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原文連結
論文資訊
- 類型：已發表論文
- 日期：2017-07-03
摘要
Hypervolume approaches are used to quantify functional 多樣性 and quantify environmental niches for 物種 distribution modelling. Recently, Qiao et al. (2016) criticized our geometrical kernel density estimation (KDE) method for measuring hypervolumes. They used a simulation analysis to argue that the method yields high error rates and makes biased estimates of fundamental niches. Here, we show that (a) KDE output depends in useful ways on dataset size and bias, (b) other 物種 distribution modelling methods make equally stringent but different assumptions about dataset bias, (c) simulation results presented by Qiao et al. (2016) were incorrect, with revised analyses showing performance comparable to other methods, and (d) hypervolume methods are more general than KDE and have other benefits for ni
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