Accounting for animal density gradients using independent information in distance sampling surveys
- 662 Downloads
Distance sampling is extensively used for estimating animal density or abundance. Conventional methods assume that location of line or point transects is random with respect to the animal population, yet transects are often placed along linear features such as roads, rivers or shorelines that do not randomly sample the study region, resulting in biased estimates of abundance. If it is possible to collect additional data that allow an animal density gradient with respect to the transects to be modelled, we show how to extend the conventional distance sampling likelihood to give asymptotically unbiased estimates of density for the covered area. We illustrate the proposed methods using data for a kangaroo population surveyed by line transects laid along tracks, for which the true density is known from an independent source, and the density gradient with respect to the tracks is estimated from a sample of GPS collared animals. For this example, density of animals increases with distance from the tracks, so that detection probability is overestimated and density underestimated if the non-random location of transects is ignored. When we account for the density gradient, there is no evidence of bias in the abundance estimate. We end with a list of practical recommendations to investigators conducting distance sampling surveys where density gradients could be an issue.
KeywordsDensity gradients Distance sampling Kangaroo Road surveys Line and point transects Wildlife abundance
Unable to display preview. Download preview PDF.
- Borchers DL, Burnham KP (2004) General formulation for distance sampling. In: Buckland ST, Anderson DR, Burnham KP, Laake JL, Borchers DL, Thomas L (eds) Advanced distance sampling. Oxford University Press, Oxford, pp 307–392Google Scholar
- ESRI (2011) ArcGIS: Release 9.3. Redlands, California: Environmental Systems Research Institute 1999–2010Google Scholar
- Fewster RM, Buckland ST (2004) Assessment of distance sampling estimators. In: Buckland ST, Anderson DR, Burnham KP, Laake JL, Borchers DL, Thomas L (eds) Advanced distance sampling. Oxford University Press, Oxford, pp 281–306Google Scholar
- Fletcher D (2007) Pest or guest: the zoology of overabundance. Chapter Managing Eastern Grey Kangaroos Macropus giganteus in the Australian capital territory: reducing the overabundance—of opinion. Royal Zoological Society of New South Wales, Mosman, NSW, pp 117–128Google Scholar
- Lancia RA, Kendall WL, Pollock KH, Nichols JD (2005) Estimating the number of animals in wildlife populations. In: Techniques for wildlife investigations and management. The Wildlife Society, Bethesda, MD, pp 106–153Google Scholar
- R Development Core Team (2012) R: a language and environment for statistical computing. R Foundation for Statistical Computing, Vienna, Austria. ISBN 3-900051-07-0Google Scholar
- Southwell C (1989) Techniques for monitoring the abundance of kangaroo and wallaby populations. In: Grigg G, Jarman P, Hume I (eds) Kangaroos, wallabies and ratkangaroos, Surrey Beatty, Sydney, pp 659–693Google Scholar
- Williams BK, Nichols JD, Conroy MJ (2002) Analysis and management of animal populations. Academic Press, San DiegoGoogle Scholar