Modelling and Quantitative Methods in Fisheries, Second Edition Review

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(More customer reviews)In my opinion, Malcolm Haddon has managed a "tour de force" with this book. He not only covered most of the modern methods of quantitative analysis and modelling in fisheries science but he did so in a clear and relatively simple language. His book is approachable to all biologists with a basic understanding of mathematics and statistics. Yet, he managed to cover both the theoretical underpinnings of the methods and the practical aspects of their use (options, pitfalls ... etc.). In addition, the book gives MS Excel examples of the methods which should allow those of us who are not programmers to fully appreciate the methods by using them interactively. The Excel spreadsheets are also available for download on two web sites.

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With numerous real-world examples, Modelling and Quantitative Methods in Fisheries, Second Edition provides an introduction to the analytical methods used by fisheries' scientists and ecologists. By following the examples using Excel, readers see the nuts and bolts of how the methods work and better understand the underlying principles. Excel workbooks are available for download from CRC Press Online.In this second edition, the author has revised all chapters and improved a number of the examples. This edition also includes two entirely new chapters:Characterization of Uncertainty covers asymptotic errors and likelihood profiles and develops a generalized Gibbs sampler to run a Markov chain Monte Carlo analysis that can be used to generate Bayesian posteriorsSized-Based Models implements a fully functional size-based stock assessment model using abalone as an exampleThis book continues to cover a broad range of topics related to quantitative methods and modelling. It offers a solid foundation in the skills required for the quantitative study of marine populations. Explaining important and relatively complex ideas and methods in a clear manner, the author presents full, step-by-step derivations of equations as much as possible to enable a thorough understanding of the models and methods.

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A Primer of Ecology with R (Use R) Review

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(More customer reviews)This primer provides an excellent, upper-level, introduction to theoretical and simulation ecology. Working through the presented R code and exercises provides a deeper understanding of the thinking of many of the most famous theoretical ecologists, while also introducing the methods by which students can examine ecological questions through simulation.

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Provides simple explanations of the important concepts in population and community ecology.Provides R code throughout, to illustrate model development and analysis, as well as appendix introducing the R language.Interweaves ecological content and code so that either stands alone. Supplemental web site for additional code.

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Mixed Effects Models and Extensions in Ecology with R Review

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(More customer reviews)Many applications in ecology clearly are not amenable to use of the general linear model due to violations of its assumptions. In fact, in most projects I work on, things like correlation among the errors, nonconstant error variance, etc., are the rule, rather than the exception. If you are looking for an applied text dealing with these types of situations with lots of examples, and demonstrations on analysis in R, then you should get this book. It does not delve into theory; there are plenty of other textbooks where you can fill in those details if you are interested. Rather, this book would be ideally suited for quantitative ecologists, biometricians, and statistical consultants who work in life sciences. Another nice thing is that the book does not assume you are an "R expert". Well done.

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This book discusses advanced statistical methods that can be used to analyse ecological data. Most environmental collected data are measured repeatedly over time, or space and this requires the use of GLMM or GAMM methods. The book starts by revising regression, additive modelling, GAM and GLM, and then discusses dealing with spatial or temporal dependencies and nested data.

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Quantitative Analysis of Movement: Measuring and Modeling Population Redistribution in Animals and Plants Review

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(More customer reviews)I wrote my Master's thesis on the movement of grasshoppers and almost wore this book out! It became my bible. While I was writing up, you'd always see me with it under my arm. It addresses all of the major movement models from an applied standpoint. The original papers are rarely useful if you wish to actually apply the model to your own research. This book is written more like a text than a review, but is still very thorough mathematically. If you're doing research on movement or are just interested in mathematical models in ecology, this book is a must have.

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This work is intended for graduate students and researchers interested in spatial ecology, including applications to conservation, pest control and fisheries. The methodological approaches discussed should be useful to ecologists working with all taxonomic groups, and the mathematics has been kept to an acceptable level for the empirical ecologist. The author has selected case studies from a variety of organisms - plants (seed dispersal, spatial spread of clonal plants), insects and vertebrates (fish, birds and mammals).

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Modeling Biological Systems:: Principles and Applications Review

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(More customer reviews)This book is a complete dissapointment.
It does not offer any real scientific physical models which then can be transformed in an algorithm and being simulated but is is merely a conglomerate of several statistical procedures commonly used in Biology for interpreting data (maybe copied by the author and collected from other books, as nothing that he presents is new!). This book does not offer any scientific, fundamental insight in how to really model and simulate properly complex biological systems, it is also written in a very unscientific, popular style. The mathematical level corresponds to High-School and as the author says in the preface: "The process of modeling biological systems is certainly not a science, but neither is it as unconstrained as the creation of a work of pure art that is evaluated solely on its esthetic content". I think that nonsense speaks for itself. This author should rather write novels instead of cobbling something together that gets the label "scientific" on the cover.
The book is not trash, the author does have collected some of the simplest "models" there are to describe collections of data in statistical terms, but this has NOTHING to do with proper scientific numerical and mathematical modeling and even less with scientific Computing in the field of biological complex systems, e.g. how to simulate membranes, proteins using Quantum Chemistry or Molecular dynamics techniques.
All in all I judge this book as a complete waste of money and as completely superfluous.

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This is the second edition of a textbook currently published by Springer for a course in mathematical modeling and computer simulation for biologists at the advanced undergraduate and introductory graduate level. The audience for this edition is similar to that of the previous one: advanced level courses in computational biology, as well as researchers retooling themselves. This new edition includes a CD-ROM with real examples of models as teaching tools.

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Structural Equation Modeling and Natural Systems Review

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(More customer reviews)This book is a wonderful introduction to the utility and conceptual simplicty of SEM for ecologists. There are plenty of deep topics that Grace covers with through explanations that will bring the novice up to speed. While the book does not offer much by the way of actual exampled for software, the associated website, http://www.jamesbgrace.com/ , containing code that works for both AMOS and MPLUS, much of which can also be implemented in R with the SEM module and a little patience and elbow-grease. The only area where the book falls short is on detail that one may desire for more in depth knowledge of just what is going on under the hood in some places. There are, however, several good texts, such as Kline's, Bollen's, and Shipley's that cover these topics (e.g., identifiability of non-recrusive models) in more detail. In summary, a great introductory text for the ecologist who wants to add one of the more powerful and intuitive statistical techniques out there to their belt.

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This book presents an introduction to the methodology of structural equation modeling, illustrates its use, and goes on to argue that it has revolutionary implications for the study of natural systems. A major theme of this book is that we have, up to this point, attempted to study systems primarily using methods (such as the univariate model) that were designed only for considering individual processes. Understanding systems requires the capacity to examine simultaneous influences and responses. Structural equation modeling (SEM) has such capabilities. It also possesses many other traits that add strength to its utility as a means of making scientific progress. In light of the capabilities of SEM, it can be argued that much of ecological theory is currently locked in an immature state that impairs its relevance. It is further argued that the principles of SEM are capable of leading to the development and evaluation of multivariate theories of the sort vitally needed for the conservation of natural systems. Supplementary information can be found at the authors website, accessible via www.cambridge.org/9780521837422.- Details why multivariate analyses should be used to study ecological systems - Exposes unappreciated weakness in many current popular analyses - Emphasises the future methodological developments needed to advance our understanding of ecological systems

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Occupancy Estimation and Modeling: Inferring Patterns and Dynamics of Species Occurrence Review

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(More customer reviews)I think this is a pretty good book. It is the only reference on this relatively new type of patch occupancy modeling. It is mainly focused on the models of MacKenzie et al. and the Royle and Nichols model. This is a great place to start if you know nothing about this method or a good reference for advanced users.
This book does not fill the need of an introductory "how-to" book. If you want to know how to set up models and run them in program PRESENCE or MARK you will need to wait. Such a book does not exist. This is not a cookbook, but a compilation of the theory and an explanantion of the methodology behind occupancy estimation.

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A Biologist's Guide to Mathematical Modeling in Ecology and Evolution Review

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(More customer reviews)Although other books may have a better presentation of the models' use and context, this is the best presentation I have seen on stability analysis, plus it presents a good quantity of model examples. The presentation of the math used is ample and clear. I highly reccomend it.

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Thirty years ago, biologists could get by with a rudimentary grasp of mathematics and modeling. Not so today. In seeking to answer fundamental questions about how biological systems function and change over time, the modern biologist is as likely to rely on sophisticated mathematical and computer-based models as traditional fieldwork. In this book, Sarah Otto and Troy Day provide biology students with the tools necessary to both interpret models and to build their own.

The book starts at an elementary level of mathematical modeling, assuming that the reader has had high school mathematics and first-year calculus. Otto and Day then gradually build in depth and complexity, from classic models in ecology and evolution to more intricate class-structured and probabilistic models. The authors provide primers with instructive exercises to introduce readers to the more advanced subjects of linear algebra and probability theory. Through examples, they describe how models have been used to understand such topics as the spread of HIV, chaos, the age structure of a country, speciation, and extinction.

Ecologists and evolutionary biologists today need enough mathematical training to be able to assess the power and limits of biological models and to develop theories and models themselves. This innovative book will be an indispensable guide to the world of mathematical models for the next generation of biologists.

A how-to guide for developing new mathematical models in biology
Provides step-by-step recipes for constructing and analyzing models
Interesting biological applications
Explores classical models in ecology and evolution
Questions at the end of every chapter
Primers cover important mathematical topics
Exercises with answers
Appendixes summarize useful rules
Labs and advanced material available

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Hierarchical Modeling and Inference in Ecology: The Analysis of Data from Populations, Metapopulations and Communities Review

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(More customer reviews)Royle & Dorazio (2008): A truly synthetic overview
This book not only illustrates, and presents R and WinBUGS code for, plenty of methods for inference about distribution and abundance in animal and plant populations and communities; it does much more. It presents a truly synthetic overview of these methods and makes the reader understand how they relate to each other. At the same time, the authors succeed extremely well in teaching a modern, "organic way" of statistical modeling -- where one first thinks hard about how the observed data might have arisen via a combination of stochastic processes (the book is about hierarchical models, remember) and then builds a custom statistical model for exactly those processes. This combination of presenting a unifying synthesis of a vast array of methods and showing how to model a study system organically in my view is unique among the currently available statistical ecology books.

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A guide to data collection, modeling and inference strategies for biological survey data using Bayesian and classical statistical methods.This book describes a general and flexible framework for modeling and inference in ecological systems based on hierarchical models, with a strict focus on the use of probability models and parametric inference. Hierarchical models represent a paradigm shift in the application of statistics to ecological inference problems because they combine explicit models of ecological system structure or dynamics with models of how ecological systems are observed. The principles of hierarchical modeling are developed and applied to problems in population, metapopulation, community, and metacommunity systems. The book provides the first synthetic treatment of many recent methodological advances in ecological modeling and unifies disparate methods and procedures.The authors apply principles of hierarchical modeling to ecological problems, including * occurrence or occupancy models for estimating species distribution* abundance models based on many sampling protocols, including distance sampling* capture-recapture models with individual effects* spatial capture-recapture models based on camera trapping and related methods* population and metapopulation dynamic models* models of biodiversity, community structure and dynamics * Wide variety of examples involving many taxa (birds, amphibians, mammals, insects, plants)* Development of classical, likelihood-based procedures for inference, as well asBayesian methods of analysis* Detailed explanations describing the implementation of hierarchical models using freely available software such as R and WinBUGS* Computing support in technical appendices in an online companion web site

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