The Temperature Dependence of Ectotherm Consumption

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). 2 Data extraction Data were extracted either directly from tables or from figures using Datathief (Tummers, 2006). A second order polynomial was fitted to each observation and all points below the optimum were used to establish the activation energy by fitting this data to the Arrhenius equation, following Irlich et al. (2009) and Englund et al. (2011). The slope of the temperature response, when the logged data is plotted as a function of where k is Boltzmann´s constant given in eV (= 8.617*10-5 eV k-1) and k absolute temperature, gives the activation energy (E) for each study. Studies that reported data on both sexes were handled separately and the mean activation energy of the two was used as one observation. Data on the functional response were first transformed into per capita consumption and the mean values of consumption from all prey densities were used for establishing the activation energy. Unimodal temperature responses To investigate the entire range of temperature responses I plotted unimodal data on standardised scales while preserving the shape of the response. This was done by standardising each response around the mean temperature optimum using Ti,s = Ti – Ti,opt + Topt, where Ti and Ti,s are vectors containing the observed and rescaled temperatures used in study i, Ti,opt is the optimal temperature in study i, and Topt is the mean optimal temperature. To standardise consumption rates I used Yi,s = Yi/Yi,max, where Yi and Yi,s are vectors containing the observed and standardised rates from study i, and Yi,max is the maximum rate estimated by fitting a second order polynomial to the data. Thus, I describe the temperature response in relative units centred on the mean optimal temperature as was done by Englund et al. (2011). To evaluate the full temperature response of consumption I fitted a unimodal extension of the Boltzmann-Arrhenius function to the full temperature range data (Dell et al., 2011, Johnson and Lewin, 1946): ( ( )) opt Where E is activation energy, ED determines the steepness of decline at values above the temperature optima (Topt) and c is a constant. This model was fitted to all standardised unimodal observations (N = 34) using nonlinear least-squares regression. Analysis of mean activation energies Weighted statistical analyses are widely used in meta-analyses since it allows for the down weighting of studies with low precision and favours

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