Fish Production Correlated with Primary Productivity, not the
Morphoedaphic Index1
John A. Downing, Celine Plante, and Sophie Lalonde
DGpartement de Sciences Biologiques, University de Montreal, C.P. 6128, Succursale 'A', Montreal, Qu4. H3C 317 Canada
Downing, J.A., C. Plante, and S. Lalonde. 1990. Fish production correlated with primary productivity, not the
morphoedaphic index. Can. J. Fish. Aquat. Sci. 47: 1929-1936.
Estimates of the biological production of entire lake fish communities were collected from the published literature
on lakes covering a wide range of geographic areas and trophic status. Correlation analysis shows that fish
production is uncorrelated with the morphoedaphic index (p>0.05) but closely correlated with annual phytoplankton production (^ = 0.79), mean total phosphorus concentration (^ = 0.67), and annual average fish stand
ing stock (r2 = 0.67). Empirically derived regression equations are presented and compared with previous models
based on catch and yield data. Analysis of these equations suggests that conversion of phytoplankton into fish
production is 100 times more efficient in oligotrophic lakes than hyper-eutrophic ones, but that a much lower
fraction of fish production can be channeled to sustainable yield in oligotrophic lakes. Sustained yields were
frequently as little as 10% of the annual community fish production.
On a releve", dans les ouvrages publics, des estimations de la production biologique de communaute's entieres
de poissons provenant de lacs couvrant une grande e"tendue ge"ographique et une vaste gamme d'e*tats trophiques.
L'analyse de correlation de"montre que la production de poissons n'est pas correMee avec I'incide morphoe"daphique (p>0,05), mais qu'elle est etroitement corre'le'e avec la production annuelle de phytoplancton (r2 = 0,79),
la concentration moyenne de phosphore total (r2 = 0,67) et la biomasse annuelle moyenne de poissons (r2 = 0,67).
On pre"sente des Equations de regression obtenues empiriquement et on les compare avec des modeles ante"rieurs
reposant sur des donne"es relatives aux captures et aux rendements. L'analyse de ces Equations revele que la
conversion de phytoplancton en production de poissons est centfois plus efficace dans les lacs oligotrophes que
dans les lacs hypereutrophes, mais qu'une fraction beaucoup plus faible de la production de poissons contribue
a un rendement gquilibre" dans les lacs oligotrophes. Dans de nombreux cas, les rendements equilibre's repre"sentaient aussi peu que 10% de la production annuelle des communaute's de poissons.
Received October 11, 1989
Accepted April 19,1990
Recu le 11 octobre 1989
Accepte" le 19 avril 1990
(JA337)
At equilibrium, unexploited fish stocks produce exactly
enough biomass to balance natural mortality. The aim
of fisheries management is to replace the greatest pos
sible fraction of natural mortality with fishing mortality without
exceeding the rate of renewal of the stock (e.g. Schaefer 1968).
The renewal of fish stocks is provided by production, which is
the "amount of tissue elaborated per unit time per unit area,
regardless of its fate'1 (Clarke 1946).
Models for the prediction of freshwater fish productivity
abound (review by Leach et al. 1987). These models disagree
about the characteristics of lakes that have the greatest influence
on fish production (Table 1). Early models were based on sim
ple morphometric measures like mean depth (Z) or lake area
(Rounsefell 1946; Rawson 1952, 1955). Later ones, like the
famous "morphoedaphic" model, augmented morphometric
data with indicators of lake fertility like alkalinity (Hayes and
Anthony 1964) and total dissolved solids (Ryder 1965). The
simplicity of the morphoedaphic index (MEI) model has made
it a staple of lake fisheries management (Ryder 1982; Leach
et al. 1987). In the 1970's, models frequently linked fish pro
duction to phytoplankton (Hrbacek 1969; Melack 1976;
McConnell et al. 1977; Oglesby 1977) and benthos (Matuszek
'Publication No. 370 of the Groupe d'Ecologie des Eaux douces,
University de Montreal.
Can. J. Fish. Aquat. Sci., Vol. 47, 1990
1978) productivity. Most recent studies have used the old
approaches on new fish production data bases (Liang et al.
1981; Jenkins 1982; Jones and Hoyer 1982; Oglesby etal. 1987)
or have reanalyzed the old data (i.e. Ryder 1965) using new
combinations of variables (e.g. Hanson and Leggett 1982;
Youngs and Heimbuch 1982) or methods (Prepas 1983; Schnei
der and Haedrich 1989). Some recent multivariate models pre
dict yields of single species fisheries (e.g. Godbout and Peters
1988). Individual models have advanced alkalinity, algal bio
mass (chlorophyll a), air temperature, area, benthos standing
crop, body size, fishing effort, mean depth, phytoplankton pro
ductivity, total dissolved solids, total nitrogen concentration,
and total phosphorus concentration as the most important pre
dictors of lake fisheries production.
Probably the most important deficiency of current lake fish
ery production models is that few actually predict rates of bio
logical production; instead, many predict variables like "catch"
or "yield" (Table 1). The many different "fish production"
models yield predictions of quite different dependent variables
and thus are difficult to compare and interpret. Short-term sport
or commercial fishing yields probably do not bear a constant
relationship to long-term or sustainable yields. Further, the
relationship between catch and actual fish production is
unknown because both the amount of fishing effort expended
1929
Table 1. Number of lakes examined, coefficients of determination (r2), and variables employed in various models for the prediction of fish
production in lakes. Year is the year of publication, Area indicates lake area, Z is the lake mean depth, Alk. is the alkalinity, TDS is total
dissolved solids, PP is the water column primary production, Chi a is the chlorophyll a concentration in the water column, Benthos indicates
the standing biomass of benthic invertebrates, TP is the water column total phosphorus concentration, TN is total nitrogen concentration, Air T
is the mean annual air temperature, and Effort is the annual fishing effort.
12
39
13
138
0.73
Area
0.40
0.80"
Area
0.67b
Area,Z,Alk
Ryder
23
0.73
TDS/Z
1969
Hrb&ek
13
0.72
TN
1969
1976
Hrb&ek
Melack
11
24
0.72c
0.56
PP
PP
1977
McConnell et al.
6
0.93
PP
1977
Oglesby
19
0.74
PP
1977
Oglesby
19
0.84
Chi a
1978
Matuszek
11
0.80
Benthos
1981
Liang et al.
18
0.76
PP
1982
Hanson and Leggett
20
0.58
Benthos/Z
1982
Hanson and Leggett
21
0.87
TP
1982
Jenkins
290
0.08
TDS/Z
1982
1982
Jones and Hoyer
Schlesinger and Regier
25
43
0.83c
0.74
Chlo
Air T
1982
Youngs and Heimbuch
27
0.72
Area
1983
1983
Prepas
Schlesinger and McCombie
23
92
0.70
0.61
Z
Effort
1946
Rounsefell
Rounsefell
1952
Rawson
1964
Hayes and Anthony
1965
1946
Production estimate
Predictor
Author
Year
Z
Commercial fishing yields
Sport fishing yields
25 yr average commercial catch
Long or short term sport or commercial
catches weighted by trophic chain length
"Catch records for several years, or from
published estimates based on intensive
fishery surveys1'
Net growth increment in stocked carp
ponds
Net growth increment in carp ponds
Three consecutive years average
commercial yield
Net growth increment in experimental
swimming pools
Fish catch "...in lakes with moderate to
intensive fishing pressure"
Fish catch "...in lakes with moderate to
intensive fishing pressure"
Average annual catch over the 15 yr
period of maximum commercial yield
Net (1-yr) growth increment in stocked
ponds and lakes
"Long term commercial and sport
harvest" (Oglesby and Ryder)
"Long term commercial and sport
harvest" (Oglesby and Ryder)
"Mean of all available annual (catch)
estimates"
Sport fish harvest from creel census
Commercial and sport yields "...assumed
to be a close approximation of the lake's
MSY."
Yield data from Ryder, Oglesby and
Matuszek
"Catch records" of Ryder
Sport fish yields from creel census.
•Computed from Rawson's (1955) table 3.
bLog of yields weighted by trophic chain length.
CFY not as logarithms.
and the capture efficiency have a large influence on the amount
offish actually landed (Schlesinger and McCombie 1983; Godbout and Peters 1988). The meaning of the variables predicted
by many of these models to either the practical management of
lake fisheries, or the theoretical study of ecology is difficult to
discern. Oglesby (1977) has stated that it would be preferable
to establish fish production models from rigorously defined and
repeatable measurements like fish production rather than rough
indices of average catch, but suggested that reliable, whole
community production data were too rare, during the mid
1970's, to permit this.
All fisheries management models assume that sustainable fish
yield is correlated with fish production. If this correlation exists,
then we would expect fish production to be correlated with the
same lake characteristics as long-term fish yield measures. This
research draws together existing measurements of production
of whole fish communities to find which of the proposed lake
characteristics is most closely correlated with fish productivity.
1930
Methods
Data on the production of entire fish communities were
gleaned from an exhaustive survey of the primary ecological
literature published since 1969. We only retained data where
the production of all significant fish species in each lake had
been measured. Fish communities that had been subject to
recent stocking were not included in the data set because pro
duction by recently stocked fish might not reflect the natural
ecosystem productivity.
Data on biotic and abiotic characteristics of lakes were
derived from published works or in some cases were completed
through direct communication with the authors of the produc
tion studies. Morphometric characteristics collected were lake
area, volume, mean and maximum depth, and area of
watershed. Lake productivity indicators were phytoplankton
productivity, total phosphorus, nitrogen and chlorophyll a con
centrations in the water column, and conductivity (as a measure
Can. J. Fish. Aquat. 5c/., Vol. 47, 1990
Table 2. Whole fish community production data and lake characteristics drawn from the published literature. Abbreviations indicate, FP: fish
production (kg-na^yr"1), FB\ fish biomass (kg-ha"1), PP: primary production (g C-m~2-yr"1), TP: total phosphorus (jxg-L"1), Cond.:
conductivity (jiS-cm"1, 20°C), Z: mean depth (m). na indicates that the datum was not available.
Lakes
Alinen Mustajarvi
Batorin
Big Turkey (1985)
Big Turkey (1988)
Botjam
Char
Dalnee
FP
FB
30.3
73.0
83.0
1.2"
3.8
17.3
2.0
Horkkajarvi
La Luisa
276.9
Little Turkey (1985)
Little Turkey (1988)
Marion
Myastro
Nakuru
Naroch
0vre Heimdalsvatn
Red deer
Sabanilla
Vitalampa
Washington
Wishart (1985)
Wishart (1988)
7.0
TP
39.3
Cond.
24.3
Z
3.0
Data sources
Rask and Arvola 1985; Arvola (pers.
comm.)
398.0
228.5
15.5
Demenets
PP
163.0
5.2"
7.3
30.3
104.0
197.0
372.2
32.2
351.6
na
na
0.3
0.3
2.6
4.2
41.1
4.6
13.5
41.1
25.0
4.1
3.8
196.0
10.2
487.0
na
na
31.5
na
na
na
3.3
5.6
114.5
43.3
7.0
1.4
6.0
3.0
12.2
12.2
3.3
145.8
325.7
10.9a
11.2
28.9
125.0
59.4
44.0
14.7
81.0
19.8
97.3
na
na
9.0
11.5
14.7
15.3
4.7
28.9
11.6
311.7
na
na
35.1
220.5
na
na
na
3.7
0.6
9.8
89.4
20.2
93.4
4.5
157.0
16.5
34.8
18.0
2.8
81.1
33.0
2.8
2.8
5.1
33.3
33.3
2.2
3.9a
4.7
21.5
64.0
5.6a
24.4a
5.7
24.4
na
na
na
0.7
0.7
8.0
314.8
881.0
6.2
5.0
38.8
na
38.8
18.2
na
na
9850.0
18166.7
5.0
6.0
2.4
5.4
2.3
2.2
Winbergetal. 1972
Kelso 1985; Lam et al. 1986
Kelso 1985, 1988; Lam et al. 1986
Nyberg 1979; Ramberg 1976
Rigler 1972, 1975
Krogius et al. 1972
Gulin and Rudenko 1973
Rask and Arvola 1985; Arvola 1983;
(pers. comm.)
Hoiafk 1970
Kelso 1985; Lam et al. 1986
Kelso 1985, 1988; Lam et al. 1986
Efford 1972
Winberg et al. 1972
Vareschi and Jacobs 1984, 1985;
Plante 1987; Vareschi (pers. comm.)
Winberg et al. 1972
Kloster 1987; Lien 1978, 1981;
Tangen and Bretum 1978
Chadwick 1976
Hoieik 1970
Nyberg 1979; Ramberg 1976
Edmondson 1977, (pers. comm.);
Eggers et al. 1978
Kelso 1985; Lam et al. 1986
Kelso 1985, 1988;
Lam et al. 1986
"Production and biomass of 1+ fish and older.
of total ion concentration). Phytoplankton production data were
converted to total net annual production per unit lake area.
Results and Discussion
Chlorophyll, nitrogen, and phosphorus concentrations were
Fish community production data were obtained for 20 lakes
(Table 2) in a wide range of geographic locations (Fig. 1). All
estimates were actual measures of biological production (as
opposed to catch or yield), and were mostly estimated using
cohort, Allen curve, or instantaneous growth methods (Chap
man 1971; Rigler and Downing 1984). Two independent pro
duction estimates were obtained for Turkey, Little Turkey, and
Wishart Lakes (Kelso 1985, 1988) yielding 23 whole com
munity fish production estimates in all. This is a number of
estimates similar to that used in the majority of previous studies
examining average catch data (Table 1). The fish communities
consisted of between one and 22 species of fish, but there was
no correlation between species richness and lake trophic status
(e.g. both the unproductive Char Lake and the tropical Lake
Nakuru had only one important fish species). Lakes varied in
mean annual fish standing stock from 5.2 kg-ha"1 in the oligotrophic Turkey Lake on the Canadian Shield (Kelso 1985) to
collected as annual averages. The chlorophyll concentrations
of lakes Alinen-Mustajarvi, Botjarn, Vitalampa and 0vre
Heimdalsvatn were estimated using the least-squares regres
sion relationship between log,o chlorophyll and log,0 total
phosphorus found in the data set (r2 = 0.82; n= 10). Total dis
solved solids (TDS) was inferred from conductivity measure
ments using Rodhe's (1949) table 2. This procedure yielded
predicted TDS values that were closely correlated (^ = 0.95;
n = 6) with TDS measurements in an independent set of data
(Oglesby 1977). Annual mean air temperature, which is closely
correlated with surface water temperature (StraSkraba 1980),
was used instead of water temperature (see also Schlesinger and
Regier 1982) because of its greater availability in climatic
reports (Wernstedt 1972). Annual average pH data were also
collected because of the possible influence of pH on the pro
duction of invertebrates (Plante and Downing 1989). Geo
graphic variables, latitude and altitude, were also noted where
possible.
Analysis of relationships between fish community produc
tion and lake characteristics were performed using simple and
multiple regression (Draper and Smith 1981; Gujarati 1978)
after logarithmic transformation, where necessary, which sta
bilized the variance, linearized the responses, and normalized
the residuals. Residuals were examined following the protocol
of Draper and Smith (1981).
Can. J. Fish. Aquat. ScL, Vol. 47, 1990
372 kg-ha"1 in the small, eutrophic Lake Demenets in the
Pskov province of the eastern USSR.
Primary Production
Only three of the 16 lake characteristics were significantly
(p<0.05) correlated with fish community production (Table 3),
thus many of the published correlations between fish yield and
various lake characteristics do not hold for fish community pro
duction. Although the top three variables in Table 3 yielded
1931
-60
-180-150-120 -90
-60
-30
DEGREES
6
30
OF
60
90
120
150
180
LONGITUDE
Fig. 1. Location of 20 lakes for which fish community production data were obtained from the published
literature.
Table 3. Coefficients of determination (r2) for the relationship:
\og,oFP = a + b logloX, for the fish production data in Table 2. n
is the number of observations included in the regression, and p is the
3-
probability that an equal or greater r2 value would be obtained by
chance alone. Values of p are corrected for the number of tests
performed (Kirk 1982).
Variable
n
r2
o
Primary production
Fish biomass
Total phosphorus
Latitude"
TDS/Z(MEI)
19
23
14
18
9
23
16
pHa
Air temperature
Conductivity
Zmax
16
22
16
23
<0.001
<0.001
0.002
0.058
0.149
0.519
0.593
Z
Lake area
Total nitrogen
Lake volume
Altitude
23
23
16
23
14
0.79
0.67
0.67
0.34
0.57
0.15
0.20
0.18
0.13
0.16
0.10
0.04
0.02
0.01
0.01
0.00
Watershed area
Chlorophyll a
0.727
0.741
0.812
0.871
1.000
1.000
1.000
1.000
1.000
"Independent variable not log,0 transformed.
r2 values that were not significantly different from each other
(r-test; Sokal and Rohlf 1981; p<0.05), the correlation coef
ficient for the relationship between annual phytoplankton pro
duction (PP; g C-m^-yr"1) and fish production (FP;
kg-ha~l-yr~1) was the highest. The coefficient of determina
tion for the relationship:
(1)
log]OFP = 0.600 + 0.575 logloPP
(Fig. 2) was 0.79 (n= 19;/?<0.001). This coefficient of deter
mination is greater than all but six of those found in studies of
correlations between lake characteristics and fish catch or yield
in the published literature (Table 1). The correlation is even
better than the relationship seen between fish yield and primary
production in stocked ponds (Hrbacek 1969; Liang et al. 1981).
The lower residual variation around Eq. (1) is probably because
1932
PRODUCTION DATA (THIS PAPER)
ooooe CATCH DATA (OGLESBY 1977)
O
O
-1
-0.5
0.0
0.5
1.0
1.5
2.0
2.5
3.0
3.5
4.0
LOG PRIMARY PRODUCTION (g C-m-yr
)
Fig. 2. Relationship between fish and phytoplankton production for
literature data on whole fish community production (closed squares)
and Oglesby's (1977) fish catch data (open circles). Dashed lines indi
cate approximate 95% confidence bands for predicted values of the
mean logloF/> (Prepas 1984). Oglesby's fish yield values were con
verted to wet mass by multiplying by four (Oglesby 1977) and the
Asian data where gross production of phytoplankton was measured or
where a range of fish catch values was given are not plotted. The solid
line is predicted from Eq. (1). The four lowest points are fisheries
yield data from Lakes Superior, Huron, Michigan and Ontario. The
four highest points are the average of three consecutive years of com
mercial yield from three Indian ponds and the annual yield from an
unnamed carp pond in Israel.
our FP data are uninfluenced by variations in fishing effort.
Confidence intervals for predicted values show that 95% of pre
dicted mean fish production rates will be not less than about
half and not more than about twice the production indicated by
Eq. (1).
Can. J. Fish. Aquat. ScL, Vol. 47, 1990
3.0
Equation (1) suggests that the conversion of phytoplankton
into fish production (assuming 1 gC = 10g fresh mass;
Oglesby 1977) is about 1% efficient in very unproductive, oli
gotrophic lakes like Big Turkey Lake, and as little as 0.002%
efficient in hyper-eutrophic lakes like Lake Nakuru. Our results
oppose those of Oglesby (1977). He found that ratios of fish
catch to primary production covered the same range as ours,
but the transfer of primary production to fish yield was most
efficient in hyper-eutrophic carp ponds. Oglesby stresses, how
ever, that his analysis does not necessarily represent true bio
logical efficiency because his yield data were subject to several
factors like fishery management practices, or incomplete catch
records. Our data on biological fish production suggest that the
transfer of energy from algae to fish production is several orders
of magnitude more efficient in oligotrophic ecosystems than in
eutrophic ones.
Oglesby's (1977) fish-catch data are nearly always lower than
our fish production rates (Fig. 2). This may be because the
sport and commercial yields in Oglesby's model do not account
for the production of all fish, only those landed by fishermen.
Another possibility is that the fishing yields summarized by
Oglesby (1977) are sub-maximal, especially considering that
the catches that were lowest, compared to measured production
values, were found in the Great Lakes (Lakes Superior, Huron,
Michigan, and Ontario) where distances are large and effort
might be small relative to the productive area. The latter seems
improbable, however, because Lawrie and Rahrer (1972) and
Wells and McLain (1972) suggest that the Great Lakes were
being overfished during the period when these catch data were
recorded. It is plausible that Rounsefell (1946) was correct, that
extremely large lakes with little perimeter, littoral zone, or allochthonous energy input yield low rates of fish production rel
ative to autochthonous primary production.
The separation between catch and actual production meas
urements seems largest in oligotrophic lakes (Fig. 2). The dif
ference between fish production and sustainable yield should
reflect either the fraction of fish community production that is
unexploited (i.e. rough fish, juveniles) or the amount of fish
production that cannot be channeled into fishing mortality.
Rough fish are generally more abundant in eutrophic lakes,
therefore it is unlikely that the difference between Oglesby's
(1977) yield measurements and our production data is only the
production of unfished populations. If Oglesby's data are an
accurate reflection of real maximum sustainable yields of
exploited populations, then it appears that sources offish mor
tality are different in oligotrophic and eutrophic lakes. Because
nearly all fish production can be harvested on a sustainable
basis when primary production is high, it appears that natural
mortality can be substantially replaced by fishing mortality
through exploitation in eutrophic lakes. This would occur if
most mortality of biomass falls upon harvestable adult fish and
reduction of adult density assuages natural mortality. In oli
gotrophic systems, however, a smaller proportion of the fish
production can be harvested on a sustainable basis, and it
appears that nearly all fish production is required to balance
natural mortality. Natural mortality therefore must fall on a part
of the population that is not harvested, and for which decreased
adult density provides little increase in growth potential or
decreased density-dependent mortality. The data therefore sug
gest that fish production in eutrophic lakes is highly dependent
on adult density, while in oligotrophic lakes a much greater
fraction of the annual community energy budget goes toward
reproduction and juvenile mortality.
Can. J. Fish. Aquat. Sci., Vol. 47, 1990
v
2.5 D
.C
(FPrRiIOkDoSUgCNH"o
o
-J
2.0 -
1.5 -
1.0 -
0.5 -
o.o
0.5
1.0
1.5
2.0
2.5
3.0
LOG FISH STANDING BIOMASS (kg-ha"1)
Fig. 3. The relationship between mean annual fish standing stock and
fish community production in lakes. The solid line represents Eq. (2).
Dashed line indicate approximate 95% confidence bands for predicted
values of the mean log1oFP (Prepas 1984).
This interpretation is speculative, however, because the
degree to which Oglesby's catch data reflect actual sustainable
yields is unknown. For example, ignoring the four lowest and
four highest points in Oglesby's data (Fig. 2), representing the
Great Lakes fisheries and Indian and Israeli pisciculture ponds,
renders the two data-sets approximately parallel, offset by about
one log unit, suggesting that fishing yields are about 10% of
net fish community production.
Standing Biomass
Fish production in lakes was found to covary with fish com
munity standing biomass (FB; annual mean kg-ha"1) as:
(2)
log10F/> = -0.42 + 1.084 logloFB
(^ = 0.67; w = 23;/><0.001). These two variables are closely
correlated (Fig. 3) because, in general, production is propor
tional to the product of growth and standing biomass (Plante
and Downing 1989). Fish production in the arctic Char Lake
falls far beneath the general trend, probably because the aver
age annual air temperature near Char Lake is - 16°C. The slope
for log10Ffi close to 1 in Eq. (2) suggests that PIB does not vary
significantly with FB. The annual production to biomass ratio
{FPIFB) ranged from 0.02 in Char Lake to 2.73 in the equa
torial Lake Nakuru (average FPIFB = 0.76; n = 23; 5£=0.15;
median = 0.52).
Total Phosphorus
Fish community production was also correlated with the total
phosphorus concentration (TP\ jxg-L~l) of the water column:
(3)
log,0F/> = 0.332 + 0.531 \o%iQTP
(t2 = 0.67;n= 14;^ = 0.002). The relationship between log, q
and \ogl0TP appears to be non-linear when the very rich Lake
Nakuru is included in the data (Fig. 4). The non-linear relation
is approximated by:
1933
,*-N
O.U
_'
2.5 -
i
-1
(4)
(^ = 0.79; n=14; ;?<0.001), but this relationship is greatly
influenced by the extreme TP concentration in Lake Nakuru.
The close correlation between TP and fish production probably
results from the close correlation of TP with most other com
ponents of lake productivity (Peters 1986) and echos the close
correlation found between long-term fish catch and TP (Hanson
and Leggett 1982). Hanson and Leggett's (1982) model
severely underestimates fish production (Fig. 4), however, but
again it is not known whether this is because these catch data
do not accurately represent sustainable yields or whether sus
tainable yields actually fall an order of magnitude beneath bio
logical fish production (Fig. 4). Equations (3) and (4) may be
of greater practical use in predicting fish production in lakes
D
f
CT 2.0 -
/
/
i—
o
gi.oo
a:
°" 0.5-
than Eq. (1) or (2) because TP is much less expensive to mea
sure than either PP or FB.
^ 0.0-
■■■■■PRODUCTION DATA
PREDICTED FISH PRODUCTION
o
HANSON AND LEGGETT (1982)
o
1
log]0FP = -0.319 + 1.441 logI077> - 0.209 (logWTP)2
-0.50
,
.
,
12
ill
3
LOG ANNUAL MEAN TOTAL P
Fig. 4. The relationship between seasonal average total phosphorus
concentration of the water column and the fish community production
in lakes. The solid curve is predicted from Eq. (4) while the dotted
curve represents predicted yields from Hanson and Leggett's (1982)
Fig. 2. Dashed lines indicate approximate 95% confidence bands for
predicted values of the mean loglo FP (Prepas 1984).
■■■■■ PRODUCTION DATA (THIS PAPER)
PREDICTED FROM RYDER (1965)
O
si
Morphoedaphic Index
We found weak correlations between FP and chlorophyll a
concentrations and watershed area, but no other variables or
combinations of variables that we tried yielded significant cor
relations (Table 3). This was especially surprising for the mor
phoedaphic index, which is reputed to be the best predictor of
fish production in lakes. There was no significant (p>0.05)
correlation between morphoedaphic index and FP (Fig. 5). This
was even true when TDS and 2 were entered separately into a
multiple regression (see Schneider and Haedrich 1989), either
alone or in combination with other likely variables. Predictions
from Ryder's morphoedaphic equation (Ryder 1965; Schneider
and Haedrich 1989) were significantly (p<0.01) beneath, and
uncorrelated (p>0.05) with actual fish production measure
ments (Fig. 5). Ryder's (1965) analysis was based on a more
geographically restricted range of lakes than ours. Ryder (1965)
has suggested that MEI might not be correlated with fish pro
duction if lakes are not all in the north temperate zone, if lakes
at high altitude (>600 m) are considered, or if lakes are not
all large (>260 ha). Excluding all tropical and high altitude
lakes from our data yields an even lower correlation between
log,0MEI and log]0F? (^ = 0.05; n= 14; p = 0.53). Our data
do not, however, cover as large a range of MEI as did Ryder's,
because we lack data on full community fish production in very
deep lakes. All of our MEI values are >1. When Ryder's data
on "moderately to intensively fished" lakes with MEI >1 (all
but very deep or very dilute lakes) are analyzed, no significant
relationship between fish yield and MEI remains (n=13;
o
Q
O
^ = 0.02; /? = 0.82). Although Ryder's (1965) equation pro
or
voked a highly influential sequence of research (e.g. Oglesby
1982), it does not make valid predictions of fish community
production within this range of basin morphometry. The anal
ysis presented here shows that fish production is more closely
correlated with primary production, phosphorus concentration,
and fish standing stock in oligotrophic to hyper-eutrophic lakes
Q_
o
o
0.2
0.4
LOG
0.6
0.8
MORPHOEDAPHIC
1.0
1.2
INDEX
Fig. 5. Relationship between fish community production and the morphoedaphic index of Ryder (1965). Not shown are data for Lake Nakuru. Where measurements of TDS were not available, they were
inferred from conductivity using Rodhe's (1949) table 2. TDS =
- 1.85 + 0.81 C + 0.0002 C2 (R2 = 1.00) where C is the conductivity
in (iS-cm"1 and TDS is the summed concentration (mg-L~') of all
major ions. The broken line is Ryder's (1965; Schneider and Haedrich
1989) equation.
1934
of moderate depth.
Acknowledgments
This research was supported by an operating grant to J.A. Downing
from the Natural Science and Engineering Research Council of Canada
(NSERC), a team grant from the Ministry of Education of the Province
of Quebec, a NSERC fellowship to Mario Henri who performed some
of the initial search for data, and a CAFIR grant from the University
de Montr6al. We are grateful to R. T. Oglesby and two anonymous
referees for suggestions on the manuscript.
Can. J. Fish. Aquat. Sci., Vol. 47, 1990
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