# Density-dependent network structuring within and across wild animal systems

Gregory Albery, Daniel Becker, Josh Firth, Delphine de Moor, Sanjana Ravindran, Matthew Silk, Amy Sweeny, Eric Vander Wal, Quinn Webber, Bryony Allen, et al.

► To cite this version:

Gregory Albery, Daniel Becker, Josh Firth, Delphine de Moor, Sanjana Ravindran, et al. Density-dependent network structuring within and across wild animal systems. Nature Ecology & Evolution, 2025, 9 (11), pp.2002-2013. 10.1038/s41559-025-02843-z. hal-05389143

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<figure><img src="image_1.png" /></figure>
1 Density-dependent network structuring within and
across wild animal systems

3 Gregory F Albery*¹,²,³, Daniel J Becker⁴, Josh A Firth⁵,⁶, Delphine De Moor⁷, Sanjana

4 Ravindran⁸, Matthew Silk⁸,⁹,¹⁰, Amy R Sweeny⁸,¹¹, Eric Vander Wal¹², Quinn Webber¹³,

5 Bryony Allen¹⁴,¹⁵, Simon A Babayan¹⁶, Sahas Barve¹⁷,¹⁸, Mike Begon¹⁴, Richard J Birtles¹⁹,

6 Theadora A Block²⁰,²¹, Barbara A Block²², Janette E Bradley²³, Sarah Budischak²⁴, Christina

7 Buesching²⁵, Sarah J Burthe²⁶, Aaron B Carlisle²⁷, Jennifer E Caselle²⁸, Ciro Cattuto²⁹, Alexis

8 S Chaine³⁰, Taylor K Chapple³¹, Barbara J Cheney³², Timothy Clutton-Brock³³, Melissa

9 Collier², David J Curnick³⁴, Richard J Delahay³⁵, Damien R Farine³⁶,³⁷,³⁸,³⁹, Andy Fenton¹⁴,

10 Francesco Ferretti⁴⁰, Laura Feyrer⁴¹,⁴², Helen Fielding⁴³,⁴⁴, Vivienne Foroughirad²,⁴⁵, Celine

11 Frere⁴⁶, Michael G Gardner⁴⁷, Eli Geffen⁴⁸, Stephanie S Godfrey⁴⁹, Andrea L Graham⁵⁰, Phil

12 S Hammond⁵¹, Maik Henrich⁵², Marco Heurich⁵²,⁵³,⁵⁴, Paul Hopwood¹⁰, Amiyaal Ilany⁵⁵,

13 Joseph A Jackson¹⁹, Nicola Jackson⁴⁶, David MP Jacoby⁵⁶, Ann-Marie Jacoby⁵⁷, Miloš

14 Ježek⁵⁸, Lucinda Kirkpatrick⁵⁹,⁶⁰, Alisa Klamm⁶¹, James A Klarevas-Irby³⁶,³⁷,⁶²,⁶³, Sarah

15 Knowles⁵, Lee Koren⁵⁵, Ewa Krzyszczyk⁶⁰, Jillian M Kusch⁶⁴,⁶⁵, Xavier Lambin⁶⁶, Jeffrey E

16 Lane⁶⁵, Herwig Leirs⁵⁹, Stephan T Leu⁶⁷, Bruce E Lyon²⁰, David W MacDonald⁶⁸, Anastasia

17 E Madsen⁶⁹,⁷⁰, Janet Mann², Marta Manser⁷¹, Joachim Mariën⁵⁹, Apia Massawe⁷², Robbie A

18 McDonald⁴⁴, Kevin Morelle⁵⁸,⁷³, Johann Mourier⁷⁴,⁷⁵,⁷⁶, Chris Newman⁶⁸, Kenneth Nussear⁷⁷,

19 Brendah Nyaguthii³⁸,³⁹,⁶²,⁷⁸, Mina Ogino³⁶,³⁷,⁶², Laura Ozella⁷⁹, Craig Packer⁸⁰, Yannis P

20 Papastamatiou⁸¹, Steve Paterson¹⁴, Eric Payne⁸², Amy B Pedersen⁸, Josephine M

21 Pemberton⁸, Noa Pinter-Wollman⁸³, Serge Planes⁷⁵,⁷⁶, Aura Raulo⁵, Rolando Rodríguez-

22 Muñoz¹⁰, Lauren Rudd⁵, Christopher Sabuni⁷², Pratha Sah², Robert J Schallert⁸⁴, Ben C

23 Sheldon⁵, Daizaburo Shizuka⁸⁵, Andrew Sih⁸², David L Sinn⁸⁶, Vincent Sluydts⁵⁹, Orr

24 Spiegel⁴⁸, Sandra Telfer⁶⁶, Courtney A Thomason⁴⁰,⁸⁷, David M Tickler⁸⁸, Tom Tregenza¹⁰,

25 Kimberley VanderWaal⁸⁹, Sam Walmsley⁴¹, Eric L Walters¹⁷, Klara M Wanelik¹⁴,⁹⁰, Hal

26 Whitehead⁴¹, Elodie Wielgus⁵², Jared Wilson-Aggarwal¹⁰,⁴⁴, Caroline Wohlfeil⁴⁷, Shweta

27 Bansal²

28 *Corresponding author: gfalbery@gmail.com

29 Affiliations can be found at the end of the document.

30 Summary

31 High population density should drive individuals to more frequently share space and interact,

32 producing better-connected spatial and social networks [1–4]. Although this theory is

33 fundamental to our understanding of disease dynamics [2,5–8], it remains unconfirmed how

34 local density generally drives individuals’ positions within their networks, which reduces our

35 ability to understand and predict density-dependent processes [4,9,10]. Here we provide the

36 first general evidence that density drives greater network connectedness at fine

37 spatiotemporal scales, at the scale of individuals within wild animal populations. We

38 analysed 36 datasets of simultaneous spatial and social behaviour in >58,000 individual

39 animals, spanning 30 species of fish, reptiles, birds, mammals, and insects. 80% of systems

40 exhibited strong positive relationships between local density and network centrality.

41 However, >80% of relationships were nonlinear and 75% became shallower at higher

42 values, signifying that demographic and behavioural processes counteract density's effects,
thereby producing saturating trends [11–15]. Density's effect was much stronger and less saturating for spatial than social networks, such that individuals become disproportionately spatially connected rather than socially at higher densities. Consequently, ecological processes that depend on spatial connections (e.g. indirect pathogen transmission, resource competition, and territory formation) are likely more density-dependent than those involving social interactions (e.g. direct pathogen transmission, aggression, and social learning). These findings reveal fundamental ecological rules governing societal structuring, with widespread implications. Identifying scaling rules based on processes that generalise across systems, such as these patterns of density dependence, might provide the ability to predict network structures in novel systems.

**Keywords:** Behavioural ecology, Spatial ecology, Disease ecology, Epidemiology, Population dynamics, Social network structure, Network analysis, Spatial analysis

## Introduction

The number of individuals occupying a given space – i.e., population density – is a central factor governing social systems. At higher densities, individuals are expected to more frequently share space, associate, and interact, producing more-connected spatial and social networks and thereby influencing downstream processes such as mating, learning, and competition. In particular, density-driven increases in network connectedness should provide more opportunities for parasites [1–4,9] or information [16] to spread between hosts[1–4,9] Despite the fundamental nature of such density-dependent processes, evidence is relatively limited that individuals inhabiting higher-density areas have more spatial and social connections. Furthermore, density effects should differ for asynchronous space sharing (e.g. home range overlap) versus social associations (e.g. den sharing or grouping) or interactions (e.g. mating or fighting). While several studies have compared animal populations at different densities to demonstrate variation in social association rates among populations (e.g., [15,17,18]) or groups (e.g., [11–13]), attempts to identify such density effects within continuous populations of individuals are rarer (but see [14,15,19–21]), and their findings have never been synthesised or compared for spatial and social behaviours. We therefore have an incomplete understanding of how density, as a fundamental ecological parameter, determines socio-spatial dynamics within and across systems. This inhibits our ability to identify and predict how changes in density – e.g. through culling, natural mortality, dispersal, or population booms – influence downstream processes that depend on shared space and social interactions.

The rate at which an individual interacts with conspecifics depends on its spatial and social behaviour within the context of the surrounding environment and population. Adding more individuals into the same space should cause them to more frequently spatially overlap and socially associate or interact (Figure 1). Often, individuals are modelled as randomly moving and interacting molecules (“mass action” or “mean field”). In this conceptualisation, direct contact between two molecules is analogous to a social interaction or association; rates of such interactions are often assumed to increase with density (“density-dependent”; e.g., [22]), and/or to be homogenous in space (e.g., [13]). In reality individuals are unlikely to behave and interact randomly in space, and instead will be influenced by spatially varying factors including local density [10] and competition for resources [15]. Changes in density may cause individuals to alter their foraging behaviour [23–25], dispersal [26,27], social
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preference or avoidance [14,28], mating behaviour [29], or preferred group size [18]. In some

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cases, density may have no effect on interaction rates, because individual animals alter their

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behaviour in a density-dependent manner to maintain a desired interaction rate [30]. These

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and related processes might produce strong nonlinearities in density-interaction

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relationships, which can complicate the predictions of density dependence models of

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pathogen transmission, for example [2,3,9]. For example, individuals or groups can learn to

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avoid where competitors might go, resulting in greater spatial partitioning under higher

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densities [31]. Nevertheless, nonlinearities such as these are poorly understood and rarely

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considered.

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Several wild animal studies have suggested relationships between density and social

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association rates are often nonlinear and saturating [11–15]. Such relationships imply that

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association rates do not increase passively with density, but rather that behavioural or

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demographic processes likely change as density increases, with the ultimate consequence

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of slowing association rates. However, these nonlinearities are difficult to examine between

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populations or between species because they introduce a range of confounders and have

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few replicates along the density axis [9]. On the other end, lower densities may provide less

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ability to exert social preferences, but low-density populations may be harder to study due to

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(for example) low return on sampling investment; alternatively, the failure to achieve

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sufficient interaction rates may result in Allee effects and ultimately drive populations toward

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decline [32,33].

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Characterising gradients of density across individuals within a population offers a

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workaround to these problems, and facilitates an appreciation of the fact that interactions

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occur between individuals rather than at the population level. Examining between-individual

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variation is one reason that social network analysis – which allows characterisation and

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analysis of individual-level social traits, amongst other things – has become so popular in

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animal ecology in recent years [34–38]. Additionally, recent years have seen a substantial

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growth in understanding of socio-spatial behaviours, including harmonising the concepts of

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spatial and social density [9,10,39]. Applying network analyses coupled with this socio-

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spatial understanding of density could provide an individual-level picture of density's effects

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on spatial and social connectedness, offering far higher resolution and statistical power and

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greater ability to detect within-system nonlinearities and between-system differences [9]. By

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providing new understanding of the correlates and emergent consequences of variation in

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density, this expansion could help to identify general rules underlying social structuring and

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network scaling in space.

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Critically, different types of interactions or associations should show different relationships

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with density: for example, the need to compete for food at higher densities could drive a

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disproportionate increase in aggression [40], but this is unlikely to be true of mating

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interactions. In contrast, higher density and food scarcity should lead to lower exclusivity in

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resources and more overlapping home ranges, thus enhancing the effect of density on

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spatial network [41]. This rationale is well-understood in disease ecology, as differences in

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density-contact relationships are thought to drive differences in density dependence of

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infection – where “contact” is defined as an interaction or association that could spread a

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pathogen (Figure 1). “Contacts” then form the basis of spatial and social networks used to

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investigate pathogen transmission dynamics, which should likewise diverge with density just

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as contacts do. For example, density should drive greater transmission of respiratory
132 pathogens but not sexually transmitted pathogens [1,42]. Establishing these density-contact
133 relationships is integral to understanding disease dynamics and developing control
134 measures [1,43], but we still have a poor understanding of how different interactions (and
135 therefore contact events for different pathogens) are driven by density. This direct/indirect
136 interaction dichotomy is most fundamental to disease ecology [39,44], but given building
137 interest in the spatial-social interface and relationships between spatial and social networks
138 in behavioural ecology [10], the framework is readily related to other fields (e.g. direct versus
139 indirect cues that can lead to social learning [45]). Previously established density-interaction
140 relationships are diverse and include feral dog bites [19], ant antennations [46] and
141 trophallaxis [30], ungulate group memberships [20,23], rodent co-trapping [11,47], and
142 agamid association patterns [14,21], but no study has yet synthesised how the rates of
143 multiple interaction or association types relate to density, within or across systems.

144 Identifying the general rules underlying density dependence requires quantifying density's
145 relationship with proxies of interaction rates at fine scales across a diversity of systems, then
146 identifying the factors determining their slope and shape. To this end, we collate a meta-
147 dataset of over 58,000 individual animals across 36 wildlife systems globally (Figure 2) to
148 ask how within-population variation in density determines between-individual interaction
149 rates based on connectedness in spatial and social networks. We fit multiple competing
150 linear and nonlinear relationships to identify the slope and shape of density effects within
151 each system, and we use meta-analyses to investigate general rules determining their slope
152 and shape across systems. In particular, we focus on comparing space sharing with social
153 interactions and associations as a cross-system case study. Ultimately, we present a de
154 novo cross-system analysis of individuals' social and spatial behaviour that traverses fields
155 of behavioural, population, and disease ecology, which could help to inform general rules
156 governing the structure of social systems, and eventually shape management and
157 conservation decisions in a wide range of systems.
<figure><img src="image_2.png" /><figcaption>Figure 1: Schematic detailing the rationale underlying this study, outlining how population density drives the formation of spatial and social networks. This depiction uses the Wytham Wood great tits as an example. In panel C, the different purple shades correspond to different individuals' home ranges. In panel D, red lines depict connections among individuals, with each individual located at their centroid. Ultimately, one of our main aims is to ask whether spatial or social connections generally show a stronger relationship with density, partly functioning as a proxy for indirect and direct contact events with the potential to transmit pathogens. This framework moves between concepts of network and contact formation traversing behavioural ecology, spatial and social network ecology, and disease ecology.</figcaption></figure>

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<figure><img src="image_3.png" /></figure>

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<caption>Figure 2: The phylogenetic (A) and geographic (B) distribution of our 36 examined datasets of spatial and social behaviour, with (C) schematic depicting the methodology for deriving local density values, using the Isle of Rum red deer data as an example. The X and Y axes are bivariate spatial coordinates. The panels within (C) show raw observations of individuals in space that we then average at the individual level to make centroids; we use the centroids to generate annual density distributions, which are then assigned to individuals in the form of local density measures. Animal silhouettes are from phylopic.org; a list of attributions is in the supplement (Supplementary Table 2). NB the Potomac dolphins are now defined as Tursiops erebennus; they are currently incorporated in Panel A as T. truncatus, following the Open Tree of Life nomenclature.</caption>
<figure><img src="image_4.png" /><figcaption>Figure 3: Meta-analysis revealed drivers of variation in linear density effects on individual network connectedness across systems. A) Our fitted linear model estimates of density effects on network strength. Each point represents the mean estimate from a given system; the error bars denote 95% confidence intervals. Opaque error bars were significant (i.e., do not overlap with 0); transparent ones were not. The estimates are in units of standard deviations for both density and network strength. The colour of the point denotes whether the network being examined was defined using spatial or social connections. B) Meta-analyses revealed that centrality in spatial networks (i.e., home range overlap; red points) had a significantly steeper relationship with density than social networks (blue points). C) We fitted linear models separately to two portions of the data within each study population (“first” and “last” represent values below and above the median). The slopes for the latter portion (pink points) were generally less positive than the former portion (purple points), implying a general saturation shape. In panels B) and C), each coloured point represents a study replicate fitted to the strength estimate; points are sized according to sample size, and jittered slightly on the x axis to reduce overplotting. The large black points represent the mean slope estimated from the meta-analysis, and the error bars represent 95% confidence intervals.</figcaption></figure>

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<figure><img src="image_5.png" /><figcaption>Figure 4: Relationships between density and network connectedness varied substantially across animal systems. Density in individuals per area is on the x axis; network connectedness (strength centrality) is on the y axis. Both values have been standardised to have a mean of zero and a standard deviation of 1 within each system; the axis ticks are in units of 1 standard deviation. Each point represents an individual-year-behaviour replicate; the lines portray the model fit from our generalised additive models (GAMs). Red lengths of the smooth=significantly positive; grey=not significantly different from zero; blue=significantly negative. Points are semi-transparent to enhance visibility. Panels are arranged phylogenetically following the tree displayed in Figure 2A; GOG=gambit of the group; HRO=home range overlap. Animal silhouettes are from phytopic.org; a set of links and attributions are in the Supplement.</figcaption></figure>
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# Results and Discussion

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We compiled a comparative meta-dataset of over ten million observations of individual animals' spatial and social behaviour, across a wide range of ecological systems and taxonomic groups of animals. We then ran a standardised pipeline to align their spatial and social observations, identifying strong and predictable relationships between local density and network connectedness at the individual level.

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We observed strong positive relationships between individuals' local population density and their connectedness in spatial and social networks across a wide range of wild animals: of our 64 replicates, 51 (78%) were significantly positive when analysed using linear models (Figure 3A). Meta-analyses identified a highly significant positive mean correlation between density and connectedness, both for social networks (Estimate 0.22; 95% CI 0.17, 0.27) and spatial networks (0.45; 0.36, 0.53; Figure 3B). Our study therefore provides fundamental evidence that high local population density broadly drives greater connectedness within ecological systems, at the individual level. Slopes were highly variable across systems for both spatial and social networks (Figure 3A; Q-test of heterogeneity across systems: $Q_{37} = 5627.33$ and $Q_{25} = 1281.83$, both $P<0.0001$), indicating that quantifying these slopes within and between multiple systems and comparing them is important for understanding animal socio-spatial structure. That is, relationships between density and individual connectedness differ substantially between populations, and the biological mechanisms underlying these divergent trends are likely important. As well as adding resolution and allowing comparisons of density effects across systems, our methodology facilitated fitting of nonlinear relationships (using generalised additive models (GAMs); see below). This approach has only rarely been applied before, and then at much coarser resolution (see [11,12,19]). As such, this study fills an important empirical gap by providing insights into the slope and shape of density-connectedness relationships for a diverse variety of animal groups and their social and spatial behaviours (Figure 4). Nevertheless, despite this diversity, we were able to identify several further general trends in our data.

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Remarkably, density's effect more than doubled in size for spatial compared to social networks (Figure 3B; $r=0.45$ versus 0.22); there was a difference of 0.26 (CI 0.16, 0.36, $P<0.0001$) for this effect when we meta-analysed the two contact types together. This finding indicates that as density increases, wild animals are more likely to share space with each other, but that social connections increase at a much slower rate. Similarly, we discovered that saturating shapes were extremely common: as density increased, its effect on connectedness decreased, such that 48/64 systems (75%) had a steeper slope at low density values than at high ones. This effect was strong for both social networks (effect on $r=$ -0.11; CI -0.19, -0.03; $P=0.01$) and for spatial networks, with substantial overlap between their estimates (-0.22; -0.37, -0.07; $P=0.0042$). Due to the greater overall effect for space sharing, the latter half of density's spatial effect was still higher than the first half of its social effect (Figure 3C). Together, these observations suggest that density-dependent processes act to limit the increase in social connectedness with density, but without limiting spatial overlaps to the same extent. Consequently, higher-density areas are characterised disproportionately by individuals asynchronously sharing space rather than socially associating, while in lower-density areas individuals are disproportionately more socially connected proportional to their shared space.

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There are many possible social reasons for saturating nonlinearity in density-dependent network structuring: for example, individuals in higher density areas may begin to avoid each other, seeking to avoid competition or aggression [40] or exposure to infectious disease [48]. For instance, Eastern water dragons (*Intellagama lesueurii*) show greater avoidance at higher densities [14], supporting avoidance-related mechanisms. Alternatively, in species with high social cognition or stable bonds, saturation could reflect lower social effort or ability to keep track of social affiliates at higher densities [49]. In general, individuals likely have a preferred social interaction rate or group size – a preference that they may increasingly exert at higher densities [18]. It remains to be seen how this preference varies among individuals, and whether individuals vary in their preferred social network position given a certain density. Given that individuals vary in their movement and spatial phenotypes [50–52], and social phenotypes [52–54] in ways that should manifest for density-dependent behaviours specifically [10], it seems likely that these slopes could vary between individuals as they do between populations. Future analyses might fit variable density-connectedness slopes among individuals to identify socio-spatial syndromes across systems, as has been done previously in single systems including caribou (*Rangifer tarandus*) [55] and American red squirrels (*Tamiasciurus hudsonicus*) [56]. Additionally, we could dissect the social network and its relationship to the spatial network to identify levels of attraction [57,58] or avoidance [59] and how they depend on density.

We considered that density-dependent changes in spatial behaviours might explain these trends: for example, density could create greater competition over resources and therefore reduce energy to roam (and contact others). Individuals may partition their niches [60], or reduce their territory or home range sizes [56,61,62], potentially driven by years of plentiful resources supporting higher densities alongside smaller home ranges sufficiently providing ones’ resource needs, which could drive lower association rates. However, our findings do not seem to support explanations related to small home ranges, because such explanations should produce an equivalent or stronger reduction in (relative) spatial connectedness. In contrast, we observed that density drove individuals to become spatially connected faster than socially, such that the underlying mechanisms likely involve behaviours and demographic processes that specifically affect social collocation in space and time. Testing the precise underlying mechanism will likely require finer-scale behavioural observations, as described below. Regardless of mechanism, these saturating density-connectedness relationships strongly support the idea that examining density effects at the individual level – rather than between populations – is highly informative. For many systems, “mean field” expectations of homogenous interactions under increasing density likely produce an inaccurate (i.e., inflated) picture of density’s effects. Importantly, our study included many examples of proximity-based social networks – most notably “gambit of the group” measures [63] – but relatively few “direct” interactions such as mating, grooming, or fighting. It is interesting that these differences manifested even among two ostensibly spatially-defined contact metrics (gambit of the group and home range overlap). This observation supports the assertion that social association metrics defined by spatiotemporal proximity are valuable for informing on social processes separately from more spatial behaviours *sensu stricto* such as ranging behaviour [20]; we expect that “more direct” interactions could show even further differences in relationships with density. Incorporating a larger number of “direct” metric-based systems could help to address this question (see Supplementary Discussion).
297 The fact that spatial networks show stronger and more linear density dependence than
298 social networks could heavily influence the ecology of animal systems. For example,
299 indirectly transmitted (i.e., environmentally latent) parasites may exhibit greater density
300 dependence than directly transmitted ones, given that individuals likely experience
301 disproportionately more indirect contact at higher densities. This observation contrasts with
302 orthodoxy that directly transmitted parasites are most likely to be density dependent [64],
303 and supports the value of investigating nonlinear changes in socio-spatial behaviour and
304 grouping patterns in response to density when considering density dependence. Saturating
305 density-connectedness functions further have implications for disease modelling and control.
306 Specifically, our findings lend behavioural support to the growing consensus that many
307 diseases are density-dependent at lower densities, but not at higher densities (i.e., that the
308 slope flattens with density) [22,65]. Rather than assuming constant behavioural mixing at
309 higher densities, epidemiological models could benefit from incorporating density-dependent
310 shifts in behaviours and demography that influence direct and indirect interaction
311 frequencies, as previously suggested empirically and by epidemiological theory [22]. These
312 relationships could influence our targets for culling or vaccination coverage [5]. Given that
313 animals at high density seem likely to have a relatively shallow relationship between density
314 and contact rates, reducing population density – for example by culling – might therefore be
315 ineffective at reducing pathogen transmission initially, particularly when considering socially
316 transmitted pathogens, where contact rates are particularly likely to have become saturated
317 (Figure 3C). Similar problems with culling have already been acknowledged in specific
318 systems – e.g. in canine rabies [43,66,67] – but our study implies that shallow nonlinear
319 density-contact trends could be more general than previously thought and could be driven by
320 flexible density-dependent changes in behaviour and demography. Conversely, culling could
321 be disproportionately effective at intermediate densities and identifying the inflection points
322 of the curve might help to design optimal management strategies. Future studies should
323 investigate whether the divergence in spatial and social connectedness with density drives a
324 concurrent divergence in the prevalence of directly and indirectly transmitted parasites, as
325 well as addressing several other biases in our selection of systems (e.g. [68]; see
326 Supplementary Discussion).

327 Beyond these general trends, we ran generalised additive models (GAMs) that revealed that
328 52/64 density effects on network connectedness (81%) were significantly nonlinear
329 (ΔAIC>2); these relationships took a wide variety of shapes, representing a range of
330 nonlinear functions that are hard to generalise (Figure 4). Notably, while many GAM
331 smooths were eventually significantly negative (Figure 4), the vast majority of linear models
332 fitted to the second half of the data were positive (Figure 3C); this result is likely an artefact
333 of restricted model fitting, rather than true downturns in connectedness with density.
334 Nonlinearity did not cluster according to connection type definitions, or according to animal
335 group. These observations were largely corroborated by our meta-analytical models, which
336 found no factors influencing the slope and shape of density effects overall (P>0.05;
337 Supplementary Table 3), including no clear phylogenetic signal (ΔAIC=2.71). This
338 observation speaks to the complexity of these relationships within and across systems, while
339 accentuating that simple functional relationships are often likely to be complicated by
340 contravening ecological factors like habitat selection [69,70], group formation [15], parasite
341 avoidance [71], and demographic structuring [72]. While we were unable to identify specific
342 between-system predictors of nonlinearity of density-connectedness relationships, the
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finding that most such relationships are strongly nonlinear is an important consideration for
future work.

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Density is a universal factor underlying the dynamics of animal populations, and its linear
and nonlinear effects on spatial and social network structure are likely to impact myriad
processes in behaviour, ecology, and evolution. Similar to other studies that have reported
general scaling patterns in network analysis [73] and in food web ecology [74], the patterns
we report strongly suggest that animal systems generally become more connected spatially
than socially under increasing density. These trends might extrapolate to human networks,
given that other scaling patterns in animal networks do [73]. As these patterns seemingly
manifest regardless of animal group and interaction type, they may reflect a generalisable
rule governing the socio-spatial structure of ecological systems. Further refining and
implementing these models could facilitate prediction of network structure in novel systems.

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Finally, this study is relatively unique in conducting an expansive meta-analysis of
behavioural data from individual animals across a diverse selection of systems. As datasets
accumulate comparative analyses are increasing in frequency in social network ecology [75],
but often revolve around analysing whole networks rather than individuals [76], and never (to
our knowledge) in conjunction with analyses of spatial behaviour. These analyses therefore
hold exceptional promise for disentangling spatial and social behaviour across diverse
systems; for example, given that our dataset includes many repeatedly sampled known
individuals, future analyses could investigate individual-level repeatability or multi-behaviour
"behavioural syndromes" across a variety of different taxa and environments [10,77].
Additionally, capitalising on the wide range of methodological approaches to behavioural
data collection (e.g. censuses, trapping, and GPS telemetry), the methodological constraints
of socio-spatial analyses could be tested in this wide meta-dataset as they have been in
other recent comparative analyses of wild ungulates [78]. As well as being diverse, our
meta-dataset had several replicate examples of (for example) marine mammals and trapped
rodents, which could be used for finer-scale and more targeted comparative analyses within
these smaller taxonomic groupings. For now, it is highly encouraging that we uncovered
general trends across these disparate animal systems, and further explorations of these
socio-spatial patterns may help to inform a wide range of exciting and longstanding
questions at the spatial-social interface [10].

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# Methods

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## Data standardisation and behavioural pipeline

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Data were manipulated and analysed using R version 4.2.3 [79], and all R code is available at https://github.com/gfalbery/DensityMetaAnalysis. Our 36 datasets each involved at least one continuous uninterrupted spatial distribution of observations in a single population; some datasets comprised multiple such populations; all systems had at least one social network measure, and two had two different types of social interaction. These datasets covered 30 different animal species, including sharks, carnivores, cetaceans, ungulates, rodents, elephants, birds, reptiles, and one orthopteran insect (Figure 2). In one case (The Firth of Tay and Moray Dolphins) we used two distinct replicates despite being composed of overlapping groups of individuals, because of their distinct spatial distributions, which made it difficult to fit a coherent density distribution.
386 To standardise the timescale across studies, all systems were analysed as annual replicates
387 – i.e., social and spatial networks were summarised within each year. Our analyses used 64
388 system-behaviour replicates, listed in Supplementary Table 1, and totalled 151,835 unique
389 system-individual-year-behaviour data points.

390 All spatial coordinates were converted to the scale of kilometres or metres to allow
391 comparison across systems. To provide an approximation of local density, following prior
392 methodology [20,80], we took each individual's average location across the year (their
393 centroid) and created a spatial density kernel using the `adehabitathr` package [81], which
394 provides a probabilistic distribution of population density across each study system based on
395 the local frequencies of observed individuals. Each individual was assigned an annual
396 estimate of local density based on their centroid's location within this spatial density
397 distribution. We made these density distributions as comparable as possible between
398 systems by incorporating the density raster using metre squares; however, there were large
399 differences in density across populations that were difficult to resolve and put on the same
400 scale (e.g. interactions per individual/km² unit of density). Consequently, we scaled and
401 centred density to have a mean of zero and a standard deviation of one within each
402 population, which allowed us to focus on differences in relative slope and shape across
403 systems.

404 To validate the local density measures estimated using the kernel density approach, we also
405 estimated local density for individuals across all populations based on the locations of
406 individual annual centroids within a designated area. To do so, we first estimated the area of
407 the minimum bounding box (MBB) within which all individuals were censused during the
408 study period based on their annual centroids. For each individuals mean location, we then
409 estimated a circular boundary of radius r=1/20 * area of MBB. We then calculated the
410 number of individuals present within this boundary as an individual's local density measure.
411 We estimated the Pearson correlation coefficients between the local density measures
412 derived using the KDE approach and the proportional area - based approach
413 (Supplementary Figure 1).

414 To provide a measure of asynchronous space sharing, we constructed home range overlap
415 (HRO) networks based on proportional overlap of two individuals' minimum convex polygon
416 (MCP; i.e., the bounding polygon around all observations of each individual in a given year).
417 These HRO networks were restricted to only individuals with five or more observations in a
418 given year to allow us to create convex polygons effectively; 10/36 (28%) systems did not
419 have sufficient sampling for this analysis. We also repeated our analyses with a series of
420 higher sampling requirements for observation numbers to ensure that our findings were
421 robust to this assumption. The MCP approach is relatively low-resolution, and assumes
422 uniform space use across an individual's home range; however, this approach is less data
423 intensive – and less sensitive to assumptions – than density kernel-based approaches that
424 would estimate variation in space use across the home range, allowing us to apply the
425 models across more systems, more generalisably, and more conservatively.

426 To provide a measure of social connectedness, we built social networks using various
427 approaches as defined by the original studies: direct observations of dyadic interactions (e.g.
428 fighting or mating); gambit of the group (GoG; i.e., membership of the same group) [63]; co-
429 trapping (i.e., trapped together or in adjacent traps within a given number of trapping
430 sessions); or direct contact measured by proximity sensors (defined by a certain distance-
431 based detection threshold). Notably some analyses use indirect interactions – i.e., spatial
432 overlap – to approximate direct interactions, which requires spatiotemporal coincidence,
433 which we caution against particularly when modelling pathogen transmission [39,82]. While
434 the two do often correlate, here we are not using HRO to approximate direct interaction
435 rates, but rather as a measure of indirect interactions (e.g., indicative of transmission of
436 environmental parasites).

437 For each social network, we scaled connection strength relative to the number of
438 observations of each individual in a dyad (i.e., simple ratio index [83]). Our response variable
439 therefore took the form of strength centrality, scaled to between 0-1 for each dyad, for each
440 social and spatial network. We focus on comparing density effects on social interactions and
441 associations with density's effects on space sharing.

442 Density-connectedness models: what forms do density effects
take?
443

444 We developed a novel workflow to allow us to derive and compare density's effects on
445 connectedness – and their drivers – in a standardised way across our animal systems. We
446 fitted models with three main forms: **linear models** fitted to the whole dataset, nonlinear
447 **Generalised additive models** fitted to the whole dataset, and linear **saturation models**
448 fitted separately to low- and high-density subsets of each dataset.

449 **Linear models:** For each system-behaviour replicate, we first fitted a linear model using the
450 `lm` function in R, fitting scaled density as an explanatory variable to estimate linear density
451 effect slopes. The linear fits are displayed in the supplement (Supplementary Figure 2), as
452 are the residuals (Supplementary Figure 3).

453 **Generalised additive models (GAMs):** We fitted GAMs in the `mgcv` package [84] to
454 identify whether each density effect was better described by a linear or nonlinear
455 relationship, and to identify the shape of these nonlinear relationships. For each model, we
456 fitted a default thin plate spline with k=4 knots. This knot number was selected to reduce
457 overfitting in our models, which formed several fits to the data that were difficult to reconcile
458 with functional formats. To assess whether nonlinear models fit better than linear models, we
459 used Akaike Information Criterion (AIC), with a contrast of 2ΔAIC designated to distinguish
460 between models.

461 **Saturation models:** To quantify whether density effects were generally saturating (i.e., that
462 density had steeper relationships with individuals' connectedness at lower density values),
463 we split the data into two portions: all values below the median density value, and all values
464 above the median. We then re-ran linear models examining the relationship between density
465 and strength in each portion. We attempted to investigate nonlinear patterns (especially
466 saturating effects) across all our systems using a range of other methods (e.g., comparing
467 specific functional relationships with nonlinear least squares), but found that they were
468 generally incapable of fitting well to the data in a standardised way across the many datasets
469 (i.e., non-convergence of nonlinear least squares using semi-automated starting estimates
470 across systems). As such, this approach represented a tradeoff between tractable,
471 generalisable model fitting, interpretability, and accurate representation of the relationship's
472 shape. All else being equal, we posit that investigating the relative slopes of two otherwise-
473 identical portions of the data is a conservative and informative method of identifying
474 saturation, which was our main hypothesis for the expected shape of density effects.

475 **Heteroskedasticity and log-log models:** To ensure that our estimates were robust to non-
476 normality and to provide another source of information concerning possible saturation
477 effects, we also conducted tests of heteroskedasticity on our linear models and
478 accompanied them with simulations and fitted log-log linear models. First, we carried out a
479 simple simulation study to test how: a) the skew in residuals; b) a saturating relationship; and
480 c) heteroscedasticity impact whether we may under- or overestimate the slope of an
481 assumed linear relationship between density and strength (See Supplementary Methods -
482 Heteroskedasticity Simulations). These demonstrated that our models were resilient to skew
483 and saturating effects, but that heteroskedasticity in residuals could drive overestimated
484 linear effects in our models.

485 To examine this possibility further, we derived the Breusch-Pagan statistic for each linear
486 model as a measure of heteroskedasticity, and then plotted it against the meta-analysis
487 covariates and fixed effects. There was no evidence that the density effect was being
488 skewed to be greater for spatial behaviours due to heteroskedasticity, and neither were the
489 second portions of the data more heteroskedastic, which would be expected if this was
490 driving the saturating effect (Supplementary Figure 4). Finally, we fitted log-log linear models
491 with the same formulations as our main linear models defined above, but with both density
492 and strength log(X+1)-transformed, rather than scaled to have a mean of 0 and a standard
493 deviation of 1 (Supplementary Figure . Our results showed broadly identical findings of
494 greater estimates for spatial behaviours, and the fact that the slopes were largely under 1 is
495 indicative of a saturating effect. As such, these tests strongly support our findings’ resilience
496 to uneven data distributions.

Meta-analysis: what factors determine the slope of density-
connectedness relationships?

499 To characterise the typical relative slope of density effects across systems and identify the
500 factors influencing their variation, we fitted hierarchical meta-analytical models using the
501 `metafor` package in R. The response variable was the standardised slope of the linear
502 density effect; because both individual network strength and density were scaled to have
503 mean of zero and standard deviation of one in the linear regression, this is equivalent to the
504 correlation coefficient (r) [85]. We converted all correlation coefficients into Fisher's Z (Z_r)
505 and computed associated sampling variance.

506 For our hierarchical meta-analysis models, we used an initial model that nested observations
507 within a system-level random effect to account for within- and between-system heterogeneity
508 [86], as 26/36 systems had more than one density effect. We used another random effect for
509 species to account for repeat observations per animal species.

510 We then added a separate random effect for animal phylogeny [87]. This effect used a
511 phylogenetic correlation matrix of our 30 animal species derived from the Open Tree of Life
512 via the `rotl` package [88], with the `ape` package used to resolve multichotomies and
513 provide branch lengths [89].
514 We then fitted intercept-only models using the `rma.mv()` function with restricted maximum likelihood (REML), weighted by inverse sampling variance, and used variance components to quantify $I^2$, the contribution of true heterogeneity to the total variance in effect size. We used Cochran's Q to test whether such heterogeneity was greater than that expected by sampling error alone.
515
516
517
518

519 We next fitted models with the same random effects structure that included explanatory variables. To detect whether some animals were more likely to experience density effects, we fitted **Animal group** as a factor with six categories, representing a combination of species' taxonomy and general ecology: aquatic (fish and dolphins), birds, large herbivores (elephants and ungulates), small mammals (rodents and hyraxes), carnivores, and ectotherms (insects and reptiles). We also fitted several explanatory variables indicative of greater statistical power that might increase the strength of density effects: **Geographic area** (km², log₁₀-transformed), **Number of years** of study, and **Number of individuals**, all of which we fitted as continuous covariates. Broadly, the animal group model was highly uninformative and competed with the other effects, and we expected that the phylogeny would be more informative, so we report the results of the model without the animal group effect fitted.
520
521
522
523
524
525
526
527
528
529
530

531 We ran several different versions of these meta-analyses: first, we fitted meta-analytical models to the **overall linear models** of spatial and social interaction types separately, and then together, to investigate differences between the spatial and social networks in terms of their mean density slope. Next, we fitted duplicated versions of these models, but with the **saturation models**. These models were identical, but each system replicate had two linear estimates: one taken from the first 50% of the data (up to the median), and one from the latter 50%. By fitting a binary fixed effect of "data portion" to the meta-analytical models, this model would tell us whether the slopes were generally higher in the first portion of the data than the last (and therefore showed generally saturating shapes). We were unable to fit meta-analytical models to our GAMMs, as methods for the meta-analysis of nonlinear estimates are not yet well defined.
532
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535
536
537
538
539
540
541

542 Acknowledgements

543 GFA and SB were supported by NSF grant number DEB-2211287. GFA acknowledges
544 support from a College for Life Sciences Fellowship at the Wissenschaftskolleg zu Berlin and
545 WAI (C-2023-00057). DJB acknowledges support from the Edward Mallinckrodt, Jr.
546 Foundation and NSF BII 2213854. JAF acknowledges funding from BBSRC
547 (BB/S009752/1), NERC (NE/S010335/1 and NE/V013483/1), and WAI (C-2023-00057). For
548 a list of system-specific acknowledgements, please see the supplement.
549

549

550 Code availability

551 All R code is available at https://github.com/gfalbery/DensityMetaAnalysis.

552
553 Affiliations

554 1. School of Natural Sciences, Trinity College Dublin, Dublin, Ireland

555 2. Department of Biology, Georgetown University, Washington, DC, USA

556 3. Wissenschaftskolleg zu Berlin, Berlin, Germany

557 4. School of Biological Sciences, University of Oklahoma, Norman, OK, USA

558 5. Department of Biology, University of Oxford, Oxford, UK

559 6. School of Biology, University of Leeds, Leeds, UK

560 7. Department of Primate Behavior and Evolution, Max Planck Institute for Evolutionary Anthropology, Leipzig, Germany

561 8. Institute of Ecology and Evolution, University of Edinburgh, Edinburgh, UK

562 9. CEFE, Univ Montpellier, CNRS, EPHE, IRD, Montpellier, France

563 10. Centre for Ecology and Conservation, University of Exeter, Penryn, UK

564 11. School of Biosciences, University of Sheffield, Sheffield, UK

565 12. Department of Biology, Memorial University of Newfoundland, 45 Arctic Avenue, St. John's NL

566 13. University of Guelph, Guelph, Canada

567 14. Institute of Infection, Veterinary and Ecological Sciences, University of Liverpool, Liverpool, UK

568 15. Institute of Zoology, Zoological Society of London, Regents Park, London, UK

569 16. University of Glasgow, Glasgow, UK

570 17. Department of Biological Sciences, Old Dominion University, Norfolk, VA, USA

571 18. Avian Ecology Program, Archbold Biological Station, Venus, FL, USA

572 19. School of Science, Engineering and Environment, University of Salford, Salford, UK

573 20. Department of Ecology and Evolutionary Biology, University of California, Santa Cruz, CA, USA

574 21. Canine Companions, 2965 Dutton Ave, Santa Rosa, CA, USA

575 22. Hopkins Marine Station, Stanford University, Stanford, CA, USA

576 23. School of Life Sciences, University of Nottingham, Nottingham, UK

577 24. Pitzer and Scripps Colleges, Claremont, CA, USA

578 25. The University of British Columbia, Irving K. Barber Faculty of Sciences, Okanagan Department of Biology, Kelowna, British Columbia, Canada

579 26. UK Centre for Ecology and Hydrology, Bush Estate, Midlothian, UK

580 27. University of Delaware, Newark, DE, USA

581 28. Marine Science Institute, UC Santa Barbara, Santa Barbara, CA, USA

582 29. ISI Foundation, Torino, Italy

583 30. Evolutionary Ecology Group, Station d'Ecologie Theorique et Experimentale du CNRS, Moulis, France

584 31. Oregon State University, Corvallis, OR, USA

585 32. Lighthouse Field Station, School of Biological Sciences, University of Aberdeen, UK

586 33. Department of Zoology, University of Cambridge, Cambridge, UK

587 34. Institute of Zoology, Zoological Society of London, London, UK
597 35. National Wildlife Management Centre, Animal and Plant Health Agency, Sand Hutton, York, UK

598 36. Department of Collective Behavior, Max Planck Institute for Animal Behavior, 78464 Konstanz, Germany

600 37. Department of Evolutionary Biology and Environmental Studies, University of Zurich, 8057 Zurich, Switzerland

603 38. Department of Ornithology, National Museum of Kenya, P.O. Box 40241-001000, Nairobi, Kenya

605 39. Division of Ecology and Evolution, Research School of Biology, Australian National University, 46 Sullivan Creek Road, Canberra, ACT 2600, Australia

606 40. Virginia Tech, Blacksburg, VA, USA

608 41. Department of Biology, Dalhousie University, Halifax, Nova Scotia, B3H 4R2, Canada

610 42. Department of Fisheries and Oceans, Bedford Institute of Oceanography, Dartmouth, Nova Scotia, B2Y 4A2, Canada

612 43. The Royal (Dick) School of Veterinary Studies, University of Edinburgh, Edinburgh, UK

613 44. Environment and Sustainability Institute, University of Exeter, Penryn, UK

615 45. Department of Marine Biology, Texas A&M University at Galveston, USA

616 46. School of the Environment, University of Queensland, Brisbane, Queensland, Australia

618 47. College of Science and Engineering, Flinders University, Adelaide, Australia

619 48. School of Zoology, Faculty of Life Science, Tel Aviv University, Tel Aviv, Israel

620 49. Department of Zoology, University of Otago, Otago, New Zealand

621 50. Department of Ecology and Evolutionary Biology, Princeton University, Princeton, NJ, USA

623 51. Sea Mammal Research Unit, Scottish Oceans Institute, University of St Andrews, UK

625 52. Department of Visitor Management and National Park Monitoring, Bavarian Forest National Park, Grafenau, Germany

627 53. Chair of Wildlife Ecology and Wildlife Management, Faculty of Environment and Natural Resources, University of Freiburg, Freiburg, Germany

629 54. Department of Forestry and Wildlife Management, Faculty of Applied Ecology, Agricultural Sciences and Biotechnology, Campus Evenstad, Inland Norway

630 55. Faculty of Life Sciences, Bar Ilan University, Ramat Gan, Israel

631 56. Lancaster University, Lancaster, UK

634 57. Division of Marine Science and Conservation, Nicholas School of the Environment, Duke University Marine Laboratory, Beaufort, NC, USA

636 58. Department of Game Management and Wildlife Biology, Faculty of Forestry and Wood Sciences, Czech University of Life Sciences, Prague, Czech Republic

639 59. Evolutionary Ecology Group, EVECO, Universiteit Antwerpen, Antwerp, Belgium

641 60. School of Environment and Natural Science, Bangor University, Bangor, Wales
643 61. Department of Nature Conservation and Research, Hainich National Park,
644 Bad Langensalza, Germany

645 62. Mpala Research Center, P.O Box 555-10400, Nanyuki, 10400, Kenya

646 63. Centre for the Advanced Study of Collective Behaviour, University of
647 Konstanz, 78464 Konstanz, Germany

648 64. Department of Biology, Memorial University of Newfoundland, St John's, NL,
649 Canada

650 65. Department of Biology, University of Saskatchewan, Saskatoon, SK, Canada

651 66. School of Biological Sciences, University of Aberdeen, Aberdeen, UK

652 67. School of Animal and Veterinary Sciences, The University of Adelaide,
653 Adelaide, Australia

654 68. Wildlife Conservation Research Unit, The Recanati-Kaplan Centre,
655 Department of Zoology, University of Oxford, Oxford, UK

656 69. School of Biological Sciences, University of Nebraska-Lincoln, NE, USA

657 70. Department of Ecology, Behavior, and Evolution, University of California, San
658 Diego, CA

659 71. Department of Evolutionary Biology and Environmental Studies, University of
660 Zurich, Zurich, Switzerland

661 72. Institute of Pest Management, Sokoine University of Agriculture, Morogoro,
662 Tanzania

663 73. Max Planck Institute of Animal Behavior, Radolfzell, Germany

664 74. MARBEC, Univ Montpellier, CNRS, Ifremer, IRD, Sète, France

665 75. PSL Research University, Université de Perpignan, Perpignan, France

666 76. Laboratoire d'Excellence "CORAIL", Papetoai, Moorea, French Polynesia

667 77. University of Nevada, Reno, NV, USA

668 78. University of Eldoret, School of Natural Resource Management, Department
669 of Wildlife, 1125-30100 Eldoret, Kenya

670 79. Department of Veterinary Sciences, University of Turin, Grugliasco (TO), Italy

671 80. Department of Ecology, Evolution and Behavior, University of Minnesota

672 81. Institute of Environment, Department of Biological Science, Florida
673 International University, FL, USA

674 82. Department of Environmental Science and Policy, University of California
675 Davis, Davis, CA, USA

676 83. Department of Ecology and Evolutionary Biology, University of California, Los
677 Angeles, CA, USA

678 84. Stanford University, Stanford, CA, USA

679 85. School of Biological Sciences, University of Nebraska-Lincoln, Lincoln, NE,
680 USA

681 86. Department of Wildlife, California State Polytechnic University, Humboldt,
682 Arcata, CA, USA

683 87. Texas Tech University, Lubbock, TX, USA

684 88. University of Western Australia, Crawley WA 6009, Australia

685 89. Department of Veterinary Population Medicine, University of Minnesota,
686 Minneapolis, US

687 90. School of Biosciences, University of Surrey, Guildford, UK

688
690

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<table><caption>971 Supplement: Albery et al. (2024): Density-
972 dependent network structuring within and across
973 wild animal systems</caption><thead><tr><th>System</th><th>Species</th><th>Host Group</th><th>Contact Type</th><th>Behaviour</th><th>N</th><th>NID</th><th>Area</th><th>NYrs</th></tr></thead><tbody><tr><td>AcornWoodpeckers [1]</td><td>Melanerpes formicivorus</td><td>Bird</td><td>GOG</td><td>Proximity</td><td>143</td><td>86</td><td>7.62775<br>7322</td><td>3</td></tr><tr><td>AcornWoodpeckers [1]</td><td>Melanerpes formicivorus</td><td>Bird</td><td>HRO</td><td>Indirect</td><td>56</td><td>37</td><td>5.00638<br>5251</td><td>3</td></tr><tr><td>AfricanDogs [2]</td><td>Canis lupus</td><td>Carnivore</td><td>HRO</td><td>Indirect</td><td>297</td><td>223</td><td>75.6352<br>8521</td><td>2</td></tr><tr><td>AfricanDogs [2]</td><td>Canis lupus</td><td>Carnivore</td><td>ProximityCollar</td><td>Proximity</td><td>297</td><td>223</td><td>75.6352<br>8521</td><td>2</td></tr><tr><td>BeakedWhales [3]</td><td>Hyperoodon<br>ampullatus</td><td>Cetacean</td><td>Census</td><td>Proximity</td><td>942</td><td>526</td><td>351.072<br>3</td><td>15</td></tr><tr><td>BeakedWhales [3]</td><td>Hyperoodon<br>ampullatus</td><td>Cetacean</td><td>HRO</td><td>Indirect</td><td>136</td><td>109</td><td>184.109</td><td>13</td></tr><tr><td>ChagosSharks [4]</td><td>Carcharhinus<br>amblyrhynchos</td><td>Shark</td><td>GOG</td><td>Proximity</td><td>103</td><td>71</td><td>1.29074<br>0108</td><td>3</td></tr><tr><td>DesertTortoises [5]</td><td>Gopherus<br>agassizii</td><td>Ectotherm</td><td>DenSharing</td><td>Proximity</td><td>6864</td><td>3544</td><td>1.05539<br>3893</td><td>4</td></tr><tr><td>DesertTortoises [5]</td><td>Gopherus<br>agassizii</td><td>Ectotherm</td><td>HRO</td><td>Indirect</td><td>3181</td><td>1519</td><td>0.62718<br>6899</td><td>4</td></tr><tr><td>EuropeanBoar [6]</td><td>Sus scrofa</td><td>LargeHerbivore</td><td>HRO</td><td>Indirect</td><td>46</td><td>46</td><td>1058.60<br>6722</td><td>1</td></tr><tr><td>EuropeanBoar [6]</td><td>Sus scrofa</td><td>LargeHerbivore</td><td>ProximityCollar</td><td>Proximity</td><td>46</td><td>46</td><td>1058.60<br>6722</td><td>1</td></tr><tr><td>FalkirkMice [7]</td><td>Apodemus<br>ylvaticus</td><td>SmallMammal</td><td>TrapProximity</td><td>Proximity</td><td>168</td><td>168</td><td>0.03777<br>55</td><td>1</td></tr><tr><td>FieldingCows [8]</td><td>Bos taurus</td><td>LargeHerbivore</td><td>HRO</td><td>Indirect</td><td>365</td><td>365</td><td>0.00788<br>627</td><td>1</td></tr><tr><td>FieldingCows [8]</td><td>Bos taurus</td><td>LargeHerbivore</td><td>ProximityCollar</td><td>Proximity</td><td>365</td><td>365</td><td>0.00788<br>627</td><td>1</td></tr></tbody></table>
<table><tr><td>GoldenCrownedSparrows [9]</td><td>Zonotrichia atricapilla</td><td>Bird</td><td>GOG</td><td>Proximity</td><td>688</td><td>341</td><td>0.113428125</td><td>5</td></tr><tr><td>GoldenCrownedSparrows [9]</td><td>Zonotrichia atricapilla</td><td>Bird</td><td>HRO</td><td>Indirect</td><td>545</td><td>285</td><td>0.103413426</td><td>5</td></tr><tr><td>KalahariMeerkats [10]</td><td>Suricata suricatta</td><td>Carnivore</td><td>GOG</td><td>Proximity</td><td>6418</td><td>2734</td><td>2.3798173</td><td>22</td></tr><tr><td>KalahariMeerkats [10]</td><td>Suricata suricatta</td><td>Carnivore</td><td>HRO</td><td>Indirect</td><td>2412</td><td>1357</td><td>2.110591914</td><td>19</td></tr><tr><td>KenyanElephants [11]</td><td>Loxodonta africana</td><td>LargeHerbivore</td><td>GOG</td><td>Proximity</td><td>215</td><td>151</td><td>11677.13864</td><td>2</td></tr><tr><td>KenyanElephants [11]</td><td>Loxodonta africana</td><td>LargeHerbivore</td><td>HRO</td><td>Indirect</td><td>77</td><td>55</td><td>10647.43727</td><td>2</td></tr><tr><td>KielderVoles [12]</td><td>Microtus agrestis</td><td>SmallMammal</td><td>HRO</td><td>Indirect</td><td>545</td><td>544</td><td>0.028251875</td><td>6</td></tr><tr><td>KielderVoles [12]</td><td>Microtus agrestis</td><td>SmallMammal</td><td>TrapProximity</td><td>Proximity</td><td>10029</td><td>9140</td><td>0.050075</td><td>6</td></tr><tr><td>LiverpoolMice [13]</td><td>Apodemus sylvaticus</td><td>SmallMammal</td><td>HRO</td><td>Indirect</td><td>243</td><td>243</td><td>0.01890773</td><td>4</td></tr><tr><td>LiverpoolMice [13]</td><td>Apodemus sylvaticus</td><td>SmallMammal</td><td>TrapProximity</td><td>Proximity</td><td>4110</td><td>4054</td><td>0.0284</td><td>4</td></tr><tr><td>MooreaSharks [14]</td><td>Carcharhinus melanopterus</td><td>Shark</td><td>GOG</td><td>Proximity</td><td>269</td><td>105</td><td>11.74987669</td><td>3</td></tr><tr><td>MorayDolphins [15]</td><td>Tursiops truncatus</td><td>Cetacean</td><td>Census</td><td>Proximity</td><td>2849</td><td>687</td><td>1894.792195</td><td>32</td></tr><tr><td>MorayDolphins [15]</td><td>Tursiops truncatus</td><td>Cetacean</td><td>HRO</td><td>Indirect</td><td>1061</td><td>224</td><td>744.7757738</td><td>27</td></tr><tr><td>MountainLake [16]</td><td>Peromyscus leucopus</td><td>SmallMammal</td><td>TrapProximity</td><td>Proximity</td><td>319</td><td>272</td><td>0.0245</td><td>2</td></tr><tr><td>MultimammateMice [17]</td><td>Mastomys natalensis</td><td>SmallMammal</td><td>HRO</td><td>Indirect</td><td>323</td><td>321</td><td>0.000234789</td><td>20</td></tr><tr><td>MultimammateMice [17]</td><td>Mastomys natalensis</td><td>SmallMammal</td><td>TrapProximity</td><td>Proximity</td><td>21142</td><td>18631</td><td>0.000261</td><td>25</td></tr><tr><td>PalmyraSharks [18]</td><td>Carcharhinus amblyrhynchos</td><td>Shark</td><td>GOG</td><td>Proximity</td><td>121</td><td>41</td><td>51.38758347</td><td>4</td></tr></table>
<table><tr><td>PalmyraSharks [18]</td><td>Carcharhinus amblyrhynchos</td><td>Shark</td><td>HRO</td><td>Indirect</td><td>88</td><td>36</td><td>48.6558</td><td>4</td></tr><tr><td>PotomacDolphins [19]</td><td>Tursiops erebennus</td><td>Cetacean</td><td>GOG</td><td>Proximity</td><td>925</td><td>861</td><td>227.787</td><td>3</td></tr><tr><td>PrairieDogs [20]</td><td>Cynomys ludovicianus</td><td>SmallMammal</td><td>GOG</td><td>Proximity</td><td>49</td><td>49</td><td>3990.29</td><td>1</td></tr><tr><td>ReticulatedGiraffes [21]</td><td>Giraffa camelopardalis</td><td>LargeHerbivore</td><td>GOG</td><td>Proximity</td><td>214</td><td>214</td><td>189.127</td><td>1</td></tr><tr><td>ReticulatedGiraffes [21]</td><td>Giraffa camelopardalis</td><td>LargeHerbivore</td><td>HRO</td><td>Indirect</td><td>204</td><td>204</td><td>134.623</td><td>1</td></tr><tr><td>RockHyraxes [22]</td><td>Procavia capensis</td><td>SmallMammal</td><td>GOG</td><td>Proximity</td><td>399</td><td>196</td><td>0.09382</td><td>14</td></tr><tr><td>RumDeer [23]</td><td>Cervus elaphus</td><td>LargeHerbivore</td><td>Dominance</td><td>Direct</td><td>2173</td><td>391</td><td>16.9160</td><td>22</td></tr><tr><td>RumDeer [23]</td><td>Cervus elaphus</td><td>LargeHerbivore</td><td>GOG</td><td>Proximity</td><td>6807</td><td>987</td><td>19.6333</td><td>47</td></tr><tr><td>RumDeer [23]</td><td>Cervus elaphus</td><td>LargeHerbivore</td><td>HRO</td><td>Indirect</td><td>8251</td><td>889</td><td>17.7771</td><td>47</td></tr><tr><td>SerengetiLions [24]</td><td>Panthera leo</td><td>Carnivore</td><td>Census</td><td>Proximity</td><td>1990</td><td>1990</td><td>3096.55</td><td>30</td></tr><tr><td>SerengetiLions [24]</td><td>Panthera leo</td><td>Carnivore</td><td>HRO</td><td>Indirect</td><td>1478</td><td>1478</td><td>2778.93</td><td>30</td></tr><tr><td>SharkBayDolphins [25]</td><td>Tursiops aduncus</td><td>Cetacean</td><td>Census</td><td>Proximity</td><td>4139</td><td>770</td><td>470.127</td><td>12</td></tr><tr><td>SharkBayDolphins [25]</td><td>Tursiops aduncus</td><td>Cetacean</td><td>HRO</td><td>Indirect</td><td>1257</td><td>328</td><td>306.226</td><td>12</td></tr><tr><td>SilwoodMice [26]</td><td>Apodemus sylvaticus</td><td>SmallMammal</td><td>CoTrapping</td><td>Proximity</td><td>542</td><td>497</td><td>0.00020</td><td>2</td></tr><tr><td>SilwoodMice [26]</td><td>Apodemus sylvaticus</td><td>SmallMammal</td><td>HRO</td><td>Indirect</td><td>93</td><td>92</td><td>0.00017</td><td>2</td></tr><tr><td>SleepyLizards [27]</td><td>Tiliqua rugosa</td><td>Ectotherm</td><td>GPSProximity</td><td>Proximity</td><td>546</td><td>199</td><td>1.67834</td><td>9</td></tr><tr><td>SleepyLizards [27]</td><td>Tiliqua rugosa</td><td>Ectotherm</td><td>HRO</td><td>Indirect</td><td>546</td><td>199</td><td>1.67834</td><td>9</td></tr></table>
<table><tr><td>SoaySheep [28]</td><td>Ovis aries</td><td>LargeHerbivore</td><td>GOG</td><td>Proximity</td><td>1568<br>3</td><td>5670</td><td>2.14523<br>8</td><td>34</td></tr><tr><td>SoaySheep [28]</td><td>Ovis aries</td><td>LargeHerbivore</td><td>HRO</td><td>Indirect</td><td>1380<br>7</td><td>5006</td><td>1.77559<br>5</td><td>34</td></tr><tr><td>TayDolphins [29]</td><td>Tursiops<br>truncatus</td><td>Cetacean</td><td>Census</td><td>Proximity</td><td>1208</td><td>266</td><td>1468.99</td><td>13</td></tr><tr><td>TayDolphins [29]</td><td>Tursiops<br>truncatus</td><td>Cetacean</td><td>HRO</td><td>Indirect</td><td>201</td><td>94</td><td>266.297<br>7</td><td>13</td></tr><tr><td>VulturineGuineafowl<br>[30]</td><td>Acryllium<br>vulturinum</td><td>Bird</td><td>GPSProximity</td><td>Proximity</td><td>3681</td><td>49</td><td>7.12259<br>9</td><td>5</td></tr><tr><td>VulturineGuineafowl<br>[30]</td><td>Acryllium<br>vulturinum</td><td>Bird</td><td>HRO</td><td>Indirect</td><td>3681</td><td>49</td><td>7.12259<br>9</td><td>5</td></tr><tr><td>WaterDragons [31]</td><td>Intellagama<br>lesueurii</td><td>Ectotherm</td><td>CensusProximity</td><td>Proximity</td><td>3685</td><td>1254</td><td>0.05441<br>5</td><td>11</td></tr><tr><td>WaterDragons [31]</td><td>Intellagama<br>lesueurii</td><td>Ectotherm</td><td>HRO</td><td>Indirect</td><td>2376</td><td>796</td><td>0.04977<br>6</td><td>11</td></tr><tr><td>WildCrickets [32]</td><td>Gryllus<br>campestris</td><td>Ectotherm</td><td>Fight</td><td>Direct</td><td>1334</td><td>1334</td><td>0.00083<br>9</td><td>10</td></tr><tr><td>WildCrickets [32]</td><td>Gryllus<br>campestris</td><td>Ectotherm</td><td>HRO</td><td>Indirect</td><td>1112</td><td>556</td><td>0.00053<br>7</td><td>10</td></tr><tr><td>WildCrickets [32]</td><td>Gryllus<br>campestris</td><td>Ectotherm</td><td>Mate</td><td>Direct</td><td>1334</td><td>1334</td><td>0.00083<br>9</td><td>10</td></tr><tr><td>WoodchesterBadgers<br>[33]</td><td>Meles meles</td><td>Carnivore</td><td>CoTrapping</td><td>Proximity</td><td>477</td><td>477</td><td>6.90092<br>6</td><td>8</td></tr><tr><td>WythamBadgers [34]</td><td>Meles meles</td><td>Carnivore</td><td>CoTrapping</td><td>Proximity</td><td>4993</td><td>1585</td><td>5.3095</td><td>32</td></tr><tr><td>WythamMice [35]</td><td>Apodemus<br>ylvaticus</td><td>SmallMammal</td><td>CoTrapping</td><td>Proximity</td><td>274</td><td>274</td><td>0.00038</td><td>1</td></tr><tr><td>WythamTits [36]</td><td>Parus major</td><td>Bird</td><td>GOG</td><td>Proximity</td><td>2874</td><td>1966</td><td>12.2911<br>7</td><td>3</td></tr><tr><td>WythamTits [36]</td><td>Parus major</td><td>Bird</td><td>HRO</td><td>Indirect</td><td>1070</td><td>857</td><td>9.52090<br>5</td><td>3</td></tr></table>

974 Supplementary Table 1: List of study system replicates and their associated traits, with an
975 example reference from each. Also available as a supplementary file. N=number of
976 individual-by-year values; NID=number of unique individuals. Area=study extent in Km².
977 NYears=number of years covered by the study.
<table><thead><tr><th>System</th><th>Species</th><th>Image credit</th><th>License link</th></tr></thead><tbody><tr><td>VulturineGuineafowl</td><td>Acrylliumvulturinum</td><td>James Klarevas</td><td>https://creativecommons.org/licenses/by/4.0/</td></tr><tr><td>WythamMice</td><td>Apodemussylvaticus</td><td>Anthony Caravaggi</td><td>https://creativecommons.org/licenses/by-nc-sa/3.0/</td></tr><tr><td>SilwoodMice</td><td>Apodemussylvaticus</td><td>NA</td><td>Public domain</td></tr><tr><td>LiverpoolMice</td><td>Apodemussylvaticus</td><td>NA</td><td>Public domain</td></tr><tr><td>FalkirkMice</td><td>Apodemussylvaticus</td><td>NA</td><td>Public domain</td></tr><tr><td>FieldingCows</td><td>Bostaurus</td><td>NA</td><td>Public domain</td></tr><tr><td>AfricanDogs</td><td>Canislupus</td><td>NA</td><td>Public domain</td></tr><tr><td>ChagosSharks</td><td>Carcharhinusamblyrhynchos</td><td>NA</td><td>Public domain</td></tr><tr><td>PalmyraSharks</td><td>Carcharhinusamblyrhynchos</td><td>Russell Engelman</td><td>https://creativecommons.org/licenses/by/4.0/</td></tr><tr><td>MooreaSharks</td><td>Carcharhinusmelanopterus</td><td>Ignacio Contreras</td><td>https://creativecommons.org/licenses/by/3.0/</td></tr><tr><td>RumDeer</td><td>Cervuselaphus</td><td>NA</td><td>Public domain</td></tr></tbody></table>
<table><tr><td>PrairieDogs</td><td>Cynomys ludovicianus</td><td>NA</td><td>Public domain</td></tr><tr><td>ReticulatedGiraffes</td><td>Giraffa camelopardalis</td><td>Cathy</td><td>https://creativecommons.org/licenses/by-nc-sa/3.0/</td></tr><tr><td>DesertTortoises</td><td>Gopherus agassizii</td><td>Andrew A. Farke, shell lines added by Yan Wong</td><td>https://creativecommons.org/licenses/by/3.0/</td></tr><tr><td>WildCrickets</td><td>Gryllus campestris</td><td>NA</td><td>Public domain</td></tr><tr><td>WaterDragons</td><td>Intellagama lesueurii</td><td>NA</td><td>Public domain</td></tr><tr><td>KenyanElephants</td><td>Loxodonta africana</td><td>Agnello Picorelli</td><td>https://creativecommons.org/licenses/by-nc-sa/3.0/</td></tr><tr><td>Multimammate Mice</td><td>Mastomys natalensis</td><td>David Liao</td><td>https://creativecommons.org/licenses/by-sa/3.0/</td></tr><tr><td>AcornWoodpeckers</td><td>Melanerpes formicivorus</td><td>NA</td><td>Public domain</td></tr><tr><td>WoodchesterBadgers</td><td>Meles meles</td><td>NA</td><td>Public domain</td></tr><tr><td>WythamBadgers</td><td>Meles meles</td><td>Anthony Caravaggi</td><td>https://creativecommons.org/licenses/by-nc-sa/3.0/</td></tr><tr><td>KielderVoles</td><td>Microtus agrestis</td><td>NA</td><td>Public domain</td></tr><tr><td>SoaySheep</td><td>Ovis aries</td><td>Gabriela Palomo-Munoz</td><td>https://creativecommons.org/licenses/by-nc/3.0/</td></tr><tr><td>WythamTits</td><td>Parus major</td><td>NA</td><td>Public domain</td></tr></table>
<table><tr><td>MountainLake</td><td>Peromyscus leucopus</td><td>Nina Skinner</td><td>https://creativecommons.org/licenses/by/3.0/</td></tr><tr><td>RockHyraxes</td><td>Procavia capensis</td><td>NA</td><td>Public domain</td></tr><tr><td>KalahariMeerkats</td><td>Suricata suricatta</td><td>NA</td><td>Public domain</td></tr><tr><td>GermanBoar</td><td>Sus scrofa</td><td>NA</td><td>Public domain</td></tr><tr><td>SleepyLizards</td><td>Tiliqua rugosa</td><td>CNZdenek</td><td>https://creativecommons.org/licenses/by-nc/3.0/</td></tr><tr><td>PotomacDolphins</td><td>Tursiops truncatus</td><td>Chris huh</td><td>https://creativecommons.org/licenses/by-sa/3.0/</td></tr><tr><td>MorayDolphins</td><td>Tursiops truncatus</td><td>NA</td><td>Public domain</td></tr><tr><td>AberdeenDolphins</td><td>Tursiops truncatus</td><td>Chris huh</td><td>https://creativecommons.org/licenses/by-sa/3.0/</td></tr><tr><td>SharkBayDolphins</td><td>Tursiops truncatus</td><td>Chris huh</td><td>https://creativecommons.org/licenses/by-sa/3.0/</td></tr><tr><td>GoldenCrowne dSparrows</td><td>Zonotrichia atricapilla</td><td>NA</td><td>Public domain</td></tr><tr><td>SerengetiLions</td><td>Panthera leo</td><td>NA</td><td>Public domain</td></tr><tr><td>BeakedWhales</td><td>Hyperoodon ampullatus</td><td>Chris huh</td><td>https://creativecommons.org/licenses/by-sa/3.0/</td></tr></table>

978

979

980

Supplementary Table 2: The phytopic.org images used in the main text and their associated licenses.

981
982

<table><thead><tr><th>Variable</th><th>Estimate</th><th>P value</th><th>CI_Lower</th><th>CI_Upper</th></tr></thead><tbody><tr><td>Intercept</td><td>0.191</td><td>7.09E-05</td><td>0.097</td><td>0.285</td></tr><tr><td>Behaviour: Spatial</td><td>0.263</td><td>5.56E-07</td><td>0.16</td><td>0.366</td></tr><tr><td>NID</td><td>0</td><td>0.217148</td><td>0</td><td>0</td></tr><tr><td>Area</td><td>0.017</td><td>0.213823</td><td>-0.01</td><td>0.043</td></tr><tr><td>NYears</td><td>0.005</td><td>0.060808</td><td>0</td><td>0.009</td></tr></tbody></table>

983

984

985

986

Supplementary Table 3: Meta-analysis effect estimates from the full meta-analytical model, providing the estimate, P value, and 95% confidence intervals.

987
<figure><img src="image_6.png" /></figure>

991 Supplementary Figure 1: Relationships between two measures of local population density
992 across animal systems. Density on the x axis was calculated using a kernel density
993 estimation approach; density on the y axis was calculated based on the number of
994 individuals located within a radius defined by the study system's area. X axis values have
995 been standardised to have a mean of zero and a standard deviation of 1 within each site; the
996 axis ticks are in units of 1 standard deviation. Each point represents an individual-year-
997 behaviour replicate; the lines portray the model fit from a generalised additive model (GAM).
998 Correlation coefficients were calculated using a Spearman's rank correlation.
<figure><img src="image_7.png" /></figure>

1000 Supplementary Figure 2: Linear relationships between density and network connectedness

1001 across animal systems. Density in individuals per area is on the x axis; network

1002 connectedness (strength centrality) is on the y axis. Both values have been standardised to

1003 have a mean of zero and a standard deviation of 1 within each system; the axis ticks are in

1004 units of 1 standard deviation. Each point represents an individual-year-behaviour replicate;

1005 the lines portray the model fit from our linear models for meta-analysis. Points are semi-

1006 transparent to enhance visibility. Panels are arranged phylogenetically following the tree

1007 displayed in Figure 2A; GOG=gambit of the group; HRO=home range overlap.
<figure><img src="image_8.png" /><figcaption>Supplementary Figure 3: Distributions of residuals from the linear models displayed in Figure 2 above.</figcaption></figure>
<figure><img src="image_9.png" /></figure>

1012

1013 Supplementary Figure 4: distribution of the Breusch-Pagan Test Statistic as a measure of
1014 heteroskedasticity (y axis) according to the type of contact being analysed (panel A) and the
1015 magnitude of the linear effect estimate (panel B). The y axis is log-transformed. These
1016 findings demonstrate no substantial difference in the levels of heteroskedasticity in the two
1017 contact types that might explain spatial models exhibiting a steeper density effect.

1018
<figure><img src="image_10.png" /><figcaption>Supplementary Figure 5: Log-linear relationships between density and network connectedness across animal systems. Density in individuals per area is on the x axis; network connectedness (strength centrality) is on the y axis, both of which are then log(x+1)-transformed. Each point represents an individual-year-behaviour replicate; the lines portray the model fit from our linear models for meta-analysis. Points are semi-transparent to enhance visibility. Panels are arranged phylogenetically following the tree displayed in Figure 2A; GOG=gambit of the group; HRO=home range overlap.</figcaption></figure>

1019

1020

1021 connectedness across animal systems. Density in individuals per area is on the x axis;

1022 network connectedness (strength centrality) is on the y axis, both of which are then log(x+1)-

1023 transformed. Each point represents an individual-year-behaviour replicate; the lines portray

1024 the model fit from our linear models for meta-analysis. Points are semi-transparent to

1025 enhance visibility. Panels are arranged phylogenetically following the tree displayed in

1026 Figure 2A; GOG=gambit of the group; HRO=home range overlap.
1027 **Supplementary discussion**

1028 **Operational implications:** Operationally, the common nature of saturating density effects
1029 will impact researchers' ability to detect density dependence: that is, density dependence
1030 could be harder to observe in higher-density areas given the shallower slopes we observed.
1031 Most of the systems in this study are relatively long-term studies of known individuals; these
1032 populations tend to be in carefully selected, high-density areas that make it convenient to
1033 study the focal animal with relatively low operational costs. For example, it has previously
1034 been noted that the badgers of Wytham Wood, the red deer of the Isle of Rum, and the Soay
1035 sheep of St Kilda are all at high densities for their respective species [37–39]. As such, we
1036 may be inherently investigating the upper end of density-connectedness relationships in the
1037 wild, and it could be difficult and costly to investigate the effects of low density so widely.

1038 **Limitations:** We acknowledge several limitations of our study, which we nevertheless
1039 believe could be remedied in the future. First, many of our social networks were formed of
1040 general spatiotemporal associations, and relatively few from specific social interactions –
1041 particularly those involving direct physical contact (only 3/36 social networks). Our current
1042 dataset could therefore benefit from supplementation with a broader range of direct
1043 interactions, particularly involving antagonism or bonding. For example, datasets concerning
1044 aggression or dominance interactions (e.g. [40]) or grooming alongside spatial behaviour
1045 could inform how density dependence affects the transmission of certain parasites such as
1046 mycobacteria [41] or tattoo skin disease [42]. The meta-dataset was also unevenly
1047 distributed across animal taxa (Figure 1): there were no primates or bony fishes and only
1048 one invertebrate, while rodents and ungulates were over-represented. These biases likely
1049 emerge through differences in data collection approaches: for example, although primate
1050 social behaviour is often studied with observations of direct interactions that could augment
1051 our data as described above (e.g. [43]), the spatial data required to build density distributions
1052 are rarely collected in these systems. This is linked to their social structures: our workflow
1053 was best suited to studies of fission-fusion societies or relatively asocial animals, rather than
1054 those with wide-ranging fixed social groups that are more common in primate systems.
1055 Finally, given that our data were observational, we could not account for (or estimate)
1056 bidirectional causality between density and social relationships (point 4 in Figure 1): that is,
1057 as well as encountering more conspecifics in areas of high density, individuals may be
1058 drawn to conspecifics, *creating* areas of high density [44]. To do so might require creating in-
1059 depth, high-resolution models of animal movement and group formation (e.g. [45]),
1060 potentially making use of telemetry approaches and drawing from large-scale open
1061 movement repositories like Movebank [46]. Using remote and next-generation approaches
1062 may help to access and incorporate more remote areas, which could also help to ameliorate
1063 the substantial geographic biases in our meta-dataset (Figure 2).

1064 **Analytical expansions:** Aside from incorporating more specific interaction types, there exist
1065 a range of potential extensions to our analysis. For example, density dependence often
1066 varies between age or sex classes (e.g. [47,48]), and age effects on infection are common
1067 and ecologically important [49,50], as are sex differences [51]. We chose not to analyse how
1068 individual animals' traits alter the shape or slope of density's effects for brevity and simplicity;
1069 however, given that many of the systems nevertheless include these data, future analyses
1070 could make use of this meta-dataset to investigate how density affects connectedness of
1071 different classes of hosts. Further, researchers could investigate other behavioural questions
1072 such as the role of observation biases; the factors influencing the correlation between spatial
1073 and social networks; and the role of environmental drivers and spatial autocorrelation in
1074 driving observed patterns of connectivity [44,52]. Finally, as our analysis approximated
1075 density-contact relationships and not host-parasite interactions specifically, important future
1076 work could investigate whether contact rates (as approximated by network connectedness)
1077 ultimately translate to greater infection risk or parasite burden. Although some previous
1078 investigations have linked density-related metrics to aspects of infection [53,54], density
1079 covaries with a range of other factors including nutrition, cooperation, and competition, all of
1080 which could complicate density-driven increases in exposure [55]. For example, in the case
1081 of ectoparasite transmission, although contact rates in general would likely increase with
1082 density, so too might grooming behaviours that remove parasites; in cases such as these,
1083 density's overall effect on ectoparasite infection may be neutral. In the future, verifying that
1084 within- and between-population variation in density-contact relationships translate to
1085 variation in infection – and whether these trends might be influenced by flexible avoidance
1086 behaviours [56] – will be a vital part of understanding and predicting density-dependent
1087 disease dynamics.
1088 Supplementary acknowledgements

1089 Acorn woodpeckers

1090 Sahas Barve and Eric L Walters were supported by funding from the National Science

1091 Foundation (Grant IOS-1455900). We would like to thank Walt Koenig, Jen Hunter, and

1092 many graduate students and field assistants who have helped contribute to this project and

1093 the Hastings Natural History Reservation for logistical support.

1094 African dogs

1095 This system acknowledges support from The Carter Center and the Natural Environment

1096 Research Council. Ciro Cattuto and Laura Ozella acknowledge partial support from the

1097 Lagrange Project of the ISI Foundation funded by CRT Foundation.

1098 Bottlenose whales

1099 Fieldwork and data management for this population were supported by Fisheries and

1100 Oceans Canada.

1101 Chagos sharks

1102 This population was funded by the Bertarelli Foundation. Ciro Cattuto and Laura Ozella

1103 acknowledge partial support from the Lagrange Project of the ISI Foundation funded by CRT

1104 Foundation.

1105 Cornish cattle

1106 This project acknowledges support from the Biotechnology and Biological Sciences

1107 Research Council.

1108 European boar

1109 Alisa Klamm (AK) is grateful to the Thuringian Ministry of Environmental, Energy and Nature

1110 Conservation and the Thuringian Ministry for Infrastructure and Agriculture for funding the

1111 research project on wild boar in the Hainich National Park. AK also thanks the people

1112 involved in the fieldwork and the copartners: Landesjagdverband Thüringen e.V. and

1113 ThüringenForst-Anstalt öffentlichen Rechts. Kevin Morelle (KM) was supported by the H2020

1114 project VACDIVA (grant ID: 862874).

1115 Golden-crowned sparrows

1116 The research was supported by an NSF Graduate Research Fellowship to Theadora A

1117 Block, a CAREER grant (NSF IOS-1750606) and National Geographic grant (WW-R012-17)

1118 to Dai Shizuka, and a Laboratoire d'Excellence grant (TULIP, ANR-10-LABX-41) and ANR-

1119 SoCo to Alexis S Chaine.
1120 Firth of Tay and Moray Dolphins

1121 BJC and PSH thank all the colleagues who have helped collect and analyse the Moray Firth
1122 and Firth of Tay bottlenose dolphin data. Funding to support these long-term projects was
1123 provided by NatureScot, Beatrice Offshore Windfarm Ltd., Moray East and Moray West
1124 Offshore Wind Farms, Vattenfall, Seagreen, Inch Cape Offshore Ltd, NnG Offshore Wind,
1125 Marine Scotland, The Crown Estate, Highlands and Islands Enterprise, BES, ASAB,
1126 Greenpeace Environmental Trust, Scottish Government, Whale and Dolphin Conservation,
1127 Talisman Energy (UK) Ltd., Department of Energy and Climate Change, Chevron, Natural
1128 Environment Research Council and the Universities of Aberdeen and St Andrews.

1129 Kalahari meerkats

1130 This analysis uses data on meerkats collected on a project supported by European
1131 Research Council Grants numbers 742808 and 294494.

1132 Kenyan elephants

1133 We would like to thank the Kenyan Office of the President and Kenya Wildlife Service (KWS)
1134 for permission to conduct this research and Felix Mdamu and David Korir for help collecting
1135 the data. KWS funded radio collars, tracking equipment, and aerial surveys. The research
1136 was approved by the Institutional Animal Care and Use Committee (IACUC) of the University
1137 of California, Davis, protocol #10087 and received the permission of the Kenya Ministry of
1138 Education, Science, and Technology (Permit No. MOEST 13/001/34C 301). Funding for this
1139 work was provided by The Lincoln Park Zoo, International Elephant Foundation, and UC
1140 Davis Animal Behavior Graduate Group.

1141 Liverpool wood mice

1142 Data were collected under funding from NERC (grants: NE/G007349/1, NE/G006830/1,
1143 NE/I024038/1 and NE/I026367/1), and data collectors: Becci Barber, Trevor Jones, Łukasz
1144 Łukomski, Stephen Price, Susan Withenshaw, Niamh Quinn and many undergraduate
1145 assistants.

1146 Mountain Lake mice

1147 Andrea Graham was funded by Defense Advanced Research Projects Agency.

1148 Potomac dolphins

1149 This population was funded by the Earth Commons Institute, Georgetown University, and
1150 under National Geographic Grant WW-022ER-17.

1151 Prairie dogs

1152 We would like to respectfully acknowledge that our research on prairie dogs occurred on
1153 Treaty 4 territory, the traditional territory of the Očhéthi Šakówin, Niitsítpiis-stahkoii
1154 ہر ہدیہ ہدیہ, and the homeland of the Michif Piyii (Métis) Nation. We are very thankful
1155 to our field technicians for assistance with data collection. We thank S. Liccioli, H. Facette,
1156 and the staff at Grasslands National Park for their assistance with permitting and logistical
1157 support. This research was supported by a Natural Sciences and Engineering Research Council Discovery Grant RGPIN 04093-2014 (J.E.L.), Parks Canada Contribution Agreement GC-794 (J.E.L.), Saskatchewan Fish and Wildlife Development Fund Student Research Award (J.M.K., and Colleen Crill Matzke), Nature Saskatchewan (J.M.K), and the University of Saskatchewan.

1162 Rum deer

1163 The authors thank NatureScot for permission to work on the Isle of Rum National Nature Reserve. The authors thank F. Guinness, M. Baker, A. Morris, S. Morris, and many others for collecting field data, and S. Albon and L Kruuk for their contributions to the long-term project. The project has been funded principally by the UK Natural Environment Research Council.

1168 Serengeti lions

1169 This study population acknowledges NSF grant DEB-1020479.

1170 Shark Bay dolphins

1171 This study population was funded by NSF grants 2146995, 2106909, 1755229, and 1172 1559380.

1173 Sleepy lizards

1174 We thank Ron and Leona Clark and Chris Mosey for access to their land and use of the homestead at Bundey Bore Station. We thank Jess Clayton and Dale Burzacott for logistical support, and we are especially grateful to the late Professor Michael Bull and the late Dale Burzacott, who established the sleepy lizard monitoring project. We are also grateful for many students that worked on the sleepy lizard system along the years. (not sure naming them all is helpfull here, so i suggest leaving as is). This work was funded by National Science Foundation of the United States Grant DEB-1456730 and Australian Research Council Grants DP0877384 and DP130100145.

1182 Soay sheep

1183 The authors thank the National Trust for Scotland for permission to work on St Kilda, and QinetiQ and Kilda Cruises for logistical support. The authors thank J. Pilkington, I. Stevenson and many others for collecting the field data and D. Nussey, J. Slate and M. Morrissey for their contributions to the long-term project. The study on St Kilda has been funded principally by the UK Natural Environment Research Council.

1188 Vulturine guineafowl

1189 The vulturine guineafowl work was funded the European Research Council (ERC) under the European Union's Horizon 2020 research and innovation programme (grant agreement number 850859) and Eccellenza Professorship Grant of the Swiss National Science Foundation (grant number PCEFP3_187058) awarded to DRF.
1193 Wild crickets

1194 WildCrickets would like to thank Luis Rodríguez and María del Carmen Muñoz for

1195 unconditional support and providing access to facilities including the WildCrickets study

1196 meadow. The following people contributed to video processing and data recording: David

1197 Fisher, Ian Skicko, Xing P. Liu, Thor Veen, Carlos Rodríguez del Valle, Alan Rees, Sophie

1198 Haugland Pedersen, Hannah Hudson, Jasmine Jenkin, Lauren Morse, Emma Rogan,

1199 Emelia Hiorns, Sarah Callow, Jamie Barnes, Chloe Mnatzaganian, Olivia Pearson, Adèle

1200 James, Robin Brown, Chris Shipway, Luke Meadows and Peter Efstratiou. The WildCrickets

1201 project was supported by the Natural Environment Research Council (NERC); standard

1202 grants: NE/E005403/1, NE/H02364X/1, NE/L003635/1, NE/R000328/1, NE/V000772/1 and

1203 the Leverhulme Trust.

1204 Woodchester badgers

1205 This study was funded by the UK Department for Environment, Farming and Rural Affairs,

1206 Animal and Plant Health Agency, and the Natural Environmental Research Council.

1207 Wytham great tits

1208 The Wytham great tit data were funded by ERC AdG 250164, NERC NE/S010335/1 and

1209 NE/D011744/1, and collected by the members of the Wytham Social Network Group.

1210
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1378

1379

1380

1381
1382 Supplementary methods – Study System Details

1383 Acorn woodpeckers

1384 We studied acorn woodpeckers at Hastings Natural History Reservation (36.387° N, 22
1385 121.551° W) in central coastal California, USA. Adults on their natal territory with their social
1386 (and genetic) parents were categorized as nonbreeding helpers. Group members living
1387 outside their natal territories, or living with birds of the opposite sex that were nonrelatives,
1388 were considered putative breeders¹. Extra-group mating, as well as incestuous mating, is
1389 rare in this study population¹,². From 1973 to 2021, the majority of the woodpecker
1390 population was color-banded (N = 6572 total individuals) and monitored continuously for
1391 group size and composition. Each year, territory quality was assigned to each acorn
1392 woodpecker social group territory based on the size of the group's granary (1: <1000 storage
1393 holes [low quality], 2: 1000–2500 [medium quality], 3: >2500 [high quality])³.

1394 Acorn woodpeckers were caught opportunistically and fitted with dorsally mounted solar-
1395 powered nanotags⁴ with leg loop harnesses adjusted for body size⁵. All tags weighed less
1396 than 1% of each bird's body mass. Radio-tagged birds (N=87) were detected by an array of
1397 43 permanently installed autonomous, solar-powered base stations during daylight hours⁶
1398 for a total of 497 days between August 2019 and July 2021.

1399 Tags produced an encoded 64-bit, 2.5 ms radio ping every 1.5 s during the day, even in
1400 cloudy weather. Each detection of an individual at the base station was accompanied with a
1401 date, time, and signal-strength stamp. All detections were stored in files created every 15
1402 min and stored on removable memory drives⁷.

1403 The raw data were first cleaned to retain only those detections that occurred with high signal
1404 strength (within ~100 m of the receiver). Next, we calculated time spent by each bird (N=87)
1405 at each receiver station (N=43). For this, we first partitioned each day into 1-min bins based
1406 on the first and the last detection of the day. Next, we assigned a bird to a receiver for a
1407 given 1-min bin if we detected that bird more than 10 times at a particular receiver within that
1408 minute. This resulted in a cumulative 821,262 minutes of bird presence at receivers.

1409 We classified birds as “associating” if they were detected in the same 1-min bin at the same
1410 receiver on a given day. We classified associations as at “home” if the receiver was within
1411 200 m of the bird’s territory or “away” if the bird was detected with high signal strength at a
1412 receiver farther than 200 m from its own territory. We detected 175,368 minutes of
1413 association between birds (range 2–16 birds associating). Most common associations were
1414 between two birds (60,299 minutes of association) and least common association was
1415 among 16 birds (only one instance detected). Seventy-eight birds were detected at territories
1416 away from their home territory. Distance away from home was highly variable (mean ± SD
1417 483.5 m ± 419 m, range 207–2898 m).

1418 References

1419 1. Koenig, W.D., Haydock, J., and Stanback, M.T. Reproductive roles in the cooperatively
1420 breeding acorn woodpecker: incest avoidance versus reproductive competition. Am. Nat.
1421 151, 243–255 (1998).
1422 2. Dickinson, J., Haydock, J., Koenig, W., Stanback, M., and Pitelka, F. Genetic monogamy
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1424 769 (1995).

1425 3. Koenig, W.D., Walters, E.L., and Haydock, J. Variable helper effects, ecological
1426 conditions, and the evolution of cooperative breeding in the acorn woodpecker. Am. Nat.
1427 178, 145-158 (2011).

1428 4. Stanback, M.T., and Koenig, W.D. Techniques for capturing birds inside natural cavities.
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1430 5. Rappole, J.H., and Tipton, A.R. New harness design for attachment of radio transmitters
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1432 6. Barve, S., Hagemeyer, N.D., Winter, R.E., Chamberlain, S.D., Koenig, W.D., Winkler,
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1435 7. Barve, S., Lahey, A.S., Brunner, R.M., Koenig, W.D., and Walters, E.L. Tracking the
1436 warriors and spectators of acorn woodpecker wars. Curr. Biol. 30, R982-R983 (2020).
1438 African dogs

1439 Field site: Dogs (*Canis familiaris*) in rural Chad were collared during the dry (5th March - 17th May 2018) and wet seasons (3rd August – 17th October 2018). Dog owners from six villages participated in the study; Medegue (11°01'48.8" N; 15°26'37.7" E), Ngakedji (9°11'16.5" N; 18°18'10.7" E), Kira (9°10'50.8"N; 18°17'00.3"E), Bembaya (9°11'33.6"N; 18°17'42.3"E), Marabodokouya (9°19'42.3" N; 18°43'20.0" E) and Tarangara; (9°08'19.8" N; 18°42'00.9" E)¹,².

1445 Spatial data: Collars were fitted with an iGotU GT600 GPS logger (Mobile Action Technology, New Taipei City, Taiwan). The fix interval was set to 10 minutes. In both the dry and wet field season, an initial two-week deployment was immediately followed by a second, longer deployment using new collars fitted with a modified GT600 unit with a larger battery. 1446 1447 1448 1449 Spatial data were cleaned by removing locations taken up to 12 h after the collar was 1450 1451 removed and data were discarded for periods when dogs were known to be tied up.

1452 Proximity sensor data: Dog collars were also fitted with a proximity sensor developed by the 1453 OpenBeacon project (http://www.openbeacon.org/) and the SocioPatterns collaboration 1454 consortium (http://www.sociopatterns.org/). The sensors exchange multiple radio packets 1455 per second and proximity is calculated by the attenuation; difference between the received 1456 and transmitted power³⁻⁵. An attenuation threshold of −70dbm was used, detecting 1457 encounters within 1–1.5m⁵,⁶. Inter-logger variability was assessed by comparing the number 1458 of packets emitted and received for all pairs of sensors. Data were discarded if there was 1459 evidence for deviations from the expected linear relationship between emitted and received 1460 radio packets. A contact event was identified when sensors exchanged radio packets for a 1461 minimum of 20 consecutive seconds. A contact event ended when the exchange of radio 1462 packets ceased in the subsequent 20s period.

1463 References

1464 1. Wilson-Aggarwal, J. K. et al. Spatial and temporal dynamics of space use by free-ranging domestic dogs *Canis familiaris* in rural Africa. *Ecological Applications* 31, e02328 1465 1466 (2021).

1467 2. Wilson-Aggarwal, J. K. et al. Spatial-temporal dynamics of contact among free-ranging domestic dogs *Canis familiaris* in rural Africa. 2024.06.26.600798 Preprint at 1468 https://doi.org/10.1101/2024.06.26.600798 (2024).

1470 3. Cattuto, C. et al. Dynamics of Person-to-Person Interactions from Distributed RFID 1471 Sensor Networks. PLOS ONE 5, e11596 (2010).

1472 4. Isella, L. et al. What's in a crowd? Analysis of face-to-face behavioral networks. 1473 Journal of Theoretical Biology 271, 166–180 (2011).

1474 5. Wilson-Aggarwal, J. K. et al. High-resolution contact networks of free-ranging 1475 domestic dogs *Canis familiaris* and implications for transmission of infection. PLOS 1476 Neglected Tropical Diseases 13, e0007565 (2019).
1477 6. Voirin, N. et al. Combining High-Resolution Contact Data with Virological Data to Investigate Influenza Transmission in a Tertiary Care Hospital. Infection Control & Hospital Epidemiology 36, 254–260 (2015).
1478
1479
1480

## Bottlenose whales

1481 Fieldwork was conducted during most summers between 1988 and 2023, and was nearly always done from a 12m sailing vessel, though other small vessels were used for brief periods of time. The vessel carried out non-systematic surveys of key northern bottlenose whale (*Hyperoodon ampullatus*) habitat on the Scotian Shelf, mostly focusing on the Gully, a large submarine canyon ~200km from the coast of Nova Scotia. The geographical distribution of effort within the Gully was haphazard, and varied between years. In a subset of years, smaller amounts of time were also spent in the adjacent Shortland and Haldimand canyons. For this analysis we consider observations from the Gully only, as data for adjacent canyons is likely too limited to estimate meaningful variation in spatial behaviour across individuals.
1482
1483
1484
1485
1486
1487
1488
1489
1490
1491
1492
1493
1494
1495
1496
1497
1498
1499
1500
1501
1502
1503
Latitude and longitude for each encounter with northern bottlenose whales, or “sighting”, were measured using various technologies over the course of the long-term study. Loran-C was used from 1988-1992, accurate within ~400 m¹, followed by various iterations of increasingly accurate GPS. During sightings, northern bottlenose whales were photographed and photo-identification of individuals was done based on distinctive markings of the dorsal fin. Film photography was used initially, with digital cameras being phased in starting in 2007. IDs are generally side-specific (i.e., left or right), except for those individuals with very distinctive fins that can be unambiguously recognized from either side. For this and most other analyses, we restrict our focus to left-sided IDs only, not considering any right-sided photographs. All observations included in this dataset were from high-quality photographs (quality ratings “3” or “4” out of 4). Additional information can be found in the publicly available photo-identification guide for the northern bottlenose whale project².
1504
1505
1506
1507
1508
1509
1510
1511
1512
Sightings of northern bottlenose whales were considered independent social events. Photo-identifications were linked to the nearest sighting by date and time, from which we drew latitude and longitude data. Any photographs lacking a sighting record within 60 minutes were excluded from further analysis. This primarily included a smaller sets of photographs provided by other research vessels for which we lacked comparable location data. This resulted in 1199 groups over 23 years and a total of 615 unique individuals. Many individuals were seen just once (N = 199). On average, individuals were observed in 5 groups, range 1-66. Observations of groups lasted 10.8 minutes on average (time between first and last photo-identification), with a range of 0-107 minutes.
1513
## References

1514 1. Hooker, S.K., Whitehead, H., Gowans, S., Baird, R.W. Fluctuations in distribution and patterns of individual range use of northern bottlenose whales. Marine Ecology Progress Series 225, 287–297 (2002).
1515
1516
1517 2. Feyrer, L. & Walmsley, S.F. A guide to northern bottlenose whale photo ID: Version 4.0.

1518 Open Science Framework doi:10.17605/osf.io/xd7ek (2024).
1519 Chagos sharks

1520 Grey reef sharks (Carcharhinus amblyrhynchos) have a broad Indo-Pacific distribution and
1521 are one of the most common elasmobranch predators on the reefs found across the Chagos
1522 Archipelago (6°00'S 71°30'E / 6.000°S 71.500°E). This study utilised acoustic telemetry to
1523 track the movements and distribution of sharks across 93 monitoring locations centred
1524 around areas of ecological interest (e.g. islands, islets, atolls and seamounts). Information
1525 on the monitoring array and tagging procedure can be found in Jacoby et al. 2020¹.

1526 While tracking of this species occurred continuously between 2013 – 2021, data for this
1527 analysis were taken from 2014-2016 when tags at liberty, and thus density of individuals
1528 monitored were highest. Tags (Innovasea V16, 69 kHz coded transmitters) acoustically
1529 transmit a unique ID code at regular intervals (nominal delay of either 30–90 s or 60–180 s)
1530 for the duration of their battery life (~10 years). Tagged animals were detected whenever
1531 they came within range (~500 m) of an acoustic receiver. Spatio-temporal co-occurrences of
1532 tagged sharks were extracted from the telemetry data stream using a Gaussian mixture
1533 modelling approach ², and implemented using the ‘gmmevents’ function in the R package
1534 asnipe ³. Group-by-individual (GBI) matrices that reflected all associations between tagged
1535 individuals within a year, were extracted from the model, alongside information on the timing
1536 and location of group to the nearest acoustic receiver location. The response variable of
1537 social network position and the associated geographic location of social behaviour were
1538 derived from these GBI matrices. A total of 142 individuals could contribute to a group
1539 throughout the duration of the study.

1540

1541 References

1542 1. Jacoby, D. M. P. et al. Shark movement strategies influence poaching risk and can guide enforcement decisions in a large, remote Marine Protected Area. J. Appl. Ecol. 1–11 (2020) doi:10.1111/1365-2664.13654.

1545 2. Jacoby, D. M. P., Papastamatiou, Y. P. & Freeman, R. Inferring animal social networks and leadership: applications for passive monitoring arrays. J. R. Soc. Interface 13, 20160676 (2016).

1548 3. Farine, D. R. Animal social network inference and permutations for ecologists in R using asnipe. Methods Ecol. Evol. 4, 1187–1194 (2013).

1550
1551 Cornish cattle

1552 Farms and cattle management

1553 Proximity and GPS data were collected from 8 groups of dairy cows on 8 commercial farms in South-West England in Summer and Autumn of 2018 for approximately 7 days per farm.
1554 One group consisted of dry cows, whereas all other groups were lactating cows. Some devices malfunctioned and therefore we were not able to obtain data for all cattle in the group (60-91% of the group recorded¹). Grazing management included rotational grazing, strip grazing, set stocking and free range¹. Mostly, groups were kept on pasture and brought into buildings only for milking (twice daily), except one group were housed at night, one group were allowed access to buildings at all times and were kept in for two nights and days in the middle of the study period due to inclement weather and one group were allowed free access to all pasture, cubicle housing, and the automated milking system at all times during the study.
1555
1556
1557
1558
1559
1560
1561
1562
1563

1564 Equipment

1565 Nylon cattle collars with a plastic clasp (Suevia Haiges, Germany) were fitted with a proximity device and a GPS logger such that one device lay at either side of the animal's neck. The GPS loggers (i-GotU GT-120 and GT-600 devices, Mobile Action Technology Inc., Taiwan) recorded fixes every ten minutes. The proximity device is based on a design by the OpenBeacon project (http://www.openbeacon.org/) and the SocioPatterns collaboration consortium (http://www.sociopatterns.org/) and has been used in contact studies of humans, horses, dogs, and sheep ²⁻⁵. The proximity sensors exchange low-power radio packets in a peer-to-peer fashion, and this exchange of radio packets is used as a proxy for the proximity of individuals wearing the sensors. To estimate distance between devices, the attenuation of the signals with distance is computed as the difference between the received and transmitted power ². A contact event occurs if at least one data packet is exchanged during a continuous 20-second time window, and a contact is considered broken if no packets are exchanged in a 20-second period ⁶, therefore, contact durations were measured in 20-second blocks.
1566
1567
1568
1569
1570
1571
1572
1573
1574
1575
1576
1577
1578

1579

1580 References

1581 1. Fielding, H. R. et al. Spatial and temporal variation in proximity networks of commercial dairy cattle in Great Britain. Prev Vet Med 194, 105443 (2021).

1583 2. Cattuto, C. et al. Dynamics of person-to-person interactions from distributed RFID sensor networks. PLoS One 5, e11596 (2010).

1585 3. Milwid, R. M., O'Sullivan, T. L., Poljak, Z., Laskowski, M. & Greer, A. L. Comparing the effects of non-homogenous mixing patterns on epidemiological outcomes in equine populations: A mathematical modelling study. Scientific Reports 9, 3227 (2019).
1586
1587

1588 4. Ozella, L. et al. The effect of age, environment and management on social contact patterns in sheep. Applied Animal Behaviour Science 225, 104964 (2020).
1589
1590 5. Wilson-Aggarwal, J. K. et al. High-resolution contact networks of free-ranging domestic dogs Canis familiaris and implications for transmission of infection. PLoS Neglected Tropical Diseases 13, 1–19 (2019).
1591
1592

1593 6. Kiti, M. C., Melegaro, A., Cattuto, C. & Nokes, D. J. Study design and protocol for investigating social network patterns in rural and urban schools and households in a coastal setting in Kenya using wearable proximity sensors [version 2; peer review: 2 approved]. Wellcome Open Research 4, 1–17 (2019).
1594
1595
1596

1597

1598

1599
1600 Desert tortoises

1601 We monitored tortoise movements using radio telemetry across multiple study sites spanning a 15-year period. Datasets were collected from nine study sites across desert tortoise habitat in the Mojave Desert of California, Nevada, and Utah from 1996 to 2014.
1602
1603 Each site was monitored over multiple years, though not all sites were surveyed every year.
1604
1605 Individuals were tracked at least weekly during their active season and at least monthly
1606
1607 during winter months. Tortoises were fitted with very high frequency (VHF) radio-transmitters
1608 (e.g., Model RI-2B [13.8–15.0 g], Holohil Systems Ltd., Carp, Ontario, Canada, or AVM
1609 models G3, SB2, or SB2-RL, AVM Colfax CA for older studies), which were attached
1610 following established methods (Boarman et al. 1998). Locations were determined using
1611 hand-held VHF receivers (e.g., Telonics TR-2, Mesa, AZ, or ICOM RC 10) and recorded with
1612 GPS units (Universal Transverse Mercator, North American Datum 1983). Periods of
1613 intensive tracking (i.e., multiple relocations per day) were conducted to obtain detailed
1613 habitat use data during peak activity for some studies.

1614 Each tortoise was uniquely identified with a numbered paper tag sealed with clear epoxy and
1615 permanent notches on the marginal scutes (Cagle 1939). During each encounter, we
1616 recorded the individual's identification number, date and time of observation, GPS location,
1617 microhabitat type (e.g., vegetation, pallet, or burrow), and any visible signs of injury or
1618 disease. Burrow use was documented by assigning a unique identification number to each
1619 burrow, with new IDs assigned when previously unmarked burrows were encountered. Den
1620 sharing was used as the contact type, where observations of individuals witnessed within the
1621 same den in the same sampling date were taken to be connected.

1622 The dataset was based on monitoring tagged individuals, so data collection was not blind.
1623 While most tortoises were monitored consistently throughout the study, logistical constraints
1624 and equipment failures occasionally altered the telemetry schedule.

1625

1626 References

1627 Boarman, W. I., T. Goodlett, G. Goodlett, and P. Hamilton. 1998. Review of radio transmitter
1628 attachment techniques for turtle research and recommendations for improvement.
1629 Herpetological Review 29: 26-27

1630 Cagle, F. R. 1939. A system for marking turtles for future identification. Copeia 1939: 170-
1631 173.

1632 Sah, P.*, Nussear, K.E., Esque, T.C., Aiello, C.M., Hudson, P.J., Bansal, S., 2016. Inferring
1633 social structure and its drivers from refuge use in the desert tortoise, a relatively solitary
1634 species. Behavioral Ecology and Sociobiology 70, 1277-1289.
1635 https://doi.org/10.1101/025494

1636
1637 Ein Gedi hyraxes

1638 The rock hyrax (Procavia capensis) population has been studied since 1999 at the Ein Gedi
1639 Nature Reserve (31°28′N, 35°24′E) in Israel (e.g. 1–4). Data used in this study were
collected continuously for the years 2000–2020, totaling approximately 75,000 observations.
1640

1641 The research focuses on two deep gorges, David and Arugot, located on the western side of
1642 the Dead Sea. Each field season, beginning in March and lasting between three and six
1643 months, rock hyraxesare trapped and observed daily. Tomahawk live box traps were
1644 deployed in secure, shaded locations, baited with cabbage and kohlrabi. Given that rock
1645 hyraxes are diurnal, the traps are opened during a fixed morning interval. Once captured, the
1646 animals are anesthetized with ketamine hydrochloride (0.1 mg/kg) and receive a
1647 subcutaneous microchip (DataMars SA) along with either an ear tag (for pups and juveniles)
1648 or a lightweight collar (weighing less than 5 g) bearing identification marks. Each individual
1649 is then sexed, weighed, and measured, and is given 90 to 150 minutes to recover from the
1650 anesthesia. Animals that are recaptured are not anesthetized; they are simply weighed and
1651 are promptly released.

1652 Hyrax activity was monitored each day during the field season using 10×42 binoculars and a
1653 telescope with 50–100× magnification. Observations were conducted in the early morning,
1654 from first light until noon, the time when the hyraxes retreat to their shelters. Every day, a
1655 randomly chosen focal group was followed. Although hyraxes primarily spend their time
1656 foraging and resting, which allows for the simultaneous monitoring of several individuals,
1657 limitations in visibility (due to rocks, trees, and bushes) and the challenge of following up to
1658 ten individuals meant that we could not capture the precise duration of every pairwise
1659 interaction. Consequently, we recorded interactions on a daily basis: if two individuals were
1660 seen interacting at any point during a day, that day was noted as an interaction event
1661 regardless of its length. In addition, we recorded the location of each observed hyrax, up to
1662 5m accuracy.

1663 Our annual marking efficiency was high, with about 95% ± 0.5 of group members identified,
1664 thus minimizing bias in assessing each group's social structure. Social interactions that
1665 involved unmarked individuals were excluded from analysis. We define positive interactions
1666 as those involving direct physical contact (such as huddling or sharing a sleeping burrow) or
1667 coordinated behavior (moving together closely or sitting side by side).

1668

1669 References

1670 1. Bar Ziv, E. et al. Individual, social, and sexual niche traits affect copulation success in
a polygynandrous mating system. Behav Ecol Sociobiol 70, 901–912 (2016).

1672 2. Barocas, A., Ilany, A., Koren, L., Kam, M. & Geffen, E. Variance in Centrality within Rock Hyrax Social Networks Predicts Adult Longevity. PLoS ONE 6, e22375 (2011).

1674 3. Ilany, A., Barocas, A., Koren, L., Kam, M. & Geffen, E. Structural balance in the social networks of a wild mammal. Animal Behaviour 85, 1397–1405 (2013).
1676 4. Bordes, C. N. M., Beukeboom, R., Goll, Y., Koren, L. & Ilany, A. High-resolution

1677 tracking of hyrax social interactions highlights nighttime drivers of animal sociality. Commun

1678 Biol 5, 1–12 (2022).

1679

1680

1681

1682
1683 European boar

1684 Behavioral data for wild boar were collected from three European study areas: Bavarian
1685 Forest National Park (BFNP, 48°59'N, 13°23'E, Germany), Hainich National Park (HNP,
1686 51°04'N, 10°26'E, Germany), and Kostelec nad Černými lesy (KNC, 50°0'N, 14°50'E, Czech
1687 Republic).

1688 BFNP data were collected between October 2021 and January 2023 as part of a research
1689 project on movement ecology and African swine fever (ASF) transmission dynamics 1. The
1690 study spanned the 250 km² BFNP and the adjacent 684 km² Šumava National Park in the
1691 Czech Republic, with elevations ranging from 570 to 1453 m. The landscape consists of
1692 mixed coniferous and mountain forests. A total of 42 wild boar were GPS-collared (Vertex
1693 Plus, Vectronic Aerospace GmbH, Berlin, Germany) to track movement patterns, home
1694 range sizes, and habitat use. Animals were captured using ~30 m² wood-clad corral traps
1695 equipped with live-monitoring cameras and counterweight-triggered gates. The captured
1696 individuals were restrained in a net tunnel, handled for approximately 5 min, and released.
1697 Ethical approval was granted by the Upper Bavaria government (permit ROB-55.2-2532.Vet-
1698 02-20-149).

1699 HNP data were collected between April 2017 and August 2019 to investigate the effects of
1700 no-hunting zones on wild boar movement and space use 2. The 25,000 ha study area
1701 comprises 54.6% agricultural land, 34.8% forests, 7.3% open land, 3.2% anthropogenic
1702 areas, and 0.4% water bodies, with elevations ranging from 180 to 500 m a.s.l. HNP is 75
1703 km² large, with 33 km² designated as a no-hunting zone, including UNESCO-listed primeval
1704 beech forests and former military sites where hunting is prohibited. A total of 63 wild boar
1705 were GPS-collared (Vertex Lite, Vectronic Aerospace GmbH, Berlin, Germany), with GPS
1706 fixes recorded at 30-minute intervals.

1707 KNC data were collected between April 2019 and October 2022 as part of a study on wild
1708 boar movement ecology, social behavior, and responses to ASF control measures (including
1709 hunting pressure, artificial food supply, and movement barriers–electric and odor fences, and
1710 sound traps) 3,4. The 2,900 ha study area, located ~30 km east of Prague, comprises mixed
1711 forests, agricultural land, water bodies, and built-up areas, with an average elevation of 430
1712 m a.s.l. Managed by the Czech University of Life Sciences Prague (Lesy ČZU), this area is
1713 heavily frequented for tourism, forestry, and hunting. A total of 84 wild boar were captured
1714 using wooden corral traps baited with corn and immobilized via anesthetic darts delivered by
1715 airguns. Each individual was GPS/GSM-collared (Vectronic Aerospace GmbH), with fixes
1716 recorded every 30–60 min. Ethical approval was granted by the Ministry of the Environment
1717 of the Czech Republic (permit MZP/2019/630/361).

1718 For all three datasets, social networks were constructed using the gambit of the group
1719 approach, where individuals were considered to be associating if they were within 100 m of
1720 each other within a 10-minute interval. GPS data were processed using the spatsoc package
1721 5,6 in R, with group_times used to assign temporal groupings and edge_dist used to
1722 calculate pairwise distances between individuals, generating an edge list of proximity-based
1723 associations.
1724 References

1725 1. Wielgus, E. et al. First-Passage Time Analysis Based on GPS Data Offers a New
1726 Approach to Estimate Restricted Zones for the Management of Infectious Diseases in
1727 Wildlife: A Case Study Using the Example of African Swine Fever. Transboundary and
1728 Emerging Diseases 2023, 4024083 (2023).

1729 2. Klamm, A. et al. Entwicklung Und Raumnutzung Eines Schwarzwild-Bestandes in
1730 Abhängigkeit von Den Naturräumlichen Gegebenheiten Des Buchenwald-Nationalparks
1731 Hainich Und Dessen Intensiv Landwirtschaftlich Genutzten Umfeldes. Abschlussbericht
1732 Dezember 2020. (2020).

1733 3. Olejarz, A., Augustsson, E., Kjellander, P., Ježek, M. & Podgórski, T. Experience
1734 shapes wild boar spatial response to drive hunts. Sci Rep 14, 19930 (2024).

1735 4. Faltusová, M. et al. Wild Boar Proves High Tolerance to Human-Caused Disruptions:  
1736 Management Implications in African Swine Fever Outbreaks. Animals 14, 2710 (2024).

1737 5. Robitaille, A. L., Webber, Q. M. R. & Wal, E. V. Conducting social network analysis with animal telemetry data: Applications and methods using spatsoc. Methods in Ecology and Evolution 10, 1203–1211 (2019).

1740 6. Robitaille, A. L., Webber, Q. & Wal, E. V. spatsoc: Group Animal Relocation Data by
1741 Spatial and Temporal Relationship. (2023).

1742

1743
1744 Falkirk wood mice

1745 Trapping for this study took place from 2015-2017 in Callendar Wood (55.990470, -3.766636; Falkirk, Scotland), a 100ha broadleaf woodland, which contain a populations of wood mice, which are naturally exposed to and infected with a wide range of parasites and pathogens. The experiment had three temporal replicates; all of which took place during the wood mouse breeding season: (i) May - July 2015 (ii) June - August 2016 and (iii) July-November 2017. Experimental design for these experiments included randomised nutrition supplementation (high quality food pellets vs unmanipulated) at the grid level and randomised parasite treatment (anthelmintic treatment vs water control) at the individual level within grids. We used a weight-adjusted of 2ml/g dose of both Pyrantel pamoate (Strongid-P, 100 mg/kg) and Ivermectin (Eqvalan, 9.4mg/kg). In 2017, treatment was re-administered 4 weeks after initial dose for all individuals still in the experiment. All animal work was carried out under the approved UK Home Office Project License 70/8543 in accordance with the UK Home Office.

1758 Trapping grids were set up with trapping stations 10m apart, and two traps at each stations, as follows: 2015: 3 grids with 8x8 trapping stations; 2016: 4, 6x5 grids; 2017: 4, 7x5 grids. In each year we live-trapped mice for 3 nights/week using Sherman live traps (H.B. Sherman 2x2.5x6.5 inch folding trap, Tallahassee, FL, USA). Trapping protocols followed (Sweeny, Clerc, et al., 2021). Each tagged individual was followed for a period of 12-16 days (2015-2016) or up to 8 weeks (2017). During this time, we collected morphometric data, blood samples, and faecal samples at every capture. Movement was minimal between grids.

1766 A total of 261 individuals were captured 882 times across this 3 year study. We used the easting and northing location of the trapping stations where individuals were trapped to estimate density and social network metrics. Edges in the social network between individuals (nodes) were defined as unique mice trapped nearby (within one adjacent trap distance by Euclidean distance) in the same trapping night.

1771

1772 References

1773 Sweeny, A. R., Clerc, M., & Pontifes, P. A. (2021). Supplemented nutrition decreases helminth burden and increases drug efficacy in a natural host-helminth system. Proceedings of the Royal Society B: Biological Sciences.
1774 https://royalsocietypublishing.org/doi/abs/10.1098/rspb.2020.2722

1777
1778 Golden-crowned sparrow

1779 Golden-crowned sparrow flocks have been monitored at the University of California-Santa Cruz Arboretum since 2009 as part of a long-term research study on their winter social behavior (Shizuka et al. 2014; Madsen et al. 2023). Each fall, first-year migrants were fitted with colored plastic and numbered metal leg bands in unique color combinations. Foraging flocks were surveyed throughout the Arboretum by recording the identities of all banded birds in a flock, which was defined as a group of birds foraging within a 5 m radius. Locations of flocks were recorded using a reference map of 10 m x 10 m grid cells (Shizuka et al. 2014). Observations at seed piles were not considered in this definition of foraging flocks and were excluded from our analysis of flocking relationships. To ensure we were observing winter flocking behaviors rather than interactions between transitory individuals on migration, we limited observations to between November 1 (when most birds had arrived from breeding grounds) and March 1 (when winter flocks begin to break apart and birds begin migration). We further limited our sample to birds that had been sighted 3 or more times throughout this period to remove transient individuals. We then inferred social associations using the 'gambit of the group'. from co-occurrences in foraging flocks and calculated the simple ratio index (SRI) to represent association strength (see Shizuka et al. 2014 for further details).

1795

1796 References

1797 Shizuka, D., Chaine, A.S., Anderson, J., Johnson, O., Laursen, I.M., Lyon, B.E., 2014. Across-year social stability shapes network structure in wintering migrant sparrows. Ecol Lett 17, 998-1007. https://doi.org/10.1111/ele.12304

1800 Madsen, A.E., Lyon, B.E., Chaine, A.S., Block, T.A., Shizuka, D., 2023. Loss of flockmates weakens winter site fidelity in golden-crowned sparrows (Zonotrichia atricapilla). Proc. Natl. Acad. Sci. U.S.A. 120, e2219939120. https://doi.org/10.1073/pnas.2219939120

1803

1804
## 1805 Kalahari meerkats

1806 An individual-based study of meerkats has been running at the Kuruman River Reserve in South Africa since 1993 (26°58′S, 21°49′E, Clutton-Brock and Manser 2016). The study area, which covers approximately 50–60 km², includes a diverse landscape of dry pans, vegetated sand dunes, and arid bushveld that is typical of a South African savannah, where livestock and game farming form the principal land use. Since the start of the project, approximately 15 groups (range 6 to 21 groups) and around 200 individuals were followed at any one time (mean ± SD per month 214.5 ± 59.4, range 101 to 359). Most individuals were born into the study population and were habituated from birth to allow close behavioural observation. Groups were visited 3–4 times per week in the morning and afternoon throughout the study, with data collected on the composition of groups and on the behaviour, reproductive status, social status, and health status of individuals, so that pregnancies, births, deaths, emigrations and immigrations could be enumerated (summarised in Clutton-Brock and Manser 2016). Most individuals in the population were also weighed at each visit by enticing them onto electronic scales with small amounts of hard-boiled egg or water, and while foraging, GPS locations were taken from the center of the group at 15 min intervals, which we use to estimate home ranges. Though the project began in 1993, all the above data were only collected on multiple groups (> 5) simultaneously from 1998 onwards. Most of our analyses therefore cover the breeding seasons from 1998–2023. The only exception is the GPS data, which were collected in the form needed for our analyses from 2002 onwards. Individuals witnessed within the same group in the same sampling date were taken to be connected.

1827

## 1828 References

1829 Clutton-Brock, Tim H; Manser, Marta B (2016). Meerkats: Cooperative breeding in the Kalahari. In: Koenig, Walter D; Dickinson, Janis L. Cooperative breeding in vertebrates : studies of ecology, evolution, and behavior. Cambridge: Cambridge University Press, 294-317.

1833
1834 Kenyan elephants

1835 Relevant methods from:

1836 The relationship between social behavior and habitat familiarity in African elephants

1837 (*Loxodonta africana*). Noa Pinter-Wollman, Lynne A. Isbell and Lynette A. Hart. Proc. R.

1838 Soc. B (2009) 276, 1009–1014. doi:10.1098/rspb.2008.1538

1839

1840 Translocation and sightings

1841 During September 2005, 150 African elephants were translocated from Shimba Hills

1842 National Reserve on the coast of Kenya (4.08 S to 4.38 S and 39.58 E to 39.38 E) to Tsavo

1843 East National Park (2.08S to 3.78S and 38.18E to 39.38 E), a distance of 160 km. This

1844 translocation was part of the Kenya Wildlife Service (KWS) effort to decrease human–

1845 elephant conflict in the vicinity of Shimba Hills. Twenty elephant groups comprising adult

1846 females, juveniles and calves (average group size 6.8) and 20 independent adult males

1847 were moved over the course of 32 days. The release site differs ecologically from the source

1848 site and is separated from it by dense human population, providing a unique opportunity for

1849 examining the social behaviour of the elephants in a novel environment.

1850 During the translocation, all the elephants were tagged with yellow zip ties on their tails to

1851 distinguish them from the local Tsavo elephant population. Unique white numbers painted on

1852 the translocated elephants' backs, natural ear marks and tusk shapes were used for

1853 individual identification of the translocated elephants (Moss 1996). Elephants' ages were

1854 estimated based on Moss (1996).

1855 The locations, their time and the identities of the translocated and local Tsavo elephants

1856 were recorded in Tsavo East for a year post-translocation using a Geko 201 GPS unit

1857 (Garmin Ltd., USA). Road transects were conducted using a vehicle four to five times a

1858 week, alternating between four routes of similar length (50–70 km) on the existing roads

1859 within Tsavo East National Park. A total of 3371 elephant sightings were recorded, of which

1860 386 and 2985 were the translocated and local elephants, respectively. Of the 150 elephants

1861 translocated, data on 83 were obtained, and are presented here. Because males leave the

1862 social unit in which they were born at the age of 15, and because the social behaviour of

1863 these independent males differs from that of females and their young offspring (Moss &

1864 Poole 1983), such translocated males were excluded from our analyses.

1865

1866 Social association

1867 Elephants were defined as associating with one another if they were sighted within 500 m

1868 from one another within a 2 hour time period, based on McComb et al. (2000, 2003). They

1869 showed that elephants can individually recognize conspecifics' vocalizations over great

1870 distances (1 km). Therefore, the definition of social association used here includes not only

1871 direct interactions but also recognizes the communicative capabilities of elephants to acquire

1872 information about the number and identities (translocated or local) of vocalizing conspecifics
1873 (McComb et al. 2000, 2003). Thus, the definition of social association used here allows for
1874 the acquisition of inadvertent social information about the new environment (Danchin et al.
1875 2004).

1876

1877 References

1878 Danchin, E., Giraldeau, L. A., Valone, T. J. & Wagner, R. H. 2004 Public information: from
1879 nosy neighbors to cultural evolution. Science 305, 487-491. (doi:10.1126/science.1098254)

1880 McComb, K., Moss, C., Sayialel, S. & Baker, L. 2000 Unusually extensive networks of vocal
1881 recognition in African elephants. Anim. Behav. 59, 1103-1109.
1882 (doi:10.1006/anbe.2000.1406)

1883 McComb, K., Reby, D., Baker, L., Moss, C. & Sayialel, S. 2003 Long-distance
1884 communication of acoustic cues to social identity in African elephants. Anim. Behav. 65,
1885 317-329. (doi:10.1006/anbe.2003.2047)

1886 Moss, C. 1996 Getting to know a population. In Studying elephants (ed. K. Kanwanga), pp.
1887 58-74. Nairobi, Kenya: African Wildlife Foundation.

1888 Moss, C. J. & Poole, J. H. 1983 Relationships and social structure of African elephants. In
1889 Primate social relations: an integrated approach (ed. R. A. Hinde), pp. 315-325. Oxford, UK:
1890 Blackwell Scientific Publications.

1891
## 1892 Kielder voles

1893 Briefly, we monitored a natural population of field voles (*Microtus agrestis*) in Kielder Forest, Northumberland, UK across three studies: 2001–2007, 2008–2010 and 2015–2017. Kielder Forest is a man-made spruce forest and field voles are found in grassy clear-cuts. Trapping was performed across different sites, each a forest clear-cut. At each site, Ugglan small mammal traps (Grahnab, Gnosjo, Sweden) were laid out in a grid and checked regularly in ‘primary’ and ‘secondary’ sessions (see below for details). Access to the sites was provided by Forestry England. Newly trapped field voles were injected with a Passive Integrated Transponder (PIT) tag (AVID, UK) for unique identification. At each subsequent capture, we recorded: date, site, trap location and PIT tag ID. This approach allowed us to build up a longitudinal record of the location of tagged voles across multiple sessions. Edges in the social network between individuals (nodes) were defined as unique voles trapped nearby (within one adjacent trap distance by Euclidean distance) in the same trapping night.

1905 Between 2001-2007, trapping was performed from March to November. Primary sessions took place at monthly intervals. At each ‘primary’ session, voles were trapped at 4 different sites. At each site, 100 Ugglan small mammal traps were laid out in a grid spaced 5 m apart. At each ‘primary’ session, traps were checked a total of five times (‘secondary sessions’). More details for the 2001-2007 study are available in¹.

1910 Between 2008-2010, trapping was performed either from February (2008–2009) or April (2009–2010) to November. Primary sessions took place at monthly intervals. Voles were trapped at a total of 4 different sites – two sites in 2008–2009 and a further two sites in 2009–2010. At each site, 150 Ugglan small mammal traps were laid out in a grid spaced 3–5 m apart. Primary sessions lasted 3 days, and traps were checked a total of five times. More details for the 2008-2010 study are available in².

1915 Between 2015-2017, trapping was performed from March to October. Primary sessions took place at approximately two-week intervals. At each primary session, voles were trapped at 4 different sites. During the study, 3 sites were reassigned due to practical constraints, giving a total of 7 different sampling sites. At each site, 150–197 Ugglan small mammal traps were laid out in a grid spaced 3–5 m apart. Primary sessions lasted 3 days, with traps checked each morning and afternoon. More details for the 2015-2017 study are available in³.

1922

## 1923 References

1924 1. Begon, M. et al. Effects of abundance on infection in natural populations: Field voles and cowpox virus. Epidemics 1, 35–46 (2009).

1926 2. Jackson, J. A. et al. An Immunological Marker of Tolerance to Infection in Wild Rodents. PLOS Biology 12, e1001901 (2014).

1928 3. Wanelik, K. M. et al. Effects of an IgE receptor polymorphism acting on immunity, susceptibility to infection, and reproduction in a wild rodent. eLife e77666 (2023).

1930
1931 Liverpool wood mice

1932 Live-trapping for this study was carried out in wild wood mouse populations located near

1933 Liverpool, UK regularly between May and December for six consecutive years (2009–2014).

1934 We sampled 16 trapping grids ranging in size from 2,500 to 10,000 m2, spread across five

1935 different woodland sites with 2–3 sites trapped per year. Sites ranged from approximately 2

1936 to 60 km apart. On each grid, trapping stations were placed every 10 m, with two live traps

1937 (H.B. Sherman 2 × 2.5 × 6.5 in. folding traps, Tallahassee, FL, USA) at each station baited

1938 with grains and bedding material. Full trapping details can be found in (Sweeny, Albery, et

1939 al., 2021).

1940 A total of 926 individuals were captured 1,609 times across this 6-year study (max captures

1941 per individual = 28, median captures per individual = 4). We used the easting and northing

1942 location of the trapping stations where individuals were trapped to estimate density and

1943 social network metrics. Edges in the social network between individuals (nodes) were

1944 defined as unique mice trapped nearby (within one adjacent trap distance by Euclidean

1945 distance) in the same trapping night. Density was calculated using individuals' centroids in

1946 the sampling year based on annual density kernels and using trapping locations.

1947

1948 References

1949 Sweeny, A. R., Albery, G. F., Venkatesan, S., Fenton, A., & Pedersen, A. B. (2021).

1950 Spatiotemporal variation in drivers of parasitism in a wild wood mouse population. Functional

1951 Ecology, 35(6), 1277–1287.

1952
1953 Moorea sharks

1954 Owner: Johann Mourier & Serge Planes

1955 Location: Moorea, French Polynesia

1956 The study was conducted at Moorea Island (17°30'S; 149°51'W) in the Society archipelago,
1957 French Polynesia. Between 2008 and 2010, a total of seven sites were surveyed on a
1958 regular basis along approximately 10 km of the Northern reef of Moorea Island. Among
1959 them, six sites were located on the outer reef and were characterized by coral structures
1960 from the barrier reef to the drop off (70 m depth) and one site was located inside the lagoon
1961 between 2 and 10 m depth within a small channel and characterized by coral patches in a
1962 sandy habitat (Mourier et al., 2012; Mourier and Planes 2021). The surveys consisted of 40
1963 min dives (~30 min dedicated to survey) during which individual blacktip reef sharks were
1964 identified by photo-identification, using unique, lifelong color-shape of the dorsal fin (Mourier
1965 et al., 2012).

1966 Associations between individuals were defined using the "Gambit of the Group", assuming
1967 that all individuals observed together are then considered as "associated." An experienced
1968 diver conducted a stationary visual census at each site monitored and identifying sharks
1969 within a ~100-m radius area (made possible by the high visibility conditions in these tropical
1970 waters). As most sharks usually remained together during the time of the dive, we
1971 considered the largest number of individuals observed within a 10-min period to be part of a
1972 group. We are confident that observed associations represented true grouping structure
1973 because groups were spatio-temporally well-defined and sharks were engaged in specific
1974 social behavior (e.g., following, parallel swimming or milling; Mourier et al., 2012). To avoid
1975 the potential for weak and nonrelevant associations between pairs of individuals with very
1976 low number of sightings, we used a restrictive observation threshold to include only
1977 individuals observed more than the median number of sightings (median = 14). Thus, all
1978 individuals seen less than 15 times were removed from the analyses to ensure that
1979 associations were estimated with high accuracy and precision.

1980 Our dataset totaled 180 groups among 105 adult blacktip reef sharks.

1981

1982 References

1983 Mourier, J. & Planes, S. Kinship does not predict the structure of a shark social network. Behavioral Ecology 32, 211-222 (2021).

1985 Mourier, J., Vercelloni, J. & Planes, S. Evidence of social communities in a spatially structured network of a free-ranging shark species. Animal Behaviour 83, 389-401 (2012).

1987
## 1988 Moray and Tay dolphins

1989 Data are from a long-term individual-based study of bottlenose dolphins on the east coast of Scotland¹. Boat-based photo-identification (photo-ID) data have been collected since 1989, initially focussing on an area that was subsequently designated as the Moray Firth Special Area of Conservation (92/43/EEC), with occasional surveys further afield²,³. During this time the population expanded its distributional range⁴,⁵ and since 2009 photo-ID surveys have also been regularly carried out around Tayside and adjacent waters⁵,⁶. Photo-ID surveys take place annually from May to September, with occasional surveys at other times of the year. Survey routes are chosen to maximise sighting probability while providing wide coverage of the study areas and all surveys were carried out under NatureScot Animal Scientific Licences. Surveys adhere to standardised protocols²,⁵ where photographs were taken of the dorsal fins of as many individual dolphins as possible, graded for quality and using unique markings matched to the Universities of Aberdeen and St Andrews catalogue of known bottlenose dolphins on the Scottish east coast. All dolphins within 100m and engaged in similar activities or travelling in the same direction were considered to be in the same group and associated. Sex was determined using genital photographs or if adults were seen in repeat association with a known calf⁷. Year of birth was estimated from photographs of calves (up to 2 years old) based on their foetal folds, size, colour and behaviour⁸.

2006 Sightings data from this population were provided for this study for every bottlenose dolphin group encountered from 1990 to 2021 including the date, location, estimated group size, unique identifier of each dolphin identified, and their sex and year of birth if known. This dataset comprised sightings of 903 individual bottlenose dolphins (males, females, unknown sex and all ages) in 3071 groups and was split into two study areas, Moray (730 dolphins in 2525 groups) and Tay (339 dolphins in 546 groups). Individuals from this population are known to travel across the east coast of Scotland and 166 dolphins were photographed in both areas.

## 2014 References

2015 1 Cheney, B. J., Arso Civil, M., Hammond, P. S. & Thompson, P. M. Site Condition Monitoring of bottlenose dolphins within the Moray Firth Special Area of Conservation: 2017-2022., (2024).

2018 2 Cheney, B. et al. Long-term trends in the use of a protected area by small cetaceans in relation to changes in population status. Global Ecology and Conservation 2, 118-128, doi:https://doi.org/10.1016/j.gecco.2014.08.010 (2014).

2021 3 Wilson, B., Hammond, P. S. & Thompson, P. M. Estimating size and assessing trends in a coastal bottlenose dolphin population. Ecol Appl 9, 288-300 (1999).

2023 4 Wilson, B., Reid, R. J., Grellier, K., Thompson, P. M. & Hammond, P. S. Considering the temporal when managing the spatial: a population range expansion impacts protected areas-based management for bottlenose dolphins. Anim. Conserv. 7, 331-338, doi:10.1017/s1367943004001581 (2004).
2027 5 Arso Civil, M. et al. Changing distribution of the east coast of Scotland bottlenose
dolphin population and the challenges of area-based management. Aquatic Conservation:
2028 Marine and Freshwater Ecosystems 29, 178-196, doi:10.1002/aqc.3102 (2019).
2029

2030 6 Arso Civil, M. et al. Variations in age- and sex-specific survival rates help explain
2031 population trend in a discrete marine mammal population. Ecology and Evolution 9, 533-544,
2032 doi:doi:10.1002/ece3.4772 (2019).

2033 7 Cheney, B. J., Dale, J., Thompson, P. M. & Quick, N. J. Spy in the sky: a method to
2034 identify pregnant small cetaceans. Remote Sensing in Ecology and Conservation n/a,
2035 doi:https://doi.org/10.1002/rse2.258 (2022).

2036 8 Grellier, K., Hammond, P. S., Wilson, B., Sanders-Reed, C. A. & Thompson, P. M.
2037 Use of photo-identification data to quantify mother-calf association patterns in bottlenose
dolphins. Can. J. Zool.-Rev. Can. Zool. 81, 1421-1427, doi:10.1139/z03-132 (2003).
2038

2039

2040
## 2041 Mountain Lake mice

2042 Data for the Mountain Lake Mice were collected as part of two 3-year deworming experiments conducted at the Mountain Lake Biological Station, Pembrooke, VA from 2016-2018 (37°22′N, 80°31′W). Eight 0.5 ha grids (8 x 8 traps with 10 m spacing) were live-trapped for 2-3 night sessions approximately every 2 weeks using Sherman live traps (H.B. Sherman 2x2.5x6.5 inch folding trap, Tallahassee, FL, USA) from May to August/early September each year. Trapping protocols followed (Sweeny et al. 2020) with each individual receiving a numbered ear tag upon first capture. Movement among grids was minimal with less than 2% of individuals captured on multiple grids. Thus, grids were treated as separate populations for local density and social network calculations. Edges in the social network between individuals (nodes) were defined as unique mice trapped nearby (within one adjacent trap distance by Euclidean distance) in the same trapping night. Animal care and use protocols were approved by Princeton University, and the research was supported by a DARPA grant to ALG (68255-LS-DRP).

2055

## 2056 References

2057 Sweeny, Amy R., Courtney A. Thomason, Edwin A. Carbajal, Christina B. Hansen, Andrea L. Graham, and Amy B. Pedersen. 2020. "Experimental Parasite Community Perturbation Reveals Associations between Sin Nombre Virus and Gastrointestinal Nematodes in a Rodent Reservoir Host." Biology Letters 16 (12): 20200604.

2061
2062 Multimammate mice

2063 *Mastomys natalensis*, the multimammate mouse, occurs throughout sub-saharan Africa and
2064 is an important agricultural pest species as well as host to a number of diseases of human
2065 health importance (e.g. plague, Lassa virus and leptospira (Fichet-calvet and Becker-ziaja,
2066 2014; Holt et al., 2006; Mccauley et al., 2015). In Morogoro, Tanzania, where field data for
2067 this study was collected, *M. natalensis* undergoes considerable population fluctuations, with
2068 densities ranging from 10ha-1 during the breeding season up to 150ha-1 outside the
2069 breeding season. Population dynamics have been monitored using a robust design open
2070 capture mark recapture on a monthly basis since 1994 and is ongoing (Leirs et al., 2023).

2071 For the purposes of this study, data was used for the period of 1994 - 2022. The study
2072 design is as follows: 300 single capture Sherman traps were placed on a 300 x 100 m grid
2073 and baited with a mixture of peanut butter and cornflour. Trapping occurred for three
2074 successive nights; trapped individuals were weighed, sex and sexual condition recorded and
2075 individually marked with a unique toe clipping code if it is the first time they were captured.

2076 More details about the study site and capture methods can be found in Leirs et. al 2023.
2077 Edges in the social network between individuals (nodes) were defined as unique mice
2078 trapped nearby (within one adjacent trap distance by Euclidean distance) in the same
2079 trapping night.

2080

2081 References

2082 Fichet-calvet, E., Becker-ziaja, B., 2014. Lassa Serology in Natural Populations of Rodents

2083 14. https://doi.org/10.1089/vbz.2013.1484

2084 Holt, J., Davis, S., Leirs, H., 2006. A model of Leptospirosis infection in an African rodent to determine risk to humans: Seasonal fluctuations and the impact of rodent control. Acta Trop.

2086 99, 218-225. https://doi.org/10.1016/j.actatropica.2006.08.003

2087 Leirs, H., Kirkpatrick, L., Sluydts, V., Sabuni, C., Borremans, B., Katakweba, A., Massawe, A., Makundi, R., Mulungu, L., Machang'u, R., Mariën, J., 2023. Twenty-nine years of continuous monthly capture-mark-recapture data of multimammate mice (*Mastomys natalensis*) in Morogoro, Tanzania. Sci. Data 10, 798. https://doi.org/10.1038/s41597-023-02700-3

2091 02700-3

2092 Mccauley, D.J., Salkeld, D.J., Young, H.S., Makundi, R., Dirzo, R., Eckerlin, R.P., Lambin, E.F., Gaffikin, L., Barry, M., Helgen, K.M., 2015. Effects of Land Use on Plague ( Yersinia pestis ) Activity in Rodents in Tanzania. Am. J. Trop. Med. Hyg. 92, 776-783.

2095 https://doi.org/10.4269/ajtmh.14-0504

2096

2097
## 2098 Palmyra sharks

2099 We tagged 41 grey reef sharks (*Carcharhinus amblyrhynchos*) that were caught on the
2100 forereefs of Palmyra Atoll (5°540 N 162°050 W) in the Central Pacific Ocean. Palmyra is a
2101 US Wildlife Refuge and includes large numbers of sharks and other predators (e.g. Bradley
2102 et al. 2017). Sharks were caught on hook and line and surgically implanted with Vemco V16
2103 acoustic transmitters (69kHz, semi-randomized delay 60-180 seconds). Transmitters had
2104 battery lives of four years and were detected by a network of 55 acoustic receivers (Vemco,
2105 VR2W) positioned around the forereef and within the lagoons and back reefs (see
2106 Papastamatiou et al. 2018 for full details). Receivers would record the date and time of
2107 sharks that swam within range of the receiver (approximately 200-300 m).

2108 We used a gambit of the group approach to generate dynamic social networks, where
2109 clusters of sharks co-occurring at receivers through time were identified using Gaussian
2110 mixture models (Psorakis et al. 2012, Jacoby et al. 2016). These clusters consisted of
2111 individual sharks that visited the same receivers at the same time. Adjacency matrices were
2112 produced based on how often dyadic pairs co-occurred within identified clusters. Full model
2113 details can be found in Papastamatiou et al. 2020.

2114

## 2115 References

2116 Bradley D, Conklin E, Papastamatiou YP, McCauley DJ, Pollock K, Pollock A, Kendall BE,
2117 Gaines SD, Caselle JE. 2017. Resetting predator baselines in coral reef ecosystems. Sci.
2118 Rep. 7:43131

2119 Jacoby DMP, Papastamatiou YP, Freeman R. 2016. Inferring animal social networks and
2120 leadership: applications for passive monitoring arrays. J. R. Soc. Interface 13:20160676

2121 Papastamatiou YP, Bodey TW, Friedlander AM, Lowe CG, Bradley D, Weng K, Priestley V,
2122 Caselle JE. 2018. Spatial separation without territoriality in shark communities. Oikos.
2123 127:767-779

2124 Papastamatiou YP, Bodey TW, Caselle JE, Bradley D, Freeman R, Friedlander AM, Jacoby
2125 DMP. 2020. Multiyear social stability and social information use in reef sharks with diel
2126 fission-fusion dynamics. Proc. R. Soc. B. 287: 20201063

2127 Psorakis I, Roberts SJ, Rezek I, Sheldon BC. 2012. Inferring social network structure in
2128 ecological systems from spatio-temporal data streams. J. R. Soc. Interface 9: 3055-3066

2129
2130 Potomac dolphins

2131 Field Site and Dolphin Population

2132 The Potomac-Chesapeake Dolphin Project has been conducting annual surveys of wild
2133 Tamanend's bottlenose dolphins (*Tursiops erebennus*) in the Potomac River and mid-
2134 Chesapeake Bay since July 2015. Approximately 550 km2 in the lower Potomac and mid-
2135 Chesapeake Bay are surveyed annually with 90% of survey effort concentrated in a 58 km2
2136 area. Since the project started, over 2000 individuals have been identified with 36% sighted
2137 more than one day and 20% returning to the area more than one year. Depending on one's
2138 definition, the populations using the area are migratory and inhabit Chesapeake mainstem
2139 waters and tributaries in the warm months (April-October) before moving south during the
2140 cold months..

2141 Data Collection

2142 All data for this study were collected between 0700 and 1800 between April to October. For
2143 the current analyses, survey data annually from 2015 to 2018 were included for a total of
2144 102 surveys with ID data on 1151 individuals (410 were re-sighted). Group sizes ranged
2145 from 1-163. The predominant group activity was determined the same way as in Shark Bay.
2146 However, group definitions differ slightly due to differences between the two systems. A
2147 survey group is defined as a close association or as an aggregation depending on the
2148 circumstances. A close association is a discrete group usually comprised of a small number
2149 of animals where the 10m chain rule can be used to determine association (as is the case
2150 for Shark Bay). An aggregation is a large number of animals that are within a 100m radius
2151 and may or may not be delineated into discrete groups as defined by the 10m chain rule. In
2152 aggregations, animals are clearly connected to each other with members moving between
2153 discrete groups or, in the absence of discrete groups, among each other. Under either
2154 circumstance (with or without discrete groups), animals move among each other in such a
2155 way that at some point during the survey they have likely been connected by the 10m chain
2156 rule.

2157

2158 References

2159 Karniski, C., Patterson, E. M., Krzyszczyk, E., Foroughirad, V., Stanton, M., and Mann, J.
2160 2015. Determining cetacean activity budgets: a comparison of surveys and individual focal
2161 follows. Marine Mammal Science. 31:839-852. DOI: 10.1111/mms.12198

2162 Mann, J. 1999. Behavioral Sampling Methods for Cetaceans: A review and critique. Marine
2163 Mammal Science, 15(1): 102-122.

2164 McEntee, M.H., Foroughirad, V., Krzyszczyk, E. and Mann, J., 2023. Sex bias in mortality
2165 risk changes over the lifespan of bottlenose dolphins. Proceedings of the Royal Society B,
2166 290(2003), p.20230675.
2167 Tsai, Y.J. & Mann, J. 2013. Dispersal, philopatry and the role of fission-fusion dynamics in
2168 bottlenose dolphins. Marine Mammal Science. 29(2) April: 261-279 DOI: 10.1111/j.1748-
2169 7692.2011.00559.x

2170 Prairie dogs

2171 We studied black-tailed prairied dogs on one colony in Grasslands National Park, Saskatchewan, Canada (49.06N, 107.36W) as a long-term project from 2015 to 2020¹. Our goal was to capture all individuals to get a full colony census of the age and sex structure within the colony as well as population dynamics. We used Tomahawk traps (Tomahawk, Hazelhurst WI) to capture each individual, and subsequently tagged each pinnae with alphanumeric ear tags (Monel #1, National Band and Tag Co., Newport, Kentucky) and painted their dorsal pelage with a unique symbol for future identification. We weighed, sexed, and aged each prairie dog at first capture.

2179 We conducted our behavioural observations during April (pre-juvenile) and June (post-
2180 juvenile emergence) 2017 within a 3-hectare area located centrally within the colony as all
2181 animals in this area were marked and readily identifiable from a distance with binoculars. We
2182 marked the 3 hectares with a 15 m Cartesian grid system to record the location of individuals
2183 during focal observations. As prairie dogs are a sedentary species, space use and social
2184 structure are not independent of each other and we recorded both social and movement
2185 behaviours. 57 individuals aged 1 year and older were observed across 9 coteries (mean =
2186 6.61 individuals per coterie, range = 2 to 14). We recorded behaviours as we sat > 40 m
2187 from marked individuals during peak activity hours for prairie dogs based on season. We
2188 recorded all affiliative (sniffing, jump-yipping², greet-kissing, mutual vigilance, shared
2189 foraging) and agonistic (fighting, chasing, standoffs, territorial defense) interactions between
2190 all individuals in 3-4 hour sessions. We could record all social encounters across all animals
2191 as encounter rates were relatively low. We also conducted focal scans to record all activities
2192 and locations (<1 m) of each individual to establish their home range. We conducted four 20-
2193 min focal scans per individual: two in April and two in June. We recorded an average of 56
2194 locations (range 41-76) for each home range per season. We recorded 380 person-hours of
2195 behavioural observation and 872 interactions between known individuals. We created two
2196 networks from weighted undirected matrices of behavioural data and home ranges from the
2197 focal scan movement data.

2198

2199 References

2200 1. Kusch, J. M. & Lane, J. E. Determinants of social structure in a northern population of black-tailed prairie dogs, Cynomys ludovicianus. Animal Behaviour 178, 1–10 (2021).

2202 2. Hare, J. F., Campbell, K. L. & Senkiw, R. W. Catch the wave: prairie dogs assess neighbours' awareness using contagious displays. Proceedings of the Royal Society B: Biological Sciences 281, 20132153 (2014).

2205

2206
2207 Reticulated giraffes

2208 This study was conducted in Ol Pejeta Conservancy (OPC), a 364 km2 semi-arid wildlife
2209 reserve located on the equator (0° N, 36°56' E) approximately 220 km north of Nairobi,
2210 Kenya. All giraffe at OPC were recognized using individually unique spot patterns along
2211 their necks, and classified to age groups as described in (1). At the time of this study, OPC
2212 had a population of 212 reticulated giraffe. At the conclusion of the study period, OPC's
2213 giraffe population consisted of 160 adults, 20 subadults, 21 juveniles, and 11 neonates. The
2214 population exhibited a 50:50 sex ratio.

2215 From January 21 to August 2, 2011, giraffe group composition and membership were
2216 recorded for all giraffe groups sighted while driving pre-determined survey routes. Observed
2217 giraffe groups were followed off-road until a complete census of the individuals present was
2218 accomplished. A group was defined as a set of individuals engaged in the same behavior, or
2219 moving in the same direction or toward a common destination, as long as each giraffe was
2220 no more than 500 m from at least one other group member. During the study period, a total
2221 of 1089 observations of giraffe groups were collected. On average, 30.7 giraffe were
2222 observed per day, distributed between four to six groups. Each individual giraffe was
2223 observed on average 31.1 times (approximately once per week). A social network was
2224 constructed from observed association patterns, using the "gambit of the group" approach.

2225

2226 References

2227 1. VanderWaal KL, Wang H, McCowan B, Fushing H, Isbell LA. 2014. Multilevel social
2228 organization and space use in reticulated giraffe (Giraffa camelopardalis). Behav Ecol 25:17-
2229 26.

2230

2231
2232 Rum deer

2233 Adapted from: Albery GF, Morris A, Morris S, Pemberton JM, Clutton-Brock TH, Nussey DH,
2234 Firth JA (2021): Multiple spatial behaviours govern social network positions in a wild
2235 ungulate. Ecology Letters 24 (4), 676-686. https://doi.org/10.1111/ele.13684

2236 The study was carried out on an unpredated long-term study population of red deer on the
2237 Isle of Rum, Scotland (57°N,6°20'W). The natural history of this matrilineal mammalian
2238 system has been studied extensively (Clutton-Brock et al. 1982), and we focussed on
2239 females aged 3+ years, as these individuals have the most complete associated census
2240 data, and few males live in the study area except during the mating period. Individuals are
2241 monitored from birth, providing substantial life history and behavioural data, and >90% of
2242 calves are caught and tagged, with tissue samples taken¹.

2243 Census data were collected for the years 1974-2017, totalling 423,070 census observations.
2244 Deer were censused by field workers five times a month, for eight months of the year, along
2245 one of two alternating routes¹. Individuals' identities, locations (to the nearest 100M), and
2246 group membership were recorded. Grouping events were estimated by seasoned field
2247 workers according to a variant of the "chain rule" (Castles et al. 2014), where individuals
2248 grazing in a contiguous group within close proximity of each other (each individual under ~10
2249 metres of at least one other individual in the group) were deemed to be associating, with
2250 mean 130.4 groups observed per individual across their lifetime (range 6-943). The mortality
2251 period falls between Jan-March, when there is least available food, and minimal mortality
2252 occurs outside this period. We only used census records in each May-December period,
2253 from which we derived annual social network position measures as response variables. We
2254 elected to investigate this seasonal period because it stretches from the spring calving
2255 period until the beginning of the mortality period, simplifying network construction and
2256 avoiding complications arising from mortality events. Our dataset totalled 3356 annual
2257 observations among 532 grown females (Figure 1).

2258 We constructed a series of 43 annual social networks using “gambit of the group,” where
2259 individuals in the same grouping event (as described above) were taken to be associating³,
2260 and using dominance interactions.

2261 References

2262 1. Clutton-Brock, T. H., Guinness, F. E. & Albon, S. D. Red Deer: Behavior and Ecology of Two Sexes. vol. 15 (University of Chicago Press, Chicago, IL, 1982).

2264 2. Castles, M. et al. Social networks created with different techniques are not comparable. Animal Behaviour 96, 59-67 (2014).

2266 3. Franks, D. W., Ruxton, G. D. & James, R. Sampling animal association networks with the gambit of the group. Behavioral Ecology and Sociobiology 64, 493-503 (2010).

2268

2269
## 2270 Serengeti lions

2271 The research data were collected on the well-studied population of African lions inhabiting a 2700km2 study area within the Serengeti National Park, Tanzania. Multiple rivers and 2272 tributaries transect this area, which encompasses both a grassland plains habitat in the 2273 southeast, and woodland habitat to the north. There are two main seasons, the wet season 2274 spanning November to May, and the dry season spanning June to October. While the 2275 woodlands habitat maintains relative stability year-round, the grasslands habitat experiences 2276 greater fluctuations in rainfall (Mosser and Packer, 2009). This leads to large seasonal 2277 migrations of prey species across the plains, with lower populations densities in the dry 2278 season. As such, lion territories shift with the seasons across the landscape, tracking prey 2279 species migrations (Packer et al., 2005). 2280

2281 Lionesses exist in egalitarian fission-fusion "prides" composed of related adults and their 2282 offspring. In the Serengeti study area these prides can range in size from 2-20 individuals. 2283 Within these prides, individuals spend large proportions of time in smaller subgroups, and 2284 frequently spend time alone. The composition and size of these subgroups is highly 2285 dynamic, fluctuating daily, but female pride-mates usually stay within 5-6 km of each other 2286 (within vocal communication range). At maturity (2-3 years of age), approximately 75% of 2287 female offspring are recruited into their natal pride, while 25% disperse to form new prides, 2288 usually in adjacent territories (Packer, 2023; Pusey and Packer, 1987).

2289 At maturity, all male offspring disperse from their mother's pride in related groups of brothers 2290 and cousins. During this nomadic life stage multiple kin cohorts may form alliances, resulting 2291 in "coalitions" of up to nine males. Once they reach full body size (~ age 4 years old), these 2292 coalitions "take over" residence of a pride of females by outcompeting the existing resident 2293 male coalition (Packer, 2023; Pusey and Packer, 1987). Male coalition membership does not 2294 change once they are resident within a pride. No evidence of a dominance hierarchy has 2295 been recorded between male coalition mates (Packer et al., 2001; Packer, 2023).

2296 The data for this study were collected over a 30-years period from 1984 to 2013. One female 2297 per pride was fitted with a GPS collar. Each collared lion was located at least once every two 2298 weeks, and individuals within 200m of each other were taken as co-occurring. These co- 2299 occurrences were recorded as part of one unique sighting event, with GPS coordinates, date 2300 and time stamps. Additionally, opportunistic sightings of individuals within the study area 2301 were recorded in the same manner. Identification of individuals with a high level of accuracy 2302 was possible using facial markings and whisker spots. For each individual lion within a 2303 sighting event, age (estimated from the first sighting as a cub or adult), sex, natal pride (if 2304 known), and current pride information was also recorded. We constructed social networks 2305 based on whether individuals were observed in the same group on the same date.

2306

## 2307 References

2308 Mosser, A., and Packer, C. (2009). Group territoriality and the benefits of sociality in the 2309 African lion, Panthera leo. Animal Behaviour 78, 359-370.
2310 Packer, C., Pusey, A.E., and Eberly, L.E. (2001). Egalitarianism in female African lions.

2311 Science 293, 690-693. https://doi.org/10.1126/science.1062320.

2312 Packer, C., Hilborn, R., Mosser, A., Kissui, B., Borner, M., Hopcraft, G., Wilmshurst, J.,

2313 Mduma, S., and Sinclair, A.R.E. (2005). Ecological Change, Group Territoriality, and

2314 Population Dynamics in Serengeti Lions. Most, 390-393.

2315 Packer, C. (2023). The Lion: Behvaiour, ecology and conservation of an iconic species

2316 (Princeton University Press).

2317 Pusey, A.E., and Packer, C. (1987). The Evolution of Sex-Biased Dispersal in Lions.

2318 Behaviour 101, 275-310.

2319
2320 **Shark Bay Dolphin Research Project**

2321 Field Site and Dolphin Population: The Shark Bay Dolphin Research Project has been conducting a longitudinal study of bottlenose dolphins (*Tursiops aduncus*) since 1984, monitoring over 1,900 individuals in the eastern gulf of Shark Bay, Australia. The relatively pristine study area extends 600 km2 east of Peron Peninsula and consists of embayment plains (5–13 m), shallow sand flats and seagrass beds (0.5–5 m), and deeper channels (6–13 m). Using boat-based sampling, field observers identify individual dolphins using dorsal fin markings and shape, and also matched photographs to a digital identification catalog. Dolphin ages were estimated based on year of birth, the birth of their first calf (Mann et al. 2000; McEntee et al. 2023), or degree of ventral speckling (Krzyszczyk and Mann 2012). Individuals were considered to be adults if they were 12 or more years of age, or for females, if they had given birth to a calf (Mann et al. 2000). Finally, sex was determined by visual observation of the genital area, genetic analysis, or the presence of a dependent calf. Both sexes remain in their natal area (bisexual philopatry) in this residential population.

2334

2335 Data Collection: All data for this study were collected between 0600 and 1900 during all seasons using survey methods.

2337 A survey is a “snapshot” of a group or individual. When dolphins were sighted, researchers instantaneously estimated initial activity and distance from the research vessel before approaching to within 100 m. Once the research vessel was close enough for observers to identify individuals a survey was initiated on all individuals in the group based on a 10 m “chain rule” (Smolker et al. 1992). For each survey, observers performed a scan of all individuals to assess their behavioral state as one of six categories: foraging, resting, socializing, traveling, other, and unknown. A “predominant group activity” was assigned for the first five minutes of the survey based on the activities of at least 50% of the individuals (Mann 1999) for at least 50% of the time. The final dataset included 7293 group sightings of 910 individuals between 2008 and 2019.

2347 **References**

2348 Karniski, C., Patterson, E. M., Krzyszczyk, E., Foroughirad, V., Stanton, M., and Mann, J. 2015. Determining cetacean activity budgets: a comparison of surveys and individual focal follows. Marine Mammal Science. 31:839-852. DOI: 10.1111/mms.12198

2351 Mann, J. 1999. Behavioral Sampling Methods for Cetaceans: A review and critique. Marine Mammal Science, 15(1): 102-122.

2353 McEntee, M.H., Foroughirad, V., Krzyszczyk, E. and Mann, J., 2023. Sex bias in mortality risk changes over the lifespan of bottlenose dolphins. Proceedings of the Royal Society B, 290(2003), p.20230675.

2356 Tsai, Y.J. & Mann, J. 2013. Dispersal, philopatry and the role of fission-fusion dynamics in bottlenose dolphins. Marine Mammal Science. 29(2) April: 261-279 DOI: 10.1111/j.1748-7692.2011.00559.x