# Synchrony in adult survival is remarkably strong among common temperate songbirds across France

Manon Ghislain, Timothée Bonnet, Ugoline Godeau, Olivier Dehorter, Olivier Gimenez, Pierre-yves Henry

► To cite this version:

Manon Ghislain, Timothée Bonnet, Ugoline Godeau, Olivier Dehorter, Olivier Gimenez, et al. Synchrony in adult survival is remarkably strong among common temperate songbirds across France. Ecology, 2024, 10.1002/ecy.4305. hal-04562377

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# Synchrony in adult survival is remarkably strong among common temperate songbirds across France

Manon Ghislain, Timothée Bonnet, Ugoline Godeau, Olivier Dehorter, Olivier Gimenez, Pierre-yves Henry

► To cite this version:

Manon Ghislain, Timothée Bonnet, Ugoline Godeau, Olivier Dehorter, Olivier Gimenez, et al. Synchrony in adult survival is remarkably strong among common temperate songbirds across France. Ecology, 2024, 10.1002/ecy.4305. hal-04562377

HAL Id: hal-04562377

https://hal.science/hal-04562377v1

Submitted on 29 Apr 2024

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

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

<figure><img src="image_1.png" /></figure>
1 **Journal name:** Ecology

2 **Manuscript type:** Article

3 **Manuscript Title:**

4 **Synchrony in adult survival is remarkably strong among common**

5 **temperate songbirds across France**

6 **Running head:** Interspecific synchrony in survival

7 **Authors:**

8 Manon Ghislain,\*¹,²,³ Timothée Bonnet,\*⁴,⁵ Ugoline Godeau,¹,²,⁶,⁷ Olivier Dehorter,² Olivier

9 Gimenez,⁸ and Pierre-Yves Henry¹,²

10 \* Co-First Authors.

11

12 ¹ Mécanismes adaptatifs et évolution (MECADEV UMR 7179), Muséum National d'Histoire

13 Naturelle, Centre National de la Recherche Scientifique, 1 avenue du Petit Château, 91800

14 Brunoy, France.

15 ² Centre de Recherches sur la Biologie des Populations d'Oiseaux (CRBPO), Centre d'Ecologie

16 et des Sciences de la Conservation (CESCO UMR 7204), Muséum National d'Histoire

17 Naturelle, Centre National de la Recherche Scientifique, Sorbonne Université, CP135, 43 rue

18 Buffon, 75005 Paris, France.
1 **Journal name:** Ecology

2 **Manuscript type:** Article

3 **Manuscript Title:**

4 **Synchrony in adult survival is remarkably strong among common**

5 **temperate songbirds across France**

6 **Running head:** Interspecific synchrony in survival

7 **Authors:**

8 Manon Ghislain,\*¹,²,³ Timothée Bonnet,\*⁴,⁵ Ugoline Godeau,¹,²,⁶,⁷ Olivier Dehorter,² Olivier

9 Gimenez,⁸ and Pierre-Yves Henry¹,²

10 \* Co-First Authors.

11

12 ¹ Mécanismes adaptatifs et évolution (MECADEV UMR 7179), Muséum National d'Histoire

13 Naturelle, Centre National de la Recherche Scientifique, 1 avenue du Petit Château, 91800

14 Brunoy, France.

15 ² Centre de Recherches sur la Biologie des Populations d'Oiseaux (CRBPO), Centre d'Ecologie

16 et des Sciences de la Conservation (CESCO UMR 7204), Muséum National d'Histoire

17 Naturelle, Centre National de la Recherche Scientifique, Sorbonne Université, CP135, 43 rue

18 Buffon, 75005 Paris, France.
19 ³PatriNat (OFB-MNHN-CNRS-IRD), Centre d'expertise et de données sur le patrimoine naturel, Muséum national d'Histoire naturelle, 36 rue Geoffroy Saint-Hilaire, CP41, 75005 Paris, France.

22 ⁴Division of Ecology and Evolution, Research School of Biology, ANU College of Science, The Australian National University, Canberra, ACT 2600, Australia.

24 ⁵Centre d'Etudes Biologiques de Chizé (CEBC UMR 7372), Centre National de la Recherche Scientifique, Villiers en Bois 79360, France.

26 ⁶Institut national de Recherche en Sciences et Technologies pour l'Environnement et l'Agriculture, Ecosystèmes Forestiers (UR EFNO), Domaine des Barres, 45290 Nogent-Sur-Vernisson, France.

29 ⁷Institut National de Recherche pour l'Agriculture l'Alimentation et l'Environnement, UR 406 Abeilles et Environnement, Avignon, France.

31 ⁸Centre d'Ecologie Fonctionnelle et Evolutive (CEFE UMR 5175), Centre National de la Recherche Scientifique, Université de Montpellier, Université Paul-Valéry Montpellier, Ecole Pratique des Hautes Etudes, 1919 Route de Mende, 34293 Montpellier Cedex 5, France.

35 **Corresponding Author**: Pierre-Yves Henry

36 ⁷E-mail: pierre-yves.henry@mnhn.fr

**Open Research:** Data necessary to reproduce the analyses are provided on the Dryad repository https://doi.org/10.5061/dryad.vx0k6djv3 (Ghislain et al. 2022a) and novel code is provided on the Zenodo repository https://doi.org/10.5281/zenodo.8172390 (Ghislain et al. 2022b).
19 ³PatriNat (OFB-MNHN-CNRS-IRD), Centre d'expertise et de données sur le patrimoine naturel, Muséum national d'Histoire naturelle, 36 rue Geoffroy Saint-Hilaire, CP41, 75005 Paris, France.

22 ⁴Division of Ecology and Evolution, Research School of Biology, ANU College of Science, The Australian National University, Canberra, ACT 2600, Australia.

24 ⁵Centre d'Etudes Biologiques de Chizé (CEBC UMR 7372), Centre National de la Recherche Scientifique, Villiers en Bois 79360, France.

26 ⁶Institut national de Recherche en Sciences et Technologies pour l'Environnement et l'Agriculture, Ecosystèmes Forestiers (UR EFNO), Domaine des Barres, 45290 Nogent-Sur-Vernisson, France.

29 ⁷Institut National de Recherche pour l'Agriculture l'Alimentation et l'Environnement, UR 406 Abeilles et Environnement, Avignon, France.

31 ⁸Centre d'Ecologie Fonctionnelle et Evolutive (CEFE UMR 5175), Centre National de la Recherche Scientifique, Université de Montpellier, Université Paul-Valéry Montpellier, Ecole Pratique des Hautes Etudes, 1919 Route de Mende, 34293 Montpellier Cedex 5, France.

35 **Corresponding Author**: Pierre-Yves Henry

36 ⁷E-mail : pierre-yves.henry@mnhn.fr

38 **Open Research**: Data necessary to reproduce the analyses are provided on the Dryad repository https://doi.org/10.5061/dryad.vx0k6djv3 (Ghislain et al. 2022a) and novel code is provided on the Zenodo repository https://doi.org/10.5281/zenodo.8172390 (Ghislain et al. 2022b).
41

42 **Keywords:** adult survival; common songbirds; demography; mark-recapture; migration; Moran

43 effect; precipitation; temporal synchrony; temperature.
41

42 **Keywords:** adult survival; common songbirds; demography; mark-recapture; migration; Moran

43 effect; precipitation; temporal synchrony; temperature.
44 Abstract

45 Synchronous variation in demographic parameters across species increases the risk of
46 simultaneous local extinction, which lowers the probability of subsequent recolonization.

47 Synchrony therefore tends to destabilize meta-populations and meta-communities. Quantifying
48 interspecific synchrony in demographic parameters, like abundance, survival or reproduction, is
49 thus a way to indirectly assess the stability of meta-populations and meta-communities.

50 Moreover, it is particularly informative to identify environmental drivers of interspecific
51 synchrony since those drivers are important across species. Using a Bayesian hierarchical multi-
52 site multi-species mark-recapture model, we investigated temporal interspecific synchrony in
53 annual adult apparent survival for 16 common songbird species across France for the period
54 2001–2016. Annual adult survival was largely synchronous among species (73%, 95% credible
55 interval [47–94%] of the variation among years was common to all species), despite species
differing in ecological niche and life-history. This result was robust to different model
57 formulations, uneven species sample sizes, and removing the long-term trend in survival.

58 Synchrony was also shared across migratory strategies, which suggests that environmental
59 forcing during the 4-month temperate breeding season has a large-scale, interspecific impact on
60 songbird survival. However, the strong interspecific synchrony was not easily explained by a set
61 of candidate weather variables we defined *a priori*. Spring weather variables explained only
62 1.4% [0.01–5.5%] of synchrony, while the contribution of large-scale winter weather indices
63 may have been stronger but uncertain, accounting for 12% [0.3–37%] of synchrony. Future
64 research could jointly model interspecific variation and covariation in breeding success, age-
44 **Abstract**

Synchronous variation in demographic parameters across species increases the risk of
simultaneous local extinction, which lowers the probability of subsequent recolonization.

Synchrony therefore tends to destabilize meta-populations and meta-communities. Quantifying interspecific synchrony in demographic parameters, like abundance, survival or reproduction, is thus a way to indirectly assess the stability of meta-populations and meta-communities.

Moreover, it is particularly informative to identify environmental drivers of interspecific synchrony since those drivers are important across species. Using a Bayesian hierarchical multi-site multi-species mark-recapture model, we investigated temporal interspecific synchrony in annual adult apparent survival for 16 common songbird species across France for the period 2001–2016. Annual adult survival was largely synchronous among species (73%, 95% credible interval [47–94%] of the variation among years was common to all species), despite species differing in ecological niche and life-history. This result was robust to different model formulations, uneven species sample sizes, and removing the long-term trend in survival.

Synchrony was also shared across migratory strategies, which suggests that environmental forcing during the 4-month temperate breeding season has a large-scale, interspecific impact on songbird survival. However, the strong interspecific synchrony was not easily explained by a set of candidate weather variables we defined *a priori*. Spring weather variables explained only 1.4% [0.01–5.5%] of synchrony, while the contribution of large-scale winter weather indices may have been stronger but uncertain, accounting for 12% [0.3–37%] of synchrony. Future research could jointly model interspecific variation and covariation in breeding success, age-
65 dependent survival and age-dependent dispersal to understand when interspecific synchrony in

66 abundance emerges and destabilizes meta-communities.

67

68

69
65 dependent survival and age-dependent dispersal to understand when interspecific synchrony in

66 abundance emerges and destabilizes meta-communities.

67

68

69
70 INTRODUCTION

71 Synchrony corresponds to the coincident change in individual, population or species
72 characteristics over extended areas, up to thousands of km (Liebhold et al. 2004). Understanding
73 the causes of synchronous variations in population size is important in ecology and conservation
74 because the greater the synchrony, the higher the risk of population, metapopulation, and
75 metacommunity extinction (Liebhold et al. 2004, Koenig and Liebhold 2016). When populations
76 go down in numbers simultaneously the risk of synchronous local extinctions increases and the
77 opportunity for subsequent demographic rescue through immigration diminishes (Tavecchia et
78 al. 2016). If extinctions occur simultaneously for multiple co-occurring species, communities
79 and ecosystem functioning can be disrupted (Loreau and de Mazancourt 2008, Kahilainen et al.
80 2018). However, differences in life-history and niche across species should increase community
81 stability by decreasing interspecific synchrony (Pandit et al. 2013).

82 A major theoretical and empirical challenge is to disentangle the respective roles of the three
83 processes that can cause synchrony. The first process, termed the Moran effect, corresponds to
84 extrinsic forcing: populations that are affected by the same environmental variables are
85 synchronized due to the spatial correlation in constraints (Liebhold et al. 2004, Olmos et al.
86 2019). In particular, climate forcing may directly drive variation in survival and reproduction
87 (Post and Forchhammer 2004, Cattadori et al. 2005, Hansen et al. 2013, Sheppard et al. 2015,
88 Koenig and Liebhold 2016, Black et al. 2018, Kahilainen et al. 2018, but see Vik et al. 2004).
89 Climate forcing in wintering areas is expected to synchronize survival among species that share
90 the same wintering range (e.g., Telensky et al. 2020). The second process is synchronization
91 through interactions with another trophic level. This includes synchrony due to variation in the
70 INTRODUCTION

71 Synchrony corresponds to the coincident change in individual, population or species
72 characteristics over extended areas, up to thousands of km (Liebhold et al. 2004). Understanding
73 the causes of synchronous variations in population size is important in ecology and conservation
74 because the greater the synchrony, the higher the risk of population, metapopulation, and
75 metacommunity extinction (Liebhold et al. 2004, Koenig and Liebhold 2016). When populations
76 go down in numbers simultaneously the risk of synchronous local extinctions increases and the
77 opportunity for subsequent demographic rescue through immigration diminishes (Tavecchia et
78 al. 2016). If extinctions occur simultaneously for multiple co-occurring species, communities
79 and ecosystem functioning can be disrupted (Loreau and de Mazancourt 2008, Kahilainen et al.
80 2018). However, differences in life-history and niche across species should increase community
81 stability by decreasing interspecific synchrony (Pandit et al. 2013).

82 A major theoretical and empirical challenge is to disentangle the respective roles of the three
83 processes that can cause synchrony. The first process, termed the Moran effect, corresponds to
84 extrinsic forcing: populations that are affected by the same environmental variables are
85 synchronized due to the spatial correlation in constraints (Liebhold et al. 2004, Olmos et al.
86 2019). In particular, climate forcing may directly drive variation in survival and reproduction
87 (Post and Forchhammer 2004, Cattadori et al. 2005, Hansen et al. 2013, Sheppard et al. 2015,
88 Koenig and Liebhold 2016, Black et al. 2018, Kahilainen et al. 2018, but see Vik et al. 2004).
89 Climate forcing in wintering areas is expected to synchronize survival among species that share
90 the same wintering range (e.g., Telensky et al. 2020). The second process is synchronization
91 through interactions with another trophic level. This includes synchrony due to variation in the
abundance of shared prey, notably mast-seeding for seed-eating species (Liebhold et al. 2004,

Klapwijk et al. 2018), predators (Huitu et al. 2004), or parasites (Cattadori et al. 2005). Variation

in abundance in the trophic level responsible for synchrony may itself be driven by climate

forcing (e.g., Dubos et al. 2018). The third process is dispersal, which may synchronize

fluctuations in population size for connected populations (Paradis et al. 1999, Liebhold et al.

2004, Martin et al. 2017; but see Sanz-Aguilar et al. 2016, Tavecchia et al. 2016). More

generally, any type of simultaneous movement of individuals, including seasonal migratory

movements, can synchronize population sizes across distant sites.

Synchrony has mainly been investigated across populations within species, i.e., as the spatial

covariance in temporal changes in population sizes or vital rates (Liebhold et al. 2004).

However, evidence accumulates that different species can show a strong degree of synchrony

over wide areas even when they are ecologically, functionally and phylogenetically divergent

(Post and Forchhammer 2004, Hansen et al. 2013, Black et al. 2018). In vertebrate animals this

interspecific synchrony (Liebhold et al. 2004) has mainly been studied using two study systems

that are subject to trivial environmental forcing: species thriving in cold-driven environments

following cyclic dynamics (Post and Forchhammer 2004, Jones et al. 2003, Hansen et al. 2013,

Koenig and Liebhold 2016, Huitu et al. 2004, Liebhold et al. 2004) and seabirds that gather at a

few colonies close to shared, large foraging areas (Lahoz-Monfort et al. 2011, 2013, 2017,

Robinson et al. 2013). Existing evidence of interspecific synchrony in population sizes across a

wide range of species comes mainly from large-scale censuses of common wintering birds: six

species from the UK (Swallow et al. 2016) and 49 species from North America (Koenig and

Liebhold 2016).
92 abundance of shared prey, notably mast-seeding for seed-eating species (Liebhold et al. 2004,

93 Klapwijk et al. 2018), predators (Huitu et al. 2004), or parasites (Cattadori et al. 2005). Variation

94 in abundance in the trophic level responsible for synchrony may itself be driven by climate

95 forcing (e.g., Dubos et al. 2018). The third process is dispersal, which may synchronize

96 fluctuations in population size for connected populations (Paradis et al. 1999, Liebhold et al.

97 2004, Martin et al. 2017; but see Sanz-Aguilar et al. 2016, Tavecchia et al. 2016). More

98 generally, any type of simultaneous movement of individuals, including seasonal migratory

99 movements, can synchronize population sizes across distant sites.

100 Synchrony has mainly been investigated across populations within species, i.e., as the spatial

101 covariance in temporal changes in population sizes or vital rates (Liebhold et al. 2004).

102 However, evidence accumulates that different species can show a strong degree of synchrony

103 over wide areas even when they are ecologically, functionally and phylogenetically divergent

104 (Post and Forchhammer 2004, Hansen et al. 2013, Black et al. 2018). In vertebrate animals this

105 interspecific synchrony (Liebhold et al. 2004) has mainly been studied using two study systems

106 that are subject to trivial environmental forcing: species thriving in cold-driven environments

107 following cyclic dynamics (Post and Forchhammer 2004, Jones et al. 2003, Hansen et al. 2013,

108 Koenig and Liebhold 2016, Huitu et al. 2004, Liebhold et al. 2004) and seabirds that gather at a

109 few colonies close to shared, large foraging areas (Lahoz-Monfort et al. 2011, 2013, 2017,

110 Robinson et al. 2013). Existing evidence of interspecific synchrony in population sizes across a

111 wide range of species comes mainly from large-scale censuses of common wintering birds: six

112 species from the UK (Swallow et al. 2016) and 49 species from North America (Koenig and

113 Liebhold 2016).
To our knowledge, until recently interspecific synchrony in vital rates, rather than population sizes, had been studied only for a small number of species at a single site or region: one study on two duck species (Péron and Koons 2012), two studies on two salmon species (Malick and Cox 2016, Malick et al. 2017), and all others on two to six seabird species (Lahoz-Monfort et al. 2011, 2013, 2017, Robinson et al. 2013). Recently, Telensky et al. (2020) estimated the synchronizing effect of weather variables on breeding sites and wintering sites for the survival of 16 songbird species across Czech Republic during 2004–2014, albeit without estimating synchrony explicitly. In addition, Morrison et al. (2022) estimated species-specific synchrony - but not overall interspecific synchrony - in reproduction and survival using 26 songbird species across 336 European sites. The lack of research on interspecific synchrony of vital rates likely results from (i) the lack of theory and methods to root (meta)community dynamics in processes operating at the individual level (but see Lahoz-Monfort et al. 2017), and (ii) the complexity of methods required to analyze multi-species, multi-site, long-term vital rate data (Grosbois et al. 2009, Lahoz-Monfort et al. 2011, 2013, 2017, Swallow et al. 2016).

In the present study, we take advantage of a Constant Effort Sites (CES) mist-netting scheme to investigate interspecific synchrony in yearly fluctuations of annual adult apparent survival for a set of 16 common songbirds species at 242 sites across France (Fig. 1) over a 16 year period. We analyzed interspecific synchrony using the variance partitioning method of Lahoz-Monfort et al. (2011), splitting between-year variance into a synchronous component common to all species, and an asynchronous component corresponding to a year by species interaction. Since our analyses involved species with distinct ecological niches (Appendix S1: Table S1) and since synchrony is expected to be lower with higher species diversity (Pandit et al. 2013), we expected
To our knowledge, until recently interspecific synchrony in vital rates, rather than population sizes, had been studied only for a small number of species at a single site or region: one study on two duck species (Péron and Koons 2012), two studies on two salmon species (Malick and Cox 2016, Malick et al. 2017), and all others on two to six seabird species (Lahoz-Monfort et al. 2011, 2013, 2017, Robinson et al. 2013). Recently, Telensky et al. (2020) estimated the synchronizing effect of weather variables on breeding sites and wintering sites for the survival of 16 songbird species across Czech Republic during 2004–2014, albeit without estimating synchrony explicitly. In addition, Morrison et al. (2022) estimated species-specific synchrony - but not overall interspecific synchrony - in reproduction and survival using 26 songbird species across 336 European sites. The lack of research on interspecific synchrony of vital rates likely results from (i) the lack of theory and methods to root (meta)community dynamics in processes operating at the individual level (but see Lahoz-Monfort et al. 2017), and (ii) the complexity of methods required to analyze multi-species, multi-site, long-term vital rate data (Grosbois et al. 2009, Lahoz-Monfort et al. 2011, 2013, 2017, Swallow et al. 2016).

In the present study, we take advantage of a Constant Effort Sites (CES) mist-netting scheme to investigate interspecific synchrony in yearly fluctuations of annual adult apparent survival for a set of 16 common songbirds species at 242 sites across France (Fig. 1) over a 16 year period. We analyzed interspecific synchrony using the variance partitioning method of Lahoz-Monfort et al. (2011), splitting between-year variance into a synchronous component common to all species, and an asynchronous component corresponding to a year by species interaction. Since our analyses involved species with distinct ecological niches (Appendix S1: Table S1) and since synchrony is expected to be lower with higher species diversity (Pandit et al. 2013), we expected
low interspecific synchrony. Further, our species differ in terms of migratory strategies and

wintering range: nine species are resident or short-distance migrants, with year-round exposure

to temperate climate, whereas seven species are trans-Saharan migrants that winter under the

west-African climate (Appendix S1). Therefore, we expected that synchrony would be stronger

within a given migratory strategy and that migratory strategy would explain a significant

proportion of year-to-year variation among species. If that was the case it would point to a

synchronizing role of conditions on wintering grounds. Winter harshness is commonly proposed

to explain a large proportion of year-round mortality in small organisms like songbirds

(Grosbois et al. 2006, Robinson et al. 2007, Salewski et al. 2013, Johnston et al. 2016, Saether et al. 2016), including in migratory species (Robinson et al. 2007, Woodworth et al. 2017b).

Factors explaining winter mortality could include water availability in arid wintering areas

(Telenský et al. 2020) and the cost of thermoregulation in extreme cold or hot conditions (Boyles

et al. 2011). On the other hand, if survival was to covary more synchronously and across

migration strategies, it would point towards a synchronizing role of conditions on the breeding

grounds, possibly due to climate forcing. In addition to factors that explain mortality during

winter, climate forcing during the breeding season could synchronize species through its effect

on the cost of reproduction through primary and secondary productivity (see details in

discussion).

To assess the role of climate forcing during the breeding season (i.e. common to all species)

vs. non-breeding season (i.e. common to species wintering in the same climatic region), we

estimated the proportion of synchronous variation in annual survival that could be ascribed to

local temperatures and precipitations at the breeding sites during the breeding season, and to
low interspecific synchrony. Further, our species differ in terms of migratory strategies and

wintering range: nine species are resident or short-distance migrants, with year-round exposure

to temperate climate, whereas seven species are trans-Saharan migrants that winter under the

west-African climate (Appendix S1). Therefore, we expected that synchrony would be stronger

within a given migratory strategy and that migratory strategy would explain a significant

proportion of year-to-year variation among species. If that was the case it would point to a

synchronizing role of conditions on wintering grounds. Winter harshness is commonly proposed

to explain a large proportion of year-round mortality in small organisms like songbirds

(Grosbois et al. 2006, Robinson et al. 2007, Salewski et al. 2013, Johnston et al. 2016, Saether et

al. 2016), including in migratory species (Robinson et al. 2007, Woodworth et al. 2017b).

Factors explaining winter mortality could include water availability in arid wintering areas

(Telenský et al. 2020) and the cost of thermoregulation in extreme cold or hot conditions (Boyles

et al. 2011). On the other hand, if survival was to covary more synchronously and across

migration strategies, it would point towards a synchronizing role of conditions on the breeding

grounds, possibly due to climate forcing. In addition to factors that explain mortality during

winter, climate forcing during the breeding season could synchronize species through its effect

on the cost of reproduction through primary and secondary productivity (see details in

discussion).

To assess the role of climate forcing during the breeding season (i.e. common to all species)

vs. non-breeding season (i.e. common to species wintering in the same climatic region), we

estimated the proportion of synchronous variation in annual survival that could be ascribed to

local temperatures and precipitations at the breeding sites during the breeding season, and to
158 global climate indices related to conditions on the wintering grounds during the non-breeding season (the winter North Atlantic Oscillation and the summer Sahel rainfalls, see Methods). We 159
160 predicted that synchronous variation in survival should be in part explained by spring weather 161
conditions. 162

162

163 METHODS

164 *Study sites and species*

165 Mark-recapture data were collected by volunteer bird banders under the French Constant- 166
ringing-Effort-Site (CES) banding survey (Julliard and Jiguet 2002). The full CES dataset is 167
available on request through https://crbpo.mnhn.fr/. We selected data over the period 2001– 168
2016, with the goal of estimating annual apparent survival and recapture probabilities. We 169
consider “apparent survival”, rather than “true survival”, as our data cannot distinguish between 170
mortality and permanent emigration. At each CES site the local bird community is sampled 3.17 171
± 1.06 SD times per breeding season (first session on May 21 ± 15 days, last session on July 4 ± 172
12 days), with 14 ± 7 mist nets (12 m long, 3-to-4 m high, 16 mm mesh size) spread over an area 173
of ~ 3 hectares (4 to 5 mist nets per hectare). A capture session typically starts at dawn and lasts 174
until midday. For every site the number, dates, hours of capture sessions, and mist net locations 175
are held constant across sessions and years. Sites are monitored for an average of six ± four 176
years. Sites are typically located in low canopy habitats such as shrublands, open woodlands and 177
reed beds, where birds are easily captured with 3.5 m high mist nets. Each bird captured is 178
marked with a metal band bearing a unique identifier, identified at the species level, aged 179
158 global climate indices related to conditions on the wintering grounds during the non-breeding season (the winter North Atlantic Oscillation and the summer Sahel rainfalls, see Methods). We 159
160 predicted that synchronous variation in survival should be in part explained by spring weather 161
conditions. 162

162

163 METHODS

164 *Study sites and species*

165 Mark-recapture data were collected by volunteer bird banders under the French Constant-
166 ringing-Effort-Site (CES) banding survey (Julliard and Jiguet 2002). The full CES dataset is
167 available on request through https://crbpo.mnhn.fr/. We selected data over the period 2001-
168 2016, with the goal of estimating annual apparent survival and recapture probabilities. We
169 consider “apparent survival”, rather than “true survival”, as our data cannot distinguish between
170 mortality and permanent emigration. At each CES site the local bird community is sampled 3.17
171 ± 1.06 SD times per breeding season (first session on May 21 ± 15 days, last session on July 4 ±
172 12 days), with 14 ± 7 mist nets (12 m long, 3-to-4 m high, 16 mm mesh size) spread over an area
173 of ~ 3 hectares (4 to 5 mist nets per hectare). A capture session typically starts at dawn and lasts
174 until midday. For every site the number, dates, hours of capture sessions, and mist net locations
175 are held constant across sessions and years. Sites are monitored for an average of six ± four
176 years. Sites are typically located in low canopy habitats such as shrublands, open woodlands and
177 reed beds, where birds are easily captured with 3.5 m high mist nets. Each bird captured is
178 marked with a metal band bearing a unique identifier, identified at the species level, aged
179 (juvenile or adult), sexed (Svensson 1992), and released at the point of capture. All recaptures of
180 marked individuals are recorded.

181 To ensure robustness of site-level parameter estimates we retained only data for species with
182 an average of at least five adult individuals captured per year. Juvenile mortality is highly
183 confounded with dispersal (Johnston et al. 2016) and so we did not consider juvenile data. After
184 excluding the first year of capture for each individual to handle transients (see below), our mark-
185 recapture dataset consists of 20,912 adults from 16 species, including 5,198 individuals
186 recaptured at least once across years (see Appendix S1), across 242 sites (Fig. 1 and Appendix
187 S2: Figure S1) over a period of 16 years (Dehorter and CRBPO 2017).

188 Bayesian survival data analysis

189 We modeled annual apparent survival and recapture probabilities using mark-recapture
190 histories of individual birds with species- and time-dependent Cormack-Jolly-Seber (CJS)
191 models (Lahoz-Monfort et al. 2011). Inter-annual adult apparent survival probability ($\phi$) is the
192 probability that a bird alive in year $t$ is still alive and present at the same CES site in year $t+1$.
193 The recapture probability ($p$) is the probability that a bird alive and present in the same CES site
194 where it was formerly captured (on year $t-1$ or before) is recaptured in year $t$. Individuals that
195 were captured only once were considered as transient individuals that do not pertain to the local
196 population (Johnston et al. 2016) and were discarded by starting mark-capture histories only at
197 the second year of capture. We also attempted to explicitly model transient rates, but the models
198 were prohibitively long to run. Therefore our estimates are conditional on each bird being alive
199 and present at a same site for at least two years. Goodness-of-fit tests for the general group-by-
179 (juvenile or adult), sexed (Svensson 1992), and released at the point of capture. All recaptures of
180 marked individuals are recorded.

181 To ensure robustness of site-level parameter estimates we retained only data for species with
182 an average of at least five adult individuals captured per year. Juvenile mortality is highly
183 confounded with dispersal (Johnston et al. 2016) and so we did not consider juvenile data. After
184 excluding the first year of capture for each individual to handle transients (see below), our mark-
185 recapture dataset consists of 20,912 adults from 16 species, including 5,198 individuals
186 recaptured at least once across years (see Appendix S1), across 242 sites (Fig. 1 and Appendix
187 S2: Figure S1) over a period of 16 years (Dehorter and CRBPO 2017).

188 Bayesian survival data analysis

189 We modeled annual apparent survival and recapture probabilities using mark-recapture
190 histories of individual birds with species- and time-dependent Cormack-Jolly-Seber (CJS)
191 models (Lahoz-Monfort et al. 2011). Inter-annual adult apparent survival probability ($\phi$) is the
192 probability that a bird alive in year $t$ is still alive and present at the same CES site in year $t+1$.
193 The recapture probability ($p$) is the probability that a bird alive and present in the same CES site
194 where it was formerly captured (on year $t-1$ or before) is recaptured in year $t$. Individuals that
195 were captured only once were considered as transient individuals that do not pertain to the local
196 population (Johnston et al. 2016) and were discarded by starting mark-capture histories only at
197 the second year of capture. We also attempted to explicitly model transient rates, but the models
198 were prohibitively long to run. Therefore our estimates are conditional on each bird being alive
199 and present at a same site for at least two years. Goodness-of-fit tests for the general group-by-
time-dependent CJS model (where group was site-by-sex) were then run separately for each species using the R2ucare package (Gimenez et al. 2018; Appendix S3).

We built a Bayesian formulation of the CJS model applying a Markov Chain Monte Carlo (MCMC) sampling procedure (Lahoz-Monfort et al. 2011) implemented in JAGS (Plummer 2003) called from R version 3.5.1 (R Development Core Team 2015) using the R-package R2jags version 4.2.0 (Su and Yajima 2015). For our base model (model 1 thereafter), the survival probability ($\phi_{i,t}$) of an individual $i$ at time $t$ was modeled as

$$ \operatorname{logit}(\phi_{i,t}) = \mu_{sp(i)} + \beta_{sex} \operatorname{sex}_i + u_{site(i)} + u_{year(t)} + u_{year:species(i,t)}, $$ where $\mu_{sp(i)}$ is a species-specific intercept, $\beta_{sex}$ is the effect of the bird being female, and $u_{site(i)}$ is a random deviation for the site $i$ on which the bird breeds; we assume that those deviations follow a normal distribution of mean zero and variance $\sigma_s^2$, which we write as $u_{site} \sim N(0, \sigma_s^2)$. Further, the random deviation for $210$ years follows $u_{year} \sim N(0, \sigma_\delta^2)$, and the random deviations for species within years follow $211$ $u_{year:species} \sim N(0, \sigma_\delta^2)$. Following Grosbois et al. (2009), the year random variance ($\sigma_\delta^2$) quantifies the amount of between-year variation that is common to all species, across all sites $212$ (synchronous, country-level, inter-annual variation), and the Year-by-Species random variance $213$ ($\sigma_\epsilon^2$) quantifies the between-year variation that differs between species (asynchronous, country-level, inter-annual variation). Note that $\sigma_\delta^2$ and $\sigma_\epsilon^2$ are parameters common to all species. $214$

Therefore, our models accounted for the variation of apparent survival and recapture probabilities between sexes (assuming effects common to all species), species and sites (see Appendix S4 and code shared on Zenodo). We addressed only synchrony across all sites, ignoring the spatio-temporal variance (i.e., Site:Year random variance) and the within species $218$
200 time-dependent CJS model (where group was site-by-sex) were then run separately for each
201 species using the R2ucare package (Gimenez et al. 2018; Appendix S3).

202 We built a Bayesian formulation of the CJS model applying a Markov Chain Monte Carlo
203 (MCMC) sampling procedure (Lahoz-Monfort et al. 2011) implemented in JAGS (Plummer
204 2003) called from R version 3.5.1 (R Development Core Team 2015) using the R-package
205 R2jags version 4.2.0 (Su and Yajima 2015). For our base model (model 1 thereafter), the
206 survival probability ($\phi_{i,t}$) of an individual $i$ at time $t$ was modeled as

207 $\operatorname{logit}(\phi_{i,t}) = \mu_{sp(i)} + \beta_{sex} \operatorname{sex}_i + u_{site(i)} + u_{year(t)} + u_{year:species(i,t)}$, where $\mu_{sp(i)}$ is a species-specific intercept, $\beta_{sex}$ is the effect of the bird being female, and $u_{site(i)}$ is a random deviation for the site $i$ on which the bird breeds; we assume that those deviations follow a normal distribution of mean zero and variance $\sigma_s^2$, which we write as $u_{site} \sim N(0, \sigma_s^2)$. Further, the random deviation for 210 years follows $u_{year} \sim N(0, \sigma_\delta^2)$, and the random deviations for species within years follow 211 $u_{year:species} \sim N(0, \sigma_\delta^2)$. Following Grosbois et al. (2009), the year random variance ($\sigma_\delta^2$) quantifies 212 the amount of between-year variation that is common to all species, across all sites 213 (synchronous, country-level, inter-annual variation), and the Year-by-Species random variance 214 ($\sigma_\epsilon^2$) quantifies the between-year variation that differs between species (asynchronous, country- 215 level, inter-annual variation). Note that $\sigma_\delta^2$ and $\sigma_\epsilon^2$ are parameters common to all species. 216

217 Therefore, our models accounted for the variation of apparent survival and recapture
218 probabilities between sexes (assuming effects common to all species), species and sites (see
219 Appendix S4 and code shared on Zenodo). We addressed only synchrony across all sites,
220 ignoring the spatio-temporal variance (i.e., Site:Year random variance) and the within species
spatio-temporal variance (i.e., Species:Site:Year random variance) in apparent adult survival

probability. Such a full, hierarchical partitioning of variance was not achievable with the amount

of mark-recapture data available within the year by site by species combinations.

We modeled recapture probability for individual *i* at time *t* as

$$ \operatorname{logit}(p_{i,t}) = v_{sp(i)} + \gamma_{sex} sex_i + \gamma_{het} het_i + v_{site(i)} + v_{year(t)}, \text{ where } v_{sp(i)} \text{ is a species-specific} $$

intercept, $\gamma_{sex}$ is the effect of the bird being female, and $\gamma_{het}$ is the effect of between-individual

heterogeneity in capture probability (detailed in Appendix S4). Finally, $v_{site}$ and $v_{year}$ are both

normally distributed with means of zero and variances to be estimated.

We chose weakly informative priors for all parameters. Details on specification of prior

distributions for the parameters and satisfactory convergence criteria are provided in Appendix

S4. We report posterior modes as point estimates and 95% highest posterior density credible

intervals to show estimation uncertainty. We report posterior probabilities, pMCMC, computed

as twice the proportion of MCMC values above 0, for a negative point estimate, or below 0, for a

positive point estimate (analog to a two-sided frequentist p-value). All calculations were done

for the full posterior distribution in order to propagate uncertainty.

## *Interspecific synchrony in survival*

From model 1, we calculated a between-species intra-class correlation (ICC) of temporal

variation to quantify country-level, between-year synchrony in adult survival across species, on

the logit scale, defined as $ICC = \sigma_{\delta}^2/(\sigma_{\delta}^2 + \sigma_{\epsilon}^2)$.
spatio-temporal variance (i.e., Species:Site:Year random variance) in apparent adult survival

probability. Such a full, hierarchical partitioning of variance was not achievable with the amount

of mark-recapture data available within the year by site by species combinations.

We modeled recapture probability for individual *i* at time *t* as

$$ \operatorname{logit}(p_{i,t}) = v_{sp(i)} + \gamma_{sex} sex_i + \gamma_{het} het_i + v_{site(i)} + v_{year(t)}, \text{ where } v_{sp(i)} \text{ is a species-specific} $$

intercept, $\gamma_{sex}$ is the effect of the bird being female, and $\gamma_{het}$ is the effect of between-individual

heterogeneity in capture probability (detailed in Appendix S4). Finally, $v_{site}$ and $v_{year}$ are both

normally distributed with means of zero and variances to be estimated.

We chose weakly informative priors for all parameters. Details on specification of prior

distributions for the parameters and satisfactory convergence criteria are provided in Appendix

S4. We report posterior modes as point estimates and 95% highest posterior density credible

intervals to show estimation uncertainty. We report posterior probabilities, pMCMC, computed

as twice the proportion of MCMC values above 0, for a negative point estimate, or below 0, for a

positive point estimate (analog to a two-sided frequentist p-value). All calculations were done

for the full posterior distribution in order to propagate uncertainty.

### *Interspecific synchrony in survival*

From model 1, we calculated a between-species intra-class correlation (ICC) of temporal

variation to quantify country-level, between-year synchrony in adult survival across species, on

the logit scale, defined as $ICC = \sigma_{\delta}^{2}/(\sigma_{\delta}^{2} + \sigma_{\epsilon}^{2})$.
240 The approach used for model 1 differs from Lahoz-Monfort et al. (2011) in that we defined $\sigma_{\epsilon}^2$
241 and ICC common to all species, while Lahoz-Monfort et al. (2011) estimated species-specific
242 parameters, $\sigma_{\epsilon species}^2$ and $ICC_{species} = \sigma_{\epsilon}^2/(\sigma_{\epsilon}^2 + \sigma_{\epsilon species}^2)$. We also fitted the model corresponding
243 to Lahoz-Monfort et al. (2011) and name it model 2. In this model, survival follows the same
244 equation as for model 1, except that $u_{year:species} \sim N(0,\sigma_{\epsilon species}^2)$, i.e. the random deviations for
245 species within years follow different distributions for each species, with one $\sigma_{\epsilon species}^2$ estimated
246 for each of the 16 species. From model 2, we calculated the average of the 16 $ICC_{species}$ point
247 estimates and compared it to the global ICC calculated from model 1. Model 2 allows us to test
248 for effects explaining variation in $ICC_{species}$ among species. Thus, we fitted a general linear
249 model of $ICC_{species}$ as a function of either of two species characteristics: migratory class, a factor
250 with two levels, long-distance vs. short-distance or resident species, or species sample size; we
251 integrated the model over each sample of the full posterior distributions of $ICC_{species}$ to propagate
252 estimation uncertainty.

253 We explain other pros and cons of model 1 and model 2 in Appendix S4. Note that we report
254 ICCs on the logit scale, not on the natural scale of survival probability. Arguably, ICC on the
255 logit scale is more biologically relevant because it expresses differences as log odds-ratios and
256 does not depend on average survival probability. For the range of parameter values considered
257 (mean survival, variance on the logit-scale, and ICC), the difference between ICCs on the two
258 scales is minimal (see Appendix S4).

259 *Migratory strategy (model 3)*
The approach used for model 1 differs from Lahoz-Monfort et al. (2011) in that we defined $\sigma_{\epsilon}^2$
and ICC common to all species, while Lahoz-Monfort et al. (2011) estimated species-specific
parameters, $\sigma_{\epsilon_{species}}^2$ and $ICC_{species} = \sigma_{\epsilon}^2/(\sigma_{\epsilon}^2 + \sigma_{\epsilon_{species}}^2)$. We also fitted the model corresponding
to Lahoz-Monfort et al. (2011) and name it model 2. In this model, survival follows the same
equation as for model 1, except that $u_{year:species} \sim N(0,\sigma_{\epsilon_{species}}^2)$, i.e. the random deviations for
species within years follow different distributions for each species, with one $\sigma_{\epsilon_{species}}^2$ estimated
for each of the 16 species. From model 2, we calculated the average of the 16 $ICC_{species}$ point
estimates and compared it to the global ICC calculated from model 1. Model 2 allows us to test
for effects explaining variation in $ICC_{species}$ among species. Thus, we fitted a general linear
model of $ICC_{species}$ as a function of either of two species characteristics: migratory class, a factor
with two levels, long-distance vs. short-distance or resident species, or species sample size; we
integrated the model over each sample of the full posterior distributions of $ICC_{species}$ to propagate
estimation uncertainty.

We explain other pros and cons of model 1 and model 2 in Appendix S4. Note that we report
ICCs on the logit scale, not on the natural scale of survival probability. Arguably, ICC on the
logit scale is more biologically relevant because it expresses differences as log odds-ratios and
does not depend on average survival probability. For the range of parameter values considered
(mean survival, variance on the logit-scale, and ICC), the difference between ICCs on the two
scales is minimal (see Appendix S4).

*Migratory strategy (model 3)*
Because of shared conditions among species wintering in the same regions, migratory strategy could explain part of the overall synchrony in annual survival. To quantify the importance of this effect, we fitted Model 3, a variation of Model 1 with a random effect for the interaction year-by-migratory strategy (long-distance vs. short-distance migrant or resident). The equation for survival thus became:

$logit(\phi_{i,t}) = \mu_{sp(i)} + \beta_{sex}sex_i + u_{site(i)} + u_{year(t)} + u_{year:species(i,t)} + u_{year:migration(i,t)},$ with $u_{year:species} \sim N(0, \sigma_{\delta}^2)$ and $u_{year:migration} \sim N(0, \sigma_{m}^2)$.

We thus modeled a year-by-migratory strategy variance $\sigma_m^2$, while $\sigma_\delta^2$ remained the Year-variance common to migratory strategies and species, and $\sigma_{\delta w}^2$ was the within-migratory strategy within-species year variance. The proportion of total annual variance attributed to migratory strategy (i.e., synchrony within migratory strategy) was estimated as $ICC_m = (\sigma_m^2)/(\sigma_\delta^2 + \sigma_m^2 + \sigma_{\delta w}^2)$. The proportion of total annual variance common to all species was estimated as $ICC = \sigma_\delta^2/(\sigma_\delta^2 + \sigma_m^2 + \sigma_{\delta w}^2)$. If migratory strategy explains no synchronous variation, the ICC from model 3 will approach the ICC calculated from model 1.

*Yearly weather covariates (models 4 and 5)*

Weather conditions during the breeding period (April to July) were characterized for each site and year using daily mean temperature and daily sum precipitation (as in Grosbois et al. 2006, Eglington et al. 2015, Gaüzère et al. 2015, Dubos et al. 2018) from the “E-OBS” meteorological dataset (available at https://www.ecad.eu/), with a 0.25° pixel (approximately 20 km by 28 km) resolution using the R-package climateExtract (available at https://github.com/RetoSchmucki). Since organisms are expected to be adapted to average local conditions (e.g. Dubos et al. 2019), we tested for an effect of departure from local average weather conditions, i.e., local spring
Because of shared conditions among species wintering in the same regions, migratory strategy could explain part of the overall synchrony in annual survival. To quantify the importance of this effect, we fitted Model 3, a variation of Model 1 with a random effect for the interaction year-by-migratory strategy (long-distance vs. short-distance migrant or resident). The equation for survival thus became:

$$ \operatorname{logit}(\phi_{i,t}) = \mu_{sp(i)} + \beta_{sex} \operatorname{sex}_i + u_{site(i)} + u_{year(t)} + u_{year:species(i,t)} + u_{year:migration(i,t)}, \text{ with} \\ u_{year:species} \sim N(0, \sigma_{\delta}^2) \text{ and } u_{year:migration} \sim N(0, \sigma_m^2). $$

We thus modeled a year-by-migratory strategy variance $\sigma_m^2$, while $\sigma_\delta^2$ remained the Year-variance common to migratory strategies and species, and $\sigma_{\delta w}^2$ was the within-migratory strategy within-species year variance. The proportion of total annual variance attributed to migratory strategy (i.e., synchrony within migratory strategy) was estimated as $ICC_m = (\sigma_m^2)/(\sigma_\delta^2 + \sigma_m^2 + \sigma_{\delta w}^2)$. The proportion of total annual variance common to all species was estimated as $ICC = \sigma_\delta^2/(\sigma_\delta^2 + \sigma_m^2 + \sigma_{\delta w}^2)$. If migratory strategy explains no synchronous variation, the ICC from model 3 will approach the ICC calculated from model 1.

## Yearly weather covariates (models 4 and 5)

Weather conditions during the breeding period (April to July) were characterized for each site and year using daily mean temperature and daily sum precipitation (as in Grosbois et al. 2006, Eglington et al. 2015, Gaüzère et al. 2015, Dubos et al. 2018) from the “E-OBS” meteorological dataset (available at https://www.ecad.eu/), with a 0.25° pixel (approximately 20 km by 28 km) resolution using the R-package climateExtract (available at https://github.com/RetoSchmucki). Since organisms are expected to be adapted to average local conditions (e.g. Dubos et al. 2019), we tested for an effect of departure from local average weather conditions, i.e., local spring
282 weather anomalies. Anomalies were computed for each variable, site and year as the difference
283 between the local value for a given spring and the mean over the 2001–2016 period (as in Dubos
284 et al. 2018). For the effect sizes for temperature and precipitation to be comparable, anomalies
285 were standardized by dividing them by the standard deviation across all sites and years.

286 However, we then used the yearly averages of anomalies across all sites to capture the
287 synchronizing effect of weather variables (Appendix S5: Figure S1).

288 To explain between-species synchrony in annual survival variations that could be attributed to
289 large-scale fluctuations in spring weather conditions, we fitted Model 4, which added terms to
290 Model 1 including fixed effects for the linear and quadratic effects of spring temperature (θ) and
291 precipitation (π) anomalies, effects known to explain bird survival (Boyles et al. 2011, Pomara
292 & Zuckerberg 2017), plus the four pairwise interactions between temperature and precipitation:

293 β<sub>θ</sub>θ<sub>i,t</sub> + β<sub>π</sub>π<sub>i</sub> + β<sub>θ2</sub>θ<sup>2</sup><sub>i,t</sub> + β<sub>π2</sub>π<sup>2</sup><sub>i,t</sub> + β<sub>θ:π</sub>(θ<sub>i,t</sub>π<sub>i,t</sub>) + β<sub>θ:π2</sub>(θ<sub>i,t</sub>π<sup>2</sup><sub>i,t</sub>) + β<sub>θ2:π</sub>(θ<sup>2</sup><sub>i,t</sub>π<sub>i,t</sub>) + β<sub>θ2:π2</sub>(θ<sup>2</sup><sub>i,t</sub>π<sup>2</sup><sub>i,t</sub>),
294 where, for instance β<sub>θ2:π</sub> is the coefficient for the interaction between squared standardized
295 temperature deviations and standardized precipitation deviations and (θ<sup>2</sup><sub>i,t</sub>π<sub>i,t</sub>) is the product of
296 squared standardized temperature deviations and standardized precipitation deviations.

297 We included quadratic and interactive effects *a priori*, without performing model selection.
298 This approach should be seen as an attempt to estimate an upper bound to the variance, and thus
299 synchrony, that can be ascribed to the weather variables available, rather than an attempt to test
300 the potential causal effects of weather presented in introduction or to produce a predictive
301 model.

302 Following Nakagawa and Schielzeth (2013), we estimated the synchronous variance
303 explained by spring weather (σ<sup>2</sup><sub>sw</sub>) as the variance in partial model predictions (that is, the linear
282 weather anomalies. Anomalies were computed for each variable, site and year as the difference
283 between the local value for a given spring and the mean over the 2001–2016 period (as in Dubos
284 et al. 2018). For the effect sizes for temperature and precipitation to be comparable, anomalies
285 were standardized by dividing them by the standard deviation across all sites and years.

286 However, we then used the yearly averages of anomalies across all sites to capture the
287 synchronizing effect of weather variables (Appendix S5: Figure S1).

288 To explain between-species synchrony in annual survival variations that could be attributed to
289 large-scale fluctuations in spring weather conditions, we fitted Model 4, which added terms to
290 Model 1 including fixed effects for the linear and quadratic effects of spring temperature (θ) and
291 precipitation (π) anomalies, effects known to explain bird survival (Boyles et al. 2011, Pomara
292 & Zuckerberg 2017), plus the four pairwise interactions between temperature and precipitation:

293 β<sub>θ</sub>θ<sub>i,t</sub> + β<sub>π</sub>π<sub>i</sub> + β<sub>θ2</sub>θ<sup>2</sup><sub>i,t</sub> + β<sub>π2</sub>π<sup>2</sup><sub>i,t</sub> + β<sub>θ:π</sub>(θ<sub>i,t</sub>π<sub>i,t</sub>) + β<sub>θ2:π</sub>(θ<sup>2</sup><sub>i,t</sub>π<sup>2</sup><sub>i,t</sub>) + β<sub>θ2:π2</sub>(θ<sup>2</sup><sub>i,t</sub>π<sub>i,t</sub>) + β<sub>θ2:π2</sub>(θ<sup>2</sup><sub>i,t</sub>π<sup>2</sup><sub>i,t</sub>),

294 where, for instance $\beta_{\theta2:\pi}$ is the coefficient for the interaction between squared standardized
295 temperature deviations and standardized precipitation deviations and $(\theta_{i,t}^2\pi_{i,t})$ is the product of
296 squared standardized temperature deviations and standardized precipitation deviations.

297 We included quadratic and interactive effects *a priori*, without performing model selection.
298 This approach should be seen as an attempt to estimate an upper bound to the variance, and thus
299 synchrony, that can be ascribed to the weather variables available, rather than an attempt to test
300 the potential causal effects of weather presented in introduction or to produce a predictive
301 model.

302 Following Nakagawa and Schielzeth (2013), we estimated the synchronous variance
303 explained by spring weather ($\sigma_{sw}^2$) as the variance in partial model predictions (that is, the linear
combination of the products of each parameter estimate by the corresponding weather variable): 304

$\sigma_{sw}^2 = \text{var}(\sum_{h=1}^{8} \beta_h \hat{x}_{ht})$ where h indexes the eight model parameters related to spring weather, $\beta_h$ 305

is the parameter estimate for the effect of h, and $\hat{x}_{ht}$ is the mean value of the weather variable h 306

in year t (across all sites). By definition $\sigma_{sw}^2$ captures only synchronous variation. Therefore we 307

calculated the proportion of synchronous variation related by spring weather as $\sigma_{sw}^2/(\sigma_\delta^2 + \sigma_{sw}^2)$, 308

and the new $ICC = (\sigma_{sw}^2 + \sigma_\delta^2)/(\sigma_{sw}^2 + \sigma_\delta^2 + \sigma_\varepsilon^2)$. 309

In Model 5, we added covariates related to winter weather to Model 1. For resident and short-distance migrants, which spend the winter in western Europe or North-Africa, we used the North Atlantic Oscillation during winter (wNAO, averaged from December to March, available at http://www.cru.uea.ac.uk/~timo/datapages/naoi.htm). The wNAO captures broad scale weather variation in Western Europe and North Africa (Forchhammer & Post 2004), which explains variations in over-winter survival in several European bird species (Robinson et al. 2007, Salewski et al. 2013). For long-distance migrants that winter in Western Africa, we used the Sahel Rainfall during summer (sSR, averaged from July to September, available at http://research.jisao.washington.edu/data_sets/sahel/). The sSR is often used as a proxy of winter Sahel suitability for wintering songbirds, considering that habitat quality in December–February is driven by rainfall during the previous summer (Robinson et al. 2007, Salewski et al. 2013). 310
311
312
313
314
315
316
317
318
319
320

As was done for spring weather covariates, we fitted linear and quadratic effects for wNAO and sSR. We estimated the synchronous variance explained by winter weather ($\sigma_{ww}^2$) as the variance in partial model predictions from the linear and quadratic effects of wNAO and sSR. The synchronous variance due to winter climate ($\sigma_{ww}^2$) is the variance among years in survival, averaged over species, predicted from differences in wNAO and sSR only, which we write as 321
322
323
324
325
combination of the products of each parameter estimate by the corresponding weather variable):

$$ \sigma_{sw}^2 = \operatorname{var}(\sum_{h=1}^{8} \beta_h \hat{x}_{ht}) $$

where $h$ indexes the eight model parameters related to spring weather, $\beta_h$ is the parameter estimate for the effect of $h$, and $\hat{x}_{ht}$ is the mean value of the weather variable $h$ in year $t$ (across all sites). By definition $\sigma_{sw}^2$ captures only synchronous variation. Therefore we calculated the proportion of synchronous variation related by spring weather as $\sigma_{sw}^2/(\sigma_\delta^2 + \sigma_{sw}^2)$, and the new $ICC = (\sigma_{sw}^2 + \sigma_\delta^2)/(\sigma_{sw}^2 + \sigma_\delta^2 + \sigma_\varepsilon^2)$.

In Model 5, we added covariates related to winter weather to Model 1. For resident and short-distance migrants, which spend the winter in western Europe or North-Africa, we used the North Atlantic Oscillation during winter (wNAO, averaged from December to March, available at http://www.cru.uea.ac.uk/~timo/datapages/naoi.htm). The wNAO captures broad scale weather variation in Western Europe and North Africa (Forchhammer & Post 2004), which explains variations in over-winter survival in several European bird species (Robinson et al. 2007, Salewski et al. 2013). For long-distance migrants that winter in Western Africa, we used the Sahel Rainfall during summer (sSR, averaged from July to September, available at http://research.jisao.washington.edu/data_sets/sahel/). The sSR is often used as a proxy of winter Sahel suitability for wintering songbirds, considering that habitat quality in December–February is driven by rainfall during the previous summer (Robinson et al. 2007, Salewski et al. 2013).

As was done for spring weather covariates, we fitted linear and quadratic effects for wNAO and sSR. We estimated the synchronous variance explained by winter weather ($\sigma_{ww}^2$) as the variance in partial model predictions from the linear and quadratic effects of wNAO and sSR. The synchronous variance due to winter climate ($\sigma_{ww}^2$) is the variance among years in survival, averaged over species, predicted from differences in wNAO and sSR only, which we write as
326 $\sigma_{ww}^2 = \text{var}_t(E_{\text{species}}(\phi|\text{winter climate}))$. We can decompose the average between the two

327 migratory strategies as

328 $\sigma_{ww}^2 = \text{var}_t(mE_{\text{migratory}}(\phi|\text{sSR}) + (1-m)E_{\text{resident}}(\phi|\text{wNAO}))$ where m is the proportion of

329 long-distance migratory species. The two conditional averages are the sum of an average

330 survival independent of winter climate and anomalies due to the effects of winter climate:

331 $\sigma_{ww}^2 = \text{var}_t$

332 $(m\alpha_1 + m(\beta_{SSR}sSR + \beta_{SSR2}sSR^2) + (1-m)\alpha_2 + (1-m)(\beta_{wNAO}wNAO + \beta_{wNAO2}wNAO^2))$, where $\alpha_1$

333 and $\alpha_2$ are baseline survival probabilities independent of winter climate and are scalars, hence

334 $\sigma_{ww}^2 = \text{var}_t(m(\beta_{SSR}sSR + \beta_{SSR2}sSR^2) + (1-m)(\beta_{wNAO}wNAO + \beta_{wNAO2}wNAO^2))$ (see Appendix S6

335 for formula validation).

336 This variance is a function of the effect of sSR among migratory species, of the effect of wNAO

337 among resident species, and of the small negative covariance between sSR and wNAO (the two

338 indices are expected to be independent, but the empirical Pearson correlation coefficient was -

339 0.12, 95% confidence interval [-0.58;0.40]). The proportion of synchronous variance related to

340 winter weather was calculated as $(\sigma_{ww}^2)/(\sigma_\delta^2 + \sigma_{ww}^2)$, and the $ICC = (\sigma_{ww}^2 + \sigma_\delta^2)/(\sigma_{ww}^2 + \sigma_\delta^2 + \sigma_\epsilon^2)$.

342

343 **RESULTS**

344 *Interspecific synchrony of annual adult apparent survival*

345 Inter-annual variation in adult apparent survival probabilities was largely synchronous across

346 the 16 study species (Fig. 2). Model 1 ICC of 73%, 95% highest posterior density credible
326 $\sigma_{ww}^2 = \text{var}_t(E_{\text{species}}(\phi|\text{winter climate}))$. We can decompose the average between the two

327 migratory strategies as

328 $\sigma_{ww}^2 = \text{var}_t(mE_{\text{migratory}}(\phi|\text{sSR}) + (1-m)E_{\text{resident}}(\phi|\text{wNAO}))$ where m is the proportion of

329 long-distance migratory species. The two conditional averages are the sum of an average

330 survival independent of winter climate and anomalies due to the effects of winter climate:

331 $\sigma_{ww}^2 = \text{var}_t$

332 $(m\alpha_1 + m(\beta_{SSR}sSR + \beta_{SSR2}sSR^2) + (1-m)\alpha_2 + (1-m)(\beta_{wNAO}wNAO + \beta_{wNAO2}wNAO^2))$, where $\alpha_1$

333 and $\alpha_2$ are baseline survival probabilities independent of winter climate and are scalars, hence

334 $\sigma_{ww}^2 = \text{var}_t(m(\beta_{SSR}sSR + \beta_{SSR2}sSR^2) + (1-m)(\beta_{wNAO}wNAO + \beta_{wNAO2}wNAO^2))$ (see Appendix S6 for formula validation).

335 This variance is a function of the effect of sSR among migratory species, of the effect of wNAO

336 among resident species, and of the small negative covariance between sSR and wNAO (the two

337 indices are expected to be independent, but the empirical Pearson correlation coefficient was -

338 0.12, 95% confidence interval [-0.58;0.40]). The proportion of synchronous variance related to

339 winter weather was calculated as $(\sigma_{ww}^2)/(\sigma_\delta^2 + \sigma_{ww}^2)$, and the $ICC = (\sigma_{ww}^2 + \sigma_\delta^2)/(\sigma_{ww}^2 + \sigma_\delta^2 + \sigma_\epsilon^2)$.

340 All parameter estimates for all models are provided in Appendix S7.

342

343 **RESULTS**

344 *Interspecific synchrony of annual adult apparent survival*

345 Inter-annual variation in adult apparent survival probabilities was largely synchronous across

346 the 16 study species (Fig. 2). Model 1 ICC of 73%, 95% highest posterior density credible
interval = [ 47–94% ] indicates that most of the temporal variance in apparent survival
347
probabilities was common to all species (Table 1: Model 1). Conversely, this implies that
348
within-species variation corresponded to only 27% [6–53%] of temporal fluctuations of survival
349
probabilities.
350

Species showed considerable variation in Model 2 *ICC<sub>species</sub>* (Fig. 3), although estimates came
351
with broad credible intervals. There was no significant association between the value of *IC*
352
*C<sub>species</sub>* and migratory strategy (Fig. 3; pMCMC=0.48) nor with species sample size
353
(pMCMC=0.31). The mean of the 16 species posterior mode estimates for *ICC<sub>species</sub>* was 76%
354
(Table 1: Model 2).
355

This strong synchrony was robust to (i) the uneven contributions of species to the mark-
recapture dataset (*ICC* = 65% [28–90%], Appendix S8), (ii) the removal of the part of synchrony
due to a potential linear trend in survival probabilities (*ICC* = 56% [23–86%], Appendix S9;
note that this calculation necessarily excludes some true synchrony), (iii) the choice of prior
distributions (Appendix S10), and (iv) the effects of weather and migratory strategy presented
below.
361

Graphically, some years seemed to deviate more from the mean survival probability (Fig. 2)
and may have contributed more to synchrony: estimates of survival probabilities between the
years 2001–2002, 2002–2003 were larger than average while estimates for the years 2005–2006
and 2008–2009 were particularly low (Appendix S11: Figure S2). However, our *ad hoc*
approach using model estimates failed at identifying statistical support for variability in yearly
contributions to interspecific synchrony (Appendix S11).

*Contributions of migratory strategy to synchrony in survival probabilities*
interval = [ 47–94% ] indicates that most of the temporal variance in apparent survival
347
probabilities was common to all species (Table 1: Model 1). Conversely, this implies that
348
within-species variation corresponded to only 27% [6–53%] of temporal fluctuations of survival
349
probabilities.
350

Species showed considerable variation in Model 2 *ICC<sub>species</sub>* (Fig. 3), although estimates came
351
with broad credible intervals. There was no significant association between the value of *IC*
352
*C<sub>species</sub>* and migratory strategy (Fig. 3; pMCMC=0.48) nor with species sample size
353
(pMCMC=0.31). The mean of the 16 species posterior mode estimates for *ICC<sub>species</sub>* was 76%
354
(Table 1: Model 2).
355

This strong synchrony was robust to (i) the uneven contributions of species to the mark-
356
recapture dataset (ICC = 65% [28–90%], Appendix S8), (ii) the removal of the part of synchrony
357
due to a potential linear trend in survival probabilities (ICC = 56% [23–86%], Appendix S9;
358
note that this calculation necessarily excludes some true synchrony), (iii) the choice of prior
359
distributions (Appendix S10), and (iv) the effects of weather and migratory strategy presented
360
below.
361

Graphically, some years seemed to deviate more from the mean survival probability (Fig. 2)
362
and may have contributed more to synchrony: estimates of survival probabilities between the
363
years 2001–2002, 2002–2003 were larger than average while estimates for the years 2005–2006
364
and 2008–2009 were particularly low (Appendix S11: Figure S2). However, our *ad hoc*
365
approach using model estimates failed at identifying statistical support for variability in yearly
366
contributions to interspecific synchrony (Appendix S11).
367

*Contributions of migratory strategy to synchrony in survival probabilities*
In Model 3 the interaction between year and migratory strategy captured only a small amount of asynchronous-variation in survival among species, with $ICC_m = 9\% [0–32\%]$ (Table 1). After removing the synchrony within migratory strategy the variance common to all species was $ICC = 65\% [30–91\%]$. This low dependence of synchrony on migratory strategy is apparent in Fig. 2 and Fig. 3.

### Contributions of weather to synchrony in survival probabilities

According to Model 4, spring weather variables taken together explained only 1.4% [0.01–5.5%] of synchrony, whereas according to Model 5, the winter weather variables explained 12% [0.3–37%] of the synchrony. Assuming spring and winter weather variables are uncorrelated, together they explain 13% [0.8–39%] of synchrony and 10% [0.6–23%] of the total temporal variance (Table 1). There was no clear evidence for an effect of any of the weather-related parameters on survival probability, with all credible intervals overlapping zero (across all species, i.e. additive effects; Appendix S5: Table S1). Higher spring precipitation and more extreme spring temperatures tended to increase survival probability (Appendix S5: Figure S1). Regarding winter weather covariates, survival probability appeared a bit higher for high Sahara rainfall values during summer (sSR), while there was no discernible effect of the North Atlantic Oscillation during winter (wNAO) across all species (i.e. additive effects; Appendix S5: Figure S2).

## DISCUSSION

Unexpectedly, annual fluctuations in adult apparent survival were largely synchronous across the 16 songbird species that dominate bird communities in France: 73% [47–94%] of between-
In Model 3 the interaction between year and migratory strategy captured only a small amount of asynchronous-variation in survival among species, with $ICC_m = 9\% [0–32\%]$ (Table 1). After removing the synchrony within migratory strategy the variance common to all species was $ICC = 65\% [30–91\%]$. This low dependence of synchrony on migratory strategy is apparent in Fig. 2 and Fig. 3.

*Contributions of weather to synchrony in survival probabilities*

According to Model 4, spring weather variables taken together explained only 1.4% [0.01–5.5%] of synchrony, whereas according to Model 5, the winter weather variables explained 12% [0.3–37%] of the synchrony. Assuming spring and winter weather variables are uncorrelated, together they explain 13% [0.8–39%] of synchrony and 10% [0.6–23%] of the total temporal variance (Table 1). There was no clear evidence for an effect of any of the weather-related parameters on survival probability, with all credible intervals overlapping zero (across all species, i.e. additive effects; Appendix S5: Table S1). Higher spring precipitation and more extreme spring temperatures tended to increase survival probability (Appendix S5: Figure S1). Regarding winter weather covariates, survival probability appeared a bit higher for high Sahara rainfall values during summer (sSR), while there was no discernible effect of the North Atlantic Oscillation during winter (wNAO) across all species (i.e. additive effects; Appendix S5: Figure S2).

## DISCUSSION

Unexpectedly, annual fluctuations in adult apparent survival were largely synchronous across the 16 songbird species that dominate bird communities in France: 73% [47–94%] of between-
year variance in survival probabilities was common to all species. This result was robust to
differences in migratory strategy among species, uneven species sample sizes, and time de-
trending. This strong synchrony is remarkable given that these species differ in various
properties linked to apparent survival: they cover the range of songbird migratory strategies,
body mass (8 to 88 g) and number of broods per year, and they partly differ in habitat and
ecological specialization (Appendix S1). Moreover, our estimates represent minimal synchrony
since they are not controlled for potentially desynchronizing factors like intra- and interspecific
negative density dependence. Estimated synchrony should be higher if we could control for
those factors (Péron and Koons 2012, Swallow et al. 2016). Our estimates of interspecific
synchrony are as high as estimates of spatial synchrony in apparent survival probabilities across
some populations of a same species, with for instance values of 67% and 71% in two long-lived
birds (Jenouvrier et al. 2009, Grosbois et al. 2009), and higher than the interspecific synchrony
in survival (49%) estimated for two closely related duck species (Péron and Koons 2012). Short-
lived vertebrate animals, such as our study species, are under stronger environment-dependence
than long-lived species (Saether et al. 2016), which may contribute to the high interspecific
synchrony observed.

The species-specific synchronies (ICC<sub>species</sub>) varied from very high to low (Figure 4). This
means that within some species, such as the Cetti's Warbler, *Cettia cetti* (with ICC = 0.12,
95%CI [0.03; 0.60]), there was considerable temporal variability in adult survival, probably due
to the species' high sensitivity to winter harshness (Moussus 2010). Conversely, in other species
such as the widespread Eurasian Blackcap, *Sylvia atricapilla* (with ICC = 0.99, 95% CI
[0.61;1]), the temporal variability in adult survival corresponded almost entirely to the average
year variance in survival probabilities was common to all species. This result was robust to
differences in migratory strategy among species, uneven species sample sizes, and time de-
trending. This strong synchrony is remarkable given that these species differ in various
properties linked to apparent survival: they cover the range of songbird migratory strategies,
body mass (8 to 88 g) and number of broods per year, and they partly differ in habitat and
ecological specialization (Appendix S1). Moreover, our estimates represent minimal synchrony
since they are not controlled for potentially desynchronizing factors like intra- and interspecific
negative density dependence. Estimated synchrony should be higher if we could control for
those factors (Péron and Koons 2012, Swallow et al. 2016). Our estimates of interspecific
synchrony are as high as estimates of spatial synchrony in apparent survival probabilities across
some populations of a same species, with for instance values of 67% and 71% in two long-lived
birds (Jenouvrier et al. 2009, Grosbois et al. 2009), and higher than the interspecific synchrony
in survival (49%) estimated for two closely related duck species (Péron and Koons 2012). Short-
lived vertebrate animals, such as our study species, are under stronger environment-dependence
than long-lived species (Saether et al. 2016), which may contribute to the high interspecific
synchrony observed.

The species-specific synchronies (ICC<sub>species</sub>) varied from very high to low (Figure 4). This
means that within some species, such as the Cetti's Warbler, *Cettia cetti* (with ICC = 0.12,
95%CI [0.03; 0.60]), there was considerable temporal variability in adult survival, probably due
to the species' high sensitivity to winter harshness (Moussus 2010). Conversely, in other species
such as the widespread Eurasian Blackcap, *Sylvia atricapilla* (with ICC = 0.99, 95% CI
[0.61;1]), the temporal variability in adult survival corresponded almost entirely to the average
412 variability across the 16 species. Morrison et al. (2022) also reported a wide range of species-
413 specific synchronies, although our estimates were generally a bit higher: Our point estimates
414 were never below 0.10 (vs. a minimum of 0.03) and were above 0.80 for 9 out of 16 species (vs.
415 2 out of 26 species). The broader geographic range covered by Morrison et al. (2022), spanning
416 eight European countries, may contribute to the lower ICC<sub>species</sub>. Differences in ICC<sub>species</sub> may be
417 influenced by heterogeneity in the spatio-temporal distribution of captures among species (see
418 Fig. 1), but we did not find a clear pattern explaining differences in synchrony among species
419 (i.e., range, migration class, sample size, habitat), and species-specific synchronies were
420 estimated with broad confidence intervals. Therefore, the determinants of interspecific
421 synchrony remain to be identified with a larger dataset, if possible involving more species, more
422 diverse life history traits, and more years. Drivers that differ between species and determine
423 local survival dynamics must occur and operate at the level of sites or regions (Giraud et al.
424 2013, Gaüzère et al. 2015, Cayuela et al. 2019, Morrison et al. 2013, 2021). In any case, our
425 moderate and high estimates of ICC<sub>species</sub> indicate that the drivers of annual variations in adult
426 apparent survival at the country level are largely common to many species.

427 Climate forcing could be responsible for synchronous events of high mortality among species,
428 either through direct or indirect effects. A few studies have shown that climate forcing was
429 responsible for at least a part of the observed interspecific synchrony in abundances or vital rates
430 in common songbirds (Jones et al. 2003, Grosbois et al. 2006, Koenig and Liebhold 2016,
431 Swallow et al. 2016, Telenský et al. 2020). However, within species, neither broad scale climatic
432 variables nor local weather variables explained synchronous survival variation in blue tit
433 populations (Bastianelli et al. 2021), and synchronous survival in little auk populations was
412 variability across the 16 species. Morrison et al. (2022) also reported a wide range of species-specific synchronies, although our estimates were generally a bit higher: Our point estimates 413 were never below 0.10 (vs. a minimum of 0.03) and were above 0.80 for 9 out of 16 species (vs. 414 2 out of 26 species). The broader geographic range covered by Morrison et al. (2022), spanning 415 eight European countries, may contribute to the lower ICC<sub>species</sub>. Differences in ICC<sub>species</sub> may be 416 influenced by heterogeneity in the spatio-temporal distribution of captures among species (see 417 Fig. 1), but we did not find a clear pattern explaining differences in synchrony among species 418 (i.e., range, migration class, sample size, habitat), and species-specific synchronies were 419 estimated with broad confidence intervals. Therefore, the determinants of interspecific 420 synchrony remain to be identified with a larger dataset, if possible involving more species, more 421 diverse life history traits, and more years. Drivers that differ between species and determine 422 local survival dynamics must occur and operate at the level of sites or regions (Giraud et al. 423 2013, Gaüzère et al. 2015, Cayuela et al. 2019, Morrison et al. 2013, 2021). In any case, our 424 moderate and high estimates of ICC<sub>species</sub> indicate that the drivers of annual variations in adult 425 apparent survival at the country level are largely common to many species. 426

427 Climate forcing could be responsible for synchronous events of high mortality among species, 428 either through direct or indirect effects. A few studies have shown that climate forcing was 429 responsible for at least a part of the observed interspecific synchrony in abundances or vital rates 430 in common songbirds (Jones et al. 2003, Grosbois et al. 2006, Koenig and Liebhold 2016, 431 Swallow et al. 2016, Telenský et al. 2020). However, within species, neither broad scale climatic 432 variables nor local weather variables explained synchronous survival variation in blue tit 433 populations (Bastianelli et al. 2021), and synchronous survival in little auk populations was
better explained by trophic interactions than climate (Reiertsen et al. 2021). In the present study we failed to identify a statistically significant role for any climatic variables, and altogether those variables explained only 13% [0.8–39%] of the interspecific synchrony. Nevertheless, we likely underestimated the importance of climate. First, the variables we used did not explicitly consider weather properties such as extreme temperature or precipitation events (e.g., Gardner et al. 2022), or species-specific periods of sensitivity to weather (van de Pol et al. 2016). Second, we used variables averaged either over spring or winter, which is unlikely to represent the cumulative effect of climatic variations experienced throughout the year. Finally, our modeling investigated the direct effects of weather whereas indirect or delayed effects may be more influential, particularly weather-driven primary and secondary productivity and their effects on the cost of reproduction (see below).

Apart from direct climate forcing other mechanisms could synchronize adult annual local survival across species (Reiertsen et al. 2021). First, synchronous variation in prey abundance could generate interspecific synchrony. Since all songbirds feed their chicks exclusively with invertebrates, a high invertebrate abundance could lower the foraging effort of breeding adults during chick rearing, decrease exposure to predators, and ultimately increase adult survival (Eglington et al. 2015). Therefore, the survival of songbird species could be synchronized by a spatial synchrony in invertebrate abundance, itself possibly caused by high precipitation and hot temperatures (Jones et al. 2003, Sheppard et al. 2015, Eglington et al. 2015, Kahilainen et al. 2018, Dubos et al. 2018, 2019). A remarkable food-supplementation experiment even demonstrated that food availability during breeding has a direct effect on annual adult survival in a long-distance migratory species: simply increasing food availability during reproduction (i.e.,
better explained by trophic interactions than climate (Reiertsen et al. 2021). In the present study we failed to identify a statistically significant role for any climatic variables, and altogether those variables explained only 13% [0.8–39%] of the interspecific synchrony. Nevertheless, we likely underestimated the importance of climate. First, the variables we used did not explicitly consider weather properties such as extreme temperature or precipitation events (e.g., Gardner et al. 2022), or species-specific periods of sensitivity to weather (van de Pol et al. 2016). Second, we used variables averaged either over spring or winter, which is unlikely to represent the cumulative effect of climatic variations experienced throughout the year. Finally, our modeling investigated the direct effects of weather whereas indirect or delayed effects may be more influential, particularly weather-driven primary and secondary productivity and their effects on the cost of reproduction (see below).

Apart from direct climate forcing other mechanisms could synchronize adult annual local survival across species (Reiertsen et al. 2021). First, synchronous variation in prey abundance could generate interspecific synchrony. Since all songbirds feed their chicks exclusively with invertebrates, a high invertebrate abundance could lower the foraging effort of breeding adults during chick rearing, decrease exposure to predators, and ultimately increase adult survival (Eglington et al. 2015). Therefore, the survival of songbird species could be synchronized by a spatial synchrony in invertebrate abundance, itself possibly caused by high precipitation and hot temperatures (Jones et al. 2003, Sheppard et al. 2015, Eglington et al. 2015, Kahilainen et al. 2018, Dubos et al. 2018, 2019). A remarkable food-supplementation experiment even demonstrated that food availability during breeding has a direct effect on annual adult survival in a long-distance migratory species: simply increasing food availability during reproduction (i.e.,
over 4–5 months) increased annual survival by 5% (Seward et al. 2013). Moreover, in highly productive years, a higher proportion of adults engage in rearing a second brood (Visser et al. 2003), potentially at a supplementary cost in terms of survival (Woodworth et al. 2017a). If facultative multi-brooding species (62% of studied species; Appendix S1) are synchronous in their choice to lay a second brood, their adult survival could be synchronized by the cost of reproduction. Schaub et al. (2015) demonstrated that the probability of double brooding was actually the most synchronous vital rate across the 9 populations of an aerial insectivorous songbird, the Barn Swallow, *Hirundo rustica*.

Second, intra- and interspecific competition for shared resources likely operate at the level of the local songbird community, potentially resulting in large-scale, weather-driven density-dependent regulations of adult survival. Nevertheless, intraspecific density-dependence can also be a source of asynchrony across species (Péron and Koons 2012, Martin et al. 2017). The net role of community-level density-dependence in synchronizing adult survival probabilities across species remains to be properly investigated (Swallow et al. 2016).

Third, breeding dispersal could contribute to interspecific synchrony in adult apparent survival. Apparent survival results from survival and residency (i.e. absence of dispersal, Saracco et al. 2010) at the scale of study sites. Interspecific synchrony in apparent survival could occur if individuals of different species tend to disperse more synchronously in some years than in others. Following a bad reproductive experience at one location (e.g., weather-driven early reproductive failure) a higher proportion of adults settles away in the following year (e.g., Arlt and Part 2008). However, only a small proportion of adults typically emigrates between breeding events, even in long-distance migrants (Greenwood and Harvey 1982), and males, the most
over 4–5 months) increased annual survival by 5% (Seward et al. 2013). Moreover, in highly productive years, a higher proportion of adults engage in rearing a second brood (Visser et al. 2003), potentially at a supplementary cost in terms of survival (Woodworth et al. 2017a). If facultative multi-brooding species (62% of studied species; Appendix S1) are synchronous in their choice to lay a second brood, their adult survival could be synchronized by the cost of reproduction. Schaub et al. (2015) demonstrated that the probability of double brooding was actually the most synchronous vital rate across the 9 populations of an aerial insectivorous songbird, the Barn Swallow, *Hirundo rustica*.

Second, intra- and interspecific competition for shared resources likely operate at the level of the local songbird community, potentially resulting in large-scale, weather-driven density-dependent regulations of adult survival. Nevertheless, intraspecific density-dependence can also be a source of asynchrony across species (Péron and Koons 2012, Martin et al. 2017). The net role of community-level density-dependence in synchronizing adult survival probabilities across species remains to be properly investigated (Swallow et al. 2016).

Third, breeding dispersal could contribute to interspecific synchrony in adult apparent survival. Apparent survival results from survival and residency (i.e. absence of dispersal, Saracco et al. 2010) at the scale of study sites. Interspecific synchrony in apparent survival could occur if individuals of different species tend to disperse more synchronously in some years than in others. Following a bad reproductive experience at one location (e.g., weather-driven early reproductive failure) a higher proportion of adults settles away in the following year (e.g., Arlt and Part 2008). However, only a small proportion of adults typically emigrates between breeding events, even in long-distance migrants (Greenwood and Harvey 1982), and males, the most
478 resident sex in birds (Amrhein et al. 2012), represent a majority (64%) of individuals in our dataset. Therefore, synchronous breeding dispersal alone seems unlikely to have caused the estimated high interspecific synchrony in adult apparent survival.
479
480

481 Could conditions during the non-breeding season explain the high synchrony in adult survival across songbirds in France? Across long-distance migrants, short-distance migrants and residents, we found that winter conditions explained 12% [0.3–37%] of the interspecific synchrony. This proportion is low, but potentially significant biologically, and, as already explained, we likely underestimate the synchronizing importance of weather. Winter harshness is commonly proposed to explain variation in year-round mortality in small organisms like songbirds (Grosbois et al. 2006, Robinson et al. 2007, Salewski et al. 2013, Johnston et al. 2016, Saether et al. 2016), including in migratory songbirds (Robinson et al. 2007, Woodworth et al. 2017b). Recent studies on multi-species population synchrony have also emphasized a major role of winter climate forcing, but these few studies were biased towards cold-driven ecosystems, like high latitude populations that are subject to extreme winter events such as rain-on-snow icing events (Post & Forchhammer 2004, Jones et al. 2003, Hansen et al. 2013, Pomara & Zuckerberg 2017). Restricting the analysis to long-distance migrant species only, Telenský et al. (2020) found that water availability on the wintering grounds explained 15% of the temporal variation in survival, but the result was not statistically significant. Overall, it seems likely that conditions on the non-breeding grounds, especially climate, contribute somewhat to the interspecific synchrony in adult survival, even though the 16 studied species spread from France to Central Africa during the winter.
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478 resident sex in birds (Amrhein et al. 2012), represent a majority (64%) of individuals in our dataset. Therefore, synchronous breeding dispersal alone seems unlikely to have caused the estimated high interspecific synchrony in adult apparent survival.
479
480

481 Could conditions during the non-breeding season explain the high synchrony in adult survival across songbirds in France? Across long-distance migrants, short-distance migrants and residents, we found that winter conditions explained 12% [0.3–37%] of the interspecific synchrony. This proportion is low, but potentially significant biologically, and, as already explained, we likely underestimate the synchronizing importance of weather. Winter harshness is commonly proposed to explain variation in year-round mortality in small organisms like songbirds (Grosbois et al. 2006, Robinson et al. 2007, Salewski et al. 2013, Johnston et al. 2016, Saether et al. 2016), including in migratory songbirds (Robinson et al. 2007, Woodworth et al. 2017b). Recent studies on multi-species population synchrony have also emphasized a major role of winter climate forcing, but these few studies were biased towards cold-driven ecosystems, like high latitude populations that are subject to extreme winter events such as rain-on-snow icing events (Post & Forchhammer 2004, Jones et al. 2003, Hansen et al. 2013, Pomara & Zuckerberg 2017). Restricting the analysis to long-distance migrant species only, Telenský et al. (2020) found that water availability on the wintering grounds explained 15% of the temporal variation in survival, but the result was not statistically significant. Overall, it seems likely that conditions on the non-breeding grounds, especially climate, contribute somewhat to the interspecific synchrony in adult survival, even though the 16 studied species spread from France to Central Africa during the winter.
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499 Differences in migratory strategy explained little temporal variation in survival
500 probability. Synchrony across all species, calculated after removing the synchrony within
501 migration classes, was similar to synchrony calculated without accounting for migratory strategy
502 (Table 1: Model 3). This high synchrony in adult survival across resident and long-distance
503 migrant species, wintering on two different continents (at a distance of 2000–3000 km and 35–
504 45° in latitude) has profound implications for our understanding of the regulation of migratory
505 bird populations. Breeding habitat degradation appears as the major cause of ongoing massive,
506 synchronous population declines (Eglington and Pearce-Higgins 2012), including in migratory
507 species (Morrison et al. 2013). Our results and these studies suggest that habitat quality or
508 productivity over breeding areas is critical for year-round songbirds population dynamics, even
509 for migratory species that spend only 3-4 months per year on their breeding grounds (Morrison
510 et al. 2013).

511 At least graphically (Fig. 2), some years appear to increase synchrony, with large
512 interspecific deviations in survival probabilities. Synchrony is expected to be heterogeneous
513 across years, or even transitory (Klapwijk et al. 2018), and to be largely attributable to the
514 occurrence of environmental disturbances in some years (Cattadori et al. 2005, Keitt 2008). For
515 instance, Jenouvrier et al. (2009) showed that the high spatial synchrony (71%) in Scopoli's
516 shearwater adult apparent survival was attributed to only two ‘low’ years out of eight. In
517 ‘normal’ years, species specificities (e.g., ecological niche differentiation) would dominate and
518 drive variation in survival probabilities (Liebhold et al. 2004, Robinson et al. 2013). As climate
519 warms and increasingly fluctuates through time with stronger and more frequent extreme
520 weather events, spatial synchrony and interspecific synchrony are likely to increase in strength
499 Differences in migratory strategy explained little temporal variation in survival
500 probability. Synchrony across all species, calculated after removing the synchrony within
501 migration classes, was similar to synchrony calculated without accounting for migratory strategy
502 (Table 1: Model 3). This high synchrony in adult survival across resident and long-distance
503 migrant species, wintering on two different continents (at a distance of 2000–3000 km and 35–
504 45° in latitude) has profound implications for our understanding of the regulation of migratory
505 bird populations. Breeding habitat degradation appears as the major cause of ongoing massive,
506 synchronous population declines (Eglington and Pearce-Higgins 2012), including in migratory
507 species (Morrison et al. 2013). Our results and these studies suggest that habitat quality or
508 productivity over breeding areas is critical for year-round songbirds population dynamics, even
509 for migratory species that spend only 3-4 months per year on their breeding grounds (Morrison
510 et al. 2013).

511 At least graphically (Fig. 2), some years appear to increase synchrony, with large
512 interspecific deviations in survival probabilities. Synchrony is expected to be heterogeneous
513 across years, or even transitory (Klapwijk et al. 2018), and to be largely attributable to the
514 occurrence of environmental disturbances in some years (Cattadori et al. 2005, Keitt 2008). For
515 instance, Jenouvrier et al. (2009) showed that the high spatial synchrony (71%) in Scopoli's
516 shearwater adult apparent survival was attributed to only two ‘low’ years out of eight. In
517 ‘normal’ years, species specificities (e.g., ecological niche differentiation) would dominate and
518 drive variation in survival probabilities (Liebhold et al. 2004, Robinson et al. 2013). As climate
519 warms and increasingly fluctuates through time with stronger and more frequent extreme
520 weather events, spatial synchrony and interspecific synchrony are likely to increase in strength
521 (Post and Forchhammer 2004, Hansen et al. 2013, Koenig and Liebhold 2016, Hansen et al.

522 2020) over wider spatial scales (Black et al. 2018). A longer timer series would be necessary to

523 properly assess the yearly contributions to synchrony and to quantify differences in synchrony

524 over different time periods. Analytical methods are also not yet readily implementable to such
525 multi-year, multi-site, multi-species mark-recapture dataset (Cattadori et al. 2005, Lahoz-

526 Monfort et al. 2017).

527 The accumulation of data over several decades should also allow fitting of more complex

528 models. In the present work we ignored interactions between site and species or time. However,

529 modeling fine-scale time by space by species variation in survival across sites would provide a

530 more nuanced view of the dynamics of survival. By exploiting the heterogeneity among sites in

531 environmental conditions and species community compositions, such modeling could be used to

532 investigate the causes for differences in interspecific synchrony among sites. It would also allow

533 disentangling the effects of global cross-sites weather effects that generate broad-scale

534 synchrony in average survival, from local site-level weather events that necessarily de-

535 synchronize survival among sites (Fig. 4 in Wan et al. 2022, Morrison et al. 2021).

536 Models could also be improved by explicitly accounting for spatial auto-correlation in

537 survival across sites. In the present study we ignored spatial auto-correlation, primarily because

538 of the necessity to limit model complexity. One might therefore expect our models to

539 underestimate uncertainty in parameter estimates. However, in the present dataset, results should

540 be robust to this simplification: a post-hoc analysis exploring the potential spatial auto-

541 correlation of survival estimates across sites, using the random site-level effect estimates

542 (BLUPs), revealed a weak and negative spatial auto-correlation (Moran's I = -0.05, null
521 (Post and Forchhammer 2004, Hansen et al. 2013, Koenig and Liebhold 2016, Hansen et al.

522 2020) over wider spatial scales (Black et al. 2018). A longer timer series would be necessary to

523 properly assess the yearly contributions to synchrony and to quantify differences in synchrony

524 over different time periods. Analytical methods are also not yet readily implementable to such
525 multi-year, multi-site, multi-species mark-recapture dataset (Cattadori et al. 2005, Lahoz-

526 Monfort et al. 2017).

527 The accumulation of data over several decades should also allow fitting of more complex

528 models. In the present work we ignored interactions between site and species or time. However,

529 modeling fine-scale time by space by species variation in survival across sites would provide a

530 more nuanced view of the dynamics of survival. By exploiting the heterogeneity among sites in

531 environmental conditions and species community compositions, such modeling could be used to

532 investigate the causes for differences in interspecific synchrony among sites. It would also allow

533 disentangling the effects of global cross-sites weather effects that generate broad-scale

534 synchrony in average survival, from local site-level weather events that necessarily de-

535 synchronize survival among sites (Fig. 4 in Wan et al. 2022, Morrison et al. 2021).

536 Models could also be improved by explicitly accounting for spatial auto-correlation in

537 survival across sites. In the present study we ignored spatial auto-correlation, primarily because

538 of the necessity to limit model complexity. One might therefore expect our models to

539 underestimate uncertainty in parameter estimates. However, in the present dataset, results should

540 be robust to this simplification: a post-hoc analysis exploring the potential spatial auto-

541 correlation of survival estimates across sites, using the random site-level effect estimates

542 (BLUPs), revealed a weak and negative spatial auto-correlation (Moran's I = -0.05, null
543 expected I = -0.004, null expected I standard deviation = 0.028, p-value=0.10): survival tends to
544 be more dissimilar among closer sites than among distant sites. Nonetheless, future models
545 investigating simultaneous intra-specific and interspecific synchrony could benefit from
546 accounting for spatial auto-correlation to ensure estimation robustness and to gain more
547 biological insight.

548 The present study focused on synchrony in adult apparent survival, but population size
549 fluctuations can be asynchronous despite synchrony in some vital rates. Indeed, vital rates taken
550 separately are expected to be more commonly synchronized than population sizes, due to
551 "compensatory mechanisms" such as life-history trade-offs, demographic buffering (Schaub et
552 al. 2015, Saether et al. 2016), and intra- and interspecific density-dependence regulations (Péron
553 and Koons 2012, Tavecchia et al. 2016, Lahoz-Monfort et al. 2017). Ultimately, it is synchrony
554 in population sizes that can cause extinction. Nevertheless, studying synchrony only in
555 population sizes does not allow inference about the mechanisms driving synchrony, and impedes
556 predicting their respective role in future extinction risk. Future research should aim to jointly
557 model synchronous variation and covariation in breeding success, age-dependent survival, and
558 age-dependent dispersal for several co-occurring species. Such modeling would not only allow
559 the study of synchrony of population growth rate and population size, but would also help
560 identify (a)synchronizing mechanisms at complementary organizational levels (individuals,
561 populations, communities).

562

563 ACKNOWLEDGMENTS
543 expected I = -0.004, null expected I standard deviation = 0.028, p-value=0.10): survival tends to
544 be more dissimilar among closer sites than among distant sites. Nonetheless, future models
545 investigating simultaneous intra-specific and interspecific synchrony could benefit from
546 accounting for spatial auto-correlation to ensure estimation robustness and to gain more
547 biological insight.

548 The present study focused on synchrony in adult apparent survival, but population size
549 fluctuations can be asynchronous despite synchrony in some vital rates. Indeed, vital rates taken
550 separately are expected to be more commonly synchronized than population sizes, due to
551 "compensatory mechanisms" such as life-history trade-offs, demographic buffering (Schaub et
552 al. 2015, Saether et al. 2016), and intra- and interspecific density-dependence regulations (Péron
553 and Koons 2012, Tavecchia et al. 2016, Lahoz-Monfort et al. 2017). Ultimately, it is synchrony
554 in population sizes that can cause extinction. Nevertheless, studying synchrony only in
555 population sizes does not allow inference about the mechanisms driving synchrony, and impedes
556 predicting their respective role in future extinction risk. Future research should aim to jointly
557 model synchronous variation and covariation in breeding success, age-dependent survival, and
558 age-dependent dispersal for several co-occurring species. Such modeling would not only allow
559 the study of synchrony of population growth rate and population size, but would also help
560 identify (a)synchronizing mechanisms at complementary organizational levels (individuals,
561 populations, communities).

562

563 ACKNOWLEDGMENTS
We are very grateful to the 226 volunteer ringers who collected the data (Appendix S12), to CRBPO for coordination of bird banding in France, to C. Vansteenwegen and R. Julliard for past survey coordination, to F. Jiguet, N. Dubos, I. Le Viol, A. Besnard and C. Barbraud for inspiring discussions, P. Acker and three anonymous reviewers for their critics that greatly improved the manuscript, to *Muséum National d'Histoire Naturelle, Centre National de la Recherche Scientifique*, and the French Ministry for Environment for continuous financial support, and to *Région Nord-Pas de Calais, Fondation pour la Recherche sur la Biodiversité* and *Agence Nationale de la Recherche* (grant ANR-16-CE02-0007) for funding the present research. TB was funded by Australian Research Council grants FL200100068 and DE210100549.

**Conflict of Interest Statement:** The authors declare no conflict of interest.
We are very grateful to the 226 volunteer ringers who collected the data (Appendix S12), to CRBPO for coordination of bird banding in France, to C. Vansteenwegen and R. Julliard for past survey coordination, to F. Jiguet, N. Dubos, I. Le Viol, A. Besnard and C. Barbraud for inspiring discussions, P. Acker and three anonymous reviewers for their critics that greatly improved the manuscript, to *Muséum National d'Histoire Naturelle, Centre National de la Recherche Scientifique*, and the French Ministry for Environment for continuous financial support, and to *Région Nord-Pas de Calais, Fondation pour la Recherche sur la Biodiversité* and *Agence Nationale de la Recherche* (grant ANR-16-CE02-0007) for funding the present research. TB was funded by Australian Research Council grants FL200100068 and DE210100549.

**Conflict of Interest Statement:** The authors declare no conflict of interest.
574 LITERATURE CITED

575 Amrhein, V., B. Scaar, M. Baumann, N. Minéry, J. P. Binnert, and F. Korner-Nievergelt. 2012.

576 Estimating adult sex ratios from bird mist netting data. Methods in Ecology and Evolution
3:713–720.
577

578 Bastianelli, O., A. Robert, C. Doutrelant, C. de Franceschi, P. Giovannini and A. Charmantier.
2021.Identifying drivers of spatio-temporal variation in survival in four blue tit
populations. Peer Community Journal 1. https://doi.org/10.24072/pcjournal.17.
579
580

581 Black, B. A., P. van der Sleen, E. Di Lorenzo, D. Griffin, W. J. Sydeman, J. B. Dunham, R. R.
582 Rykaczewski, M. Garcia-Reyes, M. Safeeq, I. Arismendi, and S. J. Bograd. 2018. Rising
583 synchrony controls western North American ecosystems. Global Change Biology 24:2305–
584 2314.

585 Boyles, J.G., F. Seebacher, B. Smit, and A. E. McKechnie. 2011. Adaptive Thermoregulation in
586 Endotherms May Alter Responses to Climate Change, Integrative and Comparative
587 Biology, 51: 676–690.

588 Cattadori, I. M., D. T. Haydon, and P. J. Hudson. 2005. Parasites and climate synchronize red
589 grouse populations. Nature 433:737.

590 Cayuela, H., R. A. Griffiths, N. Zakaria, J. W. Arntzen, P. Priol, J.-P. Léna, A. Besnard and P
591 Joly. Drivers of amphibian population dynamics and asynchrony at local and continental
592 scales. Journal of Animal Ecology 89:1350–1364

593 Dehorter, O. and CRBPO. 2017. Bird ringing and movement database for France. Muséum
574 LITERATURE CITED

575 Amrhein, V., B. Scaar, M. Baumann, N. Minéry, J. P. Binnert, and F. Korner-Nievergelt. 2012.

576 Estimating adult sex ratios from bird mist netting data. Methods in Ecology and Evolution
3:713–720.
577

578 Bastianelli, O., A. Robert, C. Doutrelant, C. de Franceschi, P. Giovannini and A. Charmantier.
2021.Identifying drivers of spatio-temporal variation in survival in four blue tit
populations. Peer Community Journal 1. https://doi.org/10.24072/pcjournal.17.
579
580

581 Black, B. A., P. van der Sleen, E. Di Lorenzo, D. Griffin, W. J. Sydeman, J. B. Dunham, R. R.
582 Rykaczewski, M. Garcia-Reyes, M. Safeeq, I. Arismendi, and S. J. Bograd. 2018. Rising
583 synchrony controls western North American ecosystems. Global Change Biology 24:2305–
584 2314.

585 Boyles, J.G., F. Seebacher, B. Smit, and A. E. McKechnie. 2011. Adaptive Thermoregulation in
586 Endotherms May Alter Responses to Climate Change, Integrative and Comparative
587 Biology, 51: 676–690.

588 Cattadori, I. M., D. T. Haydon, and P. J. Hudson. 2005. Parasites and climate synchronize red
589 grouse populations. Nature 433:737.

590 Cayuela, H., R. A. Griffiths, N. Zakaria, J. W. Arntzen, P. Priol, J.-P. Léna, A. Besnard and P
591 Joly. Drivers of amphibian population dynamics and asynchrony at local and continental
592 scales. Journal of Animal Ecology 89:1350–1364

593 Dehorter, O. and CRBPO. 2017. Bird ringing and movement database for France. Muséum
594 National d'Histoire Naturelle, Paris, France. https://crbpo.mnhn.fr/.

595 Dubos, N., O. Dehorter, P.-Y. Henry, and I. Le Viol. 2019. Thermal constraints on body size
depend on the population position within the species' thermal range in temperate songbirds.

597 Global Ecology and Biogeography 28:96–106.

598 Dubos, N., I. Le Viol, A. Robert, C. Téplitsky, M. Ghislain, O. Dehorter, R. Julliard, and P.-Y.
Henry. 2018. Disentangling the effects of spring anomalies in climate and net primary
production on body size of temperate songbirds. Ecography 41:1319–1330.

601 Eglington, S. M., R. Julliard, G. Gargallo, H. P. van der Jeugd, J. W. Pearce-Higgins, S. R.
Baillie, and R. A. Robinson. 2015. Latitudinal gradients in the productivity of European
migrant warblers have not shifted northwards during a period of climate change. Global
Ecology and Biogeography 24:427–436.

605 Eglington, S. M., and J. W. Pearce-Higgins. 2012. Disentangling the relative importance of
606 changes in climate and land-use intensity in driving recent bird population trends. PLoS
607 One 7:e30407.

608 Gardner, J. L., M. Clayton, R. Allen, J. Stein, and T. Bonnet. 2022. The effects of temperature
609 extremes on survival in two semi-arid Australian bird communities over three decades, with
610 predictions to 2104. Global Ecology and Biogeography 31:2498-2509.

611 Gaüzère, P., F. Jiguet, and V. Devictor. 2015. Rapid adjustment of bird community compositions
to local climatic variations and its functional consequences. Global Change Biology
21:3367–3378.
594 National d'Histoire Naturelle, Paris, France. https://crbpo.mnhn.fr/.

595 Dubos, N., O. Dehorter, P.-Y. Henry, and I. Le Viol. 2019. Thermal constraints on body size
depend on the population position within the species' thermal range in temperate songbirds.

597 Global Ecology and Biogeography 28:96–106.

598 Dubos, N., I. Le Viol, A. Robert, C. Téplitsky, M. Ghislain, O. Dehorter, R. Julliard, and P.-Y.
Henry. 2018. Disentangling the effects of spring anomalies in climate and net primary
production on body size of temperate songbirds. Ecography 41:1319–1330.

601 Eglington, S. M., R. Julliard, G. Gargallo, H. P. van der Jeugd, J. W. Pearce-Higgins, S. R.
Baillie, and R. A. Robinson. 2015. Latitudinal gradients in the productivity of European
migrant warblers have not shifted northwards during a period of climate change. Global
Ecology and Biogeography 24:427–436.

605 Eglington, S. M., and J. W. Pearce-Higgins. 2012. Disentangling the relative importance of
606 changes in climate and land-use intensity in driving recent bird population trends. PLoS
607 One 7:e30407.

608 Gardner, J. L., M. Clayton, R. Allen, J. Stein, and T. Bonnet. 2022. The effects of temperature
609 extremes on survival in two semi-arid Australian bird communities over three decades, with
610 predictions to 2104. Global Ecology and Biogeography 31:2498-2509.

611 Gaüzère, P., F. Jiguet, and V. Devictor. 2015. Rapid adjustment of bird community compositions
to local climatic variations and its functional consequences. Global Change Biology
21:3367–3378.
614 Ghislain, M., Bonnet, T., Godeau, U., Dehorter, O., Gimenez, O., & Henry, P.-Y. (2022). Data for: Synchrony in adult survival is remarkably strong among common temperate songbirds across France. Dryad. https://doi.org/10.5061/dryad.vx0k6djv3

617 Ghislain, M., Bonnet, T., Godeau, U., Dehorter, O., Gimenez, O., & Henry, P.-Y. (2022). Novel code for: Synchrony in adult survival is remarkably strong among common temperate songbirds across France. Zenodo. https://doi.org/10.5281/zenodo.8172390

620 Giraud, C., R. Julliard, and E. Porcher. 2013. Delimiting synchronous populations from monitoring data. Environmental and Ecological Statistics 20:337-352.

622 Gimenez, O., J. D. Lebreton, R. Choquet, and R. Pradel. 2018. R2ucare: An R package to perform goodness-of-fit tests for capture-recapture models. Methods in Ecology and Evolution 9:1749-1754.

625 Greenwood, P. J., and P. H. Harvey. 1982. The natal and breeding dispersal of birds. Annual Review of Ecology and Systematics 13:1-21.

627 Grosbois, V., M. P. Harris, T. Anker-Nilssen, R. H. McCleery, D. N. Shaw, B. J. T. Morgan, and O. Gimenez. 2009. Modeling survival at multi-population scales using mark-recapture data. Ecology 90:2922-2932.

630 Grosbois, V., P.-Y. Henry, J. Blondel, P. Perret, J.-D. Lebreton, D. W. Thomas, and M. M. Lambrechts. 2006. Climate impacts on Mediterranean blue tit survival: an investigation across seasons and spatial scales. Global Change Biology 12:2235-2249.

633 Hansen, B. B., V. Grøtan, R. Aanes, B. E. Sæther, A. Stien, E. Fuglei, R. A. Ims, N. G. Yoccoz,
614 Ghislain, M., Bonnet, T., Godeau, U., Dehorter, O., Gimenez, O., & Henry, P.-Y. (2022). Data for: Synchrony in adult survival is remarkably strong among common temperate songbirds across France. Dryad. https://doi.org/10.5061/dryad.vx0k6djv3

617 Ghislain, M., Bonnet, T., Godeau, U., Dehorter, O., Gimenez, O., & Henry, P.-Y. (2022). Novel code for: Synchrony in adult survival is remarkably strong among common temperate songbirds across France. Zenodo. https://doi.org/10.5281/zenodo.8172390

620 Giraud, C., R. Julliard, and E. Porcher. 2013. Delimiting synchronous populations from monitoring data. Environmental and Ecological Statistics 20:337-352.

622 Gimenez, O., J. D. Lebreton, R. Choquet, and R. Pradel. 2018. R2ucare: An R package to perform goodness-of-fit tests for capture-recapture models. Methods in Ecology and Evolution 9:1749-1754.

625 Greenwood, P. J., and P. H. Harvey. 1982. The natal and breeding dispersal of birds. Annual Review of Ecology and Systematics 13:1-21.

627 Grosbois, V., M. P. Harris, T. Anker-Nilssen, R. H. McCleery, D. N. Shaw, B. J. T. Morgan, and O. Gimenez. 2009. Modeling survival at multi-population scales using mark-recapture data. Ecology 90:2922-2932.

630 Grosbois, V., P.-Y. Henry, J. Blondel, P. Perret, J.-D. Lebreton, D. W. Thomas, and M. M. Lambrechts. 2006. Climate impacts on Mediterranean blue tit survival: an investigation across seasons and spatial scales. Global Change Biology 12:2235-2249.

633 Hansen, B. B., V. Grøtan, R. Aanes, B. E. Sæther, A. Stien, E. Fuglei, R. A. Ims, N. G. Yoccoz,
634 and Ashild Ø Pedersen. 2013. Climate events synchronize the dynamics of a resident
635 vertebrate community in the high Arctic. Science 339:313–315.

636 Hansen, B. B., V. Grøtan, I. Herfindal, and A. M. Lee. 2020. The Moran effect revisited: spatial
637 population synchrony under global warming. Ecography 43: 1591–1602.

638

639 Huitu, O., K. Norrdahl, and E. Korpimäki. 2004. Competition, predation and interspecific
640 synchrony in cyclic small mammal communities. Ecography 27:197–206.

641 Jenouvrier, S., J. C. Thibault, A. Viallefont, P. Vidals, D. Ristow, J. L. Mougin, P. Brichetti, J. J.
642 Borg, and V. Bretagnolle. 2009. Global climate patterns explain range-wide synchronicity
643 in survival of a migratory seabird. Global Change Biology 15:268–279.

644 Johnston, A., R. A. Robinson, G. Gargallo, R. Julliard, H. P. van der Jeugd, and S. R. Baillie.
645 2016. Survival of Afro-Palaearctic passerine migrants in western Europe and the impacts of
646 seasonal weather variables. Ibis 158:465–480.

647 Jones, J., P. J. Doran, and R. T. Holmes. 2003. Climate and food synchronize regional forest bird
648 abundances. Ecology 84:3024–3032.

649 Julliard, R., and F. Jiguet. 2002. Un suivi intégré des populations d'oiseaux communs en France.
650 Alauda 70:137–147.

651 Kahilainen, A., S. van Nouhuys, T. Schulz, and M. Saastamoinen. 2018. Metapopulation
652 dynamics in a changing climate: increasing spatial synchrony in weather conditions drives
653 metapopulation synchrony of a butterfly inhabiting a fragmented landscape. Global Change
634 and Ashild Ø Pedersen. 2013. Climate events synchronize the dynamics of a resident
635 vertebrate community in the high Arctic. Science 339:313–315.

636 Hansen, B. B., V. Grøtan, I. Herfindal, and A. M. Lee. 2020. The Moran effect revisited: spatial
637 population synchrony under global warming. Ecography 43: 1591–1602.

638

639 Huitu, O., K. Norrdahl, and E. Korpimäki. 2004. Competition, predation and interspecific
640 synchrony in cyclic small mammal communities. Ecography 27:197–206.

641 Jenouvrier, S., J. C. Thibault, A. Viallefont, P. Vidals, D. Ristow, J. L. Mougin, P. Brichetti, J. J.
642 Borg, and V. Bretagnolle. 2009. Global climate patterns explain range-wide synchronicity
643 in survival of a migratory seabird. Global Change Biology 15:268–279.

644 Johnston, A., R. A. Robinson, G. Gargallo, R. Julliard, H. P. van der Jeugd, and S. R. Baillie.
645 2016. Survival of Afro-Palaearctic passerine migrants in western Europe and the impacts of
646 seasonal weather variables. Ibis 158:465–480.

647 Jones, J., P. J. Doran, and R. T. Holmes. 2003. Climate and food synchronize regional forest bird
648 abundances. Ecology 84:3024–3032.

649 Julliard, R., and F. Jiguet. 2002. Un suivi intégré des populations d'oiseaux communs en France.
650 Alauda 70:137–147.

651 Kahilainen, A., S. van Nouhuys, T. Schulz, and M. Saastamoinen. 2018. Metapopulation
652 dynamics in a changing climate: increasing spatial synchrony in weather conditions drives
653 metapopulation synchrony of a butterfly inhabiting a fragmented landscape. Global Change
654 Biology 24:4316–4329.

655 Keitt, T. H. 2008. Coherent ecological dynamics induced by large-scale disturbance. Nature
656 454:331.

657 Klapwijk, M. J., J. A. Walter, A. Hirka, G. Csoka, C. Bjorkman, and A. M. Liebhold. 2018.
658 Transient synchrony among populations of five foliage-feeding Lepidoptera. Journal of
659 Animal Ecology 87:1058–1068.

660 Koenig, W. D., and A. M. Liebhold. 2016. Temporally increasing spatial synchrony of North
661 American temperature and bird populations. Nature Climate Change 6:614–618.

662 Lahoz-Monfort, J. J., M. P. Harris, S. Wanless, S. N. Freeman, and B. J. T. Morgan. 2017.
663 Bringing it all together: multi-species integrated population modelling of a breeding
664 community. Journal of Agricultural, Biological and Environmental Statistics 22:140–160.

665 Lahoz-Monfort, J. J., B. J. T. Morgan, M. P. Harris, F. Daunt, S. Wanless, and S. N. Freeman.
666 2013. Breeding together: modeling synchrony in productivity in a seabird community.
667 Ecology 94:3–10.

668 Lahoz-Monfort, J. J., B. J. T. Morgan, M. P. Harris, S. Wanless, and S. N. Freeman. 2011. A
669 capture-recapture model for exploring multi-species synchrony in survival. Methods in
670 Ecology & Evolution 2:116–124.

671 Liebhold, A., W. D. Koenig, and O. N. Bjornstad. 2004. Spatial synchrony in population
672 dynamics. Annual Review of Ecology Evolution and Systematics 35:467–490.

673 Loreau, M., and C. de Mazancourt. 2008. Species Synchrony and its drivers: neutral and
654 Biology 24:4316–4329.

655 Keitt, T. H. 2008. Coherent ecological dynamics induced by large-scale disturbance. Nature

656 454:331.

657 Klapwijk, M. J., J. A. Walter, A. Hirka, G. Csoka, C. Bjorkman, and A. M. Liebhold. 2018.

658 Transient synchrony among populations of five foliage-feeding Lepidoptera. Journal of

659 Animal Ecology 87:1058–1068.

660 Koenig, W. D., and A. M. Liebhold. 2016. Temporally increasing spatial synchrony of North

661 American temperature and bird populations. Nature Climate Change 6:614–618.

662 Lahoz-Monfort, J. J., M. P. Harris, S. Wanless, S. N. Freeman, and B. J. T. Morgan. 2017.

663 Bringing it all together: multi-species integrated population modelling of a breeding

664 community. Journal of Agricultural, Biological and Environmental Statistics 22:140–160.

665 Lahoz-Monfort, J. J., B. J. T. Morgan, M. P. Harris, F. Daunt, S. Wanless, and S. N. Freeman.

666 2013. Breeding together: modeling synchrony in productivity in a seabird community.

667 Ecology 94:3–10.

668 Lahoz-Monfort, J. J., B. J. T. Morgan, M. P. Harris, S. Wanless, and S. N. Freeman. 2011. A

669 capture-recapture model for exploring multi-species synchrony in survival. Methods in

670 Ecology & Evolution 2:116–124.

671 Liebhold, A., W. D. Koenig, and O. N. Bjornstad. 2004. Spatial synchrony in population

672 dynamics. Annual Review of Ecology Evolution and Systematics 35:467–490.

673 Loreau, M., and C. de Mazancourt. 2008. Species Synchrony and its drivers: neutral and
674 nonneutral community dynamics in fluctuating environments. The American Naturalist
675 172:E48–E66.

676 Malick, M. J., and S. P. Cox. 2016. Regional-Scale Declines in Productivity of Pink and Chum
677 Salmon Stocks in Western North America. PLOS ONE 11: e0146009.

678 Malick, M. J., S. P. Cox, F. J. Mueter, B. Dorner, and R. M. Peterman. 2017. Effects of the
679 North Pacific Current on the Productivity of 163 Pacific Salmon Stocks. Fisheries
680 Oceanography 26: 268–81.

681 Martin, A. E., J. W. Pearce-Higgins, and L. Fahrig. 2017. The spatial scale of time-lagged
682 population synchrony increases with species dispersal distance. Global Ecology and
683 Biogeography 26:1201–1210.

684 McKechnie, A. E., A. R. Gerson, T. J. McWhorter, E. K. Smith, W. A. Talbot, and B. O. Wolf.
685 2017. Avian Thermoregulation in the Heat: Evaporative Cooling in Five Australian
686 Passerines Reveals within-Order Biogeographic Variation in Heat Tolerance. Journal of
687 Experimental Biology 220: 2436–44.

688 Morrison, C. A., R. A. Robinson, J. A. Clark, K. Risely, and J. A. Gill. 2013. Recent
689 population declines in Afro-Palaearctic migratory birds: the influence of breeding and non-
690 breeding seasons. Diversity and Distributions 19:1051–1058.

691 Morrison, C. A., S. J. Butler, R. A. Robinson, J. A. Clark, J. Arizaga, A. Aunins, O. Baltà, J.
692 Cepák, T. Chodkiewicz, V. Escandell, R. P. B. Foppen, R. D. Gregory, M. Husby, F.
693 Jiguet, J. A. Kålås, A. Lehikoinen, Å. Lindström, C. M. Moshøj, K. Nagy, A. L. Nebot, M.
674 nonneutral community dynamics in fluctuating environments. The American Naturalist
675 172:E48-E66.

676 Malick, M. J., and S. P. Cox. 2016. Regional-Scale Declines in Productivity of Pink and Chum
677 Salmon Stocks in Western North America. PLOS ONE 11: e0146009.

678 Malick, M. J., S. P. Cox, F. J. Mueter, B. Dorner, and R. M. Peterman. 2017. Effects of the
679 North Pacific Current on the Productivity of 163 Pacific Salmon Stocks. Fisheries
680 Oceanography 26: 268-81.

681 Martin, A. E., J. W. Pearce-Higgins, and L. Fahrig. 2017. The spatial scale of time-lagged
682 population synchrony increases with species dispersal distance. Global Ecology and
683 Biogeography 26:1201-1210.

684 McKechnie, A. E., A. R. Gerson, T. J. McWhorter, E. K. Smith, W. A. Talbot, and B. O. Wolf.
685 2017. Avian Thermoregulation in the Heat: Evaporative Cooling in Five Australian
686 Passerines Reveals within-Order Biogeographic Variation in Heat Tolerance. Journal of
687 Experimental Biology 220: 2436-44.

688 Morrison, C. A., R. A. Robinson, J. A. Clark, K. Risely, and J. A. Gill. 2013. Recent
689 population declines in Afro-Palaearctic migratory birds: the influence of breeding and non-
690 breeding seasons. Diversity and Distributions 19:1051-1058.

691 Morrison, C. A., S. J. Butler, R. A. Robinson, J. A. Clark, J. Arizaga, A. Aunins, O. Baltà, J.
692 Cepák, T. Chodkiewicz, V. Escandell, R. P. B. Foppen, R. D. Gregory, M. Husby, F.
693 Jiguet, J. A. Kålås, A. Lehikoinen, Å. Lindström, C. M. Moshøj, K. Nagy, A. L. Nebot, M.
694 Piha, J. Reif, T. Sattler, J. Škorpilová, T. Szép, N. Teufelbauer, K. Thorup, C. van

695 Turnhout, T. Wenninger and J. A. Gill 2021. Covariation in Population Trends and

696 Demography Reveals Targets for Conservation Action. Proceedings of the Royal Society

697 B: Biological Sciences 288: 20202955.

698 Morrison, C. A., S. J. Butler, J. A. Clark, J. Arizaga, O. Baltà, J. Cepák, A. L.l Nebot, M. Piha,

699 K. Thorup, T. Wenninger, R. A. Robinson and J. A. Gill. 2022. Demographic Variation in

700 Space and Time: Implications for Conservation Targeting. Royal Society Open Science 9

701 (3): 211671.

702 Moussus, J.-P. (2010). Influences du climat sur la démographie des passereaux communs (p.

703 318). PhD thesis, Université Pierre et Marie Curie, Paris, France. Available at

704 https://crbpo.mnhn.fr

705 Nakagawa, S., and H. Schielzeth. 2013. A general and simple method for obtaining R2 from

706 generalized linear mixed-effects models. Methods in Ecology and Evolution 4:133–142.

707 Olmos, M, F. Massiot-Granier, E. Prévost, G. Chaput, I. R. Bradbury, M. Nevoux, E. Rivot.

708 2019. Evidence for spatial coherence in time trends of marine life history traits of Atlantic

709 salmon in the North Atlantic. Fish and Fisheries 20: 322–342.

710 Pandit, S. N., J. Kolasa, and K. Cottenie. 2013. Population synchrony decreases with richness

711 and increases with environmental fluctuations in an experimental metacommunity.

712 Oecologia 171:237–247.

713 Paradis, E., S. R. Baillie, W. J. Sutherland, and R. D. Gregory. 1999. Dispersal and spatial scale
694 Piha, J. Reif, T. Sattler, J. Škorpilová, T. Szép, N. Teufelbauer, K. Thorup, C. van

695 Turnhout, T. Wenninger and J. A. Gill 2021. Covariation in Population Trends and

696 Demography Reveals Targets for Conservation Action. Proceedings of the Royal Society

697 B: Biological Sciences 288: 20202955.

698 Morrison, C. A., S. J. Butler, J. A. Clark, J. Arizaga, O. Baltà, J. Cepák, A. L.l Nebot, M. Piha,

699 K. Thorup, T. Wenninger, R. A. Robinson and J. A. Gill. 2022. Demographic Variation in

700 Space and Time: Implications for Conservation Targeting. Royal Society Open Science 9

701 (3): 211671.

702 Moussus, J.-P. (2010). Influences du climat sur la démographie des passereaux communs (p.

703 318). PhD thesis, Université Pierre et Marie Curie, Paris, France. Available at

704 https://crbpo.mnhn.fr

705 Nakagawa, S., and H. Schielzeth. 2013. A general and simple method for obtaining R2 from

706 generalized linear mixed-effects models. Methods in Ecology and Evolution 4:133–142.

707 Olmos, M, F. Massiot-Granier, E. Prévost, G. Chaput, I. R. Bradbury, M. Nevoux, E. Rivot.

708 2019. Evidence for spatial coherence in time trends of marine life history traits of Atlantic

709 salmon in the North Atlantic. Fish and Fisheries 20: 322–342.

710 Pandit, S. N., J. Kolasa, and K. Cottenie. 2013. Population synchrony decreases with richness

711 and increases with environmental fluctuations in an experimental metacommunity.

712 Oecologia 171:237–247.

713 Paradis, E., S. R. Baillie, W. J. Sutherland, and R. D. Gregory. 1999. Dispersal and spatial scale
714 affect synchrony in spatial population dynamics. Ecology Letters 2:114–120.

715 Peron, G., and D. N. Koons. 2012. Integrated modeling of communities: parasitism, competition,
716 and demographic synchrony in sympatric ducks. Ecology 93:2456–2464.

717 Plummer, M. 2003. JAGS: A program for analysis of Bayesian graphical models using Gibbs
718 sampling. Proceedings of the 3rd international workshop on distributed statistical
719 computing 124: 10–18.

720 van de Pol, M., Bailey, L. D., McLean, N., Rijsdijk, L., Lawson, C. R., & Brouwer, L. (2016).
721 Identifying the best climatic predictors in ecology and evolution. Methods in Ecology and
722 Evolution, 7(10), 1246–1257.

723 Pomara, L. Y., and B. Zuckerberg. 2017. Climate variability drives population cycling and
724 synchrony. Diversity and Distributions 23:421–434.

725 Post, E., and M. C. Forchhammer. 2004. Spatial synchrony of local populations has increased in
726 association with the recent Northern Hemisphere climate trend. Proceedings of the National
727 Academy of Sciences of the United States of America 101:9286–9290.

728 R Development Core Team. 2015. R: A language and environment for statistical computing. R
729 Foundation for Statistical Computing, Vienna, Austria. http://www.R-project.org

730 Reiertsen, T. K., K. Layton-Matthews, K. E. Erikstad, K. Hodges, M. Ballesteros, T. Anker-
731 Nilssen, R. T. Barrett, S. Benjaminsen, M. Bogdanova, S. Christensen-Dalsgaard, F.
732 Daunt, N. Dehnhard, M. P. Harris, M. Langset, S.-H. Lorentsen, M. Newell, V. S. Bråthen,
733 I. Støyle-Bringsvor, G.-H. Systad, S. Wanless. 2021. Inter-Population Synchrony in Adult
714 affect synchrony in spatial population dynamics. Ecology Letters 2:114–120.

715 Peron, G., and D. N. Koons. 2012. Integrated modeling of communities: parasitism, competition, and demographic synchrony in sympatric ducks. Ecology 93:2456–2464.

717 Plummer, M. 2003. JAGS: A program for analysis of Bayesian graphical models using Gibbs sampling. Proceedings of the 3rd international workshop on distributed statistical computing 124: 10–18.

720 van de Pol, M., Bailey, L. D., McLean, N., Rijsdijk, L., Lawson, C. R., & Brouwer, L. (2016). Identifying the best climatic predictors in ecology and evolution. Methods in Ecology and Evolution, 7(10), 1246–1257.

723 Pomara, L. Y., and B. Zuckerberg. 2017. Climate variability drives population cycling and synchrony. Diversity and Distributions 23:421–434.

725 Post, E., and M. C. Forchhammer. 2004. Spatial synchrony of local populations has increased in association with the recent Northern Hemisphere climate trend. Proceedings of the National Academy of Sciences of the United States of America 101:9286–9290.

728 R Development Core Team. 2015. R: A language and environment for statistical computing. R Foundation for Statistical Computing, Vienna, Austria. http://www.R-project.org

730 Reiertsen, T. K., K. Layton-Matthews, K. E. Erikstad, K. Hodges, M. Ballesteros, T. Anker-Nilssen, R. T. Barrett, S. Benjaminsen, M. Bogdanova, S. Christensen-Dalsgaard, F. Daunt, N. Dehnhard, M. P. Harris, M. Langset, S.-H. Lorentsen, M. Newell, V. S. Bråthen, I. Støyle-Bringsvor, G.-H. Systad, S. Wanless. 2021. Inter-Population Synchrony in Adult
734 Survival and Effects of Climate and Extreme Weather in Non-Breeding Areas of Atlantic
735 Puffins. Marine Ecology Progress Series 676: 219–31.

736 Robinson, J. P. W., M. Dornelas, and A. F. Ojanguren. 2013. Interspecific synchrony of seabird
737 population growth rate and breeding success. Ecology and Evolution 3:2013–2019.

738 Robinson, R. A., S. R. Baillie, and H. Q. P. Crick. 2007. Weather-dependent survival:
739 implications of climate change for passerine population processes. Ibis 149:357–364.

740 Saether, B. E., V. Grotan, S. Engen, T. Coulson, P. R. Grant, M. E. Visser, J. E. Brommer, B.
741 Rosemary Grant, L. Gustafsson, B. J. Hatchwell, K. Jerstad, P. Karell, H. Pietiainen, A.

742 Roulin, O. W. Rostad, and H. Weimerskirch. 2016. Demographic routes to variability and
743 regulation in bird populations. Nature Communications 7:12001.

744 Salewski, V., W. M. Hochachka, and W. Fiedler. 2013. Multiple weather factors affect apparent
745 survival of European passerine birds. PLoS One 8:e59110.

746 Sanz-Aguilar, A., J. M. Igual, G. Tavecchia, M. Genovart, and D. Oro. 2016. When immigration
747 mask threats: the rescue effect of a Scopoli's shearwater colony in the Western
748 Mediterranean as a case study. Biological Conservation 198:33–36.

749 Saracco, J. F., Royle, J. A., DeSante, D. F., & Gardner, B. (2010). Modeling spatial variation in
750 avian survival and residency probabilities. Ecology, 91(7), 1885–1891.

751 Schaub, M., J. von Hirschheydt, and M. U. Grüebler. 2015. Differential contribution of
752 demographic rate synchrony to population synchrony in barn swallows. Journal of Animal
753 Ecology 84:1530–1541.
734 Survival and Effects of Climate and Extreme Weather in Non-Breeding Areas of Atlantic
735 Puffins. Marine Ecology Progress Series 676: 219–31.

736 Robinson, J. P. W., M. Dornelas, and A. F. Ojanguren. 2013. Interspecific synchrony of seabird
737 population growth rate and breeding success. Ecology and Evolution 3:2013–2019.

738 Robinson, R. A., S. R. Baillie, and H. Q. P. Crick. 2007. Weather-dependent survival:
739 implications of climate change for passerine population processes. Ibis 149:357–364.

740 Saether, B. E., V. Grotan, S. Engen, T. Coulson, P. R. Grant, M. E. Visser, J. E. Brommer, B.
741 Rosemary Grant, L. Gustafsson, B. J. Hatchwell, K. Jerstad, P. Karell, H. Pietiainen, A.

742 Roulin, O. W. Rostad, and H. Weimerskirch. 2016. Demographic routes to variability and
743 regulation in bird populations. Nature Communications 7:12001.

744 Salewski, V., W. M. Hochachka, and W. Fiedler. 2013. Multiple weather factors affect apparent
745 survival of European passerine birds. PLoS One 8:e59110.

746 Sanz-Aguilar, A., J. M. Igual, G. Tavecchia, M. Genovart, and D. Oro. 2016. When immigration
747 mask threats: the rescue effect of a Scopoli's shearwater colony in the Western
748 Mediterranean as a case study. Biological Conservation 198:33–36.

749 Saracco, J. F., Royle, J. A., DeSante, D. F., & Gardner, B. (2010). Modeling spatial variation in
750 avian survival and residency probabilities. Ecology, 91(7), 1885–1891.

751 Schaub, M., J. von Hirschheydt, and M. U. Grüebler. 2015. Differential contribution of
752 demographic rate synchrony to population synchrony in barn swallows. Journal of Animal
753 Ecology 84:1530–1541.
754 Seward, A. M., C. M. Beale, L. Gilbert, T. H. Jones, and R. J. Thomas. 2013. The impact of
755 increased food availability on survival of a long-distance migratory bird. Ecology 94:221-
756 230.

757 Sheppard, L. W., J. R. Bell, R. Harrington, and D. C. Reuman. 2015. Changes in large-scale
758 climate alter spatial synchrony of aphid pests. Nature Climate Change 6:610.

759 Su, Y. S., and M. Yajima. 2015. R2jags: Using R to Run "JAGS." Cran.r-project.org, Vienna,
760 Austria.

761 Svensson, L. 1992. Identification guide to European Passerines. 4th rev. Lars Svensson,
762 Stockholm.

763 Swallow, B., R. King, S. T. Buckland, and M. P. Toms. 2016. Identifying multispecies
764 synchrony in response to environmental covariates. Ecology and Evolution 6:8515-8525.

765 Tavecchia, G., S. Tenan, R. Pradel, J. M. Igual, M. Genovart, and D. Oro. 2016. Climate-driven
766 vital rates do not always mean climate-driven population. Global Change Biology 22:3960-
767 3966.

768 Telenský, T., P. Klvaňa, M. Jelínek, J. Cepák, and J. Reif. 2020. The Influence of Climate
769 Variability on Demographic Rates of Avian Afro-Palearctic Migrants. Scientific Reports
770 10: 17592.

771 Vik, J. O., N. C. Stenseth, G. Tavecchia, A. Mysterud, and O. C. Lingjærde. 2004. Living in
772 Synchrony on Greenland Coasts? Nature 427: 697-98.

773 Visser, M. E., F. Adriaensen, J. H. van Balen, J. Blondel, A. A. Dhondt, S. van Dongen, du F.
754 Seward, A. M., C. M. Beale, L. Gilbert, T. H. Jones, and R. J. Thomas. 2013. The impact of
755 increased food availability on survival of a long-distance migratory bird. Ecology 94:221-
756 230.

757 Sheppard, L. W., J. R. Bell, R. Harrington, and D. C. Reuman. 2015. Changes in large-scale
758 climate alter spatial synchrony of aphid pests. Nature Climate Change 6:610.

759 Su, Y. S., and M. Yajima. 2015. R2jags: Using R to Run "JAGS." Cran.r-project.org, Vienna,
760 Austria.

761 Svensson, L. 1992. Identification guide to European Passerines. 4th rev. Lars Svensson,
762 Stockholm.

763 Swallow, B., R. King, S. T. Buckland, and M. P. Toms. 2016. Identifying multispecies
764 synchrony in response to environmental covariates. Ecology and Evolution 6:8515-8525.

765 Tavecchia, G., S. Tenan, R. Pradel, J. M. Igual, M. Genovart, and D. Oro. 2016. Climate-driven
766 vital rates do not always mean climate-driven population. Global Change Biology 22:3960-
767 3966.

768 Telenský, T., P. Klvaňa, M. Jelínek, J. Cepák, and J. Reif. 2020. The Influence of Climate
769 Variability on Demographic Rates of Avian Afro-Palearctic Migrants. Scientific Reports
770 10: 17592.

771 Vik, J. O., N. C. Stenseth, G. Tavecchia, A. Mysterud, and O. C. Lingjærde. 2004. Living in
772 Synchrony on Greenland Coasts? Nature 427: 697-98.

773 Visser, M. E., F. Adriaensen, J. H. van Balen, J. Blondel, A. A. Dhondt, S. van Dongen, du F.
774 Chris, E. V Ivankina, A. B. Kerimov, J. de Laet, E. Matthysen, R. McCleery, M. Orell, and D. L. Thomson. 2003. Variable responses to large-scale climate change in European Parus populations. Proceedings of the Royal Society of London. Series B: Biological Sciences 270:367–372.

778 Wan, X., Holyoak, M., Yan, C., Le Maho, Y., Dirzo, R., Krebs, C. J., Stenseth, N. C., & Zhang, Z. (2022). Broad-scale climate variation drives the dynamics of animal populations: a global multi-taxa analysis. Biological Reviews 97:2174–2194

781 Woodworth, B. K., N. T. Wheelwright, A. E. M. Newman, and D. R. Norris. 2017a. Local density regulates migratory songbird reproductive success through effects on double-brooding and nest predation. Ecology 98:2039–2048.

784 Woodworth, B. K., N. T. Wheelwright, A. E. Newman, M. Schaub, and D. R. Norris. 2017b. Winter temperatures limit population growth rate of a migratory songbird. Nature Communications 8:14812.
774 Chris, E. V Ivankina, A. B. Kerimov, J. de Laet, E. Matthysen, R. McCleery, M. Orell, and D. L. Thomson. 2003. Variable responses to large-scale climate change in European Parus populations. Proceedings of the Royal Society of London. Series B: Biological Sciences 270:367–372.

778 Wan, X., Holyoak, M., Yan, C., Le Maho, Y., Dirzo, R., Krebs, C. J., Stenseth, N. C., & Zhang, Z. (2022). Broad-scale climate variation drives the dynamics of animal populations: a global multi-taxa analysis. Biological Reviews 97:2174–2194

781 Woodworth, B. K., N. T. Wheelwright, A. E. M. Newman, and D. R. Norris. 2017a. Local density regulates migratory songbird reproductive success through effects on double-brooding and nest predation. Ecology 98:2039–2048.

784 Woodworth, B. K., N. T. Wheelwright, A. E. Newman, M. Schaub, and D. R. Norris. 2017b. Winter temperatures limit population growth rate of a migratory songbird. Nature Communications 8:14812.
TABLE 1. Variance component estimates for survival probability under different models. Model 1 is the base model and captures all of the synchrony in the Year variance parameter. Other models include various effects that may or may not explain some of the synchrony, thus leaving a corrected synchrony as the Year variance parameter. Estimates are posterior modes and 95% highest probability density credible intervals.

<table><thead><tr><th>Variance component</th><th>Model 1<br>Base model</th><th>Model 2<br>Species-specific synchrony</th><th>Model 3<br>Migration:Year</th><th>Model 4<br>Spring weather</th><th>Model 5<br>Winter weather</th></tr></thead><tbody><tr><td>Year<br>(synchrony)</td><td>0.098<br>[0.031–0.234]</td><td>0.090<br>[0.016–0.203]</td><td>0.091<br>[0.009–0.205]</td><td>0.091<br>[0.023–0.216]</td><td>0.092<br>[0.019–0.214]</td></tr><tr><td>Model-specific synchrony term</td><td>-</td><td>-<sup>b</sup></td><td>0.011<br>[0–0.036]</td><td>0.001<br>[<0.001–0.0029]</td><td>0.008<br>[<0.001–0.020]</td></tr><tr><td>Year:Species<br>(asynchrony)</td><td>0.034<br>[0.012–0.068]</td><td>0.026<sup>c</sup></td><td>0.032<br>[0.007–0.065]</td><td>0.034<br>[0.007–0.062]</td><td>0.036<br>[0.010–0.066]</td></tr><tr><td>Site</td><td>0.155<br>[0.076–0.232]</td><td>0.157<br>[0.088–0.237]</td><td>0.152<br>[0.085–0.226]</td><td>0.156<br>[0.081–0.232]</td><td>0.152<br>[0.087–0.226]</td></tr><tr><td>Variance sum<sup>d</sup></td><td>0.287</td><td>-</td><td>0.286</td><td>0.287</td><td>0.286</td></tr><tr><td>ICC<sup>e</sup></td><td>73% [47–94]</td><td>Mean 76%</td><td>65% [30–91]</td><td>72% [47–93]</td><td>68% [36–95]</td></tr></tbody></table>

Notes: a. Weather variables were introduced as fixed effects and the variance they explain was computed post-hoc, while all other variance components were directly estimated as random effects.b. In model 2 synchrony is estimated separately for each of the 16 species, see Fig. 3. c.
TABLE 1. Variance component estimates for survival probability under different models. Model 1 is the base model and captures all of the synchrony in the Year variance parameter. Other models include various effects that may or may not explain some of the synchrony, thus leaving a corrected synchrony as the Year variance parameter. Estimates are posterior modes and 95% highest probability density credible intervals.

<table><thead><tr><th>Variance component</th><th>Model 1<br>Base model</th><th>Model 2<br>Species-specific synchrony</th><th>Model 3<br>Migration:Year</th><th>Model 4<br>Spring weather</th><th>Model 5<br>Winter weather</th></tr></thead><tbody><tr><td>Year<br>(synchrony)</td><td>0.098<br>[0.031–0.234]</td><td>0.090<br>[0.016–0.203]</td><td>0.091<br>[0.009–0.205]</td><td>0.091<br>[0.023–0.216]</td><td>0.092<br>[0.019–0.214]</td></tr><tr><td>Model-specific synchrony term</td><td>-</td><td>-<sup>b</sup></td><td>0.011<br>[0–0.036]</td><td>0.001<br>[<0.001–0.0029]</td><td>0.008<br>[<0.001–0.020]</td></tr><tr><td>Year:Species<br>(asynchrony)</td><td>0.034<br>[0.012–0.068]</td><td>0.026<sup>c</sup></td><td>0.032<br>[0.007–0.065]</td><td>0.034<br>[0.007–0.062]</td><td>0.036<br>[0.010–0.066]</td></tr><tr><td>Site</td><td>0.155<br>[0.076–0.232]</td><td>0.157<br>[0.088–0.237]</td><td>0.152<br>[0.085–0.226]</td><td>0.156<br>[0.081–0.232]</td><td>0.152<br>[0.087–0.226]</td></tr><tr><td>Variance sum<sup>d</sup></td><td>0.287</td><td>-</td><td>0.286</td><td>0.287</td><td>0.286</td></tr><tr><td>ICC<sup>e</sup></td><td>73% [47–94]</td><td>Mean 76%</td><td>65% [30–91]</td><td>72% [47–93]</td><td>68% [36–95]</td></tr></tbody></table>

Notes: a. Weather variables were introduced as fixed effects and the variance they explain was computed post-hoc, while all other variance components were directly estimated as random effects.b. In model 2 synchrony is estimated separately for each of the 16 species, see Fig. 3. c.
795 Mean of the 16 species-specific point estimates. d. The sum of the variances is expected to be

796 constant, but may vary slightly due to rounding, and because survival is not directly observed

797 but predicted as a latent variable (i.e., survival does not have a defined variance observable

798 independently of a model). e. Intra-class correlations (see Methods) are estimates of synchrony

799 across species, i.e., the proportion of Year variance over ‘total’ temporal variance, which varies

800 across models.

801
795 Mean of the 16 species-specific point estimates. d. The sum of the variances is expected to be

796 constant, but may vary slightly due to rounding, and because survival is not directly observed

797 but predicted as a latent variable (i.e., survival does not have a defined variance observable

798 independently of a model). e. Intra-class correlations (see Methods) are estimates of synchrony

799 across species, i.e., the proportion of Year variance over ‘total’ temporal variance, which varies

800 across models.

801
802 **Figure captions**

803 FIGURE 1. For each species, spatio-temporal distribution of the 242 sites across continental

804 France used in this study. Color corresponds to the number of years for which we estimated

805 survival in our models. The true duration of the monitoring at the site is always at least one more

806 year. Point size indicates the total number of captures used in analyses, excluding transient

807 individuals, for a given site.

808 FIGURE 2. Inter-annual fluctuation of local survival probabilities by species for adult individuals.

809 Estimates were obtained independently for each species (with resident males as intercept) from a

810 model with fully Year-dependent survival and explicitly modeling an interaction between Year

811 and Migratory behavior (resident/short-distance migrants *versus* long-distance migrants, i.e.

812 model 5).

813 FIGURE 3. Interspecific synchrony for each species and overall. Estimates of Intra-Class

814 Correlations measuring interspecific synchrony in adult survival, for each species separately, and

815 across all species taken together. A lower species-specific synchrony means that mean survival

816 probability is more variable in that species. Species-specific estimates were obtained from model

817 2, while the overall estimate was obtained from model 1. Yellow lines represent short-distance

818 migrants, blue lines represent long-distance migrants. Filled circles represent posterior means,

819 empty circles posterior modes, thick lines 50% quantile CI and thin lines 95% HPD CI.

820
802 **Figure captions**

803 FIGURE 1. For each species, spatio-temporal distribution of the 242 sites across continental

804 France used in this study. Color corresponds to the number of years for which we estimated

805 survival in our models. The true duration of the monitoring at the site is always at least one more

806 year. Point size indicates the total number of captures used in analyses, excluding transient

807 individuals, for a given site.

808 FIGURE 2. Inter-annual fluctuation of local survival probabilities by species for adult individuals.

809 Estimates were obtained independently for each species (with resident males as intercept) from a

810 model with fully Year-dependent survival and explicitly modeling an interaction between Year

811 and Migratory behavior (resident/short-distance migrants *versus* long-distance migrants, i.e.

812 model 5).

813 FIGURE 3. Interspecific synchrony for each species and overall. Estimates of Intra-Class

814 Correlations measuring interspecific synchrony in adult survival, for each species separately, and

815 across all species taken together. A lower species-specific synchrony means that mean survival

816 probability is more variable in that species. Species-specific estimates were obtained from model

817 2, while the overall estimate was obtained from model 1. Yellow lines represent short-distance

818 migrants, blue lines represent long-distance migrants. Filled circles represent posterior means,

819 empty circles posterior modes, thick lines 50% quantile CI and thin lines 95% HPD CI.

820
821
821
<figure><img src="image_2.png" /></figure>
<figure><img src="image_2.png" /></figure>
<figure><img src="image_3.png" /><figcaption>Survival probability</figcaption></figure>

Ecology
<figure><img src="image_3.png" /><figcaption>Survival probability</figcaption></figure>

Ecology
<figure><img src="image_4.png" /></figure>
<figure><img src="image_4.png" /></figure>
**Supporting Information for:**

Authors:

Manon Ghislain, Timothée Bonnet, Ugoline Godeau, Olivier Dehorter, Olivier Gimenez, and Pierre-Yves Henry

Title:

Synchrony in adult survival is remarkably strong among common temperate songbirds across France

Journal: Ecology
**Supporting Information for:**

Authors:

Manon Ghislain, Timothée Bonnet, Ugoline Godeau, Olivier Dehorter, Olivier Gimenez, and Pierre-Yves Henry

Title:

**Synchrony in adult survival is remarkably strong among common temperate songbirds across France**

Journal: Ecology
APPENDIX S1. DESCRIPTION OF STUDIED SPECIES

TABLE S1. List of species considered, with summary information on sample sizes, mean value of the individual covariate for capture heterogeneity (see Appendix S4), average apparent survival probability (with 95% credible interval), migratory strategy, habitat traits, number of broods per year and body mass.
APPENDIX S1. DESCRIPTION OF STUDIED SPECIES

TABLE S1. List of species considered, with summary information on sample sizes, mean value of the individual covariate for capture heterogeneity (see Appendix S4), average apparent survival probability (with 95% credible interval), migratory strategy, habitat traits, number of broods per year and body mass.
<table><thead><tr><th>Species</th><th>Number of adult individuals</th><th>Number of individuals with at least one recapture in another year</th><th>Individual covariate for heterogeneity in recapture probability (relative mean)<sup>1</sup></th><th>Apparent survival probability<sup>2</sup></th></tr></thead><tbody><tr><td>Acrocephalus schoenobaenus</td><td>335</td><td>60</td><td>1</td><td>0.365 [0.314–0.42]</td></tr><tr><td>Acrocephalus scirpaceus</td><td>2149</td><td>740</td><td>0.86</td><td>0.470 [0.438–0.502]</td></tr><tr><td>Cettia cetti</td><td>455</td><td>166</td><td>0.99</td><td>0.416 [0.377–0.456]</td></tr><tr><td>Erithacus rubecula</td><td>1433</td><td>269</td><td>0.84</td><td>0.360 [0.328–0.394]</td></tr><tr><td>Hippolais polyglotta</td><td>740</td><td>151</td><td>0.74</td><td>0.410 [0.369–0.451]</td></tr><tr><td>Luscinia megarhynchos</td><td>1189</td><td>441</td><td>0.74</td><td>0.503 [0.471–0.535]</td></tr><tr><td>Parus major</td><td>939</td><td>143</td><td>0.56</td><td>0.369 [0.332–0.407]</td></tr><tr><td>Phylloscopus collybita</td><td>1769</td><td>279</td><td>0.78</td><td>0.296 [0.267–0.326]</td></tr><tr><td>Phylloscopus trochilus</td><td>539</td><td>118</td><td>0.82</td><td>0.365 [0.326–0.407]</td></tr><tr><td>Prunella modularis</td><td>1408</td><td>453</td><td>0.69</td><td>0.464 [0.434–0.494]</td></tr><tr><td>Sylvia atricapilla</td><td>3890</td><td>744</td><td>0.67</td><td>0.431 [0.403–0.459]</td></tr><tr><td>Sylvia borin</td><td>1038</td><td>246</td><td>0.73</td><td>0.457 [0.421–0.494]</td></tr><tr><td>Sylvia communis</td><td>1111</td><td>244</td><td>0.83</td><td>0.370 [0.335–0.405]</td></tr><tr><td>Troglodytes troglodytes</td><td>568</td><td>99</td><td>0.74</td><td>0.307 [0.27–0.348]</td></tr><tr><td>Turdus merula</td><td>2800</td><td>936</td><td>0.57</td><td>0.499 [0.47–0.528]</td></tr><tr><td>Turdus philomelos</td><td>549</td><td>109</td><td>0.55</td><td>0.465 [0.423–0.509]</td></tr></tbody></table>
<table><thead><tr><th>Species</th><th>Number of adult individuals</th><th>Number of individuals with at least one recapture in another year</th><th>Individual covariate for heterogeneity in recapture probability (relative mean)<sup>1</sup></th><th>Apparent survival probability<sup>2</sup></th></tr></thead><tbody><tr><td>Acrocephalus schoenobaenus</td><td>335</td><td>60</td><td>1</td><td>0.365 [0.314–0.42]</td></tr><tr><td>Acrocephalus scirpaceus</td><td>2149</td><td>740</td><td>0.86</td><td>0.470 [0.438–0.502]</td></tr><tr><td>Cettia cetti</td><td>455</td><td>166</td><td>0.99</td><td>0.416 [0.377–0.456]</td></tr><tr><td>Erithacus rubecula</td><td>1433</td><td>269</td><td>0.84</td><td>0.360 [0.328–0.394]</td></tr><tr><td>Hippolais polyglotta</td><td>740</td><td>151</td><td>0.74</td><td>0.410 [0.369–0.451]</td></tr><tr><td>Luscinia megarhynchos</td><td>1189</td><td>441</td><td>0.74</td><td>0.503 [0.471–0.535]</td></tr><tr><td>Parus major</td><td>939</td><td>143</td><td>0.56</td><td>0.369 [0.332–0.407]</td></tr><tr><td>Phylloscopus collybita</td><td>1769</td><td>279</td><td>0.78</td><td>0.296 [0.267–0.326]</td></tr><tr><td>Phylloscopus trochilus</td><td>539</td><td>118</td><td>0.82</td><td>0.365 [0.326–0.407]</td></tr><tr><td>Prunella modularis</td><td>1408</td><td>453</td><td>0.69</td><td>0.464 [0.434–0.494]</td></tr><tr><td>Sylvia atricapilla</td><td>3890</td><td>744</td><td>0.67</td><td>0.431 [0.403–0.459]</td></tr><tr><td>Sylvia borin</td><td>1038</td><td>246</td><td>0.73</td><td>0.457 [0.421–0.494]</td></tr><tr><td>Sylvia communis</td><td>1111</td><td>244</td><td>0.83</td><td>0.370 [0.335–0.405]</td></tr><tr><td>Troglodytes troglodytes</td><td>568</td><td>99</td><td>0.74</td><td>0.307 [0.27–0.348]</td></tr><tr><td>Turdus merula</td><td>2800</td><td>936</td><td>0.57</td><td>0.499 [0.47–0.528]</td></tr><tr><td>Turdus philomelos</td><td>549</td><td>109</td><td>0.55</td><td>0.465 [0.423–0.509]</td></tr></tbody></table>
<table><thead><tr><td>Species</td><td>Habitat specialization index³</td><td>Habitat³</td><td>Migratory behavior³</td></tr></thead><tbody><tr><td>Acrocephalus schoenobaenus</td><td>34.1</td><td>Humid shrub land (reed)</td><td>Long-distance migrant</td></tr><tr><td>Acrocephalus scirpaceus</td><td>23.8</td><td>Humid shrub land (reed)</td><td>Long-distance migrant</td></tr><tr><td>Cettia cetti</td><td>21</td><td>Humid shrub land (reed)</td><td>Sedentary or short-distance migrant</td></tr><tr><td>Erithacus rubecula</td><td>29.7</td><td>Terrestrial shrub land / Forest</td><td>Sedentary or short-distance migrant</td></tr><tr><td>Hippolais polyglotta</td><td>43.2</td><td>Terrestrial shrub land / Forest</td><td>Long-distance migrant</td></tr><tr><td>Luscinia megarhynchos</td><td>28.4</td><td>Terrestrial shrub land / Forest</td><td>Long-distance migrant</td></tr><tr><td>Parus major</td><td>40.3</td><td>Terrestrial shrub land / Forest</td><td>Sedentary or short-distance migrant</td></tr><tr><td>Phylloscopus collybita</td><td>31.3</td><td>Terrestrial shrub land / Forest</td><td>Sedentary or short-distance migrant</td></tr><tr><td>Phylloscopus trochilus</td><td>28.3</td><td>Terrestrial shrub land / Forest</td><td>Long-distance migrant</td></tr><tr><td>Prunella modularis</td><td>38.7</td><td>Terrestrial shrub land / Forest</td><td>Sedentary or short-distance migrant</td></tr><tr><td>Sylvia atricapilla</td><td>39.6</td><td>Terrestrial shrub land / Forest</td><td>Sedentary or short-distance migrant</td></tr><tr><td>Sylvia borin</td><td>33.4</td><td>Terrestrial shrub land / Forest</td><td>Long-distance migrant</td></tr><tr><td>Sylvia communis</td><td>25.2</td><td>Terrestrial shrub land / Forest</td><td>Long-distance migrant</td></tr><tr><td>Troglodytes troglodytes</td><td>31.5</td><td>Terrestrial shrub land / Forest</td><td>Sedentary or short-distance migrant</td></tr><tr><td>Turdus merula</td><td>39.1</td><td>Terrestrial shrub land / Forest</td><td>Sedentary or short-distance migrant</td></tr><tr><td>Turdus philomelos</td><td>33.1</td><td>Terrestrial shrub land / Forest</td><td>Sedentary or short-distance migrant</td></tr></tbody></table>
<table><thead><tr><td>Species</td><td>Habitat specialization index³</td><td>Habitat³</td><td>Migratory behavior³</td></tr></thead><tbody><tr><td>Acrocephalus schoenobaenus</td><td>34.1</td><td>Humid shrub land (reed)</td><td>Long-distance migrant</td></tr><tr><td>Acrocephalus scirpaceus</td><td>23.8</td><td>Humid shrub land (reed)</td><td>Long-distance migrant</td></tr><tr><td>Cettia cetti</td><td>21</td><td>Humid shrub land (reed)</td><td>Sedentary or short-distance migrant</td></tr><tr><td>Erithacus rubecula</td><td>29.7</td><td>Terrestrial shrub land / Forest</td><td>Sedentary or short-distance migrant</td></tr><tr><td>Hippolais polyglotta</td><td>43.2</td><td>Terrestrial shrub land / Forest</td><td>Long-distance migrant</td></tr><tr><td>Luscinia megarhynchos</td><td>28.4</td><td>Terrestrial shrub land / Forest</td><td>Long-distance migrant</td></tr><tr><td>Parus major</td><td>40.3</td><td>Terrestrial shrub land / Forest</td><td>Sedentary or short-distance migrant</td></tr><tr><td>Phylloscopus collybita</td><td>31.3</td><td>Terrestrial shrub land / Forest</td><td>Sedentary or short-distance migrant</td></tr><tr><td>Phylloscopus trochilus</td><td>28.3</td><td>Terrestrial shrub land / Forest</td><td>Long-distance migrant</td></tr><tr><td>Prunella modularis</td><td>38.7</td><td>Terrestrial shrub land / Forest</td><td>Sedentary or short-distance migrant</td></tr><tr><td>Sylvia atricapilla</td><td>39.6</td><td>Terrestrial shrub land / Forest</td><td>Sedentary or short-distance migrant</td></tr><tr><td>Sylvia borin</td><td>33.4</td><td>Terrestrial shrub land / Forest</td><td>Long-distance migrant</td></tr><tr><td>Sylvia communis</td><td>25.2</td><td>Terrestrial shrub land / Forest</td><td>Long-distance migrant</td></tr><tr><td>Troglodytes troglodytes</td><td>31.5</td><td>Terrestrial shrub land / Forest</td><td>Sedentary or short-distance migrant</td></tr><tr><td>Turdus merula</td><td>39.1</td><td>Terrestrial shrub land / Forest</td><td>Sedentary or short-distance migrant</td></tr><tr><td>Turdus philomelos</td><td>33.1</td><td>Terrestrial shrub land / Forest</td><td>Sedentary or short-distance migrant</td></tr></tbody></table>
*Notes*.¹ The recapture probability heterogeneity index is defined in Appendix S4. Values were averaged across individuals of the same species; and species means were standardized across species (with *A. schoenobaenus* as reference). ²Annual, adult, apparent survival probabilities have been estimated for resident males (i.e. males recaptured at least once at the same site) with Model 1, and averaged across years. ³Habitat species specialization index values come from Godet et al. (2015); the lowest the value, the more the species is specialized. Other traits were obtained from Storchová and Hořák (2018).

**Literature Cited**

Godet, L., P. Gaüzere, F. Jiguet, and V. Devictor. 2015. Dissociating several forms of commonness in birds sheds new light on biotic homogenization. Global Ecology and Biogeography 24:416–426.

Storchová, L., and D. Hořák. 2018. Life-history characteristics of European birds. Global Ecology and Biogeography 27:400–406.
*Notes*.¹ The recapture probability heterogeneity index is defined in Appendix S4. Values were averaged across individuals of the same species; and species means were standardized across species (with *A. schoenobaenus* as reference). ²Annual, adult, apparent survival probabilities have been estimated for resident males (i.e. males recaptured at least once at the same site) with Model 1, and averaged across years. ³Habitat species specialization index values come from Godet et al. (2015); the lowest the value, the more the species is specialized. Other traits were obtained from Storchová and Hořák (2018).

**Literature Cited**

Godet, L., P. Gaüzere, F. Jiguet, and V. Devictor. 2015. Dissociating several forms of commonness in birds sheds new light on biotic homogenization. Global Ecology and Biogeography 24:416–426.

Storchová, L., and D. Hořák. 2018. Life-history characteristics of European birds. Global Ecology and Biogeography 27:400–406.
**Supporting Information for:**

Authors:

Manon Ghislain, Timothée Bonnet, Ugoline Godeau, Olivier Dehorter, Olivier Gimenez, and Pierre-Yves Henry

Title:

**Synchrony in adult survival is remarkably strong among common temperate songbirds across France**

Journal: Ecology
**Supporting Information for:**

Authors:

Manon Ghislain, Timothée Bonnet, Ugoline Godeau, Olivier Dehorter, Olivier Gimenez, and Pierre-Yves Henry

Title:

**Synchrony in adult survival is remarkably strong among common temperate songbirds across France**

Journal: Ecology
APPENDIX S2. SPECIES NUMBER PER SITE

<figure><img src="image_5.png" /><figcaption>Fig S1. Number of species per site included in the dataset. There are 242 sites spread over continental France, and 16 species.</figcaption></figure>
APPENDIX S2. SPECIES NUMBER PER SITE

<figure><img src="image_5.png" /><figcaption>Fig S1. Number of species per site included in the dataset. There are 242 sites spread over continental France, and 16 species.</figcaption></figure>
**Supporting Information for:**

Authors:

Manon Ghislain, Timothée Bonnet, Ugoline Godeau, Olivier Dehorter, Olivier Gimenez, and Pierre-Yves Henry

Title:

**Synchrony in adult survival is remarkably strong among common temperate songbirds across France**

Journal: Ecology
**Supporting Information for:**

Authors:

Manon Ghislain, Timothée Bonnet, Ugoline Godeau, Olivier Dehorter, Olivier Gimenez, and Pierre-Yves Henry

Title:

**Synchrony in adult survival is remarkably strong among common temperate songbirds across France**

Journal: Ecology
APPENDIX S3. GOODNESS-OF-FIT TESTS

<table><caption>TABLE S1. Details of goodness-of-fit tests for the Cormack-Jolly-Seber (CJS) model for each species.</caption><thead><tr><th>Species</th><th>χ<sup>2</sup></th><th>Degrees of freedom</th><th>P-value for global test</th><th>P-value for transient test (3.SR)</th><th>P-value for trap-dependence test (2.CT)</th></tr></thead><tbody><tr><td>Acrocephalus schoenobaenus</td><td>14.974</td><td>19</td><td>0.724</td><td>0.309</td><td>1.000</td></tr><tr><td>Acrocephalus scirpaceus</td><td>65.079</td><td>53</td><td>0.123</td><td>0.010</td><td>0.598</td></tr><tr><td>Cettia cetti</td><td>22.114</td><td>30</td><td>0.850</td><td>0.766</td><td>0.397</td></tr><tr><td>Erithacus rubecula</td><td>Insufficient data</td><td>40</td><td>Insufficient data</td><td>0.012</td><td>Insufficient data</td></tr><tr><td>Hippolais polyglotta</td><td>42.113</td><td>38</td><td>0.297</td><td>0.104</td><td>0.261</td></tr><tr><td>Luscinia megarhynchos</td><td>44.548</td><td>49</td><td>0.654</td><td>0.497</td><td>0.331</td></tr><tr><td>Parus major</td><td>14.322</td><td>36</td><td>1.000</td><td>0.857</td><td>0.952</td></tr><tr><td>Phylloscopus collybita</td><td>16.877</td><td>34</td><td>0.994</td><td>0.452</td><td>1.000</td></tr><tr><td>Phylloscopus trochilus</td><td>Insufficient data</td><td>30</td><td>Insufficient data</td><td>0.951</td><td>Insufficient data</td></tr><tr><td>Prunella modularis</td><td>46.721</td><td>43</td><td>0.322</td><td>0.034</td><td>0.552</td></tr><tr><td>Sylvia atricapilla</td><td>55.768</td><td>50</td><td>0.267</td><td>0.004</td><td>0.821</td></tr><tr><td>Sylvia borin</td><td>36.322</td><td>42</td><td>0.718</td><td>0.626</td><td>0.292</td></tr><tr><td>Sylvia communis</td><td>47.939</td><td>43</td><td>0.279</td><td>0.006</td><td>0.612</td></tr><tr><td>Troglodytes</td><td>12.149</td><td>21</td><td>0.936</td><td>0.747</td><td>0.857</td></tr></tbody></table>
APPENDIX S3. GOODNESS-OF-FIT TESTS

<table><caption>TABLE S1. Details of goodness-of-fit tests for the Cormack-Jolly-Seber (CJS) model for each species.</caption><thead><tr><th>Species</th><th>χ<sup>2</sup></th><th>Degrees of freedom</th><th>P-value for global test</th><th>P-value for transient test (3.SR)</th><th>P-value for trap-dependence test (2.CT)</th></tr></thead><tbody><tr><td>Acrocephalus schoenobaenus</td><td>14.974</td><td>19</td><td>0.724</td><td>0.309</td><td>1.000</td></tr><tr><td>Acrocephalus scirpaceus</td><td>65.079</td><td>53</td><td>0.123</td><td>0.010</td><td>0.598</td></tr><tr><td>Cettia cetti</td><td>22.114</td><td>30</td><td>0.850</td><td>0.766</td><td>0.397</td></tr><tr><td>Erithacus rubecula</td><td>Insufficient data</td><td>40</td><td>Insufficient data</td><td>0.012</td><td>Insufficient data</td></tr><tr><td>Hippolais polyglotta</td><td>42.113</td><td>38</td><td>0.297</td><td>0.104</td><td>0.261</td></tr><tr><td>Luscinia megarhynchos</td><td>44.548</td><td>49</td><td>0.654</td><td>0.497</td><td>0.331</td></tr><tr><td>Parus major</td><td>14.322</td><td>36</td><td>1.000</td><td>0.857</td><td>0.952</td></tr><tr><td>Phylloscopus collybita</td><td>16.877</td><td>34</td><td>0.994</td><td>0.452</td><td>1.000</td></tr><tr><td>Phylloscopus trochilus</td><td>Insufficient data</td><td>30</td><td>Insufficient data</td><td>0.951</td><td>Insufficient data</td></tr><tr><td>Prunella modularis</td><td>46.721</td><td>43</td><td>0.322</td><td>0.034</td><td>0.552</td></tr><tr><td>Sylvia atricapilla</td><td>55.768</td><td>50</td><td>0.267</td><td>0.004</td><td>0.821</td></tr><tr><td>Sylvia borin</td><td>36.322</td><td>42</td><td>0.718</td><td>0.626</td><td>0.292</td></tr><tr><td>Sylvia communis</td><td>47.939</td><td>43</td><td>0.279</td><td>0.006</td><td>0.612</td></tr><tr><td>Troglodytes</td><td>12.149</td><td>21</td><td>0.936</td><td>0.747</td><td>0.857</td></tr></tbody></table>
troglodytes

<table><tr><td>Turdus merula</td><td>64.940</td><td>54</td><td>0.146</td><td>0.100</td><td>0.111</td></tr><tr><td>Turdus philomelos</td><td>12.355</td><td>43</td><td>1.000</td><td>0.934</td><td>1.000</td></tr></table>
troglodytes

<table><tr><td>Turdus merula</td><td>64.940</td><td>54</td><td>0.146</td><td>0.100</td><td>0.111</td></tr><tr><td>Turdus philomelos</td><td>12.355</td><td>43</td><td>1.000</td><td>0.934</td><td>1.000</td></tr></table>
**Supporting Information for:**

Authors:

Manon Ghislain, Timothée Bonnet, Ugoline Godeau, Olivier Dehorter, Olivier Gimenez, and Pierre-Yves Henry

Title:

**Synchrony in adult survival is remarkably strong among common temperate songbirds across France**

Journal: Ecology
**Supporting Information for:**

Authors:

Manon Ghislain, Timothée Bonnet, Ugoline Godeau, Olivier Dehorter, Olivier Gimenez, and Pierre-Yves Henry

Title:

**Synchrony in adult survival is remarkably strong among common temperate songbirds across France**

Journal: Ecology
# APPENDIX S4. MODEL DETAILS

## 1.1 PARAMETER DEFINITIONS, PRIORS AND CONVERGENCE CRITERIA

Model parameter definitions and details on specification of prior distributions are provided in Table S1. In all models, local survival and recapture probabilities were adjusted for (i) sex-dependency (three-level fixed effect: female, male, unknown, Amrhein et al. 2012, sexing was straightforward for sexually dimorphic species, i. e. 5 species out of the 16; or the 9 other species, sexing was based on physiological evidence of reproductive activity, brood patch for females, cloacal protuberance for males, Svensson 1992), (ii) among-species differences (16 species, 16-level fixed effect), and (iii) among-site differences (random effect). Following Julliard 2004, heterogeneity across individuals (both within and across sites) was accounted for by including an individual heterogeneity index as an individual, linear covariate of recapture probabilities. This heterogeneity index was defined as $ln(1 + \frac{c-y}{y})$ where c is the total number of captures for a given individual and y is the number of years with captures for that individual. We used a heterogeneity index rather than an individual random effect because the average number of recapture per individual was very low, and the modeling was implemented at the year level (1 data per individual per year), whereas this individual heterogeneity index allows to account also for within-year recaptures (without setting the model in a Robust Design framework, which would require too many parameters, and would be computationally too demanding). When a CES site was not monitored on a given year, the corresponding recapture probability was fixed to 0, to avoid biasing survival probabilities. We assumed that sex differences were common to all species. While this assumption is certainly inexact, it appears to be a reasonable compromise between model fit and model simplicity given that among temperate songbirds in general males
# APPENDIX S4. MODEL DETAILS

## 1.1 PARAMETER DEFINITIONS, PRIORS AND CONVERGENCE CRITERIA

Model parameter definitions and details on specification of prior distributions are provided in Table S1. In all models, local survival and recapture probabilities were adjusted for (i) sex-dependency (three-level fixed effect: female, male, unknown, Amrhein et al. 2012, sexing was straightforward for sexually dimorphic species, i. e. 5 species out of the 16; or the 9 other species, sexing was based on physiological evidence of reproductive activity, brood patch for females, cloacal protuberance for males, Svensson 1992), (ii) among-species differences (16 species, 16-level fixed effect), and (iii) among-site differences (random effect). Following Julliard 2004, heterogeneity across individuals (both within and across sites) was accounted for by including an individual heterogeneity index as an individual, linear covariate of recapture probabilities. This heterogeneity index was defined as $ln(1 + \frac{c-y}{y})$ where c is the total number of captures for a given individual and y is the number of years with captures for that individual. We used a heterogeneity index rather than an individual random effect because the average number of recapture per individual was very low, and the modeling was implemented at the year level (1 data per individual per year), whereas this individual heterogeneity index allows to account also for within-year recaptures (without setting the model in a Robust Design framework, which would require too many parameters, and would be computationally too demanding). When a CES site was not monitored on a given year, the corresponding recapture probability was fixed to 0, to avoid biasing survival probabilities. We assumed that sex differences were common to all species. While this assumption is certainly inexact, it appears to be a reasonable compromise between model fit and model simplicity given that among temperate songbirds in general males
have a higher breeding philopatry (translating into higher apparent survival) and higher capture
probability (Amrhein et al. 2012).

Convergence was considered to be satisfactory when R-hat < 1.1 for all parameters. All
parameters of primary interest (i.e., variance parameters and fixed effects) had R-hat<1.05,
showed good mixing on graphical inspection and had an effective sample size of at least 100. We
run three chains of 50,000 iterations, including a burn-in of 12,500 iterations and a thinning rate
of 37 (thinning was used to reduce the computer memory used by each model). We report
posterior modes and 95% highest posterior density credible intervals.
have a higher breeding philopatry (translating into higher apparent survival) and higher capture
probability (Amrhein et al. 2012).

Convergence was considered to be satisfactory when R-hat < 1.1 for all parameters. All
parameters of primary interest (i.e., variance parameters and fixed effects) had R-hat<1.05,
showed good mixing on graphical inspection and had an effective sample size of at least 100. We
run three chains of 50,000 iterations, including a burn-in of 12,500 iterations and a thinning rate
of 37 (thinning was used to reduce the computer memory used by each model). We report
posterior modes and 95% highest posterior density credible intervals.
<table><caption>TABLE S1. Prior distributions for parameters of the base model (Model 1).</caption><thead><tr><th></th><th>Prior distribution (on logit-scale)</th></tr></thead><tbody><tr><td colspan="2"><strong>Survival probability parameters</strong></td></tr><tr><td>Species</td><td>N(0,1/1.5)</td></tr><tr><td>Sex (for female)</td><td>N(-0.5, 1/2)</td></tr><tr><td>Random(Site)</td><td>N(0, 1/σ(Site)<sup>2</sup>)</td></tr><tr><td>σ(Site)</td><td>U(0,10)</td></tr><tr><td>Random(Year)</td><td>N(0, 1/σ(Year)<sup>2</sup>)</td></tr><tr><td>σ(Year)</td><td>U(0,10)</td></tr><tr><td>Random(Year:Species)</td><td>N(0, σ(Year:Species)<sup>2</sup>)</td></tr><tr><td>σ(Year:Species)</td><td>U(0,10)</td></tr><tr><td colspan="2"><strong>Recapture probability parameters</strong></td></tr><tr><td>Species</td><td>N(0,1/3)</td></tr><tr><td>Sex (for female)</td><td>N(-0.5,1/2)</td></tr><tr><td>Individual recapture heterogeneity index</td><td>N(0,1/100)T(-10,10)</td></tr><tr><td>Random(Site)</td><td>N(0, 1/σ(Site)<sup>2</sup>)</td></tr><tr><td>σ(Site)</td><td>U(0,10)</td></tr><tr><td>Random(Year)</td><td>N(0, 1/σ(Year)<sup>2</sup>)</td></tr><tr><td>σ(Year)</td><td>U(0,10)</td></tr></tbody></table>

Notes: In JAGS, N(*m*,*p*) represents a normal distribution of mean *m* and precision *p*, where the precision is the inverse of the variance (e.g, σ(Site)<sup>2</sup>). We defined priors for standard deviations,
<table><caption>TABLE S1. Prior distributions for parameters of the base model (Model 1).</caption><thead><tr><th></th><th>Prior distribution (on logit-scale)</th></tr></thead><tbody><tr><td colspan="2"><strong>Survival probability parameters</strong></td></tr><tr><td>Species</td><td>N(0,1/1.5)</td></tr><tr><td>Sex (for female)</td><td>N(-0.5, 1/2)</td></tr><tr><td>Random(Site)</td><td>N(0, 1/σ(Site)<sup>2</sup>)</td></tr><tr><td>σ(Site)</td><td>U(0,10)</td></tr><tr><td>Random(Year)</td><td>N(0, 1/σ(Year)<sup>2</sup>)</td></tr><tr><td>σ(Year)</td><td>U(0,10)</td></tr><tr><td>Random(Year:Species)</td><td>N(0, σ(Year:Species)<sup>2</sup>)</td></tr><tr><td>σ(Year:Species)</td><td>U(0,10)</td></tr><tr><td colspan="2"><strong>Recapture probability parameters</strong></td></tr><tr><td>Species</td><td>N(0,1/3)</td></tr><tr><td>Sex (for female)</td><td>N(-0.5,1/2)</td></tr><tr><td>Individual recapture heterogeneity index</td><td>N(0,1/100)T(-10,10)</td></tr><tr><td>Random(Site)</td><td>N(0, 1/σ(Site)<sup>2</sup>)</td></tr><tr><td>σ(Site)</td><td>U(0,10)</td></tr><tr><td>Random(Year)</td><td>N(0, 1/σ(Year)<sup>2</sup>)</td></tr><tr><td>σ(Year)</td><td>U(0,10)</td></tr></tbody></table>

*Notes*: In JAGS, N(*m*,*p*) represents a normal distribution of mean *m* and precision *p*, where the precision is the inverse of the variance (e.g, σ(Site)<sup>2</sup>). We defined priors for standard deviations,
which are the square-roots of their respective variances (e.g., σ(Site)). In models considering the
difference in migratory behavior (variable “group”), we used the same prior for Year:Group as
for Year:Species. In models considering the effect of weather variables, all parameters were
given a prior N(0,1/100)T(-10,10). N(0,100) represents a normal distribution of mean 0 and
variance 100. T(-10,10) indicates that the distribution was truncated to allow only values between
-10 and 10. In Jags, this truncated distribution remains a proper distribution, in contrast to
WinBUGS. U(x,y) represents a uniform distribution between x and y. We checked that the
posterior distribution of parameters did not reach the truncation boundaries for any parameters.

1.2 PROS AND CONS OF MODEL 1 VS. MODEL 2

Our main goal was to quantify synchrony across species as a single value. A single value for synchrony corresponds directly to the parameterization of model 1 where synchrony is captured by a single *ICC*. In contrast, in model 2, to obtain the overall measure of synchrony we need to average 16 species-specific *ICC*<sub>species</sub>, which is conceptually similar to our question of synchrony across species, but less direct. Further, we rely on sparse, hard to collect, mark-recapture data, and sample size limits the precision of estimates. In model 1, the *ICC* is estimated from all the data, while in model 2 the *ICC*<sub>species</sub> are each estimated from a fraction of the data only and therefore less precisely. Finally, model 2 has more variance parameters than model 1, which makes estimation more difficult algorithmically, although these extra parameters are not of direct interest for the estimation of overall synchrony. We observed that extensions of model 2 exploring factors contributing to synchrony (see below models 3-5) had difficulty converging, while the same extensions of model 1 converged. On the other hand, model 1 assumes that within-species time deviations came from a single distribution, with a variance common to all
which are the square-roots of their respective variances (e.g., σ(Site)). In models considering the
difference in migratory behavior (variable “group”), we used the same prior for Year:Group as
for Year:Species. In models considering the effect of weather variables, all parameters were
given a prior N(0,1/100)T(-10,10). N(0,100) represents a normal distribution of mean 0 and
variance 100. T(-10,10) indicates that the distribution was truncated to allow only values between
-10 and 10. In Jags, this truncated distribution remains a proper distribution, in contrast to
WinBUGS. U(x,y) represents a uniform distribution between x and y. We checked that the
posterior distribution of parameters did not reach the truncation boundaries for any parameters.

1.2 PROS AND CONS OF MODEL 1 VS. MODEL 2

Our main goal was to quantify synchrony across species as a single value. A single value for synchrony corresponds directly to the parameterization of model 1 where synchrony is captured by a single *ICC*. In contrast, in model 2, to obtain the overall measure of synchrony we need to average 16 species-specific *ICC*<sub>species</sub>, which is conceptually similar to our question of synchrony across species, but less direct. Further, we rely on sparse, hard to collect, mark-recapture data, and sample size limits the precision of estimates. In model 1, the *ICC* is estimated from all the data, while in model 2 the *ICC*<sub>species</sub> are each estimated from a fraction of the data only and therefore less precisely. Finally, model 2 has more variance parameters than model 1, which makes estimation more difficult algorithmically, although these extra parameters are not of direct interest for the estimation of overall synchrony. We observed that extensions of model 2 exploring factors contributing to synchrony (see below models 3-5) had difficulty converging, while the same extensions of model 1 converged. On the other hand, model 1 assumes that within-species time deviations came from a single distribution, with a variance common to all
species. This assumption may lead to a poor fit to the data. Model 2 relaxes the assumption and provides a sanity check: if the estimation of synchrony in model 1 is robust to the assumption of common distribution, then most species-specific *ICC*<sub>species</sub> in model 2 should be in the same range of values, and the average of *ICC*<sub>species</sub> should be similar to the *ICC* estimated in model 1. In summary, model 2 is useful to illustrate the variability, or lack of variability in ICC across species, and to check the statistical robustness to the imbalance of species sample sizes. Nevertheless, we favor model 1 as it provides a more direct measure of synchrony and, given sparse data as available here, allows a more powerful estimation of synchrony and test of the factors contributing to synchrony.
species. This assumption may lead to a poor fit to the data. Model 2 relaxes the assumption and provides a sanity check: if the estimation of synchrony in model 1 is robust to the assumption of common distribution, then most species-specific *ICC<sub>species</sub>* in model 2 should be in the same range of values, and the average of *ICC<sub>species</sub>* should be similar to the *ICC* estimated in model 1. In summary, model 2 is useful to illustrate the variability, or lack of variability in ICC across species, and to check the statistical robustness to the imbalance of species sample sizes. Nevertheless, we favor model 1 as it provides a more direct measure of synchrony and, given sparse data as available here, allows a more powerful estimation of synchrony and test of the factors contributing to synchrony.
1.3 APPROXIMATE EQUALITY BETWEEN ICC ON THE LOGIT AND DATA SCALES.

Note that we report ratios of variances (i.e., ICCs) on the logit scale, not on the natural scale of
survival probability. Arguably, ICC on the logit scale is more biologically relevant because it
expresses differences as log odds-ratio and does not depend on average survival probability. For
instance, a change in survival probability from 0.45 to 0.50 (probability change of 0.05, logit
change of 0.2) may be considered inconsequential for population dynamics, but a change from
0.94 to 0.99 (also a probability change of 0.05, but a logit change of 1.84) would be biologically
dramatic, as would be a change in survival probabilities from 0.45 to 0.83 (also a logit change of
1.84. For mean probabilities very close to 0 or 1 (and also for very large variances on the logit
scale) ICCs can differ substantially between the logit scale and the probability scale. The
importance of the deviation depends on the logit-scale intercept and on the total variance on the
logit scale (in particular through their combined effect on the mean probability), as well as on the
ICC on the logit scale (Figure S1). Nevertheless, for most parameter values ratios of variances are
approximately invariant to logit or inverse-logit transformations, particularly for medium-lived
species as songbirds (annual adult survival in the range 0.2-0.8), and for the range of parameter
values (mean survival, variance on the logit-scale and ICC) considered the difference is minimal
(Figure S1). Therefore, the ICCs reported in the article can be thought of as close approximations
of ICCs on the natural scale for probabilities, although we favor an interpretation on the logit
scale.
1.3 APPROXIMATE EQUALITY BETWEEN ICC ON THE LOGIT AND DATA SCALES.

Note that we report ratios of variances (i.e., ICCs) on the logit scale, not on the natural scale of
survival probability. Arguably, ICC on the logit scale is more biologically relevant because it
expresses differences as log odds-ratio and does not depend on average survival probability. For
instance, a change in survival probability from 0.45 to 0.50 (probability change of 0.05, logit
change of 0.2) may be considered inconsequential for population dynamics, but a change from
0.94 to 0.99 (also a probability change of 0.05, but a logit change of 1.84) would be biologically
dramatic, as would be a change in survival probabilities from 0.45 to 0.83 (also a logit change of
1.84. For mean probabilities very close to 0 or 1 (and also for very large variances on the logit
scale) ICCs can differ substantially between the logit scale and the probability scale. The
importance of the deviation depends on the logit-scale intercept and on the total variance on the
logit scale (in particular through their combined effect on the mean probability), as well as on the
ICC on the logit scale (Figure S1). Nevertheless, for most parameter values ratios of variances are
approximately invariant to logit or inverse-logit transformations, particularly for medium-lived
species as songbirds (annual adult survival in the range 0.2-0.8), and for the range of parameter
values (mean survival, variance on the logit-scale and ICC) considered the difference is minimal
(Figure S1). Therefore, the ICCs reported in the article can be thought of as close approximations
of ICCs on the natural scale for probabilities, although we favor an interpretation on the logit
scale.
<figure><img src="image_6.png" /><figcaption>**Figure S1:** Correspondence between logit-scale and data-scale ICC depending on mean survival probability, logit-scale ICC and logit-scale total variance. The horizontal black dashed lines show the values of logit-ICC on the data-scale ICC axis. The red point in the panel logit-ICC=0.73 shows the coordinates of our point estimates for mean survival, total variance and logit-scale ICC. The curves were obtained by simulating logit-scaled data as the sum of an intercept, a grouping factor Gaussian process and a residual variance Gaussian process. Those data were back-transformed to data-scale probability using an inverse logit. The data-scale ICC was computed as the variance in the mean data-scale probability of each grouping factor, divided by the total variance in the data-scale probability. To obtain smooth curves we used 5 million points and 5000 grouping levels for each parameter combination.</figcaption></figure>

**Literature Cited**

Amrhein, V., B. Scaar, M. Baumann, N. Minéry, J. P. Binnert, and F. Korner-Nievergelt. 2012.

Estimating adult sex ratios from bird mist netting data. Methods in Ecology and Evolution
<figure><img src="image_6.png" /><figcaption>**Figure S1:** Correspondence between logit-scale and data-scale ICC depending on mean survival probability, logit-scale ICC and logit-scale total variance. The horizontal black dashed lines show the values of logit-ICC on the data-scale ICC axis. The red point in the panel logit-ICC=0.73 shows the coordinates of our point estimates for mean survival, total variance and logit-scale ICC. The curves were obtained by simulating logit-scaled data as the sum of an intercept, a grouping factor Gaussian process and a residual variance Gaussian process. Those data were back-transformed to data-scale probability using an inverse logit. The data-scale ICC was computed as the variance in the mean data-scale probability of each grouping factor, divided by the total variance in the data-scale probability. To obtain smooth curves we used 5 million points and 5000 grouping levels for each parameter combination.</figcaption></figure>

**Literature Cited**

Amrhein, V., B. Scaar, M. Baumann, N. Minéry, J. P. Binnert, and F. Korner-Nievergelt. 2012.

Estimating adult sex ratios from bird mist netting data. Methods in Ecology and Evolution
3:713–720.

Svensson, L. 1992. Identification guide to European Passerines. 4th rev. Lars Svensson, Stockholm.
3:713–720.

Svensson, L. 1992. Identification guide to European Passerines. 4th rev. Lars Svensson, Stockholm.
**Supporting Information for:**

Authors:

Manon Ghislain, Timothée Bonnet, Ugoline Godeau, Olivier Dehorter, Olivier Gimenez, and Pierre-Yves Henry

Title:

**Synchrony in adult survival is remarkably strong among common temperate songbirds across France**

Journal: Ecology
**Supporting Information for:**

Authors:

Manon Ghislain, Timothée Bonnet, Ugoline Godeau, Olivier Dehorter, Olivier Gimenez, and Pierre-Yves Henry

Title:

**Synchrony in adult survival is remarkably strong among common temperate songbirds across France**

Journal: Ecology
APPENDIX S5. DEPENDENCE OF AVERAGE ANNUAL SURVIVAL
PROBABILITY ON WEATHER VARIABLES

(a)

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

(b)

<figure><img src="image_8.png" /><figcaption>FIG. S1. Yearly variations in (a) mean temperature (in °C), and (b) mean total spring precipitation (in mm), averaged across all studied sites, for the whole study period. Note that temperature and precipitation vary across sites, but this variation is not represented here.</figcaption></figure>
APPENDIX S5. DEPENDENCE OF AVERAGE ANNUAL SURVIVAL
PROBABILITY ON WEATHER VARIABLES

(a)

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

(b)

<figure><img src="image_8.png" /><figcaption>FIG. S1. Yearly variations in (a) mean temperature (in °C), and (b) mean total spring precipitation (in mm), averaged across all studied sites, for the whole study period. Note that temperature and precipitation vary across sites, but this variation is not represented here.</figcaption></figure>
<table><caption>TABLE S1. Effects of weather covariates on average adult survival probability (additive effect across all species). In spring, covariates are common to all species; in winter, they depend on the species wintering range (i.e. migratory behavior class). Weather covariate symbols are shown for easier connection to the main text and other appendices.</caption><thead><tr><th>Season</th><th>Species</th><th>Weather covariate</th><th>Mean [95% credible interval]</th></tr></thead><tbody><tr><td rowspan="10">Spring</td><td rowspan="10">All</td><td>Precipitation (π)</td><td>0.016 [-0.060–0.091]</td></tr><tr><td>Precipitation<sup>2</sup> (π<sup>2</sup>)</td><td>0.036 [-0.006–0.077]</td></tr><tr><td>Temperatures (θ)</td><td>0.007 [-0.092–0.107]</td></tr><tr><td>Temperatures<sup>2</sup> (θ<sup>2</sup>)</td><td>0.032 [-0.017–0.084]</td></tr><tr><td>Precipitation*Temperatures (πθ)</td><td>0.036 [-0.41–0.110]</td></tr><tr><td>Precipitation<sup>2</sup>*Temperatures (π<sup>2</sup>θ)</td><td>-0.005 [-0.014–0.005]</td></tr><tr><td>Precipitation*Temperatures<sup>2</sup> (θπ<sup>2</sup>)</td><td>0.000 [-0.043–0.047]</td></tr><tr><td>Precipitation<sup>2</sup>*Temperatures<sup>2</sup> (θ<sup>2</sup>π<sup>2</sup>)</td><td>0.0175 [-0.013–0.051]</td></tr><tr><td rowspan="4">Winter</td><td rowspan="3">Residents / short-distance migrants</td><td>Winter North Atlantic Oscillation Index (wNAO)</td><td>0.009 [-0.135–0.139]</td></tr><tr><td>Winter North Atlantic Oscillation Index<sup>2</sup> (wNAO<sup>2</sup>)</td><td>-0.016 [-0.141–0.093]</td></tr><tr><td>Long-distance migrants</td><td>Summer Sahel Rainfall index (sSR)</td><td>0.022 [-0.133–0.174]</td></tr><tr><td></td><td>Summer Sahel Rainfall index<sup>2</sup> (sSR<sup>2</sup>)</td><td>0.074 [-0.067–0.219]</td></tr></tbody></table>

Notes: Posterior estimates were obtained from models 3 for spring and 4 for winter (Table 1), and are given on the logit-scale.
<table><caption>TABLE S1. Effects of weather covariates on average adult survival probability (additive effect across all species). In spring, covariates are common to all species; in winter, they depend on the species wintering range (i.e. migratory behavior class). Weather covariate symbols are shown for easier connection to the main text and other appendices.</caption><thead><tr><th>Season</th><th>Species</th><th>Weather covariate</th><th>Mean [95% credible interval]</th></tr></thead><tbody><tr><td rowspan="10">Spring</td><td rowspan="10">All</td><td>Precipitation (π)</td><td>0.016 [-0.060–0.091]</td></tr><tr><td>Precipitation<sup>2</sup> (π<sup>2</sup>)</td><td>0.036 [-0.006–0.077]</td></tr><tr><td>Temperatures (θ)</td><td>0.007 [-0.092–0.107]</td></tr><tr><td>Temperatures<sup>2</sup> (θ<sup>2</sup>)</td><td>0.032 [-0.017–0.084]</td></tr><tr><td>Precipitation*Temperatures (πθ)</td><td>0.036 [-0.41–0.110]</td></tr><tr><td>Precipitation<sup>2</sup>*Temperatures (π<sup>2</sup>θ)</td><td>-0.005 [-0.014–0.005]</td></tr><tr><td>Precipitation*Temperatures<sup>2</sup> (θπ<sup>2</sup>)</td><td>0.000 [-0.043–0.047]</td></tr><tr><td>Precipitation<sup>2</sup>*Temperatures<sup>2</sup> (θ<sup>2</sup>π<sup>2</sup>)</td><td>0.0175 [-0.013–0.051]</td></tr><tr><td rowspan="3">Winter</td><td rowspan="3">Residents / short-distance migrants</td><td>Winter North Atlantic Oscillation Index (wNAO)</td><td>0.009 [-0.135–0.139]</td></tr><tr><td>Winter North Atlantic Oscillation Index<sup>2</sup> (wNAO<sup>2</sup>)</td><td>-0.016 [-0.141–0.093]</td></tr><tr><td>Long-distance migrants</td><td>Summer Sahel Rainfall index (sSR)</td><td>0.022 [-0.133–0.174]</td></tr><tr><td></td><td></td><td>Summer Sahel Rainfall index<sup>2</sup> (sSR<sup>2</sup>)</td><td>0.074 [-0.067–0.219]</td></tr></tbody></table>

Notes: Posterior estimates were obtained from models 3 for spring and 4 for winter (Table 1), and are given on the logit-scale.
<figure><img src="image_9.png" /><figcaption>FIG. S2. Estimated survival probability according to spring weather. Local temperature anomalies ($\theta$) are represented on the x-axis, while three different predictions are given for three values (median, 10% quantile and 90% quantile) of precipitations ($\pi$). Bold lines represent the mean predictions and dashed lines represent the 95% credible interval. These predictions are based on the first-order and quadratic effects of precipitations and temperatures, and the four interactions presented in Table S1, and using the intercept for a median species. Black dots at the bottom represent data distribution over the range of observed temperature anomalies (with one value per site and year).</figcaption></figure>
<figure><img src="image_9.png" /><figcaption>FIG. S2. Estimated survival probability according to spring weather. Local temperature anomalies ($\theta$) are represented on the x-axis, while three different predictions are given for three values (median, 10% quantile and 90% quantile) of precipitations ($\pi$). Bold lines represent the mean predictions and dashed lines represent the 95% credible interval. These predictions are based on the first-order and quadratic effects of precipitations and temperatures, and the four interactions presented in Table S1, and using the intercept for a median species. Black dots at the bottom represent data distribution over the range of observed temperature anomalies (with one value per site and year).</figcaption></figure>
<figure><img src="image_10.png" /></figure>

FIG. S3. Survival probability predicted by winter weather. The summer Sahel Rainfall index (sSR) is used for predictions of survival in long-distance migratory species and the winter North Atlantic Oscillation index (wNAO) is used for predictions of survival in resident or short-distance migratory species. First-order and quadratic effects are used. Grey dots show data distributions over the range of values for each weather index (with one value per year).
<figure><img src="image_10.png" /></figure>

FIG. S3. Survival probability predicted by winter weather. The summer Sahel Rainfall index (sSR) is used for predictions of survival in long-distance migratory species and the winter North Atlantic Oscillation index (wNAO) is used for predictions of survival in resident or short-distance migratory species. First-order and quadratic effects are used. Grey dots show data distributions over the range of values for each weather index (with one value per year).
**Supporting Information for:**

Authors:

Manon Ghislain, Timothée Bonnet, Ugoline Godeau, Olivier Dehorter, Olivier Gimenez, and Pierre-Yves Henry

Title:

**Synchrony in adult survival is remarkably strong among common temperate songbirds across France**

Journal: Ecology
**Supporting Information for:**

Authors:

Manon Ghislain, Timothée Bonnet, Ugoline Godeau, Olivier Dehorter, Olivier Gimenez, and Pierre-Yves Henry

Title:

**Synchrony in adult survival is remarkably strong among common temperate songbirds across France**

Journal: Ecology
APPENDIX S6. SIMULATIONS TO CHECK THE FORMULA FOR
SYNCHRONOUS VARIANCE DUE TO WINTER WEATHER

We run simulations to check the equation computing the synchronous variance ascribed to winter climate. Code to run the simulations and produce the figure below is provided on the Zenodo repository.

<figure><img src="image_11.png" /></figure>

**Figure S1: Synchronous variance computed with the formula presented in the manuscript vs. realized synchronous variance, for a range of parameters.** The simulations assume that all synchronous variance comes from winter climate and assumes the two migratory classes represent 70% and 30% of species, respectively. For each combination of parameters (effects of NAO, NAO-squared, SSR, SSR-squared, and correlation between SSR and NAO) we ran 50 replicates. The black line represents the expectation of equality between the computed and realized synchronous variances.
APPENDIX S6. SIMULATIONS TO CHECK THE FORMULA FOR
SYNCHRONOUS VARIANCE DUE TO WINTER WEATHER

We run simulations to check the equation computing the synchronous variance ascribed to winter climate. Code to run the simulations and produce the figure below is provided on the Zenodo repository.

<figure><img src="image_11.png" /></figure>

**Figure S1: Synchronous variance computed with the formula presented in the manuscript vs. realized synchronous variance, for a range of parameters.** The simulations assume that all synchronous variance comes from winter climate and assumes the two migratory classes represent 70% and 30% of species, respectively. For each combination of parameters (effects of NAO, NAO-squared, SSR, SSR-squared, and correlation between SSR and NAO) we ran 50 replicates. The black line represents the expectation of equality between the computed and realized synchronous variances.
**Supporting Information for:**

Authors:

Manon Ghislain, Timothée Bonnet, Ugoline Godeau, Olivier Dehorter, Olivier Gimenez, and Pierre-Yves Henry

Title:

**Synchrony in adult survival is remarkably strong among common temperate songbirds across France**

Journal: Ecology
**Supporting Information for:**

Authors:

Manon Ghislain, Timothée Bonnet, Ugoline Godeau, Olivier Dehorter, Olivier Gimenez, and Pierre-Yves Henry

Title:

**Synchrony in adult survival is remarkably strong among common temperate songbirds across France**

Journal: Ecology
# APPENDIX S7. ALL PARAMETER ESTIMATES

## 1.1 MODEL 1: SIMPLE INTERSPECIFIC SYNCHRONY

<table><thead><tr><th></th><th>mean</th><th>2.50%</th><th>97.50%</th></tr></thead><tbody><tr><td>b1</td><td>-1.186</td><td>-1.335</td><td>-1.041</td></tr><tr><td>phi.sex[2]</td><td>-0.179</td><td>-0.279</td><td>-0.077</td></tr><tr><td>survival intercept S. atricapilla</td><td>-0.29</td><td>-0.521</td><td>-0.056</td></tr><tr><td>survival intercept A. schoenobaenus</td><td>-0.569</td><td>-1.006</td><td>-0.128</td></tr><tr><td>survival intercept A. scripaceus</td><td>-0.138</td><td>-0.364</td><td>0.098</td></tr><tr><td>survival intercept C. cetti</td><td>-0.361</td><td>-0.674</td><td>-0.035</td></tr><tr><td>survival intercept E. rubecula</td><td>-0.582</td><td>-0.865</td><td>-0.305</td></tr><tr><td>survival intercept H. polyglotta</td><td>-0.386</td><td>-0.715</td><td>-0.035</td></tr><tr><td>survival intercept L. megarhynchos</td><td>0.003</td><td>-0.244</td><td>0.247</td></tr><tr><td>survival intercept P. major</td><td>-0.561</td><td>-0.872</td><td>-0.251</td></tr><tr><td>survival intercept P. collybita</td><td>-0.876</td><td>-1.143</td><td>-0.601</td></tr><tr><td>survival intercept P. trochilus</td><td>-0.562</td><td>-0.887</td><td>-0.23</td></tr><tr><td>survival intercept P. modularis</td><td>-0.155</td><td>-0.4</td><td>0.091</td></tr><tr><td>survival intercept S. borin</td><td>-0.182</td><td>-0.457</td><td>0.092</td></tr><tr><td>survival intercept S. communis</td><td>-0.543</td><td>-0.828</td><td>-0.24</td></tr><tr><td>survival intercept T. troglodytes</td><td>-0.841</td><td>-1.208</td><td>-0.483</td></tr><tr><td>survival intercept T. merula</td><td>-0.02</td><td>-0.23</td><td>0.203</td></tr><tr><td>survival intercept T. philomelos</td><td>-0.146</td><td>-0.483</td><td>0.206</td></tr><tr><td>sigma.phi.site</td><td>0.39</td><td>0.295</td><td>0.496</td></tr><tr><td>sigma.phi.temps</td><td>0.303</td><td>0.177</td><td>0.484</td></tr></tbody></table>
# APPENDIX S7. ALL PARAMETER ESTIMATES

## 1.1 MODEL 1: SIMPLE INTERSPECIFIC SYNCHRONY

<table><thead><tr><th></th><th>mean</th><th>2.50%</th><th>97.50%</th></tr></thead><tbody><tr><td>b1</td><td>-1.186</td><td>-1.335</td><td>-1.041</td></tr><tr><td>phi.sex[2]</td><td>-0.179</td><td>-0.279</td><td>-0.077</td></tr><tr><td>survival intercept S. atricapilla</td><td>-0.29</td><td>-0.521</td><td>-0.056</td></tr><tr><td>survival intercept A. schoenobaenus</td><td>-0.569</td><td>-1.006</td><td>-0.128</td></tr><tr><td>survival intercept A. scripaceus</td><td>-0.138</td><td>-0.364</td><td>0.098</td></tr><tr><td>survival intercept C. cetti</td><td>-0.361</td><td>-0.674</td><td>-0.035</td></tr><tr><td>survival intercept E. rubecula</td><td>-0.582</td><td>-0.865</td><td>-0.305</td></tr><tr><td>survival intercept H. polyglotta</td><td>-0.386</td><td>-0.715</td><td>-0.035</td></tr><tr><td>survival intercept L. megarhynchos</td><td>0.003</td><td>-0.244</td><td>0.247</td></tr><tr><td>survival intercept P. major</td><td>-0.561</td><td>-0.872</td><td>-0.251</td></tr><tr><td>survival intercept P. collybita</td><td>-0.876</td><td>-1.143</td><td>-0.601</td></tr><tr><td>survival intercept P. trochilus</td><td>-0.562</td><td>-0.887</td><td>-0.23</td></tr><tr><td>survival intercept P. modularis</td><td>-0.155</td><td>-0.4</td><td>0.091</td></tr><tr><td>survival intercept S. borin</td><td>-0.182</td><td>-0.457</td><td>0.092</td></tr><tr><td>survival intercept S. communis</td><td>-0.543</td><td>-0.828</td><td>-0.24</td></tr><tr><td>survival intercept T. troglodytes</td><td>-0.841</td><td>-1.208</td><td>-0.483</td></tr><tr><td>survival intercept T. merula</td><td>-0.02</td><td>-0.23</td><td>0.203</td></tr><tr><td>survival intercept T. philomelos</td><td>-0.146</td><td>-0.483</td><td>0.206</td></tr><tr><td>sigma.phi.site</td><td>0.39</td><td>0.295</td><td>0.496</td></tr><tr><td>sigma.phi.temps</td><td>0.303</td><td>0.177</td><td>0.484</td></tr></tbody></table>