The Physics of Ecological Interactions

• INT26 • Introduction to the role pf physical factors in ecological interactions > M. Mehdi CHERIF
Content : Abstract: Ecology does not rise up to the vision spelled by Tansley, almost a century ago, that saw living organisms and their physical environment as one entangled entity. This objective is all the more actual, given that major anthropogenic perturbations affect the physical dimensions of the biosphere, such as temperature, humidity, turbulence, etc. However, it is also a very difficult task, given the high number of physical factors acting on organisms, the ubiquity of their effects, and the inner complexity of interaction networks in which organisms are embedded. No wonder that the efforts to work out the interaction between ecological systems and their physical environment has been slow and fragmented. Based on recent progress and on an extensive overview of the field, I propose a framework that recognises movement as the nexus linking physical factors with their effects on ecological interactions. All ecological interactions involve movement. Through the decomposition of the movement involved in any interaction into basic universal components, one can account for the effects of given physical factors of interest, first on those basic components, and ultimately on the original movement reconstructed from its basic components. The diversity inherent to each ecological system results from the properties of movements that are specific to the organisms engaged in the interactions as defined by the movement paradigm, i.e., their internal state, navigation capacity and motion capacity. I will present, discuss and illustrate this framework in progress with examples and hypotheses to open the floor to constructive contributions and discussions. • INT27 • Integration of environmental factors in a modular theory of trophic interactions
Content : Abstract: Trophic interactions are important for population and community dynamics. By understanding the effect of species' traits and environment on interactions, we can build more accurate and predictive trophic-interaction models. A trophic interaction occurs as a series of steps, and different traits and environmental factors have different effects on each of these steps. Many existing trophic-interaction models already use steps, such as search and pursuit, with traits having a different effect on each step. Currently, however, this is done ad hoc, with each model defining and naming steps specifically for their system. While this makes for an efficient model, it makes comparison among models, species, traits, or environments very difficult, which prevents us gaining an overarching understanding of how trophic interactions are affected by traits and environmental factors. Here, I present a comprehensive and modular approach for modeling the effect of traits and environmental factors on trophic interactions. I break the trophic interaction into 8 steps that cover all trophic interaction types and together make up the functional and numerical response functions. While 8 steps sounds like a lot, the core of this framework is that it is modular; each step can be considered as a module. To build a model for a specific system, only the most relevant steps (modules) are chosen. Each selected module is then explicitly parameterized for traits and/or environmental factors and together the modules form a dynamical model. I exemplify the approach with a terrestrial arthropod community using empirical data on temperature responses and body sizes and discuss how a model can be extended or adapted by adding more modules or traits, or applying particular modules to new interactions. This approach provides a common vocabulary for discussing the effect of traits and environmental factors on diverse interactions, and enables direct application of relevant modules to new scenarios. • INT28 • Plankton ecosystem functions emerging from first principle constraints of individual cells > K. Ken Haste ANDERSEN
Content : Abstract : Unicellular plankton span all three domains of life: primary producers, herbi-/carnivores, and decomposers (bacteria). However, individual cells often fill more than one of these functional roles, e.g., by combining phototrophy with osmo-heterotrophy or phagotrophy. This diversity in function at the cell level complicates ecosystem models. The complexity can be reduced by models organized around cell size. Here I present the size-based approach to plankton modelling. I focus on how most parameters can be derived from first principles at the level of the individual cell related to geometry, diffusion, fluid mechanics and chemical reaction kinetics. The cell level model scales directly up to the function of the entire unicellular plankton ecosystem: primary production, respiration and losses, and carbon available to production of higher trophic levels. Size-based simulation models rely on a small set of generic parameters that are universal across regions and changing climate. • INT29 • Thermal effects: linking foraging behaviour to energetic budget and species interactions > A. Arnaud SENTIS
Content : Abstract: Changes in atmospheric temperature is one of the most important drivers of global change. Despite major advances in understanding how temperature influences flows of information, energy and materials across scales from organism to ecosystem, we still lack a mechanistic framework to understand and predict how the influence of warming on individual behaviour and energetics translate to species interactions. Here, I propose to use information on the effects of temperature and body mass on animal movement and detection to predict species interaction and energy flow in food webs. I highlight this approach with a few case studies on how temperature affects predator-prey interactions and food web structure in terrestrial and aquatic systems. I then discuss the limits of the approach and the potential for integrating phenotypic and evolutionary responses to temperature changes. I conclude that considering the mechanistic links between environmental drivers and species interactions can open new avenues to better understand how global change impacts biodiversity. • INT30 • The impact of lake physics on benthic and pelagic primary production – a 2-dimensional modelling approach > S. Sebastian DIEHL
Content : Co-authors:
Hugo Harlin, hugo.harlin@umu.se, Dept. of Ecology and Environmental Science, Umea University, Umea, Sweden
Karl Larsson, karl.larsson@umu.se, Dept. of Mathematics and Mathematical Statistics, Umea University, Umea, Sweden
Åke Brännström, ake.brannstrom@umu.se, Dept. of Mathematics and Mathematical Statistics, Umea University, Umea, Sweden
Duration: 15 mins+ 5 mins questions

Abstract: Process based modelling has yielded valuable insight into the interplay of fundamental physical processes with the biogeochemistry of primary production and nutrient recycling in lakes. The vast majority of this research has focused on pelagic algae using one-dimensional (1D) models of vertical water columns. In contrast, bottom-living benthic algae have rarely been included in these models, in part because 1D models restrict benthic algae to a single depth. Overcoming this limitation, we have developed a 2D model to explore the impact of physical lake properties such as lake size (depth, surface area), bottom topography (slope) and (horizontal and vertical) mixing on the competitive interaction between benthic and pelagic algae. The model consists of coupled reaction-advection-diffusion differential equations, where primary production is limited by light and a single nutrient. The model is conceptual, so the descriptions of nutrient regeneration processes, transport processes (mixing, sedimentation, resuspension) and the geometry of the lake bottom are kept deliberately simple. We highlight two results of preliminary model explorations. (1) Under a large range of environmental conditions, the model predicts that benthic algae exhibit a biomass and production maximum at some intermediate depth within the lake, analogous to the deep chlorophyll maxima that can be found in water columns. (2) For a given depth profile and a fixed horizontal mixing intensity, the model predicts that benthic algae contribute proportionally more to total lake primary production in smaller lakes compared to larger lakes.