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Theriologia Ukrainica, 17: 112–118 (2019)
http://doi.org/10.15407/pts2019.17.112
USING SPECIES DISTRIBUTION MODELLING TO GUIDE SURVEY EFFORTS
OF THE SNOW LEOPARD (PANTHERA UNCIA) IN THE CENTRAL KYRGYZ
ALA-TOO REGION
Volodymyr Tytar1, Tolkunbek Asykulov2, Matthias Hammer3
1Schmalhausen Institute of Zoology NAS of Ukraine (Kyiv, Ukraine)
2Kyrgyz National University, Faculty of Geography and Ecology (Bishkek, Republic of Kyrgyzstan)
2Der Naturschutzbund Deutschland e. V. NABU (Bishkek, Republic of Kyrgyzstan)
3Biosphere Expeditions Deutschland (Hoechberg, Germany)
Using species distribution modelling to guide survey efforts of the Snow Leopard (Panthera uncia) in the
Central Kyrgyz Ala-Too region. — V. Tytar, T. Asykulov, M. Hammer. — Listed as Vulnerable (IUCN
2017), the snow leopard is declining across much of its present range. One of the major reasons for the snow
leopard population decline in the last two decades is a reduction in large prey species that are the cornerstone of
the conservation of the snow leopard; in the Central Kyrgyz Ala-Too region such species is primarily the Sibe-
rian ibex (Capra sibirica). Understanding factors affecting basic requirement of ibex and shaping its distribution
is essential for protecting the prey species snow leopards rely on the most. Using a niche modelling approach
we explored which environmental features are best associated with ibex occurrence, how well do models predict
ibex occurrence, and does the potential distribution of highly suitable ibex habitat correlate with records of
snow leopard. A PC analysis was used to capture aspects of ibex ecology and niche. Results of such analysis
agree with the herbivore character of the species and bioclimatic habitat requirements of the vegetation it feeds
upon, richer in flatter areas, and where plants may benefit from more sunlight. The niche model based on
maximum entropy (Maxent) had “useful” discrimination abilities (AUC = 0.746), enabling to produce a map,
where a contour line is drawn around areas of highly predicted probability (> 0.5) of ibex occurrence. In terms
of nature conservation planning and setting snow leopard research priorities these areas represent the most in-
terest. With one outlier, most of snow leopard records made in the study area (n = 15) fell within the 10 percen-
tile presence threshold (0.368). Predicted probability of ibex occurrence in places where records were made of
snow leopard presence (pugmarks, scrapes etc.) was 0.559 expectedly suggesting areas of high ibex habitat suit-
ability attract the predator.
Key words: Capra sibirica, Panthera uncia, Kyrgyz Ala-Too, species distribution models, Maxent.
Correspo ndence to : V. Tytar; Schmalhausen Institute of Zoology NASU, B. Khmelnytsky St., 15, Kyiv,
01601 Ukraine; e-mail: vtytar@gmail.com; orcid: https://orcid.org/0000-0002-0864-2548
Introduction
The snow leopard (Panthera uncia) is an icon for conservation in the mountain regions of Asia.
As a top-order predator, its presence and survival is also an indicator of intact, “healthy” eco-region.
Snow leopards are listed as Vulnerable in the IUCN Red List (IUCN 2017) and its abundance is
declining across much of its present range.
One of the major reasons for the snow leopard population decline in the last two decades is a
reduction in prey resource base. A recent investigation into prey preferences of the snow leopard
revealed three key large prey species that are cornerstone of the conservation of the snow leopard
globally; one of these species is the Siberian ibex (Capra sibirica) (Lyngdoh et al., 2014). According
to these authors, Siberian ibex were killed by snow leopards wherever they occurred, meaning the
species is a vital resource for the predator.
Siberian ibex range is spread across the mountains of Pakistan, China, India, Afghanistan, Kyr-
gyzstan, Kazakhstan, Uzbekistan, Mongolia, Russia and Tajikistan. Ibex mainly occupies rocky
mountainous regions, both open meadows and cliffs, coming down to low elevations during winter
(Fedosenko, Blank, 2001). The species avoids densely forested areas and prefers to remain near
steep and escape terrain (Fedosenko, Blank, 2001). The ibex is crepuscular in feeding, foraging in
Using species distribution modelling to guide survey efforts of the snow leopard (Panthera uncia) ... 113
evenings and mostly in early morning hours (Fedosenko, Blank, 2001). They come down from their
steep habitats during late afternoon and evenings to the alpine meadows below to feed.
Ibex lives in small groups (6–30 animals) varying considerably in size, rarely in herds of
>100 animals (Fedosenko, Blank, 2001). In the study area of the Central Kyrgyz Ala-Too groups
hardly exceed 20 individuals. Any snow leopards in this area would depend on this species as a pri-
mary food source, as far as wild sheep (argali) seem to occur here very occasionally.
The presence of species depends upon the specific environmental conditions that enable it to
survive and reproduce (Marzluff, Ewing, 2001). Understanding the factors influencing its existence
is a basic requirement for the assessment of the species distribution and devising efficient species
conservation strategies (Wein, 2002). This knowledge could help us to focus our efforts on protect-
ing the prey species snow leopards rely on the most. Additionally, Siberian ibex suffer from poach-
ing and trophy hunting, decreasing population sizes even further. In order to protect these animals,
there is a need to identify and preserve important areas for wildlife that could address the issues of
over-grazing and poaching.
Species conservation usually focused on the most suitable habitat for a species of concern, but
the challenge is to identify high-quality habitat across large areas (Bellis et al., 2008). Among the
various tools used in conservation planning to protect biodiversity, species distribution models
(SDMs), also known as climate envelope models, habitat suitability models, and ecological niche
models provide a way to identify the potential habitat of a species in an ecoregion and their applica-
tions have greatly increased. SDMs are based on the concept of the “ecological niche” (Hutchinson,
1957), which can be defined as the sum of the environmental factors that a species needs for its sur-
vival and reproduction. Many niche models are based on climate variables because these data are
readily available, covering large spatial scales. SDMs predict the potential distribution of a species
by interpolating identified relationships between presence/absence or presence-only data of a species
on one hand and environmental predictors on the other hand across an area of interest. From the ar-
ray of various applications, Maxent (Phillips et al., 2006) stands out because it has been found to
perform best among many different modeling methods (Elith et al., 2006). Maxent is a maximum
entropy based machine learning program that estimates the probability distribution for a species’
occurrence based on environmental constraints (Phillips et al., 2006). It requires only species pres-
ence data and environmental variable (continuous or categorical) layers for the study area.
Here, we addressed the following questions: (1) which environmental features are best associ-
ated with ibex occurrence? (2) How well do models predict the occurrence of this species? (3) What
is the potential distribution of highly suitable habitat for ibex in the study area and does it correlate
with records of snow leopard?
Material and methods
Study area
The chosen study area is located in the southern Kyryz Ala-Too, away from the main cities in
the north. Surveys were centered around the Karakol Mountain Pass (3,452 m) and encompassed
areas in the upper reaches of the West and East Karakol rivers. The expedition’s main access route
into the area was the Suusamyr plateau, a high steppe plateau (2,200 m) that although only some
160 km from the capital city of Bishkek, is also one of the more remote and rarely visited regions of
Kyrgyzstan. The base camp was located close to the Suusamyr-Kochkor road approximately in the
middle of the planned study area (42.359535oN, 74.737829oE, 3002 m a.s.l.). From the base camp
mostly one-day surveys, but also some two-day/one-night surveys were conducted to various por-
tions of the Kyrgyz Ala-Too Range
Species records
Between 2014 and 2018, teams of citizen scientists recruited by Biosphere Expeditions and
NABU (Kyrgyzstan) gathered 103 records of Siberian ibex in the study area according to direct
sightings and camera trap results, and 15 records of snow leopard presence.
Volodymyr Tytar, Tolkunbek Asykulov, Matthias Hammer
114
The extracted points were georeferenced using a Garmin eTrex 10 GPS receiver in combination
with GoogleEarth. All coordinates were expressed in decimal degree and converted to a point vector
file for modeling the distribution of the species. Only spatially unique ones, corresponding to a sin-
gle environmental grid cell (resolution of 30 arc seconds, ~ 1 km) were used.
Environmental and bioclimatic data
To relate the occurrence records of ibex with abiotic conditions, we downloaded 19 bioclimatic
variables for the current climate at a 30 arc second resolution and WGS84 coordinate reference sys-
tem (Hijmans et al., 2005). These variables represent annual trends of temperature and precipitation,
seasonality, and extreme or limiting environmental factors (e.g., temperature of the coldest and
warmest month).
Temperature has long been recognized as an important environmental factor in ecosystems in
regard to its pivotal role over biological (development, growth and reproduction), chemical, and
physical properties. Precipitation regimes and variation of precipitation events have broad effects on
ecosystem productivity, habitat structure, and ultimately on species’ distribution.
For the study region of the Kyrgyz Ala-Too scientific data on range of environmental resources
(other than bioclimatic) are limited which hinders sustainable management and nature conservation.
The need for update information has long been recognized and stimulated the use of earth data
using remote sensing techniques, which has become a universal and familiar instrument for assessing
natural resources (Philipson, Lindell, 2003). Information from low-altitude satellite sensors and re-
mote sensing offer an optimal path for understanding pattern and process related to rangeland condi-
tion in the area. The multi-temporal and multi-spectral data acquired by various satellite sensors are
used to identify, map and monitor rangelands, derive specific environmental variables.
We used a Landsat 8 satellite image (path 151⁄row 31) taken on 7th of August 2014, freely ac-
quired from the U.S. Geological Survey georeferenced GeoTIFF files at a 30 m resolution via the
EarthExplorer website (https://earthexplorer.usgs.gov). This image encompassing the study area was
selected because of the minimum cloud coverage (0.16 %).
Candidate predictor variables most suitable to predict the ecological niche dominance within the
landscape include tasselled cap transformation, enhanced vegetation index (EVI), and Land surface
temperature (LST). EVI is a standardized vegetation index which allows us to generate an image
showing the relative biomass. Landsat-8 thermal bands i.e., Band 10 and Band 11, were used to cal-
culate the brightness temperature over the study area. This gives an assessment of the ground tem-
perature that may be hotter than the ambient air temperature.
Tasselled cap transformations, originally developed to understand changes in agricultural lands,
generate three orthogonal bands from the six-band Landsat composite (Huang et al., 2002). The
three generated bands represent measurements of brightness (band 1, dominated by surface soils),
greenness (band 2, dominated by vegetation and correlates with EVI), and wetness (band 3, includes
interactions of soil, vegetation and moisture patterns) (Kauth, Thomas, 1976).
A digital elevation model (DEM) was used as input for capturing topographic variables. The
DEM was aggregated from the 30 seconds (~30 m) NASA Shuttle Radar Topographic Mission
(SRTM) DEM (http://srtm.csi.org). The following terrain features were extracted: slope, aspect and
terrain ruggedness. Slope is the steepness or the degree of incline of a surface. The direction a slope
faces with respect to the sun (aspect) has a profound influence on vegetation and snowpack. We split
aspect into two components: eastness = sin(aspect) and northness = cos(aspect) These indices of
northness and eastness provide continuous measures (−1 to +1) describing orientation. The topog-
raphic ruggedness index (TRI) was developed to express the amount of elevation difference between
adjacent cells of a DEM. These were selected because generally, terrain roughness and slope create a
template of risk, in which herbivores have to trade off between resource acquisition (e.g. foraging in
high quality habitats, finding mates) and predator avoidance (Schweiger et al., 2015). Ibex are very
good climbers that find protection from predators and the possibility to overview large areas in pre-
dominantly rocky terrain with steep slopes.
Using species distribution modelling to guide survey efforts of the snow leopard (Panthera uncia) ... 115
The resolution or grain of Landsat images (30 m) and the DEM is finer than the accuracy by
which we can record ibex presence in the field; for this reason all the considered environmental lay-
ers have been rescaled to a 30 arc second resolution (~ 1 km).
SAGA GIS software (v. 2.2.7) has been used for the preliminary data processing, extracting
(clipping) images for the study area (Conrad et al., 2006).
Statistical modelling
Factor analysis in Statistica 10 was used to examine the contributions and the main patterns of
inter-correlation among the potential environmental controls. Principal component (PC) was used as
the extraction method. By rotating the factors a factor solution was found that is equal to that
obtained in the initial extraction but which has the simplest interpretation, and for this purpose the
Varimax normalized type of rotation was applied. Usually a solution that explains 75-80% of the
variance is considered sufficient.
Species distribution models obtained from a large data set of associated environmental
covariates often inherently result in multi-collinearity, a statistical problem defined as a high degree
of correlation among covariates. Principal component analysis is among the statistical procedures
proposed to solve or to reduce multi-collinearity because the obtained PCs are independent of each
other, that is, they are orthogonal. Accordingly, this permits them to be used as independent, non-
correlated variables in analyses of modeling potential species distribution (Cruz-Cárdenas et al.,
2014) instead of the raw data of environmental variables.
Maxent distribution model
We used the freely available Maxent software, version 3.3.3k, which generates an estimate of
probability of presence of the species. The default settings of Maxent were used in this study. We ran
models with 25 bootstrap replicates.
Model performance was assessed using the average AUC (area under the receiver operating
curve) score to compare model performance. AUC values > 0.9 are considered to have ‘very good’,
> 0.8 ‘good’ and > 0.7 “useful” discrimination abilities (Swets, 1988). The logistic output format
was used, because it is easily interpretable with logistic suitability values ranging from 0 (lowest
suitability) to 1 (highest suitability). Applying a threshold is the last step of many species modelling
approaches. We used 10 percentile training presence because this threshold value is considered to
provide a better ecologically significant result when compared with more restricted thresholds values
(Phillips, Dudík, 2008)
Results
The PC analysis provided a comprehensive way to analyze the niche of Siberian ibex in the
study area and captures various aspects of ibex ecology and environmental requirements of the spe-
cies. PC1–5 extracted from all the variables explained ~88 % of the variance.
The first two principal components heavily correlated with temperature/altitude and greenness
(50.2 % contribution of PC1), and precipitation (21.5 % — of PC2); the following three components
collectively explained 16.3 % of the variation and correlated with one or two parameters: PC3 was
negatively associated with the inter-correlated slope and topographic ruggedness index (TRI), PC4 –
with the mean temperature of the wettest quarter, and PC5 — negatively with eastness.
These findings are in good agreement with the herbivore character of the species and biocli-
matic habitat requirements of the vegetation it feeds upon, richer in flatter areas, and where plants
may benefit from more sunlight.
Gridded data layers produced in SAGA GIS representing the first five principal components
were then used for modelling species distribution.
From the 25 model runs, the average AUC was 0.746 (meaning “useful” discrimination abilities
of the final model), with little variation in AUC between runs (SD = 0.016).
Volodymyr Tytar, Tolkunbek Asykulov, Matthias Hammer
116
Fig. 1. An elevation map of the Kyrgyz
Ala-Too study area where a contour
line is drawn around areas of predicted
probability of Siberian ibex occurrence
exceeding 0.50; triangles indicate
places where records were made of
snow leopard presence.
Рис. 1. Топографічна карта дослідженої ділянки Киргизького Ала-Тоо; контурна лінія обведена навколо
териорій, де прогнозована ймовірність перебування сибірського гірського козла складає понад 0,5;
трикутники вказують місця реєстрацій перебування снігового барса.
Fig. 2. Snow leopard pugmarks (11th
August 2016, altitude 3562 m).
Рис. 2. Сліди лап снігового барса
(11 серпня 2016 р., висота 3562 м).
Fig. 3. Snow leopard kill of a Siberian
ibex (25th August 2016, altitude
3560 m).
Рис. 3. Сибірський гірський козел,
який став жертвою снігового барса
(25-е серпня 2016 р., висота 3560 м).
Fig. 4. Camera trap record of Siberian
ibex next to the site where the kill was
discovered in 2016.
Рис. 4. Знімок сибірських гірських
козлів, зроблений за допомогою
фотопастки, встановленої поблизу
місця, де у 2016 р. була знайдена
жертва снігового барса.
Using species distribution modelling to guide survey efforts of the snow leopard (Panthera uncia) ... 117
Fig. 5. Nighttime camera trap record of a snow leopard, altitude 3720 m.
Рис. 5. Нічний знімок снігового барса, зроблений фотопасткою, висота 3720 м).
Fig. 6. Evening (zoomed) camera trap record of a snow leopard (15th of July 2019, 19:50 PM), altitude 3512 m.
Рис. 6. Вечірній (збільшений) знімок снігового барса, зроблений фотопасткою (15 липня 2019 р., 19 год.,
50 хв), висота 3512 м).
The averaged output from these model runs is shown on the map (Fig. 1), where a contour line
is drawn around areas of predicted probability of Siberian ibex occurrence exceeding 0.50. In terms
of nature conservation planning and setting snow leopard research priorities these areas of high pre-
dicted probability of Siberian ibex occurrence represent the most interest.
With one exception, most of the snow leopard records made in the study area (n = 15) fell
within the 10 percentile training presence logistic threshold of 0.368, and there were two outliers
when the 0.50 threshold was applied. One of them was a record made at the Karakol Pass, which the
animal had used for crossing from one mountain range to the other.
On average the predicted probability of Siberian ibex occurrence in places where records were
made of snow leopard presence was 0.559, with a minimum of 0.526 for the bulk of the records,
expectedly suggesting areas of high habitat suitability for Siberian ibex attract the predator (figs 2–6
are from such areas).
Volodymyr Tytar, Tolkunbek Asykulov, Matthias Hammer
118
Conclusion
Further research is needed to confirm wider snow leopard presence and monitor snow leopard
and prey population trends in the survey area. Presence/absence surveys will need to be repeated in
the coming years, using camera traps from the very beginning of the survey. Finding a trail and/or
relic scrape(s) is a high priority. If either more of these can be found, remote camera-trapping would
be enhanced as a survey tool. These efforts can be guided by modelling exercises as above, showing
places where basic requirements for Siberian ibex, upon which snow leopards rely the most, are met
to a significant degree.
References
Bellis, L. M., A. M. Pidgeon, V. C. Radeloff, V. StLouis, J. L.
Navarro, M. B. Martella. 2008. Modeling habitat suitability
for Greater Rheas based on satellite image texture. Ecologi-
cal Applications, 18 (8): 1956–1966.
Conrad, O., B. Bechtel, M. Bock, H. Dietrich, E. Fischer, L.
Gerlitz, J. Wehberg, V. Wichmann, J. Böhner. 2015. System
for Automated Geoscientific Analyses (SAGA) v. 2.1.4.
Geoscientific Model Development Discussions, 8 (2): 2271–
2312.
Cruz-Cárdenas, G., L., J. L. López-Mata, L. Villaseñor, E.
Ortiz. 2014. Potential species distribution modeling and the
use of principal component analysis as predictor variables.
Revista Mexicana de Biodiversidad, 85 (1): 189–199.
Elith, J., C. H. Graham, R. P. Anderson, M. Dudik, S. Ferrier,
A. Guisan, R. J. Hijmans, F. Huettmann, J. R. Leathwick,
A. Lehmann, J. Li, L. G. Lohmann, B. A. Loiselle, G. Man-
ion., C. Moritz, M. Nakamura, Y. Nakazawa, J. M. Overton,
A. T. Peterson, S. J. Phillips, K. Richardson, R. Scachetti-
Pereira, R. E. Schapire, J. Soberon, S. Williams, M. Wisz,
N. E. Zimmermann. 2006. Novel methods improve predic-
tion of species’ distributions from occurrence data. Ecogra-
phy, 29 (2): 129–151.
Fedosenko, A. K., D. A. Blank. 2001. Capra sibirica. Mammal-
ian Species, No. 675: 1–13.
Hijmans, R. J., S.E. Cameron, J. L. Parra, P. G. Jones, A. Jar-
vis. 2005. Very high resolution interpolated climate surfaces
for global land areas. International Journal of Climatology,
25 (15): 1965–1978.
Huang, C., W. L. Yang, H. Collin, G. Zylstra. 2002. Derivation
of a tasselled cap transformation based on Landsat 7 at-
satellite reflectance. International Journal of Remote
Sensing, 23 (8): 1741–1748.
Hutchinson, G. E. 1957. Concluding remarks. Cold Spring
Harbor Symposia on Quantitative Biology, No. 22: 415–427.
IUCN... 2017. The IUCN Red List of Threatened Species.
Version 2017-3. <http://www.iucnredlist.org>. Downloaded
on 05 May 2018.
Kauth, R. J., G. S. Thomas. 1976. The Tasselled Cap — a
graphic description of the spectral-temporal development of
agricultural crops as seen by LANDSAT. LARS Symposia.
Paper 159.
Lyngdoh, S., S. Shrotriya, S. P. Goyal, H. Clements, M. W.
Hayward, B. Habib. 2014. Prey preferences of the snow
leopard (Panthera uncia): regional diet specificity holds
global significance for conservation. PLoS ONE, 9 (2):
e88349.
Marzluff, J. M., K. Ewing. 2001. Restoration of fragmented
landscapes for the conservation of birds: a general frame-
work and specific recommendations for urbanizing land-
scapes. Restoration Ecology, 9 (3): 280–292.
Philipson, P., T. Lindell. 2003. Can coral reefs be monitored
from space? AMBIO: A Journal of the Human Environment,
32 (8): 586–593.
Phillips, S. J., R. P. Anderson, R. E. Schapire. 2006. Maximum
entropy modelling of species geographic distributions. Eco-
logical Modeling, 190 (3–4): 231–259.
Phillips, S. J., M. Dudik. 2008. Modelling of species distribu-
tions with MAXENT: new extensions and a comprehensive
evaluation. Ecography, 31 (2): 161–175.
Schweiger, A. K., M. Schütz, P. Anderwald, M. E. Schaepman,
M. Kneubühler, R. Haller, A. C. Risch. 2015. Foraging ecol-
ogy of three sympatric ungulate species Behavioural and re-
source maps indicate differences between chamois, ibex and
red deer. Movement Ecology, 3 (6): 1–12.
Swets, J. 1988. Measuring the accuracy of diagnostic systems.
Science, 240 (4857): 1285–1293.
Wein, J. 2002. Predicting species occurrences: progress, prob-
lems, and prospects. In: Scott M. J. et al. (eds). Predicting
Species Occurrences. Issues of Accuracy and Scale. Island
Press, Washington, 739–749.
