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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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Abstract

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 (\({\varvec{\beta }}_{x}{\textbf {x}}(z)\)) 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.

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Data availability

The data supporting the findings of this study are available on request from the corresponding author.

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Acknowledgements

We are grateful to the reviewers and editors for their constructive feedback, insightful comments, and the time they devoted to reviewing this manuscript.

Funding

This work was carried out as part of the “Ph.D. In-Country/In-Region Scholarship Programme FSA/UAC, 2020 (Grant number 91786077 to JABB)” of the German Federal Ministry of Economic Cooperation and Development (BMZ). JABB is grateful to the German Academic Exchange Service (DAAD) for funding his Ph.D. studies.

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  1. Laboratoire de Biomathématiques et d’Estimations Forestières-LABEF, Université d’Abomey-Calavi-UAC, 04 B.P 1525, Cotonou, Bénin

    Judi Armel Bourobou Bourobou, Mahoukpégo Luc Zinzinhedo, Adandé Belarmain Fandohan & Romain Lucas Glèlè Kakaï

  2. Unité de Recherche en Foresterie et Conservation des BioRessources-URFCBio, Université Nationale d’Agriculture-UNA, BP 43, Kétou, Bénin

    Adandé Belarmain Fandohan

  3. Institut de Recherches Agronomique et Forestière-IRAF, Centre National de Recherche Scientifique et Technologique-CENAREST, BP 2246, Libreville, Gabon

    Judi Armel Bourobou Bourobou

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  1. Judi Armel Bourobou Bourobou
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Contributions

JABB: conceptualisation, methodology, data simulation, data analysis, and writing the initial draft; MLZ: data simulation; ABF: Validation, supervision, and writing revisions; RLGK: Validation, supervision, and writing revisions; DAAD: Funding acquisition.

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Correspondence to Judi Armel Bourobou Bourobou.

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Bourobou, J.A.B., Zinzinhedo, M.L., Fandohan, A.B. 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. Model. Earth Syst. Environ. (2024). https://doi.org/10.1007/s40808-024-02017-z

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