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 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).
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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).
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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.
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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.
References
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Holling, C.S. 1959A. The components of predation as revealed by a study of small-mammal predation of the European sawfly. Canadian Entomologist. 91, 293-320. Holling, C.S. 1959B. Some characteristics of simple types of predation and parasitism. Canadian Entomologist. 91, 385-398. Huey, R. B. & Stevenson, R. D. 1979. Integrating thermal physiology and ecology of ectotherms - Discussion of approaches. American Zoologist, 19, 357-366. Irlich, U. M., Terblanche, J. S., Blackburn, T. M. & Chown, S. L. 2009. Insect Rate- Temperature Relationships: Environmental Variation and the Metabolic Theory of Ecology. American Naturalist, 174, 819-835. Jeschke, J. M., Kopp, M. & Tollrian, R. 2002. Predator functional responses: Discriminating between handling and digesting prey. Ecological Monographs, 72, 95-112. Johnson, F. H. & Lewin, I. 1946. The growth rate of E-coli in relation to temperature, quinine and coenzyme. Journal of Cellular and Comparative Physiology, 28, 47-75. Knies, J. L. & Kingsolver, J. G. 2010. Erroneous Arrhenius: Modified Arrhenius Model Best Explains the Temperature Dependence of Ectotherm Fitness. American Naturalist, 176, 227-233. Lajeunesse, M. J. 2010. Achieving synthesis with meta-analysis by combining and comparing all available studies. Ecology, 91, 2561-2564. Nilsson-Ortman, V., Stoks, R., De Block, M. & Johansson, F. 2012. Generalists and specialists along a latitudinal transect: patterns of thermal adaptation in six species of damselflies. Ecology, 93, 1340-1352. O'Connor, M. P., Kemp, S. J., Agosta, S. J., Hansen, F., Sieg, A. E., Wallace, B. P., McNair, J. N. & Dunham, A. E. 2007. Reconsidering the mechanistic basis of the metabolic theory of ecology. Oikos, 116, 1058-1072. Rosenberg, M.S., Adams, D.C. & Gurevitch, J. 1997. Metawin: statistical software for meta- analysis. Version 2.0. Sinauer Associates, Sunderland, Massachusetts. Savage, V. M., Gillooly, J. F., Brown, J. H., West, G. B. & Charnov, E. L. 2004. Effects of body size and temperature on population growth. American Naturalist, 163, 429-441. Tummers, B. 2006. Datathief III.
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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 E E fall Study Consumer (Stage) Taxon Type Resource (stage) Taxon (oC) (eV) (eV) Habitat 1 Aldridge et al. 1995 Dreissena polymorpha Mollusc Filter Algae Phytoplankton -1.45 Freshwater feeder 2 Ali 1970 Hiatella arctica Mollusc Filter Phaeodactylum Phytoplankton 13.94 0.98 Marine feeder tricornutum 3 Andersen 1986 Salpa fusiformis Tunicate Filter Various algae Phytoplankton 2.49 Marine feeder 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 Artificial Artificial 18.51 0.35 Marine feeder 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 Pavlova lutheri Phytoplankton 24.01 1.11 Marine feeder 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
1 Appendix
Topt E E fall Study Consumer (Stage) Taxon Type Resource (stage) Taxon (oC) (eV) (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 , Fish 0.72 Freshwater (Egg) Orconectes rusticus Crustacean Predator Salvelinus namaycush , Fish 1.21 Freshwater (Egg) 18 Enkegaard 1994 Encarsia formosa Insect Parasite Bemisia tabaci Insect 24,00 0.94 Terrestrial 19 Enriquez.Ocana et al. Crassostrea corteziensis Mollusc Filter Chaotocerus muelleri Phytoplankton 0.96 Marine 2012 feeder 20 Everson 1980 Phytoseiulus persimilis Mite Predator Tetranychus urticae Mite 0.63 Terrestrial 21 Fialamedioni 1978 Phallusia mammilata Tunicate Filter Monochrusis lutheri Phytoplankton 2,00 Marine feeder 22 Flinn & Hagstrum Theolax elegans Insect Parasite Rhyzopertha dominica Insect 26.94 Terrestrial 2002 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 Mite Predator Musca domestica (J) Insect 0.85 Terrestrial (A) 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 Insect 0.28 Terrestrial (J) Orius albidipennis Insect Predator Megalurothrips sjostedti Insect 0.44 Terrestrial (A) 28 Gresens 2001 Pseudochironomus richardsoni Insect Predator Diatoms Phytoplankton 1.6 Freshwater (J) 29 Gresens et al. 1982 Celethemis fasciata (J) Insect Predator Chironomus tentans (J) Insect 0.64 Freshwater
2 Appendix
Topt E E fall Study Consumer (Stage) Taxon Type Resource (stage) Taxon (oC) (eV) (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 Typhlodromis pyri (J1) Mite Predator Panonychus ulmi Mite 0.25 Terrestrial 1991 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 Stizostedion vitreum (J) Fish Predator Zooplankton Crustacean 0.34 Freshwater 1994 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 Mollusc 0.6 Marine galloprovincialis 39 Kibby 1971 Daphnia rosea Crustacean Filter Chlamydomonas sp. Phytoplankton 18.51 0.65 Freshwater feeder 40 Kishi et al. 2005 Salvelinus malma (J) Fish Predator Dead Euphasia superba Crustacean 1.68 Marine (A) 41 Kittner & Riisgard Mytilus edulis Mollusc Filter Rhodomonas sp. Phytoplankton 0.31 Marine 2005 feeder 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 Salvelinus alpinus Fish Predator Neomysis sp. Crustacean 15.88 1.27 Freshwater 1998 Salvelinus alpinus Fish Predator Pellets Artificial 15.36 1.32 Freshwater
3 Appendix
Topt E E fall Study Consumer (Stage) Taxon Type Resource (stage) Taxon (oC) (eV) (eV) Habitat 45 Larsson & Berglund Salvelinus alpinus (J) Fish Predator Pellets Artificial 14.29 1.17 Freshwater 2005 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 Electra bellula Bryozoa Filter Rhodomonas sp. Phytoplankton 1.41 Marine 2000 feeder 49 Lisbjerg & Petersen Electra crustulenta Bryozoa Filter Rhodomonas sp. Phytoplankton 0.32 Brackish 2001 feeder 50 Liu & et al. 1998 Sinniperca chuatsi (J) Fish Predator Misgurnus Fish 35.64 0.47 Freshwater anguillicaudatus Channa argus (J) Fish Predator Misgurnus Fish 29.33 1.17 Freshwater anguillicaudatus 51 Liu & Sengonca 1998 Eretmocerus longpipes Insect Parasite Aleurotuberculatus Insect 26.19 0.74 Terrestrial takahashi 52 Lu & Blake 1997 Argopecten irradians Mollusc Grazer Isochrysis galbanus Phytoplankton 0.95 Marine concentricus (J) 53 Mack & Smilowitz Coleomegilla maculata (J) Insect Predator Myzus persicae Insect 0.66 Terrestrial 1982 Coleomegilla maculata (A) Insect Predator Myzus persicae Insect 0.48 Terrestrial 54 Mackenzi 1970 Asterias forbesi Asteroidea Predator Oyster (species not Mollusc 15.28 0.43 Marine specified)
4 Appendix
Topt E E fall Study Consumer (Stage) Taxon Type Resource (stage) Taxon (oC) (eV) (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 Crustacean 19.46 Freshwater further) 57 McCaffrey & Orius insidious Insect Predator Panonychus ulmi Mite 0.67 Terrestrial Horsburgh 1986 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. Anadara Grandis Mollusc Filter Particle matter Mixed 26.7 0.71 Marine 2006 feeder 61 Murdoch et al. 1984 Notonecta hoffmani (A) Insect Predator Culex pipiens (J) Insect 1.12 Freshwater
5 Appendix
Topt E E fall Study Consumer (Stage) Taxon Type Resource (stage) Taxon (oC) (eV) (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 Platymonas suecica Phytoplankton 16.53 0.8 Marine feeder Mytilus modiolus Mollusc Filter Platymonas suecica Phytoplankton 0.49 Marine feeder 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
Topt E E fall Study Consumer (Stage) Taxon Type Resource (stage) Taxon (oC) (eV) (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 Chlorella vulgaris Phytoplankton 0.41 Freshwater feeder 76 Taylor & Collie 2003 Crangon septemspinosa Crustacean Predator Pseudopleuronectes Fish 1.27 Marine americanus 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 Green algae Phytoplankton 0.61 Freshwater feeder Oreochromis lioticus Fish Filter Cyanobacteria Phytoplankton 0.56 Freshwater feeder 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 Orconectes eupunctus Crustacean Predator Chironomus sp. Insect 25,00 1.78 Freshwater 2002 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 Artificial feed Artificial 0.78 Marine feeder 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
Topt E E fall Study Consumer (Stage) Taxon Type Resource (stage) Taxon (oC) (eV) (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
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