Big ecology

Nature uses only the longest threads to weave her patterns, so that each small piece of her fabric reveals the organization of the entire tapestry.

Richard P. Feynman

Macroecology is the field that describes and attempts to explain statistical patterns of abundance, distribution, and diversity (Brown 1995; Gaston and Blackburn 2000). Macroecologists have identified a number of distinctive patterns in these variables that seem to be telling us something important about the rules governing ecology and evolution. In the development of the field, identifying patterns constitutes what Gaston and Blackburn (1999) describe as the 'what' stage of development, and is reasonably well advanced. Ultimately, we would like to know 'how' and 'why' the patterns are generated. This is not only because the patterns themselves seem to be important features of our biotic universe, but also because underlying them are fundamental principles of the workings of ecology and evolution. We are groping for the rules behind a great ecological and evolutionary game, perhaps the greatest of them all. Since by far the most effort so far has gone into documenting the patterns, the field is very much dominated by empirical data and not by theory. Nonetheless, many hypotheses have now been formulated to explain many of the patterns, and some data allow comment on the merits of these hypotheses. One particular obstruction is that the nature of the patterns restricts tests of the hypotheses. One particular empirical option, experiment, is largely ruled out by the practical concerns of scale. As we shall see, however, this does not exclude useful tests that can distinguish between alternatives.

Several macroecological patterns rightly find prominence in ecology texts but rather fewer find their way into evolutionary texts. This is probably a mistake because, as I hope to show below, many of the patterns require us to consider evolutionary processes and mechanisms as well as ecological ones. Testing hypotheses about these mechanisms will therefore be aided by evolutionary theory and data. Before discussing some of the theory and data, however, let us outline some of the patterns we are trying to explain. The patterns fall into two categories: associations between variables and frequency

Table 15.1 Some of the best known macroecological patterns

Type of pattern

Variables concerned

Nature of pattern

Ubiquity of pattern

Frequency distribution

Latitude of occurrence

More species found at low latitudes than high latitudes

Virtually ubiquitous except at very small scales and taxa

Geographic range size

Bimodal or right skewed and unimodal

Bimodal at small scales, unimodal and right skewed at large scales

Body size

A right skewed distribution with many more small than large species

Fairly ubiquitous especially at large spatial and taxonomic scales

Abundance

Most species rare

Virtually ubiquitous across scales and taxa

Associations between variables

Body size and latitude

Larger bodied species at higher latitudes (Bergmann's rule)

Not ubiquitous but known from several bird assemblages

Body size and abundance

Negative relationship

Very common, but found less frequently at smaller scales

Geographic range and latitude

Larger geographic ranges at high latitudes (Rapoport's rule)

Not ubiquitous but known from several bird assemblages in the Holarctic

Abundance and geographic range

More widespread species achieve higher local abundance

Virtually ubiquitous across scales and taxa

Species richness and geographic area

Positive relationship

Virtually ubiquitous across scales and taxa

distributions of single variables (Table 15.1). Each relationship can be examined across a range of taxa, of different rank, and at a variety of spatial scales. Spatial scale can mean the total area over which the relationship is examined, and/or the area that comprises an individual data point (quadrat size).When relationships are examined over large geographic areas, quadrats naturally tend also to be large. For some relationships, data are relatively taxonomically restricted; for example, geographic range is only well documented in a large number of species for a few higher taxa, mostly birds and mammals. In contrast, data on local abundance or species richness come from numerous studies on many taxa. Patterns are mostly examined over quite large taxonomic ranks, such as classes. Changing the taxonomic rank under consideration, may, as we shall see below, affect the pattern. Putting together frequency distributions is relatively straightforward and requires few special considerations. However, relationships between two variables often means comparative analyses of species characteristics, and then phylo-genetically based comparative techniques should be used where possible (Chapter 4).In the remainder of the chapter,we will consider two of the most robust patterns and how evolutionary processes may contribute to them.

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