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An Introduction to Quantitative Ecology

Mathematical and Statistical Modelling for Beginners

Timothy E. Essington

September 2021

ISBN: 9780192843487

320 pages
Paperback
246x171mm

Price: £34.99

Environmental science (ecology, conservation, and resource management) is an increasingly quantitative field. This accessible textbook introduces quantitative ecology in a manner that aims to confront the limitations of the current literature and thereby appeal to a far wider audience.

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Description

Environmental science (ecology, conservation, and resource management) is an increasingly quantitative field. This accessible textbook introduces quantitative ecology in a manner that aims to confront the limitations of the current literature and thereby appeal to a far wider audience.

  • A novel integration of mathematical modelling and statistical analysis, applied to real world ecological data
  • Provides and illustrates a conceptual framework for model evaluation and interpretation
  • Includes worked examples in both spreadsheet and R platforms
  • Adopts a two tier structure (both foundational and advanced levels) to facilitate flexible tuition
  • Based on a proven and successful course structure, honed over 20 years

About the Author(s)

Timothy E. Essington, Professor, School of Aquatic and Fisheries Sciences; Director, Center for Quantitative Sciences and Director, QERM Graduate Program, University of Washington, USA, School of Aquatic and Fisheries Sciences, University of Washington, USA

Timothy E. Essington is a Professor at the School of Aquatic and Fishery Sciences, University of Washington, USA. He is also Director of the Center for Quantitative Sciences and of the University of Washington's QERM Graduate Program. His research chiefly focuses on food web interactions involving fish in marine, estuarine and freshwater habitats.

Table of Contents

    About This Book
    Part I: Fundamentals of Dynamic Models
    1:Why Do We Model?
    2:Introduction to Population Models
    3:Structured Population Models
    4:Competition and Predation Models
    5:Stochastic Population Models
    Part II: Fitting Models to Data
    6:Why Fit Models to Data?
    7:Random Variables and Probability
    8:Likelihood and Its Applications
    10:Model Selection
    10:Bayesian Statistics
    Part III: Skills
    11:Mathematics Refresher
    12:Modeling in Spreadsheets
    13:Modeling in R
    14:Skills for Dynamic Models
    15:Sensitivity Analysis
    16:Skills for Fitting Models to Data
    Part IV: Putting It All Together and Next Steps
    17:Putting It Together : Fitting a Dynamic Model
    18:Next Steps

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