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\begin{document}

\title{Individual-level patterns of resource selection do not predict hotspots
of contact}



\author[1]{Anni Yang}%
\author[2]{Raoul Boughton}%
\author[3]{Ryan  Miller}%
\author[4]{Nathan P. Snow}%
\author[4]{Kurt Vercauteren}%
\author[4]{Kim Pepin}%
\author[5]{George Wittemyer}%
\affil[1]{University of Oklahoma}%
\affil[2]{The Mosaic Company}%
\affil[3]{USDA APHIS VS}%
\affil[4]{USDA-APHIS-WS National Wildlife Research Center}%
\affil[5]{Colorado State University}%


\vspace{-1em}



  
  \date{May 17, 2024}


\begingroup
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\selectlanguage{english}
\begin{abstract}
Contact among animals is crucial for various ecological processes,
including social behaviors, disease transmission, and predator-prey
interactions. However, the distribution of contact events across time
and space is heterogeneous, influenced by environmental factors and
biological purposes. Previous studies have assumed that areas with
abundant resources and preferred habitats attract more individuals and,
therefore, lead to more contact. To examine the accuracy of this
assumption, we used a use-available framework to identify landscape
factors influencing contact locations. Our study focused on two wild pig
populations in Florida and Texas, USA. We employed a contact-resource
selection function (RSF) model, where contact locations were defined as
used points and locations without contact as available points. By
comparing the contact RSF with a population-level RSF, we assessed the
factors driving both habitat selection and contact. We found that the
landscape predictors (e.g., wetland, linear features, and food
resources) played different roles in habitat selection and contact
processes for wild pigs in both study areas. This indicates that pigs
interacted with their landscapes differently when choosing habitats
compared to when they encountered other individuals. Consequently,
relying solely on the spatial overlap of individual or population-level
RSF models may lead to a misleading understanding of contact-related
ecology. Our findings challenge prevailing assumptions about contact and
introduce innovative approaches to better understand the ecological
drivers of spatially explicit contact. By accurately predicting the
spatial distribution of contact events, we can enhance our understanding
of ecological processes and their spatial dynamics.%
\end{abstract}%



\sloppy


\textbf{Individual-level patterns of resource selection do not predict
hotspots of contact}

\textbf{Abstract}

Contact among animals is crucial for various ecological processes,
including social behaviors, disease transmission, and predator-prey
interactions. However, the distribution of contact events across time
and space is heterogeneous, influenced by environmental factors and
biological purposes. Previous studies have assumed that areas with
abundant resources and preferred habitats attract more individuals and,
therefore, lead to more contact. To examine the accuracy of this
assumption, we used a use-available framework to identify landscape
factors influencing contact locations. We employed a contact-resource
selection function (RSF) model, where contact locations were defined as
used points and locations without contact as available points. By
comparing the contact RSF with a population-level RSF, we assessed the
factors driving both habitat selection and contact. We found that the
landscape predictors (e.g., wetland, linear features, and food
resources) played different roles in habitat selection and contact
processes for wild pigs in both study areas. This indicates that pigs
interacted with their landscapes differently when choosing habitats
compared to when they encountered other individuals. Consequently,
relying solely on the spatial overlap of individual or population-level
RSF models may lead to a misleading understanding of contact-related
ecology. Our findings challenge prevailing assumptions about contact and
introduce innovative approaches to better understand the ecological
drivers of spatially explicit contact. By accurately predicting the
spatial distribution of contact events, we can enhance our understanding
of ecological processes and their spatial dynamics.

\textbf{Keywords:} resource-driven contact, resource selection, spatial
contact, wild pigs



\textbf{1 Introduction}

The spatial distribution of direct contact among animals (co-location of
animals at the same time) greatly influences the dynamics of various
ecological processes, such as disease transmission, social organization,
and human-wildlife conflict (Meijaard et al. 2011, Kurvers et al. 2014,
Craft 2015). The spatial distributions of contacts across landscapes are
often heterogeneous due to various factors, including animal movement
patterns (e.g., migration, speed), social behaviors (e.g., mating,
territoriality, fission-fusion dynamics), densities of animals, and
external factors such as food availability and predators (Kareiva and
Odell 1987, Spiegel et al. 2017). Assessing drivers of such
heterogeneity is key for mechanistic understanding of contact-driven
ecological processes.

While social factors like group membership can influence contact
structure and heterogeneity (i.e., animals within the same social groups
tend to stay together, resulting in a higher contact rate) (Silk et al.
2019), landscape features like the distribution of resources also impact
contact patterns (McClure et al. 2022). Areas with abundant resources or
preferred habitats may attract more animals, including from different
social groups, structuring contacts. Exploring the role of the landscape
in structuring contacts can facilitate understanding of the spatial
distributions and extents of contact based ecological processes and
enable the prediction of contacts or contact-driven ecological processes
on the landscape.

Frameworks to investigate environmental drivers of contacts and
probabilistically predict their distributions are limited. Koen et al.
(2017) adopted social network analysis to estimate the effects of
landscape connectivity on contact rates by comparing contact rates based
on broad-scale environmental conditions. In addition, some spatial
models (e.g., conditional autoregressive model) have also been applied
to quantify the effects of different factors on the spatial distribution
of contact rates (Yang et al. 2021a). However, these approaches have not
explicitly compared the spatial conditions where contacts occurred to
those where contacts could have occurred. As such, these approaches did
not estimate and disentangle how different landscape features drive
probability of contact occurrence across the landscape. Building off the
assumption that contacts differentially occur at the places with
abundant resources or preferred habitat, previous studies have used the
spatial overlap of individual or population-level habitat selection as a
proxy for contact probability (Habib et al. 2011). However, it remains
unclear whether patterns of resource selection can accurately predict
hotspots of contact.

In this study, we developed a method to quantify landscape factors
influential to contact locations. We applied the method to movement data
from two wild pig (\emph{Sus scrofa} ) populations in dissimilar
landscapes, exemplifying how our approach can identify geographical
attributes associated with contact events. We use our framework to test
the hypothesis that the landscape variables that drive contacts are the
same as those that drive individual-level space use, as has been assumed
in previous applications of resource selection analysis to contact
behavior. Consequently, we assess the utility of aggregating individual
resource selection functions to predict the spatial properties of
contact behavior. We discuss the implications of this work for
understanding contact dynamics in wild pig systems.

\textbf{2 Methods and Materials}

2.1 Modeling the resource-driven contacts and comparing with
individual-level resource selection

To test the hypothesis that the landscape features driving contacts are
the same as those that drive individual-level space use, we apply a
resource selection functions (RSF) framework to contact locations of
animal pairs (hereafter: contact-RSF model) and compared it with the
habitat selection RSF of the individual animals involved in a contact
pair (individual-RSF model).

2.1.1 individual-RSF model for contact pairs

Since the habitat selection of contact pairs are considered as the
reference to compare with to test our hypothesis, we first developed an
individual-level RSF model for all contact pairs and aggregated them to
estimate the spatial distributions of their habitat selection. Before
the development of individual-RSF models, we subsampled movement data
for each contact pair so as to only include the time period when both
animals were tracked, as an animal may change the area used over time.
We then combined the subsampled movement data for individuals involved
in each contact pair. This allowed time matched comparison with the
contact-RSF model predictions aggregated by all contact pairs (Figure
S1).

We applied the used-available framework, as described in Manly et al.
(2007), to develop individual-RSF models for each individual involved in
each contact pair. Specifically, the available area is defined as the
95\% home range for each individual in the contact pair for the period
when movement data collection overlapped with another in the pair (i.e.,
individual availability at pair level, e.g., HR1-2, HR1-3 in Figure 1).
We defined used points as the interpolated GPS fixes and generated 30
random (available) points per used point within the individual's home
range (Northrup et al. 2013).

For the development of RSFs, we conducted logistic regressions (Manly et
al. 2007), and implemented a model selection procedure to evaluate
candidate models using the cumulative log-likelihoods for all animals to
calculate Akaike information criteria (AIC) and select the most
parsimonious model (Burnham and Anderson 2004; Bastille-Rousseau and
Wittemyer 2019). We then calculated the population average and
confidence intervals of coefficients by weighting both the number of
times that each individual is detected in a unique contact pair and
their sample size following Murtaugh (2007). We assessed the predictive
power of the top-selected model by performing a 5-fold cross validation
to calculate the Spearman rank correlation coefficient
(r\textsubscript{s}) for each individual. We withheld 20\% of the data,
assessed the model fit, and repeated the process five times for each
individual (Boyce et al. 2002). Development of the individual RSF models
was conducted using ``IndRSA'' R-package (Bastille-Rousseau and
Wittemyer 2019).

2.1.2 pair-level contact-RSF model

To estimate the RSF for contact, we adopted a similar use-available
framework but define the location of contact for each pair of
individuals as the used points. We define the available area for contact
as the home range overlaps between a pair during an overlapping tracking
period. The available points in contact-RSF model are defined as the GPS
fixes that were not contact locations within the home range overlaps.
For pairs that have numerous available points, a subsample of available
points was randomly selected to limit the used: available points ratio
to 1:30. We used logistic regression for each contact pair to compare
the landscape features at the location of contact with those at
locations that animals used without contact.

To test the hypothesis that contact RSFs are similar to individual-level
RSFs, we assume the individual-RSF model is the null hypothesis and
reference to be compared to. Thus, we fit the logistic model for
contact-RSF using the same model structure as that of the top-selected
individual-RSF model. Similarly, we follow Murtaugh (2007) to aggregate
the pair-wise contact-RSF models to a weighted population average to
predict the spatial distribution of the occurrence of the
resource-driven contact for the animal population on the landscape.
Finally, we compare the weighted population-level contact-RSF prediction
and the weighted population-level individual RSF to examine whether
predictions of resource-based contact are equivalent to individual-RSF
patterns.

2.2 Case studies

We implemented the framework of modeling the resource-driven contacts
and comparing with individual-level resource selection in two wild pig
populations in two different ecosystems. Wild pigs, are a socially
structured species that maintain matrilineal, multigenerational social
groups of female adults with their offspring (Podgorski et al. 2014).
These groups spend most of their time moving together as a unit. Male
adults often move alone but join female groups for short periods. Based
on these behaviors, we examined contact between individuals from
different family groups or between adult males and females because these
events are distinct and rarer compared to the many within group contacts
and potentially independent of individual-level resource selection.

2.2.1 GPS Data

Our case study includes two sites, one on the Archbold's Biological
Station - Buck Island Ranch (ABIR) in Florida and one on a private
ranchland in north-central Texas. ABIR is a 42.3
km\textsuperscript{2}commercial beef cow-calf operation managed at
commercial production levels with an average standing inventory of
\textasciitilde{}3,000 head of cattle. In FL, we deployed GPS collars
(Catlog GPS device and Lotek LMRT3 VHF Collars, Lotek \selectlanguage{ngerman}©, WA, US) on 17
adult wild pigs (12 females and 5 males) from Dec 13, 2019 -- July 13,
2020. During the capture, we intended to cover most social groups of the
wild pigs across the focal pastures (given pre-collaring camera survey)
and avoid deploying multiple collars in the same social group. Such
study design aimed to measure between-group contacts to understand
potential disease transmission in the population (Yang et al. 2021a).
Collars were programmed to record GPS fixes every 10 minutes with
locational errors of 6 -- 10 meters on average. In coordination with
these collar deployments, anthropogenic cattle feed and water troughs
within the study pastures were mapped and time available recorded.

The Texas site is \textasciitilde{}52 km\textsuperscript{2} and located
within Southwest Plateau and Plains Dry Steppe and Shrub ecoregion of
North America, with vegetation communities dominated by a mosaic of
wheat croplands, grasslands, mesquite, and oak woodlands (Bailey 1998).
We deployed GPS satellite-transmitting collars (VERTEX PLUS-2 Collar,
VECTRONIC Aerospace GmbH, Berlin, Germany) to 36 adult wild pigs (22
females and 14 males) during Jan 2018. We programmed the GPS collars to
record locations every 15 min from Jan 28 -- Feb 24, with locational
errors of 5--10 m. The study was initially designed to estimate the
efficacy of toxic baiting on controlling wild pigs, therefore we
deployed bait sites targeting the collared animals. Starting on Feb 13,
the baiting was commenced with whole-kernel corn at a maximum baiting
density of 1 bait site per 0.75 × 0.75 km\textsuperscript{2}. This grid
size was selected to expose 90 -- 100\% of wild pigs to bait within the
study area. Given the behavioral and movement changes of wild pigs after
exposed to toxicants, we only used data collected during the period
before deploying toxicants. See further details about study site and
design in Texas in Snow and VerCauteren 2019.

2.2.2 Environmental variables

The environmental factors that we tested related to contact occurrence
in two wild pig populations are presented in Table 1. For both study
sites, the \emph{wetland} variable is a binary layer, with freshwater
emergent wetland and woody wetland classified as 1, and all other land
cover types classified as 0. Similarly, the \emph{water} variable is a
binary layer, with freshwater pond, riverine, lake, and water trough
classified as 1, and all other land cover types classified as 0.
Vegetation greenness was measured using the daily Normalized Difference
Vegetation Index (\emph{NDVI} ) accessed from NASA's MODIS MOD09GA
product. Tree canopy cover and daily meteorological measurements were
accessed from the U.S. Forest Service tree canopy cover product and
GridMET, respectively. In the FL site, we also included binary variables
for \emph{road, ditch, fence} , and \emph{food} , indicating whether a
grid cell includes a road, ditch, fence, or cattle supplement.
Similarly, in TX we included binary variables for \emph{road}
and\emph{trail (e.g., 2-track road)} , and the \emph{food} layer
represents the availability of pig baits. The variable \emph{cattle} in
the FL site measures the daily cattle density on each pasture. All
environmental layers were calculated or resampled to 30 * 30-meter
grids.

2.2.3 Extract resource selection of contacts based on a continuous-time
movement model of GPS data

We converted the movement data from both study areas to continuous-time
movement trajectories discretized to 5-min intervals using the ``ctmm''
R-package (Calabrese et al. 2016). The 5-min time frame was chosen based
on evidence that wild pigs have small home ranges and low rates of daily
movement (Kay et al. 2017). We followed Yang et al. (2023) to define
direct contact as the colocation of two individuals at the same time
with a spatial buffer of 10 meters to consider the GPS locational
errors.

Because wild pigs in the same group do not move independently, we
expected that contact locations among them would track their movement
patterns and thus not test our hypothesis. Since only adult wild pigs
were studied, we assumed that all female-male and male-male contacts to
be between-group contacts which may be driven by reproductive processes
and landscape features. For female-female contacts, we examined the
weekly home range core area overlap using kernel density home range
estimator to exclude pairs that were in the same family group. We
assumed that pairs with core area overlaps less than 0.5 over the
tracking period were between-group pairs, while pairs with core area
overlaps greater than 0.5 were considered within-group pairs. For pairs
with core area overlaps over 0.5 for part of the subsequential tracking
period (e.g., more than 12 weeks, a season), we assumed that the pairs
were temporarily in the same social group. We included all pairs or
timeframes of pairs (female-female, female-male, and male-male) that
were considered separate groups at each site in the following analyses.

2.2.4 Application of contact-RSF modeling framework and comparison with
individual-RSF

To ensure reliable statistical inference, we only included the wild pig
pairs with more than 10 direct contacts over the time period that
movement data from each individual overlapped. Following the modeling
framework, we first fit the individual-RSF models for each individual in
the contact pairs. The available areas were defined as the 95\% home
range estimated by the kernel density estimator. The used and available
points were defined as CTMM interpolated points and randomly generated
points at a ratio of 30 per used points, respectively., We screened
environmental variables for multicollinearity (Pearson's correlation
coefficient \textbar{}r\textbar{}\selectlanguage{english} \selectlanguage{english}[?]\selectlanguage{english} 0.6) and standardized the continuous
variables (i.e., NDVI, tmax, tmin, vp, rhum, prcp, and tree canopy)
using the scale function in R (R Core Team 2023), before the development
of logistic regression for individual-RSF models. For contact-RSF
models, we defined contact locations as used points and available points
as the CTMM interpolated movement locations that were not the contact
locations within the home range overlaps. We then followed the modeling
framework described above to implement analyses for modeling, scaling
up, and validating both individual- and contact-RSFs in the empirical
systems.

\textbf{3 Results}

Overall, our findings in two wild pig populations suggested that the
landscape predictors (e.g., wetland, linear features, and food
resources) played different roles in habitat selection and contact
processes. The spatial overlap of individuals' habitat selection does
not adequately represent the spatial distribution of contacts across the
landscape.

3.1 Female-female pairs

In the FL site, models for female wild pig pairs showed selection for
wetland, ditches, and water and avoidance of fences and roads (Figure
2). The top-selected individual-RSF model (Model 1.1 in Table S1) showed
wetland areas were the most strongly selected of the resources assessed.
Their contacts tended to happen less at food resources, suggesting use
of food resources was subject to spatial or temporal segregation.
Contacts occurred more frequently along linear features (ditches and
fences) and in pastures with higher cattle density relative to their
selection of these landscape features (Figure 3).

In the TX site, female wild pig pairs selected for water and bait sites
and avoided wetlands and roads (Model 1.1 in Table S2; Figure 2), which
resulted in strong habitat selection for riverine areas and low
selection along primary roads (Figure 4). In contrast, their contact
locations occurred more at bait sites and less at wetland and water
areas, relative to their selection for these features.

3.2 Female-male pairs

In the FL site, individuals involved in female-male contacts selected
for areas with dense tree canopy, wetlands, and locations along ditches
and roads, but avoided areas with cattle supplements (Model 2.1 in Table
S1; Figure 2). Prediction of the individual-RSF model across the
landscape showed a high probability of selection for these individuals
in wetland areas. Similar to female-female contacts, female-male
contacts tended to occur along linear features such as ditches and
roads. However, these contacts tended not to be cattle supplements.

In the TX site, individuals involved in female-male contacts selected
areas near wetlands, water, and with dense tree canopy and bait
availability, but avoided primary and secondary roads (Model 2.1 in
Table S2; Figure 2), which was reflected in the prediction map (Figure
3). Contact between female-male pairs was more likely to occur in areas
with high NDVI, along trails, and at bait sites.

3.3 Male-male pairs

In the FL site, male wild pigs selected wetland and high tree canopy
areas and avoided cattle supplements (Model 3.1 in Table S1; Figure 2).
However, their contacts happened more than expected along fences but
less at places with high NDVI and cattle supplements, as shown in Figure
2 and 3. In the TX site, males selected water areas and bait piles for
resources and avoided roads. For male-male contact in TX, the
individual-RSF was a relatively good approximation of contact RSF, but
males tended to make contact at bait sites more than habitat selection
and less at roads (Figure 2 and 3).

\textbf{4 Discussion}

Understanding the temporal and spatial processes driving contact among
animals can provide insight to factors structuring a myriad of
contact-based ecological processes. To this point, assessment of contact
structure has generally relied on simple assumptions (e.g., high habitat
resource selection area) that are often not tested. Here we assessed how
well the spatial distribution of contacts matches that of general space
use. Our findings in two wild pig populations highlight that the spatial
overlap of individuals (population-level RSF model outputs) does not
adequately represent the spatial distribution of contacts across the
landscape, which challenges the accuracy of predictions of contact rates
and spatial transmission dynamics if applied to landscapes outside the
study areas. Specifically, landscape features that drive contact between
individuals were generally different from those that drive
individual-level space-use (Figure S2 and S3) in two different systems.
This suggests that some factors related differently upon the way wild
pigs' interface with their landscapes in their daily lives compared to
how they interact with conspecifics in the areas they inhabit.

\emph{Overlap of habitat selection does not predict spatial
distributions of contact}

By comparing the contact-RSF model with a paired (overlap)
individual-RSF model, we were able to reject our null hypothesis and
found that landscape features impacted habitat selection and contact
differently. These results indicate that interactions among unmeasured
factors such as social behaviors, resource value and competition
structure contact and conspecific interactions in more complex ways than
purely selection of habitat.

The mismatch between habitat selection behavior and the spatial
distribution of contacts has ramifications for our interpretation of
contact based ecological processes. In disease systems, accurately
predicting the spatial distribution of host contacts is crucial for
assessing the risk of spillovers and estimating disease spread. Previous
research often used the distribution of host population habitat
selection as a proxy for contact to estimate transmission hotspots. For
example, in the chronic wasting disease system, overlapping areas that
are shared by deer are considered high probability areas for contact and
disease transmission, and were used to inform disease control and
surveillance priorities (Walter et al. 2011). In predator-prey systems,
the prediction of predation risk for prey species, such as the
probability of encounter, attack, and kill, is often estimated based on
the perception of risk by prey in their resource selection or by
directly assessing landscape characteristics where predation occurs
(Atwood et al. 2009). However, because landscape features may influence
habitat selection and contact differently, estimation of spatial
features influencing contact would be more accurate if done directly.

\emph{System specific biology}

In the wild pig system, pigs exhibited a higher probability of contact
at locations along certain linear landscape features, such as fences,
trails, and ditches, compared to their resource selection patterns. It
appeared that pigs used fence lines, trails and ditches as corridors to
transit between habitats, leading to encounters. In addition to transit
corridors, some large ditches in the FL site may contain water after
major rainfall events, thus providing attractive water resources for
wild pigs. Several previous studies have suggested preferences for
resources closer to linear features by pigs, such as power lines (Clontz
et al. 2021) or agricultural edges (Snow et al. 2017), indicating use of
these areas may be resource driven. Similar findings in other systems
indicate early-seral vegetation on linear features like seismic lines,
pipelines and industrial access roads provide forage for herbivore
ungulates, resulting in increased interspecific contact and competition
between these species (MacDonald et al. 2020).

Differences between contact- and individual-RSF were also found in
relation to strongly preferred landscape features including wetlands,
water, and food resources (concordant with previous work (Thurfjell et
al. 2009, Paolini et al. 2018)). Contacts, however, occurred at wetland
habitats and water less than expected by chance. In the study systems,
animals displayed a tendency to segregate their use of preferred
resources, suggesting a potential dominance or monopolization of these
resources. This observation implies a resource despotic pattern rather
than a free distribution (Harper 1982).

Supplemental feeding can provide wildlife with an abundant and
predictable food source on the landscape, leading to changes in their
foraging behaviors and population aggregation at the feeding sites
(Becker et al. 2018). Thus, supplemental food can often facilitate
direct contacts among individuals with implications for disease
transmission and other ecological interactions. In the TX sites, bait
piles significantly attracted wild pigs (Kilgo et al. 2021). In contrast
to cattle supplements in FL, contact locations were also biased towards
pig bait in TX indicating that these management techniques could affect
contact driven ecological processes, like disease transmission (Yang et
al. 2021b). In this case, baiting was used as an attractant for removal
strategies, which may reduce disease concerns.

\emph{Potential trade-offs of our contact-RSF model and limitations in
wild pig systems}

Similar to habitat selection, which often varies across different
populations, landscapes, and years because of spatial heterogeneity and
inter-annual changes in environmental suitability (Boyce 1979), a
particular landscape feature may also affect contact in different ways
across different ecosystems. In the FL site, we found selection was
strong for wetlands, but contact was depressed relative to use. However,
in the TX site, female wild pigs tended to avoid wetlands, and wetlands
were neither selected for nor avoided by males. Also, wetlands did not
have a significant impact on female-male and male-male contacts. This is
likely due to the spatial representation of wetlands in this ecosystem
of limited small, seasonal, or ephemeral wetlands (Bailey 1998). Similar
differences in findings of population, landscape, and seasonal-specific
resource selection have been suggested in other species (McCorquodale
2003, Bastille\selectlanguage{english}-Rousseau and Wittemyer 2019). Thus, scaling up local
contact-RSFs to other landscapes or larger populations requires careful
consideration of landscape-specific factors that may affect both
resource selection and contact patterns.

Challenges to modeling habitat selection with use-available frameworks
are well known, particularly around the definition of availability
(Keating and Cherry 2004, Johnson et al. 2006). Such issues also impact
interpretation of outputs from our application of RSFs to contacts.
Here, availability was defined as the areas where theoretical contacts
between two individuals could occur (i.e., the home range overlap during
the overlapped tracking period), which is different from the
availability sample used in the individual-RSFs (i.e., the home range
during the overlapped tracking period). There are alternative ways to
define the availability in the contact-RSF, such as defining it as the
union of home ranges. This would enable a scale-wise comparison between
contact-RSF and individual-RSF, but logistically, contact could only
occur in the home range overlap areas. Investigating how different
used-available designs impact contact-RSF interpretation will require
further research.

In empirical systems, one limitation is that some between-group contact
events might have resulted from mating behaviors rather than resource
acquisition. The primary mating season in the FL site seemed to be in
August, which is not covered in our study period (Buckley 2021). In TX,
some female wild pigs appeared to be farrowing during the period of this
study (N. P. Snow, Personal observation), and subsequently may have
isolated themselves from conspecifics during that time. Given that there
is a lack of information on timing of reproduction, it was not feasible
for us to filter out interactions driven by mating behaviors in the case
studies.

\textbf{5 Conclusions}

Accurate prediction of the spatial distribution of contacts is valuable
for understanding various ecological processes, including disease
systems and predator-prey encounters. Our contact-RSF model employs a
used-available framework, using contact locations as ''used'' points and
resource select locations as ''available'' points. This approach enables
us to compare the landscape characteristics at the site of contact with
those more generally used. Furthermore, it allows us to assess the
utility of predicting contact from habitat selection. Our method,
therefore, can identify multiple aspects of the spatial ecology of
contact-related processes and disentangle their environmental drivers,
which opens a door to mechanistic understanding of contact ecology.

\textbf{Conflict of Interest statement}

All authors declare no conflict of interest.

\textbf{Data Availability Statement}

Data and example codes are available at GitHub.

\textbf{Reference:}

Atwood, T. C. et al. 2009. Spatial partitioning of predation risk in a
multiple predator\selectlanguage{english}-multiple prey system. - The Journal of Wildlife
Management 73: 876--884.

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Table 1. Descriptions and sources of environmental variables used in
resource selection models.\selectlanguage{english}
\begin{longtable}[]{@{}lll@{}}
\toprule
Variables & Descriptions & Sources\tabularnewline
\midrule
\endhead
\emph{Dynamic daily variables} & \emph{Dynamic daily variables} &
\emph{Dynamic daily variables}\tabularnewline
tmax & Daily maximum temperature & GridMET\tabularnewline
tmin & Daily minimum temperature &\tabularnewline
trange & Daily difference of temperature &\tabularnewline
vp & Daily vaper pressure &\tabularnewline
prcp & Daily precipitation &\tabularnewline
rhum & Daily average relative humidity &\tabularnewline
NDVI & Daily normalized difference vegetation index & MODIS MOD09GA
products\tabularnewline
food & Daily binary layer of artificial food availability (i.e., cattle
supplements in the FL site, pig baits in TX sites) & Field
records\tabularnewline
\emph{Static variables} & \emph{Static variables} & \emph{Static
variables}\tabularnewline
water & Binary layer of surface water body (i.e., freshwater pond) and
large ditches that might hold water after rainfalls & Field data and
National Wetlands Inventory\tabularnewline
wetland & Binary layer of wetland land cover &\tabularnewline
road & Binary layer of road & Field data and Texas Department of
Transportation\tabularnewline
fence (FL only) & Binary layer of fence &\tabularnewline
ditch (FL only) & Binary layer of ditches &\tabularnewline
tree canopy & Percentage of tree canopy & NLCD\tabularnewline
trail (TX only) & Binary layer of trails & Digitalized based on
satellite image\tabularnewline
\bottomrule
\end{longtable}\selectlanguage{english}
\begin{figure}[H]
\begin{center}
\includegraphics[width=0.70\columnwidth]{figures/image1/image1}

\end{center}
\end{figure}

Figure 1. Schematics of individual and contact RSF model approaches\selectlanguage{english}
\begin{figure}[H]
\begin{center}
\includegraphics[width=0.70\columnwidth]{figures/image2/image2}

\end{center}
\end{figure}

Figure 2. Weighted population-level coefficients of individual and
contact RSF models.\selectlanguage{english}
\begin{figure}[H]
\begin{center}
\includegraphics[width=0.70\columnwidth]{figures/image3/image3}

\end{center}
\end{figure}

Figure 3. Spatial prediction of population-level resource selection and
contact in FL site. A) habitat selection for FF pairs; B) contact for FF
pairs; C) habitat selection for FM pairs; D) contact for FM pairs; E)
habitat selection for MM pairs; F) contact for MM pairs\selectlanguage{english}
\begin{figure}[H]
\begin{center}
\includegraphics[width=0.70\columnwidth]{figures/image4/image4}

\end{center}
\end{figure}

Figure 4. Spatial prediction of population-level resource selection and
contact in TX site. A) habitat selection for FF pairs; B) contact for FF
pairs; C) habitat selection for FM pairs; D) contact for FM pairs; E)
habitat selection for MM pairs; F) contact for MM pairs

\selectlanguage{english}
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\end{document}

