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预测一种稀有花栗鼠的分布( ):比较最大熵模型和占有率模型。

Predicting the distribution of a rare chipmunk (): comparing MaxEnt and occupancy models.

作者信息

Perkins-Taylor Ian E, Frey Jennifer K

机构信息

Department of Fish, Wildlife and Conservation Ecology, New Mexico State University, Las Cruces, NM, USA.

出版信息

J Mammal. 2020 Jun 16;101(4):1035-1048. doi: 10.1093/jmammal/gyaa057. eCollection 2020 Aug 31.

Abstract

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 () 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.

摘要

物种分布模型(SDMs)利用物种出现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地图。奥库拉山脉科罗拉多金花鼠()分布于新墨西哥州中部,由于其残遗分布和栖息地受到威胁,该物种受到了保护关注。我们之前为这个分类单元创建了一个占有率模型,但担心由于占有率建模对数据要求较高,该模型可能没有充分捕捉到影响金花鼠分布的生态因素。最大熵模型(MaxEnt)是另一种物种分布模型方法,在测试大量变量和处理小样本量方面特别有效。我们的目标是为奥库拉山脉科罗拉多金花鼠创建一个最大熵模型,并将其与我们之前为这个分类单元创建的占有率模型进行比较,以加强我们对相关生态因素的原始评估,或者识别出我们的占有率模型未捕捉到的其他因素。我们利用诱饵相机陷阱和机会性调查的出现记录创建了最大熵模型。我们使用一种新颖的方法调整模型复杂性,该方法用于调整正则化乘数和特征类参数,同时还进行变量选择。我们将最大熵模型选择的分布图和变量与我们为这个分类单元的占有率模型的结果进行了比较。最大熵模型和占有率模型选择了相似的环境变量,并且每个模型的总体出现空间模式相似。出现可能性与海拔、矮松林地植被类型以及与悬崖相关的地形变量呈正相关。最大熵模型和占有率模型之间的总体相似性增强了我们对影响金花鼠分布的生态因素的信心。我们得出结论,最大熵模型在预测稀有物种分布方面具有优势,这有助于为保护行动提供信息。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/19de/7528646/b7f1d2f2f395/gyaa057f0001.jpg

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