Evolution__3rd_Edition

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(a) Phenetic measurements for five species
Character 1
(length of tibia)
1 2
A
Character 2 (length of wing vein)
(b) Nearest neighbor (c) Average neighbor
1
1
Similarity can be measured for
multiple phenetic characters
2
3
4
5
3
4
B
5
2
3
4
5
CHAPTER 16 / Classification and Evolution 477
Figure 16.2
(a) The phenetic similarity between species can be expressed
graphically. Suppose five species have been measured for two
characters, for instance the length of a wing vein and the length
of the tibia. The x-axis is the measurement for each species of the
length of a wing vein, and the y-axis for the length of the tibia. The
distance between two species on the graph is the phenetic distance
between them. (Notice that the distance on the graph is different
from the measure of mean character distance in Table 16.1.)
(b) The phenetic classification by the nearest “nearest neighbor”
technique puts species 3 with the group (cluster A) that has the
nearest individual neighboring species (species 2). (c) But if it uses
the nearest “average neighbor” technique it puts species 3 with the
group (cluster B) that has the nearest average for all its species.
Species 4–5 have a nearer average distance. (The average is simply
the average of the distances of species 1–2 and of species 4–5 to
species 3.)
of phenetic characters, there is no way to decide which of the many classifications is
the best.
The next step is to define the classification not by a few characters but by many. This
became possible in the 1950s and 1960s as the statistical and computational apparatus
became available for aggregating large numbers of phenetic measures into one grand
measure of phenetic similarity. The aim of the numerical phenetic school was to measure
so many characters that the idiosyncracies of particular samples would disappear.
The resulting classification groups the units according to their whole phenotype.
How do we aggregate a large number of measures into a single combined measure
of phenetic similarity? Several methods exist, and we can illustrate one of them in a
graph. We start with the simple case of two characters, though the extension to further
dimensions is easy. Suppose that we wish to classify a group of fly species, and we have
measured two characters, such as the length of a certain wing vein, and the length of
the tibia of the hindleg. The average for each species can be represented as a point
(Figure 16.2). For any pair of species, the average difference in their wing vein lengths is
the distance between their points on the x-axis, and the difference between the lengths
of their tibiae is the distance on the y-axis. If we used either character by itself, the
classifications would differ; species 1 and 3 for instance have identical tibial lengths, but
different wing vein measurements. The aggregate difference for the two characters can
be measured simply by the distance between the two species in the two-dimensional
space. The species are then classified by putting each with the species, or group of
species, that it has the shortest distance to.