Scottish Government

Microbes play a central role in the degradation of chemical pollutants in the oceans, yet most studies investigating this are conducted in a laboratory setting which does not accurately portray and account for the prevailing physical and geochemical conditions in the deep sea. The Faroe-Shetland Channel (FSC) is a deep water, sub-Arctic region to the north of Scotland with >40-yr history of oil exploration and production. Here, we investigate the bacterioplankton community response to crude oil by enclosing water samples from ∼500m depth in dialysis bags and incubating them in-situ to account for the role of environmental conditions. Using barcoded-amplicon 16S rRNA sequencing, the community strongly and rapidly responded to the oil within 4days, particularly dominated by members of the genera Pseudoalteromonas, Alcanivorax, Mesonia, Alteromonas, and to a lesser extent Sulfitobacter, Vibrio and Thalassospira. Intriguingly, typical psychrophilic oil-degraders like Colwellia and Oleispira were not enriched, possibly due to being outcompeted by better adapted, more 'aggressive' hydrocarbon-degraders at this water depth. Using cultivation-based methods coupled with DNA-based stable isotope probing (DNA-SIP), we identified a diversity of oil-degraders, some of which (i.e. Mesonia, Spongiispira, Stutzerimonas, Vreelandella, Sulfitobacter, Paraglaciecola) had not hitherto been found in sequencing surveys or confirmed as oil-degraders in the FSC. Collectively, we show for the first time the presence of a diverse hydrocarbon-degrading community in the FSC that 'stands at the ready' to consume oil that may become entrained within the subsurface at depth in the event of a spill in this region.

In contrast to chemical pest control, biological control (biocontrol) is generally considered evolutionarily stable, with pests rarely evolving resistance to agents such as parasitoid wasps. In 1997, Holt & Hochberg outlined five principles to explain this pattern. Here, we review post-1997 case studies where resistance has contributed to the breakdown of parasitoid-based biocontrol. We evaluate how these examples align with or challenge Holt & Hochberg's framework and propose updates to reflect new findings. While resistance remains rare, reported cases suggest that breakdowns are more likely when pests possess greater standing genetic variation than their parasitoid enemies. We argue that long-term stability depends not just on host constraints but also on the potential for parasitoid virulence to evolve. Finally, we offer practical recommendations for biocontrol practitioners and regulators to minimise the risk of resistance evolution in parasitoid-based systems.

High-resolution coastal ocean modelling is essential for understanding and managing complex coastal systems under increasing environmental and socio-economic pressures. We present the development of an unstructured FVCOM numerical model for the Shetland and Orkney archipelagos in the north of Scotland, with a hindcast run covering a 30-year period at unprecedented high resolution (~70m around the Shetland coast and hourly output), nested in the Scottish Shelf Model and fully-forced with 5.5km CERRA atmospheric data at hourly frequency. The unstructured grid allows to resolve the complex coastline and bathymetry that characterizes these areas. This region is paramount for the aquaculture industry, with Shetland alone making up for more than 20% of the Scottish salmon and more than 80% of Scottish mussel production, yet its energetic circulation, complex bathymetry, and strong coastal–ocean interactions make monitoring and prediction of potential environmental impacts particularly challenging. Combining numerical modelling with data science tools, we explore the system variability and complexity. This allows the identification of emergent patterns, dominant modes and changes that may otherwise be overlooked. Our work helps supporting more effective long-term monitoring and sustainable use of marine resources in a region increasingly affected by climate change.

Objective: The paper aims to model Health-and-Social-Care (H&Sc) services as Cause–Effect (CE) groups within a Bayesian framework, using cross- and self-causation dynamics. The contribution is that the data used are open and never studied before and are posted online by National Health Services Scotland (NHSS). Method and Material: The paper contributes to the ongoing discourse on how Machine Learning(ML), and Bayesian inference, can inform the purposes of policymaking. Cause–effect relationships and Bayesian methods can associate public services as causes-effects, through suitable likelihood functions and priors that binomial and normal distributions can imlplement. The study identified the optimal predictive distribution using the Maximum-a-Posteriori (MAP) estimation method. Moreover, the CE-matrix approach, enables the representation of multiple causes linked to a single effect-target in a tabular format, that facilitates interpretability and prediction. Results: The findings indicate that services related to 'Alcohol' can be predictors of other effect-services, while home-based services were identified as causes of subsequent hospital admissions. Moreover, low-demand services were observed in earlier years, particularly those with no records after 1997, whereas higher-demand services were newly introduced in later years. These findings may offer insights into latent inter-service relationships, and inform policy development. The cross- and self-causation in a Bayesian framework, determined that the posterior can be predicted by 5 to 10 previous observations and this is significantly affected by the level of zero-padding (percentage of past no-records). In later years, the CE models yield more probable demand patterns. Cause–effect relationships were identified between smoking-related services, mental-health support, and the epidemiological index of Primary-1-Education children's Body-Mass-Index (BMI). Conclusions: The conclusions drawn from this analysis may be particularly relevant for insurance providers and public policymakers, who can leverage Bayesian CE-linked service models for long-term care planning, especially for elderly and low-income populations. The validation of ser-vice interlinkages further enhances the potential for precise and efficient resource allocation.

Abstract Black‐legged kittiwake (Rissa tridactyla) populations are in decline globally, including in Britain and Ireland, which support internationally important numbers. Although kittiwakes typically nest on coastal cliffs, they are known to colonise anthropogenic structures, including offshore oil and gas platforms. Lack of monitoring of offshore platform colonies represents a potentially important gap in our understanding of kittiwake population size and dynamics. The presence of nesting kittiwakes on oil and gas platforms presents a practical challenge for operators in relation to decommissioning and re‐purposing works. We use preliminary data from surveys of colonies on offshore oil and gas platforms to speculate on the scale of platform colonisation by kittiwakes and the potentially significant contribution this may make to the wider population. Practical Implication: We identify several research priorities to help address important evidence gaps and inform management strategies: (1) regular monitoring of offshore populations to inform national population censuses, (2) improved understanding of the ecological drivers of offshore breeding including selection of nesting sites and foraging habitats, fitness benefits and connectivity with onshore colonies and (3) collaboration with platform operators to develop practical approaches to minimise impacts of essential operational work on nesting kittiwakes.

ObjectivesThe census is among the richest available population-scale, individual-level datasets. Scotland’s Integrated Demographic Dataset (SIDD) is a population-scale individual-level dataset using only administrative data. The Administrative Data Based Ethnicity Dataset (ABED) is based on the SIDD and includes ethnicity information. MethodsSeveral administrative datasets were linked together, including those from health, education and the electoral register. The records were resolved into individuals to produce the Administrative Data Record Set. This was trimmed down using activity-based business rules to a set of individuals (the SIDD) believed to be usual residents on the reference date. The SIDD was linked to the census dataset to test its quality. Ethnicity information from the linked administrative datasets was added to the SIDD to produce the ABED. In cases where ethnicity information is not available, this was supplemented with ethnicity information from the 2011 census dataset. ResultsThe SIDD is broadly comparable to the census dataset. 95.6% of census records appear on the SIDD, while 84.7% of SIDD records appear on the census. 89.4% of individuals appear at the same address on the census and the SIDD. 98.2% appear in the same local authority, although this is lower for individuals in their 20s. LA differences are not unbiased, e.g. there are more people in their late 20s in a suburban LA on the SIDD and another LA on the census, than vice versa. When using only the currently available linked administrative data, a stated ethnicity can be found for 58.2% of individuals. When administrative data is combined with the Census 2011 data, a stated ethnicity can be found for 76.6% of individuals. ConclusionThe SIDD and ABED are large datasets, approximately covering the Scottish population, with the SIDD accurately capturing most individuals. Administrative data can provide substantially more population coverage for ethnicity than could be achieved using surveys, but lower than what could be provided by a census.

• We propose a Global Coastal Ocean Climate Model Intercomparison Project (GCO-MIP). • We explore the modelling challenges of coastal ocean climate downscaling. • Motivated by societal drivers e.g. ecosystems, coastal hazards and marine economy. • Downscaling crosses scales from global to regional to subregional and local. Resilient coastal communities and sustainable marine economies require actionable knowledge to plan for and adapt to emerging and potential future climate change, particularly in relation to ecosystem services and coastal hazards. Such knowledge necessarily draws heavily on coastal ocean modelling of future climate impacts, using a great diversity of both global and regional approaches to explore multiple societal challenges in coastal and shelf seas around the world. In this paper, we explore the challenges, solutions and benefits of developing a better coordinated and global approach to future climate impacts modelling of the coastal ocean, in the context of the UN Decade of Ocean Science for Sustainable Development project Future Coastal Ocean Climates (FLAME; part of the CoastPredict programme). Particularly, we address the need for diverse modelling approaches to meet different societal challenges, how regions can be harmonised through clustering and typology approaches, and how coordination of experimental designs can promote a better understanding of uncertainties and regional responses. Improved harmonisation of future climate impact projections in the global coastal ocean would allow sectoral and cross-sectoral global scale risk assessments, improve process understanding and help build capacity in under-represented areas such as the global south and small island developing states. We conclude with a proposed framework for a Global Coastal Ocean Model Intercomparison Project.

There is an increasing reliance on passive acoustic monitoring (PAM) as a cost-effective method for monitoring cetaceans, necessitating robust and efficient automated tools for extracting species presence. This work compares two methods, one based on the 'off-line' analysis of raw PAM data, using Convolutional Neural Networks (CNNs), and the second based on in-situ detections, implemented within the C-POD. The C-POD is a rapid, low-cost choice for monitoring of odontocetes, while CNNs, requiring large efforts to train, are gaining traction within bioacoustics as they offer performance benefits above standard detection and classification tools. This work represents the first empirical comparison of a C-POD with a system using a CNN on recorded raw acoustic data for monitoring delphinids. The comparison is based on 3000 hours of PAM data, collected off the west coast of Scotland, using a collocated C-POD and SoundTrap acoustic recorder. Results show that the system using a CNN achieves an overall accuracy of 0.82, and an effectiveness (F1-Score) of 0.78 as a click detector, whilst the C-POD achieves scores of 0.71 and 0.62, respectively. The method employing a CNN provides a lower missed detection rate, with the C-POD failing to detect > 90% delphinid positive hours at one focal site. However, the C-POD offered a lower false-positive rate across all analysis sites. This work highlights the importance of incorporating the right automated tools for long-term species monitoring, as the C-POD offers high precision rates for click detections, while the CNN based system provides a robust approach to identifying seasonal and diurnal trends in long-term dolphin occurrence.

In the published article, there was an error in the legend for Supplementary material Table S1 Step 3 as published. Where acknowledgement of the work was omitted from the legend caption. The corrected legend appears below.For the cluster analysis the chlorophyll signal from satellite data was decomposed into an interannual signal, a seasonal signal and a residual signal. The interannual signal can indicate long-term trends or regime shifts. The seasonal signal is an indication whether the blooms occur each year systematically in the same season or not. The residual signal gives an indication of the remaining variability and can give an indication of strongly varying conditions between years and seasons. With a statistical analysis using the various signals, Areas with similar phytoplankton dynamics in satellite data (courtesy of Xavier Desmit and Dimitry Van der Zande, unpublished results)" can be identified and merged (Figure A.3.1). Largely similar areas appeared in an analysis of patterns in primary production derived from satellite data.The authors apologize for this error and state that this does not change the scientific conclusions of the article in any way. The original article has been updated.