The genesis of the Llanrwst and Llanfair veinfields, North Wales: evidence from fluid inclusions and stable isotopes

The Llanrwst Pb–Zn veinfield occupies fractures in an Ordovician volcano-sedimentary sequence, with three separate mineralizing events (M₁, M₂ and M₃). The Llanfair Pb–Zn–Cu veinfield consists of sporadic mineralization in Silurian sediments. Fluid inclusion data from both veinfields indicate mineralization took place from CaCl₂-bearing brines, up to 26 wt % NaCl equivalent, between 125 and 190 °C with cooler (< 100 °C), more dilute fluids associated with later thrusting at Llanrwst. Sulphur at both veinfields was derived during the main phases of sulphide precipitation from the host rocks, with paragenetically late sulphides and sulphates deriving sulphur from ³⁴S-enriched surface waters. Carbon and oxygen isotopic data on vein carbonates shows that both deposits formed from waters that had undergone extensive water–rock interaction. Carbon was derived from at least two sources: marine carbonate dominated at Llanfair, whereas organic matter in the host rocks was more significant at Llanrwst. These data indicate that the veins formed from evolved brines which migrated along fault systems to the loci of deposition during a phase of extensional tectonics in late Devonian to Tournasian times

Does browning-induced light limitation reduce fish body growth through shifts in prey composition or reduced foraging rates?

Browning of waters, coupled to climate change and land use changes, can strongly affect aquatic ecosystems. Browning-induced light limitation may have negative effects on aquatic consumers via shifts in resource composition and availability and by negatively affecting foraging of consumers relying on vision. However, the extent to which light limitation caused by browning affects fish via either of these two pathways is largely unknown. Here we specifically test if fish growth responses to browning in a pelagic food web are best explained by changes in resource availability and composition due to light limitation, or by reduced foraging rates due to decreased visual conditions. To address this question, we set up a mesocosm experiment to study growth responses of two different fish species to browning and conducted an aquaria experiment to study species-specific fish foraging responses to browning. Furthermore, we used a space-for-time approach to analyse fish body length-at-age across >40 lakes with a large gradient in lake water colour to validate experimental findings on species-specific fish growth responses. With browning, we found an increase in chlorophyll a concentrations, shifts in zooplankton community composition, and a decrease in perch (Perca fluviatilis) but not roach (Rutilus rutilus) body growth. We conclude that fish growth responses are most likely to be linked to the observed shift in prey (zooplankton) composition. In contrast, we found limited evidence for reduced perch, but not roach, foraging rates in response to browning. This suggests that light limitation led to lower body growth of perch in brown waters mainly through shifts in resource composition and availability, perhaps in combination with decreased visibility. Finally, with the lake study we confirmed that perch but not roach body growth and length-at-age are negatively affected by brown waters in the wild. In conclusion, using a combination of experimental and observational data, we show that browning of lakes is likely to (continue to) result in reductions in fish body growth of perch, but not roach, as a consequence of shifts in prey availability and composition, and perhaps reduced foraging

Deep learning predictions of galaxy merger stage and the importance of observational realism

Machine learning is becoming a popular tool to quantify galaxy morphologies and identify mergers. However, this technique relies on using an appropriate set of training data to be successful. By combining hydrodynamical simulations, synthetic observations, and convolutional neural networks (CNNs), we quantitatively assess how realistic simulated galaxy images must be in order to reliably classify mergers. Specifically, we compare the performance of CNNs trained with two types of galaxy images, stellar maps and dust-inclusive radiatively transferred images, each with three levels of observational realism: (1) no observational effects (idealized images), (2) realistic sky and point spread function (semirealistic images), and (3) insertion into a real sky image (fully realistic images). We find that networks trained on either idealized or semireal images have poor performance when applied to survey-realistic images. In contrast, networks trained on fully realistic images achieve 87.1 per cent classification performance. Importantly, the level of realism in the training images is much more important than whether the images included radiative transfer, or simply used the stellar maps (⁠87.1 per cent compared to 79.6 per cent accuracy, respectively). Therefore, one can avoid the large computational and storage cost of running radiative transfer with a relatively modest compromise in classification performance. Making photometry-based networks insensitive to colour incurs a very mild penalty to performance with survey-realistic data (⁠86.0 per cent with r-only compared to 87.1 per cent with gri). This result demonstrates that while colour can be exploited by colour-sensitive networks, it is not necessary to achieve high accuracy and so can be avoided if desired. We provide the public release of our statistical observational realism suite, REALSIM, as a companion to this paper

Iso-Wetlands: unlocking wetland ecologies and agriculture in prehistory through sulfur isotopes

Iso-Wetlands is a new, NERC-funded collaborative research project involving researchers at UCL Institute of Archaeology, the University of Leeds and the UK Centre for Ecology and Hydrology. The project is developing sulfur isotope analysis of archaeological plants and animals as a new tool for exploring hydrological conditions under which agricultural production was taking place. This development has the potential to improve understanding of water management strategies in the past, particularly in relation to seasonal floodwater agriculture and wetland agriculture (for example, rice paddy systems). The project will open wider possibilities for the use of sulfur isotopes in archaeology and ecology to examine wetland habitat use by both people and animals

The observability of galaxy merger signatures in nearby gas-rich spirals

Galaxy mergers are crucial to understanding galaxy evolution, therefore we must determine their observational signatures to select them from large IFU galaxy samples such as MUSE and SAMI. We employ 24 high-resolution idealized hydrodynamical galaxy merger simulations based on the ‘Feedback In Realistic Environment’ (FIRE-2) model to determine the observability of mergers to various configurations and stages using synthetic images and velocity maps. Our mergers cover a range of orbital configurations at fixed 1:2.5 stellar mass ratio for two gas rich spirals at low redshift. Morphological and kinematic asymmetries are computed for synthetic images and velocity maps spanning each interaction. We divide the interaction sequence into three: (1) the pair phase; (2) the merging phase; and (3) the post-coalescence phase. We correctly identify mergers between first pericentre passage and 500 Myr after coalescence using kinematic asymmetry with 66 per cent completeness, depending upon merger phase and the field of view of the observation. We detect fewer mergers in the pair phase (40 per cent) and many more in the merging and post-coalescence phases (97 per cent). We find that merger detectability decreases with field of view, except in retrograde mergers, where centrally concentrated asymmetric kinematic features enhances their detectability. Using a cut-off derived from a combination of photometric and kinematic asymmetry, we increase these detections to 89 per cent overall, 79 per cent in pairs, and close to 100 per cent in the merging and post-coalescent phases. By using this combined asymmetry cut-off we mitigate some of the effects caused by smaller fields of view subtended by massively multiplexed integral field spectroscopy programmes

A confirmatory holding time study for purgeable VOCs in water samples

Analyte stability during pre-analytical storage is essential to the accurate quantification contaminants in environmental samples. This is particularly true for volatile organic compounds (VOCS) which can easily volatilize and/or degrade during sample storage. Recognizing this, regulatory agencies require water samples be collected in vials without headspace and stored at 4 degrees C, and that analyses be conducted within 14 days, 2048 even if samples are acid-preserved. Since the selection of a 14-day holding time was largely arbitrary, the appropriateness of this requirement must be re-evaluated. The goal of the study described here was to provide regulatory agencies with the necessary data to extend the maximum holding time for properly preserved VOC water samples to 28 days

Identification of tidal features in deep optical galaxy images with Convolutional Neural Networks

Interactions between galaxies leave distinguishable imprints in the form of tidal features which hold important clues about their mass assembly. Unfortunately, these structures are difficult to detect because they are low surface brightness features so deep observations are needed. Upcoming surveys promise several orders of magnitude increase in depth and sky coverage, for which automated methods for tidal feature detection will become mandatory. We test the ability of a convolutional neural network to reproduce human visual classifications for tidal detections. We use as training $\sim$6000 simulated images classified by professional astronomers. The mock Hyper Suprime Cam Subaru (HSC) images include variations with redshift, projection angle and surface brightness ($\mu_{lim}$ =26-35 mag arcsec$^{-2}$). We obtain satisfactory results with accuracy, precision and recall values of Acc=0.84, P=0.72 and R=0.85, respectively, for the test sample. While the accuracy and precision values are roughly constant for all surface brightness, the recall (completeness) is significantly affected by image depth. The recovery rate shows strong dependence on the type of tidal features: we recover all the images showing shell features and 87% of the tidal streams; these fractions are below 75% for mergers, tidal tails and bridges. When applied to real HSC images, the performance of the model worsens significantly. We speculate that this is due to the lack of realism of the simulations and take it as a warning on applying deep learning models to different data domains without prior testing on the actual data.Comment: 13 pages, 10 figures, accepted for publication in MNRA