Estimating GPS-based social aggregation metrics using collar data.

Area of Science:

• Wildlife ecology
• Population dynamics
• Disease ecology

Background:

• Understanding ungulate social aggregation is vital for wildlife management, disease control (e.g., chronic wasting disease in cervids), and human-wildlife conflict mitigation.
• Global Positioning System (GPS) collar data provide herd density metrics essential for management decisions, but the required sample size for accurate aggregation analysis is unclear.
• Accurate aggregation metrics are necessary for comparing herd behavior and informing disease management strategies, especially for species like elk (Cervus canadensis).

Purpose of the Study:

• To determine the minimum sample size of GPS collars needed for reliable calculation of three key aggregation metrics: pairwise inter-animal distances, daily proximity rates, and kernel density estimate (KDE) areas.
• To assess how herd characteristics (size, group structure) and seasonal density variations influence the required sample size for accurate aggregation measurements.
• To provide guidance for researchers and managers on optimizing GPS collar deployment for effective big game herd monitoring.

Main Methods:

• Utilized GPS collar data from two distinct elk herds (Jackson and West Green River) in western Wyoming, USA, differing in size and social structure.
• Analyzed data collected between 2016-2019 (Jackson) and 2005-2010 (West Green River) to calculate aggregation metrics.
• Examined the consistency and accuracy of aggregation metrics across varying GPS collar sample sizes and herd contexts.

Main Results:

• Herd-specific characteristics significantly impact the sample size needed for accurate density measurements; larger, multi-group herds require more GPS collars.
• The sample size requirement varies by metric: Kernel Density Estimate (KDE) areas generally need fewer samples, particularly in high-density situations.
• Seasonal changes in density affect sample size needs, with high-density periods requiring similar sample sizes for distance and proximity metrics across different herd types.

Conclusions:

• The optimal number of GPS collars for studying elk aggregation varies based on herd size, structure, and the specific metric being analyzed.
• Kernel density estimates (KDE) for disease risk assessment may require fewer samples than other metrics, offering potential cost savings in wildlife studies.
• These findings offer practical insights for wildlife managers to determine appropriate sample sizes for effective herd monitoring and disease risk management.