• Other Authors: The Pennsylvania State University CiteSeerX Archives
• Format: Text
• Language: English
• Subjects: Arctic; Pacific
• Online Access: http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.153.9984; http://www.ruwpa.st-and.ac.uk/distance.book/toolkit.pdf

estimating t i animal a density All too often we hear in the news statements like “there are, at most, a few hundred individuals left of this endangered species ” or “there is small hope for the persistence of this population given that so few are left”. How do scientists count animals to make such statements? Tiago Marques explains the concepts and pitfalls of distance sampling—one of the most widely used methods for estimating animal populations. Counting critters Ideally, if feasible, we would count all the animals from a population of interest. However, not much imagination is needed to realise that it is not that easy to count all the polar bears in the Arctic or all the blue whales in the Pacific Ocean. The areas involved are large, animals move around and, even if it were possible, no one would be willing to pay the bill. These are extreme examples, but they illustrate the fact that, most often, one has to rely on some sampling approach to the problem. That is, based on observations on a fraction, sometimes quite small, of the population of interest, usually covering only a subset of the entire region of interest, we wish to draw inferences about the total population. Note that even for geographically restricted scenarios, such as, for example, puffins nesting in the Isle of May, counting them all is not an option. Plot sampling There are many possible methods that allow us to estimate animal abundance or density based on sampling. Arguably one of the most widely used is distance sampling 1. But to introduce the ideas we first consider a more standard method known as strip or plot sampling: a number of plots, with total area a, are randomly allocated in a region of size A, and the total number n of animals within these plots recorded. Density in the covered area is, by definition, the number of animals per unit area, D = n, a and since a random design was used this is also a density estimate valid for the wider region. An estimate of abundance in the wider region we are interested in is obtained by ...