Cover

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Title Page, About the Series, Copyright, Dedication

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Contents

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p. vii

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Preface: Beyond the Null Hypothesis

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pp. xi-xvii

First, a word about the phrase "ecological detective," which we owe to our colleague Jon Schnute: I once found myself seated on an airplane next to a channing woman whose interests revolved primarily around the activities of her very energetic family. At one point in the conversation came the inevitable question: "What sort of work do you do?" I confess that I rather hate that question .... I replied to the woman: "Well, I work with fish populations...

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1. An Ecological Scenario and the Tools of the Ecological Detective

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pp. 3-11

The Mediterranean fruit fly (medfly), Ceratitis capitata (Wiedemann), is one of the most destructive agricultural pests in the world, causing millions of dollars of damage each year. In California, climatic and host conditions are right for establishment of the medfly; this causes considerable concern. In Southern California, populations of medfly have shown sporadic outbreaks...

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2. Alternative Views of the Scientific Method and of Modeling

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pp. 12-38

Science is a process for learning about nature in which competing ideas about how the world works are measured against observations (Feynman 1965, 1985). Because our descriptions of the world are almost always incomplete and our measurements involve uncertainty. and inaccuracy, we require methods for assessing...

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3. Probability and Probability Models: Know Your Data

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pp. 39-93

The data we encounter in ecological settings involve different kinds of randomness. Many ecological models describe only the average, or modal, value of a parameter, but when we compare models to data, we need methods for determining the probability of individual observations, given a specific model and a value for the mean or mode of the parameter. This requires that we describe...

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4. Incidental Catch in Fisheries: Seabirds in the New Zealand Squid Trawl Fishery

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pp. 94-105

It often happens that nontarget species are captured during fishing operations. These takes are called "incidental catch." In some cases, such as the high-seas driftnet fisheries (Mangel 1992), large-scale observer programs are used to monitor incidental catch. Questions arise about how to set the level of observer coverage and how to interpret the data collected during the observer...

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5. The Confrontation: Sum of Squares

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pp. 106-117

The simplest technique for the confrontation between models and data is the method of the sum of squared deviations, usually called the sum of squares. It has three selling points. First, it is simple; in particular, one need not make any assumptions about the way the uncertainty enters into the process or observation systems. Second, it has a long and successful history...

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6. The Evolutionary Ecology of Insect Oviposition Behavior

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pp. 118-130

The study of clutch size was formally initiated by David Lack about fifty years ago (Lack 1946, 1947, 1948), and continues to be a major field of interest, involving both theoretical and empirical aspects (e.g., Godfray et al. 1991; Mangel et al. 1994). Although Lack was interested in birds, his ideas have been applied widely; here we consider the oviposition behavior of insect...

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7. The Confrontation: Likelihood and Maximum Likelihood

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pp. 131-179

The method of sum of squares can be used to find the best fit of a model to the data under minimal assumptions about the sources of uncertainty. Furthermore, goodness-offit profiles and bootstrap resampling of the data sets allow us to make additional inferences about the competition between different models. All of this can be done without assumptions about how uncertainty...

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8. Conservation Biology of Wildebeest in the Serengeti

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pp. 180-202

The Serengeti ecosystem, in Tanzania and Kenya, is home to the largest migratory ungulate populations in the world, as well as many other species, some rare and endangered. This ecosystem is dominated by the wildebeest or gnu (Conochaetes taurinus), whose population size between 1978 and 1990 was about 1.5 million individuals (Figure 8.1). Longterm research in the Serengeti...

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9. The Confrontation: Bayesian Goodness of Fit

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pp. 203-234

The answer is this: because we often have prior information that is valuable and should not be lost in an analysis. For example, Reader et al. (1994) describe an intercontinental study of plant competition which involved Poa pratensis in twelve different communities. Suppose that subsequent to the study, we wanted...

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10. Management of Hake Fisheries in Namibia Motivation

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pp. 235-262

Quantitative methods have a long history in fisheries science (Smith 1994), because fisheries scientists recognized early on that their problems are in many ways much more difficult than terrestrial ones. For example, it is difficult to estimate abundance when one cannot see the population. Perhaps the major impetus was the need to set regulations; this has driven the collection...

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11. The Confrontation: Understanding How the Best Fit Is Found

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pp. 263-280

In this chapter we explore some of the fundamentals that underlie the computer methods to find the best fit. The accessibility of microcomputers, starting in the late 1970s, was a great boon for ecological modeling. Many software programs now include optimization routines to automatically find the best fit. New...

Appendix: "The Method of Multiple Working Hypotheses"

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pp. 281-294

References

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pp. 295-308

Index

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pp. 309-316