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Airborne imagery does not preclude detectability issues in estimating bird colony size.

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Drone surveys for wildlife monitoring can be biased by detectability issues. This study used capture-recapture models to assess Malagasy pond heron nest detection, revealing significant temporal variations and recommending methods to control for bias.

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Area of Science:

  • Ecological research
  • Wildlife monitoring
  • Avian population studies

Background:

  • Drone-based aerial imagery is a growing tool for ecological research, particularly wildlife monitoring.
  • Detectability issues in wildlife surveys can lead to biased population estimates and trend analyses.
  • Understanding detection probabilities is crucial for accurate wildlife census data.

Purpose of the Study:

  • To investigate nest detectability of the Malagasy pond heron (Ardeola idae) using drone imagery.
  • To identify factors influencing nest detection probability in mangrove habitats.
  • To evaluate the impact of detectability variations on population monitoring.

Main Methods:

  • Repeated drone flights over Malagasy pond heron colonies during two breeding seasons.
  • Identification of individual birds and nests from aerial images.
  • Application of closed capture-recapture models to nest-detection histories.
  • Analysis of seasonal and intra-daily variations in bird abundance and nest availability.

Main Results:

  • Nest detection probability varied between 0.58 and 0.74, influenced by flyover timing.
  • Detection probability decreased by 25% from early to late morning.
  • Seasonal and intra-daily variations in bird abundance, especially immature birds, affected nest detectability.
  • Simulations indicated three flyovers are needed to detect a 5-6% decline in colonies.

Conclusions:

  • Nest detectability for forest-canopy breeding species from aerial imagery is subject to spatial and temporal variability.
  • Capture-recapture methods are recommended to mitigate bias in drone-based wildlife population estimates.
  • Accurate population trend assessment requires accounting for detection probability variations.