Habitat suitability estimates derived from species distribution models (SDMs) are increasingly used to guide management of threatened species. Poorly estimating species’ ranges can lead to underestimation of threatened status, undervaluing of remaining habitat and misdirection of conservation funding. We aimed to evaluate the utility of a SDM, similar to the models used to inform government regulation of habitat in our study region, in estimating the contemporary distribution of a threatened and declining species. We developed a presence‐only SDM for the endangered New Holland Mouse (Pseudomys novaehollandiae) across Victoria, Australia. We conducted extensive camera trap surveys across model‐predicted and expert‐selected areas to generate an independent data set for use in evaluating the model, determining confidence in absence data from non‐detection sites with occupancy and detectability modelling. We assessed the predictive capacity of the model at thresholds based on (1) sum of sensitivity and specificity (SSS), and (2) the lowest presence threshold (LPT; i.e. the lowest non‐zero model‐predicted habitat suitability value at which we detected the species). We detected P. novaehollandiae at 40 of 472 surveyed sites, with strong support for the species’ probable absence from non‐detection sites. Based on our post hoc optimised SSS threshold of the SDM, 25% of our detection sites were falsely predicted as non‐suitable habitat and 75% of sites predicted as suitable habitat did not contain the species at the time of our survey. One occupied site had a model‐predicted suitability value of zero, and at the LPT, 88% of sites predicted as suitable habitat did not contain the species at the time of our survey. Our findings demonstrate that application of generic SDMs in both regulatory and investment contexts should be tempered by considering their limitations and currency. Further, we recommend engaging species experts in the extrapolation and application of SDM outputs.

中文翻译：

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使用随机森林测试物种分布建模对衰落物种的效用

从物种分布模型（SDM）得出的生境适宜性估计越来越多地用于指导受威胁物种的管理。对物种范围的估计不足会导致对受威胁状况的低估，对剩余生境的低估以及对保护资金的误导。我们的目的是评估SDM的效用，该模型类似于用来为研究区域的政府栖息地监管提供信息的模型，以估算濒危物种的当代分布。我们为濒临灭绝的纽荷兰老鼠（Pseudomys novaehollandiae）遍及澳大利亚维多利亚州。我们在模型预测的区域和专家选择的区域进行了广泛的相机陷阱调查，以生成用于评估模型的独立数据集，并通过占用率和可检测性建模确定了来自非检测点的缺勤数据的可信度。我们基于（1）敏感性和特异性（SSS）的总和，以及（2）最低存在阈值（LPT；即，最低的非零模型预测的生境适宜性值）评估模型在阈值处的预测能力。检测到该物种）。我们检测到了新孢子虫在472个被调查地点中有40个受到支持，强烈支持该物种可能从非检测地点消失。根据我们对SDM的事后优化SSS阈值，在我们进行调查时，我们错误地将25％的检测点预测为不合适的栖息地，而将75％的预测为合适的栖息地的地点不包含该物种。根据模型预测，一个被占领的地点的适宜性值为零，而在LPT中，在我们进行调查时，有88％的地点被认为是合适的栖息地，其中不包含该物种。我们的发现表明，在监管和投资方面，通用SDM的应用应通过考虑其局限性和时效性来加以调整。此外，我们建议让物种专家参与SDM输出的外推和应用。