This study was conducted at the Sanjay Tiger Reserve (STR), which is spread over two districts, i.e., Sidhi and Shahdol, in the Indian state of Madhya Pradesh, central India ( Figure 1 ). As per the biogeographic classification of India by Rodgers & Panwar (1988), STR comes under the “Central Highlands.” Sanjay National Park (SNP) and Dubri Wildlife Sanctuary (DWLS) together constitute the core area (831.25 km²) of STR. Surrounding the core, an additional area of 812.58 km² was demarcated as a buffer zone for STR.

The forest type of STR is dominated by Shorea robusta (Sal). It falls under the sub-group "North Indian moist deciduous forests", with subtype "Moist sal-bearing forest" and subdivision "Moist peninsular sal forest", according to the revised classification by Champion & Seth (1968). Other major tree species found in STR include Buchanania cochinchinensis (Char), Diospyros melanoxylon (Tendu), Madhuca longifolia, (Mahua), Lagerstroemia parviflora (Sedha), and Cassia fistula (Amaltas). Land use land cover types of STR are categorized into six classes, i.e., dense forest, open forest, scrubland, barren land, water bodies, and agricultural land ( Figure 1 ). The elevation of the STR varied from 239 to 770 m. The topography is generally undulating and hilly in the eastern part and moderately sloped to flat terrain in the western part with varying slopes, ranging from 0° to 61°. Temperature varies between 7.4 and 41.8°C with distinct seasonal variation viz. summer (March to June), monsoon (July to October), and winter (November to February). The mean annual precipitation in STR is 1,303 mm. Apart from sloth bear, STR harbors diverse mammal species such as the Panthera tigris (Tiger), Panthera pardus (Leopard), Hyaena hyaena (Striped hyena), Canis lupus (Indian wolf) Cuon alpinus (Asiatic wild dog), Axis axis (Chital), Rusa unicolor (Sambar), Tetracerus quadricornis (Four-horned antelope), etc. There are thirty-nine villages inside the core area of the Tiger Reserve. People living in these villages, along with their substantial livestock population, are highly dependent on forest resources, which pose immense anthropogenic pressure on the core habitat of STR.

Systematic camera trapping exercises were conducted over two consecutive years, from December 2016 to April 2017 (henceforth, 2017) and January 2018 to April 2018, in the core area of STR. The core area was gridded into 4 km² blocks (henceforth, sites), following the Indian tiger estimation protocol (Jhala et al., 2015). A total of 143 and 144 sites were selected for the deployment of camera traps during 2017 and 2018, respectively. Due to logistical constraints, we divided the core area into two blocks, viz., SNP (466.6 km²) and DWLS (364.6 km²) for the deployment of camera traps. First, we deployed camera traps at 86 sites during winter 2017, i.e., December 2016 to February 2017 in SNP, followed by 57 sites in DWLS during summer 2017, i.e., March 2017 to April 2017. During winter 2018 (January to February) and summer 2018 (March to April), camera traps were deployed in 57 sites in DWLS and 87 sites in SNP, respectively.

Double-sided automated camera traps (Cuddeback C1 and Cuddeback Ambush) were placed preferably on dirt roads, dry riverbeds, and animal trails to maximize the detectability of large carnivores (Karanth et al., 2011), including sloth bears, at a height of 30–40 cm above the ground. Furthermore, the placement of double-sided camera traps, opposite to each other, at each site would preferably increase the detection probability of animals (Pease et al., 2016). Each camera trap was set to take photographs at a 15-second interval upon detection of the animals. The survey duration of camera traps at each block was continued for 45 days, and camera traps were active on a 24 × 7 basis. However, in 2017, due to rampant forest fires at DWLS during summer, camera traps were active for an average period of 36 days. Each camera trap location was recorded using Garmin e-Trex handheld GPS units (Garmin Inc.).

After retrieving the camera trap data, detections of sloth bears and humans were considered independent events (or independent photographs) if two consecutive photographs of each species were taken at a ≥30-minute interval, to limit the risk of pseudoreplication (O’Brien et al., 2003; Xiao et al., 2018; Haswell et al., 2020). We chose a 30-minute interval for humans as we expected a substantial number of repeated photo-captures of the same individual (single and groups) within a short period, due to the curiosity of people detecting camera traps. The entire survey duration (45 days) was collapsed into 15 temporal replicates per site by considering each three days as a single temporal replicate and pooling the detection–nondetection records of species accordingly. Collapsing the survey duration into a smaller number of temporal replicates facilitated the reduction of overdispersion (variance > mean) and increased the temporal independence of each detection (Penjor et al., 2019). Lastly, we used camera trap data from two summers and two winters obtained from two blocks representing the entire core area of STR for each season (overall winter and summer).

In the first step, we used two-species conditional occupancy models (Richmond et al., 2010) to assess if sloth bears show any spatial avoidance toward human occurrence in two different seasons (winter and summer). The two-species occupancy model estimates the conditional probability of occupancy and detectability of a less dominant species in the presence of a dominant species (Richmond et al., 2010). We interpreted the “probability of occupancy” of a species as the probability of “use” since we believed that the size of the site would potentially violate the closure assumption and independence between sites (MacKenzie et al., 2017, p447). If species A and B are to co-occur and A is dominant over B, the following parameters are estimated in the two-species model:

In this study, we assumed that human (species A) activity does not depend on the occurrence of sloth bear (species B). Sloth bears probably perceive humans as a threat equivalent to predators (tigers and leopards), with which they are sympatric (Laurie and Seidensticker, 1977; Yoganand, 2005). Given the extensive anthropogenic pressure in STR, it is very likely that sloth bears would adjust their space use in accordance with human occurrence. In this view, we assumed sloth bears to be a subordinate species in the two-species occupancy model. However, considering humans as the dominant species in two-species occupancy models was not common [but see Parsons et al. (2016)], our intention was to assess the degree of spatial avoidance of sloth bears in response to the extensive occurrence of a perceived threat (i.e., humans) in the context of the human-dominated landscape.

The species interaction factor (henceforth, SIF) is a derived parameter estimated from the two-species occupancy model. A value of SIF >1 shows the degree of species aggregation rather than avoidance, and an SIF value <1 indicates spatial avoidance or segregation, where two species are less likely to occur with each other than expected under the hypothesis of species independence. An SIF value of ~1 indicates that both species can occur independently. Following Richmond et al. (2010), SIF can be calculated as:

S I F = Ψ A Ψ B A Ψ A [ Ψ A Ψ B A + ( 1 - Ψ A ) Ψ B a ]

We calculated SIF and compared it between seasons to assess the avoidance of space used by sloth bears in the presence of humans. We also carefully inspected the values of other parameter estimates (Ψ^Ba, Ψ^BA, p^B, r^BA and r^Ba), and compared them between seasons. Model comparison and selection were based on Akaike’s Information Criterion (AIC) (Burnham and Anderson, 2002). We used the program Presence (Version 2.13.11) (Hines and USGS-PWRC, 2006) to estimate occupancy and related parameters of the two species.

During winter and summer, SIF values (SIF value ± Standard Error) were found to be 0.98 ± 0.007 (95% CI: 0.97–0.99) and 1.00 ± 0.03 (95% CI: 0.93–1.07), respectively ( Table 1 ). Thus, no difference in species interaction was observed between seasons. The closeness of the SIF value toward 1 indicated no distinguishable pattern of spatial avoidance shown by sloth bears toward human occurrences. The probability of use of sites by sloth bears in the presence of humans was found to be less [Ψ^BA (SE) = 0.71 (0.04)] than when humans are absent [Ψ^Ba (SE) = 1.00 (0.00)] during winter. However, during summer, the probability of use of sites by sloth bears did not differ much [Ψ^BA (SE) = 0.79 (0.04); Ψ^Ba (SE) = 0.77 (0.17)] in the presence or absence of humans.

Two-species occupancy models showed that sloth bears could independently occur with humans irrespective of seasons, which, to our knowledge, is not an entirely novel situation. In Mudumalai Tiger Reserve, the occupancy of sloth bears was uninfluenced by the distance to the nearest human settlements (Ramesh et al., 2012a). Sloth bears can be habituated to persistent human presence and are attracted to human settlements in search of abundant seasonal fruits (Ziziphus mauritiana, Syzygium cumini), cultivated crops (groundnut, maize), and even garbage (food wastes) (Akhtar et al., 2004; Palei et al., 2020; Prajapati et al., 2021), which also increased the sloth bear–human conflict (Bargali et al., 2005; Prajapati et al., 2021). However, increased human activity or encounter with humans would facilitate the shifting of temporal activities of many mammalian species, including carnivores (Gaynor et al., 2018), instead of spatial avoidance (Nickel et al., 2020), or could induce habituation towards human activity (Albert and Bowyer, 1991; Barnes and Wilker, 2000; Elfström et al., 2014). On the other hand, bears show strong avoidance of human activities both spatially and temporally (Chi and Gilbert, 1999; Tollefson et al., 2005; Ladle et al., 2018). We believe the spatial distribution of humans or anthropogenic activities could be the governing factor of such spatial avoidance or spatial co-occurrence. In the human-dominated landscape, it is not always possible to avoid humans spatially as it would cost to miss out on potential resource-rich areas, which would negatively affect the foraging success and overall fitness of the animal. In STR, human occurrence and livestock grazing in sloth bear habitat are extensive. We found high occupancy (Ψ^A = 0.96 ± 0.02 in winter; Ψ^A = 0.84 ± 0.03 in summer) of humans in STR, which indicated the substantial level of anthropogenic pressure throughout the study area, regardless of seasons. Despite this extensive human pressure, the probability of use of sites by sloth bears was also found to be high (Ψ^BA = 0.71 ± 0.04 in winter; Ψ^BA = 0.79 ± 0.04 in summer; Table 2 ), which indicated that irrespective of seasons, sloth bears are utilizing sites independently of humans. It was also found that detection SIF (delta, δ) was slightly higher than 1 (δ = 1.05 ± 0.06 in winter; δ = 1.06 ± 0.07 in summer; Table 2 ), indicating some sort of association in detection between sloth bears and humans. It could be attributed to the relatively high preference for trail use by wide-ranging carnivores (Harmsen et al., 2010; Karanth et al., 2011; Kays et al., 2017; Nickel et al., 2020), apart from humans, which would facilitate animals to cover a larger distance in an energetically efficient way (Dickie et al., 2017).

However, spatial overlap estimation based on Pianka’s overlap index (Pianka, 1974) revealed seasonal variation between sloth bears and humans. The intensity of space use by sloth bears was lower (0.31) during summer than during winter (0.44), which indicated that bears used sites less frequently during summer in the presence of humans than in winter. Given the widespread distribution of human activities during winter and summer, it was expected to have more overlapped areas between the two without any seasonal variation. Less spatial overlap between sloth bears and humans during the summer season could be attributed to comparatively higher temporal overlap during summer with respect to winter (see Table 4 ). In STR, human activities were found to be primarily diurnal regardless of seasons. However, during summer, the activities of humans started well before 06:00 o’clock in the morning and continued till late evening hours, which substantially overlapped with the crepuscular activity of sloth bears (Yoganand, 2005; Bargali et al., 2012; Ramesh et al., 2013). Increased human activity in the early morning and crepuscular hours during summer (especially from March to the first half of April) is primarily associated with the collection of flowers of Mahua, which is used to prepare alcoholic beverages (Kumar and Rao, 2007; Mewada, 2012). The selling of dry Mahua flowers significantly helps in the income generation of indigenous people (Islam and Quli, 2017). Hence, it is widely considered an important livelihood option for indigenous people in India (Kumar et al., 2018; Mishra and Poonia, 2019). The Mahua flowers also significantly contributed to the diet of sloth bears during the onset of summer (Bargali et al., 2004; Yoganand, 2005), which, coupled with its crepuscular activity, increased the interaction between sloth bears and humans (Akhtar et al., 2004). In STR, based on our camera trap survey, it was found that the frequency of occurrence of sloth bears and humans at per hour intervals was high during the early morning (05:00–07:00) and evening (19:00–20:00) in summer ( Supplementary Figure 3 ). To avoid human encounters during the crepuscular period, it is possible that sloth bears used sites less intensively where chances of human encounters were high. Contrastingly, during winter, high spatial overlap could be associated with low temporal overlap between sloth bears and humans. In winter, human activities remained more concentrated during broad daylight hours for a comparatively short span of time, but did not start in the early morning hours, like summer (see Figure 3 ). In line with this, the morning (07:00–08:00) and early evening hours (15:00–16:00) showed the maximum frequency of occurrence of sloth bears and humans per hour interval, although the magnitude of the frequency of occurrence was less in winter compared to summer ( Supplementary Figure 3 ).

We found that the activity patterns of humans varied significantly between seasons, which was also the case for sloth bears. Our results indicated that sloth bears used sites more extensively during winter regardless of human occurrences, with a distinct temporal avoidance. Studies elsewhere have shown that carnivores could avoid human occurrences temporally (Tollefson et al., 2005), instead of avoiding them spatially, in a human-dominated landscape (Wang et al., 2015; Nickel et al., 2020). The marginal difference (p <0.05) in the activity of sloth bears between seasons indicated that sloth bears tended to avoid a more predictable pattern of human activity, despite already being strictly nocturnal and crepuscular in a human-dominated landscape (Bargali et al., 2012). Since human activity is not dependent on sloth bear activity, our study revealed that a significant shift in human activity between seasons influenced the likelihood of interaction between sloth bears and humans temporally and spatially.

However, despite distinct spatio-temporal avoidance, the conflict between sloth bears and humans in terms of human-mauling is prevalent in STR. It is attributed to the year-round existing immense anthropogenic pressure posed by human settlements inside the core area of STR. Similar observations were also made in the human-dominated landscapes of central India (Bargali et al., 2005; Dhamorikar et al., 2017) and elsewhere (Garcia et al., 2016; Debata et al., 2017; Singh et al., 2018). Based on our camera trap survey, we found that during summer, the number of detections of sloth bears and humans per hour interval was higher (n = 53) than during winter (n = 33), indicating that during summer, there is a relatively high probability of conflict incidents inside the forest, as reported by Dhamorikar et al. (2017). We assumed that sites where sloth bears and humans were detected more frequently within a specific bin (1 h in this case) of time are more prone to accidental encounters between them, while humans are engaged in different activities, especially in and around those sites. In previous studies, it has also been observed that most of the attacks occur during sudden close encounters with sloth bears (Dhamorikar et al., 2017; Singh et al., 2018), especially while collecting non-timber forest produce (NTFP) separately and silently inside the forest (Dhamorikar et al., 2017).

Finally, we have made an attempt to identify the conflict source sites and dispersion around source sites based on the present camera trap survey and records of conflict incidents obtained from the forest department. Our primary aim was to identify areas inside the forest where humans and sloth bears occurred more frequently, assuming that areas where sloth bears and humans were more likely to get exposed to each other, lead to conflict scenarios. Furthermore, records from the forest department were used to validate our model and future conflict predictions. We found that, based on detections of sloth bears and humans obtained from camera traps and validating them with registered conflict records, the GP model produced a reasonably good prediction (Gini coefficient = 0.92) of conflict source and dispersion. The GP model was found to be especially robust when fewer data points are provided and can potentially outcompete any other spatial statistics used in different contexts and scenarios of model-based predictions (Stevenson et al., 2012). However, a distinct seasonal variation was observed in the current prediction of conflict hotspots. More probable source sites (n = 9) of conflict were identified during summer, in comparison to winter (n = 5), indicating a greater chance of conflict in forest areas during summer, which was also earlier reported in the central Indian landscape (Dhamorikar et al., 2017). It could be attributed to the much higher number of detections of sloth bears (n = 312) during summer, in comparison to winter (n = 180, see Table 1 ), and associated high temporal overlap with humans. We found that seasonal variation in the temporal overlap between sloth bears and humans was also reflected in the predicted source-dispersion modeling of conflict in STR. As probable source sites and hotspots were mostly associated with villages’ locations, it was evident that the probability of conflict was also higher in areas adjacent to villages. Relocation of villages from the core zone of STR, especially those situated around the probable source sites shown here, would be a primary step for managers to reduce conflict between sloth bears and humans. However, despite being a contentious and gradual process, voluntary relocation of villages from the core zone of Tiger Reserves was proved to be one of the successful means to restore wildlife population and its habitat (Lasgorceix and Kothari, 2009), with special reference to tiger conservation in India (Jhala et al., 2021). Also, elsewhere in India, communities living inside Tiger Reserves prefer to be relocated willingly due to the decreased productivity of forests, lack of basic amenities, and human–wildlife conflict (Harihar et al., 2014). Thus, we believe that the findings of geographic profiling could be one means for forest managers to identify the villages that need to be relocated on a priority basis or rationalize resources or techniques for conflict mitigation. However, in between the process of relocation, an effort could be made to make people living in conflict hotspots aware of the avoidance of conflict scenarios based on the findings of this study and studies carried out elsewhere in central India (Bargali et al., 2005; Dhamorikar et al., 2017; Dhamorikar et al., 2018). Although we could not identify the conflict sources and hotspots outside the core zone of STR, conducting awareness programs among the local people as mitigation measures is especially important in the buffer zone and other human-dominated forest areas outside the Tiger Reserve.

Our study had several limitations. Due to logistical constraints, we could not broaden our understanding of conflict prediction in the buffer zone of STR. Also, camera traps were not deployed in the monsoon, which restricted us to drawing inferences during this season. We have deployed camera traps on the trails and forest roads in order to maximize the capture of tiger and other co-predators, including sloth bears. Previous studies showed that placement of camera traps on trails or forest roads significantly increased the detection of certain mammal species and bears as well (Wearn et al., 2013; Kolowski and Forrester, 2017; Tanwar et al., 2021). On the other hand, human activities inside the forest, such as the collection of NTFPs, are not restricted to the roads and trails but can be conducted anywhere in the forest depending upon resource availability and accessibility. In general, people do not walk alone on the forest roads or trails, especially during dark hours, as moving in groups would reduce the chance of being attacked by bears (Garrote et al., 2017). Despite knowing these facts, we assumed that in the locations where humans and bears were frequently detected, the chance of conflict could also be high in and around those locations. Although widely used, the efficacy of trail-based camera traps in estimating anthropogenic pressure has rarely been assessed. However, Miller et al. (2017) showed how wildlife monitoring cameras (on-trail) can also efficiently capture human activities and could be used for long-term wildlife–human interaction-based studies. Our study indicated fairly accurate conflict prediction through the GP model by using only camera trap data, although we recommend careful considerations while deploying camera traps (on-road and off-road) in future similar studies.

This study revealed that the interaction between sloth bears and humans was primarily governed by the seasonal variation of spatio-temporal overlap, especially the significant changes in human activity between seasons. From the species perspective, fine scale segregation of temporal and spatial activity was observed to co-exist with humans. However, extensive anthropogenic pressure throughout STR exacerbated the situation and led to conflict scenarios. Therefore, radio-telemetry-based research, along with monitoring through camera traps, is recommended to understand the spatio-temporal activity of sloth bears in a human-dominated landscape. In this study, the application of novel methods like GP models provided an opportunity to predict the source-dispersion of conflict precisely, even using secondary data sets obtained from camera traps. It would be insightful for forest managers to reduce conflict incidents derived from sloth bear–human interface with a more area-specific targeted approach and promote coexistence in a human-dominated landscape.