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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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| Published in: | Biodiversity and conservation 2024-03, Vol.33 (3), p.1051-1078 |
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| container_end_page | 1078 |
| container_issue | 3 |
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| container_title | Biodiversity and conservation |
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| creator | Jahani, Ali Saffariha, Maryam Nezhad, Zeinab Hosein |
| description | 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. |
| doi_str_mv | 10.1007/s10531-024-02783-3 |
| format | article |
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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.</description><identifier>ISSN: 0960-3115</identifier><identifier>EISSN: 1572-9710</identifier><identifier>DOI: 10.1007/s10531-024-02783-3</identifier><language>eng</language><publisher>Dordrecht: Springer Netherlands</publisher><subject>Anthropogenic factors ; Artificial intelligence ; Biodiversity ; Biodiversity loss ; Biological diversity ; Biomedical and Life Sciences ; Climate Change/Climate Change Impacts ; Conservation Biology/Ecology ; data collection ; Decision making ; Ecological conditions ; Ecology ; Ecosystems ; Grazing ; Habitats ; Human influences ; Impact prediction ; Life Sciences ; Livestock ; Livestock grazing ; Model accuracy ; Mountains ; Multilayer perceptrons ; National parks ; Neural networks ; Original Research ; Pasture ; Protection and preservation ; Range ecology ; Range management ; Rangelands ; regression analysis ; Sensitivity analysis ; Strategic management ; Support vector machines ; Sustainable development ; Tourism ; tourists ; Travel industry ; user interface ; Vegetation</subject><ispartof>Biodiversity and conservation, 2024-03, Vol.33 (3), p.1051-1078</ispartof><rights>The Author(s), under exclusive licence to Springer Nature B.V. 2024. Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.</rights><rights>COPYRIGHT 2024 Springer</rights><lds50>peer_reviewed</lds50><woscitedreferencessubscribed>false</woscitedreferencessubscribed><cites>FETCH-LOGICAL-c342t-892bf73a22a15dbaf626ce2cafcdf160ed48dad7669223b6b2a0bcbff77d4c6f3</cites></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail></links><search><creatorcontrib>Jahani, Ali</creatorcontrib><creatorcontrib>Saffariha, Maryam</creatorcontrib><creatorcontrib>Nezhad, Zeinab Hosein</creatorcontrib><title>Predicting the anthropogenic impacts on vegetation diversity of protected rangelands: an application of artificial intelligence</title><title>Biodiversity and conservation</title><addtitle>Biodivers Conserv</addtitle><description>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.</description><subject>Anthropogenic factors</subject><subject>Artificial intelligence</subject><subject>Biodiversity</subject><subject>Biodiversity loss</subject><subject>Biological diversity</subject><subject>Biomedical and Life Sciences</subject><subject>Climate Change/Climate Change Impacts</subject><subject>Conservation Biology/Ecology</subject><subject>data collection</subject><subject>Decision making</subject><subject>Ecological conditions</subject><subject>Ecology</subject><subject>Ecosystems</subject><subject>Grazing</subject><subject>Habitats</subject><subject>Human influences</subject><subject>Impact prediction</subject><subject>Life Sciences</subject><subject>Livestock</subject><subject>Livestock grazing</subject><subject>Model accuracy</subject><subject>Mountains</subject><subject>Multilayer perceptrons</subject><subject>National parks</subject><subject>Neural networks</subject><subject>Original Research</subject><subject>Pasture</subject><subject>Protection and preservation</subject><subject>Range ecology</subject><subject>Range management</subject><subject>Rangelands</subject><subject>regression analysis</subject><subject>Sensitivity analysis</subject><subject>Strategic management</subject><subject>Support vector machines</subject><subject>Sustainable development</subject><subject>Tourism</subject><subject>tourists</subject><subject>Travel industry</subject><subject>user 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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.</abstract><cop>Dordrecht</cop><pub>Springer Netherlands</pub><doi>10.1007/s10531-024-02783-3</doi><tpages>28</tpages></addata></record> |
| fulltext | fulltext |
| identifier | ISSN: 0960-3115 |
| ispartof | Biodiversity and conservation, 2024-03, Vol.33 (3), p.1051-1078 |
| issn | 0960-3115 1572-9710 |
| language | eng |
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| source | Springer Nature:Jisc Collections:Springer Nature Read and Publish 2023-2025: Springer Reading List |
| subjects | Anthropogenic factors Artificial intelligence Biodiversity Biodiversity loss Biological diversity Biomedical and Life Sciences Climate Change/Climate Change Impacts Conservation Biology/Ecology data collection Decision making Ecological conditions Ecology Ecosystems Grazing Habitats Human influences Impact prediction Life Sciences Livestock Livestock grazing Model accuracy Mountains Multilayer perceptrons National parks Neural networks Original Research Pasture Protection and preservation Range ecology Range management Rangelands regression analysis Sensitivity analysis Strategic management Support vector machines Sustainable development Tourism tourists Travel industry user interface Vegetation |
| title | Predicting the anthropogenic impacts on vegetation diversity of protected rangelands: an application of artificial intelligence |
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