Geology topics

James B. Grace

Publications and source records attributed to James B. Grace.

At least 19 recordsLinked to original sources

USGS2026

Towards a refined definition of “tidal influence” for the coastal wetland sciences

The astronomical tide relates to lunar (and to a lesser extent solar) influences on rising and falling water bodies, and is a key control of many coastal processes. However, tides manifest along coastal margins in different ways to muddle application beyond Sir Isaac Newton’s first descriptions. Here, we argue that tide, or specifically “tidal,” has not been adequately defined for the coastal wetland sciences to facilitate contemporary interdisciplinary distinction for rapidly changing coastlines and future climate adaptation planning. We provide a brief history of the tidal concept, and we describe tides in a diversity of uncommonly recognized marginal coastal environments. Common astronomical tide manifestations are described through hydrographic examples, then expanded to include those tidal environments exposed additionally to fluvial, meteorological (wind), or groundwater influences. Our expanded definition of “tidal” is intended to be a forward-looking construct as environments will inevitably change with sea-level rise, water extraction, and anthropogenic flow alteration. Our definition of wetland tidal influence, which we broaden, includes those “wetlands affected by astronomic water level fluctuations within coastally restricted environments causing frequent or infrequent surface inundation or groundwater tidal variation that affects the biogeochemistry of the soil (e.g., ion exchange capacity, oxygen state, mineralization processes)”. Astronomic water level fluctuations are often amplified or dampened by non-astronomical events, such as wind. Expanding the definition of what is considered tidal by the scientific community is critical for improved understanding of coastal wetland function and the suite of ecosystem services they provide people and society, including climate mitigation opportunity, management, and adaptation.

Wetlands↗

USGS2026

Ecological transformations of coastal wetlands of the conterminous United States in response to contemporaneous sea-level rise

Coastal wetlands are among the most important ecosystems on the planet but are increasingly imperiled by accelerating sea-level rise (SLR). In the past, biogeomorphic feedbacks have allowed coastal wetlands to adjust vertically and persist through periods of accelerated SLR. Recent rates of SLR are faster than any in recent geologic history, making the fate of coastal wetlands highly uncertain. Here, we synthesize surface elevation table marker-horizon data from 442 stations in coastal wetlands across the conterminous United States to evaluate if they are largely persisting in the face of accelerated SLR, or if they are undergoing ecological transformations of submergence and/or migration. Across the conterminous United States, 11% of coastal wetlands in this sample are on a trajectory of submergence whereas 73% of sites are lagging SLR, but may be able to migrate upslope, and 16% of sites are gaining elevation at rates which exceed SLR indicating persistence and an ability to migrate seaward. Vulnerability of these systems to ecological transformation varies across the three coasts of the conterminous United States which, span large biogeomorphic gradients. These results serve as one of the first national syntheses of coastal wetland elevation trends and may help focus conservation and restoration efforts in a rapidly changing future.

conterminous United States↗

USGS2026

Loss and transformation of coastal wetlands due to global change in the conterminous United States: Past, present, and future

Coastal wetlands are being transformed by global change, impacting the ecological and societal benefits provided by these ecosystems. Synthesizing knowledge of historical and expected future transformations in coastal wetlands can help inform forward-looking planning and stewardship efforts. Here, we review anticipated future ecological transformations in coastal wetlands of the conterminous United States and contrast them with past transformations. We examine trends at the national scale but focus in detail on the following six wetland-rich regions: (1) Everglades, (2) Mississippi River delta, (3) Chesapeake Bay, (4) San Francisco Estuary, (5) Puget Sound, and (6) Great Lakes. This review is among the first to integrate and compare coastal wetland transformations in the Great Lakes with their oceanic counterparts. Between 1850 and 1970, wetland losses across the nation were high because coastal wetlands were viewed as flooded wastelands needing to be drained, filled, or excavated to make room for other land uses. Over the last 50 years, growing public and scientific appreciation of the ecological and societal benefits provided by coastal wetlands has fostered tremendous gains in wetland restoration and legal protection. Looking to the future, climate change and accelerated sea-level rise have become critical threats to coastal wetlands and are emerging as major drivers of ecological loss and transformation. This review synthesizes regional knowledge regarding past, present, and future changes to help coastal scientists, environmental managers, and the public better anticipate and prepare for future coastal wetland transformations due to climate change, accelerated sea-level rise, land-use change, and other aspects of global change.

conterminous United States↗

USGS2025

Ecological thresholds and transformations due to climate change: The role of abiotic stress

An ecological threshold is the point at which a comparatively small environmental change triggers an abrupt and disproportionately large ecological response. In the face of accelerating climate change, there is concern that abrupt ecosystem transformations will become more widespread as critical ecological thresholds are crossed. There has been ongoing debate, however, regarding the prevalence of ecological thresholds across the natural world. While ecological thresholds are ubiquitous in some ecosystems, thresholds have been difficult to detect in others. Some studies have even concluded that threshold responses are uncommon in the natural world and overly emphasized in the ecological literature. As ecologists who work in ecosystems chronically exposed to high abiotic stress, we consider ecological thresholds and ecosystem transformations to be critical concepts that can greatly advance understanding of ecological responses to climate change and inform ecosystem management. But quantifying ecological thresholds can be challenging, if not impossible, without data that are strategically collected for that purpose. Here, we present a conceptual framework built upon linkages between abiotic stress, climate-driven ecological threshold responses, and the risk of ecosystem transformation. We also present a simple approach for quantifying ecological thresholds across abiotic stress gradients. We hypothesize that climate-driven threshold responses are especially influential in ecosystems chronically exposed to high abiotic stress, where autotroph diversity is low and foundation species play a prominent ecological role. Abiotic conditions in these environments are often near physiological tolerance limits of foundation species, which means that small abiotic changes can trigger landscape-level ecological transformations. Conversely, the alleviation of stress near thresholds can allow foundation species to thrive and spread into previously inhospitable locations. We provide examples of this climate-driven threshold behavior from four high-stress environments: coastal wetlands, coral reefs, drylands, and alpine ecosystems. Our overarching aim in this review is to clarify the strong relationships between abiotic stress, climate-driven ecological thresholds, and the risk of ecosystem transformation under climate change.

Ecosphere↗

USGS2024

Rising seas could cross thresholds for initiating coastal wetland drowning within decades across much of the United States

Accelerated sea-level rise is an existential threat to coastal wetlands, but the timing and extent of wetland drowning are debated. Recent data syntheses have clarified future relative sea-level rise exposure and sensitivity thresholds for drowning. Here, we integrate these advances to estimate when and where rising sea levels could cross thresholds for initiating wetland drowning across the conterminous United States. Our results show that there is much spatial variation in relative sea-level rise rates, which impacts the potential timing and extent of wetlands crossing thresholds. High rates of relative sea-level rise along wetland-rich parts of the Gulf of Mexico and Atlantic coasts highlight areas where wetlands are already drowning or could begin to drown within decades, including large wetland landscapes within the Mississippi River delta, Greater Everglades, Chesapeake Bay, Texas, Georgia, and the Carolinas. Collectively, our results underscore the need to prepare for transformative coastal change.

Communications Earth & Environment↗

USGS2023

Integrating remote sensing with ground-based observations to quantify the effects of an extreme freeze event on black mangroves (Avicennia germinans) at the landscape scale

Climate change is altering the frequency and intensity of extreme weather events. Quantifying ecosystem responses to extreme events at the landscape scale is critical for understanding and responding to climate-driven change but is constrained by limited data availability. Here, we integrated remote sensing with ground-based observations to quantify landscape-scale vegetation damage from an extreme climatic event. We used ground- and satellite-based black mangrove ( Avicennia germinans ) leaf damage data from the northern Gulf of Mexico (USA and Mexico) to examine the effects of an extreme freeze in a region where black mangroves are expanding their range. The February 2021 event produced coastal temperatures as low as − 10 °C in some areas, exceeding thresholds for A. germinans damage and mortality. We used Sentinel-2 surface reflectance data to assess vegetation greenness before and after the freeze, along with ground-based observations of A. germinans leaf damage. Our results show a negative, nonlinear threshold relationship between A. germinans leaf damage and minimum temperature, with a temperature threshold for leaf damage near − 6 °C. Satellite-based analyses indicate that, at the landscape scale, damage was particularly severe along the central Texas coast, where the freeze event affected > 2000 ha of A. germinans -dominated coastal wetlands. Our analyses highlight the value of pairing remotely sensed data with regional, ground-based observations for quantifying and extrapolating the effects of extreme freeze events on mangroves and other tropical, cold-sensitive plants. The results also demonstrate how extreme freeze events govern the expansion and contraction of mangroves near northern range limits in North America.

Gulf of Mexico↗

USGS2022

Direct and indirect influences of macrophyte cover on abundance and growth of juvenile Atlantic salmon

1. The relationships between macrophytes and the physical and biological characteristics of the environments that aquatic organisms inhabit are complex. Previous studies have shown that the macrophytes, Ranunculus (subgenus Batrachium ), which are dominant in lowland chalk streams and widespread across Europe, can enhance juvenile Atlantic salmon abundance and growth to a greater degree than other physical and biological habitat characteristics. However, mechanistic understanding of how this effect might arise requires consideration of the direct and indirect relationships among habitat characteristics that are likely to be influenced by the presence of macrophyte cover. 2. We applied structural equation modelling to data collected during a 2-year in-river manipulative experiment in the River Frome (southern England, U.K.) designed to quantify the magnitude and the relative importance of direct and indirect influences of Ranunculus cover and other physical and biological variables, including water velocity, water depth, prey biomass and body size, and abundance of con- and hetero-specifics, on abundance and somatic growth of 0+ salmon. 3. Results indicated a strongly positive direct influence of Ranunculus cover on salmon abundance, as well as positive influences of Ranunculus on velocity heterogeneity and water depth that are indirectly related to decreased salmon abundance. Interestingly, there was no indication of a direct influence of Ranunculus cover on salmon growth, although Ranunculus was indirectly related to increased salmon growth through its positive influence on prey biomass, an effect mediated by velocity heterogeneity and proportion of fast velocities. 4. These findings provide novel mechanistic insights into the key role of Ranunculus in their native lowland rivers to enhance abundance and improve conditions for multiple food web components. Strategies to maintain or enhance naturally occurring Ranunculus in these rivers are therefore likely to return wide ranging ecosystem benefits, including for species of high conservation value, such as salmon. These mechanistic impacts on habitat heterogeneity and ecosystem productivity could generalise to native macrophytes in other river systems, particularly where habitat is dominated by vegetation in the absence of large substrates.

Dorset County↗

USGS2022

General guidance for custom-built structural equation models

Structural Equation Modeling (SEM) represents a quantitative methodology for specifying and evaluating causal network hypotheses. The application of SEM typically involves the use of specialized software packages that implement estimation procedures and automate model checking and the output of summary results. There are times when the specification details an investigator wishes to implement to represent their data relationships are not supported by available SEM packages. In such cases, it may be desirable to develop and evaluate SE models “by hand”, using specialized regression tools. In this paper, I demonstrate a general approach to custom-built applications of SEM. The approach illustrated can be used for a wide array of specialized applications of non-linear, multi-level, and other custom specifications in SE models.

Maine↗

USGS2021

A protocol for modelling generalised biological responses using latent variables in structural equation models

In this paper we consider the problem of how to quantitatively characterize the degree to which a study object exhibits a generalized response. By generalized response, we mean a multivariate response where numerous individual properties change in concerted fashion due to some internal integration. In latent variable structural equation modeling (LVSEM), we would typically approach this situation using a latent variable to represent a general property of interest (e.g., performance) and multiple observed indicator variables that reflect the specific features associated with that general property. While ecologists have used LVSEM in a number of cases, there is substantial potential for its wider application. One obstacle is that LV models can be complex and easily over-specified, degrading their value as a means of generalization. It can also be challenging to diagnose causes of misspecification and understand which model modifications are sensible. In this paper we present a protocol, consisting of a series of questions, designed to guide the researchers through the evaluation process. These questions address (1) theoretical development, (2) data requirements, (3) whether responses to perturbation are general, (4) unique reactions by individual measures, and (5) how far generality can be extended. For this illustration, we reference a recent study considering the potential consequences of maintaining biodiversity as part of agricultural management on the overall quality of grapes used for wine making. We extend our presentation to include the complexities that occur when there are multiple species with unique reactions.

One Ecosystem↗

USGS2021

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

In a recent paper, Schoolmaster, Zirbel, and Cronin (SZC) (2020 ) claim “Formal causal analysis show[s] that biodiversity–ecosystem function (BEF) correlations are non-causal associations.” If this conclusion is accepted as true, it suggests a reconsideration of much of our current understanding of how biodiversity relates to the functioning of ecosystems. On the surface, it is easy to spot clear signs of something problematic with SZC’s presentation. They claim, for example, that (1) species richness is incapable of having a causal effect on ecosystem functioning on theoretical grounds, and (2) that trait diversity cannot be causally influenced by species diversity. These remarkable claims are counter to existing thought and evidence. We point to logical errors that lead them to a misapply causal analysis and produce erroneous conclusions.

Ecology↗

USGS2020

Scientist’s guide to developing explanatory statistical models using causal analysis principles

Recent discussions of model selection and multimodel inference highlight a general challenge for researchers, which is how to clearly convey the explanatory content of a hypothesized model or set of competing models. The advice from statisticians for scientists employing multimodel inference is to develop a well‐thought‐out set of candidate models for comparison, though precise instructions for how to do that are typically not given. A coherent body of knowledge, which falls under the general term causal analysis, now exists for examining the explanatory scientific content of candidate models. Much of the literature on causal analysis has been recently developed and we suspect may not be familiar to many ecologists. This body of knowledge comprises a set of graphical tools and axiomatic principles to support scientists in their endeavors to create “well‐formed hypotheses”, as statisticians are asking them to do. Causal analysis is complementary to methods such as structural equation modeling, which provides the means for evaluation of proposed hypotheses against data. In this paper, we summarize and illustrate a set of principles that can guide scientists in their quest to develop explanatory hypotheses for evaluation. The principles presented in this paper have the capacity to close the communication gap between statisticians, who urge scientists to develop well‐thought‐out coherent models, and scientists, who would like some practical advice for exactly how to do that.

Ecology↗

USGS2020

A 'weight of evidence' approach to evaluating structural equation models

It is possible that model selection has been the most researched and most discussed topic in the history of both statistics and structural equation modeling (SEM). The reason for this is because selecting one model for interpretive use from amongst many possible models is both essential and difficult. The published protocols and advice for model evaluation and selection in SEM studies are complex and difficult to integrate with current approaches used in biology. Opposition to the use of p-values and decision thresholds has been voiced by the statistics community, yet certain phases of model evaluation have been historically tied to reliance on p-values. In this paper, I outline an approach to model evaluation, comparison and selection based on a weight-of-evidence paradigm. The details and proposed sequence of steps are illustrated using a real-world example. At the end of the paper, I briefly discuss the current state of knowledge and a possible direction for future studies.

One Ecosystem↗

USGS2020

Climate and local environment structure asynchrony and the stability of primary production in grasslands

Aim Climate variability threatens to destabilize production in many ecosystems. Asynchronous species dynamics may buffer against such variability when a decrease in performance by some species is offset by an increase in performance of others. However, high climatic variability can eliminate species through stochastic extinctions or cause similar stress responses among species that reduce buffering. Local conditions, such as soil nutrients, can also alter production stability directly or by influencing asynchrony. We test these hypotheses using a globally distributed sampling experiment. Location Grasslands in North America, Europe and Australia. Time period Annual surveys over 5 year intervals occurring between 2007 and 2014. Major taxa studied Herbaceous plants. Methods We sampled annually the per species cover and aboveground community biomass [net primary productivity (NPP)], plus soil chemical properties, in 29 grasslands. We tested how soil conditions, combined with variability in precipitation and temperature, affect species richness, asynchrony and temporal stability of primary productivity. We used bivariate relationships and structural equation modelling to examine proximate and ultimate relationships. Results Climate variability strongly predicted asynchrony, whereas NPP stability was more related to soil conditions. Species richness was structured by both climate variability and soils and, in turn, increased asynchrony. Variability in temperature and precipitation caused a unimodal asynchrony response, with asynchrony being lowest at low and high climate variability. Climate impacted stability indirectly, through its effect on asynchrony, with stability increasing at higher asynchrony owing to lower inter‐annual variability in NPP. Soil conditions had no detectable effect on asynchrony but increased stability by increasing the mean NPP, especially when soil organic matter was high. Main conclusions We found globally consistent evidence that climate modulates species asynchrony but that the direct effect on stability is low relative to local soil conditions. Nonetheless, our observed unimodal responses to variability in temperature and precipitation suggest asynchrony thresholds, beyond which there are detectable destabilizing impacts of climate on primary productivity.

Global Ecology and Biogeography↗

USGS2020

Conceptual frameworks

The chapter starts by addressing some of the issues that come from not using a conceptual framework. This point is illustrated using an example with causal factors. The chapter then goes on to explain the mechanics of establishing conceptual frameworks. Lastly, it lays out a step-by-step guide on how to create a framework—generating a set of concepts, specifying the relations between concepts, writing a narrative for the conceptual framework, and rethinking the framework through the entire research project.

Book chapter↗

USGS2020

Structural equation modeling

This chapter introduces background and historical information on how structural equation modeling (SEM) came to be developed. Then, the main differences between SEM and earlier multivariate methods are explained. The chapter describes three main applications of SEM: path analysis, factor analysis, and hybrid models. Some computer programs are recommended for these applications. The step-by-step section goes over how to estimate structural models with AMOS and R. The chapter concludes with two example applications of SEM in the planning field.

Book chapter↗

USGS2019

Climatic controls on the distribution of foundation plant species in coastal wetlands of the conterminous United States: Knowledge gaps and emerging research needs

Foundation plant species play a critical role in coastal wetlands, often modifying abiotic conditions that are too stressful for most organisms and providing the primary habitat features that support entire ecological communities. Here, we consider the influence of climatic drivers on the distribution of foundation plant species within coastal wetlands of the conterminous USA. Using region-level syntheses, we identified 24 dominant foundation plant species within 12 biogeographic regions, and we categorized species and biogeographic regions into four groups: graminoids, mangroves, succulents, and unvegetated. Literature searches were used to characterize the level of research directed at each of the 24 species. Most coastal wetlands research has been focused on a subset of foundation species, with about 45% of publications directed at just one grass species—Spartina alterniflora. An additional 14 and 8% have been directed, respectively, at two mangrove species—Rhizophora mangle and Avicennia germinans. At the national scale, winter temperature extremes govern the distribution of mangrove forests relative to salt marsh graminoids, and arid conditions can produce hypersaline conditions that increase the dominance of succulent plants, algal mats, and unvegetated tidal flats (i.e., salt flats, salt pans) relative to graminoid and mangrove plants. Collectively, our analyses illustrate the diversity of foundation plant species in the conterminous USA and begin to elucidate the influence of climatic drivers on their distribution. However, our results also highlight critical knowledge gaps and identify emerging research needs for assessing climate change impacts. Given the importance of plant-mediated processes in coastal wetland ecosystems, there is a pressing need in many biogeographic regions for additional species- and functional group-specific research that can be used to better anticipate coastal wetland responses to rising sea levels and changing temperature and precipitation regimes.

Estuaries and Coasts↗

USGS2019

Discoveries and novel insights in ecology using structural equation modeling

As we enter the era of data science (Lortie 2018), quantitative analysis methodologies are proliferating rapidly, leaving ecologists with the task of choosing among many alternatives. The use of structural equation modeling (SEM) by ecologists has increased in recent years, prompting us to ask users a number of questions about their experience with the methodology. Responses indicate an enthusiastic endorsement of SEM. Two major elements of respondent’s experiences seem to contribute to their positive response, (1) a sense that they are obtaining more accurate explanatory understanding through the use of SEM and (2) excitement generated by the discovery of novel insights into their systems. We elaborate here on the detection of indirect effects, offsetting effects, and suppressed effects, and demonstrate how discovering these effects can advance ecology.

Ecology and Evolution↗

USGS2019

Hurricane Sandy impacts on coastal wetland resilience

The goal of this research was to evaluate the impacts of Hurricane Sandy on surface elevation trends in estuarine marshes located across the northeast region of the United States from Virginia to Maine using data from an opportunistic (in other words, not strategic) and collaborative network (from here on, an opportunistic network) of surface elevation table-marker horizon (SET-MH) stations. First, we built a data-base of metadata for 965 individual stations from 96 unique geographical locations that included the location, geomorphic setting, and wetland type for each SET-MH station. The dominant estuarine settings included in the analyses were back-barrier lagoonal marshes and emergent marshes along embayments and tidal tributaries. We then calculated prestorm elevation trends to compare to poststorm elevation measurements to determine the storm impact on each station trend. We hypothesized that the effect of Hurricane Sandy on marsh elevation trends would differ by position relative to landfall (right or left) and distance from landfall in southern New Jersey, as both of these variables influence the presence or absence of storm surge as a result of the physical characteristics of tropical cyclones (in other words, strongest winds typically occur to the right of landfall). Storm surge was spatially less extensive and less deep (~1 meter [m]) in marshes located to the left (in other words, south) of landfall compared to marshes located to the right (in other words, north) of landfall where storm surge covered a larger area and was deeper (3–4 m). About 63 percent of 223 eligible stations had a poststorm trend that was similar to the prestorm trend (in other words, less than ±5 millimeters [mm]), indicating little storm impact on elevation trends at those sites. The remaining 37 percent of stations exhibited significant poststorm deviations from the prestorm trend (in other words, greater than ±5 mm). Of these, stations located to the left of landfall had a significant and greater deviation in their elevation trend, and the deviation was more likely to be positive (elevation gain) compared to marshes located to the right of landfall, which had a significant deviation in their elevation trend that was more likely to be negative (elevation loss). This finding is directly related to storm surge impacts on marsh sediment deposition, where deep storm surge (3–4 m) results in sediment deposition in habitats inland of coastal marshes but less so in the marshes themselves. Substrate compaction by the storm surge over-burden may have contributed to elevation loss, but this was not measured because sufficient marker horizon data were not available for analysis. In contrast, to the left of landfall the wind-driven flooding of sediment laden water pushed into the headwaters of rivers and small bays with an ~1 m surge, and resulted in more prevalent sediment deposition on the marsh surfaces and elevation gain. In general, the findings support previous research showing that the physical characteristics of the storm (for example, wind speed, storm surge height, impact angle of landfall) combined with the local wetland conditions (for example, marsh productivity, groundwater level, tide height) are important factors determining a storm’s impact on soil elevation, and that the soil elevation response can vary widely among multiple wetland sites impacted by the same storm and among different storms for the same wetland site. The final objective of this project was to create a framework using metadata from the opportunistic network of SET-MH stations that could be used to develop a strategic monitoring network designed to address specific climate change impacts and related phenomena identified by land managers and stakeholders. We evaluated the spatial distribution and density of SET-MH stations in relation to geographic coverage, marsh setting, availability of public land, and historical storm surge footprints and hurricane return intervals in order to identify gaps in our understanding of risk and our ability to assess it. Analyses revealed that the general geographic coverage of SET-MH stations is limited given the low percentage of marsh patches with stations, low density of stations, the clumped distribution of stations, and the often limited and uneven distribution of stations in wetlands with a high historical frequency of hurricane strikes and storm surge impacts. These findings can be used by managers and planners to inform the creation of a strategic monitoring network that can, in turn, inform management and adaptation plans for coastal resources in the region. Final plan designs will need to consider financial and infrastructural support required for station maintenance, as well as data collection and management over the long term.

Open-File Report↗