受威胁的欺骗山蝾螈(Plethodon nettingi)的大生境发生模式

Lester O. Dillard, K. R. Russell, W. Ford
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引用次数: 21

摘要

联邦政府威胁的欺骗山蝾螈(Plethodon nettingi;在西弗吉尼亚州东部的阿勒格尼山脉,大约有70个小而分散的地点发生了这种现象。我们使用比较建模方法来解释CMS在景观水平上的分布和栖息地关系,以及在物种范围内测量的一系列生物和非生物栖息地变量。我们收集了13个解释性宏观生境变量的数据,这些变量分别位于cms占用点(n = 180)和随机点(n = 180)。然后,我们使用先验、逻辑回归模型、信息论模型选择、分类树建模和判别函数分析来检验CMS-macrohabitat之间的关系。在logistic回归模型中,包含高程、坡向、坡度和岩性变量的模型得到了最强的经验支持,尽管包含这些变量和当前植被类型的模型也得到了有限的支持。我们的分类树和判别函数建模中的变量选择与逻辑回归结果一致。所有三种方法的共同变量表明,在海拔较高和砂岩下垫的地区,在物种范围内发现CMS的可能性增加。使用保留数据对具有经验支持的模型进行验证,结果表明三种分析方法的分类准确率均≥80%。最后,我们将所有三种方法的模型输出与GIS覆盖图联系起来,这些覆盖图预测了研究区域内CMS的占用情况。研究结果表明,在大空间尺度上测量的地球物理和生态特征可能有助于量化森林景观中蝾螈的栖息地关系,更具体地说,可以提高管理者定位和规划CMS持续存在和恢复的能力。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Macrohabitat models of occurrence for the threatened Cheat Mountain salamander, Plethodon nettingi
The federally threatened Cheat Mountain salamander (Plethodon nettingi; hereafter CMS) is known to occur at approximately 70 small, scattered sites in the Allegheny Mountains of eastern West Virginia. We used a comparative modeling approach to explain the landscape-level distribution and habitat relationships of CMS in relation to a suite of biotic and abiotic habitat variables measured across the species' range. We collected data on 13 explanatory macrohabitat variables at CMS-occupied (n = 180) and random (n = 180) sites. We then examined CMS-macrohabitat relationships using a priori, logistic regression models with information-theoretic model selection, classification tree modeling, and discriminant function analysis. Among logistic regression models, a model containing the variables elevation, aspect, slope, and lithology received the strongest empirical support, although a model containing these variables and current vegetation type also received limited support. Variable selection within our classification tree and discriminant function modeling was consistent with logistic regression results. Common variables in all three approaches indicated that the probability of finding CMS across the species' range increased in areas at higher elevations and underlain by sandstone. Validation of models with empirical support using reserved data indicated that classification accuracy was ≥80% for all three analytical methods. Finally, we linked model outputs from all three methods to GIS coverage maps that predicted CMS occupancy within the study area. Our results indicate that geophysical and ecological characteristics measured at large spatial scales may be useful for quantifying salamander habitat relationships in forested landscapes, and more specifically increase the capacity of managers to locate and plan for the continued persistence and recovery of CMS.
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