Distance sampling is a widely-used group of closely related methods for estimating the density and/or abundance of biological populations. The main methods are line transects and point transects (also called variable circular plots). These have been used successfully in a very diverse array of taxa, including trees, shrubs and herbs, insects, amphibians, reptiles, birds, fish, marine and land mammals. In both cases, the basic idea is the same. The observer(s) perform a standardized survey along a series of lines or points, searching for objects of interest (usually animals or clusters of animals). For each object detected, they record the distance from the line or point to the object. Not all the objects that the observers pass will be detected, but a fundamental assumption of the basic

In this paper I outline the standard methods of distance sampling and how they are used to obtain estimates of density and abundance for species of interest. I then develop these methods following the approach of Hedley (2000) whereby waiting distances between detections are modelled to produce a density map of the area of interest. In doing so Standard distance sampling, multi-covariate distance sampling and generalized additive models are all discussed and combined together to achieve the density surface. The methods presented are then applied to a data set of fin whale provided by the Biscay Dolphin Research Programme (BDRP). Using the spatial model produced their a priori beliefs on the location of fin whale and trends in numbers are assessed. Both the density map produced (showing locations of high densities) and within season abundance estimates (together with 95 % confidence intervals) support the claims set out by the BDRP.

research on the estimation of landbird abundance from count data. Our conceptual framework includes a decomposition of the probability of detecting a bird potentially exposed to sampling efforts into four separate probabilities. Primary inference methods are described and include distance sampling, multiple observers, time of detection, and repeated counts. The detection parameters estimated by these different approaches differ, leading to different interpretations of resulting estimates of density and abundance. Simultaneous use of combinations of these different inference approaches can not only lead to increased precision but also provides the ability to decompose components of the detection process. Recent efforts to test the efficacy of these different approaches using natural systems and a new bird radio test system provide sobering conclusions about the ability of observers to detect and localize birds in auditory surveys. Recent research is reported on efforts to deal with such potential sources of error as bird misclassification, measurement error, and density gradients. Methods for inference about spatial and temporal variation in avian abundance are outlined. Discussion topics include opinions about the need to estimate detection probability when drawing inference about avian abundance, methodological recommendations based on the current state of knowledge and suggestions for future research. 3 1