ISLAND SPECIES NUMBERS AND ISARS: WHAT HAVE WE LEARNT? 95Rosenzweig (1995, 2004) has developed an
alternative typology of species–area curves,
stressing differences in scale (see figure), but which
also spans these different forms of construct. To
show how our terms relate to his scheme, see the
table below. Rosenzweig (1995) fashions a
powerful argument based on the re-analysis of
numerous data sets, and with important
implications for conservation biogeography: hence
it is important to understand his typology. In
contrast to our present treatment, he uses a single
term, species–area curve or SPAR, to refer to a
sequence of SACs and ISARs applied at different
‘scales’. The first level is local species accumulation
curves; next comes species–area relationships for a
set (archipelago) of islands; next, the construction
of a species accumulation curve within a mainland
on a regional scale; and finally, plots of overall
richness of two or more regions/provinces of
different area. This switching between different
types of relationship within his analysis is deliberate
and he comments on the implications of the
nested design. He writes (Rosenzweig 1995, p. 10)
in relation to intraprovincial SPARS:
‘you must remember to keep your subplots
contiguous when you group them to measure the
diversity of larger areas. This is called the nested
design. If you do not keep them contiguous, but
amass them from scattered subplots, the result
will have a steeper slope.’
Moreover, being a SAC, the form of such a
relationship is typically curved (even in log–logplots), initially rising swiftly and the rate of increase
slowing as more and more of the species pool are
included in the cumulative total. Hence we see that
the three types of species–area curve generalized in
Rosenzweig’s scale diagram, are each constructed
in quite different ways, with two being island or
island-type species–area plots and one a SAC.
Returning to Fig. 4.2, the mainland data
represent a regional SAC and the island data an
ISAR. Of necessity the former is a smooth plot of
a single data series, whereas the island data can
include several islands of identical or near
identical area, but varying species number,
producing scatter around the line of best fit. If we
wished to make a more direct comparison
between the two data series, it would mean
calculating an SAC for the island data, adding
islands by order of increasing area, and tallying
cumulative richness versus cumulative area. Even
then, the spatial structure of the two data series
would differ. Thus, to some extent, Wilson and
Rosenzweig are comparing apples and oranges,
but in the absence (as far as we are aware) of
comparative analyses based on multiple mainland
vs. island data sets, the implications of the
differing structures of regional SACs and ISARS
remain obscure.
The figure shows three of the four forms of
species–area curves and how they relate to each
other in respect of slopes and intersects.
Rosenzweig’s (1995, 2004) assertion that larger
provinces will necessarily contain more species than
smaller provinces, thus producing positive slopes for
the interprovincial SPAR, is dependent on careful
selection of provinces being compared so as to
eliminate variation attributable to other factors
(e.g. climate).How the alternative typologies of species numbers
analyses compare
Rosenzweig’s scheme is as given in the figure, in
which each SPAR type has a characteristic range of
slope values, and which generalizes all but his
point scale SPAR into a combined graphical model.
Our usage Rosenzweig’s scheme
Local SAC The point scale SPAR
ISAR Archipelagic SPAR
Regional SAC Intraprovincial SPAR
Inter-Regional ISAR Interprovincial SPARInterprovincialProvince AProvince BLog species numberLog areaA’s islandsB’s islands
Intraprovincial: 0.1–0.2
Archipelagic: 0.25–0.45
Interprovincial: 0.8–1.0Typical z-value rangesAn idealized set of species–area curves (from Rosenzweig 2004).