# The number of strong social bonds is linked to survival in a cooperative bird

Gabriel Munar-Delgado, Matthieu Paquet, Babette Fourie, Franck Theron, André Ferreira, Claire Doutrelant, Rita Covas

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## To cite this version:

Gabriel Munar-Delgado, Matthieu Paquet, Babette Fourie, Franck Theron, André Ferreira, et al.. The number of strong social bonds is linked to survival in a cooperative bird. 2025. hal-05289391

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Distributed under a Creative Commons Attribution 4.0 International License
The number of strong social bonds is linked to survival in a cooperative bird

1 Gabriel Munar-Delgado¹,²*, Matthieu Paquet³, Babette Fourie¹,², Franck Theron¹,², André

2 Ferreira⁴, Claire Doutrelant⁵,⁶, Rita Covas¹,²,⁶

5 ¹CIBIO, Centro de Investigação em Biodiversidade e Recursos Genéticos, Universidade do Porto,

6 Vairão, Portugal

7 ²BIOPOLIS, Program in genomics, Biodiversity and Land Planning, Vairão, Portugal

8 ³SETE, Station d'Écologie Théorique et Expérimentale, CNRS, Moulis, France

9 ⁴Department of Evolutionary Biology and Environmental Studies, University of Zurich, Zurich,
10 Switzerland

11 ⁵CEFE, CNRS, University of Montpellier, EPHE, IRD, Montpellier, France

12 ⁶FitzPatrick Institute of African Ornithology, DST-NRF Centre of Excellence, University of Cape

13 Town, Rondebosch, South Africa

14 Correspondence: gabriel.munar@gmail.com

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34 SUMMARY

35 Recent evidence from large mammals, including humans, shows that the quality of social
36 associations beyond general group size can positively influence health and survival.
37 However, whether individuals in other taxa consistently differ in sociality in ways that
38 affect fitness and thus provide a basis for selection remains largely unexplored. In this
39 study, we examined how individual sociality relates to survival probabilities in a
40 population of sociable weavers (Philetairus socius), a highly cooperative bird. We
41 combined social network analysis with multi-state capture-recapture models to quantify
42 the number of strong social bonds each individual had and to estimate survival
43 probabilities. We found that sociality was positively associated with survival: individuals
44 with medium and high sociality showed higher survival probabilities than those with low
45 sociality. Additionally, individuals were more likely to remain in the same sociality
46 category across years than to shift to another, and when transitions occurred, they
47 followed a gradual pattern, with adjacent shifts more common than abrupt changes,
48 supporting the idea of stable individual differences in sociality. These findings support a
49 link between sociality and individual fitness, and suggest that stable social traits may
50 provide a basis for evolutionary change.

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Living in groups is common across many taxa, from invertebrates to fish, birds and mammals, and has been shown to provide fitness benefits such as protection from predators and improved access to food or information (Krause et al. 2002). Much of the early work on animal sociality focused on the trade-offs associated with group size (Bilde et al. 2007; Koenig 1981). More recently, long-term social-network studies have revealed that individuals’ interactions within groups (i.e. individual sociality) also play a key role in fitness (Formica et al. 2012; Silk et al. 2010).

The link between individual sociality and survival—a key fitness component—has become central to understanding the evolution of social behaviour. A growing body of research, initially focused on humans and later extended to other mammals, shows that sociality can markedly increase survival (reviewed in Snyder-Mackler et al. 2020), underscoring that not only group living *per se*, but also specific interaction patterns—such as strong, stable social bonds—shape selection on social traits (e.g. Archie et al. 2014; Silk et al. 2010). However, for social behaviours to evolve, their expression must be at least partly stable over time (Boake 1989). While some studies have reported consistent individual differences in social network traits such as degree or strength (e.g., Aplin et al. 2015; Blumstein et al. 2013), others found lower consistency or variation across metrics (e.g., Plaza et al. 2020; Evans et al. 2021). Yet, few studies have jointly assessed the temporal stability of social traits and their relationship with fitness within the same system, constraining our understanding of their evolutionary dynamics (but see Vander Wal et al. 2015).

In this study, we assessed whether individual sociality is consistent over time and whether it is associated with survival in the sociable weaver (*Philetairus socius*), a highly cooperative, colonial passerine. Sociable weavers live in relatively stable colonies, built communally usually on acacia (*Vachellia spp*.), which harbour multiple independent chambers used for breeding and for roosting year-round (Maclean 1973). These weavers have different levels of social organisation, with the whole colony cooperating to build a large communal nest and typically foraging in large flocks that include most colony members (Lloyd *et al.* 2018). Within colonies, birds typically roost in groups inside individual nest chambers, which are used throughout the year and also for breeding (*Paquet et al.* 2016). These groups often remain stable across contexts and frequently stay together for breeding as well (*Paquet et al.* 2016; Pacheco 2022) and their social associations are further reflected in strong bonds that persist outside the breeding season while foraging (Ferreira *et al.* 2024). Sociable weavers are cooperative breeders with non-breeding group members assisting in chick provisioning. In our study population, between 30% and 80% of breeding attempts are assisted by up to nine helpers (Covas *et al.* 2008, 2022). Helpers have been associated with improved survival of the breeders and the offspring they helped (Covas *et al.* 2008; D'Amelio *et al.* 2022; Paquet *et al.* 2015) an effect also found in other cooperatively breeding species (Downing *et al.* 2021; Paquet *et al.* 2015). Altogether, these features suggest that the social structure within colonies, built upon stable group composition and strong social bonds, may be an important determinant of individual fitness in this species.
To explore this idea, we quantified individual sociality as the number of top-ranked social connections in the network (hereafter: strong social bonds), and assessed its consistency over time and association with survival. To achieve this, we combined social network analysis with multi-state capture-recapture models. We collected sociality data for 497 free-ranging individuals in a foraging context using RFID-based feeder stations over five years, and combined this with annual mist-net captures. We predicted that individual sociality—measured as the number of strong bonds—would be relatively consistent over time (Ferreira *et al.* 2024), and that individuals with higher sociality would show increased probability of survival, reflecting the fitness value of cohesive subgroups within colonies.

## 123 METHODS

### 124 Site and study species

We studied a population of sociable weavers at Benfontein Nature Reserve in South Africa (28°52'S, 24°50'E). This population has been the focus of a long-term research program initiated in 1993. Since 2008, the population has been regularly monitored through annual captures (see below), and since 2010, breeding has been consistently monitored, with all nestlings that reached nine days of age being ringed and having blood samples taken for sexing and genotyping.

Since 2017, all birds (adults and fledglings) at five colonies have also been marked with a passive integrated transponder (PIT-tag) enclosed in a plastic leg ring (3.3mm; Eccel Technology) to allow detection at RFID feeders (see below). In 2021, a fire destroyed two of those colonies, but in 2022, an additional colony was PIT-tagged, bringing the total number of marked colonies to four.

### 136 Mist-nest captures

Resident and recently migrated birds from PIT-tagged colonies are captured annually in September (i.e., at the end of the Southern Hemisphere winter). This was done by placing mist nets around the colony's nest structure before dawn (when the birds were roosting inside the nests) and later flushing them into the nets. Individuals not born in the study population were fitted with a metal ring, PIT-tagged, and had a blood sample taken for sexing (see Supplementary Material for details on age and sex determination). All individuals were released at the site of capture. For this study we used data from 2018 to 2023 (except for 2020 due to COVID-19 restrictions).

### 145 RFID data collection and social network metrics

From 2018 to 2023 (except for 2020), we collected association data in a foraging context using RFID-based feeding stations located 80–205 m away from each colony (described in Ferreira *et al.* 2020). In short, feeding stations consisted of four feeding boxes, each equipped with four small standard plastic bird feeders and four perches fitted with an RFID antenna (Priority1rfid, Melbourne, Australia) connected to a data logger which recorded the birds' visits. Each perch and feeder was designed to only allow one bird to feed at a time. Data were collected from April to June, with data collection conducted usually every three days. This period was chosen to coincide with the end of breeding and the first winter months (which is the dry season, when resources are expected to drop and
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temperatures fall to below zero, making communal roosting more important; (Paquet *et al.* 2016).

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We inferred social networks based on the time overlap between individuals at the feeders. Following Ferreira *et al.* (2020), the strength of the association between two individuals (i.e., the value of each edge) was calculated as the time two individuals spent feeding simultaneously in the same feeding box, divided by the sum of the times each individual was present at the RFID feeders. This method has been shown to capture the fine-scale social structure within a colony of sociable weavers (Ferreira *et al.* 2020) better than other more commonly used approaches in birds such as co-occurrences. This is because sociable weavers are colonial and tend to forage in cohesive flocks, usually co-occurring at the RFID feeders with most members of their colony.

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To determine social strong bonds (e.g., Gero *et al.* 2005; Kern & Radford 2021), we grouped individuals from the same colony and classified the edges between them as either strong or non-strong bonds. For each colony and year, we ranked the edges in descending order based on their association weight. Edges with weight values greater than or equal to the 80th percentile were classified as strong bonds, while the remaining edges were classified as non-strong bonds (Fourie *et al.* unpublished results). Based on this categorization, we then calculated the number of strong bonds each individual had.

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Finally, we classified individuals into three social status categories based on their number of strong bonds: low, medium, and high. For each year and colony, we determined the 33rd and 66th percentiles of the number of strong bonds each individual had. Individuals with strong bond counts above the 66th percentile were classified as having a high number of social bonds, those between the 33rd and 66th percentiles as having a medium number, and those below the 33rd percentile as having a low number of social bonds. This classification served as the social network metric for our analyses (See Supplementary material for yearly and colony-based classification).

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We discretized the social network metric variable to enable the use of a multi-state capture-recapture model (Lebreton & Cefe 2002; Schaub *et al.* 2004; see below). This model allowed us to estimate the transition probabilities from one social status to another from one year to the next, along with specific-state survival probabilities. Additionally, this model also enabled the inclusion of individuals with missing social status data, as long as data had been recorded in at least one year.

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## Multi-state capture-recapture models

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We estimated social status-specific survival, transitions, and detection probabilities of RFID-tagged sociable weavers using a Bayesian multi-state capture-recapture model. Each year (t), we conducted two capture/detection events: one using RFID-feeders (see above) and another with mist nets (see above). RFID-feeders allowed us to estimate individuals' social metrics (Figure 1), while mist-net captures ensured a higher and more consistent detection of individuals.

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This setup enabled us to estimate survival probabilities based on social status during the intervals between these two events. On the one hand, we estimated the survival
probability between the RFID-feeder data collection and the mist net captures within the same year, specifically from July to August (Figure 1). This period largely corresponds to the winter season; therefore, for simplicity, during the analysis, specifications, and results we refer to this estimate as the winter survival probability. On the other hand, we estimated the survival probability between the mist net captures and the RFID-feeder data collection of the following year (t+1), specifically from October to April (Figure 1). This period largely corresponds to the spring and summer seasons. For simplicity, and in contrast to the winter survival probability, during the analysis specifications and results we refer to this estimate as the summer survival probability.

<figure><img src="image_3.png" /><figcaption>**Figure 1.** Schematic overview of yearly data collection and the multi-state capture-recapture model. Each yearly time period (t) begins in April with RFID data collection conducted from April to June and ends in March before the next year's RFID-data collection period. During the RFID data collection period, individuals' social status is estimated based on the number of strong social bonds that they have: low (L), medium (M) or high (H). Mist-net captures were conducted in September, at the end of the Southern Hemisphere winter. Between these two periods, individuals can either survive or not, and between consecutive RFID data collection periods, individuals can either remain in the same social status or transition to a different one, provided they survive. The capture-recapture model estimates two survival probabilities: (1) winter survival probability, from the RFID data collection period to the mist-net captures within the same year, for low, medium, and highly social individuals, and (2) summer survival probability of low, medium, and highly social individuals, conditional on their transition probabilities between social statuses, from the mist-net captures to the RFID data collection period. $\phi_{X_w}$ is the winter survival probability of an individual in state X; $\phi_{X_s}$ is the summer survival probability of an individual in state X and $\psi_{XY}$ is the transition probability from state X to state Y from t to t+1.</figcaption></figure>
With the same model, we also estimated the detection probability conditional on sociable weavers' social status (estimated from RFID-feeder data) at both the RFID feeder and mist-net captures, and the social-status transition probability from one year to the next. Multi-state capture-recapture models explicitly account for state-dependent differences in detection probabilities (Lebreton et al. 2009). This adjustment prevents the underestimation of survival probabilities in groups of individuals that are less likely to be detected due to their state (e.g., low-sociality birds may visit the RFID feeders infrequently; if their lower detection probability isn't modelled, an undetected bird could be wrongly assumed dead). In our study, this approach enabled us to assess the detection probabilities of birds with different sociality levels at both mist-nets and RFID feeders, allowing the model to adjust survival and transition-probability estimates accordingly.

These different survival, transition, and detection probabilities can be represented in the corresponding survival, transition, and detection probability matrices. In the survival and detection matrices, we can represent the different detection probabilities linking the state of an individual (in rows) to the possible observations (in columns) at each data collection occasion within an observation matrix ($\Theta$). In the transition matrices, each row corresponds to the departure state, and each column corresponds to the arrival state of an individual. To improve clarity, we explicitly label the rows and columns of each matrix using the four possible states: L (low sociality), M (medium sociality), H (high sociality), and D (dead). Thus, in all matrices, the rows correspond to the individual's state at time $t$, and the columns represent either the observed state or the state at time $t+1$. In this way, for detection probabilities at the mist-net captures and winter survival probabilities, the matrices were:

$$ \Theta_n = \begin{pmatrix} L_{t+1} & M_{t+1} & H_{t+1} & D_{t+1} \\ p_{L_n} & 0 & 0 & 1-p_{L_n} \\ 0 & p_{M_n} & 0 & 1-p_{M_n} \\ 0 & 0 & p_{H_n} & 1-p_{H_n} \\ 0 & 0 & 0 & 1 \end{pmatrix} \begin{pmatrix} L_t \\ M_t \\ H_t \\ D_t \end{pmatrix} $$

$$ \Omega_w = \begin{pmatrix} L_{t+1} & M_{t+1} & H_{t+1} & D_{t+1} \\ \varphi_{L_w} & 0 & 0 & 1-\varphi_{L_w} \\ 0 & \varphi_{M_w} & 0 & 1-\varphi_{M_w} \\ 0 & 0 & \varphi_{H_w} & 1-\varphi_{H_w} \\ 0 & 0 & 0 & 1 \end{pmatrix} \begin{pmatrix} L_t \\ M_t \\ H_t \\ D_t \end{pmatrix} $$

where $\theta_n$ is the observation matrix for mist-nets matrix and $p_{X_n}$ is the detection probability at nets of an individual in state X. $\Omega_w$ is the winter survival matrix and $\varphi_{X_w}$ is the winter survival probability off an individual in state X.

On the other hand, the detection probability matrix at feeders and transition-summer survival matrix were:

$$ \Theta_f = \begin{pmatrix} L_{t+1} & M_{t+1} & H_{t+1} & D_{t+1} \\ p_{L_f} & 0 & 0 & 1-p_{L_f} \\ 0 & p_{M_f} & 0 & 1-p_{M_f} \\ 0 & 0 & p_{H_f} & 1-p_{H_f} \\ 0 & 0 & 0 & 1 \end{pmatrix} \begin{pmatrix} L_t \\ M_t \\ H_t \\ D_t \end{pmatrix} $$

$$ \Omega_s = \begin{pmatrix} L_{t+1} & M_{t+1} & H_{t+1} & D_{t+1} \\ \varphi_{L_s} \psi_{LL} & \varphi_{L_s} \psi_{LM} & \varphi_{L_s} \psi_{LH} & 1-\varphi_{L_s} \\ \varphi_{M_s} \psi_{ML} & \varphi_{M_s} \psi_{MM} & \varphi_{M_s} \psi_{MH} & 1-\varphi_{M_s} \\ \varphi_{H_s} \psi_{HL} & \varphi_{H_s} \psi_{HM} & \varphi_{H_s} \psi_{HH} & 1-\varphi_{H_s} \\ 0 & 0 & 0 & 1 \end{pmatrix} \begin{pmatrix} L_t \\ M_t \\ H_t \\ D_t \end{pmatrix} $$

where $\theta_f$ is the observation matrix for RFID-feeders and $p_{X_f}$ is the recapture probability at RFID-feeders of an individual in state X. $\Omega_s$ is the summer survival-transition matrix,
260 $\phi_{X_s}$ is the summer survival probability of an individual in state X and $\psi_{XY}$ is the transition probability from state X to state Y from $t$ to $t+1$.

262 Detection probabilities for year 2020 ($t = 3$) were set to zero both for the mist-nets and 263 RFID-feeders due to the lack of data collection for that year.

264 **Model fitting**

265 We used NIMBLE (version 1.1.0 of the *nimble* package; Valpine *et al.* 2017, 2022) to 266 implement our multistate capture-recapture models in a Bayesian framework in Program 267 R, version 4.0.2 (R Core Team, 2020). For all models, a logit link was used. We estimated 268 parameters using vague priors (Table S2). Posterior samples from three independent 269 Markov Chain Monte Carlo (MCMC) chains were based on 70000 iterations with a burn- 270 in period of 35000 iterations and a thinning interval of 20, leading to 5250 posterior 271 MCMC samples. We assessed model convergence both visually and by using the $\hat{R}$ 272 Gelman–Rubin statistic (Gelman & Rubin 1992). Finally, we also performed posterior 273 predictive checks (Figures S1 and S2). Mean differences between the state specific 274 survival, transition, and detection probabilities (and their 95% credible intervals) were 275 calculated from the posterior estimates. We considered differences statistically significant 276 when their 95% credible intervals did not overlap zero.

277 **Covariates and random effects**

278 Our multistate model incorporated several individual covariates (see Supplementary 279 Material for a detailed description) to account for their effects on social survival 280 probabilities in both winter and summer, as well as on transition probabilities and 281 detection probabilities at RFID-feeders. In contrast, we assumed that detection probability 282 at mist nets was independent of covariates because of the mist-net capture method (see 283 above).

284 For survival probabilities in winter and transition/survival probabilities in summer, we 285 included individual age and sex as covariates, with year and the individual's colony as 286 random effects. Age was included because we hypothesized that it could influence 287 survival (Martin 1995). Sex was included because we hypothesized that survival may 288 differ between males and females (Payevsky 2021). The individuals' colony and year 289 were included to account for potential inter-colony and annual variation in survival. We 290 allowed age and sex to have different effects on winter and summer survival, as these 291 effects could vary between the two periods. Year and colony random effects were also 292 allowed to differ between winter and summer. Age was centred by subtracting the mean 293 of all observed ages across individuals and years. Sex was centred by subtracting the 294 overall proportion of males across all individuals included in the model. This approach 295 improves the efficiency of MCMC sampling and allowed us to interpret the model 296 intercepts as the social status-specific survival probabilities of an individual with average 297 age and sex ratio in the population (Table S3).

298 Finally, for the detection probability at RFID-feeders, we included the presence or 299 absence of RFID-feeders at the individual's colony as a covariate since PIT-tagged 300 individuals can migrate to colonies without RFID-feeders but are expected to be less 301 likely detected if no feeders are present at their colony.
302 See Tables S1-S3 for the detailed models' description.

303 **RESULTS**

304 **Data and model overview**

305 We captured 661 individuals with mist nets over the study period. Based on RFID-306 feeders' detections, 430 individuals were included in the multi-state capture-recapture 307 model, as they had RFID-feeder-based social status data for at least one year (excluding 308 those with data only from the last year, since recapture data was not available for these 309 individuals; see Supplementary material for exclusion criteria). Our RFID-feeder social 310 status observation matrix consisted of 702 observations, with 36% corresponding to low, 311 312 313

We obtained satisfactory model performance (Figures S1 and S2)

313 **Detection probabilities**

314 We observed similar detection probabilities based on mist-net captures among individuals 315 with low, medium, and high sociality (0.88 [95 % CI: 0.83–0.93], 0.84 [0.73–0.94], and 316 0.88 [0.81–0.93], respectively; Figure 2; Tables S5 and S6).

317 Similarly, at the RFID feeders we found no evidence of significant differences in 318 detection probabilities among low-, medium-, and high-sociality individuals (0.51 [0.13– 319 0.93], 0.29 [0.06–0.73], and 0.32 [0.08–0.77], respectively; Figure 2; Tables S5 and S6), 320 321 322

<figure><img src="image_4.png" /><figcaption>**Figure 2. Detection probabilities.** Model estimates of detection probabilities for low, medium and high sociality individuals at RFID-feeders (left) and mist-nets (right). Dots represent the posterior mean estimates and bars their 95% credible intervals</figcaption></figure>

325 **Transition probabilities**

326 We found that low-sociality individuals were significantly more likely to remain in the 327 low category (0.47 [0.34, 0.59]) than to shift directly to the high category (0.19 [0.10, 328 0.29]; Tables S5 and S6). The probability of transitioning from low to medium (0.35 329 [0.23, 0.48]) fell between those two values, and the differences between remaining low 330 versus transitioning to medium and transitioning to medium versus transitioning to high 331 332
For medium-sociality individuals, we found that they were statistically significantly more likely to remain at the medium level (0.48 [0.36, 0.58]) than to transition to either the low level (0.27 [0.19, 0.36]) or the high level (0.25 [0.17, 0.35]; Tabled S5 and S6). However, there was no statistically significant difference between the likelihoods of transitioning to the low versus the high level (Figure 3).

Finally, for high-sociality individuals, we found that they were statistically significantly more likely to remain at the high level (0.60 [0.49, 0.70]) than to transition to either the low level (0.08 [0.04, 0.15]) or the medium level (0.32 [0.22, 0.43]; Tables S5 and S6). Notably, they were also statistically significantly less likely to transition to the low than to the medium level, again highlighting that abrupt changes in sociality were less common (Figure 3).

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<figure><img src="image_5.png" /><figcaption>**Figure 3. Transition probabilities.** Model estimates of transition probabilities for low (L), medium (M), and high (H) social individuals. Dots represent the posterior mean estimates and bars their 95% credible intervals.</figcaption></figure>

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## Survival probabilities

We found that, during the winter period, medium- and high-sociality individuals had statistically significantly higher survival probabilities (0.85 [0.71, 0.94] and 0.90 [0.78, 0.96], respectively) than low-sociality individuals (0.75 [0.56, 0.87]; Figure 4, Tables S5 and S6)—an average increase of about 13%.

The same pattern emerged in summer: medium- and high-sociality birds again showed statistically significantly higher survival probabilities (0.72 [0.48, 0.88] and 0.67 [0.42, 0.84], respectively) than low-sociality birds (0.53 [0.28, 0.74]; Figure 4, Tables S5 and S6), representing an average increase of roughly 17%. During both seasons, survival probabilities did not differ significantly between medium- and high-sociality individuals (Tables S5 and S6).

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<figure><img src="image_6.png" /></figure>

<figure><img src="image_7.png" /><figcaption>**Figure 4. Survival probabilities.** A) Model estimates of survival probabilities for low, medium and high sociality individuals for winter and summer intervals. Dots represent the posterior mean estimates and bars their 95% credible intervals. B) and C) Posterior mean differences in survival probabilities between low sociality individuals and medium and high sociality individuals for winter and summer, respectively. Squares represent the mean differences, and bars indicate the 95% credible intervals of the differences.</figcaption></figure>

DISCUSSION

Our results show that sociable weavers tend to maintain consistent individual sociality levels, which are based on their number of strong social bonds. Furthermore, individuals with medium and high sociality levels exhibited a higher survival probability than those with a lower level of sociality. These findings suggest that, in our population, more social individuals should be favoured by natural selection.

By incorporating state-dependent individual detectability, our model explicitly accounted for detection probability differences across sociality levels, preventing biases from incomplete detections, and ensuring that variations in survival probabilities reflected biological differences rather than methodological artefacts.
379 **Consistency of individual sociality across years**

380 We found that, for sociable weavers, remaining in the same sociality category from one
381 year to the next was the most likely outcome, although probabilities were close to 50%
382 for low- and medium-sociality individuals. When transitions occurred, they tended to be
383 between adjacent categories. The probabilities of moving from medium or high sociality
384 to other categories were significantly lower than the probability of remaining in the same
385 one. Low-sociality individuals showed the same general tendency, although the
386 difference between remaining low and transitioning to medium was not statistically
387 significant (Figure 3, Table S6).

388 The overall consistency in sociality levels observed in sociable weavers aligns with
389 previous studies reporting individual-level stability in other social network metrics—such
390 as degree, betweenness, strength, and closeness—in birds (Aplin et al. 2015; Dunning et
391 al. 2023; Plaza et al. 2020; Shizuka et al. 2014; Thys et al. 2017), mammals (Blumstein
392 et al. 2013; Evans et al. 2021; Tkaczynski et al. 2020; Vander Wal et al. 2015), and fish
393 (Jacoby et al. 2014; Krause et al. 2017). This consistency also underscores the potential
394 for sociality to evolve in sociable weavers. For a trait to evolve via natural selection, its
395 expression must be consistent over time, at least to some degree (Boake 1989). This is
396 particularly noteworthy in the case of sociality, which is influenced by a range of intrinsic
397 and extrinsic factors that can promote or constrain changes in its expression (Schradin
398 2013), and which emerges through interactions among individuals, meaning that a given
399 individual may not always be able to express their typical social behaviour, as it also
400 depends on the behaviour of others (Niemelä & Santostefano 2015). If the expression of
401 a social metric fluctuates randomly due to environmental or social factors, directional
402 selection will be hindered, preventing evolutionary change in the trait at the population
403 level, even if it has strong fitness consequences.

404 While overall patterns support consistency in individual sociality, our results also suggest
405 that low-sociality individuals may be more prone to shifting their sociality level over time.
406 Specifically, the difference between the probabilities of remaining low or transitioning to
407 medium was not statistically significant (Figure 3, Tables S5 and S6), unlike the stronger
408 stability observed in medium- and high-sociality individuals. This relative flexibility
409 could reflect successful reproduction and the formation of new bonds with offspring, as
410 discussed above. Alternatively, low-sociality individuals might actively attempt to
411 increase their number of strong bonds—potentially through social niche construction
412 (Trappes et al. 2022)—in order to gain access to the fitness benefits associated with
413 stronger social integration, such as the higher survival probabilities observed in this study.

414 **Individuals with higher sociality show increased survival**

415 We found that medium and high sociality (reflecting their relatively higher number of
416 strong social bonds) was associated with significantly higher survival probability than
417 low sociality. This pattern was observed in both winter (July–August) and summer
418 (October–March), indicating its consistency across seasons. This consistency could
419 reflect persistent factors acting throughout the year, distinct mechanisms operating in
420 each season, or a combination of both.
Although sociable weavers generally forage in cohesive colony groups—expected to provide foraging benefits to all members (Brown et al. 2003; Lloyd et al. 2018)—highly social individuals may gain additional advantages through nepotistic behaviours from group members (Rat 2015). Specifically, individuals may receive preferential access to food shared within their social group (Chiarati et al. 2011; Ekman et al. 2000), or benefit from enhanced information-sharing about novel food sources, which facilitates their discovery and exploitation (McMahon et al. 2024). Additionally, highly social individuals may experience nepotistic protection from predators, further increasing their survival (Griesser & Ekman 2004, 2005). Finally, being part of a larger and more cohesive group may reduce harassment from conspecifics, thereby lowering energy expenditure and stress (Cameron et al. 2009).

During winter, thermoregulatory benefits associated with access to roosting chambers and communal roosting are likely to play a crucial role in improving condition and survival. In sociable weavers, the communal nest provides significant insulation (Lowney et al. 2020; White et al. 1975), and night-time nest chamber temperatures are positively correlated with the number of birds roosting together, especially on cold nights (Paquet et al. 2016). Birds in larger roosting groups are therefore less exposed to extreme cold. If sociality influences group composition at night, medium- and high-social individuals may be more likely to roost in larger groups and thus benefit from improved thermoregulation, reduced energy expenditure, and lower risk of cold-related mortality.

During the breeding season, which coincides with the summer period, survival benefits may be linked to cooperative breeding behaviours. In sociable weavers, helpers-at-the-nest tend to form stronger associations with breeders and other group members (Ferreira et al. 2020, 2024). Females with more helpers show higher survival, likely due to reduced parental effort (Paquet et al. 2015; see also Hatchwell 1999), and similar patterns are found in other cooperative breeders (Downing et al. 2021). This benefit may also extend to helpers, since helping incurs a physiological cost, particularly under high effort (Covas et al. 2022). Consequently, if individual sociality (i.e., number of strong bonds) is positively associated with the number of helpers received (Ferreira et al. 2024) and the amount of help provided (Covas et al. 2008), medium- and high-sociality individuals may experience increased survival, influenced by a life-history trade-off between investment in reproduction and self-maintenance.

## Conclusions

Our results offer the first evidence of a positive association between the quality of social associations and survival in birds, suggesting that the evolutionary dynamics of sociality may be shared across taxa.

The consistency of individual sociality levels and its fitness effects suggests that this social trait is under directional selection, favouring more social individuals (i.e., those with a higher number of strong social bonds). While previous studies have demonstrated a link between sociality and survival (Archie et al. 2014; Barocas et al. n.d.; Blumstein et al. 2013; Campos et al. 2020; Ellis et al. 2017; Lehmann et al. 2016; Silk et al. 2010; Stanton & Mann 2012; Thompson & Cords 2018), few have explicitly examined the temporal consistency of social traits. Notable exceptions, such as Vander Wal et al. (2015), have addressed this by quantifying repeatability. Although our approach did not
465 directly estimate repeatability, the transition probabilities suggest a degree of temporal
466 consistency in individual sociality. Future studies should formally test repeatability in this
467 and other species to better evaluate the evolutionary potential of sociality.

468 To further investigate these dynamics, future research should explore the heritability of
469 sociality, disentangling the genetic and environmental components that shape its
470 expression (Brent *et al.* 2013; Lea *et al.* 2010). This includes considering indirect genetic
471 effects (Moore *et al.* 1997; Wolf *et al.* 1998) and the potential covariance between
472 individual sociability and the surrounding social environment (Munar-Delgado *et al.*
473 2023). Such efforts will help evaluate the evolutionary potential of sociality and clarify
474 why variation persists despite its apparent fitness benefits. The social niche specialization
475 hypothesis suggests that individuals may exhibit consistent social differences when
476 behaviours are linked to distinct trade-offs (Montiglio *et al.* 2013). Future studies could
477 test whether low-social individuals outperform high-social ones under specific
478 environmental conditions or for other key fitness components such as reproductive
479 success or disease susceptibility. Finally, stochastic developmental factors—such as
480 random early-life experiences or unpredictable environmental exposures—should also be
481 integrated to gain a more comprehensive understanding of how sociality emerges, is
482 maintained, and evolves.

483 ACKNOWLEDGEMENTS

484 We thank Annie Basson for her help building the RFID stations and all the field workers
485 that helped collecting the data: Louis Bliard, Corisande Abiven, Tshianeo Melda, Lesego
486 Moagi, Natasha Prindal, Paola Stefanini, Irene Baquero, Taylor Keet, and all persons
487 who helped with the annual captures. De Beers Consolidated Mines provided access to
488 Benfontein Reserve. This study was funded by an ERC Consolidator grant 866489 (EU)
489 to R.C. and the ANR 19CE02-0014-01 OSU OREME and SEE-Life (CNRS, France) to
490 CD. RC was funded by FCT (CEECIND/03451/2018).

491 ETHICAL STATEMENT

492 Birds were captured and handled in accordance with protocols approved by Northern
493 Cape Nature Conservation (permits FAUNA 1338/2017, 0684/2019, 0059/2021, and
494 0775/2023) and the Ethics Committee of the University of Cape Town (2014/V1/RC,
495 2018/V20/RC, 2020/2018/V22/RC/A1, and 2023/V8/RC). All efforts were made to
496 minimize handling time and disturbance, and to ensure that procedures caused the least
497 possible stress to the animals.

498 DATA AVAILABILITY STATEMENT

499 Data (https://doi.org/10.5281/zenodo.16276322) and codes are available at Zenodo
500 (https://doi.org/10.5281/zenodo.16276060).

501

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# The number of strong social bonds is linked to survival in a cooperative bird

Gabriel Munar-Delgado¹,²*, Matthieu Paquet³, Babette Fourie¹,², Franck Theron¹,², André Ferreira⁴, Claire Doutrelant⁵,⁶, Rita Covas¹,²,⁶

¹ CIBIO, Centro de Investigação em Biodiversidade e Recursos Genéticos, Universidade do Porto, Vairão, Portugal

²BIOPOLIS, Program in genomics, Biodiversity and Land Planning, Vairão, Portugal

³SETE, Station d'Écologie Théorique et Expérimentale, CNRS, Moulis, France

⁴Department of Evolutionary Biology and Environmental Studies, University of Zurich, Zurich, Switzerland

⁵CEFE, CNRS, University of Montpellier, EPHE, IRD, Montpellier, France

⁶FitzPatrick Institute of African Ornithology, DST-NRF Centre of Excellence, University of Cape Town, Rondebosch, South Africa

Correspondence: gabriel.munar@gmail.com

## SUPPLEMENTARY MATERIAL

### Parameters description as in Nimble code

<table><thead><tr><td>Parameter</td><td>Description</td></tr></thead><tbody><tr><td>phiLw</td><td>survival probability state L (Low sociality) during winter</td></tr><tr><td>phiMw</td><td>survival probability state M (Medium sociality) during winter</td></tr><tr><td>phiHw</td><td>survival probability state H (High sociality) during winter</td></tr><tr><td>phiLs</td><td>survival probability state L during summer</td></tr><tr><td>phiMs</td><td>survival probability state M during summer</td></tr><tr><td>phiHs</td><td>survival probability state H during summer</td></tr><tr><td>psiL[1]</td><td>transition probability from L to L, i.e. stay in state L</td></tr><tr><td>psiL[2]</td><td>transition probability from L to M</td></tr><tr><td>psiL[3]</td><td>transition probability from L to H</td></tr><tr><td>psiM[1]</td><td>transition probability from M to L</td></tr><tr><td>psiM[2]</td><td>transition probability from M to M i.e., stay in state M</td></tr><tr><td>psiM[3]</td><td>transition probability from M to H</td></tr><tr><td>psiH[1]</td><td>transition probability from H to L</td></tr><tr><td>psiH[2]</td><td>transition probability from H to M</td></tr><tr><td>psiH[3]</td><td>transition probability from H to H i.e. stay in state H</td></tr><tr><td>pLn</td><td>recapture probability L at nets in sep</td></tr><tr><td>pMn</td><td>recapture probability M at nets in sep</td></tr><tr><td>pHn</td><td>recapture probability H at nets in sep</td></tr><tr><td>pLf</td><td>recapture probability L at feeders</td></tr><tr><td>pMf</td><td>recapture probability M at feeders</td></tr><tr><td>pHf</td><td>recapture probability H at feeders</td></tr><tr><td>betaLw</td><td>intercept of winter survival probability for state L (logit scale)</td></tr><tr><td>betaMw</td><td>intercept of winter survival probability for state M (logit scale)</td></tr><tr><td>betaHw</td><td>intercept of winter survival probability for state H (logit scale)</td></tr></tbody></table>
<table><caption>Table S1. Description of parameters used in the Bayesian multi-state capture-recapture model, as implemented in Nimble. Notation used in the code, along with a brief explanation of each parameter's biological or modelling meaning.</caption><tr><td>betaw[1]</td><td>coefficient for the effect of sex on winter survival probability</td></tr><tr><td>betaw[2]</td><td>coefficient for the effect of age on winter survival probability</td></tr><tr><td>epstw[]</td><td>random effect of year (t) on winter</td></tr><tr><td>epscw[]</td><td>random effect of colCNA on winter</td></tr><tr><td>betaLs</td><td>intercept of summer survival probability for state L (logit scale)</td></tr><tr><td>betaMs</td><td>intercept of summer survival probability for state M (logit scale)</td></tr><tr><td>betaHs</td><td>intercept of summer survival probability for state H (logit scale)</td></tr><tr><td>betas[1]</td><td>coefficient for the effect of sex on summer survival probability</td></tr><tr><td>betas[2]</td><td>coefficient for the effect of age on summer survival probability</td></tr><tr><td>epsts[]</td><td>random effect of year (t) on summer</td></tr><tr><td>epscs[]</td><td>random effect of colCNA on summer</td></tr><tr><td>betaDL</td><td>intercept of detection probability at RFID-feeders for state L (logit scale)</td></tr><tr><td>betaDM</td><td>intercept of detection probability at RFID-feeders for state M (logit scale)</td></tr><tr><td>betaDH</td><td>intercept of detection probability at RFID-feeders for state H (logit scale)</td></tr><tr><td>betaD</td><td>coefficient for the effect of RFID_at_Col on detection probability</td></tr><tr><td>age_centered</td><td>age matrix (in years)</td></tr><tr><td>sex_centered</td><td>centered sex vector</td></tr><tr><td>colCNA</td><td>colony matrix, represent to what colony each individual belonged each year</td></tr><tr><td>RFID_at_Col</td><td>matrix of RFID feeder presence at colonies</td></tr></table>

## Specification of priors

<table><caption>Table S2. Specification of prior distributions used in the Bayesian multi-state capture-recapture model. Parameter notation corresponds to that described in Table S1. For each parameter, the prior distribution and its characteristics are provided.</caption><thead><tr><th>Parameter</th><th>Prior</th><th>Description of prior values</th></tr></thead><tbody><tr><td>psiL[1:3], psiM[1:3], psiH[1:3]</td><td>ddirch(alpha[1:3])</td><td>Dirichlet prior with concentration parameters 1, 1, 1 (i.e., equal weight to all transitions)</td></tr><tr><td>pln, pmn, phn</td><td>dunif(0, 1)</td><td>Uniform prior between 0 and 1 for detection probabilities</td></tr><tr><td>epstw[t], epsts[t]</td><td>dnorm(0, sd = sdepstw)<br>dnorm(0, sd = sdepsts)</td><td>Normal prior with mean 0 and standard deviation sdepstw/sdepsts</td></tr><tr><td>epscw[j], epscs[j]</td><td>dnorm(0, sd = sdepscw)<br>dnorm(0, sd = sdepscs)</td><td>Normal prior with mean 0 and standard deviation sdepscw/sdepscs</td></tr><tr><td>sdepstw, sdepscw, sdepsts, sdepscs</td><td>dexp(1)</td><td>Exponential prior with rate 1</td></tr><tr><td>betaw[1], betaw[2]</td><td>dnorm(mean = 0, sd = 1.5)</td><td>Normal prior with mean 0 and standard deviation 1.5</td></tr><tr><td>betaLw, betaMw, betaHw</td><td>dnorm(mean = 0, sd = 1.5)</td><td>Normal prior with mean 0 and standard deviation 1.5</td></tr><tr><td>betas[1], betas[2]</td><td>dnorm(mean = 0, sd = 1.5)</td><td>Normal prior with mean 0 and standard deviation 1.5</td></tr><tr><td>betaLs, betaMs, betaHs</td><td>dnorm(mean = 0, sd = 1.5)</td><td>Normal prior with mean 0 and standard deviation 1.5</td></tr></tbody></table>
## Model description

<table><caption>**Table S3.** Model equations used in the Bayesian multi-state capture–recapture analysis. All models used a logit link. Parameters are defined in Table S1 and priors in Table S2. See main text for rationale and code for implementation. *captured* is a dummy variable used to fix detection probability to 0 in 2020 due to missing RFID data.</caption><thead><tr><th colspan="2">Model</th></tr></thead><tbody><tr><td rowspan="3">Survival in winter</td><td>logit(phiLw[i,t]) <- betaLw + betaw[1] * sex_centered[i] + betaw[2] * age_centered[i,t] + epstw[t] + epscw[colCNA[i,t]]</td></tr><tr><td>logit(phiMw[i,t]) <- betaMw + betaw[1] * sex_centered[i] + betaw[2] * age_centered[i,t] + epstw[t] + epscw[colCNA[i,t]]</td></tr><tr><td>logit(phiLw[i,t]) <- betaLw + betaw[1] * sex_centered[i] + betaw[2] * age_centered[i,t] + epstw[t] + epscw[colCNA[i,t]]</td></tr><tr><td rowspan="3">Survival in summer and transitions</td><td>logit(phiLs[i,t]) <- betaLs + betas[1] * sex_centered[i] + betas[2] * age_centered[i,t] + epsts[t] + epscs[colCNA[i,t]]</td></tr><tr><td>logit(phiMs[i,t]) <- betaMs + betas[1] * sex_centered[i] + betas[2] * age_centered[i,t] + epsts[t] + epscs[colCNA[i,t]]</td></tr><tr><td>logit(phiHs[i,t]) <- betaHs + betas[1] * sex_centered[i] + betas[2] * age_centered[i,t] + epsts[t] + epscs[colCNA[i,t]]</td></tr><tr><td rowspan="3">Detection at RFIDs</td><td>logit(pLf[i,t]) <- (betaDL + betaD * RFID_at_Col[i,t]) * captured[t] - (1 - captured[t]) * 15</td></tr><tr><td>logit(pMf[i,t]) <- (betaDM + betaD * RFID_at_Col[i,t]) * captured[t] - (1 - captured[t]) * 15</td></tr><tr><td>logit(pHf[i,t]) <- (betaDH + betaD * RFID_at_Col[i,t]) * captured[t] - (1 - captured[t]) * 15</td></tr></tbody></table>

## Covariates and random effects

- Age: For most birds (81%), we determined their exact age through breeding monitoring. For immigrant birds first captured as adults within the studied population (sociable weavers can disperse between colonies; van Dijk *et al.* 2015), we estimated their age based on the average age of dispersal observed within our population. According to this, we assumed that immigrant individuals were 709 days old at the date of first capture (the average minimum dispersal age in our population; Silva *et al.* 2025).

- Sex: During mist-nets samplings, individual blood samples were taken to determine sex through genetic sexing (Griffiths *et al.* 1998).

- Individual's colony: Birds were sometimes registered at RFID-feeder stations from different colonies. To assign each bird to a colony, we used mist-net sampling data, associating individuals with the colony where they were captured, which corresponds to their roosting location. If an individual was not captured at mist-nets in a specific year (t) but was registered at feeders, we used mist-net data from the following year to determine its colony identity. In cases where the individual was not captured the next year either, we relied on mist-net data from the previous year. Using this approach, we were able to assign a colony identity to every individual with social status data.
## Individual exclusion criteria for multi-state capture-recapture analyses

We excluded individuals from the multi-state capture-recapture model based on three criteria. First, individuals were excluded if they only had RFID-feeder data from the last year of the study, as no recapture data could be obtained for them. Second, individuals for which sex could not be determined were excluded from the analysis, as sex was included as a covariate in the survival and transition components of the model. Third, only individuals from PIT-tagged colonies were retained. This is because social status estimates are calculated relative to the social structure of each colony, and individuals residing in non-tagged colonies could not be accurately assigned a social status. Individuals that ever migrated from PIT-tagged to non-tagged colonies were entirely excluded from the analysis, as their social status could not be determined after migration and the missing data could be wrongly interpreted by the model as mortality. Conversely, individuals that migrated from non-tagged to PIT-tagged colonies were retained, but only social status data from after migration were included.

## Sociality levels classification

<table><thead><tr><th>Year</th><th>Colony</th><th>Sociality level</th><th>Minimum strong bonds</th><th>Maximum strong bonds</th></tr></thead><tbody><tr><td rowspan="24">2018</td><td rowspan="3">11</td><td>Low</td><td>0</td><td>3</td></tr><tr><td>Medium</td><td>4</td><td>6</td></tr><tr><td>High</td><td>7</td><td>16</td></tr><tr><td rowspan="3">20</td><td>Low</td><td>0</td><td>3</td></tr><tr><td>Medium</td><td>4</td><td>7</td></tr><tr><td>High</td><td>8</td><td>16</td></tr><tr><td rowspan="3">27</td><td>Low</td><td>0</td><td>3</td></tr><tr><td>Medium</td><td>4</td><td>8</td></tr><tr><td>High</td><td>9</td><td>16</td></tr><tr><td rowspan="3">43</td><td>Low</td><td>0</td><td>1</td></tr><tr><td>Medium</td><td>2</td><td>6</td></tr><tr><td>High</td><td>7</td><td>11</td></tr><tr><td rowspan="3">71</td><td>Low</td><td>0</td><td>5</td></tr><tr><td>Medium</td><td>6</td><td>9</td></tr><tr><td>High</td><td>11</td><td>24</td></tr><tr><td rowspan="24">2019</td><td rowspan="3">11</td><td>Low</td><td>0</td><td>2</td></tr><tr><td>Medium</td><td>3</td><td>6</td></tr><tr><td>High</td><td>7</td><td>12</td></tr><tr><td rowspan="3">20</td><td>Low</td><td>0</td><td>0</td></tr><tr><td>Medium</td><td>1</td><td>1</td></tr><tr><td>High</td><td>3</td><td>4</td></tr><tr><td rowspan="3">27</td><td>Low</td><td>0</td><td>1</td></tr><tr><td>Medium</td><td>2</td><td>3</td></tr><tr><td>High</td><td>4</td><td>9</td></tr><tr><td rowspan="3">43</td><td>Low</td><td>0</td><td>0</td></tr><tr><td>Medium</td><td>1</td><td>1</td></tr><tr><td>High</td><td>2</td><td>6</td></tr><tr><td rowspan="3">71</td><td>Low</td><td>0</td><td>2</td></tr><tr><td>Medium</td><td>3</td><td>4</td></tr><tr><td>High</td><td>5</td><td>12</td></tr></tbody></table>
<table><caption>**Table S4. Classification of sociality levels by year and colony.** For each year-colony combination, the table shows the minimum and maximum numbers of strong social bonds observed within each category (see main text for classification criteria).</caption><tbody><tr><td rowspan="18">2021</td><td rowspan="3">11</td><td>Low</td><td>1</td><td>5</td></tr><tr><td>Medium</td><td>6</td><td>10</td></tr><tr><td>High</td><td>11</td><td>16</td></tr><tr><td rowspan="3">20</td><td>Low</td><td>0</td><td>2</td></tr><tr><td>Medium</td><td>3</td><td>5</td></tr><tr><td>High</td><td>8</td><td>10</td></tr><tr><td rowspan="3">27</td><td>Low</td><td>0</td><td>3</td></tr><tr><td>Medium</td><td>4</td><td>8</td></tr><tr><td>High</td><td>9</td><td>15</td></tr><tr><td rowspan="3">43</td><td>Low</td><td>0</td><td>2</td></tr><tr><td>Medium</td><td>3</td><td>4</td></tr><tr><td>High</td><td>5</td><td>9</td></tr><tr><td rowspan="3">71</td><td>Low</td><td>0</td><td>6</td></tr><tr><td>Medium</td><td>7</td><td>8</td></tr><tr><td>High</td><td>9</td><td>19</td></tr><tr><td rowspan="18">2022</td><td rowspan="3">11</td><td>Low</td><td>0</td><td>1</td></tr><tr><td>Medium</td><td>2</td><td>5</td></tr><tr><td>High</td><td>6</td><td>11</td></tr><tr><td rowspan="3">20</td><td>Low</td><td>0</td><td>2</td></tr><tr><td>Medium</td><td>3</td><td>3</td></tr><tr><td>High</td><td>4</td><td>6</td></tr><tr><td rowspan="3">21</td><td>Low</td><td>0</td><td>0</td></tr><tr><td>Medium</td><td>1</td><td>1</td></tr><tr><td>High</td><td>2</td><td>2</td></tr><tr><td rowspan="3">71</td><td>Low</td><td>0</td><td>1</td></tr><tr><td>Medium</td><td>2</td><td>4</td></tr><tr><td>High</td><td>5</td><td>10</td></tr><tr><td rowspan="18">2023</td><td rowspan="3">11</td><td>Low</td><td>0</td><td>4</td></tr><tr><td>Medium</td><td>5</td><td>12</td></tr><tr><td>High</td><td>13</td><td>23</td></tr><tr><td rowspan="3">20</td><td>Low</td><td>0</td><td>4</td></tr><tr><td>Medium</td><td>5</td><td>7</td></tr><tr><td>High</td><td>9</td><td>20</td></tr><tr><td rowspan="3">21</td><td>Low</td><td>0</td><td>3</td></tr><tr><td>Medium</td><td>4</td><td>5</td></tr><tr><td>High</td><td>6</td><td>9</td></tr><tr><td rowspan="3">71</td><td>Low</td><td>0</td><td>3</td></tr><tr><td>Medium</td><td>4</td><td>7</td></tr><tr><td>High</td><td>10</td><td>15</td></tr></tbody></table>

## Posterior predictive checks of the model

We assessed the goodness-of-fit of our model using Bayesian p-values (Gelman et al., 2014), a model-checking procedure that measures the dissimilarity between observed data and model predictions. We evaluated the dissimilarity between the observed and model-predicted means and coefficients of variation (CV) for the total number of re-detections per individual at mist
nets and RFID-feeders. The reported Bayesian p-values indicate the proportion of simulated data generated from the model in which the distance between this simulated data and the expected mean (or CV) exceeds the distance between observed data and the expected mean (or CV). Values close to 0 or 1 (commonly below 0.05 or above 0.95) suggest a lack of fit.

To perform this analysis for detections at RFID-feeders, we transformed the observed data and model-prediction matrices (which contained four states: three social-status states and non-observation) into binomial matrices with observation (any social-status) and non-observation.

We obtain Bayesian p-values of 0.33 for mean of detections at the mist-nets, 0.47 for CV of detection at mist-nets, 0.26 for mean of detections at the RFID-feeders, 0.32 for CV of detection at RFID-feeders (Figure S1) showing no evidence for a lack of fit of our model to estimate the mean, and CV of the number of detections per individual.

<figure><img src="image_8.png" /></figure>

<figure><img src="image_9.png" /></figure>

<figure><img src="image_10.png" /></figure>

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

**Figure S1.** Posterior predictive checks of detection probabilities. P-values represent Bayesian p-values. a) Mean detection at mist-nets, b) coefficient of variation (CV) of detection at mist-nets, c) mean detection at RFID-feeders, and d) CV of detection at RFID-feeders.
Model convergence and mixing of chains

<figure><img src="image_12.png" /><figcaption>**Figure S2.** Trace plots and posterior density distributions for monitored parameters in the Bayesian multi-state capture–recapture model. Parameter notation corresponds to that described in Table S1</figcaption></figure>
Model estimated values

<table><thead><tr><th></th><th></th><th>mean</th><th>SD</th><th>2.50%</th><th>97.50%</th><th>R&#770;</th><th>n.eff</th></tr></thead><tbody><tr><td rowspan="6">Survival in winter</td><td>Intercept<sub>&phi;<sub>L<sub>w</sub></sub></sub></td><td>1.16</td><td>0.41</td><td>0.24</td><td>1.89</td><td>1.00</td><td>799</td></tr><tr><td>Intercept<sub>&phi;<sub>M<sub>w</sub></sub></sub></td><td>1.82</td><td>0.46</td><td>0.89</td><td>2.74</td><td>1.00</td><td>888</td></tr><tr><td>Intercept<sub>&phi;<sub>H<sub>w</sub></sub></sub></td><td>2.24</td><td>0.51</td><td>1.29</td><td>3.30</td><td>1.00</td><td>1151</td></tr><tr><td>&beta;<sub>Sex<sub>w</sub></sub></td><td>0.29</td><td>0.28</td><td>-0.25</td><td>0.85</td><td>1.00</td><td>4436</td></tr><tr><td>&beta;<sub>Age<sub>w</sub></sub></td><td>0.07</td><td>0.08</td><td>-0.07</td><td>0.26</td><td>1.00</td><td>3224</td></tr><tr><td>SD<sub>&epsilon;<sub>t<sub>w</sub></sub></sub></td><td>0.61</td><td>0.43</td><td>0.11</td><td>1.71</td><td>1.01</td><td>726</td></tr><tr><td>SD<sub>&epsilon;<sub>Col<sub>w</sub></sub></sub></td><td>0.36</td><td>0.27</td><td>0.02</td><td>1.02</td><td>1.02</td><td>910</td></tr><tr><td rowspan="6">Survival in summer</td><td>Intercept<sub>&phi;<sub>L<sub>s</sub></sub></sub></td><td>0.11</td><td>0.51</td><td>-0.95</td><td>1.06</td><td>1.03</td><td>547</td></tr><tr><td>Intercept<sub>&phi;<sub>M<sub>s</sub></sub></sub></td><td>0.98</td><td>0.51</td><td>-0.08</td><td>1.96</td><td>1.02</td><td>590</td></tr><tr><td>Intercept<sub>&phi;<sub>H<sub>s</sub></sub></sub></td><td>0.73</td><td>0.51</td><td>-0.33</td><td>1.69</td><td>1.02</td><td>520</td></tr><tr><td>&beta;<sub>Sex<sub>s</sub></sub></td><td>-0.62</td><td>0.21</td><td>-1.05</td><td>-0.21</td><td>1.00</td><td>5250</td></tr><tr><td>&beta;<sub>Age<sub>s</sub></sub></td><td>0.01</td><td>0.05</td><td>-0.09</td><td>0.12</td><td>1.00</td><td>4495</td></tr><tr><td>SD<sub>&epsilon;<sub>t<sub>s</sub></sub></sub></td><td>1.15</td><td>0.52</td><td>0.53</td><td>2.42</td><td>1.00</td><td>2194</td></tr><tr><td>SD<sub>&epsilon;<sub>Col<sub>s</sub></sub></sub></td><td>0.52</td><td>0.28</td><td>0.14</td><td>1.23</td><td>1.00</td><td>1831</td></tr><tr><td rowspan="4">Detections at RFIDs</td><td>Intercept<sub>p<sub>L<sub>f</sub></sub></sub></td><td>0.06</td><td>1.13</td><td>-1.89</td><td>2.55</td><td>1.00</td><td>2210</td></tr><tr><td>Intercept<sub>p<sub>M<sub>f</sub></sub></sub></td><td>-1.04</td><td>0.94</td><td>-2.73</td><td>1.01</td><td>1.00</td><td>1396</td></tr><tr><td>Intercept<sub>p<sub>H<sub>f</sub></sub></sub></td><td>-0.86</td><td>0.94</td><td>-2.50</td><td>1.23</td><td>1.00</td><td>1356</td></tr><tr><td>&beta;<sub>RFID<sub>f</sub></sub></td><td>3.04</td><td>0.64</td><td>1.75</td><td>4.29</td><td>1.00</td><td>928</td></tr><tr><td rowspan="3">Detect. at nets</td><td>p<sub>L<sub>n</sub></sub></td><td>0.88</td><td>0.03</td><td>0.83</td><td>0.93</td><td>1.00</td><td>4034</td></tr><tr><td>p<sub>M<sub>n</sub></sub></td><td>0.84</td><td>0.06</td><td>0.73</td><td>0.94</td><td>1.00</td><td>3290</td></tr><tr><td>p<sub>H<sub>n</sub></sub></td><td>0.88</td><td>0.03</td><td>0.81</td><td>0.93</td><td>1.00</td><td>4018</td></tr><tr><td rowspan="8">Status Transitions</td><td>&psi;<sub>LL</sub></td><td>0.47</td><td>0.06</td><td>0.34</td><td>0.59</td><td>1.00</td><td>4497</td></tr><tr><td>&psi;<sub>LM</sub></td><td>0.35</td><td>0.06</td><td>0.23</td><td>0.48</td><td>1.00</td><td>3894</td></tr><tr><td>&psi;<sub>LH</sub></td><td>0.19</td><td>0.05</td><td>0.10</td><td>0.29</td><td>1.00</td><td>4730</td></tr><tr><td>&psi;<sub>ML</sub></td><td>0.27</td><td>0.05</td><td>0.19</td><td>0.36</td><td>1.00</td><td>4987</td></tr><tr><td>&psi;<sub>MM</sub></td><td>0.48</td><td>0.06</td><td>0.36</td><td>0.58</td><td>1.00</td><td>3728</td></tr><tr><td>&psi;<sub>MH</sub></td><td>0.25</td><td>0.05</td><td>0.17</td><td>0.35</td><td>1.00</td><td>4246</td></tr><tr><td>&psi;<sub>HL</sub></td><td>0.08</td><td>0.03</td><td>0.04</td><td>0.15</td><td>1.00</td><td>4943</td></tr><tr><td>&psi;<sub>HM</sub></td><td>0.32</td><td>0.05</td><td>0.22</td><td>0.43</td><td>1.00</td><td>3655</td></tr><tr><td>&psi;<sub>HH</sub></td><td>0.60</td><td>0.05</td><td>0.49</td><td>0.70</td><td>1.00</td><td>3735</td></tr></tbody></table>

Table S5. Multi-state capture-recapture model estimates. $\phi_{X_z}$ is the survival portability of an individual in state X (L low; M medium, H high) and time interval Z (W winter or S summer). $\beta_{C_z}$ is the effect of the covariate C (Sex or Age) at time interval Z. $SD_{\epsilon_{t_s}}$ is the standard deviation of random effect ($\epsilon_{r_z}$) where r is the identity of the random effect (t year; Col colony) for the time period Z. $p_{X_n}$ is the recapture probability at nets of an individual in state X. $p_{X_f}$ is the recapture probability at RFID-feeders of an individual in state X. $\psi_{XY}$ is the transition
probability from state X to state Y from *t* to *t+1*. Columns represent mean, standard deviation (SD), lower (2.5%) and upper (97.5%) values of 95 confidence intervals, *R̄* and effective sample size (n.eff).

## Posterior mean differences

<table><thead><tr><th></th><th>2.5%</th><th>50%</th><th>97.5%</th><th>Significance</th></tr></thead><tbody><tr><td>p<sub>Hf</sub> - p<sub>Mf</sub></td><td>-0.508</td><td>0.031</td><td>0.550</td><td>NO</td></tr><tr><td>p<sub>Hf</sub> - p<sub>Lf</sub></td><td>-0.675</td><td>-0.180</td><td>0.335</td><td>NO</td></tr><tr><td>p<sub>Mf</sub> - p<sub>Lf</sub></td><td>-0.727</td><td>-0.217</td><td>0.347</td><td>NO</td></tr><tr><td>p<sub>Hn</sub> - p<sub>Mn</sub></td><td>-0.088</td><td>0.030</td><td>0.158</td><td>NO</td></tr><tr><td>p<sub>Hn</sub> - p<sub>Ln</sub></td><td>-0.087</td><td>-0.005</td><td>0.072</td><td>NO</td></tr><tr><td>p<sub>Mn</sub> - p<sub>Ln</sub></td><td>-0.157</td><td>-0.036</td><td>0.076</td><td>NO</td></tr><tr><td>φ<sub>Hw</sub> - φ<sub>Mw</sub></td><td>-0.047</td><td>0.041</td><td>0.149</td><td>NO</td></tr><tr><td>φ<sub>Hw</sub> - φ<sub>Lw</sub></td><td>0.040</td><td>0.135</td><td>0.280</td><td>YES</td></tr><tr><td>φ<sub>Mw</sub> - φ<sub>Lw</sub></td><td>0.002</td><td>0.094</td><td>0.213</td><td>YES</td></tr><tr><td>φ<sub>Hs</sub> - φ<sub>Ms</sub></td><td>-0.172</td><td>-0.051</td><td>0.065</td><td>NO</td></tr><tr><td>φ<sub>Hs</sub> - φ<sub>Ls</sub></td><td>0.012</td><td>0.138</td><td>0.269</td><td>YES</td></tr><tr><td>φ<sub>Ms</sub> - φ<sub>Ls</sub></td><td>0.071</td><td>0.188</td><td>0.316</td><td>YES</td></tr><tr><td>ψ<sub>LL</sub> - ψ<sub>LM</sub></td><td>-0.112</td><td>0.122</td><td>0.337</td><td>NO</td></tr><tr><td>ψ<sub>LL</sub> - ψ<sub>LH</sub></td><td>0.091</td><td>0.283</td><td>0.455</td><td>YES</td></tr><tr><td>ψ<sub>LM</sub> - ψ<sub>LH</sub></td><td>-0.033</td><td>0.162</td><td>0.349</td><td>NO</td></tr><tr><td>ψ<sub>ML</sub> - ψ<sub>MM</sub></td><td>-0.374</td><td>-0.209</td><td>-0.019</td><td>YES</td></tr><tr><td>ψ<sub>ML</sub> - ψ<sub>MH</sub></td><td>-0.131</td><td>0.014</td><td>0.154</td><td>NO</td></tr><tr><td>ψ<sub>MM</sub> - ψ<sub>MH</sub></td><td>0.03</td><td>0.225</td><td>0.393</td><td>YES</td></tr><tr><td>ψ<sub>HL</sub> - ψ<sub>HM</sub></td><td>-0.367</td><td>-0.230</td><td>-0.096</td><td>YES</td></tr><tr><td>ψ<sub>HL</sub> - ψ<sub>HH</sub></td><td>-0.642</td><td>-0.518</td><td>-0.371</td><td>YES</td></tr><tr><td>ψ<sub>HM</sub> - ψ<sub>HH</sub></td><td>-0.476</td><td>-0.290</td><td>-0.063</td><td>YES</td></tr></tbody></table>

**Table S6. Posterior mean differences.** Mean differences between the survival, transition, and recapture probabilities of the different social states (and their 95% confidence intervals) calculated from the posterior estimates. See Table S5 for parameters description.

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