New NSF Funding! Coastal Marsh Response to CO2

We’ve been awarded a new grant from NSF (DEB- Ecosystem Studies)  for our project on Eco-evolutionary dynamics of coastal marsh responses to rishing CO2!

Here’s the overview of our proposed work:

Current projections of coastal marsh ecosystem responses to global environmental change (i.e., rising atmospheric carbon dioxide, sea level rise) assume static relationships between climate-related stressors and plant performance. However, the performance of ecologically dominant plants can rapidly evolve in response to climate-related stressors, which suggests that evolution could be driving ecological change in coastal marshes. Utilizing a 100+ year seed bank of the foundational sedge Schoenoplectus americanus (formerly Scirpus olneyi), we aim to examine the evolutionary dimensions of coastal marsh responses to environmental change. Building on preliminary findings, our goal is to determine whether ecosystem processes that regulate surface elevation are shaped by heritable responses of S. americanus to salinity, inundation, and carbon availability. To do so, we will first propagate and genotype modern and “ancestral” cohorts of plants from seeds retrieved from Chesapeake Bay marsh sediment cores. Taking ecological interactions into consideration, we will then conduct fully crossed exposure experiments with contemporary and ancestral cohorts to measure heritable, non-heritable and environmental contributions to plant traits, growth, and surface elevation. We will assess the aggregate importance of evolution to carbon and sediment accumulation (and allied processes) through a mechanistic model that accounts for data and model uncertainty. We will validate model predictions against independent estimates from sediment core profiles, and we will use a Bayesian assimilation framework to integrate all data streams (i.e., experiments, paleo observations, model predictions, prior studies) to determine the extent to which a century of evolution has affected Chesapeake Bay marshes, and how further data collection and model improvements might strengthen predictive forecasting.

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