气候变化领域集成服务门户(CCPortal): Evaluating spatial resolution and imperfect detection effects on the predictive performance of inhomogeneous spatial point process models trained with simulated presence-only data

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DOI	10.1007/s40808-024-02017-z
	Evaluating spatial resolution and imperfect detection effects on the predictive performance of inhomogeneous spatial point process models trained with simulated presence-only data
	Bourobou, Judi Armel Bourobou; Zinzinhedo, Mahoukpego Luc; Fandohan, Adande Belarmain; Kakai, Romain Lucas Glele
发表日期	2024
ISSN	2363-6203
EISSN	2363-6211
英文摘要	Species distribution models (SDMs) are crucial in ecology, conservation, and ecosystem management. Numerous SDMs have been developed over time, and studies have shown that these tools can be affected by a range of factors, such as data type, spatial resolution, number of explanatory variables, sample characteristics, and collinearity between environmental variables. New SDMs have often been developed to address some of these issues. Understanding the performance of new statistical tools is crucial for researchers in various fields. Thus, we assessed the predictive ability of the Poisson point process and log Gaussian Cox process models, considered as new SDMs, by simulating two factors, spatial resolution and imperfect detection, which are likely to have significant effects on SDMs, and considering Gabon as the study area. The observed model performance metrics, such as the Area Under the Receiver Operating Characteristic Curve (AUC), Mean Absolute Error (MAE), and Pearson correlation (CORR) between the true and predicted intensities, were used to evaluate the predictive performance of these models. The results showed that, although most of these models failed to estimate the intercept alpha(0) and covariate coefficients (beta(x)x(z) correctly, they at least had the merit of demonstrated good performance (AUC more than 70%, CORR more than 67%, and MAE less than 0.61%). However, the spatial resolution of the environmental variables and imperfect detection of simulated species occurrences significantly affected the predictive performance of the two models (P < .0001). This study offers important insights for ecologist modellers, environmentalists, and conservators.
英文关键词	Spatial resolution; Presence-only data; Inhomogeneous spatial point process models; Simulation; Species distribution modeling
语种	英语
WOS研究方向	Environmental Sciences & Ecology
WOS类目	Environmental Sciences
WOS记录号	WOS:001226867800001
来源期刊	MODELING EARTH SYSTEMS AND ENVIRONMENT
文献类型	期刊论文
条目标识符	http://gcip.llas.ac.cn/handle/2XKMVOVA/300240
作者单位	University of Abomey Calavi
推荐引用方式 GB/T 7714	Bourobou, Judi Armel Bourobou,Zinzinhedo, Mahoukpego Luc,Fandohan, Adande Belarmain,et al. Evaluating spatial resolution and imperfect detection effects on the predictive performance of inhomogeneous spatial point process models trained with simulated presence-only data[J],2024.
APA	Bourobou, Judi Armel Bourobou,Zinzinhedo, Mahoukpego Luc,Fandohan, Adande Belarmain,&Kakai, Romain Lucas Glele.(2024).Evaluating spatial resolution and imperfect detection effects on the predictive performance of inhomogeneous spatial point process models trained with simulated presence-only data.MODELING EARTH SYSTEMS AND ENVIRONMENT.
MLA	Bourobou, Judi Armel Bourobou,et al."Evaluating spatial resolution and imperfect detection effects on the predictive performance of inhomogeneous spatial point process models trained with simulated presence-only data".MODELING EARTH SYSTEMS AND ENVIRONMENT (2024).