Ecography 30: 749758, 2007
doi: 10.1111/j.2007.0906-7590.05259.x
# 2007 The Authors. Journal compilation # 2007 Ecography
Subject Editor: Helmut Hillebrand. Accepted 3 September 2007
Energy input and zooplankton species richness
Dag O. Hessen, Vegar Bakkestuen and Bjørn Walseng
D. O. Hessen (dag.hessen@bio.uio.no), Dept of Biology, CEES, Univ. of Oslo, P.O. Box 1066 Blindern, NO-0316 Oslo, Norway.
V. Bakkestuen, Natural History Museum, Dept of Botany, Univ. of Oslo, P.O. Box 1172 Blindern, NO-0318 Oslo, Norway, and
Norwegian Inst. for Nature Research, P.O. Box 736 Sentrum, NO-0105, Oslo, Norway. B. Walseng, Norwegian Inst. for Nature
Research, Gaustadalléen 21, NO-0349 Oslo, Norway.
What are the relative contribution of temperature and solar irradiance as types of energy deliveries for species
richness at the ecosystem level? In order to reveal this question in lake ecosystems, we assessed zooplankton
species richness in 1891 Norwegian lakes covering a wide range in latitude, altitude, and lake area. Geographical
variables could largely be replaced by temperature-related variables, e.g. annual monthly maximum temperature
or growth season. Multivariate analysis (PCA) revealed that not only maximum monthly temperature, but also
energy input in terms of solar radiation were closely associated with species richness. This was confirmed by
stepwise, linear regression analysis in which lake area was also found to be significant. We tested the predictive
power of the ‘‘metabolic scaling laws’’ for species richness by regressing Ln of species richness over the inverse of
the air temperature (in Kelvin), corrected for the activation energy (eV) as predicted by the Boltzmann constant.
A significant, negative slope of 0.78 for ln richness over temperature, given as 1/kT, was found, thus slightly
higher than the range of slopes predicted from the scaling law (0.600.70).
Temperature basically constrained the upper bound of species number, but it was only a modest predictor of
actual richness. Both PCA-analysis and linear regression models left a large unexplained variance probably due to
lake-specific properties such as catchment influence, lake productivity, food-web structure, immigration
constraints or more stochastic effects.
A large number of studies have clearly demonstrated
that ‘‘energy’’ input in one form or another is vital for
species diversity (Currie 1991). This is most clearly
demonstrated by the observation of decreasing species
diversity with decreased temperature along latitudinal
or altitudinal gradient (Gaston 2000). The actual role
of geographic or topographic variables is not straightforward, however, since these spatial variables often covary with ecosystem age (in previously glaciated areas),
with colonization constraints (e.g. presence of mountain ranges), with productivity, and not the least with
temperature.
Energy input to ecosystems includes different entities that often (but not always) co-vary. For example,
solar energy generates heat so that temperature generally
correlates well with photon flux density along a
latitudinal gradient. On the other hand, while solar
input increases with altitude, temperature tends to
decrease. Hence along altitudinal gradients, the effect
of solar input and temperature could be separated.
Temperature is important for almost all biological
processes, from enzyme kinetics to growth rate. However, the role of temperature is somewhat ancillary; on
the other hand, solar input is a direct driver for
photosynthetic C-fixation and thus related to the energy
input in terms of availability of organic matter.
Nutrient availability and concentrations, along with
solar radiation (and water for terrestrial systems), thus
determine the production of organic matter at the base
of the food web, and this organic matter serves as an
overall energy source and potential determinant of
consumer diversity. Thus, the effect of solar radiation,
per se, in promoting diversity is closely linked with the
availability of other limiting factors for autotrophs.
In the search for general determinants of processes
and patterns across ecosystems, metabolic scaling laws
749
have been advocated as a mechanistic and universal
explanation for a wide range of biological patterns from metabolic activity in individuals, to population
dynamics and richness (Gillooly et al. 2002, Brown
et al. 2004). By regressing ln of species richness over the
inverse of the temperature (in units of Kelvin and
corrected for the activation energy, eV, predicted by the
Boltzmann constant) a range of biological parameters,
including diversity, fall close to the predicted slopes of
0.60.7 (Gillooly et al. 2002, Brown et al. 2004, Savage
et al. 2004). This relationship is accounted for partly by
the fact that metabolism scales with temperature, and
partly by the idea that evolutionary rates also could
be attributed to metabolic scaling laws (Allen
et al. 2002, Gillooly et al. 2005).
In order to reveal the relative contribution of
temperature and solar irradiance as types of energy
deliveries at the ecosystem level, we have here utilized
a unique and distinctive data set of crustacean, zooplankton diversity from a large number of lakes. For
assessment of diversity patterns within ecosystems, lakes
have the advantage of fixed boundaries with restricted
immigration and emigration (yet there may be some
input and losses from inputs and outlets); homogenous
habitats (relative to most terrestrial ecosystems); and
importantly, there is no water deficiency, in contrast
with terrestrial systems where evapotranspiration generally is a stronger predictor of diversity than temperature alone (Kerr and Packer 1999).
Material and methods
This study is based on samples of microcrustaceans
from 1891 locations (Fig. 1) encompassing the entire
mainland of Norway (58.371.48N). Thus, it spanned
a wide range in altitude, latitude, longitude, temperature regime, growing season, solar radiation, water
properties (conductivity and pH), and estimated ecosystem age (Table 1). All lakes had inlets and outlets,
i.e. no true seepage lakes were included, and also
systems with high flushing rate were avoided.
The lakes were sampled for both pelagic and littoral
species, which is important since, in general, species
with littoral preference make up 60 to 70% of total
microcrustacean species number (Walseng et al. 2006).
1163 of the lakes were sampled for both pelagic and
littoral species and 728 lakes were only sampled for
littoral species. The inclusion of the latter dataset is
justified by the fact that nearly all plankton species
within a lake is represented in the littoral samples
(Walseng et al. 2006). PCA analyses run both with and
without this littoral dataset produced nearly identical
results. The lakes were sampled once or twice during
summer or early autumn. For lakes sampled over long
time spans, cumulative species number may be almost
750
50% higher than that of single samples (Arnott et al.
1998). Despite this, such cumulative numbers may not
give a good representation of the mean diversity, since
many of the contributors to the cumulative richness are
occasional visitors and not representative of resident
populations. Shurin et al. (2007) demonstrated that
daily zooplankton richness was linearly related to
annual richness and inter-annual richness in a survey
of 36 lakes over several years. Hence we believe that our
sampling regime should give a fair representation of
‘‘actual’’ species richness.
For zooplankton sampling, a net haul (27.530 cm
diameter, 90 mm mesh size) was taken from the deepest
part of the lake. This method ensures a high number
of individuals and an almost complete species list. The
littoral species were sampled by a net haul horizontally
at low speed (ca 25 m min1), both outside and inside
vegetation stands. A smaller (10 cm diameter, 90 mm)
net was used in a few cases where littoral vegetation was
too dense to allow use of the regular net.
All crustaceans were identified to species (rotifers
were not included in this survey). Cladoceran species
were identified in accordance with Flössner (2000),
and copepods were identified after Kiefer (1978).
The taxonomic affinities remain vague for some of
the cladoceran species. This holds especially for the
Daphnia group (Schwenk et al. 2004, Hobaek 2005),
and there is no doubt that further genetic screening
will reveal taxonomic revisions both for the daphnids as
well as for other groups. However, these somewhat
unclear taxonomic affinities for some species would
not have a major consequence for species richness per se
in this large data set.
Previous studies on another, independent, and
smaller data set strongly suggested that lake productivity
in terms of phosphorus concentration and algal biomass
was an important contributor to zooplankton richness
in lakes (Hessen et al. 2006). In the work we are
presenting here there are no data on lake nutrients;
instead, as variables for assessing ambient drivers of
zooplankton diversity, we include altitude, latitude,
and longitude, as well as lake area, maximum summer
air temperature, growth season, estimated potential
solar irradiance, conductivity, pH, and estimated age
(from ice-age data and land elevation). Furthermore, we
tested the observed richness against metabolic scaling
predictions.
We obtained terrain data (100 m resolution digital
elevation model DEM) from the National Map
Authorities; raster climatic data with 1-km resolution
based on the 19601990 normal compiled by the
Meteorological Inst. (Tveito et al. 2000); length of
growth season and growing degree days compiled by
the national Meteorological Inst. (Skaugen and Tveito
2002); and potential solar irradiance maps also produced from data from the Meteorological Inst.
Fig. 1. Temperature (air) distribution map of Norway, with sampled locations categorized as species-poor (B10 species, white),
medium species richness (1020, grey) and speciesrich (black).
Ecosystem age was estimated by combining land
upheaval data and Holocene deglaciation maps showing
time since deglaciation in different parts of Norway.
This gave 7 classes of estimated age. While the majority
of lakes range from 9000 to 10 000 yr, there also were
45 coastal lakes with an estimated age of 16 000 yr, i.e.
probably not permanently frozen during the last
glaciation, and there were 45 lakes of 8000 yr, and 47
of 6000 yr estimated age. Six lakes belonged to a small
group of very young, coastal lakes (estimated age 3000
yr). For the south-eastern subset of lakes, almost all
lakes were from 9000 to 10 000 yr of estimated age.
Some lakes in the southern regions were acidified
due to high deposition of long-range transported
sulphuric acids and NOx plus NH3 that may strongly
affect both fish and aquatic invertebrates. Hence, lakes
with pH B5.0 were omitted from the final analysis, as
were a few lakes with very high salinity due to marine
influence. This gave a total of 1574 lakes.
Each water polygon from the lake-zooplankton
database was superimposed on the grids from the
spatial database layers, and the corresponding mean
value aggregated over the whole lake was extracted to
the polygons. The DEM is based on interpolated data
from the contour lines of 20-m resolution from the
national map series N50. Annual monthly average
temperature data for May, June, July, August, and
September were extracted, and the warmest month for
751
Table 1. Major parameters for the surveyed lakes (n1574).
Age is estimated age since retreat of ice, max temp is
interpolated mean air temperature for the month with highest
average temperature (generally July) at the lake site.
Lake area (km2)
Altitude (m)
Latitude (8N)
Longitude (8E)
Age (yr)
Max temp (8C)
pH
Cond (mS m1)
Max
Min
Median
Mean
365
1544
71
31
16000
16.5
9.9
100
0.01
1
58
5
3000
4.5
5.0
0.4
0.19
308
62
11
9000
11.1
6.5
2.5
1.35
437
64
12
9400
11.2
6.4
4.1
each lake was selected to create the ‘‘max temperature’’
variable. Growth season is defined as number of days
with average temperature above 58C, and growing
degree-days is defined as the sum of average temperature in those days where average temperature was above
58C (Tveito et al. 2001). The potential summer sun
radiation map was rasterized from vector format with a
scale of 1:7 000 000 (unpubl.).
Principal component analysis was performed on the
set of 13 predictor variables following ter Braak and
Prentice (1988) using CANOCO v. 4.5 (ter Braak
and Smilauer 2002). PCA was run on a correlation
matrix of centred, standardized, and transformed
variables using correlation biplot scaling of the PCA
axes. Species richness variables were added by passive
ordination to identify possible relationships between
diversity and lake characteristics. Skewness and kurtosis
were standardized for all predictor variables by dividing
by their expected deviations, estimated as (6/n) 0.5
(Sokal and Rohlf 1995). Homogeneity of variance
(homoscedasticity) was achieved by transforming all
variables to zero skewness. Three transformations were
applied (Økland et al. 2001, Hessen et al. 2006):
y?kj eck xkj
(1)
y?kj ln(ck xkj )
(2)
y?kj ln(ck )ln(ck xkj )
(3)
where xkj is the original value of variable k in plot j and
ck is a variable specific parameter that gives the
transformed variable Y? {ykj? } zero skewness. Equation
(1) was applied to left-skewed variables (standardised
skewness B0), eq. (2) next equation to right-skewed
variables. Equation (3) was applied to right-skewed for
which no ck could be found eq. (2) that resulted in
standardised skewness 0. After transformation, all
variables Y? were ranged to obtain new variables Y
{ykj} on a 01 scale:
ykj [y?kj min(y?kj )]=[max(y?kj )min(y?kj )]
(4)
We also performed a stepwise, multiple linear
analysis including the same parameters as those for
752
the PCA-analysis except that growing degree days and
growth season were omitted due to their close correlation with maximum monthly temperature. We also
performed separate least square linear regression analysis for species richness versus maximum monthly
temperature and lake area. By treating irradiation as a
nominal variable, we likewise did a two-way Anova to
judge the relative contribution from temperature and
irradiation. Tests for species richness across the nominal
parameters, solar irradiation and ecosystem age, were
performed by Tukey-Kramer HSD test. All statistical analyses were performed with JMP 5.1 (SAS
Inst., Cary, NC).
Results
Because our samples were from lakes throughout the
entire mainland of Norway (Fig. 1), our dataset covered
a wide climatic range. There was a strong gradient in
maximum monthly temperatures, ranging from 5 to
nearly 178C. Maximum monthly temperature was
closely correlated with altitude, but less so with latitude
(Fig. 2a and b) due to the highly variable effect of
oceanic climate. Furthermore, maximum monthly
summer temperature does not necessarily reflect actual
growing season due to variable snow and ice cover and
variable influence of mild coastal climate for coastal
localities relative to the more continental climate in
eastern regions. Nevertheless, there was a fairly good
correlation between these two climate proxies (Fig. 2c),
justifying the use of the latter parameter in the stepwise
linear models.
Total number of species varied from 2 to 47 per
location typically with species-rich lakes in the
southern and eastern regions, and species-poor lakes
in northern and alpine areas (Fig. 1). The median
number of species was 12, and 71% of the species were
cladocerans. The fraction constituted by the cladocerans
was fairly constant and did not vary with temperature
or with lake area.
Lake area did not co-vary with latitude (p 0.29) or
altitude (Fig. 3a and b); and although there was a
significant correlation between Log area and altitude
due to high number of samples, r2 was no more than
0.01. Neither did lake area co-vary with temperate
(Fig. 3c); even though there was a slightly positive
correlation (p 0.02), r2 was only 0.006. Hence, these
parameters should be regarded as independent.
The PCA axes accounted for 42.4% of observed
variance in environmental variables (Fig. 4). Maximum
summer temperature, growth season (GS), and growing
degree days (GDD) gave high positive loadings on
PCA1; while altitude, longitude, and latitude were
negatively correlated with this axis. Consequently,
PCA1 can be interpreted as a temperature gradient
(a)
LogArea, km2
Max temp, °C
(a)
Latitude, °N
(b)
LogArea, km2
Max temp, °C
(b)
Latitude, °N
(c)
Max temperature °C
Max temp, °C
(c)
LogArea, km2
Fig. 2. Relationship between maximum monthly temperature
and altitude (a), latitude (b), and growing season (c).
Fig. 3. Scatter plots of lake area versus latitude (a), lake areas
versus altitude (b), and monthly maximum temperature over
lake area (c). Least square linear regressions are given.
that was inversely related to altitude and latitude of the
lake. Conductivity and pH, together with latitude and
longitude, were correlated with PCA2, and reflects
more dilute waters at high altitudes. When species
richness variables were added passively to the PCA
ordination, zooplankton diversity appeared to be
strongly related to PCA1, the main gradient of
temperature influence. Both temperature and irradiation thus gave independent, positive contributions to
zooplankton richness. Lake area only contributed
marginally to zooplankton richness as judged from
the PCA analysis.
By stepwise removal, the multiple linear regression
model explained 35% of the observed variability with
753
Fig. 4. PCA plot with all major variables entered (see text for further explanation).
log lake area and maximum temperature as the two
major contributors, and altitude, longitude and radiation as significant, but minor contributors (Table 2).
When regressing species richness versus log lake area
and maximum temperature, there was a pronounced
scatter for both parameters (r2 0.06 and 0.21 respectively, Fig. 5), yet both regressions were significantly
positive (p B0.0001). While temperature set the
upper bound for richness, it still was a poor predictor
of actual richness for any given temperature. This
means that while the maximum number of species
decreased with decreasing temperature, the full range
of species richness, from very low to very high, could be
found in lakes at higher temperatures. This points to
other determinants of richness besides temperature. Ln
species richness (LnR) plotted against 1/kT (Fig. 6) was
highly significant (LnR 34.2 to 0.78 1/kT, F-ratio
383, pB0.0001), but with a pronounced scatter, (R2 0.20).
When testing species richness for the various age
classes, the only significant difference between the age
Table 2. Stepwise multiple linear regression analysis for total
species richness. Full model predictions: r2: 0.35, DF: 5, SSQ:
30561, F-ratio: 163.
Source
SSQ
F-ratio
Prob(F)
log10 Area
MaxTemp
Altitude
Longitude
Radiation
5317
5252
2220
684
334
141.8
140.1
59.2
18.2
8.9
B0.0001
B0.0001
B0.0001
B0.0001
0.0029
754
categories was that the oldest lakes had significantly
lower species richness than the others (p B0.05, TukeyKramer HSD-test). But there were only a few of these
oldest locations and they were all from northern, coastal
regions. For theoretical solar radiation, we found the
highest richness for highest solar inputs (p B0.05,
Tukey-Kramer HSD-test).
Discussion
Our analysis strongly suggested that temperature per se
or temperature-related parameters were the major
determinants of richness. Lake area appeared as a
modest predictor of richness as judged from the PCAanalysis and the linear regression analysis (Fig. 5). This
is in support of other national surveys: Hessen et al.
2006, Walseng et al. 2006, but contrasts with the
general findings from other studies on richnessarea
relations (cf. Gaston 2000), including studies on
zooplankton (Browne 1981, Dodson 1992). As a
covariate in the multiple regression model, lake area
became a highly significant predictor, however, as it did
at more local, climatically homogenous scales (Hessen
et al. unpubl). This could suggest that at a sufficiently
large and climatically heterogeneous scale, temperature
and dispersal constraints could be superimposed on
lake area. The fact that both altitude and longitude
contributed significantly to observed species richness
likewise points to dispersal constraints across mountain
ridges. It should be noted, however, that even if this
MaxTemp, °C
Fig. 6. Scatter plot of species richness over the inverse of
temperature as 1/kT (see text for explanation).
Fig. 5. Scatter plots of species richness over maximum
monthly temperature and log lake area.
study covers a rather wide altitudinal and latitudinal
range, is is still locally constrained compared with
global analysis like those of Gaston (2000).
While temperature was a major predictor of richness,
there still was a wide scatter in the richness-versustemperature plot, and temperature explained no more
than 21% of observed species richness. The slope of ln
species richness was 0.78, and thus fairly close to
the range of slopes (0.600.70) predicted from the
metabolic scaling law for species richness (Allen
et al. 2002, Brown et al. 2004). Therefore our data
does not contradict the general predictions of metabolic
scaling, but again the huge scatter strongly suggests that
other parameters are superimposed on the temperature
effects.
We here report maximum air and not lake temperatures. Given the close correspondence between the
monthly max temperature, growth season, and growing
degree-days, we assume that air temperature should serve
as a good proxy of epilimnetic summer temperatures
(cf. Saloranta and Andersen 2007). On an areal basis,
there should be no a prior differences in surface water
temperatures, since the specific heat input would not be
expected to differ substantially. There are very few truly
shallow lakes (B5 m) in this survey, but lake volume and
water mixing regimes could induce deviations between
air and water temperatures. Hence, the temperature
predictions should be judged with some caution. Nevertheless, the observations that temperature constrains
the upper bound of species number, but at best is a poor
predictor of actual richness seem robust.
Solar radiation also provides an energy input, which
heats surface waters and promotes carbon fixation. In
our data, temperature (or growing season) and solar
radiation showed different spatial variation, allowing
for a separate assessment of their relative contributions
to zooplankton richness. The statistical analysis did
suggest a positive effect of direct solar radiation per se.
This effect of radiation could partly be a product of
direct heating, but may also promote primary production by increasing the euphotic zone. The solar input
peaks at high elevation sites in southern areas, but this
could be offset by low air temperatures and high
levels of detrimental UV-radiation at high altitudes
(cf. Hessen 2006). On the one hand, high light
intensities promote C-fixation, but it could also lead
to reduced food quality for zooplankton by increasing
the light: nutrient ratio (cf. Urabe and Sterner 1996) in
these nutrient-poor lakes. The net effect of radiation
could thus be subtle, but judged from this study it
seems to promote zooplankton richness.
Ecosystem age did not have any strong effect on
richness. This may be explained in three ways. First,
simply the majority of the lakes were of a rather
uniform age. Second, it may be that, for those very few
‘‘relict’’ lakes (situated in regions that were probably not
covered by the latest glaciation), extended periods of ice
755
cover and extreme cold during glacial times did not
allow support of a permanent zooplankton population.
Thirdly, a time-span of a few thousand years may not
be a major determinant of richness unless dispersal rates
were strongly constrained. The generally low species
richness in coastal areas of central and southern lakes, in
spite of mild winters and a favourable climate, could be
interpreted as an effect of migration constraint (Hessen
et al. 2006). On the other hand, apparent species
turnover rates in Norwegian lakes are fairly high, with
an estimated resident species pool of B50% of the
asymptotic species pool, i.e. in any given year no more
than 50% of the cumulative species number over a
10 yr period was found (Hessen et al. unpubl).
Colonization of species seems to occur within a decade
(Arnott et al. 1998, Shurin 2000, Shurin et al. 2000,
Havel and Shurin 2004). There is no consensus,
however, on dispersal abilities or on how much
dispersal has been achieved by zooplankton at different
regional scales (Bohanak and Jenkins 2003). The
general assumption of high dispersal abilities is also
countered by studies on local genetic affinities that
suggest rather limited gene flow at the metapopulation
scale (De Meester et al. 2002). This somewhat paradoxical situation could be caused either by a lack of
colonization abilities by newcomers that might be
competitively inferior relative to the locally adapted
population (i.e. the monopolization hypothesis by De
Meester et al. 2002), or by a fast genotypic adaptation
to the new environment (cf. Hairson 1996). On a
regional scale similar to the most constrained data set, it
is thus very unlikely that richness reflects dispersal
constraints. Hence, at least on regional scales corresponding to the smaller of the two subsets, species
richness would most likely be in equilibrium and thus
reflect biotic and abiotic properties of the given
locations. But on scales of tens to thousands of
kilometres, dispersal constraints are more likely (Havel
and Shurin 2004). This could explain why, in this data
set, southern and eastern immigrants were poorly
represented in the richness measures of the northern
and western lakes.
The ultimate reasons for the positive effects of
temperature are not straightforward. Temperature as a
driver for species richness in an evolutionary sense can
be ruled out in this context none of the species were
unique to Norway, and the time span since last
glaciation is insufficient for species radiation. Many of
these lakes have prevailing low temperatures, and
temperatures B108C are not uncommon in alpine
areas. Hence, physiological constraints do probably play
a role with regard to metabolic processes and growth
that scales with temperature. Yet the ultimate link to
species richness must be that low temperatures exclude a
number of species that may (and probably do) spread
to, but not colonize these low-temperature habitats.
756
Many species will also have problems with completing
their life cycles at low temperatures. Low temperature
could also slow down productivity among autotrophs.
Phytoplankton in cold lakes have been demonstrated to
have low photosynthetic activity per mole of photons
absorbed, which may reflect a slowing of enzymatic
activity (Markager et al. 1999, Flanagan et al. 2003);
and there is also a tendency for lower chlorophyll:
phosphorus ratios in alpine lakes (Hessen et al. 2005).
A large variance remained unexplained in this
analysis, suggesting that there might be stochastic
processes as well as water quality parameters or biotic
interactions that play major roles for zooplankton
species richness. Since each location is represented by
only one year of sampling, and there might be fairly
large inter-annual variability in species numbers, this is
likely one source of unresolved scatter. In addition, lake
productivity and fish predation could play a major role,
not only for community composition, but also for
biodiversity. A previous survey of a smaller and
different set of Norwegian lakes revealed that zooplankton species richness exhibited a strong positive response
to both productivity (concentrations of phosphorus or
phytoplankton biomass) and fish diversity (Hessen et al.
2006). Unfortunately, data on lake productivity, phosphorus or chlorophyll was not available for the present
dataset. A number of studies have pointed to a positive,
linear correlation between species richness and productivity, yet a hump-shaped curve is often invoked when
highly productive systems are included (cf. Gaston
2000, Mittelbach et al. 2001, Chase and Ryberg 2004).
Metabolic processes may scale different to ambient
temperature than to energy in terms of food. Given the
fast heat exchange in tiny animals in the aquatic
medium, ambient temperature would have a large
influence on temperature-dependent metabolic processes. But these processes also depend on food quantity
and food quality. Since metabolic rate is equal to the
respiration rate in heterotrophs (cf. Brown et al. 2004),
food quantity will affect metabolism first and foremost
via Specific Dynamic Action (SDA), i.e. the energetic
costs of processing food that is seen as an elevation of
respiration rate from basal metabolism during feeding
(Jensen and Hessen 2007). In addition, food quality in
terms of elemental composition could affect metabolic
rates. Animals, including zooplankton, when feeding
on P-deficient food, may dispose of ‘‘excess C’’ by
increasing their respiratory outputs (Darchambeau et al.
2003, Anderson et al. 2005). Thus both food quantity
and stoichiometric quality (C:P-ratio) may affect metabolic and richness responses due to temperature (Brown
et al. 2004, Jeyasingh 2007). A full evaluation of the
impact of various forms of energy input to zooplankton
richness would require combined data on temperature
(preferably integrated water temperature over the
growing season), solar radiation and potential food
supply in terms of edible seston quantity and quality.
Nevertheless, this study does suggest temperature as a
major determinant of zooplankton species richness.
While increased temperatures in general will promote increased species richness, the wide scatter in
species richness over the temperature gradient means
that predictions of effects from climate change can
hardly be made with high accuracy. It is also apparent
that global warming will not only affect summer
temperatures and extend the growth season; it will
also affect water quality in various ways, including
nutrient loading, and may thus affect both thermal
energy as well as organic energy supply.
Acknowledgements This paper is based both on a number of
published reports and unpublished data and we want to thank
all those who have contributed with data to this study. We
also thank Bror Jonsson for most helpful comments to the
manuscript. The study has received financial support from
CAS (Centre for Advanced Study), Global Diversity Information Facility Norway (Bwww.gbif.org ) and NINA (Norwegian Inst. for Nature Research).
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