Eelgrass Distribution Research Articles

Use of Landsat Imagery Time-Series and Random Forests Classifier to Reconstruct Eelgrass Bed Distribution Maps in Eeyou Istchee

The eastern coastline of James Bay is known to have been home to sizeable eelgrass beds (Zostera marina L.) which thrived in the bay’s shallow, subarctic waters. The region was subjected to substantial hydroelectric dams, large fires, and other human activities in the past half-century. To assess the impact of these factors on eelgrass beds, a historical reconstruction of eelgrass bed distribution was performed from images acquired by Landsat-5 Thematic Mapper (TM) in 1988, 1991, and 1996 and images of the Landsat-8 Operational Land Imager (OLI) in 2019. All the images were classified using the Random Forests classifier (RF) and assessed for accuracy each year on a bay-wide scale using an independent field validation dataset. The validation data were extracted from an eelgrass bed map established using aerial photos and field surveys in 1986, 1991, and 1995 and from a field survey in 2019. The overall validation accuracy of the classified images (between 72% and 85%) showed good agreement with the other datasets for most locations, providing reassurance about the reliability of the research. This makes it possible to use satellite imagery to detect past changes to eelgrass distribution within a bay. The classified images of 1988 and 1996 were also compared to aerial photos taken in years close to each other at ten sites to determine their ability to assess small eelgrass beds’ shape and presence. Such a comparison revealed that the classified images accurately portrayed eelgrass distribution even at finer scales.

Ensemble habitat suitability modeling for predicting optimal sites for eelgrass (Zostera marina) in the tidal lagoon ecosystem: Implications for restoration and conservation

Seagrass systems are in decline, mainly due to anthropogenic pressures and ongoing climate change. Implementing seagrass protection and restoration measures requires accurate assessment of suitable habitats. Commonly, such assessments have been performed using single-algorithm habitat suitability models, nearly always based on low environmental resolution information and short-term species data series. Here we address eelgrass (Zoostera marina) meadows’ large-scale decline (>80%) in Shandong province (Yellow Sea, China) by developing an ensemble habitat model (EHM) to inform eelgrass conservation and restoration strategies in the Swan Lake (SL). For this, we applied a weighted EHM derived from ten single-algorithm models including profile, regression, classification, and machine learning methods to generate a high-resolution habitat suitability map. The EHM was constructed based on the predictive performances of each model, by combining a series of present-absent eelgrass datasets from recent years coupled with oceanographic and sediment data. The model was cross-validated with independent historical datasets, and a final habitat suitability map for conservation and restoration was generated. Our EHM scheme outperformed all single models in terms of habitat suitability, scoring ∼0.95 for both true statistic skill (TSS) and area under the curve (AUC) performance criteria. Machine learning methods outperformed profile, regression and classification methods. Regarding model explanatory variables, overall, topographic characteristics such as depth (DEP) and seafloor slope (SSL) are the most significant factors determining the distribution of eelgrass. The EHM predicted that the overlapping area was almost 90% of the current eelgrass habitat. Using results from our EHM, a LOESS regression model for the relationship of the habitat suitability to both the biomass and density of Z. marina outperformed better than the classic Ordinary Least Squares regression model. The EHM is a promising tool for supporting eelgrass protection and restoration areas in temperate lagoons as data availability improves.

Fine-scale ensemble species distribution modeling of eelgrass (Zostera marina) to inform nearshore conservation planning and habitat management

Baseline data on the distribution and extent of biogenic habitat-forming species at a high spatial resolution are essential to inform habitat management strategies, preserve ecosystem integrity, and achieve effective conservation objectives in the nearshore. Model-based approaches to map suitable habitat for these species are a key tool to address this need, filling in gaps where observations are otherwise unavailable and remote sensing methods are limited by turbid waters or cannot be applied at scale. We developed a high resolution (35 m) ensemble species distribution model to predict the distribution of eelgrass (Zostera marina) along the Atlantic coast of Nova Scotia, Canada where the observational coverage of eelgrass occurrence is sparse and nearshore waters are optically complex. Our ensemble model was derived as a performance-weighted average prediction of 7 different modeling methods fit to 6 physical predictors (substrate type, depth, wave exposure, slope, and two bathymetric position indices) and evaluated with a 5-fold spatially-blocked cross-validation procedure. The ensemble model showed moderate predictive performance (Area Under the Receiver-Operating Characteristic Curve (AUC) = 0.803 ± 0.061, True Skill Statistic (TSS) = 0.531 ± 0.100; mean ± SD), high sensitivity (92.0 ± 4.5), and offered some improvement over individual models. Substrate type, depth, and relative wave exposure were the most influential predictors associated with eelgrass occurrence, where the highest probabilities were associated with sandy and sandy-mud sediments, depths ranging 0 m – 4 m, and low to intermediate wave exposure. Within our study region, we predicted a total extent of suitable eelgrass habitat of 38,130 ha. We found suitable habitat was particularly extensive within the long narrow inlets and extensive shallow flats of the South Shore, Eastern Shore, and Bras d’Or Lakes. We also identified substantial overlap of eelgrass habitat with previously identified Ecologically and Biologically Significant Areas that guide regional conservation planning while also highlighting areas of greater prediction uncertainty arising from disagreement among modeling methods. By offering improved sensitivity and insights into the fine-scale regional distribution of a habitat-forming species with associated uncertainties, our ensemble-based modeling approach provides improved support to numerous nearshore applications including conservation planning and restoration, marine spatial and emergency response planning, environmental impact assessments, and fish habitat protection.

Warming northward shifting southern limits of the iconic temperate seagrass (Zostera marina)

SummaryGlobal warming can shift the range edges of numerous species poleward. Here, eelgrass distribution was reinvestigated at its southern limits on the eastern coast of China, which indicated that there has been a northward shift in the southern limit of Z. marina. To determine if regional warming resulted in a northward shift in suitable eelgrass habitats, sixteen transplantations of adult eelgrass shoots and seeds at the historical southern distribution limit of eelgrass were conducted between 2016 and 2021. The results showed that high water temperatures in summer had negative effects on eelgrass growth, and directly triggered shoot mortality during 2016–2018. Under heat stress, antioxidant enzyme activity was initially increased, but then decreased under more stressful heat conditions; and the HSP70 protein and its molecular chaperone protein were highly expressed under heat stress. These results demonstrated that suitable eelgrass habitat was now located further north along the eastern coast of China.

Spatial overlap between lugworm (Arenicola marina) and eelgrass (Zostera marina) distribution in coastal waters: The role of environmental stressors

This study investigates the role of external physical and chemical stressors on the distribution of lugworms and eelgrass in sandy sediments at three shallow locations along the Danish coasts. The density of and spatial overlap between lugworm and eelgrass distribution were quantified during May–October in 2017 and 2018 and related to the potential role of two environmental stressors: Physical disturbance by waves and high levels of sulfide in the sediment. The inverse relationship between the density of lugworms and eelgrass shoots at the study sites suggests a general antagonistic distribution. The spatial overlap between these species was quantified by Levins’ index. The overlap was asymmetrical and much higher for lugworms than for eelgrass with the smallest overlap at the two locations mostly impacted by physical and chemical stress. Almost no eelgrass was present at these two impacted locations when lugworms exceeded 30 ind m−2, while the critical density approached 70 ind m−2 at the less impacted location. These results suggest that eelgrass is less resistant than lugworms to biological interactions when exposed to environmental stress. Eelgrass apparently require large and coherent meadows with high shoot density to tolerate multiple biological and environmental stressors. The knowledge acquired in this study improves the general understanding of species interactions in coastal ecosystems and is important for appropriate planning and management of future eelgrass restoration ventures.

USE OF LANDSAT-8 OLI IMAGERY AND LOCAL INDIGENOUS KNOWLEDGE FOR EELGRASS MAPPING IN EEYOU ISTCHEE

Abstract. The eastern coastline of James Bay (Eeyou Istchee) is known to be home to beds of subarctic eelgrass (Zostera marina L.). These eelgrass beds provide valuable habitat and food source for coastal and marine animals and contribute valuable ecosystem services such as stabilization of the shoreline all along the coast. Despite reports from Cree communities that eelgrass bed health has declined, limited research has been performed to assess and map the spatial distribution of eelgrass within the bay. This study aims to address that issue by evaluating the capability of Landsat-8 Operational Land Imager (OLI) imagery to establish a baseline map of eelgrass distribution in 2019 in the relatively turbid waters of Eeyou Istchee. Three images acquired in September 2019 were merged and classified using Random Forests into the following classes: Eelgrass, Turbid Water, Highly Turbid Water, and Optically Deep Water. The resulting classified image was validated against 108 ground truth data that were obtained from both the eelgrass health and Hydro-Quebec research team. The resulting overall accuracy was 78.7%, indicating the potential of the Random Forests classifier to estimate baseline eelgrass coverage in James Bay using Landsat-8 imagery. This project is part of a Cree driven project, the Coastal Habitat Comprehensive Research Program (CHCRP). The CHCRP aims to combine Cree's traditional knowledge with Western science to better understand environmental changes in the coastal ecosystems and ecosystem services of eastern James Bay. The study is funded by a MITACS grant sponsored by Niskamoon Corporation, an indigenous non-profit organization.

Metabarcoding of environmental samples suggest wide distribution of eelgrass (Zostera marina) pathogens in the north Pacific

Seagrass meadows provide important ecological services to the marine environment but are declining worldwide. Although eelgrass meadows in the north Pacific are thought to be relatively healthy, few studies have assessed the presence of known disease pathogens in these meadows. In a pilot study to test the efficacy of the methods and to provide foundational disease biodiversity data in the north Pacific, we leveraged metabarcoding of environmental DNA extracted from water, sediment, and eelgrass tissue samples collected from five widely distributed eelgrass meadows in Alaska and one in Japan and uncovered wide prevalence of two classes of pathogenic organisms – Labyrinthula zosterae and other associated strains of Labyrinthula, and the Phytophthora/Halophytophthora blight species complex – known to have caused decline in eelgrass (Zostera marina) elsewhere in the species’ global distribution. Although the distribution of these disease organisms is not well understood in the north Pacific, we uncovered the presence of at least one eelgrass pathogen at every locality sampled.

Using Landsat Time-Series to Monitor and Inform Seagrass Dynamics: A Case Study in the Tabusintac Estuary, New Brunswick, Canada

The recent world-wide loss of seagrasses, which are critical components of coastal ecosystems, has ignited an effort among scientists and resource managers to develop effective monitoring tools. Although Landsat time-series is considered one of the most cost-effective options for monitoring landscapes, its application to monitor seagrasses remains scarce due to many factors including difficulties obtaining accurate ground-truth data and perceived limitations in mapping nearshore marine ecosystems. Here, we report on the use of archived Landsat multispectral imagery and the automatic adaptive signature generalization (AASG) to evaluate eelgrass (Zostera marina) distribution and abundance between 1984 and 2017, in an estuary located in northeastern New Brunswick, Canada. The AASG algorithm, a novel cost-efficient approach for satellite imagery time-series analysis that requires limited ground truth data, was used to produce fourteen maps, four of which had accuracies ranging from 75 to 85%. The results indicated that eelgrass meadows near the beach barrier were highly dynamic, exhibiting high abundance fluctuations between years and a conversion of dense eelgrass to medium-low eelgrass near the main coastline. This study demonstrates the feasibility of using the AASG algorithm to map seagrass and advantages of including satellite time-series in monitoring programmes to investigate seagrass dynamics and long-term trends.

Sonar and in situ surveys of eelgrass distribution, reproductive effort, and sexual recruitment contribution in a eutrophic bay with intensive human activities: Implication for seagrass conservation

Seagrass beds are recognized as pivotal and among the most vulnerable coastal marine ecosystems globally. The eelgrass Zostera marina L. is the most widely distributed seagrass species and dominates the temperate northern hemisphere. However, an alarming decline in seagrass has been occurring worldwide due to multiple stressors. Seagrass meadow degradation is particularly serious in the Bohai Sea, in temperate China; however, large areas (> 500 ha) of seagrass meadows and population recruitment have rarely been reported in this area. In the present study, we report on a large eelgrass bed in a eutrophic bay of the Bohai Sea. Sonar and field survey methods were used to investigate the distribution of seagrass and its population recruitment. We also analyzed the major threats to this large seagrass bed. Results showed that a large Z. marina bed with an area of 694.36 ha occurred in this area of the Bohai Sea, with a peripheral area of ~25 km2. Seagrass canopy height and plant coverage had a significant correlation with water depth. Asexual reproduction principally occurred in autumn and played a dominant role in population recruitment in vegetated areas, where no seedlings successfully colonized. In contrast, a considerable number of seedlings survived in the seagrass meadow gaps, and thus played a critical role in the recruitment in these areas. The maximum reproductive shoot densities were about 100 and 70 shoots m−2 at sampling site (S)-1 and S-2 in 2018, respectively, which was about two times more than in 2019 (50 and 20 reproductive shoots m−2 at S-1 and S-2, respectively). The potential seed output per unit area in 2019 was about 1020 seeds m−2 at S-1 and 830 seeds m−2 at S-2, and the seed output in the study area was at a low level compared with global values. Overall, high spring and summer water temperature appeared to induce sexual reproduction of Z. marina in the study area, including reproductive effort, reproductive investment, and seedling development. Furthermore, eelgrass height, aboveground biomass, and density were significantly related to water temperature. Among the potential threatening factors to seagrass in this area, the activities of clam harvesting were intense with daily clam catches >2000 kg, leading to patchy seagrass meadows, especially in the fringe areas. The seagrass bed was also threatened by marine pollution (nutrient loading) and land reclamation. Therefore, the protection and restoration of this seagrass bed are strongly recommended. Our study will provide fundamental information for the conservation and management strategies of large eelgrass beds in the Bohai Sea.

Refinements in Eelgrass Mapping at Tabusintac Bay (New Brunswick, Canada): A Comparison between Random Forest and the Maximum Likelihood Classifier

Eelgrass (Zostera marina L.) is a marine angiosperm plant that grows throughout coastal areas in Atlantic Canada. Eelgrass meadows provide numerous ecosystem services while they have been acknowledged as important habitats, their location, extent, and health in Atlantic Canada are poorly understood. This study examined the effectiveness of WorldView-2 optical satellite imagery to map eelgrass presence in Tabusintac, New Brunswick, an estuarine lagoon with extensive eelgrass coverage. The imagery was classified using two supervised classifiers: the parametric Maximum Likelihood Classifier (MLC) and the nonparametric Random Forest (RF) classifier. While RF was expected to produce higher classification accuracies, it was shown not to be much better than MLC in this particular context. The overall validation accuracy was 96.36% for both the RF and MLC classifiers. Finally, the comparison of our 2014 classified image with a 2008 eelgrass distribution map shows an increase in eelgrass extent in the bay between both years.

Modeling eelgrass spatial response to nutrient abatement measures in a changing climate

For many coastal areas including the Baltic Sea, ambitious nutrient abatement goals have been set to curb eutrophication, but benefits of such measures were normally not studied in light of anticipated climate change. To project the likely responses of nutrient abatement on eelgrass (Zostera marina), we coupled a species distribution model with a biogeochemical model, obtaining future water turbidity, and a wave model for predicting the future hydrodynamics in the coastal area. Using this, eelgrass distribution was modeled for different combinations of nutrient scenarios and future wind fields. We are the first to demonstrate that while under a business as usual scenario overall eelgrass area will not recover, nutrient reductions that fulfill the Helsinki Commission’s Baltic Sea Action Plan (BSAP) are likely to lead to a substantial areal expansion of eelgrass coverage, primarily at the current distribution’s lower depth limits, thereby overcompensating losses in shallow areas caused by a stormier climate.

Impact of eelgrass bed recovery and expansion on phytoplankton growth through nutrient competition

Eelgrass beds are highly productive and support diverse faunal assemblages; they also take in nutrients from the water and prevent excessive phytoplankton growth in eutrophic coastal waters through the reduction of available nutrients. Despite its importance, the global distribution of eelgrass has declined worldwide. In eutrophic areas with high chlorophyll a (Chl.a) concentrations, natural recovery of eelgrass beds after eutrophication is possible. To facilitate this, sufficient water clarity can be reached after a large enough decrease in phytoplankton concentration. In this study, we proposed a novel indicator for the maximum possible Secchi depth (MPSD), defined as the Secchi depth when the Chl.a concentration is equal to a reference Chl.a concentration. We applied the MPSD to evaluate water clarity improvements through the reduction of terrigenous anthropogenic nutrient loading. We found that phytoplankton did not control water clarity in the study area, which was instead controlled by background factors. Therefore, improvements in water clarity would not be expected after reducing terrigenous anthropogenic nutrient loading. The habitat of Zostera marina is determined by light availability, so we investigated a potential area with ≥20% surface irradiance and Z. marina existed in 27% of it (100 of 373 ha). The maximum further recovery of eelgrass by Secchi depth improvements to the MPSD was estimated at 36 ha. The impact of eelgrass recovery and expansion on phytoplankton growth from May to September was evaluated by a mathematical model under two scenarios: the current eelgrass distribution (100 ha) and potential maximum eelgrass distribution (373 ha). A Chl.a decrease of 1.0–3.0 μg l−1 from 4.0 to 7.0 μg l−1 was achieved in an area from May to July, and the improvement decreased with time. These evaluation methods and findings could help us gain a better understanding of the nutrient management in seagrass-vegetated semi-enclosed seas subjected to anthropogenic nutrient input.

Eelgrass (Zostera marina) and benthic habitat mapping in Atlantic Canada using high-resolution SPOT 6/7 satellite imagery

Eelgrass (Zostera marina) is the dominant perennial canopy-forming vegetation along the soft-sediment shores of the Northwest Atlantic. Eelgrass is considered an ecologically significant species in Atlantic Canada as it provides essential ecosystem functions and services and is an indicator of ecosystem health. Recent declines of eelgrass habitats highlight the need for methods quantifying the large-scale distribution of eelgrass throughout Atlantic Canada to monitor for further habitat loss. We used archived, high-resolution SPOT 6/7 satellite imagery to classify where vegetated habitats exist and if eelgrass was the dominant species. We focused on three bays in Nova Scotia: Port Mouton Bay, Port Joli Bay, and Jordan Bay. In 2015, field surveys were conducted to obtain training points, which were supplemented with visually identified points to perform a supervised classification based on the maximum likelihood classifier. We also performed an unsupervised classification, where clustering algorithms were used to build training sites for a maximum likelihood classifier without using field survey data. These two pixel-based approaches provided similar results across the different images. Regardless of classification type (supervised versus unsupervised), we found different levels of success for the three bays. In Port Joli Bay, we were able to calculate where vegetated habitats occurred and what was the dominant species. This provided bay-wide distribution maps and suggested that 8.61–11.10% of the bay was covered by eelgrass. In Port Mouton Bay, we were able to calculate vegetation presence from absence, and eelgrass habitats were qualitatively differentiated from seaweed habitats by incorporating substrate data and local ecological knowledge. In contrast, benthic habitats could not be classified in Jordan Bay, highlighting the importance of sufficient water clarity for classifying satellite imagery. Our study has implications for the monitoring, conservation and management of eelgrass and other vegetated coastal habitats in Atlantic Canada by providing bay-wide distribution maps, and a classification framework which requires no field survey points for ground truthing.

Inorganic nitrogen has a dominant impact on estuarine eelgrass distribution in the Southern Gulf of St. Lawrence, Canada

AbstractEelgrass (Zostera marina) coverage and height were evaluated in 16 estuaries in the Southern Gulf of St. Lawrence, Canada, using boat‐based sonar surveys to determine the dominant factors in the decline of eelgrass in the region. Eelgrass coverage was modeled from the sonar surveys and quantified as the percentage of available habitat occupied—with habitat being defined by salinity limits (> 10 PSU) and depth (< 3 m). Estuaries showed a marked variation in eelgrass coverage ranging between 6% and 57% of available habitat, with eelgrass absent in the upper estuary of estuaries with the highest nitrate loading. The Dunk River estuary showed a decline in eelgrass coverage between 1967 and 2014, a period of increasing nitrogen loading. Measures of eelgrass height were not related to coverage endpoints, suggesting that height variables are not suitable endpoints for overall eelgrass health. Analysis of the influence of environmental factors showed that the factor that consistently correlated to eelgrass coverage was nitrate‐N loading while the factor most influencing eelgrass height was light attenuation. A nonlinear logistic loading‐effect model relating eelgrass coverage to nitrate‐N loading indicated that 10%, 25%, 50%, 75%, and 90% decline in eelgrass would be expected to occur in response to estuarine nitrate‐N loads of 1.3 kg ha−1 yr−1, 8.0 kg ha−1 yr−1, 50.0 kg ha−1 yr−1, 312 kg ha−1 yr−1, and 1947 kg ha−1 yr−1, respectively. These results suggest that inorganic nitrogen loading is the most significant factor to be addressed by environmental managers when addressing eelgrass declines in estuaries similar to those studied.

Habitat Model of Eelgrass in Danish Coastal Waters: Development, Validation and Management Perspectives

During the last century, eutrophication significantly reduced the depth distribution and density of the habitat forming eelgrass meadows (Zostera marina) in Danish coastal waters. Despite large reductions in nutrient loadings and improved water quality, Danish eelgrass meadows are currently not as widely distributed as expected from improvements in water clarity alone. This point to the importance of other environmental conditions such as sediment quality, wave exposure, oxygen conditions and water temperature that may limit eelgrass growth and contribute to constraining current distributions. Recently, detailed local models have been set up to evaluate the importance of such regulating factors in selected Danish coastal areas, but nationwide maps of eelgrass distribution and large-scale evaluations of regulating factors are still lacking. To provide such nationwide information, we applied a spatial habitat GIS modeling approach, which combines information on six key eelgrass habitat requirements (light availability, water temperature, salinity, frequency of low oxygen concentration, wave exposure and sediment type) for which we were able to obtain national coverage. The modeled potential current distribution area of Danish eelgrass meadows was 2204 km2 compared to historical estimates of around 7000 km2, indicating a great potential for further distribution. While validating the modeled eelgrass distribution area in three areas (83-111 km2) that hold large eelgrass meadows, we found an agreement of 67% with in situ monitoring data and 77% for eelgrass areas as identified from summer orthophotos. The GIS model predicted higher coverage especially in shallow waters and near the depth limits. Areas of disagreement between GIS-modelled and observed coverage generally exhibited higher exposure level, mean summer temperature and salinity compared to areas of agreement. A sensitivity analysis showed that the modeled area distribution of eelgrass was highly sensitive to light conditions, with 18 to 38% increase in coverage following an increase in light availability of 20%. Modelled coverage of eelgrass was also sensitive to wave exposure and temperature conditions while less sensitive to changes in oxygen and salinity conditions. Large regional differences in habitat conditions suggest spatial variation in the factors currently limiting the recovery of eelgrass and, hence, variations in actions required for sustainable management.

Local Regime Shifts Prevent Natural Recovery and Restoration of Lost Eelgrass Beds Along the Swedish West Coast

Along the Swedish northwest coast, over 60% of the eelgrass meadows have been lost since the 1980s. Despite improved water quality, no recovery has occurred, and restoration is presently considered to mitigate historical losses. However, the factors preventing natural recovery of eelgrass are not known, and it is not clear if conditions would allow restoration. Here, we present the results from 5 years of field studies with the aim of identifying the key processes affecting eelgrass growth and survival at historical eelgrass areas. Continuous light measurements and comparison with historic eelgrass distribution indicate that maximum depth distribution has decreased locally with 1.5–2.3 m in areas that have lost large eelgrass beds in the last 10–30 years. Field studies suggest that wind-driven local resuspension of sediments that are no longer stabilized by eelgrass beds is the main cause behind the deteriorated light conditions. Field experiments show that a combination of low light condition and disturbance from drifting algal mats prevents eelgrass recovery in these areas, whereas the sulfide intrusion from the sediment and dislodgement of shoots by waves had little effect on growth and survival. These results suggest that local regime shifts acting on a scale of 40–200 ha have occurred after the loss of eelgrass beds, where increased sediment resuspension and proliferation of drifting algal mats act as feedback mechanisms that prevent both natural recovery and restoration of eelgrass. The feedbacks appear to be interacting and causing an accelerating loss of eelgrass that is presently spreading to neighboring areas.

The effects of small-scale coastal development on the eelgrass (Zostera marina L.) distribution along the Swedish west coast – Ecological impact and legal challenges

Anthropogenic impacts on coastal areas have led to an increased degradation of marine environments globally. Eelgrass ecosystems are particularly susceptible to human induced stressors as they are sensitive to low light conditions and usually grow in shallow protected areas where pressure from coastal development is high. The extensive decline in coverage of eelgrass along the Swedish Northwest coast since the 1980s has largely been attributed to the effects of coastal eutrophication and overfishing. However, the impact on eelgrass from small-scale coastal development (docks and marinas) has never been investigated in this area. The aim of this study was to assess the local and large-scale effect of shading by docks and marinas on eelgrass habitats along the Swedish NW coast and to investigate the decision process behind small-scale exploitation to identify problems with the current legislation, which allows for continued exploitation of eelgrass. Through field assessments of eelgrass around docks and analysis of available data on eelgrass and dock distribution along the coast, the present study demonstrates that shading from docks reduced eelgrass coverage with on average 42–64% under and adjacent to the docks, and that floating docks affected larger areas and caused a much stronger reduction in eelgrass coverage (up to 100% loss) compared to docks elevated on poles (up to 70% reduction in coverage). The total eelgrass area negatively affected by docks and marinas along the NW coast was estimated to approximately 480 ha, an area corresponding to over 7% of the present areal coverage of eelgrass in the region. The analysis of decisions for dock construction showed that eelgrass was generally not assessed or considered in the decision process and that 69–88% of the applications were approved also in areas where eelgrass was present. Furthermore, marine protected areas only marginally reduced the approval of applications in eelgrass habitats. The continued small-scale development along the Swedish NW coast constitutes a significant threat to the already decimated coverage of eelgrass along the coast and changes in the management practices are needed in order to achieve both national and international goals on environmental status.

Using Acoustics to Determine Eelgrass Bed Distribution and to Assess the Seasonal Variation of Ecosystem Service.

Eelgrass beds are an important source of primary production in coastal ecosystems. Understanding seasonal variation in the abundance and distribution of eelgrass is important for conservation, and the objectives of this study were to 1) monitor seasonal variation in eelgrass beds using an acoustic monitoring method (Quantitative echo sounder) and 2) broadly quantify the carbon circulation function. We obtained acoustic data of eelgrass beds in coastal areas north and east of Ikunojima Island. Surveys were conducted nine times over the 3-year period from 2011 to 2013 in order to monitor seasonal variation. Acoustic data were obtained and used to estimate the spatial distribution of eelgrass by geostatistical methods. To determine supporting services, we determined carbon sink and carbon fixation by eelgrass beds using data from the National Research Institute of Fisheries and Environment of Inland Sea (2011). The height and distribution of eelgrass beds were at a maximum in May and at a minimum in November of each year. Distribution trends were different between the north and east areas. Supporting services showed the same patterns throughout the year. The area of distribution was considered to be coincident with the life history of eelgrass. Distribution differed by area and changed yearly due to the effects of bottom characteristics and wind direction. Quantifying the supporting services of eelgrass beds was shown to be useful for managing the conservation of coastal ecosystems.

Predicting effects of ocean warming, acidification, and water quality onChesapeake region eelgrass

AbstractAlthough environmental requirements of seagrasses have been studied for years, reliable metrics for predicting their response to current or future conditions remain elusive. Eelgrass (Zostera marinaL.) populations of the Chesapeake region lie near the southern limit of their range in the Western North Atlantic, exposing them to increasing thermal stress as the climate warms. However, CO2stimulated photosynthesis may offset some of the negative effects of temperature stress. The combined effects of temperature, CO2, and light availability controlled by water quality and epiphytes were explored usingGrassLight, a bio‐optical model that provided a predictive environment for evaluating the interaction of multiple stressors on eelgrass distribution and density across the submarine landscape. Model predictions were validated against in situ measures of spectral diffuse attenuation, eelgrass density, and distribution. The potential for photosynthesis stimulated by ocean acidification to mitigate the effects of high temperature on eelgrass populations growing near the southern limit of their distribution was explored. The model accurately reproduced the submarine light environment from measured water quality parameters, and predicted their impacts on eelgrass distribution. It also reproduced the negative effects of warm summer temperatures on eelgrass distributions, and demonstrated that CO2increases projected for the next century should stimulate photosynthesis sufficiently to offset the negative effects of thermal stress on eelgrass growing in the Chesapeake region, even in the presence of epiphytes. Thus, improved water quality should facilitate the survival of eelgrass populations in Chesapeake region, even in the face of a warming climate.

Mapping and modeling eelgrass Zostera marina distribution in the western Baltic Sea

In the northern hemisphere, eelgrass Zostera marina L. is the most important and widespread seagrass species. Despite its ecological importance, baseline data on eelgrass distribution and abundance are mostly absent, particularly in subtidal areas with relatively turbid waters. Here, we report a combined approach of vegetation mapping in the Baltic Sea coupled to a species distribution model (SDM). Eelgrass cover was mapped continuously in the summers of 2010 and 2011 with an underwater towed camera along ~400 km of seafloor. Eelgrass populated 80% of the study region and occurred at water depths between 0.6 and 7.6 m at sheltered to moderately exposed coasts. Mean patch length was 128.6 m but was higher at sheltered locations, with a maximum of >2000 m. The video observations (n = 7824) were used as empiric input to the SDMs. Using generalized additive models, 3 predictor variables (depth, wave exposure, and slope), which were selected based on Akaike’s information criterion, were sufficient to predict eelgrass presence/absence. Along with a very good overall discriminative ability (area under the receiver-operating characteristic curve ROC/AUC = 0.82), depth (as a proxy for light), wave exposure, and slope contributed 66, 29, and 5%, respectively, to the final model. The estimated total areal extent of eelgrass in the study region amounts to 140.5 km2 and comprises about 11.5% of all known Baltic seagrass beds. The present work is, to the best of our knowledge, the largest study undertaken to date on vegetation mapping and the first to assess distribution of eelgrass quantitatively in the western Baltic Sea.