Predicting the Impact of Climate Change on Threatened Species in UK Waters

Global climate change is affecting the distribution of marine species and is thought to represent a threat to biodiversity. Previous studies project expansion of species range for some species and local extinction elsewhere under climate change. Such range shifts raise concern for species whose long-term persistence is already threatened by other human disturbances such as fishing. However, few studies have attempted to assess the effects of future climate change on threatened vertebrate marine species using a multi-model approach. There has also been a recent surge of interest in climate change impacts on protected areas. This study applies three species distribution models and two sets of climate model projections to explore the potential impacts of climate change on marine species by 2050. A set of species in the North Sea, including seven threatened and ten major commercial species were used as a case study. Changes in habitat suitability in selected candidate protected areas around the UK under future climatic scenarios were assessed for these species. Moreover, change in the degree of overlap between commercial and threatened species ranges was calculated as a proxy of the potential threat posed by overfishing through bycatch. The ensemble projections suggest northward shifts in species at an average rate of 27 km per decade, resulting in small average changes in range overlap between threatened and commercially exploited species. Furthermore, the adverse consequences of climate change on the habitat suitability of protected areas were projected to be small. Although the models show large variation in the predicted consequences of climate change, the multi-model approach helps identify the potential risk of increased exposure to human stressors of critically endangered species such as common skate (Dipturus batis) and angelshark (Squatina squatina)

Climate change alters fish community size-structure, requiring adaptive policy targets

Size‐based indicators are used worldwide in research that supports the management of commercially exploited wild fish populations, because of their responsiveness to fishing pressure. Observational and experimental data, however, have highlighted the deeply rooted links between fish size and environmental conditions that can drive additional, interannual changes in these indicators. Here, we have used biogeochemical and mechanistic niche modelling of commercially exploited demersal fish species to project time series to the end of the 21st century for one such indicator, the large fish indicator (LFI), under global CO2 emissions scenarios. Our modelling results, validated against survey data, suggest that the LFI's previously proposed policy target may be unachievable under future climate change. In turn, our results help to identify what may be achievable policy targets for demersal fish communities experiencing climate change. While fisheries modelling has grown as a science, climate change modelling is seldom used specifically to address policy aims. Studies such as this one can, however, enable a more sustainable exploitation of marine food resources under changes unmanageable by fisheries control. Indeed, such studies can be used to aid resilient policy target setting by taking into account climate‐driven effects on fish community size‐structure

Toward Digitalization of Fishing Vessels to Achieve Higher Environmental and Economic Sustainability

\ua9 2024 The Authors. Published by American Chemical Society. Fishing vessels need to adapt to and mitigate climate changes, but solution development requires better information about the environment and vessel operations. Even if ships generate large amounts of potentially useful data, there is a large variety of sources and formats. This lack of standardization makes identification and use of key data challenging and hinders its use in improving operational performance and vessel design. The work described in this paper aims to provide cost-effective tools for systematic data acquisition for fishing vessels, supporting digitalization of the fishing vessel operation and performance monitoring. This digitalization is needed to facilitate the reduction of emissions as a critical environmental problem and industry costs critical for industry sustainability. The resulting monitoring system interfaces onboard systems and sensors, processes the data, and makes it available in a shared onboard data space. From this data space, 209 signals are recorded at different frequencies and uploaded to onshore servers for postprocessing. The collected data describe both ship operation, onboard energy system, and the surrounding environment. Nine of the oceanographic variables have been preselected to be potentially useful for public scientific repositories, such as Copernicus and EMODnet. The data are also used for fuel prediction models, species distribution models, and route optimization models

Practice of spirometry among physicians caring for children with asthma in Portugal - The EspiroPed survey

info:eu-repo/semantics/publishedVersio

Diffusion-weighted MR imaging findings in an isolated abscess of the clivus

We report the finding of restricted diffusion in an isolated abscess of the clivus and discuss the imaging differential diagnosis, with an emphasis on the usefulness of diffusion-weighted imaging

Primary spinal glioblastoma: A case report and review of the literature

Primary spinal glioblastoma (GBM) is a rare disease, with an aggressive course and a poor prognosis. We report a case of a 19-year-old male with a 4-week history of progressive weakness in both lower limbs, which progressed to paraparesis with a left predominance and difficulty in initiating urination over a week. Spine magnetic resonance imaging (MRI) showed an intramedullary expansile mass localised between T6 and T11. We performed a laminotomy and laminoplasty between T6 and T11 and the tumour was partially removed. Histopathological study was compatible with GBM. The patient was administered focal spine radiotherapy with chemotherapy with temozolamide. Serial MRI performed after the initial surgery demonstrated enlargement of the enhancing mass from T3 to T12 and subarachnoid metastatic deposits in C2 and C4, the pituitary stalk, interpeduncular cistern, left superior cerebellar peduncle and hydrocephalus. We review the literature with regard to the disease and treatment options, and report the unique features of this case. Primary spinal GBM is an extremely rare entity with a poor prognosis and a short survival time. An aggressive management of the different complications as they arise and improvement of current modes of treatment and new treatment options are required to improve survival and ensure better quality of life

Giant cell glioblastoma: review of the literature and illustrated case

Giant cell glioblastoma is an infrequent variety of glioblastoma (5% of the cases). It has deserved a separate category in the World Health Organization classification of grade IV tumors. The clinical, imaging, histological and immunohistochemical characteristics, and the genetic alterations are reviewed. Treatment and prognosis are discussed and updated. The case of a patient that survived 19 months and died of spinal leptomeningeal metastases is illustrated

Evaluating machine-learning techniques for recruitment forecasting of seven North East Atlantic fish species

The effect of different factors (spawning biomass, environmental conditions) on recruitment is a subject of great importance in the management of fisheries, recovery plans and scenario exploration. In this study, recently proposed supervised classification techniques, tested by the machine-learning community, are applied to forecast the recruitment of seven fish species of North East Atlantic (anchovy, sardine, mackerel, horse mackerel, hake, blue whiting and albacore), using spawning, environmental and climatic data. In addition, the use of the probabilistic flexible naive Bayes classifier (FNBC) is proposed as modelling approach in order to reduce uncertainty for fisheries management purposes. Those improvements aim is to improve probability estimations of each possible outcome (low, medium and high recruitment) based in kernel density estimation, which is crucial for informed management decision making with high uncertainty. Finally, a comparison between goodness-of-fit and generalization power is provided, in order to assess the reliability of the final forecasting models. It is found that in most cases the proposed methodology provides useful information for management whereas the case of horse mackerel is an example of the limitations of the approach. The proposed improvements allow for a better probabilistic estimation of the different scenarios, i.e. to reduce the uncertainty in the provided forecasts