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• W2792330247 abstract "Mitigating the impacts of global anthropogenic change on species is conservation's greatest challenge. Forecasting the effects of actions to mitigate threats is hampered by incomplete information on species' responses. We develop an approach to predict community restructuring under threat management, which combines models of responses to threats with network analyses of species co-occurrence. We discover that contributions by species to network co-occurrence predict their recovery under reduction of multiple threats. Highly connected species are likely to benefit more from threat management than poorly connected species. Importantly, we show that information from a few species on co-occurrence and expected responses to alternative threat management actions can be used to train a response model for an entire community. We use a unique management dataset for a threatened bird community to validate our predictions and, in doing so, demonstrate positive feedbacks in occurrence and co-occurrence resulting from shared threat management responses during ecosystem recovery." @default.
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• W2792330247 date "2018-02-05" @default.
• W2792330247 modified "2023-10-13" @default.
• W2792330247 title "Species co-occurrence analysis predicts management outcomes for multiple threats" @default.
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• W2792330247 doi "https://doi.org/10.1038/s41559-017-0457-3" @default.
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