Evaluation of the full set of habitat suitability models for vulnerable marine ecosystem indicator taxa in the South Pacific high seas

\ua9 2024 The Authors. Fisheries Management and Ecology published by John Wiley & Sons Ltd. In the high seas, regional fishery management organisations are required to implement measures to prevent significant adverse impacts on vulnerable marine ecosystems (VMEs). Our objectives were to develop habitat suitability models for use in the spatial management of bottom fisheries in the South Pacific and to evaluate these and existing models using independent data from high-quality seafloor imagery. Presence-only models for seven VME indictor taxa were developed to complement previous modelling. Evaluation of habitat suitability models using withheld data indicated high mean True Skill Statistic scores of 0.44–0.64. Most habitat suitability models performed adequately when assessed with independent data on taxon presence and absence but were poor surrogates for abundance. We therefore advocate caution when using presence-only models for spatial management and call for more systematically collected data to develop abundance models

Independent statistical validation of the New Zealand Seafloor Community Classification

\ua9 2024 The Authors. Aquatic Conservation: Marine and Freshwater Ecosystems published by John Wiley & Sons Ltd. The New Zealand Seafloor Community Classification (NZSCC) is a national-scale numerical community classification which depicts compositional turnover of 1716 taxa (demersal fish, reef fish, benthic invertebrates and macroalgae) classified into 75 groups representing seafloor communities. To ensure the continual use of the NZSCC for spatial planning and reporting, a robust maintenance framework must be set in place; key to this is being able to assess the ability of the classification to represent (discriminate between) different seafloor communities. Here we describe an approach for validating the NZSCC using temporally independent evaluation data for demersal fish and benthic invertebrates (the latter sampled via a different method), which identifies whether the NZSCC represents different seafloor communities (i.e., assesses classification strength), evaluates the underlying statistical model, and considers heterogeneity in environmental coverage and statistical uncertainty. Additionally, the availability of abundance estimates for these evaluation datasets provides an opportunity to test whether the NZSCC—which was developed using presence-absence data—can reflect abundance-weighted seafloor communities. The ANOSIM global R values (measuring classification strength) were 0.53 and 0.46 (and significant at the 1% level) for demersal fish and benthic invertebrates, respectively, indicating that the NZSCC groups define biologically distinctive environments. The proportion of significant inter-group differences were very high (95% and 97% for demersal fish and benthic invertebrates, respectively) suggesting NZSCC groups were distinct from each other in their taxonomic composition. There were positive relationships between the evaluation datasets and the underlying statistical model. There was no evidence of these relationships being affected by the statistical uncertainty of the NZSCC. NZSCC model validation metrics using abundance evaluation data were also moderately high (albeit lower than for presence-absence for invertebrates) suggesting that the NZSCC, can at least in part, represent variation in abundance-weighted communities. Results presented here suggest that the existing NZSCC is currently fit-for-purpose for informing management decisions

Sars-Cov-2 Infection in People with Type 1 Diabetes and Hospital Admission: An Analysis of Risk Factors for England

Introduction: The severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) virus (coronavirus disease 2019 [COVID-19]) pandemic revealed the vulnerability of specific population groups in relation to susceptibility to acute deterioration in their health, including hospital admission and mortality. There is less data on outcomes for people with type 1 diabetes (T1D) following SARS-CoV-2 infection than for those with type 2 diabetes (T2D). In this study we set out to determine the relative likelihood of hospital admission following SARS-CoV-2 infection in people with T1D when compared to those without T1D. Methods: This study was conducted as a retrospective cohort study and utilised an all-England dataset. Electronic health record data relating to people in a national England database (NHS England’s Secure Data Environment, accessed via the BHF Data Science Centre's CVD-COVID-UK/COVID-IMPACT consortium) were analysed. The cohort consisted of patients with a confirmed SARS-CoV-2 infection, and the exposure was whether or not an individual had T1D prior to infection (77,392 patients with T1D). The patients without T1D were matched for sex, age and approximate date of the positive COVID-19 test, with three SARS-CoV-2-infected people living without diabetes (n = 223,995). Potential factors influencing the relative likelihood of the outcome of hospital admission within 28 days were ascertained using univariable and multivariable logistic regression. Results: Median age of the people living with T1D was 37 (interquartile range 25–52) years, 47.4% were female and 89.6% were of white ethnicity. Mean body mass index was 27 (standard error [SE] 0.022) kg/m2, and mean glycated haemoglobin (HbA1c) was 67.3 (SE 0.069) mmol/mol (8.3%). A significantly higher proportion of people with T1D (10.7%) versus matched non-diabetes individuals (3.9%) were admitted to hospital. In combined analysis including individuals with T1D and matched controls, multiple regression modelling indicated that the factors independently relating to a higher likelihood of hospital admission were: T1D (odds ratio [OR] 1.71, 95% confidence interval [CI] 1.62–1.80]), age (OR 1.02, 95% CI 1.02–1.03), social deprivation (higher Townsend deprivation score: OR 1.07, 95% CI 1.06–1.08), lower estimated glomerular filtration rate (eGFR) value (OR 0.975, 95% CI 0.974–0.976), non-white ethnicity (OR black 1.19, 95% CI 1.06–1.33/OR Asian 1.21, 95% CI 1.05–1.39) and having asthma (OR 1.27, 95% CI 1.19–1.35]), chronic obstructive pulmonary disease (OR 2.10, 95% CI 1.89–2.32), severe mental illness (OR 1.83, 95% CI 1.57–2.12) or hypertension (OR 1.44, 95% CI 1.37–1.52). Conclusion: In this all-England study, we describe that, following confirmed infection with SARS-CoV-2, the risk factors for hospital admission for people living with T1D are similar to people without diabetes following confirmed SARS-CoV-2 infection, although the former were more likely to be admitted to hospital. The younger age of individuals with T1D in relation to risk stratification must be taken into account in any ongoing risk reduction strategies regarding COVID-19/future viral pandemics

Correction to: Sars-Cov-2 Infection in People with Type 1 Diabetes and Hospital Admission: An Analysis of Risk Factors for England

The article “Sars-Cov-2 Infection in People with Type 1 Diabetes and Hospital Admission: An Analysis of Risk Factors for England”, written by Adrian H. Heald, David A. Jenkins, Richard Williams, Rajshekhar N. Mudaliar, Amber Khan, Akheel Syed, Naveed Sattar, Kamlesh Khunti, Asma Naseem, Kelly A. Bowden-Davies, J. Martin Gibson, William Ollier, on behalf of the CVD-COVID-UK/COVID-IMPACT Consortium was originally published electronically on the publisher’s Internet portal (currently SpringerLink) on August 25, 2023, without open access. Now, the article is updated with open access as This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/. The original article has been corrected

Using joint species distribution modelling to predict distributions of seafloor taxa and identify vulnerable marine ecosystems in New Zealand waters

\ua9 The Author(s) 2024.Effective ecosystem-based management of bottom-contacting fisheries requires understanding of how disturbances from fishing affect seafloor fauna over a wide range of spatial and temporal scales. Spatial predictions of abundance for 67 taxa were developed, using an extensive dataset of faunal abundances collected using a towed camera system and spatially explicit predictor variables including bottom-trawl fishing effort, using a Joint Species Distribution Model (JSDM). The model fit metrics varied by taxon: the mean tenfold cross-validated AUC score was 0.70 \ub1 0.1 (standard deviation) for presence–absence and an R2 of 0.11 \ub1 0.1 (standard deviation) for abundance models. Spatial predictions of probability of occurrence and abundance (individuals per km2) varied by taxon, but there were key areas of overlap, with highest predicted taxon richness in areas of the continental shelf break and slope. The resulting joint predictions represent significant advances on previous predictions because they are of abundance, allow the exploration of co-occurrence patterns and provide credible estimates of taxon richness (including for rare species that are often not included in more commonly used single-species distribution modelling). Habitat-forming taxa considered to be Vulnerable Marine Ecosystem (VME) indicators (those taxa that are physically or functionally fragile to anthropogenic impacts) were identified in the dataset. Spatial estimates of likely VME distribution (as well as associated estimates of uncertainty) were predicted for the study area. Identifying areas most likely to represent a VME (rather than simply VME indicator taxa) provides much needed quantitative estimates of vulnerable habitats, and facilitates an evidence-based approach to managing potential impacts of bottom-trawling

Unexpected Fine-Scale Population Structure in a Broadcast-Spawning Antarctic Marine Mollusc

Several recent empirical studies have challenged the prevailing dogma that broadcast-spawning species exhibit little or no population genetic structure by documenting genetic discontinuities associated with large-scale oceanographic features. However, relatively few studies have explored patterns of genetic differentiation over fine spatial scales. Consequently, we used a hierarchical sampling design to investigate the basis of a weak but significant genetic difference previously reported between Antarctic limpets (Nacella concinna) sampled from Adelaide and Galindez Islands near the base of the Antarctic Peninsula. Three sites within Ryder Bay, Adelaide Island (Rothera Point, Leonie and Anchorage Islands) were each sub-sampled three times, yielding a total of 405 samples that were genotyped at 155 informative Amplified Fragment Length Polymorphisms (AFLPs). Contrary to our initial expectations, limpets from Anchorage Island were found to be subtly, but significantly distinct from those sampled from the other sites. This suggests that local processes may play an important role in generating fine-scale population structure even in species with excellent dispersal capabilities, and highlights the importance of sampling at multiple spatial scales in population genetic surveys

Variations in strain affect friction and microstructure evolution in copper under a reciprocating tribological load

The microstructure of the materials constituting a metallic frictional contact strongly influences tribological performance. Being able to tailor friction and wear is challenging due to the complex microstructure evolution associated with tribological loading. Here, we investigate the effect of the strain distribution on these processes. High-purity copper plates were morphologically surface textured with two parallel rectangles—referred to as membranes—over the entire sample length by micro-milling. By keeping the width of these membranes constant and only varying their height, reciprocating tribological loading against sapphire discs resulted in different elastic and plastic strains. Finite element simulations were carried out to evaluate the strain distribution in the membranes. It was found that the maximum elastic strain increases with decreasing membrane stiffness. The coefficient of friction decreases with increasing membrane aspect ratio. By analyzing the microstructure and local crystallographic orientation, we found that both show less change with decreasing membrane stiffness