letsR: a new R package for data handling and analysis in macroecology

Vilela, Bruno; Villalobos, Fabricio

doi:10.1111/2041-210x.12401

Cited by 206 publications

(161 citation statements)

References 16 publications

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“…Prior to variable selection, the WorldClim dataset was clipped to our study extent and re-scaled for our resolution, by averaging neighboring values (function = mean), using the function lets . presab in letsR package [57]. All variable selection analysis were performed using the caret package [58].…”

Section: Methodsmentioning

confidence: 99%

Model uncertainties do not affect observed patterns of species richness in the Amazon

et al. 2017

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BackgroundClimate 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.MethodsWe 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.ResultsThe 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.ConclusionsFrom 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.

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Section: Methodsmentioning

confidence: 99%

Model uncertainties do not affect observed patterns of species richness in the Amazon

et al. 2017

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“…In addition, the 0.5° resolution was chosen to counterbalance the inaccuracies associated with applying high resolution climatic data to a relatively coarse IUCN species distribution maps [37]. Calculations were done in R version 3.2.2 [38] mainly using the LetsR [39] and Raster packages and their dependencies. All figures presented in this paper are original and were created in R and ArcGIS 10.1 (ESRI, Redlands, CA, USA).…”

Section: Methodsmentioning

confidence: 99%

Assessing Mammal Exposure to Climate Change in the Brazilian Amazon

et al. 2016

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Human-induced climate change is considered a conspicuous threat to biodiversity in the 21st century. Species’ response to climate change depends on their exposition, sensitivity and ability to adapt to novel climates. Exposure to climate change is however uneven within species’ range, so that some populations may be more at risk than others. Identifying the regions most exposed to climate change is therefore a first and pivotal step on determining species’ vulnerability across their geographic ranges. Here, we aimed at quantifying mammal local exposure to climate change across species’ ranges. We identified areas in the Brazilian Amazon where mammals will be critically exposed to non-analogue climates in the future with different variables predicted by 15 global circulation climate forecasts. We also built a null model to assess the effectiveness of the Amazon protected areas in buffering the effects of climate change on mammals, using an innovative and more realistic approach. We found that 85% of species are likely to be exposed to non-analogue climatic conditions in more than 80% of their ranges by 2070. That percentage is even higher for endemic mammals; almost all endemic species are predicted to be exposed in more than 80% of their range. Exposure patterns also varied with different climatic variables and seem to be geographically structured. Western and northern Amazon species are more likely to experience temperature anomalies while northeastern species will be more affected by rainfall abnormality. We also observed an increase in the number of critically-exposed species from 2050 to 2070. Overall, our results indicate that mammals might face high exposure to climate change and that protected areas will probably not be efficient enough to avert those impacts.

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“…Smith and Patton 1999, Salazar-Bravo et al 2013, Leite et al 2014, D'Elía and Pardiñas 2015 rgdal (Bivand et al 2018), raster (Hijmans 2017), and letsR (Vilela and Villalobos 2015). Smith and Patton 1999, Salazar-Bravo et al 2013, Leite et al 2014, D'Elía and Pardiñas 2015 rgdal (Bivand et al 2018), raster (Hijmans 2017), and letsR (Vilela and Villalobos 2015).…”

Section: Hypothesesmentioning

confidence: 99%

Tracing the diversification history of a Neogene rodent invasion into South America

2018

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We investigated spatial patterns of evolutionary relatedness and diversification rates to test hypotheses about the historical biogeographic processes underlying the radiation of Neotropical rats and mice (Sigmodontinae, ~400 species). A negative correlation between mean phylogenetic distance and diversification rates of rodent assemblages reveals a pattern of species co-occurrence in which assemblages of closely related species are also the fastest diversifying ones. Subregions of the Neotropics occupied by distantly related species that are on average more slowly diversifying include Central America, northern South America, and the Atlantic forest. In southern South America, recent species turnover appears to have been higher. Ancestral locations for the main tribes of sigmodontines were also estimated, suggesting eastern South America and the Amazonian lowlands were colonized before some central Andean regions, even though the latter are now centers of species richness for these rodents. Moreover, a past connection between the tropical Andes and the Atlantic Forest is suggested by our results, highlighting a role for a hypothetical arc connecting the two biomes, which would have impacted many other groups of organisms. Whether rapid, recent speciation in some regions is related to Quaternary climatic fluctuations and the young age of sigmodontines (~12.7 Ma crown age) or instead to intrinsic traits of these rodents remains an open question. If the former is true, we hypothesize that contrasting trends will characterize older Neotropical clades.

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letsR: a new R package for data handling and analysis in macroecology

Cited by 206 publications

References 16 publications

Model uncertainties do not affect observed patterns of species richness in the Amazon

Model uncertainties do not affect observed patterns of species richness in the Amazon

Assessing Mammal Exposure to Climate Change in the Brazilian Amazon

Tracing the diversification history of a Neogene rodent invasion into South America

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