1 Tahrcountry Musings: Incorporating imperfect detection methods for estimating abundance of mountain ungulates

Wednesday, August 31, 2011

Incorporating imperfect detection methods for estimating abundance of mountain ungulates


Estimating abundance of mountain ungulates is a laborious task. It is very rarely conducted in a statistically valid manner. Rough terrain they inhabit, the group-living habits, relatively low density, and the difficulty of marking individuals contribute to making rigorous estimates of abundance logistically difficult task.

The usual way out is raw (uncalibrated). Although their drawbacks are very evident, biases are rarely quantified.

In September 2009, the authors of this paper took advantage of the presence of a radio-marked sample of argali Ovis ammon in the Ikh Nart Nature Reserve in south-central Mongolia, and the area’s comparatively forgiving topography to estimate abundance simultaneously using two independent methods: distance sampling and mark-resight sampling.

Distance sampling produced an abundance estimate of 539 (95% CI: 196-1,081) argali in a 330km2 study area on the same day the researchers visually counted189 animals. Mark-resight sampling using the Poisson log-normal model yielded an estimate of 747 (95% CI: 484-1,009) argali against a maximum223 animals observed in any given day.

The researchers say although both the techniques were imprecise, their similarity increases their confidence that neither estimator was highly biased. Because of budget or logistical restrictions, uncalibrated counts of mountain ungulates are often the only alternative. The researchers emphasize that such results should be viewed cautiously, and when possible, more rigorous approaches to estimating abundance should be taken.

Journal reference
Estimating abundance of mountain ungulates incorporating imperfect detection: argali Ovis ammon in the Gobi Desert, Mongolia
Ganchimeg J. Wingard, Richard B. Harris, Sukh Amgalanbaatar & Richard P. Reading

Volumes / 2011 - Volume 17 / 1 / Wildlife Biology

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