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Predicting the anthropogenic impacts on vegetation diversity of protected rangelands: an application of artificial intelligence

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Abstract

This study delves into anthropogenic impacts on vegetation diversity within mountainous protected rangelands, exploring habitat weakening and biodiversity loss. Employing artificial intelligence, specifically multilayer perceptron (MLP), radial basis neural network (RBFNN), and support vector regression (SVR), we predict vegetation diversity responses to ecological conditions, livestock grazing, and tourism. Assessing 305 sample plots with 21 variables, the MLP model demonstrated superior accuracy (R2 = 0.93 in training, R2 = 0.81 in the test dataset) compared to RBFNN and SVR. Sensitivity analyses highlighted anthropogenic factors like distance to tourist destinations, roads, pastures, and animal husbandries as significant influencers of vegetation diversity in mountainous protected rangelands. To enhance practical application, a user-friendly graphical interface was developed, enabling rangeland managers to utilize the MLP model. This tool facilitates estimation of livestock grazing and tourism impacts on vegetation diversity, empowering informed decision-making for the preservation and sustainability of mountainous protected rangeland ecosystems.

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Data will be available by authors on reasonable request.

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Acknowledgements

We are thankful to the Iranian National Department of Environment employees who supported us for data collection.

Funding

This research received no specific grant from any funding agency in the public, commercial, or not-for-profit sectors.

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Authors and Affiliations

  1. Research Center of Environment and Sustainable Development, Pardisan Park, Tehran, Iran

    Ali Jahani

  2. Department of Plant Sciences, University of California Davis, Davis, CA, USA

    Zeinab Hosein Nezhad

  3. Department of Environment, Natural Resources and Environment, Malayer University, Hamedan, Iran

    Maryam Saffariha

Authors
  1. Ali Jahani
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  2. Maryam Saffariha
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Contributions

A.J: ‎Data curation, Software, Validation. M.S: Conceptualization, Methodology, Writing- Original draft preparation, Software. Z.H: Visualization, Investigation.

Corresponding author

Correspondence to Ali Jahani.

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The authors have no relevant financial or non-financial interests to disclose.

Additional information

Communicated by Mukunda Dev Behera.

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Jahani, A., Saffariha, M. & Nezhad, Z.H. Predicting the anthropogenic impacts on vegetation diversity of protected rangelands: an application of artificial intelligence. Biodivers Conserv 33, 1051–1078 (2024). https://doi.org/10.1007/s10531-024-02783-3

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