R语言助力：揭开数值生态学的神秘面纱

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"《数量生态学与-R语言的应用：英文原版》是一本深入探讨生态学定量分析方法的专著，它将理论与R语言实践相结合，帮助读者掌握生态学研究中的数据处理、模型构建和可视化等核心技能。作者Daniel Borcard、François Gilbert和Pierre Legendre都是在生物科学领域有着深厚背景的专家，他们在蒙特利尔大学和法国弗朗什-孔泰大学担任教授，其中Daniel Borcard还负责生态学的数字生态学教学。本书的主要内容涵盖了数量生态学的基础概念，如种群动态、物种多样性、生态网络分析以及生态系统的量化描述。R语言在这里扮演了关键角色，作为一门强大的统计软件，R提供了丰富的生态统计方法库和图形展示工具，使得复杂的数据处理变得直观且高效。读者可以学习如何利用R进行统计建模，比如贝叶斯计算、空间数据分析、时间序列分析，以及多变量数据的可视化。《数量生态学与-R语言的应用》不仅关注理论，更注重实践，书中包含大量实例和案例，帮助读者理解并应用这些技术解决实际问题。此外，书中的交互式和动态图形部分，如《Interactive and Dynamic Graphics for Data Analysis》和《Lattice: Multivariate Data Visualization with R》，强调了图形在理解和交流科研结果中的重要作用。对于那些对生态学充满热情但对数学工具持保留态度的读者，这本书提供了桥梁，鼓励他们克服畏难心理，通过R语言的辅助，更加深入地探索生态学的量化世界。这是一本既适合生态学专业学生和研究人员，也适合希望将统计学应用于生态学实践的跨学科读者的实用指南。"

31.4 How to Use This Book

Overall, the book guides readers through an applied exploration of the major

methods of multivariate data analysis, as seen through the eye of an ecologist.

Starting with some exploratory approaches (Chap. 2), it proceeds logically with the

construction of the key building blocks of most techniques, i.e. association measures

and matrices (Chap. 3), and then submits example data to three families of approaches:

clustering (Chap. 4), ordination and canonical ordination (Chaps. 5 and 6), and finally

spatial analysis (Chap. 7). The aims of methods thus range from descriptive to

explanatory and to predictive and encompass a wide variety of approaches that should

provide readers with an extensive toolbox that can address a wide palette of questions

arising in contemporary multivariate ecological analysis.

1.4 How to Use This Book

The book is meant as a companion when working at the computer. The authors

pictured a reader studying a chapter by reading the text and simultaneously executing

the code. To fully understand the various methods, it is preferable to go through the

chapters sequentially, since each builds upon the previous ones. At the beginning of

each chapter, an empty R console is assumed to be open. All the necessary data files,

the scripts used in the chapters, as well as the R functions and packages that are not

available through the CRAN Web site, can be downloaded from a Web page acces-

sible through the Springer Web site (http://www.springer.com/978-1-4419-7975-9).

Some of the homemade functions duplicate existing ones, providing alternative solu-

tions (for instance, different or expanded graphical outputs), while others have been

written to streamline complex sequences of operations.

Although the code provided can be run in one single copy-and-paste shot within

each chapter (with some rare exceptions for interactive functions), the best proce-

dure is to proceed through the code slowly and explore each set of commands

carefully. Although the use and meaning of some arguments is explained within

the code or in the text, readers are warmly invited to use and abuse of the R help

files (function name following a question mark) to learn about and explore the

various options available. Our aim is not to describe all options of all functions,

which would be an impossible and useless task. We are confident that an avid user,

willing to go beyond the provided examples, will be kept busy for months exploring

the options that he or she deems the most interesting.

Within each chapter, after the introduction, readers are invited to import the data

for the exercises, as well as the R packages necessary for the whole chapter. The

R code used in each chapter is self-contained, i.e. it can be run in one step without

resorting to intermediate results produced in previous chapters. If such objects are

needed, they are recomputed at the beginning of the chapter.

In everyday use, one generally does not produce an R object for every single

operation, nor does one create and name a new graphical window for every plot. We

do that in the R scripts to provide readers with all the entities necessary to backtrack

the procedures, compare results and explore variants. Therefore, after having run

4 1 Introduction

most of the code in a chapter, if one decides to explore another path using some

intermediate result, the corresponding object will be available without the need to

recompute it. This is particularly handy for results of computer-intensive methods

(like some based on large numbers of random permutations), especially if one uses

a relatively slow computer.

In the code sections of the book, all calls to graphical windows have been deleted

for brevity. They are found in the electronic code scripts, however. Furthermore, the

book shows several, but not all, graphical outputs for reference. They are printed

in grey scale, although some are in colour when produced by R. This is an incentive

for readers to be active users of the book and of its code.

Sometimes, readers are made aware of some special features of the code or of

tricks used to obtain particular results, by means of hint boxes located at the bottom

of code sections.

Although many methods are applied to the example data, ecological interpreta-

tions is not provided in all cases. Sometimes, questions are left open to readers, as

an incentive to verify if she or he has correctly understood the method, and hence

its application and the numerical or graphical outputs.

Lastly, for some methods programming-oriented readers are invited to write

their own code. These incentives are placed in boxes called “code-it-yourself

corners”. When examples are readily written, they are meant for pedagogical

purposes and do not pretend at computational efficiency. The aim of these boxes is

to help interested readers code the matrix algebra equations presented in Legendre

and Legendre (1998) into R and obtain the main outputs that ready-made packages

provide. The whole idea is, of course, to reach the deepest possible understanding

of the mathematical working of some key methods.

1.5 The Data Sets

Apart from rare cases where ad hoc fictitious data are built for special purposes, the

applications rely on two main data sets that are readily available in R. However,

data provided in R packages can change over the years. Therefore, we prefer to

provide them also in the electronic material accompanying this book because this

ensures that the results obtained by the readers are exactly the same as those

presented in the book. The two data sets are briefly presented here. The first

(Doubs) data set is explored in more detail in Chap. 2, and readers are encouraged

to apply the same exploratory methods to the second one.

1.5.1 The Doubs Fish Data

In an important doctoral thesis, Verneaux (1973; see also Verneaux et al. 2003)

proposed to use fish species to characterize ecological zones along European rivers

and streams. He showed that fish communities were good biological indicators of

51.5 The Data Sets

these water bodies. Starting from the source, Verneaux proposed a typology in four

zones, and he named each one after a characteristic species: the trout zone (from

the brown trout Salmo trutta fario), the grayling zone (from Thymallus thymallus),

the barbel zone (from Barbus barbus) and the bream zone (from the common bream

Abramis brama). The two upper zones are considered as the “Salmonid region” and

the two lowermost ones constitute the “Cyprinid region”. The corresponding

ecological conditions, with much variation among rivers, range from relatively

pristine, well oxygenated and oligotrophic to eutrophic and oxygen-deprived waters.

The Doubs data set that is used in the present book consists of three matrices

containing part of the data used by Verneaux for his studies. These data have been

collected at 30 sites along the Doubs River, which runs near the France–Switzerland

border in the Jura Mountains. The first matrix contains coded abundances of

27 fish species, the second matrix contains 11 environmental variables related to

the hydrology, geomorphology and chemistry of the river, and the third matrix

contains the geographical coordinates (Cartesian, X and Y ) of the sites. These data

have already served as test cases in the development of numerical techniques

(Chessel et al. 1994).

Working with the environmental data available in the R package ade4 (at the

time of this writing, i.e. ade4 version 1.4-14), we corrected a mistake in the das

variable and restored the variables to their original units, which are presented in

Table 1.1.

1.5.2 The Oribatid Mite Data

Oribatid mites (Acari: Oribatida) are a very diversified group of small (0.2–1.2 mm)

soil-dwelling, mostly microphytophagous and detritivorous arthropods. A well-

aerated soil or a complex substrate like Sphagnum mosses present in bogs and wet

forests can harbour up to several hundred thousand (10

) individuals per square

Table 1.1 Environmental variables of the Doubs data set used

in this book and their units

Variable Code Units

Distance from source

das

Altitude alt m a.s.l.

Slope pen ‰

Mean minimum discharge deb m

−1

pH of water pH –

Calcium concentration (hardness) dur mg L

−1

Phosphate concentration pho mg L

−1

Nitrate concentration nit mg L

−1

Ammonium concentration amm mg L

−1

Dissolved oxygen oxy mg L

−1

Biological oxygen demand

dbo

mg L

−1

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