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Climate Change, Fisheries Management, and Increases in Demersal Fish Distribution in a Southern Ocean Biodiversity Hotspot
No full textThe world's oceans and their biodiversity are undergoing change driven by climate change and anthropogenic impacts such as fishing. The Kerguelen Plateau is a biodiversity hotspot with many endemic fish species; furthermore, the region has economic importance in supporting valuable fisheries. This region is also a climate change hotspot with known notable changes in the location of the polar front, ocean currents, and primary productivity. In this study, we use data from long‐term scientific trawl surveys and contemporary joint species distribution models to understand how the demersal fish assemblage of the Kerguelen Plateau has changed through time and space. The modelling indicates that most demersal species have had notable changes in their occurrence and CPUE from 2003 to 2016. This included a significant increase in species richness throughout the study period. The modelling also provides novel insights into the depth, climatic, and environmental preferences for all species, including many species that were previously data‐limited. It is unclear whether these changes reflect shifts in the fishery management or the effects of climate change, but most likely a combination of all. We also found evidence of several species' distributions responding to temperature variability, with species being exposed to the ongoing impacts of climate change. These findings will be used by managers and policy makers to inform climate change risk assessments, supporting future‐decision making to ensure the continuation of sustainable fisheries and the protection of biodiversity
Creating woodland through natural processes: current understanding and knowledge gaps in Great Britain
Full text link•1. Creating woodlands through natural processes, as opposed to traditional tree planting, is expected to result in more structurally diverse, locally adapted woodlands that enhance the resilience of existing treescapes. However, the outcomes of natural colonisation can be variable, and there is still considerable uncertainty around the ecological processes involved.
•2. To address knowledge gaps and guide a future research and policy agenda, we synthesise current knowledge of the ecology of natural colonisation in Great Britain. We combine expertise from 31 practitioners and researchers spanning varied British contexts, including insights from 15 case studies and an expert survey on the relative importance of ecological factors influencing natural colonisation.
•3. The most important determinants of successful natural colonisation, identified by practitioners and researchers, were the availability of seed sources and low levels of herbivory. However, key knowledge gaps remain around the timeframe and trajectory of woodland development and appropriate management practices. Natural colonisation and tree planting can be combined to meet diverse woodland objectives, but this has been little explored to date.
•4. Solutions . Land managers and advisors face uncertainty and many knowledge gaps when creating woodland through natural processes. Site monitoring and adaptive management can help meet site objectives that, in turn, can be supported by policies reflecting uncertainties in the process. Collaboration between researchers and land managers to monitor woodland development, use experimental approaches and share knowledge will help further applied ecological understanding, supporting informed decision‐making by land managers
Estimating the variability of deep-ocean particle flux collected by sediment traps using satellite data and machine learning
Full text linkThe gravitational pump plays a key role in the ocean carbon cycle by exporting sinking organic carbon from the surface to the deep ocean. Deep sediment trap time series provide unique measurements of this sequestered carbon flux. Sinking particles are influenced by physical short-term spatio-temporal variability, which inhibits the establishment of a direct link to their surface origin. In this study, we present a novel machine learning tool, designated as U-NetSST−SSH, which is capable of predicting the catchment area of particles captured by sediment traps moored at a depth of 3000 m above the Porcupine Abyssal Plain (PAP) based solely on surface data. The machine learning tool was trained and evaluated using Lagrangian experiments in a realistic CROCO numerical simulation. The conventional approach of assuming a static 100–200 km box over the sediment trap location only yields an average prediction for ∼25 % of the source region, whilst U-NetSST−SSH predicts ∼50 %. U-NetSST−SSH was then applied to satellite observations to create a 20-year catchment area dataset, which demonstrates a stronger correlation between the PAP site deep particle fluxes and surface chlorophyll-a concentration compared with the conventional approach. However, predictions remain highly sensitive to the local deep dynamics which are not observed in surface ocean dynamics. The improved identification of the particle source region for deep-ocean sediment traps can facilitate a more comprehensive understanding of the mechanisms driving the export of particles from the surface to the deep ocean, a key component of the biological carbon pump
Negative sensitivity of Southern Ocean Circumpolar Transport to increased wind stress controlled by residual overturning
Full text linkThe transport of the Southern Ocean’s Antarctic Circumpolar Current, closely linked to the global stratification to the north and in turn the inter-hemispheric overturning circulation, is a key metric for quantifying ocean circulation. Understanding the sensitivity of transport to changes in forcing is important in understanding the role of the Southern Ocean in past, present and future climates. Here, we report on an investigation of a negative sensitivity regime, whereby the circumpolar transport decreases with increasing wind forcing, a phenomenon previously reported in ocean modelling investigations where the residual overturning circulation is oriented opposite to the present-day configuration. The present study finds that this negative sensitivity is a subtle effect resulting from both eddy saturation and a negative residual overturning circulation, the latter referring to a poleward mass flux in the warm surface layers. The work provides an examination and rationalisation of the sensitivities relating to the Southern Ocean circumpolar transport, and additionally touches on a numerical methodology that is particularly adept for the study of equilibrium sensitivities, with implications for analogous explorations in the paleoclimate context
Alpine influences on Scottish geology: sketches by Albert Heim in Henry Cadell’s 1894 notebook
Full text linkIn 1894 the Scottish geologist and industrialist Henry Moubray Cadell (1860–1934) visited Switzerland and met with the doyen of Alpine geology, Professor Albert Heim (1849–1937). Cadell may have arranged his trip to coincide with the 6th International Geological Congress, held that year in Zurich. Heim played a leading role in the organisation of the Congress and led a field excursion to the Glarus region, for the geological structure of which he had a controversial interpretation. Whatever the circumstances, that the two men discussed details of Alpine structural geology is made clear by sketches that Heim drew in Cadell’s notebook (Figure 1), recently rediscovered in section 5381.70 of the extensive Cadell archive held by the National Library of Scotland. Some of the sketches suggest fascinating links with contemporary developments in Scottish geology
The First Three‐Dimensional Electrical Resistivity Model of the Lithosphere Beneath Britain
Full text linkMagnetotelluric data provide unique information to study the electrical resistivity of the Earth's lithosphere, enabling studies of geological structures, tectonic processes, resource exploration, and hazard monitoring. Here, we present the first fully three-dimensional (3D) electrical resistivity model of the deep lithosphere beneath Britain (BERM-2024), derived from the inversion of long-period magnetotelluric data at 69 MT sites, incorporating recently acquired data along with selected legacy data sets. Rigorous testing of the prior model design and inversion smoothing parameters led to a robust and geologically meaningful model. The model reveals significant lateral and vertical variation, with shallow conductive anomalies correlating with sedimentary basins in western Britain, such as the Cheshire Basin and the Welsh Massif, while resistive anomalies are related to granitic plutons in the Scottish Highlands and Cornwall. At mid-crustal to upper mantle depths, strong resistivity contrasts coincide with major faults that bound distinct tectono-stratigraphic terranes, including a clear signature of the Southern Uplands Fault separating the conductive Southern Uplands Terrane from the less conductive Midland Valley Terrane. A newly imaged, deep conductive anomaly (85–140 km) is detected beneath the West Midlands region. Beyond the geological insights, resistivity models are key for studying space weather impacts on ground-level infrastructure. We model geoelectric fields for the geomagnetic storm of 10 October 2024 using our model, demonstrating high correlation with measured electric fields at Eskdalemuir magnetic observatory (ESK), although amplitude discrepancies remain. This work establishes a foundation for future geophysical and geohazard studies and underscores the need for continued magnetotelluric data acquisition across Britain
A systematic review of machine learning models for groundwater level prediction
Full text linkThis study presents a comprehensive synthesis of machine learning (ML) techniques applied to groundwater level (GWL) prediction, focusing on model architectures, feature selection methods, hyperparameter tuning, optimization algorithms, and clustering techniques. A total of 223 peer-reviewed articles were systematically reviewed using the PRISMA framework to guide study identification, inclusion, and exclusion. Widely used models include artificial neural networks (ANN), support vector machines (SVM), long short-term memory networks (LSTM), and random forests (RF). More recent studies increasingly employ hybrid approaches that integrate wavelet transforms, signal decomposition, and optimization techniques such as particle swarm optimization (PSO), genetic algorithms (GA), and ant colony optimization (ACO). Transformer-based models have also begun to emerge as promising tools in this domain. A central focus of this review is feature selection, which remains one of the most underdeveloped areas in GWL modeling. Most studies rely on simple filter methods like autocorrelation and mutual information. While SHapley Additive exPlanations (SHAP) has gained some traction, more advanced techniques, such as recursive feature elimination (RFE), forward feature selection (FFS), factor analysis (FA), and self-organizing maps (SOM), are rarely used. Notably, no study systematically compared multiple feature selection strategies, limiting insights into their impact on model performance. Scientometric analysis shows that Iran, China, India, and the United States contribute the most impactful research. Despite strong predictive outcomes, trial-and-error remains the dominant approach to hyperparameter tuning. The review emphasizes the need for more systematic, interpretable, and generalizable ML approaches to support robust groundwater level (GWL) forecasting
Atmospheric patterns drive marine heatwaves in the North Atlantic and Mediterranean Sea during summer 2023
Full text linkThe year 2023 experienced record-breaking marine heatwaves (MHWs) across the North Atlantic and Mediterranean Sea, contributing to the highest global surface air and sea surface temperatures (SSTs) on record. These events were exceptional in intensity, persistence, and spatial extent, reflecting the combined influence of anthropogenic warming, short-term climate modes and complex atmosphere-ocean interactions. This study investigates the large-scale atmospheric drivers behind these extremes using NOAA OISST v2.1 and ERA5 reanalysis datasets. We characterize MHWs from May to August 2023, analyze surface and mid-tropospheric anomalies in pressure, air temperature, wind and air–sea heat flux and apply regularized generalized canonical correlation analysis (RGCCA) to study multivariate links between atmospheric variability and MHW characteristics. Our findings show that the Subtropical Atlantic experienced the longest MHW, the Northwest Atlantic the most intense, and the Western Mediterranean the most frequent events. The summer North Atlantic Oscillation (NAO) and Scandinavian Pattern (SCAN) emerged as key modulators of MHWs. Compound configurations of NAO − /SCAN + in July-August and NAO + /SCAN − in May-June generated persistent atmospheric ridges and weakened the Azores High, which in turn suppressed winds, altered heat fluxes and mixed layer depths, and promoted stratification—leading to sustained surface warming. The leading RGCCA mode explains more than 40% of the SSTA variability and shows statistically robust correlations ( r = 0.81–0.94) between atmospheric drivers and MHW evolution. This multivariate approach demonstrates how teleconnection patterns co-modulate regional MHW dynamics, underscoring the importance of compound atmospheric influences. Our results highlight the utility of RGCCA in diagnosing complex climate extremes and support the integration of large-scale atmospheric indicators into early warning systems and adaptation planning in the face of increased marine heat stress
NutGEnIE 1.0: nutrient cycle extensions to the cGEnIE Earth system model to examine the long-term influence of nutrients on oceanic primary production
Full text linkUnderstanding the nuances of the effects of nutrient limitation on oceanic primary production has been the focus of many bioassay experiments by oceanographers. A theme of these investigations is that they identify the currently limiting nutrient at a given location, or in other words they identify the proximate limiting nutrient (PLN). However, the ultimate limiting nutrient (ULN; the nutrient whose supply controls system productivity over extensive timescales) can be different from the PLN. Our motivation is to investigate the identity of the ULN. The ULN constrains oceanic primary production over extensive timescales and consequently overall ocean fertility. The rate of oceanic photosynthesis affects planetary oxygen and carbon dioxide, impacting climate. Understanding past ocean fertility is fundamental to understanding Earth system history and biological evolution.
Investigations that have considered the ULN have often utilised box models for example the work of, Tyrrell (1999) and Lenton and Watson (2000). To facilitate investigation of the ULN the carbon-centric Grid Enabled Integrated Earth system model (cGEnIE) nutrient cycles have been extended to create NutGEnIE. NutGEnIE incorporates three open nutrients cycles nitrogen, phosphorus, and iron. The impacts of diazotrophs, capable of fixing nitrogen, are represented alongside those of other phytoplankton. NutGEnIE is capable of extended duration model simulations necessary to investigate the ULN while, at the same time, including iron as a potentially limiting nutrient. NutGEnIE is described here, with particular focus on the biogeochemical cycles of iron, nitrogen and phosphorus. Model results are compared to ocean observational data to assess the degree of realism. Model-data comparisons include physical properties, nutrient concentrations, and process rates (e.g., export and nitrogen fixation). The comparisons of NutGEnIE to ocean observational data are largely positive, suggesting that the dynamics of NutGEnIE are valid. The validations, allied to the ability to run an Earth System model with open nutrients cycles of nitrogen, phosphorus, and iron over extensive time periods supports the proposed use of NutGEnIE to revisit the question of the ULN for oceanic primary production
Deep-sea biotope classification using opportunistic sampling: insights for future management
Full text linkAn iterative approach to optimise deep-sea biotope classification using a combination of acoustic data and Remotely Operated Vehicle (ROV) video footage was developed and tested at the Tropic Seamount site in the Northeast Atlantic. Two methods for biotope classification were compared: a top-down approach based on acoustic substrate classification followed by biological characterisation, and a bottom-up approach using multivariate analysis of biological assemblages only. Video transects were analysed at two spatial resolutions (200 m and 50 m segments) to assess scale effects on biotope delineation. Biotopes were classified using a combination of geological and biological data with each biotope representing a distinct combination of substrate types and their associated benthic assemblages. The bottom-up approach using 50 m segments identified 12 distinct biotopes with stronger environmental correlations compared to broader classifications at 200 m scale. This study demonstrates that shorter transects (50 m) combined with bottom-up sampling approaches are preferable for capturing the ecological heterogeneity characteristic of deep-sea seamount environments, with important implications for vulnerable marine ecosystem identification and spatial management
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