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 (rs) 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 km2commercial beef cow-calf operation managed at commercial production levels with an average standing inventory of ~3,000 head of cattle. In FL, we deployed GPS collars (Catlog GPS device and Lotek LMRT3 VHF Collars, Lotek ©, 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 ~52 km2 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 km2. 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 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 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 (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 road, ditch, fence , and food , indicating whether a grid cell includes a road, ditch, fence, or cattle supplement. Similarly, in TX we included binary variables for road andtrail (e.g., 2-track road) , and the food layer represents the availability of pig baits. The variable 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 |r| ≥ 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.