A Unifying Model for Capture-Recapture and Distance Sampling Surveys of Wildlife Populations.

Area of Science:

• Wildlife ecology
• Population dynamics
• Statistical ecology

Background:

• Estimating wildlife population size and density is crucial for effective management.
• Capture-recapture (CR) and distance sampling (DS) are dominant but separate methods.
• Existing methods lack a unified framework to leverage diverse data types.

Purpose of the Study:

• To develop a unified modeling framework for wildlife population estimation.
• To synthesize capture-recapture and distance sampling methodologies.
• To enhance inference by incorporating supplementary location data and measurement error.

Main Methods:

• Developed a flexible class of models integrating CR and DS.
• Included nonspatial CR, spatially explicit CR (SECR), DS, and mark-recapture DS (MRDS) models.
• Incorporated models with varying degrees of location data and measurement error.

Main Results:

• Demonstrated utility on acoustic surveys of gibbons and frogs using different location data.
• Applied models to a visual MRDS survey of whales, comparing exact and estimated distances.
• Showcased improved inference from SECR models with supplementary location data.

Conclusions:

• The unified framework effectively synthesizes CR and DS methods.
• The models enhance population estimation by integrating diverse data, including imperfect location information.
• This approach offers a versatile tool for wildlife ecology and management across various survey types.