当前位置: X-MOL 学术Ecol Modell › 论文详情
Our official English website, www.x-mol.net, welcomes your feedback! (Note: you will need to create a separate account there.)
Improving species distribution model predictive accuracy using species abundance: Application with boosted regression trees
Ecological Modelling ( IF 2.6 ) Pub Date : 2020-09-01 , DOI: 10.1016/j.ecolmodel.2020.109202
Hao Yu , Arthur R. Cooper , Dana M. Infante

Abstract Auxiliary information in the form of species abundance is frequently available as part of data collected for ecological investigations, yet when modeling distributions of species over large regions, species presence (and sometimes absence) are typically used. Incorporating abundances into species distribution models may greatly improve model predictive accuracy in practice. Boosted regression trees (BRT) models have been widely used in species distribution modeling, however no ecological study has been conducted to date that has assessed the predictive accuracy of BRT models that incorporates species abundance weights. We compared traditional, unweighted BRTs with species abundance-weighted BRTs for 55 fluvial fish species native to the Northeastern U.S. Overall model deviance explained and six diagnostic measures of predictive performance were compared between traditional BRTs and weighted BRTs. These comparisons indicated that unweighted BRTs performed better for fluvial fish species considered common, including those with greater numbers of presences and higher prevalence. Conversely, weighted BRTs were better suited for modeling distributions of species that had fewer presences, lower prevalence, and higher rarity, indicating the potential of species abundance-weighted distribution modeling to improve results for species of high conservation importance. Last, we offer insights into the applicability of using weighted approaches with other commonly used species distribution modeling methods.

中文翻译:

使用物种丰度提高物种分布模型预测准确性:使用增强回归树的应用

摘要 物种丰度形式的辅助信息经常作为生态调查收集数据的一部分提供,但在对大区域的物种分布进行建模时,通常使用物种存在(有时不存在)。在实践中将丰度纳入物种分布模型可能会大大提高模型预测的准确性。增强回归树 (BRT) 模型已广泛用于物种分布建模,但迄今为止还没有进行生态研究来评估包含物种丰度权重的 BRT 模型的预测准确性。我们比较了美国东北部 55 种原生河流鱼类的传统未加权 BRT 与物种丰度加权 BRT 对传统 BRT 和加权 BRT 之间的总体模型偏差进行了解释,并比较了六种预测性能的诊断措施。这些比较表明,未加权的 BRT 对被认为常见的河流鱼类表现更好,包括存在数量较多和流行率较高的鱼类。相反,加权 BRT 更适合对存在较少、流行率较低和稀有性较高的物种分布进行建模,这表明物种丰度加权分布建模有可能改善具有高度保护重要性的物种的结果。最后,我们提供了对使用加权方法与其他常用物种分布建模方法的适用性的见解。这些比较表明,未加权的 BRT 对被认为常见的河流鱼类表现更好,包括存在数量较多和流行率较高的鱼类。相反,加权 BRT 更适合对存在较少、流行率较低和稀有性较高的物种分布进行建模,这表明物种丰度加权分布建模有可能改善具有高度保护重要性的物种的结果。最后,我们提供了对使用加权方法与其他常用物种分布建模方法的适用性的见解。这些比较表明,未加权的 BRT 对被认为常见的河流鱼类表现更好,包括存在数量较多和流行率较高的鱼类。相反,加权 BRT 更适合对存在较少、流行率较低和稀有性较高的物种分布进行建模,这表明物种丰度加权分布建模有可能改善具有高度保护重要性的物种的结果。最后,我们提供了对使用加权方法与其他常用物种分布建模方法的适用性的见解。较低的流行率和较高的稀有性,表明物种丰度加权分布模型有潜力改善具有高度保护重要性的物种的结果。最后,我们提供了对使用加权方法与其他常用物种分布建模方法的适用性的见解。较低的流行率和较高的稀有性,表明物种丰度加权分布模型有潜力改善具有高度保护重要性的物种的结果。最后,我们提供了对使用加权方法与其他常用物种分布建模方法的适用性的见解。
更新日期:2020-09-01
down
wechat
bug