From population-level effects to individual response: modelling temperature dependence in Gammarus pulex

Abstract

SUMMARYPopulation-level effects of global warming result from concurrent direct and indirect processes. They are typically described by physiologically structured population models (PSPMs). Therefore, inverse modelling offers a tool to identify parameters of individual physiological processes through population-level data analysis, e.g. the temperature dependence of growth from size-frequency data of a field population. Here, we make use of experiments under laboratory conditions, in mesocosms and field monitoring to determine the temperature dependence of growth and mortality of Gammarus pulex. We found an optimum temperature for growth of approximately 17°C and a related temperature coefficient, Q 10 , of 1.5°C -1 , irrespective of whether we classically fitted individual growth curves or applied inverse modelling based on PSPMs to laboratory data. From a comparison of underlying data sets we conclude that applying inverse modelling techniques to population-level data results in meaningful response parameters for physiological processes if additional temperature-driven effects, including within-population interaction, can be excluded or determined independently. If this is not the case, parameter estimates describe a cumulative response, e.g. comprising temperature-dependent resource dynamics. Finally, fluctuating temperatures in natural habitats increased the uncertainty in parameter values. Here, PSPM should be applied for virtual monitoring in order to determine a sampling scheme that comprises important dates to reduce parameter uncertainty. Supplementary material available online at

Increased temperature has no consequence for behavioral manipulation despite effects on both partners in the interaction between a crustacean host and a manipulative parasite

“…In parallel to its effect on parasite traits, temperature also affected certain traits of the hosts. First, the survival of gammarids was decreased at high temperature, as already shown in other studies 55,56 , while infection with parasites also led to a higher mortality of gammarids. However, the influence of parasites on host survival was not affected by temperature.…”

“…As for other ectotherm species 52 , temperature has been shown to affect amphipods in several ways, influencing for instance their metabolism 53 – 55 , growth 56 , or activity 54 . Similarly, most parasites are also affected by temperature in diverse ways 57 .…”

Additive effects of temperature and infection with an acanthocephalan parasite on the shredding activity of Gammarus fossarum (Crustacea: Amphipoda): the importance of aggregative behavior

“…Variation in the seasonal distribution of acanthocephalan parasites also suggests that their prevalence could be linked to climatic conditions (VanCleave, 1916;Muzzall & Rabalais, 1975;Brown, 1989). In parallel, some effects of temperature on gammarid traits, such as their immune system (Le Moullac & Haffner, 2000), their growth (Moenickes et al, 2011), their locomotor activity (Issartel et al, 2005) or their metabolism (Roux & Roux, 1967;Issartel et al, 2005;Foucreau et al, 2014), are also likely to modify the interaction between gammarids and their parasites, such as the prevalence or the probability of encounters. For instance, Guinnee & Moore (2004) found that the effect of an acanthocephalan parasite on its insect host depended on temperature: Infection negatively impacted host fecundity only at the highest temperature tested.…”

Impaired ecosystem process despite little effects on populations: modeling combined effects of warming and toxicants

“…Background mortality was assumed to be independent of temperature (Moenickes et al, 2011). Once a day, background mortality is applied via a constant probability of dying due to, for example, predation, cannibalism, or age.…”

“…Once a day, background mortality is applied via a constant probability of dying due to, for example, predation, cannibalism, or age. Background mortality was assumed to be independent of temperature (Moenickes et al, 2011). For computational reasons, population sizes were kept at a certain level which sometimes resulted in extinctions due to the stochastic nature of background mortality implementation.…”