Analysis tools: Geolocation by light

Data from geolocators are often noisy which makes their analysis challenging. To improve the objectivity and the repeatability of the analysis we provide standard procedures for positioning.

Aims

We provide a simple step-by-step procedure from raw light level data to migration schedules and geographic positions. Our main goal is to enhance the repeatability of the analysis by reducing the effect of observer’s subjectivity and to circumvent pitfalls caused by method’s inherent errors (for a conceptual description of the procedure see supplement in Emmenegger et al. 2014,  and for an exemplary R code see supplement in Liechti et al 2015).

Approach

We use threshold-based positioning, i.e. the variation of sun rise / sun set times, to estimate migration schedules (temporal pattern) and stationary periods (spatial pattern) in terrestrial migrants. We generally distinguish between stationary periods and movement periods to finally increase the accuracy of the geographic position of each stationary site within the annual cycle, e.g. the breeding, stopover and non-breeding stationary sites.

The analysis is mostly carried out in the R package GeoLight which provides fundamental functions for a detailed analysis.

Significance

The two step discrimination between stationary and movement periods and between the temporal and the spatial aspect of migration allows for the independent analysis of temporal migration schedules (which are not affected by equinox periods) and for site-specific calibration attempts to adjust for changes in the behaviour of tracked individuals and the changing environment along the migration routes (i.e. various habitats).

Results

After launching the first version in 2012 and an update in 2013, the debugged and extended version 2.0 of the R package GeoLight is now freely available on CRAN. We organize workshops on geolocation by light to support with data analysis and to clarify method’s inherent difficulties in positioning.

Project management

Steffen Hahn, Tamara Emmenegger, Felix Liechti

Partners

Simeon Lisovski, Deakin University Geelong, Australia

Financial support

Swiss National Science Foundation (SNF) IZ32Z0_135914/1

Publications