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Donald R. Schoolmaster

Publications and source records attributed to Donald R. Schoolmaster.

At least 19 recordsLinked to original sources
USGS2026

Understanding co-invading species’ ecological strategies and interactions with causal inference and simple dynamic models: Bighead (Hypophthalmichthys nobilis) and Silver carp (H. molitrix) in the Illinois River, USA

Species interactions among multiple invasive species can present hidden challenges for management and eradication efforts. Bighead and Silver carp ( Hypophthalmichthys nobilis and H. molitrix , respectively) are closely related species that have co-invaded the Mississippi River and many of its tributaries, including the Illinois River. We used a combination of causal inference and dynamic models to test for interspecific interactions in a 10-year data set of hydroacoustic density data from the Illinois River. The results suggest that Bighead and Silver carp are exploiting different ecological strategies; where Bighead carp shows fast initial growth but low equilibrium density and Silver carp had a lower low-density growth rate but larger equilibrium population density. Further, we found evidence for a positive interspecific effect of Silver carp on Bighead carp. The mechanism leading to the observed positive interactions is unknown, but could be an effect of ecological facilitation, differential responses to harvest of both species, or hybridization and introgression. Finally, while these results provide valuable information in the ecological strategies and relationships between these species, simulations of the resulting models suggest that the magnitude of the positive interaction is likely too small to be exploited for innovative multispecies management interventions.

Illinois↗
USGS2026

Local, regional, and coastwide effects of interactions between storms and relative position in tidal frame on Louisiana coastal marshes

Context In coastal wetlands, land area is dynamic in both space and time, and it is crucial to understand the factors that may tip the balance between wetland area loss and gain. Flooding has been shown to be a key regulator of wetland resilience to land loss through effects on vegetation health and productivity. However, the relationship between elevation and land change is complex, likely interacting with lateral processes such as edge erosion to influence land change rates. Objectives We sought to determine the role of storms, major drivers of lateral erosion, to help explain the complex relationships between elevation and land change at multiple spatial scales and improve predictions of wetland loss. Methods We used long-term records of elevation change, water elevation, and surface wind stress together with remotely sensed land-area change datasets to determine the factors that contribute to land loss in Louisiana (USA) coastal wetlands. Results Our results illustrate that wetland elevation alone cannot be used as a predictor of land change. Annual time-integrated wind stress, which we used as an indicator of storminess, is an important predictor of land change and interacts with elevation to impact wetland gain or loss. The data showed high site-level variation, indicating that local factors strongly determine land change. Nonetheless, we were able to identify broad generalities at the larger scales. We found that the interaction of flooding and storminess varied among geographic regions along the coast. In the Delta Plain region of the coast, sites that are frequently flooded (low elevation) were more likely to experience land gain in stormy years and land loss in calm years. Conversely, sites that were frequently drained (high elevation) had greater land loss during stormy years, presumably due to wind-wave erosion. This trend was not observed in the heavily managed Chenier Plain region, where perpetually flooded wetlands gained land during stormy years, likely due to wind-driven drainage of the marsh. Conclusion Together, these results illustrate that models of wetland vulnerability based upon elevation change or storm effects alone are too simple and will not capture observed trends in land change. Incorporating additional factors such as wind stress and management status can improve predictions of coastal wetland loss and our understanding of the mechanisms controlling resilience of these ecosystems.

Louisiana↗
USGS2025

Decision support tools for brown pelican management in the northern Gulf of America (Gulf of Mexico)

Management plans with clear priorities can help to achieve brown pelican Pelecanus occidentalis conservation objectives in the northern Gulf of America (Gulf of Mexico). Efforts to establish clear priorities can be hindered by information gaps, especially those related to the uncertainty associated with changing conditions that influence brown pelican populations. We addressed these gaps by creating a model that uses island-specific conditions (e.g., geomorphology; predator-related conditions; brown pelican terrestrial nesting, roosting, and loafing habitats) to predict the nest count as a proxy for breeding pairs on the island. We used the model and 2000–2015 brown pelican nest count data to estimate if breeding pair targets that we identified or estimated for 10 U.S Fish and Wildlife Service Gulf Coast Biological Planning Units were met while accounting for uncertainty. Our results indicate that breeding pair targets were met in 7 of the 10 units by existing conditions. Our confidence in judging nest deficits tended to decrease from west to east because the model over-predicted total nests in the east Gulf Coast. Using an island from our data, we show how the model could be used to quantify the uncertainty of nest count outcomes under simulated changes in island conditions. The model indicated that the island's existing conditions most probably result in nests (probability = 0.51) and that increasing the area of nesting habitat (shrubs) could increase the probability of nests from 49% to 70%. Increasing shrub habitat in the model also increased nest count uncertainty by 60%, but this was due to a greater probability of larger nest counts. Our model suggests that nest count uncertainty could be reduced by improving data on island size, shrub area, and predator presence, depending on the unit and how isolated the island is from the mainland. These tools could help managers understand and incorporate the uncertainty associated with creating island conditions that are intended to help achieve brown pelican conservation objectives.

Alabama, Florida, Louisiana, Mississippi, Texas↗
USGS2025

Elevated CO2 enables brackish marsh transgression into freshwater forested wetlands while stimulating CH4 emissions

Wetlands are significant carbon (C) sinks and are expected to promote greater C assimilation as atmospheric CO 2 concentrations rise. However, the fate of C with environmental change along fresh-to-oligohaline wetland transitions is not well understood. We established an ex-situ mesocosm experiment to mimic future elevated atmospheric CO 2 concentrations ( e CO 2 , 720 ppm) versus current (380 ppm), and we exposed four co-occurring coastal wetland communities that naturally transgress (i.e., freshwater forest, mixed forest and marsh, marsh, mudflat) to these concentrations for two years. Overall, wetland communities with marsh plants in monoculture and mixed culture maintained high ecosystem C uptake with e CO 2 versus freshwater forested wetlands or mudflats, likely from superior plant species photosynthetic adjustment versus leaf area increases. e CO 2 promoted greater CO 2 uptake by leaves in all communities except mudflats, while promoting CH 4 efflux from whole ecosystems only when marsh plants were present. e CO 2 is projected to stimulate C gain 2.2-fold for forested wetlands and oligohaline marsh and 2.9-fold for forest-marsh mixture through greater CO 2 uptake. However, this comes at a cost of stimulated CH 4 flux by 1.4-to-1.7-fold in mixed and marsh communities versus reduced CH 4 fluxes with e CO 2 by forest and mudflat communities, perhaps through different oxidation pathways. Freshwater forested wetlands limited greenhouse gas emissions compared with transitional habitats, oligohaline marshes, and mudflats as atmospheric CO 2 concentrations increased. Stimulated C uptake in marshes may not offset higher methane emissions from these systems, potentially facilitating greater warming in a future with elevated atmospheric CO 2 .

Estuarine, Coastal and Shelf Science↗
USGS2024

Multi-scale effects of behavioral movement deterrents on invasive carp metapopulations

Behavioral deterrents of among-pool movement represent a promising tool for controlling invasive fish populations. To date, much of the research in this area has been focused on the direct effectiveness of different methods of deterrence. However, the effect of these structures on populations in spatially complex habitats is unknown. We combine a metacommunity model with movement data of two invasive species (bighead carp and silver carp) in a large river to assess local and river-wide scale population outcomes of deterrent locations. We calculated (1) which potential deterrent locations are most effective at reducing the growth at the invasion front (2) the river-scale population effects at each location, and (3) what, if any, are the risks imposed by altering the current spatial dynamics. We found that the effects on the population dynamics at the invasion front varied with the location of deterrents, ranging from near zero to effects equal to the reduction in an individual’s movement across the deterrent. The river-scale population growth rate was slightly increased by all potential deterrent placements because the deterrents tended to concentrate more of the river-scale population into pools with the highest recruitment rates. The short-term, transient dynamics followed a strictly decreasing pattern after deterrent placement suggesting no additional short-term risk. These results suggest that deterrents can be an effective and low-risk intervention for the control of invasive carp, although the population level effect will depend on the interaction of the traits and behavior of the species with the physical character and spatial structure of the habitat.

Illinois↗
USGS2024

Vegetation loss following vertical drowning of Mississippi River deltaic wetlands leads to faster microbial decomposition and decreases in soil carbon

Wetland ecosystems hold nearly a third of the global soil carbon pool, but as wetlands rapidly disappear the fate of this stored soil carbon is unclear. The aim of this study was to quantify and then link potential rates of microbial decomposition after vertical drowning of vegetated tidal marshes in coastal Louisiana to known drivers of anaerobic decomposition altered by vegetation loss. Profiles of potential CH 4 and CO 2 production (surface to 60 cm deep) were measured during anaerobic incubations, organic matter chemistry was assessed with infrared spectroscopy, and soil porewater nutrients and redox potentials were measured in the field along a chronosequence of wetland loss. After vertical drowning, pond soils had lower redox potentials, higher pH values, lower soil carbon and nitrogen concentrations, lower lignin: polysaccharide ratios, more NH 4 + and PO 4 3− , and higher rates of potential CO 2 release than vegetated marsh soils. Potential CH 4 production was similar in vegetated marshes and open water ponds, with depth-dependent decreases in CH 4 production as soil carbon concentrations increased. In these anoxic soils, vegetation loss exerts a primary control on decomposition rates because flooding drives sustained increases in porewater nutrient availability (NH 4 + and PO 4 3 , dissolved organic carbon) and decreases in redox potential (from −150 to −500 mV) that lead to higher potential CO 2 fluxes within a few years. Without new carbon inputs following wetland loss, the sustained decomposition in open water ponds may lead to losses of stored soil carbon and could influence global carbon budgets.

Louisiana↗
USGS2024

A new method for bioassessment of ecosystems with complex communities and environmental gradients

Bioassessment of complex and heterogeneous ecosystems is a challenge when there are multiple, strong, natural environmental gradients; unknown, or spatially varying, mixtures of stressors; and large numbers of taxa with unknown responses to both the environmental gradients and the stressors. Current methods of bioassessment are not designed for use under this set of constraints. To address this gap, we have developed an assessment method appropriate for well-sampled, heterogeneous systems with many taxa. In the bioassessment described below, we model taxa occurrence as a function of natural environmental gradients, then use residual covariance patterns between all pairs of taxa to estimate the impact of human disturbance across sites as a latent construct. The derivation of the method from an underlying causal model allows the metric value at each site and the associated taxa responses to be partitioned into contributions from a set of putative stressors. We apply this method as a case study to the subtidal benthic invertebrate community of Puget Sound, WA (USA) and demonstrate a partial decomposition of the metric values to a set of stressors including sediment organic carbon, nitrogen, metals, and organic pollutants. While this method provides new opportunities to estimate, communicate, and understand the ecological condition of complex, heterogeneous ecosystems, due to the requirement for broad, detailed data to inform its estimates, it will likely be most appropriate for monitoring programs.

Washington↗
USGS2023

Evolution of a minimal cell

Possessing only essential genes, a minimal cell can reveal mechanisms and processes that are critical for the persistence and stability of life 1,2 . Here we report on how an engineered minimal cell 3,4 contends with the forces of evolution compared with the Mycoplasma mycoides non-minimal cell from which it was synthetically derived. Mutation rates were the highest among all reported bacteria, but were not affected by genome minimization. Genome streamlining was costly, leading to a decrease in fitness of greater than 50%, but this deficit was regained during 2,000 generations of evolution. Despite selection acting on distinct genetic targets, increases in the maximum growth rate of the synthetic cells were comparable. Moreover, when performance was assessed by relative fitness, the minimal cell evolved 39% faster than the non-minimal cell. The only apparent constraint involved the evolution of cell size. The size of the non-minimal cell increased by 80%, whereas the minimal cell remained the same. This pattern reflected epistatic effects of mutations in ftsZ , which encodes a tubulin-homologue protein that regulates cell division and morphology 5,6 . Our findings demonstrate that natural selection can rapidly increase the fitness of one of the simplest autonomously growing organisms. Understanding how species with small genomes overcome evolutionary challenges provides critical insights into the persistence of host-associated endosymbionts, the stability of streamlined chassis for biotechnology and the targeted refinement of synthetically engineered cells 2,7,8,9 .

Nature↗
USGS2023

Results of validation exercise for Marine Benthic Index

Marine benthic invertebrates (benthos) are key components of the Puget Sound ecosystem. Because of their direct association living in, and sometimes consuming, sediments, benthos can be valuable sentinels of ecosystem health. Therefore, indicators of benthic invertebrate community health can serve as direct measures of sediment and water quality. In 2021, the Puget Sound Partnership funded development of a Marine Benthic Index . The Marine Benthic Index thus developed uses a novel approach that accounts for habitat preferences of the benthic invertebrate species. This report describes the design and results of the exercise conducted to validate the Marine Benthic Index . The goals of the validation exercise were to determine (a) how well the Marine Benthic Index matches more standard ways of assessing community health and (b) how finely it is possible to distinguish between levels of disturbance. A controlled experiment was devised in which simulated benthic communities were generated to correspond to predetermined levels of disturbance, and experts in benthic ecology determined which communities reflected the more-disturbed conditions. In this way, the index was directly compared to traditional methods of assessing benthic communities. The results provide strong evidence that the “latent disturbance” model used to derive the Marine Benthic Index is identifying effects that benthic experts recognize as disturbance. Not only did the model agree with the experts overall, but also the probability of agreement strongly increased with increasing difference in disturbance level. The validation exercise results indicate that the Marine Benthic Index is a reliable method of determining disturbance without the necessity of assuming a priori knowledge of the disturbance. Furthermore, the numerical approach embodied in the Marine Benthic Index has the advantage of being able to find patterns beyond the capability of individual experts to know the effects of human disturbances for all species under all environmental conditions.

Washington↗
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↗
USGS2022

Analysis of per capita contributions from a spatial model provides strategies for controlling spread of invasive carp

Metapopulation models may be applied to inform natural resource management to guide actions targeted at location-specific subpopulations. Model insights frequently help to understand which subpopulations to target and highlight the importance of connections among subpopulations. For example, managers often treat aquatic invasive species populations as discrete populations due to hydrological (e.g., lakes, pools formed by dams) or jurisdictional boundaries (e.g., river segments by country or jurisdictional units such as states or provinces). However, aquatic invasive species often have high rates of dispersion and migration among heterogenous locations, which complicates traditional metapopulation models and may not conform to management boundaries. Controlling invasive species requires consideration of spatial dynamics because local management activities (e.g., harvest, movement deterrents) may have important impacts on connected subpopulations. We expand upon previous work to create a spatial linear matrix model for an aquatic invasive species, Bighead Carp, in the Illinois River, USA, to examine the per capita contributions of specific subpopulations and impacts of different management scenarios on these subpopulations. Managers currently seek to prevent Bighead Carp from invading the Great Lakes via a connection between the Illinois Waterway and Lake Michigan by allocating management actions across a series of river pools. We applied the model to highlight how spatial variation in movement rates and recruitment can affect decisions about where management activities might occur. We found that where the model suggested management actions should occur depend crucially on the specific management goal (i.e., limiting the growth rate of the metapopulation vs. limiting the growth rate of the invasion front) and the per capita recruitment rate in downstream pools. Our findings illustrate the importance of linking metapopulation dynamics to management goals for invasive species control.

Illinois↗
USGS2022

A model of the spatiotemporal dynamics of soil carbon following coastal wetland loss applied to a Louisiana salt marsh in the Mississippi River Deltaic Plain

The potential for carbon sequestration in coastal wetlands is high due to protection of carbon (C) in flooded soils. However, excessive flooding can result in the conversion of the vegetated wetland to open water. This transition results in the loss of wetland habitat in addition to the potential loss of soil carbon. Thus, in areas experiencing rapid wetland submergence, such as the Mississippi River Delta, coastal wetlands could become a significant source of carbon emissions if land loss is not mitigated. To accurately assess the capacity of wetlands to store (or emit) carbon in dynamic environments, it is critical to understand the fate of soil carbon following the transition from vegetated wetland to open water. We developed a simple soil carbon model representing soil depths to 1 m using the data collected from a Louisiana coastal salt marsh in the Mississippi River Deltaic Plain to predict soil carbon density and stock following the transition from a vegetated salt marsh to an open water pond. While immediate effects of ponding on the distribution of carbon within the 1-m soil profile were apparent, there were no effects of ponding on the overall, integrated, carbon stocks 14 years, following wetland submergence. Rather, the model predicts that soil carbon losses in the first meter will be realized over long periods of time (∼200 years) due to changes in the source of carbon (biomass vs. mineral sediment) with minimal losses through mineralization.

Louisiana↗
USGS2020

A graphical causal model for resolving species identity effects and biodiversity–ecosystem function correlations

Identifying and clearly communicating the drivers of ecosystem function is a crucially important goal for both basic and applied ecology. This has proven difficult because the putative causes (e.g., environment, species identity, biodiversity, and functional traits) are numerous and correlated. The problem is exacerbated by a lack of a formal framework for unambiguously relating theoretical language to precise, quantitative expressions of that language. Using a formal framework for the graphical expression of complex causal hypotheses, we developed a causal diagram of the concepts required to comprehensively test whether hypothesized sets of functional traits mediate the relationship between community structure and ecosystem function. We then used causal analysis, simulations, and field data to develop and test analytical strategies for understanding how community structure influences ecosystem functions via functional traits. Formal causal analysis showed that biodiversity–ecosystem function correlations are non‐causal associations. Using simulations, we showed how biodiversity correlations and species identity effects can arise from misspecification or incomplete mediation by functional trait composites. We also found that different types of model misspecification result in different patterns of residuals, which may be used to diagnose gaps in functional trait hypotheses. Treating the model misspecifications eliminated associations between species identity or biodiversity and ecosystem function. Finally, we provide an example of the analysis of field data to demonstrate how to use these insights to conduct a research program that has the goal of understanding the mechanistic trait relationships that link community structure to ecosystem function.

Ecology↗
USGS2019

Using maintenance records from a long-term sensor monitoring network to evaluate the relationship between maintenance schedule and data quality

Sensor-based environmental monitoring networks are beginning to provide the large-scale, long-term data required to address important fundamental and applied questions in ecology. However, the data quality from deployed sensors can be difficult and costly to ensure. In this study, we use maintenance records from the 12-year history of Louisiana’s Coastwide Reference Monitoring System (CRMS) to assess the relationship between various dimensions of data quality and the frequency of field visits to the sensors. We use hierarchical Bayesian models to estimate the probability of missing data, the probability that a corrective offset of the sensor is required, and the magnitude of required offsets for water elevation and salinity data. We compared these estimates to predetermined risk thresholds to the help identify maintenance schedules that balanced the efficient use of labor resources without sacrificing data quality. We found that the relationship between data quality and increasing maintenance interval varied across metrics. Additionally, for most metrics, the maintenance interval when the metric’s credible interval and risk threshold intersected varied throughout the year and with wetland type. These results suggest that complex maintenance schedules, in which field visits vary in frequency throughout the year and with environmental context, are likely to provide the best tradeoff between labor cost and data quality. This analysis demonstrates that quantitative assessment of maintenance records can positively impact the sustainability of long-term data collection projects by helping identify new potential efficiencies in monitoring program management.

Louisiana↗
USGS2018

A causal partition of trait correlations: using graphical models to derive statistical models from theoretical language

Recent studies hypothesize various causes of species‐level trait covariation, namely size (e.g., metabolic theory of ecology and leaf economics spectrum), pace‐of‐life (e.g., slow‐to‐fast continuum; lifestyle continuum), evolutionary history (e.g., phylogenetic conservatism), and ecological conditions (e.g., stabilizing selection). Various methods have been used in attempts to partition trait correlation among these influences (e.g., univariate analysis, principal components analysis, and factor analysis). However, it is not clear that the implied causal structure assumed by these methods matches the hypothesized causal structure driving trait correlations, a situation that can potentially lead to biased estimates and incorrect partitioning among mechanisms. Here, we propose the application of graphical causal models (GCM) for across‐kingdom synthesis and to aid researchers in their selection of correct analytical strategies. Graphical causal models use causal diagrams (i.e., box‐and‐arrow graphs) to represent expert knowledge of the data‐generating processes to analytically investigate the possibility of identifying hypothesized causal associations. We developed a causal diagram that synthesizes prominent hypotheses of trait covariation. Using the causal diagram, we (1) derived a quantitative expression to partition trait covariance among its hypothesized causal elements (i.e., size, pace‐of‐life, evolutionary history, and ecological conditions) and (2) developed analytic strategies to attribute trait covariance among the hypothesized causal elements under real‐world data availability, namely unobserved variables (i.e., pace‐of‐life) and confounding variables (i.e., evolutionary history and ecological conditions). Finally, we tested each analytic strategy by simulating trait datasets and, after incorporating the data limitations, tested their ability to correctly partition trait covariance. The analytical strategies were able to correctly partition trait covariance into the hypothesized causal elements of size, pace‐of‐life, and the historical effects of evolutionary history and ecological conditions. We demonstrate the efficacy of these strategies by applying them to a widely used trait dataset. Overall, the application of GCM revealed that researchers have used inappropriate measures to represent their theoretical constructs and have relied on analytical strategies that violated their causal assumptions, likely resulting in biased estimates. We discuss how this mismatch between theoretical language and statistical methods is prevalent in species‐level, trait‐based research and call for future studies to address these limitations.

Ecosphere↗
USGS2018

Vegetation cover, tidal amplitude and land area predict short-term marsh vulnerability in Coastal Louisiana

The loss of coastal marshes is a topic of great concern, because these habitats provide tangible ecosystem services and are at risk from sea-level rise and human activities. In recent years, significant effort has gone into understanding and modeling the relationships between the biological and physical factors that contribute to marsh stability. Simulation-based process models suggest that marsh stability is the product of a complex feedback between sediment supply, flooding regime and vegetation response, resulting in elevation gains sufficient to match the combination of relative sea-level rise and losses from erosion. However, there have been few direct, empirical tests of these models, because long-term datasets that have captured sufficient numbers of marsh loss events in the context of a rigorous monitoring program are rare. We use a multi-year data set collected by the Coastwide Reference Monitoring System (CRMS) that includes transitions of monitored vegetation plots to open water to build and test a predictive model of near-term marsh vulnerability. We found that despite the conclusions of previous process models, elevation change had no ability to predict the transition of vegetated marsh to open water. However, we found that the processes that drive elevation change were significant predictors of transitions. Specifically, vegetation cover in prior year, land area in the surrounding 1 km2 (an estimate of marsh fragmentation), and the interaction of tidal amplitude and position in tidal frame were all significant factors predicting marsh loss. This suggests that 1) elevation change is likely better a predictor of marsh loss at time scales longer than we consider in this study and 2) the significant predictive factors affect marsh vulnerability through pathways other than elevation change, such as resistance to erosion. In addition, we found that, while sensitivity of marsh vulnerability to the predictive factors varied spatially across coastal Louisiana, vegetation cover in prior year was the best single predictor of subsequent loss in most sites followed by changes in percent land and tidal amplitude. The model’s predicted land loss rates correlated well with land loss rates derived from satellite data, although agreement was spatially variable. These results indicate 1) monitoring the loss of small scale vegetation plots can inform patterns of land loss at larger scales 2) the drivers of land loss vary spatially across coastal Louisiana, and 3) relatively simple models have potential as highly informative tools for bioassessment, directing future research, and management planning.

Louisiana↗
USGS2018

Resource competition model predicts zonation and increasing nutrient use efficiency along a wetland salinity gradient

A trade-off between competitive ability and stress tolerance has been hypothesized and empirically supported to explain the zonation of species across stress gradients for a number of systems. Since stress often reduces plant productivity, one might expect a pattern of decreasing productivity across the zones of the stress gradient. However, this pattern is often not observed in coastal wetlands that show patterns of zonation along a salinity gradient. To address the potentially complex relationship between stress, zonation, and productivity in coastal wetlands, we developed a model of plant biomass as a function of resource competition and salinity stress. Analysis of the model confirms the conventional wisdom that a trade-off between competitive ability and stress tolerance is a necessary condition for zonation. It also suggests that a negative relationship between salinity and production can be overcome if (1) the supply of the limiting resource increases with greater salinity stress or (2) nutrient use efficiency increases with increasing salinity. We fit the equilibrium solution of the dynamic model to data from Louisiana coastal wetlands to test its ability to explain patterns of production across the landscape gradient and derive predictions that could be tested with independent data. We found support for a number of the model predictions, including patterns of decreasing competitive ability and increasing nutrient use efficiency across a gradient from freshwater to saline wetlands. In addition to providing a quantitative framework to support the mechanistic hypotheses of zonation, these results suggest that this simple model is a useful platform to further build upon, simulate and test mechanistic hypotheses of more complex patterns and phenomena in coastal wetlands.

Louisiana↗