Dataset Information

Environmental heterogeneity explains coarse-scale ?-diversity of terrestrial vertebrates in Mexico.

ABSTRACT: We explored the hypothesis that high ?-diversity of terrestrial vertebrates of Mexico is associated with a high environmental heterogeneity (HEH) and identify the drivers of ?-diversity at different spatial scales. We used distribution range maps of 2,513 species of amphibians, reptiles, mammals, and birds occurring in Mexico. We estimated ?-diversity for each taxon at four spatial scales (grid cells of 2°, 1°, 0.5° and 0.25°) using the multiplicative formula of Whittaker ?w. For each spatial scale, we derived 10 variables of environmental heterogeneity among cells based on raw data of temperature, precipitation, elevation, vegetation and soil. We applied conditional autoregressive models (CAR) to identify the drivers of ?-diversity for each taxon at each spatial scale. CARs increased in explanatory power from fine-to-coarse spatial scales in amphibians, reptiles and mammals. The heterogeneity in precipitation including both, coefficient of variation (CV) and range of values (ROV), resulted in the most important drivers of ?-diversity of amphibians; the heterogeneity in temperature (CV) and elevation (ROV) were the most important drivers of ?-diversity for reptiles; the heterogeneity in temperature (ROV) resulted in the most important driver in ?-diversity for mammals. For birds, CARs resulted significant at fine scales (grid cells of 0.5° and 0.25°), and the precipitation (ROV and CV), temperature (ROV), and vegetation (H) and soil (H) were heterogeneity variables retained in the model. We found support for the hypothesis of environmental heterogeneity (HEH) for terrestrial vertebrates at coarse scales (grid cell of 2°). Different variables of heterogeneity, mainly abiotic, were significant for each taxon, reflecting physiological differences among terrestrial vertebrate groups. Our study revealed the importance of mountain areas in the geographic patterns of ?-diversity of terrestrial vertebrates in Mexico. At a coarse scale, specific variables of heterogeneity can be used as a proxy of ?-diversity for amphibians and reptiles.

SUBMITTER: Rodriguez P

PROVIDER: S-EPMC6347424 | biostudies-literature | 2019

REPOSITORIES: biostudies-literature

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Publications

Environmental heterogeneity explains coarse-scale β-diversity of terrestrial vertebrates in Mexico.

Rodríguez Pilar P Ochoa-Ochoa Leticia M LM Munguía Mariana M Sánchez-Cordero Víctor V Navarro-Sigüenza Adolfo G AG Flores-Villela Oscar A OA Nakamura Miguel M

PloS one 20190125 1

We explored the hypothesis that high β-diversity of terrestrial vertebrates of Mexico is associated with a high environmental heterogeneity (HEH) and identify the drivers of β-diversity at different spatial scales. We used distribution range maps of 2,513 species of amphibians, reptiles, mammals, and birds occurring in Mexico. We estimated β-diversity for each taxon at four spatial scales (grid cells of 2°, 1°, 0.5° and 0.25°) using the multiplicative formula of Whittaker βw. For each spatial sc ...[more]

PMID: 30682061

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Project description:Quantifying differences in species composition among communities provides important information related to the distribution, conservation and management of biodiversity, especially when two components are recognized: dissimilarity due to turnover, and dissimilarity due to richness differences. The ecoregions in central Mexico, within the Mexican Transition Zone, have outstanding environmental heterogeneity and harbor huge biological richness, besides differences in the origin of the biota. Therefore, biodiversity studies in this area require the use of complementary measures to achieve appropriate information that may help in the design of conservation strategies. In this work we analyze the dissimilarity of terrestrial vertebrates, and the components of turnover and richness differences, among six ecoregions in the state of Hidalgo, central Mexico. We follow two approaches: one based on species level dissimilarity, and the second on taxonomic dissimilarity. We used databases from the project "Biodiversity in the state of Hidalgo". Our results indicate that species dissimilarity is higher than taxonomic dissimilarity, and that turnover contributes more than richness differences, both for species and taxonomic total dissimilarity. Moreover, total dissimilarity, turnover dissimilarity and the dissimilarity due to richness differences were positively related in the four vertebrate groups. Reptiles had the highest values of dissimilarity, followed by mammals, amphibians and birds. For reptiles, birds, and mammals, species turnover was the most important component, while richness differences had a higher contribution for amphibians. The highest values of dissimilarity occurred between environmentally contrasting ecoregions (i.e., tropical and temperate forests), which suggests that environmental heterogeneity and differences in the origin of biotas are key factors driving beta diversity of terrestrial vertebrates among ecoregions in this complex area.

Project description:A key aim of ecology is to understand the drivers of ecological patterns, so that we can accurately predict the effects of global environmental change. However, in many cases, predictors are measured at a finer resolution than the ecological response. We therefore require data aggregation methods that avoid loss of information on fine-grain heterogeneity.We present a data aggregation method that, unlike current approaches, reduces the loss of information on fine-grain spatial structure in environmental heterogeneity for use with coarse-grain ecological datasets. Our method contains three steps: (a) define analysis scales (predictor grain, response grain, scale-of-effect); (b) use a moving window to calculate a measure of variability in environment (predictor grain) at the process-relevant scale (scale-of-effect); and (c) aggregate the moving window calculations to the coarsest resolution (response grain). We show the theoretical basis for our method using simulated landscapes and the practical utility with a case study. Our method is available as the grainchanger r package.The simulations show that information about spatial structure is captured that would have been lost using a direct aggregation approach, and that our method is particularly useful in landscapes with spatial autocorrelation in the environmental predictor variable (e.g. fragmented landscapes) and when the scale-of-effect is small relative to the response grain. We use our data aggregation method to find the appropriate scale-of-effect of land cover diversity on Eurasian jay Garrulus glandarius abundance in the UK. We then model the interactive effect of land cover heterogeneity and temperature on G. glandarius abundance. Our method enables us quantify this interaction despite the different scales at which these factors influence G. glandarius abundance.Our data aggregation method allows us to integrate variables that act at varying scales into one model with limited loss of information, which has wide applicability for spatial analyses beyond the specific ecological context considered here. Key ecological applications include being able to estimate the interactive effect of drivers that vary at different scales (such as climate and land cover), and to systematically examine the scale dependence of the effects of environmental heterogeneity in combination with the effects of climate change on biodiversity.

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Dataset Information

Environmental heterogeneity explains coarse-scale ?-diversity of terrestrial vertebrates in Mexico.

Publications

Environmental heterogeneity explains coarse-scale β-diversity of terrestrial vertebrates in Mexico.

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