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Improving species distribution model predictive accuracy using species abundance: Application with boosted regression trees

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  • Yu, Hao
  • Cooper, Arthur R.
  • Infante, Dana M.

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.

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  • Yu, Hao & Cooper, Arthur R. & Infante, Dana M., 2020. "Improving species distribution model predictive accuracy using species abundance: Application with boosted regression trees," Ecological Modelling, Elsevier, vol. 432(C).
    • Handle: RePEc:eee:ecomod:v:432:y:2020:i:c:s0304380020302726
    DOI: 10.1016/j.ecolmodel.2020.109202
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    References listed on IDEAS

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    2. Rosario Delgado & Xavier-Andoni Tibau, 2019. "Why Cohen’s Kappa should be avoided as performance measure in classification," PLOS ONE, Public Library of Science, vol. 14(9), pages 1-26, September.
    3. Peterson, A. Townsend & Papeş, Monica & Soberón, Jorge, 2008. "Rethinking receiver operating characteristic analysis applications in ecological niche modeling," Ecological Modelling, Elsevier, vol. 213(1), pages 63-72.
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    2. Jorge Sicacha-Parada & Diego Pavon-Jordan & Ingelin Steinsland & Roel May & Bård Stokke & Ingar Jostein Øien, 2022. "A Spatial Modeling Framework for Monitoring Surveys with Different Sampling Protocols with a Case Study for Bird Abundance in Mid-Scandinavia," Journal of Agricultural, Biological and Environmental Statistics, Springer;The International Biometric Society;American Statistical Association, vol. 27(3), pages 562-591, September.
    3. Yunfang Jiang & Jing Huang & Tiemao Shi & Xiaolin Li, 2021. "Cooling Island Effect of Blue-Green Corridors: Quantitative Comparison of Morphological Impacts," IJERPH, MDPI, vol. 18(22), pages 1-28, November.
    4. Yunfang Jiang & Shidan Jiang & Tiemao Shi, 2020. "Comparative Study on the Cooling Effects of Green Space Patterns in Waterfront Build-Up Blocks: An Experience from Shanghai," IJERPH, MDPI, vol. 17(22), pages 1-29, November.
    5. Barker, Justin R. & MacIsaac, Hugh J., 2022. "Species distribution models: Administrative boundary centroid occurrences require careful interpretation," Ecological Modelling, Elsevier, vol. 472(C).
    6. Chih-Wei Lin & Yu Hong & Weihao Tu & Jinfu Liu, 2022. "Multiperiod Dynamic Programming Algorithm for Optimizing a Nature Reserve," Sustainability, MDPI, vol. 14(6), pages 1-17, March.

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