The Ecological Detective

The Ecological Detective: Confronting Models with Data (MPB-28)

RAY HILBORN
MARC MANGEL
Copyright Date: 1997
Pages: 330
https://www.jstor.org/stable/j.ctt24hqnx
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  • Book Info
    The Ecological Detective
    Book Description:

    The modern ecologist usually works in both the field and laboratory, uses statistics and computers, and often works with ecological concepts that are model-based, if not model-driven. How do we make the field and laboratory coherent? How do we link models and data? How do we use statistics to help experimentation? How do we integrate modeling and statistics? How do we confront multiple hypotheses with data and assign degrees of belief to different hypotheses? How do we deal with time series (in which data are linked from one measurement to the next) or put multiple sources of data into one inferential framework? These are the kinds of questions asked and answered byThe Ecological Detective.

    Ray Hilborn and Marc Mangel investigate ecological data much as a detective would investigate a crime scene by trying different hypotheses until a coherent picture emerges. The book is not a set of pat statistical procedures but rather an approach. The Ecological Detective makes liberal use of computer programming for the generation of hypotheses, exploration of data, and the comparison of different models. The authors' attitude is one of exploration, both statistical and graphical. The background required is minimal, so that students with an undergraduate course in statistics and ecology can profitably add this work to their tool-kit for solving ecological problems.

    eISBN: 978-1-4008-4731-0
    Subjects: Ecology & Evolutionary Biology

Table of Contents

  1. Front Matter
    (pp. i-vi)
  2. Table of Contents
    (pp. vii-x)
  3. Preface: Beyond the Null Hypothesis
    (pp. xi-2)
  4. CHAPTER ONE An Ecological Scenario and the Tools of the Ecological Detective
    (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 (evidenced by trap catch) over the last two decades (Figure 1.1). Until 1991, the accepted view was that each outbreak of the medfly corresponded to a “new” invasion, started by somebody accidentally bringing flies into the state (presumably with rotten fruit). In 1991, our colleague James...

  5. CHAPTER TWO Alternative Views of the Scientific Method and of Modeling
    (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 the concordance of the competing ideas and the observations. These methods generally constitute the field of statistics (Stigler 1986). Our purpose in writing this book is to provide ecologists with additional tools to make this process more efficient. Most of the material provided in subsequent chapters deals with formal tools for evaluating the confrontation...

  6. CHAPTER THREE Probability and Probability Models: Know Your Data
    (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 the randomness in the data. Similarly, when we build a model and want to generate a distribution of some characteristic, we first need a way to quantity the probability distribution associated with this characteristic. This involves understanding both...

  7. CHAPTER FOUR Incidental Catch in Fisheries: Seabirds in the New Zealand Squid Trawl Fishery
    (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 programs. In this chapter, we analyze a particular fishery and compare the conclusions obtained using different models to describe the incidental catch. This example demonstrates the importance of knowing your data, application of the central limit theorem, and how the Monte Carlo...

  8. CHAPTER FIVE The Confrontation: Sum of Squares
    (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 in science. It is a proven winner. Third, modem computational methods (Efron and Tibshirani 1991, 1993) allow us to do remarkable calculations associated with the sum of squares. We illustrate the last point in the next chapter while...

  9. CHAPTER SIX The Evolutionary Ecology of Insect Oviposition Behavior
    (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 parasitoids. These insects, usually Hymenoptera (wasps), have a typical life history pattern in which adults are free ranging, often able to fly great distances, and lay their eggs in or on the eggs, larvae, pupae, or adults...

  10. CHAPTER SEVEN The Confrontation: Likelihood and Maximum Likelihood
    (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-of-fit 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 enters into the system. However, there are many cases in which the form of the probability distributions of the uncertain terms can be justified. For example, if the deviations of the data from the average very closely follow a...

  11. CHAPTER EIGHT Conservation Biology of Wildebeest in the Serengeti
    (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 began with the Grzimeks’ (1960) bookSerengeti Shall Not Die,which led to the creation of the Serengeti Research Institute (SRI), now known as the Serengeti Wildlife Research Centre (SWRC). Sinclair and Norton-Griffiths (1979) and Sinclair and Arcese (1995) document the history...

  12. CHAPTER NINE The Confrontation: Bayesian Goodness of Fit
    (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 involvedPoa pratensisin twelve different communities. Suppose that subsequent to the study, we wanted to model the plant dynamics in one of the communities. Should we discard relevant information from the other eleven? That seems foolish, but a method for incorporating the previous information is needed, and Bayesian methods provide a framework for using prior information. Stow et al. (1995) proposed that some of...

  13. CHAPTER TEN Management of Hake Fisheries in Namibia
    (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 and analysis of data. In some fisheries, such as those for Pacific salmon in the United States and Canada, data are collected and analyzed and regulations are set on a daily basis. Most fisheries involve large-scale perturbations of ecological systems, systematic...

  14. CHAPTER ELEVEN The Confrontation: Understanding How the Best Fit Is Found
    (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 kinds of optimization methods (genetic algorithms, neural networks, simulated annealing) are still being developed. Even so, it is good to understand at least a little bit about how such things are done—on occasion, it might even be easier for you to do it yourself than rely on a built-in routine....

  15. APPENDIX: “The Method of Multiple Working Hypotheses”
    (pp. 281-294)
    T. C. Chamberlain
  16. References
    (pp. 295-308)
  17. Index
    (pp. 309-316)