DOI: 10.5061/dryad.tdz08kqct
title: Drought-driven foraging adjustments in breeding white storks (<em>Ciconia ciconia</em>): GPS tracking insights from two French marshes

description: In the context of climate change, the increasing frequency of severe
 meteorological events, such as floods or droughts, is expected to impact
 various life history traits in organisms, primarily by altering the
 availability and quality of their trophic resources. Our study aimed to
 quantify the effects of meteorological conditions on the fine-scale space
 use of breeding white storks (Ciconia ciconia). Birds were equipped with
 GPS tags and accelerometer sensors in two breeding areas within the
 marshes of the French Atlantic coast and monitored over four years,
 including one year of drought. Specifically, we examined variations in
 home-range size, daily foraging distances, the proportion of time spent
 foraging, and daily activity levels in relation to drought conditions and
 individual state (sex, brood age and brood size). Our findings reveal that
 under drier conditions, storks increased their daily foraging distances,
 home-range size, and time spent foraging. Individuals with smaller broods
 travelled greater distances from the nest, and tended to exploit larger
 home-ranges. Their activity levels and time spent foraging increased with
 brood age and brood size, and were higher in females than in males. Our
 findings reveal how climate change, particularly drought, affects the
 foraging behaviour of a wetland top predator, and highlight the
 conservation challenges faced by wetland ecosystems.
description: # Drought-driven foraging adjustments in breeding white storks (*Ciconia
 ciconia*): GPS tracking insights from two French marshes Dataset DOI:
 [10.5061/dryad.tdz08kqct](https://doi.org/10.5061/dryad.tdz08kqct) ##
 Description of the data and file structure White Storks (Ciconia ciconia)
 were equipped with GPS tags and accelerometer sensors in two breeding
 areas within the marshes of the French Atlantic coast and monitored over
 four years, including one year of drought. Specifically, we examined
 variations in home-range size, daily foraging distances, the proportion of
 time spent foraging, and daily activity levels in relation to drought
 conditions and individual state (sex, brood age and brood size). ### Files
 and variables #### File: matrice_ciccic_2025all.csv
 **Description:** Filtered and aggregated data matrix used for the
 statistical models. ##### Variables * device: GPS device ID * id_30:
 30-day period ID (1st or 2nd part of the rearing period) * Deviceyear:
 concatenation of the device ID and the study year * num_unique: unique no.
 per Deviceyear * ID: shorter ID, concatenation of num_unique, the two last
 number of the year and the period number (1 or 2) * YEAR: study year *
 POP1: French administrative department code of the bird (44: Loire site,
 17: Gironde site) * POP: same as for POP, except that 44 is replaced by 1
 and 17 is replaced by 2. * SEXE: sex of the individuals *
 PRODUCTION_envol: number of fledglings * START: start date of the
 recordings used in the analyses * END: end date of the recordings used in
 the analyses * ECLO: hatching date * ENVOL:  fledging date * PERIOD: 1st
 or 2nd 30-day period of the chick rearing * CHICKAGE: 0 when in the 1st
 period, 1 when in the 2nd period of chick rearing * n_all_bhv: number of
 GPS locations (all behaviours) * days_available: number of days with valid
 GPS locations * n_foraging: number of GPS locations classified as
 "foraging" ("walking") * ratio_foraging_all: ratio
 between n_foraging/ n_all_behaviours * HRker95_allhadjust: Home range size
 calculated using Kernel density estimator (95%) using adjusted h *
 HRker95_foragingHref: Home range size calculated using Kernel density
 estimator (95%) using h-ref * Hrker_foraging_hadjust: Home range size only
 based on foraging locations, calculated using Kernel density estimator
 (95%) using adjusted h * Mean_nest_dist: distances between the nest and
 each foraging location were calculated for each individual-period using
 the R package ‘geosphere’ (Hijmans et al. 2022), and averaged for each day
 to obtain mean daily distance. * prop_for_month: the proportion of
 foraging activity calculated by dividing the number of walking locations
 by the total number of GPS locations for each 30-day period. *
 ODBA.mG.: daily mean Overall Dynamic Body Acceleration was calculated
 first by summing the absolute dynamic acceleration across the three
 spatial axes for each 10 s sequence, and then by averaging these values
 across all sequences recorded within the day (Qasem et al. 2012). Daily
 mean ODBA was then averaged for each 30-day rearing period. *
 daily_mean_ODBA: same as ODBA.mG. but in G  * SPEI_har_pond: the
 standardized precipitation-evapotranspiration index (SPEI). This index was
 calculated using the functions spei and hargreaves (modified form of the
 Hargreave’s equation; Droogers and Allen 2002) from the ‘SPEI’ package
 (Beguería et al. 2014, Beguería and Vicente-Serrano 2023). It was computed
 on a monthly basis, incorporating data from the two preceding months, to
 reflect time-integrated drought conditions experienced by breeding
 storks.  It was then weighted for each individual 30-day period based on
 the proportion of days from each overlapping month that fell within the
 period. #### File: Stats_JABarticle_BEGASSATetal_2025.Rmd
 **Description:** Script containing the analyses performed in the
 manuscript. ## Code/software Analyses were conducted using R ver. 4.3.2
 ([www.r-project.org](http://www.r-project.org)) with the ‘RStudio’
 interface.\ We used the following R packages: **dplyr**, **corrplot**,
 **lme4**, **nlme**, **lmerTest**, **Matrix**, **MuMIn**, **car**,
 **lubridate**, **GGally**, **mgcv**, **lmtest**, **effects**, **flexmix**,
 **performance**, **gridExtra**, **emmeans**, **ggeffects**, and
 **multcomp**.\ Full package citations are provided below. 
 
 * **dplyr** — Wickham et al. (2023). *dplyr: A Grammar of Data
 Manipulation*. * **corrplot** — Wei & Simko (2024). *corrplot:
 Visualization of a Correlation Matrix*. * **lme4** — Bates et al. (2015).
 *Fitting Linear Mixed-Effects Models Using lme4*. * **nlme** — Pinheiro
 & Bates (2000); Pinheiro et al. (2025). *nlme: Linear and Nonlinear
 Mixed Effects Models*. * **lmerTest** — Kuznetsova et al. (2017).
 *lmerTest Package: Tests in Linear Mixed Effects Models*. * **Matrix** —
 Bates et al. (2025). *Matrix: Sparse and Dense Matrix Classes and
 Methods*. * **MuMIn** — Bartoń (2025). *MuMIn: Multi-Model Inference*. *
 **car** — Fox & Weisberg (2019). *An R Companion to Applied
 Regression*. * **lubridate** — Grolemund & Wickham (2011). *Dates and
 Times Made Easy with lubridate*. * **GGally** — Schloerke et al. (2025).
 *GGally: Extension to ggplot2*. * **mgcv** — Wood (2003, 2004, 2011, 2016,
 2017) — foundational GAM references. * **lmtest** — Zeileis & Hothorn
 (2002). *Diagnostic Checking in Regression Relationships*. * **effects** —
 Fox & Weisberg (2019), plus Fox (2003), Fox & Hong (2009), Fox
 & Weisberg (2018). * **flexmix** — Grün & Leisch (2025; 2004;
 2007; 2008). *flexmix: Flexible Mixture Modeling*. * **performance** —
 Lüdecke et al. (2021). *performance: Assessment and Testing of Statistical
 Models*. * **gridExtra** — Auguie (2017). *gridExtra: Miscellaneous
 Functions for Grid Graphics*. * **emmeans** — Lenth (2025). *emmeans:
 Estimated Marginal Means*. * **ggeffects** — Lüdecke (2018). *ggeffects:
 Marginal Effects from Regression Models*. * **multcomp** — Hothorn et al.
 (2008). *Simultaneous Inference in General Parametric Models*.