Technical Explanations

The dsf() function calculates the spectrum level at each frequency of a signal. This is used to extract an animals dominant gait frequency.

Complementary Filter

The comp_filt() function separates intervals of movements into distinct frequency bands. This is used to gain insight about the locomotion and biomechanical strategies of different animals.

Depth Checks and Corrections

The fix_pressure() and fix_offset_pressure() functions correct a depth or altitude profile for offsets caused by miscalibration and temperature. This is important as biologging data are not always ready-to-use when you read them off the tag and some corrections may be needed.

Dead-Reckoned Track

The ptrack() function estimate 3D dive or flight paths by updating the estimated position of the animal using estimates of its current speed and direction of travel.

Jerk

The njerk() function estimates the differential of acceleration (m/s³). Jerk suppresses orientation changes and steady movements such as locomotion while boosting faster transient movements of typically smaller muscles that could be associated with strikes at prey.

Minimum Specific Acceleration (MSA)

The msa() function estimates the minimum specific acceleration, which is a general activity measure. MSA is particularly useful when the frequency range of specific acceleration and orientation changes can overlap, e.g., in foraging where there is both locomotion and rapid maneuvers.

Delay-Free Filtering

The fir_nodelay() function is a symmetric finite impulse response filter, which delay is cancelled out. A delay-free filter is essential if you want to preserve the temporal relationships between different sensors, e.g., accelerometer, magnetometer and depth sensors.

Spherical Calibration

The spherical_cal() function is a simple and reliable function that makes an initial calibration of the accelerometer and magnetometer. It is also a quick way to check that the sensors are working properly.

Animal Orientation

The a2pr() and m2h() functions calculate the pitch and roll from the triaxial accelerometer data and the heading from the pitch and roll and magnetometer data, respectively. These angles each describe the rotation of the body around a cardinal axis, giving an insight on an animal’s orientation.

Converting tag data into a NetCDF archive

Biologging data have many different file formats, because of the different sensors and also manufacturers. The NetCDF file format is independent of tag type (supporting the different sensor types and sampling rates) and software platform used for tag data analysis. It also allows both data and metadata in the same file.