不完全检测、个体异质性和错误识别条件下捕获-再捕获数据的概率位模型
Probit models for capture-recapture data subject to imperfect detection, individual heterogeneity and misidentification
摘要：
随着用于动物种群的非侵入性采样技术变得越来越流行，人们对开发捕获-再捕获模型越来越感兴趣，该捕获-再捕获模型可以适应个体的不完美检测和错误识别（例如，由于基因分型错误）。然而，目前的方法不允许参数的个体变化，例如检测或存活概率。在这里，我们开发了错误识别模型的捕获-再捕获数据，可以同时考虑时间变化，行为效应和个体异质性的参数。为了方便贝叶斯推断使用我们的方法，我们扩展标准的概率回归技术，潜在的多项模型的维度和零的响应不能观察到。我们还提出了一种新的Metropolis-Hastings内Gibbs算法，使用马尔可夫链蒙特卡罗拟合这些模型。使用封闭的种群丰度模型进行说明，我们重新访问了美国密歇根州黑熊的DNA捕获-再捕获种群研究，发现由于基因分型错误以及检测概率的时间，行为和个体差异而导致错误识别的证据。我们还估计了蝾螈人口已知的大小，从实验室实验评估的有效性，通常用于两栖动物和鱼类的标记技术。我们的模型能够可靠地估计这一人群的规模，并提供了证据的个体异质性的错误识别概率，这是由于变量标记质量。我们的方法比以前提出的方法计算要求更高，但它提供了必要的灵活性，可以探索更广泛的模型套件，同时适当考虑由错误识别和不完美检测引入的不确定性。在没有错误识别的情况下，我们的概率单位公式也提供了一个方便和有效的吉布斯抽样贝叶斯分析传统的封闭人口捕获-再捕获数据。
英文摘要：
As noninvasive sampling techniques for animal populations have become more popular, there has been increasing interest in the development of capture-recapture models that can accommodate both imperfect detection and misidentification of individuals (e.g., due to genotyping error). However, current methods do not allow for individual variation in parameters, such as detection or survival probability. Here we develop misidentification models for capture-recapture data that can simultaneously account for temporal variation, behavioral effects and individual heterogeneity in parameters. To facilitate Bayesian inference using our approach, we extend standard probit regression techniques to latent multinomial models where the dimension and zeros of the response cannot be observed. We also present a novel Metropolis-Hastings within Gibbs algorithm for fitting these models using Markov chain Monte Carlo. Using closed population abundance models for illustration, we re-visit a DNA capture-recapture population study of black bears in Michigan, USA and find evidence of misidentification due to genotyping error, as well as temporal, behavioral and individual variation in detection probability. We also estimate a salamander population of known size from laboratory experiments evaluating the effectiveness of a marking technique commonly used for amphibians and fish. Our model was able to reliably estimate the size of this population and provided evidence of individual heterogeneity in misidentification probability that is attributable to variable mark quality. Our approach is more computationally demanding than previously proposed methods, but it provides the flexibility necessary for a much broader suite of models to be explored while properly accounting for uncertainty introduced by misidentification and imperfect detection. In the absence of misidentification, our probit formulation also provides a convenient and efficient Gibbs sampler for Bayesian analysis of traditional closed population capture-recapture data.
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