Mapping the ghost : estimating probabilistic snow leopard distribution across Mongolia

We are grateful to Global Environment Facility, United Nations Development Program and Snow Leopard Trust for supporting the Global Snow Leopard and Ecosystem Protection Program and development of tools and methods for Population Assessment of the World's Snow leopards (PAWS).Aim Snow leopards are distributed across the mountains of 12 countries spread across 1.8 million km2 in Central and South Asia. Previous efforts to map snow leopard distributions have relied on expert opinions and modelling of presence-only data. Expert opinion is subjective and its reliability is difficult to assess, while analyses of presence-only data have tended to ignore the imperfect detectability of this elusive species. The study was conducted to prepare the first ever probabilistic distribution map of snow leopards across Mongolia addressing the challenge of imperfect detection.  Location We conducted sign-based occupancy surveys across 1,017 grid-cells covering 406,800 km2 of Mongolia's potential snow leopard range.  Methods Using a candidate model set of 31 ecologically meaningful models that used six site and seven sampling covariates, we estimate the probability of sites being used by snow leopards across the entire country.  Results Occupancy probability increased with greater terrain ruggedness, with lower values of vegetation indices, with less forest cover, and were highest at intermediate altitudes. Detection probability was higher for segments walked on foot, and for those in more rugged terrain. Our results showed broad agreement with maps developed using expert opinion and presence-only data but also highlighted important differences, for example in northern areas of Mongolia deemed largely unfavourable by previous expert opinion and presence-only analyses.  Main conclusions This study reports the first national-level occupancy survey of snow leopards in Mongolia and highlights methodological opportunities that can be taken to scale and support national-level conservation planning. Our assessments indicated that 0.5) probability of being used by snow leopards. We emphasize the utility of occupancy modelling, which jointly models detection and site use, in achieving these goals.Publisher PDFPeer reviewe

Outbreak of Peste des Petits Ruminants Virus among Criticially Endangered Mongolian Saiga and Other Wild Ungulates, Mongolia, 2016-2017

The 2016–2017 introduction of peste des petits ruminants virus (PPRV) into livestock in Mongolia was followed by mass mortality of the critically endangered Mongolian saiga antelope and other rare wild ungulates. To assess the nature and population effects of this outbreak among wild ungulates, we collected clinical, histopathologic, epidemiologic, and ecological evidence. Molecular characterization confirmed that the causative agent was PPRV lineage IV. The spatiotemporal patterns of cases among wildlife were similar to those among livestock affected by the PPRV outbreak, suggesting spillover of virus from livestock at multiple locations and time points and subsequent spread among wild ungulates. Estimates of saiga abundance suggested a population decline of 80%, raising substantial concerns for the species’ survival. Consideration of the entire ungulate community (wild and domestic) is essential for elucidating the epidemiology of PPRV in Mongolia, addressing the threats to wild ungulate conservation, and achieving global PPRV eradication

Ghostbusting - Reducing bias due to identification errors in spatial capture-recapture histories

1. Identifying individuals is key to estimating population sizes by spatial capture-recapture, but identification errors are sometimes made. The most common identification error is the failure to recognise a previously detected individual, thus creating a “ghost” (Johansson et al., 2020). This results in positively biased abundance estimates.2. Ghosts typically manifest as single detection individuals (“singletons”) in the capture history. To deal with ghosts, we develop a spatial capture-recapture method conditioned on at least K detections. The standard spatial capture-recapture (SCR) model is the special case of K = 1. Ghosts can mostly be excluded by fitting a model with K = 2 (SCR-2).3. We investigated the effect of “singleton” ghosts on the estimation of the model parameters by simulation. The SCR method increasingly over-estimated abundance with increasing percentage of ghosts, with positive bias even when only 10% of the detected individuals were ghosts, and bias between 43% and 71% when 30% were ghosts. Estimates from the SCR-2 method showed lower bias in the presence of ghosts, at the cost of a loss of precision. The mean squared error of the estimated abundance from the SCR-2 method was lower in all scenarios with ghosts under high encounter rates and for scenarios with 30% or more ghosts with low encounter rates. We also applied our method to capture histories from camera trap surveys of snow leopards (Panthera uncia) at 2 sites from Mongolia and find that the SCR method produced higher abundance estimates at both sites.4. Capture histories are susceptible to errors when generated from passive detectors such as camera traps and genetic samples. The SCR-2 method can remove bias from ghost capture histories, at the cost of some loss in precision. We recommend using the SCR-2 method in cases when there may be more than 10% ghosts or surveys with a large number of single detection capture histories, except perhaps when the sample size is very low