The challenge of modeling niches and distributions for data-poor species: a comprehensive approach to model complexity
The challenge of modeling niches and distributions for data-poor species: a comprehensive approach to model complexity
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DOI：
10.1111/ecog.02909
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发表时间：
2018-05-01
期刊：
影响因子：
5.9
通讯作者：
Anderson, Robert P.
中科院分区：
文献类型：
--
作者：
Galante, Peter J.;Alade, Babatunde;Anderson, Robert P.
物种生态位和地理分布模型是生态学、进化学和地理学中广泛使用的工具。然而，非常常见的情况下，很少有可用的发生地点的物种提出了重大挑战，这种建模技术，特别是关于模型的复杂性和评估。在这里，我们总结了关于这些问题的领域的状态，并提供了一个工作的例子，使用Maxent技术的小型哺乳动物特有的马达加斯加（nesomyine啮齿动物Eliurus majori）。文献中存在两种相关的模型选择方法（信息标准，特别是AICc;和性能预测保留的数据，通过刀切），但AICc并不严格适用于像Maxent这样的机器学习算法。我们比较模型下选择的每种选择方法与相应的Maxent默认设置，无论有和没有空间过滤的发生记录，以减少抽样偏差的影响。两种选择方法都选择了比使用默认设置更简单的模型。此外，当考虑到抽样偏差时，这些方法收敛于类似的答案，但与未过滤的发生数据明显不同。具体而言，对于该数据集，AICc选择的模型的参数远远少于通过保留数据的性能确定的参数。根据我们对研究物种的了解，在AICc和保留数据选择下选择的模型与空间过滤相结合时表现出更高的生态可持续性。该物种的结果表明，AICc可以一致地选择参数较少的模型，并且对抽样偏差更鲁棒。为了检验这些假设并得出一般性的结论，应该对各种各样的真实的和模拟物种进行全面的研究。同时，我们建议研究人员通过信息标准和保留数据的性能来评估模型复杂性的关键但未得到充分重视的问题，比较两种方法之间的结果并考虑生态可持续性。
Models of species ecological niches and geographic distributions now represent a widely used tool in ecology, evolution, and biogeography. However, the very common situation of species with few available occurrence localities presents major challenges for such modeling techniques, in particular regarding model complexity and evaluation. Here, we summarize the state of the field regarding these issues and provide a worked example using the technique Maxent for a small mammal endemic to Madagascar (the nesomyine rodent Eliurus majori). Two relevant model-selection approaches exist in the literature (information criteria, specifically AICc; and performance predicting withheld data, via a jackknife), but AICc is not strictly applicable to machine-learning algorithms like Maxent. We compare models chosen under each selection approach with those corresponding to Maxent default settings, both with and without spatial filtering of occurrence records to reduce the effects of sampling bias. Both selection approaches chose simpler models than those made using default settings. Furthermore, the approaches converged on a similar answer when sampling bias was taken into account, but differed markedly with the unfiltered occurrence data. Specifically, for that dataset, the models selected by AICc had substantially fewer parameters than those identified by performance on withheld data. Based on our knowledge of the study species, models chosen under both AICc and withheld-data-selection showed higher ecological plausibility when combined with spatial filtering. The results for this species intimate that AICc may consistently select models with fewer parameters and be more robust to sampling bias. To test these hypotheses and reach general conclusions, comprehensive research should be undertaken with a wide variety of real and simulated species. Meanwhile, we recommend that researchers assess the critical yet underappreciated issue of model complexity both via information criteria and performance on withheld data, comparing the results between the two approaches and taking into account ecological plausibility.