Coastal Wetland Monitoring Research Articles

Increasing marsh bird abundance in coastal wetlands of the Great Lakes, 2011–2021, likely caused by increasing water levels

AbstractWetlands of the Laurentian Great Lakes of North America (i.e., lakes Superior, Michigan, Huron, Erie, and Ontario) provide critical habitat for marsh birds. We used 11 years (2011–2021) of data collected by the Great Lakes Coastal Wetland Monitoring Program at 1,962 point-count locations in 792 wetlands to quantify the first-ever annual abundance indices and trends of 18 marsh-breeding bird species in coastal wetlands throughout the entire Great Lakes. Nine species (50%) increased by 8–37% per year across all of the Great Lakes combined, whereas none decreased. Twelve species (67%) increased by 5–50% per year in at least 1 of the 5 Great Lakes, whereas only 3 species (17%) decreased by 2–10% per year in at least 1 of the lakes. There were more positive trends among lakes and species (n = 34, 48%) than negative trends (n = 5, 7%). These large increases are welcomed because most of the species are of conservation concern in the Great Lakes. Trends were likely caused by long-term, cyclical fluctuations in Great Lakes water levels. Lake levels increased over most of the study, which inundated vegetation and increased open water-vegetation interspersion and open water extent, all of which are known to positively influence abundance of most of the increasing species and negatively influence abundance of all of the decreasing species. Coastal wetlands may be more important for marsh birds than once thought if they provide high-lake-level-induced population pulses for species of conservation concern. Coastal wetland protection and restoration are of utmost importance to safeguard this process. Future climate projections show increases in lake levels over the coming decades, which will cause “coastal squeeze” of many wetlands if they are unable to migrate landward fast enough to keep pace. If this happens, less habitat will be available to support periodic pulses in marsh bird abundance, which appear to be important for regional population dynamics. Actions that allow landward migration of coastal wetlands during increasing lake levels by removing or preventing barriers to movement, such as shoreline hardening, will be useful for maintaining marsh bird breeding habitat in the Great Lakes.

Deep Learning of High-Resolution Unmanned Aerial Vehicle Imagery for Classifying Halophyte Species: A Comparative Study for Small Patches and Mixed Vegetation

Recent advances in deep learning (DL) and unmanned aerial vehicle (UAV) technologies have made it possible to monitor salt marshes more efficiently and precisely. However, studies have rarely compared the classification performance of DL with the pixel-based method for coastal wetland monitoring using UAV data. In particular, many studies have been conducted at the landscape level; however, little is known about the performance of species discrimination in very small patches and in mixed vegetation. We constructed a dataset based on UAV-RGB data and compared the performance of pixel-based and DL methods for five scenarios (combinations of annotation type and patch size) in the classification of salt marsh vegetation. Maximum likelihood, a pixel-based classification method, showed the lowest overall accuracy of 73%, whereas the U-Net classification method achieved over 90% accuracy in all classification scenarios. As expected, in a comparison of pixel-based and DL methods, the DL approach achieved the most accurate classification results. Unexpectedly, there was no significant difference in overall accuracy between the two annotation types and labeling data sizes in this study. However, when comparing the classification results in detail, we confirmed that polygon-type annotation was more effective for mixed-vegetation classification than the bounding-box type. Moreover, the smaller size of labeling data was more effective for detecting small vegetation patches. Our results suggest that a combination of UAV-RGB data and DL can facilitate the accurate mapping of coastal salt marsh vegetation at the local scale.

Marsh birds as ecological performance indicators for Lake Ontario outflow regulation

Water-level regulation can have significant impacts on coastal wetland ecosystems. In this study we sought to update marsh-bird-based ecological performance indicators (PIs) that support adaptive management of long-term outflow regulation for Lake Ontario. Previous PIs established in the mid-2000 s were based on single species and monitoring them required data not currently being collected at broad scales. We therefore focused on developing and validating community-level PIs using data from an ongoing, long-term, basin-wide monitoring program, the Coastal Wetland Monitoring Program (CWMP). After identifying species with documented responses to variation in water levels in the literature, we considered a suite of potential PIs by first examining correlations with both annual mean water levels and measures of interannual water-level fluctuations. We then used a mixed-modelling framework to determine which highly correlated PIs exhibited statistically significant relationships with water-level variables. Having established significant effects of water levels on the candidate PIs, we performed a power-sensitivity analysis to determine the degree of change in each PI that can be detected based on current CWMP sampling. From these analyses, we propose six potential marsh-bird based PIs: sum total abundance of sensitive marsh-obligate species, richness of sensitive marsh-obligate species, and abundance of each of red-winged blackbird (Agelaius phoeniceus), marsh wren (Cistothorus palustris), common gallinule (Gallinula galeata), and least bittern (Ixobrychus exilis). Of these, the community-based PIs of sum total abundance and richness of sensitive species appear most suitable for assessing the marsh-bird community response to outflow regulation on Lake Ontario.

Coastal Wetland Vegetation Classification Using Pixel-Based, Object-Based and Deep Learning Methods Based on RGB-UAV

The advancement of deep learning (DL) technology and Unmanned Aerial Vehicles (UAV) remote sensing has made it feasible to monitor coastal wetlands efficiently and precisely. However, studies have rarely compared the performance of DL with traditional machine learning (Pixel-Based (PB) and Object-Based Image Analysis (OBIA) methods) in UAV-based coastal wetland monitoring. We constructed a dataset based on RGB-based UAV data and compared the performance of PB, OBIA, and DL methods in the classification of vegetation communities in coastal wetlands. In addition, to our knowledge, the OBIA method was used for the UAV data for the first time in this paper based on Google Earth Engine (GEE), and the ability of GEE to process UAV data was confirmed. The results showed that in comparison with the PB and OBIA methods, the DL method achieved the most promising classification results, which was capable of reflecting the realistic distribution of the vegetation. Furthermore, the paradigm shifts from PB and OBIA to the DL method in terms of feature engineering, training methods, and reference data explained the considerable results achieved by the DL method. The results suggested that a combination of UAV, DL, and cloud computing platforms can facilitate long-term, accurate monitoring of coastal wetland vegetation at the local scale.

Considerations and tradeoffs of UAS-based coastal wetland monitoring in the Southeastern United States

Coastal wetlands of the Southeastern United States host a high abundance and diversity of critical species and provide essential ecosystem services. A rise in threats to these vulnerable habitats has led to an increased focus on research and monitoring in these areas, which is traditionally performed using manual measurements of vegetative characteristics. As these methods require substantial time and effort, they are often limited in scale and infeasible in areas of dense or impassable habitat. Unoccupied Aircraft Systems (UAS) provide an advantage over traditional ground-based methods by serving as a non-invasive alternative that expands the scale at which we can understand these ecosystems. While recent interest in UAS-based monitoring of coastal wetland habitats has grown, methods and parameters for UAS-based mapping lack standardization. This study addresses variability introduced by common UAS study techniques and forms recommendations for optimal survey designs in vegetated coastal habitats. Applying these parameters, we assess alignment of computed estimations with manually collected measurements by comparing UAS-SfM mapping products to ground-based data. This study demonstrates that, with careful consideration in study design and analysis, there exists great potential for UAS to provide accurate, large-scale estimates of common vegetative characteristics in coastal salt marshes.

An Improved Submerged Mangrove Recognition Index-Based Method for Mapping Mangrove Forests by Removing the Disturbance of Tidal Dynamics and S. alterniflora

Currently, it is a great challenge for remote sensing technology to accurately map mangrove forests owing to periodic inundation. A submerged mangrove recognition index (SMRI) using two high- and low-tide images was recently proposed to remove the influence of tides and identify mangrove forests. However, when the tidal height of the selected low-tide image is not at the lowest tidal level, the corresponding SMRI does not function well, which results in mangrove forests below the low tidal height being undetected. Furthermore, Spartina alterniflora Loisel (S. alterniflora) was introduced to China in 1979 and rapidly spread to become the most serious invasive plant along the Chinese coastline. The current SMRI has failed to distinguish S. alterniflora from submerged mangrove forests because of their similar spectral signatures. In this study, an SMRI-based mangrove forest mapping method was developed using the time series of Sentinel-2 images to mitigate the two aforementioned issues. In the proposed method, quantile synthesis was applied to the time series of Sentinel-2 images to generate a lowest-tide synthetic image for creating SMRI to identify submerged mangrove forests. Unsubmerged mangrove forests were classified using a support vector machine, and a preliminary mangrove forest map was created by merging them. In addition, S. alterniflora was distinguished from the mangrove forests by analyzing their phenological differences. Finally, mangrove forest mapping was performed by masking S. alterniflora. The proposed method was applied to the entire coastline of the Guangxi Province, China. The results showed that it can reliably and accurately identify submerged mangrove forests derived from SMRI by synthesizing low- and high-tide images using quantile synthesis, and the differentiation of S. alterniflora using phenological differences results in more accurate mangrove mapping. This work helps to improve the accuracy of mangrove forest mapping using SMRI and its feasibility for coastal wetland monitoring. It also provides data for sustainable management, ecological protection, and restoration of vegetation in coastal zones.

Satellite-Based Monitoring of Coastal Wetlands in Yancheng, Jiangsu Province, China

The dynamic process of the wetland can reflect its impact on the environment, and finding a balance point supporting harmonious coexistence between man and nature has become an issue of increasing concern. On the basis of previous studies that have focused on local coastal wetlands, the temporal and spatial changes and driving forces of wetlands in the Yancheng coastal area from 1991 to 2021 were analyzed over a larger area. According to the study findings: (1) The results of the study of the Yancheng coastal wetland with a larger scope differed significantly from findings resulting from a study of coastal wetland only. This difference was mainly reflected in the relatively stable situation of wetland ecology as a whole, while the changes in local surface features were more significant. (2) Natural wetlands were transformed into artificial wetlands and non-wetland types, and artificial wetlands were transformed into non-wetland types; additionally, reverse transformations and internal transformations of surface features also took place. For instance, the saltpan was transformed into mudflats (86.26 km2), and some mudflats into herbaceous vegetation (193.47 km2). (3) When analyzing the impact intensity of human activities on the Yancheng wetland, it was found that this factor has experienced a process of first rising and then falling. The index was 0.650, 0.653, 0.664, 0.661, and 0.641 in 1991, 2000, 2008, 2016, and 2021, respectively. (4) Lastly, an analysis of factors driving wetland change revealed that human factors were the most critical reasons for wetland landscape change. Our work can play a reference and inspiration role in the monitoring and protection of similar coastal wetlands.

Environmental predictors of phytoplankton chlorophyll-a in Great Lakes coastal wetlands

Coastal wetlands of the Laurentian Great Lakes are diverse and productive ecosystems that provide many ecosystem services, but are threatened by anthropogenic factors, including nutrient input, land-use change, invasive species, and climate change. In this study, we examined one component of wetland ecosystem structure – phytoplankton biomass – using the proxy metric of water column chlorophyll-a measured in 514 coastal wetlands across all five Great Lakes as part of the Great Lakes Coastal Wetland Monitoring Program. Mean chlorophyll-a concentrations increased from north-to-south from Lake Superior to Lake Erie, but concentrations varied among sites within lakes. To predict chlorophyll-a concentrations, we developed two random forest models for each lake – one using variables that may directly relate to phytoplankton biomass (“proximate” variables; e.g., dissolved nutrients, temperature, pH) and another using variables with potentially indirect effects on phytoplankton growth (“distal” variables; e.g., land use, fetch). Proximate and distal variable models explained 16–43% and 19–48% of variation in chlorophyll-a, respectively, with models developed for lakes Erie and Michigan having the highest amount of explanatory power and models developed for lakes Ontario, Superior, and Huron having the lowest. Land-use variables were important distal predictors of chlorophyll-a concentrations across all lakes. We found multiple proximate predictors of chlorophyll-a, but there was little consistency among lakes, suggesting that, while chlorophyll-a may be broadly influenced by distal factors such as land use, individual lakes and wetlands have unique characteristics that affect chlorophyll-a concentrations. Our results highlight the importance of responsible land-use planning and watershed-level management for protecting coastal wetlands.

Hyperspectral and Multispectral Classification for Coastal Wetland Using Depthwise Feature Interaction Network

The monitoring of coastal wetlands is of great importance to the protection of marine and terrestrial ecosystems. However, due to the complex environment, severe vegetation mixture, and difficulty of access, it is impossible to accurately classify coastal wetlands and identify their species with traditional classifiers. Despite the integration of multisource remote sensing data for performance enhancement, there are still challenges with acquiring and exploiting the complementary merits from multisource data. In this article, the depthwise feature interaction network (DFINet) is proposed for wetland classification. A depthwise cross attention module is designed to extract self-correlation and cross correlation from multisource feature pairs. In this way, meaningful complementary information is emphasized for classification. DFINet is optimized by coordinating consistency loss, discrimination loss, and classification loss. Accordingly, DFINet reaches the standard solution-space under the regularity of loss functions, while the spatial consistency and feature discrimination are preserved. Comprehensive experimental results on two hyperspectral and multispectral wetland datasets demonstrate that the proposed DFINet outperforms other competitive methods in terms of overall accuracy.

Multilayer Global Spectral–Spatial Attention Network for Wetland Hyperspectral Image Classification

Coastal wetland monitoring plays an important role in the protection and restoration of ecosystems in this world. UAV-hyperspectral imaging, as an emerging technique for Earth observation and space exploration, provides the huge potential ability to identify different wetland species. In this work, a multilayer global spectral–spatial attention network (MGSSAN) is proposed for mapping coastal wetlands, which mainly consists of two major steps. First, a two-branch convolutional neural network (CNN) framework with residual connection is developed to obtain an initial classification probability map, in which one branch is used to capture the spectral information, the other branch is used to extract spatial information, and a global spectral–spatial attention module is designed to guide networks focusing on those features that are more discriminative. Second, an extended random walker method is utilized to optimize the initial classification probabilities, so as to yield the final map. Experiments performed on three wetland HSI datasets created by ourselves verify that the proposed method can obtain superior performance with respect to several state-of-the-art hyperspectral image classification methods.

Classification ofTypha-dominated wetlands using airborne hyperspectral imagery along Lake Ontario, USA

AbstractShoreline wetlands along Lake Ontario are valuable, multi-functional resources that have historically provided large numbers of important ecosystem goods and services. However, alterations to the lake’s natural hydrologic regime have impacted traditional meadow marsh in the wetlands, resulting in competition and colonization by dense and aggressive Typha angustifolia and Typha x glauca (Cattails). The shift to a Typha-dominated landscape resulted in an array of negative impacts, including increased Typha density, substantial decreases in plant species richness and diversity, and altered habitat and changes in associated ecosystem services. Successful long-term adaptive management of these wetland resources requires timely and accurate monitoring. Historically, wetland landscapes have been surveyed and mapped using field-based surveys and/or photointerpretation. However, given their resource- and cost-intensive nature, these methods are often prohibitively time- and labor-consuming or geographically limited. Other remote sensing applications can provide more rapid and efficient assessments when evaluating wetland change trajectories or analyzing direct and indirect impacts across larger spatial and temporal scales. The primary goal of this study was to develop and describe methodology using U.S. Army Corps of Engineers National Coastal Mapping Program hyperspectral imagery, light detection and ranging data, and high-spatial resolution true-color imagery to provide updated wetland classifications for Lake Ontario coastal wetlands. This study used existing field-collected vegetation survey data (Great Lakes Coastal Wetland Monitoring Program), ancillary imagery, and existing classification information as training data for a supervised classification approach. These data were used along with a generalized wetland schema (classes based on physical and biological gradients: elevation, Typha, meadow marsh, mixed emergent, upland vegetation) to generate wetland classification data with Kappa values near 0.85. Ultimately, these data and methods provide helpful knowledge elements that will allow for more efficient inventorying and monitoring of Great Lake resources, forecasting of resource condition and stability, and adaptive management strategies.

Great Lakes Coastal Wetland Monitoring Program (EPA)

Federal Grants & ContractsVolume 44, Issue 20 p. 5-5 Grants Alerts Great Lakes Coastal Wetland Monitoring Program (EPA) First published: 14 September 2020 https://doi.org/10.1002/fgc.31299Read the full textAboutPDF ToolsRequest permissionExport citationAdd to favoritesTrack citation ShareShare Give accessShare full text accessShare full-text accessPlease review our Terms and Conditions of Use and check box below to share full-text version of article.I have read and accept the Wiley Online Library Terms and Conditions of UseShareable LinkUse the link below to share a full-text version of this article with your friends and colleagues. Learn more.Copy URL Share a linkShare onFacebookTwitterLinkedInRedditWechat No abstract is available for this article. Volume44, Issue2017 September 2020Pages 5-5 RelatedInformation

Coastal Wetland Monitoring and Mapping along the Turkish Mediterranean: Determining the Impact of Habitat Inundation on Breeding Bird Species

Yılmaz, K.T.; Alphan, H.; Kosztolányi, A.; Ünlükaplan, Y., and Derse, M.A., 2020. Coastal wetland monitoring and mapping along the Turkish Mediterranean: Determining the impact of habitat inundation on breeding bird species Journal of Coastal Research, 36(5), 961–972. Coconut Creek (Florida), ISSN 0749-0208.Within this study, bird species composition, spatial distribution of nests of the most common breeding bird species—the Kentish plover (Charadrius alexandrinus L.)—coastal habitat types, and spatiotemporal change on lagoon surface area were monitored. Field work and water-level monitoring occurred during a 2-year period form 2009 to 2011. Habitat mapping was accomplished through a combination of remote sensing (unsupervised classification of vegetation types) combined with ground truthing. Eleven habitat types were mapped in GIS software to create a dynamic habitat map for analyzing distribution and abundance of waterfowl. Annual changes in water levels and its effect on adjacent habitats are explored. Fifty-two nests were located in 2009 on places that were inundated by the spring flood and were, thus, potentially endangered by the flood. For change detection of the wetland, 13 Landsat Thematic Mapper and Enhanced Thematic Mapper Plus scenes, covering the period between January 2009 and October 2009, were used for pair-wise comparisons. For land cover classification, a WorldView-2 data was used and the information obtained was used in delineating boundaries of habitats. During the point surveys at the six observation points, 44 bird species were observed, whereas 65 bird species in total were recorded in the course of the project. At the salt marshes, 36 species were recorded, whereas at the freshwater marsh habitat, there were 28 species. In total, 247 Kentish plover nests were found during 2 years. The date eggs were laid was known for 198 nests; most nests were laid during May. The overall objective of this article was to develop a monitoring methodology for predicting the effect of inundation on reproductive success, understanding the habitat features of nesting waterfowl species, and estimating their population size for future monitoring of coastal wetlands.

Relationships between the distribution and abundance of the invasive faucet snail (Bithynia tentaculata) and environmental factors in Laurentian Great Lakes coastal wetlands

The faucet snail (Bithynia tentaculata) was introduced to the Great Lakes region in the late 1800s. Faucet snails alter native community dynamics and are an intermediate host for multiple trematode parasites that can be lethal to waterfowl when the snails are consumed. Although faucet snails have been established in the Great Lakes for over a century, their populations appear to have remained small for most of this period and their known distribution was limited to the lower Great Lakes, though basin-wide surveys were lacking until recently, so snails may have gone undetected. We compiled data from a five-year coastal wetland monitoring program spanning all of the Great Lakes, providing a basin-wide inventory of faucet snail populations and confirming the snail’s presence in every Great Lake. Further, we identified potential drivers of faucet snail occurrence and abundance (individuals per sampling replicate) across the basin and within individual lakes to identify factors that could lead to elevated risk of range expansion. Across the basin and within individual lakes, faucet snail occurrence was related to human recreational transport (proximity to a boat launch) and eutrophication (anthropogenic land-use, elevated nutrient concentrations). In addition, at sites where faucet snails occurred, they were most abundant at wetlands with surrounding forest cover, suggesting that the species can thrive in a range of environmental conditions. Our results suggest that limiting passive transport of faucet snails is vital to minimizing their spread to remote wetlands where the snails may thrive once established.

Leveraging a Landscape-Level Monitoring and Assessment Program for Developing Resilient Shorelines throughout the Laurentian Great Lakes

Traditionally, ecosystem monitoring, conservation, and restoration have been conducted in a piecemeal manner at the local scale without regional landscape context. However, scientifically driven conservation and restoration decisions benefit greatly when they are based on regionally determined benchmarks and goals. Unfortunately, required data sets rarely exist for regionally important ecosystems. Because of early recognition of the extreme ecological importance of Laurentian Great Lakes coastal wetlands, and the extensive degradation that had already occurred, significant investments in coastal wetland research, protection, and restoration have been made in recent decades and continue today. Continued and refined assessment of wetland condition and trends, and the evaluation of restoration practices are all essential to ensuring the success of these investments. To provide wetland managers and decision makers throughout the Laurentian Great Lakes basin with the optimal tools and data needed to make scientifically-based decisions, our regional team of Great Lakes wetland scientists developed standardized methods and indicators used for assessing wetland condition. From a landscape perspective, at the Laurentian Great Lakes ecosystem scale, we established a stratified random-site-selection process to monitor birds, anurans, fish, macroinvertebrates, vegetation, and physicochemical conditions of coastal wetlands in the US and Canada. Monitoring of approximately 200 wetlands per year began in 2011 as the Great Lakes Coastal Wetland Monitoring Program. In this paper, we describe the development, delivery, and expected results of this ongoing international, multi-disciplinary, multi-stakeholder, landscape-scale monitoring program as a case example of successful application of landscape conservation design.

Great Lakes coastal fish habitat classification and assessment

Abstract Basin-scale assessment of fish habitat in Great Lakes coastal ecosystems would increase our ability to prioritize fish habitat management and restoration actions. As a first step in this direction, we identified key habitat factors associated with highest probability of occurrence for several societally and ecologically important coastal fish species as well as community metrics, using data from the Great Lakes Aquatic Habitat Framework (GLAHF), Great Lakes Environmental Indicators (GLEI) and Coastal Wetland Monitoring Program (CWMP). Secondly, we assessed whether species-specific habitat was threatened by watershed-level anthropogenic stressors. In the southern Great Lakes, key habitat factors for determining presence/absence of several species of coastal fish were chlorophyll concentrations, turbidity, and wave height, whereas in the northern ecoprovince temperature was the major habitat driver for most of the species modeled. Habitat factors best explaining fish richness and diversity were bottom slope and chlorophyll a. These models could likely be further improved with addition of high-resolution submerged macrophyte complexity data which are currently unavailable at the basin-wide scale. Proportion of invasive species was correlated primarily with increasing maximum observed inorganic turbidity and chlorophyll a. We also demonstrate that preferred habitat for several coastal species and high-diversity areas overlap with areas of high watershed stress. Great Lakes coastal wetland fish are a large contributor to ecosystem services as well as commercial and recreational fishery harvest, and scalable basin-wide habitat models developed in this study may be useful for informing management actions targeting specific species or overall coastal fish biodiversity.

Remote Sensing for Wetland Mapping and Historical Change Detection at the Nisqually River Delta

Coastal wetlands are important ecosystems for carbon storage and coastal resilience to climate change and sea-level rise. As such, changes in wetland habitat types can also impact ecosystem functions. Our goal was to quantify historical vegetation change within the Nisqually River watershed relevant to carbon storage, wildlife habitat, and wetland sustainability, and identify watershed-scale anthropogenic and hydrodynamic drivers of these changes. To achieve this, we produced time-series classifications of habitat, photosynthetic pathway functional types and species in the Nisqually River Delta for the years 1957, 1980, and 2015. Using an object-oriented approach, we performed a hierarchical classification on historical and current imagery to identify change within the watershed and wetland ecosystems. We found a 188.4 ha (79%) increase in emergent marsh wetland within the Nisqually River Delta between 1957 and 2015 as a result of restoration efforts that occurred in several phases through 2009. Despite these wetland gains, a total of 83.1 ha (35%) of marsh was lost between 1957 and 2015, particularly in areas near the Nisqually River mouth due to erosion and shifting river channels, resulting in a net wetland gain of 105.4 ha (44%). We found the trajectory of wetland recovery coincided with previous studies, demonstrating the role of remote sensing for historical wetland change detection as well as future coastal wetland monitoring.

Coastal wetland vegetation community classification and distribution across environmental gradients throughout the Laurentian Great Lakes

Abstract Between 2011 and 2013, more than 300 coastal wetland sites were surveyed throughout the Laurentian Great Lakes as part of a coastal wetland monitoring project. Twenty-one wetland vegetation communities were identified through cluster analysis and indicator species analysis. Non-metric multidimensional scaling was used to ordinate sites based on species composition, and measured and calculated environmental parameters were used to evaluate environmental correlation with groups of sites (vegetation communities). Latitude, agricultural intensity, substrate, geomorphology, and water depth were found to account for the majority of the three-axis solution in the non-metric multidimensional scaling. Comparison with earlier studies indicates major increases in extent for several community types dominated by the common invasive species Phragmites australis, Typha angustifolia and T. × glauca. These invasive plants often formed similar species assemblages in both the meadow and emergent zones. Similarities in species composition between meadow and emergent communities indicate that species assemblages may be responding to fluctuations in water levels. This paper presents a unique classification of Great Lakes coastal wetlands with greater geographic range and increased detail in the meadow and emergent zones based on vegetation species composition. The classification is useful as a comparison to past and future ecological and restoration studies.

Standardized Measures of Coastal Wetland Condition: Implementation at a Laurentian Great Lakes Basin-Wide Scale

Since European settlement, over 50 % of coastal wetlands have been lost in the Laurentian Great Lakes basin, causing growing concern and increased monitoring by government agencies. For over a decade, monitoring efforts have focused on the development of regional and organism-specific measures. To facilitate collaboration and information sharing between public, private, and government agencies throughout the Great Lakes basin, we developed standardized methods and indicators used for assessing wetland condition. Using an ecosystem approach and a stratified random site selection process, birds, anurans, fish, macroinvertebrates, vegetation, and physico-chemical conditions were sampled in coastal wetlands of all five Great Lakes including sites from the United States and Canada. Our primary objective was to implement a standardized basin-wide coastal wetland monitoring program that would be a powerful tool to inform decision-makers on coastal wetland conservation and restoration priorities throughout the Great Lakes basin.

Applying a regional coastal wetland monitoring framework to refine and report on wildlife and habitat delisting criteria in the Bay of Quinte Area of Concern

In most Canadian Areas of Concern, fish and wildlife populations and their habitats (i.e. Beneficial Use Impairments 3 and 14) have been listed as impaired. While much work has addressed other Beneficial Use Impairments, there has often been a lack of specific data and methodologies for evaluating fish and wildlife populations and their habitats. This article presents a methodology for refining delisting criteria for wildlife and habitat Beneficial Use Impairments in the Bay of Quinte Area of Concern using indices of condition in a coastal wetland monitoring framework. Data have been collected to provide information on loss of fish and wildlife habitat (water quality and submerged aquatic vegetation and aquatic macroinvertebrate communities) and degradation of fish and wildlife populations (fish, amphibian and breeding bird communities). Three potential models for delisting are presented using the submerged aquatic vegetation community data as an example. For all coastal wetland attributes considered through the framework, Bay of Quinte coastal wetlands were generally in better condition than other Canadian sites along the Lake Ontario shoreline.