208 T. Wacher et al.
livestock, and all routes driven, and mapped perimeters of bushfires. We then overlaid locations of dama gazelle observations and fires on livestock encounter rate heat maps, using a 10 × 10 km grid in Surfer 11.6.1159 (Golden Software, Golden, USA). On line and reconnaissance transects, survey routes pre-
pared with GPS software (Mapsource 6.16.3 and Basecamp 4.7.2; both Garmin Ltd., Olathe, USA) were displayed on dashboard mounted units. In the open terrain, the vehicle remained mostly well within 25 m of the planned transect line, with an average speed of 12–15 km/h. We recorded wildlife and livestock observations as
groups using Cybertracker 3 (Cybertracker Conservation, 2019). Dorcas and dama gazelles often move away on being sighted. To fulfil distance sampling assumptions, we selected landmark reference points (isolated trees, small shrubs, grass tussocks or bare patches of ground) near the central position of herds when first seen. Wethen measured the distance from the transect line to these landmarks with a laser rangefinder once the vehicle was positioned perpen- dicular to the landmark. At right angle turns, observations originating in the external quadrant of the turn were ex- cluded and observations originating in the interior quadrant were not double counted. Livestock (camels, sheep, goats, cattle, horses and don-
keys) were counted (or estimated for large, dense herds) in- dividually by species, and distances measured to the central location of the assembly.Weconverted these counts to trop- ical livestock units (equivalent to amature animal weighing 250 kg), using correction factors: 1.25 for camels, 0.7 for cat- tle (in herds), 0.1 for sheep, 0.08 for goats, 0.5 for donkeys and 1 for horses (Le Houerou & Hoste, 1977;FAO, 2011), to provide standardized measures. We used 16 kg as the unit weight for the dorcas gazelle (Yom-Tov et al., 1995). Weobtained satellite data on locations of fires within the
survey area from ESDS (2020). As a proxy for the impact of fires, we summed the number of detected fires in each 10 × 10 km grid cell over 1–60 days and 61–180 days prior to the start date of each survey. We required 3–5 days to complete each survey, operating
during 07.30–17.00 each day, with a break during 12.00– 14.00. Detectability of animals may thus have varied over the course of a survey as animal activity changes with time of day, cloud cover and temperature, but all surveys were conducted with as much standardization as was feasible.
Data analysis
Wecalculated estimates for numbers and densities of dorcas gazelles and livestock using Distance 6.0 (Research Unit for Wildlife Population Assessment, University of St Andrews, St Andrews, UK). Uniform, half-normal and hazard rate key functions were compared, with cosine, simple polynomial
and hermite polynomial adjustment terms constrained, to ensure the detection function decreased monotonically. We used size-bias regression to account for larger groups being more likely to be sighted at longer distances than smaller groups, and maximized sample size by using a glo- bal detection function across all surveys, with truncation at 400mfor the smaller dorcas gazelle and 490mfor livestock. We used Akaike’s information criterion (AIC; Sakamoto et al., 1986) to select the best models, and confirmed an acceptable fit to the data using a χ2 test. We assessed the trend of the dorcas gazelle population
for 2013–2019, when the sample design was standardized, by regressing the natural log of the density estimates against survey year in R 3.4.4 (R Core Team, 2019). We used den- sity rather than estimated numbers because of variations in survey area (Table 1). We performed a Bayesian analysis (Crome et al., 1996) to estimate the probability of decline or increase, assuming a flat prior and treating the scaled likelihood curve as the posterior probability. We examined the distribution of livestock and dorcas
gazelles in relation to artificial water sources by calculating minimumlinear distance from the centre of each 10 × 10 km transect grid cell to the nearest artificial water point using the NNJoin package in QGIS 3.12 (QGIS, 2020). There were three types of artificial water sources (in order of in- creasing cost to use them): hafiris (natural water hole de- pressions, artificially enlarged to prolong surface water availability), cement-lined wells (hand-drawn using live- stock) and boreholes (with mechanized pumps). We mod- elled dorcas gazelle encounter rate in each grid cell against livestock encounter rate, distance to nearest water point of each type, season, survey and fire detections in the recent and longer-term period prior to a survey, using multiple regression in R 3.4.4 (R Core Team, 2019). Livestock encoun- ter rate was modelled against distance to nearest water point of each type, season, survey, and recent and longer-term fire detections. We used AIC for model selection. To examine sampling efficiency, we used observed encounter rates to predict sample effort required (total transect line length) in relation to targeted level of precision, measured as the coefficient of variation (CV; Buckland et al., 2001). Meteorological data for the study site were obtained
from a HOBO 21-USB weather station established at the oryx release site base camp in 2017, as no other data close to the study area were available.
Results
Dorcas gazelle numbers and trend Estimated numbers of dorcas gazelles in the line transect study zone ranged from c. 7,700 to c. 18,000 individuals; associated mean densities were 3.5–7.0 dorcas/km2 (Fig. 2a). Precision (CV) averaged 18%(11–29%) across all surveys (Table 1). The large differences
Oryx, 2023, 57(2), 205–215 © The Author(s), 2022. Published by Cambridge University Press on behalf of Fauna & Flora International doi:10.1017/S0030605321001629
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