Aim: An assessment method that can precisely represent human-vectored long-distance dispersals (HVLDD) is currently in need for the effective management of invasive species. Here, we focussed on HVLDD happening along roads and proposed a path-finding algorithm as a more precise dispersal assessment tool than the most widely used Euclidean distance method by using pine wilt disease (PWD) as a case study. Location: Busan Metropolitan City, Republic of Korea. Methods: A path-finding algorithm, which calculates distances by considering the spatial distribution of road networks, was tested for its effectiveness in estimating dispersal distances of HVLDD events. To this end, annual HVLDD cases were classified from entire PWD occurrence data from 2016 to 2019, and their dispersal distances were calculated using the path-finding algorithm and the Euclidean distance method. We constructed potential dispersal ranges based on the occurrence points in 2016, 2017 and 2018 using the respective year's mean dispersal distance for both methods, and their performances in accounting for each subsequent year's HVLDD cases were compared to determine which method calculated more precise distances. The information on which road class contributed more to dispersal occurrences and distances was analysed as well using the proposed algorithm. Results: The potential dispersal ranges of the path-finding algorithm accounted for more future anthropogenic infection cases than the ones that used the Euclidean distance method, validating its higher functionality. It also revealed that most HVLDDs started and ended on small roads, and large roads constituted the majority of the total dispersal length. Main conclusions: The path-finding algorithm has proven to be a more effective dispersal assessment method for HVLDD events. It can help design effective control strategies. Thus, we encourage using the path-finding algorithm for the dispersal assessment of invasive species that move along road networks, and for the development of more powerful HVLDD prediction models.
Bibliographical noteFunding Information:
This work was supported by Korea Environment Industry & Technology Institute (KEITI) through the Decision Support System Development Project for Environmental Impact Assessment, funded by the Korea Ministry of Environment (MOE) (No. 2020002990009) and Korea University grant.
© 2022 The Authors. Diversity and Distributions published by John Wiley & Sons Ltd.
- dispersal assessment
- human-vectored dispersal
- invasive species
- long-distance dispersal
- path-finding algorithm
- pinewood nematodes
ASJC Scopus subject areas
- Ecology, Evolution, Behavior and Systematics