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\begin{document}

\title{How do genetic relatedness and spatial proximity shape African swine
fever infections in wild boar?}



\author[1]{Tomasz Podgórski}%
\author[2]{Kim Pepin}%
\author[3]{Anna Radko}%
\author[3]{Angelika Podbielska}%
\author[4]{Magdalena Łyjak}%
\author[5]{Grzegorz  Wozniakowski}%
\affil[1]{Instytut Biologii Ssakow Polskiej Akademii Nauk}%
\affil[2]{USDA-APHIS National Wildlife Research Center}%
\affil[3]{Instytut Zootechniki Panstwowy Instytut Badawczy}%
\affil[4]{Panstwowy Instytut Weterynaryjny - Panstwowy Instytut Badawczy w Pulawach}%
\affil[5]{Uniwersytet Mikolaja Kopernika w Toruniu Wydzial Filozofii i Nauk Spolecznych}%


\vspace{-1em}



  
  \date{May 19, 2024}


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\selectlanguage{english}
\begin{abstract}
The importance of social and spatial structuring of wildlife populations
for disease spread, though widely recognized, is still poorly understood
in many host-pathogen systems. In particular, system specific kin
relationships among hosts can create contact heterogeneities and
differential disease transmission rates. Here, we investigate how
distance-dependent infection risk is influenced by genetic relatedness
in a novel wild boar ( \emph{Sus scrofa}) - African swine fever (ASF)
system. We hypothesized that the infection risk would correlate
positively with proximity and relatedness to ASF-infected individuals
but expected those relationships to weaken with distance between
individuals due to decay in contact rates and genetic similarity. ASF
infection risk was shaped by the number of infected animals throughout
the zone of potential contact (0-10 km) but not beyond it. This effect
was the strongest at close distances (0-2 km) and weakened further on
(2-10 km), consistent with decreasing probability of contact. Overall,
there was a positive association between genetic relatedness to
infectees and infection risk within the contact zone but this effect
varied in space. In the high-contact zone (0-2 km), infection risk was
not influenced by relatedness when controlled for the number of
ASF-positive animals. However, infections were more frequent among close
relatives indicating that familial relationships could have played a
role in ASF transmission. In the medium-contact zone (2-5 km), infection
risk and frequency of paired infections were associated with
relatedness. Relatedness did not predict infection risk in low- and
no-contact zones (5-10 and \textgreater{}10 km, respectively). Together,
our results indicate that the number of nearby infected individuals
overrides the effect of relatedness in shaping ASF transmission rates
which nevertheless can be higher among close relatives. Highly localized
transmission highlights the possibility to control the disease if
containment measures are employed quickly and efficiently.%
\end{abstract}%



\sloppy


\textbf{How do genetic relatedness and spatial proximity shape African
swine fever infections in wild boar?}

Tomasz Podg\selectlanguage{ngerman}órski\textsuperscript{1,2, *}, Kim M.
Pepin\textsuperscript{3}, Anna Radko\textsuperscript{4}, Angelika
Podbielska\textsuperscript{4}, Magdalena \selectlanguage{polish}Ł\selectlanguage{english}yjak\textsuperscript{5},
Grzegorz Wo\selectlanguage{polish}ź\selectlanguage{english}niakowski\textsuperscript{6}

\textsuperscript{1} Mammal Research Institute, Polish Academy of
Sciences, Stoczek 1, 17-230 Bia\selectlanguage{polish}ł\selectlanguage{english}owie\selectlanguage{polish}ż\selectlanguage{english}a, Poland

\textsuperscript{2} Department of Game Management and Wildlife Biology,
Faculty of Forestry and Wood Sciences, Czech University of Life
Sciences, Kam\selectlanguage{ngerman}ýcká 129, 165 00~Praha 6, Czech Republic

\textsuperscript{3}National Wildlife Research Center, USDA, APHIS,
Wildlife Services, 4101 Laporte Ave., Fort Collins, CO, 80526

\textsuperscript{4}Department of Animal Molecular Biology, National
Research Institute of Animal Production, Krakowska 1, 32-083 Balice,
Poland

\textsuperscript{5}Department of Swine Diseases, National Veterinary
Research Institute, Partyzantów 57, 24-100 Pulawy, Poland

\textsuperscript{6}Deparment of Diagnosis and Clinical Sciences, Faculty
of Biological and Veterinary Sciences, Nicolaus Copernicus University,
Lwowska 1,87-100 Torun, Poland

*Corresponding author: Tomasz Podgórski, Department of Game Management
and Wildlife Biology, Faculty of Forestry and Wood Sciences, Czech
University of Life Sciences, Kamýcká 129, 165 00~Praha 6, Czech
Republic;

podgorski@fld.czu.cz

\textbf{Summary}

The importance of social and spatial structuring of wildlife populations
for disease spread, though widely recognized, is still poorly understood
in many host-pathogen systems. In particular, system specific kin
relationships among hosts can create contact heterogeneities and
differential disease transmission rates. Here, we investigate how
distance-dependent infection risk is influenced by genetic relatedness
in a novel wild boar (\emph{Sus scrofa} ) - African swine fever (ASF)
system. We hypothesized that the infection risk would correlate
positively with proximity and relatedness to ASF-infected individuals
but expected those relationships to weaken with distance between
individuals due to decay in contact rates and genetic similarity.

ASF infection risk was shaped by the number of infected animals
throughout the zone of potential contact (0-10 km) but not beyond it.
This effect was the strongest at close distances (0-2 km) and weakened
further on (2-10 km), consistent with decreasing probability of contact.
Overall, there was a positive association between genetic relatedness to
infectees and infection risk within the contact zone but this effect
varied in space. In the high-contact zone (0-2 km), infection risk was
not influenced by relatedness when controlled for the number of
ASF-positive animals. However, infections were more frequent among close
relatives indicating that familial relationships could have played a
role in ASF transmission. In the medium-contact zone (2-5 km), infection
risk and frequency of paired infections were associated with
relatedness. Relatedness did not predict infection risk in low- and
no-contact zones (5-10 and \textgreater{}10 km, respectively). Together,
our results indicate that the number of nearby infected individuals
overrides the effect of relatedness in shaping ASF transmission rates
which nevertheless can be higher among close relatives. Highly localized
transmission highlights the possibility to control the disease if
containment measures are employed quickly and efficiently.

Key words: ASF, disease ecology, kinship, social behavior, transmission,
wild boar

\textbf{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 \emph{Sus
scrofa}which 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 \selectlanguage{polish}Ś\selectlanguage{english}mietanka, 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 \textless{} 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 (Podg\selectlanguage{ngerman}órski 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 (\textgreater{} 10 kilometers) (Podgórski et al., 2020),
while transmission rates appear to be the highest at fine scales
(\textless{} 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
(\textgreater{}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.\selectlanguage{english}
\begin{longtable}[]{@{}ll@{}}
\toprule
\begin{minipage}[b]{0.47\columnwidth}\raggedright\strut
Hypotheses\strut
\end{minipage} & \begin{minipage}[b]{0.47\columnwidth}\raggedright\strut
Predictions\strut
\end{minipage}\tabularnewline
\midrule
\endhead
\begin{minipage}[t]{0.48\columnwidth}\raggedright\strut
H1. Infection risk increases with proximity to infected individuals due
to higher chances of contact\strut
\end{minipage} & \begin{minipage}[t]{0.48\columnwidth}\raggedright\strut
P1.1: positive and strongest effect of the proportion of infected
individuals in the 'high-contact' (0-2km) P1.2: positive but weaker
effects of the proportion of infected individuals in 'medium-contact'
(2-5km) and 'low-contact' (5-10km) zones P1.3: no effect of the
proportion of infected individuals in the 'no-contact'
(\textgreater{}10km) zone\strut
\end{minipage}\tabularnewline
\begin{minipage}[t]{0.48\columnwidth}\raggedright\strut
H2. Infection risk increases with relatedness to infected individuals
due to kin-based social structure\strut
\end{minipage} & \begin{minipage}[t]{0.48\columnwidth}\raggedright\strut
P2.1: positive effect of relatedness to infected individuals across
contact zones (0-10km) P2.2: no effect of relatedness to infected
individuals in the 'no-contact' zone (\textgreater{}10km)\strut
\end{minipage}\tabularnewline
\begin{minipage}[t]{0.48\columnwidth}\raggedright\strut
H3. Effect of relatedness on infection risk will decline with distance
to infected individuals due to social structure and spatial
clustering\strut
\end{minipage} & \begin{minipage}[t]{0.48\columnwidth}\raggedright\strut
P3.1: positive effect of relatedness and proportion of infected
individuals in the 'high-contact' and potentially 'medium-contact' zones
P3.2: positive effect of the proportion of infected individuals and no
effect of relatedness in the 'low-contact' zone P3.3 no effects of
relatedness and proportion of infected individuals in the 'no-contact'
zone\strut
\end{minipage}\tabularnewline
\bottomrule
\end{longtable}

\textbf{Materials and methods}

\textbf{Study area and sample collection}

The study was conducted in north-eastern Poland where ASF was introduced
in February 2014, \textless{}1 km from the border with Belarus, and has
subsequently spread through the region in wild boar (Podg\selectlanguage{ngerman}órski et al.,
2020) and domestic pigs (Taylor et al., 2021). By mid 2016, ASF cases in
wild boar were distributed continuously in the infected region covering
about 4500 km\textsuperscript{2} and this is where samples used in this
study originate (Fig. 1). The area is mostly field-woodland mosaic
characterized by extensive agriculture and low human population density
(60 people/km\textsuperscript{2}) and relatively high forest cover
(31\%). The landscape is flat (highest elevation 298 ms a.s.l.) with no
significant natural or anthropogenic barriers to wild boar movement.
Wild boar are distributed continually throughout the area with densities
at the start of the ASF epidemic ranging between 0.5 and 5
ind./km\textsuperscript{2} at the forest district level (Regional
Directorate of State Forests, Bia\selectlanguage{polish}ł\selectlanguage{english}ystok, Poland).

Following the introduction of ASF, an intensive surveillance program was
implemented in the affected area. The program conducted laboratory tests
of all hunted wild boar (active surveillance) and all wild boar found
dead (passive surveillance). We used surveillance data routinely
collected by the National Reference Laboratory for ASF at the National
Veterinary Research Institute in Pu\selectlanguage{polish}ł\selectlanguage{english}awy, Poland. Wild boar samples were
classified as ASF-positive (herefater ''case'') if presence of viral DNA
was confirmed in real-time PCR or antibodies were detected using ELISA
test and confirmed with a immunoperoxidase test (IPT). Detailed
description of the surveillance design and laboratory procedures can be
found in (Wo\selectlanguage{polish}ź\selectlanguage{english}niakowski et al., 2016). A total of 5487 wild boars were
sampled in the study area from February 2014 to July 2016 and 168 of
them tested positive for ASF. It was not technically possible to
genotype all of the sampled individuals. Instead, we selected a
subsample of 1078 individuals (including all ASF-positive) evenly
distributed across the study area for further analyses. However, many
samples, particularly originating from the carcasses, were of poor
quality and yielded too few microsatellite loci (those yielding less
than 13 out of 16 loci were excluded). Finally, we were able to
satisfactorily genotype 323 samples (including 80 ASF cases) which
comprised a final dataset.

\textbf{Microsatellite genotyping}

DNA was extracted from tissue and blood using the Sherlock AX Kit (A\&A
Biotechnology, Gdynia, Poland), following the manufacturer's protocol.
The extracts were quantified with a NanoDrop 2000 spectrophotometer
(Thermo Scientific, Wilmington, DE, USA). All individuals were genotyped
with a panel of 16 polymorphic microsatellite loci (S090, SW72, S155,
S026, S355, S215, SW951, SW857, SW24, SW122, IGF1, SW461, SW1492,
SW2021, SW2496, SW2532), which had been successfully used to study
relatedness and genetic variation in wild boar populations from the
study area (Podg\selectlanguage{ngerman}órski et al., 2014a; Podgórski et al., 2014b). The 16
autosomal STR loci were amplified in two independent multiplexed mixes
using Qiagen Master Mix (Qiagen Inc, Hilden, Germany) reagents and
fluorescently label primers. For the first panel, the fluorochromes were
used: 6-FAM for the loci SW72, SW857; VIC for SW1492, IGF1; NED for
S026, S215; PET for SW2021, SW2532 and for second panel 6-FAM for SW122,
S0355 ; VIC for SW461, SW2496; NED for W24, S0155 and PET for SW951 and
S0090. PCR was performed on Veriti® Thermal Cycler amplifier (Applied
Biosystems, Foster City, CA, USA) using the following thermal profile: 5
min. of initial DNA denaturation at 95°C, followed by 35 cycles of
denaturation at 95°C for 30 s, annealing at 56°C for 90 s, elongation of
starters at 72°C for 30 s and final elongation of starters at 60°C for
30 min. Analysis of the obtained PCR products was performed using an ABI
3130\emph{xl} capillary sequencer \emph{(} Applied Biosystems, Foster
City, CA, USA) at the Institute of National Research Institute of Animal
Production (Cracow, Poland). The amplified DNA fragments were subjected
to electrophoresis in 7\% denaturing POP-7 polyacrylamide gel in the
presence of a standard length of 500 Liz and a reference sample. The
results of the electrophoretic separation were analysed automatically
using the \emph{GeneMapper ® Software 4.0 (} Applied Biosystems, Foster
City, CA, USA).

\textbf{Analysis of relatedness and spatial-genetic structure}

Basic parameters of microsatellite polymorphism and genetic diversity
were calculated using GENALEX 6.5 (Peakall \& Smouse, 2006) and FSTAT
(Goudet, 1995). GENEPOP 4.7 (Raymond \& Rousset, 1995) was used to test
loci for departures from linkage equilibrium and Hardy--Weinberg
equilibrium (HWE) using the Markov chain method (parameters: 1000
dememorization steps, 100 batches, 1000 iterations per batch). The
significance level was adjusted for multiple testing across loci using
the sequential Bonferroni's correction (Rice, 1989).

Pairwise genetic relatedness (Queller and Goodnight, 1989) was
calculated with GENALEX 6.5 among all individuals (n = 323) and
subsequently used in all analyses. The Queller and Goodnight estimator
is an unbiased measure of relatedness based on population allele
frequencies. It ranges from -1 to 1, where positive values indicate
pairs more related than average and negative less related than average.
We used the Queller and Goodnight estimator to allow comparison with
previous studies of wild boar social structure which we build upon
(Podgórski et al., 2014a) . We used a dyadic relatedness as continuous
predictor in models explaining variation in infection probability. We
also dychotomized highly related (hereafter ''kin'') and unrelated
(herefater ''non-kin'') pairs of individuals using previously published
mean relatedness between social group members of 0.247 (Podgórski et
al., 2014a). Analysis of spatial-genetic structure was performed with
GENALEX 6.5. Autocorrelation coefficients (\emph{r} ) between pairwise
genetic and geographic distance matrices were calculated for pre-defined
Euclidean distance classes (Supplementary Information: Fig. S1) which
correspond to spatial genetic structure previously observed in wild boar
populations (Pepin et al., 2016; Podgórski et al., 2014a; Podgórski et
al., 2018; Poteaux et al., 2009). Spatial-genetic structure was examined
among all individuals as well as among ASF-positive and ASF-negative
individuals separately. Spatial autocorrelation coefficients were
compared across distance classes using randomization tests (10 000
permutations) (Manly 1997). When estimating probability of infection and
comparing relatedness within distance classes, we merged the 10+ km
classes into one due to weak genetic structure between pairs and
negligible contact rates beyond 10 km. We compared relatedness of
individuals with paired infections (pairs where both individuals were
infected) and with no paired infection (none or one individual of the
pair were infected) within the same distance classes as in models
estimating probability of infection (0-2 km, 2-5 km, 5-10 km,
\textgreater{}10 km) and using the same temporal windows of potential
transmission to select pairs for comparison. Relatedness levels were
compared using randomization tests (10 000 permutations) (Manly 1997).

\textbf{Estimating probability of infection}

For every sampled individual (n = 323), we selected ASF-positive
individuals that could have been the source of infection using a
temporal window (30 days) that reflects the upper limit of lifespan
after ASF infection, i.e. infection-to-death time (Gallardo et al.,
2017; Pietschmann et al., 2015). For samples originating from carcasses,
the window was set to 60 days prior to sampling and included
infection-to-death time and carcass decomposition time during which the
sample was taken. A decomposition time of 30 days was chosen arbitrarily
because the decomposition status of most carcasses was unknown and the
exact date of death could not be determined. This time frame was
conservative given usually longer decomposition times of wild boar
tissues (Probst et al., 2020) and ASF virus persistence in them (Fischer
et al., 2020). Additionally, the temporal window was extended by 7 days
post-sampling of the focal individual to account for the retrieval and
reporting lag of the ASF-positives. ASF-positive individuals selected
within those time frames were then categorized into four distance
classes (0-2 km, 2-5 km, 5-10 km, \textgreater{}10 km). The proportion
of ASF cases in each distance class were used as covariates explaining
the binary response of ASF infection status of the focal individual (0 -
negative, 1- positive).

We analysed the effects of explanatory variables on the probability of
ASF infection using a generalized linear model with a binomial response
and ''logit'' link function (Zuur, 2009). The probability of ASF
infection reflects the likelihood of any sampled individual from the
study area testing positive for ASF within the 2.5 years of the study
period based on the covariates. Proportion of ASF-positive individuals
within the four aforementioned distance classes and relatedness to
ASF-positive individuals within 0-10 km was used as covariates to verify
the predictions (Table 1). Sample sizes across covariate categories were
unbalanced and precluded formulation of a global model including all
explanatory variables. Therefore, the structure of the models was
developed progressively, starting with simple formulations where each
model included just one covariate explaining infection risk
(Supplementary Information: Table S1). We then contrasted, in four
separate models (Table 2), information on relatedness to ASF-positive
individuals within 0-10 km with the proportion of ASF-positive
individuals in each of the four distance classes. This approach allowed
us to test the relative effect of relatedness and spatial proximity to
infectees on infection risk while accounting for the greater number of
highly related individuals at close distances resulting from wild boar
social structure. Explanatory variables did not show significant
collinearity (variance inflation factors - VIFs \textless{} 1.3 for all
models). We did not perform model selection for reduced models within
each hypothesis as we were interested in testing specific predictions
represented by each of the candidate explanatory variables in our full
models and we could not compare across models because the response
variable was different for each model. We quantified variation explained
by the fixed effects of the model by calculating marginal\emph{R}
\textsuperscript{2}\textsubscript{GLMM} (Nakagawa et al., 2017) using
the MuMIn R-package (Barto\selectlanguage{polish}ń \selectlanguage{english}2020). All statistical analyses were
performed in R 4.0.2 (R Core Team 2020). The models were computed using
the ''lme4'' package (Bates et al. 2015).

\textbf{Results}

\textbf{Spatial-genetic structure and relatedness}

In total, 135 alleles were detected across 16 analysed loci. All loci
were polymorphic with the number of alleles per locus ranging from 3 to
18 (mean \selectlanguage{ngerman}± SE: 8.4 ± 0.94). Missing data (i.e. \% of missing alleles)
amounted to 2.4 \% of the dataset and no individual was typed at less
than 13 loci. Expected and observed heterozygosity averaged 0.61 ± 0.06
and 0.56 ± 0.06, respectively. Following sequential Bonferroni's
corrections, the overall population showed deviation from HWE at eight
loci and from linkage equilibrium in 2 out of 120 pairs of loci. Such
deviation from equilibrium was most likely attributed to the inherent
substructure of the population (i.e. presence of kin groups) and all
loci were retained for statistical analyses.

Spatial autocorrelation analysis revealed the presence of genetic
structure, as indicated by an autocorrelation coefficient (\emph{r)}
greater than at random, at a distance of up to 20 km at the population
level and up to 10 km among ASF-positive individuals, with genetic
similarity between individuals declining from the smallest distance
(Fig. S1). Genetic structuring was stronger among ASF-positive
individuals compared to ASF-negative ones within each distance class up
to 10 km (0-2.5 km distance class: \emph{r\textsubscript{pos}} = 0.078 ±
0.003 and \emph{r\textsubscript{neg}} = 0.033 ± 0.001, p \textless{}
0.001; 2.5-5 km distance class: \emph{r\textsubscript{pos}} = 0.028 ±
0.002 and \emph{r\textsubscript{neg}} = 0.015 ± 0.001, p \textless{}
0.001; 5-10 km distance class: \emph{r\textsubscript{pos}} = 0.019 ±
0.001 and \emph{r\textsubscript{neg}} = 0.011 ± 0.0004, p \textless{}
0.001; Fig. S1).

Considering the entire study area, the coefficient of relatedness in the
population averaged 0.0003 ± 0.002 and relatedness among individuals
with paired infections was much higher than among those without paired
infections (0.049 ± 0.009 and -0.003 ± 0.002, respectively,
p\textless{}0.001). The differences were influenced by the distance
between pairs (Fig. 2). Relatedness among animals with paired infections
was highest in the 0-2 km and 2-5 km distance classes (0.129 ± 0.054 and
0.129 ± 0.055, respectively) and lower in the subsequent classes (5-10
km: 0.018 ± 0.017; \textgreater{}10 km: 0.042 ± 0.009). Relatedness
between individuals with paired infections was consistently higher than
among those without paired infections, except within the 5-10 km
distance class (Fig. 2).

\textbf{Probability of infection}

We found a positive relationship between the probability of ASF
infection and the proportion of infected individuals in high-, medium-,
and low-contact zones (0-2km, 2-5km, and 5-10 km, respectively)
(Supplementary Information, Table S1, Fig. S2). The strongest effect of
the proportion of infectees was observed in the high-contact zone, where
infection risk increased from 2.5 \% if no infected individuals were
present to 96 \% if all other individuals were ASF-positive. Proportion
of infected individuals at distances over 10 km did not influence the
probability of infection (Table S1, Fig. S2). Relatedness was a positive
predictor of infection risk within 10 km but had no effect at larger
distances (Table S1, Fig. S2). Probability of infection among kin (i.e.
presumably group members) across the study area was on average 77 ±
1.5\% (min-max: 69-87) within 10 km, while infection risk among non-kin
averaged 52 ± 0.9\% (min-max: 28-68) within 10km (Fig. S2a). Models
combining relatedness and proximity to infected individuals (Table 2)
showed that the relative contribution of relatedness and proportion of
infected individuals to explaining variation in infection probability
varied with distance. In the high-contact zone, variation in infection
risk was shaped overwhelmingly by the proportion of infected individuals
and not by relatedness (Table 2, Fig 3). However, infection risk among
kin within 2 km distance was high. All of the animals which were
surrounded by ASF-positive kin (n = 17) tested positive, while none of
the animals surrounded by ASF-negative kin (n = 7) tested positive.
Among animals surrounded only by infected non-kin between 0 and 2 km (n
= 18), the proportion of ASF-positive individuals was 83\%, indicating
they were 17\% less likely to become infected by non-kin relative to kin
on average. At further distances of 2-5 km (medium-contact zone)
infection risk increased with both relatedness and proportion of
ASF-positive individuals at this distance (Table 2, Fig.3), indicating
that ASF transmission was significantly more likely between related
individuals. In the medium-contact zone, infection risk averaged 84 ±
3.5\% (min-max: 58-93) among kin and 47 ± 2.5\% (min-max: 16-81) among
non-kin. At even further distances of 5-10 km (low-contact zone),
probability of infection increased with the number of ASF-positive
individuals while relatedness to infectees did not shape infection risk.
Here, infection risk increased from 38 \% if at least one infected
individual was present to 75 \% if all other individuals were
ASF-positive (Fig. 3). At distances \textgreater{}10 km (no-contact
zone), neither relatedness to infected individuals nor their number
influenced infection risk (Table 2, Fig.3).

\textbf{Discussion}

Our results showed that the risk of ASF infection was shaped by
interacting effects of spatial proximity and genetic relatedness to
infected individuals. Proximity and number of infected animals had
consistently positive effect on infection risk throughout the zone of
potential contact (0-10 km) but not beyond it, thus fully supporting H1.
The number of ASF-positive individuals had the strongest effect on
infection risk in the high-contact zone (0-2 km; P1.1), weaker but
similar within medium- (2-5 km) and low-contact (5-10km) zones (P1.2),
and no effect in the no-contact zone (\textgreater{}10 km; P1.3). There
was a positive association between genetic relatedness to infectees and
infection risk within the contact zone (0-10 km), supporting H2, but
this effect varied in space (H3). In the high-contact zone, infection
risk was not influenced by relatedness when controlled for the number of
ASF-positive animals. However, infections were more frequent among close
relatives, i.e. kin or group members, indicating that familial
relationships could have played a significant role in ASF transmission.
In the medium-contact zone, infection risk was associated with
relatedness and paired infections were more frequent among relatives.
For the most part, those results provide support of P3.1. Relatedness
did not predict infection risk in low- and no-contact zones (P3.2 and
P3.3, respectively).

Social contacts directed towards relatives can lead to increased
pathogen transmission within familial groups and local clustering of
disease prevalence (Blanchong et al., 2007; Delahay et al., 2000).
Social relationships in wild boar tend to be the strongest among closely
related group members (Gabor et al., 1999; Podgórski et al., 2018;
Podgórski et al., 2014b). Therefore, we predicted a strong effect of
relatedness on disease transmission at close distances (0-2-km) due to
socially-driven contact heterogeneity. This prediction was not supported
in our regression model possibly because at this distance most
individuals are highly related and there are not enough unrelated
individuals to find statistical significance. However, our descriptive
analysis found that individuals which were infected simultaneously (i.e.
paired infections) tended to be more related than those uninfected. This
trend was particularly noticeable at the upper range of relatedness
distribution, i.e. among close kin or group members. Such a pattern is
consistent with kin-biased associations in wild boar, particularly among
females and young animals, manifested in more regular and longer lasting
contacts (Podgórski et al., 2014b; Poteaux et al., 2009) which
facilitate disease transmission. Similarly, kinship was shown to drive
bovine tuberculosis infections in badger cubs exposed to infectious
females in a natal sett (Benton et al., 2016). Inter-group contacts in
wild boar occur most frequently at a spatial scale similar to
interactions within groups (Podgórski et al., 2018; Yang et al., 2020).
This spatial overlap in social connectivity may confound the effects of
inter- vs. intra-group transmission. Using relatedness as an indicator
of group membership could potentially help untangle those effects.
However, in our study the number of ASF-positive individuals in close
vicinity was a much stronger predictor of infection risk compared to
relatedness. It thus appears that at close distances kin relationships
do not have a strong impact on structuring contacts and shaping ASF
transmission beyond the closest relatives. Our analysis did not
discriminate between infections originating from the contact with
infectious carcass and infected live animals. Therefore, it is possible
that indirect transmission through infected carcasses and contaminated
environment (Chenais et al. 2018) contributed significantly to recorded
infections. In fact, indirect transmission accounts for more than half
of ASF infections in wild boar population (Pepin et al., 2020). Animals
could have come into contact with nearby infected carcass of the
individual which was a member of a different but spatially overlapping
social group, i.e. was not closely related. Abundance of infectious
carcasses in the surrounding environment could thus lead to infection
regardless of relatedness and produce confounding effects that masked
actual transmission contacts among live animals.

Interestingly, infection risk correlated with relatedness in the
medium-contact zone (2-5 km) suggesting that social connectivity with
relatives extended beyond the closest group members. This pattern is
unlikely to have resulted from the dispersing individuals infected in
the natal groups because it typically takes longer to disperse
(Podgórski et al., 2014a) than it takes the disease to hamper movements
(Blome et al., 2013). To alleviate symptoms of the disease, infected
wild boar seek specific habitats which differ from those regularly used
(Morelle et al., 2019). Those preferences and restricted mobility of
sick animals can separate them from the group and result in dispersion
of diseased group members and wide distribution of samples from related
individuals. Additionally, kin-directed interactions over larger
distances could be maintained by temporal fission-fusion events of core
groups, similar to observed in African elephants (Archie et al., 2006)
or giraffes (Carter et al., 2013). These dynamics could provide a
mechanism for disease transmission among distant relatives. However,
fission-fusion dynamics in wild boar has not been systematically studied
and it is difficult to tell whether temporal scales of social and
disease dynamics would match and help explain patterns observed in our
study. Besides relatedness to infected individuals, their number had a
strong impact on infection risk within the medium-contact zone which
indicates that contacts between groups and/or with infected carcasses
played an important role in transmission.

Relatedness did not play a major role in shaping ASF infections beyond 5
km distance. In the low-contact zone (5-10 km), infection risk was
influenced only by the number of infected individuals. It is unlikely
that direct transmission played a significant role in producing this
effect since inter-group contacts are very rare at those distances
(Pepin et al., 2016; Podgórski et al., 2018). While a distance of 5-10
km exceeds the size of typical home range, it is within a range of daily
travel (Podgorski et al., 2013) and could be covered during dispersal,
foraging or mating forays leading to distant transmission events. Those
behaviors, however, are not typically seeking contact with kin and
particularly in case of mating or dispersal are often seeking non-kin to
avoid inbreeding (Archie et al., 2007; Biosa et al., 2015; Hoffman et
al., 2007). The observed effect of distant infected individuals on
infection risk could be also a by-product of correlated local enzootic
dynamics (i.e. spatial and temporal co-occurrence of cases) rather than
direct transmission. Spatial clusters of increased ASF prevalence
identified previously were measured at 20-40km (Podgórski et al., 2020;
Taylor et al., 2021). However, those studies used data aggregated over
time periods (months-years) exceeding temporal windows of ASF
transmission (days-weeks) and thus could not capture fine-scale disease
dynamics well. Our results indicate that ASF outbreaks are even more
localized and with realistic transmission times do not exceed 10 km
distance. This is supported by strong genetic structuring among infected
animals (Fig. S2) and no effect of the number of infected individuals
located at \textgreater{}10km on infection risk (Table 2, Fig.3). The
spatial limits of transmission highlight the possibility to control
outbreaks if containment measures, such as fencing, zoning and carcass
removal, are employed immediately and early detection is ensured by
effective surveillance.

Together our results show that ASF infection risk declines with
distance, matching spatial changes in contact intensity.
Infection-causing contacts were structured by relatedness particularly
in medium- and, to lesser degree, high-contact zones. At close distances
infections were more frequent only among close kin while at medium
distances relatedness predicted infections risk more consistently. This
indicates that physical kin relationships can extend beyond the
immediate social environment and induce differential transmission rates,
similarly to transmission of chronic wasting disease in white-tailed
deer (Grear et al., 2010). However, infection risk was primarily
influenced by the number of infected individuals throughout the high-,
medium-, and low-contact zones. This effect was particularly strong at
close distances conforming to the previous modeling study which found
that most transmission events occur within \textless{}1.5 km with some
rare events at longer distances (Pepin et al., 2021). Spatially limited
transmission is further supported by an insignificant effect of the
number of infected individuals present at long distances (\textgreater{}
10 km).

\textbf{Acknowledgements}

This work was supported by the National Science Centre, Poland (grant
number 2014/15/B/NZ9/01933) and Ministry of Agriculture, Czech Republic
(grant number

QK1910462). We thank M. Niedzia\selectlanguage{polish}ł\selectlanguage{english}kowska from the Mammal Research
Institute, Polish Academy of Sciences, for help in genetic analyses.

\textbf{Conflict of interests}

Authors declare no conflict of interest.

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Table 2. Coefficients of the models explaining variation in ASF
infection risk. Each model is formulated to test predictions of the
hypothesis H3 outlined in Table 1 (model 1 and 2: P3.1; model 3: P3.2;
model 4: P3.3.\selectlanguage{english}
\begin{longtable}[]{@{}llllll@{}}
\toprule
Model & N & Coefficient & Estimate (95\% CI) & p-value &
\emph{R\textsuperscript{2}}\tabularnewline
\midrule
\endhead
1 & 55 & Intercept & -3.57 (-6.65; -1.72) & 0.004 & 0.76\tabularnewline
& & Proportion of infected 0-2 km & 6.80 (4.03; 11.30) &
\textless{}0.001 &\tabularnewline
& & Relatedness to infected \textless{}10 km & -0.94 (-8.81; 9.00) &
0.823 &\tabularnewline
2 & 63 & Intercept & -0.76 (-1.59; -0.3\textsuperscript{-4}) & 0.058 &
0.27\tabularnewline
& & Proportion of infected 2-5 km & 1.29 (0.08; 2.55) & 0.039
&\tabularnewline
& & Relatedness to infected \textless{}10 km & 4.16 (0.33; 8.64) & 0.046
&\tabularnewline
3 & 96 & Intercept & -1.13 (-1.99; -0.33) & 0.007 & 0.16\tabularnewline
& & Proportion of infected 5-10 km & 2.01 (0.75; 3.36) & 0.002
&\tabularnewline
& & Relatedness to infected \textless{}10 km & 2.13 (-1.11; 5.51) &
0.201 &\tabularnewline
4 & 276 & Intercept & -1.56 (-2.05; -1.08) & \textless{}0.001 &
0.03\tabularnewline
& & Proportion of infected \textgreater{}10 km & 1.50 (-0.25; 3.23) &
0.085 &\tabularnewline
& & Relatedness to infected \textgreater{}10 km & 1.91 (-0.92; 4.79) &
0.187 &\tabularnewline
\bottomrule
\end{longtable}\selectlanguage{english}
\begin{figure}[H]
\begin{center}
\includegraphics[width=0.70\columnwidth]{figures/image1/image1}

\end{center}
\end{figure}

Fig. 1. Distribution of wild boar samples used in the study.\selectlanguage{english}
\begin{figure}[H]
\begin{center}
\includegraphics[width=0.70\columnwidth]{figures/image2/image2}

\end{center}
\end{figure}

Fig. 2. Distribution of relatedness values among pairs of individuals
with or without paired ASF infection (both individuals ASF positive vs.
one/none individual ASF positive) across four distance classes (0-2km:
78 dyads; 2-5km: 183 dyads; 5-10km: 511 dyads; \textgreater{}10km: 6339
dyads). The violin plots show smoothed histogram of the data with mean
(circle) and standard error (horizontal line). Statistical significance
of the differences between the means (***p\textless{}0.001,
**p\textless{}0.01, n.s. p\textgreater{}0.05) was obtained with
randomization tests.\selectlanguage{english}
\begin{figure}[H]
\begin{center}
\includegraphics[width=0.70\columnwidth]{figures/image3/image3}

\end{center}
\end{figure}

Fig. 3. Relationship between a) proportion of ASF-infected individuals
and b) relatedness to ASF-infected individuals and ASF infection risk
predicted across three proximity categories varying in frequency of
contact (0-2km: high-contact; 2-5km: medium contact; 5-10km:
low-contact). Solid lines represent the effects estimated with
generalized linear model (Table 2) and dashed lines represent 95\%
confidence limits around the model estimates. Only significant effects
are plotted.

\selectlanguage{english}
\FloatBarrier
\end{document}

