3.1. Population Pattern of Livestock overtime and Depredation losses
The livestock population pattern, year-wise population trend, predation count and losses caused by predators and non-predatory factors from 2014 to 2019 was presented in (Fig. 3A,B). Our study revealed a positive increase in the population of goats and sheep than cattle. The study revealed that the loss of livestock by predators and non-predatory factors in three pasture including Kilike, Murkushi, and Dardee. Data collected during the year 2019 showed a higher population of livestock compared with the year-wise data from 2014 to 2018 (Table 1). Interestingly, a total of 300 livestock were killed by predators from 2014 to 2018 and the major species were sheep, goats, yak, and cows. The last five years’ data indicated that wolf was found a dangerous predator to damage the sheeps followed goats, cows and yaks in the study area. (Fig.3B). Inversely, in the year 2019, a total of 64 livestock damaged due to predation. The most affected were Yak (22.7%) than Sheep (1.08%), Goat (0.97%), and Cattle (3.92%). The recorded population in the year 2019 was 10 to 11% increase from the average percentage to the population from 2014 to 2018 (Table 1).
3.2 Economic Value of Livestock Losses
We census livestock population, predation count and also to evaluate the total financial losses due to predation and non - predatory factors in the Misgar valley. The total economic loss arising from livestock depredation during the year 2014 to 2019 was estimated at (13,195,000) PKR (82,468.75(US$). The cost for cattle was approximately (2,480,000 PKR), (15,500 US$), goats (1,860,000 PKP), (11,625 (US$) for Sheep (1,815,000 PKR), (11,343.75 US$) and for yaks (7,040,000 PKP), (44,000 US$). Most losses were attributed to wolves 1, 815,000 PKR (11,343.75 US$) followed by snow leopards and diseases (Table. 2). This study shows cows and oxen were the least choices of predators in the area. Our study indicates that death caused by disease slightly higher in cows and yaks compared with goats and sheep (Figure 3B). In addition, a total of 364 livestock preyed during the last six-year by both predators and diseases, that leads to major economic loss and invoked a retaliatory killing of the carnivores.
3.3. Depredation counts and Patterns for Snow leopard and Wolf
We have considered more than 22 different factors from demographics, seasonal information, government policies, ecosystem and economic sectors to model the predation count. Snow leopard and wolf predation counts were separately modelled through poison regression and the final parsimonious model for both species is presented in (Table. 3,4). The final poison model for both snow leopard and wolf contains 22 influential factors and predation distribution is presented in (Fig.2 A-V)
3.4 Socio-Economics and Demographic Characteristics of Respondents
The variation in snow leopard’s predation counts was modelled through stepwise poison regression. Influential factors affecting the snow leopard and wolf predation counts are presented in (Table.3,4). In our study the responded age was categorized into three groups; 55 % were adults, 40 % were aged while 5 % were young (Fig.2A). Compared to adults, the young respondent had observed 0.016 times lesser the snow leopard predation counts with the p-value <0.001. Study contains 60 % male and 40% female (Fig.2B). Compared to the female respondent’s male respondent observed 0.652 times less snow leopard predation counts. Models for wolf showed the same kind of results (Table. 4). Of the total respondents, those who perceived increasing predation by snow leopard comprised 44% farmers, 29 % of government employees and 27% private employees. Farmers were considered as reference profession, as profession gets government employee the snow leopard predation is likely to increase by 6.081. Similarly when profession get private sector employ the snow leopard predation gets increased by 7.173 times (Table 3). We observed the same outcome in a wolf model for occupation (Table.4). The education of the respondents was divided into three groups, 44% illiterate and 38% with basic education and 18 % were holding higher education qualification (data was not shown). The model showed that basic education was considered as the reference category and higher and illiterate, the predation of the wolf was increased 0.032 compared with higher education and increased 0.11 with illiterate (Table. 4).
3.5 Estimate Income from Livestocks
The income came from agriculture and livestock and its impact on predation modelled against the predation count. Here, we evaluate the economic status of respondents and investigated their yearly income, livestock owns and livestock sold. In modelling the yearly income of the respondent was categorized into two groups, below 50,000 income were 4% and above 50,000 were 96%. (Fig. 2D). In the model below 50,000 income considered as the reference category and the yearly income of the respondent appeared significant compared to the above 50,000 income of the respondent, the snow leopard predation increased by 0.055 times (Table 3,4).
Factor such as reason to sell livestock was categorized into three categories, for migration 28%, for basic need 13% and education 59% (Fig.2E). In the model, the reference category was migration compared with the basic need and appeared non-significant with basic need and weak significant with education (Table 3). In wolf predation model education shown highly significant with both categories with p<0.000. (Table.4). Estimated income from livestock categorized into three groups, 50,000-100,000 income occupy 43%, above-100,000 income 22% and below-50,000 income 35%. (Fig.2F). In the model, 50,000-100,000 used as reference category shown non-significant with above-100,000 and highly significant with below 50,000.(Table.3). However, the model designed for wolf shown 50,000 -100,000 income was statistically significant with above 100,000 income of the respondents (Table.4). Livestock treatment and vaccination (LTV) last six years categorized into two, No vaccinated 72% and Yes vaccinated 28%. In the model, the reference category was NO, statistically significant with Yes at P< 0.05 (Table 3,4)
3.6. Predators and their Perceived Population trend
The respondents' perceptions about the current status and population trends of mammalian predators are given in (Fig 2K). The perceived predators status and populations were categorized into two groups, the respondents who sighted snow leopard 1-5 time were 82% and above-5 time was 18% (Fig.2 J). As the model illustrate that 1-5 time used as the reference category and compared with above-5 time with P<0.000 (Table 3). Similarly, the estimated population of wolf categorized in two, 20-30 times seen the wolf was 79% and above 30 were 21%. The reference category was 20-30 times, compared with above-30 predation increased 0.017 times showed significant at P<0.05 (Table 4). We categorized the presence of predators into common, rare and absent. Out of 100 respondents, 85% respondent believed Snow leopard is common, 2% believed absent and 13% believed rare, similarly for wolf 2% said absent, 89% said common and 9% said rarely. Likewise, 77% of respondent thought brown bear is rare, 10% thought common and 13% thought absent. In the model, we chose absent as reference category and compared with common and rare has revealed highly significant with common at P<0.000.
3.7. Perceptions towards Human-Carnivores Conflict
Out of 100 respondents, most of the respondents 63% ranked snow leopard as the dangerous predator, 37% sited the wolf as a dangerous predator (Fig 2L). In the model, we used snow leopard as the reference category and compared with the wolf, predation increased 1.335 times and statistically significant at P<0.000 (Table3,4). Of the total respondents, 24% respondent agreed on livestock exposed to predator, 23% said livestock is the favourite food and 53% said its natural prey reduced (Fig. 2M). The model showed that exposure to predator considered as the reference category and predation was increased 3.207 times compared with favourite food and statistically significant at P< 0.000 and non-significant in comparison with natural prey reduction (Table 3).Of the total respondents who perceive the importance of predators comprised 51% consider no importance and 49% considered important. Among them 9% of respondents thought predators are important to balance ecosystem, 21% believed predators have economic importance and the majority of the respondents 52% assumed that predators have no importance. However, 18% said that the presence of predators is important for tourism (Fig. 2N). In the model, we used the balanced ecosystem as the reference category and compared with economic importance and found that predation increased 9.12 time and also increased 0.356 times with tourism. Statistically, it was highly significant at P<0.000 (Table 3). Our analysis revealed similar results in the model for the wolf shown in (Table 4).
3.8. Threats towards Wild Predators
A total of 100 respondents ranked the predators into three kinds of threats index, low, high and medium. Among them, 69 % respondents believed there is low threat for the snow leopard in the study area, 5% respondents believed there is a medium threat and 26% responded thought there is a high threat to the snow leopard (data not shown). In the model, the high threat was chosen as the reference category and compared with low and medium depredation decreased 1.179 and 0.578 with p-value 0.346 and 0.076 respectively (Table 3). Similarly, 57% of respondents ranked wolf as low threatened, 13% ranked medium and 30% ranked high (Figure 2P). The model for wolf indicated depredation decreased 50.7 times compared with low with P<0.001. Similarly, predation decreases 3000 times compared with medium with P< 0.001 (Table. 4).In response to the question about major threats, 14% of respondents believed climate change, 5% habitat destruction, and 54 % believed illegal hunting and 27% both illegal hunting and habitat destruction. In our model, climate change was used as a reference category and compared with illegal hunting and both illegal hunting and habitat destruction, depredation increased 0.391 and 0.185 times with P<0.001 (Table. 3,4)
3.9. Temporal Predation
In order to evaluate the temporal predation, we divided our responses into five groups. Of the total respondent's perceived predation occurred in winter season 22%, in summer 21%, in autumn 17%, in spring 15%, in both spring and summer 13%, summer and autumn 12% in spring (Fig. 2Q).In the poison regression model autumn used as the reference category and compared with summer and autumn predation by snow leopard increased 2.208 times and also increased 1.799 times compared with winter (Table 3). Autumn used as the reference category in the wolf model and compared with summer and autumn predation by wolf increased 0.002 times and predation also increased 0.006 times compared with winter with P<0.01 (Table 4).
3.10. Conservation Strategies
The conservation approaches adopted to protected livestock from the attack of predators during grazing. Of the total respondents, those who perceived decreasing predation by snow leopard and wolf comprised predator-proof corrals pens 49%, guard dog 32%, and guarded grazing 19% (Fig.2R). In the model, we used the guarded dog as a reference category and compared with predator-proof corrals pens and guarded grazing predation decreased 0.549 and 1.638 times with P<0.001 (Table.3). In the model of the wolf, Predation decrease 6.23 time compared with guarded grazing and 0.43 times with predator’s proof corrals. These both factors were highly significant at p<0.001 (Table.4). Shelters of livestock recognized as protected and unprotected. Of the total respondents' majority, 86% thought livestock unprotected in the pasture and only 14% of thought livestock protected in the pasture (Fig. 2S). In model protected used as a reference category and predation was increased 0.466 times compared with unprotected and statistically highly significant at p<0.00 (Table.3). In response to the question of what steps need to maintain wildlife,50%, preferred government rules need to implement, 37% denoted local community involvement and 13% favoured enforcement of laws for conservation (Fig. 2T). In the model for the snow leopard laws for conservation used as reference category compared with government rules that indicate the killing of snow leopard decreased 1.646 times with P<0.044. However, in the model for wolf laws for conservation compared with government rules that indicate 0.036 times decreased in the killing of the wolf with P<0.001 (Table.3, 4). The locally developed organization managed compensation to the communities to maintain the mammalian predators in the valley. Of the total respondent's those who did not receive compensation (87%) and received was (14%) from a locally based community organization. The respondents who replied Yes, furthered categorized into two groups, those who compensated 1000-6000 (13%) and above-6000 (1%) (Fig.2V). In the model, No used as a reference category and compared with yes then the predation increased 0.42 times and shown significant at P<0.000 (Table 3,4).