pamlr

pamlr analyzes multisensor geolocator data in R to infer animal behavioral patterns from pressure, acceleration, temperature, magnetism, and light sensors.

Key Features:

• Multisensor Integration: Integrates data from pressure (barometers), acceleration (accelerometers), temperature (thermometers), magnetism (magnetometers), and light (light-level sensors) for combined analyses.
• Data Handling and Visualization: Provides functions for importing, visualizing, and formatting multisensor geolocator datasets.
• Behavioral Pattern Identification: Derives behavioral classifications using cluster analysis and hidden Markov models.
• Calibration and Error Estimation: Implements methods for calibrating sensor data and estimating error in behavioral analyses.
• Flexibility Across Species: Contains functions tailored for avian studies while remaining applicable to a wide range of other species.
• Comparative Analysis: Enables comparison of classification accuracy between different models.

Scientific Applications:

• Migration Tracking: Infers migration timing and broad-scale movement patterns from multisensor signals.
• Foraging Behavior Analysis: Identifies foraging-related behaviors using combined acceleration, pressure, temperature, magnetism, and light data.
• Habitat Use and Movement Patterns: Characterizes fine-scale movement patterns and habitat use.
• Species-Specific Responses to Environmental Change: Assesses behavioral responses to environmental changes using calibrated multisensor data.

Methodology:

Employs statistical techniques and machine learning models, including cluster analysis and hidden Markov models, integrates multiple sensor inputs, and applies sensor calibration and error estimation.