Stakeholder support is vital for achieving conservation success, yet there are few reliable mechanisms to monitor stakeholder attitudes towards conservation. Importantly, few approaches account for bias arising from reporting errors; that is, reporting a positive attitude towards conservation when the respondent actually does not have one (a false positive error), or not reporting a positive attitude when the respondent is positive towards conservation (a false negative error). We borrow from developments in applied conservation science to use a Bayesian hierarchical model to quantify stakeholder attitudes as the probability of having a positive attitude towards wildlife, notionally (or in abstract terms) and at localized scales. The model allows us to assess stakeholder attitudes, and factors influencing these attitudes, while accounting for false negative and false positive reporting errors. We show through simulations that this method has lower bias than naïve estimates of the proportion of respondents who are positive towards wildlife, or Likert-scores. We demonstrate the utility of the model by applying it to questionnaire surveys on Asian elephants Elephas maximus in the Kaziranga-Karbi Anglong landscape, Northeast India. After accounting for reporting errors, we estimated the probability of being positive towards elephants notionally as 0.85; at a localized scale, however, the proportion of respondents that were positive towards elephants was 50%. In comparison, without accounting for reporting errors, the proportion of respondents professing positive attitudes towards elephants in at least one of the certain questions, was 0.69 and 0.23, notionally and at local scales, respectively. False (positive and negative) reporting probabilities were consistently non-zero (0.22-0.68). We submit that regular and reliable assessment of stakeholder attitudes--combined with an understanding of factors contributing to variation in attitudes--can feed into participatory conservation monitoring programs, help assess the success of initiatives aimed at facilitating human behavioral change, and inform conservation decision-making. This article is protected by copyright. All rights reserved.

中文翻译：

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在存在报告错误的情况下量化利益相关者对保护的态度的贝叶斯分层方法

利益相关者的支持对于实现保护成功至关重要，但很少有可靠的机制来监测利益相关者对保护的态度。重要的是，很少有方法可以解释报告错误引起的偏差；也就是说，当受访者实际上没有保护态度时报告对保护的积极态度（假阳性错误），或者当受访者对保护持积极态度时不报告积极态度（假阴性错误）。我们借鉴应用保护科学的发展，使用贝叶斯分层模型将利益相关者的态度量化为对野生动物持有积极态度的可能性，概念上（或抽象术语）和局部尺度。该模型使我们能够评估利益相关者的态度以及影响这些态度的因素，同时考虑假阴性和假阳性报告错误。我们通过模拟表明，与对野生动物持积极态度的受访者比例或李克特分数的天真估计相比，这种方法具有更低的偏差。我们通过将模型应用于印度东北部 Kaziranga-Karbi Anglong 景观中的亚洲象 Elephas maximus 的问卷调查来证明该模型的实用性。在考虑报告错误后，我们估计对大象持积极态度的概率为 0.85；然而，在局部范围内，对大象持积极态度的受访者比例为 50%。相比之下，在不考虑报告错误的情况下，至少在某个特定问题中对大象表示积极态度的受访者比例分别为 0.69 和 0.23，分别在概念上和局部尺度上。错误（阳性和阴性）报告概率始终为非零 (0.22-0.68)。我们认为，对利益相关者态度的定期和可靠评估——结合对导致态度变化的因素的理解——可以提供给参与式保护监测计划，帮助评估旨在促进人类行为改变的举措的成功，并为保护决策提供信息-制作。本文受版权保护。版权所有。我们认为，对利益相关者态度的定期和可靠评估——结合对导致态度变化的因素的理解——可以提供给参与式保护监测计划，帮助评估旨在促进人类行为改变的举措的成功，并为保护决策提供信息-制作。本文受版权保护。版权所有。我们认为，对利益相关者态度的定期和可靠评估——结合对导致态度变化的因素的理解——可以提供给参与式保护监测计划，帮助评估旨在促进人类行为改变的举措的成功，并为保护决策提供信息-制作。本文受版权保护。版权所有。