The temperature dependence of ectotherm …588173/FULLTEXT01.pdfThe Metabolic Theory of Ecology (MTE) uses the Arrhenius equation to link the biology of individuals to the ecology

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.

References

Angiletta, M. J., Niewiarowski, P. H. & Navas, C. A. 2002. The evolution of thermal

physiology in ectotherms. Journal of Thermal Biology, 27, 249-268.

Angiletta, M.J. 2009. Thermal Adaption, a Theoretical and Empirical Synthesis. Oxford

University Press, Oxford.

Brown, J. H., Gillooly, J. F., Allen, A. P., Savage, V. M. & West, G. B. 2004. Toward a

metabolic theory of ecology. Ecology, 85, 1771-1789.

Bulte, G. & Blouin-Demers, G. 2006. Cautionary notes on the descriptive analysis of

performance curves in reptiles. Journal of Thermal Biology, 31, 287-291.

Clarke, A. 2004. Is there a Universal Temperature Dependence of metabolism? Functional

Ecology, 18, 252-256.

Clarke, A. & Fraser, K. P. P. 2004. Why does metabolism scale with temperature? Functional

Ecology, 18, 243-251.

Cornish-Bowden, A. 2004. Fundamentals of enzyme kinetics. Portland press, London.

Dell, A. I., Pawar, S. & Savage, V. M. 2011. Systematic variation in the temperature

dependence of physiological and ecological traits. Proceedings of the National

Academy of Sciences of the United States of America, 108, 10591-10596.

Deutsch, C. A., Tewksbury, J. J., Huey, R. B., Sheldon, K. S., Ghalambor, C. K., Haak, D. C. &

Martin, P. R. 2008. Impacts of climate warming on terrestrial ectotherms across

latitude. Proceedings of the National Academy of Sciences of the United States of

America, 105, 6668-6672.

Englund, G., Ohlund, G., Hein, C. L. & Diehl, S. 2011. Temperature dependence of the

functional response. Ecology Letters, 14, 914-921.

Englund, G., Sarnelle, O. & Cooper, S. D. 1999. The importance of data-selection criteria:

Meta-analyses of stream predation experiments. Ecology, 80, 1132-1141.

Gillooly, J. F., Allen, A. P., Savage, V. M., Charnov, E. L., West, G. B. & Brown, J. H. 2006.

Response to Clarke and Fraser: effects of temperature on metabolic rate. Functional

Ecology, 20, 400-404.

Gillooly, J. F., Brown, J. H., West, G. B., Savage, V. M. & Charnov, E. L. 2001. Effects of size

and temperature on metabolic rate. Science, 293, 2248-2251.

8

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. <http://datathief.org/>

Vucic-Pestic, O., Ehnes, R. B., Rall, B. C. & Brose, U. 2011. Warming up the system: higher

predator feeding rates but lower energetic efficiencies. Global Change Biology, 17,

1301-1310.

Whittaker, R. J. 2010. Meta-analyses and mega-mistakes: calling time on meta-analysis of

the species richness-productivity relationship. Ecology, 91, 2522-2533.

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


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


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


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


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




Podisus maculiventris (A) Insect Predator Spodoptera littoralis (J) Insect 0.63 Terrestrial

Podisus maculiventris (J1) Insect Predator Spodoptera littoralis (J) Insect -0.89 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


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


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


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

References

Aldridge, D. W., Payne, B. S. & Miller, A. C. 1995. Oxygen-consumption, nitrogeneous excretion, and filtration-rates of Dreissena polymorpha at acclimation temperatures between 20 and 32-degrees-C. Canadian Journal of Fisheries and Aquatic Sciences, 52, 1761-1767.

Ali, R. M. 1970. Influence of suspension density and temperature on filtration rate of Hiatella arctica. Marine Biology, 6, 291-&.

Andersen, V. 1986. Effect of temperature on the filtration rate and percentage of assimilation of Salpa fusiformis cuvier (Tunicata, Thalliacea). Hydrobiologia, 137, 135-140.

Bailey, P. C. E. 1989. The effect of water temperature on the functional-response of the water stick insect Ranatra dispar (Heteroptera, Nepidae). Australian Journal of Ecology, 14, 381-386.

Bergman, E. 1987. Temperature-dependent deifferences in foraging ability of 2 percids, Perca fluviatilis and Gymnocephalus cernuus. Environmental Biology of Fishes, 19, 45-53.

Britz, P. J., Hecht, T. & Mangold, S. 1997. Effect of temperature on growth, feed consumption and nutritional indices of Haliotis midae fed a formulated diet. Aquaculture, 152, 191-203.

Cave, R. D. & Gaylor, M. J. 1989. Functional response of Telenomus reynoldsi Hym, scelionidae at 5 constant temperatures and in an artificial plant arena. Entomophaga, 34, 3-10.

Chipps, S. R. 1998. Temperature-dependent consumption and gut-residence time in the opossum shrimp Mysis relicta. Journal of Plankton Research, 20, 2401-2411.

Chiverton, P. A. 1988. Searching behavior and cereal aphid consumption by Bembidion lampros and Pterostichus cupreus, in relation to temperature and prey density. Entomologia Experimentalis Et Applicata, 47, 173-182.

Christoffersen, K. 2001. Predation on Daphnia pulex by Lepidurus arcticus. Hydrobiologia, 442, 223-229.

Cockrell, B. J. 1984. Effects of temperature and oxygenation on predator prey overlap and prey choice of Notonecta glauca. Journal of Animal Ecology, 53, 519-532.

Crisp, D. J., Yule, A. B. & White, K. N. 1985. Feeding by oyster larvae - The functional-response, energy budget and a comparison with mussel larvae. Journal of the Marine Biological Association of the United Kingdom, 65, 759-783.

Croll, S. L. & Watts, S. A. 2004. The effect of temperature on feed consumption and nutrient absorption in Procambarus clarkii and Procambarus zonangulus. Journal of the World Aquaculture Society, 35, 478-488.

Dreisig, H. 1981. The rate of predation and its temperature-dependence in a Tiger beetle, Cicindela hybrida. Oikos, 36, 196-202.

Eggleston, D. B. 1990. Behavioral mechanisms underlying variable functional-responses of Blue crabs, Callinectes sapidus feeding on juvenile oysters, Crassostrea virginica. Journal of Animal Ecology, 59, 615-630.

Elliott, J. K. & Leggett, W. C. 1996. The effect of temperature on predation rates of a fish (Gasterosteus aculeatus) and a jellyfish (Aurelia aurita) on larval capelin (Mallotus villosus). Canadian Journal of Fisheries and Aquatic Sciences, 53, 1393-1402.

Ellrott, B. J., Marsden, J. E., Fitzsimons, J. D., Jonas, J. L. & Claramunt, R. M. 2007. Effects of temperature and density on consumption of trout eggs by Orconectes propinquus and O-rusticus. Journal of Great Lakes Research, 33, 7-14.

Enkegaard, A. 1994. Temperature-dependent functional-response of Encarsia formosa parasitizing the Poinsettia-strain of the Cotton whitefly, Bemisia tabaci, on Poinsettia. Entomologia Experimentalis Et Applicata, 73, 19-29.

Enriquez-Ocana, L. F., Nieves-Soto, M., Pina-Valdez, P., Martinez-Cordova, L. R. & Medina-Jasso, M. A. 2012. Evaluation of the combined effect of temperature and salinity on the filtration, clearance rate and assimilation efficiency of the Mangrove Oyster Crassostrea corteziensis (Hertlein, 1951). Archives of Biological Sciences, 64, 479-488.

10

Everson, P. 1980. Relative activity and functional-response of Phytoseiulus persimilis (Acarina, Phytoseiidae) and Tetranychus urticae (Acarina, Tetranychidae) - Effect of temperature. Canadian Entomologist, 112, 17-24.

Fialamedioni, A. 1978. Filter-feeding ethology of benthic invertebrates (Ascidians). Influence of temperature on pumping, filtration and digestion rates and rythms in Phallucia mamillata. Marine Biology, 48, 251-259.

Flinn, P. W. 1991. Temperature-dependent functional-response of the parasitoid Cephalonomia waterstoni (Gahan) (Hymenoptera, Bethylidae) attacking Rusty grain beetle larvae (Coleoptera, Cucujidae). Environmental Entomology, 20, 872-876.

Flinn, P. W. & Hagstrum, D. W. 2002. Temperature-mediated functional response of Theocolax elegans (Hymenoptera : Pteromalidae) parasitizing Rhyzopertha dominica (Coleoptera : Bostrichidae) in stored wheat. Journal of Stored Products Research, 38, 185-190.

Garton, D. & Stickle, W. B. 1980. Effects of salinity and temperature on the predation rate of Thais haemastoma on Crassostrea virginica spat. Biological Bulletin, 158, 49-57.

Geden, C. J. & Axtell, R. C. 1988. Predation by Carcinops pumilio (Coleoptera, Histeridae) and Macrocheles muscadomesticae (Acarina, Macrochelidae) on the House fly (Diptera, Muscidae) - Functional-response, effects of temperature, and availability of alternative prey. Environmental Entomology, 17, 739-744.

Gerald, V. M. 1976. Effects of temperature on consumption, absorption and conversion of food in Ophiocephalus punctatus Bloch. Hydrobiologia, 49, 87-93.

Gitonga, L. M., Overholt, W. A., Lohr, B., Magambo, J. K. & Mueke, J. M. 2002. Functional response of Orius albidipennis (Hemiptera : Anthocoridae) to Megalurothrips sjostedti (Thysanoptera : Thripidae). Biological Control, 24, 1-6.

Gresens, S. E. 2001. Thermal sensitivity of ingestion and digestion in larvae of a eurythermal chironomid. Journal of the North American Benthological Society, 20, 68-83.

Gresens, S. E., Cothran, M. L. & Thorp, J. H. 1982. The influence of temperature on the functional-response of the Dragonfly Celithemis fasciata (Odonata, Libellulidae). Oecologia, 53, 281-284.

Handeland, S. O., Imsland, A. K. & Stefansson, S. O. 2008. The effect of temperature and fish size on growth, feed intake, food conversion efficiency and stomach evacuation rate of Atlantic salmon post-smolts. Aquaculture, 283, 36-42.

Hanks, J. E. 1957. The rate of feeding of the common oyster drill, urosalpinx cinerea (Say), at controlled water temperatures. Biological Bulletin, 112, 330-335.

Hardman, J. M. & Rogers, M. L. 1991. Effects of temperature and prey density on survival, development, and feeding rates of immature Typhlodromus pyri (Acari, Phytoseiidae). Environmental Entomology, 20, 1089-1096.

Heiman, D. R. & Knight, A. W. 1975. Influence of temperature in bioenergetics on carnivorous stonefly nymph, Acroneuria californica Banks (Plecoptera, Perlidae). Ecology, 56, 105-116.

Hooff, R. C. & Bollens, S. M. 2004. Functional response and potential predatory impact of Tortanus dextrilobatus, a carnivorous copepod recently introduced to the San Francisco Estuary. Marine Ecology-Progress Series, 277, 167-179.

Johnston, T. A. & Mathias, J. A. 1994. The effects of temperature on feeding in zooplanktivorous Walley, Stizostedion vitreum, larvae. Environmental Biology of Fishes, 40, 189-198.

Jones, D. B., Giles, K. L., Berberet, R. C., Royer, T. A., Elliott, N. C. & Payton, M. E. 2003. Functional responses of an introduced parasitoid and an indigenous parasitoid on greenbug at four temperatures. Environmental Entomology, 32, 425-432.

Jones, D. B., Giles, K. L., Elliott, N. C. & Payton, M. E. 2007. Parasitism of greenbug, Schizaphis graminum, by the parasitoid Lysiphlebus testaceipes at winter temperatures. Environmental Entomology, 36, 1-8.

Kemp, J. O. G. & Britz, P. J. 2008. The effect of temperature on the growth, survival and food consumption of the east coast rock lobster Panulirus homarus rubellus. Aquaculture, 280, 227-231.

11

Kibby, H. V. 1971. Effect of temperature on feeding behavior of Daphnia rosea. Limnology and Oceanography, 16, 580-&.

Kishi, D., Murakami, M., Nakano, S. & Maekawa, K. 2005. Water temperature determines strength of top-down control in a stream food web. Freshwater Biology, 50, 1315-1322.

Kittner, C. & Riisgaard, H. U. 2005. Effect of temperature on filtration rate in the mussel Mytilus edulis: no evidence for temperature compensation. Marine Ecology-Progress Series, 305, 147-152.

Koskela, J., Pirhonen, J. & Jobling, M. 1997. Feed intake, growth rate and body composition of juvenile Baltic salmon exposed to different constant temperatures. Aquaculture International, 5, 351-360.

Largen, M. J. 1967. Influence of water temperature upon life of Dog-whelk Thais lapillus (Gastropoda, Prosobranchia). Journal of Animal Ecology, 36, 207-&.

Larsson, S. & Berglund, I. 1998. Growth and food consumption of 0+ Arctic charr fed pelleted or natural food at six different temperatures. Journal of Fish Biology, 52, 230-242.

Larsson, S. & Berglund, I. 2005. The effect of temperature on the energetic growth efficiency of Arctic charr (Salvelinus alpinus L.) from four Swedish populations. Journal of Thermal Biology, 30, 29-36.

Li, D. X., Tian, J. & Shen, Z. R. 2007. Functional response of the predator Scolothrips takahashii to hawthorn spider mite, Tetranychus viennensis: effect of age and temperature. Biocontrol, 52, 41-61.

Linlokken, A. N., Bergman, E. & Greenberg, L. 2010. Effect of temperature and roach Rutilus rutilus group size on swimming speed and prey capture rate of perch Perca fluviatilis and R-rutilus. Journal of Fish Biology, 76, 900-912.

Lisbjerg, D. & Petersen, J. K. 2000. Clearance capacity of Electra bellula (Bryozoa) in seagrass meadows of Western Australia. Journal of Experimental Marine Biology and Ecology, 244, 285-296.

Lisbjerg, D. & Petersen, J. K. 2001. Feeding activity, retention efficiency, and effects of temperature and particle concentration on clearance rate in the marine bryozoan Electra crustulenta. Marine Ecology-Progress Series, 215, 133-141.

Liu, B. & Sengonca, C. 1998. Functional responses of the parasitoid, Eretmocerus longipes Compere (Hym, Aphelinidae) to densities of the whitefly, Aleurotuberculatus takahashi David et Subramaniam (Hom, Aleyrodidae) at different temperatures. Anzeiger Fur Schadlingskunde Pflanzenschutz Umweltschutz, 71, 53-56.

Liu, J. & Cui, Y. 1998. Food consumption and growth of two piscivorous fishes, the mandarin fish and the Chinese snakehead. Journal of Fish Biology, 53, 1071-1083.

Lu, Y. T. & Blake, N. J. 1997. Clearance and ingestion rates of Isochrysis galbana by larval and juvenile bay scallops, Argopecten irradians concentricus (Say). Journal of Shellfish Research, 16, 47-54.

Mack, T. P. & Smilowitz, Z. 1982. Using temperature-mediated functional-response models to predict the impact of Coleomegilla maculata (Coleoptera, Coccinellidae) (Degeer) adults and 3rd-instar larvae on Green peach aphids Myzus persicae (Sulzer) (Homoptera, Aphididae). Environmental Entomology, 11, 46-52.

Mackenzi. C. L. 1970. Feeding rates of Starfish, Asterias forbesi (Desor), at controlled water temperatures and during different seasons of the year.. United States Fish and Wildlife Service Fishery Bulletin, 68, 67-&.

Mahdian, K., Vantornhout, I., Tirry, L. & De Clerq, P. 2006. Effects of temperature on predation by the stinkbugs Picromerus bidens and Podisus maculiventris (Heteroptera : Pentatomidae) on noctuid caterpillars. Bulletin of Entomological Research, 96, 489-496.

Marchand, F., Magnan, P. & Boisclair, D. 2002. Water temperature, light intensity and zooplankton density and the feeding activity of juvenile brook charr (Salvelinus fontinalis). Freshwater Biology, 47, 2153-2162.

McCaffrey, J. P. & Horsburgh, R. L. 1986. Functional-response of Orius insidiosus (Hemiptera, Anthocoridae) to the european red mite, Panonychus ulmi (Acari, Tetranychidae), at different constant temperatures. Environmental Entomology, 15, 532-535.

12

McCoull, C., Swain, R. & Barnes, R. W. 1998. Effect of temperature on the functional response and components of attack rate in Naucoris congrex Stal (Hemiptera : Naucoridae). Australian Journal of Entomology, 37, 323-327.

Menon, A., Flinn, P. W. & Dover, B. A. 2002. Influence of temperature on the functional response of Anisopteromalus calandrae (Hymenoptera : Pteromalidae), a parasitoid of Rhyzopertha dominica (Coleoptera : Bostrichidae). Journal of Stored Products Research, 38, 463-469.

Miranda-Baeza, A., Voltolina, D. & Cordero-Esquivel, B. 2006. Filtration and clearance rates of Anadara grandis juveniles (Pelecypoda, Arcidae) with different temperatures and suspended matter concentrations. Revista De Biologia Tropical, 54, 787-792.

Murdoch, W. W., Scott, M. A. & Ebsworth, P. 1984. Effects of the general predator, Notonecta (Hemiptera) upon a fresh-water community. Journal of Animal Ecology, 53, 791-808.

Nishi, A., Imamura, T., Miyanoshita, A., Morimoto, S., Takahashi, K., Visarathanonth, P., Kengkanpanich, R., Shazali, M. E. & Sato, K. 2004. Predatory abilities of Amphibolus venator (Klug) (Hemiptera : Reduviidae), a predator of stored-product insect pests. Applied Entomology and Zoology, 39, 321-326.

Osborne, J. A. & Riddle, R. D. 1999. Feeding and growth rates for triploid grass carp as influenced by size and water temperature. Journal of Freshwater Ecology, 14, 41-45.

Parajulee, M. N., Shrestha, R. B., Leser, J. F., Wester, D. B. & Blanco, C. A. 2006. Evaluation of the functional response of selected arthropod predators on bollworm eggs in the laboratory and effect of temperature on their predation efficiency. Environmental Entomology, 35, 379-386.

Persson, L. 1986. Temperature-induced shift in foraging ability in 2 fish species, Roach (Rutilus rutilus) and Perch (Perca fluviatilis) - Implications for coexistence between poikilotherms. Journal of Animal Ecology, 55, 829-839.

Rassoulzadegan, F. 1982. Dependence of grazing rate, gross growth efficiency and food size range on temperature in a pelagic oligotrichous ciliate lohmanniella-spiralis Leeg, fed on naturally occurring particulate matter. Annales De L Institut Oceanographique, 58, 177-184.

Roy, J. K. & Raut, S. K. 1994. Factors influencing predation of the waterbugs Sphaerodema annulatum (Fab) and S-rusticum (Fab) on the disease transmitting snail Lymnaea (Radix) luteola (Lamarck). Memorias Do Instituto Oswaldo Cruz, 89, 11-20.

Sanford, E. 1999. Regulation of keystone predation by small changes in ocean temperature. Science, 283, 2095-2097.

Schulte, E. H. 1975. Influence of algal concentration and temperature on filtration-rate of Mytilus edulis. Marine Biology, 30, 331-341.

Sell, A. F., Van Keuren, D. & Madin, L. P. 2001. Predation by omnivorous copepods on early developmental stages of Calanus finmarchicus and Pseudocalanus spp. Limnology and Oceanography, 46, 953-959.

Sirvin, D. J. & Fenlon, J. S. 2003. The effect of temperature on the functional response of Phytoseiulus persimilis (Acari : Phytoseiidae). Experimental and Applied Acarology, 31, 37-49.

Song, Y. H. & Heong, K. L. 1997. Changes in searching responses with temperature of Cyrtorhinus lividipennis Reuter (Hemiptera : Miridae) on the eggs of the brown planthopper, Nilaparvata lugens (Stal.) (Homoptera : Delphacidae). Researches on Population Ecology, 39, 201-206.

Specziar, A. 2002. An in situ estimate of food consumption of five cyprinid species in Lake Balaton. Journal of Fish Biology, 60, 1237-1251.

Spitze, K. 1985. Functional-response of an ambush predator - Chaoborus americanus predation on Daphnia pulex. Ecology, 66, 938-949.

Sylvester, F., Dorado, J., Boltovskoy, D., Juarez, A. & Cataldo, D. 2005. Filtration rates of the invasive pest bivalve Limnoperna fortunei as a function of size and temperature. Hydrobiologia, 534, 71-80.

Taylor, D. L. & Collie, J. S. 2003. Effect of temperature on the functional response and foraging behavior of the sand shrimp Crangon septemspinosa preying on juvenile winter

13

flounder Pseudopleuronectes americanus. Marine Ecology-Progress Series, 263, 217-234.

Thomas, C. W., Crear, B. J. & Hart, P. R. 2000. The effect of temperature on survival, growth, feeding and metabolic activity of the southern rock lobster, Jasus edwardsii. Aquaculture, 185, 73-84.

Thompson, D. J. 1978. Towards a realistic predator-prey model - Effect of temperature on functional response and life history of larvae of Damselfly, Ischnura elegans. Journal of Animal Ecology, 47, 757-767.

Turker, H., Eversole, A. G. & Brune, D. E. 2003. Effect of temperature and phytoplankton concentration on Nile tilapia Oreochromis niloticus (L.) filtration rate. Aquaculture Research, 34, 453-459.

Verity, P. G. 1985. Grazing, respiration, excretion, and growth-rates of tintinnids. Limnology and Oceanography, 30, 1268-1282.

Wang, B. & Ferro, D. N. 1998. Functional responses of Trichogramma ostriniae (Hymenoptera : Trichogrammatidae) to Ostrinia nubilalis (Lepidoptera : Pyralidae) under laboratory and field conditions. Environmental Entomology, 27, 752-758.

Watts, S. A., Hofer, S. C., Desmond, R. A., Lawrence, A. L. & Lawrence, J. M. 2011. The effect of temperature on feeding and growth characteristics of the sea urchin Lytechinus variegatus fed a formulated feed. Journal of Experimental Marine Biology and Ecology, 397, 188-195.

Whitledge, G. W. & Rabeni, C. F. 2002. Maximum daily consumption and respiration rates at four temperatures for five species of crayfish from Missouri, USA (Decapoda, Orconectes spp.). Crustaceana, 75, 1119-1132.

Wyban, J., Walsh, W. A. & Godin, D. M. 1995. Temperature effects on growth, feeding rate and feed conversion of the Pacific white shrimp (Penaeus vannamei). Aquaculture, 138, 267-279.

Xia, J. Y., Rabbinge, R. & Van Der Werf, W. 2003. Multistage functional responses in a ladybeetle-aphid system: Scaling up from the laboratory to the field. Environmental Entomology, 32, 151-162.

Yee, E. H. & Murray, S. N. 2004. Effects of temperature on activity, food consumption rates, and gut passage times of seaweed-eating Tegula species (Trochidae) from California. Marine Biology, 145, 895-903.

Zamani, A., Talebi, A., Fathipour, Y. & Baniameri, V. 2006. Temperature-dependent functional response of two aphid parasitoids, Aphidius colemani and Aphidius matricariae (Hymenoptera : Aphidiidae), on the cotton aphid. Journal of Pest Science, 79, 183-188.

Dept. of Ecology and Environmental Science (EMG)

S-901 87 Umeå, Sweden

Telephone +46 90 786 50 00

Text telephone +46 90 786 59 00

www.umu.se

Documents

The temperature dependence of ectotherm …588173/FULLTEXT01.pdfThe Metabolic Theory of Ecology (MTE) uses the Arrhenius equation to link the biology of individuals to the ecology