Jessica L. O’Connell

Jessica L. O’Connell
University of Texas at Austin | UT · Department of Marine Science

Ph.D

About

21
Publications
4,027
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348
Citations
Introduction
I am an ecologist, remote sensing specialist, and data scientist. I have broad experience in wetland science. I work on applied ecological topics, but incorporate fundamental ecological principles from multiple areas, including community ecology, ecosystem ecology, landscape ecology and global change biology. You can learn more about my projects, find copies of my publications, and more at my lab website: https://landscapemodeling.net
Additional affiliations
August 2020 - June 2021
University of Texas at Austin
Position
  • Professor (Assistant)
August 2019 - August 2020
University of Georgia
Position
  • Researcher
Description
  • Modeling productivity of wetland plants

Publications

Publications (21)
Article
Full-text available
Remote sensing can provide critical information about the health and productivity of coastal wetland ecosystems, including extent, phenology, and carbon sequestration potential. Unfortunately, periodic inundation from tides dampens the spectral signal and, in turn, causes remote sensing-based models to produce unreliable results, altering estimates...
Article
Full-text available
The Prairie Pothole Region (PPR) contains numerous depressional wetlands known as potholes that provide habitats for waterfowl and other wetland-dependent species. Mapping these wetlands is essential for identifying viable waterfowl habitat and conservation planning scenarios, yet it is a challenging task due to the small size of the potholes, and...
Article
Spatiotemporal patterns of Spartina alterniflora belowground biomass (BGB) are important for evaluating salt marsh resiliency. To solve this, we created the BERM (Belowground Ecosystem Resiliency Model), which estimates monthly BGB (30‐m spatial resolution) from freely available data such as Landsat‐8 and Daymet climate summaries. Our modeling fram...
Article
Full-text available
Light use efficiency (LUE) of salt marshes has not been well studied but is central to production efficiency models (PEMs) used for estimating gross primary production (GPP). Salt marshes are typically dominated by a species monoculture, resulting in large areas with distinct morphology and physiology. We measured eddy covariance atmospheric CO2 fl...
Article
Full-text available
Phenology studies mostly focus on variation across time or landscapes. However, phenology can vary at fine spatial scales, and these differences may be as important as long-term change from climate warming. We used high-frequency “PhenoCam” data to examine phenology of Spartina alterniflora, a foundation species native to salt marshes on the US Eas...
Article
Full-text available
Elevation differences in salt marshes result in numerous ecological consequences as a result of variation in tidal flooding. We demonstrate here that elevation differences are also negatively correlated with soil temperature on the marsh platform, irrespective of tidal flooding. Field observations of soil temperature at 10‐cm depth in a Georgia mar...
Article
Full-text available
Despite the importance of tidal ecosystems in the global carbon budget, the relationships between environmental drivers and carbon dynamics in these wetlands remain poorly understood. This limited understanding results from the challenges associated with in situ flux studies and their correlation with satellite imagery which can be affected by peri...
Article
Abstract Remote sensing in tidal marshes can provide regional assessments of wetland extent, phenology, primary production, and carbon sequestration. However, periodic tidal flooding reduces spectral reflectance, especially in the near and short-wave infrared wavelengths. Consequently, marsh vegetation time-series products that lack tidal filtering...
Article
PhenoCams are part of a national network of automated digital cameras used to assess vegetation phenology transitions. Effectively analyzing PhenoCam time-series involves eliminating scenes with poor solar illumination or high cover of non-target objects such as water. We created a smart classifier to process images from the “GCESapelo” PhenoCam, w...
Article
Soil organic carbon (SOC) is an important reservoir for atmospheric CO2 associated with climate warming. The High Plains, USA, lacks region-wide SOC estimates within playa wetlands and their adjacent watershed. Croplands often have less SOC than grasslands, and the Conservation Reserve Program (CRP; former croplands planted to grass) may return SOC...
Article
Full-text available
Broad-scale estimates of belowground biomass are needed to understand wetland resiliency and C and N cycling, but these estimates are difficult to obtain because root:shoot ratios vary considerably both within and between species. We used remotely-sensed estimates of two aboveground plant characteristics, aboveground biomass and % foliar N to explo...
Article
There is a need to quantify large-scale plant productivity in coastal marshes to understand marsh resilience to sea level rise, to help define eligibility for carbon offset credits, and to monitor impacts from land use, eutrophication and contamination. Remote monitoring of aboveground biomass of emergent wetland vegetation will help address this n...
Article
Full-text available
Coastal marshes depend on belowground biomass of roots and rhizomes to contribute to peat and soil organic carbon, accrete soil and alleviate flooding as sea level rises. For nutrient-limited plants, eutrophication has either reduced or stimulated belowground biomass depending on plant biomass allocation response to fertilization. Within a freshwat...
Article
Identifying community assembly filters is a primary ecological aim. The High Plains, a 30 million ha short-grass eco-region, is intensely cultivated. Cultivation disturbance, including plowing and eroded soil deposition down-slope of plowing, influences plant composition in depressional wetlands, such as playas, within croplands. We evaluated influ...
Article
Isolated wetlands are often degraded by agriculture, increasing sediment accretion and altering plant composition. Two common opposing wetland restoration practices are self-design vs. intensive revegeta-tion. Self-design restores hydrogeomorphology without inoculating wetland taxa into restoration sites. Self-design may not meet restoration target...
Article
Full-text available
We evaluated the influence of marsh terracing on waterbirds in Louisiana’s Chenier Plain. Terracing is a novel technique used to slow coastal marsh loss. Terracing increases marsh edge and is assumed to slow erosion, decrease pond depth, and encourage vegetation production. From April to September 2005, we monitored waterbirds in paired terraced an...
Article
Full-text available
Depressional wetlands are predominant surface hydrological features providing critical societal ecosys-tem services in the semiarid United States High Plains. Critical wetland properties may be threatened because this 30 million ha short-grass prairie largely was converted from grassland to cropland. Further, the United States Department of Agricul...
Article
Full-text available
From February to March 2005-2006, we surveyed wintering waterbirds to test effects of terracing on coastal pond use before and after Hurricane Rita. Marsh terracing is intended to slow coastal marsh loss in the Chenier Plain by slowing marsh erosion and encouraging vegetation expansion. Terraces also increase marsh edge in ponds, possibly benefitin...

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Projects

Project (1)
Project
Coastal marshes are atmospheric carbon sinks, depositing as much as 1713 g C m-2 yr-1 in soils,which has been termed "blue carbon". However, marshes are threatened by multiple causes,especially sea-level rise. Remote monitoring of landscape Gross Primary Productivity (GPP), a proxy for carbon sequestration potential, helps assess C sinks and facilitate prioritization of restoration and conservation. We propose to create a novel MODIS GPP algorithm for coastal marshes that accounts for the species specific influence of tidal inundation on plant production. We will calibrate our models across four salt and brackish marsh sites across three states (Louisiana, Mississippi, Georgia) and covering three species (Spartina alterniflora, Spartina patens, Juncus roemerianus). Models will be based on a combination of eddy covariance carbon flux data, monitoring of plant level photosynthesis, field and spectral estimates of species composition and plant biophysical variables, as well as accurate measures of marsh surface inundation. The ultimate product will be regional maps of GPP that assists with monitoring coastal marsh blue carbon across the southeastern United States. This work relies on MODIS based remote sensing to scale from carbon flux and field data to regional GPP assessments. These remote sensing GPP models will be based on two approaches: production efficiency models (PEM) which compute GPP from absorbed solar radiation and canopy photosynthesis models (CPM), based on biophysical variables including leaf area index. We will adapt PEM and CPM GPP models to tidal marshes from 500 m tide-indexed MODIS daily surface reflectance data. As part of this study, we will solve problems that complicate tidal marsh GPP estimates. For example, we will improve marsh surface flooding estimates, generate a plant-centered GPP model that reduces the influence of tidal exchange on carbon accounting, adapt new spectral biophysical indices that account for the influence of wetland moist soils on reflectance, and use chlorophyll flouremetry to measure plant level productivity during high tides, a time when eddy covariance towers can not estimate carbon flux. We also will map species composition and generate light use efficiency estimates for common coastal marsh species. Consequently, we will be able to generate species invariant and tide robust CPM and PEM plant centered GPP. The crux of this proposal is to combine multiple sources of information to generate our ultimate product, regional maps of MODIS derived plant GPP spanning 2000-2020 and perform a comprehensive phenological analysis. End-users engagement is also important. We will make end-users, such as coastal managers,aware of the tools we will develop, provide access to tools and instructional documents, and train staff to use the tools to inform decision-making. To ease this task, we will develop a Python plug-in for QGIS, an open source geospatial software and host our source code on GitHub to facilitate future and community model development. Applications of our work include estimating CO2 exchange after natural and anthropogenic disasters, modeling the influence of sea level rise on marsh health, understanding coastal C sources and sinks, and use by government agencies to assess restoration trajectories for conservation and management of critical coastal ecosystems.