Species distribution model performance improves when habitat characterizations are centered on detected individuals instead of observers

IF 7 2区 环境科学与生态学 Q1 ENVIRONMENTAL SCIENCES
Fang-Yu Shen , Fiona Victoria Stanley Jothiraj , Rebecca A. Hutchinson , Tyler A. Hallman , Jenna R. Curtis , W. Douglas Robinson
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Abstract

Species distribution models (SDMs) link species occurrence to environmental characteristics to predict suitable habitats beyond known occurrences. The conventional procedure to fit SDMs for individual organisms detected at some distance away from observers is to characterize species’ associated habitat based on observer’s survey location. However, each surveyed individual may be detected in habitats distinct from those where observers are located. Here, we compared environmental variables centered on the observer and individual bird locations and the consequent effects on SDMs performance. We utilized remote sensing data on observer- and bird-locations to characterize habitat at three radii (pixel radius: 30-m; fixed radius: 100-m; species-specific effective detection radius). We trained Poisson boosted regression tree models for 105 bird species from structured professional surveys. We evaluated models’ predictability with Kendall’s rank correlation coefficient and used linear mixed-effect models to measure the effect of characterization locations and radii. Models based on bird locations exhibited a median increase of 22.9% in predictive performance, demonstrating higher Kendall’s rank correlation coefficients than those based on observer locations, leading to more reliable prediction maps. SDMs of habitat specialists and generalists performed better when habitat characterization was centered on bird instead of surveyor locations. A higher percentage of habitat specialists (72%) than generalists (55%) showed better model performance in bird-location than in observer-location models. Across radii, fixed radius generally performed better than species-specific effective and pixel radii. Our findings emphasize the importance of prioritizing habitat characterizations based on detected individuals’ locations to enhance model performance and improve species distribution predictions.
当栖息地特征以被探测个体为中心而不是以观察者为中心时,物种分布模型的性能得到改善
物种分布模型(SDMs)将物种发生与环境特征联系起来,以预测已知事件之外的适宜栖息地。对于距离观测者一定距离的个体生物,拟合sdm的传统方法是根据观测者的调查位置表征物种的相关栖息地。然而,每一个被调查的个体可能在不同于观察员所在的生境中被发现。在这里,我们比较了以观察者和个体鸟的位置为中心的环境变量及其对sdm性能的影响。我们利用观测者和鸟类位置的遥感数据在三个半径(像素半径:30 m;固定半径:100m;物种特异性有效探测半径)。我们训练了105种鸟类的Poisson增强回归树模型。我们使用肯德尔等级相关系数评估模型的可预测性,并使用线性混合效应模型来衡量表征位置和半径的影响。基于鸟类位置的模型的预测性能中位数提高了22.9%,显示出比基于观测者位置的模型更高的肯德尔等级相关系数,从而导致更可靠的预测图。当栖息地特征以鸟类为中心而不是以测量员的位置为中心时,栖息地专家和通才的SDMs表现更好。栖息地专家(72%)比通才(55%)在鸟类定位模型中表现出比观察者-位置模型更好的模型性能。在整个半径范围内,固定半径通常优于物种特异性有效半径和像素半径。我们的研究结果强调了基于被检测个体位置的栖息地特征优先化对于提高模型性能和改善物种分布预测的重要性。
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来源期刊
Ecological Indicators
Ecological Indicators 环境科学-环境科学
CiteScore
11.80
自引率
8.70%
发文量
1163
审稿时长
78 days
期刊介绍: The ultimate aim of Ecological Indicators is to integrate the monitoring and assessment of ecological and environmental indicators with management practices. The journal provides a forum for the discussion of the applied scientific development and review of traditional indicator approaches as well as for theoretical, modelling and quantitative applications such as index development. Research into the following areas will be published. • All aspects of ecological and environmental indicators and indices. • New indicators, and new approaches and methods for indicator development, testing and use. • Development and modelling of indices, e.g. application of indicator suites across multiple scales and resources. • Analysis and research of resource, system- and scale-specific indicators. • Methods for integration of social and other valuation metrics for the production of scientifically rigorous and politically-relevant assessments using indicator-based monitoring and assessment programs. • How research indicators can be transformed into direct application for management purposes. • Broader assessment objectives and methods, e.g. biodiversity, biological integrity, and sustainability, through the use of indicators. • Resource-specific indicators such as landscape, agroecosystems, forests, wetlands, etc.
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