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

Coat Polymorphism in Eurasian Lynx: Adaptation to Environment or Phylogeographic Legacy?

We studied the relationship between the variability and contemporary distribution of pelage phenotypes in one of most widely distributed felid species and an array of environmental and demographic conditions. We collected 672 photographic georeferenced records of the Eurasian lynx throughout Eurasia. We assigned each lynx coat to one of five phenotypes. Then we fitted the coat patterns to different environmental and anthropogenic variables, as well as the effective geographic distances from inferred glacial refugia. A majority of lynx were either of the large spotted (41.5%) or unspotted (uniform, 36.2%) phenotype. The remaining patterns (rosettes, small spots and pseudo-rosettes) were represented in 11.0%, 7.4%, and 3.9% of samples, respectively. Although various environmental variables greatly affected lynx distribution and habitat suitability, it was the effect of least-cost distances from locations of the inferred refugia during the Last Glacial Maximum that explained the distribution of lynx coat patterns the best. Whereas the occurrence of lynx phenotypes with large spots was explained by the proximity to refugia located in the Caucasus/Middle East, the uniform phenotype was associated with refugia in the Far East and Central Asia. Despite the widely accepted hypothesis of adaptive functionality of coat patterns in mammals and exceptionally high phenotypic polymorphism in Eurasian lynx, we did not find well-defined signs of habitat matching in the coat pattern of this species. Instead, we showed how the global patterns of morphological variability in this large mammal and its environmental adaptations may have been shaped by past climatic change.publishedVersio

Mapping the ghost:estimating probabilistic snow leopard distribution across Mongolia

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

Insights into the spatial ecology of severely injured free‐living felids: Iberian lynx, bobcat, and snow leopard

Abstract Severe musculoskeletal diseases, such as those associated with congenital or traumatic events, that result in missing limbs may compromise the fitness and survival of free‐living felids. Here we report the space use of four amputee individuals from three felid species captured from 2017 to 2022 in Missouri (USA), Toledo and Badajoz (Spain), and Suitai Khairkhan Mountain (Mongolia). We describe home ranges and daily travel distances post‐release of free‐living felids that had either suffered a traumatic amputation or following a surgical amputation. We compared these data with those reported in the literature for felids without amputations. Forelimb or hindlimb amputation did not affect the hunting, mating, or territory patrolling behavior of any of the individuals. However, we recorded significant differences in the daily movement before and after the traumatic event of the Iberian lynx forelimb amputee. We attribute this difference to the physical impairment, although we consider other variables that may have played a role. Nevertheless, all animals appeared to cope well with their limb loss, showing home ranges and daily distances within those recorded for their sex and species. Unless amputee felids represent a threat to domestic livestock or humans, our data suggest these individuals may remain free‐living as they contribute to local population persistence and appear to maintain good general health and welfare

Genome-wide diversity loss in reintroduced Eurasian lynx populations urges immediate conservation management

Reintroductions may produce populations that suffer from decreasing genetic diversity due to isolation, genetic drift and inbreeding if not assisted by careful management. To assess the genetic outcomes of reintroductions in large carnivores, we used the Eurasian lynx (Lynx lynx) as a case study, which was the subject of several reintroduction attempts over the last 50 years. Although some restocking actions initially appeared successful, lynx recovery has stagnated in recent years. To reveal potential genetic causes of slow lynx recovery in Europe, we examined genome-wide patterns of genetic diversity and inbreeding using single nucleotide polymorphisms (SNPs) in all six successfully reintroduced populations in central Europe, as well as twelve natural populations across Europe and Asia. All reintroduced populations showed lower genetic diversity and elevated levels of inbreeding compared to source and other natural populations. Recent inbreeding is prevalent in all reintroduced populations with varying degrees of severity; the most severe cases are those with the lowest number of founding individuals. Interestingly, we found evidence of lower genetic diversity and recent inbreeding in the source population for five reintroduced populations, begging the question if individuals taken from these source populations can safeguard sufficient genetic diversity for future reintroductions. Given the observed genetic consequences, we advocate for standardized regular genomic assessment of source and target populations as well as individuals prior to release. Our study provides compelling evidence for the serious consequences of founder population size on the genetic diversity of reintroduced large carnivore populations, which has broad implications for their conservation. Conservation genomics Inbreeding Large carnivore Runs of homozygosity Species translocation Population management Reintroduction biolog

Genome-wide diversity loss in reintroduced Eurasian lynx populations urges immediate conservation management

Reintroductions may produce populations that suffer from decreasing genetic diversity due to isolation, genetic drift and inbreeding if not assisted by careful management. To assess the genetic outcomes of reintroductions in large carnivores, we used the Eurasian lynx (Lynx lynx) as a case study, which was the subject of several reintroduction attempts over the last 50 years. Although some restocking actions initially appeared successful, lynx recovery has stagnated in recent years. To reveal potential genetic causes of slow lynx recovery in Europe, we examined genome-wide patterns of genetic diversity and inbreeding using single nucleotide polymorphisms (SNPs) in all six successfully reintroduced populations in central Europe, as well as twelve natural populations across Europe and Asia. All reintroduced populations showed lower genetic diversity and elevated levels of inbreeding compared to source and other natural populations. Recent inbreeding is prevalent in all reintroduced populations with varying degrees of severity; the most severe cases are those with the lowest number of founding individuals. Interestingly, we found evidence of lower genetic diversity and recent inbreeding in the source population for five reintroduced populations, begging the question if individuals taken from these source populations can safeguard sufficient genetic diversity for future reintroductions. Given the observed genetic consequences, we advocate for standardized regular genomic assessment of source and target populations as well as individuals prior to release. Our study provides compelling evidence for the serious consequences of founder population size on the genetic diversity of reintroduced large carnivore populations, which has broad implications for their conservation. Conservation genomics Inbreeding Large carnivore Runs of homozygosity Species translocation Population management Reintroduction biologypublishedVersio

Data from: Range-wide snow leopard phylogeography supports three subspecies

The snow leopard, Panthera uncia, is an elusive high-altitude specialist that inhabits vast, inaccessible habitat across Asia. We conducted the first range-wide genetic assessment of snow leopards based on noninvasive scat surveys. Thirty-three microsatellites were genotyped and a total of 683-bp of mitochondrial DNA sequenced in 70 individuals. Snow leopards exhibited low genetic diversity at microsatellites (AN = 5.8, HO = 0.433, HE = 0.568), virtually no mtDNA variation, and underwent a bottleneck in the Holocene (~8,000 years ago) coinciding with increased temperatures, precipitation, and upward treeline shift in the Tibetan Plateau. Multiple analyses supported three primary genetic clusters: (1) Northern (the Altai region), (2) Central (core Himalaya and Tibetan Plateau), and (3) Western (Tian Shan, Pamir, trans-Himalaya regions). Accordingly, we recognize three subspecies, P. u. irbis (Northern group), P. u. uncia (Western group), and P. u. uncioides (Central group) based upon genetic distinctness, low levels of admixture, unambiguous population assignment, and geographic separation. The patterns of variation were consistent with desert-basin "barrier effects" of the Gobi isolating the northern subspecies (Mongolia), and the trans-Himalaya dividing the central (Qinghai, Tibet, Bhutan, and Nepal) and western subspecies (India, Pakistan, Tajikistan, and Kyrgyzstan). Hierarchical Bayesian clustering analysis revealed additional subdivision into a minimum of six proposed management units: western Mongolia, southern Mongolia, Tian Shan, Pamir-Himalaya, Tibet-Himalaya, and Qinghai, with spatial autocorrelation suggesting potential connectivity by dispersing individuals up to ~ 400 km. We provide a foundation for global conservation of snow leopard subspecies, and set the stage for in-depth landscape genetics and genomic studies

Data for "Mapping the ghost: Estimating probabilistic snow leopard distribution across Mongolia"

Data and code used for a country-wide occupancy survey of snow leopards in Mongolia, accompanying the paper "Mapping the ghost: Estimating probabilistic snow leopard distribution across Mongolia". This data contains the results of a survey of 1017 20x20km sampling units, out of a total of 1200 sampling units identified as potential snow leopard habitat (183 could not be sampled for various reasons), a near complete survey of potential snow leopard habitat in Mongolia, nearly 500,000 square kilometers, and an enormous effort by many researchers. If you make use of the data, please cite the following sources: Data for "Mapping the ghost: Estimating probabilistic snow leopard distribution across Mongolia". (2021). Gantulga Bayandonoi, Koustubh Sharma, Justine Shanti Alexander, Purevjav Lkhagvajav, Ian Durbach, Darryl MacKenzie, Chimeddorj Buyanaa, Bariushaa Munkhtsog, Munkhtogtokh Ochirjav, Sergelen Erdenebaatar, Bilguun Batkhuyag, Nyamzav Battulga, Choidogjamts Byambasuren, Bayartsaikhan Uudus, Shar Setev, Lkhagvasuren Davaa, Khurel-Erdene Agchbayar, Naranbaatar Galsandorj, David Borchers. doi: https://doi.org/10.5281/zenodo.5257572 Mapping the ghost: Estimating probabilistic snow leopard distribution across Mongolia. (2021). Gantulga Bayandonoi, Koustubh Sharma, Justine Shanti Alexander, Purevjav Lkhagvajav, Ian Durbach, Darryl MacKenzie, Chimeddorj Buyanaa, Bariushaa Munkhtsog, Munkhtogtokh Ochirjav, Sergelen Erdenebaatar, Bilguun Batkhuyag, Nyamzav Battulga, Choidogjamts Byambasuren, Bayartsaikhan Uudus, Shar Setev, Lkhagvasuren Davaa, Khurel-Erdene Agchbayar, Naranbaatar Galsandorj, David Borchers. To appear in Diversity and Distributions Contents of zip file Data The main dataset is contained in `data\Mongolia_occupancy_inputs.Rdata` . Please see the paper for more detail on data collection. The following objects are contained in the file: - Pres: presence/absence occupancy survey results, used for model fitting - Site_Cov: unit-specific covariates, used for model fitting - SurvCov: survey-specific covariates, used for model fitting - Mongolia_studyarea: covariates for whole survey area, used for prediction - Mongolia_fullrange: covariates across whole expected snow leopard range, used for prediction Code Code is cloned from the GitHub repository https://github.com/iandurbach/mongolia-occupancy, which may contain updates. The version here reproduces the analyses in the paper above. The run these analyses: - run *occupancy-analysis.R* to fit the main occupancy models (these are also saved in the `\output` folder), do model selection, and plot covariate effects - run *occupancy-goodness-of-fit.R* to calculate the c-hat statistic giving an indication of model fit for the best model - run *comparing-maps.R* to compare the occupancy results with similar metrics generated using a presence-only analysis (using MaxEnt) or an expert map generated through qualitative discussion (reproduces Figure 3 in the paper). Code in *occupancy-data-preproc.R* is not needed but included for completeness. It converts the csv files in `data\csv`, which contain various input datasets used by the occupancy model, into a single .Rdata file (`data\Mongolia_occupancy_inputs.Rdata`), which is then used by the scripts above. Some minimal pre-processing (excluding ununsed variables, renaming for consistency, etc) is performed