Geology ReportsSearch

USGS · 5211306

Estimating the number of animals in wildlife populations

Abstract

INTRODUCTION In 1938, Howard M. Wight devoted 9 pages, which was an entire chapter in the first wildlife management techniques manual, to what he termed 'census' methods. As books and chapters such as this attest, the volume of literature on this subject has grown tremendously. Abundance estimation remains an active area of biometrical research, as reflected in the many differences between this chapter and the similar contribution in the previous manual. Our intent in this chapter is to present an overview of the basic and most widely used population estimation techniques and to provide an entree to the relevant literature. Several possible approaches could be taken in writing a chapter dealing with population estimation. For example, we could provide a detailed treatment focusing on statistical models and on derivation of estimators based on these models. Although a chapter using this approach might provide a valuable reference for quantitative biologists and biometricians, it would be of limited use to many field biologists and wildlife managers. Another approach would be to focus on details of actually applying different population estimation techniques. This approach would include both field application (e.g., how to set out a trapping grid or conduct an aerial survey) and detailed instructions on how to use the resulting data with appropriate estimation equations. We are reluctant to attempt such an approach, however, because of the tremendous diversity of real-world field situations defined by factors such as the animal being studied, habitat, available resources, and because of our resultant inability to provide detailed instructions for all possible cases. We believe it is more useful to provide the reader with the conceptual basis underlying estimation methods. Thus, we have tried to provide intuitive explanations for how basic methods work. In doing so, we present relevant estimation equations for many methods and provide citations of more detailed treatments covering both statistical considerations and field applications. We have chosen to present methods that are representative of classes of estimators, rather than address every available method. Our hope is that this chapter will provide the reader with enough background to make an informed decision about what general method(s) will likely perform well in any particular field situation. Readers with a more quantitative background may then be able to consult detailed references and tailor the selected method to suit their particular needs. Less quantitative readers should consult a biometrician, preferably one with experience in wildlife studies, for this 'tailoring,' with the hope they will be able to do so with a basic understanding of the general method, thereby permitting useful interaction and discussion with the biometrician. SUMMARY Estimating the abundance or density of animals in wild populations is not a trivial matter. Virtually all techniques involve the basic problem of estimating the probability of seeing, capturing, or otherwise detecting animals during some type of survey and, in many cases, sampling concerns as well. In the case of indices, the detection probability is assumed to be constant (but unknown). We caution against use of indices unless this assumption can be verified for the comparison(s) of interest. In the case of population estimation, many methods have been developed over the years to estimate the probability of detection associated with various kinds of count statistics. Techniques range from complete counts, where sampling concerns often dominate, to incomplete counts where detection probabilities are also important. Some examples of the latter are multiple observers, removal methods, and capture-recapture. Before embarking on a survey to estimate the size of a population, one must understand clearly what information is needed and for what purpose the information will be used. The key to derivin

Explore related subjects

Keep this discovery

Explore connections, maps & timelines

BibTeXRIS

R.A. Lancia, W. L. Kendall, K. H. Pollock, J.D. Nichols. 2005. Estimating the number of animals in wildlife populations. https://pubs.usgs.gov/publication/5211306

Cite the original work for its findings. Save a collection to share your selection of sources.

KEEP EXPLORING

Related USGS reports

Loma salmonae and related species

Loma salmonae is a microsporidium that infects Pacific salmon and causes a gill inflammatory syndrome known as microsporidial gill disease of salmon. This disease has been mostly associated with netpen-farmed Chinook salmon ( Oncorhynchus tshawytscha ) in British Columbia, Canada. Clinical, diagnostic, pathological aspects of disease, as well as approaches for disease avoidance in salmon aquaculture are discussed. A laboratory infection model in rainbow trout was used to determine life cycle-stages, transmission dynamics, pathophysiology, influence of temperature, and to test therapeutics applicable to aquaculture. This experimental model has been informative on various approaches of disease control, including the development of a promising vaccine. In addition to improving fish health in salmon farming in North America, this L. salmonae model will be applicable to other microsporidial diseases that may be encountered in emerging aquaculture regions.

Book chapter

Cumulative effects of multiple stressors on marine mammals: Elephant seals as a model system

Noise exposure is a potential stressor for free-ranging marine mammals and is often studied in the absence of other environmental factors. Here, a multi-investigator, interdisciplinary effort was undertaken to examine the response of elephant seals to multiple stressors. An integrated physiological and ecological approach was taken, including immunology, stress physiology, toxicology, animal behavior, population biology, and life history theory, to examine the cumulative effects of exposure to multiple stressors in elephant seals. While we measured the response of individual animals, a population response can be predicted by incorporating these results into the long-term data on elephant seal demographics.

Book chapter

When is a parasite a problem?

A parasite’s perceived societal impact depends on the disease it causes and the perception of the affected host species. For instance, doctors and veterinarians have a mission to treat parasites that infect humans or that impact host species that have some utilitarian or aesthetic value for society. Marine scientists have different concerns than doctors. Although the number of parasites that marine scientists should be concerned about may vary, only 13% of parasites and 6% of host–parasite links might be considered “problematic” in a kelp forest food web. With regard to the many threats to marine ecosystems, these percentages suggest that most parasites and infectious diseases are inconsequential. A related issue is the common expectation that parasites and the impacts that they cause are increasing under stress as ocean environments across the globe degrade. Yet, reports of disease have not increased due to human impacts on the marine environment, where the factors that influence parasitism are more complex. Thus, the expectation that marine parasites create problems, and that the diseases they cause are getting worse, is more likely the exception than the rule.

Book chapter