Different types of social links contrastingly shape reproductive wellbeing in a multi-level society of wild songbirds

Different types of social links contrastingly shape reproductive wellbeing in a multi-level society of wild songbirds | bioRxiv /* */ /* */ <!-- <!-- /*! * yepnope1.5.4 * (c) WTFPL, GPLv2 */ (function(a,b,c){function d(a){return"[object Function]"==o.call(a)}function e(a){return"string"==typeof a}function f(){}function g(a){return!a||"loaded"==a||"complete"==a||"uninitialized"==a}function h(){var a=p.shift();q=1,a?a.t?m(function(){("c"==a.t?B.injectCss:B.injectJs)(a.s,0,a.a,a.x,a.e,1)},0):(a(),h()):q=0}function i(a,c,d,e,f,i,j){function k(b){if(!o&&g(l.readyState)&&(u.r=o=1,!q&&h(),l.onload=l.onreadystatechange=null,b)){"img"!=a&&m(function(){t.removeChild(l)},50);for(var d in y[c])y[c].hasOwnProperty(d)&&y[c][d].onload()}}var j=j||B.errorTimeout,l=b.createElement(a),o=0,r=0,u={t:d,s:c,e:f,a:i,x:j};1===y[c]&&(r=1,y[c]=[]),"object"==a?l.data=c:(l.src=c,l.type=a),l.width=l.height="0",l.onerror=l.onload=l.onreadystatechange=function(){k.call(this,r)},p.splice(e,0,u),"img"!=a&&(r||2===y[c]?(t.insertBefore(l,s?null:n),m(k,j)):y[c].push(l))}function j(a,b,c,d,f){return q=0,b=b||"j",e(a)?i("c"==b?v:u,a,b,this.i++,c,d,f):(p.splice(this.i++,0,a),1==p.length&&h()),this}function k(){var a=B;return a.loader={load:j,i:0},a}var l=b.documentElement,m=a.setTimeout,n=b.getElementsByTagName("script")[0],o={}.toString,p=[],q=0,r="MozAppearance"in l.style,s=r&&!!b.createRange().compareNode,t=s?l:n.parentNode,l=a.opera&&"[object Opera]"==o.call(a.opera),l=!!b.attachEvent&&!l,u=r?"object":l?"script":"img",v=l?"script":u,w=Array.isArray||function(a){return"[object Array]"==o.call(a)},x=[],y={},z={timeout:function(a,b){return b.length&&(a.timeout=b[0]),a}},A,B;B=function(a){function b(a){var a=a.split("!"),b=x.length,c=a.pop(),d=a.length,c={url:c,origUrl:c,prefixes:a},e,f,g;for(f=0;f<d;f++)g=a[f].split("="),(e=z[g.shift()])&&(c=e(c,g));for(f=0;f<b;f++)c=x[f](c);return c}function g(a,e,f,g,h){var i=b(a),j=i.autoCallback;i.url.split(".").pop().split("?").shift(),i.bypass||(e&&(e=d(e)?e:e[a]||e[g]||e[a.split("/").pop().split("?")[0]]),i.instead?i.instead(a,e,f,g,h):(y[i.url]?i.noexec=!0:y[i.url]=1,f.load(i.url,i.forceCSS||!i.forceJS&&"css"==i.url.split(".").pop().split("?").shift()?"c":c,i.noexec,i.attrs,i.timeout),(d(e)||d(j))&&f.load(function(){k(),e&&e(i.origUrl,h,g),j&&j(i.origUrl,h,g),y[i.url]=2})))}function h(a,b){function c(a,c){if(a){if(e(a))c||(j=function(){var a=[].slice.call(arguments);k.apply(this,a),l()}),g(a,j,b,0,h);else if(Object(a)===a)for(n in m=function(){var b=0,c;for(c in a)a.hasOwnProperty(c)&&b++;return b}(),a)a.hasOwnProperty(n)&&(!c&&!--m&&(d(j)?j=function(){var a=[].slice.call(arguments);k.apply(this,a),l()}:j[n]=function(a){return function(){var b=[].slice.call(arguments);a&&a.apply(this,b),l()}}(k[n])),g(a[n],j,b,n,h))}else!c&&l()}var h=!!a.test,i=a.load||a.both,j=a.callback||f,k=j,l=a.complete||f,m,n;c(h?a.yep:a.nope,!!i),i&&c(i)}var i,j,l=this.yepnope.loader;if(e(a))g(a,0,l,0);else if(w(a))for(i=0;i (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0];var j=d.createElement(s);var dl=l!='dataLayer'?'&l='+l:'';j.src='//www.googletagmanager.com/gtm.js?id='+i+dl;j.type='text/javascript';j.async=true;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-M677548'); Skip to main content Home About Submit ALERTS / RSS Search for this keyword Advanced Search New Results Different types of social links contrastingly shape reproductive wellbeing in a multi-level society of wild songbirds Samin Gokcekus , View ORCID Profile Josh A. Firth , Ella F. Cole , View ORCID Profile Ben C. Sheldon , View ORCID Profile Gregory F. Albery doi: https://doi.org/10.1101/2024.02.02.578606 Samin Gokcekus 1 Department of Biology, University of Oxford , United Kingdom Find this author on Google Scholar Find this author on PubMed Search for this author on this site For correspondence: samingokcekus{at}gmail.com gfalbery{at}gmail.com Josh A. Firth 1 Department of Biology, University of Oxford , United Kingdom 2 School of Biology, University of Leeds , United Kingdom Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Josh A. Firth Ella F. Cole 1 Department of Biology, University of Oxford , United Kingdom Find this author on Google Scholar Find this author on PubMed Search for this author on this site Ben C. Sheldon 1 Department of Biology, University of Oxford , United Kingdom Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Ben C. Sheldon Gregory F. Albery 3 Department of Biology, Georgetown University , Washington, DC, USA Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Gregory F. Albery For correspondence: samingokcekus{at}gmail.com gfalbery{at}gmail.com Abstract Full Text Info/History Metrics Preview PDF Abstract The social environment has diverse consequences for individuals’ welfare, health, reproductive success, and survival. This environment consists of different kinds of dyadic bonds that exist at different levels; in many social species, smaller social units come together in larger groups, creating multilevel societies. In great tits ( Parus major ), individuals have four major types of dyadic bonds: pair mates, breeding neighbours, flockmates, and spatial associates, all of which have been previously linked to fitness outcomes. Here, we show that these different types of dyadic bonds are differentially linked with subsequent reproductive success metrics in this wild population and that considering spatial effects provides further insights into these relationships. We provide evidence that more social individuals had a higher number of fledglings, and individuals with more spatial associates had smaller clutch sizes. We also show individuals with stronger bonds with their pair mate had earlier lay dates. Our study highlights the importance of considering different types of dyadic relationships when investigating the relationship between wellbeing and sociality, and the need for future work aimed at experimentally testing these relationships, particularly in spatially structured populations. Introduction Many animals live in complex societies ( Ward & Webster 2016 ). The social behavior of individuals within populations gives rise to structured social systems, and social interactions influence individuals’ decisions and behaviors, including foraging activity, mate choice, survival, and reproduction ( Snyder-Mackler et al . 2020 ). Consequently, variation in sociality is an important determinant of overall fitness ( Bond et al . 2021 ; Ellis et al . 2019 ; Menz et al . 2020 ; Siracusa et al . 2020 ; Turner et al . 2020 ). While social relationships come in many forms, sociality is often quantified using a single type of dyadic bond between individuals, for example by quantifying the strength of bonds between reproductive pairs, foraging partners, or group members. A key remaining question is how individual variation in different types of dyadic relationships simultaneously shape fitness outcomes in these complex societies. Different kinds of dyadic relationships within populations can pave the way for hierarchically nested social relationships that form multilevel societies ( Papageorgiou & Farine 2021 ). Indeed, all representations of social relationships are ultimately built upon dyadic units. Multi-tiered social structures often consist of breeding pairs or families that are nested within higher-order groups, allowing for flexibility in social interactions ( Grueter et al . 2017 ). Smaller, more stable units may come together to form larger fluctuating groups, such that individuals may maintain and keep track of relationships at multiple levels ( Papageorgiou et al . 2019 ). Assessing how these layers influence fitness is crucial to gaining a more complete understanding of understanding how individual-level selection on behaviours build up to produce societal structure. However, quantifying these effects in wild populations can be difficult, as it requires fine-scale behavioural data from multiple social contexts across years alongside fitness outputs. Long-term, individual-based studies with high-resolution data are particularly useful in this regard ( Clutton-Brock & Sheldon 2010 ; Sheldon et al . 2022 ). The long-term study system of great tits ( Parus major ) at Wytham Woods provides an ideal opportunity to link different kinds of social relationships with fitness outcomes. Individuals are monitored during winter and spring over multiple years and in multiple social contexts, with associated data collected on reproductive success. As detailed below, four broad types of associations exist within this population that are known to be biologically meaningful: pair mates ( Culina et al . 2015b , a , 2020 ; Firth et al . 2018a ; Firth & Sheldon 2016 ), breeding neighbours ( Gokcekus et al . 2023 ; Grabowska-Zhang et al . 2012a , b ), winter flockmates ( Firth et al . 2015 , 2017 , 2018b ), and winter spatial associates ( Farine et al . 2015 ; Firth et al . 2018b ; Firth & Sheldon 2016 ). In this monogamous species, pair mates bond and defend their territory during the breeding season, while also staying close during the non-breeding season ( Firth et al . 2015 ). Pair bonds can be understood as the core unit of this multilevel society ( Grueter et al . 2020 ). Spatial and social associations often have an influence on mate choice ( Beck et al . 2020 ), and mate choice will in turn have an influence on fitness ( Fayet et al . 2017 ; Griffith et al . 2011 ; Ihle et al . 2015 ). Stronger associations within the pair bond, often assessed in terms of familiarity over time, seem to come with fitness benefits in many species ( Ausband 2019 ; Van De Pol et al . 2006 ; Sánchez-Macouzet et al . 2014 ). In this population, pairs who meet earlier in the winter have higher reproductive success ( Culina et al . 2020 ), pairs that stay together longer are more likely to survive ( Culina et al . 2015a ), and individuals that are unsuccessful are more likely to divorce ( Culina et al . 2015b ; Gokcekus et al . 2023 ). During the breeding season, territorial great tits have breeding neighbours, and these neighbours often also coexist in winter flocks ( Firth & Sheldon 2016 ). Studies have shown that familiarity among neighbours can lead to mutual tolerance and decreases in aggression and energy required for territorial defence, potentially optimizing the timing of reproduction and facilitating coordination and cooperation ( Bebbington et al. 2017 ; Beletsky & Orians 19s89; Booksmythe et al. 2012 ; Grabowska-Zhang et al. 2012a ; Liebgold & Cabe 2008 ; Riehl & Strong 2018 ; Siracusa et al. 2020 ) (but see Müller & Manser 2007 ; Temeles 1994 ). Great tits in Wytham Woods benefit from having familiar neighbours (individuals that were neighbours in previous years), through increases in cooperation ( Grabowska-Zhang et al . 2012a ) and, presumably consequentially, higher reproductive success ( Grabowska-Zhang et al . 2012c ). Outside of the breeding season, individuals also have flock mates that they forage with in relatively large fission-fusion winter flocks ( Massen et al . 2010 ). Studies in mammalian and avian species have demonstrated that having more ( Cameron et al . 2009 ; Ellis et al . 2019 ; McFarland et al . 2017 ; Turner et al . 2020 ) or stronger ( Cheney et al . 2016 ; Kohn 2017 ; Silk et al . 2010 ) social associations can come with fitness benefits (but see Menz et al . 2020 ; Sabol et al . 2020 ). It is possible that more/stronger bonds allow for more appropriate individual or group responses to social and environmental stressors ( Campbell et al . 2018 ; Micheletta et al . 2012 ; Young et al . 2014 ), higher rates of cooperation ( Berghänel et al . 2011 ; Samuni et al . 2018 , 2021 ), and/or increases in dominance that lead to more successful reproduction ( Bray et al . 2021 ; Feldblum et al . 2021 ; Schülke et al . 2010 ; Strauss & Holekamp 2019 ). In this system, an individual’s flock mates influence patterns of social information acquisition during winter foraging ( Firth et al . 2016 ). During the winter, individuals are also spatially associated with others while foraging, which may indicate some form of association (or non-independence) but not necessarily direct social interactions. The effects of such associations can vary based on factors such as predation risk and competition ( Alberts 2019 ). The configuration and constraints of the external environment can also influence spatial proximity, and therefore associations, between individuals ( He et al . 2019 ; Papageorgiou et al . 2019 ). Partitioning out these purely spatial associations can help to gain a clearer understanding of how different dyadic relationships contribute to the formation of multi-level societies and their fitness consequences. Finally, all of these links occur within the context of the spatial environment , which plays a role in “bottom-up” determination of the social network itself ( Albery et al . 2021 ; Webber et al . 2023 ). Because the social network’s formation can be confounded with a variety of environmental ( Fisher et al . 2021 ) and demographic drivers ( Shizuka & Johnson 2019 ), many of which can also influence fitness, it is important to take the spatial environment – and particularly population density – into account when understanding sociality-fitness links. Although the link between sociality and fitness has been considered for several types of dyadic relationships separately, considering them together and in concert with spatial effects is necessary to gain a better understanding of how natural selection operates on individual social behaviour, how this varies for different types of relationships, and therefore how evolution shapes social organization. What are the consequences of different types of sociality? Here, we examine how these four types of relationships contribute to multiple components of fitness within a multi-level society of wild songbirds, while considering spatial effects based on breeding position. Methods Study system Our study was conducted in Wytham Woods, Oxford, UK (51°46’N, 1°20’W) on the long- term study population of great tits ( Parus major ). These 385ha of mixed deciduous woodland house ∼1018 fixed nest boxes, with known GPS coordinates, that are monitored yearly to record breeding attempts and performance ( Perrins 1965 ). Either during the breeding season (at the nest) or throughout the year (through mist netting) individuals are caught, fitted with a unique BTO (British Trust for Ornithology) metal leg ring and a plastic leg ring with a passive integrated transponder (PIT) tag, aged and sexed, and measured to record standard morphometric information. It is estimated that 90% of the population is tagged ( Aplin et al . 2013 ). Data collection Winter social network data collection During the winter, great tits feed from patches of resources that are distributed throughout the woodland. Sunflower seed feeding stations with two opposing access holes equipped with RFID antennae (Dorset ID, Aalten, The Netherlands) were placed in a stratified grid at 65 fixed locations approximately 250m apart. Feeders opened automatically every weekend over the winter (December-February, 2011-2014) from pre-dawn to after dusk. When birds used one of these feeders, tags allowed the detection of the time and date of their visit, creating a spatio-temporal data stream consisting of each individual visit to a feeder. Breeding season data collection Fitness measures During the breeding season (March-June), birds breeding in nest boxes were monitored through the stages of nest building, egg laying, incubation, and rearing offspring. Nests were checked weekly until eggs were found. Based on the assumption that one egg is laid each day, lay date was recorded (if one egg was found) or estimated (if more than one eggs were found) as the date the first egg of the clutch was laid. After incubation began, clutch size was recorded as the maximum number of eggs within the nest. Parents were either identified or ringed a week after eggs hatched, and all chicks were ringed and weighed a week later. Subsequently, nests were checked to determine the number of chicks that had fledged in each breeding attempt. Labelling neighbours In order to label territorial neighbours during the spring, we used the spatial arrangement of occupied nest boxes to estimate individual territories and their boundaries. For each occupied box, a Voronoi diagram (Thiessen polygon) was drawn to include all of the points that were closer to the focal box than any other occupied box ( Adams 2001 ; Schlicht et al . 2014 ). This method of estimating territories and neighbours accurately accounts for population density and is highly correlated with territory sizes and boundaries that are manually determined ( Grabowska-Zhang et al . 2012c ; Schlicht et al . 2014 ; Wilkin et al . 2006 ) as well as with the winter social structure ( Farine & Sheldon 2015 ; Firth & Sheldon 2016 ). Calculating the four measures of dyadic associations Each of the four measures of dyadic bonds was calculated based on the social interactions of individuals during the winter. In the spatio-temporal data stream created by the feeder visits, intermittent bursts of clustered activity can be detected which denote flocks that are arriving to feed together. These flocks were detected using a Gaussian mixture model (GMM) that statistically assigns each individual visit to the group that it is most likely to belong to ( Psorakis et al . 2012 ). We created a social network (association matrix) for each winter based on co-occurrences in flocks using the simple ratio index (SRI). The SRI values range from 0 to 1 and can be defined by the following equation: SAB = x/(x + yAB + yA + yB), where: SAB is the strength of association between A and B x is the number of times A and B were in the same flock yAB is the number of times A and B were detected at the same time but not together yA is the number of times A was detected in a flock without B yB is the number of times B was detected in a flock without A To quantify the relationship between pair mates , we only retained records where the identity of both individuals in the breeding pair was known. Based on the winter social network, we calculated the strength of each pair’s bond during the winter prior to the breeding season (following Culina et al . 2020 ; Firth et al . 2018a ). The relationship between breeding neighbours was quantified based on the territory and neighbour estimates described above and winter social networks. For every focal and each of their first-order neighbours, we calculated the bond strength between them from the previous winter (following Firth & Sheldon 2016 ). For each individual, we calculated 3 metrics: the average bond strength to their male neighbours, female neighbours, and all neighbours (male and female combined). To represent flockmate associations, we used two social network metrics, weighted degree (the number of connections an individual had weighted by the strength of those connections i.e. the sum of their SRI scores to others) and average bond strength (the average strength of connections i.e. the mean of their non-zero SRI scores to others). In other words, these metrics are based on co-occurrence within the foraging groups identified through the GMMs described above. To estimate winter spatial associations , we calculated the spatial temporal overlap for every possible dyad by accounting for the number of times individuals were observed at each feeder regardless of the specific foraging flocks they occurred in whilst at the feeder (following Firth et al . 2018a ). So, for example, if two individuals spent equal proportions of time in the same location on the same day (not necessarily at the same time), they would be assigned a 1; if they were never at the same location on the same day, they would be assigned a 0. Proportions for each focal individual were summed to create a single variable accounting for both the number of individuals each focal was in the same location as and the amount of time they each spent there. In contrast to the flockmate associations, this measure is based only on spatial temporal overlap in location. To examine the role of socio-spatial demographic distributions, we additionally calculated local density . Using the adehabitathr package in R, we created annual space use distribution kernels for the population based on their breeding locations and assigned each individual a local density measure based on the individual’s location on that distribution. This follows a protocol established for badgers and deer ( Albery et al . 2020 , 2021 ). Data analysis We analyzed three years (2011-2014) of data from 754 individuals. We ran separate models for males and females for each of the five fitness variables derived from the breeding data (binary success, clutch size, lay date, mean chick weight, and number of fledglings), as each individual has different social characteristics (following Gokcekus et al . 2023 ). We fitted Generalized Linear Mixed Models (GLMMs) using the Integrated Nested Laplace Approximation (INLA) R package and fitted the SPDE random effect (for breeding season locations). This models similarity emerging from the distance between points to account for spatial autocorrelation in the response variable ( Albery et al . 2019 ; Rue et al . 2009 ); we used locations of nest boxes. Continuous variables were scaled to have a mean of 0 and a standard deviation of 1. The base model included year (as a categorical fixed effect), categorical age (juvenile vs. adult), and habitat quality (the number of oak trees within a 75m radius of the nest box). We also included individual identity as a random effect. To investigate the effect of each of the dyadic relationship measures, we iteratively added the social effects (one measure of the social pair bond, three measures of breeding neighbour bonds, two measures of flockmate bonds, and three measures of spatial associations; correlation matrix in Supplementary Figure 1 ) the base model, and used Deviance Information Criterion (DIC) to identify the best fit. In each round, social effects were individually added until all had been included or their addition did not improve the model, using a cutoff of 2 DIC (following Albery et al . 2022 ; Gokcekus et al . 2023 ). Fixed effect estimates were provided by the mean and 95% credibility intervals of the posterior estimate distribution. Significance was determined by examining each effect’s overlap of the 2.5% and 97.5% posterior estimates with zero. Results We analysed data on 754 individuals embedded within 377 mated pairs with a total of 4,498 neighbour bonds and flockmate associations for each individual determined from 204,838 winter foraging flocking events. Our measures of sociality (in terms of the four types of dyadic bonds) influenced three of the five fitness-related variables (i.e. were retained by the model selection process for these models; lay date, clutch size, and number of fledglings). Four levels of dyadic bonds Individuals with stronger winter bonds to their pair mate had earlier lay dates, and this effect remained significant when controlling for breeding spatial autocorrelation ( Figure 1 ; Female: -.13, CI: -.19, -.07; Male: -.13, CI: -.19, -.07). Download figure Open in new tab Figure 1 – Summary of the social effects for all ten models, for females and males, with an SPDE effect to account for spatial autocorrelation. Points represent the estimate for each effect that was retained in the model selection process; error bars denote 95% credibility intervals. Individuals with stronger bonds to their male breeding neighbours had fewer fledglings. This effect was not significant after controlling for breeding spatial autocorrelation for males (-.08, CI: -.19, .04), but remained significant for females (-.13, CI: -.24, -.02; Figure 1 ). Individuals with more bonds to flock mates (higher degree) had a higher number of fledglings (F: .19, CI: .07, .31; M: .18, CI: .06, .29), but this effect was not significant after controlling for spatial autocorrelation (F: .10, CI: -.03, .23; M: .09, CI: -.04, .22). Individuals with more bonds to flock mates had later lay dates, and this effect remained significant after controlling for breeding spatial autocorrelation (F: .10, CI: .03, .18; M: .10, CI: .03, .18; Figure 1 ). Individuals with more winter spatial associates had smaller clutches, but this effect was only significant after controlling for breeding spatial autocorrelation (F: -.14, CI: -.26, -.03; M: - .14, CI: -.26, -.03; Figure 1 ). Although it was retained in the models, there were no significant effects of density on lay date (F: .06, CI: -.01, .13; M: .05, CI: -.02, .13). Non-social spatial drivers Individuals who bred in areas with a better habitat quality had earlier lay dates (F: -.09, CI: - .16, -.02; M: -.09, CI: -.16, -.02) and larger clutches (F: .11, CI: .01, .21; M: 11, CI: .01, .21; Figure 3 ), but this effect was no longer significant after controlling for breeding spatial autocorrelation. The spatial distribution of each of the response (fitness) variables is graphically illustrated by projecting the SPDE random effect onto a two-dimensional plane ( Figure 2 ). Overall, the southern part of the woods has lower reproductive success than the northern part, and lay dates are the latest in the central portion of the woods. Adding the SPDE random effect (accounting for breeding spatial autocorrelation) improves all of the models substantially, with the exception of the binary success model ( Table 1 ). Download figure Open in new tab Download figure Open in new tab Figure 2 - 2D projection of the spatial distribution of the z-scores for each fitness variable (for females), when accounting for both the fixed and random effects in each model. For a) lay date, b) clutch size, c) mean chick weight, and d) number of fledglings. Darker shading denotes higher success. View this table: View inline View popup Download powerpoint Table 1 - DIC for base and SPDE models Other non-social drivers Year had an influence on all of the variables except for mean chick weight (see Figure 3 ). There were no significant differences in terms of numeric age. However, juveniles had significantly later lay dates than adults (F: .35, CI: .21, .48; M: .35, CI: .17, .54). All social and non-social drivers were included in the same models ( Table 2 ). Download figure Open in new tab Figure 3 - Summary of the non-social effects with the SPDE effect to control for spatial autocorrelation. Points represent the estimate for each effect that was retained in the model selection process and error bars denote 95% credibility intervals View this table: View inline View popup Table 2 - model statistics for female main models (same information for males can be found in Supplementary Table 1 ); bold variables are significant in both models, italic variables are only significant before controlling for breeding spatial autocorrelation, * variables are only significant after controlling for breeding spatial autocorrelation Discussion Although individuals exist in complex multi-level societies, much work exploring the relationship between sociality and fitness has focused on a single type of dyadic bond. Using three years of behavioural and reproductive data in wild songbirds, we demonstrate the potential fitness consequences of four types of dyadic relationships that are found within multilevel societies. Individuals with a stronger bond to their pair mate had earlier lay dates, consistent with previous findings ( Culina et al . 2020 ). Although more social individuals (higher winter network degree) had later lay dates, they also had a higher number of fledglings, suggesting that social associations with flock mates influence fitness outcomes in multiple dimensions, rather than simply improving fitness overall. When controlling for other social and spatial effects, bonds with breeding neighbours were not additionally beneficial. In fact, having stronger bonds with neighbouring males was associated with having slightly fewer fledglings. Finally, individuals with more winter spatial associates had smaller clutch sizes – suggesting that it is important to control for spatial relationships when considering other, more traditionally studied, aspects of sociality. Accounting for breeding position spatial autocorrelation in fitness outcomes improved nearly all of the models, highlighting the importance of accounting for spatial effects. Our results demonstrate support for a consistent finding in this population - the importance of the pair bond in determining lay dates, with individuals that met earlier in the winter ( Culina et al . 2020 ) or had bred together in the past ( Gokcekus et al . 2023 ) having earlier lay dates. Here, we show this relationship still holds even when simultaneously considering other kinds of social links. Historically, many external factors have been used to predict lay dates ( Perrins & McCleery 1989 ), including temperature (Noordwijk et al . 1995; Visser et al . 1998 ), habitat quality ( Wilkin et al . 2007 ), caterpillar growth ( Visser et al . 2006 ), and presence of parasites ( Oppliger et al . 1994 ) among others. Yet the strength of the pair bond seems to consistently be an important predictor that is not usually examined. Lay date has often been considered as an individual trait, but these results suggest that it may also be a social trait (and therefore dependent on others in the social system; see discussion below on flock mate relations and lay date)( Evans et al . 2020 ). Previous studies in this population have shown that individuals breed close to their highly associated former flock mates ( Firth & Sheldon 2016 ) and that neighbours who are socially familiar (due to year-to-year territory sharing) have higher fitness ( Gokcekus et al . 2023 ). However, when controlling for other types of dyadic bonds, having strong winter bonds to subsequent breeding neighbours was not actually beneficial in this sample. Females with stronger bonds to neighbouring males had fewer fledglings. It is possible that tolerating males may actually come at a cost if it leads to having fewer resources. Additionally, if females with stronger bonds to male neighbours are more likely to have extra-pair copulations, males may subsequently provide less parental care in the nest or spend more time investing in other females ( Eliassen & Jørgensen 2014 ; Magrath & Komdeur 2003 ). This finding on breeding neighbours raises the possibility that the previously observed positive effect of neighbour familiarity ( Gokcekus et al . 2023 ; Grabowska-Zhang et al . 2012c ) is actually due to general sociality and/or early establishing of territories and not the result of cooperation that arises due to specific bonds with particular individuals. However, it is also possible that breeding neighbour familiarity is most important for an aspect of fitness that we cannot measure: whether individuals are able to establish a territory at all. In support of this possibility, a previous study on this population showed that immigrants are more likely to fail in establishing a territory (and therefore establishing a breeding attempt) than integrated members ( Kidd et al . 2015 ). Furthermore, it is possible that individuals that are familiar over multiple years are easier to identify because they become associated with territory boundaries. If there is a limit on the number of individuals that great tits can identify, it is possible that having stronger bonds with neighbours is not as beneficial as having neighbours that are familiar over the years simply because birds are more likely to be aware of this between-year familiarity ( Gokcekus et al . 2021 ). Individuals with a higher degree during the winter have more fledglings, which is in line with the general idea that being more social comes with fitness benefits ( Snyder-Mackler et al . 2020 ). However, this effect is weakened when considering breeding location spatial effects, raising the possibility that part of this relationship may be explained by higher-quality areas supporting stronger bonds. On the other hand, individuals with higher degree also had later lay dates, which may represent that dedicating social time to other flockmates trades off against the social bond with their pair mate and thus potentially pushes back the lay date. When considering winter spatial relations, we find that individuals with more winter spatial associates have smaller clutches. Individuals who spent the winter in higher-density areas had slightly later lay dates but this effect was not significant. As resources are provided only on weekends at these feeding stations, it is unlikely that the reduced clutch sizes are the result of resource competition at the feeders themselves; instead, females who spend the winter in areas with more conspecifics might anticipate greater competition and lay smaller clutches accordingly to avoid overstretching the available resources. This result also highlights the importance of explicitly testing and controlling for spatial associations when investigating more apparent measures of sociality. In this multi-level society, four types of social relationships measured across seasons influenced breeding success when tracked over multiple years. However, future experimental work is likely necessary to understand the causation behind these effects. For example, sociality’s influence on reproductive success could be investigated by manipulating the ability of some individuals to form social bonds. Detailed data on different kinds of social associations across multiple circumstances and years is necessary for a more thorough knowledge of the fitness consequences of sociality within multi-level societies. Disentangling the effects of different types of dyadic bonds within such societies and separating them from spatial patterns is important for gaining a more thorough understanding of the factors that drive the evolution of social stability within populations. Supplementary material Download figure Open in new tab Supplementary Figure 1 - Correlation matrix of social variables Download figure Open in new tab Supplementary Figure 2 - model results (only social effects) without SPDE spatial autocorrelation correction Download figure Open in new tab Supplementary Figure 3 - model results (only non-social effects without SPDE spatial autocorrelation correction View this table: View inline View popup Supplementary Table 1 - Male model results Acknowledgements This work was supported by grants from BBSRC (BB/L006081/1, BB/S009752/1), ERC (AdG250164), and NERC (NE/K006274/1, NE/S010335/1, NE/V013483/1) and the Wild Animal Initiative (C-2023-00057). References 1. ↵ Adams , E.S . ( 2001 ). Approaches to the study of territory size and shape . Annu Rev Ecol Syst . 2. ↵ Alberts , S.C. ( 2019 ). Social influences on survival and reproduction: Insights from a long-term study of wild baboons . Journal of Animal Ecology , 88 , 47 – 66 . 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