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Measuring Light-Switching Behavior Using an Occupancy and Light Data Logger
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Predicting wildlife road-crossing probability from roadkill data using occupancy-detection models.

Rodrigo A L Santos1, Mário Mota-Ferreira2, Ludmilla M S Aguiar3

  • 1Department of Ecology, University of Brasília-UnB, Brasília, Federal District, Brazil; IBRAM - Instituto Brasília Ambiental, Brasília, Federal District, Brazil.

The Science of the Total Environment
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Summary
This summary is machine-generated.

Wildlife-vehicle collisions (WVC) pose a significant threat. This study uses occupancy models to accurately assess roadkill risk, identifying high-risk areas near open habitats and agricultural land.

Keywords:
Bayesian modelsCrossing locationImperfect detectionRoad ecologyRoadkill risk

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Area of Science:

  • Wildlife ecology
  • Conservation biology
  • Road ecology

Background:

  • Wildlife-vehicle collisions (WVC) are a major threat to biodiversity.
  • Roadkill surveys are often biased due to undetected carcasses, potentially hindering conservation efforts.
  • Understanding the relationship between WVC patterns and land cover is crucial for effective mitigation strategies.

Purpose of the Study:

  • To assess roadkill risk along roads while accounting for imperfect detection.
  • To relate roadkill risk to surrounding land cover information.
  • To develop a robust framework for predicting high-mortality road sections.

Main Methods:

  • Conducted biweekly roadkill surveys over 114 km for five years (484 surveys).
  • Developed a Bayesian hierarchical occupancy model to estimate roadkill risk and detectability.
  • Analyzed data for six major road-killed taxa across different road sections and seasons (WET and DRY).

Main Results:

  • Roadkill risk was higher in road sections adjacent to agricultural and open habitats.
  • Detectability of carcasses was higher on 4-lane roads.
  • The occupancy model successfully accounted for imperfect detection in assessing roadkill risk.

Conclusions:

  • Occupancy models provide a valuable tool to overcome imperfect detection limitations in roadkill assessments.
  • Identifying high-risk road sections based on land cover and detectability can inform targeted conservation actions.
  • This modeling framework has significant potential for predicting and mitigating wildlife mortality on roads.