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Multistate Dail-Madsen models effectively estimate wildlife abundance, survival, and recruitment from camera trap data. Increasing sample size improves accuracy, while missing data and fewer sites reduce precision for population monitoring.

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Area of Science:

  • Wildlife ecology
  • Population dynamics
  • Ecological modeling

Background:

  • Remote cameras are crucial for wildlife population studies, enabling demographic rate estimation.
  • Hierarchical models can track unmarked animals, but are computationally intensive and complex.
  • Multistate Dail-Madsen (DM) models offer advanced analysis for camera trap data.

Purpose of the Study:

  • To evaluate the efficacy of multistate DM models for estimating wildlife abundance, survival, and recruitment using camera trap data.
  • To assess the impact of varying sample sizes and missing data on model performance.
  • To validate camera-based estimates against telemetry data for moose (Alces alces).

Main Methods:

  • Simulation studies with varying abundance, missing data, camera sites, surveys, and years.
  • Analysis of empirical moose camera trap data using multistate DM models.
  • Comparison of camera-derived estimates with existing moose telemetry data.

Main Results:

  • Multistate DM models accurately recovered simulated demographic parameters.
  • Increased sites, surveys, and years enhanced estimate accuracy and precision.
  • High missing data and fewer sites decreased accuracy and precision, particularly for survival and recruitment.

Conclusions:

  • Multistate DM models are valuable for estimating demographic parameters from camera data when developmental stages are identifiable.
  • Model performance is sensitive to data completeness and sampling effort.
  • These models show promise for large-scale wildlife population monitoring.