Improved sampling of wildlife populations using airborne surveys

Abstract

Parameter estimates, obtained from airborne surveys of wildlife populations, often have large bias and large standard errors. Sampling error is one of the major causes of this imprecision and the occurrence of many animals in herds violates the common assumptions in traditional sampling designs like systematic or simple random sampling even when stratification is used. In this paper, we present an adaptive sampling design that uses criteria of observed animal counts to maximise sample information and that is independent of the usual assumption of a uniform distribution for wildlife populations. For illustration, the design is applied to data derived from a survey carried out in the Masai Mara ecosystem (Mara) of Kenya, with a focus on three species: elephant ( Loxodonta africana ), kongoni ( Alcelaphus buselaphus ) and wildebeest ( Connochaetes taurinus ). The sampling design's more efficient estimates show an improvement on those from conventional systematic design, with a greater than 10 times reduction in estimated bias and a 37% lowering of the standard error. The adaptive design, however, underestimates population totals for species in large herds, while a multivariate extension gives only marginal improvements.