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首页> 外文期刊>Conservation Biology >Use of Spatial Capture-Recapture Modeling and DNA Data to Estimate Densities of Elusive Animals
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Use of Spatial Capture-Recapture Modeling and DNA Data to Estimate Densities of Elusive Animals

机译：使用空间捕获-捕获模型和DNA数据来估计难以捉摸的动物的密度

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Abstract: Assessment of abundance, survival, recruitment rates, and density (i.e., population assessment) is especially challenging for elusive species most in need of protection (e.g., rare carnivores). Individual identification methods, such as DNA sampling, provide ways of studying such species efficiently and noninvasively. Additionally, statistical methods that correct for undetected animals and account for locations where animals are captured are available to efficiently estimate density and other demographic parameters. We collected hair samples of European wildcat (Felis silvestris) from cheek-rub lure sticks, extracted DNA from the samples, and identified each animals’ genotype. To estimate the density of wildcats, we used Bayesian inference in a spatial capture-recapture model. We used WinBUGS to fit a model that accounted for differences in detection probability among individuals and seasons and between two lure arrays. We detected 21 individual wildcats (including possible hybrids) 47 times. Wildcat density was estimated at 0.29/km2 (SE 0.06), and 95% of the activity of wildcats was estimated to occur within 1.83 km from their home-range center. Lures located systematically were associated with a greater number of detections than lures placed in a cell on the basis of expert opinion. Detection probability of individual cats was greatest in late March. Our model is a generalized linear mixed model; hence, it can be easily extended, for instance, to incorporate trap- and individual-level covariates. We believe that the combined use of noninvasive sampling techniques and spatial capture-recapture models will improve population assessments, especially for rare and elusive animals.
机译：摘要：对于最需要保护的难以捉摸的物种（例如稀有食肉动物），评估丰度，存活率，募集率和密度（即种群评估）尤其具有挑战性。个体识别方法（例如DNA采样）提供了有效且无创地研究此类物种的方法。此外，可以使用校正未发现的动物并说明捕获动物的位置的统计方法来有效地估计密度和其他人口统计参数。我们从脸颊诱饵棒中收集了欧洲野猫（Felis silvestris）的毛发样本，从样本中提取了DNA，并确定了每只动物的基因型。为了估计野猫的密度，我们在空间捕获-捕获模型中使用了贝叶斯推断。我们使用WinBUGS拟合了一个模型，该模型考虑了个体和季节之间以及两个诱饵阵列之间的检测概率差异。我们检测到21只野猫（包括可能的杂种）47次。野猫密度估计为0.29 / km 2 （SE 0.06），据估计，野猫活动的95％发生在距他们家乡中心1.83公里内。根据专家意见，系统放置的诱饵比放置在牢房中的诱饵具有更多的检测结果。三月下旬，个别猫的检测概率最大。我们的模型是广义线性混合模型。因此，它可以很容易地扩展，例如合并陷井级和个体级协变量。我们认为，无创采样技术与空间捕获-捕获模型的结合使用将改善种群评估，尤其是对稀有和难以捉摸的动物。

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