Movement Analysis
We calibrated the filtered data from the day after capture to the end of July (i.e., when birds were at the breeding grounds). Each bird’s capture location was used as the known location for calibration to estimate the error in sunset/sunrise times. Limited summer darkness in the subarctic can make sunset time challenging to estimate with light-loggers; the number of calibration days was increased by extending the calibration period to the end of July to augment the total number of days. A threshold model estimated latitude and longitude after correcting for twilight bias during the calibration period. Data collection began too late in the summer for one Arctic Warbler (USGS band 1780-53921) to effectively calibrate the geolocator, so we used calibration data from the other Arctic Warbler (1760-53520) as the best estimate of twilight detection bias for that individual.
The Markov Chain Monte Carlo (MCMC) modeling process used to estimate position was implemented using the SGAT package in R (Sumner et al. 2009; Lisovski & Hahn 2012; R Core Team 2020). These models combine a position estimation model with a movement model to determine the animal’s path in a Bayesian framework. We used vague priors on the movement speed and bearing parameters of the movement model. The distribution of terrestrial habitat contributed to the posterior estimates of position. We created a raster that categorized each cell as land or water and built a probability mask where overland travel was given a higher prior probability of use (log(2) vs. log(1), e.g. (Hill & Renfrew 2019). However, position estimation was unrestricted across the globe. While the probability mask increases the likelihood of terrestrial position estimates, it does not prevent overwater position estimates. Three MCMC chains were run with a 5,000 iteration burn-in, then a 10,000-iteration posterior sample. Posterior estimates of locations were visually checked for chain convergence.
Final location estimates are the mean of the three-chain posterior for each position, and the uncertainty in each is displayed using point intensity of the location estimate MCMC posteriors across a grid system. We estimated arrival and departure dates from the breeding and wintering grounds using multiple changepoint analysis to detect shifts in modeled latitude with a segmented neighborhood method (Killick & Eckley 2014). We used a cumulative probability test for non-normal data, with a penalty value of 0.9 to identify changes in mean longitude (for departing the breeding grounds) and latitude (for arriving at the wintering grounds). Estimated transition dates were visually assessed to determine accuracy. R scripts are documented in Appendix B.