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

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 regres...

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Bibliographic Details
Published in:Biodiversity and conservation 2024-03, Vol.33 (3), p.1051-1078
Main Authors: Jahani, Ali, Saffariha, Maryam, Nezhad, Zeinab Hosein
Format: Article
Language:English
Subjects:
Anthropogenic factors
Artificial intelligence
Biodiversity
Biodiversity loss
Biomedical and Life Sciences
Climate Change/Climate Change Impacts
Conservation Biology/Ecology
Decision making
Ecological conditions
Ecology
Grazing
Habitats
Human influences
Impact prediction
Life Sciences
Livestock
Livestock grazing
Model accuracy
Mountains
Multilayer perceptrons
National parks
Neural networks
Original Research
Pasture
Range management
Rangelands
Sensitivity analysis
Strategic management
Support vector machines
Tourism
Vegetation
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Summary: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 (R 2  = 0.93 in training, R 2  = 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.
ISSN:0960-3115
1572-9710
DOI:10.1007/s10531-024-02783-3