Unusual data in conservation science: searching for validation
Unusual data in conservation science: searching for validation
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保护科学中的异常数据：寻找验证
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发表时间：
2013
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通讯作者：
A. Keane
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作者：
A. Keane
保护本质上是多学科的(Sandbrook等人，2013年)，在有限的资源下运作(Waldron等人，2013年)，并越来越认识到行动需要有坚实的经验证据作为基础(Pullin&Knight，2009年)。总而言之，这些特点意味着我们领域的研究利用了异常广泛的数据源。保护已经包含了来自生物科学和社会科学的研究技术，我们不断寻找新的方法，我们可以适应我们的目的，以及新的方式来利用在正式科学框架外收集的数据。例如，最近的研究使用了专门的采访技术(旨在减少因不回答和“告诉采访者他们想听到的话”而产生的偏见)来检查野生动物偷猎和其他敏感、非法行为的模式(St John等人，2012年；Nuno等人，2013年)；对机会主义收集的数据进行分析(例如，业余鸟类学家的记录；Beale等人，2013年)和“公民科学”倡议正在为以前不可能在背景和规模上进行研究开辟令人兴奋的新途径(Dickinson等人，2012年)；当地生态知识被宣传为关于种群数量和趋势的信息来源(Anadón等人，2009年)。保护中使用的方法和数据类型的绝对多样性使其成为最吸引人的科学领域之一。“不同寻常”的数据来源具有产生有价值的新见解的巨大潜力，但它们也带来了重大挑战：收集这些数据是一回事，但学会欣赏它们可以和不能告诉我们的则完全是另一回事。为了最大限度地利用这些方法的好处并避免误解的危险，保护研究的许多领域中的一个共同挑战是了解不同的数据源提供了多少信息，并发现它们可能受到的偏见。简而言之，有希望的信息来源只有在得到适当验证的情况下才是真正有用的。Golden，Wrangham&Brashares(2013)做了一项令人钦佩的工作，深入研究了这样一个不同寻常的数据来源的复杂性：马达加斯加偏远地区对丛林肉类物种的回顾消费。在各种保护背景下，召回数据被视为有用的信息来源，既用于快速评估当前的威胁(Jones等人，2008年)，也允许评估历史趋势(Turvey等人，2012年)。为了检查召回数据的属性，Golden等人。收集了两个关于几种野生动物家庭消费水平的可比数据集。每个家庭的女户主被要求每天使用消费日记保持详细的记录，而同一家庭的男户主被要求回忆更长时间的消费，时间长达一个月或一年。然后，这些日记被用作衡量“真相”的指标，用来评估较长期的回忆。这种方法允许Gold等人。(2013)为召回数据的使用提供有用的实用指导，包括一些令人惊讶的结果。虽然较短时间的回忆可能较少受到遗忘的影响，但一个重要的发现是，当消费随着时间的推移存在显著变化时，与基于每月回忆的推断相比，年度回忆提供的情况似乎没有那么有偏见。令人欣慰的是，每年的回忆与日常日记之间的相关性很好，这表明回忆数据可能是一个有价值的信息来源。戈登等人。(2013)还观察到家庭之间年度回忆准确性的差异相对较小，但考虑到样本相对较少，来自单一社区，这或许并不令人惊讶。更广泛的比较将有助于确定这一发现是否更普遍地适用。这项研究还提出了一些有价值的新问题。预计召回数据中的一个重要偏差源于被召回物品的突出程度的差异(即它们突出的程度)。如果有人问我去年吃了什么，我想我会比其他人更容易地回忆起我特别喜欢或不喜欢的东西，或者可能在某个特殊场合吃过的东西。戈登等人。假设显着性仅仅是稀有性的函数，但Reis和Judd(2000)认为，在强度、情绪性、非同寻常或个人重要性方面，那些有特色的事件往往更有影响力。发现是否可以使用特定bs_bs_banner的显著程度的简单测量来校准召回数据将是一件很有趣的事情
Conservation is inherently multidisciplinary (Sandbrook et al., 2013), operates with restricted resources (Waldron et al., 2013) and increasingly recognizes the need for actions to be underpinned by solid, empirical evidence (Pullin & Knight, 2009). Together, these characteristics mean that research in our field makes use of an unusually broad array of data sources. Conservation already embraces research techniques from both the biological and social sciences, and we continually look for new methodologies that we can adapt for our purposes and new ways to make use of data collected outside of formal scientific frameworks. For example, recent studies have used specialized interview techniques (designed to reduce biases arising from non-response and ‘telling the interviewer what they want to hear’) to examine patterns of wildlife poaching and other sensitive, illegal behaviours (St John et al., 2012; Nuno et al., 2013); analyses of opportunistically collected data (e.g. the records of amateur ornithologists; Beale et al., 2013) and ‘citizen science’ initiatives are opening exciting new avenues for research in contexts and at scales that were not previously possible (Dickinson et al., 2012); and local ecological knowledge is promoted as a source of information on the abundance of and trends in populations (Anadón et al., 2009). The sheer diversity of approaches and data types used in conservation make it one of the most fascinating areas of science to work in. ‘Unusual’ sources of data have huge potential to produce valuable new insights, but they also present significant challenges: collecting them is one thing, but learning to appreciate what they can and cannot tell us is quite another. To maximize the benefits of these approaches and to avoid the dangers of misinterpretation, a common challenge in many areas of conservation research is to learn how much information different data sources provide and to discover the biases that they might be subject to. Put simply, promising sources of information are only truly useful if they are appropriately validated. Golden, Wrangham & Brashares (2013) do an admirable job of delving into the complexities of one such unusual data source: recalled consumption of bushmeat species in a remote area of Madagascar. Recall data are seen as a useful source of information in a variety of conservation contexts, both for rapid appraisal of current threats (Jones et al., 2008) and also to allow historical trends to be evaluated (Turvey et al., 2012). To examine the properties of recall data, Golden et al. collected two comparable data sets about householdlevel consumption of several species of wildlife. The female heads of each household were asked to keep detailed records on a daily basis using consumption diaries, while the male heads of the same households were asked for their recall of consumption over longer periods of 1 month or a year. The diaries were then used as a measure of ‘truth’ against which the longer-term recall could be evaluated. This approach allows Golden et al. (2013) to provide useful practical guidance for the use of recall data, including some surprising results. While recall over shorter periods is likely to suffer less from forgetting, an important finding is that when there is significant variability in consumption over time, annual recall appears to provide a less biased picture than extrapolations based on monthly recall. Reassuringly, the correlation between annual recall and the daily diaries was found to be good, suggesting that recall data can be a valuable source of information. Golden et al. (2013) also observed relatively low levels of variation in the accuracy of annual recall between households, but this is perhaps not surprising given the relatively small sample, drawn from a single community. Wider comparisons would be useful to determine whether this finding holds more generally. This study also suggests some valuable new questions. An important bias to be expected in recall data stems from differences in the salience of items being recalled (i.e. the degree to which they stand out). If someone asked me to remember what I had eaten over the last year, I imagine I would recall things that I had particularly liked or disliked, or perhaps had eaten during a special occasion, more readily than others. Golden et al. assume that salience is simply a function of rarity, but Reis & Judd (2000) argue that ‘[m]ore distinctive events in terms of intensity, emotionality, unusualness or personal significance, tend to be more influential’. It would be fascinating to discover whether recall data could be calibrated using simple measures of the salience of particular bs_bs_banner