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Assessment of a large number of empirical plant species niche models by elicitation of knowledge from two national experts

机译:通过两位国家专家的知识启发评估了大量的经验植物物种生态位模型

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摘要

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.
机译:定量模型在探索全球变化对生物多样性的影响方面起着越来越重要的作用。为了赢得信誉和信任,他们需要进行验证。我们将展示如何将专家知识用于评估为英国维管植物和苔藓植物区系构建的大量经验物种生态位模型。主要成果是(a)对每个建模物种和生态位轴组合进行评分评估,(b)需要进一步开发的模型的指导,(c)探索提出更复杂的模型摘要之间的取舍,这可能导致更彻底的验证,而不是用更长的时间进行评估;(d)基于对两位专家评估的随机模型子集的评估得分的比较,量化专家意见的内部一致性。总体而言,专家们评估了39%的物种和小生境轴组合为“差”和61%,以显示出在“中度”(30%),“良好”(25%)和“优秀”之间划分的可靠性程度( 6%)。两位专家仅在43%的案例中表示同意,就较差的模型达成了更大的共识,而对任何一位专家认为更好的模型都持不同意见。较低的一致率表明需要更多的专家来进行可靠的评估,并更充分地理解缺乏共识的原因。虽然曲线下面积(AUC)统计数据通常显示出模型具有很好的预测数据的随机保留样本的能力,但这些与专家给出的分数之间没有对应关系,AUC与物种流行率之间也没有明显的相关性。通过允许基于网络的模型拟合访问,众包进一步评估是显而易见的下一步。为此,我们开发了一个在线应用程序,用于检查和评估每个利基表面与其训练数据的拟合度。

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