Spatial and temporal activity patterns of Golden takin (Budorcas taxicolor bedfordi) recorded by camera trapping

Abstract

Understanding animals’ migration, distribution and activity patterns is vital for the development of effective conservation action plans; however, such data for many species are lacking. In this study, we used camera trapping to document the spatial and temporal activity patterns of golden takins (Budorcas taxicolor bedfordi) in Changqing National Nature Reserve in the Qinling mountains, China, from April 2014 to October 2017. Our study obtained 3,323 independent detections (from a total of 12,351 detections) during a total camera trapping effort of 93,606 effective camera trap days at 573 sites. Results showed that: (1) the golden takin’s utilization distributions showed seasonal variation, with larger utilization distributions during spring and autumn compared to summer and winter; (2) the species was recorded at the highest elevations in July, and lowest elevations in December, with the species moving to higher-elevations in summer, lower-elevations in spring and autumn; (3) during all four seasons, golden takins showed bimodal activity peaks at dawn and dusk, with activity intensity higher in the second peak than the first, and overall low levels of activity recorded from 20:00–06:00; and (4) there were two annual activity peaks, the first being in April and the second in November, with camera capture rate during these two months higher than in other months, and activity levels in spring and autumn higher than in summer and winter. This study is the first application of camera traps to assess the spatial and temporal activity patterns of golden takins at a population level. Our findings suggest that the proposed national park should be designed to include golden takin habitat and that ongoing consistent monitoring efforts will be crucial to mitigating novel and ongoing threats to the species.

Temporal and Spatial Activity Patterns of Sympatric Wild Ungulates in Qinling Mountains, China

“…For each location, we considered whether each ungulate species was detected (One) or non-detection (Zero) during each 30-days period, then created detection matrix using six periods (0-30, 31-60, 61-90, 91-120, 121-150, >150 days). We selected four environment variables to estimate occupancy probability based on previous studies of habitat selection/activity patterns by ungulates [15,43,50], distance to river (Disriv), distance to settlement (Disset), elevation (Ele) and vegetation type (Veg), and used vegetation type and season as covariates for ungulate detection probability analysis. We obtained rivers, settlements and vegetation type data from Changqing administration, elevation was derived from a digital elevation model with a resolution of 30 m from Resource and Environment Science and Data Center (https://www.resdc.cn/Default.aspx (assessed on 20 October 2021)).…”

“…Wild ungulates are highly integrated with components of grassland food webs that exert strong direct and indirect influences on vegetation composition, which may alter plant communities through the extensive grazing, browsing, trampling and defecation [10], not only shaping the structure and distribution of the vegetation, but also affecting nutrient flows and the responses of associated fauna [11][12][13]. Additionally, wild ungulates often forage and damage cropland, as well as compete for resources with livestock, potentially creating sources of conflicts between humans and wild ungulates [14,15]. The widespread reduction in apex predators and more restricted hunting management has contributed to an increase in the abundance of wild ungulates, sometimes resulting in an intensifying interspecific competition pressure [16,17].…”

“…However, most previous behavioral studies of wild ungulates have focused on single species ecology, with fewer studies having addressed the interactions between species. Studies conducted across several seasons to evaluate both spatial and temporal activity patterns of wild ungulates are still scanty [15,29]. Traditional methods depending on direct observations and tagging to survey animal behaviors have limitations (such as complex terrain, dense vegetation and physical capture), impeding the understanding of behavioral ecology [30].…”

Camera traps reveal overlap and seasonal variation in the diel activity of arboreal and semi-arboreal mammals

“…The date and time data (observations between 1/12/2019 and 30/11/2020 selected) were transformed into solar time to reduce the bias associated with local time (Vazquez et al 2019), using the 'Activity' package (Rowcliffe et al 2014). Detections for each species were considered independent if taken at intervals of greater than one hour (Porfirio et al 2017;Li et al 2020;Marinho et al 2020). This dataset with observations separated by one hour was extracted using the assess temporal independence function in the R package 'camtrapR' (Niedballa et al 2016).…”

Diversity and vertical distribution patterns of wildlife in Tianzhushan, Shaanxi Province, China

“… Mori et al (2020) investigated the activity rhythms of invasive coypus ( Myocastor coypus ) using camera trapping. Scientists have also explored the vertical distribution of mammals using infrared cameras ( Yan et al 2019 , Li et al 2020 ). This technology has become a routine monitoring method for large- and medium-sized terrestrial mammals and birds ( Rovero et al 2010 , Bridges and Noss 2011 ).…”