Submerged Aquatic Vegetation Coverage Research Articles

A novel strategy for estimating biomass of submerged aquatic vegetation in lake integrating UAV and Sentinel data

Submerged aquatic vegetation (SAV) plays a fundamental ecological role in mediating carbon cycling within lakes, and its biomass is essential to assess the carbon sequestration potential of lake ecosystems. Remote sensing (RS) offers a powerful tool for large-scale SAV biomass retrieval. Given the underwater location of SAV, the spectral signal in RS data often exhibits weakness, capturing primarily horizontal structure rather than volumetric information crucial for biomass assessment. Fortunately, easily-measured SAV coverage can serve as an intermediary variable for difficultly-quantified SAV biomass inversion. Nevertheless, obtaining enough SAV coverage samples matching satellite image pixels for robust model development remains problematic. To overcome this challenge, we employed a UAV to acquire high-precision data, thereby replacing manual SAV coverage sample collection. In this study, we proposed an innovative strategy integrating unmanned aerial vehicle (UAV) and satellite data to invert large-scale SAV coverage, and subsequently estimate the biomass of the dominant SAV population (Potamogeton pectinatus) in Ulansuhai Lake. Firstly, a coverage-biomass model (R2 = 0.93, RMSE = 0.8 kg/m2) depicting the relationship between SAV coverage and biomass was developed. Secondly, in a designed experimental area, a high-precision multispectral image was captured by a UAV. Based on the Normalized Difference Water Index (NDWI), the UAV-based image was classified into non-vegetated and vegetated areas, thereby generating an SAV distribution map. Leveraging spatial correspondence between satellite pixels and the UAV-based SAV distribution map, the proportion of SAV within each satellite pixel, referred to as SAV coverage, was computed, and a coverage sample set matched with satellite pixels was obtained. Subsequently, based on the sample set, a satellite-scale SAV coverage estimation model (R2 = 0.78, RMSE = 14.05 %) was constructed with features from Sentinel-1 and Sentinel-2 data by XGBoost algorithm. Finally, integrating the coverage-biomass model with the obtained coverage inversion results, fresh biomass of SAV in Ulansuhai Lake was successfully estimated to be approximately 574,600 tons.

Changes in submerged aquatic vegetation cover off the northern Yucatán Peninsula detected with Sentinel-2 imagery using a fuzzy classification system

Submerged aquatic vegetation (SAV) plays a major role in various coastal ecological processes, providing habitat and food to a marine community and environmental benefits to people. However, seagrass meadow extensions around the world are shrinking. The objective of this study was to analyze recent variation in SAV coverage along the northern coast of the Yucatán Peninsula. Using Sentinel-2 satellite imagery, acoustic echosounder field surveys, and a fuzzy classification system, we produced binary cartographic models of SAV coverage for 2016, 2018, and 2019. In a study area of approximately 16342 km2, we estimated SAV coverage of 5957.90 km2 in 2016, with a decline to 2873.55 km2 by 2019. Coverage in protected areas decreased from 2376.03 km2 to 1690.89 km2 over the same period. The Federal Protected Area for Flora and Fauna of Yum-Balam showed an estimated gain of 48.3 km2 (r = 11.63%), but that of Laguna de Términos an estimated reduction of 508.50 km2 (r = -29.77%), and coverage in the Los Petenes Biosphere Reserve decreased by 186.5 km2 (r = −5.35%). These changes are likely part of a complex dynamic in the study area, one that may include anthropic effects.

Multi-year landscape-scale efficacy analysis of fluridone treatment of invasive submerged aquatic vegetation in the Sacramento–San Joaquin Delta

Invasive aquatic macrophytes are a major threat to estuarine ecosystems globally, posing difficult control challenges for resource managers. This study examined the efficacy of a fluridone treatment program to control invasive submerged aquatic vegetation (SAV). We leveraged four datasets to examine efficacy of fluridone treatment in the Sacramento–San Joaquin Delta, a freshwater tidal estuary in California, from 2014 to 2018. Annual SAV coverage maps derived from spectroscopy data were used in conjunction with fluridone application data and water current speed predicted by a 3-D hydrodynamic model of the study system. Our objective was to determine if the probability of SAV occurrence in treated sites was significantly different from untreated sites. We examined whether efficacy was influenced by the amount of herbicide applied at a site, the local current speed, and the treatment history of the site. We found that treatment efficacy was positively associated with the amount of herbicide applied per unit area, but this effect was mediated by a negative association with current speed. The efficacy of treatment did not improve when a site was treated for multiple consecutive years. Finally, we did not detect legacy effects one year after cessation of treatment. Our results suggest the need for careful selection of treatment sites to ensure that the amount of fluridone applied is likely to have significant impact given local current speeds. Our study underscores the need for development of additional treatment methodologies for hydrologically dynamic systems such as estuaries, particularly when control is needed year-round and for multiple years.

Six decades of field observations reveal how anthropogenic pressure changes the coverage and community of submerged aquatic vegetation in a eutrophic lake

Six decades field observation data series on submerged aquatic vegetation (SAV), water level and water quality from Lake Taihu were compiled to reveal the dynamics in coverage and species composition of SAV and their anthropogenic drivers. We found that both SAV species composition and coverage area declined significantly in Lake Taihu during the period, and the increasing nutrient levels and water level as well as decreasing water clarity were responsible for these change trends. Specifically, the decrease in species richness could be particularly well predicted by total nitrogen (TN) and the ratio of water clarity (i.e., Secchi disk depth (SDD)) to water level (WL), contributing 47.3 % and 32.3 %, respectively, while the coverage of macrophytes was most strongly related to the water level, accounting for 70.1 % of the variation. A classification tree analysis revealed a threshold of TN of 3.2 mg/L and SDD/WL of 0.14 that caused a shift to a eutrophic low-macrophyte dominated state. Our results highlight that SDD/WL must be improved for SAV recolonization, rather than merely reducing nutrient input and regulating water level. Our findings provide scientific information for lake managers to prevent plant degradation in macrophyte-dominant lakes and facilitate a shift to a macrophyte-dominant state in eutrophic lakes.

Open-Source Analysis of Submerged Aquatic Vegetation Cover in Complex Waters Using High-Resolution Satellite Remote Sensing: An Adaptable Framework

Despite being recognized as a key component of shallow-water ecosystems, submerged aquatic vegetation (SAV) remains difficult to monitor over large spatial scales. Because of SAV’s structuring capabilities, high-resolution monitoring of submerged landscapes could generate highly valuable ecological data. Until now, high-resolution remote sensing of SAV has been largely limited to applications within costly image analysis software. In this paper, we propose an example of an adaptable open-sourced object-based image analysis (OBIA) workflow to generate SAV cover maps in complex aquatic environments. Using the R software, QGIS and Orfeo Toolbox, we apply radiometric calibration, atmospheric correction, a de-striping correction, and a hierarchical iterative OBIA random forest classification to generate SAV cover maps based on raw DigitalGlobe multispectral imagery. The workflow is applied to images taken over two spatially complex fluvial lakes in Quebec, Canada, using Quickbird-02 and Worldview-03 satellites. Classification performance based on training sets reveals conservative SAV cover estimates with less than 10% error across all classes except for lower SAV growth forms in the most turbid waters. In light of these results, we conclude that it is possible to monitor SAV distribution using high-resolution remote sensing within an open-sourced environment with a flexible and functional workflow.

Performance of acoustic telemetry in relation to submerged aquatic vegetation in a nearshore freshwater habitat

Acoustic telemetry is a powerful tool for learning about the movements and ecology of aquatic animals, but proper use requires evaluation of its performance in different environments. Nearshore freshwater habitats are important to many fishes; however, submerged aquatic vegetation (SAV) in these areas influences the performance of acoustic telemetry through attenuation of the transmissions. Despite this, few studies have quantified the influence of SAV on the detection efficiency and range. We conducted range testing and hydroacoustic surveys to assess the seasonal influence of SAV biovolume on the detection efficiency of 180kHz transmitters in the nearshore (<1.5m) habitats of a temperate freshwater riverine ecosystem. The interaction of transmitter–receiver distance and SAV biovolume significantly reduced the detection efficiency of transmitters, which varied with seasonal growth and senescence of SAV. Daily effective detection range (mean±s.e.) varied from 6.85 m±1.98 when SAV coverage was high (mean biovolume 0.98) to 196.08 m±51.89 when SAV was largely absent (mean biovolume 0.01). This study demonstrated the impact of SAV on the detection range of acoustic transmitters, illustrating the need for range testing and consideration in study design and analysis to improve the quality of interpretation of data in vegetated habitats.

Development and spatial application of a submerged aquatic vegetation model for Cootes Paradise Marsh, Ontario, Canada

Cootes Paradise Marsh (CP) is an urban wetland and is part of the Hamilton Harbour Area of Concern (AOC). Anthropogenic stressors have degraded the system’s water quality. Submerged aquatic vegetation (SAV) provides critical fish habitat, and its recovery is crucial to this AOC’s delisting efforts. We developed predictive models to recommend water clarity (Secchi depth) targets that can potentially achieve a minimum SAV presence of 230 ha in CP, using macrophyte monitoring data that have been collected since 1996 by the Royal Botanical Gardens (RBG). A random forest approach was used for modelling SAV presence and SAV % cover. The final model for predicting presence of SAV consisted of Secchi depth, west wind fetch, and water level; the model had high accuracy (accuracy = 0.88, kappa = 0.77). For predicting SAV cover, the final model consisted of water depth, Secchi depth, percent slope, average fetch, water level, and substrate type; it had moderate accuracy (σ2explained = 0.66, root mean square error = 26.09, and weighted absolute percentage error = 58.96). Both models were then applied spatially using a digital elevation model to predict areas of CP where SAV would likely occur under different water level and water clarity scenarios. We recommend a delisting Secchi depth target of greater than 0.75 m to achieve the maximum potential of SAV areal extent under different water level scenarios.

Shallow lake management enhanced habitat and attracted waterbirds during fall migration

Lake water levels can be managed in shallow lakes in order to improve water quality and promote aquatic vegetation that presumably benefits waterbirds. We aimed to understand whether waterbird abundance and species richness during fall migrations were positively influenced by managed lake water levels at 32 shallow lakes over 10 years. We conducted annual waterbird surveys that counted 6 million birds and repeatedly measured several in-lake habitat variables. Lakes with water level management had lower water depths, greater water quality and clarity, more submerged aquatic vegetation (SAV), and more wild rice (Zizania palustris L.) compared to unmanaged lakes. Redundancy analysis and regressions revealed that the waterbird community and several waterbird species were positively correlated to water level management and SAV; however, waterbirds were apparently responding principally to abundant SAV regardless of water depth or management. Two presented case studies of turbid-state lakes also highlighted that water level management rehabilitated lake habitat and waterbird use for a few years. We concluded that water level management can be an effective tool for increasing SAV and migrating waterbirds, but noted that large bird communities can also occur on unmanaged, deeper wetlands with existing SAV coverage of > 80%.

Submerged aquatic vegetation cover and complexity drive crustacean zooplankton community structure in a large fluvial lake: An in situ approach

Submerged aquatic vegetation (SAV) is considered an important driver of the zooplankton community, owing to the refuge offered by its structural complexity. However, non-destructive in situ approaches to assess quantitatively the features of SAV beds and their effects on zooplankton community are still lacking. This study aims to: 1) estimate SAV cover and complexity using subaquatic upward photographs (SUP) and 2) analyse the relationship between SAV variables and crustacean zooplankton composition and functional diversity (FD). SAV biomass and zooplankton were sampled at 52 stations in a large fluvial lake. Two metrics based on SUP were used to quantify SAV cover and complexity: 1) the percentage of the underwater landscape occupied by SAV (PLAND: SAV cover), and 2) its perimeter-area fractal dimension (PAFRAC: SAV complexity). Zooplankton composition and functional diversity were estimated based on crustacean species assemblages and functional traits. We used linear models to describe the relationships between SAV indices, environmental variables and zooplankton abundance, biomass and FD. SUP was an effective method to estimate in situ SAV cover and complexity. PLAND and PAFRAC were positively related to SAV biomass and zooplankton abundance, yielding a non-destructive assessment of the SAV-zooplankton relationships. Our study partially supports the habitat complexity-ecological niche availability hypothesis, as SAV cover and complexity influenced zooplankton functional groups and diversity indices. However, water transparency and depth also had important effects in interaction with PLAND and PAFRAC, and it remains difficult to fully disentangle the effects of SAV complexity from SAV cover on crustacean zooplankton community structure.

Evaluating the influences of harvesting activity and eutrophication on loss of aquatic vegetations in Taihu Lake, China

A rapid degradation of aquatic vegetations in Taihu Lake has roused a wide attention in recent years. Giving large-scale harvesting activity on aquatic vegetation since 2012, whether water eutrophication or the human harvest activity induced the degradation remains controversial and unclear. In this study, based on Landsat and HJ-CCD data acquired from 1984 to 2016 and a 12-year field observation (2005–2016) of water quality, a method was proposed to quantitatively assess impacts of harvesting activity and water quality change on degradations of both floating-leaved aquatic vegetation (FAV) and submerged aquatic vegetation (SAV) in Taihu Lake. First, areas of FAV and SAV covers from 1984 to 2016 in Taihu Lake were mapped using the satellite data, and then the mapped areas were modified to those on a reference date by using phenological curves of FAV and SAV covers. Next, correlations between water quality data and FAV and SAV covers were analyzed by using Pearson correlation analysis based on the data before implementing the human harvesting activity (i.e., before 2012), and multiple general linear models were established based on the selected water quality variables with p-value <0.01 for estimating covers of FAV and SAV from 2012 to 2016. Finally, based on the predicted areas of FAV and SAV covers by the models and the modified areas mapped from satellite data, the influences of water eutrophication and the human harvesting activity on the degradation of FAV and SAV covers were quantitatively assessed. The results indicated that (1) FAV cover exhibited a significant increase from 1984 to 2011 and then a rapid decrease, while SAV cover increased significantly before 2003 and then obviously declined; (2) water level (WL) and total nitrogen (TN) showed significantly negative correlations with FAV and SAV covers, while secchi disk depth (SDD) and SDD/WL had significantly positive correlations with FAV and SAV covers; (3) the human harvesting activity made a major contribution to the loss of FAV cover, and the degradation of SAV cover was mainly due to an increased lake eutrophication and deteriorated underwater light environment. The findings derived from this study could offer a guidance for Taihu Lake ecological restoration and effective management.

Using Recreation‐Grade Side‐Scan Sonar to Produce Classified Maps of Submerged Aquatic Vegetation

AbstractThe routine assessment and monitoring of aquatic habitat characteristics, such as submerged aquatic vegetation (SAV), is a fundamental tool in fisheries management. The relative low cost and availability of recreation‐grade side‐scan sonar (SSS) systems has made capturing high‐resolution (&lt;10 cm) underwater imagery a practical and viable tool for improving these assessments. Using Humminbird SSS and a boat‐mounted transducer, we determined our ability to produce classified maps of SAV within three reservoirs in Texas. Sonar recordings of photic zone habitat were collected and processed into georeferenced mosaic images by using SonarTRX SSS software for use in geographical information systems (GISs). The resulting raster images were interpreted to identify and map SAV, and a classification scheme was developed. Random‐point data was also used to develop a traditional error matrix and an accuracy assessment for each classified map. In the three reservoirs, 485 ha were classified, including 298 ha of SAV. The area estimates for 11 of 12 classes fell within the 95% CIs that were estimated from concurrent random‐point surveys. Overall classification accuracy ranged from 78% to 88% for the three reservoirs. The methods that were developed demonstrate an accurate procedure for calculating SAV coverage and producing a high‐quality map product for distribution to stakeholders.

Using the random forest algorithm to integrate hydroacoustic data with satellite images to improve the mapping of shallow nearshore benthic features in a marine protected area in Jamaica

Hydroacoustic and optical remote sensing have been commonly used to map shallow nearshore benthic features. However, the number, type, scale, and accuracy of the mapping products that can be obtained from the two sensors differ; as such, there can be limited agreement between their mapping products. These differences can be further accentuated if the hydroacoustic data are interpolated to produce a map. Interpolation introduces spatial uncertainty and reduces map accuracy. Consequently, maps generated from the two sensors may provide dissimilar spatial and temporal representations of the same benthic features. We therefore compared the performance of a random forest regression (RFr) and a universal kriging (UK) interpolation method and a post-classification enhancement that can be used to increase the accuracy and complementarity of benthic habitat maps derived from hydroacoustic data. First, we used single beam echosounder (SBES) survey bathymetry data from the Bluefields Bay marine protected area (MPA) in western Jamaica (13.82 km2 in size), to create a bathymetric surface model (BSM), from which rugosity and bathymetric position index (BPI) maps were generated. Next, the RFr was used to create submerged aquatic vegetation (SAV) percentage cover maps from the SBES SAV cover data by predicting cover at un-sampled locations. Predictors included auxiliary data such as depth, BPI, survey points coordinates and radiometrically corrected, deglinted and water column corrected image reflectance index values from each of the following: WorldView-2, Geoeye-1 and Landsat 8. Additionally, a SAV map was created using the UK. The most accurate SAV cover thresholds were identified and were used to create binary maps from the RFr and UK maps. A rugosity derived coral reef map was then added to the binary maps. The resulting benthic habitat maps had comparable accuracies and class coverage to benthic maps classified from GeoEye-1 and WorldView-2 images using pixel and object-based classifiers. However, map accuracies were calculated using a suboptimal number of reference points (<50) for two of the benthic map classes (SAV absent and coral reef). This was not considered to be problematic as the addition of the coral reef class to the binary maps resulted in a significant decrease in uncertainty (standard error and confidence interval width of the overall accuracy) and a significant increase in the user’s accuracy of the SAV absent map class. Also, the difference in uncertainty and accuracy between the map classes did not change. The methods used in this study can therefore be used to increase the accuracy (and to decrease the uncertainty) and the complementarity of maps derived from hydroacoustic data.

A New Method for Mapping Aquatic Vegetation Especially Underwater Vegetation in Lake Ulansuhai Using GF-1 Satellite Data

It is difficult to accurately identify and extract bodies of water and underwater vegetation from satellite images using conventional vegetation indices, as the strong absorption of water weakens the spectral feature of high near-infrared (NIR) reflected by underwater vegetation in shallow lakes. This study used the shallow Lake Ulansuhai in the semi-arid region of China as a research site, and proposes a new concave–convex decision function to detect submerged aquatic vegetation (SAV) and identify bodies of water using Gao Fen 1 (GF-1) multi-spectral satellite images with a resolution of 16 meters acquired in July and August 2015. At the same time, emergent vegetation, “Huangtai algae bloom”, and SAV were classified simultaneously by a decision tree method. Through investigation and verification by field samples, classification accuracy in July and August was 92.17% and 91.79%, respectively, demonstrating that GF-1 data with four-day short revisit period and high spatial resolution can meet the standards of accuracy required by aquatic vegetation extraction. The results indicated that the concave–convex decision function is superior to traditional classification methods in distinguishing water and SAV, thus significantly improving SAV classification accuracy. The concave–convex decision function can be applied to waters with SAV coverage greater than 40% above 0.3 m and SAV coverage 40% above 0.1 m under 1.5 m transparency, which can provide new methods for the accurate extraction of SAV in other regions.

Rivers to reservoirs: hydrological drivers control reservoir function by affecting the abundance of submerged and floating macrophytes

Lakes and reservoirs are important sites for biogeochemical cycling on both regional and global scales. Shallow lakes often have higher coverage of submerged aquatic vegetation (SAV) because of increased light penetration to the sediment as well as stronger interactions between the sediment and the water column. These biotic and abiotic interactions can strongly affect nutrient cycling. This study evaluated how hydrologically driven changes in SAV coverage affected nutrient processing within a relatively shallow reservoir. To assess these effects, a comprehensive water quality sampling network was established that quantified nutrient concentrations in the inflows and outflow of the lake. Annual vegetation surveys quantified the spatial coverage of SAV. Annual inflow was significantly lower in the first year of the study compared to the following 2 years. Consequently, SAV coverage was also highest during the lowest flows in the first year and was lower in the following 2 years when flows were greater. NO3-N concentrations were also lowest within Hydrilla beds and in the outflow during the growing season of the first year. Our results suggest that hydrological variation was the main driving variable of SAV coverage, and that the extent of SAV coverage strongly controlled nutrient processing at the whole-reservoir scale.

Changes in submerged aquatic vegetation (SAV) coverage caused by extended drought and flood pulses

ABSTRACTShivers SD, Golladay SW, Waters MN, Wilde SB, Ashford PD, Covich AP. 2018. Changes in submerged aquatic vegetation (SAV) coverage caused by extended drought and flood pulses. Lake Reserv Manage. 34:199—210.Previous studies demonstrated that submerged aquatic vegetation (SAV) affects productivity and biogeochemical cycling within freshwater ecosystems. Some invasive submerged macrophytes dominate shallow water bodies and are known to compete for nutrients with native macrophytes and phytoplankton. To evaluate variation in SAV coverage, annual whole-reservoir vegetation surveys were conducted during the peak of the growing season over a 3-yr period that included drought and years with seasonal flood pulses. Physical parameters were directly measured (Photosynthetically Active Radiation: PAR) or obtained from USGS river gauges (river discharge and turbidity) to investigate the relationship between hydrology and SAV coverage. Precipitation was lower in the first year of the study compared to the following 2 yr causing the lowest river discharge observed over the 3-yr study. First-year discharge was also lower than the 50-yr median daily Q (discharge). SAV coverage, particularly of dioecious Hydrilla verticillata, was the greatest during the reduced flow period (35.5 km2). With increasing precipitation and river discharge, SAV coverage was reduced during subsequent years (22.9 km2 and 18.3 km2, respectively). Increased discharge caused turbidity to increase, which reduced light availability during the early growing season, causing a delay in germination and subsequent reduction in SAV coverage. In shallow reservoirs, SAV is capable of extensive coverage. Thus, large variation in coverage can alter ecosystem functionality. In regulated river systems, managing late spring flood pulses may provide some control of SAV coverage in shallow reservoirs, in addition to providing other environmental flow benefits.

A Satellite-Based Assessment of the Distribution and Biomass of Submerged Aquatic Vegetation in the Optically Shallow Basin of Lake Biwa

Assessing the abundance of submerged aquatic vegetation (SAV), particularly in shallow lakes, is essential for effective lake management activities. In the present study we applied satellite remote sensing (a Landsat-8 image) in order to evaluate the SAV coverage area and its biomass for the peak growth period, which is mainly in September or October (2013 to 2016), in the eutrophic and shallow south basin of Lake Biwa. We developed and validated a satellite-based water transparency retrieval algorithm based on the linear regression approach (R2 = 0.77) to determine the water clarity (2013–2016), which was later used for SAV classification and biomass estimation. For SAV classification, we used Spectral Mixture Analysis (SMA), a Spectral Angle Mapper (SAM), and a binary decision tree, giving an overall classification accuracy of 86.5% and SAV classification accuracy of 76.5% (SAV kappa coefficient 0.74), based on in situ measurements. For biomass estimation, a new Spectral Decomposition Algorithm was developed. The satellite-derived biomass (R2 = 0.79) for the SAV classified area gives an overall root-mean-square error (RMSE) of 0.26 kg Dry Weight (DW) m-2. The mapped SAV coverage area was 20% and 40% in 2013 and 2016, respectively. Estimated SAV biomass for the mapped area shows an increase in recent years, with values of 3390 t (tons, dry weight) in 2013 as compared to 4550 t in 2016. The maximum biomass density (4.89 kg DW m-2) was obtained for a year with high water transparency (September 2014). With the change in water clarity, a slow change in SAV growth was noted from 2013 to 2016. The study shows that water clarity is important for the SAV detection and biomass estimation using satellite remote sensing in shallow eutrophic lakes. The present study also demonstrates the successful application of the developed satellite-based approach for SAV biomass estimation in the shallow eutrophic lake, which can be tested in other lakes.

Shifts in coastal fish communities: Is eutrophication always beneficial for sticklebacks?

Following declines of predatory fish, mesopredators such as sticklebacks have been linked to shifts in coastal trophic networks through both top-down (preying on mesograzers and facilitating algal blooms) and bottom-up (benefitting from eutrophicated conditions) processes. Here, we tested whether the association between eutrophication effects (filamentous algae and turbidity) and sticklebacks held true in the Finnish Archipelago Sea where predatory fish populations have remained stable. If so, sticklebacks should be more abundant in the middle archipelago, where eutrophic conditions have led to increased turbidity, higher filamentous algal loads, and decreased cover of submerged aquatic vegetation (SAV), than in the outer archipelago, where environmental conditions are better. We measured the spatial and seasonal variation of sticklebacks (three-spined Gasterosteus aculeatus and nine-spined Pungitius pungitius) in middle and outer archipelago sites, as well as environmental variables potentially affecting their abundance.Adults and juveniles of both species were more abundant in the outer than middle archipelago. The outer archipelago was characterized by greater Secchi depth throughout the summer and higher SAV cover in late summer. Secchi depth was positively correlated with stickleback abundance of both species, while SAV cover was also positively correlated in late summer. Filamentous algal cover was high in both the middle and outer archipelago, but not consistently associated with stickleback abundance throughout the summer. While sticklebacks have been thought to both contribute to, and benefit from, eutrophication, our results instead suggest that the resulting environmental changes may have adverse effects on sticklebacks, especially if predators are present. This may lead them to shift their breeding grounds and spatial distribution to less eutrophicated areas where lower turbidity and the resulting increased availability of SAV provide refuge from predators for juveniles, and higher quality breeding and feeding grounds for adults.

Where's the Grass? Disappearing Submerged Aquatic Vegetation and Declining Water Quality in Lake Mattamuskeet

Abstract Major threats to aquatic systems such as shallow lakes can include declining water quality, the loss of macrophyte beds, and the occurrence of harmful algal blooms. Often, these changes go unnoticed until a shift from a clear, oligotrophic system dominated by macrophyte beds to a turbid, eutrophic system dominated by phytoplankton and associated harmful algal blooms has occurred. Lake Mattamuskeet, which mostly lies within the boundary of Mattamuskeet National Wildlife Refuge, North Carolina, is a shallow lake that has recently experienced a reduction in water clarity and macrophyte beds, also referred to as submerged aquatic vegetation (SAV), and an increase in nutrients, phytoplankton, harmful algal blooms, and cyanotoxin production. At Lake Mattamuskeet, SAV coverage and water clarity declined between the 1980s and 2015. During the same time, significantly increasing trends in nitrogen, phosphorus, turbidity, suspended sediments, chlorophyll a, and pH occurred. Current water-quality conditions (2012–2015) are not conducive to SAV survival and, in some cases, do not meet North Carolina water-quality standards for the protection of aquatic life. Water clarity declines appear to predate the SAV die-offs on the east side. Moving forward, SAV will serve as a primary indicator for lake health; and lake monitoring, research, and management efforts will focus on the restoration of aquatic grasses and water quality at Lake Mattamuskeet.

Biogeography of Submerged Aquatic Vegetation (SAV) in the Pontchartrain Basin: Species Salinity Zonation and 1953–2016 Lake Pontchartrain Trends

A biogeographic study of submerged aquatic vegetation (SAV) in the Pontchartrain Basin of southeastern Louisiana was conducted. These plants provide numerous essential ecosystem services. Eighteen species occurred in different salinity zones ranging from freshwater to the saline waters of the Gulf of Mexico. Lake Pontchartrain SAV coverage was reduced by 70 percent from 1953 to 1990 due to increased turbidity from urbanization and shell dredging. A La Niña drought (1998–2001) increased SAV to 80 percent of the 1953 coverage, but declines occurred after the drought and there was extensive damage from Katrina and other hurricanes between 2005 and 2012. Coverage in 2016 is about 10 percent of the 1953 level. In these subtropical waters, some species were abundant during the winter and spring, but declined during the summer. Lower Basin SAV is also changing due to high rates of relative sea level rise and erosion of marsh and barrier islands.

The Implicit Price of Aquatic Grasses

Submerged aquatic vegetation (SAV) in the Chesapeake Bay is well below half of its historic levels, largely due to excessive nutrient and sediment loads degrading water quality. SAV provides important ecosystem functions, many of which are beneficial to local residents. To understand the implicit value residents place on SAV and the ecosystem services it provides, we undertake a hedonic property value study using residential transactions in 11 Maryland counties adjacent to the Chesapeake Bay. These data are matched to high-resolution maps of SAV coverage. We pose a quasi-experimental comparison and examine how the prices of homes near the waterfront vary with the presence of SAV. On average, waterfront and near-waterfront homes within 200 meters of the shore sell at about a 6.5% premium when SAV is present. Applying these estimates to the 185,000-acre SAV attainment goal suggests total property value gains on the order of $436 million.