Evaluating, partitioning, and mapping the spatial autocorrelation component in ecological niche modeling: a new approach based on environmentally equidistant records
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2013-11-15
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Most species data display spatial autocorrelation that can affect ecological niche models (ENMs) accuracy-statistics,
affecting its ability to infer geographic distributions. Here we evaluate whether the spatial autocorrelation underlying
species data affects accuracy-statistics and map the uncertainties due to spatial autocorrelation effects on species
range predictions under past and future climate models. As an example, ENMs were fitted to Qualea grandiflora
(Vochysiaceae), a widely distributed plant from Brazilian Cerrado. We corrected for spatial autocorrelation in ENMs
by selecting sampling sites equidistant in geographical (GEO) and environmental (ENV) spaces. Distributions were
modelled using 13 ENMs evaluated by two accuracy-statistics (TSS and AUC), which were compared with uncorrected
ENMs. Null models and the similarity statistics I were used to evaluate the effects of spatial autocorrelation. Moreover,
we applied a hierarchical ANOVA to partition and map the uncertainties from the time (across last glacial maximum,
pre-insustrial, and 2080 time periods) and methodological components (ENMs and autocorrelation corrections). The
GEO and ENV models had the highest accuracy-statistics values, although only the ENV model had values higher
than expected by chance alone for most of the 13 ENMs. Uncertainties from time component were higher in the core
region of the Brazilian Cerrado where Q. grandiflora occurs, whereas methodological components presented higher
uncertainties in the extreme northern and southern regions of South America (i.e. outside of Brazilian Cerrado). Our
findings show that accounting for autocorrelation in environmental space is more efficient than doing so in geographical
space. Methodological uncertainties were concentrated in outside the core region of Q. grandiflora’s habitat. Conversely,
uncertainty due to time component in the Brazilian Cerrado reveals that ENMs were able to capture climate change
effects on Q. grandiflora distributions.
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OLIVEIRA, G.; RANGEL, Thiago F.; RIBEIRO, Matheus de Souza Lima; TERRIBILE, L. C.; DINIZ FILHO , J. A. F. Evaluating, partitioning, and mapping the spatial autocorrelation component in ecological niche modeling: a new approach based on environmentally equidistant records. Ecography, Copenhagen, v. 37, p. 637-647, Jul. 2014. Disponível em: < file:///C:/Users/Luanna/Downloads/Oliveira_et_al-2014-Ecography.pdf>.