predicting scaling exponents (Cyr & Walker, 2004 ). For our analysis, we selected
the recently formalized metabolic theory of Brownet al.(2004) as a framework
for posing testable predictions about the relationship between components of
production and body size. Their metabolic theory is grounded on the assump-
tion that processes driven by metabolism at all ecological scales are subject to
quarter-power scaling, and that individual metabolism scales as M0.75due to the
fractal-like architecture of internal distribution systems (Woodwardet al.,
2005a). For our test of selected predictions of this theory, we specifically focused
on several independent communities because factors constraining bioenergetic
relationships among animals should be most effectively expressed at the com-
munity scale (Cyr & Walker, 2004 ).
The results of our analysis indicate that the scaling exponents describing the
relationship between N, B, P/B and P for the communities of Ball Creek, Sutton
Stream and Stony Creek showed a high level of concordance with the predicted
quarter-power scaling relationships. Among the analyses of the different pro-
duction variables, the relationship between P/B and M (summed taxa) showed
the highest level of precision, with the scaling exponent varying from only
0.24 to0.27, which bracketed the predicted exponent (0.25), and with
0
1
2
3
4
5
0
1
2
3
4
5
log
10
annual P(mg/m
2 )
Ogeechee River
log 10 P = 4.83 – 0.37 log 10 M
(r^2 = 0.60, p < 0.02)
log 10 M (∝g)
Sutton Stream
log 10 P = 2.77 – 0.03 log 10 M
(r^2 = 0.00, p = 0.887)
Stony Creek
log 10 P = 3.09 – 0.13 log 10 M
(r^2 = 0.07, p = 0.556)
Ball Creek
log 10 P = 2.94 – 0.02 log 10 M
(r^2 = 0.03, p = 0.705)
01234567 01234567
0
1
2
3
4
5
0
1
2
3
4
5
01234567 01234567
Figure 4.6Log-log plots of annual
P(P¼mg m^2 yr^1 ) against M (mg/
individual) for four stream
communities (see Fig.4.2for
details). The grey line indicates the
predicted slope of the relationship
between log 10 Pandlog 10 M
(P/M^0 ). The black line indicates
the slope derived from least-
squares regression of the data.
64 A. D. HURYN AND A. C. BENKE