Farwell Laura S, Elsen Paul R, Razenkova Elena, Pidgeon Anna M, Radeloff Volker C
SILVIS Lab, Department of Forest and Wildlife Ecology, University of Wisconsin-Madison, 1630 Linden Drive, Madison, Wisconsin, 53706, USA.
Wildlife Conservation Society, Bronx, New York, 10460, USA.
Ecol Appl. 2020 Dec;30(8):e02157. doi: 10.1002/eap.2157. Epub 2020 Jun 1.
Species loss is occurring globally at unprecedented rates, and effective conservation planning requires an understanding of landscape characteristics that determine biodiversity patterns. Habitat heterogeneity is an important determinant of species diversity, but is difficult to measure across large areas using field-based methods that are costly and logistically challenging. Satellite image texture analysis offers a cost-effective alternative for quantifying habitat heterogeneity across broad spatial scales. We tested the ability of texture measures derived from 30-m resolution Enhanced Vegetation Index (EVI) data to capture habitat heterogeneity and predict bird species richness across the conterminous United States. We used Landsat 8 satellite imagery from 2013-2017 to derive a suite of texture measures characterizing vegetation heterogeneity. Individual texture measures explained up to 21% of the variance in bird richness patterns in North American Breeding Bird Survey (BBS) data during the same time period. Texture measures were positively related to total breeding bird richness, but this relationship varied among forest, grassland, and shrubland habitat specialists. Multiple texture measures combined with mean EVI explained up to 41% of the variance in total bird richness, and models including EVI-based texture measures explained up to 10% more variance than those that included only EVI. Models that also incorporated topographic and land cover metrics further improved predictive performance, explaining up to 51% of the variance in total bird richness. A texture measure contributed predictive power and characterized landscape features that EVI and forest cover alone could not, even though the latter two were overall more important variables. Our results highlight the potential of texture measures for mapping habitat heterogeneity and species richness patterns across broad spatial extents, especially when used in conjunction with vegetation indices or land cover data. By generating 30-m resolution texture maps and modeling bird richness at a near-continental scale, we expand on previous applications of image texture measures for modeling biodiversity that were either limited in spatial extent or based on coarse-resolution imagery. Incorporating texture measures into broad-scale biodiversity models may advance our understanding of mechanisms underlying species richness patterns and improve predictions of species responses to rapid global change.
全球物种丧失正以前所未有的速度发生，有效的保护规划需要了解决定生物多样性模式的景观特征。栖息地异质性是物种多样性的一个重要决定因素，但使用成本高昂且在后勤方面具有挑战性的实地方法在大面积区域进行测量很困难。卫星图像纹理分析为在广泛空间尺度上量化栖息地异质性提供了一种经济高效的替代方法。我们测试了从30米分辨率增强植被指数（EVI）数据得出的纹理测量值捕捉栖息地异质性并预测美国本土鸟类物种丰富度的能力。我们使用了2013 - 2017年的陆地卫星8号卫星图像来得出一系列表征植被异质性的纹理测量值。在同一时期，单个纹理测量值解释了北美繁殖鸟类调查（BBS）数据中鸟类丰富度模式高达21%的方差。纹理测量值与繁殖鸟类总丰富度呈正相关，但这种关系在森林、草原和灌木丛生境专家中有所不同。多个纹理测量值与平均EVI相结合解释了总鸟类丰富度高达41%的方差，并且包含基于EVI的纹理测量值的模型比仅包含EVI的模型多解释了高达10%的方差。还纳入地形和土地覆盖指标的模型进一步提高了预测性能，解释了总鸟类丰富度高达51%的方差。一种纹理测量值提供了预测能力，并表征了仅靠EVI和森林覆盖无法表征的景观特征，尽管后两者总体上是更重要的变量。我们的结果凸显了纹理测量值在绘制广泛空间范围内的栖息地异质性和物种丰富度模式方面的潜力，特别是当与植被指数或土地覆盖数据结合使用时。通过生成30米分辨率的纹理图并在近大陆尺度上对鸟类丰富度进行建模，我们扩展了以往图像纹理测量值在生物多样性建模中的应用，以往的应用要么在空间范围上有限，要么基于粗分辨率图像。将纹理测量值纳入大规模生物多样性模型可能会推进我们对物种丰富度模式潜在机制的理解，并改善对物种对快速全球变化反应的预测。
