Road-based line distance surveys overestimate densities of olive baboons

Abstract

Estimating population density and population dynamics is essential for understanding primate ecology and relies on robust methods. While distance sampling theory provides a robust framework for estimating animal abundance, implementing a constrained, non-systematic transect design could bias density estimates. Here, we assessed potential bias associated with line distance sampling surveys along roads based on a case study with olive baboons (Papio anubis) in Lake Manyara National Park (Tanzania). This was achieved by comparing density estimates of olive baboons derived from road transect surveys with density estimates derived from estimating the maximum number of social groups (via sleeping site counts) and multiplying this metric with the estimated average size of social groups. From 2011 to 2019, we counted olive baboons along road transects, estimated survey-specific densities in a distance sampling framework, and assessed temporal population trends. Based on the fitted half-normal detection function, the mean density was 132.5 baboons km-2 (95% CI: 110.4-159.2), however, detection models did not fit well due to heaping of sightings on and near the transects. Density estimates were associated with relatively wide confidence intervals that were mostly caused by encounter rate variance. Based on a generalized additive model, baboon densities were greater during the rainy seasons compared to the dry seasons but did not show marked annual trends. Compared to estimates derived from the alternative method (sleeping site survey), distance sampling along road transects overestimated the abundance of baboons more than threefold. Possibly, this overestimation was caused by the preferred use of roads by baboons. While being a frequently used technique (due to its relative ease of implementation compared to spatially randomized survey techniques), inferring population density of baboons (and possibly other species) based on road transects should be treated with caution. Beyond these methodological concerns and considering only the most conservative estimates, baboon densities in LMNP are among the highest across their geographic distribution range.

Fig 1. Part of an olive baboon (Papio anubis) group using a road in Lake Manyara National Park, Tanzania (Photo: Filipa M.D. Paciência).

Fig 2

Outline of Lake Manyara National Park (LMNP), positions of transects (straight lines), baboon sleeping sites (black dots), and baboon sightings during surveys from late 2015 to 2019 (red dots). The boundaries of the park shown here depict the boundaries of the park before its extension in the southwest (Marang Forest) in 2012. The inset in the top-left indicates the location of the study area in northern Tanzania. All transects and sleeping sites are located inside the borders of LMNP.

Fig 3

Detection functions (red line) of conventional distance sampling models for olive baboons sighted along road transects in Lake Manyara National Park (Tanzania). The histograms (blue bars) show the observed frequency of sightings in each distance bin; detection functions were modeled using a) hazard rate, b) negative exponential, c) half-normal, and d) uniform key functions with cosine series extensions.

Fig 4

Box-plots (bold midline indicates the median, the upper and lower limits of the box represent the third and first quartiles) of observed baboon cluster sizes during seasonal (LR: Long rains; Dry: Dry season; SR: Short rains) line distance surveys in Lake Manyara National Park (Tanzania). The jittered grey points indicate observed cluster sizes.

Fig 5. Seasonal (LR: Long rains; Dry: Dry season; SR: Short rains) density estimates and associated 95% confidence intervals of olive baboons in Lake Manyara National Park (Tanzania) from 2011–2019 (one survey in 2011; three surveys per year from 2012–2018, two surveys in 2019) and seasonal trend lines based on a general additive model.