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Sample Survey Strategies

Part of the Springer Series on Environmental Management book series (SSEM)

The goal of wildlife ecology research is to learn about wildlife populations and their use of habitats. The objective of this chapter is to provide a description of the fundamentals of sampling for wildlife and other ecological studies. We discuss a majority of sampling issues from the perspective of design-based observational studies where empirical data are collected according to a specific study design. We end the chapter with a discussion of several common model-based sampling approaches that combine collection of new data with parameters from the literature or data from similar studies by way of a theoretical mathematical/statistical model. This chapter draws upon and summarizes topics from several books on applied statistical sampling and wildlife monitoring and we would encourage interested readers to see Thompson and Seber (1996), Thompson (2002b), Thompson et al. (1998), Cochran (1977), and Williams et al. (2002).

Typically, the availability of resources is limited in wildlife studies, so researchers are unable to carry out a census of a population of plants or animals. Even in the case of fixed organisms (e.g., plants), the amount of data may make it impossible to collect and process all relevant information within the available time. Other methods of data collection may be destructive, making measurements on all individuals in the population infeasible. Thus, in most cases wildlife ecologists must study a subset of the population and use information collected from that subset to make statements about the population as a whole. This subset under study is called a sample and is the focus of this section. We again note that there is a significant difference between a statistical population and a biological population (Chap. 1).

Keywords

Simple Random Sample Line Transect Adaptive Sampling Resource Selection Aerial Survey 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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