Reliable estimates of population size and a knowledge of determinants of detectability and density estimates are crucial for effective conservation of species. Using Multiple Covariate Distance Sampling (MCDS), we modelled the influence of covariates on detection probability and density estimates of native blackbuck Antilope cervicapra and the invasive feral-horse Equus caballus at Point Calimere Wildlife Sanctuary. Grids of 1 × 1 km size were overlaid on the study area with a 1-km line transect in alternate grid cells. Sixteen transects were walked four times each, which detected 199 blackbuck and 152 feral-horse clusters. On each sighting, the climatic, habitat and anthropogenic covariates were recorded, which are likely to affect detection probability. At first, exploratory analyses were made using Conventional Distance Sampling (CDS) to arrive at estimates. Later, in Multiple Covariate Distance Sampling (MCDS), key models were fit into the dataset after selecting the potential covariates that had a significant effect on detection distances obtained from FAMD/PCA, and the best one was selected based on AIC. The MCDS analysis in blackbuck included covariates, viz., distance to water in the best model (29 individuals/km 2 ; CI 22 – 36; detection probability = 0.58), followed by distance to feral-horse, sampling time and principal diet. The covariate distance to water emerged as the best model for feral-horse as well (13 individuals/km 2 ; CI 9 – 18; detection probability = 0.36), followed by distance to cattle and principal diet. While the MCDS approach outperformed the CDS global and survey-strata estimates in blackbuck, both the approaches had a marginal difference in feral-horse. Post-stratification analysis showed that blackbuck density increased significantly with distance to water and feral-horse, but insignificantly with the absence of principal diet; whereas, the density of the feral-horse increased significantly with distance to water and cattle in the presence of principal diet. These findings suggest the ability of feral-horse to keep the blackbuck away from optimal areas, where the principal diet is abundant. Our study, thus, illustrates the need for the use of MCDS approach that ascertained (i) reliable population density estimates, (ii) spatiotemporal constraint on large herbivores caused by overabundance of water during the wet season, and (iii) competitive interaction of the invasive feral-horse with the native blackbuck and its likely effect on the latter keeping away from the former and the principal diet to overcome competition. Thus, the study highlights the impact of feral-horse on the native species and suggests measures for the long-term conservation of the blackbuck.

中文翻译：

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在印度南部 Point Calimere Wildlife Sanctuary 使用多协变量距离采样模拟协变量对原生黑羚羊和入侵野马的可检测性和密度的影响

对种群规模的可靠估计以及对可探测性和密度估计的决定因素的了解对于有效保护物种至关重要。我们使用多协变量距离采样 (MCDS) 对协变量对 Point Calimere 野生动物保护区的原生黑羚羊角羚和入侵野马 Equus caballus 的检测概率和密度估计的影响进行建模。1 × 1 km 大小的网格覆盖在研究区域上，并在交替网格单元中使用 1 公里的线横断面。16 条横断面各走四次，检测到 199 只黑羚羊和 152 只野马群。每次目击时，都会记录气候、栖息地和人为协变量，这可能会影响检测概率。首先，使用常规距离采样 (CDS) 进行探索性分析以得出估计值。后来，在多协变量距离采样（MCDS）中，在选择对 FAMD/PCA 获得的检测距离有显着影响的潜在协变量后，将关键模型拟合到数据集中，并基于 AIC 选择最好的一个。blackbuck 中的 MCDS 分析包括协变量，即最佳模型中与水的距离（29 个体/km 2 ；CI 22 – 36；检测概率 = 0.58），其次是与野马的距离、采样时间和主要饮食。与水的协变量距离也成为野马的最佳模型（13 个人/km 2 ；CI 9 – 18；检测概率 = 0.36），其次是与牛的距离和主要饮食。虽然 MCDS 方法在 blackbuck 中的表现优于 CDS 全球和调查层估计，但两种方法在野马方面存在边际差异。分层后分析表明黑羚羊密度随着与水和野马的距离显着增加，但在没有主要饮食的情况下不显着；而在主要饮食存在的情况下，野马的密度随着与水和牛的距离的增加而显着增加。这些发现表明，野马有能力让黑羚羊远离主要饮食丰富的最佳区域。因此，我们的研究表明需要使用 MCDS 方法来确定 (i) 可靠的种群密度估计，(ii) 由于雨季水量过多而对大型食草动物造成的时空限制，(iii) 入侵野马与本地黑羚羊的竞争性互动及其对后者远离前者和主要饮食以克服竞争的可能影响。因此，该研究强调了野马对本地物种的影响，并提出了长期保护黑羚羊的措施。