Buffalo close association matrices: Kruger National Park, South Africa

Many infectious pathogens are shared through social interactions, and examining host connectivity has offered valuable insights for understanding patterns of pathogen transmission across wildlife species. African buffalo are social ungulates and important reservoirs of directly-transmitted pathogens that impact numerous wildlife and livestock species.  

Here, we analyzed African buffalo social networks to quantify variation in close contacts, examined drivers of contact heterogeneity, and investigated how the observed contact patterns affect pathogen invasion likelihoods for a wild social ungulate.  

We collected continuous association data using proximity collars and sampled host traits approximately every two months during a 15-month study period in Kruger National Park, South Africa.  

Although the observed herd was well connected, with most individuals contacting each other during each bimonthly interval, our analyses revealed striking heterogeneity in close-contact associations among herd members. Network analysis showed that individual connectivity was stable over time and that individual age, sex, reproductive status, and pairwise genetic relatedness were important predictors of buffalo connectivity. Calves were the most connected members of the herd, and adult males were the least connected. These findings highlight the role susceptible calves may play in the transmission of pathogens within the herd. We also demonstrate that, at time scales relevant to infectious pathogens found in nature, the observed level of connectivity affects pathogen invasion likelihoods for a wide range of infectious periods and transmissibilities.  

Ultimately, our study identifies key predictors of social connectivity in a social ungulate and illustrates how contact heterogeneity, even within a highly connected herd, can shape pathogen invasion likelihoods.  

Methods

Study site and population 

Kruger National Park (KNP) spans nearly 19,485km2 (22.5° - 25.5°S, 31.0° - 31.57°E) and hosts a diversity of wildlife, including wild African buffalo. Our study population included a wild buffalo herd, which was captured in Northern KNP during the early 2000's and relocated to a 900-hectare enclosure in the center of the park, near Satara camp (Fig. S1). The buffalo sample size varied during our study (N = 60–70) due to births and deaths. On average, 25.60 ± 0.10 % of the herd were calves, 22.95 ± 0.09 % were juveniles and 51.45 ± 0.19 % were adults. Buffalo were free to graze and breed in the enclosure, and during extreme droughts, they had access to supplemental grass hay. Water was available to buffalo at a natural pan and a manmade water point (Fig. S1). This “nearly natural” enclosure included numerous other species typical of the ecosystem (e.g., giraffe, zebra, warthogs, and small predators), while excluding megaherbivores (e.g., rhinos, elephants) and large predators (e.g., lions, leopards). 

Buffalo captures and sedation procedures 

Data collection spanned six observation periods (OPs) that occurred during March 2014 through May 2015 (See Rushmore et al 2023, Table S1). We captured buffalo to collect biological data and download association data from proximity-logging collars at the end of each OP. Captures occurred five times per year, at two to three-month intervals. We performed three active captures, in which buffalo were darted from a helicopter, and four passive captures during the dry season in which researchers filled man-made water troughs that attracted buffalo into a fenced area with a remote-controlled gate closure (See Rushmore et al 2023, Fig. S1). At each capture, we darted small groups of buffalo using chemical immobilization procedures described by Couch et al. (2017).  

Data collection: behavioral association data   

In February 2014, we fitted the majority of buffalo aged over 6 months with Sirtrack proximity-logging collars (Sirtrack Tracking Solutions, Havelock North, New Zealand), which record the identity of collars in close proximity in addition to the date, time, and duration of each encounter. Percentage coverage across the herd is provided in Table 1 of Supplemental information. Calves < 6 months in age were not collared for ethical considerations, as their growth rate exceeded collar re-fitting schedules. We programmed collars with a UHF range coefficient of 20 and a separation time of 240s, which in a laboratory setting initiated an association when collars were within 1.22m ± 0.46m (mean ± SD), and terminated the association when collars exceeded a distance of 1.70m ± 0.67m for more than 240s. We deemed this a reasonable representation of transmission distances for pathogens spread via close contact (e.g., respiratory viruses and bacteria) (Wells 1934, Olsen et al. 2003). While proximity collars allowed for near-complete data collection without observation bias, we note that close contacts were inferred from data on physical proximity (Farine 2015, Farine and Whitehead 2015) rather than directly-observed interactions.  

Estimating association indices and social networks  

We analyzed association data for 69 buffalo (males = 22, females = 47), including 18 calves ( < 1.5y). We created a matrix of pairwise association indices for each of the six observation periods (OPs). The number of buffalo varied across OPs due to births, deaths, and collar malfunctions. Overall, matrices included an average of 39.33 buffalo (± SD: 14.11) and ranged from 17–53 buffalo (Table S1).  

When cleaning association data, we excluded capture days and a two-day buffer after each intervention, and we removed 1s encounters shown to skew results (Drewe et al. 2012). To reduce asymmetries in association matrices, we excluded encounters logged after one individual in a pair had a full proximity logger memory. For each pair of individuals i and j, we calculated an association index (AI) for a given OP as follows:  

???? = ??? / (??? + ???) 

in which Cij refers to the summed duration (hours) of association logged between individuals i and j during the observation period, and Nij refers to the total hours during the observation period in which i and j were not in contact. Thus Tij, in which Tij = ??? + ???, refers to the total hours during the observation period in which individuals i and j could associate with each other (i.e., both had collars with available memory to record data). Because data were collected continuously by proximity collars with little to no observation bias or missing groups within the herd (Davis et al. 2018), we used the simple ratio index of proportion of time a dyad spent in close proximity (Farine and Whitehead 2015) with the resultant index being a value between 0 and 1 that indicates the proportion of time a dyad spent in close proximity. These indices were used to create adjacency matrices.  

When recording association data, ideally both collars in a pair would record identical information; however, previous studies demonstrate that collars vary in their abilities to transfer information (Drewe et al. 2012; Boyland et al. 2013). We reduced inter-collar variation biases, specifically conflation of individual ID and error/strength of proximity collar, using a method similar to that proposed by Boyland et al. (2013). In brief, we assessed variation in reciprocal AIs in a given matrix to evaluate each collar’s relative performance and to develop a measure of collar bias. We then corrected matrix AIs by scaling each collar’s data according to its average bias across collars, resulting in a nearly identical pre- and post-correction average AI for the matrix. Further details about collar corrections are provided in the Supplementary Information for the associated manuscript, Rushmore et al. 2023.