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Article

Spatio-Temporal Overlap of Cattle, Feral Swine, and White-Tailed Deer in North Texas

by
Jacob G. Harvey
1,†,
Aaron B. Norris
1,
John M. Tomeček
2 and
Caitlyn E. Cooper-Norris
1,*
1
Department of Natural Resources Management, Texas Tech University, Lubbock, TX 79409, USA
2
Department of Rangeland, Wildlife, and Fisheries Management, Texas A&M University, College Station, TX 77843, USA
*
Author to whom correspondence should be addressed.
Current address: Barta Brothers Ranch, Eastern Nebraska Research, Extension & Education Center, Bassett, NE 68714, USA.
Sustainability 2025, 17(18), 8354; https://doi.org/10.3390/su17188354
Submission received: 9 July 2025 / Revised: 11 August 2025 / Accepted: 14 September 2025 / Published: 17 September 2025

Abstract

Livestock interactions with wildlife have been a concern for managers historically. Invasive feral swine represent an additional management concern in the realm of resource competition as well as zoonotic disease spread between livestock and wildlife. Our study deployed game cameras on a ranch in the Rolling Plains of North Texas to obtain a better understanding of the possibility of interspecies interactions among cattle, feral swine, and white-tailed deer across spatial, temporal, and seasonal variables. Species’ use of bottomlands, shallow uplands, and deep uplands within the ranch were monitored continuously over the course of a year. Cattle and feral swine exhibited high diel activity overlap with the greatest overlap estimates occurring in bottomlands (Δ = 0.889) and wintertime (Δ = 0.875). Cattle and deer exhibited lower diel overlap (Δ = 0.596–0.836, depending on the season and vegetation type), which could be a sign of niche partitioning between the two ungulates. Image captures and overlap estimates suggest interactions between cattle and the other two species occur less frequently in shallow upland sites relative to the other vegetation types. Though image captures of the three species were 17–69% lower in summer relative to fall, indirect interactions may remain high due to competition for shared resources and greater reliance on watering sites. Results suggest that land managers should focus on bottomland sites for feral swine eradication efforts and as areas of increased contact among species. Results can be used to guide livestock and wildlife management and herd health decisions, which can improve ranch economic, environmental, and social sustainability.

1. Introduction

Pastoralists across the world have historically struggled with interactions between livestock and wildlife, as predation, resource competition, and disease spread all can negatively affect livestock producers and native ecosystems. The diets of most native North American ungulates do not overlap significantly with cattle [1,2], though at certain times of the year species that typically consume greater amounts of browse rely more heavily on herbaceous vegetation [3,4]. However, because of shared habitats across various landscapes, resource competition and disease transmission between cattle, native ungulates, and exotic ungulates have become major areas of interest in recent decades [5,6,7,8]. Diseases spread by livestock can harm natural biodiversity and serve as an additional threat to endangered species, and conversely, diseases spread from wildlife to livestock negatively affect agricultural economies and threaten food security [9,10].
In Texas, the cattle industry is the largest in the country, with an estimated 12.2 million head in January 2025 [11]. Similarly, white-tailed deer (Odocoileus virginianus) and feral swine (Sus scrofa) populations are also high in Texas, with an estimated 5.4 and 2.5 million animals, respectively, found throughout the state [12]. Feral swine are reported to consume more grasses and forbs than browse [13,14], and therefore, may have greater dietary overlap with cattle. During times of low forage availability, dietary overlap may be exacerbated. Additionally, there is increasing interest in developing and participating in voluntary programs such as Secure Food Supply Plans and Quality Assurance programs that require livestock herd health records and biosecurity protocols detailing how operations will respond to outbreaks of foreign animal diseases, such as foot-and-mouth disease and others which are transmissible to and from native and exotic wildlife species [15,16,17,18]. Recent concerns about the threat of New World screwworm (Cochliomyia hominivorax) reintroduction to the United States involve both livestock and wildlife [19]. While these insects do not represent a bacterial or viral zoonotic disease threat, host animals for their larvae include wild, exotic, and domestic ungulates [20]. The U.S. cow herd size is currently at a historic low [21], and threats of zoonotic disease spread could destabilize a vulnerable market. Thus, better information about when and where livestock, native wildlife, and exotic species are likely to come in contact is desired.
Due to increasing interest in better understanding interactions between livestock, native wildlife, and exotic wildlife species regarding resource competition and disease transmission, our objectives were to compare the spatial, diel, and seasonal activity of cattle, feral swine, and white-tailed deer in the Texas Rolling Plains to identify periods and locations where species have the greatest probability of encountering one another. Feral swine have not been as extensively researched within the Rolling Plains as other ecoregions within Texas [22], though high numbers of feral swine inhabit the region [23]. Assessments of landscape use of the three species in the Rolling Plains of north Texas will help improve the sustainability of livestock and wildlife management in the region and in similar semiarid areas with a mix of rangeland and cropland vegetation. We expect vegetation types will be a predictor for feral swine encounters with other species as bottomland sites received greater photo captures of swine than the shallow upland sites in a previous study at the site [22].

2. Materials and Methods

2.1. Study Area

We performed the study on a 216,500 ha ranch in Wilbarger County, Texas, USA, located within the Rolling Plains ecoregion of Texas. The climate is characterized by mild, dry winters and hot summers. Mean temperatures range from 3.8 °C in January to 29.1 °C in July with annual precipitation averaging approximately 665 mm. This region was historically warm-season mid-grass dominated with species such as sideoats grama (Bouteloua curtipendula (Michx.) Torr.) and buffalograss (Bouteloua dactyloides (Nutt.) J.T. Columbus) making up the majority of biomass. Due to fire exclusion and overgrazing, much of this region is now dominated by mesquite (Prosopis glandulosa Torr.) savannas. Continuous grazing and low fire frequencies have shifted much of the study site to a plant community characterized by mesquite, Texas wintergrass (Nassella leucotricha (Trin. & Rupr.) Pohl), prickly pear (Opuntia spp.), and dropseed species (Sporobolus spp.) with patches of sod-bound buffalograss in the intensively used areas.
We chose two adjoining pastures for placement of the survey areas. The pastures were approximately 2000 ha each, and the two survey areas were about 405 ha each. The survey areas were located >16 km from the ranch headquarters. Survey areas were grouped into three vegetatively and structurally distinct “vegetation types”: bottomland, deep upland, and shallow upland (Figure 1). For a more detailed description of how the vegetation types were designated and chosen for the study, see Harvey et al. [22]. Bottomland sites were characterized by cottonwood (Populus deltoides W. Bartram ex Marshall)/hackberry (Celtis spp.) woodlands. Shallow upland and deep upland sites consisted of mesquite savannas, with deep upland sites having more and larger mesquite trees, fewer warm-season midgrass species, and greater amounts of Texas wintergrass [22]. At the time of the study, the stocking rate varied between 16–25 ha per animal unit in the 2000 ha pastures containing the survey areas, and the herd primarily consisted of cow-calf pairs in a continuous grazing system. Yearling steers and bulls were moved into the pastures during the dormant season of late fall through early spring. The pastures that contained the survey areas are part of a larger complex designated by the ranch for white-tailed deer and northern bobwhite (Colinus virginianus) (an important game bird species in North America) habitat management and hunting. Though a portion of the ranch has a high fence and contains protein feeders for intensive management of white-tailed deer, there were no high fences or game feeders within the pastures used for this study. The closest planted wildlife food plot was 0.5 km from the border of the surveyed area. Similarly, supplementation of cattle via protein cubes and minerals took place away from camera trap sites. The ranch’s swine management can be characterized by occasional trapping by ranch staff and sport hunts targeting edges of cropland fields and mesquite thickets. We asked ranch staff to keep swine hunts outside of the study pastures for the duration of the study to not cause shifts in feral swine activity. The combination of multiple uses within these survey areas provided a unique opportunity to monitor cattle landscape use in the presence of native wildlife and feral swine.

2.2. Animal Monitoring

We placed game cameras (Bushnell Core DS Low Glow and Aggressor models, Bushnell Performance Optics, Overland Park, KS, USA) randomly across the three vegetation types in the survey areas via stratified random sampling and random point generation within ArcMap (ArcGIS Version 10.7.1; Environmental Systems Research Institute, Inc., 2019; Redlands, CA, USA). Cameras were distributed proportionally by model among the vegetation types in each survey area. Further details regarding camera placement, distances between cameras, and camera deployment dates can be found in Harvey et al. [22]. Camera spacing was not determined due to home range of any of the study species. The vegetation types were the monitoring focus. Due to resource availability, stratified random sampling was chosen over a grid. All camera traps were considered independent because no two cameras were recording the same field of view. This definition of camera trap independence has been used in multiple other studies, with distances between cameras as low as 50 m [24,25,26]. Stock ponds and the stream running through the two pastures were digitized (Figure 1), and the minimum distance between each camera and water was calculated. Bottomland, shallow upland, and deep upland cameras were 80 ± 69, 217 ± 69, and 418 ± 49 m from water, respectively.
Cameras were programmed to record all movement detections 24 h per day. Upon activity detection, cameras were programmed to take a three-photo burst with a five-minute wait period between successive bursts [27]. We checked cameras every 30–45 days to retrieve SD cards and swap batteries. Spacing the camera checks once per month–1.5 months helped to minimize human disturbance of the study area. A 12,000 ha wildfire engulfed the study sites in May 2022 and resulted in the conclusion of the study. For a more detailed description of the camera trap methodologies, image analysis, and rationale see Harvey et al. [22].

2.3. Image Analysis

When a camera captured animal movement, we classified animal(s) in the photo by species with a presence/absence marking (1 or 0) alongside the camera ID, date, time, and temperature (Figure 2). Only cattle, feral swine, and white-tailed deer image captures were analyzed in this study, although other species [e.g., coyote (Canis latrans), wild turkey (Meleagris gallopavo), and javelina (Dicotyles tajacu)] were captured on camera as well. When individuals of multiple species of interest were captured in the same image, the presence of all relevant species were recorded. Two species were documented in the same image on 15 occasions throughout the study (i.e., 3 cattle-swine, 11 cattle-deer, and 1 swine-deer). Specific behaviors in images were not categorized, and individual animals were not identified to provide any estimates of population size, survival, reproduction, or home range as these variables were beyond the scope of this study. Similarly, we did not count the number of individual animals in photos. We only recorded species presence/absence. Duplicate or triplicate images within a three-picture burst were removed to reduce overestimations of species’ presence. Animal observations made ≥5 min apart were considered independent [28,29]. For example, if the same species was documented in images ≥5 min apart, these images were considered to be distinct sightings. Animal capture by camera was used as a sign of vegetation type-association and landscape use around that camera. Image dates were categorized into four three-month periods: summer (June–August 2021), fall (September–November 2021), winter (December 2021–February 2022), and spring (March–May 2022), For each photo, the time stamp was then categorized into one of six four-hour time blocks (0:00–3:59; 4:00–7:59; 8:00–11:59; 12:00–15:59; 16:00–19:59; 20:00–23:59).

2.4. Statistical Analysis

The effects of species, season, time, vegetation type, and their interactions were tested for mean number of image captures per camera using generalized linear mixed models (Proc GLIMMIX, SAS 9.4, SAS Institute Inc., Cary, NC, USA), with individual cameras within pastures considered random effects in the models. We used a negative binomial distribution and the log link function to account for overdispersion in the count data. The denominator degrees of freedom were adjusted with the SATTERTHWAITE option to account for heteroscedasticity, and we used the GROUP option in the RANDOM statement to estimate treatment variances separately. Values were considered significantly different at p ≤ 0.05, and tendencies were assumed at 0.10 ≥ p > 0.05. All significant main effects and interactions were analyzed with Tukey’s honestly significant difference (HSD) post hoc analysis. In all tables containing mean image captures, letters indicating statistical differences reflect differences among means determined using Tukey’s HSD.
We used the coefficient of overlap, Δ4, to compare the overlap of diel activity between cattle and swine, cattle and deer, and deer and swine [30]. We calculated coefficients of overlap using the ‘overlap’ package in R version 4.3.1 [31] and used 1000 bootstrap samples to calculate 95% confidence intervals for the coefficients of overlap [32].

3. Results

3.1. Vegetation Type, Time of Day, and Season Influence Image Captures Across Species

Swine were captured on camera most often in bottomland sites and least often in deep upland sites (p = 0.02; Table 1). Similarly, deer tended to be caught on camera more often in bottomland than deep upland sites (p = 0.07). Cattle were caught on camera more frequently than swine in every vegetation type (p < 0.01) and were caught on camera more frequently than deer in all vegetation types except bottomland (p < 0.01). Deer were captured on camera more often than swine at two of the three vegetation types (p < 0.05) but were observed in similar proportions to swine in bottomland sites.
The average number of captures per camera differed over time for the three species (Table 2). During every four-hour time block, cattle were caught most often on camera, followed by deer, and lastly swine (p < 0.01; Table 2). Most animal images were captured during the 8:00–11:59 period. For all three species, more images were captured during this time period than at 0:00–3:59 (p < 0.05). The least number of cattle images were caught overnight (blocks 20:00–23:59, 0:00–3:59, and 4:00–7:59). Cattle images were low at 0:00–7:59, reached a peak at 8:00–11:59, dipped at 12:00–15:59, rose back up to peak levels at 16:00–19:59, and then decreased back to the lowest levels at 20:00–23:59. Feral swine tended to be active during the same periods of the day as cattle throughout the year (Table 2 and Table 3). Both species exhibited two peaks in activity, with the first occurring mid- to late morning, followed by a dip around noon, and then a second peak in the late afternoon. Specifically, swine were captured on camera less during the 0:00–3:59 and 20:00–23:59 periods than the 8:00–11:59 period (p < 0.05; Table 2). The number of swine image captures was similar during the 4:00–7:59 and 8:00–11:59 periods, while cattle image captures were lower at 4:00–7:59 than 8:00–11:59 (p < 0.01). Deer exhibited crepuscular activity overall, though activity overlap with cattle and swine was still relatively high with overlap estimates of 0.765 and 0.829, respectively (Table 2 and Table 3). The number of deer images was lowest at 0:00–3:59 and reached the greatest quantity at 8:00–11:59 (p < 0.05). Though statistically the number of deer images in four-hour time blocks was similar from the 4:00–7:59 time block to the 20:00–23:59 time block, diel activity curves il-lustrate that deer activity declined to low levels in the late afternoon and then reached a second peak around dusk.
The average number of captures per camera varied seasonally for each species (Table 4). Cattle were captured on camera more often than swine and deer in every season, except summer (p < 0.01; Table 4). The number of deer image captures was greater than the number of swine image captures in every season except winter (p < 0.01). Cattle were captured least frequently in the summer (p < 0. 01). Similarly, there were a greater number of swine images captured in fall and winter than in summer (p < 0.01), with the number of swine images captured in spring being intermediate. Deer image captures were greatest in the fall and lowest in the winter (p < 0.01). Unlike the other two species, the number of deer images captured in the summer was similar to the number captured in their peak season (fall). Almost one-third of the total deer images were captured in the summer, relative to approximately 10% each for cattle and swine.

3.2. Temporal Overlap Among Species

Diel activity overlap was more similar among cattle and swine in deep upland (Δ = 0.842) and bottomland sites (Δ = 0.889) than in shallow upland sites (Δ = 0.755) (Table 5).
A similar pattern was observed between cattle and deer, with the lowest overlap of all species and site combinations occurring in shallow upland sites (Δ = 0.596). Diel activity overlap between cattle and the other two species was greatest in the fall (cattle and swine Δ = 0.808; cattle and deer Δ = 0.836) and winter (cattle and swine Δ = 0.875; cattle and deer Δ = 0.806) and lowest in the spring (cattle and swine Δ = 0.738; cattle and deer Δ = 0.739) (Table 6). Swine and deer diel activity overlap was greatest in the spring (Δ = 0.818) and lowest in the summer (Δ = 0.680).

4. Discussion

4.1. Site Selection by the Species and Potential for Interaction

Unsurprisingly, cattle were caught on camera more often than white-tailed deer and feral swine, as cattle were confined to the pastures where our study areas were placed. White-tailed deer and feral swine can be more selective of vegetation types and their associated food and habitat resources than cattle, due to being able to jump over or dig under barbed wire fences, respectively [33,34,35]. High woven fence (up to 2.5 m tall) characteristically used for wildlife containment or exclusion was not used on this part of the ranch. Therefore, movement of white-tailed deer and feral swine into and out of the study area likely occurred.
Results suggest that of the three vegetation types, bottomland sites may have greater potential as locations for contact and disease transfer among the three species, while upland sites appear to have less potential. Feral swine exhibited a clear preference for bottomland sites in our study area. Many other studies have reported similar preferences of feral swine for bottomlands, riparian areas, and sites near bodies of water (e.g., stock ponds) relative to upland areas [36,37,38,39]. Feral swine likely selected bottomland areas at our study site due to their provision of thermal refuge and preferred food sources. Swine thermoregulate by wallowing in shallow water and mud [40] and are known to visit wallows multiple times a day for thermoregulation in extreme temperatures [41]. Bottomland sites also commonly contained sedges [22], and their tubers have been reported to be a preferred food of feral swine [42,43].
Cattle did not exhibit a preference between vegetation types during this study, but we can expect greater interactions between cattle and swine at bottomland sites due to swine preferences for those areas and cattle selection of shaded bottomland sites during times when temperatures are high [44]. In contrast to our results, other studies have documented cattle using bottomlands in proportion to their availability, while they exhibited a greater preference for grassland, cropland fields, and areas near roads and stock ponds [36,45]. A possible explanation for the similar use of all vegetation types by cattle was the distribution of water sources throughout the vegetation types. Large stock tanks (catch ponds) in both survey areas were located within shallow and deep upland vegetation types (Figure 1). Though bottomland camera trap locations were closest to live water sources [22], the steep banks observed alongside the stream could be a potential barrier to use of the stream itself and bottomland vegetation by larger grazers such as cattle [46].
Like cattle, white-tailed deer were captured on camera proportionally across all vegetation types. Numerically, however, white-tailed deer captures were greatest in bottomland sites. White-tailed deer are known to select bottomland sites and areas with hardwood canopy cover due to prevalence of preferred foods [3,42,47], protection from predators [48], and thermal cover [49]. White-tailed deer are also known to prefer browse and mast from several species common to the bottomlands at the research site, such as cottonwood, western soapberry (Sapindus saponaria L.), and netleaf hackberry (Celtis laevigata var. reticulata Willdenow) [3,50]. Greater numbers of feral swine and white-tailed deer images in bottomland sites suggest that this vegetation type may provide greater potential for interactions between the two species than the other vegetation types.
Because neither white-tailed deer nor cattle exhibited distinct preferences among vegetation types, opportunities for interactions between the two species may be equal across all vegetation types, though these interactions are likely infrequent. Diel activity overlap suggests that when interactions occur, they may be more likely on bottomland and deep upland sites than shallow upland sites. Similarly, Cooper et al. found that both species tended to prefer productive riparian, clay loam, and deep upland sites [44].
Results suggest direct interactions among the three species may occur least frequently in deep uplands where lower numbers of images were captured for every species. Cattle movements may be restricted on deep upland sites with high densities of mesquite trees [51,52]. Additionally, deep upland sites had the greatest average distance to water (417.78 ± 54.29 m) compared to shallow upland (217.32 ± 79.09 m) and bottomland sites (80.31 ± 26.80 m). Distance to water is considered a limiting factor for cattle distribution [46] and was considered as a primary driver of feral swine distribution on these sites by Harvey et al. [22].
Though we did not monitor species presence around stock ponds in our study pastures, we assume direct and indirect contact among species could occur at a high rate near those features. Direct and indirect contact (defined as two species using the same location within a set time period) between cattle and feral swine has been reported to occur most frequently at watering sites [36,53].
Although direct contact between the three species should occur relatively infrequently at our sites, indirect contact at watering sites and shaded areas may result in the greatest opportunity for disease transfer. While direct contact is the most likely route of zoonotic disease transmission, the environment plays a role in increasing indirect contact. Fecal contamination of shared water sources can serve as a concern for livestock managers, crop producers, and wildlife biologists alike [54,55]. Drought in particular can increase visits to water, thereby increasing indirect contact and elevating risk for disease transmission [36,53,56]. Similarly, in times of the year when food resources and thermal cover are limited, increases in spatial and temporal overlap are to be expected, leading to increased frequency of indirect interactions [57,58].

4.2. Temporal Movements of Species and Potential for Interaction

Diel activity patterns suggest feral swine and cattle have similar activity at this site, while deer diel activity curves suggest that this species might exhibit temporal niche partitioning against the other two species. Swine and cattle were primarily caught on camera during the day, while deer were primarily crepuscular. Cattle typically exhibit diurnal activity [59,60], while swine are more commonly reported to be nocturnal or crepuscular [61,62,63]. Therefore, studies that have compared activity and interactions of feral swine and cattle have typically reported that most direct and indirect interactions occur in the crepuscular hours [24,44,64]. In studies reporting diurnal activity in both species, interactions around sunrise and sunset were greater due to increases in feral swine activity at those times [64]. Increases in swine diurnal activity may be attributed to reduced hunting pressure and human disturbance and greater distances from structures and roads [65,66,67]. Because swine exhibited an overall diurnal activity pattern, we suspect that swine located on this ranch may not be exposed to hunting pressure or human interaction at the threshold level needed to shift activity to nocturnal periods [62,68]. It is possible that animal presence at night and degree of night-time activity were underestimated due to cameras’ reduced range of detection at night. This may influence feral swine captures to a greater degree than the other two species since feral swine are reported to have greater nocturnal activity in other studies [61,63]. However, for assessment of presence of nocturnal species and their spatio-temporal overlap with other species, camera traps utilizing night-time images are commonly used [29,58,61] and are preferred to other methods that use only daylight images or daylight surveys [69].
While crepuscular activity is common in white-tailed deer [70], we observed relatively high camera captures during 12:00–19:59 at this study site. We suspect that they may be adjusting their daytime activities to travel and forage when cattle and swine activity is reduced in the middle of the day and afternoon, though we can only speculate at this time. Due to the low number of images of swine (242) and deer (1093) captured, it is difficult to determine if the activity patterns observed are due to potential negative interactions or if the temporal partitioning of images simply reflect the natural movements of the species in the absence of the others. Temporal separation between cattle and white-tailed deer [44] and other wild herbivores [71,72] has been reported in some studies. While wild herbivores have typically increased crepuscular or nocturnal activity in the presence of cattle [71,72], some herbivores have been seen to increase activity during the daytime periods when cattle activity is low [72]. Alternative explanations of relatively high amounts of deer images during the daytime could be due to reduced hunting pressure and human interaction [70] or avoidance of nocturnal predators [73,74]. Coyotes are the primary nocturnal predators in the study region; however, we do not believe that coyotes are significantly influencing deer activity at the site. We presume coyote abundance to be relatively low due to low numbers (488) of coyote images captured on camera over the duration of the study. In contrast, 242, 1093, and 4602 images of feral swine, white-tailed deer, and cattle were captured over the same time period.
Diel activity patterns suggest that there is lower potential for direct interactions between cattle and the other two species in shallow upland sites. Chances of direct interactions between cattle and deer appear particularly low in this vegetation type. Reduced canopy cover in shallow upland sites may have influenced deer diel activity. White-tailed deer have been reported to utilize more closed canopy vegetation during the daytime and more open woodland and grassland during crepuscular periods and at night [75]. They are also reported to use open grassland areas less frequently than areas with greater cover [76]. We believe that these factors coupled with increased cattle use of shallow upland sites may have driven greater temporal partitioning in this vegetation type.

4.3. Seasonal Effects on Species’ Movements and Habitat Selection

Differences in image captures across the four seasons likely reflect the species’ relative tolerances to seasonal weather conditions. We believe that cattle and swine were likely captured on camera the least during the summer due to reducing movements relative to thermal stress [22,77,78,79]. Cattle are known to increase time spent near water and under shade as temperatures increase [36,80], so we expected that cattle would spend greater amounts of time under the shade of riparian species in the bottomland sites and mesquites on the deep upland sites during the summer than the other seasons. We did not find an interaction between species, season, and vegetation type, however. Cheleuitte-Nieves et al. reported that cattle aggregated more closely and increased use of shade at midday more than any other time of day [79]. In every season, cattle were found under shade patches at midday more than any other time of day. Reduced image captures of cattle at 12:00–15:59 may be due to cattle congregating under shade and resting at that time. As Cheleuitte-Nieves et al. documented, congregating under shade at midday may be a typical behavior regardless of season [79]. However, it is important to note that their study was conducted in South Texas where midday temperatures in winter may still approach the upper limit of cattle’s thermoneutral zone (~25 °C). Our results suggest that deer may be less affected by high temperatures than swine and cattle. While swine and cattle were captured on camera the least during the summer, deer were captured in relatively high amounts. White-tailed deer are known to decrease movement and select shaded areas during mid-day in the summer [49,81], but less is known about the extent to which they reduce movements relative to cattle and feral swine under similar conditions. While thermal stress may be greater for deer in the summer relative to other seasons, late gestation and lactation may drive does to maintain relatively high activity during this period to meet nutritional demands [70]. In the Northern Rolling Plains, peak fawning typically occurs during the third week of June.
Increased camera captures of cattle and swine during the fall and winter likely reflect more optimal temperatures for activity of the two species. Increased deer image captures in the fall could reflect increased movements associated with the rut. In the Northern Rolling Plains where our studied site was located, most does are bred between late October to early January with the peak occurring during the first week of December [82]. Diel activity curves indicate that activity overlap was greatest between cattle and the other two species in the fall and winter. The combination of relatively high camera captures of all three species along with greater activity overlap among the species suggests that direct interactions between the three species may increase in the fall. Other studies have reported increased diel activity overlap and direct interactions of cattle with cervids and swine in the fall [64,83]. Triguero-Ocana et al. reported that most direct and indirect interactions between cattle and feral swine occurred in the crepuscular hours with greater activity overlap occurring in the spring and fall than the summer [64]. While we similarly observed high diel activity overlap between cattle and swine in the fall, activity overlap in the spring and summer was lower.
Declines in deer images in the winter could be attributed to decreases in activity post-rut and a scarcity of quality forage when animals are attempting to conserve energy [75]. Both bucks and does have been reported to be less active following the rut; studies have found that diel home ranges, distances traveled, and rates of travel are greater during the pre-rut and rut periods than post-rut [70,84]. Additionally, low precipitation during the fall and winter combined with warmer than average temperatures [22] may have reduced forage quantity and quality to a greater degree than usual which in turn may have reduced site use. The vegetation surveys conducted by Harvey et al. at this site did not include nutrient analysis or browse surveys [22], so we can only speculate about lower forage quality and its possible influences on site use. Additionally, spring vegetation surveys were limited by excessive rainfall in 2021 and wildfire in 2022 [22]. The inclusion of vegetation data from these time periods could better inform how forage availability and cover may have influenced species presence in the three vegetation types.
Less images of deer in the winter and spring and swine in the spring may indicate movement of these two species off-site. Wheat is a prominent crop in the region, and white-tailed deer and feral swine routinely utilize wheat fields to access high-quality forage during the winter and spring [85,86,87]. Wheat is one of the ten most frequent food items found in the diet of deer in the northern Rolling Plains [87]. On the upland areas at this site, browse is limited primarily to honey mesquite with lesser amounts of netleaf hackberry present. Though these species are among the preferred browse species in the region, browse quantity and quality is generally lacking in the Rolling Plains region [87,88], leading deer to sometimes travel great distances to access more desirable cropland fields during the winter and spring. Feral swine are also known to increase travel distances to access desirable crops [89], particularly during seasons of food scarcity [86,90]. Though captures of individual species did not differ seasonally across vegetation types, the availability of distinct seasonal resources likely played a role in vegetation type preferences. The significant interaction of vegetation type and season suggests that all three species used specific vegetation types similarly relative to seasonal changes in weather and availability of shelter and food. Deep upland and bottomland camera trap sites on the ranch had similar production of C3 grasses, sedges, and forbs in October 2021 [22]. Shallow upland sites produced the greatest amounts of warm-season grasses and were characterized by species such as buffalograss, sideoats grama, and dropseeds [22]. In the Rolling Plains, cattle have been reported to consume these species in high proportions in the summer and fall when availability and live–dead tissue ratios are relatively high [91,92]. Burke (2003) reported that grasses comprised around 19% of white-tailed deer’s annual diet in the Rolling Plains and were particularly prevalent in diets in fall and winter [3]. Schlichting et al. found that grasses were more abundant in swine diets in the region in spring and summer and were lower in fall and winter [42].
The mesquite understory at the research site was dominated by Texas wintergrass (i.e., the Prosopis/Nassella association; [93]), a preferred forage species for cattle and white-tailed deer in the study region. Texas wintergrass contains high amounts of crude protein relative to other common grasses and is one of the most important forages in livestock and wildlife diets in the region, especially during the cool-season when C4 grasses are dormant [87,92,94]. In the winter and spring, cool-season grass species may have attracted the three species to the upland sites, while in the late summer and fall mesquite beans and prickly pear may have attracted animals to the same vegetation types.
Mesquite is the most common browse and mast species on the ranch. Mesquite dominates much of the upland, especially the deep upland sites [95,96]. All three species are known to consume mesquite beans in high proportions during the late summer and early fall, particularly when herbaceous material is in low supply [97,98]. Therefore, availability of mesquite beans may have drawn animals to upland sites in late summer and early fall. Deer and swine are reported to have similar preferences for mesquite pods, consuming pods at similar rates and bite sizes [98,99]. However, feral swine are suspected of displacing white-tailed deer and other species from feeding sites [100,101]. Due to this, the two species may separate themselves spatially and/or temporally at specific sites and during particular seasons when competition for common food resources occurs [45,101,102]. Although visits may be temporally segregated at commonly used sites, indirect contact at feeding sites such as locations of supplemental feed and minerals, bait sites, and natural foraging areas can result in intra- and inter-species disease spread if visiting intervals occur within the environmental survival time of shared zoonotic diseases [83,101,102,103,104,105].

5. Conclusions

Results suggest more direct and indirect interactions between cattle and invasive and native wildlife likely occur at bottomland and deep upland sites than shallow upland sites. Additionally, temporal data and diel curves indicate that the probability of interactions occurring among the three species is greatest during the mid- to late morning. This was the time of the first peak in diel activity by cattle and feral swine, and image captures of white-tailed deer were high during this time period as well. Increased image captures of all species in the fall and high diel overlap between cattle and the other two species during the fall suggest that interactions among the species may increase in that season. Though less images of cattle and swine were captured in the summer relative to other seasons, we suspect that the species may experience greater overlap in site use at this time in areas with temperature-ameliorating characteristics such as the presence of shade and water.
Monitoring of shared resources can be strategically focused in areas expected to receive greater visitation by the target species. Similarly, monitoring can be tailored to the expected use of seasonal resources (e.g., watering sites in summer or sites with mast production in fall). Management of feral swine via hunting or trapping should be focused on bottomland and shallow upland sites. Summer hunts to manage feral swine should ideally take place in the morning when temperatures are cooler. Similarly, targeting saturated areas used for wallowing might increase success of locating feral swine during hot conditions. Increased success in trapping and hunting of feral swine will reduce costs associated and increase economic sustainability. Monitoring of feral swine and their movements around livestock and wildlife in the Rolling Plains region should continue as research on zoonotic disease transmission improves and interest in linking livestock, wildlife, and human health continues to grow. Game cameras are an inexpensive way for landowners to monitor their properties and are often already being used where hunting enterprises and management of white-tailed deer and other game species exist. Additionally, private land managers could collaborate with state agencies, universities, and university extension services to receive assistance with standardizing data collection and cost-share opportunities for purchasing sampling and monitoring equipment to help build capacity for feral swine monitoring across the region. Management efforts should focus on eradication of feral swine in high-use areas and mitigation of cross species interactions through the improvement and addition of supplemental water sources and fencing off sensitive areas with woven wire to exclude feral swine. Although we suspect that white-tailed deer might be avoiding feral swine and cattle at particular times of the day due to inverse peaks in camera captures observed in the afternoon, we cannot confirm with certainty that behaviors of any of the species were altered by the presence of any of the other species in the study. We also acknowledge that future studies including sampling of shared areas (e.g., water, bedding sites, wallows, supplemental feeding locations) and individual animals for pathogens are necessary to evaluate potential disease transfer at this site.

Author Contributions

Conceptualization, C.E.C.-N. and A.B.N.; methodology, C.E.C.-N., A.B.N., J.M.T. and J.G.H.; investigation, J.G.H.; data curation, J.G.H.; data analysis, C.E.C.-N.; resources, C.E.C.-N. and A.B.N.; writing—original draft preparation, J.G.H. and C.E.C.-N.; writing—reviewing and editing, C.E.C.-N., J.G.H., A.B.N. and J.M.T.; project administration, C.E.C.-N. and A.B.N.; funding acquisition, C.E.C.-N. All authors have read and agreed to the published version of the manuscript.

Funding

This research was supported by Texas Tech University and the US Department of Agriculture NIFA Capacity Building Grants for Non-Land-Grant Colleges of Agriculture Program, 2020-70001-31281.

Institutional Review Board Statement

Not applicable.

Informed Consent Statement

Not applicable.

Data Availability Statement

The raw data supporting the conclusions of this article will be made available by the authors on request.

Acknowledgments

We would like to thank H.S. Iida, B. Sagraves, T. Brimager, A. Pearson, W. Pfeiffer, and B. Richardson for their assistance in field data collection and image review.

Conflicts of Interest

The authors declare no conflicts of interest. The funders had no role in the design of the study; in the collection, analyses, or interpretation of data; in the writing of the manuscript; or in the decision to publish the results.

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Figure 1. Locations of cameras at the study site. A1–A12 and B1–B12 indicate camera identification numbers. Reprinted from [22], p. 3, Copyright (2023), with permission from Elsevier.
Figure 1. Locations of cameras at the study site. A1–A12 and B1–B12 indicate camera identification numbers. Reprinted from [22], p. 3, Copyright (2023), with permission from Elsevier.
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Figure 2. Images of a feral pig at a shallow upland site (A), a white-tailed deer buck at a bottomland site (B), and a feral pig and cow together at a bottomland site (C).
Figure 2. Images of a feral pig at a shallow upland site (A), a white-tailed deer buck at a bottomland site (B), and a feral pig and cow together at a bottomland site (C).
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Table 1. Mean image captures 1 of species within vegetation types.
Table 1. Mean image captures 1 of species within vegetation types.
Vegetation Type
SpeciesBottomlandDeep UplandShallow Upland
Cattle149.9 ± 45.8 X109.8 ± 23.8 X208.2 ± 63.5 X
Deer59.1 ± 20.5 X,Y22.3 ± 5.6 Y34.5 ± 12.0 Y
Swine 216.1 ± 7.0 A,Y2.8 ± 1.0 B,Z7.8 ± 3.5 A,B,Z
1 Values include mean number of images captured per camera ± standard error of the mean. 2 Veg X Species was not significant (p = 0.3), but vegetation type affected swine image captures (p = 0.02). A, B Differences among vegetation types within a species (p ≤ 0.05) are indicated with different letters (A, B). X,Y,Z Differences among species within a vegetation type (p ≤ 0.05) are indicated by different letters (X,Y,Z).
Table 2. Mean image captures 1 of species within diel time blocks.
Table 2. Mean image captures 1 of species within diel time blocks.
Species
Time BlockCattleDeerSwine
0:00–3:5929.3 ± 6.0 C,X10.8 ± 2.6 B,Y1.0 ± 0.4 C,Z
4:00–7:5941.5 ± 8.4 C,X18.0 ± 4.2 A,B,Y2.3 ± 0.8 A,B,C,Z
8:00–11:59135.1 ± 26.4 A,X21.7 ± 5.0 A,Y5.0 ± 1.6 A,Z
12:00–15:5979.7 ± 15.7 B,X16.9 ± 4.0 A,B,Y3.3 ± 1.1 A,B,Z
16:00–19:5999.8 ± 19.6 A,B,X13.7 ± 3.3 A,B,Y3.7 ± 1.2 A,B,Z
20:00–23:5936.8 ± 7.4 C,X14.2 ± 3.4 A,B,Y2.1 ± 0.7 B,C,Z
1 Values include mean number of images captured per camera ± standard error of the mean. A,B,C Differences among time blocks within a species (p ≤ 0.05) are indicated by different letters (A,B,C). X,Y,Z Differences among species within a time block (p ≤ 0.05) are indicated by different letters (X,Y,Z).
Table 3. Coefficient of overlap (Δ) and 95% bootstrapped confidence intervals (in parentheses) of diel activity of cattle, feral swine, and white-tailed deer between June 2021–May 2022.
Table 3. Coefficient of overlap (Δ) and 95% bootstrapped confidence intervals (in parentheses) of diel activity of cattle, feral swine, and white-tailed deer between June 2021–May 2022.
SpeciesΔ
Cattle and Swine0.920 (0.879–0.960)
Cattle and Deer0.765 (0.734–0.795)
Swine and Deer0.829 (0.772–0.888)
Table 4. Mean species image captures 1 within seasons.
Table 4. Mean species image captures 1 within seasons.
Season
SpeciesSummerFallWinterSpring
Cattle40.6 ± 8.0 B,X128.0 ± 24.3 A,X137.3 ± 26.1 A,X117.7 ± 22.4 A,X
Deer29.2 ± 6.3 A,B,X35.1 ± 7.5 A,Y11.0 ± 2.5 C,Y20.3 ± 4.4 B,Y
Swine2.0 ± 0.7 B,Y6.5 ± 1.9 A,Z5.3 ± 1.6 A,Y3.5 ± 1.1 A,B,Z
1 Values include mean number of images captured per camera ± standard error of the mean. A,B,C Differences among seasons within a species (p ≤ 0.05) are indicated by different letters (A,B,C). X,Y,Z Differences among species within a season (p ≤ 0.05) are indicated by different letters (X,Y,Z).
Table 5. Coefficient of overlap (Δ) and 95% bootstrapped confidence intervals (in parentheses) of diel activity of cattle, feral swine, and white-tailed deer in shallow upland, deep upland, and bottomland vegetation types.
Table 5. Coefficient of overlap (Δ) and 95% bootstrapped confidence intervals (in parentheses) of diel activity of cattle, feral swine, and white-tailed deer in shallow upland, deep upland, and bottomland vegetation types.
Vegetation TypeSpeciesΔ
BottomlandCattle and Swine0.889 (0.832–0.934)
Cattle and Deer0.779 (0.735–0.824)
Swine and Deer0.828 (0.754–0.899)
Shallow UplandCattle and Swine0.755 (0.656–0.855)
Cattle and Deer0.596 (0.537–0.650)
Swine and Deer0.823 (0.724–0.914)
Deep UplandCattle and Swine0.842 (0.754–0.913)
Cattle and Deer0.825 (0.779–0.866)
Swine and Deer0.814 (0.708–0.901)
Table 6. Coefficient of overlap (Δ) and 95% bootstrapped confidence intervals (in parentheses) of diel activity of cattle, feral swine, and white-tailed deer in each season.
Table 6. Coefficient of overlap (Δ) and 95% bootstrapped confidence intervals (in parentheses) of diel activity of cattle, feral swine, and white-tailed deer in each season.
SeasonSpeciesΔ
SummerCattle and Swine0.744 (0.609–0.853)
Cattle and Deer0.796 (0.742–0.847)
Swine and Deer0.680 (0.533–0.814)
FallCattle and Swine0.808 (0.724–0.874)
Cattle and Deer0.836 (0.790–0.880)
Swine and Deer0.766 (0.674–0.846)
WinterCattle and Swine0.875 (0.803–0.937)
Cattle and Deer0.806 (0.744–0.859)
Swine and Deer0.715 (0.600–0.825)
SpringCattle and Swine0.738 (0.630–0.835)
Cattle and Deer0.739 (0.683–0.796)
Swine and Deer0.818 (0.716–0.916)
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Harvey, J.G.; Norris, A.B.; Tomeček, J.M.; Cooper-Norris, C.E. Spatio-Temporal Overlap of Cattle, Feral Swine, and White-Tailed Deer in North Texas. Sustainability 2025, 17, 8354. https://doi.org/10.3390/su17188354

AMA Style

Harvey JG, Norris AB, Tomeček JM, Cooper-Norris CE. Spatio-Temporal Overlap of Cattle, Feral Swine, and White-Tailed Deer in North Texas. Sustainability. 2025; 17(18):8354. https://doi.org/10.3390/su17188354

Chicago/Turabian Style

Harvey, Jacob G., Aaron B. Norris, John M. Tomeček, and Caitlyn E. Cooper-Norris. 2025. "Spatio-Temporal Overlap of Cattle, Feral Swine, and White-Tailed Deer in North Texas" Sustainability 17, no. 18: 8354. https://doi.org/10.3390/su17188354

APA Style

Harvey, J. G., Norris, A. B., Tomeček, J. M., & Cooper-Norris, C. E. (2025). Spatio-Temporal Overlap of Cattle, Feral Swine, and White-Tailed Deer in North Texas. Sustainability, 17(18), 8354. https://doi.org/10.3390/su17188354

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