# Drought-driven foraging adjustments in breeding white storks Ciconia ciconia: GPS tracking insights from two French marshes

Océane Bégassat, Jean-marc Paillisson, Françoise Amélineau, Akiko Kato, Céline Rousselle, Hubert Dugué, Christophe Barbraud, Raphaël Musseau

<figure><img src="image_1.png" /></figure>

## To cite this version:

Océane Bégassat, Jean-marc Paillisson, Françoise Amélineau, Akiko Kato, Céline Rousselle, et al. "Drought-driven foraging adjustments in breeding white storks Ciconia ciconia: GPS tracking insights from two French marshes." Journal of Avian Biology, 2026, 2026 (1), pp.e03504. 10.1002/jav.03504 hal-05477084

HAL Id: hal-05477084

https://hal.science/hal-05477084v1

Submitted on 26 Jan 2026

HAL is a multi-disciplinary open access archive for the deposit and dissemination of scientific research documents, whether they are published or not. The documents may come from teaching and research institutions in France or abroad, or from public or private research centers.

L'archive ouverte pluridisciplinaire HAL, est destinée au dépôt et à la diffusion de documents scientifiques de niveau recherche, publiés ou non, émanant des établissements d'enseignement et de recherche français ou étrangers, des laboratoires publics ou privés.

<figure><img src="image_2.png" /></figure>

Distributed under a Creative Commons CC BY 4.0 - Attribution - International License
Research Article

# Drought-driven foraging adjustments in breeding white storks
## Ciconia ciconia: GPS tracking insights from two French marshes

Océane Bégassat<sup>1</sup>, Jean-Marc Paillisson<sup>1</sup>, Françoise Amélineau<sup>1</sup>, Akiko Kato<sup>2</sup>, Céline Rousselle<sup>3</sup>, Hubert Dugué<sup>4</sup>, Christophe Barbraud<sup>2</sup> and Raphaël Musseau<sup>3</sup>

**Correspondence:** Océane Bégassat (oceane.begassat@free.fr)

Journal of Avian Biology

2026: e03504

doi: 10.1002jav.03504

Subject Editor: Yali Si

Editor-in-Chief: Jan-Åke Nilsson

Accepted 21 November 2025

<figure><img src="image_3.png" /></figure>

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/ acceleration loggers 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.

Keywords: accelerometer, behavioural classification, biologging, breeding, wetland

Introduction

Climate change, including the global increase in temperature and the rising frequency of extreme events such as floods and droughts, alongside profound changes in land-use (AghaKouchak et al. 2020, IPCC 2023), is recognized for its impacts on food resources for wildlife, particularly for high trophic-level consumers (Durant et al. 2007, Thomas et al. 2017, Amarasekare 2019). For example, food depletion during severe

<figure><img src="image_4.png" /></figure>

<footnote n="1"><sup>1</sup>ECOBIO (Ecosystèmes, Biodiversité, Evolution), Université de Rennes, CNRS, Rennes, France</footnote>

<footnote n="2"><sup>2</sup>Centre d'Etudes Biologiques de Chizé, UMR7372 CNRS-La Rochelle Université, Villiers en Bois, France</footnote>

<footnote n="3"><sup>3</sup>BioSphère Environnement, Mortagne-sur-Gironde, France</footnote>

<footnote n="4"><sup>4</sup>Association pour la Connaissance et la Recherche Ornithologique Loire et Atlantique (ACROLA), Donges, France</footnote>
drought events impacts the breeding success and population size of waterbirds (Peach et al. 2004, Pearce-Higgins et al. 2010, Pearce-Higgins and Green 2014). This is particularly relevant for species that depend on invertebrate communities, which are strongly influenced by soil desiccation and altered permeability (Carroll et al. 2011, Thomas et al. 2017). However, short-term food shortages caused locally by the effects of climate change are too often overlooked in studies on animal foraging behaviour (Cox et al. 2019).

In response to food depletion, waterbirds are expected to adjust their foraging behaviour in various ways, mainly through changes in space use (e.g. based on biotelemetry studies; Kays et al. 2015). This is particularly expected during the breeding period, when birds 1) can be spatially constrained to forage around their nests, such as in the case of central-place foragers (Bell 1990), and 2) must provision their chicks during periods of high energy demand, making them particularly sensitive to environmental changes. Thus, breeding individuals can travel longer distances to reach more productive foraging areas when local food resources are depleted (e.g. in the white stork *Ciconia ciconia*: Alonso et al. 1991, Moritzi et al. 2001, Soriano-Redondo et al. 2021; the bean geese *Anser fabalis* and the lesser white-fronted geese *Anser erythropus*: Lei et al. 2019) and exploit larger home ranges (e.g. the white stork: Zurell et al. 2018; goose species: Lei et al. 2019). Birds can also switch to habitats richer in food, such as in the case of the white stork (Alonso et al. 1991, Golawski and Kasprzykowski 2021, Standfuß et al. 2022), the wood stork *Mycteria americana* (Gaines et al. 2000), wading birds (Lantz et al. 2010), the white ibis *Eudocimus albus* and the great egret *Ardea alba* (Beerens et al. 2011), and the ring-billed gull *Larus delawarensis* (Patenaude-Monette et al. 2014). Such findings are generally derived from bird presence data, whereas focusing on foraging behaviour could provide more subtle information, such as time allocation in foraging and foraging locations (Moritzi et al. 2001, Lantz et al. 2010, Wood et al. 2021, Ghimire et al. 2021). Finally, as a consequence of behavioural responses to poor food-resource availability, birds can increase their activity levels (Soriano-Redondo et al. 2016, Golawski and Kasprzykowski 2021). The majority of the aforementioned literature focuses on large waterbirds, which have high energetic demands and are known for their rapid responses to short-term fluctuations in prey availability (Kushlan 1981). As such, they can act as ecosystem sentinels (i.e. biological indicators of environmental quality and change; Clark-Wolf et al. 2024). Consequently, exploring plasticity in the foraging behaviour of waterbirds in response to changes in habitat conditions, particularly in relation to meteorological conditions, can provide valuable insights into the effects of global change on wetland ecosystems.

However, little is known about the extent to which plasticity in foraging behaviour enables individuals to cope with shifts in food abundance due to climate change (Ghalambor et al. 2010), especially in wetlands. Although wetlands support biodiversity (Gibbs 2000), including birds (Ramírez et al. 2018), and benefit from different conservation strategies

(including the Ramsar Convention), their degradation and loss is still ongoing worldwide (Davidson 2014, Davidson and Finlayson 2018, McInnes et al. 2020). Among the main threats to wetlands, climate change leads to severe disruptions of the natural hydrological regime by altering precipitation patterns and all their ecosystem functions (Zhang et al. 2015).

In the present study, we assessed the plasticity of foraging behaviour in white storks (hereafter refered to as 'storks'), opportunist top predators that mainly feed on aquatic macro-invertebrates, amphibians, and small mammals (Hoyo et al. 1992, Cramp and Simmons 1994, Barbraud et al. 2002). The white stork is among the most energy-demanding avian predators (Johst et al. 2001) and generally exploits vast areas (Alonso et al. 1991, Hoyo et al. 1992, Zurell et al. 2018). Its sensitivity to extreme temperatures (Hoyo et al. 1992) and reliance on drought-sensitive invertebrates (Kosicki et al. 2006), especially when breeding, make it an excellent wetland sentinel species. Despite a considerable body of literature on the foraging behaviour of storks in various habitats (e.g. agricultural landscapes; Golawski and Kasprzykowski 2021, Standfuß et al. 2022; landfills: Gilbert et al. 2016, Soriano-Redondo et al. 2021), no study has yet examined foraging plasticity in wetland-breeding populations at a fine spatio-temporal scale and under drought conditions.

We studied daily activity of breeding storks from two different marshes of the French Atlantic coast during four years (2020–2023). We measured the daily movements of breeding storks equipped with GPSs and accelerometers and assessed the effects of drought intensity on their activity. The year 2022 was characterised by prolonged drought and three heatwaves in spring across Europe. These conditions were comparable to climate projections that suggest an increase by 2°C around 2060 under the SSP2–4.5 scenario (intermediate greenhouse gas emissions; IPCC 2023, Sorel et al. 2023).

According to the studies that have been conducted to date, we hypothesized that the assumed low food abundance during dry conditions would lead to: 1) an increase in the daily distances travelled by storks to forage and in the size of their foraging home ranges (Rolando 2002), 2) an increase in the proportion of bird activity allocated to foraging behaviour (Moritzi et al. 2001), and consequently, 3) an increase in their daily activity levels measured with the help of overall dynamic body acceleration (ODBA; Halsey et al. 2009, Qasem et al. 2012). We also explored the influence of individual state (sex, brood age and brood size) on the four foraging metrics listed above.

# Material and methods

## Study sites and logger deployment

The study was conducted in two coastal marshes along the French Atlantic coast: one in the floodplain of the Loire River (Loire-Atlantique, 47.25°N, 1.80°W, hereafter referred to as 'Loire' supporting 155–227 stork breeding pairs during the period under study) and the second along the Gironde
estuary (Charente Maritime, 45.31°N, 0.77°W, named 'Gironde' supporting 550-560 breeding pairs), located 215 km apart from each other (Fig. 1, Barbraud et al. 2024).

Adult storks were captured from 2018 to 2023, either at the beginning of the breeding season (March), using spring nets or cannon nets at locations prepared with materials for nest construction/restoration, or at the end of the breeding season (July), using net guns triggered from tractors during the mowing period. A total of 31 individuals (including non-breeders) were captured, banded with a metal ring and a plastic Darvic ring and equipped with solar-charged GPS-GSM devices with 3-dimensional acceleration sensors (OrniTrack-L40, Ornitela, 40 g, approximately 1% of the average body mass of captured storks) all attached in the same standardized manner to the left tarsus to avoid any bias related to attachment technique. GPS and accelerometry data were recorded every 30 min from dawn to dusk (when the centre of the Sun was ≥ 6° under the Earth's horizon; maximum time window from 3:30 to 20:50 UTC) since storks forage during daylight hours. This setting balances the trade-off between minimizing temporal autocorrelation in consecutive data points collected and maximizing the daily sample size. Consecutive GPS locations were recorded for 20 s at 1 Hz, immediately followed by x-, y-, and z-axes acceleration data recorded for 10 s at 10 Hz. The last GPS location of each 20-location sequence was used for analysis, as it is considered to be the most accurate due to signal stabilization in GPS devices (Tomkiewicz et al. 2010).

We used only tracking data from the chick-rearing period (see calculation method below, Table 1), focusing on breeders with known phenology (hatching date) and breeding success. For birds equipped after fledging, we excluded data from the current breeding event, focusing instead on their subsequent breeding attempts. In contrast, we retained a few periods with incomplete data coverage from device failures (Table 1), after confirming that their exclusion did not improve model performance. Overall, we analyzed a dataset consisting of 40 individual-year units (i.e. birds × breeding attempts from 21 distinct individuals monitored from 2020 to 2023; Table 1). Birds were sexed either by direct observation (based on clear sex-size dimorphism; Kwieciński and Tryjanowski 2009, Gadenne 2012: one individual; or sex-specific behaviours, including copulation: 18 individuals), or molecularly from feather samples collected during logger deployment (two individuals; Table 1), including eight females and 13 males.

## Reproductive parameters

We assigned an individual-specific rearing period to each tracked stork, in order to reflect annual, site-level, and individual differences in breeding phenology. When chicks of equipped storks were ringed, hatching dates were back-calculated based on the age of the largest (presumed oldest) chick within the brood (n=23/40 broods) using its tarsus length and the chick growth equation developed by Benharzallah et al. (2015). When measurements were not possible due to nest inaccessibility (n=17/40 broods), the

<figure><img src="image_5.png" /><figcaption>Figure 1. Foraging home ranges (KDE95) of breeders, calculated from GPS positions associated with foraging behaviour during the first 30-day period of chick rearing, in the Loire and Gironde estuaries. CLC: CORINE land cover (European Union's Copernicus Land Monitoring Service information).</figcaption></figure>
<table><caption>Table 1. Summary of the individual number of GPS locations associated with foraging behaviour during the chick-rearing period (subdivided into two 30-day periods), for each study site and year. A dash (-) indicates no available data. *: periods with GPS and/or accelerometry arrhythmias or delayed accelerometry activation resulting in incomplete data coverage (15, 17, 20, 21, 26 and 28 days of data, respectively). **: brood failure recorded at the end of the first 30-day period.</caption><thead><tr><th rowspan="2">Site</th><th colspan="2">Device</th><th rowspan="2">Sex</th><th colspan="4">No. of GPS locations (rearing period: period 1/period 2)</th></tr><tr><th>ID</th><th>Deployment year</th><th>2020</th><th>2021</th><th>2022</th><th>2023</th></tr></thead><tbody><tr><td>Loire</td><td>181909</td><td>2020</td><td>F</td><td>391*/486*</td><td>411/645</td><td>395/682</td><td>345/539</td></tr><tr><td>Loire</td><td>190659</td><td>2020</td><td>F</td><td>-</td><td>338/518</td><td>430/718</td><td>377/570</td></tr><tr><td>Loire</td><td>190664</td><td>2019</td><td>M</td><td>294/449</td><td>259/443</td><td>349/551</td><td>-</td></tr><tr><td>Loire</td><td>190658</td><td>2020</td><td>M</td><td>-</td><td>-</td><td>320/526</td><td>354/326</td></tr><tr><td>Loire</td><td>190665</td><td>2019</td><td>M</td><td>206*/504</td><td>521/472</td><td>-</td><td>-</td></tr><tr><td>Gironde</td><td>190657</td><td>2019</td><td>F</td><td>416/505</td><td>436/329*</td><td>-</td><td>-</td></tr><tr><td>Gironde</td><td>190657b</td><td>2023</td><td>F</td><td>-</td><td>-</td><td>-</td><td>416/562</td></tr><tr><td>Gironde</td><td>200015</td><td>2020</td><td>F</td><td>181*/444</td><td>411/629</td><td>373/579</td><td>404/571</td></tr><tr><td>Gironde</td><td>203562</td><td>2021</td><td>F</td><td>-</td><td>-</td><td>360/508</td><td>-</td></tr><tr><td>Gironde</td><td>210358</td><td>2022</td><td>F</td><td>-</td><td>-</td><td>476/628</td><td>406/674</td></tr><tr><td>Gironde</td><td>226969</td><td>2023</td><td>F</td><td>-</td><td>-</td><td>-</td><td>569/-**</td></tr><tr><td>Gironde</td><td>200013</td><td>2020</td><td>M</td><td>355/355</td><td>405/425</td><td>441/593</td><td>316/366*</td></tr><tr><td>Gironde</td><td>200014</td><td>2020</td><td>M</td><td>410/427</td><td>-</td><td>-</td><td>-</td></tr><tr><td>Gironde</td><td>200017</td><td>2020</td><td>M</td><td>-</td><td>-</td><td>418/552</td><td>-</td></tr><tr><td>Gironde</td><td>200018</td><td>2020</td><td>M</td><td>380/611</td><td>-</td><td>-</td><td>-</td></tr><tr><td>Gironde</td><td>203563</td><td>2021</td><td>M</td><td>-</td><td>-</td><td>-</td><td>247/319</td></tr><tr><td>Gironde</td><td>210280</td><td>2022</td><td>M</td><td>-</td><td>-</td><td>-</td><td>361/584</td></tr><tr><td>Gironde</td><td>210281</td><td>2022</td><td>M</td><td>-</td><td>-</td><td>356/539</td><td>309/493</td></tr><tr><td>Gironde</td><td>210359</td><td>2022</td><td>M</td><td>-</td><td>-</td><td>361/475</td><td>362/492</td></tr><tr><td>Gironde</td><td>226972</td><td>2023</td><td>M</td><td>-</td><td>-</td><td>-</td><td>312/479</td></tr><tr><td>Gironde</td><td>226973</td><td>2023</td><td>M</td><td>-</td><td>-</td><td>-</td><td>356/486</td></tr></tbody></table>

age of the largest chick was visually estimated using either a drone or a telescope. An average difference of 3.05 ± 2.83 (standard deviation) days was observed between the two methods (tarsus-based calculation versus visual estimation) when both were available (n=22/40 broods), which comforted us in the use of these two methods to age chicks. For subsequent analyses, we included brood size (recorded during chick ringing or remotely observed during regular monitoring), brood age (considering an average rearing period of 60 days, which was divided in two 30-day periods; Hoyo et al. 1992) and the sex of the equipped individual as covariates, since all of these factors may influence the foraging activity of adults (Bertram et al. 1996, Erikstad et al. 1997, García-Navas and Sanz 2010, Gwiazda et al. 2017, Kwieciński et al. 2017). The use of 30-day periods represented a good balance between the monthly resolution of the meteorological variables (below) and the need for a sufficient number of GPS locations to calculate foraging descriptors.

## Behavioural classification from tri-axial accelerometers

Storks mainly feed by slowly walking to capture macroinvertebrates or amphibians encountered (Carrascal et al. 1990, Bocheński et al. 2006). Using biologging data, Barbraud et al. (2024) found that the time spent walking is a good proxy for the time spent foraging in the white stork. We therefore developed a behavioural classification model to discriminate walking from other behaviours using accelerometry data and machine learning ('rabc' package in R; Yu and Klaassen 2021), with the aim of estimating a series of foraging descriptors. We

present here a short version of the methodology (a full version is detailed in the Supporting information).

We trained the model in 'rabc' using 1036 accelerometric sequences from a subset of our dataset (six days of recordings: 01/05/21-06/05/21) from six randomly selected storks and labelled using eight behaviours: take-off, walking (used as a proxy for foraging), resting with the leg carrying the GPS sensor down, resting with the leg carrying the GPS sensor lifted, flapping flight, gliding flight, sitting and sitting with the leg inclined. The identification of accelerometry pattern for each behaviour (see the Supporting information) was partially based on observations of captive storks conducted by Barbraud et al. (2024).

A total of 36 features (specific mathematical descriptors of the accelerometry signal; Brown et al. 2013) was calculated for each accelerometry sequence to discriminate between the labelled behaviours. These included minimum specific acceleration (MSA; Simon et al. 2012), pitch angle (leg inclination from a vertical axis; Wilson et al. 2008), and overall dynamic body acceleration (ODBA; Halsey et al. 2009, Gleiss et al. 2011), along with the minimum, maximum, mean, variance, standard deviation and range values of x-, y- and z-axes (value ranges are provided in the Supporting information). Four non-redundant features were then selected to categorise behaviours: y-axis maximum, MSA standard deviation, y-axis minimum, and z-axis variance. A supervised machine learning model (XGBoost) was then trained using these four features, with 75% of the labeled dataset used to train the model and the remaining 25% to test it. A confusion matrix was generated to assess model accuracy (up to 96%, p < 0.001,
see also model performance in discriminating walking in the Supporting information). The final model was then used to predict behaviours for each accelerometry sequence in the full dataset. Predicted behaviours were then assigned to the corresponding GPS location.

## Foraging descriptors

GPS data (geographic coordinates) were filtered to remove imprecise or erroneous locations. Specifically, we excluded data with a horizontal dilution of precision (HDOP) ≥ 5 (Specht 2021), a commonly used threshold (e.g. Ferrarini and Gustin 2022, Bracey et al. 2024), as well as locations with both latitude and longitude equal to 0, altogether totaling less than 0.20% of data removed. No excessive speed values between consecutive locations were found (maximum value of 83 km h⁻¹ largely lower than the maximum of 110 km h⁻¹ reported in BirdLife International and Stichting BirdLife Europe 2019). We then calculated four foraging descriptors for each of the two 30-day rearing periods: the foraging home-range size based on the walking activity of the birds, the average distance between the nest and each foraging (i.e. walking) location, the average ODBA as a proxy for activity level (Halsey et al. 2009, Schreven et al. 2021), and the proportion of all GPS locations classified as foraging behaviour (walking).

To estimate home-range size, we applied two complementary methods: 1) kernel density estimation (KDE95%), a commonly-used conservative method (Kernohan et al. 2001, Hemson et al. 2005, Katakisto and Moilanen 2006, Zurell et al. 2018), and 2) the multi-nuclear outlier-exclusive range core method defined by Kenward et al. (2001), which is particularly suited for species exploiting patchy habitats, such as the white stork (Johst et al. 2001). Preliminary tests showed a strong correlation between estimates from the two methods (Spearman correlation, $r_s=0.84$, p < 0.001). We therefore used only KDE95% in the subsequent analyses to facilitate comparison with the existing literature. Kernel contour analyses were performed using the *kernelUD* function from the 'adehabitatHR' package (Calenge 2024). To calculate the 95% fixed kernels density representing the foraging area (in hectares; Worton 1989), we used a data-driven approach to determine the smoothing parameter (bandwidth, *b*) thereby avoiding both over- and under-smoothing (O'Hanlon et al. 2022). For each individual and 30-day rearing period, we therefore initially calculated the reference bandwidth (*href*) based on the sample's standard deviation and an automatically defined grid size. We then used the ad hoc method developed by Kie (2013) for selecting the most suitable bandwidth by iteratively applying multipliers to *href* and visually inspecting the resulting 95% KDE contours. The optimal multiplier was defined as the minimum value at which the number of 95% contour polygons matches that obtained with the initial *href* (i.e. before home-range fragmented into multiple smaller polygons). The final KDE95% was then computed using an adjusted bandwidth ($h = href \times$ optimal multiplier, ranging from 0.3 to 1.1, depending on the 30-day period).

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. To account for the variable number of accelerometry sequences per day and per individual, daily mean ODBA 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 distances and ODBA were then averaged for each 30-day rearing period. Finally, the proportion of foraging activity (hereafter '%foraging') was calculated by dividing the number of walking locations by the total number of GPS locations for each 30-day period.

Despite positive correlations between certain foraging variables (home-range size and distance from the nest: Spearman correlation, $r_s=0.88$, p < 0.001; %foraging and daily mean ODBA: $r_s=0.87$, p < 0.001; Supporting information), we retained all four foraging descriptors in subsequent analyses to facilitate comparison with the existing literature.

## Drought index calculation

To explore whether variation in the foraging behaviour of breeding storks was influenced by interannual meteorological conditions, we calculated the standardized precipitation-evapotranspiration index (SPEI) using data from two local Météo France stations: Saint-Nazaire-Montoir (47.31383°N, 2.15450°W) for the Loire estuary marshes and Mortagne-sur-Gironde (45.47767°N, 0.78983°W) for the Gironde estuary marshes. SPEI incorporates evapotranspiration and, as such, reflects drought intensity, soil moisture and permeability (Vicente-Serrano et al. 2010). It is therefore a valuable proxy for food supply in wetlands (Pearce-Higgins et al. 2010, Anderson et al. 2021). 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 (Bateman et al. 2015, Gorzo et al. 2016). SPEI values were categorized using four classes as recommended by (McKee et al. 1993): mild drought [−0.99, 0], moderate drought [−1.49, −1.00], severe drought [−1.99, −1.50], and extreme drought [< −2.00]. Finally, the SPEI was weighted for each individual 30-day period based on the proportion of days from each overlapping month that fell within the period.

## Statistical analysis

First, we tested for differences in SPEI values between years using Kruskal-Wallis tests, followed by post hoc pairwise comparisons (R package 'agricolae', false discovery rate method; Mendiburu 2023). These tests examined year effects on SPEI values across the entire rearing season (pooling individual-periods) separately for the Loire and Gironde estuaries, using a significance threshold of 0.05. Second, we tested the effects of several candidate predictors on variation
in foraging variables using mixed models. Brood age (first or
second 30-day period), brood size (four categories: one, two,
three, and ≥ four chicks) and adult sex were included as cat-
egorical fixed factors. Bird identity and study area (Loire or
Gironde estuary) were included as random factors to account
for pseudo-replication (we were not interested in exploring
whether foraging behaviour of storks varied between the two
study areas). To select the random structure of the modelling
framework, each random factor was added independently
to the full linear model up to the most complex structure
including bird identity nested within its breeding area (Loire
or Gironde). Selection of the random structure was based
on AICc and marginal R² criteria (below) resulting in the
inclusion of the individual identity in all models except for
ODBA, for which study area was used. We then built all can-
didate models by testing combinations of main and additive
effects, including interactions between SPEI and biological
factors (the full model being: foraging descriptor ~ SPEI ×
BROOD AGE + SPEI × BROOD SIZE + SPEI × SEX + ran-
dom effects). Interactions were tested because environmental
conditions - here drought intensity - may affect individuals
differently depending on their sex (e.g. through differences
in physiological traits or parental roles; Sasvári and Hegyi
2001, Wu et al. 2025), or brood age and size (e.g. increasing
energetic demands imposed by a larger number of chicks).
Limited sample sizes in some predictor combinations (e.g.
only two females with two chicks), prevented us from testing
simultaneously SPEI × SEX with SPEI × BROOD SIZE and
SPEI × BROOD AGE with SPEI × BROOD SIZE in order
to maintain statistical power and avoid model instability due
to unbalanced sample sizes. Models were then ranked by the
Akaike information criterion corrected for small sample sizes
(AICc; Anderson and Burnham 2002, Burnham et al. 2011)
calculated using the 'MuMln' package (Barton 2010). When
several models were within ΔAICc < 2, we discarded those
including an additional non-informative parameter to the top
model (i.e. a parameter for which its 95% confidence interval

included zero, as recommended in Arnold 2010; model-
averaging was ultimately not necessary). Both marginal and
conditional R² (R²m and R²c, respectively) were calculated
using the r.squaredGLMM function of the 'MuMln' package.
Finally, post hoc comparisons for brood size were conducted
after type II ANOVA ('car' package; Fox and Weisberg 2018)
using estimated marginal means with Tukey correction
('emmeans' package; Lenth et al. 2025).

Since non-linear relationships between candidate predictors
and foraging descriptors were possible, we initially explored
data using generalized additive mixed models (GAMM, using
the 'mgcv' package; Wood 2011, 2017, 2023) with an auto-
matic definition of the 'knots' number (k). Except for the
%foraging (modelled with a GAMM), other foraging vari-
ables were modelled using linear mixed models (LMMs, using
the 'lme4' package; Bates et al. 2003, 2015) since the effective
degrees of freedom (edf) were less than 2 for all explanatory
variables in GAMMs (Zuur et al. 2009, Pedersen et al. 2019).
Foraging home-range sizes and distances were ln-transformed
to satisfy normality and homoskedasticity assumptions. Due
to overfitting issues in GAMMs (with a binomial family and
a logit link function) for the %foraging, we retained a model
with k=3 (better explained variance and lower complex-
ity penalty compared to the initial GAMM, k-index=0.84,
p=0.16, using the k.check function; Wood 2017).

All statistical analyses were conducted using R ver. 4.3.2
(www.r-project.org) with the 'RStudio' interface.

Results

Drought conditions experienced by storks varied over the
study period, with mild drought conditions in both areas in
2022 (Fig. 2). In the Gironde estuary, all other years pre-
sented normal meteorological conditions (Kruskal–Wallis test,
χ²=31.04, p < 0.001). In contrast, mild drought conditions
also occurred in 2021 in the Loire estuary.

<figure><img src="image_6.png" /><figcaption>Figure 2. Comparison of standardized precipitation-evapotranspiration index (SPEI) range values during the chick-rearing period over four years in each area. Different letters indicate significant differences between years. Boxplots show the first quartile, median, and third quartile, with whiskers extended to 1.5 × the interquartile range.</figcaption></figure>
A total of 77 180 GPS locations were recorded over the four-year study (mean: 977 ± 95 locations per individual-period), of which 34 701 (i.e. 44.9%) were classified as walking behaviour (mean: 439 ± 113 locations, range=181–718 locations per individual 30-day period; Table 1). On average, storks foraged at a distance of 1890 ± 1714 m from their nest (range=189–7963 m). The mean foraging home-range size per 30-day period was 2345 ± 3886 ha (51–22 882 ha; Fig. 1). On average, the daily mean ODBA per individual-period was 291.5 mG (range=196.5–438.6 mG). Storks of the two study areas spent, on average, 44.9 ± 10.3% of their daily activity walking (range=24.4–70.8%).

Variation in the mean daily distance to the nest was best explained by SPEI and brood size (despite the fact that the model exhibited a low R²m; Supporting information). Distances increased as drought conditions intensified (a 20.2% increase in distance per decrease in one SPEI unit; estimate [95% CI] = -0.22 [-0.40, -0.05]; Fig. 3a). When SPEI was null, birds foraged, on average, at 1979 m from their nest. Individuals with broods of four or more chicks foraged 53% closer to the nest than those with a single chick (-0.76 [-1.30, -0.24]; Fig. 3b), while no significant

effect was found for broods of two or three chicks. Results were broadly similar for home-range sizes; they increased as SPEI decreased (-0.34 [-0.71, -0.03]; Fig. 3c, Supporting information) and decreased with brood size (the ANOVA test tend to be statistically significant; χ²=7.47, df=3, p=0.058). On average, home ranges were 73% smaller for birds rearing four or more chicks compared to those with a single chick (95% CI=29–90%; Fig. 3d, post hoc comparisons not shown). No other explanatory variables explained variation in either the mean daily distance to the nest or the home-range size.

Brood age (with the first 30-days rearing period as reference level) had a positive effect on the daily mean ODBA (estimate=69.66 [53.01, 86.35]; Supporting information, Fig. 4). Individuals with broods of two or three chicks displayed a higher daily mean ODBA than those with a single chick (36.23 [3.78, 66.25] and 45.93 [19.15, 72.67], respectively; Supporting Information, Fig. 4), while no significant difference was found for broods with four and more chicks relative to single-chick broods (Supporting information). In addition, females exhibited significatively higher daily ODBA than males (-36.11 [-54.45, -18.59]).

<figure><img src="image_7.png" /></figure>

Figure 3. Relationships between the natural-log-transformed daily mean distance from the nest (m) and (a) standardized precipitation-evapotranspiration index – SPEI – (the black line shows the predicted marginal effect of SPEI, averaged across levels of brood size), or (b) brood size, and the natural-log-transformed foraging home-range size (ha), calculated with the Kernel method (95%), and (c) SPEI (same process), or (d) brood size. The black line and blue ribbon (or whiskers) represent the fitted model and its 95% confidence interval; black points show raw data. Letters indicate significant differences between brood size levels based on pair comparisons between estimated marginal means with Tukey correction.
<figure><img src="image_8.png" /><figcaption>Figure 4. Relationship between daily mean overall dynamic body acceleration (ODBA) and the rearing period (split into the first and second 30-day intervals as a proxy for brood age), shown by brood size and sex (red=female, blue=male). Points represent raw data; whiskers indicate the 95% confidence intervals. Letters indicate significant differences between brood size levels.</figcaption></figure>

Concerning the %foraging, the retained model included the combination of SPEI, sex, brood age, and brood size (adjusted R² > 0.64; Supporting information). Females spent a greater proportion of their activity foraging than males (estimate with female as reference level = -0.36 [-0.67, -0.32]; Fig. 5a). The proportion of foraging increased during the second month of rearing (estimate with the first 30-day period as reference level = 0.59 [0.56, 0.62]), when the brood was composed of more than two chicks (0.15 [0.06, 0.24], 0.34 [0.27, 0.41], and 0.19 [0.12, 0.26] for two, three and four or more chicks compared to one chick; Fig. 5a), and under drier conditions (SPEI as a smoothed explanatory variable with edf = 1.5, F = 42.26, p < 0.001; Supporting information, Fig. 5b).

## Discussion

Using fine-scale biologging data collected over four years from two populations of breeding white storks, our study highlighted that, as expected, under drier conditions, storks foraged at longer distances from their nest, exploited larger foraging home ranges, and spent a higher proportion of their activity walking. Foraging distances and home-range sizes also decreased with increasing brood size. Contrary to our prediction, their daily mean activity levels (ODBA) were not impacted by drought conditions, but, instead, increased with brood age and brood size. Finally, females spent a higher

proportion of time foraging and had greater activity levels than males.

## Effects of drought on the foraging behaviour of storks

Since distances travelled from the nest and foraging home-range sizes were highly correlated, the same interpretation regarding the effects of SPEI applies to both descriptors. Our findings align with seminal theoretical studies, notably the territory theory and the optimal foraging theory, which suggest that ‘the size of the necessary territories (of a central-place forager) is adjusted to its exact ecological requirements and its specific food’ (Mayr 1935, Andersson 1978) and that home-range size is inversely related to both the maximum resource density and resources renewal rate (Ford 1983). For species such as the white stork, which rely heavily on wetland habitats, several empirical studies have shown that drought conditions increase the patchiness of suitable foraging areas (Middleton and Kleinebecker 2012, Wen et al. 2016, Wang et al. 2025). This increased fragmentation likely compels individuals to travel farther to access resources, resulting in larger foraging home ranges (Rolando 2002), a pattern observed in several diurnal top predators, such as white storks (Alonso et al. 1991, Moritzi et al. 2001) and Swainson's hawks *Buteo swainsoni* (Babcock 1995).

Despite a significant effect of drought according to our model – with foraging home-range size increasing by 31.3%
<figure><img src="image_9.png" /><figcaption>Figure 5. Relationships between the proportion of foraging activity and (a) the rearing period (split into the first and second 30-day intervals as a proxy for brood age), shown by brood size and sex (red=female, blue=male) and (b) standardized precipitation-evapotranspiration index – SPEI – (additionnal factors are fixed to their reference levels, i.e. brood size = 1, brood age = first 30-day period, and sex = F). Points represent raw data. Letters indicate significant differences between brood size levels in (a) and the line and blue ribbon represent the fitted model and its 95% confidence interval in (b).</figcaption></figure>

for each one-unit decrease in SPEI – the daily foraging dis-
tances and the home-range sizes we calculated were consis-
tent with values reported in the literature, suggesting that
drought effects were not dramatic. The mean daily distance
between foraging sites and the nest was 1890 m (range = 189–
7963 m), compared to 1324 m (range = 40–12 500 m) in
Alonso et al. (1994), and 1436 m in Chenchouni (2017).
Their mean foraging home-range size was 2345 ha (range:
51–22 881 ha), or 3 384 ha (range: 5–73 923 ha) when
considering all GPS locations regardless of behavioural filter-
ing (see the Supporting information), which falls within the
100 to 3 500 ha range reported by Lakeberg (1995, cited in

Moritzi et al. 2001) and the 2 140 ± 2 900 ha reported in
Zurell et al. (2018).

Contrary to expectations, we found no correlation between
space-use descriptors (distance from the nest and foraging
home-range size) and either the proportion of time spent for-
aging or the daily ODBA. One possible explanation is that
increased heterogeneity in the quality of foraging patches dur-
ing droughts may decouple space use from actual foraging
effort. Storks may either concentrate their activity in nearby
patches of sufficient quality or expand their range without
necessarily increasing foraging effort. However, our findings
remain consistent with the broader pattern that foraging
activity tends to be influenced by adverse meteorological conditions. For example, Soriano-Redondo et al. (2016) reported increased foraging time in Eurasian cranes *Grus grus* following a flooding episode, which they attributed to the need to spend more time searching for food in suboptimal areas.

Contrary to our prediction, ODBA was not influenced by SPEI. Since this measure reflects overall activity levels rather than foraging specifically (despite a significant correlation with %foraging), it may not be a reliable descriptor for foraging behaviour. However, given that distances travelled, home-range sizes, and %foraging tend to increase under drier conditions, we would have expected a similar trend for ODBA (see Soriano-Redondo et al. 2021 for a comparable pattern under resource-limited conditions). Since ODBA includes all behaviours, including both flight and foraging (together accounting for 51.3% of all recorded behaviours), a rise in activity under drier conditions seemed likely due to increased transit flights between resource patches. The absence of such a pattern remains unexplained and warrants further investigations, for instance by investigating varying accelerometry resolutions (Halsey et al. 2009) or by using alternative energy expenditure proxies (Green et al. 2009).

## Effects of other predictors on the foraging activity of storks

Interestingly, we did not detect any effect of sex or brood age on the space use of storks. In his review, Rolando (2002) highlighted the inconsistent effects of sex on home-range size across various terrestrial bird species. This also applies to the white stork (Crank and Simmons 1977, Sasvári and Hegyi 2001, Bocheński et al. 2006), where low sexual dimorphism may make sex-related differences in foraging ecology difficult to detect (Kwieciński et al. 2017). However, we did observe higher ODBA values than a greater proportion of foraging behaviour in females than in males. This is consistent with an experimental study on captive breeding storks which reported a male-to-female foraging ratio of 1:4 (Kwieciński et al. 2017). This sex difference may reflect lower foraging efficiency in females, potentially due to more selective prey preferences (Bocheński et al. 2006), resulting in reduced prey capture rates (Sasvári and Hegyi 2001). Regardless of the underlying mechanisms, our results also indicate that both sexes increased their foraging effort as chicks aged. This likely reflects the increasing energetic demand of their offspring (e.g. in Gaines et al. 2000 for the wood stork), possibly involving a shift toward a greater number of prey items, and/or larger, more energy-rich prey (Chenchouni 2017). Although brood size significantly affected most foraging variables, home-range size and daily distances decreased as brood size increased – contrasting with previous studies suggesting greater parental effort, and thus larger foraging areas, in larger broods (Moritzi et al. 2001, Djerdali et al. 2008). We suggest that this finding may result from parents of larger broods making more frequent trips, thereby limiting foraging distances. Overall, the studied foraging variables appeared to plateau beyond a certain brood size, indicating potential limits to behavioural adjustment.

Finally, no interaction terms between the drought index and intrinsic factors were retained in the final models. This does not imply that interactions do not exist. As mentioned earlier, it is plausible that parental sensitivity to drought depends on biological traits such as sex-related physiological or behavioural differences (Kwieciński and Tryjanowski 2009, Kwieciński et al. 2017), brood age (due to changing energetic constraints; Katzenberger et al. 2015), or brood size (with larger broods at fledging potentially reflecting better parental ability to cope with environmental pressures). This absence of significance could result from the small sample size for some factor combinations. Future work incorporating larger datasets, may therefore provide a clearer assessment of how drought effects vary with intrinsic parental traits.

## Conclusion

Our results provide evidence of subtle but consistent foraging responses of breeding white storks to drought conditions. The standardized precipitation-evapotranspiration index (SPEI) appears to be a relevant and biologically meaningful proxy of deteriorating environmental conditions and food availability for birds (Wilson et al. 2018, Cady et al. 2019). We also highlighted significant variation in the foraging behaviour of storks related to individual state (notably parent sex, brood size, and brood age) across varying meteorological conditions.

By combining a drought index and individual state variables, we revealed nuanced responses in the space use of breeding white storks: they increased both foraging distances and foraging time in response to resource scarcity and brood size, while adjusting only their foraging effort and time allocation (but not foraging distances) in response to offspring development stage. This likely reflects a trade-off, consistent with the dual foraging strategies described by Zurell et al. (2015) whereby white storks alternate between a time-minimization strategy (i.e. restricting their home range to reduce travel time) and an energy-maximization strategy (i.e. enlarging their home range to exploit distant but profitable foraging grounds). Finally, our results suggest inter-individual and site-level variability in foraging behaviour, which could be further explained by considering landscape and habitat characteristics around nests. Additionally, factors such as individual age, experience, and social interactions (Alonso et al. 1994, Nevoux et al. 2008) may also shape foraging strategies. Future studies investigating these dimensions could provide valuable insights into how white storks respond to drought episodes.

To conclude, our study demonstrates that storks exhibit foraging behavioural plasticity under drought conditions. However, as Pearce-Higgins and Green (2014) pointed out, the demographic consequences of such plasticity remain to be assessed. A key next step will be to evaluate how projected warming scenarios may affect stork fitness and population dynamics in marshland ecosystems. Under repeated drought conditions, breeders may be forced to prioritize foraging over other essential behaviours (such as resting) in response to
reduced food availability, as shown in guillemots *Uria aalge*
(Uttley et al. 1994), potentially compromising their fitness
(Johst et al. 2001, Moritzi et al. 2001). This could occur
either through parental exhaustion – leading to reduced nest
vigilance and increased chick predation – or through par-
tial brood loss (Mock 1994). Ultimately, reduced foraging
opportunities under extreme climatic conditions could nega-
tively impact the reproductive output and long-term viability
of white stork populations (Martín et al. 2021).

*Acknowledgements* – We would particularly like to thank all the field assistants for their valuable help during the field sessions and data collection, and especially Amandine Barles (from ACROLA) for her support in data acquisition and processing, as well as for her valuable contributions during discussions. We thank Marie-Claire Martin for her assistance with the molecular sexing of individuals. We are also grateful to the subject editor and two anonymous reviewers for their constructive comments on a previous version of the manuscript. We thank Mindaugas Dagys (Ornitela company) for his technical assistance regarding the GPS devices used as part of this study.

*Funding* – We would like to express our sincere gratitude for the generous financial support provided by the Région Nouvelle-Aquitaine, the Communauté de Communes de Haute-Saintonge, ENEDIS, the Fondation Jardin Majorelle, the Ville de Jonzac, the Crédit Agricole Charente-Maritime Deux-Sèvres endowment fund, the Sturia company (French caviar production), the Palmyre Conservation endowment fund managed by the La Palmyre Zoo, as well as all the people who participated in the crowdfunding campaign that enabled the acquisition of a significant portion of the GPS loggers used for this study. This research was financially supported by ECOBIO through a Master’s thesis grant awarded to OB.

*Conflict of interest* – The authors declare no conflict of interest.

*Permits* – All protocols performed in the study involving animals comply with the ethical standards and French laws on animal welfare. Birds were ringed and equipped under the autorization no. 493 granted by the Centre de Recherches sur la Biologie des Populations d'Oiseaux (CRBPO – French National Museum of Natural History).

Author contributions

**Océane Bégassat:** Data curation (supporting); Formal analysis (lead); Methodology (lead); Visualization (lead); Writing – original draft (lead); Writing – review and editing (lead). **Jean-Marc Paillisson:** Conceptualization (equal); Funding acquisition (equal); Methodology (equal); Supervision (equal); Validation (equal); Writing – review and editing (equal). **Françoise Amélineau:** Conceptualization (equal); Funding acquisition (equal); Methodology (equal); Supervision (equal); Validation (equal); Writing – review and editing (equal). **Akiko Kato:** Validation (supporting); Writing – review and editing (supporting). **Céline Rousselle:** Data curation (equal); Investigation (equal); Writing – review and editing (supporting). **Hubert Dugué:** Data curation (equal); Funding acquisition (equal); Investigation (equal); Writing – review and editing (supporting). **Christophe Barbraud:** Conceptualization (equal); Data curation (supporting); Funding acquisition (supporting); Investigation

(equal); Methodology (equal); Supervision (equal); Validation
 equal); Writing – review and editing (equal). **Raphaël Musseau:** Conceptualization (equal); Data curation (supporting); Funding acquisition (equal); Investigation (equal); Methodology (equal); Supervision (equal); Validation (equal); Writing – review and editing (equal).

Transparent peer review

The peer review history for this article is available at https://www.webofscience.com/api/gateway/wos/peer-review/10.1002 jav.03504.

Data availability statement

Data and R scripts are available from the Dryad Digital
Repository: https://doi.org/10.5061/dryad.tdz08kqct
(Bégassat et al. 2025). R scripts are also available from
https://doi.org/10.5281/zenodo.18171774 (Bégassat et al.
2026).

Supporting information

The Supporting information associated with this article is
available with the online version.

References

AghaKouchak, A., Chiang, F., Huning, L. S., Love, C. A., Mal-
lakpour, I., Mazdiyasni, O., Moftakhari, H., Papalexiou, S. M.,
Ragno, E. and Sadegh, M. 2020. Climate extremes and com-
pound hazards in a warming world. – Annu. Rev. Earth Planet.
Sci. 48: 519–548.

Alonso, J. C., Alonso, J. A. and Carrascal, L. M. 1991. Habitat selection by foraging white storks, *Ciconia ciconia*, during the breeding season. – Can. J. Zool. 69: 1957–1962.

Alonso, J. A., Alonso, J. C., Carrascal, L. M. and Muñoz-Pulido,
R. 1994. Flock size and foraging decisions in central place for-
aging white storks, *Ciconia Ciconia*. – Behav. 129: 279–292.

Amarasekare, P. 2019. Effects of climate warming on consumer-resource interactions: a latitudinal perspective. – Front. Ecol. Evol. 7: 146.

Andersson, M. 1978. Optimal foraging area: size and allocation of search effort. – Theor. Popul. Biol. 13: 397–409.

Anderson, D. R. and Burnham, K. P. 2002. Avoiding pitfalls when using information-theoretic methods. – J. Wildl. Manage. 66: 912–918.

Anderson, A. M., Friis, C., Gratto-Trevor, C. L., Harris, C. M., Love, O. P., Morrison, R. I. G., Prosser, S. W. J., Nol, E. and Smith, P. A. 2021. Drought at a coastal wetland affects refueling and migration strategies of shorebirds. – Oecologia 197: 661–674.

Arnold, T. W. 2010. Uninformative parameters and model selection using Akaike's information criterion. – J. Wildl. Manage. 74: 1175–1178.

Babcock, K. W. 1995. Home range and habitat use of breeding Swainson's hawks in the Sacramento. – J. Raptor Res. 29: 193–197.

Barbraud, C., Barbraud, J.-C., Barbraud, M. and Delord, K. 2002. Changements récents dans le régime alimentaire des poussins de cigogne blanche *Ciconia ciconia* en Charente-Maritime (centre-ouest, France). – Alauda 70: 437–443.
Barbraud, C., Kato, A., Trehou, G., Dugué, H. and Musseau, R. 2024. Factors affecting time activity budgets of breeding white storks *Ciconia ciconia* assessed using bio-logging. – Ardeola 71: 321–335.

Bartoń, K. 2010. MuMIn: multi-model inference. – R package ver. 1.48.4, https://CRAN.R-project.org/package=MuMIn.

Bateman, B. L., Pidgeon, A. M., Radeloff, V. C., Allstadt, A. J., Resit Akçakaya, H., Thogmartin, W. E., Vavrus, S. J. and Heglund, P. J. 2015. The importance of range edges for an irruptive species during extreme weather events. – Landsc. Ecol. 30: 1095–1110.

Bates, D., Maechler, M., Bolker, B. and Walker, S. 2003. lme4: linear mixed-effects models using "eigen" and S4. – R package ver. 1.1.37, https://CRAN.R-project.org/package=lme4.

Bates, D., Mächler, M., Bolker, B. and Walker, S. 2015. Fitting linear mixed-effects models using lme4. – J. Stat. Softw. 67: 1–48.

Beerens, J. M., Gawlik, D. E., Herring, G. and Cook, M. I. 2011. Dynamic habitat selection by two wading bird species with divergent foraging strategies in a seasonally fluctuating wetland - Selección de hábitat dinámica por dos especies de aves zancudas con estrategias divergentes de forrajeo en un humedal con fluctuaciones estacionales. – Auk 128: 651–662.

Bégassat, O., Paillisson, J.-M., Amélineau, F., Kato, A., Rousselle, C., Dugué, H., Barbraud, C. and Musseau, R. 2025. Data from: Drought-driven foraging adjustments in breeding white storks *Ciconia ciconia*: GPS tracking insights from two French marshes. – Dryad Digital Repository, https://doi.org/10.5061/dryad.tdz08kqct.

Bégassat, O. 2026. Rcode_WhiteStork_Foraging: v1.0.0. Rcode for movement analyses. – Zenodo. https://doi.org/10.5281/zenodo.18171774.

Beguería, S. and Vicente-Serrano, S. M. 2023. SPEI: calculation of the standardized precipitation-evapotranspiration index. – R package ver. 1.8.1, https://CRAN.R-project.org/package=SPEI.

Beguería, S., Vicente-Serrano, S. M., Reig, F. and Latorre, B. 2014. Standardized precipitation evapotranspiration index (SPEI) revisited: parameter fitting, evapotranspiration models, tools, datasets and drought monitoring. – Int. J. Climatol. 34: 3001–3023.

Bell, W. J. 1990. Central place foraging. – In: Bell, W. J. (ed.), Searching behaviour: the behavioural ecology of finding resources. – Springer, pp. 171–187.

Benharzallah, N., Si Bachir, A., Taleb, F. and Barbraud, C. 2015. Factors affecting growth parameters of white stork nestlings in eastern Algeria. – J. Ornithol. 156: 601–612.

Bertram, D. F., Welham, C. V. J. and Ydenberg, R. C. 1996. Flexible effort in breeding seabirds: adjustment of provisioning according to nestling age and mass. – Can. J. Zool. 74: 1876–1881.

BirdLife International and Stichting BirdLife Europe 2019. Flight for survival: white stork. – https://flightforsurvival.org/birds/white-stork/.

Bocheński, M., Jerzak, L., Tryjanowski, P. and Sparks, T. H. 2006. Behaviour of the white stork *Ciconia ciconia*: a review. – In: Sparks, T. H., Tryjanowski, P. and Jerzak L. (eds), The white stork in Poland: studies in biology, ecology and conservation. – Bogucki Wydawnictwo Naukowe, pp. 301–330.

Bracey, A., Strand, F., Grinde, A., Cuthbert, F., McKellar, A. E., Moore, D., Craig, E. and Lisovski, S. 2024. Stopover regions, phenology, and spatiotemporal group dynamics of adult and juvenile common terns *Sterna hirundo* from inland lakes in North America. – J. Avian Biol. 2025: e03308.

Brown, D. D., Kays, R., Wikelski, M., Wilson, R. and Klimley, A. P. 2013. Observing the unwatchable through acceleration logging of animal behavior. – Anim. Biotelem. 1: 20.

Burnham, K. P., Anderson, D. R. and Huyvaert, K. P. 2011. AIC model selection and multimodel inference in behavioral ecology: some background, observations, and comparisons. – Behav. Ecol. Sociobiol. 65: 23–35.

Cady, S. M., O'Connell, T. J., Loss, S. R., Jaffe, N. E. and Davis, C. A. 2019. Species-specific and temporal scale-dependent responses of birds to drought. – Global Change Biol. 25: 2691–2702.

Calenge, C. 2024. Home range estimation in R: the adehabitatHR package. – R package ver. 0.4.22, https://CRAN.R-project.org/package=adehabitatHR.

Carrascal, L., Alonso, J. C. and Alonso, J. 1990. Aggregation size and foraging behaviour of white storks *Ciconia ciconia* during the breeding season. – Ardea 78: 399–404.

Carroll, M. J., Dennis, P., Pearce-Higgins, J. W. and Thomas, C. D. 2011. Maintaining northern peatland ecosystems in a changing climate: effects of soil moisture, drainage and drain blocking on craneflies. – Global Change Biol. 17: 2991–3001.

Chenchouni, H. 2017. Contribution à l'étude de la bio-écologie de la Cigogne blanche (*Ciconia ciconia*) dans la région de Batna (Nord-est algérien). – PhD thesis, Batna 2 University, Algeria.

Clark-Wolf, T. J., Holt, K. A., Johansson, E., Nisi, A. C., Rafiq, K., West, L., Boersma, P. D., Hazen, E. L., Moore, S. E. and Abrahms, B. 2024. The capacity of sentinel species to detect changes in environmental conditions and ecosystem structure. – J. Appl. Ecol. 61: 1638–1648.

Cox, A. R., Robertson, R. J., Lendvai, Á. Z., Everitt, K. and Bonier, F. 2019. Rainy springs linked to poor nestling growth in a declining avian aerial insectivore (*Tachycineta bicolor*). – Proc. R. Soc. B 286: 20190018.

Cramp, S. and Simmons, K. E. L. (eds) 1977. White stork. – In: Handbook of the birds of the western Palearctic. – Oxford Univ. Press, pp. 328–335.

Cramp, S. and Simmons, K. E. L. 1994. Handbook of the birds of Europe the Middle East and North Africa. 1: Ostrich to ducks. – Oxford Univ. Press.

Davidson, N. 2014. How much wetland has the world lost? Long-term and recent trends in global wetland area. – Mar. Freshw. Res. 65: 934–941.

Davidson, N. C. and Finlayson, C. M. 2018. Extent, regional distribution and changes in area of different classes of wetland. – Mar. Freshw. Res. 69: 1525–1533.

Djerdali, S., Tortosa, F. S., Hillstrom, L. and Doumandji, S. 2008. Food supply and external cues limit the clutch size and hatchability in the white stork *Ciconia ciconia*. – Acta Ornithol. 43: 145–150.

Droogers, P. and Allen, R. G. 2002. Estimating reference evapotranspiration under inaccurate data conditions. – Irrig. Drain. Syst. 16: 33–45.

Durant, J., Hjermann, D., Ottersen, G. and Stenseth, N. 2007. Climate and the match or mismatch between predator requirements and resource availability. – Clim. Res. 33: 271–283.

Erikstad, K. E., Asheim, M., Fauchald, P., Dahlhaug, L., Tveraa, T. and Dahlhaug, P. 1997. Adjustment of parental effort in the puffin: the roles of adult body condition and chick size. – Behav. Ecol. Sociobiol. 40: 95–100.

Ferrarini, A. and Gustin, M. 2022. Unsupervised ethograms of a vulnerable bird species: the red-footed falcon in Northern Italy. – Ecologies 3: 435–445.

Ford, R. G. 1983. Home range in a patchy environment: optimal foraging Predictions 1. – Am. Zool. 23: 315–326.
Fox, J. and Weisberg, S. 2018. An R companion to applied regression. – SAGE Publications.

Gadenne, H. 2012. Les effets des changements climatiques et des changements d'usages sur les oiseaux d'eau migrateurs : une approche mécaniste chez un oiseau emblématique, la Cigogne blanche. – PhD thesis, Univ. de Poitiers, France.

Gaines, K. F., Bryan, A. L. and Dixon, P. M. 2000. The effects of drought on foraging habitat selection of breeding wood storks in coastal Georgia. – Waterbirds 23: 64–73.

García-Navas, V. and Sanz, J. J. 2010. Flexibility in the foraging behavior of blue tits in response to short-term manipulations of brood size. – Ethology 116: 744–754.

Ghalambor, C., Angeloni, L. M. and Carroll, S. 2010. Behavior as phenotypic plasticity. – Evol. Behav. Ecol. 2010: 90–107.

Ghimire, P., Pandey, N., Timilsina, Y. P., Bist, B. S. and Sundar, K. S. G. 2021. Woolly-necked stork (*Ciconia episcopus*) activity budget in lowland Nepal's farmlands: the influence of wetlands, seasonal crops, and human proximity. – Waterbirds 44: 415–424.

Gibbs, J. P. 2000. Wetland Loss and biodiversity conservation. – Conserv. Biol. 14: 314–317.

Gilbert, N. I., Correia, R. A., Silva, J. P., Pacheco, C., Catry, I., Atkinson, P. W., Gill, J. A. and Franco, A. M. A. 2016. Are white storks addicted to junk food? Impacts of landfill use on the movement and behaviour of resident white storks (*Ciconia ciconia*) from a partially migratory population. – Mov. Ecol. 4: 7.

Gleiss, A. C., Wilson, R. P. and Shepard, E. L. C. 2011. Making overall dynamic body acceleration work: on the theory of acceleration as a proxy for energy expenditure. – Methods Ecol. Evol. 2: 23–33.

Golawski, A. and Kaspryzkowski, Z. 2021. Alternative foraging strategies in the white stork *Ciconia ciconia*: the effect of mowing meadows. – Agric. Ecosyst. Environ. 319: 107563.

Gorzo, J. M., Pidgeon, A. M., Thogmartin, W. E., Allstadt, A. J., Radeloff, V. C., Heglund, P. J. and Vavrus, S. J. 2016. Using the North American Breeding Bird Survey to assess broad-scale response of the continent's most imperiled avian community, grassland birds, to weather variability. – Condor 118: 502–512.

Green, J. A., Halsey, L. G., Wilson, R. P. and Frappell, P. B. 2009. Estimating energy expenditure of animals using the accelerometry technique: activity, inactivity and comparison with the heart-rate technique. – J. Exp. Biol. 212: 471–482.

Gwiazda, R., Ledwoń, M. and Neubauer, G. 2017. Sex-specific foraging behaviour of adult whiskered terns *Chlidonias hybrida* in response to body mass and offspring age. – AORN 52: 81–92.

Halsey, L. G., Green, J. A., Wilson, R. P. and Frappell, P. B. 2009. Accelerometry to estimate energy expenditure during activity: best practice with data loggers. – Physiol. Biochem. Zool. 82: 396–404.

Hemson, G., Johnson, P., South, A., Kenward, R., Ripley, R. and Macdonald, D. 2005. Are kernels the mustard? Data from Global Positioning System (GPS) collars suggests problems for kernel home-range analyses with least-squares cross-validation. – J. Anim. Ecol. 74: 455–463.

Hijmans, R. J., Karney, C., Williams, E. and Vennes, C. 2022. geosphere: spherical trigonometry. – R package ver. 1.5.20, https://CRAN.R-project.org/web/packages/geosphere.

Hoyo, J. del, Elliott, A., Sargatal, J. and Cabot, J. (eds) 1992. Handbook of the birds of the world. – Lynx Edicions.

IPCC 2023. – In: Core Writing Team, Lee, H. and Romero, J. (eds), Climate change 2023: synthesis report. – Contribution of Working Groups I, II and III to the sixth assessment report of the Intergovernmental Panel on Climate Change.

Johst, K., Brandl, R. and Pfeifer, R. 2001. Foraging in a patchy and dynamic landscape: human land use and the white stork. – Ecol. Appl. 11: 60–69.

Katajisto, J. and Moilanen, A. 2006. Kernel-based home range method for data with irregular sampling intervals. – Ecol. Modell. 194: 405–413.

Katzenberger, J., Tate, G., Koeslag, A. and Amar, A. 2015. Black sparrowhawk brooding behaviour in relation to chick age and weather variation in the recently colonised Cape Peninsula, South Africa. – J. Ornithol. 156: 903–913.

Kays, R., Crofoot, M. C., Jetz, W. and Wikelski, M. 2015. Terrestrial animal tracking as an eye on life and planet. – Science 348: aaa2478.

Kenward, R. E., Clarke, R. T., Hodder, K. H. and Walls, S. S. 2001. Density and linkage estimators of home range: nearest-neighbor clustering defines multinuclear cores. – Ecology 82: 1905–1920.

Kernohan, B. J., Gitzen, R. A. and Millspaugh, J. J. 2001. Chapter 5: Analysis of animal space use and movements. – In: Millspaugh, J. J. and Marzluff, J. M. (eds), Radio tracking and animal populations. – Academic Press, pp. 125–166.

Kie, J. G. 2013. A rule-based ad hoc method for selecting a bandwidth in kernel home-range analyses. – Anim. Biotelem. 1: 13.

Kosicki, J., Profus, P., Dolata, P. and Tobólka, M. 2006. Food composition and energy demand of the white stork *Ciconia ciconia* breeding population. Literature survey and preliminary results from Poland. – In: Sparks, T. H., Tryjanowski, P. and Jerzak L. (eds), The white stork in Poland: studies in biology, ecology and conservation. – Bogucki Wydawnictwo Naukowe, pp. 169–183.

Kushlan, J. A. 1981. Resource use strategies of wading birds. – Wilson Bull. 93: 145–163.

Kwieciński, Z. and Tryjanowski, P. 2009. Differences between sexes in digestive efficiency of the white stork *Ciconia ciconia* under experimental conditions. – Folia Biol. 57: 193–198.

Kwieciński, Z., Rosin, Z. M., Dylewski, Ł. and Skórka, P. 2017. Sexual differences in food preferences in the white stork: an experimental study. – Naturwissenschaften 104: 39.

Lakeberg, H. 1995. Feeding ecology of the White stork *Ciconia ciconia* in Oberschwaben (S-Germany): feeding area, time budget, development of the young, and territoriality. – Ökologie der Vögel 17: 1–1590.

Lantz, S. M., Gawlik, D. E. and Cook, M. I. 2010. The effects of water depth and submerged aquatic vegetation on the selection of foraging habitat and foraging success of wading birds. – Condor 112: 460–469.

Lei, J., Jia, Y., Wang, Y., Lei, G., Lu, C., Saintilan, N. and Wen, L. 2019. Behavioural plasticity and trophic niche shift: how wintering geese respond to habitat alteration. – Freshw. Biol. 64: 1183–1195.

Lenth, R. V., Banfai, B., Bolker, B., Buerkner, P., Giné-Vázquez, I., Herve, M., Jung, M., Love, J., Miguez, F., Piaskowski, J., Riebl, H. and Singmann, H. 2025. emmeans: estimated marginal means, aka least-squares means. – R package ver. 1.11.2.8, https://CRAN.R-project.org/web/packages/emmeans.

Martín, B., Onrubia, A. and Ferrer, M. 2021. Climate change and the spatiotemporal variation in survival of a long-distance migrant (white stork, *Ciconia ciconia*) across Western Europe. – Birds 2: 362–380.
Mayr, E. 1935. Bernard Altum and the territory theory. – PYOC. Linn. Sot. N.Y. 45,46: 24–38.

McInnes, R. J., Davidson, N. C., Rostron, C. P., Simpson, M. and Finlayson, C. M. 2020. A citizen science state of the World's wetlands survey. – Wetlands 40: 1577–1593.

McKee, T. B., Doesken, N. J. and Kleist, J. 1993. The relationship of drought frequency and duration to time scales. – Proc. 8th Conf. Appl. Climatol. 17: 179–183.

Mendiburu, F. de 2023. agricolae: statistical procedures for agricul-
tural research. – R package ver. 1.3.7, https://CRAN.R-proje-
ct.org/web/packages/agricolae.

Middleton, B. A. and Kleinebecker, T. 2012. The effects of climate-
change-induced drought and freshwater wetlands. – In: Mid-
dleton, B. A. (ed.), Global change and the function and distri-
bution of wetlands. – Springer, pp. 117–147.

Mock, D. W. 1994. Brood reduction: narrow sense, broad sense.
– J. Avian Biol. 25: 3–7.

Moritzi, M., Maumary, L., Schmid, D., Steiner, I., Vallotton, L.,
Spaar, R. and Biber, O. 2001. Time budget, habitat use and
breeding success of white storks *Ciconia ciconia* under variable
foraging conditions during the breeding season in Switzerland.
– Ardea 89: 457–470.

Nevoux, M., Barbraud, J.-C. and Barbraud, C. 2008. Breeding experience and demographic response to environmental variability in the white stork. – Condor 110: 55–62.

O'Hanlon, N. J., Thaxter, C. B., Burton, N. H. K., Grant, D., Clark, N. A., Clewley, G. D., Conway, G. J., Barber, L. J., McGill, R. A. R. and Nager, R. G. 2022. Habitat selection and specialisation of herring gulls during the non-breeding season. – Front. Mar. Sci. 9: 816–881.

Patenaude-Monette, M., Bélisle, M. and Giroux, J.-F. 2014. Balancing energy budget in a central-place forager: which habitat to select in a heterogeneous environment? – PLoS One 9: e102162.

Peach, W. J., Denny, M., Cotton, P. A., Hill, I. F., Gruar, D., Bar-
ritt, D., Impey, A. and Mallord, J. 2004. Habitat selection by
song thrushes in stable and declining farmland populations. – J.
Appl. Ecol. 41: 275–293.

Pearce-Higgins, J. W. and Green, R. E. 2014. Birds and climate change: impacts and conservation responses. – Cambridge Univ. Press.

Pearce-Higgins, J. W., Dennis, P., Whittingham, M. J. and Yal-
den, D. W. 2010. Impacts of climate on prey abundance
account for fluctuations in a population of a northern wader
at the southern edge of its range. – Global Change Biol. 16:
12–23.

Pedersen, E. J., Miller, D. L., Simpson, G. L. and Ross, N. 2019. Hierarchical generalized additive models in ecology: an introduction with mgcv. – PeerJ 7: e6876.

Qasem, L., Cardew, A., Wilson, A., Griffiths, I., Halsey, L. G., Shepard, E. L. C., Gleiss, A. C. and Wilson, R. 2012. Tri-axial dynamic acceleration as a proxy for animal energy expenditure: should we be summing values or calculating the vector? – PLoS One 7: e31187.

Ramírez, F., Rodríguez, C., Seoane, J., Figuerola, J. and Busta-
mante, J. 2018. How will climate change affect endangered
Mediterranean waterbirds? – PLoS One 13: e0192702.

Rolando, A. 2002. On the ecology of home range in birds. – Rev. Écologie 57: 53–73.

Sasvári, L. and Hegyi, Z. 2001. Condition-dependent parental effort and reproductive performance in the white stork *Ciconia ciconia*. – Ardea 89: 281–291.

Schreven, K. H. T., Stolz, C., Madsen, J. and Nolet, B. A. 2021. Nesting attempts and success of Arctic-breeding geese can be derived with high precision from accelerometry and GPS-tracking. – Anim. Biotelem. 9: 25.

Simon, M., Johnson, M. and Madsen, P. T. 2012. Keeping momentum with a mouthful of water: behavior and kinematics of humpback whale lunge feeding. – J. Exp. Biol. 215: 3786–3798.

Sorel, M., Mittelberger, S., Berne, C. and Ribes, A. 2023. Bilan climatique de l'année 2022. – Météorologie 120: 2–4.

Soriano-Redondo, A., Bearhop, S., Cleasby, I. R., Lock, L., Votier, S. C. and Hilton, G. M. 2016. Ecological responses to extreme flooding events: a case study with a reintroduced bird. – Sci. Rep. 6: 28595.

Soriano-Redondo, A., Franco, A. M. A., Acácio, M., Martins, B. H., Moreira, F. and Catry, I. 2021. Flying the extra mile pays-off: foraging on anthropogenic waste as a time and energy-saving strategy in a generalist bird. – Sci. Total Environ. 782: 146843.

Specht, M. 2021. Experimental studies on the relationship between HDOP and position error in the GPS system. – Metrol. Meas. Syst. 2021: 17–36.

Standfuß, I., Geiß, C., Nathan, R., Rotics, S., Scacco, M., Kerr, G. and Taubenböck, H. 2022. Time series enable the characterization of small-scale vegetation dynamics that influence fine-scale animal behavior: an example from white storks' foraging behavior. – Remote Sens. Ecol. Conserv. 8: 391–408.

Thomas, R. J., Vafidis, J. O. and Medeiros, R. J. 2017. Climatic impacts on invertebrates as food for vertebrates. – In: Johnson, S. N. and Jones, T. H. (eds), Global climate change and terrestrial invertebrates, 1st edn. – Wiley, pp. 295–316.

Tomkiewicz, S. M., Fuller, M. R., Kie, J. G. and Bates, K. K. 2010. Global positioning system and associated technologies in animal behaviour and ecological research. – Philos. Trans. R. Soc. B 365: 2163–2176.

Uttley, J. D., Walton, P., Monaghan, P. and Austin, G. 1994. The effects of food abundance on breeding performance and adult time budgets of guillemots *Uria aalge*. – Ibis 136: 205–213.

Vicente-Serrano, S. M., Beguería, S. and López-Moreno, J. I. 2010. A multiscalar drought index sensitive to global warming: the standardized precipitation evapotranspiration index. – J. Clim. 23: 1696–1718.

Wang, C., Xia, S., Yu, X. and Wen, L. 2025. Responses to extreme drought in wintering waterbirds: a multi-species approach. – Front. Zool. 22: 3.

Wen, L., Saintilan, N., Reid, J. R. W. and Colloff, M. J. 2016. Changes in distribution of waterbirds following prolonged drought reflect habitat availability in coastal and inland regions. – Ecol. Evol. 6: 6672–6689.

Wheeler, M., Ton, R., Brandl, H. B., Schuett, W. and Griffith, S. C. 2025. Reduced parental brood visit rate in wild zebra finches *Taeniopygia castanotis* is correlated with high maximum daily ambient temperature. – J. Avian Biol. 2025: e03152.

Wilson, R., Shepard, E. and Liebsch, N. 2008. Prying into the intimate details of animal lives: use of a daily diary on animals. – Endang. Species Res. 4: 123–137.

Wilson, S., Smith, A. C. and Naujokaitis-Lewis, I. 2018. Opposing responses to drought shape spatial population dynamics of declining grassland birds. – Divers. Distrib. 24: 1687–1698.

Wood, S. N. 2011. Fast stable restricted maximum likelihood and marginal likelihood estimation of semiparametric generalized linear models. – J. R. Stat. Soc. B 73: 3–36.
Wood, S. N. 2017. Generalized additive models: an introduction with R. – CRC Press/Taylor & Francis Group.

Wood, S. 2023. mgcv: mixed GAM computation vehicle with automatic smoothness estimation. – R package ver. 1.9.3, https://CRAN.R-project.org/web/packages/mgcv.

Wood, K. A., Stillman, R. A., Newth, J. L., Nuijten, R. J. M., Hilton, G. M., Nolet, B. A. and Rees, E. C. 2021. Predicting avian herbivore responses to changing food availability and competition. – Ecol. Modell. 441: 109421.

Worton, B. J. 1989. Kernel methods for estimating the utilization distribution in home-range studies. – Ecology 70: 164–168.

Wu, J. X., Harbison, M. A., Beilke, S., Saha, P. and Bateman, B. L. 2025. A focus on females can improve science and conservation. – Ibis 167: 819–827.

Yu, H. and Klaassen, M. 2021. R package for animal behavior classification from accelerometer data—rabc. – Ecol. Evol. 11: 12364–12377.

Zhang, D., Zhou, L. and Song, Y. 2015. Effect of water level fluctuations on temporal-spatial patterns of foraging activities by the wintering hooded crane (*Grus monacha*). – Avian Res. 6: 16.

Zurell, D., Eggers, U., Kaatz, M., Rotics, S., Sapir, N., Wikelski, M., Nathan, R. and Jeltsch, F. 2015. Individual-based modeling of resource competition to predict density-dependent population dynamics: a case study with white storks. – Oikos 124: 319–330.

Zurell, D., von Wehrden, H., Rotics, S., Kaatz, M., Groß, H., Schlag, L., Schäfer, M., Sapir, N., Turjeman, S., Wikelski, M., Nathan, R. and Jeltsch, F. 2018. Home range size and resource use of breeding and non-breeding white storks along a land use gradient. – Front. Ecol. Evol. 6: 6–79.

Zuur, A. F., Ieno, E. N., Walker, N. J., Saveliev, A. A. and Smith, G. M. (eds) 2009. Things are not always linear; additive modelling. – In: Mixed effects models and extensions in ecology with R. – Springer, pp. 35–69.