ORCID Profile
0000-0002-2324-2518
Current Organisation
Boston University
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Ecological Impacts of Climate Change | Ecological Applications | Conservation and Biodiversity
Sparseland, Permanent Grassland and Arid Zone Flora, Fauna and Biodiversity |
Publisher: American Geophysical Union (AGU)
Date: 05-2014
DOI: 10.1002/2013JG002553
Publisher: Wiley
Date: 30-03-2012
Publisher: Wiley
Date: 25-03-2013
DOI: 10.1111/GCB.12164
Abstract: Predicted responses of transpiration to elevated atmospheric CO2 concentration (eCO2 ) are highly variable amongst process-based models. To better understand and constrain this variability amongst models, we conducted an intercomparison of 11 ecosystem models applied to data from two forest free-air CO2 enrichment (FACE) experiments at Duke University and Oak Ridge National Laboratory. We analysed model structures to identify the key underlying assumptions causing differences in model predictions of transpiration and canopy water use efficiency. We then compared the models against data to identify model assumptions that are incorrect or are large sources of uncertainty. We found that model-to-model and model-to-observations differences resulted from four key sets of assumptions, namely (i) the nature of the stomatal response to elevated CO2 (coupling between photosynthesis and stomata was supported by the data) (ii) the roles of the leaf and atmospheric boundary layer (models which assumed multiple conductance terms in series predicted more decoupled fluxes than observed at the broadleaf site) (iii) the treatment of canopy interception (large intermodel variability, 2-15%) and (iv) the impact of soil moisture stress (process uncertainty in how models limit carbon and water fluxes during moisture stress). Overall, model predictions of the CO2 effect on WUE were reasonable (intermodel μ = approximately 28% ± 10%) compared to the observations (μ = approximately 30% ± 13%) at the well-coupled coniferous site (Duke), but poor (intermodel μ = approximately 24% ± 6% observations μ = approximately 38% ± 7%) at the broadleaf site (Oak Ridge). The study yields a framework for analysing and interpreting model predictions of transpiration responses to eCO2 , and highlights key improvements to these types of models.
Publisher: Wiley
Date: 21-05-2014
DOI: 10.1111/NPH.12847
Abstract: Elevated atmospheric CO 2 concentration ( eCO 2 ) has the potential to increase vegetation carbon storage if increased net primary production causes increased long‐lived biomass. Model predictions of eCO 2 effects on vegetation carbon storage depend on how allocation and turnover processes are represented. We used data from two temperate forest free‐air CO 2 enrichment ( FACE ) experiments to evaluate representations of allocation and turnover in 11 ecosystem models. Observed eCO 2 effects on allocation were dynamic. Allocation schemes based on functional relationships among biomass fractions that vary with resource availability were best able to capture the general features of the observations. Allocation schemes based on constant fractions or resource limitations performed less well, with some models having unintended outcomes. Few models represent turnover processes mechanistically and there was wide variation in predictions of tissue lifespan. Consequently, models did not perform well at predicting eCO 2 effects on vegetation carbon storage. Our recommendations to reduce uncertainty include: use of allocation schemes constrained by biomass fractions careful testing of allocation schemes and synthesis of allocation and turnover data in terms of model parameters. Data from intensively studied ecosystem manipulation experiments are invaluable for constraining models and we recommend that such experiments should attempt to fully quantify carbon, water and nutrient budgets.
Publisher: Springer Science and Business Media LLC
Date: 21-05-2015
DOI: 10.1038/NCLIMATE2621
Publisher: Wiley
Date: 24-10-2017
DOI: 10.1111/GCB.13910
Abstract: Numerous current efforts seek to improve the representation of ecosystem ecology and vegetation demographic processes within Earth System Models (ESMs). These developments are widely viewed as an important step in developing greater realism in predictions of future ecosystem states and fluxes. Increased realism, however, leads to increased model complexity, with new features raising a suite of ecological questions that require empirical constraints. Here, we review the developments that permit the representation of plant demographics in ESMs, and identify issues raised by these developments that highlight important gaps in ecological understanding. These issues inevitably translate into uncertainty in model projections but also allow models to be applied to new processes and questions concerning the dynamics of real-world ecosystems. We argue that stronger and more innovative connections to data, across the range of scales considered, are required to address these gaps in understanding. The development of first-generation land surface models as a unifying framework for ecophysiological understanding stimulated much research into plant physiological traits and gas exchange. Constraining predictions at ecologically relevant spatial and temporal scales will require a similar investment of effort and intensified inter-disciplinary communication.
Publisher: Wiley
Date: 19-10-2020
DOI: 10.1002/ECM.1423
Publisher: Wiley
Date: 2023
DOI: 10.1002/ECS2.4342
Abstract: Synthesis research in ecology and environmental science improves understanding, advances theory, identifies research priorities, and supports management strategies by linking data, ideas, and tools. Accelerating environmental challenges increases the need to focus synthesis science on the most pressing questions. To leverage input from the broader research community, we convened a virtual workshop with participants from many countries and disciplines to examine how and where synthesis can address key questions and themes in ecology and environmental science in the coming decade. Seven priority research topics emerged: (1) ersity, equity, inclusion, and justice (DEIJ), (2) human and natural systems, (3) actionable and use‐inspired science, (4) scale, (5) generality, (6) complexity and resilience, and (7) predictability. Additionally, two issues regarding the general practice of synthesis emerged: the need for increased participant ersity and inclusive research practices and increased and improved data flow, access, and skill‐building. These topics and practices provide a strategic vision for future synthesis in ecology and environmental science.
Publisher: Springer Science and Business Media LLC
Date: 29-06-2023
Publisher: Wiley
Date: 28-11-2016
DOI: 10.1111/NPH.14283
Abstract: Accurate representation of photosynthesis in terrestrial biosphere models ( TBM s) is essential for robust projections of global change. However, current representations vary markedly between TBM s, contributing uncertainty to projections of global carbon fluxes. Here we compared the representation of photosynthesis in seven TBM s by examining leaf and canopy level responses of photosynthetic CO 2 assimilation ( A ) to key environmental variables: light, temperature, CO 2 concentration, vapor pressure deficit and soil water content. We identified research areas where limited process knowledge prevents inclusion of physiological phenomena in current TBM s and research areas where data are urgently needed for model parameterization or evaluation. We provide a roadmap for new science needed to improve the representation of photosynthesis in the next generation of terrestrial biosphere and Earth system models.
Publisher: Wiley
Date: 11-08-2022
Abstract: Ecological forecasting provides a powerful set of methods for predicting short‐ and long‐term change in living systems. Forecasts are now widely produced, enabling proactive management for many applied ecological problems. However, despite numerous calls for an increased emphasis on prediction in ecology, the potential for forecasting to accelerate ecological theory development remains underrealized. Here, we provide a conceptual framework describing how ecological forecasts can energize and advance ecological theory. We emphasize the many opportunities for future progress in this area through increased forecast development, comparison and synthesis. Our framework describes how a forecasting approach can shed new light on existing ecological theories while also allowing researchers to address novel questions. Through rigorous and repeated testing of hypotheses, forecasting can help to refine theories and understand their generality across systems. Meanwhile, synthesizing across forecasts allows for the development of novel theory about the relative predictability of ecological variables across forecast horizons and scales. We envision a future where forecasting is integrated as part of the toolset used in fundamental ecology. By outlining the relevance of forecasting methods to ecological theory, we aim to decrease barriers to entry and broaden the community of researchers using forecasting for fundamental ecological insight.
Publisher: Elsevier BV
Date: 03-2021
Publisher: American Geophysical Union (AGU)
Date: 18-08-2021
DOI: 10.1029/2020RG000715
Abstract: A reanalysis is a physically consistent set of optimally merged simulated model states and historical observational data, using data assimilation. High computational costs for modeled processes and assimilation algorithms has led to Earth system specific reanalysis products for the atmosphere, the ocean and the land separately. Recent developments include the advanced uncertainty quantification and the generation of biogeochemical reanalysis for land and ocean. Here, we review atmospheric and oceanic reanalyzes, and more in detail biogeochemical ocean and terrestrial reanalyzes. In particular, we identify land surface, hydrologic and carbon cycle reanalyzes which are nowadays produced in targeted projects for very specific purposes. Although a future joint reanalysis of land surface, hydrologic, and carbon processes represents an analysis of important ecosystem variables, biotic ecosystem variables are assimilated only to a very limited extent. Continuous data sets of ecosystem variables are needed to explore biotic‐abiotic interactions and the response of ecosystems to global change. Based on the review of existing achievements, we identify five major steps required to develop terrestrial ecosystem reanalysis to deliver continuous data streams on ecosystem dynamics.
Publisher: Wiley
Date: 28-01-2014
DOI: 10.1111/NPH.12697
Abstract: We analysed the responses of 11 ecosystem models to elevated atmospheric [ CO 2 ] (e CO 2 ) at two temperate forest ecosystems ( D uke and Oak Ridge National Laboratory ( ORNL ) F ree‐ A ir CO 2 E nrichment ( FACE ) experiments) to test alternative representations of carbon ( C )–nitrogen ( N ) cycle processes. We decomposed the model responses into component processes affecting the response to e CO 2 and confronted these with observations from the FACE experiments. Most of the models reproduced the observed initial enhancement of net primary production ( NPP ) at both sites, but none was able to simulate both the sustained 10‐yr enhancement at D uke and the declining response at ORNL : models generally showed signs of progressive N limitation as a result of lower than observed plant N uptake. Nonetheless, many models showed qualitative agreement with observed component processes. The results suggest that improved representation of above‐ground–below‐ground interactions and better constraints on plant stoichiometry are important for a predictive understanding of e CO 2 effects. Improved accuracy of soil organic matter inventories is pivotal to reduce uncertainty in the observed C – N budgets. The two FACE experiments are insufficient to fully constrain terrestrial responses to eCO 2 , given the complexity of factors leading to the observed erging trends, and the consequential inability of the models to explain these trends. Nevertheless, the ecosystem models were able to capture important features of the experiments, lending some support to their projections.
Publisher: Wiley
Date: 05-2014
DOI: 10.1890/ES13-00273.1
Location: United States of America
Start Date: 2021
End Date: 12-2024
Amount: $371,000.00
Funder: Australian Research Council
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