions at the larger spatial scales typically considered in distribution models (Wisz et al, 2013).…”

Spatial Interpolation of Meteorologic Variables in Vietnam using the Kriging Method

“…Knowledge of the spatial temporal variability of climatic conditions is required for assessing the recent climate change and greenhouse effect [1]. Spatially and temporally continuous gridded meteorologic datasets are important in many applications, such as in forest fire risk modeling, soil sciences, and ecological studies [2,3].…”