# Interspecies synchrony in departure decisions from stopover sites: evidence using a multi-species capture-recapture model

Sébastien Roques, Pierre-yves Henry, Hubert Dugué, Sébastien Gautier, Jean-luc Chil, Pascal Provost, Daniel Turek, Emmanuelle Cam, Roger Pradel

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Sébastien Roques, Pierre-yves Henry, Hubert Dugué, Sébastien Gautier, Jean-luc Chil, et al.. Inter-species synchrony in departure decisions from stopover sites: evidence using a multi-species capture-recapture model. Oikos, 2025, 2025 (9), pp.e11270. 10.1002/oik.11270. hal-05100411

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OIKOS

Research article

# Interspecies synchrony in departure decisions from stopover sites: evidence using a multi-species capture-recapture model

Sébastien Roques<sup>1,2</sup>, Pierre-Yves Henry<sup>3,4</sup>, Hubert Dugué<sup>5</sup>, Sébastien Gautier<sup>6</sup>, Jean-Luc Chil<sup>7</sup>, Pascal Provost<sup>8</sup>, Daniel Turek<sup>9</sup>, Emmanuelle Cam<sup>2,10</sup> and Roger Pradel<sup>1</sup>

**Correspondence:** Sébastien Roques (se.roques@gmail.com)

Oikos
2025: e11270

doi: 10.1002/oik.11270

Subject Editor: Jiri Reif

Editor-in-Chief: Pedro Peres-Neto

Accepted 21 April 2025

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www.oikosjournal.org

During migration, migratory bird species often aggregate at the same stopover sites due to geographical features that channel migratory routes (coasts, valleys) or locally abundant food resources (e.g. reedbeds, fruiting bushes). In migration ecology, however, stopover behavior is often studied on a single species, limiting the generality of inferences and their application to habitat management. If stopover decisions are similar across co-occurring species, characterizing stopover ecology at a community level could provide more comprehensive insights. Using recent advances in modelling, we adapted a multi-species capture-recapture model to 1) quantify synchrony in stopover departure probabilities across species and 2) identify covariates driving this synchrony. We applied this model to three migratory songbirds commonly captured at French stopover sites: the sedge warbler *Acrocephalus schoenobaenus*, reed warbler *Acrocephalus scirpaeus* and bluethroat *Luscinia svecica*. Departure decisions were largely synchronous across species, with time since arrival (TSA) as the primary synchronizing factor and weather conditions as a secondary influence. High synchrony in departure timing produced waves of migrant departures and consistent patterns in departure decisions across species, suggesting shared fuelling and timing strategies. Although this phenomenon has been documented via visual counts and radar, it has not been formally quantified at the species-level using capture-recapture methods. Our model is flexible and can test hypotheses regarding spatial synchrony in departure decisions. With decades of capture-recapture data available across Europe and North America, our approach offers new potential for studying stopover ecology at the community level, over large geographic and temporal scales.

Keywords: stopover ecology, bird migration, capture-recapture, Bayesian, synchrony

© 2025 The Author(s). Oikos published by John Wiley & Sons Ltd on behalf of Nordic Society Oikos. This is an open access article under the terms of the Creative Commons Attribution License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited.

<footnote n="1"><sup>1</sup>CEFE, Univ Montpellier, CNRS, EPHE, IRD, Montpellier, France</footnote>

<footnote n="2"><sup>2</sup>Laboratoire Evolution et Diversité Biologique, UMR CNRS 5171, Université Toulouse, Paul Sabatier, Toulouse Cedex, France</footnote>

<footnote n="3"><sup>3</sup>Mécanismes adaptatifs et Evolution (MECADEV UMR 7179), Muséum National d'Histoire Naturelle, Centre National de la Recherche Scientifique, Brunoy, France</footnote>

<footnote n="4"><sup>4</sup>Centre de Recherches sur la Biologie des Populations d'Oiseaux (CRBPO), Centre d'Ecologie et des Sciences de la Conservation (CESCO UMR 7204), Muséum National d'Histoire Naturelle, Centre National de la Recherche Scientifique, Sorbonne Université, Buffon, Paris, France</footnote>

<footnote n="5"><sup>5</sup>Association pour la Connaissance et la Recherche Ornithologique Loire et Atlantique (ACROLA), Cordemais, France</footnote>

<footnote n="6"><sup>6</sup>Office Français de la Biodiversité (OFB) Service départemental du Morbihan, Vannes, France</footnote>

<footnote n="7"><sup>7</sup>Office Français de la Biodiversité (OFB), Service départemental de la Loire-atlantique, Nantes cedex, France</footnote>

<footnote n="8"><sup>8</sup>Réserve Naturelle Nationale Estuaire de la Seine, Maison de l'estuaire, Le Havre, France</footnote>

<footnote n="9"><sup>9</sup>Lafayette College, Department of Mathematics, Easton, PA, USA</footnote>

<footnote n="10"><sup>10</sup>Univ. Brest, CNRS, IRD, Ifremer, LEMAR, Plouzané, France</footnote>
Introduction

Each year, billions of migrating birds travel thousands of kilo-
metres between breeding and wintering areas. Long-distance
travel requires regular rest and refilling of energy stores, and
most species can only perform this journey by alternating
short episodes of endurance flight with long episodes of stop-
over, when they can refuel (Alerstam 1990, Hedenström and
Alerstam 1997). The time spent at stopovers represents most
of the overall migration time and is critical for the success of
the journey (Schmaljohann et al. 2022). Many species con-
verge towards common stopover areas, where food resources
are abundant (McGrath et al. 2009, Chudzińska et al. 2015,
La Sorte and Graham 2021) or because ecological barri-
ers (mountains, desert, sea) channel birds towards those
areas (Buler and Moore 2011, Schmaljohann et al. 2022).
Consequently, tens of different species frequently co-occur
at the same stopover site (Kirby et al. 2008, Cohen and
Satterfield 2020).

Species co-occurring at stopover areas can be considered
as communities (Bellisario et al. 2023, DeSimone et al.
2024). These species are subject to the same environ-
mental conditions (weather conditions, food availability,
predators) and may interact with one another (e.g. com-
pete for food; Oestreich et al. 2022, Aikens et al. 2022,
Zimin et al. 2023). This may imply similar responses among
species to their environment during stopover, including
refuelling strategies and departure timing (de Zwaan et al.
2022). However, cross-species similarity of stopover behav-
iour has received little attention (DeSimone et al. 2024).
Migration ecology would benefit from approaches recog-
nizing that migratory species at a given site are subject to
common constraints, leading to shared mechanisms govern-
ing refuelling and departure decisions (Klinner et al. 2020,
Schmaljohann et al. 2022, de Zwaan et al. 2022). Peaks
of arrivals and departures have already been documented
by field ornithologists and birdwatchers (McGrath et al.
2009, La Sorte and Graham 2021), but also through radar
studies (Nussbaumer et al. 2021, 2024), suggesting that
stopover use and departure timing may not be purely spe-
cies-specific but instead influenced by generalizable factors
(Schmaljohann et al. 2022, Nussbaumer et al. 2024). For
instance, radio-tracking studies have shown that departure
decisions across multiple species are shaped by a combina-
tion of intrinsic factors, such as fat reserves and body con-
dition, and extrinsic factors, such as weather conditions
(Deppe et al. 2015, Anderson et al. 2019). Given that most
migratory birds follow time- and energy-optimized strate-
gies, species with similar migratory schedules and fuelling
needs may respond in parallel to environmental cues, lead-
ing to synchronized departures (Alerstam and Lindström
1990, Bayly 2006, Schmaljohann and Eikenaar 2017,
Klinner et al. 2020). Favourable weather conditions could
trigger peaks of departure across species, while shared con-
straints on refuelling rates, such as food availability and
metabolic limits on fat deposition, may result in species

reaching departure readiness simultaneously. Additionally,
birds stopping over at the same location often have com-
parable migration distances ahead, further aligning their
timing of refuelling completion and departure (Bayly 2006,
Anderson et al. 2019). These findings support the idea that
stopover decisions at the site-level are not only species-spe-
cific but are also shaped by broader ecological constraints
that apply to multiple species (DeSimone et al. 2024). As a
result, the timing of refuelling and departures may be more
predictable at the community level, with synchronization
emerging naturally from the shared challenges of migration.
However, while these studies suggest that species experienc-
ing similar constraints may exhibit synchronized departure
from stopover sites, this synchrony and its drivers have
rarely been formally quantified.

In the capture–mark–recapture (hereafter CR) frame-
work, estimating synchrony of departures from stopover
sites has received less attention than synchrony of demo-
graphic parameters such as survival or breeding probability
(Grosbois et al. 2009, Lahoz-Monfort et al. 2011, 2013,
Ghislain et al. 2024). Comparisons of departure probability
from a stopover site among species have been conducted in
the CR framework (Schaub et al. 2008, Moore et al. 2017).
However, these comparisons were based on models that esti-
mate departure probability separately for each species, which
are subsequently compared. This two-step approach does
not allow to account for uncertainty in synchrony estimates.
CR models integrating data from multiple migratory species
have never been developed, and the synchrony in the varia-
tion of departure probability has never been quantified. In
the present study, we have adapted the multi-species capture–
recapture models developed by Grosbois et al. (2009) and
Lahoz-Monfort et al. (2011) to estimate between-species syn-
chrony in daily departure probability, which is our primary
parameter of interest. This approach offers the advantage of
directly quantifying the variation in departure probability
across multiple levels (days, species, sites) within the model.
This framework enables the estimation of synchrony and its
associated uncertainty while also assessing the contribution
of covariates to synchrony.

Here, we investigate cross-species synchrony in daily depar-
ture probability and its drivers among three migratory song-
birds: the sedge warbler *Acrocephalus schoenobaenus*, the reed
warbler *Acrocephalus scirpaeus* and the bluethroat *Luscinia sve-
cica*. The stopover behaviour of these species has been stud-
ied independently (Schaub et al. 2001, 2008, Arizaga et al.
2010, 2011b, Roques et al. 2022), but to date no attempt
has been made to address inter-species synchrony in depar-
ture probability for these species and reedbed communities,
nor to identify synchronizing agents. These species largely
depend on reed habitats for stopovers, have similar ecologi-
cal requirements and mostly feed on a variety of small insects
such as aphids (Bibby and Green 1981, Bibby and Thomas
1985, Fontanilles et al. 2020). We believe that if the prey of
one species is abundant, the preferred prey of the other species
will also be abundant, since they all result from the primary
production of the local reedbed (a largely monospecific habitat, dominated by *Phragmites australis*). Reed productivity may thus synchronize resource availability for multiple species at particular stopover sites (McGrath et al. 2009, La Sorte and Graham 2021). Consequently, we expect a relatively high synchrony in the variation of their departure probabilities across days because most of the individuals arrive at the same time when resource availability is peaking. The main life history differences between the three species are that the sedge warbler (SW) and the reed warbler (RW) are trans-Saharan migrants (Bibby and Green 1981), while the bluethroat (BT) includes both trans-Saharan and non-trans-Saharan migrants (Ellegren 1990, 1991). We hypothesize that between-day variation in departure probability should exhibit greater synchronization between SW and RW than between either species and BT. Beyond expected synchrony, we also hypothesize that the primary driver of departure synchrony should reflect refueling progress and restedness, as stopover duration is primarily dictated by an individual's internal state (Roques et al. 2022). Additionally, favourable weather conditions may act secondarily, enabling mass departures once birds have accumulated sufficient energy reserves. Testing these hypotheses will help clarify whether departure synchrony is primarily governed by weather conditions or by intrinsic state-dependent processes common to migratory songbirds.

By formally quantifying synchrony in daily departure probability and identifying its key drivers through a multispecies capture-recapture model, this study aims to provide new insights into the mechanisms shaping shared stopover departure decisions across migratory songbirds species.

# Material and methods

## Data sampling

Data collection was conducted at three ringing sites in France (Donges, France: 47°18'30.9"N, 02°01'45.2"W, Chenaval et al. 2011; Île du Massereau: 47°15'14.6"N, 01°53'32.8"W, Caillat et al. 2005; Saint-Vigor-d'Ymonville: 49°27'18.9"N, 00°21'39.9"E, Péron et al. 2007, Dehorter and CRBPO 2015). The first two sites are located in the Loire Estuary, the third in the Seine Estuary and are all suitable reed stopover habitats for songbirds. Data from marked individuals has been collected using a standardized mist-netting protocol, including tape-luring which was consistently used across sites and days (Bargain et al. 2002). Mist-nets are deployed at various locations within each stopover site to

reduce potential biases arising from temporal or local emigration. Each captured bird was identified and ringed. The three most abundant songbirds were the sedge warbler, the reed warbler and the bluethroat. These nocturnal migrant species stop at these sites on their way to wintering areas located in sub-Saharan Africa for the two species of warblers, and North Africa and Iberian Peninsula for the BT (Arizaga et al. 2016, Shirihai and Svensson 2018). The SW and the RW weigh on average 12 g, and the BT weighs on average 16 g. These species mostly depend on reedbeds to feed during stopovers.

Among all migrant individuals that stop at a site, only a small fraction stay several days to rest and refuel (i.e. actual stopover; Warnock 2010). The majority either continue migration by the following night, or move to a nearby stopover site (i.e. transients; Bächler and Schaub 2007, Schaub et al. 2008). As we aimed to study the departure probability of birds that stay over at the site, we selected capture-recapture data from birds that were caught at least twice, in two different days. This restriction excludes most transients (Mills et al. 2011, Taylor et al. 2011, Sjöberg et al. 2015). Hence, the stopover duration estimated here applies only to the part of the population passing by the site and staying for at least two days. Final sample sizes and minimum stopover durations (number of days between first and last capture) are summarized in Table 1.

## Weather variables

Based on previous studies on stopover departure decisions (Deppe et al. 2015, Sjöberg et al. 2015, Roques et al. 2021, 2022), we used daily records of wind speed (m s⁻¹), wind direction (°), atmospheric pressure (Hpa) and relative humidity (%) as weather covariates in our model. Weather data comes from local meteorological stations close to ringing sites. Depending on its direction, wind can either facilitate flight (tailwind) or hinder flight (headwind). To account for contrasting wind effects, the wind covariate was computed as in (Arizaga et al. 2011a):

$$ \text{wind} = V \cos[\alpha_T - (180^\circ + \alpha_W)], $$

where *V* is wind speed (in m s⁻¹), α_T the assumed departure direction (180° for Donges and Île du Massereau; 210° for Saint-Vigor-d'Ymonville according to recovery data and south migration direction, Dehorter and CRBPO 2015), and α_W the direction the wind (Åkesson et al. 2002).

To reduce collinearity between weather variables, we used principal component analysis (PCA) at each site (Supporting

<table><caption>Table 1. Sample size and mean time elapsed in days (with standard deviation ±) between first and last capture for the sedge warbler, reed warbler and bluethroat at the three studied sites.</caption><thead><tr><th></th><th>Donges</th><th>Saint-Vigor-d'Ymonville</th><th>Île du Massereau</th></tr></thead><tbody><tr><td>Sedge warbler</td><td>3.75 ± 3.48<br>n=1349</td><td>3.91 ± 3.02<br>n=317</td><td>4.48 ± 3.84<br>n=580</td></tr><tr><td>Reed warbler</td><td>7 ± 5.62<br>n=277</td><td>6.08 ± 4.91<br>n=163</td><td>5.52 ± 4.91<br>n=188</td></tr><tr><td>Bluethroat</td><td>6.46 ± 5.29<br>n=230</td><td>7.08 ± 5.01<br>n=73</td><td>7.06 ± 5.19<br>n=87</td></tr></tbody></table>
information) with the following weather variables: relative humidity (in %), atmospheric pressure (in Hpa) and wind (Eq. 1). As the first PCA component accounted for more than 50% of the variation in weather conditions at each site, we used first PCA component at each site as the weather covariate in the CR model.

## Statistical analysis

We used a Bayesian, state-space formulation of an open population capture-recapture model that incorporates entry probabilities to a sampling area (Pledger et al. 2008, Lyons et al. 2016, Roques et al. 2022). This formulation allows modeling both birds' arrival at and departure from the stopover site. The parameters of the model are:

$\phi_{i,y,t,s}$ probability of staying in the sampling area between sampling occasions (days) $t$ and $t+1$, in year $y$, for individual $i$ of species $s$

$\eta_{i,y,t,s}$ probability of arriving at the stopover area on sampling occasion $t$, in year $y$, for individual $i$ of species $s$ given that the individual was not present the prior day

$p_{i,y,t,s}$ probability of capture on the sampling occasion $t$ in year $y$, for individual $i$ of species $s$ given that the individual has arrived and not yet departed

Our model contains one submodel for the state process (states are 'not yet arrived', 'present in the study area' or 'departed'), and another submodel for the observations in individual capture histories. The observation process is conditional on the value of the state process. For each individual capture history $h_i$, the true state history is given by the vector $z_i$. This vector is composed of binary state variables, where $z_{i,t}=1$ if an individual $i$ is present on day $t$, and $z_{i,t}=0$ otherwise.

The state process is defined as:

$$z_{i,t} | z_{i,t-1} \sim \text{Bernoulli} \left( \phi_{i,t-1} z_{i,t-1} + \eta_t \prod_{j=1}^{t-1} (1 - z_{i,j}) \right)$$

The product $\prod_{j=1}^{t-1} (1 - z_{i,j})$ models the availability of the individual to enter in the stopover area, and is equal to one when the individual has not yet entered the stopover area, and zero when it has already entered.

As the binary observations are conditionally independent Bernoulli random variables, the link between the state and observation processes is given as:

$$h_{i,t} | z_{i,t} \sim \text{Bernoulli}(z_{i,t}, p_t)$$

This means that if an individual $i$ has not yet entered the stopover area or has already left it ($z_{i,t}=0$), then $h_{i,t}=0$. If $z_{i,t}=1$, then the capture history $h_{i,t}$ is modelled as a Bernoulli trial with probability $p_t$, which is the probability of capture at time $t$. This formulation allows estimation of the time since arrival (TSA) for each individual (Pledger et al. 2008, Roques et al. 2022). TSA can be computed using the sum of the true states $z_{i,t}$, which represents the presence of the individual $i$ at time $t$ at the stopover area:

$$TSA_{i,t} = \sum_{s=1}^{t-1} z_{i,s}$$

Lahoz-Monfort et al. (2011) and Grosbois et al. (2009) addressed synchrony in between-year survival across species. We used the same definitions, changing only the time lag, analysing synchrony in between-day survival (i.e. the complement to departure probability), instead of between-year survival. We treated year-specific ($y$) day effect ($t$) as a random effect that is common to all the species: $\delta_{y,t} \sim N(0, \sigma^2_{\delta})$. The second random effect depends on year ($y$), day ($t$), and species ($s$), and accounts for species-specific variation in departure probability: $\varepsilon_{y,t,s} \sim N(0, \sigma^2_{\varepsilon_s})$. We ran three models, one without covariate (A), one with a covariate representing time already spent at a site (B), and one with a covariate representing weather (C):

$$\operatorname{logit}(\phi_{i,y,t,s}) = \alpha_{y,t} + \delta_{y,t} + \varepsilon_{y,t,s} \quad (A)$$

$$\operatorname{logit}(\phi_{i,y,t,s}) = \alpha_{y,t} + \delta_{y,t} + \varepsilon_{y,t,s} + \beta_s \times \text{TSA}_{i,t} \quad (B)$$

$$\operatorname{logit}(\phi_{i,y,t,s}) = \alpha_{y,t} + \delta_{y,t} + \varepsilon_{y,t,s} + \beta_1 \times \text{PCA1}_t \quad (C)$$

$\alpha_{y,t}$ is the intercept for species $s$ in year $y$, $\beta_s$ is the species-specific slope for TSA, and $\beta_1$ is the slope for weather conditions. This formulation allows the variation of synchrony between years but needs sufficient yearly data to be used. Consequently, we used the site with the largest sample size during different years (Donges) to allow the variances to vary between years, and obtain year-specific estimates of synchrony (Table 3). Here the random effects are $\delta_{y,t} \sim N(0, \sigma^2_{\delta_y})$ for the common day and year effect, and $\varepsilon_{y,t,s} \sim N(0, \sigma^2_{\varepsilon_{y,s}})$ for the species-specific term.

The formulation of the model also allows integrating data from multiple years (and considered them as replicates) to estimate the variances of the common $(\sigma^2_{\delta})$ and species-specific random terms $(\sigma^2_{\varepsilon_s})$. In this context, the common day random effect is $\delta_t \sim N(0, \sigma^2_{\delta})$ and the species-specific random effect is $\varepsilon_{t,s} \sim N(0, \sigma^2_{\varepsilon_s})$. This approach therefore maximizes the amount of data used to estimate variances. For each analysis, the three models (A, B, C) were each fitted independently to the data.

Using the estimated parameters, we calculated the species-specific intra-class correlation coefficient ($ICC_s$) to quantify synchrony:

$$ICC_s = \frac{\sigma^2_{\delta}}{\sigma^2_{\delta} + \sigma^2_{\varepsilon_s}}$$
ICC<sub>s</sub> quantifies the synchrony in variation of departure probability of species *s* with the other species, or equivalently, the between-day variance that is common to all species (i.e. accounted by the common random term δ<sub>j</sub>). When σ<sup>2</sup><sub>δ</sub> is large compared with σ<sup>2</sup><sub>ε<sub>j</sub></sub>, then ICC<sub>s</sub> is large and the between-day variation for that species is mostly synchronous with the other species. This formulation of ICC<sub>s</sub> can be extended to calculate a year- and species-specific intra-class correlation coefficient:

$$ICC_{y,s} = \frac{\sigma_{\delta_y}^2}{\sigma_{\delta_y}^2 + \sigma_{\varepsilon_{y,s}}^2}$$

To quantify the contribution of environmental covariates to the synchrony between species, we used the method in Grosbois et al. (2009) and Lahoz-Monfort et al. (2011). All models (A, B and C) include random effects, but model A does not include covariates and contains only an annual species-specific intercept (α<sub>j</sub>). For model A the residual variance is denoted by σ̂<sup>2</sup><sub>δ</sub> (res), and the total variance for model B and C is denoted by σ̂<sup>2</sup><sub>δ</sub> (tot). From these estimated variances, we can compute a coefficient C<sub>δ</sub> which quantifies the contribution of the focal covariate to the interspecies synchrony in departure probability:

$$C_{\delta} = 1 - \frac{\hat{\sigma}_{\delta}^{2}(\text{res})}{\hat{\sigma}_{\delta}^{2}(\text{tot})}$$

As previously, this coefficient can be extended to a year-specific contribution of covariates to synchrony. Here, the residual variance σ̂<sup>2</sup><sub>δy</sub> from model A is denoted by σ̂<sup>2</sup><sub>δy</sub> (res), and the total variance for model B and C is denoted as σ̂<sup>2</sup><sub>δy</sub> (tot). The new coefficient is denoted C<sub>δy</sub> and quantifies the contribution of the focal covariate to the interspecies synchrony in departure probability in year *y*:

$$C_{\delta_y} = 1 - \frac{\hat{\sigma}_{\delta_y}^2 (\text{res})}{\hat{\sigma}_{\delta_y}^2 (\text{tot})}$$

We used ‘Nimble’ (de Valpine et al. 2017) in R ver. 3.6.1 (www.r-project.org) with RStudio ver. 1.2.5001 to run models. Models were fit to the data using MCMC chains of 300 000 iterations with a burnin of 200 000. Chain mixing and convergence of the parameters were checked (Kéry and Schaub 2011). The hierarchical model specifications are available in the Supporting information.

## Results

### Between sites comparisons

Interspecific synchrony (ICC<sub>s</sub>) and the fraction of variation accounted for by the covariates (C<sub>δ</sub>) were calculated from estimates of model A (without covariates). For SW, ICC<sub>s</sub> values range between 0.6 and 0.8 at the three sites while ICC<sub>s</sub> values are higher (around 0.9) in RW and BT (Table 2). Results are consistent among sites, and the credible intervals are narrow enough to give confidence about the mean values (Table 2). This provides evidence of a high degree of synchrony in the variation of daily departure probability between the three species at the three sites. The between-day variation in departure probability explained by TSA (C<sub>δ</sub>TSA) is high at the three sites, with a mean equal to 0.921 at Donges, 0.901 at Saint-Vigor-d’Ymonville and 0.77 at l’île du Massereau (Table 2). Conversely, the fraction of variation explained by the environmental covariates is generally low, with a mean value of 0.012 at Donges, 0.137 at Saint-Vigor-d’Ymonville and 0.001 at l’île du Massereau. Overall, the number of individuals captured does not seem to affect the estimation of ICC<sub>s</sub> because the credible intervals for RW and BT are generally narrower than for SW, which is the species with the largest sample size.

### Multiple years at the Donges station

The values of species-specific ICC<sub>s</sub> vary among years, and are generally similar to what we found in the previous section: ICC<sub>s</sub> for SW is between 0.5 and 0.7 but was higher in RW and BT (except in years 2010 and 2013, Table 3). These results reflect what we see when looking at the daily departure probabilities Fig. 1, with visible peaks of departure which appear to be temporally synchronized. However, the standard deviations of these estimated ICC<sub>s</sub> are three to four times larger than what we found previously when we estimated a species-specific ICC<sub>s</sub> each year (Table 3). The estimation of

<table><caption>Table 2. Estimates of species-specific intra-class correlation coefficients (ICC<sub>s</sub>), and covariate contributions (C<sub>δ</sub>) from the estimated residual and total variance of the common (δ<sub>j</sub>) and species-specific (ε<sub>j,s</sub>) random effect terms. Standard deviations of the ICC<sub>s</sub> and the C<sub>δ</sub> are given with ±.</caption><thead><tr><th></th><th>Donges</th><th>Saint-Vigor-d'Ymonville</th><th>Île du Massereau</th></tr></thead><tbody><tr><td>Sedge warbler</td><td>0.61 ± 0.13<br>n=1349</td><td>0.78 ± 0.11<br>n=317</td><td>0.85 ± 0.13<br>n=580</td></tr><tr><td>Reed warbler</td><td>0.93 ± 0.10<br>n=277</td><td>0.96 ± 0.07<br>n=163</td><td>0.97 ± 0.08<br>n=188</td></tr><tr><td>Bluetroat</td><td>0.95 ± 0.07<br>n=240</td><td>0.94 ± 0.08<br>n=73</td><td>0.81 ± 0.12<br>n=87</td></tr><tr><td>C<sub>δ</sub>TSA</td><td>0.921 ± 0.01</td><td>0.901 ± 0.03</td><td>0.770 ± 0.15</td></tr><tr><td>C<sub>δ</sub>Weather</td><td>0.012 ± 0.36</td><td>0.137 ± 0.50</td><td>0.001 ± 0.62</td></tr></tbody></table>
<table><caption>Table 3. Estimates of species and year-specific intra-class correlation coefficients (ICC<sub>s</sub>) and year-specific covariates contributions (C<sub>δ,y</sub>) from the estimated residual and total variance of the common (δ<sub>y,t</sub>) and species-specific (ε<sub>y,t,x</sub>) random effect terms for the Donges station. Standard deviations of the ICC<sub>s</sub> and the C<sub>δ,y</sub> values are shown with ±.</caption><thead><tr><th></th><th>2009</th><th>2010</th><th>2011</th><th>2012</th><th>2013</th><th>2014</th></tr></thead><tbody><tr><td>Sedge warbler</td><td>0.59 ± 0.31<br>n=54</td><td>0.45 ± 0.32<br>n=29</td><td>0.74 ± 0.21<br>n=777</td><td>0.46 ± 0.31<br>n=376</td><td>0.26 ± 0.24<br>n=45</td><td>0.69 ± 0.24<br>n=68</td></tr><tr><td>Reed warbler</td><td>0.65 ± 0.30<br>n=79</td><td>0.37 ± 0.34<br>n=29</td><td>0.83 ± 0.22<br>n=27</td><td>0.78 ± 0.28<br>n=36</td><td>0.45 ± 0.35<br>n=31</td><td>0.78 ± 0.28<br>n=75</td></tr><tr><td>Bluethroat</td><td>0.53 ± 0.27<br>n=81</td><td>0.30 ± 0.33<br>n=45</td><td>0.82 ± 0.25<br>n=18</td><td>0.73 ± 0.28<br>n=21</td><td>0.62 ± 0.34<br>n=10</td><td>0.80 ± 0.21<br>n=55</td></tr><tr><td>C<sub>δ</sub>TSA</td><td>0.97 ± 0.04</td><td>0.69 ± 0.40</td><td>0.89 ± 0.28</td><td>0.91 ± 0.11</td><td>0.79 ± 0.33</td><td>0.91 ± 0.13</td></tr><tr><td>C<sub>δ</sub>Environment</td><td>0.52 ± 0.51</td><td>0 ± 1.23</td><td>0.17 ± 1.31</td><td>0 ± 1.45</td><td>0 ± 1.52</td><td>0.3 ± 0.71</td></tr></tbody></table>

synchrony is thus more precise when we integrated all years to estimate a single ICC<sub>s</sub> for each species. Contrary to what we found in the previous section, the number of individuals seems to affect the precision of the estimation; the smallest standard deviation corresponds to SW in year 2011, and to the largest number of individuals (Table 3, Fig. 1). The fraction of the variation accounted for by the covariates is consistent with the results in the previous section: TSA represents most of the variation of the year-specific common random term (δ<sub>y</sub>). In other words, it mainly contributes to high synchrony in departure decisions between species. However, weather conditions seemed to account for a high proportion of synchrony in some years (50% and 17% respectively in

2009 and 2011; Table 3). Hence, TSA appears to be a generic synchronizing factor, operating in all years, whereas weather conditions tend to increase synchrony in departure probability in some years but not all (PCA first component yearly values in the Supporting information).

## Discussion

### Synchrony in departure probability

Overall, we found high synchrony in daily departure probability from stopover sites between three songbird species. This high synchrony of the between-day variation of

<figure><img src="image_5.png" /><figcaption>Figure 1. Daily departure probability of the three species in August, from 2009 to 2014. Red: sedge warbler, blue: reed warbler, green: bluethroat. Solid lines represent the mean and the shaded areas around the mean are the standard deviations around the mean.</figcaption></figure>
departure probability suggests that birds typically leave the stopover area on the same nights, generating 'waves' of departure (Fig. 1). Even if nocturnal long distant songbird species do not form flocks during night migration (Gauthreaux Jr. 1972, Balcomb 1977), their departures from stopover sites seem to largely coincide in time across individuals. These peaks of departures, synchronous across many migratory species, result in high migration intensity on certain nights. This phenomenon has first been detected by ornithologists during moon-watching sessions (Nisbet and Drury, 1969, Gauthreaux Jr. 1972, Liechti et al. 1995, Zehtindjiev and Liechti 2003) and subsequently by radar studies (Richardson 1979, Lupi et al. 2016, Weisshaupt et al. 2016) but without the ability to identify species (Schmaljohann et al. 2008, Zaugg et al. 2008, Nilsson et al. 2018, Schmid et al. 2019). One may think that weather conditions explain this cross-species synchrony of departure: when weather conditions are favourable for migratory flight, species with similar aerodynamic constraints might tend to depart simultaneously (and conversely, when weather conditions are unfavourable to migration). However, even if weather conditions play a role in the decision to depart from stopover sites (Erni et al. 2002, Schaub et al. 2004, Ma et al. 2011, Grönroos et al. 2012, Deppe et al. 2015, Sjöberg et al. 2015, Dossman et al. 2016), our results suggest that weather is not the main driver of synchrony in departure decisions in our study. Only particularly bad conditions seem to affect departure decisions: heavy rain or strong headwind (Wikelski et al. 2003, Schmaljohann et al. 2017). The secondary role of weather conditions in generating synchrony may be attributed to the relatively stable weather in western and northwestern France during August. If weather remains stable for most of the time, it is unlikely to be the primary driver of synchrony. However, the influence of weather on synchrony in departure probability could be more pronounced at stopover sites with greater weather variability or at different locations along the flyway (Bayly 2007, Anderson et al. 2019).

Our results show that time since arrival (TSA) is indeed the most synchronizing agent of departures. TSA can be considered an integrative metric reflecting changes in a bird's internal state, encompassing physiological and metabolic adjustments that occur during stopover. Its relationship with other individual-level covariates, such as fat stores or body weight, has been discussed in the literature (Roques et al. 2021, 2022). Considered as such, our findings align with recent studies suggesting that internal state variables (fuel stores, body condition, restlessness) are primary determinants of departure decisions, while weather conditions influence this process mainly by imposing constraints rather than actively driving synchronization (Eikenaar and Schläfke 2013, Cohen et al. 2014, Schmaljohann and Eikenaar 2017, Moore et al. 2017, Anderson et al. 2019, Klinner et al. 2020, Linscott and Senner 2021, Roques et al. 2022). Interestingly, the similarity in slope coefficients of the TSA effect across RW, BT, and SW (Supporting information) suggests a shared response to TSA. This indicates that, beyond merely synchronizing their

departure decisions, these species exhibit convergent strategies in managing energy reserves during stopover. Such synchronization may reflect underlying constraints imposed by metabolic processes, whereby individuals accumulate and metabolize fuel at comparable rates, leading to a predictable departure pattern (Schmaljohann and Eikenaar 2017, Schmaljohann et al. 2022). Moreover, these results highlight the importance of intrinsic factors over environmental ones in shaping migration timing, reinforcing the idea that birds follow a relatively fixed, species-specific energy budget that governs their stopover duration and readiness to depart. The fact that TSA-driven synchrony is maintained across years, sites and species further suggests a robust, evolutionary conserved strategy that likely optimizes energy expenditure and migratory success. These findings contribute to a broader understanding of stopover ecology, emphasizing that while weather conditions can modulate migration timing, the primary drivers of departure remain deeply rooted in common physiological and energetic constraints (Schmaljohann et al. 2022).

Extending this approach to mixed communities of long-distance and partially migratory songbirds could provide deeper insights into departure synchrony and migration ecology. Long-distance migrants, constrained by strict schedules, primarily rely on intrinsic factors like fuel accumulation, with weather playing a secondary role (Moore et al. 2017, Roques et al. 2022). In contrast, partial migrants have more flexible movement schedules and may respond more directly to local conditions such as food availability, habitat quality, and weather variability (Chapman et al. 2011). As a result, we would expect departure synchrony in mixed communities to vary significantly across species and environmental contexts. In areas with unpredictable or highly variable weather conditions, partial migrants may display less synchronized departures, as individuals adjust their timing based on short-term fluctuations in local conditions (Pulido 2007). Conversely, in more stable environments, partial migrants could exhibit greater synchrony, particularly if food availability follows predictable seasonal trends (Zhemchuzhnikov et al. 2021). Studying such mixed communities could enhance our understanding of how different migratory strategies interact within shared habitats, shedding light on the ecological and evolutionary pressures shaping departure decisions. Given ongoing climate change, examining these dynamics is particularly relevant, as shifts in environmental conditions may alter the balance between intrinsic and extrinsic factors in migration timing (Pulido 2007, Visser et al. 2009). Understanding these patterns could also inform conservation efforts by identifying species or populations that may be more vulnerable to environmental change due to their reliance on specific departure cues (Linscott and Senner 2021).

## Modelling and possible developments

We modified existing capture-recapture methodology estimating synchrony in survival (Grosbois et al. 2009, Lahoz-Monfort et al. 2011) in order to study the synchrony in departure probability from stopover sites. This
model allows disentangling the underlying processes which generate synchrony across species in departure probability. We also extended these models to integrate multiple replicates (here, different years) to obtain more precise estimates of synchrony indices. Also, it would be interesting to extend this model to integrate data from multiple sources, such as telemetry and radar data (Liechti et al. 2019, Anderson et al. 2019). Combining different data sources in the same model would improve the accuracy of estimates and daily variations. The advantages of data integration have already been widely discussed in the literature (Korner-Nievergelt et al. 2017, Miller et al. 2019, Tucker et al. 2023).

Apart from detecting synchrony in departure probability, this model permits further extensions. For example, this model could be transformed to study synchrony in departure probability at the spatial level by adding a site dependent random effect ($\gamma_{t,l}$) as:

$$\log it(\phi_{t,l}) = \alpha_s + \delta_t + \gamma_{t,l}$$

Where $\gamma_{t,l} \sim N(0, \sigma^2)$ is the site-specific random effect at time $t$ and at location $l$. The synchrony index between sites can be computed as follows:

$$ICC_l = \frac{\sigma^2}{\sigma^2 + \sigma^2}$$

$ICC_l$ would then represent the synchrony in variation of departure probability between sites for the studied species. However, this excludes the possibility to integrate multiple species in the model and hence to consider how the variations of departure probabilities of a specific species $s$ at a site $l$ are synchronized with the variations of departure probability of the same species at other sites. Doing so would require an additional random effect. This random effect $\lambda_{t,s,l}$ would be species- ($s$) and site-specific ($l$) and denoted by $\lambda_{t,s,l} \sim N(0, \sigma^2)$). The new model also including the species-specific random effect $\varepsilon_{t,s}$ and the site and species-specific intercept $\alpha_{s,l}$ would be:

$$\log it(\phi_{t,s,l}) = \alpha_{s,l} + \varepsilon_{t,s} + \lambda_{t,s,l}$$

This model would support evaluating how species $s$ at site $l$ behaves compared to the same species at the other locations:

$$ICC_{s,l} = \frac{\sigma^2}{\sigma^2 + \sigma^2}$$

This extension would allow inclusion of all species and all the locations in the same model, and also computing multiple synchrony indexes within the same framework by changing the random effects included in the model. The estimation and the convergence of random effects is often time-consuming and difficult to obtain but different parametrization (e.g. non-centered, Bass and Sahu 2017) could help to achieve convergence of such parameters.

As competition between species is thought to influence behaviour at stopover sites, especially if food resources are not sufficient (Jenni and Schaub 2003), the model could also be extended to test hypotheses concerning the relationship between the presence (or abundance) of one species and the decisions of others, by adding a term in the departure probability equation to quantify its effect on synchrony. Finally, given the hypothesis that closely situated stopover sites are more likely to exhibit synchronized departure probabilities, a distance effect can be readily incorporated into the departure probability equation.

## Conclusion

In this study, by leveraging a newly adapted modelling framework, we demonstrated through data from ringed birds that species with seemingly similar ecological requirements during stopover exhibit broadly synchronized departure behavior. This study is the first to explicitly estimate synchrony in departure probability from a stopover site from the monitoring of ringed birds and quantify the contribution of internally driven processes compared to external drivers (e.g. weather conditions) to synchrony.

Applying such tools to compare departure decisions across multiple species enables new hypotheses about bird migration to be tested at unprecedented scales involving thousands of individuals, spanning decades, and covering multiple countries, far beyond the current reach of telemetry alone (du Feu et al. 2016). However, the complex processes driving individual departure decisions and synchrony highlight the need for further data on local movements (Mills et al. 2011, Taylor et al. 2011), body condition (Anderson et al. 2019, Klinner et al. 2020), and migration distance (Anderson et al. 2019). The modelling framework adapted here is highly adaptable; it can be applied to capture-recapture data across various spatial and temporal scales, supporting diverse taxonomic groups and migratory strategies (e.g. partial versus obligate migrants). This flexibility opens new avenues for advancing our understanding of migration dynamics in bird populations.

**Acknowledgements** – This study was made possible thanks to tens of bird ringers and hundreds of assistants who volunteered in these long-term monitoring efforts. This study was supervised by the Centre de Recherches sur la Biologie des Populations d'Oiseaux (CRBPO), supported by Muséum National d'Histoire Naturelle, CNRS and Ministère de l'Environnement.

**Permits** – This work was carried out in accordance with standard animal care protocols approved by the CRBPO.
**Author contributions**

**Sébastien Roques:** Conceptualization (lead); Formal analysis (lead); Methodology (lead); Writing – original draft (lead).

**Pierre-Yves Henry:** Conceptualization (equal); Formal analysis (equal); Methodology (equal); Supervision (equal); Writing – review and editing (equal). **Hubert Dugué:** Resources (equal). **Sébastien Gautier:** Resources (equal).

**Jean-Luc Chil:** Resources (equal). **Pascal Provost:** Resources (equal). **Daniel Turek:** Formal analysis (equal); Methodology (equal); Writing – review and editing (equal). **Emmanuelle Cam:** Conceptualization (equal); Methodology (equal); Supervision (equal); Writing – review and editing (equal).

**Roger Pradel:** Conceptualization (equal); Formal analysis (equal); Methodology (equal); Supervision (equal); Writing – review and editing (equal);

**Data availability statement**

Data are available from Github: https://github.com/sebroques/multisp_stopover.

**Supporting information**

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

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