Databases used to test the model described in the article "Inferring size-based functional responses from the physical properties of the medium", Frontiers in Ecology and Evolution. Please read the "Readme.pdf" file for detailed information. This file explains all the variables and provides full references for the data in each of the datasets. "Portalier_et_al_2021_Species_Speeds.csv" provides species speeds according to body size for numerous species in aquatic systems. "Portalier_et_al_2021_Predator_Prey_Interactions.csv" provides attack rates, capture probabilities and handling times for numerous predator-prey interactions in aquatic systems.
Abstract Growth is a process fundamental to life. It implies an increase in not only energy and information but also matter content. Recent advances in ecology have demonstrated that the elemental composition of organisms – their stoichiometry – is inextricably linked to their growth rate. Unbalances between the demands of elements for growth and their relative availabilities often result in elemental limitation. Also, different cellular components have different elemental compositions, and thus changes in allocation between uptake and assembly machineries affect both growth rate and elemental composition at the organismal level. Osmotrophs (including autotrophs) acquire essential elements through a vast set of separate molecules, resulting in more flexible stoichiometries compared to non‐osmotrophs that ingest their preys in one package. Relationships between elemental composition and growth rate should be considered differently for individuals and for populations, as processes and mechanisms differ between the two scales, and more generally among the various biological scales. Key Concepts Growth for organisms is by nature a stoichiometric process that involves multiple currencies: energy, information and matter, itself made of multiple essential elements. Most organisms are stoichiometrically homeostatic, that is, they need to keep the ratios of elements in their protoplasm within narrow limits. However, some organisms use storage structures, such as vacuoles, to further modulate their stoichiometry. According to Liebig's law of the minimum, growth is mostly limited by the element that is in least supply compared to the demand of the growing organism. Organisms can resort to a set of behavioural and physiological strategies when facing elemental limitation. Osmotrophs (including autotrophs), which can regulate the stoichiometry of their diet at the uptake level, differ from non‐osmotrophs (including some large protists and all metazoans), which ingest all the essential elements at once. Another strategy is to adapt the relative investment into cellular machineries that differ in their elemental composition, but this comes with important repercussions on cellular functions. Excretion of the elements in excess is another strategy, but there are associated costs, too, leading to only a narrow range of diet elemental composition that optimises growth. A priori, elemental limitation at the level of populations should differ from limitation of individual growth because of demographic processes. Even if differences between the two biological levels are ignored, including stoichiometry into classical population models yields interesting novel predictions, confirming the importance of stoichiometry to understand the growth process.
Databases used to test the model described in the article "Inferring size-based functional responses from the physical properties of the medium", Frontiers in Ecology and Evolution. Please read the "Readme.pdf" file for detailed information. This file explains all the variables and provides full references for the data in each of the datasets. "Portalier_et_al_2021_Species_Speeds.csv" provides species speeds according to body size for numerous species in aquatic systems. "Portalier_et_al_2021_Predator_Prey_Interactions.csv" provides attack rates, capture probabilities and handling times for numerous predator-prey interactions in aquatic systems.
Abstract The mineralization of nitrogen (N) and especially the regeneration of ammonium are critical processes performed by bacteria in aquatic ecosystems. Quantifying these processes is complicated because bacteria simultaneously consume and produce ammonium. Here we use experimental data on the effects of the molecular composition of the supplied substrates, combined with a classical stoichiometric model of ammonium regeneration, to demonstrate how the quantification of these processes can be improved. We manipulated a batch culture experiment with an isolated bacterial community by adding three different types of N substrates: dissolved inorganic nitrogen (DIN, nitrate), dissolved organic nitrogen (DON, amino acid) and a mixture of DIN and DON. With such experiment set-up, the ammonium regeneration per se could be easily tracked without using complicated methods (e.g. isotope dilution). We compared the experimental data with the predictions of Goldman et al ’ model (1987) as well as with a revised version, using the measured consumption carbon:nitrogen ratio (C:N ratio), rather than an estimated consumption ratio. We found that, for all substrates, and in particular, mixed substrates where C and N are partially dissociated between different molecules, estimates of ammonium regeneration rates can be improved by measuring the actual consumption C: N ratio. Importance Measuring bacterial ammonium regeneration in natural aquatic ecosystem is difficult because bacteria in the field simultaneously consume and produce ammonium. In our experimental design, we used nitrate as the inorganic nitrogen substrate. This way, we could measure separately the uptake and excretion of inorganic nitrogen by bacteria without incorporating cumbersome methods such as isotope dilution. Our experiment allowed us to evaluate the accuracy of various stoichiometric models for the estimation of net bacterial nitrogen regeneration. We found that: The exact distribution of C and N among the various molecules that make the bulk of DOM is a crucial factor to consider for bacterial net nitrogen regeneration. For all substrates, and in particular, mixed substrates where C and N are partially dissociated between different molecules, estimates of net nitrogen regeneration rates can be improved by measuring the actual C: N ratio of bacterial consumption.
First derivations of the functional response were mechanistic, but subsequent uses of these functions tended to be phenomenological. Further understanding of the mechanisms underpinning predator-prey relationships might lead to novel insights into functional response in natural systems. Because recent consideration of the physical properties of the environment has improved our understanding of predator-prey interactions, we advocate the use of physics-based approaches for the derivation of the functional response from first principles. These physical factors affect the functional response by constraining the ability of both predators and prey to move according to their size. A physics-based derivation of the functional response should thus consider the movement of organisms in relation to their physical environment. One recent article presents a model along these criteria. As an initial validation of our claim, we use a slightly modified version of this model to derive the classical parameters of the functional response (i.e., attack rate and handling time) of aquatic organisms, as affected by body size, buoyancy, water density and viscosity. We compared the predictions to relevant data. Our model provided good fit for most parameters, but failed to predict handling time. Remarkably, this is the only parameter whose derivation did not rely on physical principles. Parameters in the model were not estimated from observational data. Hence, systematic discrepancies between predictions and real data point immediately to errors in the model. An added benefit to functional response derivation from physical principles is thus to provide easy ways to validate or falsify hypotheses about predator-prey relationships.
In aquatic microplankton food webs, the relative availability of dissolved inorganic nitrogen (DIN) and organic nitrogen (DON) can shape trophic interactions, food web structure and the stoichiomet ...
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