Upscaling biodiversity: Estimating the species-area relationship from small samples
Upscaling biodiversity: Estimating the species-area relationship from small samples
复制标题
升级生物多样性：从小样本估计物种与面积的关系
DOI：
10.1002/ecm.1284
复制
发表时间：
2018
影响因子：
6.1
通讯作者：
Varun Varma
中科院分区：
文献类型：
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作者：
W. Kunin;J. Harte;F. He;C. Hui;R. Jobe;A. Ostling;C. Polce;A. L. Šizling;Adam B. Smith;Krister T. Smith;S. Smart;D. Storch;E. Tjørve;K. I. Ugland;W. Ulrich;Varun Varma
近年来，生物多样性升级的挑战越来越引起人们的兴趣，产生了一系列相互竞争的方法。这种方法如果成功，可能会在多尺度生物多样性评估和监测中有重要的应用。在这里，我们使用高质量的植物数据集测试了19种技术：1999年英国乡村调查，对英国景观的分层随机样本进行了详细调查。除了完整的数据集之外，还创建了一组地理和统计子集，允许在具有不同特征的多个数据集上测试每种方法。根据同期调查的国家地图集数据得出的英国植物的“真实”物种-面积关系，对模型的预测进行了检验。这代表着一种远比通常采用的更雄心勃勃的测试，需要5到10个数量级的升级。这些方法在性能上有很大差异；虽然在重点区域记录了2326个重点植物分类群，但放大的物种丰富度估计从62到11593不等。有几个模型在16个测试数据集上提供了相当可靠的结果：沈和和Ulrich和Ollik模型提供了对总物种丰富度的最可靠估计，前者提供的估计通常在真实值的10%以内。被测试的方法在估计物种-区域关系(SAR)的整体形状方面被证明不太准确；最好的单一方法是许氏的占有率等级曲线法，平均误差为20%。将总物种丰富度估计值(沈和河模型)和降尺度方法(Sizling模型)相结合的混合方法在预测SAR方面被证明比测试的任何纯尺度方法都更准确(平均相对误差15.5%)。在提升尺度的方法上仍有很大的改进空间，但我们的结果表明，现有的几种方法在粗略空间尺度上估计物种丰富度具有很高的实际应用潜力。这些方法将极大地促进对研究较少的类群和地区的生物多样性估计，以及在多个空间尺度上监测生物多样性的变化。
The challenge of biodiversity upscaling, estimating the species richness of a large area from scattered local surveys within it, has attracted increasing interest in recent years, producing a wide range of competing approaches. Such methods, if successful, could have important applications to multi‐scale biodiversity estimation and monitoring. Here we test 19 techniques using a high quality plant data set: the GB Countryside Survey 1999, detailed surveys of a stratified random sample of British landscapes. In addition to the full data set, a set of geographical and statistical subsets was created, allowing each method to be tested on multiple data sets with different characteristics. The predictions of the models were tested against the “true” species–area relationship for British plants, derived from contemporaneously surveyed national atlas data. This represents a far more ambitious test than is usually employed, requiring 5–10 orders of magnitude in upscaling. The methods differed greatly in their performance; while there are 2,326 focal plant taxa recorded in the focal region, up‐scaled species richness estimates ranged from 62 to 11,593. Several models provided reasonably reliable results across the 16 test data sets: the Shen and He and the Ulrich and Ollik models provided the most robust estimates of total species richness, with the former generally providing estimates within 10% of the true value. The methods tested proved less accurate at estimating the shape of the species–area relationship (SAR) as a whole; the best single method was Hui's Occupancy Rank Curve approach, which erred on average by <20%. A hybrid method combining a total species richness estimate (from the Shen and He model) with a downscaling approach (the Sizling model) proved more accurate in predicting the SAR (mean relative error 15.5%) than any of the pure upscaling approaches tested. There remains substantial room for improvement in upscaling methods, but our results suggest that several existing methods have a high potential for practical application to estimating species richness at coarse spatial scales. The methods should greatly facilitate biodiversity estimation in poorly studied taxa and regions, and the monitoring of biodiversity change at multiple spatial scales.