The temperature dependence of
ectotherm consumption
Sven Norman
Student
Degree Thesis in Ecology 60 ECTS
Master’s Level
Report passed: 12 November 2012
Supervisor: Göran Englund
Abstract
The effect of temperature on predator and herbivore consumption is an important factor for
predicting the effects of climate warming on ecosystems. The Metabolic Theory of Ecology
(MTE) describes the temperature dependence of biological and ecological rates and states
that metabolism is the fundamental biological mechanism that governs most observed
patterns in ecology. This statement has been criticized empirically for a number of
organismal traits and systematic deviations have been found. Here, a meta-analysis is
performed on published temperature responses of ectotherm consumption. The mean effect
of temperature on consumption was higher than the mean value predicted by proponents of
the MTE and was highly variable. Some of this variation is explained by habitat type, where
the consumption rates of marine organisms displayed stronger temperature dependence than
for terrestrial and freshwater organisms. The frequency distribution of temperature
dependencies is right skewed for consumption. Here, this skewness is explained by a
methodological artefact as values close to “no effect” are more unlikely to be sampled than
others when fitting the Arrhenius equation. In conclusion, the assumptions of the MTE do
not hold for rates of consumption and marine organisms display a stronger temperature
dependence compared to terrestrial and freshwater organisms.
Key words: Meta-analysis, Ectotherm, Consumption Rate, Temperature, Response Curve.
Introduction
Many physiological and ecological processes are strongly affected by temperature. This is
especially true for ectothermic organisms, as their ability to thermoregulate is more limited
than that of endotherms (Angilletta, 2009, Deutsch et al., 2008). A warmer climate is
therefore expected to have profound effects on the structure and function of ecosystems. A
process of particular importance for our ability to predict such effects is the consumption of
resources by predators and herbivores. The relationship between temperature and most
biological rates, including consumption, are unimodal with a left skew (Huey and Stevenson,
1979, Bulte and Blouin-Demers, 2006, Angilletta et al., 2002). Nevertheless, temperature
responses are by convention described by the Arrhenius equation, which was originally
formulated for the kinetics of chemical reactions; The reaction rate (y) is given by
where k is the Boltzmann constant, T is absolute temperature and E is the activation energy
that determines the strength of the temperature dependence (Cornish-Bowden, 2004). Thus,
the Arrhenius model predicts that biological rates increase exponentially with increasing
temperature.
The Metabolic Theory of Ecology (MTE) uses the Arrhenius equation to link the biology of
individuals to the ecology of populations, communities and ecosystems (Brown et al., 2004).
Proponents of this theory argue that the Arrhenius equation provides an accurate description
of temperature responses at temperatures lower than the optimal temperature. This range is
termed the biologically relevant temperature range (BTR) (Savage et al., 2004). Proponents
of the MTE also argue that there is a Universal Temperature Dependence (UTD) for traits
linked to metabolism such as growth, development and maximal consumption rate.
Specifically, according to the MTE, the activation energy (E) of biological rates should vary
between 0.6 and 0.7 with a mean value of 0.65 (Gillooly et al., 2006, Gillooly et al., 2001,
Brown et al., 2004). This prediction has been heavily criticized on both theoretical and
1
empirical grounds (Clarke, 2004, Clarke and Fraser, 2004, O'Connor et al., 2007, Knies and
Kingsolver, 2010) and several recent studies have found that reported activation energies for
growth and consumption in most cases are outside of the predicted range (Englund et al
2011, Dell et al. 2011). It has also been shown that there are systematic variation in activation
energies depending on latitude, taxonomic groups, the relative mobility of predators and
prey, and the motivation of different behaviours (Nilsson-Ortman et al., 2012, Englund et al.,
2011, Dell et al., 2011, Irlich et al., 2009, Vucic-Pestic et al., 2011). These results suggest that
the UTD may be replaced by more detailed generalizations. Providing an empirical basis for
such generalizations requires that factors influencing the temperature responses of different
biological rates are identified. Here I investigate factors that could potentially influence
relationship between consumption rate and temperature. Consumption rates are often
described by Hollings type II functional response model, which contains two parameters,
attack rate and handling time (i.e. maximum intake rate) (Holling, 1959a, Holling, 1959b).
Attack rate is a measure of per capita prey mortality at low prey densities and maximum
intake rate is limited by the rate of gut evacuation (Jeschke et al., 2002). In a recent metaanalysis of studies providing data on the temperature dependence of functional responses, it
was found that the temperature dependence of attack rate was significantly stronger than
that of maximum intake rate (Englund et al., 2011). However, the difference was small,
suggesting that the much larger literature reporting consumption rates at different
temperatures can be used to search for more detailed generalizations.
In this thesis I examine if the activation energies for consumption are within the range
proposed by the MTE (E = 0.65 ± 0.05), and I test if habitat, functional groups of predators
and prey or predator strategy could account for any of the variation found in activation
energies. Because recent studies have proposed that the distribution of activation energies
are skewed (Dell et al. 2011), I also test whether the distribution of activation energies for
consumption is skewed.
Methods
Literature search
The literature search was conducted with the Web of Science and reference lists of published
papers. 83 studies that reported consumption at different temperatures were found and
included in this study. Some of these reported data for several consumers or different
combinations of consumer and resource yielding a total number of observations of 122. The
studied habitats comprised of marine (N = 35), freshwater (N = 47) and terrestrial (N = 39).
A complete description of the studied consumer/resource taxa, consumer type, and habitat is
listed in fig. 1.
The use of meta-analyses has received some criticism as several studies on the same body
of literature have been shown to differ in their conclusions largely dependent on differences
in the criteria used for selecting studies (Englund et al., 1999, Whittaker, 2010). Therefore, I
used an inclusive approach that allowed for a wide variety of reported consumption to be
included (e.g. rates of consumption, attack, filtration, clearance and intake) as well as
including all studies with at least 2 distinct temperatures and thereby following the
recommendations of Lajeunesse, (2010).
2
Data extraction
Data were extracted either directly from tables or from figures using Datathief (Tummers,
2006). A second order polynomial was fitted to each observation and all points below the
optimum were used to establish the activation energy by fitting this data to the Arrhenius
equation, following Irlich et al. (2009) and Englund et al. (2011).
The slope of the temperature response, when the logged data is plotted as a function of
where k is Boltzmann´s constant given in eV (= 8.617*10-5 eV k-1) and k absolute
temperature, gives the activation energy (E) for each study. Studies that reported data on
both sexes were handled separately and the mean activation energy of the two was used as
one observation. Data on the functional response were first transformed into per capita
consumption and the mean values of consumption from all prey densities were used for
establishing the activation energy.
Unimodal temperature responses
To investigate the entire range of temperature responses I plotted unimodal data on
standardised scales while preserving the shape of the response. This was done by
standardising each response around the mean temperature optimum using Ti,s = Ti – Ti,opt +
Topt, where Ti and Ti,s are vectors containing the observed and rescaled temperatures used in
study i, Ti,opt is the optimal temperature in study i, and Topt is the mean optimal temperature.
To standardise consumption rates I used Yi,s = Yi/Yi,max, where Yi and Yi,s are vectors
containing the observed and standardised rates from study i, and Yi,max is the maximum rate
estimated by fitting a second order polynomial to the data. Thus, I describe the temperature
response in relative units centred on the mean optimal temperature as was done by Englund
et al. (2011).
To evaluate the full temperature response of consumption I fitted a unimodal extension
of the Boltzmann-Arrhenius function to the full temperature range data (Dell et al., 2011,
Johnson and Lewin, 1946):
(
opt
(
))
Where E is activation energy, ED determines the steepness of decline at values above the
temperature optima (Topt) and c is a constant. This model was fitted to all standardised
unimodal observations (N = 34) using nonlinear least-squares regression.
Analysis of mean activation energies
Weighted statistical analyses are widely used in meta-analyses since it allows for the down
weighting of studies with low precision and favours studies with high replication. Weighted
statistical analyses of differences between groups in mean activation energies were done with
a random effects model and the randomisation test provided in Metawin (Rosenberg et al.,
2007). The sample size of each observation was used as weight and the average weight across
groups was given to those observations were no sample size could be extracted (3 % of
observations). Metawin use the inverse of the sample size (1/N) as weight.
Results
The overall mean value of 0.77 eV (± 0.08 CI95%) is significantly different from 0.65 eV (but
not 0.7 eV) that was suggested by the MTE. Furthermore, 86.9 % of the total observations lie
3
outside of the predicted range (0.6-0.7 eV). Some of this variation was explained by habitat
where marine studies had a mean activation energy of 0.93 eV (±0.21 CI95%) compared to
0.74 eV (±0.1 CI95%) and 0.68 eV (±0.11 CI95%) for freshwater and terrestrial studies
(randomisation test, p<0,05) (fig. 1).
The activation energies in figure 2 are normally distributed when plotted with the
excluded negative observations. Furthermore, there are very few observations at -0.2 – 0.2
eV. However, the distribution is significantly right skewed when only the positive
observations are allowed (D'Agostino skewness test: Skew = 2.0876, p<0.01).
The general shape of the temperature response is unimodal where consumption reaches
an optima and falls sharply after that (fig. 3). The overall mean temperature optimum is
22.07 oC ± 1.05 (mean ± SE) and varies with habitat. Terrestrial organisms had a mean
temperature optima of 27.11 oC ± 0.86, marine 18.78 oC ± 1.4 and freshwater 20.97 oC ± 1.92.
Figure 1. Mean activation energies (± CI95%) for the investigated categories. The dotted lines depict the interval
where activation energies should lie (0.6-0.7 eV), suggested by the MTE and the UTD. Significant differences were
found in the category habitat. The values within the parentheses are the sample size of each group. * Brackish is
excluded (N = 1). ** Taxa included are Mite (N = 5), Bryozoa (N = 2), Asteroidea (N = 2), Ciliate (N = 2) and
Tunicate (N = 2). *** Taxa included are Mite (N = 6), Mixed (N = 7) and Algae (N = 3).
4
Figure 2. The distribution of activation energies exhibits a normal distribution when analysed with the excluded
negative observations (from fall section, see fig. 3) (D'Agostino skewness test: Skew = 0.5996, n.s.). When the
negative values are excluded, the histogram shows a clear right skewness (D'Agostino skewness test: Skew =
2.0876, p<0.01). The columns to the left of the striped line are the excluded observations (see methods section for
the inclusion criteria). The total observations are N = 135 (included N = 122, excluded N = 13)
Figure 3. The data points are the standardised values of consumption and absolute temperature from 34 studies
with a unimodal response. The solid line is the fitted unimodal extended Boltzmann-Arrhenius function.
Parameter values are E = 0.97 ± 0.14 and ED = 2.57 ± 0.2 (Mean ± SE). The striped line at Topt delimits the two
sections of the response curve; the rise component and the fall component. The standardisation of consumption
and temperature is described in the methods section. Mean overall temperature optimum is 22.07 oC ± 1.05 (295
K ± 1.05).
Although an intuitive way of describing the temperature response of biological rates, no
differences for the slopes of the habitat groups could be found when fitting the extended
unimodal Boltzmann-Arrhenius function to the data (not shown), possibly because of small
sample size - only about 28 % of the total number of observations was used as no
temperature optima could be found in most observations.
5
Discussion
The data does not support a universal temperature dependence of consumption rate and as
many as 86.9% of the total observations, as well as the mean activation energy of 16 out of 19
groups, lies outside of the range (0.6-0.7) suggested by proponents of the MTE (fig. 1). Other
studies have reached similar conclusions for rates of development and metabolism (Irlich et
al., 2009), for attack rate and maximum intake rate (Englund et al., 2011) and for fitness
curves (Knies & Kingsolver, 2010). This large variation in trait activation energies seems to be
pervading all levels of organization, taxa, habitats and trophic groups as exemplified by Dell
et al. (2011) for a variety of traits. Englund et al. (2011) also showed that an additional source
of variation is that the (log)rate vs. inverse temperature response were concave downwards
rather than linear as would be expected if the true response is exponential. Thus, indicating
that the BTR might not be as exponential as earlier suggested. As it currently stand, the MTE
and the UTD cannot explain the scope of the variation in activation energies. Gillooly et al.
(2001) acknowledge that some of the variation in activation energies may reside in
differences in the ecology between species but the extent of the variation seen for most traits
implies that other mechanisms, other than the relationship between temperature and
metabolism, probably are at play. The assumption of a UTD is fundamental for the MTE and
without it, one have to address issues such as acclimatization and evolutionary adaption
(Clarke and Fraser, 2004). For instance, Nilsson-Ortman et al. (2012) have shown that
damselflies differ in their temperature responses of growth rate at a latitudinal scale. Thus,
indicating adaption to local or regional temperature regimes.
Some of the variation of the temperature response of consumption could be explained by
type of habitat where marine organisms displayed a stronger response than terrestrial and
freshwater organisms (fig. 1). The relatively high mean activation energy of marine organisms
may indicate that they are closer to their Topt making them more vulnerable to climate
warming. However, my data did not provide a sufficient number of unimodal observations to
test this hypothesis as such a test would require measurements of the breadth of the
temperature response as well as estimation of habitat temperatures (see Deutsch et al.,
2008). Thus, the issue of habitat warming and its impact on organisms remains speculative
here but of paramount importance. Therefore, I strongly implore researchers to, when
possible, measure the entire temperature range of trait responses to allow for further studies
of the warming tolerance of organisms. However, it is clear that marine organisms
experiencing elevated temperatures will generally experience a stronger initial increase in
consumption rates.
The distribution of activation energies is right skewed but it is important to keep in mind
that this distribution is based on the rise section of the thermal performance curve (fig. 2, fig.
3). When the negative activation energies are added, the data display a normal distribution.
Dell et al. (2011) propose that their right skewness, observed across all levels of organization,
taxa, habitats and trophic groups, is an indication of some “unexplained biological signal”. It
may very well be so, but one has to be cautious when drawing general conclusions from the
shape of the distribution while assuming that values above Topt are unimportant. I argue that
the biological signal could potentially be explained in the typically left skewed unimodal
shape of thermal performance curves where sampled values of E near 0 eV (at and around
Topt) are unlikely especially since only half (the rise component) of the performance curve is
used when fitting the Boltzmann-Arrhenius model (see fig. 3). Thus, the low number of
6
activation energies found in this study at 0 eV ± 0.2 can potentially be explained by the
typically left skewed shape of the rate-temperature relationship (fig. 2).
Describing the temperature response of biological rates with an exponential model, such
as the Arrhenius equation, presents a couple of problems. First, the notion that the true
response in the “biologically relevant temperature range” is exponential presents a problem
in the definition of the upper limit of the BTR as the response begin to curve downward well
before the response optimum (see fig. 3). This may introduce variation depending on the
location of the measured range on the TPC (Englund et al., 2011). Second, only measuring the
rise component leaves out important information of the response shape and breadth that can
potentially be important for assessing the warming tolerance of organisms, an issue that
surely will affect future ecosystems (see Deutsch et al., 2008). It is important to point out
that the Arrhenius equation may provide a good estimation for species living at their lower
temperature range. However, a unimodal model, such as the extended Boltzmann-Arrhenius
model, would circumvent issues mentioned earlier and is therefore preferable to the
exponential version as it stands.
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8
Appendix
Table 1. Summary of studies included in the analysis. Consumer/resource species and stage is listed when available in the category Consumer (stage) and Resource (stage). Topt
is the temperature of the maximum consumption in each observation, estimated by fitting a second order polynomial to the data. E and E (fall) is the activation energy calculated from
fits to the Boltzmann-Arrhenius model. Each observation is also categorised by habitat. In the category Prey taxon, algae refers to large types or macro algae (e.g. Kelp) whereas
phytoplankton refer to smaller sizes of algae or micro algae (e.g. Diatoms).
Topt
(oC)
E
(eV)
13.94
0.98
Marine
Phytoplankton
2.49
Marine
Anisops deanei
Chaoborus obscuripes (J)
Insect
Insect
0.66
0.2
Freshwater
Freshwater
Predator
Chaoborus obscuripes (J)
Insect
0.67
Freshwater
Artificial
Insect
Filter
feeder
Parasite
1
Study
Aldridge et al. 1995
Consumer (Stage)
Dreissena polymorpha
Taxon
Mollusc
2
Ali 1970
Hiatella arctica
Mollusc
3
Andersen 1986
Salpa fusiformis
Tunicate
4
5
Bailey 1989
Bergman 1987
Ranatra dispar
Gymnocephalus cernuus (A)
Perca fluviatilis (A)
Resource (stage)
Algae
Taxon
Phytoplankton
Phaeodactylum
tricornutum
Various algae
Phytoplankton
Insect
Fish
Type
Filter
feeder
Filter
feeder
Filter
feeder
Predator
Predator
Fish
Mollusc
6
Britz et al. 1997
Haliotis midae
7
Cave & Gaylor 1989
Telenomus reynoldsi
8
9
Chipps 1998
Chiverton 1988
Mysis relicta
Bembidion lampros (A)
Crustacean
Insect
Predator
Predator
Bembidion lampros (A)
Insect
10 Christoffersen 2001
Lepidurus arcticus (A)
11 Cockrell 1984
12 Crisp et al. 1985
E fall
(eV)
-1.45
Habitat
Freshwater
Artificial
18.51
0.35
Marine
Insect
30.02
1.38
Terrestrial
Daphnia pulex
Rhopalosiphum padi (J)
Crustacean
Insect
11.36
0.87
0.7
Freshwater
Terrestrial
Predator
Rhopalosiphum padi (A)
Insect
0.73
Terrestrial
Crustacean
Predator
Daphnia pulex
Crustacean
0.36
Freshwater
Notonecta glauca (A)
Ostrea edulis
Insect
Mollusc
Culex pipiens (J)
Pavlova lutheri
0.82
1.11
Freshwater
Marine
13 Croll & Watts 2004
Procambarus clarkii
Crustacean
Predator
Filter
feeder
Grazer
Artificial feed
Artificial
1.03
Freshwater
14 Dreisig 1981
Procambarus zonangulus
Cicindela hybrida
Crustacean
Insect
Grazer
Predator
Artificial feed
random encounter
Artificial
Insect
0.76
1.46
Freshwater
Terrestrial
Geocoris sp. (Eggs)
1
Insect
Phytoplankton
24.01
33.8
Appendix
Study
15 Eggleston 1990
Consumer (Stage)
Callinectes sapidus (A)
16 Elliot & Leggett 1996
17 Ellrott et al. 2007
E
(eV)
1.37
E fall
(eV)
Type
Predator
Resource (stage)
Crassostrea virginica (J)
Gasterosteus aculeatus
Fish
Predator
Mallotus villosus
Fish
0.08
Marine
Aurelia aurita
Orconectes propinquus
Cnidaria
Crustacean
Predator
Predator
Fish
Fish
0.15
0.72
Marine
Freshwater
Orconectes rusticus
Crustacean
Predator
Mallotus villosus
Salvelinus namaycush ,
(Egg)
Salvelinus namaycush ,
(Egg)
Bemisia tabaci
Chaotocerus muelleri
Fish
1.21
Freshwater
Insect
Phytoplankton
0.94
0.96
Terrestrial
Marine
Tetranychus urticae
Monochrusis lutheri
Mite
Phytoplankton
0.63
2,00
Terrestrial
Marine
18 Enkegaard 1994
19 Enriquez.Ocana et al.
2012
20 Everson 1980
21 Fialamedioni 1978
Encarsia formosa
Crassostrea corteziensis
Insect
Mollusc
Phytoseiulus persimilis
Phallusia mammilata
Mite
Tunicate
22 Flinn & Hagstrum
2002
23 Flinn 1991
Theolax elegans
Insect
Parasite
Filter
feeder
Predator
Filter
feeder
Parasite
Chephalonomia waterstoni
Insect
24 Garton & Stickle 1980 Thais haemostoma
Taxon
Mollusc
Topt
(oC)
Taxon
Crustacean
24,00
26.94
Habitat
Marine
Rhyzopertha dominica
Insect
Terrestrial
Parasite
Cryptocelestes ferrugineus
Insect
0.61
Terrestrial
Mollusc
Predator
Crassostrea virginica
Mollusc
3.52
Marine
Insect
Predator
Musca domestica (J)
Insect
0.8
Terrestrial
Insect
0.85
Terrestrial
0.55
0.28
Freshwater
Terrestrial
25 Geden & Axtell 1988
Carcinops pumilio (A)
Mite
Predator
Musca domestica (J)
26 Gerald 1976
27 Gitonga et al. 2002
Macrocheles muscadomesticae
(A)
Ophiocephalus punctatus
Orius albidipennis
Fish
Insect
Predator
Predator
Orius albidipennis
Insect
Predator
Insect
0.44
Terrestrial
Pseudochironomus richardsoni
(J)
Celethemis fasciata (J)
Insect
Predator
Artificial
Megalurothrips sjostedti
(J)
Megalurothrips sjostedti
(A)
Diatoms
Phytoplankton
1.6
Freshwater
Insect
Predator
Chironomus tentans (J)
Insect
0.64
Freshwater
28 Gresens 2001
29 Gresens et al. 1982
2
Artificial
Insect
26.93
Appendix
Topt
(oC)
14.01
E
(eV)
0.78
20.4
0.92
1.66
0.25
Marine
Marine
Terrestrial
Mite
Insect
0.33
0.39
Terrestrial
Freshwater
Simulium spp. (J)
Oithona davisae
Insect
Crustacean
0.14
Predator
Zooplankton
Crustacean
0.34
Freshwater
Insect
Insect
Insect
Crustacean
Parasite
Parasite
Parasite
Predator
Insect
Insect
Insect
Mollusc
0.21
0.76
0.63
0.6
Terrestrial
Terrestrial
Terrestrial
Marine
Crustacean
Filter
feeder
Predator
Schizaphis graminum
Schizaphis graminum
Schizaphis graminum
Perna perna & Mytilus
galloprovincialis
Chlamydomonas sp.
0.65
Freshwater
Crustacean
1.68
Marine
Phytoplankton
0.31
Marine
Freshwater
Marine
Marine
Freshwater
Freshwater
Study
Consumer (Stage)
30 Handeland et al. 2008 Salmo salar
Taxon
Fish
Type
Predator
Resource (stage)
Pellets
Taxon
Artificial
31 Hanks 1957
Mollusc
Mollusc
Mite
Predator
Predator
Predator
Crassostrea virginica
Mytilus edulis
Panonychus ulmi
Mollusc
Mollusc
Mite
Mite
Insect
Predator
Predator
Panonychus ulmi
Hydropsyche spp. (J)
Insect
Crustacean
Predator
Predator
Fish
37 Jones et al. 2007
38 Kemp & Britz 2008
Aphidius colemani
Lysiphlebus testaceipes
Lysiphlebus testaceipes
Panuliros humaros rubellus
39 Kibby 1971
Daphnia rosea
40 Kishi et al. 2005
Salvelinus malma (J)
41 Kittner & Riisgard
2005
42 Koskela et al. 1997
43 Largen 1967
Mytilus edulis
Mollusc
Salmo salar (J)
Nucella lapillus (A)
Nucella lapillus (A)
Salvelinus alpinus
Fish
Mollusc
Mollusc
Fish
Filter
feeder
Predator
Predator
Predator
Predator
Fish
Predator
32 Hardman & Rogers
1991
Urosalpinx cinerea
Urosalpinx cinerea
Typhlodromis pyri (J1)
Typhlodromis pyri (J2)
33 Heiman & Knight 1975 Acroneuria californica (J)
34 Hooff & Bollens 2004
35 Johnston & Mathias
1994
36 Jones et al. 2003a
44 Larsson & Berglund
1998
Acroneuria californica (J)
Tortanus dextrilobatus (A)
Stizostedion vitreum (J)
Salvelinus alpinus
Fish
Dead Euphasia superba
(A)
Rhodomonas sp.
Pellets
Mytilus edulis (A)
Cirripedia sp.
Neomysis sp.
Pellets
3
Phytoplankton
E fall
(eV)
-0.1
18.51
Artificial
Mollusc
Crustacean
Crustacean
18.07
15.88
0.54
0.93
1.5
1.27
Artificial
15.36
1.32
Habitat
Marine
Freshwater
Marine
Appendix
Study
45 Larsson & Berglund
2005
46 Li et al. 2007
47 Linlokken et al. 2010
48 Lisbjerg & Petersen
2000
49 Lisbjerg & Petersen
2001
50 Liu & et al. 1998
Mite
Insect
27.71
0.77
0.77
Terrestrial
Freshwater
Chironomidae sp.
Rhodomonas sp.
Insect
Phytoplankton
12.48
0.58
1.41
Freshwater
Marine
Rhodomonas sp.
Phytoplankton
0.32
Brackish
Type
Predator
Resource (stage)
Pellets
Taxon
Artificial
Scolothrips takahashii
Perca fluviatilis
Insect
Fish
Predator
Predator
Tetranychus viennensis
Chironomidae sp.
Rutilus rutilus
Electra bellula
Fish
Bryozoa
Electra crustulenta
Bryozoa
Sinniperca chuatsi (J)
Fish
Predator
Filter
feeder
Filter
feeder
Predator
Channa argus (J)
Fish
Predator
Insect
Parasite
Mollusc
Grazer
Insect
Predator
Myzus persicae
Insect
Asteroidea
Predator
Predator
Myzus persicae
Oyster (species not
specified)
Eretmocerus longpipes
52 Lu & Blake 1997
Argopecten irradians
concentricus (J)
Coleomegilla maculata (J)
54 Mackenzi 1970
E
(eV)
1.17
Taxon
Fish
51 Liu & Sengonca 1998
53 Mack & Smilowitz
1982
Topt
(oC)
14.29
Consumer (Stage)
Salvelinus alpinus (J)
Coleomegilla maculata (A)
Asterias forbesi
Misgurnus
anguillicaudatus
Misgurnus
anguillicaudatus
Aleurotuberculatus
takahashi
Isochrysis galbanus
4
E fall
(eV)
Habitat
Freshwater
Fish
35.64
0.47
Freshwater
Fish
29.33
1.17
Freshwater
Insect
26.19
0.74
Terrestrial
Phytoplankton
0.95
Marine
Insect
0.66
Terrestrial
0.48
0.43
Terrestrial
Marine
Insect
Mollusc
15.28
Appendix
Study
55 Mahdian et al.
2006
Topt
(oC)
E
(eV)
0.46
E fall
(eV)
Consumer (Stage)
Picromerus bidens (A)
Taxon
Insect
Type
Predator
Resource (stage)
Spodoptera littoralis (J)
Taxon
Insect
Picromerus bidens (J1)
Insect
Predator
Spodoptera littoralis (J)
Insect
-0.27
Terrestrial
Picromerus bidens (J2)
Insect
Predator
Spodoptera littoralis (J)
Insect
-0.51
Terrestrial
Picromerus bidens (J3)
Insect
Predator
Spodoptera littoralis (J)
Insect
-0.28
Terrestrial
Picromerus bidens (J4)
Insect
Predator
Spodoptera littoralis (J)
Insect
-0.42
Terrestrial
Picromerus bidens (J5)
Insect
Predator
Spodoptera littoralis (J)
Insect
-0.31
Terrestrial
Podisus maculiventris (A)
Insect
Predator
Spodoptera littoralis (J)
Insect
Podisus maculiventris (J1)
Insect
Predator
Spodoptera littoralis (J)
Insect
-0.89
Terrestrial
Podisus maculiventris (J2)
Insect
Predator
Spodoptera littoralis (J)
Insect
-0.62
Terrestrial
Podisus maculiventris (J3)
Insect
Predator
Spodoptera littoralis (J)
Insect
-0.34
Terrestrial
Podisus maculiventris (J4)
Insect
Predator
Spodoptera littoralis (J)
Insect
-0.28
Terrestrial
Podisus maculiventris (J5)
Insect
Predator
Spodoptera littoralis (J)
Insect
-0.34
Terrestrial
Fish
Predator
56 Marchand et al. 2002
Salvelinus fontinalis (J)
57 McCaffrey &
Horsburgh 1986
58 McCoull 1998
59 Menon et al. 2002
Orius insidious
Insect
Predator
Zooplankton (Not specified
further)
Panonychus ulmi
Naucoris congrex (A)
Anisopteromalus calandrae
Insect
Insect
Predator
Parasite
60 Miranda-Baeza et al.
2006
61 Murdoch et al. 1984
Anadara Grandis
Mollusc
Filter
feeder
Predator
Notonecta hoffmani (A)
Insect
Crustacean
0.63
19.46
Habitat
Terrestrial
Terrestrial
Freshwater
Mite
0.67
Terrestrial
Culicidae sp. (J)
Rhyzopertha dominica
Insect
Insect
0.56
1.05
Freshwater
Terrestrial
Particle matter
Mixed
0.71
Marine
Culex pipiens (J)
Insect
1.12
Freshwater
5
26.7
Appendix
Study
62 Nishi et al. 2004
65 Persson 1986
Resource (stage)
Tribolium confusum (J1)
Taxon
Insect
Amphibolus venator (A)
Insect
Predator
Tribolium confusum (J2)
Insect
0.71
Terrestrial
Amphibolus venator (A)
Insect
Predator
Tribolium confusum (A)
Insect
0.56
Terrestrial
Fish
Grazer
Hydrilla verticillata
Plant
1.41
Freshwater
Collops sp.
Insect
Predator
Helicoverpa zea (Egg)
Insect
0.37
Terrestrial
Hippodamia convergens (J)
Insect
Predator
Helicoverpa zea (Egg)
Insect
0.52
Terrestrial
Hippodamia convergens (A)
Insect
Predator
Helicoverpa zea (Egg)
Insect
0.73
Terrestrial
Geocoris sp.
Chrysopidae sp. (J)
Orius insidiosus
Perca fluviatilis (A)
Insect
Insect
Insect
Fish
Predator
Predator
Predator
Predator
Helicoverpa zea (Egg)
Helicoverpa zea (Egg)
Helicoverpa zea (Egg)
Chaoborus obscuripes (J)
Insect
Insect
Insect
Insect
0.64
0.42
0.51
0.45
Terrestrial
Terrestrial
Terrestrial
Freshwater
Fish
Predator
Chaoborus obscuripes (J)
Insect
0.85
Freshwater
Protist
Grazer
Particle matter
Mixed
0.94
Marine
Insect
Insect
Asteroidea
Mollusc
Lymnaea luteola
Lymnaea luteola
Mytilus californianus
Platymonas suecica
Mollusc
Mollusc
Mollusc
Phytoplankton
0.14
0.23
0.79
0.8
Freshwater
Freshwater
Marine
Marine
Platymonas suecica
Phytoplankton
0.49
Marine
Crustacean
Mite
Predator
Predator
Predator
Filter
feeder
Filter
feeder
Predator
Predator
Calanus nauplii
Tetranychus urticae
Crustacean
Mite
26.7
1.22
Marine
Terrestrial
Insect
Predator
Nilaparvata lugens
Insect
28.4
0.79
Terrestrial
66 Rassoulzadegan 1982
Lohmanniella spiralis
67 Roy & Raut 1994
Sphaerodema annulatum
Sphaerodema rusticum
Pisaster ochraceus
Mytilus edulis
Mytilus modiolus
70 Sell et al. 2001
Metridia lucens
71 Skirvin & Fenlon 2003 Phytoseiulus persimilis
72 Song & Heong 1997
E fall
(eV)
Type
Predator
Rutilis rutilus (A)
68 Sanford 1999
69 Schulte 1975
E
(eV)
0.63
Taxon
Insect
63 Osborne & Riddle 1999 Ctenopharyngodon idella
64 Parajulee et al. 2006
Topt
(oC)
Consumer (Stage)
Amphibolus venator (A)
Cyrtorhinus lividipennis
Mollusc
6
16.53
-0.73
Habitat
Terrestrial
Appendix
Study
73 Specziar 2002
Taxon
Fish
Fish
Fish
Fish
Fish
Insect
Mollusc
Topt
(oC)
E
(eV)
0.17
0.81
0.68
0.82
1.12
0.71
0.41
E fall
(eV)
74 Spitze 1985
75 Sylvester et al. 2005
Consumer (Stage)
Abramis brama
Blicca bjoerkna
Rutilis rutilus
Carassius auratus gibelio
Cyprinus carpio
Chaoborus americanus
Limnoperna furtonei
76 Taylor & Collie 2003
Crangon septemspinosa
Crustacean
Type
Predator
Predator
Predator
Predator
Predator
Predator
Filter
feeder
Predator
Resource (stage)
Mixed
Mixed
Mixed
Mixed
Mixed
Daphnia pulex
Chlorella vulgaris
Taxon
Mixed
Mixed
Mixed
Mixed
Mixed
Crustacean
Phytoplankton
77 Thomas et al. 2000
Jasus Edwardsii
Crustacean
Predator
Pseudopleuronectes
americanus
Mytilus edulis & pellets
Fish
1.27
Marine
Mixed
0.27
Marine
78 Thompson 1978
Ischnura elegans elegans (J)
Insect
Predator
Daphnia magna (A)
Crustacean
0.69
Freshwater
79 Turker et al. 2003
Oreochromis lioticus
Fish
Green algae
Phytoplankton
0.61
Freshwater
Oreochromis lioticus
Fish
Cyanobacteria
Phytoplankton
0.56
Freshwater
80 Wang & Ferro 1998
Trichogramma ostriniae
Insect
Filter
feeder
Filter
feeder
Parasite
81 Watts et al. 2011
82 Verity 1985
Lytechinus variegatus
Tintinnopsis acuminata
Tintinnopsis vasculum
Orconectes eupunctus
Echinoid
Ciliate
Ciliate
Crustacean
Grazer
Grazer
Grazer
Predator
Artificial
Isochrysis galbanus
Dicraterie incornata
Chironomus sp.
84 Wyban et al. 1995
Orconectes hylas
Orconectes vinlis
Orconectes luteus
Orconectes punctimanus
Penaeus vannamei
Crustacean
Crustacean
Crustacean
Crustacean
Crustacean
Chironomus sp.
Chironomus sp.
Chironomus sp.
Chironomus sp.
Artificial feed
85 Xia et al. 2003
86 Yee & Murray 2004
Cocinella Septempunctata
Tegula aureotincta
Insect
Mollusc
Predator
Predator
Predator
Predator
Filter
feeder
Predator
Grazer
Tegula brunnea
Mollusc
Tegula funebralis
Mollusc
83 Whitledge & Rabeni
2002
Ostrinia nubilalis
Habitat
Freshwater
Freshwater
Freshwater
Freshwater
Freshwater
Freshwater
Freshwater
Insect
23,00
1.86
Terrestrial
Artificial
Phytoplankton
Phytoplankton
Insect
23.13
0.51
0.49
0.52
1.78
Marine
Marine
Marine
Freshwater
0.62
0.93
0.45
2.66
0.78
Freshwater
Freshwater
Freshwater
Freshwater
Marine
0.58
0.8
Terrestrial
Marine
Insect
Insect
Insect
Insect
Artificial
25,00
22.9
Aphis gossypii
Kelp (species not specified)
Insect
Algae
Grazer
Kelp (species not specified)
Algae
17.92
0.57
Marine
Grazer
Kelp (species not specified)
Algae
19.03
0.56
Marine
7
Appendix
Study
87 Zamani et al.
2006
Consumer (Stage)
Aphidius colemani
Aphidius matricariae
Taxon
Insect
Insect
Type
Parasite
Parasite
Resource (stage)
Aphis gossypii
Aphis gossypii
8
Taxon
Insect
Insect
Topt
(oC)
25.75
E
(eV)
0.29
0.23
E fall
(eV)
Habitat
Terrestrial
Terrestrial
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