Introduction
Spatial and social behavior of the hosts play major role in shaping patterns of pathogen spread in animal populations (Albery et al., 2021; Altizer et al., 2003; Dougherty et al., 2018; Sah et al., 2018). Host movements define the spatial dimension of pathogen transmission while social structure determines the encounter rate between infected and susceptible individuals. In social systems with stable group membership, individual contacts and pathogen transmission occur mainly within social groups, potentially limiting the speed of disease spread (Pepin et al., 2020). This type of social structure is typically based on familial (e.g. matrilineal) groups where social interactions and disease transmission rates are correlated with genetic relatedness (Carter et al., 2013; Grear et al., 2010). In contrast, in fission-fusion societies with dynamic group membership, contact rates and disease transmission tend to be independent of relatedness (Hirsch et al., 2013; Mejía-Salazar et al., 2017; Vander Wal et al., 2012). Social system can thus affect the rate and mechanisms of pathogen transmission (Altizer et al., 2003; Sah et al., 2018). Contact heterogeneity due to social structure may be particularly relevant for disease transmission at a local scale, where groups are already in spatial proximity allowing for contact. Understanding relative contribution of spatial and social process to disease transmission at such scales is important for modeling and managing wildlife diseases (Dougherty et al., 2018; Pepin et al., 2021). However, investigating the role of host social and spatial behavior in disease transmission is challenging, ideally requiring simultaneous host contact, movement and infection data and studies addressing this issue with relevant data are limited. Additionally, host social system and pathogen characteristics (e.g. infectiousness, transmission mode, lethality) interact to produce varying spatial infection patterns and epidemiological outcomes (Pepin and VerCauteren, 2016; VanderWaal and Ezenwa, 2016).
African swine fever (ASF) is a contagious viral disease with both direct and environmental transmission in Eurasian wild boar Sus scrofawhich is the sole reservoir of the disease in Eastern Europe (Chenais et al. 2018). The ASF virus strain currently circulating in Eastern Europe (genotype II) is highly virulent, causing lethality approaching 100% within 1-3 weeks post-infection (Blome et al., 2013) and the disease causes high mortality in the susceptible host populations which can reduce wild boar numbers by as much as 90% during the initial phase of an outbreak (Morelle et al., 2020). ASF virus is resistant to environmental factors and can remain active in contaminated tissues from several weeks to months (Fischer et al., 2020). Transmission through infected carcasses has been estimated to account for about a half of all infections and contribute to long-term persistence of the disease particularly at low host densities (Pepin et al., 2020). Because diseased animals tend to die locally (i.e. within their home range), they will be a source of infections mainly for the most proximate individuals from their own or neighboring social groups. However, transmission rates via indirect and direct (i.e. through social interactions) routes will probably differ due to varying contact dynamics and long availability of infectious carcasses (Cukor et al., 2020; Probst et al., 2017; Probst et al., 2020). Thus, carcass-based transmission interacts with direct transmission to shape local infection patterns (Lange and Thulke, 2016). The spread of infectious diseases occurs over multiple spatial scales (Riley, 2007). On the landscape level, ASF prevalence and spread correlate positively with wild boar density (Nurmoja et al., 2017; Podgórski et al., 2020), proportion of forest cover (Dellicour et al., 2020; Podgórski et al., 2020) and negatively with distance to previous cases (Podgórski et al., 2020) and physical barriers to wild boar movement (Dellicour et al., 2020). At fine scales, ASF transmission is likely influenced by a combination of social interactions, movements, and spatial distribution of individuals (Pepin et al., 2021).
Wild boar social structure is based on cohesive, matrilineal social units (Gabor et al., 1999; Kaminski et al., 2005; Podgórski et al., 2014b). Contact rates are strongly structured socially and spatially (Pepin et al., 2016; Podgórski et al., 2018; Yang et al., 2020). The rate of inter-group interactions is relatively low and declines sharply with distance between the groups. The highest contact rates are between immediately adjacent groups (0-2 km) and drop to very low levels at a distance as close as 4 km. Such type of social structure is not conducive to rapid spread of infectious diseases (Pepin and VerCauteren, 2016). Social behavior of wild boar, next to its sedentary lifestyle, is probably one of the factors responsible for slow natural spread of ASF in wild boar populations. While wild boar movements were shown to be poor predictors of ASF spread (Podgórski and Śmietanka, 2018), the role of genetic relatedness as a predictor of social interaction rates has received little attention as a potential driver of ASF transmission. A recent model of ASF transmission in wild boar highlighted a significant role of social structure in shaping spatial and temporal dynamics of ASF spread and showed that most transmission events occurred within family groups and within close distance < 1.5 km (Pepin et al., 2021). However, real time infection and contact tracing data that could validate predictions from models of surveillance data are notoriously difficult to obtain from field studies and no such data exist for the wild boar - ASF system. Here we use genetic relatedness as a proxy of social interactions as those two have been shown to correlate in the kin-based wild boar society (Podgórski et al., 2014b). Kinship has been shown to predict infection risk in other wildlife disease systems, e.g. chronic wasting disease in white-tailed deer (Grear et al., 2010) or bovine tuberculosis in badgers (Benton et al., 2016).
Previous studies have shown that probability of ASF occurrence in wild boar populations increases with proximity to previous cases at a coarse spatial scale (> 10 kilometers) (Podgórski et al., 2020), while transmission rates appear to be the highest at fine scales (< 2 kilometers (Pepin et al., 2021). Here, we investigate whether distance-dependent infection risk is influenced by genetic relatedness at a local spatial scale where relatedness might influence contact structure and thus impact disease transmission. Because genetic relatedness and distance between hosts are correlated, we designed the analysis to examine the role of genetic relatedness among hosts within fixed spatial distances among hosts (‘proximity’), with the proximity categories based on previous work of wild boar spatial contact ecology (see below). We hypothesized that the infection risk would correlate positively with proximity (H1, Table 1) and relatedness (H2, Table 1) to ASF-positive individuals. We expected relationships of infection risk and proximity or genetic relatedness to become weaker with increasing distance between individuals due to decay in contact rates (Pepin et al., 2016; Podgórski et al., 2018) and genetic similarity (Podgórski et al., 2014a; Poteaux et al., 2009) (H3, Table 1). Based on current knowledge of wild boar socio-spatial ecology, we analysed infection risk in four distance classes: 1) ’high-contact’ zone (0-2 km): social contacts among individuals are most frequent, both within and between groups (Podgórski et al., 2018; Yang et al., 2020) , 2) ’medium-contact’ zone (2-5 km): interactions among neighbouring social groups (Pepin et al., 2016; Podgórski et al., 2018) , 3) ’low-contact’ zone (5-10 km): sporadic contacts between distant groups with non-overlapping home ranges, distance of most natal dispersal (Keuling et al., 2010; Podgórski et al., 2014a; Prévot and Licoppe, 2013), 4) ’no-contact’ zone (>10 km): groups do not interact, occasional long-distance movements (Andrzejewski and Jezierski, 1978; Podgórski et al., 2014a). We predicted that variation in infection risk would be explained by relatedness and proximity to infected individuals in high-contact and medium-contact zones (P3.1). In the low-contact zone infection risk would be predicted by the distance to infectees but not by relatedness to them (P3.2.). In the no-contact zone, neither relatedness nor proximity would shape infection risk (P3.3).
Table 1. Hypotheses and corresponding predictions on relationships between proximity and relatedness to ASF-positive individuals and infection risk.