Species distribution models (SDMs) use presence records to determine the relationship between species occurrence and various environmental variables to create predictive maps describing the species' distribution. The Oscura Mountains Colorado chipmunk (Neotamias quadrivittatus oscuraensis) occurs in central New Mexico and is of conservation concern due to its relict distribution and threats to habitat. We previously created an occupancy model for this taxon, but were concerned that the model may not have adequately captured the ecological factors influencing the chipmunk's distribution because of the data hungry nature of occupancy modeling. MaxEnt is another SDM method that is particularly effective at testing large numbers of variables and handling small sample sizes. Our goal was to create a MaxEnt model for the Oscura Mountains Colorado chipmunk and to compare it with our previous occupancy model for this taxon, either to strengthen our original assessment of the relevant ecological factors or identify additional factors that were not captured by our occupancy model. We created MaxEnt models using occurrence records from baited camera traps and opportunistic surveys. We adjusted model complexity using a novel method for tuning both the regularization multiplier and feature class parameters while also performing variable selection. We compared the distribution maps and variables selected by MaxEnt to the results of our occupancy model for this taxon. The MaxEnt and occupancy models selected similar environmental variables and the overall spatial pattern of occurrence was similar for each model. Likelihood of occurrence was positively related to elevation, piñon woodland vegetation type, and topographic variables associated with escarpments. The overall similarities between the MaxEnt and occupancy models increased our confidence of the ecological factors influencing the distribution of the chipmunk. We conclude that MaxEnt offers advantages for predicting the distribution of rare species, which can help inform conservation actions.

中文翻译：

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预测稀有花栗鼠（Neotamias quadrivittatus oscuraensis）的分布：比较 MaxEnt 和占用模型

物种分布模型 (SDM) 使用存在记录来确定物种出现与各种环境变量之间的关系，以创建描述物种分布的预测图。奥斯库拉山脉科罗拉多花栗鼠（Neotamias quadrivittatus oscuraensis）生活在新墨西哥州中部，由于其遗存分布和对栖息地的威胁而受到保护。我们之前为这个分类群创建了一个占用模型，但担心该模型可能没有充分捕捉影响花栗鼠分布的生态因素，因为占用建模的数据饥渴性质。MaxEnt 是另一种 SDM 方法，它在测试大量变量和处理小样本量方面特别有效。我们的目标是为 Oscura Mountains Colorado 花栗鼠创建 MaxEnt 模型，并将其与我们之前针对该分类群的占用模型进行比较，以加强我们对相关生态因素的原始评估或识别我们的占用模型未捕获的其他因素. 我们使用来自诱饵相机陷阱和机会主义调查的发生记录创建了 MaxEnt 模型。我们使用一种新颖的方法来调整模型复杂性，以调整正则化乘数和要素类参数，同时执行变量选择。我们将 MaxEnt 选择的分布图和变量与我们对该分类群的占用模型的结果进行了比较。MaxEnt 和 occupancy 模型选择了相似的环境变量，并且每个模型的总体发生空间格局相似。发生的可能性与海拔、piñon 林地植被类型和与悬崖相关的地形变量呈正相关。MaxEnt 和占用模型之间的整体相似性增加了我们对影响花栗鼠分布的生态因素的信心。我们得出结论，MaxEnt 为预测稀有物种的分布提供了优势，这有助于为保护行动提供信息。