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    53713 research outputs found

    Zinc and copper have the greatest relative importance for river macroinvertebrate richness at a national scale

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    It is important to discover what change led to the improvement in European macroinvertebrate biodiversity in the period from 1990−2000s and what prevents further desirable gains from taking place today. A 30-year data set from 1,457 macroinvertebrate monitoring sites spread across England, with 65,032 discrete observations was combined with 41 chemical, physical, habitat, and geographic variables. This data set was analyzed using generalized linear mixed-effect models and generalized additive mixed models. To include all combinations of the variables required to address each question, required over 20,000 model runs. It was found that no variables were more consistently and strongly associated with the overall family richness than Zn and Cu. Zn and Cu led both for the era of large gains in richness up to 2005 and also in the later period of 2006–2018 when few further gains were made

    Global urbanization benefits food security and nature restoration

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    Urbanization is often viewed as a threat to food security and environmental restoration due to extensive land use. However, by integrating urban and rural land perspectives, a different narrative emerges. Using data from 214 countries, we demonstrate that the projected urbanization of 2 billion people between 2020 and 2050 could unlock approximately 52 million hectares (Mha) of land, due to higher urban population densities. In scenarios with increased urban density, potential land savings could reach 80 Mha, meeting 55 % of the additional global cropland demand by 2050. If allocated for ecological restoration, this land could protect 1,437 species and sequester 21 billion tonnes (14–27 billion tonnes, 90 % confidence interval) of carbon by 2050. These findings underscore the positive impact that strategic urbanization can have on land use and conservation goals

    Evaluating the acclimation capacity of two keystone Antarctic echinoderms to coastal freshening

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    Coastal freshening in the Southern Ocean is expected to increase under projected climate scenarios. As a major environmental stressor, prolonged reduced salinity could pose a significant challenge to Antarctica's endemic echinoderms. Acclimatising to low salinity may be crucial for their continued survival as climate change accelerates, yet little is currently known about their capacity to do so. The sea star Odontaster validus and sea urchin Sterechinus neumayeri, two of the most ecologically important and abundant echinoderms of the shallow Antarctic seas, were exposed to reduced salinities (29 ‰ and 24 ‰) for at least 71 days after a stepwise dilution from 34.5 ‰. Feeding, faecal production (S. neumayeri only) and activity coefficient were significantly impacted at 24 ‰ and did not recover to control levels in either species. Oxygen consumption remained similar to control levels (34.5 ‰) across both treatments and species until day 85, when a significant increase was observed in S. neumayeri at 24 ‰. Coelomic fluid osmolality was near isosmotic with external salinities in both species, while coelomocyte composition and concentration were unaffected by reduced salinities (S. neumayeri only). Both species demonstrated the capacity to tolerate lower salinities that may be expected with climate change, with successful acclimation demonstrated at 29 ‰. Although survival rates were high at 24 ‰, significant reductions in mass and the failure of metrics to return to control levels suggest that long-term survival at 24 ‰ is unlikely, potentially impacting Antarctic food-web dynamics and ecological interactions

    UK hydrological outlook - March 2025

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    The Hydrological Outlook provides an insight into future hydrological conditions across the UK. Specifically, it describes likely trajectories for river flows and groundwater levels on a monthly basis, with a particular focus on the next three months. Well established monitoring programmes provide the current status of both river flows and groundwater levels at many sites across the UK, and data from these programmes provide the starting point for the Outlook. A number of techniques are used to project forwards from the current state and results from these are used to produce a summary that includes a highlights map

    Machine learning for stochastic parametrization

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    Atmospheric models used for weather and climate prediction are traditionally formulated in a deterministic manner. In other words, given a particular state of the resolved scale variables, the most likely forcing from the subgrid scale processes is estimated and used to predict the evolution of the large-scale flow. However, the lack of scale separation in the atmosphere means that this approach is a large source of error in forecasts. Over recent years, an alternative paradigm has developed: the use of stochastic techniques to characterize uncertainty in small-scale processes. These techniques are now widely used across weather, subseasonal, seasonal, and climate timescales. In parallel, recent years have also seen significant progress in replacing parametrization schemes using machine learning (ML). This has the potential to both speed up and improve our numerical models. However, the focus to date has largely been on deterministic approaches. In this position paper, we bring together these two key developments and discuss the potential for data-driven approaches for stochastic parametrization. We highlight early studies in this area and draw attention to the novel challenges that remain

    Bayesian views of generalized additive modelling

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    •Generalized additive models (GAMs) are a frequently used, flexible framework applied to many problems in statistical ecology. They are commonly used to incorporate smooth effects into models via splines, including spatial components in species distribution models. •GAMs are often considered to be a purely frequentist framework (‘generalized linear models with wiggly bits’), however links between frequentist and Bayesian approaches to these models were highlighted early‐on in the literature. From a practical perspective, Bayesian thinking underlies many parts of the implementation in the popular R package mgcv , so understanding these underpinnings can be informative during model building and assessment. •This article aims to highlight useful links (and differences) between Bayesian and frequentist approaches to smoothing, as detailed in the statistical literature, in accessible way, with a focus on the mgcv implementation. By harnessing these links we can expand the set of modelling tools we have at our disposal, as well as our understanding of how existing methods work. •Two important topics for quantitative ecologists are covered in detail: model term selection and uncertainty estimation. Taking Bayesian viewpoints for these problems makes them much more tractable in many applied settings. Examples are given using data from the NOAA Alaska Fisheries Science Center's groundfish assessment program

    Shared environmental similarity between relatives influences heritability of reproductive timing in wild great tits

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    Intraspecific variation is necessary for evolutionary change and population resilience, but the extent to which it contributes to either depends on the causes of this variation. Understanding the causes of individual variation in traits involved with reproductive timing is important in the face of environmental change, especially in systems where reproduction must coincide with seasonal resource availability. However, separating the genetic and environmental causes of variation is not straightforward, and there has been limited consideration of how small-scale environmental effects might lead to similarity between individuals that occupy similar environments, potentially biasing estimates of genetic heritability. In ecological systems, environments are often complex in spatial structure, and it may therefore be important to account for similarities in the environments experienced by individuals within a population beyond considering spatial distances alone. Here, we construct multi-matrix quantitative genetic animal models using over 11,000 breeding records (spanning 35 generations) of individually-marked great tits (Parus major) and information about breeding proximity and habitat characteristics to quantify the drivers of variability in two key seasonal reproductive timing traits. We show that the environment experienced by related individuals explains around a fifth of the variation seen in reproductive timing, and accounting for this leads to decreased estimates of heritability. Our results thus demonstrate that environmental sharing between relatives can strongly affect estimates of heritability and therefore alter our expectations of the evolutionary response to selection

    An updated landslide susceptibility model and a log-Gaussian Cox process extension for Scotland

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    At the time of its development, GeoSure was created using expert knowledge based on a thorough understanding of the engineering geology of the rocks and soils of Great Britain. The ability to use a data-driven methodology to develop a national-scale landslide susceptibility was not possible due to the relatively small size of the landslide inventory at the time. In the intervening 20 years, the National Landslide Database has grown from around 6000 points to over 18,000 records today and continues to be added to. With the availability of this additional inventory, new data-driven solutions could be utilised. Here, we tested a Bernoulli likelihood model to estimate the probability of debris flow occurrence and a log-Gaussian Cox process model to estimate the rate of debris flow occurrence per slope unit. Scotland was selected as the test site for a preliminary experiment, which could potentially be extended to the whole British landscape in the future. Inference techniques for both of these models are applied within a Bayesian framework. The Bayesian framework can work with the two models as additive structures, which allows for the incorporation of spatial and covariate information in a flexible way. The framework also provides uncertainty estimates with model outcomes. We also explored consideration on how to communicate uncertainty estimates together with model predictions in a way that would ensure an integrated framework for master planners to use with ease, even if administrators do not have a specific statistical background. Interestingly, the spatial predictive patterns obtained do not stray away from those of the previous GeoSure methodology, but rigorous numerical modelling now offers objectivity and a much richer predictive description

    Drivers of interspecific spatial segregation in two closely-related seabird species at a Pan-Atlantic scale

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    Aim: Ecologically similar species living in sympatry are expected to segregate to reduce the effects of competition where re-sources are limiting. Segregation from heterospecifics commonly occurs in space, but it is often unknown whether such segregation has underlying environmental causes. Indeed, species could segregate because of different fundamental environmental requirements (i.e., ‘niche divergence’), because competitive exclusion at sympatric sites can force species to either change the habitat use they would have at allopatric sites (i.e., ‘niche displacement’) or to avoid certain areas, independently of habitat (i.e.,‘spatial avoidance’). Testing these hypotheses requires the comparison between sympatric and allopatric sites. Understanding the competitive mechanisms that underlie patterns of spatial segregation could improve predictions of species responses to environmental change, as competition might exacerbate the effects of environmental change. Location: North Atlantic and Arctic. Taxa: Common guillemots Uria aalge and Brünnich's guillemots Uria lomvia

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