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  • The study was conducted in the protected areas and adjoining forests of northwestern Yunnan China. The Yunnan Forest Department provided necessary research permits for the study. Since the methods used were non-invasive and relied completely on recording indirect signs of animals, animal care and use committee approval was not required. As a cryptic, crepuscular species, musk deer are difficult to observe, rendering monitoring programs for conservation purposes a challenge [18]. For elusive ungulate species living in dense forests and/or complex terrain, pellet counts have proved to be a more practical means of estimating population density and abundance [23–26]. Accurate estimates of two parameters, defecation rate and decay rate, are needed to convert fecal pellet group density to population density. These two parameters are influenced by season, climate, habitat type and animal behavior, leading to poor precision and biases in different conditions [25, 26]. Thus, fecal pellet group density, rather than population density, frequently has been used to assess differences among habitats [24, 27–29]. In our study we used fecal group density on transects as an index of relative abundance of musk deer in the region. We located 55 straight line transects totaling 207.7 km in length within 6 separate forest sites in northwest Yunnan. Transects were designed to cover variation in both forest habitat type and elevation within each sample site. Transect lines were situated in each forest type, each following a compass bearing parallel to slope aspect. Transects were located approximately 2 km apart, which we believe was sufficient for independent sampling across transects [30]. We conducted transect survey only in dry season (February to June in 2011 and 2012) to avoid climatic influences on the dung decay rates. Musk deer fecal pellets can be accurately discriminated from other sympatric ungulate droppings [30]. Previous study showed that habitats of the three musk deer species in northwest Yunnan overlap [31]. As it impossible to distinguish the three musk deer species from their dung pellets, we pooled the data together to reflect the overall musk deer status in the study areas. The methodology for fecal pellet surveys was adapted from Webbon, Baker [24] and Acevedo, Ferreres [27]. Fecal pellet groups were considered to have decayed totally when six or fewer pellets remained, after which they were excluded from the data. Musk deer relative abundance (R) within each transect was calculated as R = N/L, where N is the number of fecal pellet groups recorded per transect, L denotes length of transect. Although fecal pellet counts have been used successfully to estimate ungulate populations [23, 27, 29], results should be interpreted with caution, as they may have been subject to false negatives due to degradation [25]. In our study we conducted all transect counts in the dry, cold season, and the effects of varying rates of fecal decomposition should therefore be limited. All indications of human activity and presence were noted. On the transects, sightings of people, hunting snares, spent gun cartridge cases, livestock herds, active or abandoned camp sites and signs of medicinal plant gathering were recorded. Human disturbances were classified into 3 categories: gathering, grazing and poaching according to encounters with people or signs observed on transects. Frequencies of disturbances were scored as number of encounters/signs per km walked. We carried out data analysis in R v. 3.2.1 (R Development Core Team, 2013). We used Independent t-tests to examine differences in musk deer pellet group density between protected and non-protected areas and used One Way ANOVA to test for differences among study areas. We used line transects within the 6 study areas as the sampling unit. We used generalized linear models (error distribution family = Gaussian, S1 Fig) to document associations between human disturbance, conservation status of the study areas, main ethnic group living around the study areas and habitat variables, and the abundance of musk deer pellet groups. We examined pair-wise Spearman correlation tests between all variables to check for multicollinearity; if variables were correlated at rs≥0.70, only the variables with the lower Akaike’s Information Criterion value were included in further analyses to reduce redundancy [32–34]. We used multimodel inference based on information theory (Akaike’s Information Criterion corrected for small sample sizes, AICc) to assess the relative importance of each predictor. Akaike model weights, wi, were calculated as the weight of evidence in favor of model i among the models being compared. The top competing models (within △AICc = 3.00 of the top model) were included in model averaging (S1 Text).
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