Machine learning models have been used extensively in hydrology, but issues persist with regard to their transparency, and there is currently no identifiable best practice for forcing variables in streamflow or flood modeling. In this paper, using data from the Centre for Ecology & Hydrology’s National River Flow Archive and from the European Centre for Medium-Range Weather Forecasts, we present a study that focuses on the input variable set for a neural network streamflow model to demonstrate how certain variables can be internalized, leading to a compressed feature set. By highlighting this capability to learn effectively using proxy variables, we demonstrate a more transferable framework that minimizes sensing requirements and that enables a route toward generalizing models.

Antarctica is populated by a diverse array of terrestrial fauna that have successfully adapted to its extreme environmental conditions. The origins and diversity of the taxa have been of continuous interest to ecologists since their discovery. Early theory considered contemporary populations as descendants of recent arrivals; however, mounting molecular evidence points to firmly established indigenous taxa far earlier than the Last Glacial Maximum, thus indicating more ancient origins. Here we present insights into Antarctica's terrestrial invertebrates by synthesizing available phylogeographic studies. Molecular dating supports ancient origins for most indigenous taxa, including Acari (up to 100 million years ago; Ma), Collembola (21–11 Ma), Nematoda (~30 Ma), Tardigrada (> 1 Ma) and Chironomidae (> 49 Ma), while Rotifera appear to be more recent colonizers (~130 Ka). Subsequent population bottlenecks and rapid speciation have occurred with limited gene transfer between Continental and Maritime Antarctica, while repeated wind- or water-borne dispersal and colonization of contiguous regions during interglacial periods shaped current distributions. Greater knowledge of Antarctica's fauna will focus conservation efforts to ensure their persistence.

Environmental data science for spatial extremes has traditionally relied heavily on max-stable processes. Even though the popularity of these models has perhaps peaked with statisticians, they are still perceived and considered as the “state of the art” in many applied fields. However, while the asymptotic theory supporting the use of max-stable processes is mathematically rigorous and comprehensive, we think that it has also been overused, if not misused, in environmental applications, to the detriment of more purposeful and meticulously validated models. In this article, we review the main limitations of max-stable process models, and strongly argue against their systematic use in environmental studies. Alternative solutions based on more flexible frameworks using the exceedances of variables above appropriately chosen high thresholds are discussed, and an outlook on future research is given. We consider the opportunities offered by hybridizing machine learning with extreme-value statistics, highlighting seven key recommendations moving forward.

Selenolaurite, ideally RuSe2, is a new mineral, the first natural ruthenium selenide. It was discovered in an assemblage with Se-bearing moncheite. Both form xenomorphic inclusions in the crystal aggregates of Os–Ir–Ru minerals found at the Ingul gold placer, Urals, Russia. In addition a mineral with selenolaurite composition was found as a euhedral inclusion within grains of Pt–Fe alloy with isoferroplatinum composition at the Kazan gold placer. These placers are situated in the Chelyabinsk district, South Urals, Russia. The selenolaurite from the Ingul placer forms interstitial grains with maximum size of section of 0.05–0.1 mm. Crystals of the selenolaurite from the Kazan placer reach 20 µm in size. Selenolaurite is grey with metallic lustre and is isotropic. Reflectance values [R (%) for COM approved wavelengths (nm)] are 45.8(470), 44.3(546), 43.8(589) and 43.1(650). The chemical composition of the holotype from the Ingul placer corresponds to the empirical formula (Ru0.99Ir0.05)Σ1.04(Se1.92Te0.03S0.01)Σ1.96. Selenolaurite is the selenium-dominant analogue of laurite, RuS2 with a pyrite-type structure. It is cubic, space group Pa3̅, a = 5.9424(2) Å, V = 209.84 2) Å3, Z = 4 and Dcalc. = 8.415 g·cm–3 (calculated on the basis of empirical formula and unit-cell parameters refined by the Rietveld method). The crystal structure has been refined from the powder data to RB = 0.0067. The strongest lines of the powder X-ray diffraction pattern [d(Å), (I), (hkl)] are: 3.434(41)(111), 2.973(90)(200), 2.6580(100)(210), 2.4264(84)(211) and 1.7913(87)(311). The possible sources of a Ru–Se mineralisation in the South Urals are ophiolitic ultramafic rocks enriched in Ru and depleted with sulfur.

A rare silver mineral, dervillite (ideally Ag2AsS2), has been found in specimens from the famous Jáchymov mining district, Czech Republic. It occurs as very rare long-prismatic crystals up to 0.4 mm across in association with proustite, bismuth and native silver in the thin arsenic veinlets within the Trojická vein (Svornost mine). Dervillite is monoclinic, space group Pc, with a = 9.6375(3), b = 12.9462(4), c = 6.8497(2) Å, β = 99.510(2)° and V = 842.88(2) Å3 (Z = 8). The new structure refinement, R1 = 2.94% for 18767 reflections with [I > 3σ(I)] and wR2 = 7.93% for all 20050 reflections, provided a better fit to the data compared to earlier studies, revealing that silver (8 symmetrically independent atomic sites), which adopts various coordinations (from quasi-linear to tetrahedral) in the structure of dervillite vibrates non-harmonically at room temperature. The Gram-Charlier development, describing the atomic displacement parameters of silver atoms, was used to model their non-harmonic behaviour. A discussion on the use of the approach to the data with limited quality is also provided.

Diamonds are found occasionally in the United States of America. Diamonds from the Prairie Creek lamproite in Arkansas, USA occur within a north to south corridor of Neoproterozoic-to-mid-Cretaceous magmatism that extends across North America. These diamond-bearing lamproites are unusual because they intrude adjacent to sutured and strongly thinned lithosphere rather than stable within-plate settings and the diamonds themselves provide physical evidence of processes related to diamond formation at the cratonic margin. Indeed, A review of previously published geophysical data, isotopic compositions, inclusion suites and inclusion geochemistry suggest most diamonds were formed in subducted and eclogitic rocks within a highly localised diamondiferous lithosphere beneath the cratonic margin.

The morphology and spectroscopic character of 155 diamonds from the Prairie Creek lamproite suggest typical diamond formation conditions in an otherwise thinned continental lithosphere. Most diamonds examined during this investigation have spectroscopic features indicating strong nitrogen aggregation, a history of thermal perturbation and plastic deformation. Nitrogen contents range up to 1882 ppm and the diamonds preserve ∼70% aggregated nitrogen in the B aggregation state. Furthermore, inclusion elastic barometry and time-averaged mantle residence temperatures suggest most Arkansas diamonds formed at 5.2±0.2 GPa and 1205±63°C (1σ). However, a subpopulation of ∼4% of relatively large and inclusion free, colourless, flattened-to-irregular habit Arkansas diamonds are Type IIa with <5 at.ppm nitrogen. Those stones size, morphology, colour and N content might warrant their inclusion in the class of Cullinan-like, Large, Inclusion-Poor, Pure, Irregular and Resorbed or ‘CLIPPIR’ diamonds. Other diamonds examined commonly exhibit physical evidence of plastic deformation, including brown body colour and deformation lamellae.

A new model for the interpretation of radiocarbon (14C) dates of Holocene marine shells is presented. For the Netherlands, the size of reservoir effect is difficult to assess, as these shells often lived in an environment of mixed marine- and river waters. Both stable isotopes 13C and 18O of the shell carbonate give insight in the environmental conditions the shells lived in. River water occurs in two main categories, distinguished by 18O: the Rhine which is dominant, and other rivers. This leads to two estuary mixing lines between the North Sea and rivers. The stable isotopes of the shell carbonate are also indicative for additional processes, such as uptake of secondary carbonate from the soil by shells, and exchange of C isotopes between atmosphere and water. Extensions of the main model deal with special cases such as pools of stagnant water and lakes. The model leads to an assessment of the recent 14C activities of the system the shells lived in, called 14aSYS. The measured 14C activities relative to these 14aSYS values determine the 14C age of the shells and include the reservoir effect. This way we circumvent normalizing to δ13C = –25‰, i.e. the terrestrial timescale and the subsequent correction for reservoir effects. The model is applied to a large legacy dataset of marine shells from the Netherlands, obtained during the last 7 decades. It contains 1116 14C dated shells; for the majority of these, the 3 isotopes 13C, 14C and 18O are measured.

Tri-octahedral clay minerals have the potential to be used as CO2 sorbents at intermediate temperatures (200–400°C) owing to their thermal stability in this temperature range. In this study, Laponite RD®, a commercially synthesized hectorite (with Na+ as the exchangeable cation) was used to investigate its capacity of CO2 adsorption at 200°C and ambient pressure. Various cations such as Co2+, Ni2+, Mg2+ and Ca2+ were employed to exchange Na+, with the aim being to study their effects on the capacity for adsorbing CO2. The commercial sample showed an adsorption capacity of 144 µmolCO2 g–1. Most of the other exchanged samples displayed a lower quantity of CO2 adsorbed. An exception was the Ca2+-saturated sample, which exhibited a better performance (163 µmolCO2 g–1) compared with Laponite RD®. Thus, with its greater affinity towards CO2, such a sample could be a good candidate for CO2 capture. For all of the samples, most of the CO2 was desorbed, and the formation of carbonate bonds was not observed using Fourier-transform infrared spectroscopy, suggesting that the CO2 was mainly physisorbed.

The spotted hyena (Crocuta crocuta) was an important large carnivore of Pleistocene ecosystems in Africa and Eurasia. Like its modern relatives, this obligate carnivore was adapted to crush and digest bones of its prey and absorb organic matter from bones more efficiently than any other carnivore. This difference in the nutrient resource use between hyenas and most other carnivores led to differences in the isotope flux and variation in the carbon and nitrogen isotopic composition. In our paper, we assess the prey-to-hyena collagen-to-collagen Δ13C and Δ15N trophic discrimination factor (TDF), a key parameter needed in mixing models used for quantitative reconstruction of diet. We analyzed a Pleistocene hyena den bone accumulation in Perspektywiczna Cave (Poland), with a preserved assemblage of remains containing both hyenas and a wide spectrum of their prey represented by digested bones. With the use of proteomics-based taxonomic identification (ZooMS), we estimated the proportion of prey species in the hyena diet. The modeled collagen-to-collagen TDFs are around +1.6‰ to +1.7‰ for δ13C and around +3.4‰ to +3.5‰ for δ15N. This study provides new data on the dietary habits of this large carnivore and allows for a more accurate use of isotopic signals in modeling past hyena diets.

The global record of fossil hyenid tracks is sparse—the only formal reports that can be considered reliable are of trackways from Tanzania and a single track from Greece. However, trackway and track patterns of the four extant members of the Hyaenidae are distinctive among the tracks of carnivorans. A Pleistocene trackway comprising five manus–pes pairs has been identified on an aeolianite surface on the Cape south coast of South Africa, and is attributed to a hyena, most likely the brown hyena (Parahyaena brunnea). The diagnostic approach followed involves a combination of the knowledge of Indigenous Master Trackers and the methods of modern ichnology.