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      Predictive sampling effort and species-area relationship models for estimating richness in fragmented landscapes

      1 , 2 , * , 3

      PLoS ONE

      Public Library of Science

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          Abstract

          Loss of habitat, specifically deforestation, is a major driver of biodiversity loss. Species-area relationship (SAR) models traditionally have been used for estimating species richness, species loss as a function of habitat loss, and extrapolation of richness for given areas. Sampling-species relationships (SSRs) are interrelated yet separate drivers for species richness estimates. Traditionally, however, SAR and SSR models have been used independently and not incorporated into a single approach. We developed and compared predictive models that incorporate sampling effort species-area relationships (SESARS) along the entire Atlantic Forest of South America, and then applied the best-fit model to estimate richness in forest remnants of Interior Atlantic Forest of eastern Paraguay. This framework was applied to non-volant small mammal assemblages that reflect different tolerances to forest loss and fragmentation. In order to account for differences in functionality we estimated small mammal richness of 1) the entire non-volant small mammal assemblage, including introduced species; 2) the native species forest assemblage; and 3) the forest-specialist assemblage, with the latter two assemblages being subsets of the entire assemblage. Finally, we geospatially modeled species richness for each of the three assemblages throughout eastern Paraguay to identify remnants with high species richness. We found that multiple regression power-law interaction-term models that only included area and the interactions of area and sampling as predictors, worked best for predicting species richness for the entire assemblage and the native species forest assemblage, while several traditional SAR models (logistic, power, exponential, and ratio) best described forest-specialist richness. Species richness was significantly different between assemblages. We identified obvious remnants with high species richness in eastern Paraguay, and these remnants often were geographically isolated. We also found relatively high predicted species richness (in relation to the entire range of predicted richness values) in several geographically-isolated, medium-size forest remnants that likely have not been considered as possible priority areas for conservation. These findings highlight the importance of using an empirical dataset, created using sources representing diverse sampling efforts, to develop robust predictive models. This approach is particularly important in geographic locations where field sampling is limited yet the geographic area is experiencing rapid and dramatic land cover changes. When combined, area and sampling are powerful modeling predictors for questions of biogeography, ecology, and conservation, especially when addressing habitat loss and fragmentation.

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          Most cited references 69

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          The Statistics and Biology of the Species-Area Relationship

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            The status of the world's land and marine mammals: diversity, threat, and knowledge.

            Knowledge of mammalian diversity is still surprisingly disparate, both regionally and taxonomically. Here, we present a comprehensive assessment of the conservation status and distribution of the world's mammals. Data, compiled by 1700+ experts, cover all 5487 species, including marine mammals. Global macroecological patterns are very different for land and marine species but suggest common mechanisms driving diversity and endemism across systems. Compared with land species, threat levels are higher among marine mammals, driven by different processes (accidental mortality and pollution, rather than habitat loss), and are spatially distinct (peaking in northern oceans, rather than in Southeast Asia). Marine mammals are also disproportionately poorly known. These data are made freely available to support further scientific developments and conservation action.
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              Multiple causes of high extinction risk in large mammal species.

              Many large animal species have a high risk of extinction. This is usually thought to result simply from the way that species traits associated with vulnerability, such as low reproductive rates, scale with body size. In a broad-scale analysis of extinction risk in mammals, we find two additional patterns in the size selectivity of extinction risk. First, impacts of both intrinsic and environmental factors increase sharply above a threshold body mass around 3 kilograms. Second, whereas extinction risk in smaller species is driven by environmental factors, in larger species it is driven by a combination of environmental factors and intrinsic traits. Thus, the disadvantages of large size are greater than generally recognized, and future loss of large mammal biodiversity could be far more rapid than expected.
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                Author and article information

                Contributors
                Role: ConceptualizationRole: Data curationRole: Formal analysisRole: Funding acquisitionRole: InvestigationRole: MethodologyRole: Project administrationRole: ResourcesRole: SoftwareRole: SupervisionRole: ValidationRole: Writing – original draftRole: Writing – review & editing
                Role: ConceptualizationRole: Formal analysisRole: Funding acquisitionRole: ResourcesRole: SoftwareRole: VisualizationRole: Writing – original draftRole: Writing – review & editing
                Role: Editor
                Journal
                PLoS One
                PLoS ONE
                plos
                plosone
                PLoS ONE
                Public Library of Science (San Francisco, CA USA )
                1932-6203
                31 December 2019
                2019
                : 14
                : 12
                Affiliations
                [1 ] Department of Biological Sciences, Chicago State University, Chicago, Illinois, United States of America
                [2 ] Integrative Research Center, The Field Museum of Natural History, Chicago, Illinois, United States of America
                [3 ] Department of Biology, Rhodes College, Memphis, Tennessee, United States of America
                University of Waikato, NEW ZEALAND
                Author notes

                Competing Interests: The authors have declared that no competing interests exist.

                Article
                PONE-D-19-19143
                10.1371/journal.pone.0226529
                6938349
                31891589
                © 2019 de la Sancha, Boyle

                This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

                Page count
                Figures: 3, Tables: 6, Pages: 22
                Product
                Funding
                Funded by: funder-id http://dx.doi.org/10.13039/100010629, Fulbright Association;
                Award Recipient :
                Partial financial support to NUD comes from Fulbright Fellowship; The Marshall Field Collection Fund; Grainer Bioinformatics Center at The Field Museum; Department of Biological Sciences, Texas Tech University; the Mary Rice Foundation; The Lewis and Clark Exploration Fund; and American Society of Mammalogists. Funding for the collection of the Tapytá data was provided by Rhodes College to SAB. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.
                Categories
                Research Article
                Biology and Life Sciences
                Ecology
                Ecological Metrics
                Species Diversity
                Ecology and Environmental Sciences
                Ecology
                Ecological Metrics
                Species Diversity
                Biology and Life Sciences
                Ecology
                Ecosystems
                Forests
                Ecology and Environmental Sciences
                Ecology
                Ecosystems
                Forests
                Ecology and Environmental Sciences
                Terrestrial Environments
                Forests
                Biology and Life Sciences
                Organisms
                Eukaryota
                Animals
                Vertebrates
                Amniotes
                Mammals
                Biology and Life Sciences
                Conservation Biology
                Species Extinction
                Ecology and Environmental Sciences
                Conservation Science
                Conservation Biology
                Species Extinction
                Biology and Life Sciences
                Evolutionary Biology
                Evolutionary Processes
                Species Extinction
                Research and Analysis Methods
                Mathematical and Statistical Techniques
                Statistical Methods
                Forecasting
                Physical Sciences
                Mathematics
                Statistics
                Statistical Methods
                Forecasting
                People and places
                Geographical locations
                South America
                Paraguay
                Ecology and Environmental Sciences
                Deforestation
                Biology and Life Sciences
                Ecology
                Biodiversity
                Ecology and Environmental Sciences
                Ecology
                Biodiversity
                Custom metadata
                All relevant data are within the manuscript and Supporting Information files.

                Uncategorized

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