Leveraging eDNA metabarcoding to characterize nearshore fish communities in Southeast Alaska: Do habitat and tide matter?

Nearshore marine habitats are critical for a variety of commercially important fish species, but assessing fish communities in these habitats is costly and time-intensive. Here, we leverage eDNA metabarcoding to characterize nearshore fish communities near Juneau, Alaska, USA, a high-latitude environment with large tidal swings, strong currents, and significant freshwater input. We investigated whether species richness and community composition differed across three habitat types (sand beaches, eelgrass beds, and rocky shorelines) and between high and low tides. Additionally, we tested whether replication of field samples and PCR reactions influenced either species richness or composition. We amplified a 12S mitochondrial locus in our samples and identified 188 fish amplicon sequence variants (ASVs), corresponding to 21 unique taxa, with approximately half of these resolved to single species. Species richness and composition inferred from eDNA differed substantially among habitats, with rock habitats containing fewer taxa and fewer overall detections than sand and eelgrass habitats. The effect of tide was more subtle and suggested a habitat-tide interaction, with differences in taxa between tides largely isolated to sand habitats. Power analyses indicated that additional field sampling is useful to detect subtle changes in species richness such as those due to tide. PCR replicates typically identified a small number of additional taxa. The most notable result from our study was that shore morphology appeared to substantially influence community structure. Rocky shorelines sloped quickly into deep water, while sand and eelgrass habitats descended much more gradually. We hypothesize that differences in taxa observed among habitats were largely due to lack of mixing between bottom and surface water, providing further evidence that eDNA transport is minimal and that many marine eDNA detections are derived from highly localized sampling locations. We suggest that future studies could explore the extent to which habitat and nearshore physical processes influence eDNA detections.

Response of eelgrass (Zostera marina) to an adjacent Olympia oyster restoration project

Recent restoration efforts for the native Olympia oyster, Ostrea lurida, are commonly motivated by potential return of oyster-associated ecosystem services, including increased water filtration. The potential impact of such restoration on another species of ecological concern, eelgrass, Zostera marina, is unclear, but has been hypothesized to be positive if oyster filter feeding increases light penetration to eelgrass. For two years after construction of an oyster restoration project, we assessed the response of adjacent eelgrass (impact) compared to control and reference eelgrass beds by monitoring changes in light intensity, eelgrass shoot density, biomass, leaf morphometrics, and epiphyte load. We observed lower light intensity consistently over time, including prior to restoration, near the constructed oyster bed relative to the control and one of the reference locations. We also observed minor variations between control and impact eelgrass morphology and density. However, the changes observed were not outside the range of natural variation expected in this system, based upon comparisons to reference eelgrass beds, nor were they detrimental. This limited impact to eelgrass may be due in part to the incorporation of a buffer distance between the restored oyster bed and the existing eelgrass bed, which may have dampened both positive and negative impacts. These findings provide evidence that Olympia oyster restoration and eelgrass conservation goals can be compatible and occur simultaneously.

A Hotspot in the Romanian Black Sea: Eelgrass Beds Drive Local Biodiversity in Surrounding Bare Sediments

Ecosystem engineers create habitat and provide conditions otherwise unavailable for the development of diverse communities. In marine soft-bottoms in particular, the biodiversity sustained by a matrix of relatively uniform sediments can be drastically enhanced by the presence of ecosystem engineers such as seagrasses. Unfortunately, the influence of seagrass meadows on the diversity of surrounding sediments is often unrecognized in spite of its importance, especially in coastlines exposed to multiple sources of pollution. This study examined composition and diversity associated with a bed of Zostera noltei Hornemann, 1832, and its surrounding bare sediments in a highly urbanized coastal area of the Romanian Black Sea. Dissimilarity levels were quantified and key species driving the differences between uniform (bare) and complex (eelgrass) sedimentary habitats were identified. 48 taxa were collected and counted, with epifaunal and infaunal species each accounting for nearly half of that diversity. Abundance, richness and diversity were strikingly higher in eelgrass-associated sediments, a difference driven primarily by various species of snails, crustaceans, polychaetes and bivalves. Between-habitat differences remained significant even after the removal of epifaunal species and each dataset undergoing strong data transformation. These results suggest that even small eelgrass beds, located in the vicinity of multiple sources of stress, can act as hotspots and make a substantial contribution to local benthic diversity.

EELGRASS MAPPING WITH SENTINEL-2 AND UAV DATA IN PRINCE EDWARD ISLAND (CANADA)

Eelgrass (Zostera marina L.) is a marine angiosperm that grows throughout coastal regions in Atlantic Canada. Eelgrass beds provide a variety of important ecosystem services, and while it is considered an important marine species, little research has been done to understand its distribution and location within Atlantic Canada. The purpose of this study was to assess the capability of Sentinel-2 and UAV imagery to map the presence of eelgrass beds within the Souris River in Prince Edward Island. Both imageries were classified using the non-parametric Random Forests (RF) supervised classifier and the resulting classification was validated using sonar data. The Sentinel-2 classified image had a lower validation accuracy at 77.7%, while the UAV classified image had a validation accuracy of 90.9%. The limitations of the study and recommendations for future work are also presented.

Mapping and Quantification of the Dwarf Eelgrass Zostera noltei Using a Random Forest Algorithm on a SPOT 7 Satellite Image

The dwarf eelgrass Zostera noltei Hornemann (Z. noltei) is the most dominant seagrass in semi-enclosed coastal systems of the Atlantic coast of Morocco. The species is experiencing a worldwide decline and monitoring the extent of its meadows would be a useful approach to estimate the impacts of natural and anthropogenic stressors. Here, we aimed to map the Z. noltei meadows in the Merja Zerga coastal lagoon (Atlantic coast of Morocco) using remote sensing. We used a random forest algorithm combined with field data to classify a SPOT 7 satellite image. Despite the difficulties related to the non-synchronization of the satellite images with the high tide coefficient, our results revealed, with an accuracy of 95%, that dwarf eelgrass beds can be discriminated successfully from other habitats in the lagoon. The estimated area was 160.76 ha when considering mixed beds (Z. noltei-associated macroalgae). The use of SPOT 7 satellite images seems to be satisfactory for long-term monitoring of Z. noltei meadows in the Merja Zerga lagoon and for biomass estimation using an NDVI–biomass quantitative relationship. Nevertheless, using this method of biomass estimation for dwarf eelgrass meadows could be unsuccessful when it comes to areas where the NDVI is saturated due to the stacking of many layers.

USE OF UAV IMAGERY FOR EELGRASS MAPPING IN ATLANTIC CANADA

Eelgrass beds are critical in coastal ecosystems and can be useful as a measure of nearshore ecosystem health. Population declines have been seen around the world, including in Atlantic Canada. Restoration has the potential to aid the eelgrass population. Traditionally, field-level protocols would be used to monitor restoration; however, using unmanned aerial vehicles (UAVs) would be faster, more cost-efficient, and produce images with higher spatial resolution. This project used RGB UAV imagery and data acquired over five sites with eelgrass beds in the northern part of the Shediac Bay (New Brunswick, Canada). The images were mosaicked using Pix4Dmapper and PCI Geomatica. Each RGB mosaic was tested for the separability of four different classes (eelgrass bed, deep water channels, sand floor, and mud floor), and training areas were created for each class. The Maximum-likelihood classifier was then applied to each mosaic for creating a map of the five sites. With an average and overall accuracy higher than 98% and a Kappa coefficient higher than 0.97, the Pix4D RGB mosaic was superior to the PCI Geomatica RGB mosaic with an average accuracy of 89%, an overall accuracy of 87%, and a Kappa coefficient of 0.83. This study indicates that mapping eelgrass beds with UAV RGB imagery is possible, but that the mosaicking step is critical. However, some factors need to be considered for creating a better map, such as acquiring the images during overcast conditions to reduce the difference in sun illumination, and the effects of glint or cloud shadow on the images.