Face Value: Towards Robust Estimates of Snow Leopard Densities

Abstract

When densities of large carnivores fall below certain thresholds, dramatic ecological effects can follow, leading to oversimplified ecosystems. Understanding the population status of such species remains a major challenge as they occur in low densities and their ranges are wide. This paper describes the use of non-invasive data collection techniques combined with recent spatial capture-recapture methods to estimate the density of snow leopards Panthera uncia. It also investigates the influence of environmental and human activity indicators on their spatial distribution. A total of 60 camera traps were systematically set up during a three-month period over a 480 km2 study area in Qilianshan National Nature Reserve, Gansu Province, China. We recorded 76 separate snow leopard captures over 2,906 trap-days, representing an average capture success of 2.62 captures/100 trap-days. We identified a total number of 20 unique individuals from photographs and estimated snow leopard density at 3.31 (SE = 1.01) individuals per 100 km2. Results of our simulation exercise indicate that our estimates from the Spatial Capture Recapture models were not optimal to respect to bias and precision (RMSEs for density parameters less or equal to 0.87). Our results underline the critical challenge in achieving sufficient sample sizes of snow leopard captures and recaptures. Possible performance improvements are discussed, principally by optimising effective camera capture and photographic data quality.

A spotlight on snow leopard conservation in China

“…In terms of thematic coverage, there are gaps in all key framework dimensions, particularly those related to human interactions; and understanding the influence on snow leopards of livestock herding practices, and an assessment of the effectiveness of protected areas; the impact of large economic and infrastructural developments, that have direct implications for national policy development. Priority is also needed for setting criteria for more systematic population assessments (Alexander et al, 2015a(Alexander et al, , 2015b) and large-scale occupancy studies, and the assessment of threats (Taubmann et al, 2015). Large-scale sites should ensure wide range coverage within and outside protected areas across China, and allow for snow leopard corridor assessments (Taubmann et al, 2015).…”

“…The wide variety of methods applied makes comparing absolute numbers or densities across sites problematic. There is an urgent need for more systematic and coordinated research efforts to allow meaningful comparisons across regions (Alexander et al 2015a).…”

Spatial density estimates of Eurasian lynx (Lynx lynx) in the French Jura and Vosges Mountains

“…The multi‐site dimension of our study allows exploring variability in the density estimates across landscapes. Our study is yet another example of the potential of combining SCR methods and noninvasive sampling techniques to estimate abundance and density for elusive and wide‐ranging species, like large carnivores (Alexander et al, ; Broekhuis & Gopalaswamy, ; Goldberg et al, ; López‐Bao et al, ; Pesenti & Zimmermann, ; Stetz et al, ).…”

“…In addition, individual heterogeneity in detection due to spatial variation in the distance of home ranges to the sampling devices may lead to biased density estimates (Otis et al, 1978). Spatial capture-recapture (SCR) models deal with these issues by explicitly incorporating spatial locations of detections (Borchers, 2012;Borchers & Efford, 2008;Efford, 2004;Royle & Young, 2008), and they are increasingly used to estimate densities of large carnivores (Alexander, Gopalaswamy, Shi, & Face, 2015;Broekhuis & Gopalaswamy, 2016;Goldberg et al, 2015;López-Bao et al, 2018;Pesenti & Zimmermann, 2013;Stetz, Mitchell, & Kendall, 2018).…”

Toward reliable population density estimates of partially marked populations using spatially explicit mark–resight methods

“…If no individuals are identifiable, options include random encounter modeling (Rowcliffe, Field, Turvey, & Carbone, 2008), spatial presence-absence (Ramsey, Caley, & Robley, 2015), and N-Mixture models (Jiménez et al, 2017;Royle, 2004). If a proportion of the population is identified individually, spatially explicit mark-resight (SEMR) methods are suitable (Rich et al, 2014;Sollmann, Gardner, Chandler et al, 2013;Sollmann, Gardner, Parsons et al, 2013), while spatially explicit capture-recapture (SECR) is appropriate if all animals recorded are identifiable (Alexander, Gopalaswamy, Shi, & Riordan, 2015;Bahaa-el-din et al, 2016;Borchers & Efford, 2008;Hearn et al, 2016;Royle & Young, 2008). Models are also available that combine data collection methods, for example, when animals cannot be identified uniquely (i.e., providing an encounter rate) but telemetry movement data are available for a portion of the population (e.g., Potts, Buckland, Thomas, & Savage, 2012;White & Shenk, 2001).…”