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

AbstractSpecies 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. MethodsWe 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 fully independent presence and absence data to calculate area under the receiver operating characteristic curve (AUC) and true positive rate (TPR). We compiled data on plant traits, including seed weight, dispersal mechanism, woody vs. non-woody from the literature and from the Kew Seed Information Database.摘要 物种分布模型（Species Distribution Models，SDMs）可用于验证生态学理论，并指导受保护物种的针对性调查。多项研究已探讨物种性状对SDMs预测精度的影响，但多数研究均为所有物种使用同一套环境预测因子，且/或未使用真正独立的数据来检验SDM精度。我们为24种受保护植物物种各构建了8个SDMs，每个版本的SDM所包含的环境预测因子各不相同。随后，我们使用完全独立的出现与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）和真阳性率（True Positive Rate，TPR），以此衡量每个SDM的精度。我们采用广义线性混合模型（Generalized Linear Mixed Models，GLMMs）检验物种性状与SDM精度之间的关联，同时控制不同预测因子集可能引入的SDM性能差异。所有性状均对一项或两项SDM精度指标产生影响。在其他条件一致的情况下，种子更轻、种子由动物传播、出现点密度更高的物种，其AUC和TPR均高于其他物种。长寿木本植物的AUC高于草本植物，但TPR更低。上述结果支持这一假说：物种-环境关联的强度受物种自身特征或其地理分布特征的影响。然而，由于每个物种均具有多重性状，且AUC与TPR所受影响存在差异，因此无法通过物种性状先验地直接判断哪些物种能够构建出实用的SDMs。多数物种均可构建出至少一个实用的SDM。因此，无论受保护植物物种具备何种性状，为其构建并测试SDMs以发现新种群均具有实际价值。 方法 我们为24种受保护植物物种各构建了8个SDMs，每个版本的SDM所包含的环境预测因子各不相同。随后，我们使用完全独立的出现与缺失数据计算AUC和TPR，以此衡量每个SDM的精度。我们从文献及邱园种子信息数据库（Kew Seed Information Database）中收集了植物性状数据，包括种子重量、传播机制、木本与非木本属性。