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      Modelling the distribution of Mustela nivalis and M. putorius in the Azores archipelago based on native and introduced ranges

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          Abstract

          The aims of this study were to predict the potential distribution of two introduced Mustelidae, Mustela nivalis and M. putorius in the Azores archipelago (Portugal), and evaluate the relative contribution of environmental factors from native and introduced ranges to predict species distribution ranges in oceanic islands. We developed two sets of Species Distribution Models using MaxEnt and distribution data from the native and introduced ranges of the species to project their potential distribution in the archipelago. We found differences in the predicted distributions for the models based on introduced and on native occurrences for both species, with different most important variables being selected. Climatic variables were most important for the introduced range models, while other groups of variables (i.e., human-disturbance) were included in the native-based models. Most of the islands of the Azorean archipelago were predicted to have suitable habitat for both species, even when not yet occupied. Our results showed that predicting the invaded range based on introduced range environmental conditions predicted a narrower range. These results highlight the difficulty to transfer models from native to introduced ranges across taxonomically related species, making it difficult to predict future invasions and range expansion.

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          Selecting pseudo-absences for species distribution models: how, where and how many?

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            Sample selection bias and presence-only distribution models: implications for background and pseudo-absence data.

            Most methods for modeling species distributions from occurrence records require additional data representing the range of environmental conditions in the modeled region. These data, called background or pseudo-absence data, are usually drawn at random from the entire region, whereas occurrence collection is often spatially biased toward easily accessed areas. Since the spatial bias generally results in environmental bias, the difference between occurrence collection and background sampling may lead to inaccurate models. To correct the estimation, we propose choosing background data with the same bias as occurrence data. We investigate theoretical and practical implications of this approach. Accurate information about spatial bias is usually lacking, so explicit biased sampling of background sites may not be possible. However, it is likely that an entire target group of species observed by similar methods will share similar bias. We therefore explore the use of all occurrences within a target group as biased background data. We compare model performance using target-group background and randomly sampled background on a comprehensive collection of data for 226 species from diverse regions of the world. We find that target-group background improves average performance for all the modeling methods we consider, with the choice of background data having as large an effect on predictive performance as the choice of modeling method. The performance improvement due to target-group background is greatest when there is strong bias in the target-group presence records. Our approach applies to regression-based modeling methods that have been adapted for use with occurrence data, such as generalized linear or additive models and boosted regression trees, and to Maxent, a probability density estimation method. We argue that increased awareness of the implications of spatial bias in surveys, and possible modeling remedies, will substantially improve predictions of species distributions.
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              A balanced view of scale in spatial statistical analysis

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                Author and article information

                Contributors
                Role: ConceptualizationRole: Formal analysisRole: InvestigationRole: MethodologyRole: SoftwareRole: VisualizationRole: Writing – original draftRole: Writing – review & editing
                Role: InvestigationRole: MethodologyRole: SoftwareRole: SupervisionRole: Visualization
                Role: ResourcesRole: SupervisionRole: Writing – review & editing
                Role: ConceptualizationRole: SupervisionRole: Writing – review & editing
                Role: ConceptualizationRole: Writing – review & editing
                Role: ConceptualizationRole: SupervisionRole: 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
                7 August 2020
                2020
                : 15
                : 8
                Affiliations
                [1 ] cE3c - Centre for Ecology, Evolution and Environmental Changes / Azorean Biodiversity Group, Faculty of Agriculture and Environment, Department of Environmental Sciences and Engineering, University dos Azores, Azores, Portugal
                [2 ] Department of Cellular Biology and Ecology, University of Santiago de Compostela, Santiago, Spain
                [3 ] cE3c –Centre for Ecology, Evolution and Environmental Changes, Faculty of Sciences, University of Lisbon, Lisbon, Portugal
                [4 ] cE3c –Centre for Ecology, Evolution and Environmental Changes / Azorean Biodiversity Group and University of Azores, Azores, Portugal
                [5 ] University Research Priority Program in Global Change and Biodiversity, University of Zürich, Zürich, Switzerland
                [6 ] Department of Geography, University of Zürich, Zürich, Switzerland
                University of Minnesota, UNITED STATES
                Author notes

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

                Article
                PONE-D-19-33731
                10.1371/journal.pone.0237216
                7413552
                32764786
                © 2020 Lamelas-López et al

                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: 4, Pages: 19
                Product
                Funding
                Funded by: Fundação para a Ciência e Tecnologia (FCT)
                Award ID: SFRH/BD/115022/2016
                Award Recipient :
                Funded by: Fundação para a Ciência e Tecnologia (FCT)
                Award ID: UID/BIA/00329/2019
                Award Recipient :
                Funded by: Fundação para a Ciência e Tecnologia (FCT)
                Award ID: SFRH/BPD/102804/2014
                Award Recipient :
                LLL (SFRH/BD/115022/2016) and PAVB and MSR (UID/BIA/00329/2019) and IAR (SFRH/BPD/102804/2014) were supported by the Fundação para a Ciência e Tecnologia - FCT. MJS was supported by the University Research Priority Program in Global Change and Biodiversity from the University of Zürich.
                Categories
                Research Article
                Ecology and Environmental Sciences
                Species Colonization
                Invasive Species
                Earth Sciences
                Geomorphology
                Topography
                Landforms
                Islands
                Biology and Life Sciences
                Ecology
                Ecosystems
                Forests
                Ecology and Environmental Sciences
                Ecology
                Ecosystems
                Forests
                Ecology and Environmental Sciences
                Terrestrial Environments
                Forests
                People and Places
                Geographical Locations
                Europe
                Biology and Life Sciences
                Organisms
                Eukaryota
                Animals
                Vertebrates
                Amniotes
                Mammals
                Biology and Life Sciences
                Zoology
                Animals
                Vertebrates
                Amniotes
                Mammals
                Biology and Life Sciences
                Ecology
                Biodiversity
                Ecology and Environmental Sciences
                Ecology
                Biodiversity
                Biology and Life Sciences
                Ecology
                Community Ecology
                Trophic Interactions
                Predation
                Ecology and Environmental Sciences
                Ecology
                Community Ecology
                Trophic Interactions
                Predation
                Biology and Life Sciences
                Organisms
                Eukaryota
                Animals
                Vertebrates
                Amniotes
                Birds
                Seabirds
                Biology and Life Sciences
                Zoology
                Animals
                Vertebrates
                Amniotes
                Birds
                Seabirds
                Custom metadata
                All relevant data are within the manuscript and its Supporting Information files.

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