McCune Jenny L, Rosner-Katz Hanna, Bennett Joseph R, Schuster Richard, Kharouba Heather M
Geomatics and Landscape Ecology Research Laboratory Department of Biology Carleton University Ottawa ON Canada.
Present address: Department of Biological Sciences University of Lethbridge Lethbridge AB Canada.
Ecol Evol. 2020 May 12;10(11):5001-5014. doi: 10.1002/ece3.6254. eCollection 2020 Jun.
Species distribution models (SDMs) are used to test ecological theory and to direct targeted surveys for species of conservation concern. Several studies have tested for an influence of species traits on the predictive accuracy of SDMs. However, most used the same set of environmental predictors for all species and/or did not use truly independent data to test SDM accuracy. We built eight SDMs for each of 24 plant species of conservation concern, varying the environmental predictors included in each SDM version. We then measured the accuracy of each SDM using independent presence and absence data to calculate area under the receiver operating characteristic curve (AUC) and true positive rate (TPR). We used generalized linear mixed models to test for a relationship between species traits and SDM accuracy, while accounting for variation in SDM performance that might be introduced by different predictor sets. All traits affected one or both SDM accuracy measures. Species with lighter seeds, animal-dispersed seeds, and a higher density of occurrences had higher AUC and TPR than other species, all else being equal. Long-lived woody species had higher AUC than herbaceous species, but lower TPR. These results support the hypothesis that the strength of species-environment correlations is affected by characteristics of species or their geographic distributions. However, because each species has multiple traits, and because AUC and TPR can be affected differently, there is no straightforward way to determine a priori which species will yield useful SDMs based on their traits. Most species yielded at least one useful SDM. Therefore, it is worthwhile to build and test SDMs for the purpose of finding new populations of plant species of conservation concern, regardless of these species' traits.
物种分布模型（SDMs）用于检验生态理论，并指导针对具有保护意义的物种进行有针对性的调查。多项研究检验了物种特征对物种分布模型预测准确性的影响。然而，大多数研究对所有物种使用相同的一组环境预测变量，和/或未使用真正独立的数据来检验物种分布模型的准确性。我们为24种具有保护意义的植物物种分别构建了8个物种分布模型，每个模型版本中包含的环境预测变量各不相同。然后，我们使用独立的存在和缺失数据来测量每个物种分布模型的准确性，以计算受试者工作特征曲线下面积（AUC）和真阳性率（TPR）。我们使用广义线性混合模型来检验物种特征与物种分布模型准确性之间的关系，同时考虑不同预测变量集可能引入的物种分布模型性能差异。所有特征都影响了一种或两种物种分布模型准确性指标。在其他条件相同的情况下，种子较轻、靠动物传播种子且出现密度较高的物种，其AUC和TPR高于其他物种。长寿木本物种的AUC高于草本物种，但TPR较低。这些结果支持了以下假设：物种与环境的相关性强度受物种特征或其地理分布特征的影响。然而，由于每个物种都有多个特征，并且由于AUC和TPR可能受到不同的影响，因此没有直接的方法可以根据物种特征先验地确定哪些物种将产生有用的物种分布模型。大多数物种至少产生了一个有用的物种分布模型。因此，无论这些植物物种的特征如何，为了寻找具有保护意义的植物物种的新种群而构建和检验物种分布模型都是值得的。
