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At least 1,009 records · Page 56Linked to original sources

USGS2006

Electrical resistance sensors record spring flow timing, Grand Canyon, Arizona

Springs along the south rim of the Grand Canyon, Arizona, are important ecological and cultural resources in Grand Canyon National Park and are discharge points for regional and local aquifers of the Coconino Plateau. This study evaluated the applicability of electrical resistance (ER) sensors for measuring diffuse, low-stage (<1.0 cm) intermittent and ephemeral flow in the steep, rocky spring-fed tributaries of the south rim. ER sensors were used to conduct a baseline survey of spring flow timing at eight sites in three spring-fed tributaries in Grand Canyon. Sensors were attached to a nearly vertical rock wall at a spring outlet and were installed in alluvial and bedrock channels. Spring flow timing data inferred by the ER sensors were consistent with observations during site visits, with flow events recorded with collocated streamflow gauging stations and with local precipitation gauges. ER sensors were able to distinguish the presence of flow along nearly vertical rock surfaces with flow depths between 0.3 and 1.0 cm. Laboratory experiments confirmed the ability of the sensors to monitor the timing of diffuse flow on impervious surfaces. A comparison of flow patterns along the stream reaches and at springs identified the timing and location of perennial and intermittent flow, and periods of increased evapotranspiration.

Ground Water↗

USGS2018

Evaluating GPS biologging technology for studying spatial ecology of large constricting snakes

Background : GPS telemetry has revolutionized the study of animal spatial ecology in the last two decades. Until recently, it has mainly been deployed on large mammals and birds, but the technology is rapidly becoming miniaturized, and applications in diverse taxa are becoming possible. Large constricting snakes are top predators in their ecosystems, and accordingly they are often a management priority, whether their populations are threatened or invasive. Fine-scale GPS tracking datasets could greatly improve our ability to understand and manage these snakes, but the ability of this new technology to deliver high-quality data in this system is unproven. In order to evaluate GPS technology in large constrictors, we GPS-tagged 13 Burmese pythons ( Python bivittatu s) in Everglades National Park and deployed an additional 7 GPS tags on stationary platforms to evaluate habitat-driven biases in GPS locations. Both python and test platform GPS tags were programmed to attempt a GPS fix every 90 min. Results : While overall fix rates for the tagged pythons were low (18.1%), we were still able to obtain an average of 14.5 locations/animal/week, a large improvement over once-weekly VHF tracking. We found overall accuracy and precision to be very good (mean accuracy = 7.3 m, mean precision = 12.9 m), but a very few imprecise locations were still recorded (0.2% of locations with precision > 1.0 km). We found that dense vegetation did decrease fix rate, but we concluded that the low observed fix rate was also due to python microhabitat selection underground or underwater. Half of our recovered pythons were either missing their tag or the tag had malfunctioned, resulting in no data being recovered. Conclusions : GPS biologging technology is a promising tool for obtaining frequent, accurate, and precise locations of large constricting snakes. We recommend future studies couple GPS telemetry with frequent VHF locations in order to reduce bias and limit the impact of catastrophic failures on data collection, and we recommend improvements to GPS tag design to lessen the frequency of these failures.

Florida↗

USGS2020

Environmental contamination and unusual snake mortality in an urban national wildlife refuge

The National Wildlife Refuge (NWR) System protects ~150 million acres of land and water in the United States and provides habitat for >2,000 native vertebrates species. Although legally protected, wildlife populations within these refuges can be threatened by anthropogenic activities. The lack of knowledge about such threats has the potential to undermine biodiversity conservation. We investigated patterns of wildlife mortality among three NWRs in the northern U.S. Gulf Coast. Visual surveys (958 total search-hours) were conducted for live or dead amphibians and reptiles at Big Branch, Bogue Chitto, and Bayou Sauvage NWRs. An exceptionally high incidence of snake mortality (>800% above background, P < 0.001) was observed at one site (Haul Road, Bayou Sauvage NWR). Mortality affected seven species and recurred for 29 months. Intact carcasses were subject to histopathological examination and diagnostic testing. Cause of death was undetermined (n = 9) or attributed to various pathogens (n = 5). To investigate possible underlying causes of this unusual mortality, water exposure challenges and soil toxicological analyses (n = 6 and 11 sites, respectively) were conducted on samples from Bayou Sauvage NWR, given the known potential for anthropogenic contamination in the refuge. In a controlled experiment, survival of Anaxyrus fowlerii embryos exposed to water from Recovery Road (i.e., 250m from a landfill) and Haul Road was reduced 92% and 48%, respectively, compared to four reference sites in the refuge. Sediment analyses suggested at least seven distinct sources of contaminants in Bayou Sauvage NWR. Potential sources included a landfill, illegal dumping, vehicle emissions, pesticide migration, and an active hydrocarbon pipeline leak discovered 600 m from Haul Road. Synthesis and applications: Although further research is needed to determine whether the observed mortality and contamination are related, these collective findings suggest that anthropogenic factors may threaten the ecological integrity of one of the United States largest urban wildlife refuges. More broadly, our study highlights the critical need for species inventories, baseline data, and systematic monitoring in NWRs. Such information is essential to detecting and mitigating anthropogenic threats to biodiversity conservation in protected areas.

Louisiana↗

USGS2024

Integrating social-ecological outcomes into invasive species management: The Tamarix case

Incorporating societal considerations into decisions related to invasive species management is desirable, but can be challenging because it requires a solid understanding of the ecological functions and socio-cultural and economic benefits and values of the invaded environment before and after invasion. The ecosystem service ( ES ) concept was designed to facilitate such decision-making by establishing direct connections between ecosystem properties and human well-being, but its application in invasive species management has not been systematic. In this Discussion paper, we propose the adoption of the ES cascade model as a framework for understanding the environmental effects, costs and benefits associated with controlling an invasive shrub ( Tamarix spp.) in riparian systems of the western United States. The cascade model has the advantage of explicitly dissecting social-ecological systems into five components: ecosystem structure and processes, ecological functions, ecosystem services, benefits and the economic and socio-cultural valuation of these services and benefits. The first two have received significant attention in the evaluation of Tamarix control effectiveness. The last three have long been implicitly acknowledged over decades of Tamarix management in the region, but have not been formally accounted for, which we believe would increase the effectiveness, accountability and transparency of management efforts.

NeoBiota↗

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↗

USGS2021

Transient disease dynamics across ecological scales

Analyses of transient dynamics are critical to understanding infectious disease transmission and persistence. Identifying and predicting transients across scales, from within-host to community-level patterns, plays an important role in combating ongoing epidemics and mitigating the risk of future outbreaks. Moreover, greater emphases on non-asymptotic processes will enable timely evaluations of wildlife and human diseases and lead to improved surveillance efforts, preventive responses, and intervention strategies. Here, we explore the contributions of transient analyses in recent models spanning the fields of epidemiology, movement ecology, and parasitology. In addition to their roles in predicting epidemic patterns and endemic outbreaks, we explore transients in the contexts of pathogen transmission, resistance, and avoidance at various scales of the ecological hierarchy. Examples illustrate how (i) transient movement dynamics at the individual host level can modify opportunities for transmission events over time; (ii) within-host energetic processes often lead to transient dynamics in immunity, pathogen load, and transmission potential; (iii) transient connectivity between discrete populations in response to environmental factors and outbreak dynamics can affect disease spread across spatial networks; and (iv) increasing species richness in a community can provide transient protection to individuals against infection. Ultimately, we suggest that transient analyses offer deeper insights and raise new, interdisciplinary questions for disease research, consequently broadening the applications of dynamical models for outbreak preparedness and management.

Theoretical Ecology↗

USGS2020

Spatial conservation planning under uncertainty using modern portfolio theory and nash bargaining solution

In recent years, researchers from interdisciplinary teams involving ecologists, economists and operations re- searchers collaborated to provide decision support tools to address the challenges of preserving biodiversity by optimizing the design of reserves. The goal of this paper is to further advance this area of research and provide new solutions to solve complex Spatial Conservation Planning (SCP) problems under uncertainty that consider risk preferences of decision makers. Our approach employs modern portfolio theory to address uncertainties in SCP problems, and involves two conflicting objectives: maximizing return and minimizing risk. We apply concepts from game theory such as the Nash bargaining solution to directly compute a desirable Pareto-optimal solution for the proposed bi-objective optimization formulation in natural resource management problems. We demonstrate with numerical examples that by directly computing a Nash bar- gaining solution, a Binary Quadratically Constrained Quadratic Program (BQCQP) can be solved. We show that our approach (implementable with commercial solvers such as CPLEX) can effectively solve the proposed BQCQP for much larger problems than previous approaches published in the ecological literature. Optimal solutions for problems with less than 400 parcels can be computed within a minute. Near optimal solutions (within at most 0.2% gap from an optimal solution) for high-dimensional problems involving up to 800 parcels can be computed within 8 hours on a standard computer. We have presented a new approach to solve SCP optimization problems while considering uncertainty and risk tolerance of decision makers. Our new approach expands considerably the applicability of such SCP optimization methods to address real conservation problems.

Ecological Modelling↗

USGS2015

Implementation of a framework for multi-species, multi-objective adaptive management in Delaware Bay

Decision analytic approaches have been widely recommended as well suited to solving disputed and ecologically complex natural resource management problems with multiple objectives and high uncertainty. However, the difference between theory and practice is substantial, as there are very few actual resource management programs that represent formal applications of decision analysis. We applied the process of structured decision making to Atlantic horseshoe crab harvest decisions in the Delaware Bay region to develop a multispecies adaptive management (AM) plan, which is currently being implemented. Horseshoe crab harvest has been a controversial management issue since the late 1990s. A largely unregulated horseshoe crab harvest caused a decline in crab spawning abundance. That decline coincided with a major decline in migratory shorebird populations that consume horseshoe crab eggs on the sandy beaches of Delaware Bay during spring migration. Our approach incorporated multiple stakeholders, including fishery and shorebird conservation advocates, to account for diverse management objectives and varied opinions on ecosystem function. Through consensus building, we devised an objective statement and quantitative objective function to evaluate alternative crab harvest policies. We developed a set of competing ecological models accounting for the leading hypotheses on the interaction between shorebirds and horseshoe crabs. The models were initially weighted based on stakeholder confidence in these hypotheses, but weights will be adjusted based on monitoring and Bayesian model weight updating. These models were used together to predict the effects of management actions on the crab and shorebird populations. Finally, we used a dynamic optimization routine to identify the state dependent optimal harvest policy for horseshoe crabs, given the possible actions, the stated objectives and our competing hypotheses about system function. The AM plan was reviewed, accepted and implemented by the Atlantic States Marine Fisheries Commission in 2012 and 2013. While disagreements among stakeholders persist, structured decision making enabled unprecedented progress towards a transparent and consensus driven management plan for crabs and shorebirds in Delaware Bay.

Biological Conservation↗

USGS2025

A transdisciplinary approach to growing an applied science of cultural evolution for a sustainable future

Addressing sustainability challenges requires an integrative approach that bridges scientific research with practical application. The field of cultural evolution (CE) offers a perspective that may guide transitions and cultural transformations for a sustainable future. However, there have been few efforts to apply this field to sustainability challenges. This study explores how CE can inform sustainability practices through mechanisms of social learning, processes of cultural adaptation and conditions that promote cooperative governance. Through a two-phase research design—comprising online discussions with professionals from six domains, and an international workshop—we examined the perceived benefits, challenges and opportunities for applying CE in real-world contexts. Our findings indicate that professionals recognize CE’s potential to enhance knowledge dissemination, foster adaptation to social–ecological changes, improve governance structures and generate cooperation. However, barriers such as complex terminology, unfamiliarity with CE, the lack of clear, context-specific evidence of added value and competition with existing frameworks hinder its application. To overcome these challenges, we propose simplifying CE concepts, demonstrating its unique contributions, and continuing to foster co-production with practitioners to refine its applicability. This study is an initial step in building an applied science of CE that can support sustainability transformations across diverse domains.

Philosophical Transactions of the Royal Society B ↗

USGS2017

Application of an unstructured 3D finite volume numerical model to flows and salinity dynamics in the San Francisco Bay-Delta

A linked modeling approach has been undertaken to understand the impacts of climate and infrastructure on aquatic ecology and water quality in the San Francisco Bay-Delta region. The Delft3D Flexible Mesh modeling suite is used in this effort for its 3D hydrodynamics, salinity, temperature and sediment dynamics, phytoplankton and water-quality coupling infrastructure, and linkage to a habitat suitability model. The hydrodynamic model component of the suite is D-Flow FM, a new 3D unstructured finite-volume model based on the Delft3D model. In this paper, D-Flow FM is applied to the San Francisco Bay-Delta to investigate tidal, seasonal and annual dynamics of water levels, river flows and salinity under historical environmental and infrastructural conditions. The model is driven by historical winds, tides, ocean salinity, and river flows, and includes federal, state, and local freshwater withdrawals, and regional gate and barrier operations. The model is calibrated over a 9-month period, and subsequently validated for water levels, flows, and 3D salinity dynamics over a 2 year period. Model performance was quantified using several model assessment metrics and visualized through target diagrams. These metrics indicate that the model accurately estimated water levels, flows, and salinity over wide-ranging tidal and fluvial conditions, and the model can be used to investigate detailed circulation and salinity patterns throughout the Bay-Delta. The hydrodynamics produced through this effort will be used to drive affiliated sediment, phytoplankton, and contaminant hindcast efforts and habitat suitability assessments for fish and bivalves. The modeling framework applied here will serve as a baseline to ultimately shed light on potential ecosystem change over the current century.

California↗

USGS1986

A population model for a long-lived, resprouting chaparral shrub: Adenostoma fasciculatum

Extensive stands of Adenostoma fasciculatum H.&A. (chamise) in the chaparral of California are periodically rejuvenated by fire. A population model based on size-specific demographic characteristics (thinning and fire-caused mortality) was developed to generate probable age distributions within size classes and survivorship curves for typical stands. The model was modified to assess the long term effects of different mortality rates on age distributions. Under observed mean mortality rates (28.7%), model output suggests some shrubs can survive more than 23 fires. A 10% increase in mortality rate by size class slightly shortened the survivorship curve, while a 10% decrease in mortality rate by size class greatly elongated the curve. This approach may be applicable to other long-lived plant species with complex life histories.

Ecological Modelling↗

USGS2013

Advances and applications of occupancy models

Summary: The past decade has seen an explosion in the development and application of models aimed at estimating species occurrence and occupancy dynamics while accounting for possible non-detection or species misidentification. We discuss some recent occupancy estimation methods and the biological systems that motivated their development. Collectively, these models offer tremendous flexibility, but simultaneously place added demands on the investigator. Unlike many mark–recapture scenarios, investigators utilizing occupancy models have the ability, and responsibility, to define their sample units (i.e. sites), replicate sampling occasions, time period over which species occurrence is assumed to be static and even the criteria that constitute ‘detection’ of a target species. Subsequent biological inference and interpretation of model parameters depend on these definitions and the ability to meet model assumptions. We demonstrate the relevance of these definitions by highlighting applications from a single biological system (an amphibian–pathogen system) and discuss situations where the use of occupancy models has been criticized. Finally, we use these applications to suggest future research and model development.

Methods in Ecology and Evolution↗

USGS2016

A multistate dynamic site occupancy model for spatially aggregated sessile communities

Estimation of transition probabilities of sessile communities seems easy in principle but may still be difficult in practice because resampling error (i.e. a failure to resample exactly the same location at fixed points) may cause significant estimation bias. Previous studies have developed novel analytical methods to correct for this estimation bias. However, they did not consider the local structure of community composition induced by the aggregated distribution of organisms that is typically observed in sessile assemblages and is very likely to affect observations. We developed a multistate dynamic site occupancy model to estimate transition probabilities that accounts for resampling errors associated with local community structure. The model applies a nonparametric multivariate kernel smoothing methodology to the latent occupancy component to estimate the local state composition near each observation point, which is assumed to determine the probability distribution of data conditional on the occurrence of resampling error. By using computer simulations, we confirmed that an observation process that depends on local community structure may bias inferences about transition probabilities. By applying the proposed model to a real data set of intertidal sessile communities, we also showed that estimates of transition probabilities and of the properties of community dynamics may differ considerably when spatial dependence is taken into account. Results suggest the importance of accounting for resampling error and local community structure for developing management plans that are based on Markovian models. Our approach provides a solution to this problem that is applicable to broad sessile communities. It can even accommodate an anisotropic spatial correlation of species composition, and may also serve as a basis for inferring complex nonlinear ecological dynamics.

Methods in Ecology and Evolution↗

USGS2009

Isolation and characterization of microsatellite loci in the whale shark (Rhincodon typus)

In preparation for a study on population structure of the whale shark (Rhincodon typus), nine species-specific polymorphic microsatellite DNA markers were developed. An initial screening of 50 individuals from Holbox Island, Mexico found all nine loci to be polymorphic, with two to 17 alleles observed per locus. Observed and expected heterozygosity per locus ranged from 0.200 to 0.826 and from 0.213 to 0.857, respectively. Neither statistically significant deviations from Hardy–Weinberg expectations nor statistically significant linkage disequilibrium between loci were observed. These microsatellite loci appear suitable for examining population structure, kinship assessment and other applications.

Molecular Ecology Resources↗

USGS2024

Nontarget effects of pre-emergent herbicides and a bioherbicide on soil resources, processes, and communities

Community-type conversions, such as replacement of perennials by exotic annual grasses in semiarid desert communities, are occurring due to plant invasions that often create positive plant–soil feedbacks, which favor invaders and make restoration of native perennials difficult. Exotic annual grass control measures, such as pre-emergent herbicides, can also alter soil ecosystems directly or indirectly (i.e. via the plant community), yet there are few studies on the topic in natural, non-cropped landscapes. We asked how spray treatments applied to soil post-fire with the intention of inhibiting invasive annual grasses (such as Bromus tectorum L.) and releasing existing native perennial grasses affected soil resources, a microbial process, and invertebrates in three climatically varied sagebrush steppe sites. Spray treatments included chemical herbicides (imazapic and rimsulfuron) that strongly affected plant communities and a bioherbicide ( Pseudomonas fluorescens strain D7) that did not. Chemical herbicides increased soil mineral nitrogen in proportion to their negative effects on plant cover for 2 years after treatments in all sites and increased soil water and net N mineralization (measured at one site) but did not affect total carbon, nitrogen, or organic matter. Invertebrate responses to herbicides varied by site, and invertebrates increased with chemical herbicides at the highest, wettest site. We show that herbicide treatments can exacerbate pulses of mineral nutrients, which previous studies have shown can weaken ecosystem resistance to invasion. Thus, restoration strategies that increase the likelihood that desired plants can capture mineralized nutrients after herbicide application will likely be more successful.

Restoration Ecology↗

USGS2024

North Central Climate Adaptation Science Center regional science plan—2023–28

The U.S. Geological Survey North Central Climate Adaptation Science Center (NC CASC), established in 2012, is part of a national network supporting climate-informed decisions that benefit wildlife and habitats. The NC CASC provides climate science for the U.S. Department of the Interior, State agencies, and Tribal nations to support effective resource management. Collaborating with the National and Regional Climate Adaptation Science Center network, the NC CASC addresses multiregion challenges through a partnership involving the U.S. Geological Survey and additional regional partners. Input is collected from an Advisory Committee of State, Federal, and Tribal Government representatives. The center hosts Tribal Climate Resilience Liaisons, which are funded by the Bureau of Indian Affairs Branch of Tribal Climate Resilience and aligned with a Tribal Engagement Strategy. Positioned at the meeting point of scientists and managers and guided by core values, the NC CASC studies diverse ecosystems affected by climate change and empowers resource managers with actionable data and innovative science. This report outlines the 5-year Science Plan (hereafter “the Plan”) for the NC CASC. The center uses stakeholder engagement and coproduction of knowledge for effective collaboration, including engagement of Advisory Committee members and other management partners in the development of the Plan. The Plan provides a high-level overview of the NC CASC, covering the center’s geographical scope, key collaborators, stakeholders, and the strategic framework guiding the application of climate science to make informed management decisions. The Plan also summarizes management challenges and scientific objectives related to the impact of climate change on habitat, ecological drought, wildlife disease, invasives and encroachment, fire, and phenology.

Colorado, Kansas, Montana, North Dakota, Nebraska,↗

USGS2015

FORUM: Effective management of ecological resilience – are we there yet?

Ecological resilience is developing into a credible paradigm for policy development and environmental management for preserving natural capital in a rapidly changing world. However, resilience emerges from complex interactions, limiting the translation of theory into practice. Main limitations include the following: (i) difficulty in quantification and detection of changes in ecological resilience, (ii) a lack of empirical evidence to support preventative or proactive management and (iii) difficulties in managing processes operating across socio-ecological systems that vary in space and time. We highlight recent research with the potential to address these limitations including new and/or improved indicators of resilience and tools to assess scale as a driver of resilience. Synthesis and applications . Effective resilience-based management must be adaptive in nature. To support this, we propose an operational model using resilience-based iterative management actions operating across scales.

Journal of Applied Ecology↗

USGS2023

Quantifying uncertainty in coastal salinity regime for biological application using quantile regression

Salinity regimes in coastal ecosystems are highly dynamic and driven by complex geomorphic and hydrological processes. Estuarine biota are generally adapted to salinity fluctuation, but are vulnerable to salinity extremes. Characterizing coastal salinity regime for ecological studies therefore requires representing extremes of salinity ranges at time scales relevant to ecology (e.g., daily, monthly, and seasonally). Here, we propose a framework for modeling coastal salinity with these overall goals: (1) quantify uncertainty in salinity associated with important terrestrial and oceanographic drivers, (2) examine time scales of salinity response to river streamflow events, and (3) predict salinity continuously over space at key time scales. Salinity is modeled as quantile surfaces related to river discharge, tidal dynamics, wind, and spatial location, applied to Suwannee Sound estuary, FL, USA, where salinity has been monitored spatially since 1981. Each quantile level is regressed independently, and together they comprise a distribution of salinity uncertainty across space, with upper and lower quantiles describing salinity extremes. Effects of physical drivers on salinity are compared through four base models with various combinations of tide and wind variables, each including spatial coordinates and a single streamflow metric (in cubic meters per second). Multiple time scales of streamflow are considered by taking means across various periods, from 1 to 12 days, and at various lagged intervals prior to salinity sample, totaling 144 streamflow metrics. We found that the Suwannee coastal salinity regime is dynamic at multiple time scales and varies nonlinearly across space from the river effluence outward. Salinity increases nonlinearly with decreasing river flow rates below 200 m 3 /s, most prominently in the lower quantiles of salinity (τ = 0.05–0.25). Wind appears to have a stronger influence on salinity than astronomic tides for this estuary. The regression approach developed here can be applied to any coastal system that has sufficient spatial and temporal monitoring coverage to capture multiple flood and drought events. It is implemented with a simple R routine, and is less computationally-intensive than finite difference hydrodynamic modeling. The characterizations of salinity uncertainty developed in these analyses can be directly applied to future studies of fish and wildlife responses to changes in watershed management.

Florida↗