Uncertainty in United States coastal wetland greenhouse gas inventorying

© The Author(s), 2018. This article is distributed under the terms of the Creative Commons Attribution License. The definitive version was published in Environmental Research Letters 13 (2018): 115005, doi:10.1088/1748-9326/aae157.Coastal wetlands store carbon dioxide (CO2) and emit CO2 and methane (CH4) making them an important part of greenhouse gas (GHG) inventorying. In the contiguous United States (CONUS), a coastal wetland inventory was recently calculated by combining maps of wetland type and change with soil, biomass, and CH4 flux data from a literature review. We assess uncertainty in this developing carbon monitoring system to quantify confidence in the inventory process itself and to prioritize future research. We provide a value-added analysis by defining types and scales of uncertainty for assumptions, burial and emissions datasets, and wetland maps, simulating 10 000 iterations of a simplified version of the inventory, and performing a sensitivity analysis. Coastal wetlands were likely a source of net-CO2-equivalent (CO2e) emissions from 2006–2011. Although stable estuarine wetlands were likely a CO2e sink, this effect was counteracted by catastrophic soil losses in the Gulf Coast, and CH4 emissions from tidal freshwater wetlands. The direction and magnitude of total CONUS CO2e flux were most sensitive to uncertainty in emissions and burial data, and assumptions about how to calculate the inventory. Critical data uncertainties included CH4 emissions for stable freshwater wetlands and carbon burial rates for all coastal wetlands. Critical assumptions included the average depth of soil affected by erosion events, the method used to convert CH4 fluxes to CO2e, and the fraction of carbon lost to the atmosphere following an erosion event. The inventory was relatively insensitive to mapping uncertainties. Future versions could be improved by collecting additional data, especially the depth affected by loss events, and by better mapping salinity and inundation gradients relevant to key GHG fluxes. Social Media Abstract: US coastal wetlands were a recent and uncertain source of greenhouse gasses because of CH4 and erosion.Financial support was provided primarily by NASA Carbon Monitoring Systems (NNH14AY67I) and the USGS Land Carbon Program, with additional support from The Smithsonian Institution, The Coastal Carbon Research Coordination Network (DEB-1655622), and NOAA Grant: NA16NMF4630103

Using gene co-expression network analysis to predict biomarkers for chronic lymphocytic leukemia

<p>Abstract</p> <p>Background</p> <p>Chronic lymphocytic leukemia (CLL) is the most common adult leukemia. It is a highly heterogeneous disease, and can be divided roughly into indolent and progressive stages based on classic clinical markers. Immunoglobin heavy chain variable region (IgV_H) mutational status was found to be associated with patient survival outcome, and biomarkers linked to the IgV_H status has been a focus in the CLL prognosis research field. However, biomarkers highly correlated with IgV_H mutational status which can accurately predict the survival outcome are yet to be discovered.</p> <p>Results</p> <p>In this paper, we investigate the use of gene co-expression network analysis to identify potential biomarkers for CLL. Specifically we focused on the co-expression network involving ZAP70, a well characterized biomarker for CLL. We selected 23 microarray datasets corresponding to multiple types of cancer from the Gene Expression Omnibus (GEO) and used the frequent network mining algorithm CODENSE to identify highly connected gene co-expression networks spanning the entire genome, then evaluated the genes in the co-expression network in which ZAP70 is involved. We then applied a set of feature selection methods to further select genes which are capable of predicting IgV_H mutation status from the ZAP70 co-expression network.</p> <p>Conclusions</p> <p>We have identified a set of genes that are potential CLL prognostic biomarkers IL2RB, CD8A, CD247, LAG3 and KLRK1, which can predict CLL patient IgV_H mutational status with high accuracies. Their prognostic capabilities were cross-validated by applying these biomarker candidates to classify patients into different outcome groups using a CLL microarray datasets with clinical information.</p

Use of historical remote sensing to link watershed land use change and wetland vegetation response in a California estuary

Elkhorn Slough, a National Estuarine Research Reserve, is one of the most important estuarine systems in California. Despite the protection status of Elkhorn Slough, agriculture in its watershed has increased dramatically since 1970. Associated with this agriculture is an increase in sedimentation, which deposits in creek bottoms, marshes, mudflats, and channels. This sedimentation may alter wetland structural features, which are a major component of habitat for rare species, and drive patterns of plant and animal community assemblages. This research addresses the questions: Has the structure of salt marsh wetlands within Elkhorn Slough changed since 1970? If yes, can these changes be linked to increased agriculture? Are there differences in wetland changes between sub-catchments of the watershed? The following wetland features will be examined: plant community type and distribution, width of tidal channels, length of channels, and sinuosity of the channel network. If the research identifies changes to these features, effort will be made to link them to changes in upland features indicative of increased sediment through multiple regression analysis. Remote sensing technologies will be used to quantify historic modifications to the Elkhorn Slough watershed and wetlands. Landsat Multispectral Scanner (MSS) and Thematic Mapper (TM) imagery will be analyzed to quantify the extent of land use change from 1973 to 2000, through the process of land use classification and a post-classification change analysis. Color and color infra-red aerial photos will be used in conjunction with Geographic Information Systems (GIS) to measure changes in wetland structural features over this same time period. This research will assess the extent of habitat degradation in tidal channels, which serve as a nursery for juvenile fish in Elkhorn Slough and Monterey Bay. The research will also provide information about wetland conditions prior to intensive agriculture, and the extent to which changes have occurred, which the Natural Resources Conservation Service and the Elkhorn Slough Foundation will use to guide decisions related to wetland restoration and management

Use of remote sensing to link watershed land use change and wetland vegetation response in a California coastal watershed

While Elkhorn Slough wetlands are protected by a State ecological reserve and NOAA research reserve, intensified farming in the watershed has led to high soil erosion rates and sedimentation into the slough, where several sediment fans have formed at the base of slopes. The goal of this study was to determine how watershed land use change (i.e., increasing agriculture) and associated sedimentation over 30 years influenced changes in salt marsh soil physical properties, and in turn, plant composition through the use of remote sensing, GIS, and field methods. Objectives were to use current and historic aerial photographs to map historic wetland plant communities and quantify the extent and type of salt marsh vegetation change on sediment fans from 1971 to the present, and to explore the driving factors of this change through field sampling of marsh soil properties and topographic features. Analysis of historic aerial photos demonstrated that the formation of sediment fans in pickleweed salt marsh led to the expansion of arroyo willow into the marsh plain. A cattail and bulrush plant community also formed on the sediment fans, forming a band between the willow and pickleweed. This vegetation change occurred as sedimentation led to lower salinity levels, lower soil moisture, higher bulk density, higher elevation, and an increase in soil sand content. Resource managers are actively working with farmers to reduce soil erosion and off-farm sediment. Evidence of habitat change from sedimentation documented in this study supports the need for these continued erosion control efforts. As these efforts continue to reduce sedimentation into the slough, it would be valuable to continue to monitor the sediment fans to determine if any recovery in salt marsh vegetation occurs

A Hybrid Model for Mapping Relative Differences in Belowground Biomass and Root: Shoot Ratios Using Spectral Reflectance, Foliar N and Plant Biophysical Data within Coastal Marsh

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 explore biomass allocation in low diversity freshwater impounded peatlands (Sacramento-San Joaquin River Delta, CA, USA). We developed a hybrid modeling approach to relate remotely-sensed estimates of % foliar N (a surrogate for environmental N and plant available nutrients) and aboveground biomass to field-measured belowground biomass for species specific and mixed species models. We estimated up to 90% of variation in foliar N concentration using partial least squares (PLS) regression of full-spectrum field spectrometer reflectance data. Landsat 7 reflectance data explained up to 70% of % foliar N and 67% of aboveground biomass. Spectrally estimated foliar N or aboveground biomass had negative relationships with belowground biomass and root:shoot ratio in both Schoenoplectus acutus and Typha, consistent with a balanced growth model, which suggests plants only allocate growth belowground when additional nutrients are necessary to support shoot development. Hybrid models explained up to 76% of variation in belowground biomass and 86% of variation in root:shoot ratio. Our modeling approach provides a method for developing maps of spatial variation in wetland belowground biomass

Remote Sensing for Wetland Mapping and Historical Change Detection at the Nisqually River Delta

Coastal wetlands are important ecosystems for carbon storage and coastal resilience to climate change and sea-level rise. As such, changes in wetland habitat types can also impact ecosystem functions. Our goal was to quantify historical vegetation change within the Nisqually River watershed relevant to carbon storage, wildlife habitat, and wetland sustainability, and identify watershed-scale anthropogenic and hydrodynamic drivers of these changes. To achieve this, we produced time-series classifications of habitat, photosynthetic pathway functional types and species in the Nisqually River Delta for the years 1957, 1980, and 2015. Using an object-oriented approach, we performed a hierarchical classification on historical and current imagery to identify change within the watershed and wetland ecosystems. We found a 188.4 ha (79%) increase in emergent marsh wetland within the Nisqually River Delta between 1957 and 2015 as a result of restoration efforts that occurred in several phases through 2009. Despite these wetland gains, a total of 83.1 ha (35%) of marsh was lost between 1957 and 2015, particularly in areas near the Nisqually River mouth due to erosion and shifting river channels, resulting in a net wetland gain of 105.4 ha (44%). We found the trajectory of wetland recovery coincided with previous studies, demonstrating the role of remote sensing for historical wetland change detection as well as future coastal wetland monitoring

Remotely-Sensed Indicators of N-Related Biomass Allocation in <i>Schoenoplectus acutus</i>

<div><p>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 freshwater wetland impoundment receiving minimal sediments, we used experimental plots to explore growth models for a common freshwater macrophyte, <i>Schoenoplectus acutus</i>. We used N-addition and control plots (4 each) to test whether remotely sensed vegetation indices could predict leaf N concentration, root:shoot ratios and belowground biomass of <i>S. acutus</i>. Following 5 months of summer growth, we harvested whole plants, measured leaf N and total plant biomass of all above and belowground vegetation. Prior to harvest, we simulated measurement of plant spectral reflectance over 164 hyperspectral Hyperion satellite bands (350–2500 nm) with a portable spectroradiometer. N-addition did not alter whole plant, but reduced belowground biomass 36% and increased aboveground biomass 71%. We correlated leaf N concentration with known N-related spectral regions using all possible normalized difference (ND), simple band ratio (SR) and first order derivative ND (FDN) and SR (FDS) vegetation indices. FDN_{1235, 549} was most strongly correlated with leaf N concentration and also was related to belowground biomass, the first demonstration of spectral indices and belowground biomass relationships. While <i>S. acutus</i> exhibited balanced growth (reduced root:shoot ratio with respect to nutrient addition), our methods also might relate N-enrichment to biomass point estimates for plants with isometric root growth. For isometric growth, foliar N indices will scale equivalently with above and belowground biomass. Leaf N vegetation indices should aid in scaling-up field estimates of biomass and assist regional monitoring.</p></div