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Method in macroecology
Tim M. Blackburn
School of Biosciences, University of Birmingham, Edgbaston, Birmingham B15 2TT, UK
KEYWORDS
Observation;
Natural
experiments;
Manipulative
experiments;
Null-hypothesis;
Autocorrelation
Summary
Macroecology is concerned with understanding the abundance and distribution of
species at large spatial and temporal scales. Understanding pattern and process in
macroecology thus presents a considerable methodological challenge, as the scales of
interest are simply too large for the traditional ecological approach of experimental
manipulation to be possible or ethical. Here, I summarise the methods that have been
most commonly employed to explore macroecological questions, discuss important
methodological issues that need to be considered when interpreting macroecological
data, and suggest likely future developments in macroecological methodology.
& 2004 Elsevier GmbH. All rights reserved.
Zusammenfassung
Die Makroökologie befasst sich mit dem Verständnis der Abundanz und der Verbreitung
der Arten in groXen räumlichen und zeitlichen MaXstäben. Das Verständnis von Mustern
und Prozessen in der Makroökologie stellt deshalb eine beträchtliche methodische
Herausforderung dar, weil die Skalen von Interesse für die traditionelle ökologische
Vorgehensweise der experimentellen Manipulation einfach zu groX sind, als dass sie
möglich oder ethisch vertretbar wäre. Hier fasse ich die Methoden zusammen, die am
häufigsten zum Einsatz kommen, um makroökologische Fragen zu erforschen,
diskutiere wichtige methodische Punkte, die beachtet werden müssen, wenn
makroökologische Daten interpretiert werden, und deute auf wahrscheinliche
zukünftige Entwicklungen in der makroökologischen Methodik hin.
& 2004 Elsevier GmbH. All rights reserved.
Introduction
The aim of this paper is to present an overview of
how macroecology has been done to date. There is
nothing unusual about the macroecological ap-
proach in the framework of the scientific method
(Gaston & Blackburn, 1999), yet the issue of
method has been important in the development
of macroecology. As will be seen, the discipline
depends on techniques different to those used in
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doi:10.1016/j.baae.2004.08.002
ARTICLE IN PRESS
2
the majority of ‘traditional’ ecological studies. In
consequence, it has been criticised by those
unfamiliar with these alternative methods of asking
and answering questions. These criticisms seem to
have been prompted less by a genuine disregard for
macroecology than by a lack of understanding of
the logic underlying the approach. The response of
macroecologists has been more clearly to explain
this logic (e.g. Brown, 1995, 1999; Gaston &
Blackburn, 1999; Maurer, 1999; Gaston & Blackburn, 2000), and there is evidence that the field
appears to be gaining wider acceptance as a result
(e.g. Brown, 1999; Faaborg, 2001; Marquet, 2001).
The paper is divided into three sections. In the
first, I summarise the methods that scientists have
most commonly employed to explore macroecological questions. Then, I go on to discuss some
important issues that these methods require scientists to consider when interpreting macroecological
data. This will not be an exhaustive review of such
issues, but rather an overview of those I consider to
be most significant. More extensive or specific
considerations of methodological issues can be
found elsewhere (e.g. Diamond, 1986; Maurer,
1994; Brown, 1995; Gotelli & Graves, 1996; Harvey,
1996; McArdle, 1996; Blackburn & Gaston, 1997b,
1998; Gaston & Blackburn, 1999; Maurer, 1999;
Gaston & Blackburn, 2000; Blackburn & Gaston,
2002). Finally, I conclude with a few remarks about
likely future developments in macroecological
methodology.
Methodological approaches to
macroecology
Macroecology is concerned with patterns expressed
by ecological systems over extensive spatial and
temporal scales, and with the processes that
determine these patterns (Brown & Maurer, 1989;
Brown, 1995; Gaston & Blackburn, 2000), and thus
presents a significant problem: How is it possible to
study scientifically the characteristics of an ecological system over such scales? The traditional
approach to the study of complex ecological
systems has been to attempt to reduce the
complexity by isolating and studying fragments of
it. This is a standard strategy in science. The
workings of simple systems are easier to elucidate
than those of complex ones. The implicit aim of
such a ‘reductionist’ approach is ultimately to
understand the workings of the whole system by
piecing together the understanding gained about
each constituent part (e.g. Dunbar, 1995).
T.M. Blackburn
However, there are at least two shortcomings of
such an approach. First, it is difficult to put the
constituent pieces back together with no idea of
the system that is being recreated. For example,
study of individual communities may give a clear
picture of the forces determining how local
abundances are distributed amongst species, but
to understand how those communities combine to
produce the species-abundance distribution of a
region, one needs to know the regional distribution
itself. Second, by isolating a fragment of a system,
you may be excluding the very influences that are
the most important determinants of its character.
For example, an important determinant of the
number of gall wasp species utilising an individual
oak tree is the total number of gall wasp species
that utilise that particular oak species (Cornell,
1985). Any attempt to understand the richness of
the gall wasp assemblage on a specific tree by
considering that tree alone is unlikely to succeed.
To understand ecological systems, therefore, it is
clear that we need to study the structure and
function of such systems in full as well as in part.
Sidestepping the issue of complexity by adopting
solely a reductionist approach is not an option for
macroecologists, as it is the whole that is of
interest. There are several consequences of this,
but one of the most important is that the option of
utilising manipulative experiments is reduced.
Macroecological systems are simply too large for
most such experiments to be possible (practically
or financially) or ethical. This inability to apply
experimental manipulations is one reason why the
macroecological approach has been criticised (see
also Diamond, 1986). However, there are still ways
to study pattern and process in large-scale ecological systems.
Observation
Macroecologists have to date principally relied on
observational data to generate and test hypotheses
(see references in Gaston & Blackburn, 2000).
Indeed, most scientific fields can be viewed as
relying principally on such data early in their
development, when the field is inevitably largely
descriptive and concerned with the establishment
of the phenomena which it will eventually try to
explain (Wiegert, 1988; Gaston & Blackburn, 1999).
In macroecology, the foundation of the field has
been laid in the form of studies documenting the
existence of particular patterns in the structure of
species assemblages at geographical scales (e.g.
Fig. 1). The principal of these patterns are the
interspecific frequency distributions of abundance,
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Method in macroecology
3
Natural experiments and experiments in
nature
2.25
Log population density
2
1.75
1.5
1.25
1
.75
.5
.25
0
0
.5
1
1.5
2
2.5
3
3.5
4
4.5
Log body mass
Figure 1. The relationship between log population
density (mean number of individuals per Breeding Bird
Survey (Bystrak, 1981) census route) and log body mass
(g) for North American bird species. N ¼ 380: From Brown
and Maurer (1987).
range size and body size, and the relationships
between these variables (Cotgreave, 1993; for
recent reviews, see Blackburn & Gaston, 1994;
Gaston, 1994; Brown, 1995; Brown, Stevens, &
Kaufman, 1996; Gaston, 1996a, b; Gaston & Blackburn, 1996; Blackburn & Gaston, 1997a; Gaston,
Blackburn, & Lawton, 1997; Gaston & Blackburn,
2000; Holt, Gaston, & He, 2002; Storch & Gaston,
2004).
Macroecological patterns, such as Fig. 1, constitute a basic description of facets of the distribution of life across the Earth (Lawton, 1996).
However, the primary goal of macroecology is to
explain their existence. Unfortunately, observational data generally lack power to distinguish
between similar hypotheses for the origin of such
patterns (McArdle, 1996). In addition, the large
spatial and temporal scales relevant to macroecology mean that it is usually beyond the capabilities
of a single scientist, or even group of scientists, to
collect all the observational data required to
explore any given pattern. Hence, such data
sometimes derive from disparate uncoordinated
and unplanned sets of observations by multiple
observers from different sites over a range of
dates. This means that their use must be tempered
by caution if spurious and biased conclusions are to
be avoided (Gotelli & Graves, 1996; Blackburn &
Gaston, 1998, 2002). Nevertheless, with careful
attention to their limitations, observational
data can be valuable in hypothesis testing (McArdle, 1996), and so inform about process as well as
about pattern.
Although macroecologists have to date principally
relied on observational data, experimental approaches are available. In particular, macroecological studies can draw on conclusions from ‘natural
experiments’, and the conceptually similar ‘experiments in nature’ (Diamond, 1986). Natural experiments are changes in systems brought about by
natural events, such as earthquakes, volcanic
eruptions, or natural colonisations. Experiments in
nature are changes in systems brought about as an
intentional or accidental product of human activities, such as introductions of alien species, and
changes in atmospheric composition. Diamond
(1986) also distinguishes between natural snapshot
experiments (NSEs), which compare different systems at a point in time, and natural trajectory
experiments (NTEs), which compare the same
system at different points in time. All these kinds
of experiments provide an opportunity to observe
and test the consequences of changes in systems
that we would not otherwise be able to manipulate
for the purpose of testing macroecological theory
(Gaston & Blackburn, 1999). They have been a
useful source of information on a variety of largescale questions about ecological systems, such as
those concerning extinction (e.g. Karr, 1982; Pimm,
Jones, & Diamond, 1988; Hinsley, Bellamy, &
Newton, 1995; Bellamy, Hinsley, & Newton, 1996;
Lomolino, 1996; Manne, Pimm, Diamond, & Reed,
1998; Spiller, Losos, & Schoener, 1998; Duncan &
Young, 2000; Terborgh et al., 2001) and invasion
(Moulton & Pimm, 1983, 1986; Moulton & Lockwood, 1992; Thornton, 1996; Veltman, Nee, &
Crawley, 1996; Duncan, 1997; Blackburn & Duncan,
2001b).
Natural experiments and experiments in nature
have both advantages and disadvantages in comparison to traditional manipulative experiments.
The main disadvantages are that the experimental
manipulation is not chosen by the investigator, and
control treatments usually are much less precise, if
they are possible at all (Diamond, 1986). A
consequence of the first problem is that often the
question that can be tested is not necessarily
exactly the one that the scientist would like to ask.
Alternatively, the scientist may be forced to answer
a question using a system with which they are less
familiar.
The lack of adequate controls means that while
responses of natural systems to natural or anthropogenic perturbations may be clear, the precise
aspect of the perturbation to which they are
responding often is not. Natural or anthropogenic
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4
effects rarely involve changes in only one aspect of
the system. Thus, natural experiments do not have
the same power to link pattern to process, as do
manipulative experiments. Further, a lack of
experimental control means that the response of
the system in the absence of the perturbation is
often unknown. Typically, statistics quantifying
relevant features of the system following the
change are compared with the values of those
statistics at the start of (for NTEs, or in the absence
of, in the case of NSEs) the perturbing event.
However, the parameters of all ecological systems
tend to vary over time even in the absence of
unusual perturbing events, so that robust conclusions may be hard to draw in such cases. For
example, Gonzalez (2000) showed that the number
of species of micro-arthropods inhabiting moss
patches declined following habitat fragmentation,
but that changing environmental conditions also
caused fluctuations in the species richness observed
in control patches. Without the controls, it would
have been hard to separate the effects of fragmentation and weather on the communities in the
fragmented moss patches.
The main advantages of natural experiments and
experiments in nature are twofold. First, they are
probably the only ways in which experiments can
be directly performed at the temporal and spatial
scales of concern to macroecologists (Gaston &
Blackburn, 1999). Second, they have the advantage
that the experimental system is likely to be more
realistic than that of any laboratory or field
manipulation (Diamond 1986, 2001): the study
system is a natural entity. Thus, while it is harder
to draw firm conclusions from natural experiments,
those conclusions that can be drawn are probably
more relevant to natural systems. In contrast,
while manipulative experiments can give very
precise tests of specific questions, they do so in
systems that are generally greatly simplified relative to the ‘real’ world. Such results may lack wider
generality. Natural experiments and experiments in
nature are not, therefore, simply poor relations of
more controlled approaches.
Manipulative experiments
Although manipulative experiments are impossible
to perform on most of the ecological systems that
have attracted the attention of macroecologists,
they are not entirely precluded, however. They can
be used to address macroecological questions in
systems where the manipulations are large-scale
for the organisms but not for the experimenter,
such as laboratory and field-based microcosms (e.g.
T.M. Blackburn
Cotgreave, Middleton, & Hill, 1993; Naeem,
Thompson, Lawler, Lawton, & Woodfin, 1994;
Gaston & Warren, 1997; Warren & Gaston, 1997;
Gilbert, Gonzalez, & Evans-Freke, 1998; Gonzalez,
Lawton, Gilbert, Blackburn, & Evans-Freke, 1998;
Fox, McGrady-Steed, & Petchey, 2000). A good
example is the study by Gonzalez et al. (1998) of
abundance and distribution changes in the fauna of
moss microecosystems following fragmentation.
Since manipulative experiments offer probably
the most powerful way of differentiating between
alternate hypotheses, the use of microcosms to
elucidate process in macroecology is a potentially
valuable tool. However, the wider relevance of this
approach is as yet unclear, as the mechanisms
structuring communities of organisms amenable to
use in microcosms may be different to those for
groups such as birds and mammals on which most of
our knowledge of macroecological patterns is based
(Blackburn & Gaston, 2003a). The issues of scaling
between microcosms and geographical landscapes
remain to be resolved (Peterson & Parker, 1998).
Consequences of the methodological
approaches
The extent of the reliance of macroecology on
observational data and unplanned natural experiments has general consequences for the way in
which the data so obtained are subsequently
treated and interpreted. The usual lack of control
treatments accompanying observations means that
thought needs to be given to the relationship that
the controls would express were they to exist. This
is termed the ‘null hypothesis’. Lack of control
treatments also means that analyses frequently
have to be more sophisticated to extract useful
information. The greater reliance on statistical
manipulation of macroecological data means that
particular attention has to be paid to how the data
might violate the assumptions of the statistics. An
important assumption of standard statistics is that
each data point contributes an independent piece
of information to the analysis. Since many macroecological studies rely on comparisons between
species, one way in which the assumption of
independence may be violated is because species
tend to share characteristics through common
evolutionary ancestry, termed ‘phylogenetic autocorrelation’. Another way the assumption may be
violated is when the ecological characteristics of
locations are compared, because locations closer
together in space tend to be more similar. This is
termed ‘spatial autocorrelation’. In this section,
ARTICLE IN PRESS
Method in macroecology
I briefly discuss these issues in the context of
macroecology.
The null hypothesis
Lacking controls, observational studies and natural
experiments must rely instead on a null hypothesis.
This has been defined as one that ‘‘entertain(s) the
possibility that nothing has happened, that a
process has not occurred, or that change has not
been produced by a cause of interest’’ (Strong,
1980). The importance of a good null hypothesis to
macroecological studies has been repeatedly emphasised (Gotelli & Graves, 1996; Blackburn &
Gaston, 1998; Gaston & Blackburn, 1999, 2000).
That an observed pattern is a simple consequence
of the null expectation should always be the first
hypothesis considered by the macroecologist (Gaston & Blackburn, 2000; see also Maurer & McGill,
this issue).
The failure to frame an adequate null hypothesis
for any particular test may result in the acceptance
of a pattern as the result of a biological process,
when in fact it is no more than an artefact of a
particular methodology, or would be expected in
the absence of any biological mechanism. An
example of the first problem is provided by the
study of the relationship between niche breadth
and abundance (reviewed by Gaston, 1994). It has
been suggested that abundant species may be
common because they can use a wider spectrum
of resources (they have a broader niche) than rare
species (e.g. Brown, 1984). However, early demonstrations of a positive association between abundance and niche breadth were later called in
question when it was noted that we would expect
the fewer individuals of the rarer species to be
encountered in fewer habitats, and hence appear
to have a narrower niche, by chance alone (Burgman, 1989; Gaston, 1994). The positive association
may have been little more than a methodological
artefact. Indeed, subsequent studies that address
the issue of sample size find much weaker evidence
for any such relationship (Gaston et al., 1997;
Gregory & Gaston, 2000).
The importance of considering the possibility
that a pattern may be expected in the absence of
any biological mechanism is illustrated by study of
the relationship between species number and area.
Species–area relationships tend to be positive, so
that larger areas hold more species, and a variety
of biological mechanisms have been suggested as
the cause (reviewed by Rosenzweig, 1995). However, the random distribution of individuals across
the environment will also tend to result in a
5
positive relationship. Thus, before searching for a
biological driver, we need to test whether or not
observed positive relationships differ from the null
expectation. In fact, they generally do (Rosenzweig, 1995; Storch, Sizling, & Gaston, 2003).
This example highlights a further point about the
null hypothesis: it is easy to fall into the trap of
thinking that it will be that there is no relationship
between the variables of interest. In fact, that will
often not be the case. Note that Strong’s definition
includes the notion that a ‘‘change has not been
produced by a cause of interest’’. It follows that
null relationships may be non-zero, and in macroecological systems they frequently are (e.g. Blackburn, Harvey, & Pagel, 1990; Wright, 1991; Colwell
& Hurtt, 1994; Rosenzweig, 1995; Blackburn &
Gaston, 1996; Wright, Patterson, Mikkelson, Cutler,
& Atmar, 1998; Colwell & Lees, 2000; Jetz &
Rahbek, 2001).
Statistical tests
The outcome of a well-formulated manipulative
experiment would ideally be a situation where the
only difference between the control and experimental treatments is in the variable or process
under test. Changes in the treatment relative to
the control can then be assigned to this difference,
and its effect easily assessed using simple statistics
(typically, ANOVA: e.g. Underwood, 1997).
Although this ideal is rarely achieved in practice
in ecology (even simple ecological systems are
complicated in comparison to those of most other
scientific disciplines), the existence of controls
makes the interpretation of data from manipulative
experiments much easier than data from observational studies and natural experiments, where a
variety of processes could potentially be driving
any observed changes. In these latter, the absence
of controls can to some degree be compensated for
using statistical techniques. However, a greater
variety of statistical approaches typically need to
be adopted to extract meaningful information from
the raw data.
Statistics can aid interpretation of macroecological data in at least three ways. First, statistical
null models can help identify significant features of
macroecological data (Blackburn et al., 1990;
Gotelli & Graves, 1996; Gaston & Blackburn,
1999; Colwell & Lees, 2000; Gotelli, 2000). An
example was discussed in the previous section,
where the comparison of real species–area relationships with those expected from random distributions of individuals confirmed that the real
pattern differed from the null expectation.
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6
However, given that a variety of processes may
potentially generate macroecological patterns
(Gaston & Blackburn, 1999), the field may particularly benefit from the application of informationtheoretic approaches to hypothesis testing (Burnham & Anderson, 2001; Johnson & Omland, 2004).
This provides an alternative to null hypothesis
testing in which, instead of a single model
being compared to a null hypothesis, a variety of
a priori candidate models can be tested simultaneously and ranked in terms of the support
provided for them by the data. Pursuing the
example above, a variety of mechanisms have
been proposed to generate species–area relationships (including random distribution of individuals,
habitat diversity, colonisation-extinction dynamics,
neutral biodiversity theory). Testing the predictions
of each hypothesis separately against a null
hypothesis would tell us whether each was a
better predictor of the relationship than expected
by chance, but would not tell us which was the
best model. The information-theoretic approach,
in contrast, would allow the shape of the species–area relationship predicted by the different
models to be compared to observed data. The
models could then be weighted and ranked in
terms of fit to provide a quantitative measure of
relative support for each hypothesis (Johnson &
Omland, 2004).
Second, multivariate techniques can help factor
out uncontrolled variation in natural systems (e.g.
Mac Nally, 1996; Thomson, Weiblen, Thomson,
Alfaro, & Legendre, 1996). For example, Blackburn
and Duncan (2001a) included a variable coding for
biogeographic region in a multivariate analysis of
introduction success in birds, to account for the
fact that introduction success varies significantly
among regions. Since Blackburn & Duncan were
analysing an unplanned (as an experiment) set of
species translocated by humans, any significant
results found could have been a spurious consequence of uncontrolled differences between regions, rather than being due to the effect of the
predictor variables in their analysis.
Third, macroecologists can use statistics to
simplify complex environmental or biological variation into more manageable summaries of the data
(e.g. Preston, 1948; Rapoport, 1982; Williamson &
Lawton, 1991; Maurer, 1994; Ruggiero, Lawton, &
Blackburn, 1998; Gregory & Gaston, 2000). For
example, both Gaston (1994) and Maurer (1994)
show how complex variation in the structure of a
species’ geographic distribution can be summarised
in terms of a fractal dimension that quantifies the
degree of range fragmentation (see also Williamson
& Lawton, 1991).
T.M. Blackburn
In many cases the data or questions of interest to
macroecologists are difficult to analyse using
traditional statistics (Blackburn & Gaston, 1998),
presenting a whole extra set of problems. Some
studies simply present standard statistical analyses
while acknowledging the short-comings of the
approach. An example is provided by a study of
how the mean (across species) range of depths
inhabited by marine fish varies with latitude
(Stevens, 1996). However, mean depth ranges
calculated at different latitudes are not independent, because neighbouring latitudes share many
species in common, and so standard estimates of
the significance of statistics calculated for these
data are invalid. Thus, while Stevens (1996)
presents regression lines and correlation coefficients, he acknowledges that the statistical significance of some of these relationships cannot
reliably be assessed. However, in some cases, the
lack of suitable statistics to test macroecological
ideas has been the spur for theoretical developments to attempt to solve the problem. For
example, the idea that polygonal relationships
such as that in Fig. 1 are caused by boundary
constraints has led to the search for methods to
estimate the values of the slopes of such boundaries (Blackburn, Lawton, & Perry, 1992; Thomson
et al., 1996; Cade, Terrell, & Schroder, 1999).
Phylogenetic autocorrelation
An obvious feature of most macroecological patterns is that they comprise observational data on
characteristics exhibited by large numbers of
ecological ‘particles’, typically species (Brown,
1995, Brown, Gillooly, West, & Savage, 2003). A
problem encountered when attempting to identify
the processes generating such multi-species patterns is that the characteristics of species are not
independent. Phylogenetically related species
share characteristics through their common evolutionary ancestry (Felsenstein, 1985). Two closely
related species will therefore tend to be more
similar to each other in a range of characters than
will two species chosen at random from the entire
assemblage being compared. This may be termed
‘phylogenetic autocorrelation’. It is a problem
because it makes it harder to identify whether
associations between variables are causal or due to
shared characteristics.
For example, Nee, Read, Greenwood, and Harvey
(1991) showed that the population size of a British
bird species is negatively related to its body mass
(Fig. 2). However, they demonstrated that this
negative relationship was due to a simple differ-
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Method in macroecology
7
7
7
A
20
22
8
passerines
non-passerines
Log population size
6
5
17
4
9
B
24
14
C
30
20
D
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VALUES OF Y
40
VALUES OF X
3
A
2
B
d1
Y
X
d1
2
4
C
d2
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10
d2
d3
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d3
1
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.5
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1
1.5
2
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Log body mass
10
d3
Y VARIABLE
COMPARISONS
d2
5
Figure 2. The relationship between log population size
(number of individuals) and log body mass (g) for British
bird species. The equation of the regression line through
all species is y ¼ 0:749x þ 5:641: N ¼ 147 species. From
Nee et al. (1991).
ence between passerines and non-passerines: the
former tend to be small-bodied and common,
whereas the latter tend to be large-bodied and
rare. Within these groups, there was no relationship between population size and body mass. This
suggests that a causal relationship between population size and body mass is unlikely, and that the
overall negative relationship between them could
be a consequence of any characteristic that differs
between passerines and non-passerines.
One way in which the problem of phylogenetic
relatedness can be circumvented, and the ability of
observational data for multi-species assemblages to
test hypotheses simultaneously improved, is
through application of a phylogenetically controlled comparative method (Harvey & Pagel,
1991; Harvey, 1996). A variety of such methods is
now available, together with computer programmes for their implementation, which account
for the relatedness of species in several different
ways (e.g. Purvis & Rambaut, 1995; Agapow &
Isaac, 2002). For example, the phylogenetically
independent contrast (PIC) method solves the
relatedness problem by performing comparisons
between characteristics only for related taxa (Fig.
3 and Purvis & Rambaut, 1995). Any differences
between the taxa compared must have developed
since they last shared a common ancestor. If such
comparisons repeatedly find associations between
characteristics in related species, that provides
much stronger evidence of a causal link between
the characteristics than a simple comparison across
d1
0
5
10
X VARIABLE COMPARISONS
15
Figure 3. The top box shows the values of two traits, X
and Y, for each of four species, A to D. Higher nodes, in
this simplified illustration, are calculated as the average
value of lower nodes. Species A and B diverged at the
higher node, so any differences between them, d1, must
have evolved since then. Similarly, the differences
between C and D, d2, must have arisen since those
lineages split. These two sets of differences are
independent. Furthermore, the differences between
the higher nodes, d3, makes up a third independent
comparison. These three independent comparisons are
shown and tabulated in the second box. If the Y-variable
comparisons are plotted against the X-variable comparisons, as shown in the third box, we can see whether
evolutionary change in the two traits has been correlated. From Purvis & Rambaut (1994).
all species. These methods require some hypothesis
about the phylogenetic relationships of the taxa
compared, but these now exist for many animal and
plant groups, even if just in terms of a simple
taxonomy.
Phylogenetically controlled comparative methods, such as PICs, are now routinely applied in
macroecology (e.g. Blackburn, Gates, Lawton, &
Greenwood, 1994; Blackburn, Lawton, & Gregory,
1996; Blackburn, Gaston, Quinn, Arnold, & Gregory,
1997; Fa & Purvis, 1997; Jones & Purvis, 1997;
Johnson, 1998; Gregory & Gaston, 2000; Poulin,
2004), although some controversy about their
utility persists (see e.g. Harvey, Read, & Nee,
1995a, b; Rees, 1995; Westoby, Leishman, & Lord,
1995a–c; Harvey, 1996; Ricklefs & Starck, 1996;
Harvey & Rambaut, 2000; Freckleton, Pagel, &
Harvey, 2003).
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8
Spatial autocorrelation
The standard statistical assumption that each data
point contributes an independent piece of information to the analysis may also be violated if data
points are associated in space, or spatially autocorrelated. Sites close together will share many
characteristics through that proximity, such as the
presence of certain species, habitats, or climatic
conditions, and so not provide independent information about ecological processes. Spatial autocorrelation can seriously bias statistical tests that
assume that data are independent (e.g. Legendre,
1993; Carroll & Pearson, 2000; Lennon, 2000).
Failure to account for it may result in either Type
I or Type II errors, or in misleading estimates of
statistical parameters such as correlation coefficients; predictions from the subsequent models
may be highly inaccurate as a result. Lennon (2000)
further notes that spatial autocorrelation will bias
analyses such that highly autocorrelated predictor
variables are most frequently identified as significantly related to an autocorrelated response
variable, even if the spatial patterns are completely independent. Macroecological studies frequently concern spatially or temporally explicit
data, and so necessitate controls for associated
autocorrelation (Carroll & Pearson, 2000; Lennon,
2000; Lennon, Koleff, Greenwood, & Gaston, 2001;
Brewer & Gaston, 2002; Perry et al., 2002; DinizFilho, Bini, & Hawkins, 2003; Storch, Konvicka,
Benes, Martinkova, & Gaston, 2003).
Several examples exist in the macroecological
literature of statistical solutions to the problem of
spatial autocorrelation. For example, Lennon et al.
(2001) apply the modified correlation test of
Clifford, Richardson, and Hemon (1989) to correct
the significance of the Pearson correlation coefficients they calculate between measures of species
richness calculated across Britain at two different
spatial scales. The correction reduces the degrees
of freedom used to calculate significance for each
correlation by an amount that reflects the degree
of redundancy in the original data (i.e. the fact
that sites that are close together effectively
provide duplicate information for the analysis). In
Lennon et al.’s study, the extent of redundancy
meant that the corrected sample size was typically
one or two orders of magnitude less than the actual
number of raw data points. A different approach
was taken by Carroll and Pearson (1998), who
explicitly modelled spatial non-independence in
relationships between the number of butterfly
species and the number of tiger beetle species in
grid squares covering North America, to investigate
the utility of the latter for predicting the former.
T.M. Blackburn
They found that the estimated relationship changed substantially when estimates with and without
spatial autocorrelation accounted for were compared. This difference would critically affect
predictions based on the model (Carroll & Pearson,
1998, 2000).
Closing remarks and future directions
I think it fair to say that any scientist that has tried
to investigate ecological systems at the macroecological scale will at some point in their career
have encountered some prejudice from fellow
ecologists about this approach. In part, that relates
to the methods that macroecologists have had to
use, perhaps because some ecologists equate
science with manipulative experiments. Yet, methodological approaches other than manipulative
experiments are just as scientific, and indeed can
produce novel and exciting insights. Our understanding of ecological systems will only benefit
from a greater variety of lines of enquiry, and
macroecology offers great potential as one such
line. However, if the field is to develop and evolve,
its future will see changes. I finish by briefly noting
four ways in which this evolution may occur in the
near future.
First, the frequency of manipulative experiments
in macroecological studies should increase. The
technique is simply too powerful not to use.
Although the large scale of study presents significant obstacles, there are ways to circumvent them,
as has already been demonstrated by studies
exploiting field and laboratory microcosms (see
above). Finding new ways to sidestep the problems
poses a major challenge for the future, but one
that offers great potential benefits.
Second, natural experiments and experiments in
nature should continue to start and develop, which
will provide new and different opportunities to
derive insights about large-scale patterns in processes in ecology. Increasing information on introduced species, for example, offers the opportunity
to gain insights about the factors determining the
limits to species distributions (e.g. Blackburn &
Duncan, 2001a). The responses of species to global
climate change will be an experiment in nature on
a grand scale, one that macroecologists should not
fail to exploit (see also Lawton, 2000), albeit one
that few would have wished for.
Third, the explosive growth in the availability of
phylogenetic data will allow a much greater
integration of historical information into macroecological studies than currently occurs. Patterns in
ARTICLE IN PRESS
Method in macroecology
the distribution and abundance of species across
the planet can be viewed as the end point of
processes of evolution spanning many millions of
years (these processes also leave their imprint in
patterns of macroevolution). Therefore, it makes
sense to consider the relationship between evolutionary history and current macroecological patterns. Phylogenies provide a map of that evolution.
Finally, I expect the future to bring a general
growth in the number of macroecological studies.
That growth is already underway (Brown, 1995,
1999; Maurer, 1999; Gaston & Blackburn, 2000;
Blackburn & Gaston, 2003b), but wider understanding of macroecology, its aims and its methods,
will help not only by encouraging more people to
consider large-scale questions and research, but
also by reducing the frequency of ill-informed
criticism of such studies (hopefully leaving more
room for constructive criticism). All of ecology will
benefit as a result.
Acknowledgements
I thank B.A. Maurer and S. Nee for supplying data on
which Figs. 1 and 2 were based, Paul Harvey for
allowing me to use Fig. 3, David Storch for helpful
criticism, and Kevin Gaston both for commenting on
this manuscript and for a decade of inspiration on
macroecological questions.
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