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AbundanceR: A Novel Method for Estimating Wildlife Abundance Based on Distance Sampling and Species Distribution Models

Author

Listed:
  • Xinhai Li

    (Key Laboratory of Animal Ecology and Conservation Biology, Institute of Zoology, Chinese Academy of Sciences, Beijing 100101, China
    College of Life Sciences, University of Chinese Academy of Sciences, Beijing 100049, China
    These authors contributed equally to this work.)

  • Ning Li

    (Institute of Applied Ecology, Nanjing Xiaozhuang University, Nanjing 211171, China
    These authors contributed equally to this work.)

  • Baidu Li

    (Lassonde School of Engineering, York University, 4700 Keele Street, Toronto, ON M3J 1P3, Canada)

  • Yuehua Sun

    (Key Laboratory of Animal Ecology and Conservation Biology, Institute of Zoology, Chinese Academy of Sciences, Beijing 100101, China)

  • Erhu Gao

    (Academy of Inventory and Planning, National Forestry and Grassland Administration, Beijing 100714, China)

Abstract

Appropriate field survey methods and robust modeling approaches play an important role in wildlife protection and habitat management because reliable information on wildlife distribution and abundance is important for conservation planning and actions. However, accurately estimating animal abundance is challenging in most species, as usually only a small proportion of the population can be detected during surveys. Species distribution models can predict the habitat suitability index, which differs from species abundance. We designed a method to adjust the results from species distribution models to achieve better accuracy for abundance estimation. This method comprises four steps: (1) conducting distance sampling, recording species occurrences, and surveying routes; (2) performing species distribution modeling using occurrence records and predicting animal abundance in each quadrat in the study area; (3) comparing the difference between field survey results and predicted abundance in quadrats along survey routes, adjusting model prediction, and summing up to obtain total abundance in the study area; (4) calculating uncertainty from three sources, i.e., distance sampling (using detection rate), species distribution models (using R squared), and differences between the field survey and model prediction [using the standard deviation of the ratio (observation/prediction) at different zones]. We developed an R package called abundanceR to estimate wildlife abundance and provided data for the Tibetan wild ass ( Equus kiang ) based on field surveys at the Three-River-Source National Park, as well as 29 layers of environmental variables covering the terrestrial areas of the planet. Our method can provide accurate estimation of abundance for animals inhabiting open areas that can be easily observed during distance sampling, and whose spatial heterogeneity of animal density within the study area can be accurately predicted using species distribution models.

Suggested Citation

  • Xinhai Li & Ning Li & Baidu Li & Yuehua Sun & Erhu Gao, 2022. "AbundanceR: A Novel Method for Estimating Wildlife Abundance Based on Distance Sampling and Species Distribution Models," Land, MDPI, vol. 11(5), pages 1-13, April.
    • Handle: RePEc:gam:jlands:v:11:y:2022:i:5:p:660-:d:805280
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    References listed on IDEAS

    as
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    3. Xinhai Li & Liming Ma & Dazhi Hu & Duifang Ma & Renqiang Li & Yuehua Sun & Erhu Gao, 2022. "Potential Range Shift of Snow Leopard in Future Climate Change Scenarios," Sustainability, MDPI, vol. 14(3), pages 1-14, January.
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    Cited by:

    1. Xinhai Li & Jiayu Fu & Tianqing Zhai & Yazu Zhang & Michael W. Bruford & Yuehua Sun & Xiangjiang Zhan, 2022. "Understanding Recovery Is as Important as Understanding Decline: The Case of the Crested Ibis in China," Land, MDPI, vol. 11(10), pages 1-14, October.
    2. Juan F. Beltrán & John A. Litvaitis & Pedro Abellán, 2022. "Seeking Sustainable Solutions in a Time of Change," Land, MDPI, vol. 11(6), pages 1-2, June.

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