Map 1 Study Area
Data collection
The International Centre for Integrated Mountain Development’s (ICIMOD)
land use map (http://geoportal.icimod.org/) was used to identify forest
cover area within elevation range: 2000 m to 4000 m in potential red
panda habitat. The DEM 90 m resolution image was used for elevation. The
identified study area was divided into 504 grids of 9.6
km2 based on animal maximum home range (Yonzon 1989)
using the Geospatial Modeling Environment built-in ArcGIS 10.2 version.
We selected 50% of grids which were further divided into 6 sub-grids
(Area=1.6 km2). Altogether, 252 sub-grids were
selected randomly for sampling across the habitat. We followed the red
panda field survey and protocol for community base monitoring for data
collection (MoFSC 2015). Ensuring these selected sub-grids cover the
entire potential habitat, including elevation range and water
availability, in the particular grid. All the available transects with
an average length of 1 km at an interval of 100 m contour were traversed
in each sub-grid (MoFSC 2015). We recorded the red panda presence
evidences based on indirect signs, such as droppings, foot prints,
foraging sign and remains of dead body parts, and direct sighting while
walking along the transects. Additionally, we also recorded the
occurrence data opportunistically when encountered beside designed
transects. Additionally, the habitat variables were collected in a
concentric sampling plots with a radius 10 m. Such sampling plots were
also established in the red panda sign/sighting recorded site. Tree
canopy cover and bamboo cover within a subplot of 10 m radius (A =
314.28 m2) and 1 m radius (3.14 m2)
respectively also were recorded (MoFSC 2015). In total, we covered 1213
plots including 970 occurrence and
243 non-occurrence plots along 100.68 km long transects. All the field
survey was conducted in June-July and September-October in 2016.
Data filtering and
processing
Mostly, fecal pellets of red panda scat were used as indicative evidence
of Himalayan red panda presence. The occurrence records (n=331) were
used to predict distribution of Himalayan red panda using species
distribution modeling techniques. Also, we categorized other
field-collected data into three separate groups: topographic, habitat,
and disturbance variables. All data were imported into excel
spreadsheets and further statistical analysis was performed in R (Lê et
al. 2008). Species absence record was nine times higher than the
presence record, which could influence further statistical analysis. To
address this inconsistency, we excluded those records consisting of more
than 80% zero input values, and elevation below 2000 m and above 4000 m
in further analysis.
Red pandas are relatively more abundant within these altitudinal range
of (Choudhury 2001; Pradhan et al. 2001; Yonzon et al. 1991). At the
same time, we also removed all the outliers from the data.
Potential habitat and
corridors
Occurrence data of red panda and bamboo species were extracted from the
vegetation survey and used for distribution modeling based on the
Maximum Entropy Algorithm (MaxEnt 3.3.3k). All 19 bioclimatic variables
(11 temperature and 8 precipitation metrics) were downloaded from the
WorldClim website
(http://www.worldclim.org)
(Hijmans et al. 2005). Our data were
spatially distributed covering entire Western Nepal. All variables were
converted into the ascii raster images with a cell size of 30 arc
seconds (~1 km) and masked by study area boundary for
the modeling process. We run 5000 repetitions with a convergence
threshold of 0.00001, a regularization multiplier of 1, a maximum number
of 100,000 background points, the output grid format as “logistic,”
algorithm parameters set to auto features, and all other parameters at
their default settings. Random test percentage was 25% of presence
locations to test the performance of the model. The model outcome was
evaluated by the Area Under Curve (AUC) of the Receiver-Operating
Characteristic (ROC) plot. The training and test AUC above 0.75
indicated a reasonable to high model discrimination ability and good
model performance (Elith et al. 2006). The habitat suitability map was
built by combining the habitat model, bamboo distribution model, and
forest cover using raster calculator in ARC GIS 10.2. We reclassified
the habitat into three suitability classes: low (0.10.50), moderate
(0.50–0.75), and high (>0.75) (Shrestha & Bawa 2014;
Thapa et al. 2018). Forest and bamboo habitat within 2000 m and 4000 m
that consist river/stream, (sign recoded with 0.5), occurrence of low
human footprint
(https://sedac.ciesin.columbia.edu/data/set
/wildareas-v2-human-footprint-geographic), and away from human
settlements (excluded cattle sheds)
(http://sedac.ciesin.columbia.edu/data/set/gpw-v4-population-count-rev11),
and habitat patches (>9.6km2) were
considered as an area of potential corridor. All these layers were built
and overlay using a spatial analysis tool in ArcGIS 10.2.