A ERSF-VIPA framework: scalable wildlife movement modelling for conflict mitigation

1. Effective conservation planning and conflict mitigation can hinge on accurately modelling wildlife movement paths (WMPs), yet progress is hindered by both a shortage of reliable methods and limited data. The critical challenge, therefore, is to devise limited-data models that faithfully reproduce...

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Vydané v:	Movement ecology Ročník 13; číslo 1; s. 78 - 14
Hlavní autori:	Chen, Xiaoyi, Li, Jie, Cao, Xinyu, Yang, Yin, Chapman, Colin A., Li, Xiao, Qiao, Ruijing, Wang, Xiaohuan, Yang, Feiling, Kong, Dejun
Médium:	Journal Article
Jazyk:	English
Vydavateľské údaje:	London BioMed Central 30.10.2025 BioMed Central Ltd BMC
Predmet:	Algorithms Animal Ecology Asian elephants (elephas maximus) Biomedical and Life Sciences Conflict management Conservation Biology/Ecology Enhanced resource selection function–vector network iterative pathfinding algorithm Human-wildlife conflict Life Sciences Limited data Methodology Protection and preservation Terrestial Ecology Wildlife Wildlife movement path prediction China Wildlife movement path prediction Human-wildlife conflict ) Enhanced resource selection function–vector network iterative pathfinding algorithm Asian elephants Limited data Asian elephants (elephas maximus)
ISSN:	2051-3933, 2051-3933
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Abstract	1. Effective conservation planning and conflict mitigation can hinge on accurately modelling wildlife movement paths (WMPs), yet progress is hindered by both a shortage of reliable methods and limited data. The critical challenge, therefore, is to devise limited-data models that faithfully reproduce elusive species’ movements and deliver actionable insights for human–wildlife conflict management. 2. We introduce the Enhanced Resource Selection Function–Vector-network Iterative Pathfinding Algorithm (ERSF-VIPA), a novel framework for simulating WMPs with limited data. Drawing on historical occurrence records of Asian elephants ( Elephas maximus ), we assume individuals make rational, goal-driven decisions based on local environmental knowledge. The ERSF employs a random forest on a hexagonal grid to estimate nonlinear resource-selection probabilities, while VIPA conducts an iterative, node-to-node search across that hexagonal vector network—scoring each candidate by combining selection probability with cubic distance coefficients to ensure ecological validity and energetic efficiency. 3. The model demonstrates high accuracy, with 90.3% of the 68 simulated paths approximating the observed paths with an average maximum deviation of 418 m. These findings underscore the model’s robustness and its capacity to translate limited tracking data into actionable insights for conservation. 4. ERSF-VIPA operates using only coarse, non-continuous historical data that lack precise timestamps or spatial accuracy. By operating with minimal data requirements, ERSF-VIPA demonstrates exceptional extensibility and broad applicability for reconstructing movement paths of elusive wildlife species. Its proven accuracy in simulating Asian elephant paths further positions it as a potentially powerful decision-support framework for real-time animal monitoring and proactive human–wildlife conflict mitigation.
AbstractList	1. Effective conservation planning and conflict mitigation can hinge on accurately modelling wildlife movement paths (WMPs), yet progress is hindered by both a shortage of reliable methods and limited data. The critical challenge, therefore, is to devise limited-data models that faithfully reproduce elusive species' movements and deliver actionable insights for human-wildlife conflict management. 2. We introduce the Enhanced Resource Selection Function-Vector-network Iterative Pathfinding Algorithm (ERSF-VIPA), a novel framework for simulating WMPs with limited data. Drawing on historical occurrence records of Asian elephants (Elephas maximus), we assume individuals make rational, goal-driven decisions based on local environmental knowledge. The ERSF employs a random forest on a hexagonal grid to estimate nonlinear resource-selection probabilities, while VIPA conducts an iterative, node-to-node search across that hexagonal vector network-scoring each candidate by combining selection probability with cubic distance coefficients to ensure ecological validity and energetic efficiency. 3. The model demonstrates high accuracy, with 90.3% of the 68 simulated paths approximating the observed paths with an average maximum deviation of 418 m. These findings underscore the model's robustness and its capacity to translate limited tracking data into actionable insights for conservation. 4. ERSF-VIPA operates using only coarse, non-continuous historical data that lack precise timestamps or spatial accuracy. By operating with minimal data requirements, ERSF-VIPA demonstrates exceptional extensibility and broad applicability for reconstructing movement paths of elusive wildlife species. Its proven accuracy in simulating Asian elephant paths further positions it as a potentially powerful decision-support framework for real-time animal monitoring and proactive human-wildlife conflict mitigation. 1. Effective conservation planning and conflict mitigation can hinge on accurately modelling wildlife movement paths (WMPs), yet progress is hindered by both a shortage of reliable methods and limited data. The critical challenge, therefore, is to devise limited-data models that faithfully reproduce elusive species' movements and deliver actionable insights for human-wildlife conflict management. 2. We introduce the Enhanced Resource Selection Function-Vector-network Iterative Pathfinding Algorithm (ERSF-VIPA), a novel framework for simulating WMPs with limited data. Drawing on historical occurrence records of Asian elephants (Elephas maximus), we assume individuals make rational, goal-driven decisions based on local environmental knowledge. The ERSF employs a random forest on a hexagonal grid to estimate nonlinear resource-selection probabilities, while VIPA conducts an iterative, node-to-node search across that hexagonal vector network--scoring each candidate by combining selection probability with cubic distance coefficients to ensure ecological validity and energetic efficiency. 3. The model demonstrates high accuracy, with 90.3% of the 68 simulated paths approximating the observed paths with an average maximum deviation of 418 m. These findings underscore the model's robustness and its capacity to translate limited tracking data into actionable insights for conservation. 4. ERSF-VIPA operates using only coarse, non-continuous historical data that lack precise timestamps or spatial accuracy. By operating with minimal data requirements, ERSF-VIPA demonstrates exceptional extensibility and broad applicability for reconstructing movement paths of elusive wildlife species. Its proven accuracy in simulating Asian elephant paths further positions it as a potentially powerful decision-support framework for real-time animal monitoring and proactive human-wildlife conflict mitigation. Keywords: Asian elephants (elephas maximus), Enhanced resource selection function-vector network iterative pathfinding algorithm, Human-wildlife conflict, Limited data, Wildlife movement path prediction 1. Effective conservation planning and conflict mitigation can hinge on accurately modelling wildlife movement paths (WMPs), yet progress is hindered by both a shortage of reliable methods and limited data. The critical challenge, therefore, is to devise limited-data models that faithfully reproduce elusive species’ movements and deliver actionable insights for human–wildlife conflict management. 2. We introduce the Enhanced Resource Selection Function–Vector-network Iterative Pathfinding Algorithm (ERSF-VIPA), a novel framework for simulating WMPs with limited data. Drawing on historical occurrence records of Asian elephants ( Elephas maximus ), we assume individuals make rational, goal-driven decisions based on local environmental knowledge. The ERSF employs a random forest on a hexagonal grid to estimate nonlinear resource-selection probabilities, while VIPA conducts an iterative, node-to-node search across that hexagonal vector network—scoring each candidate by combining selection probability with cubic distance coefficients to ensure ecological validity and energetic efficiency. 3. The model demonstrates high accuracy, with 90.3% of the 68 simulated paths approximating the observed paths with an average maximum deviation of 418 m. These findings underscore the model’s robustness and its capacity to translate limited tracking data into actionable insights for conservation. 4. ERSF-VIPA operates using only coarse, non-continuous historical data that lack precise timestamps or spatial accuracy. By operating with minimal data requirements, ERSF-VIPA demonstrates exceptional extensibility and broad applicability for reconstructing movement paths of elusive wildlife species. Its proven accuracy in simulating Asian elephant paths further positions it as a potentially powerful decision-support framework for real-time animal monitoring and proactive human–wildlife conflict mitigation. Abstract 1. Effective conservation planning and conflict mitigation can hinge on accurately modelling wildlife movement paths (WMPs), yet progress is hindered by both a shortage of reliable methods and limited data. The critical challenge, therefore, is to devise limited-data models that faithfully reproduce elusive species’ movements and deliver actionable insights for human–wildlife conflict management. 2. We introduce the Enhanced Resource Selection Function–Vector-network Iterative Pathfinding Algorithm (ERSF-VIPA), a novel framework for simulating WMPs with limited data. Drawing on historical occurrence records of Asian elephants (Elephas maximus), we assume individuals make rational, goal-driven decisions based on local environmental knowledge. The ERSF employs a random forest on a hexagonal grid to estimate nonlinear resource-selection probabilities, while VIPA conducts an iterative, node-to-node search across that hexagonal vector network—scoring each candidate by combining selection probability with cubic distance coefficients to ensure ecological validity and energetic efficiency. 3. The model demonstrates high accuracy, with 90.3% of the 68 simulated paths approximating the observed paths with an average maximum deviation of 418 m. These findings underscore the model’s robustness and its capacity to translate limited tracking data into actionable insights for conservation. 4. ERSF-VIPA operates using only coarse, non-continuous historical data that lack precise timestamps or spatial accuracy. By operating with minimal data requirements, ERSF-VIPA demonstrates exceptional extensibility and broad applicability for reconstructing movement paths of elusive wildlife species. Its proven accuracy in simulating Asian elephant paths further positions it as a potentially powerful decision-support framework for real-time animal monitoring and proactive human–wildlife conflict mitigation. 1. Effective conservation planning and conflict mitigation can hinge on accurately modelling wildlife movement paths (WMPs), yet progress is hindered by both a shortage of reliable methods and limited data. The critical challenge, therefore, is to devise limited-data models that faithfully reproduce elusive species' movements and deliver actionable insights for human-wildlife conflict management. 2. We introduce the Enhanced Resource Selection Function-Vector-network Iterative Pathfinding Algorithm (ERSF-VIPA), a novel framework for simulating WMPs with limited data. Drawing on historical occurrence records of Asian elephants (Elephas maximus), we assume individuals make rational, goal-driven decisions based on local environmental knowledge. The ERSF employs a random forest on a hexagonal grid to estimate nonlinear resource-selection probabilities, while VIPA conducts an iterative, node-to-node search across that hexagonal vector network-scoring each candidate by combining selection probability with cubic distance coefficients to ensure ecological validity and energetic efficiency. 3. The model demonstrates high accuracy, with 90.3% of the 68 simulated paths approximating the observed paths with an average maximum deviation of 418 m. These findings underscore the model's robustness and its capacity to translate limited tracking data into actionable insights for conservation. 4. ERSF-VIPA operates using only coarse, non-continuous historical data that lack precise timestamps or spatial accuracy. By operating with minimal data requirements, ERSF-VIPA demonstrates exceptional extensibility and broad applicability for reconstructing movement paths of elusive wildlife species. Its proven accuracy in simulating Asian elephant paths further positions it as a potentially powerful decision-support framework for real-time animal monitoring and proactive human-wildlife conflict mitigation.1. Effective conservation planning and conflict mitigation can hinge on accurately modelling wildlife movement paths (WMPs), yet progress is hindered by both a shortage of reliable methods and limited data. The critical challenge, therefore, is to devise limited-data models that faithfully reproduce elusive species' movements and deliver actionable insights for human-wildlife conflict management. 2. We introduce the Enhanced Resource Selection Function-Vector-network Iterative Pathfinding Algorithm (ERSF-VIPA), a novel framework for simulating WMPs with limited data. Drawing on historical occurrence records of Asian elephants (Elephas maximus), we assume individuals make rational, goal-driven decisions based on local environmental knowledge. The ERSF employs a random forest on a hexagonal grid to estimate nonlinear resource-selection probabilities, while VIPA conducts an iterative, node-to-node search across that hexagonal vector network-scoring each candidate by combining selection probability with cubic distance coefficients to ensure ecological validity and energetic efficiency. 3. The model demonstrates high accuracy, with 90.3% of the 68 simulated paths approximating the observed paths with an average maximum deviation of 418 m. These findings underscore the model's robustness and its capacity to translate limited tracking data into actionable insights for conservation. 4. ERSF-VIPA operates using only coarse, non-continuous historical data that lack precise timestamps or spatial accuracy. By operating with minimal data requirements, ERSF-VIPA demonstrates exceptional extensibility and broad applicability for reconstructing movement paths of elusive wildlife species. Its proven accuracy in simulating Asian elephant paths further positions it as a potentially powerful decision-support framework for real-time animal monitoring and proactive human-wildlife conflict mitigation.
ArticleNumber	78
Audience	Academic
Author	Yang, Yin Chen, Xiaoyi Chapman, Colin A. Wang, Xiaohuan Yang, Feiling Kong, Dejun Li, Jie Li, Xiao Cao, Xinyu Qiao, Ruijing
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Keywords	Wildlife movement path prediction Human-wildlife conflict ) Enhanced resource selection function–vector network iterative pathfinding algorithm Asian elephants Limited data Asian elephants (elephas maximus)
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Snippet	1. Effective conservation planning and conflict mitigation can hinge on accurately modelling wildlife movement paths (WMPs), yet progress is hindered by both a... Abstract 1. Effective conservation planning and conflict mitigation can hinge on accurately modelling wildlife movement paths (WMPs), yet progress is hindered...
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SubjectTerms	Algorithms Animal Ecology Asian elephants (elephas maximus) Biomedical and Life Sciences Conflict management Conservation Biology/Ecology Enhanced resource selection function–vector network iterative pathfinding algorithm Human-wildlife conflict Life Sciences Limited data Methodology Protection and preservation Terrestial Ecology Wildlife Wildlife movement path prediction
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Title	A ERSF-VIPA framework: scalable wildlife movement modelling for conflict mitigation
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