Assessment of a large number of empirical plant species niche models by elicitation of knowledge from two national experts.
Assessment of a large number of empirical plant species niche models by elicitation of knowledge from two national experts.
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通过引用两位国家专家的知识来评估大量经验植物物种生态位模型。
DOI：
10.1002/ece3.5766
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发表时间：
2019
影响因子：
2.6
通讯作者：
Smart SM
中科院分区：
文献类型：
--
作者：
Smart SM
定量模型在探索全球变化对生物多样性的影响方面发挥着越来越重要的作用。为了赢得信誉和信任，他们需要得到认可。我们展示了如何专家知识，可以用来评估大量的经验物种生态位模型构建的英国维管植物和britrete植物群。主要成果是：（a）对每个模拟物种和生态位轴组合进行评分评估，（B）对需要进一步开发的模型提供指导，（c）探索提出更复杂的模型摘要之间的权衡，这可能导致更彻底的验证，而这些需要更长的时间进行评估，（d）根据对由两位专家评价的一组随机模型的评估分数进行比较，量化专家意见的内部一致性。总体而言，专家们评估了39%的物种和生态位轴组合为“差”，61%的组合显示出“中等”（30%），“良好”（25%）和“优秀”（6%）之间的可靠性。两位专家仅在43%的情况下达成一致，对较差的模型达成了更大的共识，而对两位专家认为更好的模型则意见不一。这一低一致率表明，需要更多的专家提出可靠的评估，并更充分地了解缺乏共识的原因。虽然曲线下面积（AUC）统计数据显示，模型预测数据随机保留样本的能力通常非常好，但这些数据与专家给出的评分之间没有对应关系，AUC与物种流行率之间也没有明显的相关性。通过允许基于网络访问模型拟合来众包进一步评估是一个明显的下一步。为此，我们开发了一个在线应用程序，用于检查和评估每个小生境表面对其训练数据的拟合。
Quantitative models play an increasing role in exploring the impact of global change on biodiversity. To win credibility and trust, they need validating. We show how expert knowledge can be used to assess a large number of empirical species niche models constructed for the British vascular plant and bryophyte flora. Key outcomes were (a) scored assessments of each modeled species and niche axis combination, (b) guidance on models needing further development, (c) exploration of the trade‐off between presenting more complex model summaries, which could lead to more thorough validation, versus the longer time these take to evaluate, (d) quantification of the internal consistency of expert opinion based on comparison of assessment scores made on a random subset of models evaluated by both experts. Overall, the experts assessed 39% of species and niche axis combinations to be “poor” and 61% to show a degree of reliability split between “moderate” (30%), “good” (25%), and “excellent” (6%). The two experts agreed in only 43% of cases, reaching greater consensus about poorer models and disagreeing most about models rated as better by either expert. This low agreement rate suggests that a greater number of experts is required to produce reliable assessments and to more fully understand the reasons underlying lack of consensus. While area under curve (AUC) statistics showed generally very good ability of the models to predict random hold‐out samples of the data, there was no correspondence between these and the scores given by the experts and no apparent correlation between AUC and species prevalence. Crowd‐sourcing further assessments by allowing web‐based access to model fits is an obvious next step. To this end, we developed an online application for inspecting and evaluating the fit of each niche surface to its training data.