@Article{SalesNeveMarcLoyo:2017:MoUnNo,
author = "Sales, Lilian Patr{\'{\i}}cia and Neves, Ol{\'{\i}}via Viana
and Marco Junior, Paulo de and Loyola, Rafael",
affiliation = "{Universidade Federal de Goi{\'a}s (UFG)} and {Universidade
Federal de Goi{\'a}s (UFG)} and {Instituto Nacional de Pesquisas
Espaciais (INPE)} and {Instituto Nacional de Pesquisas Espaciais
(INPE)}",
title = "Model uncertainties do not affect observed patterns of species
richness in the Amazon",
journal = "PLoS One",
year = "2017",
volume = "12",
number = "10",
pages = "e0183785",
month = "Oct.",
abstract = "Background Climate change is arguably a major threat to
biodiversity conservation and there are several methods to assess
its impacts on species potential distribution. Yet the extent to
which different approaches on species distribution modeling affect
species richness patterns at biogeographical scale is however
unaddressed in literature. In this paper, we verified if the
expected responses to climate change in biogeographical
scale-patterns of species richness and species vulnerability to
climate change-are affected by the inputs used to model and
project species distribution. Methods We modeled the distribution
of 288 vertebrate species (amphibians, birds and mammals), all
endemic to the Amazon basin, using different combinations of the
following inputs known to affect the outcome of species
distribution models (SDMs): 1) biological data type, 2) modeling
methods, 3) greenhouse gas emission scenarios and 4) climate
forecasts. We calculated uncertainty with a hierarchical ANOVA in
which those different inputs were considered factors. Results The
greatest source of variation was the modeling method. Model
performance interacted with data type and modeling method.
Absolute values of variation on suitable climate area were not
equal among predictions, but some biological patterns were still
consistent. All models predicted losses on the area that is
climatically suitable for species, especially for amphibians and
primates. All models also indicated a current East-western
gradient on endemic species richness, from the Andes foot
downstream the Amazon river. Again, all models predicted future
movements of species upwards the Andes mountains and overall
species richness losses. Conclusions From a methodological
perspective, our work highlights that SDMs are a useful tool for
assessing impacts of climate change on biodiversity. Uncertainty
exists but biological patterns are still evident at large spatial
scales. As modeling methods are the greatest source of variation,
choosing the appropriate statistics according to the study
objective is also essential for estimating the impacts of climate
change on species distribution. Yet from a conservation
perspective, we show that Amazon endemic fauna is potentially
vulnerable to climate change, due to expected reductions on
suitable climate area. Climate-driven faunal movements are
predicted towards the Andes mountains, which might work as climate
refugia for migrating species.",
doi = "10.1371/journal.pone.0183785",
url = "http://dx.doi.org/10.1371/journal.pone.0183785",
issn = "1932-6203",
language = "en",
targetfile = "sales_model.pdf",
urlaccessdate = "27 abr. 2024"
}