The Research Groups
Scaling Imbalances: Modeling the Consequences of Stoichiometric Mismatch for Populations and Nutrient Cycling
Group leader: LM Bradley, Emory University
Consumers live in a nutritionally dynamic world, in contrast to the stoichiometric homeostasis they must maintain. Resource fluctuations can alter the nutritional quality of resources by inducing a stark imbalance between the composition of resource and consumer biomass: a stoichiometric mismatch. These can greatly impact consumer population dynamics, even if the fluctuations are temporally short (i.e., resource pulses). Yet, ecological models generally impose nutritionally static conditions onto these organisms, limiting our understanding of the underlying ecological processes and capacity to predict population dynamics and nutrient cycling under nutrient imbalances. This project aims to build a consumer-resource model at the ecological community-level to predict how consumer populations and environmental nutrient cycling are altered under (1) various intensities of stoichiometric mismatch and (2) variable temporal fluctuations. To achieve this, we will create an Agent Based Model (ABM) using a stoichiometrically-explicit individual-level bioenergetic model to address fundamental questions about the relationships between consumer/resource stoichiometric mismatches and consumer dynamics. This could provide insight into feedbacks between consumer nutrition and the environment with energetic “memory”—if a consumer ate a nitrogen-rich but phosphorus-poor meal last week, how will its population grow and cycle nutrients tomorrow?
phytoStoich: Use of model-data integration framework to better estimate phytoplankton stoichiometry and primary productivity
Group leader: Carly Olson, University of Nebraska-Lincoln
Predicting primary productivity using ecosystem models is important because it is a globally important ecosystem function that couples carbon (C), nitrogen (N), and phosphorus (P) biogeochemical cycles. Currently, aquatic ecosystem models lack mechanisms that explain the patterns and drivers of phytoplankton stoichiometry (PS) – a trait that is paramount to quantifying rates of primary productivity. We propose to leverage the forthcoming STOICH database (i.e., a database compiling field-based inland aquatic organism C, N, and P stoichiometric data) to explore spatiotemporal variation in lake PS. Specifically, we will collate both lab and field stoichiometric measurements to be used as priors in a model-data integration framework. Such efforts will improve estimates of PS – a quantity that is difficult to discern from field data. Finally, we will conduct a rigorous model comparison experiment with aquatic ecosystem models that vary in their representation of phytoplankton physiology and stoichiometric traits. The use of a model-data integration framework in combination with a model comparison analysis will simultaneously constrain our uncertainty in stoichiometric parameterization and bolster our confidence in model predictions of aquatic primary productivity.
SPACE-Stoich: Spectral Acquisition of the Composition of Elements
Group Leader: Jamie Reeves, Oklahoma State University
A central tenet of ecological stoichiometry theory asserts that elements are distributed unevenly across space. This distribution affects the ability of organisms to acquire the appropriate amount of matter in the correct elemental balance, and a rich body of work provides a foundational understanding of how organismal spatial dynamics relate to macro-elemental (e.g., carbon, nitrogen, phosphorus, etc.) fluxes. Recent technological advances in remote sensing have increased the capacity of researchers to quantify elemental composition of landscape features at broad spatial extents. Yet, few studies have taken advantage of these methods to relate the distribution of organisms to the distribution of resource stoichiometry. Given the broad array of available remote sensing products and analytical methods, this project aims to synthesize remote sensing techniques quantifying variation in stoichiometry across space and how these tools may advance understanding of ecological systems by testing stoichiometric hypotheses.
Mixo-Stoich: Ecological stoichiometry in the planktonic mixotroph paradigm
Group Leader: Luca Schenone, University of Konstanz
Mixotrophy is currently recognized as a widespread strategy among planktonic protists, allowing them to combine photo-autotrophy with nutrient uptake through phago-heterotrophy. On the other hand, the ecological stoichiometry theory has provided a framework for different studies in plankton since its foundation. However, how ecological stoichiometry is considered in the study of mixotrophic protists remains unclear. Therefore, the goal of this project is to understand how ecological stoichiometry and mixotrophy are coupled in current plankton research. We will perform a meta-analysis on literature looking for papers studying plankton at different organization levels (individuals, populations, community, and food web coupled with nutrient dynamics) considering both mixotrophy and ecological stoichiometry. We want to address: if different mixotrophic functional groups involve different homeostatic and stoichiometric strategies; how elemental stoichiometry (or stoichiometric homeostasis) of planktonic mixotrophs affect higher food web levels; and how ecological stoichiometry of mixotrophs is linked with environmental conditions.
Stoichi-omics: Ecological Stoichiometry in the Age of Omics
Group Leader: Catriona Jones, Purdue University
Historically, microbes have been the ‘black boxes’ of ecological stoichiometry; we can infer their considerable effects on nutrient dynamics in the ecosystem using data on ambient nutrient fluxes but the fate of elements within the cell, and the effect of external processes on intra-cellular nutrient dynamics, remain largely unexplored. In ecological stoichiometry, mass balance modeling has been a foundational tool for quantifying nutrient fluxes at an ecosystem level and has been vital in generating and testing hypotheses since the field’s inception. We propose to take the principles of ecosystem mass balance modeling and scale them to an individual microbial cell. We will combine mass balance modeling with single-cell stoichiometry data, metabolic pathway maps, and data on the chemical stoichiometry of metabolic pathways to produce a single-cell mass balance model. By combining cellular omics with ecological stoichiometry, we can gain powerful insights into microbial constraints and controls on global biogeochemical cycles.