NOAA Office for Coastal Management

Wetlands and deepwater habitats are essential breeding, rearing, and feeding grounds for many species of fish and wildlife.They may also perform important flood protection and pollution control functions.Increasing national and international recognition of these values has intensified the need for reliable information on the status and extent of wetland resources.To develop comparable information over large areas, a clear definition and classification of wetlands and deepwater habitats is required.The classification system contained in this report was developed by wetland ecologists, with the assistance of many private individuals and organizations and local, State, and Federal agencies.An operational draft was published in October 1977, and a notice of intent to adopt the system for all pertinent Service activities was published December 12, 1977 (42 FR 62432).The Fish and Wildlife Service is officially adopting this wetland classification system.Future wetland data bases developed by the Service, including the National Wetlands Inventory, will utilize this system.A one-year transition period will allow for training of Service personnel, amendment of administrative manuals, and further development of the National Wetlands Inventory data base.During this period, Service personnel may continue to use the old wetland classification described in Fish and Wildlife Service Circular 39 for Fish and Wildlife Coordination Act reports, wetland acquisition priority determinations, and other activities in conjunction with the new system, where immediate conversion is not practicable.Upon completion of the transition period, the Circular 39 system will no longer be officially used by the Fish and Wildlife Service except where applicable laws still reference that system or when the only information available is organized according to that system and cannot be restructured without new field surveys.Other Federal and State agencies are encouraged to convert to the use of this system.No specific legal authorities require the use of this system-or any other system for that matter.However, it is expected that the benefits of National consistency and a developing wetland data base utilizing this system will result in ac- ceptance and use by most agencies involved in wetland management.Training can be provided to users by the Service, depending on availability of resources.Congressional committees will be notified of this adoption action and will be encouraged to facilitate general adoption of the new system by amending any laws that reference the Circular 39 system.This is a new system and users will need to study and learn the terminology.The Service is preparing a document to aid in comparing and translating the new system to the Service's former classification system.In the coming year, the Fish and Wildlife Service, in conjunction with the Soil Conservation Service, also plans to develop initial lists of hydrophytic plants and hydric soils that will support interpretation and use of this system.We believe that this system will provide a suitable basis for information gathering for most scientific, educational, and administrative purposes; however, it will not fit all needs.For instance, historical or potentially restorable wetlands are not included in this system, nor was the system designed to accommodate all the requirements of the many recently passed wetland statutes.No attempt was made to define the proprietary or jurisdictional boundaries of Federal, State, or local agencies.Nevertheless, the basic design of the classification system and the resulting data base should assist substantially in the administration of these programs.This report represents the most current methodology available for wetland classification and culminates a long-term effort involving many wetland scientists.Although it may require revision from time to time, it will serve us well in the years ahead.We hope all wetland personnel in all levels of government and the private sector come to know it and use it for the ultimate benefit of America's wetlands.

Commercial-scale mining for polymetallic nodules could have a major impact on the deep-sea environment, but the effects of these mining activities on deep-sea ecosystems are very poorly known. The first commercial test mining for polymetallic nodules was carried out in 1970. Since then a number of small-scale commercial test mining or scientific disturbance studies have been carried out. Here we evaluate changes in faunal densities and diversity of benthic communities measured in response to these 11 simulated or test nodule mining disturbances using meta-analysis techniques. We find that impacts are often severe immediately after mining, with major negative changes in density and diversity of most groups occurring. However, in some cases, the mobile fauna and small-sized fauna experienced less negative impacts over the longer term. At seven sites in the Pacific, multiple surveys assessed recovery in fauna over periods of up to 26 years. Almost all studies show some recovery in faunal density and diversity for meiofauna and mobile megafauna, often within one year. However, very few faunal groups return to baseline or control conditions after two decades. The effects of polymetallic nodule mining are likely to be long term. Our analyses show considerable negative biological effects of seafloor nodule mining, even at the small scale of test mining experiments, although there is variation in sensitivity amongst organisms of different sizes and functional groups, which have important implications for ecosystem responses. Unfortunately, many past studies have limitations that reduce their effectiveness in determining responses. We provide recommendations to improve future mining impact test studies. Further research to assess the effects of test-mining activities will inform ways to improve mining practices and guide effective environmental management of mining activities.

A change in the spectral shape at 681 nm is used to distinguish blooms of cyanobacteria from blooms of other phytoplankton via MERIS satellite sensor imagery. During large cyanobacterial blooms, the spectral shape around 681 nm is not a positive quantity as scattering due to cyanobacteria overwhelms the fluorescence signal, thus creating a negative spectral shape. This relationship is consistent in both remotely sensed and in situ data.

Results of a single‐blind round‐robin comparison of satellite primary productivity algorithms are presented. The goal of the round‐robin exercise was to determine the accuracy of the algorithms in predicting depth‐integrated primary production from information amenable to remote sensing. Twelve algorithms, developed by 10 teams, were evaluated by comparing their ability to estimate depth‐integrated daily production (IP, mg C m −2 ) at 89 stations in geographically diverse provinces. Algorithms were furnished information about the surface chlorophyll concentration, temperature, photosynthetic available radiation, latitude, longitude, and day of the year. Algorithm results were then compared with IP estimates derived from 14 C uptake measurements at the same stations. Estimates from the best‐performing algorithms were generally within a factor of 2 of the 14 C‐derived estimates. Many algorithms had systematic biases that can possibly be eliminated by reparameterizing underlying relationships. The performance of the algorithms and degree of correlation with each other were independent of the algorithms’ complexity.

Abstract. Nearly half of the earth's photosynthetically fixed carbon derives from the oceans. To determine global and region specific rates, we rely on models that estimate marine net primary productivity (NPP) thus it is essential that these models are evaluated to determine their accuracy. Here we assessed the skill of 21 ocean color models by comparing their estimates of depth-integrated NPP to 1156 in situ 14C measurements encompassing ten marine regions including the Sargasso Sea, pelagic North Atlantic, coastal Northeast Atlantic, Black Sea, Mediterranean Sea, Arabian Sea, subtropical North Pacific, Ross Sea, West Antarctic Peninsula, and the Antarctic Polar Frontal Zone. Average model skill, as determined by root-mean square difference calculations, was lowest in the Black and Mediterranean Seas, highest in the pelagic North Atlantic and the Antarctic Polar Frontal Zone, and intermediate in the other six regions. The maximum fraction of model skill that may be attributable to uncertainties in both the input variables and in situ NPP measurements was nearly 72%. On average, the simplest depth/wavelength integrated models performed no worse than the more complex depth/wavelength resolved models. Ocean color models were not highly challenged in extreme conditions of surface chlorophyll-a and sea surface temperature, nor in high-nitrate low-chlorophyll waters. Water column depth was the primary influence on ocean color model performance such that average skill was significantly higher at depths greater than 250 m, suggesting that ocean color models are more challenged in Case-2 waters (coastal) than in Case-1 (pelagic) waters. Given that in situ chlorophyll-a data was used as input data, algorithm improvement is required to eliminate the poor performance of ocean color NPP models in Case-2 waters that are close to coastlines. Finally, ocean color chlorophyll-a algorithms are challenged by optically complex Case-2 waters, thus using satellite-derived chlorophyll-a to estimate NPP in coastal areas would likely further reduce the skill of ocean color models.

The performance of 36 models (22 ocean color models and 14 biogeochemical ocean circulation models (BOGCMs)) that estimate depth‐integrated marine net primary productivity (NPP) was assessed by comparing their output to in situ 14 C data at the Bermuda Atlantic Time series Study (BATS) and the Hawaii Ocean Time series (HOT) over nearly two decades. Specifically, skill was assessed based on the models' ability to estimate the observed mean, variability, and trends of NPP. At both sites, more than 90% of the models underestimated mean NPP, with the average bias of the BOGCMs being nearly twice that of the ocean color models. However, the difference in overall skill between the best BOGCM and the best ocean color model at each site was not significant. Between 1989 and 2007, in situ NPP at BATS and HOT increased by an average of nearly 2% per year and was positively correlated to the North Pacific Gyre Oscillation index. The majority of ocean color models produced in situ NPP trends that were closer to the observed trends when chlorophyll‐ a was derived from high‐performance liquid chromatography (HPLC), rather than fluorometric or SeaWiFS data. However, this was a function of time such that average trend magnitude was more accurately estimated over longer time periods. Among BOGCMs, only two individual models successfully produced an increasing NPP trend (one model at each site). We caution against the use of models to assess multiannual changes in NPP over short time periods. Ocean color model estimates of NPP trends could improve if more high quality HPLC chlorophyll‐ a time series were available.

Cancer treatment has been revolutionized by immunotherapy and nanomedicine, offering innovative strategies to overcome the tumor microenvironment (TME) complexities. However, challenges such as therapeutic resistance, off-target effects, and immune suppression necessitate advanced delivery systems and combination approaches. Recent advancements in nanoparticle-based therapies, biomimetic platforms, and personalized immunotherapy provide promising solutions to enhance therapeutic efficacy while minimizing systemic toxicity. This review explores recent nanoparticle-mediated immunotherapy developments, highlighting strategies to optimize drug delivery, remodel the TME, and improve patient-specific treatment outcomes. A comprehensive review of recent literature focused on nanoparticle-based drug delivery, stimuli-responsive systems, biomimetic nanoplatforms, and personalized immunotherapy approaches. The effectiveness of combination therapies integrating physical and immunological strategies was also analyzed. Nanoparticle-mediated immunotherapy enables precise targeting and controlled drug release, significantly improving therapeutic outcomes. Biomimetic nanoplatforms enhance immune modulation and drug bioavailability, while personalized immunotherapy, guided by predictive biomarkers, tailors treatment to individual patients. Advanced nanomedicine strategies, including TME remodeling, targeted genome editing, and combination immunotherapies, offer promising avenues for overcoming limitations in conventional cancer treatments. Future research should optimize nanoformulations, integrate multi-modal treatment strategies, and refine biomarker-driven personalization to enhance clinical outcomes.

A new 30-m spatial resolution global shoreline vector (GSV) was developed from annual composites of 2014 Landsat satellite imagery. The semi-automated classification of the imagery was accomplished by manual selection of training points representing water and non-water classes along the entire global coastline. Polygon topology was applied to the GSV, resulting in a new characterisation of the number and size of global islands. Three size classes of islands were mapped: continental mainlands (5), islands greater than 1 km2 (21,818), and islands smaller than 1 km2 (318,868). The GSV represents the shore zone land and water interface boundary, and is a spatially explicit ecological domain separator between terrestrial and marine environments. The development and characteristics of the GSV are presented herein. An approach is also proposed for delineating standardised, high spatial resolution global ecological coastal units (ECUs). For this coastal ecosystem mapping effort, the GSV will be used to separate the nearshore coastal waters from the onshore coastal lands. The work to produce the GSV and the ECUs is commissioned by the Group on Earth Observations (GEO), and is associated with several GEO initiatives including GEO Ecosystems, GEO Marine Biodiversity Observation Network (MBON) and GEO Blue Planet.

Recommended methods for the statistical analysis of extreme waves are presented. Proper data selection is stressed. Data from different seasons and/or of different type or origin should be analysed separately. Use of the peaks over threshold method is recommended. It is advised that a three parameter Weibull distribution is fitted to the observed storm peak data giving due consideration to data censoring.

Abstract. Nearly half of the earth's photosynthetically fixed carbon derives from the oceans. To determine global and region specific rates, we rely on models that estimate marine net primary productivity (NPP) thus it is essential that these models are evaluated to determine their accuracy. Here we assessed the skill of 21 ocean color models by comparing their estimates of depth-integrated NPP to 1156 in situ 14C measurements encompassing ten marine regions including the Sargasso Sea, pelagic North Atlantic, coastal Northeast Atlantic, Black Sea, Mediterranean Sea, Arabian Sea, subtropical North Pacific, Ross Sea, West Antarctic Peninsula, and the Antarctic Polar Frontal Zone. Average model skill, as determined by root-mean square difference calculations, was lowest in the Black and Mediterranean Seas, highest in the pelagic North Atlantic and the Antarctic Polar Frontal Zone, and intermediate in the other six regions. The maximum fraction of model skill that may be attributable to uncertainties in both the input variables and in situ NPP measurements, was nearly 72%. Contrary to prior studies, ocean color models were not highly challenged in extreme conditions of surface chlorophyll-a and sea surface temperature, nor in high-nitrate low-chlorophyll waters. On average, the simplest depth/wavelength integrated models performed no worse than the more complex depth/wavelength resolved models. Water column depth (distance to coastlines) was the primary influence on ocean color model performance such that average skill was significantly higher at depths greater than 250 m, suggesting that ocean color models are more challenged in Case-2 waters (coastal) than in Case-1 (pelagic) waters. Given that in situ chlorophyll-a data was used as input data, algorithm improvement is required to eliminate the poor performance of ocean color models in Case-2 waters that are close to coastlines. Finally, ocean color chlorophyll-a algorithms are challenged by optically complex Case-2 waters, thus using satellite-derived chlorophyll-a to estimate NPP in coastal areas would likely further reduce the skill of ocean color models.

Coastal wetlands are not only among the world's most valued ecosystems but also among the most threatened by high greenhouse gas emissions that lead to accelerated sea level rise. There is intense debate regarding the extent to which landward migration of wetlands might compensate for seaward wetland losses. By integrating data from 166 estuaries across the conterminous United States, we show that landward migration of coastal wetlands will transform coastlines but not counter seaward losses. Two-thirds of potential migration is expected to occur at the expense of coastal freshwater wetlands, while the remaining one-third is expected to occur at the expense of valuable uplands, including croplands, forests, pastures, and grasslands. Our analyses underscore the need to better prepare for coastal transformations and net wetland loss due to rising seas.

The development phases of coastal management in tropical countries are described. Precolonial, centralized, community based, and collaborative coastal management are identified as forms of management which have evolved to what today is called integrated coastal management. Centralized management began during colonial times when any attempt at management was orchestrated from the top of government. Community based coastal management, in response to the failures of more centralized approaches, began in the Philippines through several projects, the experience of which spread to other countries in Southeast Asia and to Ecuador in the 1980s. The concept of collaborative management is now accepted as a description of variations in joint management arrangements between government and community or with the private sector stakeholders. It is suggested that collaborative management and integrated coastal management are more or less synonymous. The trends identified within tropical coastal management include interdisciplinary research and management integration, valuing traditional knowledge and management systems, increasing reliance on local participation, and using participation oriented research approaches. It is seen that the capacity for improved coastal management is increasing dramatically with a strong emphasis on training through academe and field learning trials. The authors are positive about current developments in coastal management, while cautioning practitioners that global economics will increasingly impact the use and conservation of coastal resources. A theme is that local government and community accountability in coastal management will help make efforts effective and more sustainable.

Abstract We investigated 32 net primary productivity (NPP) models by assessing skills to reproduce integrated NPP in the Arctic Ocean. The models were provided with two sources each of surface chlorophyll‐ a concentration (chlorophyll), photosynthetically available radiation (PAR), sea surface temperature (SST), and mixed‐layer depth (MLD). The models were most sensitive to uncertainties in surface chlorophyll, generally performing better with in situ chlorophyll than with satellite‐derived values. They were much less sensitive to uncertainties in PAR, SST, and MLD, possibly due to relatively narrow ranges of input data and/or relatively little difference between input data sources. Regardless of type or complexity, most of the models were not able to fully reproduce the variability of in situ NPP, whereas some of them exhibited almost no bias (i.e., reproduced the mean of in situ NPP). The models performed relatively well in low‐productivity seasons as well as in sea ice‐covered/deep‐water regions. Depth‐resolved models correlated more with in situ NPP than other model types, but had a greater tendency to overestimate mean NPP whereas absorption‐based models exhibited the lowest bias associated with weaker correlation. The models performed better when a subsurface chlorophyll‐ a maximum (SCM) was absent. As a group, the models overestimated mean NPP, however this was partly offset by some models underestimating NPP when a SCM was present. Our study suggests that NPP models need to be carefully tuned for the Arctic Ocean because most of the models performing relatively well were those that used Arctic‐relevant parameters.

ABSTRACT: Urban runoff contributes to nonpoint source pollution, but there is little understanding of the way that pattern and extent of urbanization contributes to this problem. Indicators of type and density of urbanization and access to municipal services were examined in six urban watersheds in Durham, North Carolina. Principal components analysis (PCA) was used to identify patterns in the distribution of these variables across the urban landscape. While spatial variation in urban environments is not perfectly captured by any one variable, the results suggest that most of the variation can be explained using several variables related to the extent and distribution of urban development. Multiple linear regression models were fit to relate these urbanization indicators to total phosphorus, total kjeldahl nitrogen, total suspended solids, and fecal coliforms. Development density was correlated to decreased water quality in each of the models. Indicators of urbanization type such as the house age, amount of contiguous impervious surface, and stormwater connectivity explained additional variation. In the nutrient models, access to city services was also an important factor. The results indicate that while urbanization density is important in predicting water quality, indicators of urbanization type and access to city services help explain additional variation in the models.

The level of genetic variation throughout the entire worldwide range of the mangrove species Avicennia marina (Forsk.) Vierh. was examined using microsatellite markers. Three microsatellite loci detected high levels of allelic diversity (70 alleles in total), essential for an accurate estimation of population genetic parameters. The informativeness of the microsatellite loci tended to increase with increasing average number of repeats. The levels of heterozygosity detected for each population, over all loci, ranged from 0.0 to 0.8, with an average of 0.407, indicating that some populations had little or no genetic variation, whereas others had a large amount. Populations at the extremes of the distribution range showed reduced levels of heterozygosity, and significant levels of inbreeding. This is not unexpected as these populations may be subject to founder effects and environmental constraints. The presence of genetic structure was tested in A. marina populations using three models: (i) a single panmictic model; (ii) the discrete subpopulation model; and (iii) the isolation by distance model. The discrete subpopulations model was supported by the overall measures of population differentiation based on the infinite alleles model (F-statistics), and the stepwise mutation model (R statistics). In addition, an analysis of molecular variance (AMOVA), using both theoretical models, found that most of the variation was between populations (41-71%), and within individuals in the total population (31-49%). There was little variation among individuals within populations (0-10%). There was no significant isolation by distance. The high levels of genetic differentiation observed among populations of A. marina may be due to environmental and ecological factors, particularly past sea level and climatic changes.

Satellite monitoring of thermal stress on coral reefs has become an essential component of reef management practice around the world. A recent development by the U.S. National Oceanic and Atmospheric Administration’s Coral Reef Watch (NOAA CRW) program provides daily global monitoring at 5 km resolution—at or near the scale of most coral reefs. In this paper, we introduce two new monitoring products in the CRW Decision Support System for coral reef management: Regional Virtual Stations, a regional synthesis of thermal stress conditions, and Seven-day Sea Surface Temperature (SST) Trend, describing recent changes in temperature at each location. We describe how these products provided information in support of management activities prior to, during and after the 2014 thermal stress event in the Commonwealth of the Northern Mariana Islands (CNMI). Using in situ survey data from this event, we undertake the first quantitative comparison between 5 km satellite monitoring products and coral bleaching observations. Analysis of coral community characteristics, historical temperature conditions and thermal stress revealed a strong influence of coral biodiversity in the patterns of observed bleaching. This resulted in a model based on thermal stress and generic richness that explained 97% of the variance in observed bleaching. These findings illustrate the importance of using local benthic characteristics to interpret the level of impact from thermal stress exposure. In an era of continuing climate change, accurate monitoring of thermal stress and prediction of coral bleaching are essential for stakeholders to direct resources to the most effective management actions to conserve coral reefs.

Ecological restoration programs often attempt to maintain or enhance ecosystem services (ES), but fine‐scale maps of multiple ES are rarely available to support prioritization among potential projects. Here we use agency reports, citizen science, and social media as data sources to quantify the spatial distribution of five recreational elements of cultural ES (CES) – sport fishing, recreational boating, birding, beach use, and park visitation – across North America's Laurentian Great Lakes, where current restoration investments exceed US$1.5 billion. These recreational CES are widely yet unevenly distributed, and spatial correlations among all except park visitation indicate that many locations support multiple CES benefits. Collectively, these five service metrics correlate with tourism gross domestic product, indicating that local economies benefit from ecosystem conditions that support CES. However, locations of high recreational CES delivery are often severely affected by environmental stressors, suggesting that either ecosystem condition or human enjoyment of these recreational CES is resilient even to substantial levels of stress. Our analyses show that spatial assessments of recreational CES are an informative complement to ecosystem stress assessments for guiding large‐scale restoration efforts.

Weight‐of‐evidence is the process by which multiple measurement endpoints are related to an assessment endpoint to evaluate whether significant risk of harm is posed to the environment. In this paper, a methodology is offered for reconciling or balancing multiple lines of evidence pertaining to an assessment endpoint. Weight‐of‐evidence is reflected in three characteristics of measurement endpoints: (a) the weight assigned to each measurement endpoint; (b) the magnitude of response observed in the measurement endpoint; and (c) the concurrence among outcomes of multiple measurement endpoints. First, weights are assigned to measurement endpoints based on attributes related to: (a) strength of association between assessment and measurement endpoints; (b) data quality; and (c) study design and execution. Second, the magnitude of response in the measurement endpoint is evaluated with respect to whether the measurement endpoint indicates the presence or absence of harm; as well as the magnitude. Third, concurrence among measurement endpoints is evaluated by plotting the findings of the two preceding steps on a matrix for each measurement endpoint evaluated. The matrix allows easy visual examination of agreements or divergences among measurement endpoints, facilitating interpretation of the collection of measurement endpoints with respect to the assessment endpoint. A qualitative adaptation of the weight‐of‐evidence approach is also presented.

Concerns are being raised about high failure rates of community-based small-scale no-take marine reserves that are proliferating in the Southeast Asian region. Factors hypothesized to influence success include intrinsic community characteristics, project input levels, and change agent characteristics. An empirical analysis of these hypotheses was conducted using a sample of 24 villages in North Sulawesi, Indonesia, where marine reserves were in early phases of establishment. Factors found to influence the rate of progress were village complexity, level of development, project input levels, characteristics of community organizers, and degree of community organizer homophily relative to the community. These findings are important for community-based marine conservation initiatives conducting simultaneous interventions in multiple communities. It provides insights in how project strategies can be adjusted to increase the probability of success, obtain economies of scale, target communities more amenable to community-based interventions and result in a better return on project investments.

Communities must identify exposure to hazard impacts to proactively address emergency response, disaster recovery, and hazard mitigation, and incorporate sustainable development practices into comprehensive planning. Hazard mitigation, an important part of sustainable development, eliminates or minimizes disaster-related damages and empowers communities to respond to and recover more quickly from disasters. The Community Vulnerability Assessment Tool (CVAT) is a risk and vulnerability assessment methodology designed by the National Oceanic and Atmospheric Administration’s Coastal Services Center to assist emergency managers and planners in their efforts to reduce hazard vulnerabilities through hazard mitigation, comprehensive land-use, and development planning. CVAT analysis results provide a baseline to prioritize mitigation measures and to evaluate the effectiveness of those measures over time. This methodology is flexible, as results may be achieved using a geographic information system or static maps with overlays and handwritten data. This paper outlines how to engage stakeholders and explains the CVAT process. Several case studies also highlight some of the challenges/problems and best practices/opportunities associated with applying the CVAT methodology.