https://cpw.cvlcollections.org/files/original/3372fab85acaf59c34d88d3a2d999a7e.pdf c8f56b20a3549d054650b8e740de13c3 PDF Text Text | Accepted: 22 May 2020 DOI: 10.1111/1365-2656.13294 ANIMAL SOCIAL NETWORKS Research Article Group density, disease, and season shape territory size and overlap of social carnivores Ellen E. Brandell1 | Nicholas M. Fountain-Jones2 | Marie L. J. Gilbertson2 | Paul C. Cross3 | Peter J. Hudson1 | Douglas W. Smith4 | Daniel R. Stahler4 | Craig Packer5 | Meggan E. Craft2 1 Center for Infectious Disease Dynamics & Department of Biology, Huck Institute for Life Sciences, Pennsylvania State University, University Park, PA, USA 2 Department of Veterinary Population Medicine, University of Minnesota, St Paul, MN, USA 3 U.S. Geological Survey, Northern Rocky Mountain Science Center, Bozeman, MT, USA 4 Yellowstone Center for Resources, Yellowstone National Park, WY, USA 5 Department of Ecology, Evolution and Behavior, University of Minnesota, St Paul, MN, USA Correspondence Ellen E. Brandell Email: ebrandell08@gmail.com Funding information College of Veterinary Medicine University of Minnesota; U.S. Geological Survey, Grant/ Award Number: G17AC00427; National Science Foundation, Grant/Award Number: DEB-1413925, DEB-1654609 and LTREB DEB–1245373; National Institutes of Health, Grant/Award Number: T32OD010993 Handling Editor: Damien Farine Abstract 1. The spatial organization of a population can influence the spread of information, behaviour and pathogens. Group territory size and territory overlap and components of spatial organization, provide key information as these metrics may be indicators of habitat quality, resource dispersion, contact rates and environmental risk (e.g. indirectly transmitted pathogens). Furthermore, sociality and behaviour can also shape space use, and subsequently, how space use and habitat quality together impact demography. 2. Our study aims to identify factors shaping the spatial organization of wildlife populations and assess the impact of epizootics on space use. We further aim to explore the mechanisms by which disease perturbations could cause changes in spatial organization. 3. Here we assessed the seasonal spatial organization of Serengeti lions and Yellowstone wolves at the group level. We use network analysis to describe spatial organization and connectivity of social groups. We then examine the factors predicting mean territory size and mean territory overlap for each population using generalized additive models. 4. We demonstrate that lions and wolves were similar in that group-level factors, such as number of groups and shaped spatial organization more than population-level factors, such as population density. Factors shaping territory size were slightly different than factors shaping territory overlap; for example, wolf pack size was an important predictor of territory overlap, but not territory size. Lion spatial networks were more highly connected, while wolf spatial networks varied seasonally. We found that resource dispersion may be more important for driving territory size and overlap for wolves than for lions. Additionally, canine distemper epizootics may have altered lion spatial organization, highlighting the importance of including infectious disease epizootics in studies of behavioural and movement ecology. 5. We provide insight about when we might expect to observe the impacts of resource dispersion, disease perturbations, and other ecological factors on spatial organization. Our work highlights the importance of monitoring and managing social carnivore populations at the group level. Future research should elucidate the J Anim Ecol. 2021;90:87–101. wileyonlinelibrary.com/journal/jane� © 2020 British Ecological Society | 87 13652656, 2021, 1, Downloaded from https://besjournals.onlinelibrary.wiley.com/doi/10.1111/1365-2656.13294 by Colorado Parks & Wildlife, Wiley Online Library on [13/03/2023]. See the Terms and Conditions (https://onlinelibrary.wiley.com/terms-and-conditions) on Wiley Online Library for rules of use; OA articles are governed by the applicable Creative Commons License Received: 7 November 2019 �| Journal of Animal Ecology BRANDELL et al. complex relationships between demographics, social and spatial structure, abiotic and biotic conditions and pathogen infections. KEYWORDS disease, lion, network, resource dispersion, spatial oganization, territoriality, territory overlap, wolf 1 | I NTRO D U C TI O N population dynamics or individual fitness (Armansin et al., 2019; He, Maldonado-Chaparro, & Farine, 2019; Paniw, Maag, Cozzi, Clutton- The spatial organization of free-ranging animal populations, such Brock, & Ozgul, 2019). For example, Yellowstone wolves infected as territory size and overlap (Arden-Clarke, 1986; Belcher & with sarcoptic mange have substantially higher survival when they Darrant, 2004), emerges from patterns in resource availability and are associated with larger packs and there are higher prey densities distribution (Macdonald, 1983), and can drive a population's de- (Almberg et al., 2015); however, access to prey and hunting suc- mographic rates (Cantor et al., 2012; Pasinelli et al., 2011), consumer- cess is influenced by territory location and topography (Kauffman resource dynamics (Murdoch, Briggs, & Nisbet, 2003) and disease et al., 2007; Nelson et al., 2012). Thus, the survival of an infected transmission (Cross, Lloyd-Smith, Johnson, & Getz, 2005; Hess, wolf is necessarily linked to the spatial characteristics of its pack. 1996). Similarly, mating systems (Gosden & Svensson, 2008), kin For highly territorial social species, such as lions and wolves, social relations (VanderWaal, Mosser, & Packer, 2009) and human pres- behaviour like territory defence, infanticide and scent marking plays sures (Lesmerises, Dussault, & St-Laurent, 2013) can also influence an important role in maintaining boundaries and limiting the amount a population's spatial organization. How predators are distributed of space that groups share (Cubaynes et al., 2014; Packer, Scheel, & on the landscape has repercussions for competing predators and Pusey, 1990; Smith et al., n.d; Spong & Creel, 2004). Here we explore prey (Kittle, Bukombe, Sinclair, Mduma, & Fryxell, 2016; Kohl variables shaping space use more comprehensively. et al., 2019). Thus, recognizing the ecology of observed spatial pat- Spatial organization may also be affected by biotic and abiotic terns of a population is important for understanding population dy- perturbations, including extreme weather events (Loe et al., 2016; namics and relationships among species in a community. Here we Paniw et al., 2019), harvest (Woodroffe et al., 2006) or infectious ask, which factors drive spatial organization in territorial carnivore disease epizootics (reviewed in Binning, Shaw, & Roche, 2017). For populations? We investigate this question by leveraging 60 cumu- instance, wolves decrease their daily distance travelled as the sever- lative years of observations to examine the covariates influencing ity of their mange infection intensifies, likely because of the increas- territory size and territory overlap of African lions Panthera leo in ing energetic demands of thermoregulation as hair loss increases Serengeti National Park and grey wolves Canis lupus in Yellowstone (Cross et al., 2016). Parasites can manipulate intermediate hosts to National Park. travel to habitats where they are more likely to be predated by the Spatial organization is commonly characterized by territori- definitive hosts—these are areas that hosts often avoid when un- ality, which is a product of both biotic and abiotic processes in an infected (Lafferty & Morris, 1996; Thomas et al., 2002). Thus the environment. A territory is an area encompassing vital resources consequences of an infection may manifest in changes in space use; for an individual's fitness (Macdonald, 1983); this predominantly for example, male wood mice infected with nematode parasites have includes an area to give birth and raise young (e.g. nests, burrows) larger territories than uninfected males (Brown, Macdonald, Tew, and food resources. There is a range in territorial behaviour, from & Todd, 1994), and female Tasmanian devils decreased their home acute protection of a transient resource (e.g. speckled wood but- range size and overlap following a wide-spread facial tumour disease terfly: Davies, 1978), to intense defence year-round (e.g. Eurasian outbreak (Comte, Carver, Hamede, & Jones, 2020). The relationship otters: Erlinge, 1968; gray wolves: Mech, 1994; African lions: between space use and epizootics is a new area of research for ter- Heinsohn, 1997). ritorial, social carnivores. Literature from the last few decades emphasizes how food Network analysis can be used to describe spatial organization availability predominantly influences a population's spatial orga- (Croft, Madden, Franks, & James, 2011). When groups in a popula- nization (Davies & Hartley, 1996; Fuller, Mech, & Cochrane, 2003; tion (i.e. nodes) interact with each other, these relationships can be Lack, 1954; Ostfeld, 1985; Simon, 1975). However, sociality and be- quantified as ‘edges’ in a network, and edges can be weighted based haviour can also shape space use, and space use and habitat quality on frequency, intensity, duration or type of interaction. Networks together can impact demography (Alberts, 2019; Thompson, 2019). using spatial overlap to form edges connecting nodes have demon- Research focusing on the relationship between spatial organi- strated utility in elucidating non-random relationships (Godfrey, zation, sociality, and the abiotic and biotic environment has fed a Moore, Nelson, & Bull, 2010; Perkins, Cagnacci, Stradiotto, Arnoldi, central debate in ecology, and recent work highlights that spa- & Hudson, 2009; VanderWaal, Atwill, Isbell, & McCowan, 2014), in- tial and social organization cannot be ignored when considering cluding the identification of parasite transmission pathways via the 13652656, 2021, 1, Downloaded from https://besjournals.onlinelibrary.wiley.com/doi/10.1111/1365-2656.13294 by Colorado Parks & Wildlife, Wiley Online Library on [13/03/2023]. See the Terms and Conditions (https://onlinelibrary.wiley.com/terms-and-conditions) on Wiley Online Library for rules of use; OA articles are governed by the applicable Creative Commons License 88 �relationship between home range overlap and the spatiotemporal spread of parasites (i.e. Fenner, Godfrey, & Bull, 2011). Here we use a network approach to describe connectivity among lion prides in | 89 2 | M ATE R I A L S A N D M E TH O DS 2.1 | African lion and grey wolf sociality Serengeti and wolf packs in Yellowstone. These exceptionally well-monitored lion and wolf populations Lions and wolves reside in familial social groups. African lion provide a unique opportunity to compare and contrast the spatial or- prides are comprised of related females and their offspring typi- ganization of group-living carnivores that occur in similar ecosystems cally <3 years old (Packer, Gilbert, Pusey, & O’Brieni, 1991; Pusey and with similar ecology. Serengeti and Yellowstone are both vast & Packer, 1987). Prides range in size from 2 to 21 adult females expanses of protected land that contain suites of carnivores, meso- (Pusey & Packer, 1987). Prides are highly territorial and, although predators and ungulate prey. Both systems experience extreme sea- rare, interpride interactions are aggressive and can be deadly sonality, which drives migratory ungulate and predator movement. (Grinnell, Packer, & Pusey, 1995; Schaller, 1972). Male lions live Lions and wolves are apex predators that live in highly territorial, in smaller groups (1–9 individuals) and fight for access to females; familial groups (i.e. prides and packs, respectively). Generally, larger males may have access to more than one female pride territory at groups have higher fitness, higher hunting success and access to bet- a time (Bygott, Bertram, & Hanby, 1979; Pusey & Packer, 1987). ter quality habitat (lions: Mosser & Packer, 2009; Packer et al., 1990; Male lions disperse from their natal prides before sexual maturity, wolves: MacNulty, Tallian, Stahler, & Smith, 2014; Stahler, MacNulty, and female lions may leave if the pride grows too large and may Wayne, vonHoldt, & Smith, 2013; Tallents, Randall, Williams, & occupy a neighbouring territory, but territories remain exclusive MacDonald, 2012; but see Kittle et al., 2015). Serengeti lions and (VanderWaal et al., 2009). Yellowstone wolves have both experienced population-wide ex- Wolf packs are typically comprised of a breeding pair, their de- posure to canine distemper virus (CDV)—lions in 1976, 1981, pendent offspring and a few unrelated individuals. Packs typically 1994, 1998 and 2007, and wolves in 1999, 2005 and 2008 (Cross range from 3 to 12 members. Wolves disperse from their packs et al., 2018; Packer et al., 1999; Viana et al., 2015). when the pack is too large to be supported. To avoid inbreeding, We assess how group living, resource abundance, epizootics and packs rely on the emigration of related individuals out of their natal environmental factors drive the spatial organization of territorial packs and the immigration of unrelated individuals into new packs carnivore populations. First, we describe lion pride and wolf pack (Mech & Boitani, 2003; VonHoldt et al., 2008). On average, dis- spatial organization using network analysis; second, we examine the persers in the Rocky Mountains relocated about 90–100 km away group, population and seasonal covariates influencing these popula- from their previous pack's territory (straight-line distance, Jimenez tions’ spatial organization using generalized additive models (GAMs). et al., 2017). Wolf pack territories are distinct and interpack conflicts Figure 1 displays a workflow connecting the covariates we used in are often aggressive and can result in death, even between related our models, the potential mechanisms and processes underlying individuals from different packs (Cassidy, MacNulty, Stahler, Smith, their relationships, and our model types. & Mech, 2015; Cubaynes et al., 2014). F I G U R E 1 Workflow diagram displaying how our dataset (covariates, grey boxes) can relate to spatial organization (response variables, pink box) and the selected analyses (blue boxes). Arrows represent mechanisms or processes that may link the variables and models; direction of the arrow implies the direction of the process 13652656, 2021, 1, Downloaded from https://besjournals.onlinelibrary.wiley.com/doi/10.1111/1365-2656.13294 by Colorado Parks & Wildlife, Wiley Online Library on [13/03/2023]. See the Terms and Conditions (https://onlinelibrary.wiley.com/terms-and-conditions) on Wiley Online Library for rules of use; OA articles are governed by the applicable Creative Commons License Journal of Animal Ecology BRANDELL et al. �| Journal of Animal Ecology 2.2 | Study sites and location data BRANDELL et al. Wolves were monitored using global positioning system (GPS) collar locations and visual observations aided by VHF collars. Wolves Serengeti National Park (Serengeti) is a vast protected area were located approximately biweekly via aerial monitoring through- (14,750-km2) located in northern Tanzania and can be roughly out the year (1997–2016; Kohl et al., 2019; Smith & Bangs, 2009). divided into grassland plains (southern portion) and woodland Individually identifiable wolves were members of packs with known (northern portion) habitats. The Serengeti ecosystem is recog- membership and composition. We excluded any singular wolves or nized for the massive annual wildebeest Connochaetes taurinus and transient groups from our analysis, defined as groups surviving <2 zebra Equus burchelli migrations where ungulates move south in consecutive months. We also removed the years 1995–1996 when the wet season and north in the dry season. Buffalo Syncerus caffer the population was being reintroduced. are a common resident ungulate, making them an important dry season food source. Suites of predators also reside in Serengeti, including African lions P. leo. 2.3 | Calculating territory size and overlap The lion population increased from the start of monitoring in 1966 to 2014; increases in population size occurred every 5–10 years All individual locations were aggregated by group (Kittle et al., 2015). as numbers jump from a lower equilibrium value to a higher value, Kernel density estimation was used to construct seasonal territo- with one notable decrease in population size which began after the ries with a limited extent and the standard degree of smoothing. 1994 CDV outbreak and persisted for 5 years (Packer et al., 2005; Peripheral locations (5%) were discarded to calculate each group's Figure S1a). The CDV epizootic in 1994 led to a one-third reduc- 95% territory size and removed extreme outlier positions from ter- tion in the lion population size across all age classes (Roelke-Parker ritories that were probably incorrectly recorded locations; we then et al., 1996). There were also multiple periods of widespread CDV averaged the 95% kernel densities for all observed prides/packs exposure (1976, 1981, 1994, 1998, 2007), determined via opportu- over the season to create a variable called territory size. Seasons nistic serological testing, where population-wide declines were not were considered to be 6 months long based on mean temperature observed (Munson et al., 2008; Viana et al., 2015). We considered and precipitation (rain in Serengeti and snow in Yellowstone). In the five CDV exposure periods as CDV epizootics. Serengeti, the wet season was defined as November–April and the Lions were monitored weekly (1973–2014) by recording the loca- dry season was May–October (Boone, Thirgood, & Hopcraft, 2006; tions of individually identified lions; the years 1966–1972 were ex- McNaughton, 1985; Pascual & Hilborn, 1995). In Yellowstone, winter cluded due to a low number of sightings and few monitored prides. was defined as October–March and summer was April–September. These sightings were either opportunistic or aided by the use of We assessed whether lion or wolf territories were inflated with very-high-frequency (VHF) telemetry. Individuals were members small numbers of location coordinates. In general, territory size and of prides with known compositions and all lions within observed overlap were fairly stable as the number of locations approached 50, groups were recorded. In order to retain a focus on the comparison and were fairly stable or slightly declined as the number of locations of group-living social carnivores, we did not include solitary lions in exceeded 50 (Supporting Information Effects of sample size on net- this analysis. work estimation, Figures S3 and S4). There is a trade-off between re- Yellowstone National Park (Yellowstone) is a large protected taining enough groups to realistically represent the population while area (8,991 km2) characterized by long, harsh winters when elk ensuring the groups were appropriately sampled (Cross et al., 2012; Cervus canadensis and bison Bison bison migrations occur in northern Gilbertson, White, & Craft, 2020). Thus, we discarded any groups Yellowstone. Elk are one of the most abundant ungulates and grey with <21 locations per season. wolves’ C. lupus main prey source (Metz, Smith, Vucetich, Stahler, To ensure we compared territories with approximately uniform & Peterson, 2012). Central and southern Yellowstone are referred amounts of data, we randomly selected up to 400 locations annu- to as the Interior, which is characterized by higher elevations and ally from each group without replacement, aiming for 200 locations higher snow levels, and it is more heavily forested. per group per season. Estimating a territory using random sampling Wolves were reintroduced into Yellowstone in 1995, decades reduces autocorrelation and provides reasonably small bias and ex- after extirpation. Their population increased rapidly, peaking in ceptional precision when the smoothing parameter is appropriate 2008 at nearly 200 wolves, before it declined and subsequently sta- (Fieberg, 2007). Lion locations were occasionally identical due to bilized at about 90 wolves between 2010 and 2016 (Smith, Stahler, sampling methodology, prohibiting territory estimation. Therefore, et al., 2017; Figure S1b). The wolf population experienced three if a pride contained <11 unique locations in a given season, we ex- major CDV epizootics between 1997 and 2016 that were associated panded the territory to 1 km2 around those locations. This expansion with juvenile mortality up to ~80%: 1999, 2005 and 2008 (Almberg, allowed for territory and overlap estimation and retention of extant Mech, Smith, Sheldon, & Crabtree, 2009). CDV epizootic years were prides. We do not expect territories to differ according to collar type determined using a combination of juvenile seropositivity, observed or time of day (Demma & Mech, 2011; lions sleep for ~22 hr/day). host symptoms and subsequent mortality, and, for a subset of indi- Territory overlap was estimated using the volume of intersec- viduals suspected to be infected during outbreaks, the confirmation tion between each group and its neighbours. The volume of in- of CDV infection was achieved via PCR (Almberg et al., 2009). tersection index is a common overlap metric that ranges from 0 13652656, 2021, 1, Downloaded from https://besjournals.onlinelibrary.wiley.com/doi/10.1111/1365-2656.13294 by Colorado Parks & Wildlife, Wiley Online Library on [13/03/2023]. See the Terms and Conditions (https://onlinelibrary.wiley.com/terms-and-conditions) on Wiley Online Library for rules of use; OA articles are governed by the applicable Creative Commons License 90 �| 91 (complete exclusion) to 1 (complete overlap) while it accounts for the weighted and unfiltered, which is generally preferred to removing three-dimensional shape of the territory kernel (Millspaugh, Gitzen, edges below an arbitrary weight threshold, or binary edges (Farine Kernohan, Larson, & Clay, 2004). Volume of intersection can be in- & Whitehead, 2015). terpreted as the proportion of space shared between two groups We explored the population attributes: network size (total num- within a season. We summed all overlap measurements per group ber of groups), mean degree (mean number of groups each group (i.e. total group-level overlap) and averaged them to calculate ter- spatially overlaps with), proportion disconnected (the proportion of ritory overlap, which describes how much space is shared between groups that did not overlap with other groups), network density (the that group and neighbouring groups in the population that season proportion of possible edges that are realized), betweenness central- (i.e. average group-level overlap). ity (how important a group is in spatially connecting the population All group-level attributes were averaged by season before anal- based on the shortest paths between groups) and closeness centrality yses. In summary, the data were calculated for each group, per sea- (a group's connectivity to the rest of the population calculated as son, per year and then averaged. Our two response variables (mean the inverse of the average shortest distance between a group and territory size and mean territory overlap) were calculated in the r all other groups in the network). Centrality measures were weighted package adehabitatHR (Calenge, 2006) with the functions ‘kernelUD’ by territory overlap (volume of intersection ≥0.001), and density and (‘kern = bivnorm’), ‘getverticeshr’, and ‘kerneloverlap’. Although not degree were unweighted; attributes were calculated in the r package a main goal of this manuscript, we also assessed the validity of using igraph territory overlap as a surrogate for direct contact between members averaged across each season before network analyses (i.e. centrality, of different groups in the lion and wolf populations (see Supporting degree). (Csardi & Nepusz, 2006). Again, group-level attributes were Information: Spatial overlap as a surrogate for direct contact). 2.4 | Network analysis 2.5 | Statistical analysis Covariates considered to be potentially important in shaping ter- We used network analysis to more broadly describe how well the ritory size and territory overlap among groups included: population populations were spatially connected (Figure 2; Figure S2). Weighted density (population count within study areas), group size (group size edges were constructed using territory overlap; networks were observed), number of groups (number of distinct groups observed F I G U R E 2 Wolf seasonal territories (85% isopleths of kernel density estimates for visualization purposes) for packs in summer (top left) and winter (bottom left) for the year 2011, where the thin black outline is the boundary of Yellowstone National Park. The right column shows the corresponding seasonal networks. Each pack was assigned a colour, edge width represents total overlap between territories and network configurations reflect approximate spatial organization (r package igraph; Csardi & Nepusz, 2006). Figure S2 is the equivalent figure for lions 13652656, 2021, 1, Downloaded from https://besjournals.onlinelibrary.wiley.com/doi/10.1111/1365-2656.13294 by Colorado Parks & Wildlife, Wiley Online Library on [13/03/2023]. See the Terms and Conditions (https://onlinelibrary.wiley.com/terms-and-conditions) on Wiley Online Library for rules of use; OA articles are governed by the applicable Creative Commons License Journal of Animal Ecology BRANDELL et al. �| Journal of Animal Ecology BRANDELL et al. within study areas), season (lion: dry/wet, wolf: summer/winter), function (tensor product) that is recommended for variables rainfall (lions) or snow level (wolf), habitat (lion: woodlands/plains, with temporal correlation (Wood & Augustin, 2002). Then, CDV wolf: northern range/interior), CDV epizootic (number of years since epizootic was added as a numeric covariate (model 2). Finally, the last period of exposure) and prey population densities (annual we constructed the complete model (model 3) adding the covari- counts within study areas). It is likely that territory size and terri- ates listed in Supporting Information Table S1 (excluding popu- tory overlap are not independent, and so we used these variables lation density in the lion model). We used the Gaussian process as covariates in respective models: territory overlap was used as a basis function for population density, number of groups, group predictor of territory size, and territory size was used as a predic- size and elk abundance as these were also temporally autocor- tor of territory overlap. Prey population sizes from the Serengeti related. The default basis functions were used for the remaining were only available at the ecosystem level for buffalo, Thomson's covariates. gazelle, wildebeest and zebra from aerial surveys conducted every Each model was fitted using restricted maximum likelihood 2–6 years. These parameters were obtained from previously pub- and models were evaluated using the ‘gam.check’ function. p val- lished studies; see Supporting Information Table S1 for details on ues were calculated using the ‘ANOVA’ function since season was each covariate. a covariate in all of our models. We used the package mgcv (Wood We used GAMs to understand how our covariates shaped terri- & Augustin, 2002) to construct each GAM. Model selection was tory size and overlap, while accounting for temporal autocorrelation. performed using the integrated penalized spline approach (Wood Prior to analysis, we screened for variable collinearity, and, in the & Augustin, 2002) and the temporal trend model and temporal lion dataset, excluded population density as it was strongly collinear trend + CDV model were compared to the complete model based with the number of prides on the landscape (Pearson's correlation on AIC values. The model with the smallest AIC was selected; we coefficient = 0.79). To screen for variable interactions to include in discuss models within two ΔAIC of the top model (Burnham & our GAM models, we employed two machine learning algorithms Anderson, 2002). All analyses were performed in Program R version (using the default parameters): support vector machines (Hastie, 3.6.1 (R Core Team, 2019). See Supporting Information and Data Rosset, Tibshirani, & Zhu, 2004) and boosted regression trees (Elith, Accessibility statement for further details. Leathwick, & Hastie, 2008). We used the support vector machines for the wolf dataset because they are better suited for dealing with smaller datasets, and boosted regression trees for the lion dataset because they are more powerful and require a larger dataset. These approaches account for missing data, which were common with 3 | R E S U LT S 3.1 | Network analysis some lion variables (i.e. prey species abundance). All covariates and pairwise interactions were evaluated, and any covariate or interac- The lion dataset comprised 42 years (1973–2014). The number of tion of no predictive value in either machine learning model was ex- prides per season ranged from 2 to 26, with the lowest counts in cluded from subsequent GAMs, including all prey variables from the the earlier years. Prides had, on average, 6.3 members (range: 2–21, lion models. See Fountain-Jones et al. (2019) for more detail about SD = 3.59). Most prides shared space to some extent with other this analytical pipeline. prides—on average each pride overlapped with 7–9 neighbouring We anticipated temporal trends would account for a notable prides (Figure 3a), and centrality and overlap estimates were moder- portion of the variation in territory size and overlap. We were also ate (Table S10). Closeness and betweenness centrality (Figure 3b,c), particularly interested in the effect of CDV epizootics on spatial or- and proportion disconnected (Figure 3e) did not differ substantially ganization. Therefore, we constructed and compared three GAMs between the wet and dry seasons. Mean degree (Figure 3a) and net- to determine what covariates best captured variability in territory work density (Figure 3d), however, were slightly higher in the wet size and territory overlap. Our models were (in order of increasing season. complexity): The wolf dataset comprised 20 years (1997–2016). The number of packs per season ranged from 4 to 16, with the lowest counts in 1. Temporal trend only (temporal trend model, ‘null’ model), earlier and later years. Packs had, on average, 10.0 members (range: 2. Temporal trend with CDV epizootic as a covariate (temporal 2–37; SD = 5.52). Most packs spatially overlapped with others; spa- trend + CDV model), and 3. The complete model with all covariates considered to be of predictive value in the machine learning models (complete model). tial overlap was greater in winter, which corresponds to a higher network density (Figure 3d). Each pack's territory overlapped with 3–5 neighbouring packs on average (Figure 3a), yet closeness centrality was low (Figure 3c), indicating that overlap was generally low. The seasonal component (wet/dry or winter/summer) was There were stark differences in population attributes in the summer modelled as a fixed effect. Tensor products were used as the versus winter (Figure 2), with a higher mean degree (Figure 3a), den- smoothing functions for each covariate because the covariates sity (Figure 3d) and fewer disconnected packs (Figure 3e) in winter were all on different scales. For the temporal trend model (model (Table S10), but betweenness centrality was less variable across sea- 1) we allowed the year to be a smoothed Gaussian process basis sons (Figure 3b). 13652656, 2021, 1, Downloaded from https://besjournals.onlinelibrary.wiley.com/doi/10.1111/1365-2656.13294 by Colorado Parks & Wildlife, Wiley Online Library on [13/03/2023]. See the Terms and Conditions (https://onlinelibrary.wiley.com/terms-and-conditions) on Wiley Online Library for rules of use; OA articles are governed by the applicable Creative Commons License 92 �| 93 out across the landscape, whereas in the dry season, lions congregate at watering holes or at good hunting locations with reliable prey sources (Hopcraft, Sinclair, & Packer, 2005; Kittle et al., 2016). Wolves are more disconnected in summer likely because they purposefully den away from other packs thus reducing contact with neighbours, and prey are more spread out on the landscape thereby reducing the need to hunt in the same areas (Kauffman et al., 2007). In both populations, there was a wide spread of seasonal betweenness centrality values, and about half of the values were zero, indicating that peripheral groups were highly connected in some seasons but not others (Figure 3b). The distribution of network density was fairly predictable within a season but differed between seasons (Figure 3d); a greater proportion of edges were realized in the wet (lions) and winter (wolves) seasons. In general, the lion population had a higher centrality and mean degree than the wolf population, and some prides served as central hubs in the network while other prides were weakly connected. The wolf population in Yellowstone is characterized by many stable but weak connections among packs, with a few important packs serving as central hubs. In both populations, the most connected groups were either the most centrally located or were groups with a small territory that was predominantly shared with neighbouring groups. 3.2 | Lion statistical modelling results Lion territories were larger when there were more prides on the landscape and as overlap among prides increased. Mean territory size also grew the longer it had been since a CDV epizootic, especially when 5 or more years had passed (Figure 4a; Figure S8a). Territories were slightly larger in the wet season, and territories generally became larger through the time series. The complete model explained significantly more deviance (82.5%) in the data compared to the temporal trend model (25.4%) or temporal trend + CDV model (49.7%); thus the complete model was our top model (Tables S2a–S5a). The amount of territory overlap between a lion pride and neighbouring prides increased when the pride had more members, a larger territory size, and when there were more prides on the landscape. Mean territory overlap was also higher the longer it had been since a CDV epizootic, especially when 4 or more years had passed F I G U R E 3 Density distributions of five network attributes: (a) mean degree, (b) betweenness centrality, (c) closeness centrality, (d) network density, (e) proportion disconnected, by season (wet/ winter = grey, dry/summer = black) for lions in Serengeti National Park (1973–2014, left column) and wolves in Yellowstone National Park (1997–2016, right column) (Figure 4c; Figure S8b). The complete model was the top model (Table S5b) and explained a large amount of deviance in pride overlap (89.5%). The temporal trend model explained 39.6% of the deviance, and the temporal trend + CDV model explained 63.8% of the deviance (Tables S3b and S4b). The relationship between lion territory size and territory Lions and wolves both experienced seasonal decreases in the overlap was positive, and both territory size and overlap were proportion of groups spatially connected—wet season in Serengeti important predictors of the other (Figure S8). Adding CDV to the and winter in Yellowstone (Figure 3e). Lion prides are more discon- temporal models nearly doubled the deviance explained for both nected in the wet season likely because prey and water are spread territory size and overlap (Tables S3 and S4). Territory overlap 13652656, 2021, 1, Downloaded from https://besjournals.onlinelibrary.wiley.com/doi/10.1111/1365-2656.13294 by Colorado Parks & Wildlife, Wiley Online Library on [13/03/2023]. See the Terms and Conditions (https://onlinelibrary.wiley.com/terms-and-conditions) on Wiley Online Library for rules of use; OA articles are governed by the applicable Creative Commons License Journal of Animal Ecology BRANDELL et al. �| Journal of Animal Ecology BRANDELL et al. F I G U R E 4 The percentage of deviance explained by each term used in the (a, b) territory size and (c, d) territory overlap complete models (model 3) for (a, c) lions in Serengeti and (b, d) wolves in Yellowstone. Colour indicates the scale or type of term: red, group-level; dark grey, population-level; light blue, temporal trends. Deviance explained was calculated as the difference between the complete model and the model with the covariate of interest removed; the smoothing terms from the complete model were used for continuity F I G U R E 5 (Top row) Serengeti lion networks during the 1994 canine distemper virus (CDV) epizootic and the year post-epizootic (1995); coloured nodes represent different prides, node size represents territory size, and edge width corresponds to total overlap between territories. (bottom row) Boxplots comparing four spatial or network attributes— territory size, territory overlap, degree and betweenness centrality—between the epizootic year (E, grey) and the post-epizootic year (P, white). Network layouts are forcedirected (r package igraph; Csardi & Nepusz, 2006) tended to increase as rainfall increased, and rain explained quite expand their territories during the wet season and are more likely a bit of the deviance; however, rain was not statistically signif- to overlap with neighbouring prides. Interestingly, territory size icant (Table S2b). Taken together, we can conclude that lions and overlap increased as the number of prides grew, suggesting 13652656, 2021, 1, Downloaded from https://besjournals.onlinelibrary.wiley.com/doi/10.1111/1365-2656.13294 by Colorado Parks & Wildlife, Wiley Online Library on [13/03/2023]. See the Terms and Conditions (https://onlinelibrary.wiley.com/terms-and-conditions) on Wiley Online Library for rules of use; OA articles are governed by the applicable Creative Commons License 94 �that lions do not readily contract their territories as pride density increases. | 95 top models; this was the most notable similarity between lion and wolf modelling results (Figures S8 and S10). The population-level co- We were particularly interested in the effect of CDV on lion variates with the strongest influence on territory size and overlap spatial organization, so we explored how spatial and network met- included CDV epizootics (lions only) and precipitation as rain (lions rics (i.e. territory size, territory overlap, degree and betweenness only) or snow (wolves only). Generally, covariates shaping territory centrality) changed the year following each CDV exposure period size were similar to covariates shaping territory overlap, although (Figure 5; Figure S5; Supporting Information: Post hoc mecha- the magnitude of the covariates’ importance differed. nism exploration). We found that changes in territory size, overlap and degree were the most extreme following the 1994 epizootic (Figure 5; Figure S5); for instance, about 85% prides decreased ter- 4 | D I S CU S S I O N ritory overlap with other prides by at least 10%, and about 62% of prides decreased their degree and territory size by at least 10%. We assessed how environmental factors, biotic interactions and in- After the 1998 epizootic, lions similarly decreased their territory fectious disease epizootics alter network topology and drive the sea- size, overlap and degree. sonal territory patterns of two social carnivores—lions in Serengeti National Park and wolves in Yellowstone National Park. Lion popula- 3.3 | Wolf statistical modelling results tions were highly connected with some prides serving as hubs connecting peripheral prides; wolf packs were weakly connected, but these connections were quite stable through time. Covariates shap- Temporal trends (season and year) were the only significant covari- ing territory size were similar to covariates shaping territory overlap, ates predicting wolf territory size (Figure 4b; Tables S6a–S8a), and although the magnitude of the covariates’ importance differed in they explained 79.4% of the deviance in territory sizes (Table S6a). each model. The results of this study provide insights into how: (a) The complete model had the lowest AIC value, but all models were the number of groups in a population, (b) territory size and overlap within ~3 ΔAIC because temporal trends dominated (Table S9a). and (c) disease perturbations might impact space use. Wolf pack territory overlap increased when there were more packs First, we found that the number of groups in a study area or pop- on the landscape, population density increased, and pack size was larger ulation was unequivocally important in shaping spatial organization. (Figure 4d; Table S6b). The relationship between territory overlap and This is important because, for social species like lions and wolves, population density was nonlinear such that overlap increased as popula- groups act as the functional units comprising the population. Groups tion density increased until about 90 wolves/1,000-km2, then stabilized. raise offspring and compete for territories and resources, and the Mean territory overlap was higher in the winter, which corresponded fitness of solitary individuals is often drastically reduced (Almberg with larger territory size and snow, both of which were significant predic- et al., 2015; Packer & Ruttan, 1988; Packer et al., 1990). More re- tors. The complete model was the top model (Table S9b) and it explained cent research describing the spatial and social processes of group- 95.0% of the deviance, which was a large improvement from the tempo- living territorial species has benefitted from explicit consideration of ral trend model (52.4%, Table S7b) and the temporal trend + CDV model groups (e.g. spinner dolphins: Karczmarski, Würsig, Gailey, Larson, & (53.8%, Table S8b). Vanderlip, 2005; orcas: Baird & Whitehead, 2000, primates: Kasper There was a slight increasing trend in territory overlap throughout & Voelkl, 2009, spotted hyenas: Ilany, Booms, & Holekamp, 2015; the time series. Territory size declined in the early years as wolves es- meerkats: Bateman, Lewis, Gall, Manser, & Clutton-Brock, 2015 and tablished territories following reintroduction, and then territory sizes some birds: Ke, Deng, Guo, & Huang, 2017). Our results therefore slowly increased and potentially stabilized in recent years (Figures S10 support the growing focus of conservation and management for so- and S11). This pattern tracks trends in population size: rapid increase cial species on maintaining viable groups. followed by a decline and stabilization (Figure S1). Wolf spatial organi- Second, lion and wolf territory size and overlap had a complex zation was driven seasonally such that territory size and overlap were relationship that suggested that as a territory grows, so does terri- both greater in the winter, yet as snow levels increased and limited wolf tory overlap and vice versa. If groups had more neighbouring groups, movement, overlap declined. The relationship between territory size then overlap was more likely to increase. This implies that groups and overlap was positive, although territory size was more important were not as good at maintaining territory boundaries when they for predicting territory overlap than overlap was for predicting size. had to travel more widely across larger territories and when there These results suggest that territory size is highly predictable by season, were more potential intruders in close proximity. Importantly, this whereas territory overlap generally increases as territory size increases relationship was influenced by season, which likely relates to re- but is modulated by the number of packs in the population, snow level source availability and resource dispersion since prey are migratory and population size. in both systems. In particular, prey are aggregated on the landscape Generally, group-level covariates shaped territory attributes within specific habitats with respect to season. For example, when more than population-level for both lions and wolves (Figure 4; elk are scarce, wolf packs in Yellowstone utilize overlapping hunting Tables S2 and S6). We found that the number of groups in the popu- areas due to favourable topography and elk occurrence (Kauffman lation was significant with relatively large effects in three of the four et al., 2007), and this a likely mechanism for increasing territory 13652656, 2021, 1, Downloaded from https://besjournals.onlinelibrary.wiley.com/doi/10.1111/1365-2656.13294 by Colorado Parks & Wildlife, Wiley Online Library on [13/03/2023]. See the Terms and Conditions (https://onlinelibrary.wiley.com/terms-and-conditions) on Wiley Online Library for rules of use; OA articles are governed by the applicable Creative Commons License Journal of Animal Ecology BRANDELL et al. �| Journal of Animal Ecology BRANDELL et al. overlap. Yet elk density was not significant in our wolf territory size for lions following the 1994 epizootic, female Tasmanian devils de- and overlap models, indicating that elk density does not necessarily creased their home range size and home range overlap following a capture the heterogeneous distribution of elk. large-scale outbreak of facial tumour disease (Comte et al., 2020). Resource dispersion is the prevailing hypothesis for the evolu- Such disease-induced behavioural changes can have significant im- tion of territoriality (Johnson, Kays, Blackwell, & Macdonald, 2002; pacts on subsequent transmission dynamics. For instance, when Maher & Lott, 2000; Ostfeld, 1985). Yet there are other plausible driv- raccoons are infected with rabies, individual movement may de- ers of spatial organization, such as kinship (Frase & Armitage, 1984; cline due to impaired mobility, which can alter epizootic size and Pravosudova, Grubb, & Parker, 2001; Rogers, 1987), land tenure spread (Reynolds, Hirsch, Gehrt, & Craft, 2015). While we lack the (Benson, Chamberlain, & Leopold, 2004; Elbroch, Lendrum, Quigley, data to distinguish between mechanisms underlying changes in & Caragiulo, 2016) and protection of young or infanticide (Smith spatial organization, our results support the importance of incor- et al., 2015; Wolff & Peterson, 1998). These drivers are not mutually porating spatial and behavioural ecology into studies of pathogen exclusive and their relationship can be complex. For example, cougar transmission dynamics. (Puma concolor) territories are well described by resource dispersion, Changes in space use may also result from competition (Apps, and individuals often utilize territory vacated by a cougar of the same McLellan, & Woods, 2006). Top predators have been shown to sex (Elbroch et al., 2016). The strong influence of season in shaping alter sympatric carnivore species’ space use (reviewed in Davis wolf spatial organization implies that resource dispersion is an im- et al., 2018; Wang, Allen, & Wilmers, 2020). Both lions and wolves portant driver of space use. African lions are tolerant of dispersing have a larger effect on the space use patterns of other carnivores daughters, which frequently bud off from existing prides and occupy (e.g. spotted hyenas, cheetahs, coyotes, cougars) rather than the neighbouring territories where they show considerable overlap with other way around; this has been described for lions in the Serengeti their mothers’ range for the first 2–5 years but decline by 10 years system (Kittle et al., 2016; Swanson, Arnold, Kosmala, Forester, & post-dispersal (VanderWaal et al., 2009); this indicates kin relation- Packer, 2016; Vanak et al., 2013) and wolves in the northern Rocky ships among lions may be an important driver of spatial organization. Mountains (Bartnick, Van Deelen, Quigley, & Craighead, 2013; Third, we identified changes in lion spatial organization following Berger & Gese, 2007; Kortello, Hurd, & Murray, 2007). Therefore, the 1994 CDV epizootic that were not observed for wolves (Figure 5). we do not believe that the occurrence of other species would have a We postulate that differences in space use arose in part from the substantial impact on lion and wolf spatial organization. fact that the major die-off in the lion population killed individuals of all age classes, at least partially due to a coinfection (Munson et al., 2008; Roelke-Parker et al., 1996), whereas CDV epizootics 4.1 | Data limitations and future directions in the wolf population primarily affected mostly pups and yearlings (Almberg et al., 2009). The loss of adult lions that maintain terri- Our analyses might have been strengthened by more locations per tory boundaries may have been a contributing covariate to prides’ group per season, which could increase the precision of our territory contraction in space (Heinsohn, Packer, & Pusey, 1996; McComb, estimates (Seaman et al., 1999). Additionally, more detailed data on Packer, & Pusey, 1994; Mosser & Packer, 2009). As social species, prey distribution in both Serengeti and Yellowstone may have im- which individuals die within a group can have disproportionate im- proved our models. Other studies demonstrated a shift in Serengeti pacts on the behaviour of group members (i.e. ‘social disruption’; lion territories as they follow prey migration, resulting in annual e.g. Borg, Brainerd, Meier, & Prugh, 2015). Social species such as territory overlap that is not necessarily simultaneous, or territories lions (Davidson, Valeix, Loveridge, Madzikanda, & Macdonald, 2011; with little overlap where prey abundance is high (reviewed in Hanby, Heinsohn & Packer, 1995), wolves (Borg et al., 2015), whales (Wade, Bygott, & Packer, 1995). Wolves in Yellowstone may shift their space Reeves, & Mesnick, 2012) and badgers (Carter et al., 2007) exhibit use patterns to improve access to elk (Kauffman et al., 2007). We behavioural changes (e.g. territory reconfiguration, dispersal, group were unable to assess this because our prey data were at the scale dissolution) following the death (e.g. due to harvest, cull, disease) of of the study area, and annual prey counts may not explain the spatial group members. However, social disruption mechanisms would not and seasonal distribution of prey on the landscape and hence access explain the effect we observed of CDV epizootics with limited mor- to prey for these territorial species. Gathering more detailed data at tality in lions, such as in 1998. the group-level is an area of future research. Infectious diseases such as CDV can alter the behaviour of in- Another area for future research is the impact of human ac- fected individuals. CDV infection may impede movement through tivities and tourism on lion and wolf space use. Human activity symptoms such as muscle tremors and lethargy, which could subse- has been shown to affect carnivore movement and space use quently lead to the reductions in territory size and overlap we ob- (Boydston, Kapheim, Szykman, & Holekamp, 2003; Foster, Harmsen, served. Sick or weakened animals may behaviourally avoid healthy & Doncaster, 2010; Ordiz, Støen, Delibes, & Swenson, 2011; Smith, animals in order to reduce their risk of injury during an aggressive Suraci, et al., 2017; Zeller, Wattles, Conlee, & Destefano, 2019), how- intergroup encounter. Alternatively, infectious diseases may also ever, this has not been comprehensively assessed in either Serengeti alter the behaviour of healthy individuals, as observed in guppies lions or Yellowstone wolves and we were unable to include it in our Poecilia reticulata (Houde & Torio, 1992). Similar to our observations analysis. 13652656, 2021, 1, Downloaded from https://besjournals.onlinelibrary.wiley.com/doi/10.1111/1365-2656.13294 by Colorado Parks & Wildlife, Wiley Online Library on [13/03/2023]. See the Terms and Conditions (https://onlinelibrary.wiley.com/terms-and-conditions) on Wiley Online Library for rules of use; OA articles are governed by the applicable Creative Commons License 96 �5 | CO N C LU S I O N S In summary, we identified ecological drivers shaping the seasonal space use of two apex territorial species. While there is abundant literature about the effects of spatial or social organization on population dynamics in free-ranging animals (e.g. pathogen transmission, reviewed in White, Forester, & Craft, 2017; carnivore sociality, Tichon, Gilchrist, Rotem, Ward, & Spiegel, 2020), there have been few attempts to elucidate drivers of mammalian spatial organization as we have done here. Our work highlights the importance of monitoring and managing social carnivore populations at the group level. We found that resource dispersion may be more important for driving territory size and overlap for wolves than for lions. Finally, canine distemper epizootics may alter lion spatial organization, suggesting the importance of including infectious disease epizootics in studies of behavioural and movement ecology. We hope that future research builds off of this work to elucidate the complex relationships between a population's demographics, social and spatial structure, abiotic and biotic conditions and pathogen infections. AU T H O R S ' C O N T R I B U T I O N S E.E.B., N.M.F.-J., M.L.J.G. and M.E.C. designed the project; E.E.B. created the networks, performed all network analyses and wrote the manuscript; N.M.F.-J. developed and analysed the GAMs; C.P., D.W.S. and D.R.S. provided long-term datasets. All authors contributed substantially to the intellectual development of the project and manuscript revision. The authors claim no conflicts of interest. DATA AVA I L A B I L I T Y S TAT E M E N T Data available from the Dryad Digital Repository https://doi.org/ 10.5061/dryad.vq83b​k3qd (Brandell et al., 2020). ORCID https://orcid.org/0000-0002-2698-7013 Ellen E. Brandell Nicholas M. Fountain-Jones https://orcid. org/0000-0001-9248-8493 Paul C. Cross https://orcid.org/0000-0001-8045-5213 Craig Packer https://orcid.org/0000-0002-3939-8162 REFERENCES Alberts, S. C. (2019). Social influences on survival and reproduction: Insights from a long-term study of wild baboons. Journal of Animal Ecology, 88, 47–66. https://doi.org/10.1111/1365-2656. 12887 Almberg, E. S., Cross, P. C., Dobson, A. P., Smith, D. W., Metz, M. C., Stahler, D. R., & Hudson, P. J. (2015). Social living mitigates the costs of a chronic illness in a cooperative carnivore. Ecology Letters, 18, 660–667. https://doi.org/10.1111/ele.12444 Almberg, E. S., Mech, L. D., Smith, D. W., Sheldon, J. W., & Crabtree, R. L. (2009). A serological survey of infectious disease in Yellowstone National Park’s canid community. PLoS ONE, 4. https://doi.org/ 10.1371/journ​al.pone.0007042 Apps, C. D., McLellan, B. N., & Woods, J. G. (2006). Landscape partitioning and spatial inferences of competition between black and grizzly | 97 bears. Ecography, 29, 561–572. https://doi.org/10.1111/j.0906-7590. 2006.04564.x Arden-Clarke, C. H. G. (1986). Population density, home range size and spatial organization of the Cape clawless otter, Aonyx capensis, in a marine habitat. Journal of Zoology, 209, 201–211. https://doi. org/10.1111/j.1469-7998.1986.tb035​76.x Armansin, N. C., Stow, A. J., Cantor, M., Leu, S. T., Klarevas-Irby, J. A., Chariton, A. A., & Farine, D. R. (2019). Social barriers in ecological landscapes: The social resistance hypothesis. Trends in Ecology & Evolution, 35(2), 137–148. https://doi.org/10.1016/j.tree. 2019.10.001. Baird, R. W., & Whitehead, H. (2000). Social organization of mammaleating killer whales: Group stability and dispersal patterns. Canadian Journal of Zoology, 78, 2096–2105. https://doi.org/10.1139/ z00-155 Bartnick, T. D., Van Deelen, T. R., Quigley, H. B., & Craighead, D. (2013). Variation in cougar (Puma concolor) predation habits during wolf (Canis lupus) recovery in the southern Greater Yellowstone Ecosystem. Canadian Journal of Zoology, 91, 82–93. https://doi.org/10.1139/cjz2012-0147 Bateman, A. W., Lewis, M. A., Gall, G., Manser, M. B., & CluttonBrock, T. H. (2015). Territoriality and home-range dynamics in meerkats, Suricata suricatta: A mechanistic modelling approach. Journal of Animal Ecology, 84, 260–271. https://doi.org/10.1111/ 1365-2656.12267 Belcher, C. A., & Darrant, J. P. (2004). Home range and spatial organization of the marsupial carnivore, Dasyurus maculatus maculatus (Marsupialia: Dasyuridae) in south-eastern Australia. Journal of Zoology, 262, 271–280. https://doi.org/10.1017/S0952​8 3690​ 3004631 Benson, J. F., Chamberlain, M. J., & Leopold, B. D. (2004). Land tenure and occupation of vacant home ranges by bobcats (Lynx rufus). Journal of Mammalogy, 85, 983–988. https://doi.org/10.1644/BWG-132 Berger, K. M., & Gese, E. M. (2007). Does interference competition with wolves limit the distribution and abundance of coyotes? Journal of Animal Ecology, 76, 1075–1085. https://doi.org/10.1111/ j.1365-2656.2007.01287.x Binning, S. A., Shaw, A. K., & Roche, D. G. (2017). Parasites and host performance: Incorporating infection into our understanding of animal movement. Integrative and Comparative Biology, 57, 267–280. https:// doi.org/10.1093/icb/icx024 Boone, R. B., Thirgood, S. J., & Hopcraft, J. G. C. (2006). Serengeti wildebeest migratory patterns modeled from rainfall and new vegetation growth. Ecology, 87, 1987–1994. https://doi.org/10.1890/00129658(2006)87[1987:SWMPM​F ]2.0.CO;2 Borg, B. L., Brainerd, S. M., Meier, T. J., & Prugh, L. R. (2015). Impacts of breeder loss on social structure, reproduction and population growth in a social canid. Journal of Animal Ecology, 84, 177–187. https://doi. org/10.1111/1365-2656.12256 Boydston, E. E., Kapheim, K. M., Szykman, M., & Holekamp, K. E. (2003). Individual variation in space use by female spotted hyenas. Journal of Mammalogy, 84, 1006–1018. https://doi.org/10.1644/BOS-038 Brandell, E. E., Fountain-Jones, N. M., Gilbertson, M. L. J., Cross, P. C., Hudson, P. J., Smith, D. W., … Craft, M. E. (2020). Data from: Group density, disease, and season shape territory size and overlap of social carnivores. Dryad Digital Repository, https://doi.org/10.5061/dryad. vq83b​k3qd Brown, E. D., Macdonald, D. W., Tew, T. E., & Todd, I. A. (1994). Apodemus sylvaticus infected with Heligmosomoides polygyrus (Nematoda) in an arable ecosystem: Epidemiology and effects of infection on the movements of male mice. Journal of Zoology, 234, 623–640. https:// doi.org/10.1111/j.1469-7998.1994.tb048​69.x Burnham, K. P., & Anderson, D. R. (2002). Model selection and multimodel inferences: A practical-theoretic approach (2nd ed.). New York, NY: Springer. 13652656, 2021, 1, Downloaded from https://besjournals.onlinelibrary.wiley.com/doi/10.1111/1365-2656.13294 by Colorado Parks & Wildlife, Wiley Online Library on [13/03/2023]. See the Terms and Conditions (https://onlinelibrary.wiley.com/terms-and-conditions) on Wiley Online Library for rules of use; OA articles are governed by the applicable Creative Commons License Journal of Animal Ecology BRANDELL et al. �| Journal of Animal Ecology Bygott, J. D., Bertram, B. C., & Hanby, J. P. (1979). Male lions in large coalitions gain reproductive advantages. Nature, 282, 839–841. https:// doi.org/10.1038/282839a0 Calenge, C. (2006). The package ‘adehabitat’ for the R software: A tool for the analysis of space and habitat use by animals. Ecological Modelling, 7, 516–519. https://doi.org/10.1016/j.ecolm​ odel.2006.03.017 Cantor, M., Wedekin, L. L., Guimaraes, P. R., Daura-Jorge, F. G., RossiSantos, M. R., & Simoes-Lopes, P. C. (2012). Disentangling social networks from spatiotemporal dynamics: The temporal structure of a dolphin society. Animal Behaviour, 84, 641–651. https://doi. org/10.1016/j.anbeh​av.2012.06.019 Carter, S. P., Delahay, R. J., Smith, G. C., Macdonald, D. W., Riordan, P., Etherington, T. R., … Cheeseman, C. L. (2007). Culling-induced social perturbation in Eurasian badgers Meles meles and the management of TB in cattle: An analysis of a critical problem in applied ecology. Proceedings of the Royal Society B: Biological Sciences, 274, 2769–2777. Cassidy, K. A., MacNulty, D. R., Stahler, D. R., Smith, D. W., & Mech, L. D. (2015). Group composition effects on aggressive interpack interactions of gray wolves in Yellowstone National Park. Behavioral Ecology, 26, 1352–1360. https://doi.org/10.1093/behec​o/arv081 Comte, S., Carver, S., Hamede, R., & Jones, M. (2020). Changes in spatial organization following an acute epizootic: Tasmanian devils and their transmissible cancer. Global Ecology and Conservation, 22, e00993. https://doi.org/10.1016/j.gecco.2020.e00993 Croft, D. P., Madden, J. R., Franks, D. W., & James, R. (2011). Hypothesis testing in animal social networks. Trends in Ecology & Evolution, 26, 502–507. https://doi.org/10.1016/j.tree.2011.05.012 Cross, P. C., Almberg, E. S., Haase, C. G., Hudson, P. J., Maloney, S. K., Metz, M. C., … Smith, D. W. (2016). Energetic costs of mange in wolves estimated from infrared thermography. Ecology, 97, 1938– 1948. https://doi.org/10.1890/15-1346.1 Cross, P. C., Creech, T. G., Ebinger, M. R., Heisey, D. M., Irvine, K. M., & Creel, S. (2012). Wildlife contact analysis: Emerging methods, questions, and challenges. Behavioral Ecology and Sociobiology, 66, 1437– 1447. https://doi.org/10.1007/s0026​5-012-1376-6 Cross, P. C., Lloyd-Smith, J. O., Johnson, P. L. F., & Getz, W. M. (2005). Duelling timescales of host movement and disease recovery determine invasion of disease in structured populations. Ecology Letters, 8, 587–595. https://doi.org/10.1111/j.1461-0248.2005.00760.x Cross, P. C., van Manen, F. T., Viana, M., Almberg, E. S., Bachen, D., Brandell, E. E., … Smith, D. W. (2018). Estimating distemper virus dynamics among wolves and grizzly bears using serology and Bayesian state-space models. Ecology and Evolution, 8, 8726–8735. https://doi. org/10.1002/ece3.4396 Csardi, G., & Nepusz, T. (2006). The igraph software package for complex network research. InterJournal, Complex Systems, 1695, 1–9. Cubaynes, S., Macnulty, D. R., Stahler, D. R., Quimby, K. A., Smith, D. W., & Coulson, T. (2014). Density-dependent intraspecific aggression regulates survival in northern Yellowstone wolves (Canis lupus). Journal of Animal Ecology, 83, 1344–1356. https://doi. org/10.1111/1365-2656.12238 Davidson, Z., Valeix, M., Loveridge, A. J., Madzikanda, H., & Macdonald, D. W. (2011). Socio-spatial behaviour of an African lion population following perturbation by sport hunting. Biological Conservation, 144, 114–121. https://doi.org/10.1016/j.biocon.2010.08.005 Davies, N. B. (1978). Territorial defence in the speckled wood butterfly (Pararge aegeria): The resident always wins. Animal Behaviour, 26, 138–147. https://doi.org/10.1016/0003-3472(78)90013​-1 Davies, N. B., & Hartley, I. R. (1996). Food patchiness, territory overlap and social systems: An experiment with dunnocks Prunella modularis. The Journal of Animal Ecology, 65, 837. https://doi.org/10.2307/5681 Davis, C. L., Rich, L. N., Farris, Z. J., Kelly, M. J., Di Bitetti, M. S., Di Blanco, Y., … Miller, D. A. W. (2018). Ecological correlates of the BRANDELL et al. spatial co-occurrence of sympatric mammalian carnivores worldwide. Ecology Letters, 21, 1401–1412. https://doi.org/10.1111/ele. 13124 Demma, D. J., & Mech, L. D. (2011). Accuracy of estimating wolf summer territories by daytime locations. The American Midland Naturalist, 165, 436–445. https://doi.org/10.1674/0003-0031-165.2.436 Elbroch, L. M., Lendrum, P. E., Quigley, H., & Caragiulo, A. (2016). Spatial overlap in a solitary carnivore: Support for the land tenure, kinship or resource dispersion hypotheses? Journal of Animal Ecology, 85, 487–496. https://doi.org/10.1111/1365-2656.12447 Elith, J., Leathwick, J. R., & Hastie, T. (2008). A working guide to boosted regression trees. Journal of Animal Ecology, 77, 802–813. https://doi. org/10.1111/j.1365-2656.2008.01390.x Erlinge, S. (1968). Territoriality of the Otter Lutra lutra L. Oikos, 19(1), 81–98. https://doi.org/10.2307/3564733 Farine, D. R., & Whitehead, H. (2015). Constructing, conducting and interpreting animal social network analysis. Journal of Animal Ecology, 84, 1144–1163. https://doi.org/10.1111/1365-2656.12418 Fenner, A. L., Godfrey, S. S., & Bull, M. C. (2011). Using social networks to deduce whether residents dispersers spread parasites in a lizard population. Journal of Animal Ecology, 80, 835–843. https://doi. org/10.1111/j.1365-2656.2011.01825.x Fieberg, J. (2007). Kernel density estimators of home range: Smoothing and the autocorrelation red herring. Ecology, 88, 1059–1066. https:// doi.org/10.1890/06-0930 Foster, R. J., Harmsen, B. J., & Doncaster, C. P. (2010). Habitat use by sympatric jaguars and pumas across a gradient of human disturbance in Belize. Biotropica, 42, 724–731. https://doi.org/10.1111/ j.1744-7429.2010.00641.x Fountain-Jones, N., Machado, G., Carver, S., Packer, C., Mendoza, M., & Craft, M. E. (2019). How to make more from exposure data? An integrated machine learning pipeline to predict pathogen exposure. Journal of Animal Ecology, 88(10), 1447–1461. Frase, B. A., & Armitage, K. B. (1984). Foraging patterns of yellow-bellied marmots: Role of kinship and individual variability. Behavioral Ecology and Sociobiology, 16, 1–10. Fuller, T. K., Mech, L. D., & Cochrane, J. F. (2003). Wolf population dynamics. In L. D. Mech & L. Boitani (Eds.), Wolves: Behavior, ecology, and conservation (pp. 161–191). Chicago, IL: University of Chicago Press. Gilbertson, M. L. J., White, L. A., & Craft, M. E. (2020). Trade-offs with telemetry-derived contact networks for infectious disease studies in wildlife. Methods in Ecology and Evolution, 12, 76–87. https://doi. org/10.1111/2041-210X.13355 Godfrey, S. S., Moore, J. A., Nelson, N. J., & Bull, C. M. (2010). Social network structure and parasite infection patterns in a territorial reptile, the tuatara (Sphenodon punctatus). International Journal for Parasitology, 40, 1575–1585. https://doi.org/10.1016/j.ijpara.2010. 06.002 Gosden, T. P., & Svensson, E. I. (2008). Spatial and temporal dynamics in a sexual selection mosaic. Evolution: International Journal of Organic Evolution, 62, 845–856. https://doi.org/10.1111/j.15585646.2008.00323.x Grinnell, J., Packer, C., & Pusey, A. E. (1995). Cooperation in male lions: Kinship, reciprocity or mutualism? Animal Behavior, 49, 95–105. Hanby, J. P., Bygott, J. D., & Packer, C. (1995). Ecology, demography, and behavior of lions in two contrasting habitats: Ngorongoro crater and the serengeti plains. In A. R. E. Sinclaire & P. Arcese (Eds.), Serengeti II: Dynamics, management, and conservation of an ecosystem (pp. 315– 331). Chicago, IL: The University of Chicago Press. Hastie, T., Rosset, S., Tibshirani, R., & Zhu, J. (2004). The entire regularization path for the support vector domain description. Journal of Machine Learning Research, 5, 1391–1415. He, P., Maldonado-Chaparro, A. A., & Farine, D. R. (2019). The role of habitat configuration in shaping social structure: A gap in studies 13652656, 2021, 1, Downloaded from https://besjournals.onlinelibrary.wiley.com/doi/10.1111/1365-2656.13294 by Colorado Parks & Wildlife, Wiley Online Library on [13/03/2023]. See the Terms and Conditions (https://onlinelibrary.wiley.com/terms-and-conditions) on Wiley Online Library for rules of use; OA articles are governed by the applicable Creative Commons License 98 �of animal social complexity. Behavioral Ecology and Sociobiology, 73. https://doi.org/10.1007/s0026​5-018-2602-7 Heinsohn, R. (1997). Group territoriality in two populations of African lions. Animal Behaviour, 53, 1143–1147. https://doi.org/10.1006/ anbe.1996.0316 Heinsohn, R., & Packer, C. (1995). Complex cooperative strategies in group-territorial African lions. Science, 269, 1260–1262. https://doi. org/10.1126/scien​ce.7652573 Heinsohn, R., Packer, C., & Pusey, A. E. (1996). Development of cooperative territoriality in juvenile lions. Proceedings of the Royal Society of London Series B: Biological Sciences, 263, 475–479. https://doi. org/10.1098/rspb.1996.0071 Hess, G. (1996). Disease in metapopulation models: Implications for conservation. Ecology, 77, 1617–1632. https://doi.org/10.2307/2265556 Hopcraft, J. G. C., Sinclair, A. R. E., & Packer, C. (2005). Planning for success: Serengeti lions seek prey accessibility rather than abundance. Journal of Animal Ecology, 74, 559–566. https://doi.org/10.1111/ j.1365-2656.2005.00955.x Houde, A. E., & Torio, A. J. (1992). Effect of parasitic infection on male color pattern and female choice in guppies. Behavioral Ecology, 3, 346–351. https://doi.org/10.1093/behec​o/3.4.346 Ilany, A., Booms, A. S., & Holekamp, K. E. (2015). Topological effects of network structure on long-term social network dynamics in a wild mammal. Ecology Letters, 18, 687–695. https://doi.org/10.1111/ele. 12447 Jimenez, M. D., Bangs, E. E., Boyd, D. K., Smith, D. W., Becker, S. A., Ausband, D. E., … Laudon, K. (2017). Wolf dispersal in the Rocky Mountains, Western United States: 1993–2008. Journal of Wildlife Management, 81, 581–592. https://doi.org/10.1002/jwmg.21238 Johnson, D. D. P., Kays, R., Blackwell, P. G., & Macdonald, D. W. (2002). Does the resource dispersion hypothesis explain group living? Trends in Ecology & Evolution, 17, 563–570. https://doi.org/10.1016/S0169​5347(02)02619​-8 Karczmarski, L., Würsig, B., Gailey, G., Larson, K. W., & Vanderlip, C. (2005). Spinner dolphins in a remote Hawaiian atoll: Social grouping and population structure. Behavioral Ecology, 16, 675–685. https:// doi.org/10.1093/behec​o/ari028 Kasper, C., & Voelkl, B. (2009). A social network analysis of primate groups. Primates, 50, 343–356. https://doi.org/10.1007/s1032​9009-0153-2 Kauffman, M. J., Varley, N., Smith, D. W., Stahler, D. R., MacNulty, D. R., & Boyce, M. S. (2007). Landscape heterogeneity shapes predation in a newly restored predator-prey system. Ecology Letters, 10, 690–700. https://doi.org/10.1111/j.1461-0248.2007.01059.x Ke, D.-H., Deng, Y.-H., Guo, W.-B., & Huang, Z.-H. (2017). A quadratic correlation between long-term mean group size and group density in a cooperatively breeding passerine. Ecology and Evolution, 7, 8719– 8729. https://doi.org/10.1002/ece3.3405 Kittle, A. M., Anderson, M., Avgar, T., Baker, J. A., Brown, G. S., Hagens, J., … Fryxell, J. M. (2015). Wolves adapt territory size, not pack size to local habitat quality. Journal of Animal Ecology, 84, 1177–1186. https://doi.org/10.1111/1365-2656.12366 Kittle, A. M., Bukombe, J. K., Sinclair, A. R. E., Mduma, S. A. R., & Fryxell, J. M. (2016). Landscape-level movement patterns by lions in western Serengeti: Comparing the influence of inter-specific competitors, habitat attributes and prey availability. Movement Ecology, 4, 1–19. https://doi.org/10.1186/s4046​2-016-0082-9 Kohl, M. T., Ruth, T. K., Metz, M. C., Stahler, D. R., Smith, D. W., White, P. J., & MacNulty, D. R. (2019). Do prey select for vacant hunting domains to minimize a multi-predator threat? Ecology Letters, 22, 1724–1733. https://doi.org/10.1111/ele.13319 Kortello, A. D., Hurd, T. E., & Murray, D. L. (2007). Interactions between cougars (Puma concolor) and gray wolves (Canis lupus) in Banff National Park, Alberta. Ecoscience, 14, 214–222. https://doi.org/10 .2980/1195-6860(2007)14[214:IBCPC​A]2.0.CO;2 Journal of Animal Ecology | 99 Lack, D. (1954). The natural regulation of animal numbers. In The Natural Regulation of Animal Numbers. Oxford, UK: Clarendon Press. Lafferty, K. D., & Morris, A. K. (1996). Altered behavior of parasitized killifish increases susceptibility to predation by bird final hosts. Ecology, 77, 1390–1397. https://doi.org/10.2307/2265536 Lesmerises, F., Dussault, C., & St-Laurent, M. H. (2013). Major roadwork impacts the space use behaviour of gray wolf. Landscape and Urban Planning, 112, 18–25. https://doi.org/10.1016/j.landu​ rbplan.2012.12.011 Loe, L. E., Hansen, B. B., Stien, A., Albon, S. D., Bischof, R., Carlsson, A., … Mysterud, A. (2016). Behavioral buffering of extreme weather events in a high-Arctic herbivore. Ecosphere, 7, 1–13. https://doi. org/10.1002/ecs2.1374 Macdonald, D. W. (1983). The ecology of social behaviour. Nature, 301, 379–384. MacNulty, D. R., Tallian, A., Stahler, D. R., & Smith, D. W. (2014). Influence of group size on the success of wolves hunting bison. PLoS ONE, 9, 1–8. https://doi.org/10.1371/journ​al.pone.0112884 Maher, C. R., & Lott, D. F. (2000). A review of ecological determinants of territoriality within vertebrate species. The American Midland Naturalist, 143, 1–29. https://doi.org/10.1674/0003-0031(2000)143 [0001:AROED​O]2.0.CO;2 McComb, K., Packer, C., & Pusey, A. (1994). Roaring and numerical assessment in contests between groups of female lions, Panthera leo. Animal Behaviour, 47, 379–387. https://doi.org/10.1006/anbe. 1994.1052 McNaughton, S. J. (1985). Ecology of a grazing ecosystem: The Serengeti. Ecological Monographs, 55, 259–294. https://doi.org/10.2307/194 2578 Mech, L. D. (1994). Buffer zones of territories of gray wolves as regions of intraspecific strife. Journal of Mammalogy, 75, 199–202. https:// doi.org/10.2307/1382251 Mech, L. D., & Boitani, L. (2003). Wolves: Behavior, ecology, and conservation. Chicago, IL: University of Chicago Press. Metz, M. C., Smith, D. W., Vucetich, J. A., Stahler, D. R., & Peterson, R. O. (2012). Seasonal patterns of predation for gray wolves in the multiprey system of Yellowstone National Park. Journal of Animal Ecology, 81, 553–563. https://doi.org/10.1111/j.1365-2656.2011.01945.x Millspaugh, J. J., Gitzen, R. A., Kernohan, B. J., Larson, M. A., & Clay, C. L. (2004). Comparability of three analytical techniques to assess joint space use. Wildlife Society Bulletin, 32, 148–157. https://doi.org/10.2 193/0091-7648(2004)32[148:COTAT​T ]2.0.CO;2 Mosser, A., & Packer, C. (2009). Group territoriality and the benefits of sociality in the African lion, Panthera leo. Animal Behaviour, 78, 359– 370. https://doi.org/10.1016/j.anbeh​av.2009.04.024 Munson, L., Terio, K. A., Kock, R., Mlengeya, T., Roelke, M. E., Dubovi, E., … Packer, C. (2008). Climate extremes promote fatal co-infections during canine distemper epidemics in African lions. PLoS ONE, 3, 5–10. https://doi.org/10.1371/journ​al.pone.0002545 Murdoch, W. W., Briggs, C. J., & Nisbet, R. M. (2003). Consumer-resource dynamics. Princeton, NJ: Princeton University Press. Nelson, A. A., Kauffman, M. J., Middleton, A. D., Jimenez, M. D., Mcwhirter, D. E., Barber, J., & Gerow, K. (2012). Elk migration patterns and human activity influence wolf habitat use in the Greater Yellowstone Ecosystem. Ecological Applications, 22, 2293–2307. https://doi.org/10.1890/11-1829.1 Ordiz, A., Støen, O.-G., Delibes, M., & Swenson, J. E. (2011). Predators or prey? Spatio-temporal discrimination of human-derived risk by brown bears. Oecologia, 166, 59–67. https://doi.org/10.1007/s0044​ 2-011-1920-5 Ostfeld, R. S. (1985). Limiting resources and territoriality in Microtine Rodents. The American Naturalist, 126, 1–15. https://doi.org/10.1086/ 284391 Packer, C., Altizer, S., Appel, M., Brown, E., Martenson, J., O’Brien, S. J., … Lutz, H. (1999). Viruses of the Serengeti: Patterns of infection and 13652656, 2021, 1, Downloaded from https://besjournals.onlinelibrary.wiley.com/doi/10.1111/1365-2656.13294 by Colorado Parks & Wildlife, Wiley Online Library on [13/03/2023]. See the Terms and Conditions (https://onlinelibrary.wiley.com/terms-and-conditions) on Wiley Online Library for rules of use; OA articles are governed by the applicable Creative Commons License BRANDELL et al. �| Journal of Animal Ecology mortality in African lions. Journal of Animal Ecology, 68, 1161–1178. https://doi.org/10.1046/j.1365-2656.1999.00360.x Packer, C., Gilbert, D. A., Pusey, A. E., & O’Brieni, S. J. (1991). A molecular genetic analysis of kinship and cooperation in African lions. Nature, 351, 562–565. https://doi.org/10.1038/351562a0 Packer, C., Hilborn, R., Mosser, A., Kissui, B., Borner, M., Hopcraft, G., … Sinclair, A. R. E. (2005). Ecological change, group territoriality, and population dynamics in Serengeti lions. Science, 307, 390–393. https://doi.org/10.1126/scien​ce.1105122 Packer, C., & Ruttan, L. (1988). The evolution of cooperative hunting. The American Naturalist, 132, 159–198. https://doi.org/10.1086/284844 Packer, C., Scheel, D., & Pusey, A. E. (1990). Why lions form groups: Food is not enough. The American Naturalist, 136, 1–19. https://doi. org/10.1086/285079 Paniw, M., Maag, N., Cozzi, G., Clutton-Brock, T., & Ozgul, A. (2019). Life history responses of meerkats to seasonal changes in extreme environments. Science, 363, 631–635. https://doi.org/10.1126/scien​ ce.aau5905 Pascual, M. A., & Hilborn, R. (1995). Conservation of harvested populations in fluctuating environments: The case of the Serengeti wildebeest. Journal of Applied Ecology, 32, 468–480. https://doi. org/10.2307/2404645 Pasinelli, G., Runge, J. P., Schiegg, K., Liu, J., Hull, V., Morzillo, A. T., & WiensWiens, J. A. (2011). Source-sink status of small and large wetland fragments and growth rate of a population network. In Sources, sinks and sustainability (pp. 216–238). Cambridge, UK: Cambridge University Press. Perkins, S. E., Cagnacci, F., Stradiotto, A., Arnoldi, D., & Hudson, P. J. (2009). Comparison of social networks derived from ecological data: Implications for inferring infectious disease dynamics. Journal of Animal Ecology, 78, 1015–1022. https://doi.org/10.1111/j.1365-2656.2009.01557.x Pravosudova, E. V., Grubb Jr., T. C., & Parker, P. G. (2001). The influence of kinship on nutritional condition and aggression levels in winter social groups of tufted titmice. The PasinelliCondor, 103, 821–828. https://doi.org/10.1093/condo​r/103.4.821 Pusey, A. E., & Packer, C. (1987). The evolution of sex-biased dispersal in lions. Behavior, 101, 275–310. https://doi.org/10.1163/15685​3987X​ 00026 R Core Team. (2019). R: A language and environment for statistical computing. Vienna, Austria: R Foundation for Statistical Computing. Retrieved from https://www.R-proje​c t.org/ Reynolds, J. J. H., Hirsch, B. T., Gehrt, S. D., & Craft, M. E. (2015). Raccoon contact networks predict seasonal susceptibility to rabies outbreaks and limitations of vaccination. Journal of Animal Ecology, 84, 1720– 1731. https://doi.org/10.1111/1365-2656.12422 Roelke-Parker, M. E., Munson, L., Packer, C., Kock, R., Cleaveland, S., Carpenter, M., … Appel, M. J. G. (1996). A canine distemper virus epidemic in Serengeti lions (Panthera leo). Nature, 379, 441–445. https:// doi.org/10.1038/379441a0 Rogers, L. L. (1987). Effects of food supply and kinship on social behavior, movements, and population growth of black bears in Northeastern Minnesota. Wildlife Monographs, 97, 3–72. Schaller, G. B. (1972). The Serengeti lion: A study of predator-prey relations. Chicago, IL: University of Chicago Press. Seaman, D. E., Millspaugh, J. J., Kernohan, B. J., Brundige, G. C., Raedeke, K. J., & Gitzen, R. A. (1999). Effects of sample size on kernel home range estimates. The Journal of Wildlife Management, 63, 739–747. https://doi.org/10.2307/3802664 Simon, C. A. (1975). The influence of food abundance on territory size in the iguanid lizard Sceloporus jarrovi. Ecology, 56, 993–998. https://doi. org/10.2307/1936311 Smith, D. W., & Bangs, E. E. (2009). Reintroduction of wolves to Yellowstone National Park: history, values, and ecosystem restoration. In M. W. Hayward & M. J. Somers (Eds.), Reintroduction of top-order predators (pp. 92–125). Oxford: Wiley-Blackwell. BRANDELL et al. Smith, D. W., Cassidy, K. A., Stahler, D. R., MacNulty, D. R., Harrison, Q., Balmford, B., … Coulson, T. (n.d.). Population dynamics and demography. In D. W. Smith, D. R. Stahler, & D. R. MacNulty (Eds.), Yellowstone wolves: Science and discovery in the World's First National Park. Chicago, IL: University of Chicago Press. Smith, D. W., Metz, M. C., Cassidy, K. A., Stahler, E. E., McIntyre, R. T., Almberg, E. S., & Stahler, D. R. (2015). Infanticide in wolves: Seasonality of mortalities and attacks at dens support evolution of territoriality. Journal of Mammalogy, 96, 1174–1183. https://doi. org/10.1093/jmamm​al/gyv125 Smith, D., Stahler, D., Stahler, E., Metz, M., Cassidy, K., Cassidy, B., … McIntyre, R. (2017). Yellowstone Wolf Project Annual Report. Yellowstone National Park Wolf Project Annual Report 2016. Yellowstone National Park, WY: National Park Service. Smith, J. A., Suraci, J. P., Clinchy, M., Crawford, A., Roberts, D., Zanette, L. Y., & Wilmers, C. C. (2017). Fear of the human ‘super predator’ reduces feeding time in large carnivores. Proceedings of the Royal Society B: Biological Sciences, 284, 20170433. https://doi.org/10.1098/ rspb.2017.0433 Spong, G., & Creel, S. (2004). Effects of kinship on territorial conflicts among groups of lions, Panthera leo. Behavioral Ecology and Sociobiology, 55, 325–331. https://doi.org/10.1007/s0026​5 -0030723-z Stahler, D. R., MacNulty, D. R., Wayne, R. K., vonHoldt, B., & Smith, D. W. (2013). The adaptive value of morphological, behavioural and lifehistory traits in reproductive female wolves. Journal of Animal Ecology, 82, 222–234. https://doi.org/10.1111/j.1365-2656.2012.02039.x Swanson, A., Arnold, T., Kosmala, M., Forester, J., & Packer, C. (2016). In the absence of a ‘landscape of fear’: How lions, hyenas, and cheetahs coexist. Ecology and Evolution, 6, 8534–8545. https://doi. org/10.1002/ece3.2569 Tallents, L. A., Randall, D. A., Williams, S. D., & MacDonald, D. W. (2012). Territory quality determines social group composition in Ethiopian wolves Canis simensis. Journal of Animal Ecology, 81, 24–35. https:// doi.org/10.1111/j.1365-2656.2011.01911.x Thomas, F., Schmidt-Rhaesa, A., Guilhaume, M., Manu, C., Durand, P., & Renaud, F. (2002). Do hairworms (Nematomorpha) manipulate the water seeking behaviour of their terrestrial hosts? Journal of Evolutionary Biology, 15, 356–361. https://doi.org/10.1046/ j.1420-9101.2002.00410.x Thompson, N. A. (2019). Understanding the links between social ties and fitness over the life cycle in primates. Behaviour, 156, 859–908. https://doi.org/10.1163/15685​39X-00003552 Tichon, J., Gilchrist, J. S., Rotem, G., Ward, P., & Spiegel, O. (2020). Social interactions in striped hyena inferred from camera trap data: Is it more social than previously thought? Current Zoology, 66(4), 345– 353. https://doi.org/10.1093/cz/zoaa003 Vanak, A. T., Fortin, D., Thaker, M., Ogden, M., Owen, C., Greatwood, S., & Slotow, R. (2013). Moving to stay in place: Behavioral mechanisms for coexistence of African large carnivores. Ecology, 94, 2619–2631. https://doi.org/10.1890/13-0217.1 VanderWaal, K. L., Atwill, E. R., Isbell, L. A., & McCowan, B. (2014). Linking social and pathogen transmission networks using microbial genetics in giraffe (Giraffa camelopardalis). Journal of Animal Ecology, 83, 406–414. VanderWaal, K. L., Mosser, A., & Packer, C. (2009). Optimal group size, dispersal decisions and postdispersal relationships in female African lions. Animal Behaviour, 77, 949–954. https://doi.org/10.1016/j. anbeh​av.2008.12.028 Viana, M., Cleaveland, S., Matthiopoulos, J., Halliday, J., Packer, C., Craft, M. E., … Lembo, T. (2015). Dynamics of a morbillivirus at the domestic–wildlife interface: Canine distemper virus in domestic dogs and lions. Proceedings of the National Academy of Sciences of the United States of America, 112, 1464–1469. https://doi.org/10.1073/ pnas.14116​23112 13652656, 2021, 1, Downloaded from https://besjournals.onlinelibrary.wiley.com/doi/10.1111/1365-2656.13294 by Colorado Parks & Wildlife, Wiley Online Library on [13/03/2023]. See the Terms and Conditions (https://onlinelibrary.wiley.com/terms-and-conditions) on Wiley Online Library for rules of use; OA articles are governed by the applicable Creative Commons License 100 �Vonholdt, B. M., Stahler, D. R., Smith, D. W., Earl, D. A., Pollinger, J. P., & Wayne, R. K. (2008). The genealogy and genetic viability of reintroduced Yellowstone grey wolves. Molecular Ecology, 17, 252–274. https://doi.org/10.1111/j.1365-294X.2007.03468.x Wade, P. R., Reeves, R. R., & Mesnick, S. L. (2012). Social and behavioural factors in cetacean responses to overexploitation: Are odontocetes less ‘Resilient’ than mysticetes? Journal of Marine Biology, 2012, 1–15. https://doi.org/10.1155/2012/567276 Wang, Y., Allen, M. L., & Wilmers, C. C. (2020). Mesopredators display behaviourally plastic responses to dominant competitors when scavenging and communicating. bioRxiv, preprint. https://doi.org/ 10.1101/2020.01.20.913335 White, L. A., Forester, J. D., & Craft, M. E. (2017). Using contact networks to explore mechanisms of parasite transmission in wildlife. Biological Reviews, 92, 389–409. https://doi.org/10.1111/brv.12236 Wolff, J. O., & Peterson, J. A. (1998). An offspring-defense hypothesis for territoriality in female mammals. Ethology Ecology & Evolution, 10, 227–239. https://doi.org/10.1080/08927​014.1998. 9522854 Wood, S. N., & Augustin, N. H. (2002). GAMs with integrated model selection using penalized regression splines and applications to environmental modelling. Ecological Modelling, 157, 157–177. https://doi. org/10.1016/S0304​-3800(02)00193​-X | 101 Woodroffe, R., Donnelly, C. A., Cox, D. R., Bourne, F. J., Cheeseman, C. L., Delahay, R. J., … Morrison, W. I. (2006). Effects of culling on badger Meles meles spatial organization: Implications for the control of bovine tuberculosis. Journal of Applied Ecology, 43, 1–10. https://doi. org/10.1111/j.1365-2664.2005.01144.x Zeller, K. A., Wattles, D. W., Conlee, L., & Destefano, S. (2019). Black bears alter movements in response to anthropogenic features with time of day and season. Movement Ecology, 7, 19. https://doi. org/10.1186/s4046​2-019-0166-4 S U P P O R T I N G I N FO R M AT I O N Additional supporting information may be found online in the Supporting Information section. How to cite this article: Brandell EE, Fountain-Jones NM, Gilbertson MLJ, et al. Group density, disease, and season shape territory size and overlap of social carnivores. J Anim Ecol. 2021;90:87–101. https://doi.org/10.1111/1365-2656.13294 13652656, 2021, 1, Downloaded from https://besjournals.onlinelibrary.wiley.com/doi/10.1111/1365-2656.13294 by Colorado Parks & Wildlife, Wiley Online Library on [13/03/2023]. See the Terms and Conditions (https://onlinelibrary.wiley.com/terms-and-conditions) on Wiley Online Library for rules of use; OA articles are governed by the applicable Creative Commons License Journal of Animal Ecology BRANDELL et al. � Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Title A name given to the resource Journal Articles Description An account of the resource CPW peer-reviewed journal publications Text A resource consisting primarily of words for reading. Examples include books, letters, dissertations, poems, newspapers, articles, archives of mailing lists. Note that facsimiles or images of texts are still of the genre Text. Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Title A name given to the resource Group density, disease, and season shape territory size and overlap of social carnivores Description An account of the resource <ol> <li>The spatial organization of a population can influence the spread of information, behaviour and pathogens. Group territory size and territory overlap and components of spatial organization, provide key information as these metrics may be indicators of habitat quality, resource dispersion, contact rates and environmental risk (e.g. indirectly transmitted pathogens). Furthermore, sociality and behaviour can also shape space use, and subsequently, how space use and habitat quality together impact demography.</li> <li>Our study aims to identify factors shaping the spatial organization of wildlife populations and assess the impact of epizootics on space use. We further aim to explore the mechanisms by which disease perturbations could cause changes in spatial organization.</li> <li>Here we assessed the seasonal spatial organization of Serengeti lions and Yellowstone wolves at the group level. We use network analysis to describe spatial organization and connectivity of social groups. We then examine the factors predicting mean territory size and mean territory overlap for each population using generalized additive models.</li> <li>We demonstrate that lions and wolves were similar in that group-level factors, such as number of groups and shaped spatial organization more than population-level factors, such as population density. Factors shaping territory size were slightly different than factors shaping territory overlap; for example, wolf pack size was an important predictor of territory overlap, but not territory size. Lion spatial networks were more highly connected, while wolf spatial networks varied seasonally. We found that resource dispersion may be more important for driving territory size and overlap for wolves than for lions. Additionally, canine distemper epizootics may have altered lion spatial organization, highlighting the importance of including infectious disease epizootics in studies of behavioural and movement ecology.</li> <li>We provide insight about when we might expect to observe the impacts of resource dispersion, disease perturbations, and other ecological factors on spatial organization. Our work highlights the importance of monitoring and managing social carnivore populations at the group level. Future research should elucidate the complex relationships between demographics, social and spatial structure, abiotic and biotic conditions and pathogen infections.</li> </ol> Bibliographic Citation A bibliographic reference for the resource. Recommended practice is to include sufficient bibliographic detail to identify the resource as unambiguously as possible. Brandell, E. E., N. M. Fountain‐Jones, M. L. Gilbertson, P. C. Cross, P. J. Hudson, D. W. Smith, D. R. Stahler, C. Packer, and M. E. Craft. 2021. Group density, disease, and season shape territory size and overlap of social carnivores. Journal of Animal Ecology, 90(1):87-101. https://doi.org/10.1111/1365-2656.13294 Creator An entity primarily responsible for making the resource Brandell, Ellen E. Fountain-Jones, Nicholas M. Gilbertson, Marie L. J. Cross, Paul C. Hudson, Peter J. Smith, Douglas W. Stahler, Daniel R. Packer, Craig Craft, Meggan E. Subject The topic of the resource Resource dispersion Wolf Lion Spatial organization Territoriality Extent The size or duration of the resource. 15 pages Rights Information about rights held in and over the resource <a href="http://rightsstatements.org/vocab/InC-NC/1.0/">IN COPYRIGHT - NON-COMMERCIAL USE PERMITTED</a> Format The file format, physical medium, or dimensions of the resource application/pdf Language A language of the resource English Is Part Of A related resource in which the described resource is physically or logically included. Journal of Animal Ecology Date Accepted Date of acceptance of the resource. Examples of resources to which a Date Accepted may be relevant are a thesis (accepted by a university department) or an article (accepted by a journal). 2020/05/22 Date Issued Date of formal issuance (e.g., publication) of the resource. 2020/07/12 Date Submitted Date of submission of the resource. Examples of resources to which a Date Submitted may be relevant are a thesis (submitted to a university department) or an article (submitted to a journal). 2019/11/07 Type The nature or genre of the resource Article