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The Tibetan Plateau (TP) hosts a variety of mountain peatlands that are sensitive to the amplified warming in this region. However, we still lack a basic understanding of environmental and climatic factors controlling peatland distribution in the region. Here we use a bioclimatic envelope model (PeatStash) and environmental analysis that utilise three peatland datasets-(a) the well-studied Zoige peatland complex, (b) a literature-based dataset of TP peatlands sites, and (c) an existing global peatland map (PEATMAP)-to investigate major drivers of peatland distribution in the TP. The Zoige peatland complex is defined by gentle slopes (< 2°), mean annual temperature at 0-2 °C, and soil moisture index > 1.7, much narrower thresholds than those stemming from PEATMAP. Using these narrower thresholds to predict future changes, we found that the Zoige peatland complex will shrink greatly under full-range future warming scenarios (both SSP1-2.6 and SSP5-8.5). Modelling peatland distribution in the entire TP remains challenging because accurate environmental and climate data at high resolution and a reliable peatland distribution map are still lacking. Improved peatland mapping supported by ground-truthing is necessary to understand drivers of peatland distribution, assess carbon storage and other ecosystem services, and predict the TP's peatlands fate under climate change., (© 2023. Springer Nature Limited.)

Antimicrobial resistance (AMR) is a global health and development threat, with calls for the optimisation of antimicrobial use (AMU) in the treatment of both humans and animals prevalent across national and international policy. Rapid, low-cost and readily available diagnostics that specifically identify pathogens and their antimicrobial susceptibility profiles have been identified as essential parts of this optimisation process, yet questions over the assumed utility of novel rapid technology as a cornerstone of tackling agricultural AMU still exist. To understand whether this technology may support the optimisation of AMU in the treatment of animal disease, this study qualitatively examines the discourse between veterinarians, laboratory representatives, veterinary researchers and (cattle) farmers within three participatory events concerning diagnostic testing on UK farms, to offer a critical examination of the interaction between veterinary diagnostic practice and agricultural AMU. Veterinarian-led discussion suggested that veterinary rationales for engaging with diagnostic testing are nuanced and complex, where veterinarians (i) were driven by both medical and non-medical motivators; (ii) had a complex professional identity influencing diagnostic-test engagement; and (iii) balanced a multitude of situated contextual factors that informed "gut feelings" on test choice and interpretation. In consequence, it is suggested that data-driven diagnostic technologies may be more palatable for veterinarians to promote to their farm clients in the pursuit of better and more sustainable AMU, whilst also being in synergy with the emerging preventative role of the farm veterinarian.

When a peatland is drained and cultivated, it behaves as a notable source of CO 2 However, we lack temporally and spatially explicit estimates of carbon losses from cultivated peatlands. Using a process-based land surface model that explicitly includes representation of peatland processes, we estimate that northern peatlands converted to croplands emitted 72 Pg C over 850-2010, with 45% of this source having occurred before 1750. This source surpassed the carbon accumulation by high-latitude undisturbed peatlands (36 to 47 Pg C). Carbon losses from the cultivation of northern peatlands are omitted in previous land-use emission assessments. Adding this ignored historical land-use emission implies an 18% larger terrestrial carbon storage since 1750 to close the historical global carbon budget. We also show that carbon emission per unit area decrease with time since drainage, suggesting that time since drainage should be accounted for in inventories to refine land-use emissions from cultivated peatlands., (Copyright © 2021 The Authors, some rights reserved; exclusive licensee American Association for the Advancement of Science. No claim to original U.S. Government Works. Distributed under a Creative Commons Attribution NonCommercial License 4.0 (CC BY-NC).)

The carbon (C) accumulation histories of peatlands are of great interest to scientists, land users and policy makers. Because peatlands contain more than 500 billion tonnes of C, an understanding of the fate of this dynamic store, when subjected to the pressures of land use or climate change, is an important part of climate-change mitigation strategies. Information from peat cores is often used to recreate a peatland's C accumulation history from recent decades to past millennia, so that comparisons between past and current rates can be made. However, these present day observations of peatlands' past C accumulation rates (known as the apparent rate of C accumulation - aCAR) are usually different from the actual uptake or loss of C that occurred at the time (the true C balance). Here we use a simple peatland model and a more detailed ecosystem model to illustrate why aCAR should not be used to compare past and current C accumulation rates. Instead, we propose that data from peat cores are used with existing or new C balance models to produce reliable estimates of how peatland C function has changed over time.

In this paper we consider the shifting role, practice and context of veterinary diagnosis in addressing concerns over what is, in the context of the growing threat of antimicrobial resistance, considered unnecessary or excessive antimicrobial medicine use in UK livestock farms. With increasing policy and regulatory interest in diagnostic practices and technologies, coupled with an expanding focus on the development and deployment of new rapid and point-of-care on-farm diagnostic testing, this paper investigates current diagnostic practices amongst veterinarians working on dairy, pig and poultry farms in Great Britain (England, Wales, and Scotland) and, more specifically, veterinarians' use and perceptions of new and emerging rapid and point-of-care diagnostic tests. Drawing on a series of 30 semi-structured interviews with farm animal veterinary professionals across the three sectors, this paper examines the manner in which such tests are both used and anticipated in clinical farm animal veterinary practice and the possible impact rapid test technologies might have on broader farm animal health management and disease control. Analysis of the transcribed interviews reveals a number of complexities around the use of rapid and point-of-care diagnostic tests. The relative rapidity and simplification of such tests, facilitating immediate treatment responses, is held in balance against both the accuracy and the more detailed and documented procedures of established laboratory testing routes. In situations of multifaceted on-farm etiologies, respondents maintained that rapid tests may offer restricted diagnostic capabilities, though in other situations they were found to offer ready confirmation of disease presence. A third complexity arising from the growth of rapid and point-of-care testing and revealed in this study relates to the shifting distribution of responsibilities in animal health care within contemporary food chains. The growing availability of rapid and point-of-care tests effectively diversifies the range of diagnostic actors with consequences for the flow of diagnostic and disease information. The veterinarians in this study identified areas where new rapid and point-of-care tests would be of particular value to them in their clinical practice particularly in addressing concerns over inappropriate antimicrobial use in animal treatment. However, despite the considerable policy advocacy on rapid and point-of-care tests as key tools in shifting diagnostic practice and reducing unnecessary antimicrobial use, veterinarians in this study, while recognizing the potential future role of such tools and technologies, nonetheless viewed diagnostic practice as a far more complex process for which rapid tests might constitute only a part., (Copyright © 2020 Buller, Adam, Bard, Bruce, (Ray) Chan, Hinchliffe, Morgans, Rees and Reyher.)

Higher levels of taxonomic and evolutionary diversity are expected to maximize ecosystem function, yet their relative importance in driving variation in ecosystem function at large scales in diverse forests is unknown. Using 90 inventory plots across intact, lowland, terra firme, Amazonian forests and a new phylogeny including 526 angiosperm genera, we investigated the association between taxonomic and evolutionary metrics of diversity and two key measures of ecosystem function: aboveground wood productivity and biomass storage. While taxonomic and phylogenetic diversity were not important predictors of variation in biomass, both emerged as independent predictors of wood productivity. Amazon forests that contain greater evolutionary diversity and a higher proportion of rare species have higher productivity. While climatic and edaphic variables are together the strongest predictors of productivity, our results show that the evolutionary diversity of tree species in diverse forest stands also influences productivity. As our models accounted for wood density and tree size, they also suggest that additional, unstudied, evolutionarily correlated traits have significant effects on ecosystem function in tropical forests. Overall, our pan-Amazonian analysis shows that greater phylogenetic diversity translates into higher levels of ecosystem function: tropical forest communities with more distantly related taxa have greater wood productivity.

Accurate representation of photosynthesis in terrestrial biosphere models (TBMs) is essential for robust projections of global change. However, current representations vary markedly between TBMs, contributing uncertainty to projections of global carbon fluxes. Here we compared the representation of photosynthesis in seven TBMs by examining leaf and canopy level responses of photosynthetic CO 2 assimilation (A) to key environmental variables: light, temperature, CO 2 concentration, vapor pressure deficit and soil water content. We identified research areas where limited process knowledge prevents inclusion of physiological phenomena in current TBMs and research areas where data are urgently needed for model parameterization or evaluation. We provide a roadmap for new science needed to improve the representation of photosynthesis in the next generation of terrestrial biosphere and Earth system models., (No claim to original US Government works New Phytologist © 2016 New Phytologist Trust.)

Many studies have highlighted significant interactions between soil C reservoir dynamics and global climate and environmental change. However, in order to estimate the future soil organic carbon sequestration potential and related ecosystem services well, more spatially detailed predictions are needed. The present study made detailed predictions of future spatial evolution (at 250 m resolution) of topsoil SOC driven by climate change and land use change for France up to the year 2100 by taking interactions between climate, land use and soil type into account. We conclude that climate change will have a much bigger influence on future SOC losses in mid-latitude mineral soils than land use change dynamics. Hence, reducing CO 2 emissions will be crucial to prevent further loss of carbon from our soils.

The temperature dependence of the reaction kinetics of the Rubisco enzyme implies that, at the level of a chloroplast, the response of photosynthesis to rising atmospheric CO2 concentration (Ca ) will increase with increasing air temperature. Vegetation models incorporating this interaction predict that the response of net primary productivity (NPP) to elevated CO2 (eCa ) will increase with rising temperature and will be substantially larger in warm tropical forests than in cold boreal forests. We tested these model predictions against evidence from eCa experiments by carrying out two meta-analyses. Firstly, we tested for an interaction effect on growth responses in factorial eCa  × temperature experiments. This analysis showed a positive, but nonsignificant interaction effect (95% CI for above-ground biomass response = -0.8, 18.0%) between eCa and temperature. Secondly, we tested field-based eCa experiments on woody plants across the globe for a relationship between the eCa effect on plant biomass and mean annual temperature (MAT). This second analysis showed a positive but nonsignificant correlation between the eCa response and MAT. The magnitude of the interactions between CO2 and temperature found in both meta-analyses were consistent with model predictions, even though both analyses gave nonsignificant results. Thus, we conclude that it is not possible to distinguish between the competing hypotheses of no interaction vs. an interaction based on Rubisco kinetics from the available experimental database. Experiments in a wider range of temperature zones are required. Until such experimental data are available, model predictions should aim to incorporate uncertainty about this interaction., (© 2015 John Wiley & Sons Ltd.)