Contact and contagion: Probability of transmission given contact varies with demographic state in bighorn sheep Authors: Kezia R. Manlove, E. Frances Cassirer, Raina K. Plowright, Paul C. Cross, and Peter J. Hudson This is the peer reviewed version of the following article: citation below, which has been published in final form in Journal of Animal Ecology at https://dx.doi.org/10.1111/1365-2656.12664. This article may be used for non-commercial purposes in accordance with Wiley Terms and Conditions for Self-Archiving. Manlove, Kezia R. , E. Frances Cassirer, Raina K. Plowright, Paul C. Cross, and Peter J. Hudson. "Contact and contagion: Probability of transmission given contact varies with demographic state in bighorn sheep." Journal of Animal Ecology (May 2017). DOI: 10.1111/1365-2656.12664. Made available through Montana State University’s ScholarWorks scholarworks.montana.edu Contact and contagion: Probability of transmission given contact varies with demographic state in bighorn sheep Kezia R. Manlove*,1 , E. Frances Cassirer2, Raina K. Plowright3, Paul C. Cross4 and Peter J. Hudson1 1Department of Biology, Center for Infectious Disease Dynamics, Pennsylvania State University, 208 Mueller Labs, University Park, PA 16802, USA; 2Idaho Department of Fish and Game, 3316 16th St., Lewiston, ID 83501, USA; 3Department of Microbiology and Immunology, Montana State University, PO Box 173520, Bozeman, MT 59717, USA; and 4U.S. Geological Survey, Northern Rocky Mountain Research Center, 2327 University Way Ste. 2, Bozeman, MT 59715, USA Abstract 1. Understanding both contact and probability of transmission given contact are key to managing wildlife disease. However, wildlife disease research tends to focus on contact heterogeneity, in part because the probability of transmission given contact is notoriously dif- ficult to measure. Here, we present a first step towards empirically investigating the probabil- ity of transmission given contact in free-ranging wildlife. 2. We used measured contact networks to test whether bighorn sheep demographic states vary systematically in infectiousness or susceptibility to Mycoplasma ovipneumoniae, an agent responsible for bighorn sheep pneumonia. 3. We built covariates using contact network metrics, demographic information and infection sta- tus, and used logistic regression to relate those covariates to lamb survival. The covariate set con- tained degree, a classic network metric describing node centrality, but also included covariates breaking the network metrics into subsets that differentiated between contacts with yearlings, ewes with lambs, and ewes without lambs, and animals with and without active infections. 4. Yearlings, ewes with lambs, and ewes without lambs showed similar group membership patterns, but direct interactions involving touch occurred at a rate two orders of magnitude higher between lambs and reproductive ewes than between any classes of adults or yearlings, and one order of magnitude higher than direct interactions between multiple lambs. 5. Although yearlings and non-reproductive bighorn ewes regularly carried M. ovipneumoniae, our models suggest that a contact with an infected reproductive ewe had approximately five times the odds of producing a lamb mortality event of an identical contact with an infected dry ewe or yearling. Consequently, management actions targeting infected animals might lead to unnecessary removal of young animals that carry pathogens but rarely transmit. 6. This analysis demonstrates a simple logistic regression approach for testing a priori hypotheses about variation in the odds of transmission given contact for free-ranging hosts, and may be broadly applicable for investigations in wildlife disease ecology. Key-words: bighorn sheep, disease ecology, force of infection, Mycoplasma ovipneumoniae, pathogen transmission, probability of transmission given contact, social network, wildlife disease Introduction Understanding factors that drive variation in host trans- mission propensity is critical for predicting and managing infectious disease events (Lloyd-Smith et al. 2005). For directly transmitted pathogens, transmission can be dis- tilled into two stages. First, a susceptible and an infected host must contact one another; and second, the pathogen must take advantage of that contact to move between hosts and establish in a new individual. Partitioning*Correspondence author. E-mail: kezia.manlove@gmail.com observed heterogeneity into separate stages attributable to contact, and pathogen movement and establishment is a first step towards understanding and managing pathogen transmission. Theoretical and empirical studies describe how contact heterogeneity shapes transmission (e.g. Ban- sal, Grenfell & Meyers 2007; Craft et al. 2011), but a key open question is whether measured networks can also offer insights about the probabilities of transmission given contact. To date, network-based studies of disease transmission fall into two general groups. The first group simulates epi- demics on empirical or simulated contact networks to assess the relative importance of well-connected individu- als or groups for pathogen transmission (e.g. Cross et al. 2005; Craft et al. 2011). Inferences usually focus on popu- lation-level epidemic outcomes, such as epidemic size (Cross et al. 2004) or duration (Keeling et al. 2001). The second group of studies directly measures transmission using physically or genetically marked pathogens (e.g. Zohdy et al. 2012; VanderWaal et al. 2014), and empiri- cally evaluates the relationship between host behaviour and transmission. Both groups attribute transmission heterogeneity to the contact process, so that epidemic fea- tures are treated as functions of contact, with the proba- bility of transmission given contact assumed constant. Recent studies have begun to link heterogeneity in duration, quality, or order of contact to disease transmis- sion in wildlife (e.g. VanderWaal & Ezenwa 2016). For example, experimental contacts between desert tortoises (Gopherus agassizii) underscored the importance of con- tact quality in transmission of Mycoplasma agassizii (Aiello et al. 2016); and Quevillon et al. (2015) demon- strated how interaction patterns between black carpenter ants (Camponotus pennsylvanicus) belonging to different functional grounds (foragers, nest ants, queen, etc.) could theoretically constrain pathogen transmission. For some human pathogens, knowledge of how proba- bility of transmission given contact shapes epidemic pro- gression is even more explicit. The probability of HIV transmission during a single sexual contact is much higher if the infected individual is within the first 15 months of infection than if the same contact occurred during the subsequent period of viral latency (Wawer et al. 2005), indicating a critical role for infection age in determining transmission. While the factors leading to superspreader hosts are often unclear, in some cases, specific demo- graphic attributes relate directly to increased transmission risk. For instance, pregnant women infected with Plas- modium falciparum malaria experience higher Plasmodium loads in their blood (Bouyou-Akotet et al. 2005) and higher vector bite rates (Lindsay et al. 2000) than other human host groups, therefore disproportionately con- tributing to the force of infection. In these human disease examples, variation in host infectiousness or susceptibility was established by directly measuring pathogen load or immune dynamics within individual hosts, either longitudinally or in widespread cross-sectional sampling pulses. In either case, acquiring sufficient measurements requires extensive animal sam- pling, which is often not possible in free-ranging wildlife systems; a key next step in epidemiological modelling in wildlife is to account for the contact network with empiri- cal observations, and assess the probability of transmis- sion given contact. Although the probability of transmission given contact is assumed constant in models attributing heterogeneity to contact, models could instead condition on empirically measured contacts, allowing direct inquiry about probability of transmission and establishment given contact. Here, we outline a general approach for drawing such inferences simply by using social contact information and disease outcomes. We demonstrate the method through a case-study of Myco- plasma ovipneumoniae transmission in bighorn sheep. general approach Individual-level network metrics are not independent of one another, in that one animal’s connections are not independent of those of its neighbours (e.g. Whitehead 2008). Although network dependencies are usually viewed as inferentially problematic, here, we capitalize on their existence to evaluate how well shared contacts predict common epidemic outcomes among a set of hosts. Our approach is inspired by adaptive evolutionary models that infer selective pressures over different regions of phylogenetic trees across evolutionary time (Butler & King 2004). These models treat the networks – in their case, phylogenetic trees and here, social contact networks – as known. Node-specific outcomes – in their case, observed phenotypes; here, individual disease outcomes – are assumed to covary in proportion to shared edge attri- butes. If outcomes for nodes with many edges in common do not covary strongly, this suggests that their shared edges contribute only weakly to the observed outcome. Hypotheses about edge-specific effects can be presented as alternative weightings of the network by categorizing all edges connected to each individual according to hypothe- sized type, summing edge-weights for each type, and recasting those sums as covariates driving the model’s mean so that the corresponding hypotheses can be tested with standard linear modeling techniques (Fig. 1). Consider an epidemic progressing along a weighted net- work where nodes represent individuals and edges repre- sent contact intensities. The epidemic produces disease occurrences along the network, with individual outcomes depending on both contact structure (edges and edge- weights) and the probability of transmission given con- tact. If the probability of transmission given contact does not vary, individual outcomes should covary in propor- tion to their shared edges (contacts). Alternatively, if some host groups have systematically higher probabilities of transmission given contact, then a more detailed model allowing probabilities to vary for different host groups will better describe the epidemic. A pure contact model might treat an individual’s risk as a function of its network centrality. For individual i whose contacts are listed in the set I = {1,2, . . ., In} with edge-weights linking i to j2I denoted eij, a pure contact model postulates that risk of infection is a function of a single covariate equal to the sum of i’s edge-weights, Σj2Ieij. In this case, all edges of a given weight impose the same transmission risk, regardless of the particular ani- mals involved. However, if load data are available, then the contact-driven edge-weights can be rescaled to also account for neighbour j’s current pathogen load, lj. Under this new load-weighted risk model, i’s risk becomes a function of a new covariate, Σj2Ieijlj. When outcomes and pathogen loads are known, these two hypotheses can be compared with standard model selection methods for logistic regression models whose outcomes uniquely depend on the first covariate vs. the second (Fig. 1). Indi- vidual-specific attributes hypothesized to alter susceptibil- ity (e.g. antibody titres or nutritional condition) can also be incorporated as additional covariates, providing a means to directly compare how factors specific to the physiological condition of the recipient host individual and factors specific to that individual’s set of infectious contacts shape transmission. Several studies have already taken this approach with- out investigating the probability of transmission given contact per se. Grear, Luong & Hudson (2013) used a model conditioned on contacts to find optimal temporal lags for capturing transmission dynamics in two classes of macroparasite, and Godfrey et al. (2010) used a model accounting for contacts to examine host sex differences in transmission of three tuatara parasites. Nevertheless, stud- ies that condition on measured contacts to better estimate other aspects of transmission remain rare. application: inferring transmission risk in bighorn sheep Bighorn sheep experience recurrent spillover and prolonged persistence of pathogens causing population-limiting pneu- monia (Cassirer et al. 2013; Manlove et al. 2016). Disease events start with an all-age die-off typically killing between 15% and 100% of animals in affected herds. Following all-age die-offs, some adults continue chronically carrying M. ovipneumoniae, a key agent underlying disease (Besser et al. 2013). Chronically infected adults apparently act as reservoirs for transmission to na€ıve lambs, initiating lamb disease events that severely reduce lamb survival to weaning (b)(a) (d) (e) (c) Fig. 1. Conceptual underpinnings. When all animals impose the same infection force on their neighbours, the probability a focal animal (Animal i) gets infected is simply a function of the sum of its edge-weights (a). However, when different types of animals impose differ- ent probabilities of transmission given contact, the edges are differentially weighted depending on individual identities (b). When individ- ual outcomes and contacts are known, individual outcomes can be regarded as binary response variables, Y, and summed edge-weights can be treated as covariates, X. This structure allows estimation of an individual’s conditional probability of transmission given contact through transformation of the linear predictor from a logistic regression model relating Y and X, as shown in (c) and (d). Transmission coefficients, b, can be transformed to obtain estimates of the odds ratio of transmission from animals of different types under identical contact conditions. Additionally, this structure allows estimation of conditional probability of transmission given contact with indivi- duals of a particular type (e). in the years-to-decades following die-offs (Cassirer et al. 2013; Manlove et al. 2016). Currently, it is unclear whether all chronically infected animals impose equivalent transmis- sion risks on lambs, and whether all lambs are equally vul- nerable to death from pneumonia. We apply our general approach to understand M. ovi transmission in bighorn sheep during summer lamb pneu- monia outbreaks. Our analysis hinges on the assumption that infected animals with low odds of transmission given contact will have relatively little effect on disease dynam- ics, even if their contact rates with susceptible lambs are high. In contrast, highly infectious animals may influence disease dynamics even if their contact rates with suscepti- ble lambs are low. We arrange the investigation around three research questions. First, are association and inter- action patterns consistent across three different demo- graphic classes of bighorn sheep? Second, does the probability of transmission given contact differ between animals in these same demographic states? Finally, how does an individual’s infection status relate to its transmis- sion propensity in the bighorn pneumonia system? Materials and methods study populations and field data collection Health and behavioural data were collected from three bighorn sheep populations in southeastern Washington and northeastern Oregon during the summers of 2013–2015 over a total of 6 popula- tion-years (Table 1). The Asotin Creek, Black Butte and Mountain View populations were established through translocations con- ducted between 1977 and 1997 due to efforts from the Washington and Oregon Departments of Fish and Wildlife (WDFD and ODFW). The populations experienced all-age pneumonia epi- demics between 1988 and 2012, followed by pneumonia outbreaks of varying severity in lambs. Each population consisted of 42–65 animals during the study period, and the study relies specifically on observational field data from 88 marked ewes and yearlings, moni- tored for 129 animal-years. Nasal swabs were obtained from most (82 of 129, accounting for 74% of all observations) of the marked animals in the winter preceding summer observations. Samples were analysed at the Washington Disease Diagnostic Laboratory, who measured per cent inhibition by an anti-M. ovi antibody using a competitive ELISA, and identified active current infections on a categorical basis (an animal was infected or not) using real-time M. ovi-specific PCR (Ziegler et al. 2014). We assume PCR status during the preceding winter was a reasonable proxy for an active infection throughout this manuscript. Animals greater than 1 year of age were fitted with colour-coded radiocollars, and individually marked with numbered and coloured ear tags. Animals captured under 1 year of age received only ear tags. Capture and handling were conducted according to protocols approved by the collaborat- ing State agencies. We compared contact patterns and probability of transmission given contact for individuals in three different demographic states. These states were “dams” (ewes with lambs), “dry ewes” (non- reproductive adult ewes that do not have lambs), “yearlings” (1- year olds). We tracked an additional fourth state, “lambs”, though we assumed that lambs could not be chronically infected at the out- set of the study, as they are typically born uninfected. Crews located radiocollared individuals approximately every 3 days from 1 May to 15 July and recorded group location, group age composi- tion (number of lambs, ewes and yearlings) and health status of all group members. Number of relocations and sampling intensities for each population-year are reported in Table 1. Observational data collection was conducted under protocols approved by Penn State (IACUC #40292) and Montana State University (IACUC #2014-59). “Association” data, or information on how frequently each pair of marked animals occurred in the same group, came from 4548 individual relocation events, and were derived using the social contact network methods described below. Direct contact, or “interaction” data came from 3234 ten- minute group follows on 1131 unique spatiotemporal aggregations (“groups”), during which all direct touching events were timed and classified. Contacts were attributed to specific individuals whenever individual identities were known. Unmarked individuals involved in interactions were recorded by their demographic state (ewes with lambs, dry ewes that were never observed with lambs or lost their lambs prior to 1 June, yearlings, and lambs). Adult rams occurred in less than 10 of the groups included in this analysis, and therefore were excluded. Lambs could not be uniquely identified, so nursing and bedded contacts were assumed to involve dam–lamb pairs unless otherwise noted by the observer; other interactions involving lambs were attributed to “unknown” lambs. Table 1. Study population sample sizes. In “Disease status”, “I” indicates that a disease event occurred in lambs; “P” indicates that live pathogens were detected in some hosts in the population during that population-year; “S” indicates that some adult animals actively dis- played symptoms in that population-year; “E” indicates that some collected lamb carcasses showed evidence of pathogen infection in that population-year; and “He” indicates that no signs of clinical disease were observed in lambs Year Population Population size* Ewes (n) Yearlings (n) Lambs (n) Number recognizable adults and yearlings Number relocations Disease status 2013 Asotin Creek 64 32 5 11 12 554 P 2013 Black Butte 13† 13 0 4 4 124 I, P, S, E 2014 Asotin Creek 65 29 9 15 28 1136 He, P, S 2014 Black Butte 13† 13 0 11 13 718 I, P, S, E 2015 Asotin Creek 54 24 13 23 33 1442 He 2015 Mtn View 42 24 14 11 16 574 He, P *Population estimates, ewe counts and yearling counts are based on counts in March, just prior to the birth pulse. †The full Black Butte population consisted of 52 animals (30 ewes and 8 yearlings) in 2013, and 36 animals (26 ewes and 2 yearlings) in 2014, but the group of 13 adult ewes studied were completely spatially and socially closed during this study. © 2017 The Authors. Journal of Animal Ecology © 2017 British Ecological Society, Journal of Animal Ecology, 86, 908–920 (aj) for each of the j population-years. Formally, the average degree in time window t of individual i from reproductive state g living in population-year j was ygijt = aj + fg(t) + ɛijt. Since a fit to this model had a relatively symmetric and uni- modal residual distribution (Fig. S1, Supporting Information), we assumed that egijt~N(0, r 2). GAMs were fit using R’s mgcv package (Wood 2011). We evaluated whether association rate and interaction rate were reasonable proxies for one another by assessing the linear relationship between an animal’s centrality within the association network (measured here with eigenvector centrality; Bonacich 1987) and its interaction rate, in a model that also contained intercept adjustments for population-year and demographic state. Interaction rate was our measure of interaction centrality, since that metric allowed us to normalize over unbalanced numbers of animal-specific focal follows. l inking network measures to lamb survival Lamb survival until 1 October was the binary response variable in all transmission models, under the assumption that lambs that were infected died of disease prior to 1 October, and all other lambs survived. This structure casts lamb mortalities as sentinel events indicating pathogen transmission, an assumption partially justified by previous work in the Hells Canyon system that showed 88% of summer lamb mortality was attributable to dis- ease (Cassirer et al. 2013). The analyses relied on lamb survival and covariate measures for 56 lambs born to 43 separate ewes over five population-years. Lambs born to Black Butte ewes in 2013 were excluded, since fewer than 50% of ewes were marked during that population-year, and none had been tested for M. ovi. Marked animals never observed with lambs did not con- tribute response values, but did contribute to the covariate sets. Static association (together in same group) and interaction (di- rect contacts) network metrics provided a suite of covariates asso- ciated with transmission risk. Covariates were calculated for each lamb based on the set of individuals observed interacting or asso- ciating with that lamb (or that lamb’s dam). We also included one individual-specific covariate hypothesized to alter lamb suscepti- bility, maternal per cent antibody inhibition. From the outset, we wanted to compare transmission risk posed by dry ewes and year- lings with transmission risk posed by reproductive ewes. To that end, some models allowed separate risks for contacts with repro- ductive ewes vs. contacts with dry ewes or yearlings. Covariates, along with details of their construction, are outlined in Table 2. Contacts with yearlings and dry ewes were combined in all trans- mission analyses, due to their similar interaction rates (Fig. 2). Bivariate plots of all covariate pairs are shown in Fig. S2. Our objective was to model lamb survival, but limited knowl- edge of the incubation and infection periods, as well as the assumption that most transmission likely stems from newly infected lambs once the epidemic is underway, prevented us from using traditional Cox-like hazard models. Instead, we fit a suite of logistic regressions that captured all combinations of the hypotheses in Table 2. The association centrality, interaction intensity, and infection-weighted association and interaction hypotheses were all tested using variants on the same theme: that the risk imposed on a given individual by a certain peer group can be measured by summing over total contacts or interactions with members of that peer group (Table 2). Models with more conventional approaches to infection risk, for example, a model that treated an individual’s risk as a function of the sum of that individual’s edge-weights in the interaction or association network, were also included, as was a model that treated risk as a function of estimated population size (PopEst). All models included a random effect for population-year. Mod- els conditioned on contact were compared to models that over- looked contact, and instead used the number of reproductive ewes, dry ewes and yearlings in each population as covariates. Dam per cent antibody inhibition was also accounted for, so that results were directly comparable. AIC was used for model com- parison throughout, which a 2-point difference in AIC regarded as substantial improvement in model fit (Burnham & Anderson 2003). Results from the transmission models prompted us to conduct three follow-up analyses. The first was a detailed exploration of antibody patterns in adults as a function of age and population- year. The second was a Fisher’s exact test comparing infection prevalence (as determined by M. ovi PCR status in the preceding winter) in yearlings (n = 22) to infection prevalence in adult females (n = 60). The third was a logistic regression of lamb sur- vival status (0 or 1 for each lamb) as a function of two indicator variables: one indicating whether a lamb had any associations with infected reproductive ewes, and one indicating whether a lamb had any associations with infected yearlings or dry ewes. Results are association and interaction patterns consistent across demographic states? Association networks based on shared group membership were generally well-connected, with all animals exhibiting similar connection numbers and strengths (Fig. S3). All animals accumulated associates at a relatively constant rate (Fig. 3a and Table S1; although demographic states showed statistically significant differences in patterns, qualitatively their degree accumulation trajectories were similar). While groups were large and stable prior to the mid-May birth pulse, all demographic classes showed diminished average degree during the lambing period. Average degree increased again about a week after the peak of the birth pulse, and remained high throughout summer (Fig. 3b and Table S2; demographic state differ- ences are statistically significant, but qualitatively similar), consistent with formation of nursery groups. Field crews recorded data on 1131 groups, and observed an average of 274 identifiable animals and 66 groups per 48-h period. Interaction patterns varied dramatically between demographic classes, with all the most common forms of interactions involving lambs (Fig. 2 and Table S3). The most common and longest-duration interac- tions were between lambs and their dams, and typically consisted of nursing or bedding together. Yearlings and adults of all demographic classes almost never directly con- tacted other post-juvenile animals. Interactions and associ- ations were not significantly related to one another at the individual level for identifiable animals (b < 001; standard error <001; conditional R2 from the regression of total interactions on association eigencentrality = 23%; Fig. S4), suggesting individuals with high association centralities (those regularly observed with many other individuals within their populations) were not necessarily the same individuals who engaged in the most, or the longest, direct contacts. This is not overly surprising, as individuals with weak social ties may be more likely to switch groups, but less likely to actually touch other animals within those groups. does the probabil ity of transmission given contact differ between demographic states? Transmission models were fit to binary survival data from the 56 lambs documented during our study period for which dam M. ovi PCR and antibody inhibition data and contact data were available. Of these lambs, 17 died and 39 survived, forming the basis for our logistic regression models of transmission. The best-performing transmission model (AIC weight = 059) included interaction-weighted, demo- graphic class-specific contacts with infected animals, as well as dam’s per cent antibody inhibition (Fig. 4c, AIC weights relative to other models shown in Table 3, and model coefficient estimates shown in Table S5). A second model that included group-specific associations with infected animals and dam’s per cent antibody inhibition was competitive with the top model (AIC weight = 039; Table 3). Both high-performing models supported higher transmission coefficients for infected dams than for infected dry ewes and yearlings, suggesting that the prob- ability of transmission given contact varied with demo- graphic class. Models that overlooked neighbour infection status, or accounted for connectedness (either through associations or interactions) without adjusting for differ- ences between demographic classes, and models that did not include dam’s antibody values received little-to-no support (Table 3). There were not significant differences Table 2. Hypotheses, covariate definitions, and expected relationships. Ix denotes all of lamb i’s edges connected to individuals of group X. Si,j is the social affinity index for individuals i and j, c^State1State2 is the estimated of interaction minutes per day between individuals of demographic state 1 and demographic state 2 (reported in Table S3). Association and interaction metrics that are not group- specific sum over all associates, regardless of demographic state Lamb survival predictors Covariate Calculation Expectation Associations with specific groups of animals, regardless of infection status CReproEwe log 1þ X j2IReproEwe Si; j 0 @ 1 A Lambs with many reproductive ewe associates have high P(death) CDryYrl log 1þ X j2IDryYrl Si; j 0 @ 1 A Lambs with many dry ewe or yearling associates have high P(death) Interactions with specific groups of animals, regardless of infection status DReproEwe log 1þ X j2IReproEwe Si; j  c^ReproEweLamb 0 @ 1 A Lambs with high interaction rates with reproductive ewes have high P(death) DDryYrl log 1þ X j2IDryYrl Si; j  c^DryYrlLamb 0 @ 1 A Lambs with high interaction rates with yearlings and dry ewes have high P(death) Associations with specific groups of infected animals PAReproEwe log 1þ X j2IPCRþReproEwe Si; j 0 @ 1 A Many infected associate reproductive ewes increases P(death) PADryYrl log 1þ X j2IPCRþDryYrl Si; j 0 @ 1 A Many infected associate dry ewes and yearlings increases P(death) Interactions with specific groups of infected animals PIReproEwe log 1þ X j2IPCRþReproEwe Si; j  c^ReproEweLamb 0 @ 1 A Many interactions with infected reproductive ewes increase P(death) PIDryYrl log 1þ X j2IPCRþDryYrl Si; j  c^DryYrlLamb 0 @ 1 A Many interactions with infected dry ewes or yearlings increases P(death) Ewe’s antibodies M Ewe’s per cent antibody inhibition (measured in preceding winter) Lambs born to seropositive ewes have lower P(death) as important local pathogen reservoirs due to their rela- tively high rates of infection (Fig. 6a), our analyses sug- gested that in fact yearlings and dry ewes rarely transmit pathogens to lambs. We estimated that a contact with an infected dam has approximately five times the odds of producing a lamb mortality event than an identical con- tact with an infected dry ewe or yearling. Average number of associates declined during the birth pulse, and then rapidly increased (Fig. 3b), corresponding to “creching”, the formation of ewe–lamb nursery groups which structure disease mortalities in this system (Man- love et al. 2014). From a disease transmission standpoint, creching elevates an animal’s number of potential infec- tious contacts. Whether total number of associates is the best measure for rate of acquiring new potentially infec- tious contacts likely depends on the intensity of contact necessary for transmission, as well as the interaction rates, pathogen loads and symptoms of the particular animals involved. While each of these mechanisms clearly con- tributes to disease dynamics, our data are insufficient to differentiate among them at this time. A ewe’s per cent antibody inhibition value was associ- ated with small but significant increases in the odds that her lamb survived, but we hesitate to overemphasize this relationship for several reasons. First, a model consisting of only dam’s antibody value was not competitive with models that incorporated contact (Table 3); the effect was only evident in models accounting for contact structure. This does not necessarily preclude a dam antibody effect, but it does suggest that antibody values cannot overcome – and may confounded with – contact structure. Second, antibody inhibition measures were based on field samples collected 2–6 months prior to lamb birth, and are there- fore likely subject to both process error from antibody waning in the dam, and measurement error associated with the sampling protocol and subsequent cELISAs. Finally, we saw similar antibody inhibition levels across all older dams, regardless of their current infection status (Fig. 5a), suggesting that immune response is not strongly associated with pathogen clearance (in which case we would expect to see higher per cent inhibition among uninfected animals). Despite these reservations, we never- theless include the antibody result here, as we believe it merits future exploration. Our analysis relies on a few additional assumptions that could shape the results. First, we used lamb survival as a proxy for transmission and infection, assuming all infected lambs died and all uninfected lambs survived. In reality, we know that some infected lambs survive, and that some lambs die of other causes. However, previous studies found that most (88%) of pre-weaning lamb mor- tality in these populations is attributable to disease, and that median summer lamb survival in the absence of dis- ease is in excess of 80% (Cassirer et al. 2013). Further- more, although misclassification error undoubtedly adds noise to this analysis, we see no reason to suspect that noise to systematically bias our findings (but see Vander Wal et al. 2015, who found an association between indi- vidual fitness and social network centrality in a different bighorn sheep system). Additionally, some of our data are drawn from a year with a novel M. ovi strain introduc- tion event (described in Cassirer et al. 2017), which may have resulted in particularly severe disease and marginally altered behavioral signals. Second, this analysis is likely subject to measurement error and confounding in the covariates, since our predic- tor values derive from field observations of association. While this form of error is present yet unaccounted for in many ecological studies, recent work suggests it may fac- tor disproportionately into inappropriate detection of effects (Westfall & Yarkoni 2016). In this system, we have no clear means of validating the reliability of our associa- tion indices (that is, there is no “gold standard” to which we can compare our field-estimated association indices). The role of measurement error, both in social network analysis and in ecological studies more generally, requires further consideration in future work. An additional factor that might confound our results is the assumption that direct contact patterns during focal follows reflected direct contact patterns at other times of day. Importantly, we have no observations during night. We assume that bighorn sheep decrease their activity levels, and spend most of their time bedded in groups. Since lambs probably bed preferentially with their dams and may nurse at night, the data presented here likely underestimate direct dam–lamb contact rates. This might explain the much higher force of infection from infected dams than from infected yearlings and non-reproductive ewes. However, we would expect that force of infection to apply disproportionately to lambs with infected dams (as opposed to lambs that were associated with infected ewes, but that were born to uninfected ewes), and that effect did not emerge in our analyses. Finally, we assumed that contact rates were homoge- neous within demographic states. This assumption was made partially out of necessity, since we could not uniquely identify lambs. For the demographic states in which ani- mals were uniquely identifiable, rare contacts paired with unbalanced observation times complicated formal assess- ments of within-group heterogeneities in interaction pat- terns. Therefore, while within-group heterogeneities in interaction rates likely exist, we overlook them here. Despite these caveats, our results strongly suggest that pathogen transmission risk is not constant across all infected hosts, even after accounting for differences in association patterns and interaction rates. We saw major differences in transmission probabilities between demo- graphic states after accounting for differences in contact intensity, with yearlings and dry ewes apparently imposing a much lower force of infection on lambs than the force imposed by infected dams. Our results suggest that per- haps test-removal strategies aimed at reducing M. ovi prevalence should primarily target infected adult ewes. Incorporating age-structured removals as one potential management action in a broad-scale experimental adap- tive management framework could clarify this effect. This analysis is an early effort to leverage empirically measured contacts in order to test hypotheses about prob- ability of transmission given contact. We anticipate that this line of inquiry will increase in importance as the set of measured animal contact networks continues to grow, and research efforts shift to the remaining variation in transmission not attributable to contacts alone. Key next steps include evaluating how measurement error might drive these results, and incorporating additional aspects of individual heterogeneity. Inferring probability of trans- mission given contact using social network analyses may be more plausible for many wildlife systems than conduct- ing the analogous infection and transmission trials in cap- tivity. As such, network-based inferences like the ones presented here could fill an inferential gap with important implications for wildlife disease management. Authors’ contributions All authors designed the project, and K.R.M. and E.F.C. collected data. K.R.M. developed the inferential framework with input from P.C.C., and drafted the manuscript. All authors contributed to manuscript revisions. Acknowledgements Funding was provided by a grant for Federal Aid to Wildlife Restoration, Shikar-Safari International, the Washington and Oregon Chapters of the Wild Sheep Foundation, and the Idaho Wildlife Disease Research Over- sight Committee. We thank Michael Lerch, Johanna Ohm, Logan Weyand, K.C. Hill, Carrie Lowe, and Nick Fortin for assistance with data collection, and Christina Aiello for providing comments on an early draft. We are especially grateful to Bob Dice and Paul Wik of WDFW for their ongoing support of the Hells Canyon bighorn monitoring effort. Funding was provided by Morris Animal Foundation grant D13ZO-081 and Mon- tana University System Research Initiative: 51040-MUSRI2015-03. K.R.M. was supported through a Penn State Academic Computing Fel- lowship. R.K.P. was supported by National Institutes of Health IDeA Program grants P20GM103474 and P30GM110732, and P. Thye. 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Fig. S3. Node strength and degree for each population-year. Fig. S4. Relationship between interaction rate and eigencentrality. Table S1. Fits to logistic growth curve in degree accumulation for dams, dry ewes and yearlings. Table S2. Generalized additive model fits for moving average of degree in each demographic groups. Table S3. Contact rate estimates for dyads of various demographic groups. Table S4. Models and information criteria for models fit without lambs born to 2-year-old dams. Table S5. Coefficient estimates from the top model in a fit that excludes 2-year-old ewes.