Geology topics

Kristin DeMarco

Publications and source records attributed to Kristin DeMarco.

6 recordsLinked to original sources

USGS2023

2023 Coastal master plan: ICM-wetlands – Submerged aquatic vegetation (SAV) updates

Submerged aquatic vegetation (SAV) provides critical structural habitat for valuable nekton and wildlife species across coastal ecosystems and can buffer the negative effects of land loss. Landscape change and restoration efforts across coastal Louisiana can impact the occurrence, coverage, and species assemblages of SAV, and changes to these foundational species can have cascading impacts across food webs. To support the 2023 Coastal Master Plan efforts, a unique SAV model was developed to assess coverage and occurrence of SAV across aquatic waterbodies in response to environmental variables evaluated. This effort created a spatial model describing the probability of presence of SAV across the study area in response to changing conditions over the modeled time period. To develop the initial coverage data layer, we used remotely sensed Normalized Difference Vegetation Index (NDVI) and modified Normalized Difference Water Index (mNDWI) data from 2015-2018 to identify areas containing variable vegetation and water spectral reflectance. Key environmental variables evaluated included total suspended sediments (TSS), salinity, and physical exposure. Seasonal estimates for TSS and salinity were used, as research indicates that seasonal environmental variability is a significant driver for SAV establishment. Seasonal salinity was derived from Coast-wide Reference Monitoring Station (CRMS) data, and seasonal TSS was estimated from hyperspectral imagery. Estimates of physical exposure have previously been provided by calculating fetch (the distance across water over which waves can propagate), but this proved to be too computationally intensive to be feasible, and we found distance to land to be a reasonable proxy for exposure. To represent geographic conditions and historical factors influences on SAV establishment and occurrence (e.g., variables too numerous and complex to model) we developed a basin variable that served as a proxy for complex historical, or prior, conditions, determined by the forested, fresh, intermediate, brackish, or saline (FFIBS) score. The final model included spring TSS, spring salinity, distance to land, and the basin prior. The model performed well for the area evaluated, correctly classifying SAV (as present or absent) 89% of the time (Kappa = 580). SAV probability of presence responded as expected to change in these environmental variables, with likelihood of occurrence decreasing in response to increasing spring TSS, spring salinity, and distance to land. However, the model was more accurate at predicting absence (true negative = 0.940) than predicting presence (true positive = 0.626), suggesting that the scale of the model may limit the ability to predict presence. Moreover, the simplicity of the model limited the accuracy in highly dynamic environments, for example near the outflow of diversions or areas of significant changes in salinity or TSS. Through incorporating underwater communities like SAV, this master plan provides a holistic view of coastal change and restoration. To create healthy ecological structure and function in wetland habitats, the submergent communities must be considered alongside the emergent habitats. As the benefits of SAV are increasingly recognized, both here in Louisiana and beyond, SAV restoration and the use of SAV communities in assessing and improving ecological condition are becoming more common.

Louisiana↗

USGS2020

2023 Coastal master plan: Model improvement plan, ICM-wetlands, vegetation, and soil

As part of the model improvement effort for the 2023 Coastal Master Plan, the wetland processes captured by the morphology and vegetation models used during previous master plans were reevaluated to assess how Integrated Compartment Model (ICM) subroutines could be improved. This process considered technical reviews, comments, and suggested improvements provided by model developers, advisory groups, and other experts during previous master plan cycles. The availability of new data and information that could be used to make model improvements was also considered. In many cases, the team considered and tested multiple options or approaches. As a result of this effort, recommended improvements are provided here. The improvements recommended to be included in the 2023 Coastal Master Plan include: adjusting marsh collapse thresholds, refining organic matter accretion calculations, developing an unstructured grid for modeling vegetation, improving flotant marsh and forested wetlands algorithms, creating and applying an updated map of existing vegetation, adjusting model code, and updating the submerged aquatic vegetation (SAV) module. This report describes the team’s work through a series of 7 distinct activities to identify and test options for model improvements to ensure the updated ICM used for the 2023 Coastal Master Plan appropriately captures ecological and morphological processes observed in Coastal Louisiana. As appropriate, relevant literature and data are discussed. Test runs to evaluate how changes influence model outputs are also documented. A final list of recommended updates, taking into account consideration of all options and results from test runs, is summarized at the end of the report. A later report will describe the final ICM-LAVegMod and ICM-Morph subroutines for the 2023 Coastal Master Plan, detailing the updates that have been incorporated.

Louisiana↗

USGS2019

Salinity and water clarity dictate seasonal variability in coastal submerged aquatic vegetation in subtropical estuarine environments

Spatial and temporal variability characterize submerged aquatic vegetation (SAV) assemblages, but understanding the complex interactions of environmental drivers of SAV assemblages remains elusive. We documented SAV composition and biomass across a salinity gradient in a coastal estuary over 12 mo. Ten macrophyte species were identified. The dominant species, Ceratophyllum demersum and Myriophyllum spicatum, accounted for over 40% of total biomass. Only Ruppia maritima occurred across the salinity gradient. Salinity, water depth and clarity delineated 3 assemblages: a saline assemblage, and 2 groups of fresher-water species, one associated with deeper water and lower water clarity and the other associated with shallow water and higher water clarity. These assemblages exhibited intra-annual variation, with at least 5 times more biomass in late spring/mid-summer compared to early winter. This pattern was consistent across the estuary, although the difference between peak and low biomass varied by habitat type; brackish exhibited the greatest magnitude. This variation is likely due to higher variation in salinity and the species composition of this habitat. As climate change and coastal restoration impact timing and range of salinity, water depth and clarity in this region, these data can be used to help inform predictive models and management decisions.

Louisiana↗

USGS2018

Submerged aquatic vegetation mapping in coastal Louisiana through development of a spatial likelihood occurrence (SLOO) model

Determining the spatial distribution of coastal foundation species is essential to accurately determine restoration goals, predict the ecological effects of climate change, and develop habitat management strategies. Mapping the distribution of submerged aquatic vegetation (SAV) species assemblages, which provide important habitat resource and ecological services in Louisiana, has been difficult due to the dynamic nature of SAV occurrence and the limited water clarity across much of the coast. Species distribution models (SDMs) link ecological conditions species occurrence across landscapes, and can predict the distribution of species across un-sampled or hard to sample areas and support the development of habitat maps. To predict SAV distribution in coastal Louisiana, a SDM was developed and projected across the landscape to create a spatial likelihood of occurrence (SLOO) model describing the probability of SAV presence in aquatic habitats. SAV presence and absence data were examined from over 500 field observations in relation to physical and hydrologic variables, including exposure, turbidity, water level, and salinity. A binary logistic regression model (p < 0.0001) identified three significant predictors of SAV presence: mean winter salinity, exposure, and turbidity. As each of these variables increased, the probability of SAV presence in the summer growing season decreased. The spatial application of this SDM helps to predict the likelihood of occurrence across the coastal landscape, creating a valuable tool to describe un-sampled SAV habitat and estimate future changes in habitat availability.

Louisiana↗

USGS2016

Establishing a baseline of estuarine submerged aquatic vegetation resources across salinity zones within coastal areas of the northern Gulf of Mexico

Coastal ecosystems are dynamic and productive areas that are vulnerable to effects of global climate change. Despite their potentially limited spatial extent, submerged aquatic vegetation (SAV) beds function in coastal ecosystems as foundation species, and perform important ecological services. However, limited understanding of the factors controlling SAV distribution and abundance across multiple salinity zones (fresh, intermediate, brackish, and saline) in the northern Gulf of Mexico restricts the ability of models to accurately predict resource availability. We sampled 384 potential coastal SAV sites across the northern Gulf of Mexico in 2013 and 2014, and examined community and species-specific SAV distribution and biomass in relation to year, salinity, turbidity, and water depth. After two years of sampling, 14 species of SAV were documented, with three species (coontail [Ceratophyllum demersum], Eurasian watermilfoil [Myriophyllum spicatum], and widgeon grass [Ruppia maritima]) accounting for 54% of above-ground biomass collected. Salinity and water depth were dominant drivers of species assemblages but had little effect on SAV biomass. Predicted changes in salinity and water depths along the northern Gulf of Mexico coast will likely alter SAV production and species assemblages, shifting to more saline and depth-tolerant assemblages, which in turn may affect habitat and food resources for associated faunal species.

Gulf of Mexico↗

USGS2016

Brackish marsh zones as a waterfowl habitat resource in submerged aquatic vegetation beds in the northern Gulf of Mexico

Submerged aquatic vegetation (SAV) beds are shallow coastal habitats that are increasingly exposed to the effects of sea-level rise (SLR). In the northern Gulf of Mexico (nGoM), an area especially vulnerable to SLR, the abundance and distribution of SAV food resources (seeds, rhizomes, and tissue) can influence the carrying capacity of coastal marshes to support wintering waterfowl. Despite the known importance of SAV little is known about their distribution across coastal landscapes and salinity zones or how they may be impacted by SLR. We estimated SAV cover and seed biomass in coastal marshes from Texas to Alabama from 1 June – 15 September 2013 to assess variation in SAV and seed resource distribution and abundance across the salinity gradient. Percent cover of SAV was similar among salinity zones (10%–20%) although patterns of distribution differed. Specifically, SAV occurred less frequently in saline zones, but when present the percent coverage was greater than in fresh, intermediate and brackish. Mean seed biomass varied greatly and did not differ significantly among salinity zones. However, when considering only seed species identified as waterfowl foods, the mean seed biomass was lower in saline zones (1.2 g m–2). Alteration of nGoM marshes due to SLR will likely shift the distribution and abundance of SAV resources, and these shifts may affect carrying capacity of coastal marshes for waterfowl and other associated species.

Gulf of Mexico↗