Size-Fractionated Microbiome Structure in Subarctic Rivers and a Coastal Plume Across DOC and Salinity Gradients

Little is known about the microbial diversity of rivers that flow across the changing subarctic landscape. Using amplicon sequencing (rRNA and rRNA genes) combined with HPLC pigment analysis and physicochemical measurements, we investigated the diversity of two size fractions of planktonic Bacteria, Archaea and microbial eukaryotes along environmental gradients in the Great Whale River (GWR), Canada. This large subarctic river drains an extensive watershed that includes areas of thawing permafrost, and discharges into southeastern Hudson Bay as an extensive plume that gradually mixes with the coastal marine waters. The microbial communities differed by size-fraction (separated with a 3-μm filter), and clustered into three distinct environmental groups: (1) the GWR sites throughout a 150-km sampling transect; (2) the GWR plume in Hudson Bay; and (3) small rivers that flow through degraded permafrost landscapes. There was a downstream increase in taxonomic richness along the GWR, suggesting that sub-catchment inputs influence microbial community structure in the absence of sharp environmental gradients. Microbial community structure shifted across the salinity gradient within the plume, with changes in taxonomic composition and diversity. Rivers flowing through degraded permafrost had distinct physicochemical and microbiome characteristics, with allochthonous dissolved organic carbon explaining part of the variation in community structure. Finally, our analyses of the core microbiome indicated that while a substantial part of all communities consisted of generalists, most taxa had a more limited environmental range and may therefore be sensitive to ongoing change.

SI-STSAR-7: A Large SAR Images Dataset with Spatial and Temporal Information for Classification of Winter Sea Ice in Hudson Bay

Remote sensing satellites have been broadly applied to sea ice monitoring. The substantial increase in satellite imagery provides a large amount of data support for deep learning methods in the sea ice classification field. However, there is a lack of public remote sensing datasets to facilitate sea ice classification with spatial and temporal information and to benchmark the deep learning methods. In this paper, we provide a labeled large sea ice dataset derived from time-series sentinel-1 SAR images, dubbed SI-STSAR-7, and a validated dataset construction method for sea ice classification research. The SI-STSAR-7 dataset includes seven different sea ice types corresponding to different sea ice development stages in Hudson Bay during winter, and its samples are time sequences of SAR image patches in order to embody the differences of backscattering intensity and textures between different sea ice types, as well as the change of sea ice with time. We construct the dataset by first performing noise reduction and mitigation of incidence angle dependence on SAR images, and then producing data samples and labeling them based on our proposed sample-producing principles and the weekly regional ice charts provided by Canadian Ice Service. Three baseline classification methods are developed on SI-STSAR-7 to establish benchmarks, which are evaluated with accuracy and kappa coefficient. The sample-producing principles are verified through experiments. Based on the experimental results, sea ice classification can be implemented well on SI-STSAR-7.

The annual cycle for whimbrel populations using the Western Atlantic Flyway

Many long-distance migratory birds use habitats that are scattered across continents and confront hazards throughout the annual cycle that may be population-limiting. Identifying where and when populations spend their time is fundamental to effective management. We tracked 34 adult whimbrels (Numenius phaeopus) from two breeding populations (Mackenzie Delta and Hudson Bay) with satellite transmitters to document the structure of their annual cycles. The two populations differed in their use of migratory pathways and their seasonal schedules. Mackenzie Delta whimbrels made long (22,800 km) loop migrations with different autumn and spring routes. Hudson Bay whimbrels made shorter (17,500 km) and more direct migrations along the same route during autumn and spring. The two populations overlap on the winter grounds and within one spring staging area. Mackenzie Delta whimbrels left the breeding ground, arrived on winter grounds, left winter grounds and arrived on spring staging areas earlier compared to whimbrels from Hudson Bay. For both populations, migration speed was significantly higher during spring compared to autumn migration. Faster migration was achieved by having fewer and shorter stopovers en route. We identified five migratory staging areas including four that were used during autumn and two that were used during spring. Whimbrels tracked for multiple years had high (98%) fidelity to staging areas. We documented dozens of locations where birds stopped for short periods along nearly all migration routes. The consistent use of very few staging areas suggests that these areas are integral to the annual cycle of both populations and have high conservation value.

Parameter Estimation to Improve Coastal Accuracy in a Global Tide Model

Global tide and surge models play a major role in forecasting coastal flooding due to extreme events or climate change. The model performance is strongly affected by parameters such as bathymetry and bottom friction. In this study, we propose a method that estimates bathymetry globally and the bottom friction coefficient in the shallow waters for a Global Tide and Surge Model (GTSMv4.1). However, the estimation effect is limited by the scarcity of available tide gauges. We propose to complement sparse tide gauges with tide time-series generated using FES2014. The FES2014 dataset outperforms GTSM in most areas and is used as observations for the deep ocean and some coastal areas, such as Hudson Bay/Labrador, where tide gauges are scarce but energy dissipation is large. The experiment is performed with a computation and memory efficient iterative parameter estimation scheme applied to Global Tide and Surge Model (GTSMv4.1). Estimation results show that model performance is significantly improved for deep ocean and shallow waters, especially in the European Shelf directly using the CMEMS tide gauge data in the estimation. GTSM is also validated by comparing to tide gauges from UHSLC, CMEMS, and some Arctic stations in the year 2014.

Atmospherically forced sea-level variability in western Hudson Bay, Canada

In recent years, significant trends toward earlier breakup and later freeze-up of sea ice in Hudson Bay have led to a considerable increase in shipping activity through the Port of Churchill, which is located in western Hudson Bay and is the only deep-water ocean port in the province of Manitoba. Therefore, understanding sea-level variability at the port is an urgent issue crucial for safe navigation and coastal infrastructure. Using tidal gauge data from the port along with an atmospheric reanalysis and Churchill River discharge, we assess environmental factors impacting synoptic to seasonal variability of sea level at Churchill. An atmospheric vorticity index used to describe the wind forcing was found to correlate with sea level at Churchill. Statistical analyses show that, in contrast to earlier studies, local discharge from the Churchill River can only explain up to 5 % of the sea-level variability. The cyclonic wind forcing contributes from 22 % during the ice-covered winter–spring season to 30 % during the ice-free summer–fall season due to cyclone-induced storm surges generated along the coast. Multiple regression analysis revealed that wind forcing and local river discharge combined can explain up to 32 % of the sea-level variability at Churchill. Our analysis further revealed that the seasonal cycle of sea level at Churchill appears to be impacted by the seasonal cycle in atmospheric circulation rather than by the seasonal cycle in local discharge from the Churchill River, particularly post-construction of the Churchill River diversion in 1977. Sea level at Churchill shows positive anomalies for September–November compared to June–August. This seasonal difference was also revealed for the entire Hudson Bay coast using satellite-derived sea-level altimetry. This anomaly was associated with enhanced cyclonic atmospheric circulation during fall, reaching a maximum in November, which forced storm surges along the coast. Complete sea-ice cover during winter impedes momentum transfer from wind stress to the water column, reducing the impact of wind forcing on sea-level variability. Expanding our observations to the bay-wide scale, we confirmed the process of wind-driven sea-level variability with (i) tidal-gauge data from eastern Hudson Bay and (ii) satellite altimetry measurements. Ultimately, we find that cyclonic winds generate sea-level rise along the western and eastern coasts of Hudson Bay at the synoptic and seasonal timescales, suggesting an amplification of the bay-wide cyclonic geostrophic circulation in fall (October–November), when cyclonic vorticity is enhanced, and Hudson Bay is ice-free.

Assessing spatial segregation of beluga whales (Delphinapterus leucas) in Western Hudson Bay estuaries

Segregation of older adult males from females and immature males is known to occur in some beluga whale populations, but it is unclear if adults accompanied by calves segregate in Hudson Bay, where the largest summering population is found. Using imagery from a photographic aerial survey conducted in August 2015, we considered a number of environmental variables that might explain distribution by age class of beluga near two of three main estuaries (Churchill and Seal River) used by Western Hudson Bay belugas in the summer season. Belugas were identified and classified by age manually using an identification decision tree and GPS coordinates were plotted in ArcGIS. Distribution by age class was examined in relation to distance to coastal habitat and bathymetry to test the predation risk hypothesis, sea surface temperature (thermal advantage hypothesis), and extent of river plume (forge-selection hypothesis). Habitat characteristics and the proportion of age classes in both estuaries were similar between age class groups (with and without calves) indicating no segregation and suggesting the environmental data assessed were not driving patterns of distribution and density of age classes at the spatial and temporal scale being investigated. Results provide a greaterunderstanding of spatial patterns of beluga whale habitat use in western Hudson Bay and information useful in conservation and management advice.