Research
Keywords: ecosystem modeling, terrestrial carbon cycle, eddy covariance, forest management, data assimilation, climate change, scaling, structure-function relationships
I specialize in the patterns, mechanistic relationships, and scale dependencies that characterize interactions between the land surface and the overlying atmosphere, as well as the influence of management on those interactions, particularly within the context of ongoing global climate change.
I specialize in the patterns, mechanistic relationships, and scale dependencies that characterize interactions between the land surface and the overlying atmosphere, as well as the influence of management on those interactions, particularly within the context of ongoing global climate change.
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Current Projects
Interactive effects of management and climate change on forest function
Forests blanket approximately 30% of the land surface and interact with the atmosphere to provide a suite of complimentary climate feedbacks, including serving as a significant global carbon sink. Human management fundamentally alters land-atmosphere exchanges of carbon, water, and energy, as well as resultant interactions with climate. However, gaps remain in our understanding of the long-term impacts of management on forest function, how impacts will interact with climate change, and the spatial scales that define these relationships.
Here, we assessed the response of forest function, defined primarily as carbon, water, and energy cycling, to four management regimes (production, preservation, passive, and ecological forestry) and two climate change scenarios (RCP4.5 and RCP8.5), across two U.S. regions, the Southeast and Great Lakes regions. We used the Ecosystem Demography model to simulate forest dynamics from 2006-2100, and defined the dominant axes of functional variability through principal component analysis. Random forests were used to determine the relative importance of climate and management as drivers of functional variability and test for regional dependencies.
Paper in prep, stay tuned...
Mechanistic links between forest structure and function
Structurally complex forests optimize light and water resources to assimilate carbon more effectively, leading to higher productivity. Information obtained from Light Detection and Ranging (LiDAR)-derived canopy structural complexity (CSC) metrics across spatial scales serves as a powerful indicator of ecosystem-scale functions such as gross primary productivity (GPP). However, our understanding of mechanistic links between forest structure and function, and the impact of disturbance on the relationship, is limited.
Here, we paired eddy covariance measurements of carbon and water fluxes in temperate forests collected in the Chequamegon Heterogeneous Ecosystem Energy-balance Study Enabled by a High-density Extensive Array of Detectors (CHEESEHEAD) field campaign with drone LiDAR measurements of CSC to establish which CSC metrics were strong drivers of GPP, and tested potential mediators of the relationship. Mechanistic relationships were inspected at five metric calculation resolutions (0.25m, 2m, 10m, 25m, and 50m) to determine whether relationships persisted with scale. Vertical heterogeneity metrics were the most influential in predicting productivity for forests with a significant degree of heterogeneity in management, forest type, and species composition. CSC metrics included in the structure-function relationship as well as the strength of drivers was dependent on metric calculation resolution. Structural equation modeling showed that the relationship was not direct, but was mediated by light use efficiency (LUE) and water use efficiency (WUE), with WUE being a stronger mediator and driver of GPP.
These findings allow us to improve representation in ecosystem models of how CSC impacts light and water-sensitive processes, and ultimately GPP. Improved models enhance our ability to simulate true ecosystem responses to management, resulting in a more accurate assessment of forest responses to management regimes and furthering our ability to assess climate mitigation and strategies.
Paper link (Murphy et al., 2022): https://doi.org/10.1029/2021JG006748
Master's Thesis
Emulation-based data assimilation to constrain a dynamic ecosystem model
I utilized novel emulation-based Bayesian parameter data assimilation (PDA) techniques (first applied by Fer et al., 2018) to constrain the Ecosystem Demography model version 2.2 (ED2.2), a dynamic ecosystem model. The model was calibrated to my northern Wisconsin research site using the data assimilation modules built into the Predictive Ecosystem Analyzer (PEcAn), an open-source ecoinformatics workflow, and observations collected by the WLEF EC flux tall tower. I sought to determine whether PDA using a Bayesian emulator approach could reduce individual parameter uncertainty, and if this method could reduce overall model predictive uncertainty related to carbon cycle dynamics in a structurally complex model.
Interactive effects of management and climate change on forest function
Forests blanket approximately 30% of the land surface and interact with the atmosphere to provide a suite of complimentary climate feedbacks, including serving as a significant global carbon sink. Human management fundamentally alters land-atmosphere exchanges of carbon, water, and energy, as well as resultant interactions with climate. However, gaps remain in our understanding of the long-term impacts of management on forest function, how impacts will interact with climate change, and the spatial scales that define these relationships.
Here, we assessed the response of forest function, defined primarily as carbon, water, and energy cycling, to four management regimes (production, preservation, passive, and ecological forestry) and two climate change scenarios (RCP4.5 and RCP8.5), across two U.S. regions, the Southeast and Great Lakes regions. We used the Ecosystem Demography model to simulate forest dynamics from 2006-2100, and defined the dominant axes of functional variability through principal component analysis. Random forests were used to determine the relative importance of climate and management as drivers of functional variability and test for regional dependencies.
Paper in prep, stay tuned...
Mechanistic links between forest structure and function
Structurally complex forests optimize light and water resources to assimilate carbon more effectively, leading to higher productivity. Information obtained from Light Detection and Ranging (LiDAR)-derived canopy structural complexity (CSC) metrics across spatial scales serves as a powerful indicator of ecosystem-scale functions such as gross primary productivity (GPP). However, our understanding of mechanistic links between forest structure and function, and the impact of disturbance on the relationship, is limited.
Here, we paired eddy covariance measurements of carbon and water fluxes in temperate forests collected in the Chequamegon Heterogeneous Ecosystem Energy-balance Study Enabled by a High-density Extensive Array of Detectors (CHEESEHEAD) field campaign with drone LiDAR measurements of CSC to establish which CSC metrics were strong drivers of GPP, and tested potential mediators of the relationship. Mechanistic relationships were inspected at five metric calculation resolutions (0.25m, 2m, 10m, 25m, and 50m) to determine whether relationships persisted with scale. Vertical heterogeneity metrics were the most influential in predicting productivity for forests with a significant degree of heterogeneity in management, forest type, and species composition. CSC metrics included in the structure-function relationship as well as the strength of drivers was dependent on metric calculation resolution. Structural equation modeling showed that the relationship was not direct, but was mediated by light use efficiency (LUE) and water use efficiency (WUE), with WUE being a stronger mediator and driver of GPP.
These findings allow us to improve representation in ecosystem models of how CSC impacts light and water-sensitive processes, and ultimately GPP. Improved models enhance our ability to simulate true ecosystem responses to management, resulting in a more accurate assessment of forest responses to management regimes and furthering our ability to assess climate mitigation and strategies.
Paper link (Murphy et al., 2022): https://doi.org/10.1029/2021JG006748
Master's Thesis
Emulation-based data assimilation to constrain a dynamic ecosystem model
I utilized novel emulation-based Bayesian parameter data assimilation (PDA) techniques (first applied by Fer et al., 2018) to constrain the Ecosystem Demography model version 2.2 (ED2.2), a dynamic ecosystem model. The model was calibrated to my northern Wisconsin research site using the data assimilation modules built into the Predictive Ecosystem Analyzer (PEcAn), an open-source ecoinformatics workflow, and observations collected by the WLEF EC flux tall tower. I sought to determine whether PDA using a Bayesian emulator approach could reduce individual parameter uncertainty, and if this method could reduce overall model predictive uncertainty related to carbon cycle dynamics in a structurally complex model.
