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PreprintCC BY 4.0arXiv

Bayesian Wombling for Tracking Invasive Species

  • Aritra Halder
  • Joseph R. Stinziano
  • Joshua Persi
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Abstract

This paper develops Bayesian wombling, or boundary analysis for tracking invasive species. We focus on three invasive species of concern in North America: Lycorma delicatula, Adelges tsugae, and Microstegium vimineum. We aim to demonstrate the applicability of wombling for supporting invasive pest management activities. In spatial science wombling is used widely to track regions of rapid change within the spatial reference domain. We use Gaussian process models for point-referenced spatial data followed by predictive inference on rates of change over curves using line integrals. We extend the wombling model based Bayesian inference framework to feature simple analytic forms that avoid high dimensional quadrature while offering uncertainty quantification over curvilinear spatial boundaries.

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Cite

BibTeXRIS
BibTeX
@misc{halder2026bayesian,
  title = {{Bayesian Wombling for Tracking Invasive Species}},
  author = {Aritra Halder and Joseph R. Stinziano and Joshua Persi},
  howpublished = {arXiv},
  year = {2026},
  month = nov,
  doi = {10.48550/arxiv.2611.10871},
  eprint = {2611.10871},
  archivePrefix = {arXiv},
  url = {https://arxiv.org/abs/2611.10871},
  note = {CC BY 4.0},
}
RIS
TY  - UNPB
TI  - Bayesian Wombling for Tracking Invasive Species
AU  - Aritra Halder
AU  - Joseph R. Stinziano
AU  - Joshua Persi
PY  - 2026
DA  - 2026/11/07
PB  - arXiv
DO  - 10.48550/arxiv.2611.10871
UR  - https://arxiv.org/abs/2611.10871
AB  - This paper develops Bayesian wombling, or boundary analysis for tracking invasive species. We focus on three invasive species of concern in North America: Lycorma delicatula, Adelges tsugae, and Microstegium vimineum. We aim to demonstrate the applicability of wombling for supporting invasive pest management activities. In spatial science wombling is used widely to track regions of rapid change within the spatial reference domain. We use Gaussian process models for point-referenced spatial data followed by predictive inference on rates of change over curves using line integrals. We extend the wombling model based Bayesian inference framework to feature simple analytic forms that avoid high dimensional quadrature while offering uncertainty quantification over curvilinear spatial boundaries.
ER  -