# Habitat availability is insufficient to explain regional variations in white stork breeding habitat preference

Anne-sophie Bonnet-lebrun, Remi Lemaire-Patin, Nicolas Gendre, Hubert Dugué, Jérémy Dupuy, Achille Pedespan, Anaëlle Brand, Lisa Garnier, Christophe Barbraud

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Anne-sophie Bonnet-lebrun, Remi Lemaire-Patin, Nicolas Gendre, Hubert Dugué, Jérémy Dupuy, et al.. Habitat availability is insufficient to explain regional variations in white stork breeding habitat preference. Oikos, 2025, 10.1002/oik.11214 . hal-05183640

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OIKOS

Research article

# Habitat availability is insufficient to explain regional variations in white stork breeding habitat preference

Anne-Sophie Bonnet-Lebrun<sup>1</sup>,<sup>2</sup> Rémi Lemaire-Patin<sup>2</sup>, Nicolas Gendre<sup>3</sup>, Hubert Dugué<sup>4</sup>, Jérémy Dupuy<sup>3</sup>, Achille Pedespan<sup>5</sup>, Anaëlle Brand<sup>5</sup>, Lisa Garnier<sup>5</sup> and Christophe Barbraud<sup>1</sup>

**Correspondence:** Anne-Sophie Bonnet-Lebrun (anne-sophie.bonnet-lebrun@cnrs.fr)

Oikos

2025: e11214

doi: 10.1002/oik.11214

Subject Editor: Pedro Peres-Neto

Editor-in-Chief:

Paulo R. Guimaraes

Accepted 09 April 2025

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Understanding species–habitat associations is key for making predictions of species distributions of relevance to ecology and conservation. Regional differences in species habitat preferences can hinder the transferability of habitat models in space and time, but our ability to account for these differences will depend on the mechanisms underlying them (differences in habitat availability, genetics, culture). Here, we modelled the large-scale breeding distribution of an expanding species, the white stork *Ciconia ciconia* in France, applying machine-learning algorithms to an extensive dataset of the distribution of nests spanning the whole country. Specifically, we assessed the transferability of the models across different geographic zones and contrasted the modelled nesting habitat preferences of the species across these zones. Finally, we assessed whether local differences in model transferability were related to habitat availability in each zone. Our models generally had good calibration performances, but were not equally transferable to all zones. Additionally, environmental variables did not have the same effects in the different zones, with particularly striking differences between Alsace and the rest of France. This included a certain preference for urban areas in Alsace – absent from other zones - that was consistent with their tendency to nesting on buildings in that zone. Differences in habitat availability between Alsace and the rest of France, as well as connectivity within the French white stork metapopulation appeared to be insufficient to explain the lack of transferability of models to this zone, suggesting some possible local historical and cultural effects on habitat selection.

Keywords: Habitat preferences, nesting habitat selection, species distribution models, model transferability, *Ciconia ciconia*

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

<footnote n="1"><sup>1</sup>Centre d'Études Biologiques de Chizé, CNRS - La Rochelle Université, Villiers-en-Bois, France</footnote>

<footnote n="2"><sup>2</sup>INRAE, Bordeaux Science Agro, ISPA, avenue Edouard Bourleaux, Villenave d'Ornon, France</footnote>

<footnote n="3"><sup>3</sup>LPO France - Fonderies royales, Rochefort, France</footnote>

<footnote n="4"><sup>4</sup>Association ACROLA, Cordemais, France</footnote>

<footnote n="5"><sup>5</sup>R&D RTE, Paris La Défense CEDEX, France</footnote>
Introduction

Predictions in time and/or space of species distributions
are key in ecology and for purposes of conservation plan-
ning or ecosystem management, e.g. for identifying areas
where species are in need for protection, or areas of poten-
tial human-wildlife conflicts (Elith and Leathwick 2009,
Guisan et al. 2013). Our ability to make predictions relies
on a good understanding of the drivers of such distributions.
For this purpose, habitat preferences of the species of interest
are often inferred using species distribution models (SDMs;
Guisan and Zimmermann 2000, Elith and Leathwick 2009).
However, regional differences in habitat preferences are
commonly reported (Graf et al. 2006, Randin et al. 2006,
Roach et al. 2017, Péron et al. 2018, Linner and Chen 2024),
raising issues on the transferability of SDMs to unstudied
regions (Yates et al. 2018). In particular, widespread general-
ist species commonly inhabit a variety of different habitats
across their geographic range, which could hinder predictions
of their potential distribution, bias our understanding of the
processes driving their distributions and alter the efficiency of
management plans (Pearman et al. 2010).

Regional differences in habitat preferences – and associated
lack of transferability between regions – can emerge for differ-
ent reasons. Habitat availability might differ between regions,
and extrapolation to new environmental conditions beyond
those used for model building is likely to produce unreliable
predictions (Fitzpatrick and Hargrove 2009, Elith et al. 2010,
Meyer and Pebesma 2021). In addition, individuals might
respond differently to different levels of habitat availability.
For example, if a preferred habitat is rare, it might be more
strongly preferred than if it is abundant (Matthiopoulos et al.
2011). Accordingly, controlling for such an effect, for exam-
ple using interactions between environmental variables and
indicators of habitat availability, can improve the transferabil-
ity of models across regions (Matthiopoulos et al. 2011). If
habitat availability drives region-specific habitat preferences,
then similarity in habitat preferences between regions would
reflect similarity in available habitats in those regions.

Region-specific habitat preferences might also emerge
regardless of differences in habitat availability. If individu-
als retain habitat preferences throughout their life, either
because of genetic drivers of habitat preferences through local
adaptation (DeMarche et al. 2019, Lachmuth et al. 2024)
or because of natal habitat preference induction (Davis and
Stamps 2004), similarity in habitat preferences between
regions would reflect the connectivity between those regions.
In the case of expanding populations, habitat preferences
might reflect those in the region of origin. Alternatively,
region-specific habitat preferences might emerge locally, then
spread and be maintained through social transmission, i.e.
there might be cultural differences between regions (Keith
and Bull 2017). In this case, similarity in habitat preferences
between regions would be fully explained neither by similar-
ity in habitat availability nor by connectivity between regions.
An important step towards disentangling the causal mecha-
nisms of region-specific habitat preferences is to quantify the

differences in habitat preferences between regions and their
drivers at the intraspecific level. Such studies across the tree
of life and in species showing range expansion are necessary
for meta-analyses seeking to understand the drivers of region-
specific habitat preferences and the factors affecting SDM
transferability (DeMarche et al. 2019, Liu et al. 2020).

The white stork *Ciconia ciconia* is a widespread highly mobile species, its breeding distribution spanning ca 60° of longitude and ca 30° of latitude in the Palearctic. In the last decades its range and population size have expanded in western Europe, mainly in Italy, France, Switzerland, Germany and the Netherlands (Keller et al. 2020). Across its range, studies of its nesting distribution have highlighted the importance of various habitats – e.g. grassland (Carrascal et al. 1993, Moritzi et al. 2001, Radović and Tepić 2009, Gadenne et al. 2014, Pestka et al. 2023), rivers or streams (Onmuş et al. 2012) or urban areas (Si Bachir et al. 2013, Radović et al. 2015). The species has a generalist diet, ranging from invertebrates to small mammals, small birds and amphibians, and variable across regions (Tsachalidis and Goutner 2002, Hušek et al. 2013, Ferreira et al. 2019, Golawski and Kasprzykowski 2021), as well as within and across years (Barbraud and Barbraud 1997, Barbraud et al. 2002). Although unstudied for nesting habitat preferences, there is evidence of cultural transmission of behaviour in the species, in particular of migratory behaviour (Chernetsov et al. 2004).

In France, breeding white storks almost disappeared in the
20th century (9 pairs in 1974; Schierer 1986) and a popula-
tion reinforcement program, based on captive breeding and
supplementary feeding of native individuals and of nestlings
imported from Morocco, Algeria and Spain (Schierer 1992),
was conducted in Alsace to prevent local extinction. From the
late 1970s to the 1990s, the species progressively colonized
coastal marshes in western France from wild individuals orig-
inating from Spain, Switzerland, the Netherlands, Germany
and Poland (Barbraud et al. 1999), before spreading out in
multiple regions in France (Rojas et al. 2016). The breed-
ing population reached around 5500 breeding pairs in 2022.
Connectivity varies across the country, with more exchange of
individuals between western regions and between north-east-
ern and central regions (Rojas et al. 2016). Understanding the
drivers of the distribution of the species is of management rel-
evance, given the increasing number of breeding pairs nesting
on electric pylons and other linear infrastructures in France
and elsewhere (Gadenne et al. 2014, Janiszewski et al. 2015,
Moreira et al. 2018, Burdett et al. 2022) and the potential
impact both for storks (risks of collision with power lines and
electrocution; Kaluga et al. 2011) and for electricity quality
(failure in power transmission caused by nest materials and
bird guano; Maricato et al. 2016). Maps of suitable breeding
habitat for the species would help plan the deployment of
linear infrastructures and anticipate the need for mitigation
measures (e.g. removing nests from problematic parts of elec-
tric pylons and installing nest replacement platforms in safer
areas in the close vicinity of the removed nest).

Here, we modelled the spatial variations in habitat prefer-
ences of white storks in France, using an extensive dataset
of the distribution of nests spanning the whole country. Specifically, we 1) define a set of spatial zones using an objective density-based spatial clustering approach, 2) model habitat preferences in each zone using machine-learning algorithms, 3) generate ensemble models for each zone to examine regional differences in habitat preferences, and 4) assess whether differences in model transferability across zones was related to habitat availability in each zone. We also discuss alternative explanations of regional differences in habitat preferences.

# Methods

## White stork nest locations

We obtained white stork nest locations and nest supports from a national collaborative survey in metropolitan France performed during the 2021 breeding season and coordinated by two ornithological associations, the Ligue pour la Protection des Oiseaux and Acrola. The survey was finalized during the 2022 breeding season for a few areas not surveyed in 2021, representing only 1.4% of the total number of nests found. All observers distributed in known sites or sites potentially favourable for the white stork throughout France were systematically contacted. Observers were asked to make three visits at each site during the breeding period, i.e. between March and June, and record all nests occupied, a sampling effort ensuring that most occupied nests would be detected (Aguirre and Vergara 2009). Most observers respected this protocol, and in regions where no observer communicated data, data were completed by exports from the Faune France database (www.faune-france.org/). Occupied nests mainly (93.4%) included breeding pairs (i.e. pairs that were observed incubating eggs or rearing chicks), the rest being pairs that occupied a nest site but without signs of breeding. For each recorded nest, the dataset contained coordinates and additional information, including nest support, a facultative complement on nest support, department, town and observer/observing team name. We performed spatial checks to identify errors in reported coordinates (e.g. a missing digit in projected coordinates, missing minus sign in front of negative coordinates, wrong spatial projection used to submit the data). This consisted in cross-checking information from our dataset and from a spatial layer of French departments (Contour des départements français issus d'OpenStreetMap - Export de mars 2014 simplifié à 100 m, downloaded from www.data.gouv.fr) and considering coordinates that led to mismatches between reported and spatially identified departments as erroneous. We corrected those erroneous coordinates by identifying potential issues in the coordinates or reaching back to the observer or data provider, and further checking that the corrected nest locations fell in the correct town. We could correctly geo-reference 5463 nests.

We grouped nests in five broad categories of nest supports – trees, buildings, platforms or poles, pylons and others – based on the rules in the Supporting information applied to the nest support and complement to nest support columns.

In addition, we further distinguished high-voltage pylons in the Réseau de Transport d'Électricité (RTE) network by spatially overlaying nests recorded as 'on pylons' with a spatial layer of RTE pylons (layer Pylônes RTE (au 30 juin 2023), downloaded from the OpenData Réseau-Énergies website (odre.opendatasoft.com). For each nest, we identified the closest RTE pylon using the R package 'sf' (www.r-project.org, Pebesma et al. 2023), and considered nests further than 100 m away from a RTE pylon as belonging to the 'platforms or poles' category. Nest support data was available for 5368 nests (98.3% of all nests included in analyses).

## Environmental layers

We considered environmental variables on the basis of previous studies on the distribution of white stork nests, notably in the French department Charente-Maritime (Gadenne et al. 2014). We obtained land use data from the 2018 version of the Corine Land Cover database (CLC 2018 level 2, Metropolitan France; downloaded from www.data.gouv.fr/fr/datasets/corine-land-cover-edition-2018-france-metropolitaine/), and generated variables corresponding to groupings of land use categories, as described in Table 1. We obtained data on stream length, hydrographic surface and vegetation from BD TOPO (BD TOPO 2021 in GeoPackage format, downloaded from https://geoservices.ign.fr/bdtopo), using sublayers described in Table 1. We calculated all variables – with the exception of distance variables – within a 3 km radius buffer around each location (hereafter called 'potential foraging territory'), to account for storks moving around their nests in search of food during the breeding season. This threshold was a compromise between different studies showing variable foraging distances (Alonso et al. 1991, Moritzi et al. 2001, Gilbert et al. 2016). We calculated these variables by rasterizing the vector data (using a common 100 × 100 m resolution raster) and extracting summary (e.g. mean, total length) values within each potential foraging territory using the R packages 'terra' (www.r-project.org, Hijmans et al. 2023a) and 'exactextractr' (Baston et al. 2022).

## Habitat models

Modelling of habitat preferences was performed using the following workflow. First, we defined zones, to which we assigned nest locations (hereafter, presences) matched with randomly generated pseudo-absences (Fig. 1). Second, for each focal zone, we separated presences/pseudo-absences dataset into a training dataset (all zones except the focal zone) and a validation dataset (the focal zone only). Third, we built habitat models using three machine-learning algorithms with equivalent pseudo-absence recommendations (Barbet-Massin et al. 2012), and calculated performance metrics, variable importance and response curves for each zone (Fig. 1), using the 'biomod2' package ver. 4.2-4 (www.r-project.org, Thuiller et al. 2023) in R.

We defined zones using a combination of the *density-based spatial clustering of applications with noise* (DBSCAN) algorithm and manual refinement to reflect coherent spatial entities. To run the DBSCAN algorithm, we used the R
<table><caption>Table 1. Description and source of the variables included in the habitat models. The Source column contains the name of the database, followed that the names of the spatial layers or subcategories used to define the variable.</caption><thead><tr><th>Variable (code)</th><th>Variable (full name)</th><th>Description</th><th>Source</th></tr></thead><tbody><tr><td>AgriHetero</td><td>Hetero-geneous agriculture</td><td>Proportion of each type of land cover in the 3 km buffer around each location (potential foraging territory)</td><td>CLC 2018 – Complex cultivation patterns (2.4.2.) and Land principally occupied by agriculture, with significant areas of natural vegetation (2.4.3.)</td></tr><tr><td>Agrilntens</td><td>Intensive agriculture</td><td></td><td>CLC 2018 – Non-irrigated arable land (2.1.1.)</td></tr><tr><td>AgriPerm</td><td>Permanent agriculture</td><td></td><td>CLC 2018 – Permanently irrigated land (2.1.2.) and Fruit trees and berry plantations (2.2.2.)</td></tr><tr><td>AgriPast</td><td>Pastures</td><td></td><td>CLC 2018 – Pastures, meadows and other permanent grasslands under agricultural use (2.3.1.) and Natural grasslands (3.2.1.)</td></tr><tr><td>Urban</td><td>Urban areas</td><td></td><td>CLC 2018 – all sub-categories of Urban fabric (1.1.), of Industrial, commercial and transport units (1.2.), of Artificial, non-agricultural vegetated areas (1.4.), plus Mineral extraction sites (1.3.1.) and Construction sites (1.3.3.)</td></tr><tr><td>Aquatic</td><td>Aquatic zones</td><td></td><td>CLC 2018 – all sub-categories of Inland waters (5.1.)</td></tr><tr><td>Shrub</td><td>Shrublands</td><td></td><td>CLC 2018 – Moors and heathland (3.2.2.) and Transitional woodland-shrub (3.2.4.)</td></tr><tr><td>Forest</td><td>Forests</td><td></td><td>CLC 2018 – all sub-categories of Forests (3.1.)</td></tr><tr><td>Marsh</td><td>Marshes</td><td></td><td>CLC 2018 – all sub-categories of Inland wetlands (4.1.)</td></tr><tr><td>Water surface</td><td>–</td><td></td><td>BD TOPO 2021 – surface_hydrographique layer</td></tr><tr><td>Dist. to marsh</td><td>Distance to the nearest marsh</td><td>Distance to the nearest marsh (in m)</td><td>CLC 2018 – all sub-categories of Inland wetlands (4.1.)</td></tr><tr><td>Dist. to pasture</td><td>Distance to the nearest pasture</td><td>Distance to the nearest pasture (in m)</td><td>CLC 2018 – Pastures, meadows and other permanent grasslands under agricultural use (2.3.1.) and Natural grasslands (3.2.1.)</td></tr><tr><td>Stream length</td><td>–</td><td>Total stream length (in m) in each potential foraging territory</td><td>BD TOPO 2021 – troncon_hydrographique layer</td></tr><tr><td>Dist. to stream</td><td>Distance to the nearest stream</td><td>Distance to the nearest stream (in m)</td><td>BD TOPO 2021 – troncon_hydrographique layer</td></tr></tbody></table>

package 'dbscan' (Hahsler et al. 2019), with a search radius $\varepsilon$ of 50 km and a threshold minPts of 50 points within the $\varepsilon$-neighbourhood for these points to be considered a cluster. Manual refinement mostly consisted in assigning a zone to points not considered to be in any cluster. Zones were then named to reflect meaningful French geography. The aim of the approach was not to accurately reflect biological populations but rather to obtain subsets of the data to train and test models, with minimal spatial auto-correlation between those subsets. Within each zone, we randomly generated 10 sets of as many pseudo-absences as presences (as recommended in Barbet-Massin et al. 2012), within a 150 km radius of all presences in the zone but excluding a 3 km radius around each presence, using the R package 'dismo' (www.r-project.org, Hijmans et al. 2023b). We then extracted environmental variables around each location (presences and pseudo-absences).

We built habitat models using three machine-learning algorithms: generalised boosting models (GBM), random forests (RF) and extreme gradient boosting (XGBOOST), using the 'Bigboss' pre-defined option values in 'biomod2' (full list of option values in the Supporting information). For each zone, we built a model per pseudo-absence set and algorithm (30 models in total), by training models on all data except the focal zone (keeping data from the focal zone for validation). We evaluated the performance of each model using the true skill statistic (TSS; true positive rate + true

negative rate – 1) and the area under the receiver operating curve (ROC). We calculated calibration metrics (testing predictions on the training dataset, i.e. all data excluding the focal zone) and validation metrics (testing predictions on the validation dataset, i.e. the focal zone only; Fig. 1C).

We then generated ensemble models for each zone (averaging probabilities of all models trained without the zone, across all pseudo-absence sets and algorithms) to evaluate variable importance and build response curves for each zone, and an overall ensemble model (averaging probabilities across all pseudo-absence sets, algorithms and zones) to generate spatial predictions for the whole country. First, we estimated variable importance for each zone-specific ensemble model, using the *bm_VariableImportance* function in 'biomod2'. For each focal variable, the function randomly shuffles the original variable and computes model predictions using the shuffled variable. The variable importance score then corresponds to one minus the Pearson's correlation between predictions using the original variable and predictions using the shuffled variable. Variable importance therefore varies between 0 (variable with no influence on model predictions) and 1 (the variable alone is enough to generate model predictions). Second, we built response curves for each variable using the adaptation of the evaluation strip method (Elith et al. 2005) available in 'biomod2'. For each focal variable, we calculated predicted values of probability of presence for each possible value of the focal variable, by setting all other variables to
<figure><img src="image_5.png" /><figcaption>Figure 1. Modelling framework. (A) General modelling workflow. (B) Nest locations separated into thirteen zones, with number of nests in each zone. (C) Performance evaluation framework: training of each model on a dataset excluding one of the thirteen zones, and calculation of calibration metrics on zones included in the training dataset and of validation metrics on the zone excluded from the training dataset.</figcaption></figure>

their median value. Finally, we projected the overall ensemble model on a 1 × 1 km grid covering the whole metropolitan France, using the centroid of each grid cell as locations around which to extract environmental variables.

## Environmental dissimilarity between zones

We tested whether model transferability (here estimated using validation metrics) to a new zone was poorer when the environment was more dissimilar between training and testing zones. We followed the approach in (Meyer and Pebesma 2021) to estimate a dissimilarity index (DI) between training and testing zones. We scaled each predictor variable and weighted it according to its importance in the habitat models as follows:

$$X_{ij}^{sw} = w_j \frac{(X_{ij} - \bar{X}_{.j})}{\sigma_j}$$

with $X_{ij}$ the ith observation of the predictor variable j, $w_j$ the importance of predictor variable j in habitat models and $\bar{X}_{.j}$ and $\sigma_j$ respectively the average value and the standard deviation of predictor variable j across all observations in the training set.

Given that the multivariate Euclidian distance between two points a and b in the multidimensional space of all P

scaled and weighted predictor variables in the models (hereafter, predictor space) is calculated as follows:

$$d(a,b) = \sqrt{\sum_{j=1}^{p} (X_{dj}^{sw} - X_{bj}^{sw})^2}$$

we then calculated the dissimilarity index as:

$$DI_k = \frac{d_k}{d}$$

with $d_k$ the multivariate Euclidian distance of observation k in the testing set to the nearest training point in the predictor space, and $\bar{d}$ the average multivariate Euclidian distance between observations from the training set in the same predictor space. We used two approaches to weight predictor variables, using $w_j$ the importance of predictor variable j in 1) the ensemble model for all zones except the focal zone, and 2) the overall ensemble model (all zones combined).

We then averaged validation metrics across algorithms for each zone and pseudo-absence set separately, and related these values to the average DI values in each zone and pseudo-absence set (considering presence observations in combination with each pseudo-absence set).
# Results

Using the density-based spatial clustering algorithm we were able to define 13 zones within the country (Supporting information). Manual refinement consisted in creating a new zone in the Pyrenées and assigning isolated points to close-by zones (Fig. 1B, Supporting information).

## Performance of habitat models

Overall, models had high calibration performance values (Fig. 2), with median ROC and TSS values at 0.978 and 0.832 respectively. Validation performance values were lower, particularly in terms of TSS, with median ROC and TSS values at 0.931 and 0.674 respectively. If variations in calibration ROC values between zones were small, there were

marked variations in validation ROC values (Fig. 2). In particular, the median validation ROC value for models trained without Alsace was 0.653, followed by 0.881 for Centre-Est and for Pyrénées, and 0.882 for Ardennes, while median validation ROC values for all other zones were all above 0.900. Similarly, validation TSS values varied considerably between zones, with the lowest median value in Alsace (0.037), followed by Pyrénées (0.244), Centre-Est (0.409), and Sud-Ouest (0.536), all other median values being above 0.600. For certain zones (particularly Alsace), models trained without the focal zone poorly predicted observations of occupied nests in the zone, suggesting contrasted white stork breeding habitat preferences between the focal zone and other zones. However, adding the focal zone in the training dataset did not deteriorate model predictions for other zones (similar

<figure><img src="image_6.png" /><figcaption>Figure 2. Performance metrics for the different models. ROC - area under the receiver operating curve, TSS - true skill statistic. Top panels, blue: calibration performance metrics (each box: 120 models) – for each zone, the performance of all models trained with the focal zone (3 × 10 × 12 = 360 models) at predicting the observations in that zone. Bottom panel, red: validation performance metrics (each box: 10 models) – for each zone, the performance of all models trained without the focal (3 × 10 = 30 models) at predicting the observations in that zone. Shades of red and blue correspond to the three different algorithms: generalised boosting models (GBM), random forests (RF) and extreme gradient boosting (XGBOOST).</figcaption></figure>
<figure><img src="image_7.png" /><figcaption>Figure 3. Spatial projection of the final countrywide ensemble model: predicted probability of presence in each 1 × 1 km grid cell (left) and coefficient of variation of probability of presence (right).</figcaption></figure>

calibration values in the different zones for models trained
with and without Alsace; Supporting information). This sug-
gests that the three machine-learning algorithms were able
to capture these zone-specific habitat preference patterns
and provides support for the use of a countrywide ensemble
model to generate spatial predictions (Fig. 3).

Dissimilarity

Even though differences in validation scores across models
appear to reflect differences in dissimilarity between vali-
dation zone and the rest of the dataset (both for ROC and
TSS), Alsace was a clear outlier in these relationships (Fig. 4,
Supporting information). Indeed, probability of presence in
Alsace was more poorly predicted by models calibrated with-
out it than one would expect from the level of environmen-
tal dissimilarity of the regions from the rest of the dataset.
This result holds regardless of whether predictor variables
were weighted according to their importance in ensemble
models for each zone separately (Fig. 4), or in the overall
ensemble model (Supporting information). Linear models of

validation metrics as functions of dissimilarity indices only
identified significant relationships when metrics for Alsace
were removed (Supporting information).

Importance of environmental variables

The most important variable across ensemble models (Fig. 5A) was the total stream length within each potential foraging territory. The probability of presence increased with total stream length until reaching a plateau at around 15 km of total stream length, but this effect was less marked when Charente-Maritime/Gironde was absent from the training dataset (Fig. 5B). The second and third most important variables were the percentage of urban areas and water surface within each potential foraging territory. The percentage of urban areas was important in all models except when Alsace was absent from the training dataset (Fig. 5A). Indeed, adding Alsace to the training dataset strongly increased the importance of urban areas in the models (Fig. 5A), and led to a preference for potential foraging territories containing urban areas (with a peak at 10–15% of urban areas). The probability

<figure><img src="image_8.png" /><figcaption>Figure 4. Relationship between average validation scores across zones and average dissimilarity index. Average validation scores for each zone were obtained by averaging validation scores (ROC – left; TSS – right) over the 30 models calibrated without the focal zone (3 algorithms × 10 sets of pseudo-absences). Average dissimilarity indices for each zone and pseudo-absence set were obtained by averaging dissimilarity index values across all presence points and pseudo-absence points of the focal zone. Dissimilarity index was calculated by weighting variables according to their importance in the ensemble model for all zones except the focal zone.</figcaption></figure>
<figure><img src="image_9.png" /><figcaption>Figure 5. Contrasted habitat preferences across zones. (A) Variable importance and (B) response curves for six important variables. Colours correspond to models trained without each corresponding focal zone. Models are ensemble models across pseudo-absence sets and algorithms.</figcaption></figure>

of presence increased with the proportion of water surface, until reaching a plateau at around 15%. In most models, the probability of presence decreased with increasing proportion of forest in each potential foraging territory (Fig. 5B); and with increasing distance to the nearest marsh (Fig. 5B) until reaching a plateau around 25 km (note that this effect was weaker when Alsace was absent from the training set). The effect of distance to the nearest stream was more local, with a steep decrease before reaching a plateau around 500 m. Supporting information shows response curves for less important variables, including a preference for high amounts of pastures and permanent agriculture. Finally, differences in preferences between zones appeared not only in terms of habitat surrounding nests (Fig. 5A–B), but also in terms of nest supports (Supporting information). Trees were mostly used as nest supports in seven out of the 13 zones, whereas man-made structures (mainly pylons, platforms or poles) were used in the other zones.

Discussion

We modelled the large-scale breeding distribution of an
expanding species, the white stork in France, and contrasted
the modelled nesting habitat preferences of the species across
different geographical zones. Our models generally had good
calibration performances, but were not equally transferable
to all zones. Additionally, environmental variables did not
have the same effects in the different zones, with particularly
striking differences between Alsace and the rest of France.
Differences in habitat availability were insufficient to explain
these regional variations in habitat preferences.

In general, the variables identified as important in our
models were coherent with previous studies of white stork

nesting habitat elsewhere. First, most models confirmed
the importance of aquatic variables found in previous
studies (Gadenne et al. 2014, Radović et al. 2015, Fasolă-
Mătăsaru et al. 2018, Pestka et al. 2023). Storks generally
favoured nest sites surrounded by longer stream networks
(stream length was the most important variable in most
models), higher amounts of water surface, and closer to
streams (up to ~ 500 m) and/or marshes (up to ~ 25 km).
This is consistent with the presence of freshwater and wet-
land habitat prey generally abundant in the diet of breed-
ing storks (e.g. molluscs, crustaceans, amphibians, aquatic
insects; Barbraud and Barbraud 1997, Barbraud et al. 2002,
Tsachalidis and Goutner 2002, Ferreira et al. 2019). Stream
length, for example, was particularly important in Charente-
Maritime (models built with Charente-Maritime showed a
strong response to this variable), a zone where white storks
are known to consume high amounts of invasive crayfish
(Barbraud et al. 2002). Second, we found consistent nega-
tive responses to forests, an effect consistently found across
previous studies (Carrascal et al. 1993, Radović et al. 2015,
Fasolă-Mătăsaru et al. 2018, Pestka et al. 2023), apart from
a rare exception (Tryjanowski et al. 2018). Third, in most
models the probability of nest presence was high in areas with
high proportions of pastures, which were previously found
to be important white stork habitats (Alonso et al. 1991,
Carrascal et al. 1993, Moritzi et al. 2001, Radović and Tepić
2009, Bialas et al. 2020, Pestka et al. 2023). This is also con-
sistent with the presence of grassland prey often consumed by
breeding storks (e.g. orthoptera, voles, earthworms; Cramp
and Simmons 1977, Barbraud et al. 2002). Finally, in all
models including Alsace in their training set, urban areas
were important, an effect found previously in certain regions
of the species range (Si Bachir et al. 2013, Radović et al.
2015, Fasolă-Mătăsaru et al. 2018).
If our models had overall good calibration performance, validation performance varied between zones. This finding is in line with a recent multispecies analysis on North American passerines suggesting that between-regions predictions are generally poorer than models making predictions within regions (Rousseau and Betts 2022). In particular, if transferability of our models to new zones was generally high, as would be expected for such a species with high longevity and at the spatial scale considered here (Rousseau and Betts 2022), Alsace stood at as a zone in which nest distribution was poorly predicted by models built without it. This echoes the fact that variable importance response curves differed between models built with and without Alsace. Most strikingly, the importance of urban areas dropped drastically when Alsace was not included in the models. This preference for moderate to high amounts of urban areas around nests (10–15%) found in models including Alsace is consistent with the nesting supports chosen by storks in this zone: in Alsace, a high proportion (27.5%) of storks nested on buildings, which can be typical urban nest supports. Storks also nest on buildings in other countries (Wuczyński 2006, Onmuş et al. 2012, Bialas et al. 2020), and in two other zones in our dataset (Cotentin and Lorraine with 14.1% of nests on buildings), but not in the rest of France. In addition, including Alsace or not in models also had consequences on the importance of other variables, including distance to the nearest marsh (including Alsace in the model lowered the importance of this variable), or the proportion of intensive agriculture (with a preference for this land cover type only in models including Alsace). There were other differences between zones (e.g. water surface more important in models with Centre-Est than models without it), but these were not as marked as differences with Alsace. These results derived from validation performance metrics obtained from species distribution models fitted at the intraspecific level in different areas of the species range suggests specificities in several zones, and particularly in Alsace, that make nesting habitat preferences differ regionally at the scale of the country.

However, what makes Alsace so different from the other zones remains uncertain. First, differences in habitat availability between Alsace and the rest of France seem insufficient to explain the lack of transferability of models to this region. Indeed, even though we found models to more poorly transfer to zones that are more environmentally dissimilar, Alsace was an outlier in this relationship, and nest presence in the zone was more poorly predicted than we would expect based on environmental dissimilarity alone. We are also unaware of any other unmeasured habitat variable that could explain this discrepancy. These region-specific patterns seem more likely to reflect local individual preferences, regardless of habitat availability. These preferences could reflect native habitat preferences and emerge either because of genetic drivers or because of cultural processes such as learned preferences from early life (Camacho et al. 2015). Although nest placement appears poorly heritable in birds (Yeh et al. 2007), tolerance to human presence and disturbance in urban areas has been found to be heritable in some bird species (Carrete et al.

2016). These local preference mechanisms would be consistent with the fact that the population reinforcement program that started ~ 50 years ago in Alsace to halt the strong national decline included captive breeding and supplementary feeding (Schierer 1986), and relied on a relatively large number of nestlings (> 200) imported from Morocco, Algeria and Spain (Muller and Schierer 2002). In these countries, white storks often breed close to or within urban areas (Molina 2013, Si Bachir et al. 2013, Hmamouchi et al. 2020). All these could have contributed to a particularly strong habituation to humans in Alsatian storks, helping to explain their urban preferences. These mechanisms alone however would not easily explain why these preferences are much less displayed elsewhere in France. Part of this pattern could be explained by the fact that wild individuals who colonised western France (Barbraud et al. 1999) came from countries in which storks are not particularly known for their urban preferences (Alonso et al. 1991, Moritzi et al. 2001, Nowakowski 2003). However, the contrast in habitat selection between Alsace and the rest of France seems to be unrelated to connectivity within the French white stork metapopulation. Indeed, the most recent connectivity estimates found Alsace (and Lorraine) to be most strongly connected with the Centre-Est zone (Rojas et al. 2016). Not including Alsace in models and not including Centre-Est had different consequences for variable importance and response curves to these variables. The maintenance of differences between Alsace and other regions (Centre-Est in particular), might therefore be better explained by conformity-biased cultural transmission, copying local adults. Social learning traditions have been documented in avian nest construction (Breen 2021) and foraging behaviours (Aplin 2022) – both likely to have consequences for breeding habitat choices – and information on conspecific success has been shown to be used by prospecting individuals when choosing a breeding site in some species (Doligez et al. 2003). Finally, there might be other mechanisms that we could not test here – for example, Gadenne et al. (2014) found habitat selection to change between the beginning of the colonisation of the study area and later on as stork density increased. Similarly, López-García and Aguirre (2023) identified temporal changes in nesting habitat selection.

From a broader perspective, these results highlight the importance of training species distribution models across different zones, and raise general questions for our ability to predict distributions in unstudied areas or in areas yet to be colonised. The algorithms used in this study seem robust to the addition of zones with different habitat preferences in the training dataset: adding Alsace to the models did not deteriorate calibration performance in other zones. We are thus confident that our prediction maps reflect suitable breeding white stork habitat and therefore potential colonisation areas. However, it is possible that in unstudied areas, birds would have an even different habitat selection behaviour. If habitat preferences were dependent only on habitat availability (which was not the case here), such an effect could be taken into account explicitly in model building (Matthiopoulos et al. 2011) or by building models for
different zones and aggregating these in an ensemble model
by giving a higher weight to models built in zones that are
more environmentally similar to the new areas. If habitat
preferences were more similar in populations that exchange
more migrants (which was also not the case here), one would
have to take into account the potential origin of colonisers to
make predictions. Depending on the species behaviour and
ecology, some proxies for this might include proximity to
existing populations, or migration routes (e.g. in white storks
for example breeding dispersal has been shown to be affected
by spring migration routes; Itonaga et al. 2010). In contrast,
with regional differences in habitat selection behaviour that
cannot easily be predicted by habitat availability or by con-
nectivity (as in our study), perhaps the safest approach would
be to build models in a large variety of regions to identify
all possible suitable habitats for a given species. Such maps
of all possible suitable habitats for the species can be useful
to identify areas of potential human-wildlife conflicts, and
can be used in a precautionary way for planning (e.g. limit-
ing the establishment of new aerial power lines in potential
white stork habitat) and for spatially prioritising mitigation
efforts (e.g. installing deterrents to prevent nest establishment
on certain sections of existing pylons or anticipating the need
for nest relocation in these areas; Moreira et al. 2023). The
realised distribution of the species would obviously then be
influenced by other factors that we cannot currently predict:
accessibility of new areas for colonisation, local dynamics
(which will determine whether all suitable sites will be occu-
pied, or perhaps whether less suitable sites will have to be
occupied because of high density, as in Gadenne et al. 2014),
or behavioural plasticity leading to the emergence of new
nesting habitat preferences.

In conclusion, by modelling the nesting habitat preferences of white storks in France, we found regional differences in habitat preferences that could not be explained by differences in habitat availability or by connectivity between regions. Our work highlights the importance of explicitly considering how habitat selection might differ between regions, and paves the way for a further understanding of such regional differences and for improvements in model transferability across regions.

*Acknowledgements* – We thank all the volunteers who have contributed to the 2021-2022 white stork survey in all regions of France. Without them, this work would have not been possible.

*Funding* – We thank R.T.E. (Réseau de Transport d'Électricité) for funding the study and the salary of ASB-L.

*Conflict of interest* – The salary of ASB-L was funded by R.T.E., and LKMG is a permanent member of R.T.E. staff.

Author contributions

**Anne-Sophie Bonnet-Lebrun**: Conceptualization (equal); Formal analysis (lead); Methodology (lead); Writing – original draft (lead); Writing – review and editing (lead).

**Rémi Lemaire-Patin**: Formal analysis (supporting); Methodology (equal); Writing – review and editing (equal).

**Nicolas Gendre**: Conceptualization (equal); Data curation (equal);

Methodology (supporting); Writing – review and edit-
ing (supporting). **Hubert Dugué:** Conceptualization
(equal); Data curation (equal); Methodology (support-
ing); Writing – review and editing (supporting). **Jeremy
Dupuy:** Conceptualization (equal); Data curation (equal);
Methodology (supporting); Writing – review and edit-
ing (supporting). **Achille Pedespan:** Data curation (sup-
porting); Methodology (supporting); Writing – review and
editing (supporting). **Anaëlle Brand:** Data curation (sup-
porting); Methodology (supporting); Writing – review
and editing (supporting). **Lisa Garnier:** Conceptualization
(equal); Funding acquisition (equal); Methodology (sup-
porting); Writing – review and editing (equal). **Christophe
Barbraud:** Conceptualization (lead); Funding acquisition
(lead); Methodology (equal); Project administration (lead);
Supervision (lead); Writing – original draft (supporting);
Writing – review and editing (equal).

Data availability statement

Data are available from the Dryad Digital Repository: https://doi.org/10.5061/dryad.fxnpx148 (Bonnet-Lebrun et al. 2025).

Supporting information

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

References

Aguirre, J. I. and Vergara, P. 2009. Census methods for white stork (*Ciconia ciconia*): bias in sampling effort related to the frequency and date of nest visits. - J. Ornithol. 150:147-153.

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.

Aplin, L. 2022. Culture in birds. - Curr. Biol. 32: R1136-R1140.

Barbet-Massin, M., Jiguet, F., Albert, C. H. and Thuiller, W. 2012. Selecting pseudo-absences for species distribution models: how, where and how many? - Methods Ecol. Evol. 3: 327-338.

Barbraud, C. and Barbraud, J.-C. 1997. Le régime alimentaire des poussins de cigogne blanche *Ciconia ciconia*, en Charente-Maritime: importance des insectes. - Alauda 65: 259-262.

Barbraud, C., Barbraud, J.-C. and Barbraud, M. 1999. Population dynamics of the white stork *Ciconia ciconia* in western France. - Ibis 141: 469-479.

Barbraud, C., Barbraud, J.-C., Monique, B. 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: 437-444.

Baston, D. 2022. ISciences, and LLC. 2022, November 16. exact-extractr: fast extraction from raster datasets using polygons. - https://cran.r-project.org/web/packages/exactextractr/index.html.

Bialas, J. T., Dylewski, Ł. and Tobolka, M. 2020. Determination of nest occupation and breeding effect of the white stork by human-mediated landscape in western Poland. - Environ. Sci. Pollut. Res. Int. 27: 4148-4158.

Bonnet-Lebrun, A. S., Lemaire-Patin, R., Gendre, N., Dugué, H., Dupuy, J., Pedespan, A., Brand, A., Garnier, L. and Barbraud,
C.. 2025. Data from: Habitat availability is insufficient to explain regional variations in white stork breeding habitat preference. - Dryad Digital Repository, https://doi.org/10.5061/dryadfxpnvx148.

Breen, A. J. 2021. Animal culture research should include avian nest construction. - Biol. Lett. 17: 20210327.

Burdett, E. M., Muriel, R., Morandini, V., Kolnegari, M. and Ferrier, M. 2022. Power lines and birds: drivers of conflict-prone use of pylons by nesting white storks (*Ciconia ciconia*). - Diversity 14: 984.

Camacho, C., Canal, D. and Potti, J. 2015. Testing the matching habitat choice hypothesis in nature: phenotype-environment correlation and fitness in a songbird population. - Evol. Ecol. 29: 873-886.

Carrascal, L. M., Bautista, L. M. and Lázaro, E. 1993. Geographical variation in the density of the white stork *Ciconia ciconia* in Spain: influence of habitat structure and climate. - Biol. Conserv. 65: 83-87.

Carrete, M., Martínez-Padilla, J., Rodríguez-Martínez, S., Reboló-Ifrán, N., Palma, A. and Tella, J. L. 2016. Heritability of fear of humans in urban and rural populations of a bird species. - Sci. Rep. 6: 31060.

Chernetsov, N., Berthold, P. and Querner, U. 2004. Migratory orientation of first-year white storks (*Ciconia ciconia*): inherited information and social interactions. - J. Exp. Biol. 207: 937-943.

Cramp, S. and Simmons, K. E. L. 1977. The birds of the western Palearctic, Vol. I. - Oxford Univ. Press.

Davis, J. M. and Stamps, J. A. 2004. The effect of natal experience on habitat preferences. - Trends Ecol. Evol. 19: 411-416.

DeMarche, M. L., Doak, D. F. and Morris, W. F. 2019. Incorporating local adaptation into forecasts of species' distribution and abundance under climate change. - Global Change Biol. 25: 775-793.

Doligez, B., Cadet, C., Danchin, E. and Boulinier, T. 2003. When to use public information for breeding habitat selection? The role of environmental predictability and density dependence. - Anim. Behav. 66: 973-988.

Elith, J. and Leathwick, J. R. 2009. Species distribution models: ecological explanation and prediction across space and time. - Annu. Rev. Ecol. Evol. Syst. 40: 677-697.

Elith, J., Ferrier, S., Huettmann, F. and Leathwick, J. 2005. The evaluation strip: a new and robust method for plotting predicted responses from species distribution models. - Ecol. Modell. 186: 280-289.

Elith, J., Kearney, M. and Phillips, S. 2010. The art of modelling range-shifting species: the art of modelling range-shifting species. - Methods Ecol. Evol. 1: 330-342.

Fasolă-Mătăsaru, L., Baltag, E. Ş., Ichim, P. and Cojocaru, D. 2018. Factors influencing the breeding success of white storks *Ciconia ciconia* in eastern Romania. - Ardea 65: 271-282.

Ferreira, E., Grilo, F., Mendes, R. C., Lourenço, R., Santos, S. and Petrucci-Fonseca, F. 2019. Diet of the white stork (*Ciconia ciconia*) in a heterogeneous Mediterranean landscape: the importance of the invasive red swamp crayfish (*Procambarus clarkii*). - Airo 26: 27-41.

Fitzpatrick, M. and Hargrove, W. 2009. The projection of species distribution models and the problem of non-analog climate. - Biodivers. Conserv. 18: 2255-2261.

Gadenne, H., Cornulier, T., Eraud, C., Barbraud, J.-C. and Barbraud, C. 2014. Evidence for density-dependent habitat occupancy at varying scales in an expanding bird population. - Popul. Ecol. 56: 493-506.

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.

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

Graf, R. F., Bollmann, K., Sachot, S., Suter, W. and Bugmann, H. 2006. On the generality of habitat distribution models: a case study of capercaillie in three Swiss regions. - Ecography 29: 319-328.

Guisan, A. and Zimmermann, N. E. 2000. Predictive habitat distribution models in ecology. - Ecol. Modell. 135: 147-186.

Guisan, A. et al. 2013. Predicting species distributions for conservation decisions. - Ecol. Lett. 16: 1424-1435.

Hahsler, M., Piekenbrock, M. and Doran, D. 2019. dbscan: fast density-based clustering with R. - J. Stat. Softw. 91: 1-30.

Hijmans, R. J., Bivand, R., Pebesma, E. and Sumner, M. D. 2023a. terra: spatial data analysis. - https://cran.r-project.org/web/packages/terra/index.html.

Hijmans, R. J., Leathwick, J. and Elith, J. 2023b. dismo: species distribution modeling. - R package ver. 1.3-16, https://CRAN.R-project.org/package=dismo.

Hmamouchi, M.-J., Agharroud, K., Dahmani, J. and Hanane, S. 2020. Landscape and coloniality are robust predictors of white stork nest habitat selection in a coastal urban environment. - Estuarine Coast. Shelf Sci. 242: 106835.

Hušek, J., Adamík, P., Albrecht, T., Cepák, J., Kania, W., Mikolášková, E., Tkadlec, E. and Stenseth, N. Chr. 2013. Cyclicity and variability in prey dynamics strengthens predator numerical response: the effects of vole fluctuations on white stork productivity. - Popul. Ecol. 55: 363-375.

Itonaga, N., Köppen, U., Plath, M. and Wallschläger, D. 2010. Breeding dispersal directions in the white stork (*Ciconia ciconia*) are affected by spring migration routes. - J. Ethol. 28: 393-397.

Janiszewski, T., Minias, P. and Wojciechowski, Z. 2015. Selective forces responsible for transition to nesting on electricity poles in the white stork *Ciconia ciconia*. - Ardea 103: 39-50.

Kaluga, I., Sparks, T. H. and Tryjanowski, P. 2011. Reducing death by electrocution of the white stork *Ciconia ciconia*. - Conserv. Lett. 4: 483-487.

Keith, S. A. and Bull, J. W. 2017. Animal culture impacts species' capacity to realise climate-driven range shifts. - Ecography 40: 296-304.

Keller, V., Herrando, S., Voříšek, P., Franch, M., Kipson, M., Milanesi, P., Martí, D., Anton, M., Klvaňová, A., Kalyakin, M. V., Bauer, H.-G. and Foppen, R. P. B. 2020. European breeding bird. Atlas 2: Distribution, abundance and change. - European Bird Census Council and Lynx Edicions.

Lachmuth, S., Capblancq, T., Prakash, A., Keller, S. R. and Fitzpatrick, M. C. 2024. Novel genomic offset metrics integrate local adaptation into habitat suitability forecasts and inform assisted migration. - Ecol. Monogr. 94: e1593.

Linner, R. M. and Chen, Y. 2024. Evaluating the spatial transferability of habitat suitability models: implications for conservation and management. - Can. J. Fish. Aquat. Sci. 81: 589-599.

Liu, C., Wolter, C., Xian, W. and Jeschke, J. M. 2020. Species distribution models have limited spatial transferability for invasive species. - Ecol. Lett. 23: 1682-1692.

López-García, A. and Aguirre, J. I. 2023. White storks nest at high densities near landfills changing stork nesting distributions in
the last four decades in central Spain. – Ornithol. Appl. 125: 160009.

Maricato, L., Faria, R., Madeira, V., Carreira, P. and de Almeida, A. T. 2016. White stork risk mitigation in high voltage electric distribution networks. – Ecol. Eng. 91: 212–220.

Matthiopoulos, J., Hebblewhite, M., Aarts, G. and Fieberg, J. 2011. Generalized functional responses for species distributions. – Ecology 92: 583–589.

Meyer, H. and Pebesma, E. 2021. Predicting into unknown space? Estimating the area of applicability of spatial prediction models. – Methods Ecol. Evol. 12: 1620–1633.

Molina, B. 2013. Results of the 6th international white stork census in Spain. – NABU-BundesverbandURL: https://bergenhusen/spain_zensus_2004.pdf.

Moreira, F., Martins, R. C., Aguilar, F. F., Canhoto, A., Martins, J., Moreira, J. and Bernardino, J. 2023. Long-term management practices successfully reduce bird-related electrical faults in a transmission grid increasingly used by white storks for nesting. – J. Environ. Manage. 327: 116897.

Moreira, F., Martins, R. C., Catry, I. and D'Amico, M. 2018. Drivers of power line use by white storks: a case study of birds nesting on anthropogenic structures. – J. Appl. Ecol. 55: 2263–2273.

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.

Muller, Y. and Schierer, A. 2002. La cigogne blanche. – Eveil Nature.

Nowakowski, J. J. 2003. Habitat structure and breeding parameters of the white stork *Ciconia ciconia* in the Kolno upland (NE Poland). – Acta Ornithol. 38: 39–46.

Onmuş, O., Ağaoğlu, Y. and Gül, O. 2012. Environmental factors affecting nest-site selection and breeding success of the white stork (*Ciconia ciconia*) in western Turkey. – Wilson J. Ornithol. 124: 354–361.

Pearman, P. B., D'Amen, M., Graham, C. H., Thuiller, W. and Zimmermann, N. E. 2010. Within-taxon niche structure: niche conservatism, divergence and predicted effects of climate change. – Ecography 33: 990–1003.

Pebesma, E., Bivand, R., Racine, E., Sumner, M., Cook, I., Keitt, T., Lovelace, R., Wickham, H., Ooms, J., Müller, K., Pedersen, T. L., Baston, D. and Dunnington, D. 2023. sf: simple features for R. – https://cran.r-project.org/web/packages/sf/index.html.

Péron, C., Authier, M. and Grémillet, D. 2018. Testing the transferability of track-based habitat models for sound marine spatial planning. – Divers. Distrib. 24: 1772–1787.

Pestka, Z., Zbyryt, A., Menderski, S. and Jakubas, D. 2023. Habitat suitability mapping of white stork *Ciconia ciconia* in one of its key European breeding areas. – Ecol. Indic. 151: 110278.

Radović, A. and Tepić, N. 2009. Using Corine Land Cover Habitat Database for the analysis of breeding bird habitat: case study

of white storks (*Ciconia ciconia*) from northern Croatia. – Biologia 64: 1212–1218.

Radović, A., Kati, V., Perčec Tadić, M., Denac, D. and Kotrošan, D. 2015. Modelling the spatial distribution of white stork *Ciconia ciconia* breeding populations in southeast Europe. – Bird Study 62: 106–114.

Randin, C. F., Dirnböck, T., Dullinger, S., Zimmermann, N. E., Zappa, M. and Guisan, A. 2006. Are niche-based species distribution models transferable in space? – J. Biogeogr. 33: 1689–1703.

Roach, N. S., Hunter, E. A., Nibbelink, N. P. and Barrett, K. 2017. Poor transferability of a distribution model for a widespread coastal marsh bird in the southeastern United States. – Ecosphere 8: e01715.

Rojas, E. R., Sueur, C., Henry, P.-Y., Doligez, B., Wey, G., Dehorter, O., Massemin, S. and Groupe Cigognes France 2016. Network analysis shows asymmetrical flows within a bird metapopulation. – PLoS One 11: e0166701.

Rousseau, J. S. and Betts, M. G. 2022. Factors influencing transferability in species distribution models. – Ecography 2022: e06060.

Schierer, A. 1986. Quarante années d'observations et de recherches sur la cigogne blanche en Alsace. – Ciconia 10: 1–12.

Schierer, A. 1992. Réintroduction de la Cigogne blanche et politique des enclos en Alsace. Page Les Cigognes d'Europe: cigogne blanche et Cigogne noire - Alimentation - Migrations - Populations - Facteurs létaux. Colloque. International, Metz 3,4 et 5 juin 1991. – Institut Européen d'Ecologie-Association Multi-disciplinaire des Biologistes de l'Environnement.

Si Bachir, A., Chenchouni, H., Djeddou, N., Barbraud, C., Céréghino, R. and Santoul, F. 2013. Using self-organizing maps to investigate environmental factors regulating colony size and breeding success of the white stork (*Ciconia ciconia*). – J. Ornithol. 154: 481–489.

Thuiller, W., Georges, D., Gueguen, M., Engler, R., Breiner, F., Lafourcade, B. and Patin, R. 2023. biomod2: ensemble platform for species distribution modeling. – https://cran.r-projekt.org/web/packages/biomod2/index.html.

Tryjanowski, P., Grzywaczewski, G. and Zbyryt, A. 2018. Foraging of white stork *Ciconia ciconia* in forests – the heritage of an ancient behaviour? – Pol. J. Ecol. 66: 250–256.

Tsachalidis, E. P. and Goutner, V. 2002. Diet of the white stork in Greece in relation to habitat. – Waterbirds 25: 417–423.

Wuczyński, A. 2006. Colonization of new territories: the white stork *Ciconia ciconia* distribution and population changes in the Sudeten Mountains (Poland). – In: Tryjanowski, P., Sparks, T. H. and Jerzak, L. (eds), The white stork in Poland: studies in biology, ecology and conservation. Bogucki Wydawnictwo Naukowe, Poznań, Poland.

Yates, K. L. et al. 2018. Outstanding challenges in the transferability of ecological models. – Trends Ecol. Evol. 33: 790–802.

Yeh, P. J., Hauber, M. E. and Price, T. D. 2007. Alternative nesting behaviours following colonisation of a novel environment by a passerine bird. – Oikos 116: 1473–1480.