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Incorporating local habitat heterogeneity and productivity measures when modelling vertebrate richness

机译：在模拟脊椎动物丰富度时，结合当地栖息地的异质性和生产力措施

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摘要

Declining species richness is a global concern; however, the coarse-scale metrics used at regional or landscape levels might not accurately represent the important habitat characteristics needed to estimate species richness. Currently, there exists a lack of knowledge with regard to the spatial extent necessary to correlate remotely sensed habitat metrics to species richness and animal surveys. We provide a protocol for determining the best scale to use when merging remotely sensed habitat and animal survey data as a step towards improving estimates of vertebrate species richness on broad scales. We test the relative importance of fine-resolution habitat heterogeneity and productivity metrics at multiple spatial scales as predictors of species richness for birds, frogs and mammals using a Bayesian approach and a combination of passive monitoring technologies. Model performance was different for each taxonomic group and dependent on the scale at which habitat heterogeneity and productivity were measured. Optimal scales included a 20-m radius for bats and frogs, an 80-m radius for birds and a 180-m radius for terrestrial mammals. Our results indicate that optimal scales do exist when merging remotely sensed habitat measures with ground-based surveys, but they differ between vertebrate groups. Additionally, the selection of a measurement scale is highly influential to our understanding of the relationships between species richness and habitat characteristics.
机译：物种丰富度下降是全球关注的问题；但是，在区域或景观级别使用的粗略度量标准可能无法准确代表估算物种丰富度所需的重要栖息地特征。当前，关于将遥感栖息地指标与物种丰富度和动物调查相关联所需的空间范围尚缺乏知识。我们提供了一个协议，用于确定合并遥感栖息地和动物调查数据时使用的最佳比例，以此作为在较大范围内改进脊椎动物物种丰富度估计的一步。我们使用贝叶斯方法和被动监测技术的组合，在多个空间尺度上测试了高分辨率的栖息地异质性和生产率指标作为鸟类，青蛙和哺乳动物物种丰富度预测指标的相对重要性。每个分类组的模型性能都不同，并取决于测量栖息地异质性和生产力的规模。最佳比例包括蝙蝠和青蛙的半径为20米，鸟类为80毫米，陆生哺乳动物为180毫米。我们的结果表明，将遥感生境措施与地面调查相结合时，确实存在最佳尺度，但脊椎动物群体之间存在差异。此外，测量尺度的选择对我们对物种丰富度与栖息地特征之间关系的理解有很大的影响。

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  • 来源
    《Environmental Conservation》 |2020年第1期|7-14|共8页
  • 作者

    W Justin Cooper; William J McShea; David A Luther; Tavis Forrester;

  • 作者单位

    Smithsonian Conservation Biology Institute 1500 Remount Road Front Royal VA 22630 USA Biology Department George Mason University 4400 University Drive Fairfax VA 22030 USA;

    Smithsonian Conservation Biology Institute 1500 Remount Road Front Royal VA 22630 USA;

    Biology Department George Mason University 4400 University Drive Fairfax VA 22030 USA Smithsonian Mason School of Conservation 1500 Remount Road Front Royal VA 22630 USA;

    Smithsonian Conservation Biology Institute 1500 Remount Road Front Royal VA 22630 USA Oregon Department of Fish and Wildlife 1401 Gekeler Lane La Grande OR 97850 USA;

  • 收录信息 美国《科学引文索引》(SCI);美国《工程索引》(EI);
  • 原文格式 PDF
  • 正文语种 eng
  • 中图分类
  • 关键词

    acoustics monitoring; camera trap; habitat heterogeneity; hyperspectral; LiDAR; productivity; remote sensing; scale; species richness; vertebrate;

    机译：声学监控;相机陷阱生境异质性高光谱激光雷达生产率;遥感;规模;物种丰富度;脊椎动物;

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