Describing the structure of biological communities

Community Parameters

These are indices and/measures which can be used to give some indication of community structure. Four approaches (a - d) are included here.

a) Numerical abundance, N: total number of organisms recorded in a sample.

b) Species richness, S: number of species recorded in a sample.

c) Species diversity, D (not a standard symbol): depends upon the distribution of individuals between the various species, i.e. n_i for i = 1 to S, where i indicates a species.

Diversity depends on the number of species (i.e. S), but also on the evenness (E); if all species are equally abundant (E max) then diversity is high, but if one species is especially abundant and the rest are rare (E min) then diversity is low. There are many diversity indices, but two were proposed by Simpson, both of which relate to Dominance:-

i. D′ = 1 - Σp_i² , and

ii. D = 1/Σp_i² (known as Simpson's Inverse Index of Diversity),

where p_i is the proportional abundance of species i, i.e. n_i/Σn_i .

So, to calculate D, you simply do this:-

add up the species' abundances (n_i) to give the total (N = Σn_i );

divide each n_i by N to give p_i ;

square each p_i , before you

add them up to give you a value for Dominance (= Σp_i² ),

the reciprocal of which is Simpson's Inverse Index of Diversity.

The Inverse Index is conceptually useful, as its maximal value when all species are evenly represented equals S, the value you get for any sample could be interpreted as "the number of species there are effectively if they were evenly abundant/important".

Note that the maximal value of Dominance equals 1, when there is only one species present.

Evenness can be calculated as obs. D / max. D, or D/S for Simpson's Inverse Index.

d) Expected Species Richness is an alternative approach in which you calculate the expected number of species, E(S), you would expect to find in a standard sample size, N. This value of E(S)_N may then be used to make comparisons between samples of different sizes, since S varies with N (see graphical methods below). So, if you have samples with different abundances (N), you can better compare their species diversity by calculating how many species you would find in the same number of individuals taken from each, (e.g. how many species in a random selection of 100 individuals).

Graphical Methods

To describe community structure, these relate to ways of describing either (a) the distribution of individual organisms between species, or (b) how many species you might expect in a given sample size.

(a) Species Frequency curves - plot (possibly histogram) of number of species with particular abundance values, i.e. S(n) against n.

(b) Species Importance curves - the species are arranged along the x axis in decreasing order of abundance; the y axis is their abundance (n_i). A steeply falling curve indicates low diversity, whereas a gently falling curve involving more species of moderate abundance indicates higher diversity.

(c) Species/Sample size curves - a plot of the number of species recorded (species richness, S) against total number of organisms in the samples (sample size, N); more diverse communities will show a steep rise of S with N, or a more gentle rise to a high asymptote, whereas a low-diversity community will quickly reach a low asymptote.

[examples in prep.]

A Note of Caution

Remember, though, that the way you take your samples can influence your data. Different sampling approaches may be used for different kinds of organisms in different environments. In making comparisons between communities you need to be sure that you have either (a) standardised your sampling procedures so that they don't distort the data or (b) know enough about the distortions caused by variations in sampling efficacy that you can compensate mathematically for them somehow. Obviously, (a) is preferable to (b)!

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