As geographic range estimates for the IUCN Red List guide conservation actions, accuracy and ecological realism are crucial. IUCN’s extent of occurrence (EOO) is the general region including the species’ range, while area of occupancy (AOO) is the subset of EOO occupied by the species. Data‐poor species with incomplete sampling present particular difficulties, but species distribution models (SDMs) can be used to predict suitable areas. Nevertheless, SDMs typically employ abiotic variables (i.e., climate) and do not explicitly account for biotic interactions that can impose range constraints. We sought to improve range estimates for data‐poor, parapatric species by masking out areas under inferred competitive exclusion. We did so for two South American spiny pocket mice: Heteromys australis (Least Concern) and Heteromys teleus (Vulnerable due to especially poor sampling), whose ranges appear restricted by competition. For both species, we estimated EOO using SDMs and AOO with four approaches: occupied grid cells, abiotic SDM prediction, and this prediction masked by approximations of the areas occupied by each species’ congener. We made the masks using support vector machines (SVMs) fit with two data types: occurrence coordinates alone; and coordinates along with SDM predictions of suitability. Given the uncertainty in calculating AOO for low‐data species, we made estimates for the lower and upper bounds for AOO, but only make recommendations for H. teleus as its full known range was considered. The SVM approaches (especially the second one) had lower classification error and made more ecologically realistic delineations of the contact zone. For H. teleus, the lower AOO bound (a strongly biased underestimate) corresponded to Endangered (occupied grid cells), while the upper bounds (other approaches) led to Near Threatened. As we currently lack data to determine the species’ true occupancy within the post‐processed SDM prediction, we recommend that an updated listing for H. teleus include these bounds for AOO. This study advances methods for estimating the upper bound of AOO and highlights the need for better ways to produce unbiased estimates of lower bounds. More generally, the SVM approaches for post‐processing SDM predictions hold promise for improving range estimates for other uses in biogeography and conservation.

中文翻译：

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使用分布模型和支持向量机改进旁域物种的占用面积估计

由于 IUCN 红色名录的地理范围估计指导保护行动，准确性和生态现实主义至关重要。IUCN的出现范围（EOO）是包括物种分布范围的一般区域，而占有面积（AOO）是物种占据的EOO的子集。数据匮乏且采样不完整的物种特别困难，但物种分布模型 (SDM) 可用于预测合适的区域。然而，SDM 通常使用非生物变量（即气候），并且没有明确考虑可能施加范围限制的生物相互作用。我们试图通过掩盖推断出的竞争排斥区域来改进对数据贫乏的、近亲物种的范围估计。我们为两只南美多刺袖珍老鼠这样做：Heteromys australis（最不关心）和Heteromys teleus（由于采样特别差而易受攻击），其范围似乎受到竞争的限制。对于这两个物种，我们使用 SDM 和 AOO 以四种方法估计 EOO：占用网格单元、非生物 SDM 预测，以及该预测被每个物种同源占据的区域的近似值所掩盖。我们使用支持向量机 (SVM) 制作了适合两种数据类型的掩码：仅出现坐标；并与 SDM 的适用性预测一起进行协调。鉴于计算低数据物种 AOO 的不确定性，我们对 AOO 的下限和上限进行了估计，但仅对H. teleus提出建议因为考虑了它的全部已知范围。SVM 方法（尤其是第二种方法）具有较低的分类误差，并且对接触区进行了更生态现实的描绘。对于H. teleus，AOO 下限（强烈偏向的低估）对应于濒危（被占用的网格单元），而上限（其他方法）导致近危。由于我们目前缺乏数据来确定该物种在后处理 SDM 预测中的真实占有率，我们建议更新H. teleus列表包括 AOO 的这些界限。本研究提出了估计 AOO 上限的方法，并强调需要更好的方法来产生无偏估计的下限。更一般地说，用于后处理 SDM 预测的 SVM 方法有望改善生物地理学和保护中其他用途的范围估计。