McClintock Brett T
National Marine Mammal Laboratory Alaska Fisheries Science Center NOAA-NMFS 7600 Sand Point Way NE Seattle Washington 98115.
Ecol Evol. 2015 Oct 13;5(21):4920-31. doi: 10.1002/ece3.1676. eCollection 2015 Nov.
I describe an open-source R package, multimark, for estimation of survival and abundance from capture-mark-recapture data consisting of multiple "noninvasive" marks. Noninvasive marks include natural pelt or skin patterns, scars, and genetic markers that enable individual identification in lieu of physical capture. multimark provides a means for combining and jointly analyzing encounter histories from multiple noninvasive sources that otherwise cannot be reliably matched (e.g., left- and right-sided photographs of bilaterally asymmetrical individuals). The package is currently capable of fitting open population Cormack-Jolly-Seber (CJS) and closed population abundance models with up to two mark types using Bayesian Markov chain Monte Carlo (MCMC) methods. multimark can also be used for Bayesian analyses of conventional capture-recapture data consisting of a single-mark type. Some package features include (1) general model specification using formulas already familiar to most R users, (2) ability to include temporal, behavioral, age, cohort, and individual heterogeneity effects in detection and survival probabilities, (3) improved MCMC algorithm that is computationally faster and more efficient than previously proposed methods, (4) Bayesian multimodel inference using reversible jump MCMC, and (5) data simulation capabilities for power analyses and assessing model performance. I demonstrate use of multimark using left- and right-sided encounter histories for bobcats (Lynx rufus) collected from remote single-camera stations in southern California. In this example, there is evidence of a behavioral effect (i.e., trap "happy" response) that is otherwise indiscernible using conventional single-sided analyses. The package will be most useful to ecologists seeking stronger inferences by combining different sources of mark-recapture data that are difficult (or impossible) to reliably reconcile, particularly with the sparse datasets typical of rare or elusive species for which noninvasive sampling techniques are most commonly employed. Addressing deficiencies in currently available software, multimark also provides a user-friendly interface for performing Bayesian multimodel inference using capture-recapture data consisting of a single conventional mark or multiple noninvasive marks.
我介绍了一个开源的R包multimark，用于根据由多个“非侵入性”标记组成的捕获-标记-重捕数据来估计生存率和种群数量。非侵入性标记包括天然的皮毛或皮肤图案、伤疤以及能够用于个体识别而非实际捕获的基因标记。multimark提供了一种方法，用于合并和联合分析来自多个非侵入性来源的遭遇历史记录，否则这些记录无法可靠匹配（例如双侧不对称个体的左右侧照片）。该软件包目前能够使用贝叶斯马尔可夫链蒙特卡罗（MCMC）方法，拟合具有多达两种标记类型的开放种群Cormack-Jolly-Seber（CJS）模型和封闭种群数量模型。multimark还可用于对由单一标记类型组成的传统捕获-重捕数据进行贝叶斯分析。该软件包的一些功能包括：（1）使用大多数R用户已经熟悉的公式进行通用模型规范；（2）能够在检测和生存概率中纳入时间、行为、年龄、队列和个体异质性效应；（3）改进的MCMC算法，在计算上比以前提出的方法更快、更高效；（4）使用可逆跳跃MCMC进行贝叶斯多模型推断；（5）用于功效分析和评估模型性能的数据模拟功能。我展示了如何使用从南加州偏远单相机站点收集的山猫（Lynx rufus）左右侧遭遇历史记录来使用multimark。在这个例子中，有证据表明存在一种行为效应（即陷阱“偏好”反应），而使用传统的单侧分析则无法识别这种效应。对于寻求通过合并难以（或无法）可靠协调的不同捕获-重捕数据源来进行更强推断的生态学家来说，该软件包将非常有用，特别是对于那些最常采用非侵入性采样技术的稀有或难以捉摸物种的典型稀疏数据集。针对当前可用软件的不足，multimark还提供了一个用户友好的界面，用于使用由单一传统标记或多个非侵入性标记组成的捕获-重捕数据进行贝叶斯多模型推断。
