Flexibility in the face of climate change? A rapid and dramatic shift toward later spring migration in Hudsonian godwits (Limosa haemastica)

With rapid environmental change, shifts in migration timing are vitally important for maintaining population stability in migratory species and have been widely documented. However, little remains known about how migrants make these shifts and what factors influence the utilization of these strategies, limiting our ability to assess their vulnerability to climate change. Hudsonian godwits (Limosa haemastica) are an extreme long-distance migratory shorebird that (1) have previously shifted their population-level migration timing and (2) are sexually dimorphic. We combined over a decade of tracking data from one breeding population with a historical predictive model to assess on-going shifts in migration timing and investigate potential sex-specific migration strategies. We found that godwit departure and arrival timing shifted 6 days later from 2010-2023 with no differences in timing between the sexes. The population maintained an average migratory duration of 24 days and drove shifts in..., , # Flexibility in the face of climate change? A rapid and dramatic shift toward later spring migration in Hudsonian godwits (Limosa haemastica)

Dataset DOI: [10.5061/dryad.12jm63z90](10.5061/dryad.12jm63z90)

## Description of the data and file structure

We deployed light-level geolocators on Hudsonian godwits breeding in Beluga, Alaska, USA, during two time periods, 2009-2012 and 2020-2023. We analyzed the resulting movement trajectories of those geolocators to assess whether there were changes in the nonbreeding ground departure and breeding ground arrival timing of individuals and the population over time.

### Files and variables

#### File: 2009-2012\_Tags.csv

**Description:** Migratory timing data from the first period of geolocator deployment

##### Variables

* Flag code: Individual Identifier
* Time period: Year deployed and year recovered
* Year: Actual year of migratory timing data
* USGS_Band: Individual Identifier
* Sex: Individual's sex based on plumage
* Multi year? Y/N...,