ECOLOGICAL MODELS AND DATA IN R PDF

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book August 29, Ecological Models and Data in R. Ben Bolker. PRINCETON UNIVERSITY PRESS. PRINCETON AND OXFORD. book cover: Ecological Models and Data in R (The PDF and HTML files are self -explanatory; XML may render math better in some browsers. The R files are. This book is about combining models with data to answer ecological ques- tions. . statistics package like SPSS or SAS, although you should definitely use R.


Ecological Models And Data In R Pdf

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DOWNLOAD PDF. book August 29, Ecological Models and Data in R book August 29, book August 29, Ecological Models and Data in R. Ecological Models and Data in R is the first truly practical introduction to modern statistical methods for ecology. In step-by-step detail, the book teaches ecology. Ecological Models and Data in R is the first truly practical introduction to modern statistical methods for ecology. In step-by-step detail, the book teaches e.

Practical, beginner-friendly introduction to modern statistical techniques for ecology using the programming language R Step-by-step instructions for fitting models to messy, real-world data Balanced view of different statistical approaches Wide coverage of techniques--from simple distribution fitting to complex state-space modeling Techniques for data manipulation and graphical display Companion Web site with data and R code for all examples Benjamin M.

More about this book. Exercise solutions and other supplementary materials.

A Practical Guide to Ecological Modelling

Bolker's Home Page. Introduction [PDF].

Chapter 2 [PDF]. Chapter 3 [PDF]. This book is a tour de force for anyone who studied ecology for his or her interest of nature's working. But it is the one single book that can propel the statistical novice to the cutting edge of statistical ecology--albeit with blood, sweat and tears.

Dormann, Basic and Applied Ecology. Numerous enlightening footnotes, meaningful graphics and direct speech are evidence of substantial classroom experience of the author.

The book addresses students and researchers who have or have had some basic knowledge in ecology, mathematics and statistics. Delivering many examples and profound details on numerical aspects of maximum likelihood estimation, the book may also give a red line for a course in computational statistics. I expect to refer to it often.

Prior, Austral Ecology.

This alone will make it more useful than previous books. To solve both problems simultaneously, many have moved to doing their statistics on the R platform. R is a high-level, open-source programming language strong on both statistical computing and graphic output.

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Mathematica and Stella have focused on user friendliness of basic functions and graphing, making it easy to learn as you go. Matlab has a somewhat steeper learning curve, but is blazingly fast with matrix operations on large data sets and sets of simulated data. All are fairly expensive to license and upgrade.

I find R less intuitive than any of these other programs. If you have already come in through the back door to R for its statistical uses, then the steep learning-curve argument is behind you.

Soetaert and Herman develop a very deliberate set of switchbacks up the learning curve by emphasizing model diversity and relatively easy examples. The examples are the ones that have survived the sometimes brutally Darwinian process of teaching a class based on the draft book.

Examples are diverse enough that others can teach from the book and still develop an emphasis of their own choosing. Part of the challenge of learning the material in this book is a chicken-and-egg problem: in order to know why certain technical details are important, you need to know the big picture, but the big picture itself involves knowing some of those technical details. Iterating, or cycling, is the best way to handle this problem.

Most of the material introduced in this chapter will be covered in more detail in later chapters. Each column contrasts a different qualitative style of modeling.

The loose association of descriptors in each column gets looser as you work downwards. Theoretical models are often mathematically difficult and ecologically oversimplified, which is the price of generality. Paradoxically, although theoretical models are defined in terms of precise numbers of individuals, because of their simplicity they are usually only used for qualitative predictions. Applied models are often mathematically simpler although they can require complex computer code , but tend to capture more of the ecological complexity and quirkiness needed to make book August 29, 8 CHAPTER 1 detailed predictions about a particular place and time.

Because of this complexity their predictions are often less general. The dichotomy of mathematical vs. A mathematician is more likely to produce a deterministic, dynamic process model without thinking very much about noise and uncertainty e. A statistician, on the other hand, is more likely to produce a stochastic but static model, that treats noise and uncertainty carefully but focuses more on static patterns than on the dynamic processes that produce them e.

The important difference between phenomenological pattern and mechanistic process models will be with us throughout the book. As usual, there are shades of gray; the same function could be classified as either phenomenological or mechanistic depending on why it was chosen. All other things being equal, mechanistic models are more powerful since they tell you about the underlying processes driving patterns. They are more likely to work correctly when extrapolating beyond the observed conditions.

Finally, by making more assumptions, they allow you to extract more information from your data — with the risk of making the wrong assumptions. Further reading: books on ecological modeling overlap with those on ecological theory listed on p.

A Practical Guide to Ecological Modelling

Other good sources include Nisbet and Gurney a well-written but challenging classic Gurney and Nisbet a lighter version Haefner broader, including physiological and ecosystem perspectives Renshaw good coverage of stochastic models , Wilson simulation modeling in C , and Ellner and Guckenheimer dynamics of biological systems in general.

An analytical model is made up of equations solved with algebra and calculus. A computational model consists of a computer program which you run for a range of parameter values to see how it behaves.

Most mathematical models and a few statistical models are dynamic; the response variables at a particular time the state of the system feed back to affect the response variables in the future.

Integrating dynamical and statistical models is challenging see Chapter Most statistical models are static; the relationship between predictor and response variables is fixed.

One can specify how models represent the passage of time or the structure of space both can be continuous or discrete ; whether they track continuous population densities or biomass or carbon densities or discrete individuals; whether they consider individuals within a species to be equivalent or divide them by age, size, genotype, or past experience; and whether they track the properties of individuals individual-based or Eulerian or the number of individuals within different categories population-based or Lagrangian.

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Deterministic models represent only the average, expected behavior of a system in the absence of random variation, while stochastic models incorporate noise or randomness in some way. A purely deterministic model allows only for qualitative comparisons with real systems; since the model will never match the data exactly, how can you tell if it matches closely book August 29, 10 CHAPTER 1 enough?Based on a model for both the deterministic and stochastic aspects of the data, we can compute the likelihood the probability of the observed outcome given a particular choice of parameters.

Haefner , pp. If we assume that the probabilities are uniform on one scale linear or logarithmic , they must be non-uniform on the other.

Organisms might decide to switch from growth to reproduction, or to migrate between locations, when they reach a certain size or when resource supply drops below a threshold.

Note that you may also need to install the Hmisc package from CRAN for generating some of the tabular output. Ecological statistics has gotten much more complicated in the last few decades. Many distributions binomial, Poisson, negative binomial, gamma become approximately normal in some limit Figure 4.

Inspection copies are only available to verified university faculty. The next section briefly reviews a much broader range of modeling frameworks, and gives some starting points in the modeling literature in case you want to learn more about other kinds of ecological models.

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