The ecosystem effects of rapidly evolving invasive species: A novel framework for the experimental study of nutrient fluxes


Main funder

Funder's project number: 295941


Funds granted by main funder (€)

  • 455 810,00


Funding program


Project timetable

Project start date: 01/09/2016

Project end date: 31/08/2020


Summary

Ecosystem services to humans, from productivity to water quality, are ultimately governed by nutrient cycling, which determines the supply of elements necessary for life. Managing ecosystems in a changing world thus requires our ability to understand how nutrient cycles respond to disturbance, such of the spread of an invasive species. When they colonize new ecosystems, invasive species alter the structure of food webs, and with it nutrient cycling. However, our ability to predict the direction and magnitude of their effects on nutrient cycling is poor. One reason is that invasive species often evolve rapidly to adapt to their new environment, changing the way they affect the ecosystem. Another reason is that we lack a robust statistical and mathematical framework to empirically link species evolution to nutrient cycles. In this proposal we develop a new toolbox of statistical and mathematical methods to analyze data from tracer additions experiments in order to understand the effects of single species, and their changes, on the dynamics of nutrients. We will do so by using as a case study the effects of Trinidadian guppies, and their rapid evolution, to the nitrogen cycle as they invade headwater streams. Studying the effects of species evolution on ecosystems is not only of practical importance to the management of invasive species in a changing world, but also of central relevance to eco-evolutionary theory. Our framework combines the statistical reconstruction of matrix representations of nutrient cycles, with the the analysis of those matrices to derive ecosystem properties. The method allows: (1) the estimation of nutrient uptake, retention, and excretion rates between compartments, (2) the statistical estimation of nutrient fluxes and derivation of emergent properties of interest such as cycling rates, and (3) the quantification of different compartment (or species) contributions to those properties. By making our approach explicitly statistical, it calculates uncertainty measures for all estimates and is well suited for formal hypothesis testing and model comparison. This allows, for example, the statistical comparison of ecosystems, or the testing of assumptions regarding the presence of a given trophic link. While developing this toolbox, applicable to a wide range of systems and questions, the project will also make an important contribution to the study of species invasions and the effects of evolution on ecosystem processes.


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Last updated on 2021-17-03 at 12:07