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Exploring hybrid consensus models to assess roadkill.

Panagiotis Karanasios1, Rainer Ferdinand Wunderlich1, Hussnain Mukhtar1

  • 1Department of Bioenvironmental Systems Engineering, National Taiwan University, No. 1, Sec. 4, Roosevelt Rd., Taipei, 10617, Taiwan.

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|June 15, 2021
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Summary

A new hybrid consensus modeling (HCM) approach effectively identifies critical roadkill hotspots for herpetofauna (amphibians and reptiles). HCM outperforms traditional kernel density estimation (KDE) and maximum entropy modeling (ME) in prioritizing mitigation efforts.

Keywords:
ConsensusHotspotKernel densityMaximum entropyMitigationRoadkill

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Area of Science:

  • Wildlife Ecology
  • Conservation Biology
  • Road Ecology

Background:

  • Roadkill is a significant threat to wildlife, necessitating accurate identification of high-risk areas (hotspots) for effective mitigation.
  • Existing methods like kernel density estimation (KDE) and maximum entropy modeling (ME) have limitations in detecting all suitable mitigation locations.
  • KDE may miss hotspots with low local animal abundance, while ME can partially identify hotspots due to imperfect discrimination.

Purpose of the Study:

  • To introduce and evaluate a hybrid consensus modeling (HCM) approach for identifying roadkill hotspots.
  • To compare the statistical performance and theoretical mitigation efficiency of HCM against KDE and ME.
  • To analyze spatial clustering and environmental conditions within identified hotspots for targeted conservation.

Main Methods:

  • Collected herpetofauna (amphibians and reptiles) roadkill data (N=839) across four roads in Taiwan.
  • Applied hybrid consensus modeling (HCM), kernel density estimation (KDE), and maximum entropy modeling (ME) to the dataset.
  • Evaluated models based on discrimination skill (AUC) and theoretical mitigation efficiency, considering spatial clustering (Moran's I).

Main Results:

  • HCM demonstrated superior theoretical mitigation efficiency (2.89) compared to KDE (2.58) and ME (1.91).
  • KDE and ME models showed good to excellent discrimination skill (AUCKDE=0.944, AUCME=0.822).
  • Theoretical mitigation efficiency increased with decreased spatial clustering (lower Moran's I).

Conclusions:

  • Hybrid consensus modeling (HCM) is a robust approach for identifying core roadkill hotspots, leveraging the strengths of KDE and ME.
  • HCM provides a more efficient allocation strategy for limited conservation resources, prioritizing permanent mitigation at consensus hotspots.
  • Recommendations include using HCM for permanent measures, KDE for temporary measures, and ME for additional monitoring based on budget constraints.