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...

Full description

Saved in:
Bibliographic Details
Published in:Movement ecology Vol. 13; no. 1; pp. 78 - 14
Main Authors: Chen, Xiaoyi, Li, Jie, Cao, Xinyu, Yang, Yin, Chapman, Colin A., Li, Xiao, Qiao, Ruijing, Wang, Xiaohuan, Yang, Feiling, Kong, Dejun
Format: Journal Article
Language:English
Published: London BioMed Central 30.10.2025
BioMed Central Ltd
BMC
Subjects:
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
Online Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary: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.
Bibliography:ObjectType-Article-1
SourceType-Scholarly Journals-1
ObjectType-Feature-2
content type line 23
ISSN:2051-3933
2051-3933
DOI:10.1186/s40462-025-00602-0