Comparing distance sampling and spatial point process models for estimating wide-ranging, aggregated ungulate populations

Résumé fourni par la source

Ungulate populations are declining globally due to increasing anthropogenic pressures on habitats and migration routes, highlighting the importance of protected areas for their conservation. The protected areas in the Mongolian Gobi provide a critical stronghold for several threatened ungulate species, including the khulan (Asiatic wild ass, Equus hemionus ) and goitered gazelle ( Gazella subgutturosa ). Although long-term ungulate population monitoring is essential to assess the effectiveness of conservation measures, it is challenging due to low population densities, fission-fusion social dynamics, and highly variable environmental conditions. We analysed data from large-scale ground surveys conducted in 2010, 2015, and 2022 in the Great Gobi B Strictly Protected Area (GGB), comparing two analytical approaches: deign-based point transect distance sampling and Bayesian point process modelling. The latter produces spatially explicit density surfaces that account for spatial autocorrelation alongside abundance estimates. Both methods revealed consistent population trends: khulan numbers declined over the study period, while the gazelle population increased threefold. The Bayesian approach provided greater precision and identified aggregation hotspots. These findings underscore the benefits of modelling the spatial structures within a point process framework for aggregated species in landscapes with uneven and temporally variable resource distributions. We recommend continuing the point transect surveys at least every 5 years, ideally expanding the coverage to suitable habitats adjacent to the protected area. In addition, ranger observations on distribution, group size, reproduction and mortality are needed to help identify drivers of population change and to obtain annual minimum population counts.

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