Impacts of Snow Leopards on co-occurence Species Diversity and Spatial-Temporal Interactions in Snowy Ecosystems: Conservation Insights and Implications

The presence of apex predators is crucial for ecosystem structuring, yet the impacts of rapid climate change on these species and their ecological communities remain poorly understood. This study investigates the cascading effects of the snow leopard (Panthera uncia) on the diversity and abundance of mesopredators and prey in the high-altitude ecosystems of Baltistan, Pakistan. Using extensive camera trapping data (November 2018 - April 2023) and statistical analyses (SEMs, GAMMs, overlap models), we assessed spatio-temporal interactions modulated by climatic factors. Our results demonstrate that snow cover is a critical driver of species activity and distribution. During snow presence, snow leopards were most active in the late afternoon, while foxes preferred twilight hours, with significant temporal overlap between leopards, martens (p<0.001), and markhor (p<0.022). In the absence of snow, activity patterns shifted significantly: leopards became diurnal, foxes were active at dawn (p=0.041), martens at night (p<0.001), ibex at dawn (p=0.021), and markhor during midday (p=0.012). Abundance was also climate-dependent; without snow, leopard detection was linked to cloud cover and temperature, foxes to wind at lower elevations, markhor preferred higher elevations, and martens avoided lower elevations. These findings reveal that the snow leopard, as an apex predator, exerts complex top-down and bottom-up forces that regulate mammalian community structure across temporal scales. This research provides critical baseline data on predator-prey dynamics, highlighting the importance of spatio-temporal considerations for effective conservation and habitat management of this vulnerable ecosystem under a changing climate.

Spatio-temporal scales an apex predators play a crucial role in shaping the behavior, diversity and abundance of prey and smaller carnivores in ecosystems [1, 2]. Their actions can have cascading effects at various scales from individual to population levels [3, 4]. In ecological studies, resource partitioning, including temporal segregation , is a fundamental concept helping explain how species minimize competition by sharing resources namely food, space, or time [1, 2]. Temporal segregation refers to the division of resource use over time allowing species to coexist by accessing the same resources but at different periods, thus reducing direct competition [5]: an as a result ultimately influencing the biology and ecology of organisms at lower trophic levels [6], and spatial separation is a significant factor that can impact species coexistence [7]. Moreover, understanding the dynamics of competition between different species within the same ecological guild as well as their interactions within specific landscapes is crucial in the development of successful conservation strategies for mammalian communities across different spatial and temporal scales [5]. This knowledge is essential for effectively preserving biodiversity and promoting long-term sustainability of mammal populations in their natural habitats [8, 9]. In addition, influence of top predators on lower trophic levels in the food chain can have significant implications for carnivores at intermediate trophic levels due to intricate interactions within the same ecological guild [6], and competition for resources or direct predation by large carnivores can restrict the populations of mesopredators [10, 11]. Consequently, the absence or decline of apex predators may lead to an increase in mesopredator abundance within the ecosystem [12], for instance, the mesopredator release hypothesis [13]. Mesocarnivores occupying intermediate trophic levels [14], are known to exhibit a high level of species diversity and utilize a wide range of resources and habitats [15]. These mesocarnivores also play a crucial role in significantly reducing the abundance of vulnerable prey species [12]. However, mesocarnivores can also benefit from scavenging on remains left by apex predators thereby being indirectly supported by them [16], and contributing to the maintenance of biodiversity ecosystem structure and overall health [11, 17, 18]. Furthermore, the presence of predators often prompts prey to develop various anti-predator behavioral strategies such as increasing vigilance postures, altering group size and adjusting behaviors to minimize encounters with predators [19, 20]. Prey have been observed adjusting their behavior to balance the need to forage with the risk of predator encounters [21].This balancing act may involve temporal avoidance of areas where perceived predation risk is high [21, 22]. Conversely, predators are also reliant on their prey and are expected to adapt their hunting behavior and activity based on their prey’s activity patterns [22, 23]. As a result, the temporal and spatial overlap between predators and prey is likely influenced by the trade-off between feeding opportunities or abundance for both species and predator avoidance specifically for the prey [24, 25].This dynamic is particularly evident when resources are limited [26, 27]. The spatial and temporal activity dynamics of apex predators, mesopredators and their prey are influenced by a multitude of ecological factors and many researchers failed to explore the drivers behind the top-down patterns of these key players in the ecosystem referable deficiency of data [28, 29]. For example climate warming in cold environments can impact vulnerability to predation and circadian activities such as ambient temperatures, altitudes, snow depth and wind pressure and clouds conditions influence the behavior of various animals like arctic fox ( Alopex lagopus ) [30, 31], American martens ( Martes americana ) [32], swiss ibex ( Capra ibex ) [33], moose ( Alces alces ) [34], felids [35, 36]. The snow leopard is the top predator in the mountainous regions of South and Central Asia, spanning across 12 countries with vast territories to roam [37, 38], these majestic creatures are also found in the northern mountains of Pakistan, which presents various conservation challenges and threats for recovery [39]. As a result, snow leopards in Northern Pakistan have been placed in Appendix I of CITES [39]. As such they play a top-down vital role in regulating the population sizes of their prey species, which primarily consist of seven ungulates such as flare-horned markhor ( Capra falconeri ), himalayan ibex ( Capra ibex sibirica ) [40], and smaller herbivores such as the Indian pika ( Ochotona spp ), also inhabit the area [41], and competitors such as small carnivores like weasels ( Mustela spp ) and stone marten ( Martes foina ) coexist with other carnivores including the red fox ( Vulpes vulpes ), grey wolf ( Canis lupus ), and himalayan lynx ( Lynx lynx) [41-43]. The regulation of snow leopard populations can be achieved through various methods like lethal or non-lethal means, aimed at managing their numbers and ensuring a harmonious balance in the mammalian ecosystem preventing any species from dominating and potential harm to the environment [44]. The rare and low populated wildlife areas the camera trapping has become a highly effective method for monitoring the spatio-temporal distribution [45], abundance or diversity of species [46], and their activities in the remote mountainous landscapes [28, 47]. This non-invasive technique is commonly used for studying various feline species [46-48] .In Baltistan there is a significant lack of information regarding the conservation status of apex predators and competitors at the community level [39]. The ecological impact of these large top predators during the presence or absences of snow days remains largely unquantified in natural ecosystems, impeding the implementation of more effective conservation measures for these apex predators as well as mesopredator and their prey species and their habitats. This research seeks to investigate how apex predators, such as the snow leopard (here after leopards), influence the diversity and distribution of mesopredators and prey species through interactions with environmental factors [49, 50], and understanding these cascading effects on spatio-temporal scale is crucial for ecology [51-53] and conservation biology [54, 55]. It was speculated that leopards could have various top-down effects on mesopredators and prey due to environmental challenges and competition for resources [36, 56]. It is expected that stone martens (here after martens) and red foxes (here after foxes) will display spatio-temporal avoidance behavior toward leopards [56], but it can be alter in the presence of snow days or either absence of snow days. Moreover, the abundance or diversity of ungulate prey like himalayan ibex (here after ibexes) and astor markhor ( Capra falconeri falconeri ) (here after markhor) and their activity patterns can driven by putting the landscape at risk from carnivores or causing lethal consequences [38, 57]. . However, the potential impact of the declining population of leopards [58], mesopredator and their prey on the mountainous ecosystem remains unexplored (as far as we know). Our main objectives are to understand how mesopredators and apex predators coexist or niche segregation phenomena focusing on factors like prey availability and trophic flexibility that influence species diversity.We evaluate that the abundance of leopards will be influenced by the spatial diversity of two main preys, ibexes and markhor, two mesopredator species foxes or martens. Temporal factors can influence the activity patterns of ibex and markhor to avoid predators, while leopards adjust their peak activity times to pursue prey. Similar effects may be observed with mesopredators such as foxes or martens. We propose that mesopredators like martens, with more varied diets, may outmaneuver foxes by being more adaptable in finding food sources. They are most active during the late night when fox activity is minimal, giving them a competitive edge [56]. We anticipate that ibex and markhor will have fewer interactions with foxes and martens than what would occur by random chance. This will be especially noticeable in snowy conditions, where there will be less overlap at three different time scales. However, leopards may continue to have a significant impact regardless. Moreover, we expect that varying climatic factors, including temperature, cloud cover, and wind pressure will have different effects at various altitudinal levels. For example, the presence of snow at lower altitudes may be more favorable compared to higher altitudes for leopards and other co-occurring species [59]. During days without snow, higher temperatures might encourage nighttime activity, while lower temperatures could lead to peak activity shifting to daytime when snow is present [36]. Our study seeks to address these questions and shed light on the complex relationships within this ecosystem.

Study Area The research was carried out in various fragmented landscapes in four districts: Shigar, Gangche, Skardu, SKB Rondo, and Basho (Figure1 a) as described in details in our previous research[39]. Our previous research also involved identifying marking sites and examining reproductive evidence of snow leopards. Additionally, we described the study areas and investigated the causes of their predation(see in details)[60, 61]. Figures 1 : Map indicating camera trapping stations for all project sites and community controlled hunting areas in Baltistan, including areas under management by village conservation community (VCC) and community controlled hunting area (CCHA). Data Collection Camera placement procedures We examined co-occurrence relationships between snow leopards and other mammalian species in the same habitat by analyzing a comprehensive dataset collected through multiple camera trap sessions across different s landscapes northern Pakistan [60]. In high-quality habitats, an estimated minimum home range for an adult females is approximately 2 trap stations per 16-30 km 2 [47]. Camera traps were strategically placed based on indicators such as depredation sites, footprints, scats and territorial markings along different trails. We collaborated with village conservation committee (VICs) members to achieve more effective monitoring. Despite challenges such as inaccessible terrains, glaciers, land sliding risks, floods and snow damage during winter the camera trapping methods remain unsystematic [47]. A grid system (5 x 5 km) with two or one infrared cameras (Bushnell© and Browning©, manufactured by Shenzhen Weixin Science and Technology Development Co. Ltd., Shenzhen China) supported data collection based on previous methodologies described by zaman et al.,2024 [39]. This set-up aimed to capture images from both sides of visiting snow leopards at trapping stations increasing the likelihood of identifying different individuals [25] and prey species as well as sympatric carnivores. Cameras were programmed to record either short videos (30-second duration) or take three photos per trigger event. Vegetation hindering camera views or generating false triggering was removed as recommended by Zaman et al. (2022). Videos were time-stamped and location-tagged, with 136 camera trap locations active between November 2018 and April 2023 in various villages. Monitoring took place over six consecutive winter sampling stages with camera checks every 15 days for data retrieval and maintenance (see details in table S1). Climatic factors The study focused on documenting captured events of leopards and their co-occurrence species through video records gathered in the presence or absence of snowcover or snowfall. We divided the winter season into two distinct periods: one with extreme cold temperatures and snowfall from December 25th to February 25th, and the other with warmer temperatures but lacking notable snowfall in November, March, and April [41]. These environmental conditions are known to influence the temporal activity patterns of leopards and their co-occurrence species [36]. We also recorded average temperature, cloud cover (assigned as 1 for clear and 0 for overcast or lack of sun) [28, 31] and wind intensity (assigned as 1 for presence of winds, irrespective of its velocity and 0 for absence of winds based on video records) were calculated at each study site using time-stamp information recorded in the video footage (see figure 1b)[62]. These factors have been used by previous researchers in similar studies [28, 31] and other factors such as elevations (m) day length of camera traps also noted for each camera station locations. Abundances data The number of videos along with the total number of camera functioning days was recorded at each site during the monitoring period. To quantify the relative abundance index (RAI) of leopards and co-occurrence species spatio-temporal events during day or night at each trap site in the presence or absence of snow and from different habitats, we aggregated the number of units of camera trap 24 hours following the methodology described by [63]. The RAI for a specific marking event was calculated using the formula: RAI = (Ni/TRAPDAYi) × 100, where Ni represents the number of independent valid videos of marking events I, and TRAPDAYi indicates a camera working day.These activities were grouped into three categories (1) nocturnal, with activity mainly between 1 hour after sunset and 1 hour before sunrise; (2) diurnal, with activity primarily between 1 hour after sunrise and 1 hour before sunset; and (3) crepuscular or twilights, occurring 1 hour before sunrise to 1 hour after sunrise and 1 hour before sunset to 1 hour after sunset [62]. Moreover, we documented the frequency of captured sightings for each species at every camera site,but without measuring the actual population sizes. Ibexes and markhor are known for their social behavior, often being seen in groups of 2 – 10 [64, 65]; multiple animals together were also classified as significant single events (see figure 1b).The diagram in Figure 1b illustrates conceptual models that depict the influence of climatic factors such as snow cover, temperature, cloud cover, and wind on the spatial and temporal top-bottom interactions among snow leopards, mesopredators, and their prey [31]. These interactions are examined at three distinct time scales: absence of snow (AS), presence of snow (PS), and across night (N), day (D), and twilight (T) periods. In the diagram, black solid circles indicate a preference for nocturnal activity and interactions, while blue solid circles represent daytime interactions. Unfilled circles denote interactions during twilight or dawn and dusk. Black dotted circles signify snow leopard abundance and activity, red circles denote mesopredator presence, and green dotted circles represent prey species. A horizontal dotted green line represents days without snow, and a solid blue vertical line signifies cloudy days with snow.These three different models represent various study sites and the complex dynamics between snow leopards, mesopredators, and prey across different temporal scales and climatic conditions [54].Camera picture is represented numbers of camera traps day in each sites. Species diversity and accumulation We carefully recorded the number of individual species detected through camera footage across multiple camera trap sites. We organized the data from five different sites and documented the detection rates of each mammalian species, noting whether snow was present or absent during each observed event.Videos containing humans, unidentifiable species, non-animal or bird subjects (such as triggered by motion of vegetation or wind or snow) and livestock were eliminated from the dataset as recommended by [45].This assisted us estimate the individual abundance size of each species enabling us to quantify and compare species abundance. We precisely registered the number of individuals of each species captured by cameras for the purpose of calculating species diversity and accumulation following the methodology outlined by [66]. In order to prevent duplicating sample recordings, we established clear criteria for defining independent events. These criteria include: 1) capturing consecutive videos of unique individuals, whether they belong to the same species or not, 2) taking consecutive videos of individuals from the same species with a time gap of more than 30 minutes, and 3) videos non-consecutive individuals of the same species. Because many cameras were battered not work due to low temperatures in winter. Figure b1 Data analysis Abundance and Interspecific relationships For interspecific temporal overlap interaction, we employed kernel density estimates to examine the daily activity patterns of specific pairs of target species based on the time stamps of individual sightings [31]. The coefficients of temporal niche overlap, ranging from 0 (no overlap) to 1 (complete overlap) were used to evaluate the temporal synchronization between each pair of target species during each winter season with and without snow cover [67]. We utilized method deltas (D4) for large sample sizes (minimum sample size > 75), method D1 for small sample sizes (minimum sample size < 50), and method D5 for sample sizes ranging from 50 to 75 [31, 67]. Confidence intervals (CI) were derived from 10,000 bootstrap samples [28]. Furthermore, we determined the frequency occurrence rate of species pairs through the utilization of 999 smoothed bootstrap values (SBV) for every sample, visually represented through a histogram, the averages means and differences (delta values) for each shared species. Each camera trap location was treated as a spatial point to assess the activity records of leopards and four co-occurring species between sunset and sunrise. As sunrise and sunset times vary slightly throughout the year depending on the distance from the equator and the season, we used the ”sunTime” function from the ”overlap” R package version 0.3.2 to convert times to radians for analysis (refer to [28] for more details). Subsequently, we employed the circular test (CT) to explore synchronous relationships between the activity rhythms of pairs species and their interaction with and without snow cover by utilizing Watson’s (W) 2-sample test 0.05 in the ”circular”(version 0.4- 94)” package in R 4.0.5 [28, 38]. Effect of climatic factor on the abundance of species In order to examine the impact of climatic factors on the spatial and temporal detection rate of leopards and four their co-occurring species,we used Generalized Additive Mixed Effect Models (GAMMs) as described by Panigada et al. [68], and conducted two separate analyses for the influence of climatic variables such as temperatures, clouds, winds and elevation, trapping days on the independent spatio-temporal detection rate of snow leopards and four co-occurring species during the presence or absences of snow [28, 31]. Each species independent recorded events—whether occurring in the presence or absence of snow—were used as response variables. Camera ID served as a random factor, while climatic variables as explanatory variables and analyzed the maximum likelihood value (ML) functioned using a negative poisson distribution log-link function. Model selection was based on criteria such as adjusted R-squared, the proportion of deviance explained, and chi-square tests, all evaluated at a significance level of 95% (p < 0.05) [56].The negative Poisson distribution model was applied using the MASS package in combination with the mgcv package for GAMMs using a Poisson (gamm function in the R ), following the approach outlined by Verbeke [69] . Species diversity and accumulation The sampling effort was determined by multiplying the total number of camera-traps by the number of days sampled. Each video of a species within a 24-hour period was considered a single independent capture. In cases where groups of individuals were captured each individual was counted as an independent capture [2]. Community diversity was assessed based on four components: species richness, camera-trapping rate, diversity indices and trophic guilds in five sites [2]. Species richness was calculated as the total number of species recorded by camera-traps in five various sites and these sites habitats encompass bare lands, alpine and sub-alpine meadows, herbaceous and shrubby areas, mixed forests, as well as regions used for grazing [39]. They are primarily involved in activities such as livestock depredation and trophy hunting, which mostly occur away from human settlements. For a detailed and repeated analysis of these aspects, please refer to our previous publications [39, 60]. The camera-trapping rate (CTR) was determined by dividing the total number of independent captures by the sampling effort, and then multiplying by 100. and diversity, where D =(∑S(i=1) pi )1/(1-i)), S considering the number of species in each sites, pi is abundance of each species in each sties ith and the order of diversity with the value of i [70]. Shannon’s and Simpson’s indices were used to characterize species diversity accounting for both evenness and richness and accumulation curves [71]. Species rarefaction curves were generated to analyze species richness or camera detection rates among different study sites for comparison [2, 71]. Confidence intervals (95%) were computed based on unconditional variance following the method of [72] with 1000 permutations. In our analysis, we generated accumulation curves by employing the ”specaccum” function from the vegan package, utilizing the method parameter set to ”random”, and visualizing the plots with ggplot2. Analysis was conducted using the “Vegan” “picante’ ’ “knitr” and “dplyr” packages, following similar methodologies as cited [72]. The structural equation modelling We used structural equation modelling (SEMs) to assess the impact of snow cover on the spatio-temporal independent detections events of leopards, mesopredator and their two main prey species at top-down and bottom-up interaction during different periods of the day, night and twilight [73] and the relationships among predator-prey and competitor interactions also varied across three different time scales [24]. To evaluate potential collinearity among all spatio-temporal detection events of five co-occurrence species, we conducted variance inflation factor (VIF) and Pearson correlation tests [38]. Variables with VIF values less than 2 and Pearson correlation coefficients |r| less than 0.5 were retained in the analysis [38]. VIF calculations were performed using the ”see”, along with the ”performance” packages.The Shapiro-Wilk statistic from the ”mvnormalTest” package [74] was utilized to test for multivariate normality in the data. Furthermore, six distinct SEMs were developed to analyze the presence or absence of snow by using a latent variables (LV) in maximum likelihood (ML) regression models with the default optimization method (NLMINB). The independent spatial-temporal detection events of each species were treated as the response variables, while the presence of other co-occurring species served as explanatory factors. The count of detection events for each species during different times of day—day, night, or twilight—was considered as a constant (latent variable). The SEMS were conducted separately for three temporal scales: daytime, nighttime and twilight for snow leopards, two preys and two mseopredator to investigate spatio-temporal top-down or bottom-up segregations or co-occurrences interaction among the mammalian guilds [38]. The path coefficients were estimated using the bootstrapping method specifically the naïve bootstrap and the Bollen-Stine bootstrap. The fit of each model was assessed using standardized root-mean-square residual (SRMR), root-mean-square error of approximation (RMSEA), and the chi-square test. A good fit was considered when RMSEA and SRMR values were less than 0.05, and the chi-square test was significant (p-value < 0.05) with the correct degrees of freedom. Additionally, coefficient 95% confidence intervals (CI) and covariances or variances (z value p < 0.05) were calculated using the (Hmisc;ciTools;dplyr) packages. The sem.mod function was used to create latent variables and predictors, and top-down and bottom-up network figures were constructed using the semPlot, latticeExtra, and tidySEM packages. The SEMs analysis was performed in R 4.0.5 using the lavaan package (version 0.6.3) developed by [75]. Abundances of speceis We used 2773 camera trap days and detecting 5008 occurrences of 10 mammalian species. Leopards, ibexes, markhor, martens and foxes were the main focus with 1719 detections during winter and 880 detections in absence of snow, compared to 839 observations with snow present. Foxes were frequently independently detected in snow condition, while martens were less commonly seen overall. Leopards were more often documented on snow days (n=78; RAI; 4.537) or nights (n= 92; RAI; 5.351) when there was no snow; ibexes were most commonly detected in absence of snow (n = 213) and markhor were higher detected during days without snow (n=101; RAI; 5.875) in the absence of snow (Table 1). Effect of Snow covers on Interspecific relationships In the presence of snow, leopards are most active in the late afternoon (around 14:00-17:00) and at dawn (around 4:00-5:00;Figure 2a). Foxes peak during twilight (around 17:00-18:00;Figure 2a), while martens are more active at night (around 18:00) or at dawn (around 5:00; Figure 2b). Ibexes are active in the early morning (around 5:00) and late afternoon (around 17:00;Figure 2c), while markhor prefer to active at midday (12:00) or twilight (18:00;Figure 2d). The overlap between leopards and foxes was not significant based on the Watson test (w = 0.302, p = 0.100; D=0.75; CI: 0.564 to 0.833), but they had a significant overlap with martens (D=0.82, CI: 0.718 to 0.934; w = 0.022; p < 0.001). There was also a noticeable overlap between leopards and ibexes (D=0.871,CI: 0.764 to 0.978; w = 0.043; p = 0.320), although this was found to be insignificant, as well as leopards and markhor (D=0.806,CI: 0.685 to 0.927; w = 0.131; p < 0.022). Differences in temporal co-occurrence were observed among species, with foxes and martens showing less overlap with leopards, and ibexes and markhor having higher rates of co-occurrence ( see Figure 2e-h). The leopards was most active in the morning (6:00;Figure 3a) and midday (around 10:00-12:00) when there was no snow present. Foxes, were most active during early dawn (around 4:00-5:00) and midday (around 10:00-12:00;Figure 3a). marten preferred to be active at night (around 2:00-24:00) and less at daytime (1:00) (Figure 3b). Ibexes were more active in the early dawn (5:00) and dusk (around 15:00-17:00 and 18:00; Figure 3c). Markhor showed a clear preference for early morning (6:00) or either midday (12:00) or twilight (18:00;Figure 4d). The overlap in activity between leopards and foxes was significant (D=0.804;CI :0.680 to 0.928;w = 0.022; p = 0.041). leopards also exhibited a high overlap with martens (D=0.779;CI ;0.556 to 0.870; w = 0.041; p < 0.001). The overlap between leopards and ibexes was lower but still significant (D=0.657; CI: 0.538 to 0.779; w = 0.051; p = 0.021). Similarly, the overlap between leopards and markhor was less, but the interaction was significant (D=0.561;CI:0.340 to 0.628; w = 0.440; p = 0.012). The frequency of co-occurrence detections varied for each species, with foxes (Figure 3e) and martens (Figure 3f) showing higher co-occurrence with leopards. Ibexes (Figure 3g) had the highest co-occurrence with leopards as compared to markhor (Figure 3h). Figure 2a-d. Temporal overlap in daily activity patterns was examined among snow leopards, mesopredators and two prey species in the presence of snow (Figure 2a-d). A solid line represents the activity of snow leopards, while a dotted line represents the activity of the coexisting species and a vertical dotted line indicating dawn or dusk. The comparisons were made between snow leopard and fox (Figure 2a), marten (Figure 2b), ibex (Figure 2c) and markhor (Figure 2d). Figure 2e-h. The frequency of snow leopard sightings and co-occurrences with other species in snowy conditions. The dotted blue line represents the mean co-occurrence, while the solid red line indicates the overlaps with a 95% confidence interval. Figure 3a-d.Temporal niche overlap was examined between snow leopards, mesopredators, and two prey species during snowy conditions. The vertical dotted line indicates either dawn or dusk. The solid line corresponds to the activities of snow leopards, while the dotted line represents the activities of co-existing species. The pairs compared include (a) leopards and foxes, (b), martens(c), ibex, (d), markhores. In Figure 3e-h, The frequency of snow leopard detection and the presence of other species when snow is present. The dotted blue line represents the mean co-occurrence, while the solid red line indicates the overlaps with a 95% confidence interval. Impact of climatic condition on spatio-temporal detection events of species The GAMMs model incorporating camera identity as a random factor revealed a significant effect (X2 = 0.313; p = 0.051). In snow-free conditions, leopard detection rates were positively associated with cloud cover, temperature and camera identity. Fox detection rates increased in windy conditions at lower elevations. Markhor detection was higher at higher elevations, while ibex detection rates were significantly linked to cloud cover. Marten detection rates showed no significant association with lower elevations (see Table 2a). During snowy conditions (which explained 45.5% of deviance; X2 = 3.055; p = 0.033), leopard detection rates tended to avoid windy conditions, low temperatures, and higher elevations. Fox detection rates decreased with lower temperatures. Markhor detection was negatively affected by cloud cover but showed a preference for windy conditions. Ibex detection rates were associated with cloud cover but avoided higher elevations. The detection rates for martens also indicated that higher elevations were avoided during snow conditions (refer to Table 2b). Species richness and assemblage During snowy conditions, the detection of markhor was low at Trabathing site 1, 57 with a CTR of 2.055, but highest at Skoyo 491 with a CTR of 17.92. Ibex sightings were minimal at Site 1 (CTR 0.036) and highest at Site 2 (CTR 5.681). Leopard detections peaked at Site 1 (CTR 8.186, 227 events) and were lowest at Site 5 with only 10 detections (CTR 0.360). Foxes were most frequently recorded at Site 1 (CTR 0.360, 10 events), and no foxes were observed at Sites 4 and 5 . Martens had the fewest detections at Site 1,(CTR 0.180,5 events) and the most at Site 2 (CTR 0.865, 24 events); none were recorded at Site 4. Species diversity varied across locations with Shannon’s index indicating the highest mammal diversity at Site 2 compared to Site 5. Simpson’s index showed notable diversity at Sites 1, 2, and 3. Rarefaction curves confirmed sufficient sampling effort for detecting species richness at these sites(Figure 4a). Species accumulation curves showed the greatest number of species at Sites 1 and 2, with fewer at Sites 4 and 5 (Table 3a; Figure 4 b). Overall, the patterns observed in Shannon’s and Simpson’s indexes were consistent across all sites (Figure 4c-d). Data were analyzed using a random accumulation method with 1,000 permutations . The lack of snowfall resulted in the highest detection rate of markhors at site 1, 176, with a CTR of 2.740. In contrast, ibexes were least detected at site 1 (CTR 0.937) and most at site 2, 129 events (CTR 4.652). Martens were highest observed at site 2, 14 events (CTR 0.504). Foxes showed the lowest detection in site 1 (4 events; CTR 0.144), while site 2 recorded the highest number, with 196 foxes (CTR 7.068). Leopards had their lowest detection at site 5 (CTR 0.180) and the highest at site 1, with 76 events(CTR 2.740) .We found that the diversity analysis using Shannon’s index indicated mammals were most diverse at site 1 compared to site 5. Simpson’s index confirmed the highest mammal diversity at sites 1 and 3 ((Table 3b). Rarefaction curves revealed variations in species richness across sites and camera operation days. Species accumulation curves identified the greatest species richness at sites 3 and 4, consistent with Shannon’s and Simpson’s indices based on 1000 permutations of the random accumulation data (Figure 4e-h). Figure 4; The presence of snow leopards and four other co-occurring species across five study sites. The graph displays the rarefaction curve depicting the number of species detected on the y-axis, with each study site represented on the x-axis. The species richness is shown on the y-axis against different study sites on the x-axis. The rarefaction curve and species accumulation curves are divided into sections for presence and absence of snow: (a-d) representing the presence of snow, with rarefaction curve (a-d) and species accumulation curves for Shannon’s index (b) and Simpson’s index (c); (e-h) representing the absence of snow, with rarefaction curve (e-h) and species accumulation curves for Shannon’s index (f) and Simpson’s index (g) Top-down and bottom-up effects Throughout the day, snow presence was well aligned with the data (see Table S2). Events of fox detections and their daytime activity did not exhibit top-down interactions with martens and leopards. Similarly, leopard detection events and their daytime activities showed no bottom-up influence on martens and foxes. Marten detection events and their daytime activities did not demonstrate top-down effects on leopards and foxes (Figure S1a; see Table S2). Additionally, ibex detection events indicated top-down interactions with leopards but did not show such effects with foxes, martens, or markhor. Notably, the detection rate of markhors suggested avoidance of leopards from a top-down perspective; interactions with foxes were observed, whereas no significant interactions occurred with martens or ibexes. During twilight or crepuscular hours, SEMS data explained a significant portion of the variation (Table S2). Fox detection events and their activity during twilight did not display top-down interactions with martens or leopards. Leopard detection events and their activities showed no bottom-up effects on martens and foxes (Table S2). Conversely, marten detection events and activity periods engaged in top-down interactions with leopards but not with foxes. Ibex detection events did not reveal top-down interactions with leopards, foxes, martens, or markhor. Furthermore, markhor detection rates exhibited top-down avoidance behavior toward leopards and foxes, with no such interactions observed with martens (Figure S1b; see Table S2). At night, combined SEM observations and statistical analyses indicated that fox detection events and their activity were top-down positively correlated with martens and leopards (Table S1). Leopard detection events, characterized by bottom-up influence, showed a positive relationship with martens and foxes (Table S2). Marten detection events did not demonstrate significant top-down interactions with leopards and foxes. The detection rate of ibexes was top-down positively correlated with leopards but showed no significant association with foxes, martens, or markhor. Similarly, markhor detection rates exhibited top-down avoidance behavior toward leopards and foxes, with no significant interactions observed with martens or ibexes (see Figure S1c; Table S2). In the absence of snow, daytime activity patterns were analyzed using Structural Equation Models (SEMs) (Figure S1d) (Table S3). The fox detection rate did not show any top-down influence from martens, but instead appeared to reflect avoidance behavior towards leopards. Leopard detection rates did not significantly have bottom-up effects on martens or foxes (Table S3). Martens demonstrated a positive top-down interaction with leopards but showed no such relationship with foxes. Similarly, ibex detection exhibited a top-down influence from leopards, while no significant interactions were observed with foxes, martens, or markhors. Markhor detection events did not display any top-down or bottom-up interactions with leopards, foxes, martens, or ibexes. During twilight or crepuscular periods, SEMs were used to analyze activity data (Figure S1e) (Table S3). Fox detection events did not reveal any top-down interactions with martens or leopards. Likewise, leopard detection rates did not show bottom-up effects on martens and foxes. Martens’ detection events did not significantly interact with the activity of leopards or foxes from a top-down perspective. Ibex detection showed no strong top-down association with leopards but indicated avoidance behavior toward foxes and martens. Additionally, markhor detection events exhibited top-down avoidance of leopards and a positive interaction with foxes, but showed no significant relationship with martens and a negative interaction with ibexes (Table S3). At night, SEM analysis revealed further important findings (Figure S1f). Fox detection rates did not demonstrate any top-down correlations with martens or leopards. Leopard detection from a bottom-up perspective was not linked to activities of martens but showed a positive interaction with foxes. Marten detection events exhibited top-down interactions with their activity levels but were not significantly associated with leopards or foxes. Ibex detection events operated independently, showing no top-down interactions with leopards, foxes, martens, or markhors. Markhor detection events displayed top-down avoidance behavior toward foxes and did not show significant interactions with leopards, martens, or ibexes (Table S3). Discussions This study investigated the role of leopards as apex predators in shaping the diversity and spatial-temporal interactions of mesopredators and prey across multiple sites. Results indicated that species and functional groups exhibited distinct, time-dependent responses. Additionally, climatic variables like snow, cloud cover, temperature, and wind significantly affected the presence and activity patterns of leopards, ungulates, and mesopredators [61]. This paper discusses how government and NGO conservation initiatives have helped protect these sites by reducing illegal hunting, although human-wildlife conflicts continue to pose challenges [39]. Previous studies suggest that reduced human disturbances may have led to growth in community group populations [41]. Our study did not thoroughly explore seasonal habitat variables due to limited human disturbance, although grazing activity was still evident [39]. Previous research suggests that leopards prefer open bare areas and herb-shrublands for territorial marking and hunting. Prey species tend to favor these regions due to the abundant foraging opportunities, despite the elevated risk of predation [60]. Additionally, foxes and martens utilize the same travel paths as leopards. In one observed case, a leopard predated a markhor, and subsequently, both foxes and martens regularly visited the carcass to scavenge, consistent with findings from our previous research [61]. Notably, foxes were detected more often in proximity to leopard habitats compared to martens [76]. In our study, we did not compare seasonal variations in diversity or consider factors such as age, sex, and habitat, which may introduce bias at our current sites. The sampling period was limited and not ideally suited for camera trapping across multiple seasons, especially given ongoing grazing activities, mining operations, and the movement of target species to higher elevations, making monitoring more challenging and less accessible to humans[39], for exmaples seasons [28], seasonals movement [77], and group size, sex [78], and ages as well as habitat factors showed influnce on ecoystem webs [36]. Both ibex and markhor repeatedly avoided leopard pathways. During camera trap surveys, we recorded two male markhor examining a rock face tagged with leopard urine, then swiftly retreating, likely driven by fear [61]. This finding supports existing research indicating that the presence of top predators can influence herbivore populations and mesopredators by heightening the landscape of fear [79, 80]. Our findings indicate that the availability of snow plays a crucial role in modulating the movement patterns and interactions among leopards, foxes, martens, ibexes, and markhors within their shared habitats, a trend consistently observed across various global landscapes[81]. In addition to suppressing mesocarnivore populations numerically, larger predators can also influence their behavior, leading to reduced predation efficiency. This behavioral regulation aligns with the mesopredator release hypothesis, highlighting the non-lethal ways apex predators impact smaller carnivores [82].In habitats dominated by large predators, martens often resort to avoidance strategies to minimize the risk of aggression and predation [79]. Abundance and interspecific interactions Our findings indicate that human activities significantly impact leopard detection rates and species co-occurrence, with camera sites in areas of higher human presence—such as fuelwood collection zones, grazing areas, and livestock enclosures—showing reduced encounter frequencies. Additionally, leopard behavior varies with seasonal changes; they display more scent-marking and roaming during snow-free periods, likely to avoid peak activity during snowfall, possibly influenced by prey behavior and environmental factors [60]. Additionally, leopards were most active in the late afternoon and twilight times when human disturbance is minimal. In areas with significant human presence these felids are generally more active at night [24, 83]. These seasonal shifts in activity patterns during different times of the day suggest that leopards regulate their daily activities to avoid extreme temperatures [36, 84]. According to Hunter et al. [85], the findings suggest that climate change poses a greater threat to the survival and long-term conservation of leopards in mountainous regions. In our study, we observed that both ibexes and markhor tend to form social groups comprising either solely males or mixed sexes. However, due to limitations in sex identification and group size data from camera traps, we did not analyze activity patterns based on sex. Overall, ibexes were most active during early mornings and late afternoons, while markhor showed a preference for midday and twilight activity periods. These activity patterns remained consistent across conditions with or without snow cover. Notably, markhor occupy lower elevations compared to ibexes in our study sites, likely reflecting adaptive responses to seasonal snow conditions. Both species seem to adjust their activity based on snow presence, for instance moving to warmer, lower-elevation slopes during severe winter conditions—possibly as an avoidance strategy for predators or human disturbance. For examples, the temporal activity shifts of ungulates in response to snow cover fluctuations or either predator avoidances have also been observed in other regions such as India [20], China [86], and Mongolia [36]. Prey species employ different strategies to reduce predation risk including adjusting foraging times, selecting safer areas and being more vigilant in high-risk zones [80]. In Pakistan ibex are classified as Least Concern on the IUCN Red List [20], while markhor was previously listed as Endangered from 1994–2015, but was rank to Near Threatened in 2015 [64] and both are trophy animals in northern pakistan. The red foxes were frequently observed regardless of snow conditions, whereas martens appeared less often. Our findings indicate that snow presence affected the activity patterns of both species, with martens modifying their activity times—either during twilight or in response to interactions with co-occurring species [87]. Research conducted in northern Pakistan [56], showed that martens were strictly nocturnal, while foxes were mainly active during the night or twilight hours, with some sightings during the day and some caused founded both species to be nocturnal [88]. This study reveals that snow cover, leopards, and their prey species such as markhor tend to exhibit overlapping activity periods, indicating positive interactions. Similarly, the presence or absence of snow influences the behavior of martens in a comparable manner. Notably, without snow, leopards show increased interactions with key prey and mesocarnivores. These findings highlight the complex behavioral dynamics between predators and prey, where prey behavior aims to evade predation while predators continuously adapt their hunting strategies. [59]. According to optimal foraging theory, both predators and prey balance foraging costs and opportunities for survival and reproduction [89]. As per prior research conducted in the Baltistan region of Northern Pakistan certain co-occurring species have been recognized for impacting the dietary preferences of leopards [57]. Additionally, interactions between apex predators and mesopredators are common, with apex predators regulating their temporal activity through fear tactics or direct predation [49]. Effect of climatic factor on leopards and four co-occurrence species Our research discovered that the spatial and temporal distribution of leopards and co-occurring species are influenced by climatic conditions [79, 90]. Factors such as cloud cover and ambient temperature have a significant impact on the activity of leopards, while foxes do not avoid windy conditions. Markhor tend to preferred moderates elevations, while martens favour lower elevations[11]. Ibexes activity is linked to cloud cover particularly during snow-free periods and similar result also occurred in others studies [79]. Previous studies have shown that seasonality plays a crucial role in animal physiology, [91], movement ecology, foraging strategies [92], and survival in response to temperature changes, precipitation, and food availability [93]. Additionally, wind flows and cloud cover can also impact the behavior of different species[28]. In snowy environments, leopards tend to steer clear of windy areas, cold temperatures, and high-altitude regions. Foxes are adversely impacted by low temperatures, whereas Markhor prefer windy conditions over cloudy days. Ibex are more active during overcast weather but avoid higher elevations, similar to martens. Research indicates that rising temperatures may influence predator-prey dynamics by elevating predator metabolic rates and their foraging needs [94, 95] and According to [96] warming has been proven to lower prey activity in case of predation. In snowy conditions, leopards tended to evade areas with strong winds, low temperatures, and higher altitudes. Foxes experienced reduced activity in colder weather, while markhor preferred clear days over cloudy ones but showed a preference for windy conditions. Ibexes were more active on cloudy days but tended to avoid higher elevations. Similarly, martens avoided higher altitudes. Overall, severe weather phenomena such as strong winds, snowstorms, and overcast skies appear to diminish the activity levels of species inhabiting extreme environments [97].These climatic factor also linked to Mountain-dwelling animals tend to seek higher elevations in the summer months and move to lower altitudes during winter to ensure survival and access to food [98, 99]. Species richness and diversity Our findings indicate that snow cover significantly affects the abundance of leopards and other species in the area. Notably, fox populations were notably higher and observed across most study sites, aligning with previous research findings [43]. Markhor was found in only one site, while martens showed lower abundance compared to ibexes and other species. To effectively achieve conservation goals accurate estimation of species richness, abundance, and activity is crucial [66]. It is essential to evaluate the accuracy, precision, replicability and cost-effectiveness of techniques before recommending them on a larger scale [100]. Camera trapping surveys are commonly used to document species richness [101], occupancy [102], abundance indices [103], human disturbances [63],estimate population sizes of identifiable species in capture-recapture studies [25], and determine activity patterns [104]. While there is limited research on using Shannon’s and Simpson’s index to estimate the diversity of leopards, prey, and mesocarnivores in northern Pakistan, camera traps have proven to be valuable tools in calculating the abundance and diversity of mammals and birds [45].The diversity and abundance of species are influenced by climatic conditions. For example, during winter, leopards and their co-occurrence move from colder to warmer areas, impacting the abundance and diversity of species in the presence or absence of snow [36, 54]. Our research indicated that Study Area 1 and Site 2, exhibited a higher diversity and abundance of leopards and co-occurring species. This can be attributed to the focus on community-based conservation efforts in these areas, which involve community participation and incentive programs to promote conservation attitudes [105, 106]. Despite the presence of foxes and martens throughout the study area, foxes are known for their adaptability and non-threatening behavior [43], while martens typically do not pose a threat to livestock, thus reducing conflicts [56]. Markhor are predominantly found in lower elevation areas [64], while ibexes tend to inhabit higher elevations to avoid predators and human disturbances [65]. The presence of apex predators like the leopards plays a crucial role in maintaining the abundance and diversity of prey and mesopredators in the ecosystem. This highlights the importance of apex predators in regulating ecosystem balances, as observed in other studies [79, 107]. Spatio-temporal top-bottom and bottom-up interactions During snow-covered days, foxes showed no clear avoidance or preference for snow leopards or martens across different seasonal activity periods. Additionally, leopard sightings during the daytime were independent of fox and marten detection rates. Marten detection did not exhibit any bottom-up influence from foxes or leopards, aligning with theories of temporal segregation between top predators and mesopredators[104]. In our study, ibex prey showed a strong bottom-up response to leopard activity but did not react similarly to mesopredators. Conversely, markhor adjusted their spatial behavior to avoid areas with high leopard abundance, yet showed no significant response to mesopredators or ibex. These findings suggest that top predator influence primarily shapes prey behavior, reflecting how prey respond to apex predators at various temporal scales [73]. In snow-free conditions, foxes exhibited top-down avoidance of leopards, consistent with findings from previous studies. [15]. Marten detetion rate showed interaction with leopards, but in the former research it was concluded that marten avoid leopard [56].Apex predators can also control mesopredators through intraguild interactions [108]. Ibexes indicated top-bottom interaction with leopards as observed in China [109]. Makhore did not show any interactions within species. For example, predator regulation of herbivore populations [110]. The removal of apex predators can disrupt intraguild interactions leading to mesopredator occurrence and increased predation on smaller prey [12]. For example, martens and ibexes are less active during the daytime compared to foxes. These interactions have significant impacts on the spatiotemporal niches of both predators and prey [111]. Sympatric species, especially herbivores must strategically balance their spatial and temporal niches to optimize fitness and minimize interspecific competition showing more distinct differences in temporal niches than spatial differentiation [3]. In contrast, carnivores tend to exhibit more spatial differentiation than temporal differentiation [90].There is growing recognition of the important roles played by predators in regulating ecosystems and sustaining biodiversity [3]. Furthermore, in the snowy condition during nighttime, foxes detetion rate did not exhibit top-bottom interactions with leopards and martens. However, leopards suppressed mesopredator activity by positively interacting with both foxes and martens as similar with the previous work [56], but did not show interactions with leopards or foxes. Ibex abundance showed no any bottom-up interactions with other species. Markhors exhibited bottom-up avoided with leopards and foxes on a temporal scale .When snow was absent at nighttime, foxes did not top-bottom interact with other coexisting species and to reduced interaction guilds at temporal-spatial scales [14]. The detection rates for leopards exhibited a bottom-up relationship with fox detections, while marten detection rates were linked to their nocturnal activity patterns consistent with findings from previous studies[11]. Former research suggested that apex predators bring fear causing mesopredators like coyote ( Canis latrans ) and bobcats ( Lynx rufus ) to adjust their activity patterns to avoid conflicts with in areas with and without an apex predator predators like gray wolf ( Canis lupus ) [112]. Ibexes did not show interactions with co-occurrence species but in Mongolia that ibex showed dodging to leopards at spatio-temporal scales [36]. Interestingly, markhor avoided encounters with foxes due to their related distribution ranges in northern Pakistan [42]. During the presence of snow, crespecular time the foxes did not show any interaction with leopards or co-occurrence species in a top-down manner but mostly former study explored that foxes were more active during the dawn or dusk [56, 100] and snow depth alters their interaction [113] .Leopards showed no specific preference for interacting with any particular species. In contrast, martens engaged in top-down interactions with leopards but did not interact with foxes. While ibexes largely avoided other species, markhors demonstrated avoidance behaviors toward both leopards and foxes, likely driven by predator-induced fear in their shared habitat [80, 111]. In the absence of snow, foxes and leopard abundance did not indicated interactions to other species but absent of snow cause more overlap apex predator and mesopredator in the former work [100, 104]. Similarly, martens displayed a preference for their temporal activity during crepuscular also somehow but mostly researcher considered nocturnal or crespecular but that depending on habitat factors [14, 104]. Ibexes did not associate with leopards for obvious reasons, but showed avoidance behavior towards foxes this related to the mesopredator released hypothesis [12] and non-lethal effect by carnivores [114]. Further analysis revealed that markhor avoided the temporal activity of leopards as well as ibexes and but no avoidance showed against the foxes. These variations in activity are influenced by climate change [35], impacting top-down interactions [77]. Implications for management Our findings highlight the role of top-down ecological regulation, with leopards as apex predators, influencing species diversity and wildlife activity patterns in northern Pakistan. Additionally, climate change is increasingly affecting snow leopards’ foraging and behavior, underscoring the need for habitat restoration and careful management to mitigate human impacts. The coexistence of humans and wildlife presents a complex conservation challenge that requires balancing ecological needs with human interests. We emphasize the importance of integrating both top-down and bottom-up strategies in conservation efforts, advocating for proactive and adaptive approaches to ensure the sustainable future of snow leopards and other regional wildlife populations.Despite efforts by local non-profit organizations and governments to protect the top predator population in mountainous regions [115], the complete recovery of this species still poses significant challenges in the region [105]. Prey species like ibexes and markhor are also facing declining populations due to illegal hunting and a lack of conservation measures [64, 65]. For example, the markhor was locally extinct from the Shigar valley 30 years ago due to a lack of conservation plans [41]. Our research highlights several key implications for the better management of top predators, mesopredators and prey species along with their habitats. Firstly, areas with high abundance and quality of pasture land play a crucial role in maintaining animal populations and ecosystem stability [20, 54]. Secondly, our research demonstrated the importance of preserving the diversity and abundance levels of leopards, mesopredators, and their prey across different spatial and temporal scales: species diversity is associated with pristine and robust ecosystems. Areas with lower abundance of these species required prioritized efforts to enhance habitat quality, especially involving local communities in conservation work including mitigating human-wildlife conflicts [2]. This highlights the critical implications for effective management strategies outlined in the study.Thirdly, our findings also examined how changes in climatic conditions affect the spatio-temporal activity patterns of species in different regions. Particularly, human disturbance can influence the daytime activity of each species, with distinct behavior observed in the presence or absence of snow. When snow is present, species tend to move to lower elevations and exhibit increased daytime activity to regulate their body temperature effectively [35, 77] .Top predators play a crucial role in ecosystem recovery through top-down effects, such as the ”landscape of fear” phenomenon or direct predation [116].

We want to express our gratitude to all the village field researchers for their genuine support during data collection in our study site. We also thank the Snow Leopard Conservancy (SLC) and the NABU (Nature and Biodiversity Conservation Union) for their financial support of projects. Conflict of interest statement On behalf of all authors, the corresponding author states that there is no conflict of interest. AUTHOR CONTRIBUTIONS MZ formulated the research inquiry and designed the analytical framework, conducted data processing and analysis, and authored the manuscript. MZ and SH collaborated on drafting the initial funding proposals and crafting the camera trap sampling strategy. NJ oversaw the funding for fieldwork operations, with MZ providing input. SH collected the data, while FL and RJ aided in data interpretation and offered conceptual insights. All authors contributed feedback on various drafts of the manuscript . Funding information Funding: none DATA AVAILABILITY STATEMENT The data input for the model and the R script provided as additional supplementary material for the manuscript.

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Figure S1, In the presence of snow the data from camera traps was utilized for the Structural Equation Modelling Analysis. The time of sightings for each species was considered as Latent Variables, while the abundance of each species was used in the SEM models, with the inclusion of Covariances and Variances labels, along with a 95% confidence interval for the structural equation models. The study focused on the interactions between snow leopards, mesopredators, and prey species across three different time scales: top-down, bottom-up, and reciprocal interactions. These interactions were examined during daytime spatio-temporal interactions (a), as well as during crepuscular(b) and c, nighttime periods. Figure S1, In the absence of snow the data from camera traps was utilized for the Structural Equation Modeling Analysis. The time of sightings for each species was considered as Latent Variables, while the abundance of each species was used in the SEM models, with the inclusion of Covariances and Variances labels, along with a 95% confidence interval for the structural equation models. The study focused on the interactions between snow leopards, mesopredators, and prey species across three different time scales: top-down, bottom-up, and reciprocal interactions. These interactions were examined during daytime spatio-temporal interactions (d), as well as during crepuscular(e) and f, nighttime periods. Table 1. The number of independent captured videos events and relative abundance index (RAI per 100 camera trap days) for snow leopards and mesopredator and their preys species during the presence and absence of snow at three time scale day,night and twilight at various study sites in Northern Pakistan. | Names | events | Absence of snow | events | Presence of snow | |||| | Day | Night | twilight | Day | Night | twilight | ||| | leopard | 202 | 73(4.246) | 92(5.351) | 37(2.152) | 171 | 78(4.537) | 56(3.257) | 37(2.152) | | ibex | 213 | 51(2.966) | 117(6.806) | 45(2.617) | 183 | 62(3.606) | 81(4.712) | 40(2.326) | | markhor | 176 | 101(5.875) | 44(2.559) | 31(1.803) | 159 | 95(5.526) | 36(2.094) | 28(1.628) | | fox | 231 | 70(4.072) | 128(7.446) | 33(1.919) | 254 | 96(5.584) | 134(7.795) | 24(1.396) | | marten | 58 | 12(0.698) | 29(1.687) | 17(0.988) | 72 | 13(0.756) | 38(2.210) | 21(1.221) | | Presence or absence of snow total | 880 | 307(17.859) | 410(23.851))) | 163(9.482) | 839 | 344(20.011) | 345(20.069) | 150(8.726) | | study total | 1719 | Table 2a. The negative posson model of generalized additive mixed effect models (GAMMs) was utilized to explore how climatic factors influence the abundance of snow leopards and other species when there is no snow present. Camera identity (ID) was accounted for as a random factor in the analysis | Variables absence of snow | NB | df | -ML | DE (%) | R2 | Ref | df | Chi.sq | P | | leopard ~clouds (+***)+temperature(-*)+S(camera.id,.) | 2.175 | 4 | 96.631 | 6.32 | -0.016 | 9.059 | 1 | 0 .313 | 0.510 | | Fox~winds(**)+elevation(*)+S(camera.id**) | 4.749 | 3 | 96.631 | 28.30 | 0.267 | 0.856 | 1 | 6.761 | 0.004 | | Markhor~elevation (***)+S(camera.id)(.) | 13.93 | 2 | 84.087 | 26.70 | 0.171 | 3.915 | 1 | 0.020 | 0.561 | | Ibex~clouds (+**)+temperature (-*)+traps day(+***)+S(camera.id)(*) | 3.785 | 4 | 13.93 | 27.00 | 0.199 | 5.731 | 1 | 0.001 | 0.045 | | Marten~ elevation (-**)+S(camera.id)(.) | 8.158 | 2 | 52.766 | 29.80 | 0.31 | 6.085 | 1 | 0.112 | 0.413 | Hence, Negative posson (NB) ;significant. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘. ’ 1; and approximate significance of smooth terms(S); random effect with degree of freedom (Ref.df), chi-square test with significant level ( Chi.sq p-value),adjusted R square (R2);deviance explained (DE);maximum livelihood value (ML);significant level positive (+) and negative (-). Table 2b. The negative binomial model of generalized additive mixed effect models (GAMMs) was utilized to analyze the impact of various climatic factors on the abundance of snow leopards and other co-occurring species in the presence of snow. Camera identity (ID) was included as a random factor in the analysis | Variables presence of snow | NB | df | -ML | DE(%) | R2 | Ref | df | Chi.sq | P | | leopard~winds(-**)+temperature(-*)+elevation(-** )+trap days (-.)+S(camera.id)(*) | 4.949 | 5 | 76.57 | 45.50 | 0.348 | 0.670 | 1 | 3.055 | 0.033 | | Fox~temperature(***)+trapdays(**)+S(camera.id)(.) | 0.853 | 3 | 125.47 | 53.41 | 0.17 | 1.116 | 1 | 0 .031 | 0.373 | | Markhor~clouds(-**)+winds (+*)+S(camera.id)(**) | 1.053 | 3 | 237.44 | 30.30 | 0.145 | 0.865 | 1 | 7.309 | 0.003 | | Ibex~clouds(+**)+elevation (-*)+traps day(-*)+S(camera.id)(.) | 2.694 | 4 | 134.75 | 56.61 | 0.198 | 1.650 | 1 | 0.001 | 0.381 | | Marten~ elevation (-**)+S(camera.id)(.) | 9.331 | 2 | 79.246 | 52.30 | 0.63 | 2.428 | 1 | 0.010 | 0.541 | Hence, Negative posson (NB) ;significant. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘. ’ 1; and approximate significance of smooth terms(S); random effect with degree of freedom (Ref.df), chi-square test with significant level ( Chi.sq p-value),adjusted R square (R2);deviance explained (DE);maximum livelihood value (ML);significant level positive (+) and negative (-). Table 3a.This table displays the camera traps employed in determining the simpson and shannon diversity indices for the abundance of snow leopard and four other co-occurring species across five study sites. These sites include snow, basha, thaly, skoyo, trabathang, basingo (SKB), as well as the community conservation hunting areas of Hussainabad (CCHHs) and Hushe. | species diversity index | Absence of snow estimation of species diversity | |||| | Study sites | Basha | Thaly | SKB | CCHH | Hushe | | Simpson(D) | 0.42 | 0.50 | 0.69 | 0.51 | 0.53 | | Shannon(H) | 0.81 | 0.69 | 1.32 | 0.69 | 0.89 | | Richenss | 3.19 | 4.79 | 0.56 | 4.21 | 4.61 | | sd | 1.17 | 0.43 | 0.001 | 0.75 | 0.49 | | Estimate diversity | specpool | |||| | Species chao | 5 | |||| | chao.se | 0.35 | |||| | jack1 | 5.8 | |||| | jack1.se | 0.8 | |||| | jack2 | 6.4 | |||| | boot | 5.34 | |||| | boot.se | 0.51 | |||| | n | 5 | Table 3b . The camera traps utilized for assessing simpson and shannon diversity indices to determine the abundance of snow leopards and four co-occurring species in five research sites containing snow, basha, thaly, skoyo, trabathang, basingo (SKB), as well as the community conservation hunting areas Hussainabad (CCHAs) and Hushe. | Species diversity index | presence of snow species diversity | |||| | Study sites | Basha | Thaly | SKB | CCHH | Hushe | | Simpson(D) | 0.52 | 0.47 | 0.59 | 0.51 | 0.45 | | Shannon(H) | 0.94 | 0.83 | 1.18 | 0.69 | 0.84 | | Richenss | 3.59 | 4.79 | 0.51 | 4.31 | 4.61 | | sd | 1.02 | 0.41 | 0.01 | 0.64 | 0.49 | | Estimate diversity | specpool | |||| | Species chao | 0.5 | |||| | chao.se | 0.46 | |||| | jack1 | 5.9 | |||| | jack1.se | 0.9 | |||| | jack2 | 6.5 | |||| | boot | 6.33 | |||| | boot.se | 0.49 | |||| | n | 0.5 | Suuplementary Information Table S1.the numbers of captured of all mammalian species during study periods with villages names,traps days and number of camera point information. | Villages | years | Traps days | Cam stations | leopard | ibex | markhor | fox | marten | wolf | lynx | pika | squirrel | weasel | | Basha | 2018 to 2019 | 123 | 8 | 42 | 544 | 0 | 217 | 11 | 9 | 0 | 6 | 0 | 2 | | Skoyo | 2018 to 2019 | 91 | 11 | 142 | 24 | 18 | 138 | 21 | 15 | 0 | 0 | 0 | 0 | | Hussainabad | 2018 to 2019 | 50 | 3 | 0 | 0 | 0 | 3 | 3 | 0 | 0 | 0 | 0 | 0 | | Basho | 2019 t0 2020 | 179 | 2 | 16 | 0 | 0 | 0 | 8 | 3 | 0 | 0 | 0 | 0 | | Krabathang | 2019 t0 2020 | 92 | 10 | 45 | 21 | 12 | 111 | 4 | 0 | 0 | 0 | 0 | 0 | | Hushe | 2019 t0 2020 | 157 | 20 | 95 | 0 | 0 | 132 | 1 | 21 | 13 | 0 | 0 | 0 | | Skoyo | 2020 t0 2021 | 100 | 4 | 111 | 3 | 0 | 124 | 3 | 0 | 0 | 0 | 0 | 0 | | Krabathang | 2020 t0 2021 | 96 | 8 | 169 | 80 | 751 | 937 | 7 | 2 | 0 | 0 | 0 | 0 | | Mendi | 2020 t0 2021 | 71 | 3 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | | Hushe | 2020 t0 2021 | 192 | 8 | 40 | 0 | 0 | 210 | 2 | 0 | 0 | 1 | 0 | 0 | | Khumerah | 2020 t0 2021 | 123 | 3 | 1 | 27 | 0 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | | Skoyo | 2021 t0 2022 | 201 | 8 | 36 | 9 | 6 | 105 | 22 | 0 | 0 | 0 | 3 | 1 | | Krabathang | 2021 t0 2022 | 192 | 16 | 26 | 65 | 52 | 72 | 3 | 3 | 0 | 1 | 0 | 0 | | Basho | 2021 t0 2022 | 142 | 2 | 0 | 1 | 0 | 78 | 0 | 0 | 0 | 0 | 0 | 0 | | Khumerah | 2021 t0 2022 | 213 | 3 | 0 | 2 | 0 | 3 | 2 | 0 | 0 | 1 | 0 | 0 | | Thallay | 2021 t0 2022 | 100 | 3 | 5 | 0 | 0 | 6 | 11 | 0 | 0 | 0 | 0 | 1 | | Hushe | 2021 t0 2022 | 151 | 6 | 18 | 143 | 0 | 43 | 1 | 0 | 0 | 0 | 0 | 0 | | Skoyo | 2022 t0 2023 | 145 | 5 | 44 | 0 | 5 | 13 | 19 | 0 | 0 | 0 | 0 | 0 | | Mendi | 2022 t0 2023 | 123 | 3 | 5 | 0 | 0 | 0 | 3 | 0 | 0 | 1 | 0 | 0 | | Hushe | 2022 t0 2023 | 100 | 6 | 15 | 7 | 0 | 13 | 3 | 0 | 0 | 0 | 0 | 0 | | Thallay | 2022 t0 2023 | 132 | 4 | 3 | 0 | 0 | 1 | 8 | 0 | 0 | 1 | 0 | 0 | | Total | 2773 | 136 | 813 | 926 | 845 | 2207 | 133 | 53 | 13 | 11 | 0 | 4 | | | Total captured | 5008 | Table S2.Snow leopard and co-occurrence species temporal interaction during the presence of snow at the nigh,day and twilights and also showed significant levels by using the SEMs | Top-down and bottom-up interaction | Presence of snow | Coeff | Z | p | X2 | df | p | | Day time | Fox ¬ activity time | 0.661 | 0.114 | 0.768 | 30.041 | 13 | 0.005 | | Fox ¬ marten | -0.310 | -0.301 | 0.763 | |||| | Fox ¬ leopard | -1.731 | -1.045; | 0.296 | |||| | leopard ¬ activity time | 0.822 | 5.282 | 0.000 | |||| | leopard ¬ marten | -0.151 | -1.435 | 0.151 | |||| | leopard ¬ fox | 0.313 | 0.123 | 0.173 | |||| | Marten ¬ activity time | 0.050 | 0.829 | 0.407 | |||| | Marten ¬ leopard | 0.131 | 1.363 | 0.173 | |||| | Marten ¬ fox | 0.079 | 0.600 | 0.549 | |||| | ibex ¬ leopard | 1.871 | 1.920 | 0.055 | |||| | ibex ¬ fox | 0.640 | 0.483 | 0.629 | |||| | ibex ¬ marten | -0.309 | -0.520 | 0.603 | |||| | ibex ¬ markhor | -1.401 | -1.845 | 0.065 | |||| | markhor ¬ leopard | -7.584 | -2.732 | 0.006 | |||| | markhor ¬ foxes | 1.089 | 0.279 | 0.124 | |||| | markhor ¬ marten | -0.154 | 0.582 | 0.168 | |||| | markhor ¬ ibex | 0.403 | 0.146 | 0.143 | |||| | Twilight | Fox ¬ activity time | 0.888 | 0.407 | 0.684 | 33.956 | 13 | 0.001 | | Fox ¬ marten | -0.747 | -0.424 | 0.672 | |||| | Fox ¬ leopard | 4.019 | 1.233 | 0.218 | |||| | leopard ¬ activity time | 0.497 | 1.272 | 0.203 | |||| | leopard ¬ marten | 0.082 | 0.394 | 0.218 | |||| | leopard ¬ fox | 0.176 | 0.685 | 0.493 | |||| | Marten ¬ activity time | 0.151 | 1.498 | 0.134 | |||| | Marten ¬ leopard | 0.400 | 2.159 | 0.031 | |||| | Marten ¬ fox | 0.066 | 0.526 | 0.599 | |||| | ibex ¬ leopard | -0.674 | -0.338 | 0.735 | |||| | ibex ¬ fox | -0.355 | -0.274 | 0.784 | |||| | ibex ¬ marten | -0.403 | -0.384 | 0.701 | |||| | ibex ¬ markhor | -3.937 | -1.277 | 0.202 | |||| | markhor ¬ leopard | -0.676 | -0.115 | 0.001 | |||| | markhor ¬ foxes | -2.473 | -0.636 | 0.011 | |||| | markhor ¬ marten | -0.767 | -0.236 | 0.813 | |||| | Night-time | Fox ¬ activity time | 1.859 | 1.292 | 0.196 | 19.344 | 13 | 0.013 | | Fox ¬ marten | 1.518 | 0.940 | 0.274 | |||| | Fox ¬ leopard | 2.015 | 1.063 | 0.288 | |||| | leopard ¬ activity time | 0.137 | 0.832 | 0.405 | |||| | leopard ¬ marten | 0.442 | 2.116 | 0.034 | |||| | leopard ¬ fox | 0.458 | 2.854 | 0.004 | |||| | Marten ¬ activity time | 0.050 | 0.829 | 0.007 | |||| | Marten ¬ leopard | -0.007 | 1.363 | 0.173 | |||| | Marten¬ fox | 0.079 | 0.600 | 0.549 | |||| | ibex ¬ leopard | 1.871 | 1.920 | 0.055 | |||| | ibex ¬ fox | 0.640 | 0.483 | 0.629 | |||| | ibex ¬ marten | 0.309 | -0.520 | 0.603 | |||| | ibex ¬ markhor | -1.401 | -1.845 | 0.065 | |||| | markhor¬ leopard | -7.584 | -2.732 | 0.006 | |||| | markhor¬ foxes | 1.089 | 0.279 | 0.000 | |||| | markhor ¬ marten | 1.065 | 0.582 | 0.274 | |||| | markhor ¬ ibexes | 0.403 | 0.146 | 0.719 | Table S3.Snow leopard and co-occurrence species top-down and bottom-up temporal interaction during the absence of snow at the nigh,day and twilights and also showed significant levels by using the SEMs | Temporal interaction | Absence of snow | Coeff | Z | p | X2 | df | p | | Day time | Fox ¬ activity time | -0.372 | -0.308 | 0.581 | 62.639 | 13 | 0.00 | | Fox ¬ marten | -0.616 | -0.604 | 0.546 | |||| | Fox ¬ leopard | -0.969 | 0.117 | 0.053 | |||| | leopard ¬ activity time | -0.224 | -0.137 | 0.300 | |||| | leopard ¬ marten | 0.070; | 0.128; | 0.857 | |||| | leopard ¬ fox | 0.305 | 0.123 | 0.219 | |||| | Marten ¬ activity time | 0.455 | 0.107 | 0.052 | |||| | Marten ¬ leopard | 0.514 | 0.095 | 0.00 | |||| | Marten ¬ fox | 0.134 | 0.107 | 0.392 | |||| | ibex ¬ leopard | 0.717 | 0.114 | 0.012 | |||| | ibex ¬ fox | 0.111 | 0.121 | 0.392 | |||| | ibex ¬ marten | 0.717 | 0.114 | 0.012 | |||| | ibex ¬ markhor | -0.549 | 0.118 | 0.062 | |||| | markhor ¬ leopard | 0.471 | 0.114 | 0.136 | |||| | markhor ¬ foxes | 0.645 | 0.118 | 0.124 | |||| | markhor ¬ marten | 0.864 | 0.117 | 0.168 | |||| | markhor ¬ ibex | 0.453 | 0.113 | 0.143 | |||| | Twilight | Fox ¬ activity time | -0.912 | -0.119 | 0.421 | 53.276 | 13 | 0.000 | | Fox ¬ marten | -0.912 | 0.112 | 0.145 | |||| | Fox ¬ leopard | 1.288 | 1.459 | 0.242 | |||| | leopard ¬ activity time | 0.124 | 0.124 | 0.712 | |||| | leopard ¬ marten | 1.288 | 1.459 | 0.242 | |||| | leopard ¬ fox | -0.392 | 0.122 | 0.352 | |||| | Marten ¬ activity time | 0.314 | 2.710 | 0.004 | |||| | Marten ¬ leopard | -0.177 | 0.116 | 0.439 | |||| | Marten ¬ fox | 0.289 | 0.115 | 0.318 | |||| | ibex ¬ leopard | -0.363 | 0.119 | 0.479 | |||| | ibex ¬ fox | -1.239 | -0.119 | 0.057 | |||| | ibex ¬ marten | -0.945 | -0.115 | 0.058 | |||| | markhor¬ leopard | -1.036 | 0.116 | 0.053 | |||| | markhor ¬ foxes | 0.431 | 0.117 | 0.001 | |||| | markhor ¬ marten | 0.964 | 0.116 | 0.084 | |||| | markhor ¬ ibex | -0.434 | -0.118 | 0.031 | |||| | Night-time | Fox ¬ activity time | -0.859 | 0.127 | 0.090 | 29.523 | 13 | 0.006 | | Fox ¬ marten | -0.912 | 0.112 | 0.145 | |||| | Fox ¬ leopard | 0.033 | 0.128 | 0.949 | |||| | leopard ¬ activity time | 0.204 | 0.187 | 0.498 | |||| | leopard ¬ marten | -0.041 | 0.132 | 0.110 | |||| | leopard ¬ fox | 0.458 | 2.854 | 0.051 | |||| | Marten ¬ activity time | -0.249 | 0.085 | 0.000 | |||| | Marten ¬ leopard | -0.050 | 0.089 | 0.595 | |||| | Marten ¬ fox | 0.204 | 0.137 | 0.455 | |||| | ibex ¬ leopard | -0.526 | 0.127 | 0.073 | |||| | ibex ¬ fox | 0.227 | 0.131; | 0.455 | |||| | ibex ¬ marten | -0.097 | 0.142 | 0.495 | |||| | ibex ¬ markhor | 0.857 | 0.119 | 0.097 | |||| | markhor ¬ leopard | -1.093 | -0.112 | 0.000 | |||| | markhor ¬ foxes | 0.062 | 0.119 | 0.602 | |||| | markhor ¬ marten | 0.407 | 0.130; | 0.274 | |||| | markhor ¬ ibexes | -0.103 | -0.108 | 0.719 | Information & Authors Information Version history Copyright This work is licensed under a Non Exclusive No Reuse License.

Authors Metrics & Citations Metrics Article Usage 539views 149downloads Citations Download citation Muhammad Zaman, Longcheng Fan, Rodney Jackson, et al. Impacts of Snow Leopards on co-occurence Species Diversity and Spatial-Temporal Interactions in Snowy Ecosystems: Conservation Insights and Implications. Authorea. 21 November 2025. DOI: https://doi.org/10.22541/au.176371445.57060920/v1 DOI: https://doi.org/10.22541/au.176371445.57060920/v1 If you have the appropriate software installed, you can download article citation data to the citation manager of your choice. Simply select your manager software from the list below and click Download. For more information or tips please see 'Downloading to a citation manager' in the Help menu.