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.