Department of Forest Resource Management, 2424 Main Mall, University of British Columbia, Vancouver, British Columbia, V6T 1Z4, Canada.
SILVIS Lab, Department of Forest and Wildlife Ecology, University of Wisconsin, Madison, USA.
Sci Rep. 2018 Nov 2;8(1):16261. doi: 10.1038/s41598-018-34162-8.
Ecological regionalisations delineate areas of similar environmental conditions, ecological processes, and biotic communities, and provide a basis for systematic conservation planning and management. Most regionalisations are made based on subjective criteria, and can not be readily revised, leading to outstanding questions with respect to how to optimally develop and define them. Advances in remote sensing technology, and big data analysis approaches, provide new opportunities for regionalisations, especially in terms of productivity patterns through both photosynthesis and structural surrogates. Here we show that global terrestrial productivity dynamics can be captured by Dynamics Habitat Indices (DHIs) and we conduct a regionalisation based on the DHIs using a two-stage multivariate clustering approach. Encouragingly, the derived clusters are more homogeneous in terms of species richness of three key taxa, and of canopy height, than a conventional regionalisation. We conclude with discussing the benefits of these remotely derived clusters for biodiversity assessments and conservation. The clusters based on the DHIs explained more variance, and greater within-region homogeneity, compared to conventional regionalisations for species richness of both amphibians and mammals, and were comparable in the case of birds. Structure as defined by global tree height was also better defined by productivity driven clusters than conventional regionalisations. These results suggest that ecological regionalisations based on remotely sensed metrics have clear advantages over conventional regionalisations for certain applications, and they are also more easily updated.
生态区域划分划定了具有相似环境条件、生态过程和生物群落的区域，为系统的保护规划和管理提供了基础。大多数区域划分都是基于主观标准进行的，而且不能轻易修改，因此对于如何最佳地开发和定义这些区域划分，仍然存在一些悬而未决的问题。遥感技术和大数据分析方法的进步为区域划分提供了新的机会，特别是在通过光合作用和结构替代物来描述生产力模式方面。在这里，我们表明全球陆地生产力动态可以通过动态生境指数（DHIs）来捕捉，并且我们使用两阶段多元聚类方法基于 DHIs 进行了区域划分。令人鼓舞的是，与传统的区域划分相比，基于 DHIs 得出的聚类在三个关键分类群的物种丰富度、冠层高度方面更加同质。最后，我们讨论了这些由远程衍生的聚类对于生物多样性评估和保护的好处。与传统区域划分相比，基于 DHIs 的聚类在两栖动物和哺乳动物的物种丰富度方面解释了更多的方差，并且在鸟类的情况下，区域内的同质性更高。由全球树高定义的结构也比传统区域划分更能由生产力驱动的聚类来定义。这些结果表明，基于遥感指标的生态区域划分在某些应用中比传统区域划分具有明显的优势，而且它们也更容易更新。
