Home range is defined as the area traversed by an individual during its normal activities of feeding, mating and caring for its offspring (Burt 1943). It provides a variety of necessary natural resources and conditions for wild animals (Gareshelis 2000), studies on animal home range variation can shed lights on the distribution and utilization of resources, and habitat selection in time and space, hence contributing to better understanding about animal behavior and ecology (Pebsworth et al. 2012; Noonan et al. 2018). Previous studies had shown that ecological factors such as the condition of the animal itself, food in the habitat, topography and shelter conditions can all affect the size of the home range (Bowers et al 1996; Guarino 2002). The characteristics of home range, such as spatial distribution, shape and home range overlap, have their specific formation causes and potential biological significance. Meanwhile, home range size is an important parameter for estimating the minimum active area needed to predict the habitat carrying capacity (Baber 2003) that is valuable in managing the minimum viable populations (Kang and Paek 2005) and developing effective conservation strategies (Macdonald 2016; Wilson et al. 2018).
The basis of home range analysis is the collection of activity sites of the studied animals. GPS tracking provides convenience for site location and consequently has obvious advantages over other data collection methods in animal spatial behavior study (Walter et al. 2015). The most important advantage of GPS tracking seems to be the continuous recording of locations during study period, and providing large number of accurate locations that may be obtainable without interfering the normal life of animals that is being tracked (Pebsworth et al. 2012; Dvořák et al. 2014; Cohen et al. 2018).
Home range can be calculated with plenty of methods (Getz et al. 2007; Laver and Kelly 2008). These methods have different merits and weakness (Cumming and Cornélis 2012; Reinecke et al. 2014; Halbrook and Petach 2018), and no standardized method for home range analysis exists (Signer and Balkenhol 2015). Minimum convex polygon (MCP) is a simple and most widely used method in home range estimation, but it is sensitive to outlier locations and the number of fixes, and poor fit to data if the shape of the home range is non-convex. Moreover, MCP is not capable of providing data concerning density distribution (Laver and Kelly 2008; Nilsen et al. 2008). Kernel density estimation (KDE) has been considered the most commonly used method which constructs home range based on a probability of distribution. (Wartmann et al. 2010; Lichti and Swihart 2011; Cumming and Cornélis 2012). However, home range estimates generated by KDE is difficult to compare with those resulted from other methods due to its sensitive trait to the types of smoothing schemes and no optimal process for determining the bandwidth (Hemson at al. 2005; Laver and Kelly 2008). In addition, both the MCP and KDE methods share common shortcomings, one among which is the invalid active areas, i.e. regions that animals do not frequently used (Getz and Wilmers 2004). These invalid regions might include areas containing distinct boundaries or physically inaccessible landscapes, such as steep cliffs, and fenced domains, that may not be utilized by the tracked animals (Getz et al. 2007). Fortunately, Getz et al. (2007) refined the local convex hull (LoCoH) method which preserves the simple and intuitive idea of MCP and introduced the concept of contours in KDE, which solved the problem of sensitivity to abnormal points in the MCP and addressed the problem of invalid areas.
The Chinese goral (Naemorhedus griseus) is a small ungulate with a goat-like appearance that inhabits steep rocky terrain and timberland throughout the northern, central, and southern parts of China (Hrabina 2015; Liu and Zhang 2018). In its northern distribution, the Chinese gorals normally favor steep slopes as shelters to avoid predators and may move to lower altitude areas in cold season (Chen et al. 2012; Yang et al. 2019). It is categorized as the second class of state key protected species in China, a vulnerable species by the IUCN Red List, and is included in Appendix Ⅰ list by CITES for protection (Jiang et al. 2016). The goral population in Saihanwula National Nature Reserve in Inner Mongolia was the source population of this area in the 1960s and 1970s, and the surrounding small populations mainly came from this population (National Forestry Administration 2009; Liu and Zhang 2018). Under the disturbance of increased human activities in this region, the surrounding small populations had disappeared and the source population had also been further isolated and localized in this nature reserve. Therefore, understanding its basic ecological traits and behavior is critical in planning appropriate conservation measures. Previous studies have focused on observations of diet analysis, habitat suitability assessment and preliminary molecular biology (Zhou 2015; Tang et al. 2018; 2019; Liu and Zhang 2018), but there is no report on home range estimation for this species.
The Chinese goral population in Saihanwula National Nature Reserve of Inner Mongolia lived in a limited cliff landscape which was isolated from the populations in central China (Tang et al. 2019). In addition, a study found that this goral population maintained a moderate genetic diversity and diverged with its southern conspecifics in Beijing region (Yang et al. 2019). Therefore, revealing the goral’s adaptation strategy to limited space resources, and clarifying the overlapping use and segmentation behavior of home range and microhabitat spaces are of great significance to effectively protect and improve the quality of animal habitats. This study aimed to detect home range variations of the gorals by GPS tracking and select a better data analysis method in the rugged terrain environment which would be helpful for the conservation of this isolated population.