Sea Ice and Substratum Shape Extensive Kelp Forests in the Canadian Arctic

The coastal zone of the Canadian Arctic represents 10% of the world’s coastline and is one of the most rapidly changing marine regions on the planet. To predict the consequences of these environmental changes, a better understanding of how environmental gradients shape coastal habitat structure in this area is required. We quantified the abundance and diversity of canopy forming seaweeds throughout the nearshore zone (5–15 m) of the Eastern Canadian Arctic using diving surveys and benthic collections at 55 sites distributed over 3,000 km of coastline. Kelp forests were found throughout, covering on average 40.4% (±29.9 SD) of the seafloor across all sites and depths, despite thick sea ice and scarce hard substrata in some areas. Total standing macroalgal biomass ranged from 0 to 32 kg m–2 wet weight and averaged 3.7 kg m–2 (±0.6 SD) across all sites and depths. Kelps were less abundant at depths of 5 m compared to 10 or 15 m and distinct regional assemblages were related to sea ice cover, substratum type, and nutrient availability. The most common community configuration was a mixed assemblage of four species: Agarum clathratum (14.9% benthic cover ± 12.0 SD), Saccharina latissima (13% ± 14.7 SD), Alaria esculenta (5.4% ± 1.2 SD), and Laminaria solidungula (3.7% ± 4.9 SD). A. clathratum dominated northernmost regions and S. latissima and L. solidungula occurred at high abundance in regions with more open water days. In southeastern areas along the coast of northern Labrador, the coastal zone was mainly sea urchin barrens, with little vegetation. We found positive relationships between open water days (days without sea ice) and kelp biomass and seaweed diversity, suggesting kelp biomass could increase, and the species composition of kelp forests could shift, as sea ice diminishes in some areas of the Eastern Canadian Arctic. Our findings demonstrate the high potential productivity of this extensive coastal zone and highlight the need to better understand the ecology of this system and the services it provides.

Kelp in the Eastern Canadian Arctic: Current and Future Predictions of Habitat Suitability and Cover

Climate change is transforming marine ecosystems through the expansion and contraction of species’ ranges. Sea ice loss and warming temperatures are expected to expand habitat availability for macroalgae along long stretches of Arctic coastlines. To better understand the current distribution of kelp forests in the Eastern Canadian Arctic, kelps were sampled along the coasts for species identifications and percent cover. The sampling effort was supplemented with occurrence records from global biodiversity databases, searches in the literature, and museum records. Environmental information and occurrence records were used to develop ensemble models for predicting habitat suitability and a Random Forest model to predict kelp cover for the dominant kelp species in the region – Agarum clathratum, Alaria esculenta, and Laminariaceae species (Laminaria solidungula and Saccharina latissima). Ice thickness, sea temperature and salinity explained the highest percentage of kelp distribution. Both modeling approaches showed that the current extent of arctic kelps is potentially much greater than the available records suggest. These modeling approaches were projected into the future using predicted environmental data for 2050 and 2100 based on the most extreme emission scenario (RCP 8.5). The models agreed that predicted distribution of kelp in the Eastern Canadian Arctic is likely to expand to more northern locations under future emissions scenarios, with the exception of the endemic arctic kelp L. solidungula, which is more likely to lose a significant proportion of suitable habitat. However, there were differences among species regarding predicted cover for both current and future projections. Notwithstanding model-specific variation, it is evident that kelps are widespread throughout the area and likely contribute significantly to the functioning of current Arctic ecosystems. Our results emphasize the importance of kelp in Arctic ecosystems and the underestimation of their potential distribution there.

What and where? Predicting invasion hotspots in the Arctic marine realm

The risk of aquatic invasions in the Arctic is expected to increase with climate warming, greater shipping activity and resource exploitation in the region. Planktonic and benthic marine aquatic invasive species (AIS) with the greatest potential for invasion and impact in the Canadian Arctic were identified and the 23 riskiest species were modelled to predict their potential spatial distributions at pan-Arctic and global scales. Modelling was conducted under present environmental conditions and two intermediate future (2050 and 2100) global warming scenarios. Invasion hotspots—regions of the Arctic where habitat is predicted to be suitable for a high number of potential AIS—were located in Hudson Bay, Northern Grand Banks/Labrador, Chukchi/Eastern Bering seas and Barents/White seas, suggesting that these regions could be more vulnerable to invasions. Globally, both benthic and planktonic organisms showed a future poleward shift in suitable habitat. At a pan-Arctic scale, all organisms showed suitable habitat gains under future conditions. However, at the global scale, habitat loss was predicted in more tropical regions for some taxa, particularly most planktonic species. Results from the present study can help prioritize management efforts in the face of climate change in the Arctic marine ecosystem. Moreover, this particular approach provides information to identify present and future high-risk areas for AIS in response to global warming.

Analysis of Bio-Oracle data

Objective

While running some brief quality control tests on Bio-Oracle layers before using them for a recent project it was detected that some of the layers in the current version of the Bio-Oracle product appear to have very large errors. Specifically the error is that there are layers where the minimum values are greater than the maximum values. It is unclear how this could be possible, so in the following text and code we will look into how we go about investigating these data layers and we will discuss which layers are fine, and which are not. This error was first detected in the current velocity layers but a brief search turned up errors in other layers, too. So in this post we will be going through each individual layer to test for this max less than min error. We will look at all of the different depths as well as the future projections.