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• W2970890648 abstract "Abstract Despite much discussion about the utility of remote sensing for effective conservation, the inclusion of these technologies in species recovery plans remains largely anecdotal. We developed a modeling approach for the integration of local, spatially measured ecosystem functional dynamics into a species distribution modeling (SDM) framework in which other ecologically relevant factors are modeled separately at broad scales. To illustrate the approach, we incorporated intraseasonal water‐vegetation dynamics into a cross‐scale SDM for the Common Snipe ( Gallinago gallinago ), which is highly dependent on water and vegetation dynamics. The Common Snipe is an Iberian grassland waterbird characteristic of European agricultural meadows and a member of one of the most threatened bird guilds. The intraseasonal dynamics of water content of vegetation were measured using the standard deviation of the normalized difference water index time series computed from bimonthly images of the Sentinel‐2 satellite. The recovery plan for the Common Snipe in Galicia (northwestern Iberian Peninsula) provided an opportunity to apply our modeling framework. Model accuracy in predicting the species’ distribution at a regional scale (resulting from integration of downscaled climate projections with regional habitat–topographic suitability models) was very high (area under the curve [AUC] of 0.981 and Boyce's index of 0.971). Local water‐vegetation dynamic models, based exclusively on Sentinel‐2 imagery, were good predictors (AUC of 0.849 and Boyce's index of 0.976). The predictive power improved (AUC of 0.92 and Boyce's index of 0.98) when local model predictions were restricted to areas identified by the continental and regional models as priorities for conservation. Our models also performed well (AUC of 0.90 and Boyce's index of 0.93) when projected to updated water‐vegetation conditions. Our modeling framework enabled incorporation of key ecosystem processes closely related to water and carbon cycles while accounting for other factors ecologically relevant to endangered grassland waterbirds across different scales, allowed identification of priority areas for conservation, and provided an opportunity for cost‐effective recovery planning by monitoring management effectiveness from space." @default.
• W2970890648 created "2019-09-05" @default.
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• W2970890648 date "2019-10-24" @default.
• W2970890648 modified "2023-09-27" @default.
• W2970890648 title "Integrating intraseasonal grassland dynamics in cross‐scale distribution modeling to support waterbird recovery plans" @default.
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• W2970890648 doi "https://doi.org/10.1111/cobi.13415" @default.
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• W2970890648 hasPublicationYear "2019" @default.
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