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At least 55 records · Page 3Linked to original sources
USGS2024

Resilience is not enough: Toward a more meaningful rangeland adaptation science

Rangeland ecosystems, and their managers, face the growing urgency of climate change impacts. Researchers are therefore seeking integrative social-ecological frameworks that can enhance adaptation by managers to these climate change dynamics through tighter linkages among multiple scientific disciplines and manager contexts. Social-ecological framings, including resilience and vulnerability, are popular in such efforts, but their potential to inform meaningful rangeland adaptation science is limited by traditional disciplinary silos. Here, we provide reflective lessons learned from a multidisciplinary Rangelands, Ranching, and Resilience (R3) project on U.S. western rangelands that addressed 1) biophysical science projections of forage production under future climate scenarios, 2) ranchers’ views of resilience using social science methods, and 3) outreach efforts coordinated through extension professionals. Despite the project's initial intentions, human dimensions and ecological researchers largely worked in parallel sub-teams during the project, rather than weaving their expertise together with managers. The R3 project was multidisciplinary, but it provides a case study on lessons learned to suggest how social and ecological researchers can move towards approaches that transcend individual disciplines. Transdisciplinary science and management in rangelands requires more than just conceptual social-ecological frameworks. Additional methodological concepts need to include: 1) relationship building; 2) shared meaning making; and 3) a commitment to continual conversations and learning, or staying with the trouble, following Haraway (2016). If the goal is to address meaningful rangeland adaptation science rather than just produce academic products, researchers, outreach professionals, and rangeland-based communities should address a series of critical troubling questions. In the process of addressing these, deeper engagement among and beyond disciplines will occur as relationship building, shared meaning, and continual conversations and learning facilitate staying with the trouble.

Rangeland Ecology & Management↗
USGS2026

How Indigenous socio-cultural systems resist, accept, or direct ecological transformation: A research agenda

Anthropogenic ecological transformations are challenging conventional environmental management paradigms, as maintaining historical ecological structures and functions becomes increasingly untenable. The resist–accept–direct (RAD) framework offers a strategic tool to navigate these shifts. However, as a product of the dominant socio-cultural system, the RAD framework risks perpetuating the same patterns of thinking that have contributed to unsustainable social-ecological dynamics. Indigenous socio-cultural systems, considered here through the lens of long-term coevolution with their environments, offer valuable empirical models for adaptive responses to environmental change. Such Indigenous socio-cultural responses, grounded in narratives, governance institutions, value systems, and other cultural elements collectively referred to as Indigenous Knowledge (IK), hold relevance for addressing today’s intertwined environmental and social challenges. We provide a targeted review of existing literature at the intersection of IK and the RAD framework, along with a broader conceptual overview of relevant insights across related fields. These reveal contrasting assumptions about Indigenous responses to environmental change, such as consistently resisting to preserve cultural fidelity or uniformly accepting, reflecting deeper cultural, temporal, and ecological complexity. We propose a structured research agenda to investigate Indigenous socio-cultural responses to environmental change through empirical research, theoretical synthesis, hypothesis development, and practical application. This agenda is grounded in comprehensive collaboration, emphasizing shared power and integrating both emic and etic perspectives throughout all stages of research and implementation. By bridging the adaptive dynamics of coevolved social-ecological systems and mainstream management strategies, an IK–RAD framework could enhance the efficacy of responses to global environmental change.

Human Ecology↗
USGS2001

Lessons learned from long-term ecosystem research and monitoring in alpine and subalpine basins of the Colorado Rocky Mountains, USA

Long-term ecosystem research and monitoring was begun in the Loch Vale watershed of Rocky Mountain National Park in 1983, after extensive survey work to identify the best location. Then, as now, our scientific objectives were to understand natural biogeochemical cycles and variability, so that we could differentiate ecosystem changes from human-caused disturbances, such as atmospheric deposition of pollutants and climate change. We have learned many lessons, often through our mistakes, that are worth passing on. Clear scientific objectives, even for long-term monitoring, are essential. Standardized methods, including rigorous quality assurance procedures should be adhered to from the beginning of the program. All data, even those collected routinely for background records, should be scrutinized and summarized at least once a year. Freely share basic information such as weather, hydrologic, chemical, and descriptive records with other researchers who can build upon your efforts. Use many tools when asking complex ecological questions, in order to minimize bias toward specific results. Publish frequently; long-term studies do not imply there are no interim conclusions or interesting findings. Interpret findings frequently to policy makers and citizens; increased understanding of the environment and human-caused changes may improve natural resource management, and build support for ecological research. And finally, be persistent. Long-term ecological research can be frustrating and difficult to maintain, yet is often the best way to observe and understand ecological change on a meaningful time scale.

Colorado↗
USGS2010

QuickBird satellite imagery as a tool for restoration and rehabilitation of Lake Sevan, Armenia

Lake Sevan in Armenia is the largest freshwater lake in the Transcaucasus region and is one of the largest high-altitude freshwater lakes in Eurasia (Babayan et al., 2006 ). Armenia is a small (29,743 km 2 ), land- locked country that is comparatively poor in natural resources and nearly surrounded by borders that are blockaded by its neighbors (Babayan et al., 2006 ; Rhoades, 2008 and references therein; CIA, 2010 ). Despite these obstacles, ecological research and environmental restoration efforts continue. For the most part, ecological research in Lake Sevan during the twentieth century was restricted to Soviet-era journals, or was otherwise unavailable to western scientists.

Lake Sevan↗
USGS2020

Unfamiliar territory: Emerging themes for ecological drought research and management

Novel forms of drought are emerging globally, due to climate change, shifting teleconnection patterns, expanding human water use, and a history of human influence on the environment that increases the probability of transformational ecological impacts. These costly ecological impacts cascade to human communities, and understanding this changing drought landscape is one of today’s grand challenges. By using a modified horizon-scanning approach that integrated scientists, managers, and decision-makers, we identified the emerging issues in ecological drought that represent key challenges to timely and effective responses. Here we review the themes that most urgently need attention, including novel drought conditions, the potential for transformational drought impacts, and the need for anticipatory drought management. This horizon scan and review provides a roadmap to facilitate the research and management innovations that will support forward-looking, co-developed approaches to reduce the risk of drought to our socio-ecological systems during the 21 st century.

One Earth↗
USGS2002

Fragmentation: Is the message clear?

In this paper, we briefly discuss some of the fundamental problems arising from the inherent complexity of larger-scale ecological systems. We examine the tenuous assumption of a direct correspondence between ecological data and theory, we comment on a recent report that evaluated the efficacy of fragmentation experiments, and we briefly assess its implications for ecological research and conservation practice on the landscape scale.

Ecology and Society↗
USGS2021

A machine learning approach to modeling streamflow with sparse data in ungaged watersheds on the Wyoming Range, Wyoming, 2012–17

Scant availability of streamflow data can impede the utility of streamflow as a variable in ecological models of aquatic and terrestrial species, especially when studying small streams in watersheds that lack streamgages. Streamflow data at fine resolution and broad extent were needed by collaborators for ecological research on small streams in several ungaged watersheds of southwestern Wyoming, where streamflow data are sparse. To improve the utility of sparse streamflow data to ecological research in ungaged watersheds, we developed a machine learning approach in R for modeling spatially and temporally continuous monthly streamflow from 2012 through 2017 in three semiarid montane-steppe watersheds (with drainage areas of 26–55 square miles and mean elevations of 8,031–8,455 feet) on the Wyoming Range in the upper Green River Basin. A machine learning streamflow (MLFLOW) model was calibrated and validated with 971 discrete streamflow observations and 24 static and dynamic predictor variables derived from geospatial and time series data on climatic, physiographic, and anthropogenic characteristics affecting streamflow. The predictor variables were temporally and spatially conditioned to amplify the relation of predictor variables to monthly streamflow. The MLFLOW model had satisfactory agreement between observed and predicted streamflow (coefficient of determination [ R 2 ]=0.80, Nash-Sutcliffe efficiency [NSE]=0.79, NSE with log-transformed data [logNSE]=0.82, and percent bias [PBIAS]=0.7 percent). NSE and logNSE indicated the MLFLOW model performed equally well for high and low flows, and PBIAS indicated the MLFLOW model did not overpredict or underpredict monthly streamflow. Streamflow predictions seemed to well represent the annual hydrograph within the study area during the study period. The most important variables (statistically important in the MLFLOW model) for explaining monthly streamflow were temporally and spatially conditioned dynamic climatic variables, mostly precipitation and snow water equivalent. Importance of the static and dynamic variables did not differ substantially among the three watersheds but differed considerably among the 6 years. Monthly streamflow increased with increasing precipitation, snow water equivalent, and drainage area but decreased with increasing forest cover, elevation, evapotranspiration, and temperature. The MLFLOW model was most sensitive to selection of dynamic climatic variables. Unconditioned dynamic climatic variables alone explained 54 percent of the variance ( R 2 =0.54) in monthly streamflow, whereas adding static physiographic and anthropogenic variables only explained 12 percent more of the variance ( R 2 =0.66). Also, spatial conditioning of all variables together with temporal conditioning of dynamic variables increased the variance explained in the MLFLOW model by another 14 percent ( R 2 =0.80). The MLFLOW model also had greater sensitivity to temporal than to spatial differences in the data. For the MLFLOW model trained with observations from all watersheds and years or for models trained with observations from all except one watershed or 1 year left out sequentially, performance was better in testing on observations from each watershed than from each year separately. Also, performance was better for models fitted to fewer sites than to fewer months of observations. The greatest utility of the modeling approach is the ease of use and the speed of processing input data, running the model, and interpreting the model output, whereas the greatest limitation is the need for spatially and temporally representative streamflow observations to drive the model. Although familiarity with R is necessary, only a working knowledge of hydrology (for selecting appropriate predictor variables and evaluating the quality of streamflow observations) and a rudimentary understanding of machine learning models are needed. Therefore, this modeling approach is practicable for other scientists who work with water but who are not hydrologists.

Wyoming↗
USGS2010

Persistent organic pollutants in the blood of free-ranging sea otters (Enhydra lutris ssp.) in Alaska and California

As part of tagging and ecologic research efforts in 1997 and 1998, apparently healthy sea otters of four age-sex classes in six locations in Alaska and three in California were sampled for persistent organic pollutants (POPs) and other chemicals of ecologic or environmental concern (COECs). Published techniques for the detection of POPs (specifically Σpolychlorinated biphenyls [PCBs], ΣDDTs, Σhexachlorocyclohexanes [HCHs], Σpolycyclic aromatic hydrocarbons [PAHs], Σchlordanes [CHLs], hexachlorobenzene [HCB], dieldrin, and mirex) in the tissue of dead otters were modified for use with serum from live sea otters. Toxic equivalencies (TEQs) were calculated for POPs with proven bioactivity. Strong location effects were seen for most POPs and COECs; sea otters in California generally showed higher mean concentrations than those in Alaska. Differences in contaminant concentrations were detected among age and sex classes, with high levels frequently observed in subadults. Very high levels of ΣDDT were detected in male sea otters in Elkhorn Slough, California, where strong freshwater outflow from agricultural areas occurs seasonally. All contaminants except mirex differed among Alaskan locations; only ΣDDT, HCB, and chlorpyrifos differed within California. High levels of ΣPCB (particularly larger, more persistent congeners) were detected at two locations in Alaska where associations between elevated PCBs and military activity have been established, while higher PCB levels were found at all three locations in California where no point source of PCBs has been identified. Although POP and COEC concentrations in blood may be less likely to reflect total body burden, concentrations in blood of healthy animals may be more biologically relevant and less influenced by state of nutrition or perimortem factors than other tissues routinely sampled.

Alaska, California↗
USGS2008

Using an ecological ethics framework to make decisions about the relocation of wildlife

Relocation is an increasingly prominent conservation tool for a variety of wildlife, but the technique also is controversial, even among conservation practitioners. An organized framework for addressing the moral dilemmas often accompanying conservation actions such as relocation has been lacking. Ecological ethics may provide such a framework and appears to be an important step forward in aiding ecological researchers and biodiversity managers to make difficult moral choices. A specific application of this framework can make the reasoning process more transparent and give more emphasis to the strong sentiments about non-human organisms held by many potential users. Providing an example of the application of the framework may also increase the appeal of the reasoning process to ecological researchers and biodiversity managers. Relocation as a conservation action can be accompanied by a variety of moral dilemmas that reflect the interconnection of values, ethical positions, and conservation decisions. A model that is designed to address moral dilemmas arising from relocation of humans provides/demonstrates/illustrates a possible way to apply the ecological ethics framework and to involve practicing conservationists in the overall decision-making process. ?? 2008 Springer Science+Business Media B.V.

Science and Engineering Ethics↗
USGS2017

Do you hear what I see? Vocalization relative to visual detection rates of Hawaiian hoary bats (Lasiurus cinereus semotus)

B ats vocalize during flight as part of the sensory modality called echolocation, but very little is known about whether flying bats consistently call. Occasional vocal silence during flight when bats approach prey or conspecifics has been documented for relatively few species and situations. Bats flying alone in clutter-free airspace are not known to forgo vocalization, yet prior observations suggested possible silent behavior in certain, unexpected situations. Determining when, why, and where silent behavior occurs in bats will help evaluate major assumptions of a primary monitoring method for bats used in ecological research, management, and conservation. In this study, we recorded flight activity of Hawaiian hoary bats ( Lasiurus cinereus semotus ) under seminatural conditions using both thermal video cameras and acoustic detectors. Simultaneous video and audio recordings from 20 nights of observation at 10 sites were analyzed for correspondence between detection methods, with a focus on video observations in three distance categories for which accompanying vocalizations were detected. Comparison of video and audio detections revealed that a high proportion of Hawaiian hoary bats “seen” on video were not simultaneously “heard.” On average, only about one in three visual detections within a night had an accompanying call detection, but this varied greatly among nights. Bats flying on curved flight paths and individuals nearer the cameras were more likely to be detected by both methods. Feeding and social calls were detected, but no clear pattern emerged from the small number of observations involving closely interacting bats. These results may indicate that flying Hawaiian hoary bats often forgo echolocation, or do not always vocalize in a way that is detectable with common sampling and monitoring methods. Possible reasons for the low correspondence between visual and acoustic detections range from methodological to biological and include a number of biases associated with the propagation and detection of sound, cryptic foraging strategies, or conspecific presence. Silent flight behavior may be more prevalent in echolocating bats than previously appreciated, has profound implications for ecological research, and deserves further characterization and study.

Hawai'i↗
USGS2020

AMMonitor: Remote monitoring of biodiversity in an adaptive framework with R

Ecological research and management programs are increasingly using autonomous monitoring units (AMUs) to collect large volumes of acoustic and/or photo data to address pressing management objectives or research goals. The data management requirements of an AMU-based monitoring effort are often overwhelming, with a considerable amount of processing to translate raw data into models and analyses that have research and management utility. We created the r package AMMonitor to simplify the process of moving from remotely collected data to analysis and results, using a comprehensive SQLite database for data management that tracks all components of a remote monitoring program. This framework enables the tracking of analyses and research/management objectives through time. We illustrate the AMMonitor approach with the example of evaluating an occurrence-based management objective for a target species. First, we provide an overview of the database and data management approach. Next, we illustrate a few available workflows: temporally adaptive sampling, automated detection of species sounds from acoustic recordings and aggregation of automated detections into an encounter history for use in an occupancy analysis, the outcome of which can be analysed with respect to the motivating management objective. Without a comprehensive framework for efficiently moving from raw remote monitoring data collection to results and analysis, monitoring programs are limited in their capacity to systematically characterize ecological processes and inform management decisions through time. AMMonitor provides an option for such a framework. Code, comprehensive documentation, and step-by-step examples are available online at https://code.usgs.gov/vtcfwru/AMMonitor

Methods in Ecology and Evolution↗
USGS2026

A comprehensive inventory of communication tower infrastructure across the range of greater and Gunnison sage-grouse

We compiled and verified a comprehensive inventory dataset of communication tower infrastructure across the range of the greater sage-grouse ( Centrocercus urophasianus ) and Gunnison sage-grouse ( Centrocercus minimus ), two species of conservation concern that are viewed as ecosystem health indicators for the entire sagebrush biome within the United States. Our dataset includes all known towers with emphasis on validating construction year and month for towers built between 1990–2023. The annual spatial time series format of the data allows users to visualize, assess, and download tower locations and duration (including dates of construction and dismantlement) across the sagebrush biome of the western U.S. Tower data were acquired from publicly available infrastructure databases and records were filtered to include communication tower structures within the area of interest. Data records were validated and checked for accuracy with high resolution aerial and satellite imagery, and a subset were verified during field visits. The final filtered dataset comprises 4,322 tower sites, of which 3,528 tower site locations were verified via satellite imagery or field visits, and 794 were unverified tower sites (tower presence could not be confirmed via satellite imagery). Each tower record includes geographic coordinates, structure height, estimated date of construction, number of towers at each site, and, if applicable, date of dismantlement. The data product closes spatiotemporal gaps and resolves discrepancies present in other public versions of similar data and can be used in ecological research, infrastructure planning/siting/permitting, decision support tools for biological or landscape management, environmental assessments, or general use pertaining to the historic and current locations of communication infrastructure across sagebrush ecosystems.

western united States↗
USGS2017

A guide to multi-objective optimization for ecological problems with an application to cackling goose management

Choices in ecological research and management are the result of balancing multiple, often competing, objectives. Multi-objective optimization (MOO) is a formal decision-theoretic framework for solving multiple objective problems. MOO is used extensively in other fields including engineering, economics, and operations research. However, its application for solving ecological problems has been sparse, perhaps due to a lack of widespread understanding. Thus, our objective was to provide an accessible primer on MOO, including a review of methods common in other fields, a review of their application in ecology, and a demonstration to an applied resource management problem. A large class of methods for solving MOO problems can be separated into two strategies: modelling preferences pre-optimization (the a priori strategy), or modelling preferences post-optimization (the a posteriori strategy). The a priori strategy requires describing preferences among objectives without knowledge of how preferences affect the resulting decision. In the a posteriori strategy, the decision maker simultaneously considers a set of solutions (the Pareto optimal set) and makes a choice based on the trade-offs observed in the set. We describe several methods for modelling preferences pre-optimization, including: the bounded objective function method, the lexicographic method, and the weighted-sum method. We discuss modelling preferences post-optimization through examination of the Pareto optimal set. We applied each MOO strategy to the natural resource management problem of selecting a population target for cackling goose ( Branta hutchinsii minima ) abundance. Cackling geese provide food security to Native Alaskan subsistence hunters in the goose's nesting area, but depredate crops on private agricultural fields in wintering areas. We developed objective functions to represent the competing objectives related to the cackling goose population target and identified an optimal solution first using the a priori strategy, and then by examining trade-offs in the Pareto set using the a posteriori strategy. We used four approaches for selecting a final solution within the a posteriori strategy; the most common optimal solution, the most robust optimal solution, and two solutions based on maximizing a restricted portion of the Pareto set. We discuss MOO with respect to natural resource management, but MOO is sufficiently general to cover any ecological problem that contains multiple competing objectives that can be quantified using objective functions.

Ecological Modelling↗
USGS2017

Moving forward in circles: Challenges and opportunities in modeling population cycles

Population cycling is a widespread phenomenon, observed across a multitude of taxa in both laboratory and natural conditions. Historically, the theory associated with population cycles was tightly linked to pairwise consumer–resource interactions and studied via deterministic models, but current empirical and theoretical research reveals a much richer basis for ecological cycles. Stochasticity and seasonality can modulate or create cyclic behaviour in non-intuitive ways, the high-dimensionality in ecological systems can profoundly influence cycling, and so can demographic structure and eco-evolutionary dynamics. An inclusive theory for population cycles, ranging from ecosystem-level to demographic modelling, grounded in observational or experimental data, is therefore necessary to better understand observed cyclical patterns. In turn, by gaining better insight into the drivers of population cycles, we can begin to understand the causes of cycle gain and loss, how biodiversity interacts with population cycling, and how to effectively manage wildly fluctuating populations, all of which are growing domains of ecological research.

Ecology Letters↗
USGS2013

A non-marine source of variability in Adélie Penguin demography

A primary research objective of the Palmer Long Term Ecological Research (LTER) program has been to identify and understand the factors that regulate the demography of Adélie penguins ( Pygoscelis adeliae ). In this context, our work has been focused on variability in the marine environment on which this species depends for virtually all aspects of its life history (Ainley, 2002). As we show here, however, there are patterns evident in the population dynamics of Adélie penguins that are better explained by variability in breeding habitat quality rather than by variability in the marine system. Interactions between the geomorphology of the terrestrial environment that, in turn, affect patterns of snow deposition, drive breeding habitat quality.

Oceanography↗
USGS2016

Foreword: The dynamics of change in Alaska’s boreal forests: Resilience and vulnerability in response to climate warming

Long-term research by the Bonanza Creek (BNZ) Long Term Ecological Research (LTER) program has documented natural patterns of interannual and successional variability of the boreal forest in interior Alaska against which we can detect changes in system behavior. Between 2004 and 2010 the BNZ LTER program focused on understanding the dynamics of change through studying the resilience and vulnerability of Alaska's boreal forest in response to climate warming. The overarching question in this endeavor has been “How are boreal ecosystems responding, both gradually and abruptly, to climate warming, and what new landscape patterns are emerging?”

Alaska↗
USGS2026

Advances and applications of Unoccupied Aerial Systems (UAS) research in landscape ecology

Landscape ecologists have long depended on satellite and aerial remote sensing to address questions about landscape pattern and process, structure, and change (Foody 2023 ). Unoccupied aerial systems/vehicles (UAS/UAV, a.k.a. drones) technology is becoming an increasingly popular research tool in environmental sciences allowing scientists to generate low-cost, high-quality, and high-resolution imagery on demand that can be tailored to specific research questions. While satellite data are of a fixed resolution and temporal interval, UAS offer researchers control and flexibility to design studies and collect data at resolutions and scales that provide ecologically relevant information at finer spatial resolutions (e.g., < 30 cm) than what is currently available from satellite platforms (typically > 3m), thus helping capture objects such as individual plant canopies, micro-topography, and individual animals. Unlike satellites with fixed orbits, UAS can be deployed at more optimal temporal frequencies for ecological monitoring. We organized the special collection “Advances and Applications of Unoccupied Aerial Systems (UAS) Research in Landscape Ecology” to showcase the many ways that UAS tools and technologies are currently applied to advance landscape ecological research. When we announced the collection in 2023, only 11 papers published in the journal Landscape Ecology used UAS data, which was a notably small number compared to many other general ecology, environmental science and remote sensing journals. In an attempt to understand why UAS were not more widely used in landscape ecology and provide possible solutions, we published a review article (Villarreal et al. 2025) that identified the challenges, knowledge gaps, and obstacles for the adoption of UAS technologies in landscape ecology research. The main issues we identified include: (1) an abundance of UAS methods papers in the existing literature, with comparatively few studies demonstrating how UAS can be applied to address ecological questions; (2) a perceived scale mismatch between the geographic extent of UAS data collection (local) compared to larger study areas (landscapes) and a need to design robust scaling approaches to connect fine-scale UAS data with broader ecological patterns; and (3) a need for improved integration of UAS data with other commonly used remote sensing datasets including historical high resolution aerial imagery. Additionally, researchers new to UAS remote sensing may be discouraged or overwhelmed by the general lack of scientific consensus and standardized protocols for typical tasks such as data collection, vegetation classification, and change detection, as well as restrictive and/or confusing policy, regulatory, and legal issues surrounding UAS operations (Villarreal et al. 2025).

Landscape Ecology↗
USGS2014

Freshwater availability and coastal wetland foundation species: ecological transitions along a rainfall gradient

Climate gradient-focused ecological research can provide a foundation for better understanding critical ecological transition points and nonlinear climate-ecological relationships, which is information that can be used to better understand, predict, and manage ecological responses to climate change. In this study, we examined the influence of freshwater availability upon the coverage of foundation plant species in coastal wetlands along a northwestern Gulf of Mexico rainfall gradient. Our research addresses the following three questions: (1) what are the region-scale relationships between measures of freshwater availability (e.g., rainfall, aridity, freshwater inflow, salinity) and the relative abundance of foundation plant species in tidal wetlands; (2) How vulnerable are foundation plant species in tidal wetlands to future changes in freshwater availability; and (3) What is the potential future relative abundance of tidal wetland foundation plant species under alternative climate change scenarios? We developed simple freshwater availability-based models to predict the relative abundance (i.e., coverage) of tidal wetland foundation plant species using climate data (1970-2000), estuarine freshwater inflow-focused data, and coastal wetland habitat data. Our results identify regional ecological thresholds and nonlinear relationships between measures of freshwater availability and the relative abundance of foundation plant species in tidal wetlands. In drier coastal zones, relatively small changes in rainfall could produce comparatively large landscape-scale changes in foundation plant species abundance which would affect some ecosystem good and services. Whereas a drier future would result in a decrease in the coverage of foundation plant species, a wetter future would result in an increase in foundation plant species coverage. In many ways, the freshwater-dependent coastal wetland ecological transitions we observed are analogous to those present in dryland terrestrial ecosystems.

Gulf Of Mexico↗