Drawbacks to palaeodistribution modelling: the case of South American seasonally dry forests

Abstract

Aim Species distribution modelling (SDM) has increasingly been used to predict palaeodistributions at regional and global scales in order to understand the response of vegetation to climate change and to estimate palaeodistributions for the testing of biogeographical hypotheses. However, there are many sources of uncertainty in SDM that may restrict the ability of models to hindcast palaeodistributions and provide a basis for hypothesis testing in molecular phylogenetics and phylogeographical studies.Location Seasonally dry forests (SDFs) in South America. MethodsWe addressed the problem of using palaeodistribution modelling for SDFs based on the projection of their current distribution into past environments (21 ka) using 11 methods for SDMs and five coupled atmosphere-ocean global circulation models (AOGCMs) for 16 species. ResultsWe observed considerable uncertainty in the hindcasts, with the most important effects for AOGCM (median 12.2%), species (median 15.6%) and their interaction (median 13.6%). The effects of AOGCMs were stronger in the Amazon region, whereas the species effect occurred primarily in the dry areas of central Brazil. The log-linear model detected significant effects of the three sources of variation and their interaction on the classification of each map in supporting alternative hypotheses. An expansion scenario combining the Pleistocene arc and Amazonian expansion, and Pennington's Amazonian expansion alone, were the most frequently supported palaeodistribution scenarios.Main conclusions As a basis for evaluating a given hypothesis, hindcast distributions must be used in direct association with other evidence, such as molecular variation and the fossil record. We propose an alternative framework concerning hypothesis testing that couples SDM and phylogeographical work, in which palaeoclimatic distributions and other sources of information, such as the pollen fossil record and coalescence modelling, must be weighted equally.

EcoClimate: a database of climate data from multiple models for past, present, and future for macroecologists and biogeographers

“…Using a scenario based on observed climate (see example in Hijmans and Graham, 2006) would reduce variability among AOGCM outputs by relating downscaled past and future layers to the same unique baseline, and could hide potential macroecological and biogeographic responses or underestimate their variation or uncertainty. Not considering the full diversity of potential responses to climate change may compromise many research questions, or even lead to invalid results in many cases (e.g., see the importance of using intact climate uncertainty for phylogeographic inference as discussed in Collevatti et al, 2013b;.…”

“…This suite of methods and ideas has been applied to diverse research purposes: guiding discovery of populations of known (Bourg et al, 2005;Guisan et al, 2006) and unknown (Raxworthy et al, 2003) species; understanding distributional dynamics under past (Banks et al, 2008a;2008b) and future (Dormann, 2007;Anderson, 2013) climates; anticipating climate change impacts on agricultural (Fraga et al, 2013) and natural (Nabout et al, 2011) extraction; mapping invasion risk (Peterson, 2003;Jiménez-Valverde et al, 2011), pest distributions (Venette et al, 2010;Estay et al, 2014), and disease trasmission (Peterson, 2014); estimating population parameters (Tôrres et al, 2012;Lima-Ribeiro and DinizFilho, 2013;Thuiller et al, 2014), species richness (Wisz and Rahbeck, 2007;LimaRibeiro et al, 2013b), and community composition (Pellissier et al, 2012); analyzing biotic interactions (Anderson et al, 2002;Wheeler et al, 2015); illuminating patterns and processes of diversification and speciation (Silva et al, 2014); characterizing dispersal (Génard and Lescourret, 2013;Saltré et al, 2015); highlighting extinction (Nogués-Bravo et al, 2008;Lima-Ribeiro et al, 2013a); testing niche conservatism (Martínez-Meyer et al, 2004;Peterson and Nyári, 2007;Jakob et al, 2010) and phylogeographic hypotheses (Collevatti et al, 2013b;Alvarado-Serrano and Knowles, 2014); establishing historical refugia (Waltari et al, 2007;…”

Recovering the demographical history of a Brazilian Cerrado tree species Caryocar brasiliense: coupling ecological niche modeling and coalescent analyses

“…described in Collevatti et al (2012b). Four independent demographic scenarios were modeled (Figure 1), derived from the hypothesis supported by ENM, 'Range Stability' , 'Range Expansion' , 'Range Retraction' , or from a priori biogeographical hypothesis, 'Multiple Refugia' , following Ab'Saber (2000), Collevatti et al (2003) and evidences from the pollen fossil record (see Supporting Information).…”

A large historical refugium explains spatial patterns of genetic diversity in a Neotropical savanna tree species

“…Together with coalescent models, ENMs provide the framework to reconstruct species' demographic history (e.g. Collevatti et al, 2012aCollevatti et al, , 2013a and to better understand the effects of past colonization and the role of Quaternary climate changes in shaping the current spatial pattern of genetic diversity, especially for species with incomplete or no fossil records (e.g. Collevatti et al, 2013aCollevatti et al, , 2015a.…”

“…Collevatti et al, 2012aCollevatti et al, , 2013a and to better understand the effects of past colonization and the role of Quaternary climate changes in shaping the current spatial pattern of genetic diversity, especially for species with incomplete or no fossil records (e.g. Collevatti et al, 2013aCollevatti et al, , 2015a. The identification of areas with low climate change velocity through time (Quaternary refugia) is important to establish adequate conservation measures to ensure the maintenance of biodiversity in the face of ongoing climate change.…”