DOI: 10.5061/dryad.x0k6djhgx
title: Seasonal niche tracking of climate emerges at the population level in a migratory bird

description: Seasonal animal migration is a widespread phenomenon. At the species
 level, it has been shown that many migratory animal species track similar
 climatic conditions throughout the year. However, it remains unclear
 whether such niche tracking pattern is a direct consequence of individual
 behaviour or emerges at the population or species level through
 behavioural variability. Here, we estimated seasonal niche overlap and
 seasonal niche tracking at the individual and population level of Central
 European White Storks (Ciconia ciconia). We quantified niche tracking for
 both weather and climate conditions to control for the different
 spatio-temporal scales over which ecological processes may operate. Our
 results indicate that niche tracking is a bottom-up process. Individuals
 mainly track weather conditions while climatic niche tracking mainly
 emerges at the population level. This result may be partially explained by
 a high degree of intra- and inter-individual variation in niche overlap
 between seasons. Understanding how migratory individuals, populations and
 species respond to seasonal environments is key for anticipating the
 impacts of global environmental changes.
description: We trapped 62 adult white storks in the state of Saxony-Anhalt,
 Germany, and equipped them with solar GPS-ACC transmitters (e-obs GmbH;
 Munich, Germany) that weighed 55 g including harness, ca. 2% of the
 average stork’s weight (see  [25]). The transmitters recorded GPS fixes
 every 5 min when solar conditions were good (95% of the time) or every 20
 min otherwise. This dataset include a set of maximum 100 GPS
 locations randomly selected per day and individual to estimate the
 seasonal niche and to avoid over-fitting the data to some
 locations. In addition, each datapoint were associated
 to three type of environmental variables at two scales, weather and
 climate. For weather variables, the datapoints were annotated with
 environmental data of temperature (Land Surface Temperature &
 Emissivity 1-km Daily Terra; MOD11A1 V6), precipitation (ECMWF Interim
 Full Daily SFC-FC Total Precipitation; 0.75 deg.; 3 hourly)  and
 Normalized Difference Vegetation Index (NDVI; MODIS Land Vegetation
 Indices 1km 16 days Terra) using the Env-DATA track annotation tool of
 MoveBank. For the climate data, we used long-term averaged monthly
 temperature and precipitation patterns for the time period 1979-2013 at 1
 km resolution (CHELSA), and monthly NDVI for the time period 1982-2000
 (GIMMS AVHHR Global NDVI), and extracted the values of each variable for
 all selected points using the “raster” package.
  
description: There is two csv files for each environmental scale (weather and
 climate). Both data files have the GPS information for
 the 100 randomly points selected for each individual and day. Longitude
 ("location.long"), Latitude ("location.lat"), date
 (day_format"), and id for the individual
 ("tag.local.identifier"). Besides, three
 columns for each climate variable: 1- For climate data,
  tmean_value (temperature mean, from the CHELSA dataset),
 prec_value (precipitation mean, from the CHELSA dataset), and ndvi_value
 (monthly NDVI for the time period 1982-2000). 2- For
 weather data,  tmean_value (temperature mean, Land Surface Temperature
 & Emissivity 1-km Daily Terra; MOD11A1 V6),
 prec_value (precipitation from ECMWF Interim Full Daily SFC-FC Total
 Precipitation; 0.75 deg.; 3 hourly), and ndvi_value (MODIS Land Vegetation
 Indices 1km 16 days Terra).