A spatial capture-recapture model for group-living species

Area of Science:

• Ecology
• Population Biology
• Conservation Science

Background:

• Group-living species exhibit complex behaviors impacting populations and ecosystems.
• Estimating group density and size is vital for understanding population dynamics and conservation needs.
• Standard spatial capture-recapture (SCR) models face biases when modeling group-living species due to cohesive movement and variable group sizes.

Purpose of the Study:

• To develop and validate a novel cluster SCR model capable of simultaneously estimating group density, individual density, and group size.
• To address limitations of existing SCR methods that model individual or group detection separately, leading to biases.
• To provide a more accurate tool for studying group-living species.

Main Methods:

• Developed a cluster SCR model utilizing clustered point processes.
• Compared the cluster SCR model against standard SCR models through simulation studies.
• Applied the model to camera trap data of African wild dogs (Lycaon pictus) in Botswana.

Main Results:

• The cluster SCR model outperformed standard group-level SCR models when group sizes varied.
• The model accurately recovered estimates for African wild dog group density, individual density, and group size.
• Model performance improved with increased correlation between individual and group movements.

Conclusions:

• The cluster SCR model offers a robust framework for estimating key population parameters in group-living species.
• This approach enhances our ability to investigate ecological questions linking group size to population dynamics.
• The model effectively accounts for cohesive movement patterns inherent in group-living animals, improving ecological inference.