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Real-time species distribution models for conservation and management of natural resources in marine environments

机译:海洋环境中自然资源保护和管理的实时物种分布模型

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ABSTRACT: Species distribution models in marine environments typically use static variables, partly due to the limited availability of fine-resolution dynamic predictor variables and sufficiently detailed species abundance data. Our aim was to describe and quantify the dynamic coupling between the distribution of marine species (seabirds) and the natural variability of their habitat in real time through the combination of a high-resolution hydrodynamic model, aerial digital surveys and real-time species distribution modelling. We used a 2-step (delta) generalized additive model at 500 m spatial resolution for assessment and prediction of the changing patterns of wintering red-throated divers (RTDs) Gavia stellata in the outer Thames estuary, United Kingdom. Our dynamic species distribution models successfully resolved the major oscillations in the distribution of RTDs and confirmed their tight association with frontal zones where the probability of prey encounter was higher. The relative model standard errors (%) were generally below 30% in the high-density areas. Area under the curve (AUC) values indicated that the models were capable of distinguishing presence from absence about 75% of the time. The predictive power of the achieved distribution models made it possible to accurately identify areas where RTDs were concentrated. Comparisons between visual aerial and digital stills aerial surveys documented that, in spite of similar patterns, the aerial digital surveys generally recorded significantly higher densities of RTDs than the visual aerial surveys. This study demonstrates how marine distribution models with assimilation of habitat variables from a well-calibrated fine-resolution hydrodynamic model coupled with the use of digital aerial surveys can facilitate the capture of detailed associations between seabirds and their dynamic habitats.
机译:摘要:海洋环境中的物种分布模型通常使用静态变量,部分原因是高分辨率的动态预测变量的可用性有限以及足够详细的物种丰度数据。我们的目的是通过高分辨率水动力模型,航空数字调查和实时物种分布模型的结合,实时描述和量化海洋物种(海鸟)分布与其栖息地自然变异之间的动态耦合。 。我们使用两步(增量)广义加性模型在500 m的空间分辨率上评估和预测了美国泰晤士河外河口的越冬红喉潜水员(RTD) Gavia stellata 的变化模式王国。我们的动态物种分布模型成功地解决了RTD分布中的主要振荡问题,并确认了它们与被捕食可能性更高的额叶区域紧密相关。在高密度区域中,相对模型标准误差(%)通常低于30%。曲线下面积(AUC)值表明,该模型能够在大约75%的时间内区分存在与不存在。所获得的分布模型的预测能力使准确识别RTD集中的区域成为可能。视觉航空照片和数字静态照片航空测量之间的比较表明,尽管模式相似,但航空数字测量普遍记录的RTD密度明显高于视觉航空测量。这项研究表明,将具有良好校准的高分辨率水动力模型的栖息地变量同化的海洋分布模型,以及使用数字航空勘测,可以如何帮助捕获海鸟与其动态栖息地之间的详细关联。

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