| Summary: | Dissertation (Ph.D.) University of Alaska Fairbanks, 2022 Migration is one of the world's great natural wonders and the scale of some migratory journeys is astounding. Yet migration is globally imperiled and effective conservation of the remaining migrations will require a thorough understanding of the drivers and mechanisms underlying how migrants complete such journeys. In this dissertation, I present three chapters that sought to better understand spring and autumn migration for the Western Arctic Herd, a population of barren-ground caribou (Rangifer tarandus) that complete some of the longest terrestrial migrations on the planet. In the first chapter, I applied and validated an analytical method to infer parturition events from GPS data with robust statistical confidence. In the second chapter, I examined the parturition events detected with these methods to better understand the drivers and mechanisms of spring migration because the calving grounds are the destination for pregnant females in spring. I quantified annual spatial patterns of calving and assessed what environmental factors influenced calving site selection by caribou through time. I found evidence of both memory and perception influencing spring migration, such that caribou use memory to return to an area of generally high-quality forage at the time of calving, and consequently adjust calving sites each year based on experienced conditions. In the third chapter, I sought to understand the environmental cues caribou respond to in deciding when to migrate in autumn. I found that decreasing temperatures and the timing of first snowfall events of the season had the greatest influence on migratory movements, but notably, caribou re-assessed decisions throughout the migration period as the conditions they experience changed. I also found that the cues caribou used are similar across individuals despite the herd being broadly dispersed in late summer, and the variability in migration timing observed each year is likely due to variability in ...
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