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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
1
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
2
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 meta-
analysis 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).
3
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
4
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).
5
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.
6
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
7
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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Appendix
Study Consumer (Stage) Taxon Type Resource (stage) Taxon
Topt
(oC)
E
(eV)
E fall
(eV) Habitat
1 Aldridge et al. 1995 Dreissena polymorpha Mollusc Filter
feeder
Algae Phytoplankton -1.45 Freshwater
2 Ali 1970 Hiatella arctica Mollusc Filter
feeder
Phaeodactylum
tricornutum
Phytoplankton 13.94 0.98 Marine
3 Andersen 1986 Salpa fusiformis Tunicate Filter
feeder
Various algae Phytoplankton 2.49 Marine
4 Bailey 1989 Ranatra dispar Insect Predator Anisops deanei Insect 0.66 Freshwater
5 Bergman 1987 Gymnocephalus cernuus (A) Fish Predator Chaoborus obscuripes (J) Insect 0.2 Freshwater
Perca fluviatilis (A) Fish Predator Chaoborus obscuripes (J) Insect 0.67 Freshwater
6 Britz et al. 1997 Haliotis midae Mollusc Filter
feeder
Artificial Artificial 18.51 0.35 Marine
7 Cave & Gaylor 1989 Telenomus reynoldsi Insect Parasite Geocoris sp. (Eggs) Insect 30.02 1.38 Terrestrial
8 Chipps 1998 Mysis relicta Crustacean Predator Daphnia pulex Crustacean 11.36 0.87 Freshwater
9 Chiverton 1988 Bembidion lampros (A) Insect Predator Rhopalosiphum padi (J) Insect 0.7 Terrestrial
Bembidion lampros (A) Insect Predator Rhopalosiphum padi (A) Insect 0.73 Terrestrial
10 Christoffersen 2001 Lepidurus arcticus (A) Crustacean Predator Daphnia pulex Crustacean 0.36 Freshwater
11 Cockrell 1984 Notonecta glauca (A) Insect Predator Culex pipiens (J) Insect 0.82 Freshwater
12 Crisp et al. 1985 Ostrea edulis Mollusc Filter
feeder
Pavlova lutheri Phytoplankton 24.01 1.11 Marine
13 Croll & Watts 2004 Procambarus clarkii Crustacean Grazer Artificial feed Artificial 1.03 Freshwater
Procambarus zonangulus Crustacean Grazer Artificial feed Artificial 0.76 Freshwater
14 Dreisig 1981 Cicindela hybrida Insect Predator random encounter Insect 33.8 1.46 Terrestrial
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).
1
Appendix
Study Consumer (Stage) Taxon Type Resource (stage) Taxon
Topt
(oC)
E
(eV)
E fall
(eV) Habitat
15 Eggleston 1990 Callinectes sapidus (A) Crustacean Predator Crassostrea virginica (J) Mollusc 1.37 Marine
16 Elliot & Leggett 1996 Gasterosteus aculeatus Fish Predator Mallotus villosus Fish 0.08 Marine
Aurelia aurita Cnidaria Predator Mallotus villosus Fish 0.15 Marine
17 Ellrott et al. 2007 Orconectes propinquus Crustacean Predator Salvelinus namaycush ,
(Egg)
Fish 0.72 Freshwater
Orconectes rusticus Crustacean Predator Salvelinus namaycush ,
(Egg)
Fish 1.21 Freshwater
18 Enkegaard 1994 Encarsia formosa Insect Parasite Bemisia tabaci Insect 24,00 0.94 Terrestrial
19 Enriquez.Ocana et al.
2012
Crassostrea corteziensis Mollusc Filter
feeder
Chaotocerus muelleri Phytoplankton 0.96 Marine
20 Everson 1980 Phytoseiulus persimilis Mite Predator Tetranychus urticae Mite 0.63 Terrestrial
21 Fialamedioni 1978 Phallusia mammilata Tunicate Filter
feeder
Monochrusis lutheri Phytoplankton 2,00 Marine
22 Flinn & Hagstrum
2002
Theolax elegans Insect Parasite Rhyzopertha dominica Insect 26.94 Terrestrial
23 Flinn 1991 Chephalonomia waterstoni Insect Parasite Cryptocelestes ferrugineus Insect 0.61 Terrestrial
24 Garton & Stickle 1980 Thais haemostoma Mollusc Predator Crassostrea virginica Mollusc 3.52 Marine
25 Geden & Axtell 1988 Carcinops pumilio (A) Insect Predator Musca domestica (J) Insect 0.8 Terrestrial
Macrocheles muscadomesticae
(A)
Mite Predator Musca domestica (J) Insect 0.85 Terrestrial
26 Gerald 1976 Ophiocephalus punctatus Fish Predator Artificial Artificial 26.93 0.55 Freshwater
27 Gitonga et al. 2002 Orius albidipennis Insect Predator Megalurothrips sjostedti
(J)
Insect 0.28 Terrestrial
Orius albidipennis Insect Predator Megalurothrips sjostedti
(A)
Insect 0.44 Terrestrial
28 Gresens 2001 Pseudochironomus richardsoni
(J)
Insect Predator Diatoms Phytoplankton 1.6 Freshwater
29 Gresens et al. 1982 Celethemis fasciata (J) Insect Predator Chironomus tentans (J) Insect 0.64 Freshwater
2
Appendix
Study Consumer (Stage) Taxon Type Resource (stage) Taxon
Topt
(oC)
E
(eV)
E fall
(eV) Habitat
30 Handeland et al. 2008 Salmo salar Fish Predator Pellets Artificial 14.01 0.78 Marine
31 Hanks 1957 Urosalpinx cinerea Mollusc Predator Crassostrea virginica Mollusc 20.4 0.92 Marine
Urosalpinx cinerea Mollusc Predator Mytilus edulis Mollusc 1.66 Marine
32 Hardman & Rogers
1991
Typhlodromis pyri (J1) Mite Predator Panonychus ulmi Mite 0.25 Terrestrial
Typhlodromis pyri (J2) Mite Predator Panonychus ulmi Mite 0.33 Terrestrial
33 Heiman & Knight 1975 Acroneuria californica (J) Insect Predator Hydropsyche spp. (J) Insect 0.39 Freshwater
Acroneuria californica (J) Insect Predator Simulium spp. (J) Insect 0.14 Freshwater
34 Hooff & Bollens 2004 Tortanus dextrilobatus (A) Crustacean Predator Oithona davisae Crustacean -0.1 Marine
35 Johnston & Mathias
1994
Stizostedion vitreum (J) Fish Predator Zooplankton Crustacean 0.34 Freshwater
36 Jones et al. 2003a Aphidius colemani Insect Parasite Schizaphis graminum Insect 0.21 Terrestrial
Lysiphlebus testaceipes Insect Parasite Schizaphis graminum Insect 0.76 Terrestrial
37 Jones et al. 2007 Lysiphlebus testaceipes Insect Parasite Schizaphis graminum Insect 0.63 Terrestrial
38 Kemp & Britz 2008 Panuliros humaros rubellus Crustacean Predator Perna perna & Mytilus
galloprovincialis
Mollusc 0.6 Marine
39 Kibby 1971 Daphnia rosea Crustacean Filter
feeder
Chlamydomonas sp. Phytoplankton 18.51 0.65 Freshwater
40 Kishi et al. 2005 Salvelinus malma (J) Fish Predator Dead Euphasia superba
(A)
Crustacean 1.68 Marine
41 Kittner & Riisgard
2005
Mytilus edulis Mollusc Filter
feeder
Rhodomonas sp. Phytoplankton 0.31 Marine
42 Koskela et al. 1997 Salmo salar (J) Fish Predator Pellets Artificial 18.07 0.54 Freshwater
43 Largen 1967 Nucella lapillus (A) Mollusc Predator Mytilus edulis (A) Mollusc 0.93 Marine
Nucella lapillus (A) Mollusc Predator Cirripedia sp. Crustacean 1.5 Marine
44 Larsson & Berglund
1998
Salvelinus alpinus Fish Predator Neomysis sp. Crustacean 15.88 1.27 Freshwater
Salvelinus alpinus Fish Predator Pellets Artificial 15.36 1.32 Freshwater
3
Appendix
Study Consumer (Stage) Taxon Type Resource (stage) Taxon
Topt
(oC)
E
(eV)
E fall
(eV) Habitat
45 Larsson & Berglund
2005
Salvelinus alpinus (J) Fish Predator Pellets Artificial 14.29 1.17 Freshwater
46 Li et al. 2007 Scolothrips takahashii Insect Predator Tetranychus viennensis Mite 27.71 0.77 Terrestrial
47 Linlokken et al. 2010 Perca fluviatilis Fish Predator Chironomidae sp. Insect 0.77 Freshwater
Rutilus rutilus Fish Predator Chironomidae sp. Insect 12.48 0.58 Freshwater
48 Lisbjerg & Petersen
2000
Electra bellula Bryozoa Filter
feeder
Rhodomonas sp. Phytoplankton 1.41 Marine
49 Lisbjerg & Petersen
2001
Electra crustulenta Bryozoa Filter
feeder
Rhodomonas sp. Phytoplankton 0.32 Brackish
50 Liu & et al. 1998 Sinniperca chuatsi (J) Fish Predator Misgurnus
anguillicaudatus
Fish 35.64 0.47 Freshwater
Channa argus (J) Fish Predator Misgurnus
anguillicaudatus
Fish 29.33 1.17 Freshwater
51 Liu & Sengonca 1998 Eretmocerus longpipes Insect Parasite Aleurotuberculatus
takahashi
Insect 26.19 0.74 Terrestrial
52 Lu & Blake 1997 Argopecten irradians
concentricus (J)
Mollusc Grazer Isochrysis galbanus Phytoplankton 0.95 Marine
53 Mack & Smilowitz
1982
Coleomegilla maculata (J) Insect Predator Myzus persicae Insect 0.66 Terrestrial
Coleomegilla maculata (A) Insect Predator Myzus persicae Insect 0.48 Terrestrial
54 Mackenzi 1970 Asterias forbesi Asteroidea Predator Oyster (species not
specified)
Mollusc 15.28 0.43 Marine
4
Appendix
Study Consumer (Stage) Taxon Type Resource (stage) Taxon
Topt
(oC)
E
(eV)
E fall
(eV) Habitat
55 Mahdian et al. Picromerus bidens (A) Insect Predator Spodoptera littoralis (J) Insect 0.46 Terrestrial
2006 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 0.63 Terrestrial
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
56 Marchand et al. 2002 Salvelinus fontinalis (J) Fish Predator Zooplankton (Not specified
further)
Crustacean 19.46 Freshwater
57 McCaffrey &
Horsburgh 1986
Orius insidious Insect Predator Panonychus ulmi Mite 0.67 Terrestrial
58 McCoull 1998 Naucoris congrex (A) Insect Predator Culicidae sp. (J) Insect 0.56 Freshwater
59 Menon et al. 2002 Anisopteromalus calandrae Insect Parasite Rhyzopertha dominica Insect 1.05 Terrestrial
60 Miranda-Baeza et al.
2006
Anadara Grandis Mollusc Filter
feeder
Particle matter Mixed 26.7 0.71 Marine
61 Murdoch et al. 1984 Notonecta hoffmani (A) Insect Predator Culex pipiens (J) Insect 1.12 Freshwater
5
Appendix
Study Consumer (Stage) Taxon Type Resource (stage) Taxon
Topt
(oC)
E
(eV)
E fall
(eV) Habitat
62 Nishi et al. 2004 Amphibolus venator (A) Insect Predator Tribolium confusum (J1) Insect 0.63 Terrestrial
Amphibolus venator (A) Insect Predator Tribolium confusum (J2) Insect 0.71 Terrestrial
Amphibolus venator (A) Insect Predator Tribolium confusum (A) Insect 0.56 Terrestrial
63 Osborne & Riddle 1999 Ctenopharyngodon idella Fish Grazer Hydrilla verticillata Plant 1.41 Freshwater
64 Parajulee et al. 2006 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. Insect Predator Helicoverpa zea (Egg) Insect 0.64 Terrestrial
Chrysopidae sp. (J) Insect Predator Helicoverpa zea (Egg) Insect 0.42 Terrestrial
Orius insidiosus Insect Predator Helicoverpa zea (Egg) Insect 0.51 Terrestrial
65 Persson 1986 Perca fluviatilis (A) Fish Predator Chaoborus obscuripes (J) Insect 0.45 Freshwater
Rutilis rutilus (A) Fish Predator Chaoborus obscuripes (J) Insect 0.85 Freshwater
66 Rassoulzadegan 1982 Lohmanniella spiralis Protist Grazer Particle matter Mixed 0.94 Marine
67 Roy & Raut 1994 Sphaerodema annulatum Insect Predator Lymnaea luteola Mollusc 0.14 Freshwater
Sphaerodema rusticum Insect Predator Lymnaea luteola Mollusc 0.23 Freshwater
68 Sanford 1999 Pisaster ochraceus Asteroidea Predator Mytilus californianus Mollusc 0.79 Marine
69 Schulte 1975 Mytilus edulis Mollusc Filter
feeder
Platymonas suecica Phytoplankton 16.53 0.8 Marine
Mytilus modiolus Mollusc Filter
feeder
Platymonas suecica Phytoplankton 0.49 Marine
70 Sell et al. 2001 Metridia lucens Crustacean Predator Calanus nauplii Crustacean -0.73 Marine
71 Skirvin & Fenlon 2003 Phytoseiulus persimilis Mite Predator Tetranychus urticae Mite 26.7 1.22 Terrestrial
72 Song & Heong 1997 Cyrtorhinus lividipennis Insect Predator Nilaparvata lugens Insect 28.4 0.79 Terrestrial
6
Appendix
Study Consumer (Stage) Taxon Type Resource (stage) Taxon
Topt
(oC)
E
(eV)
E fall
(eV) Habitat
73 Specziar 2002 Abramis brama Fish Predator Mixed Mixed 0.17 Freshwater
Blicca bjoerkna Fish Predator Mixed Mixed 0.81 Freshwater
Rutilis rutilus Fish Predator Mixed Mixed 0.68 Freshwater
Carassius auratus gibelio Fish Predator Mixed Mixed 0.82 Freshwater
Cyprinus carpio Fish Predator Mixed Mixed 1.12 Freshwater
74 Spitze 1985 Chaoborus americanus Insect Predator Daphnia pulex Crustacean 0.71 Freshwater
75 Sylvester et al. 2005 Limnoperna furtonei Mollusc Filter
feeder
Chlorella vulgaris Phytoplankton 0.41 Freshwater
76 Taylor & Collie 2003 Crangon septemspinosa Crustacean Predator Pseudopleuronectes
americanus
Fish 1.27 Marine
77 Thomas et al. 2000 Jasus Edwardsii Crustacean Predator Mytilus edulis & pellets 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 Filter
feeder
Green algae Phytoplankton 0.61 Freshwater
Oreochromis lioticus Fish Filter
feeder
Cyanobacteria Phytoplankton 0.56 Freshwater
80 Wang & Ferro 1998 Trichogramma ostriniae Insect Parasite Ostrinia nubilalis Insect 23,00 1.86 Terrestrial
81 Watts et al. 2011 Lytechinus variegatus Echinoid Grazer Artificial Artificial 23.13 0.51 Marine
82 Verity 1985 Tintinnopsis acuminata Ciliate Grazer Isochrysis galbanus Phytoplankton 0.49 Marine
Tintinnopsis vasculum Ciliate Grazer Dicraterie incornata Phytoplankton 0.52 Marine
83 Whitledge & Rabeni
2002
Orconectes eupunctus Crustacean Predator Chironomus sp. Insect 25,00 1.78 Freshwater
Orconectes hylas Crustacean Predator Chironomus sp. Insect 0.62 Freshwater
Orconectes vinlis Crustacean Predator Chironomus sp. Insect 22.9 0.93 Freshwater
Orconectes luteus Crustacean Predator Chironomus sp. Insect 0.45 Freshwater
Orconectes punctimanus Crustacean Predator Chironomus sp. Insect 2.66 Freshwater
84 Wyban et al. 1995 Penaeus vannamei Crustacean Filter
feeder
Artificial feed Artificial 0.78 Marine
85 Xia et al. 2003 Cocinella Septempunctata Insect Predator Aphis gossypii Insect 0.58 Terrestrial
86 Yee & Murray 2004 Tegula aureotincta Mollusc Grazer Kelp (species not specified) Algae 0.8 Marine
Tegula brunnea Mollusc Grazer Kelp (species not specified) Algae 17.92 0.57 Marine
Tegula funebralis Mollusc Grazer Kelp (species not specified) Algae 19.03 0.56 Marine
7
Appendix
Study Consumer (Stage) Taxon Type Resource (stage) Taxon
Topt
(oC)
E
(eV)
E fall
(eV) Habitat
87 Zamani et al. Aphidius colemani Insect Parasite Aphis gossypii Insect 0.29 Terrestrial
2006 Aphidius matricariae Insect Parasite Aphis gossypii Insect 25.75 0.23 Terrestrial
8
9
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