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Model-Based Selection of Areas for the Restoration of Acrocephalus paludicola Habitats in NE Germany

机译:基于模型的东北德国Acrocephalus paludicola生境恢复区域选择

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The global Aquatic Warbler (Acrocephalus paludicola, Vieillot, 1817) population has suffered a major decline due to the large-scale destruction of its natural habitat (fen mires). The species is at risk of extinction, especially in NE Germany/NW Poland. In this study, we developed habitat suitability models based on satellite and environmental data to identify potential areas for habitat restoration on which further surveys and planning should be focused. To create a reliable model, we used all Aquatic Warbler presences in the study area since 1990 as well as additional potentially suitable habitats identified in the field. We combined the presence/absence regression tree algorithm Cubist with the presence-only algorithm Maxent since both commonly outperform other algorithms. To integrate the separate model results, we present a new way to create a metamodel using the initial model results as variables. Additionally, a histogram approach was applied to further reduce the final search area to the most promising sites. Accuracy increased when using both remote sensing and environmental data. It was highest for the integrated metamodel (Cohen's Kappa of 0.4, P < 0.001). The final result of this study supports the selection of the most promising sites for Aquatic Warbler habitat restoration.
机译:由于其自然栖息地的大规模破坏,全球水莺(Acrocephalus paludicola,Vieillot,1817年)的人口大幅减少。该物种有灭绝的危险,特别是在德国东北部/西北波兰。在这项研究中,我们基于卫星和环境数据开发了栖息地适应性模型,以识别可能需要进一步调查和规划的栖息地恢复的潜在区域。为了创建可靠的模型,我们使用了1990年以来研究区域中所有水生莺的栖息地,以及在野外发现的其他可能合适的栖息地。我们将在场/不在场回归树算法Cubist与仅在场算法Maxent结合在一起,因为两者通常都胜过其他算法。为了集成单独的模型结果,我们提出了一种使用初始模型结果作为变量来创建元模型的新方法。另外,使用直方图方法将最终搜索区域进一步缩小到最有希望的站点。同时使用遥感和环境数据可提高准确性。对于集成元模型,它最高(Cohen Kappa为0.4,P <0.001)。这项研究的最终结果支持选择水生莺栖息地恢复最有希望的地点。

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