Read an Excerpt
CHAPTER 1
1. Starting to look at communities
The study of community ecology is complicated and challenging, which makes it all the more fun, of course. Ecology is a science and like all science subjects there is an approach to study that helps to facilitate progress.
1.1 A scientific approach
Science is a way of looking at the natural world. In short, the process goes along the following lines:
• You have an idea about something.
• You come up with a hypothesis.
• You work out a way of testing this hypothesis.
• You collect appropriate data in order to apply a test.
• You test the hypothesis and decide if the original idea is supported or rejected.
• If the hypothesis is rejected, then the original idea is modified to take the new findings into account.
• The process then repeats.
In this way, ideas are continually refined and our knowledge of the natural world is expanded. You can split the scientific process into four parts (more or less): planning, recording, analysing and reporting.
• Planning: This is the stage where you work out what you are going to do. Formulate your idea(s), undertake background research, decide what your hypothesis will be and determine a method of collecting the appropriate data and a means by which the hypothesis may be tested.
• Recording: The means of data collection is determined at the planning stage although you may undertake a small pilot study to see if it works out. After the pilot stage you may return to the planning stage and refine the methodology. Data are finally collected and arranged in a manner that allows you to begin the analysis.
• Analysing: The method of analysis should have been determined at the planning stage. Analytical methods (often involving statistics) are used to test the null hypothesis. If the null hypothesis is rejected then this supports the original idea/hypothesis.
• Reporting: Disseminating your work is vitally important. Your results need to be delivered in an appropriate manner so they can be understood by your peers (and often by the public). Part of the reporting process is to determine what the future direction needs to be.
In community ecology the scientific process operates in the same way as in any other branch of science. Generally you are dealing with complicated situations with many species and samples – methods of analysis in community ecology are specialised because of this complexity.
1.2 The topics of community ecology
There are many ways to set about analysing community data. The subject can be split into several broad themes, which can help you to determine the best approach for your situation and requirements.
1.2.1 Diversity
Diversity is concerned with how many different species there are in a given area. Strictly speaking there are two main strands of diversity – in the first you simply count the number of different species in an area. In the second case you take into account the abundance of the species – this leads to the notion of the diversity index.
The term diversity (or biodiversity) is a much used term both in science and in general use. Its meaning in science is not necessarily the same as that understood by the general public. You can think of diversity as being expressed in two forms:
• The number of different species in a given area.
• The number of species and their relative abundance in a given area.
The first form, number of species in an area, is called species richness (see Chapter 7). This is an easy measure to understand and you can calculate it from simple species lists. The second form, involving relative abundance of species, is more complicated because of course you have an extra dimension, abundance information (see Chapter 8).
Whichever measure of diversity is under question, the scale of measurement is particularly important. Diversity is usually expressed at three scales (see Chapter 10):
• Alpha diversity – this is diversity measured in a single habitat or sampling unit (e.g. a quadrat); it is the smallest unit of measurement.
• Beta diversity – this is the diversity between habitats.
• Gamma diversity – this is the diversity of a larger sampling unit, such as a landscape that is composed of many habitats.
The three scales of measurement of diversity are linked by a simple relationship:
Alpha × beta = gamma
In some measures of diversity however, the relationship can be additive rather than multiplicative (see Chapter 10).
The species richness measure of diversity can be used when you do not have abundance information – which can be useful. Species richness can also be used as the response variable in analyses in certain circumstances (see Section 7.1).
When you have abundance information you are able to carry out different analyses, for example:
• Diversity indices.
• Species abundance curves.
A diversity index is a way to take into account the evenness of a community – if a single species dominates a community the index is smaller, if the species are all more even in abundance the index is larger (see Chapter 8).
Species abundance curves are another way to look at the evenness of a community – the abundance of each species is plotted on a graph, with the most abundant being plotted first (see Chapter 11).
1.2.2 Similarity and clustering
Similarity and clustering: this is where you look to see how similar things are based on their composition (see Section 12.1). In community ecology this tends to be the similarity of sites or habitats based on the species present. The idea of clustering stems from this – you form clusters of things based on how similar they are.
There are two main approaches to clustering:
• Hierarchical clustering – in this approach the data are repeatedly split into smaller units until you end up with a kind of 'family tree', which shows the relationship between items (see Section 12.2.1).
• Clustering by partitioning – in this approach you take the data and build clusters based on how similar they are; the data are clumped around so-called medoids, which are the centres of the various groups (see Section 12.2.2).
You can explore similarity and create clusters of samples even if you do not have species abundance information – simple presence-absence data can be used.
1.2.3 Association analysis
Association analysis is a way to link species together to find out which species tend to be found in the same samples and which ones tend to be found in different samples. This is one way to identify communities – species that tend to be found together will likely be from the same community. You can set about sampling in two main ways:
• By area – in this approach you sample in a geographical area and identify the various associations (which can be positive or negative) and so identify the communities in that area (see Section 13.1).
• By transect – in this approach you sample along a transect, usually because of some underlying environmental gradient. Often this will lead to a succession of communities and your association analysis will help you to identify them (see Section 13.2).
The association analysis gives you values for the 'strength' of the various associations – this can be thought of as akin to the similarity and clustering kind of analyses (Chapter 12). A spin-off from association analysis is the idea of indicator species (see Section 13.4). Here you look to see if certain species can be regarded as indicative of a particular community. An ideal indicator species would be one that shows great specificity for a single community.
1.2.4 Ordination
The term ordination covers a range of methods that look to simplify a complicated situation and present it in a simpler fashion (see Chapter 14). This sounds appealing! In practice you are looking at communities of species across a range of sites or habitats and the methods of ordination look to present your results in a kind of scatter plot. Things that appear close are more similar to one another than things that are far apart. Think of it as being an extension to the similarity and clustering idea.
There are several methods of ordination (see Chapter 14) but you can split the general idea of ordination into two broad themes:
• Indirect gradient analysis – in this approach you analyse the species composition and the patterns you observe allow you to infer environmental gradients that the species may be responding to (see Section 14.2).
• Direct gradient analysis – in this approach you already have environmental data which you use to help reorder the samples and species data into meaningful patterns (see Section 14.3). A spin-off from this approach is that you can test hypotheses about the effects of the environmental variable(s) that you measured.
Ordination is a very commonly used analytical approach in community ecology because the main aim of the various methods is to distil the complicated community data into a simpler and more readily understood form.
1.3 Getting data – using a spreadsheet
A spreadsheet is an invaluable tool in science and data analysis. Learning to use one is a good skill to acquire. With a spreadsheet you are able to manipulate data and summarise details in different ways quite easily. You can also use a spreadsheet to prepare data for further analysis in other computer programs. It is important that you formalise the data into a standard format, as you shall see later (in Chapter 3). This will make the analysis run smoothly and allow others to follow what you have done. It also allows you to see what you did later on (it is easy to forget the details).
Your spreadsheet is useful as part of the planning process. You may need to look at old data; these might not be arranged in an appropriate fashion so using the spreadsheet will allow you to organise your data. The spreadsheet will allow you to perform some simple manipulations and run some straightforward analyses, looking at means for example, as well as producing simple summary graphs. This will help you to understand what data you have and what they might show. You will see a variety of ways of manipulating data as you go along (e.g. Section 4.2).
If you do not have past data and are starting from scratch, then your initial site visits and pilot studies will need to be dealt with. The spreadsheet should be the first thing you look to, as this will help you arrange your data into a format that facilitates further study. Once you have some initial data (be it old records or pilot data) you can continue with the planning process.
1.4 Aims and hypotheses
A hypothesis is your idea of what you are trying to determine but phrased in a specific manner. The hypothesis should relate to a single testable item.
In reality you cannot usually 'prove' your hypothesis – it is like a court of law when you do not have to prove your innocence, you are assumed innocent until proven otherwise. In statistics, the equivalent is the null hypothesis. This is often written as H0 (or H0) and you aim to reject your null hypothesis and therefore, by implication, accept the alternative (usually written as H1 or H1).
The H0 is not simply the opposite of what you thought (called the alternative hypothesis, H1) but is written as such to imply that no difference, no pattern, exists (I like to think of it as the dull hypothesis).
Getting your hypotheses correct (and also the null hypotheses) is an important step in the planning process as it allows you to decide what data you will need to collect in order to reject the H0. You will examine hypotheses again later (Section 5.2).
Allied to your hypothesis is the analytical method you will use later to help test and support (or otherwise) your hypothesis. Even at this early stage you should have some idea of the statistical test or analytical approach you are going to apply. Certain statistical tests are suitable for certain kinds of data and you can therefore make some early decisions. You may alter your approach, change the method of analysis and even modify your hypothesis as part of your planning process.
Some kinds of analysis do not lend themselves to a hypothesis test – this is particularly so in community ecology. When you have several species and several habitats your analysis may be concerned with looking for patterns in the data to highlight relationships that were not evident from the raw data. These analytical methods are important but you cannot always perform a hypothesis test. However, you still need to plan your approach and decide what method of analysis is best to help you make sense of the ecological situation (see Chapter 5) – if the best approach is to carry out an analysis that does not test a null hypothesis then that is what you go with.
1.6 Exercises
1.1 What are the main topics in community ecology, as set out in this book?
1.2 Diversity can be measured at various scales, from simple samples to whole landscapes. What are the 'units' of diversity and how are they related?
1.3 What are the main reasons for carrying out association analysis?
1.4 With indirect gradient analysis you can test hypotheses about the relationship between species composition and environment – TRUE or FALSE?
1.5 If you had an idea regarding the number of species and an environmental variable your hypothesis might run along these lines 'there is a positive correlation between species richness and soil moisture'. What would an appropriate null hypothesis be?
The answers to these exercises can be found in Appendix 1.
CHAPTER 2
Software tools for community ecology
Learning to use your spreadsheet is time well spent. It is important that you can manipulate data and produce summaries, including graphs. You will see later how the spreadsheet is used for a variety of aspects of data manipulation as well as for the production of graphs. Many statistical tests can be performed using a spreadsheet but there comes a point when it is better to use a dedicated computer program for the job. The more complicated the data analyses are the more cumbersome it is to use a spreadsheet and the more sensible it is to use a dedicated analytical program. There are many on the market, some are cheap (or even free) and others are expensive. Some programs will interface with your spreadsheet and others are totally separate. Some programs are specific to certain types of analysis and others are more general.
In this book you will focus on two programs:
• Microsoft Excel: this spreadsheet is common and widely available. There are alternatives and indeed the Open Office spreadsheet uses the same set of formulae and can be regarded as equivalent. The Libre Office spreadsheet is a derivative of Open Office and similarly equivalent to Excel.
• R: the R project for statistical computing is a huge open-source undertaking that is fast becoming the de facto standard for analysis in many fields of science, engineering and business, to name just a few. It is a powerful and flexible system.
Excel is particularly useful as a data management system, and throughout this book you will see it used mainly in that fashion although it is capable of undertaking some statistical analyses and producing various graphs. The R program is very powerful and flexible, and you will see this used for the majority of the analyses. Once you learn how to use R it is almost as easy to create a complicated community analysis as it is to carry out a simple t-test.
2.1 Excel
A spreadsheet in an invaluable tool. The most common is Microsoft Excel and it has many uses:
• For data storage.
• As a database.
• For preliminary summary.
• For summary graphs.
• For simple (and not so simple) statistical analyses.
Generally the more complicated the analysis you are going to undertake, the less likely it is that you will use a spreadsheet to do the analysis. However, when you have more complicated data it is really important to manage the data carefully and this is a strength of the spreadsheet. It can act like a database. Part of your planning process should be to determine how you are going to arrange your data – getting the layout correct from the start can save an immense amount of time later on.
2.1.1 Getting Excel
There are many versions of Excel and your computer may already have a version installed when you purchased it. The basic functions that Excel uses have not changed for quite some while so even if your version is older than described here, you should be able to carry out the same manipulations. You will mainly see Excel 2007 for Windows described here. If you have purchased a copy of Excel (possibly as part of the Office suite) then you can install this following the instructions that came with your software. Generally, the defaults that come with the installation are fine although it can be useful to add extra options, especially the Analysis ToolPak, which will be described next.
(Continues…)
Excerpted from "Community Ecology"
by .
Copyright © 2014 Mark Gardener.
Excerpted by permission of Pelagic Publishing.
All rights reserved. No part of this excerpt may be reproduced or reprinted without permission in writing from the publisher.
Excerpts are provided by Dial-A-Book Inc. solely for the personal use of visitors to this web site.
