DOI: 10.5061/dryad.vmcvdnd34
title: Data from: Habitat conditions during winter explain movement among subpopulations of a declining migratory bird

description: Understanding animal movement patterns among subpopulations is crucial for
 identifying spatiotemporal patterns in animal abundance. Quantifying such
 movement rates enables a better understanding of population dynamics and
 how animals decide to move between habitats throughout their range.
 However, detailed assessments of animal movements among subpopulations are
 often difficult to obtain with typical capture-mark-recapture methods,
 limiting our ability to incorporate movement information into conservation
 planning. We used six years of high-resolution Global Positioning System
 (GPS) tracking data in a Bayesian multistate model to quantify habitat
 drivers of monthly intra- (i.e., from one month to the next within a
 winter) and inter-winter (i.e., from the last month of one winter to the
 first month of the following winter) movements among eight different
 subpopulations of a declining migratory bird, the Greenland white-fronted
 goose (Anser albifrons flavirostris). We found that while Greenland
 white-fronted geese were highly philopatric to geographically distinct
 wintering subpopulations, individuals made intra- and inter-winter
 movements based on locally available foraging habitat. These decisions
 changed within and among winters; geese were more likely to make
 intra-winter movements to areas with fewer croplands and boglands,
 potentially in response to local food depletion, and more likely to make
 inter-winter movements to areas with more boglands and “greener”
 grasslands. We demonstrated a framework for using high-frequency GPS
 tracking data to discover movement patterns and test hypotheses about
 environmental drivers of movements, which could be linked with population
 dynamics and applied to other species of concern for an improved
 understanding of metapopulations across space and time. Implementing
 habitat management strategies that optimize foraging conditions throughout
 winter, including rewetting degraded peatlands to provide bogland food
 plants, provision of cereal stubbles in early winter and high-quality
 grasslands throughout winter may therefore help improve conservation
 outcomes for Greenland white-fronted geese.
description: # Data from: Habitat conditions during winter explain movement among
 subpopulations of a declining migratory bird All analyses were completed
 using R. See manuscript for details. Please contact the corresponding
 author (A. Schindler) with questions about this data package or to seek
 potential collaborations using these data. --- ### Description Model code
 and associated data files to quantify habitat drivers of both intra- and
 inter-winter movements among Greenland white-fronted goose (*Anser
 albifrons flavirostris*) wintering subpopulation groups. --- ### Code
 files * `winter_multistate_model.R`: Multistate model code *
 `nimble_restart_functions.R`: Functions used in
 `winter_multistate_model.R` to continue model run from a new session (for
 improving model convergence) ### Data files (See manuscript for further
 details) * `monthly_ch.csv`: Monthly multistate capture histories for all
 Greenland white-fronted geese captured during the study * rows: individual
 Greenland white-fronted goose capture histories * columns: month/year
 (i.e., 2018_1 = first month [October] of winter 2018/2019) * states: 1 =
 observed at Lough Swilly, 2 = observed at Sheskinmore, 3 = observed at
 Connemara, 4 = observed at Lough Iron, 5 = observed at Wexford, 6 =
 observed at Islay, 7 = observed at West Freugh, 8 = observed at Loch Ken,
 9 = observed elsewhere, 10 = not observed * `covariate_data.csv`:
 Covariate data used in the multistate model to quantify the effects of
 habitat on movement probabilities * `state`: 1 = observed at Lough Swilly,
 2 = observed at Sheskinmore, 3 = observed at Connemara, 4 = observed at
 Lough Iron, 5 = observed at Wexford, 6 = observed at Islay, 7 = observed
 at West Freugh, 8 = observed at Loch Ken * `prop_farm`: Average proportion
 of land within the 95% minimum convex polygons associated with the
 corresponding state managed through a farm plan. * `prop_farm_scaled`:
 Average proportion of land (scaled) within the 95% minimum convex polygons
 associated with the corresponding state managed through a farm plan. *
 `prop_grass`: Average proportion of land within the 95% minimum convex
 polygons associated with the corresponding state classified as grassland.
 * `prop_grass_scaled`: Average proportion of land (scaled) within the 95%
 minimum convex polygons associated with the corresponding state classified
 as grassland. * `prop_ag`: Average proportion of land within the 95%
 minimum convex polygons associated with the corresponding state classified
 as agriculture. * `prop_ag_scaled`: Average proportion of land (scaled)
 within the 95% minimum convex polygons associated with the corresponding
 state classified as agriculture. * `prop_bog`: Average proportion of land
 within the 95% minimum convex polygons associated with the corresponding
 state classified as bog. * `prop_bog_scaled`: Average proportion of land
 (scaled) within the 95% minimum convex polygons associated with the
 corresponding state classified as bog. * `max_evi`: Maximum enhanced
 vegetation index value for all land classified as grass within each
 minimum convex polygon. * `evi_scaled`: Maximum enhanced vegetation index
 value (scaled) for all land classified as grass within each minimum convex
 polygon.