1 Scientific RepoRts | 6:23381 | DOI: 10.1038/srep23381 www.nature.com/scientificreports thermal niche estimators and the capability of poor dispersal species to cope with climate change David sánchez-Fernández 1,2 , Valeria Rizzo 1 , Alexandra Cieslak 1 , Arnaud Faille 3 , Javier Fresneda 4,5 & Ignacio Ribera 1 For management strategies in the context of global warming, accurate predictions of species response are mandatory. However, to date most predictions are based on niche (bioclimatic) models that usually overlook biotic interactions, behavioral adjustments or adaptive evolution, and assume that species can disperse freely without constraints. the deep subterranean environment minimises these uncertainties, as it is simple, homogeneous and with constant environmental conditions. It is thus an ideal model system to study the efect of global change in species with poor dispersal capabilities. We assess the potential fate of a lineage of troglobitic beetles under global change predictions using diferent approaches to estimate their thermal niche: bioclimatic models, rates of thermal niche change estimated from a molecular phylogeny, and data from physiological studies. Using bioclimatic models, at most 60% of the species were predicted to have suitable conditions in 2080. Considering the rates of thermal niche change did not improve this prediction. However, physiological data suggest that subterranean species have a broad thermal tolerance, allowing them to stand temperatures never experienced through their evolutionary history. these results stress the need of experimental approaches to assess the capability of poor dispersal species to cope with temperatures outside those they currently experience. Climate change has become one of the main threats to global biodiversity 1,2 . For management strategies in the context of global warming, accurate predictions of species response are mandatory. However, to date most assessments of species’ vulnerability under climate change have been derived from niche (bioclimatic) mod- els 3 . Tese approaches relate observed geographic distribution of a species to current climate; resultant models are then applied to climate projections to infer potential climatically suitable areas for a given species in the future. Tis allows detecting populations likely to remain stable over coming decades, as compared with others that are likely to be lost 4–6 . However, the application of these approaches has been debated as they present some important uncertainties 7,8 . It is assumed that i) species are able to disperse freely without any constraint; ii) are perfectly adapted to the environmental conditions of their current distributions; iii) the variables determining species distribution are known; iv) environmental conditions (usually estimated for grid cells at diferent spatial resolution) are homogeneous through the habitat, ignoring both temporal (daily and ofen seasonal) and spa- tial (micro-habitat) heterogeneity 9–12 ; and v) organisms have no control over the conditions to which they are exposed, ignoring both behavioural and phenological accommodation at diferent environmental conditions 13,14 and the possibility of evolutionary adaptation 15–17 . Tere is, however, a system in which all these uncertainties are minimised: the deep subterranean environ- ment. Contrary to what happens in epigean (surface) environments, the range of variables afecting species in this environment is minimal. Te humidity in the deep parts of a cave is always near the saturation point and the temperature is highly constant through the day and the year. It is also possible to have a reliable estimate of this temperature, which is approximately equal to the mean annual temperature of the surface 18–20 (see Supplementary Fig. S1). Besides, environmental conditions are practically homogeneous through all possible microhabitats within a cave system, so small-scale spatial heterogeneity and the possibility of behavioural adjustments or 1 inst itute of evolutionary Biology (cSic-Universitat Pompeu fabra), Barcelona, Spain. 2 inst ituto de ciencias Ambientales, Universidad de castilla-La Mancha, toledo, Spain. 3 Zoologische Staatsammlung, Muenchhausenstrasse, Munich, Germany. 4 ca de Massa, Llesp – el Pont de Suert, Spain. 5 Museu de ciències naturals, Barcelona, Spain. correspondence and requests for materials should be addressed to D.S.f. (email: david.sanchez@ibe.upf-csic.es) received: 23 November 2015 Accepted: 04 March 2016 Published: 17 March 2016 opeN