Dataset Information

Patterns of Species Richness and Turnover for the South American Rodent Fauna.

ABSTRACT: Understanding the spatial distribution of species sheds light on the group's biogeographical history, offers clues to the drivers of diversity, and helps to guide conservation strategies. Here, we compile geographic range information for South America's diverse rodents, whose 14 families comprise ~50% of the continent's mammalian species. The South American rodent fauna is dominated by independent and temporally staggered radiations of caviomorph and sigmodontine groups. We mapped species richness and turnover of all rodents and the principal clades to identify the main predictors of diversity patterns. Species richness was highest in the Andes, with a secondary hotspot in Atlantic Forest and some regions of considerable richness in Amazonia. Differences in richness were evident between the caviomorphs and sigmodontines, the former showing the greatest richness in tropical forests whereas the latter show-and largely determine-the all-rodent pattern. Elevation was the main predictor of sigmodontine richness, whereas temperature was the principal variable correlated with richness of caviomorphs. Across clades, species turnover was highest along the Andes and was best explained by elevational relief. In South America, the effects of the familiar latitudinal gradient in species richness are mixed with a strong longitudinal effect, triggered by the importance of elevation and the position of the Andes. Both latitudinal and elevational effects help explain the complicated distribution of rodent diversity across the continent. The continent's restricted-range species-those seemingly most vulnerable to localized disturbance-are mostly distributed along the Andes and in Atlantic Forest, with the greatest concentration in Ecuador. Both the Andes and Atlantic Forest are known hotspots for other faunal and floral components. Contrasting patterns of the older caviomorph and younger sigmodontine radiations underscore the interplay of both historical and ecological factors in determining present-day diversity patterns.

SUBMITTER: Maestri R

PROVIDER: S-EPMC4801412 | biostudies-literature | 2016

REPOSITORIES: biostudies-literature

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Publications

Patterns of Species Richness and Turnover for the South American Rodent Fauna.

Maestri Renan R Patterson Bruce D BD

PloS one 20160321 3

Understanding the spatial distribution of species sheds light on the group's biogeographical history, offers clues to the drivers of diversity, and helps to guide conservation strategies. Here, we compile geographic range information for South America's diverse rodents, whose 14 families comprise ~50% of the continent's mammalian species. The South American rodent fauna is dominated by independent and temporally staggered radiations of caviomorph and sigmodontine groups. We mapped species richne ...[more]

PMID: 26999278

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Project description:Aim:Common species contribute more to species richness patterns (SRPs) than rare species in most studies. Our aim was to test this hypothesis using a novel model system, species living exclusively in subterranean habitats. They consist of mainly rare species (small ranges), only a few of them being common (large ranges), and challenge whether rare species are less important for the development of SRPs in this environment. We separately analyzed aquatic and terrestrial species. Location:Western Balkans in southeastern Europe. Methods:We assembled two datasets comprising 431 beetle and 145 amphipod species, representing the model groups of subterranean terrestrial and aquatic diversity, respectively. We assessed the importance of rare and common species using the stepwise reconstruction of SRPs and subsequent correlation analyses, corrected also for the cumulative information content of the subsets based on species prevalence. We applied generalized linear regression models to evaluate the importance of rare and common species in forming SRPs. Additionally, we analyzed the contribution of rare and common species in species-rich cells. Results:Patterns of subterranean aquatic and terrestrial species richness overlapped only weakly, with aquatic species having larger ranges than terrestrial ones. Our analyses supported higher importance of common species for forming overall SRPs in both beetles and amphipods. However, in stepwise analysis corrected for information content, results were ambiguous. Common species presented a higher proportion of species than rare species in species-rich cells. Main Conclusion:We have shown that even in habitats with the domination of rare species, it is still common species that drive SRPs. This may be due to an even spatial distribution of rare species or spatial mismatch in hotspots of rare and common species. SRPs of aquatic and terrestrial subterranean organisms overlap very little, so the conservation approaches need to be habitat specific.

Project description:BackgroundThe quantification of species-richness and species-turnover is essential to effective monitoring of ecosystems. Wetland ecosystems are particularly in need of such monitoring due to their sensitivity to rainfall, water management and other external factors that affect hydrology, soil, and species patterns. A key challenge for environmental scientists is determining the linkage between natural and human stressors, and the effect of that linkage at the species level in space and time. We propose pixel intensity based Shannon entropy for estimating species-richness, and introduce a method based on statistical wavelet multiresolution texture analysis to quantitatively assess interseasonal and interannual species turnover.Methodology/principal findingsWe model satellite images of regions of interest as textures. We define a texture in an image as a spatial domain where the variations in pixel intensity across the image are both stochastic and multiscale. To compare two textures quantitatively, we first obtain a multiresolution wavelet decomposition of each. Either an appropriate probability density function (pdf) model for the coefficients at each subband is selected, and its parameters estimated, or, a non-parametric approach using histograms is adopted. We choose the former, where the wavelet coefficients of the multiresolution decomposition at each subband are modeled as samples from the generalized Gaussian pdf. We then obtain the joint pdf for the coefficients for all subbands, assuming independence across subbands; an approximation that simplifies the computational burden significantly without sacrificing the ability to statistically distinguish textures. We measure the difference between two textures' representative pdf's via the Kullback-Leibler divergence (KL). Species turnover, or [Formula: see text] diversity, is estimated using both this KL divergence and the difference in Shannon entropy. Additionally, we predict species richness, or [Formula: see text] diversity, based on the Shannon entropy of pixel intensity.To test our approach, we specifically use the green band of Landsat images for a water conservation area in the Florida Everglades. We validate our predictions against data of species occurrences for a twenty-eight years long period for both wet and dry seasons. Our method correctly predicts 73% of species richness. For species turnover, the newly proposed KL divergence prediction performance is near 100% accurate. This represents a significant improvement over the more conventional Shannon entropy difference, which provides 85% accuracy. Furthermore, we find that changes in soil and water patterns, as measured by fluctuations of the Shannon entropy for the red and blue bands respectively, are positively correlated with changes in vegetation. The fluctuations are smaller in the wet season when compared to the dry season.Conclusions/significanceTexture-based statistical multiresolution image analysis is a promising method for quantifying interseasonal differences and, consequently, the degree to which vegetation, soil, and water patterns vary. The proposed automated method for quantifying species richness and turnover can also provide analysis at higher spatial and temporal resolution than is currently obtainable from expensive monitoring campaigns, thus enabling more prompt, more cost effective inference and decision making support regarding anomalous variations in biodiversity. Additionally, a matrix-based visualization of the statistical multiresolution analysis is presented to facilitate both insight and quick recognition of anomalous data.

Dataset Information

Patterns of Species Richness and Turnover for the South American Rodent Fauna.

Publications

Patterns of Species Richness and Turnover for the South American Rodent Fauna.

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