Feature photo: The Hornøya cliffs with nesting seabirds, including Kittiwakes, Common Guillemots and possibly Razorbills © Biotope
In their latest research, Sam Hodges and colleagues present a novel solution that may help guide ecosystem management practices by predicting the effects of climate change and yearly variation in sea surface temperature on foraging seabird hotspots in the Barents Sea.
Seabirds have historically been shown to be especially vulnerable to oil spill disasters. Depending on the level of penetration of the toxic oil compounds into the ecosystem, the birds are at risk of poisoning through their food sources through bioaccumulation. Further, direct exposure to oil slick has been shown to foul their wings, preventing flight, and crucially, diving; birds exposed in this way often die of starvation, due to their inability to feed.
This has become a pressing concern for seabird colonies on the coast of East Finmark as the Norwegian and Russian governments have sought to industrialise the Barents Sea, a shallow sea region enclosed by the Arctic Ocean, Norwegian and Kara Seas.
The region has historically been exploited for its abundant fisheries, where three separate Capelin stock collapses ushered in existing agreements and management practices between Norway and Russia. In 2010, the Norwegian and Russian governments stated a change of policy where the Barents Sea would be ‘opened up for investment’ to encourage construction of oil and deep sea mining rigs.
In order to implement mitigation measures, it is crucial for policymakers to understand where the greatest biodiversity hotspots exist in the Barents Sea ecosystem and how these hotspots are influenced by the surrounding ocean. These hotspots can be modelled indirectly by analysing recorded presence of a sample of the population against environmental variables known to contribute to hotspot formation.
In this study, we focus on the role of temperature and temperature anomalies.
Species Distribution Modelling in a fluid environment
Species distribution modelling was originally developed to highlight regions of land to discover new populations belonging to a given species of interest. These models were run under the assumption that the study environment would not undergo significant change in the near future. When considering terrestrial conditions over short timescales (e.g. 5 years), this is a reasonable assumption given that these changes are typically brought about through geological activity, population genetics and extreme events.
However, the physical environment in the oceans is much more dynamic due to fluid physics.
The Barents Sea can be divided into bodies of water with distinct electrochemical and temperature properties, but these bodies are constantly intermixing and being disturbed by current inflow from the North Atlantic and Kara seas. While some physical patterns occur seasonally, there is significant edge variability from day to day, and patterns can vary considerably on a monthly basis.
Assuming a stable short-term environment was clearly impractical for making predictions so we aimed to characterise the physical environment instead, creating a model that could be applied repeatedly to similar seasonal conditions and would adapt the predicted ranges in response to the changing environment.
In practical terms, this involved tracking individual bird paths using geolocation sensors attached to leg rings before mapping these positions to rasters of Sea Surface Temperature (SST) from the HYCOM ocean model. We then carried out MaxENT modelling to generate Habitat Suitability – Temperature curves, which were sorted into monthly groups and characterised in a separate model using the standard gaussian formula. This last model could then be applied to independent temperature data from the same monthly group to discover its Habitat Suitability.
Organism Forecasting and the role of SST Anomalies
This method allowed us to create monthly predicted species distributions that allowed for fluidity since the model is fixed in parameter space rather than real space. Furthermore, it allowed us to apply models directly to independent data, highlighting the possibility for future work designing ‘organism forecast models’.
Our findings confirmed patterns of seasonality in the wintering movements of Auk species which are probably linked with the ebb and flow of major water bodies and current fronts in the Barents Sea. It was interesting to observe distinctive occupancy and habitat suitability patterns emerge from our four study species which is likely a reflection of their feeding preferences.
Furthermore, our study established that basin-wide variation in ocean climate has the potential to complicate SDMs and niche modelling in marine ecology; we found that models based on raw SST performed poorly in predicting future distributions. However, this could be corrected by standardising the SST values against their respective monthly mean. This led us to speculate that the importance of SST variability/anomalies could be related to the organisms sensing temperature gradients during travel, indicative of ocean front formation and corresponding with ecological hotspots at sea.
Management implications
Marine habitat suitability models are important to convey an understanding of the dynamic nature of the seas to policymakers: it is especially important to communicate that regions outside of typical species distributions can have a strong influence over the immediate region of interest.
For example, an oil spill disaster at the western mouth of the Barents Sea could result in oil slick being spread throughout the interior by the prevailing North Atlantic current inflow, so affecting areas of ecological interest through indirect means. Highlighting high-habitat-suitability regions which are not typically inhabited can also be suggestive of secondary habitats, where populations may relocate under increasing environmental pressures: these are ideal for designating secondary conservation sites, alongside the government’s main effort.
It unfortunately remains unlikely that the central Norwegian government’s development plans will undergo significant revision, owing to rising earnings from gas sales resulting from the current energy crisis. It is also unlikely that region-wide conservation plans will be prioritised due to an accelerating deterioration of relations with Russia over the last twenty years, arising chiefly from conflicts over territory within the Barents Sea.
However, regional and local policymakers have previously shown considerable support for translating conservation imperatives into functioning management practices. Continued development of ecological modelling will remain a cornerstone in providing authoritative and scientifically validated advice to these bodies, as well as informing the continuing public debate over Norway’s energy future.
Read the full article: “Predicting the foraging patterns of wintering Auks using a sea surface temperature model for the Barents Sea” in Issue 3:4 of Ecological Solutions and Evidence.
Further Reading
BBC article discussing agreements on the disputed midline of the Barents Sea and industrialisation proposals in 2010.
The Conversation and The Barents Observer articles on industrialisation prospects around the time we were carrying out this research.
Articles fromMarine and Terrestrial Ecology discussing future research to move Habitat Suitability and SDM modelling from 2D to 3D. This is especially helpful for understanding the emerging field of Aeroecology.
SEATRACK/SEAPOP is an umbrella organisation for several research groups working on the seabird ecology and conservation of the Barents Sea region, they list publications and pool data on observed seabird presence, usually from the GLS loggers we describe in the article
