Using Activity Measures and GNSS Data from a Virtual Fencing System to Assess Habitat Preference and Habitat...

Area of Science:

• Livestock management
• Animal behaviour
• Ecological monitoring

Background:

• Sustainable livestock grazing requires monitoring cattle habitat use and behavior.
• High-frequency accelerometer and Global Navigation Satellite System (GNSS) data are energy-intensive, limiting long-term monitoring in virtual fencing systems.
• Developing methods using low-frequency data is crucial for efficient, long-term animal tracking.

Purpose of the Study:

• To assess cattle habitat preference and utilization patterns using low-frequency activity and location data.
• To determine daily activity patterns and infer grazing/resting sites in cattle.
• To evaluate the effectiveness of combining GNSS data, activity measures, and habitat mapping for livestock management.

Main Methods:

• Calculated habitat selection ratios for cattle in a coastal environment.
• Analyzed daily activity patterns using low-frequency data from virtual fencing collars.
• Inferred grazing and resting behaviors using a Gaussian mixture model and habitat mapping.

Main Results:

• Cattle demonstrated clear habitat preferences, favoring salt meadows and avoiding wooded dunes and humid dune slacks.
• Distinct daily activity patterns were identified.
• Salt meadows were predominantly used for resting and ruminating, as inferred from activity data and behavioral modeling.

Conclusions:

• Low-frequency GNSS and activity data, coupled with accurate habitat mapping, provide an effective tool for assessing cattle habitat preference and utilization.
• This approach supports sustainable livestock grazing by informing management decisions.
• The study demonstrates the feasibility of long-term, low-energy monitoring for understanding animal behavior and habitat use.