Applying the double observer methodology for assessing blue sheep population size in Nar Phu Valley, Annapurna Conservation Area, Nepal
Thapa, Kamal et al. (2021), Applying the double observer methodology for assessing blue sheep population size in Nar Phu Valley, Annapurna Conservation Area, Nepal, Dryad, Dataset, https://doi.org/10.5061/dryad.47d7wm3dz
This study was undertaken in spring, 2019 to assess the applicability of the Double-Observer survey method for estimating blue sheep Pseudois nayaur abundance in Nar-Phu valley of Manang District located in Annapurna Conservation Area of northern Nepal. Since counting large mammals in rugged mountain habitat poses a special challenge, we tested the efficacy of the Double Observer method for generating robust population estimates for this important protected area. The overall detection probability for observers (O1 and O2) was 0.94 and 0.91 for a total of 106 groups comprised of 2,059 individual blue sheep. We estimated the area’s blue sheep population at 2,070 (SE ±168.77; 95% CI 2,059 – 2,405) for the 246.2 km² of sampled habitat. We determined blue sheep to be widely distributed within the study area with a mean density of 8.4 individuals per km2 based on a total study area of 246.2 km2. We discuss demographic population structure and identify limitations when applying the Double Observer approach, along with recommending viewshed mapping for ensuring more robust density estimates of mountain-dwelling ungulates like blue sheep or ibex that inhabit extremely heterogeneous terrain which strongly influences sighting distances and overall animal detection rates.
Delineation of study area and survey blocks
Blue sheep habitat ranges from about 3,000-5,400 meter (m) above sea level across much of Nepal (Jackson and Hunter 1996), and we selected this altitudinal range using Government of Nepal (Governmentof Nepal 2001)1:50,000 topographic maps to delineate sampling blocks (see below). Within the Nar- Phu Valley (total area 835.9 km2) we estimated suitable habitat for blue sheep at 322.4 km² (Figure 1), of which we surveyed 246.2 km² after excluding areas with excessively steep terrain (> 40 degrees) judged too inaccessible for surveying.
Prior to ﬁeld visits, potential blue sheep habitat was outlined on topographic maps by team members familiar with the study area, including the first observer (O1) who had conducted several intensive blue sheep surveys earlier (see Thapa 2005, Sharma et al. 2006). By plotting livestock pasture boundaries using ArcView (10.7.1 Desktop version software, ESRI, Inc, California), and examining contour maps along with Google Earth images, we identified 11 watershed-based survey blocks for sampling (Figure 2). Each survey block is separated by high mountain ridges or a large river, with the expectation that such geographic features limit blue sheep movement between different survey blocks during sampling intervals.
To ensure the entire study area was adequately covered, each survey unit (hereby termed, sampled block) ranging in size from 10.4 km² to 36.8 km², was divided into relatively small sections for survey purposes (see Figure 2 & Table 1) as recommended by Suryawanshi et al. 2012 and the Government of Nepal (MoFSC 2017). Additional information included dominant habitat type, presence/absence of topographic barriers, dominant aspect and slope steepness and major landform type(s) along with a generalized map of habitat suitability for blue sheep covering the entire survey region. We also utilized a WWF-Nepal generated habitat suitability map for snow leopard (DNPWC 2017). We excluded non-habitat (rock, ice, snowfields), with prime and fair blue sheep suitability habitats comprising 49% and 51% respectively of the sampled area. We considered open forest, alpine grassland and barren habitats to offer suitable foraging habitat for blue sheep. The proportions of land use were barren land (56.3%), grassland (40.8%), forest cover (1.4%), and shrubland (0.89%). We excluded agricultural land, permanent water bodies and snow cover/glaciers from the areal calculations.
Census scheduling and protocols
Using the DO method, each sample block was sampled sequentially, following from one sample block to the adjacent block for ensuring consistency and to minimize double counting between consecutive days by identifying individually distinctive animals in order to distinguish between different herds to the extent feasible. In each sampled block, we laid a transect using existing herder or livestock trails that offered the best viewing opportunities and provided practical altitudinal gradients, starting from the lowest elevation (typically a river or large stream), and terminating on or near cliffs, rocky outcrops or ridgelines at upper elevations. This chosen uphill pattern also generally coincided with the daily movement patterns of blue sheep (Thinley et al. 2018), which are known to descend to valleys in early morning for drinking water and then progressively ascend (while foraging along the way) and eventually reaching more secure areas (often ridge tops) by dusk where they bed down for the night (Schaller 1973). During spring, most blue sheep are concentrated in or near valleys, uninhibited herder camps or the vacated agricultural lands, the main places for foraging on grass at this time of year. We also scanned the opposite site of valleys from vantage points, using binoculars and spotting scope. We set a conservative upper limit of about one kilometer for the distance at which blue sheep can consistently be detected (Filla et al. 2020).
On each survey day, the first observer (O1) walked slowly (about 2 km/hour) uphill, commencing at 06:30 hours (Nepal Standard Time; dawn 06:00 hrs) and ﬁnishing at around 11:30 hours. The second observer (O2) followed the same trail after an interval of one hour, as suggested by Suryawanshi et al. (2012). Although O1 and O2 commenced their surveys an hour apart, they often completed the transect around the same time, then taking a 2-hour lunch while the blue sheep also bedded down to rest (and are thus harder to detect than when moving and foraging). The second daily survey, following the same pattern, was initiated around 14:30 hrs and continued until about 17:30 hrs. Whenever a blue sheep group was sighted, each observer stopped to record details, assisted by a village assistant serving as local guide, and who was provided with on-the-job training.
In order to meet assumptions of the DO method, including population closure, we completed all 11 study area block counts within 22 consecutive days including travel time. As noted above, each survey block was separated by high ridges, large glaciers or rivers, thus reducing likelihood of individual or group movement in or out of the sampling area, but not entirely eliminating that possibility. Separation of O1 and O2 observer surveys by 1-hour is aimed at fulﬁlling the second assumption of independence, namely that the two observer surveys represent independent samples of the entire population. Based on computer modeling of field data Suryawanshi et al. (2012) deemed this temporal spacing adequate for minimizing detectability responses associated with O1’s presence ahead of O2.
The DO survey method requires that each blue sheep group detected be uniquely identiﬁable based on herd composition and presence of individuals with distinctive body features (e.g., one animal with only one horn, another with a broken horn tip, furless patches on the body or other injuries and separable physical features). Each observer made note of such characteristics when classifying individuals to sex and age class, along with documenting key characteristics for each observation site (e.g., dominant geophysical feature, presence/absence of herder’s camp, distance nearest cliff or other prominent landmark). These elements supported the post hoc evaluation of uniquely detected blue sheep groups by helping O1 and O2 match (or mismatch) each sighting and/or group through cross-referencing them with distinctive individuals and site-locations. Binoculars (10 x 50 power) and, whenever possible, a 15- 30- 45 X spotting scope were used for validating total herd size and individual sex-age classes. We based the classes on size, body pattern and coloration, and size and shapes of horns following Wegge (1979, as simplified by Thapa, 2007), recognizing the following classes: Young (male & female) (<1 year of age); Yearling (male & female) (1–2 years age); Adult Female (> 2 years age); Young-aged Male (2–4 years); Medium-aged Male (4–6 years); and Adult-aged Male (>7 years age) with fully grown horns.
The geographical center for each blue sheep group was estimated to the nearest 100m using a Global Position System (GPSmap 62s, Gamin, Inc., Kansas USA). We also recorded the habitat type(s), topographic feature, landform ruggedness class, aspect, slope and distance to the nearest escape cover along with the distance between observer and center of each group (Jackson and Hunter 1996; however, the compass direction from observer to each group was not taken).
At the conclusion of each day’s field survey, the two observers met to reconcile and agree which groups should be designated as uniquely or commonly observed, aware that slight variations with respect to group number and/or sex-age composition may influence consensual decision-making. Therefore, for each herd tallied, the survey team evaluated total herd size, sex and age classes along with individuals with distinctive features (like misshapen horns found in female blue sheep), sighting location and the time of observation to help determine which blue sheep groups were likely sighted by both observers (common), or conversely which could be classified as unique and observed by only one observer (also see Harris 1994).
Using the example of the Pangre survey block, we evaluated the utility of viewshed mapping for defining the extent of each survey block visible to observers as they walked along the transect scanning for blue sheep. A viewshed is defined as the geographical area that is visible from each location or a series of linked locations (i.e., along the entire transect length). Viewsheds include all surrounding areas that fall within line-of-sight from the transect centerline while observing from multiple locations, but excludes points falling beyond the horizon or areas obstructed by terrain, rocky outcrops and other large obstacles.
Using the ArcMap GIS software tools (ESRI, Inc. Redlands, California) Spatial Analyst Viewshed tool, with the 30m DEM from the Shuttle Radar Topography Mission (SRTM)
(https://earthexplorer.usgs.gov/) as input, we generated viewsheds for 8 randomly selected observation points along the 6.7-kilometer Pangre Pasture transect (see Figure 3). The viewshed rasters of each observation point were merged into a single coverage representing the cumulative land area visible along the entire length of Pangre transect, but clipped to the Pangre survey block boundary. A 2-kilometer buffer was placed around this transect, by adding one kilometer to either side, thereby indicating the areal extent most likely visible to each observer, along with areas hidden by ridgelines, hillsides or other large topographic features. We then computed the total visible land area and reported it as a percentage of the survey block deemed visible to both observers (Table 4).
We used the computer simulation Excel spreadsheet provided by Suryawanshi et al. (2012) for estimating blue sheep population size. Observer group detection rates and the ratio of the sum of the number of groups were derived from the number of groups seen by both observers, and the number of groups seen by only one of the two observers. Densities were computed assuming a 246.2 km2 survey area.
United Kingdom Darwin Initiative Program, Award: 25-027, Round 25
United Kingdom Darwin Initiative Program, Award: 25-027, Round 25