Data from: Do traits of plant species predict the efficacy of species distribution models for finding new occurrences?

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（Species Distribution Models, SDMs）可用于验证生态学理论，并为受保护关切物种开展针对性调查提供指引。已有多项研究探讨了物种性状对物种分布模型预测精度的影响。然而，绝大多数研究均为所有物种采用了同一套环境变量，且/或未使用真正独立的数据来检验物种分布模型的精度。本研究为24种受保护关切的植物物种各构建了8个物种分布模型，每个模型版本所纳入的环境变量均有所差异。随后，本研究利用独立的存在-缺失数据计算受试者工作特征曲线下面积（Area Under the Receiver Operating Characteristic Curve, AUC）与真阳性率（True Positive Rate, TPR），以此评估每个物种分布模型的精度。本研究采用广义线性混合模型检验物种性状与物种分布模型精度之间的关联，同时控制由不同环境变量集所引入的模型性能差异。所有被测物种性状均对至少一项物种分布模型精度指标产生了影响。在其他条件一致的情况下，种子更轻、种子由动物传播以及出现点密度更高的物种，其模型的AUC与TPR值均高于其他物种。长寿木本植物的模型AUC值高于草本植物，但TPR值更低。上述结果支持“物种-环境关联的强度受物种自身特征或其地理分布格局影响”这一假说。然而，由于每个物种兼具多重性状，且AUC与TPR所受影响存在差异，因此无法通过物种性状直接先验地判断哪些物种能够构建出具有实用价值的物种分布模型。绝大多数被测物种均可构建出至少一个具有实用价值的物种分布模型。因此，无论受保护关切的植物物种具备何种性状，为其构建并检验物种分布模型以发现新的种群，均具有重要价值。