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Historical trends and controlling factors of isoprene emissions in CMIP6 Earth system models
Full text link•Terrestrial isoprene, a biogenic volatile organic compound emitted by many plants, indirectly influences Earth's radiative balance through its interactions with atmospheric oxidants, affecting ozone formation, methane lifetime, and secondary aerosol production. Elucidating its historical changes is therefore important for predicting climate change and air quality. Isoprene emissions can respond to climate (e.g. temperature, shortwave radiation, precipitation), land use and land cover change (LULCC), and atmospheric CO2 concentrations. However, historical trends of isoprene emissions and the relative influences of the respective drivers of those trends remain highly uncertain. This study addresses uncertainty in historical isoprene emission trends and their influential factors, particularly the roles of climate, LULCC, and atmospheric CO2 (via fertilization and inhibition effects). The findings are expected to reconcile discrepancies among different modelling approaches and to improve predictions of isoprene emissions and their climate change effects.
•To investigate isoprene emission trends, controlling factors, and discrepancies among models, we analysed long-term (1850–2014) global isoprene emissions from online simulations of CMIP6 Earth system models and offline simulations using the Vegetation Integrative SImulator for Trace gases (VISIT) dynamic vegetation model driven by climate reanalysis data.
•Mean annual global present-day isoprene emissions agree well among models (434–510 TgC yr−1) with a 5 % inter-model spread (24 TgC yr−1), but regional emissions differ greatly (9 %–212 % spread). All models show an increasing trend in global isoprene emissions in recent decades (1980–2014), but their magnitudes vary (+1.27 ± 0.49 TgC yr−2, 0.28 ± 0.11 % yr−1). Long-term trends of 1850–2014 show high uncertainty among models (−0.92 to +0.31 TgC yr−2).
•Results of emulated sensitivity experiments indicate meteorological variations as the main factor of year-to-year fluctuations, but the main drivers of long-term isoprene emission trends differ among models. Models without CO2 effects implicate climate change as the driver, but other models with CO2 effects (fertilization only/fertilization and inhibition) indicate CO2 and LULCC as the primary drivers. The discrepancies arise from how models account for CO2 and LULCC alongside climate effects on isoprene emissions. Aside from LULCC-induced reductions, differences in CO2 inhibition representation (strength and presence or absence of thresholds) were able to mitigate or reverse increasing trends because of rising temperatures or in combination with CO2 fertilization. Net CO2 effects on global isoprene emissions show the highest inter-model variation (σ=0.43 TgC yr−2), followed by LULCC effects (σ=0.17 TgC yr−2), with climate change effects exhibiting more or less variation (σ=0.06 TgC yr−2).
•The critical drivers of isoprene emission trends depend on a model's emission scheme complexity. This dependence emphasizes the need for models with accurate representation of CO2 and LULCC effects alongside climate change influences for robust long-term predictions. Important uncertainties remain in understanding the interplay between CO2, LULCC, and climate effects on isoprene emissions, mainly for CO2. More long-term observations of isoprene emissions across various biomes are necessary, along with improved models with varied CO2 responses. Moreover, instead of reliance on the current models, additional emission schemes can better capture isoprene emissions complexities and their effects on climate
Understanding digitalization’s environmental impact: why LCA is essential for informed decision-making [Comment]
Full text linkThis comment critiques Gritsenko et al.‘s dismissal of environmental assessments such as Life Cycle Analysis (LCA) in analyzing digitalization’s environmental impacts. While acknowledging the need for action amidst uncertainty, we argue that LCA yet provides valuable insights into potential impacts, trade-offs, and areas to focus on in a supply chain. Especially in the rapidly evolving digital landscape, LCA helps manage decision-makers’ uncertainty and informs targeted measures for sustainable digital infrastructure deployment and use
Urban greening for climate resilient and sustainable cities: grand challenges and opportunities
Full text link• Urban greening can improve air quality, health, biodiversity, and climate resilience.
• Collaboration among communities and stakeholders is key to urban greening success.
• Community engagement ensures acceptance and long-term success of green infrastructure.
• Barriers like funding, regulations, and resistance impede green infrastructure projects.
• Accurate data and planning are essential for maximizing green infrastructure benefits
Polar ectotherms more vulnerable to warming than expected
No full textPolar regions are heavily impacted by climate change. Yet, vulnerability assessments suggest little concern about heat-related challenges for polar terrestrial ectotherms. These conclusions are based, however, on assumptions and extrapolation from temperate regions; the limited data available suggest that polar ectotherms are more sensitive to warming than previously recognized
DNA extraction methodology has a limited impact on multitaxa riverine benthic metabarcoding community profiles
Full text linkThere is an expanding body of evidence that environmental DNA (eDNA) can serve as a reliable alternative to traditional assessments of biodiversity and ecological quality. Riverine benthic ecosystems represent one such habitat, holding significant promise for ecological health evaluations using eDNA. Diatoms have typically been assessed in these environmental biofilms through both molecular and conventional methods. However, a wide diversity of life has not been targeted previously, which may serve as important indicators of water quality. To be fully integrated into existing monitoring programs, it is essential to demonstrate the reliability of eDNA‐based assessments. This entails developing unbiased methodologies that capture total DNA across the entire community. DNA extraction from environmental samples is critical in analyzing microbial communities; nevertheless, current workflows often focus on individual kingdoms or communities. In this study, we investigated how extraction methodologies can bias the analysis of microbial community composition using amplicon sequencing at a cross‐kingdom level in river phytobenthos samples. We tested four commercially available DNA extraction methodologies on 23 freshwater benthic biofilm samples collected across a pH and conductivity gradient. Quantitative PCR and metabarcoding of four amplicons (16S, 18S, ITS, and rbcL), targeting bacterial, eukaryotic, fungal, and phototrophic communities, were employed to assess the impact of the DNA extraction kits on community evaluation. This study revealed a high level of similarity between methods incorporating mechanical lysis, which exhibited higher PCR and sequencing success rates as well as increased cross‐kingdom richness and differential abundance compared to chemical and enzymatic lysis alone. However, the origin of the samples, rather than the extraction methodology, emerged as the most significant factor linking them. We recommend utilizing mechanical lysis to optimize cross‐kingdom recovery from environmental samples. Nonetheless, the strong correlation between sample origin and extraction method implies that existing data gathered through alternative methodologies remain valid for informing future monitoring practices
Spatially-varying parametrization of the Total Runoff Integrating Pathways (TRIP) scheme for improved river routing at the global scale
No full text•Land Surface Models (LSMs), such as the Joint UK Land Environment Simulator (JULES), are pivotal in simulating biophysical processes on Earth’s surface, with key applications in assessing ecological impacts of climate change on hydrology. However, both model structural and parametric limitations persist in aspects like surface runoff routing, where the Total Runoff Integrating Pathways (TRIP) scheme used in JULES typically represents no sub-grid variability in river pathways by parametrizing uniform velocity and meander ratios. These uniform in space parameters often fail to capture the diverse hydrological characteristics of different river systems, leading to inaccuracies in predicting river flow patterns, especially in regions with complex topographical and hydrological dynamics.
•This study addresses this limitation by introducing a spatially varying parameterization scheme for these parameters, using data from 360 river basins around the globe. The optimization involved a factorial experiment, exploring different velocity and meander ratio setups. A function-fitting neural network was employed to correlate the optimal parameters with river basin physiographic attributes, utilizing data from the GRDC and HydroATLAS databases. The neural network’s results were then extrapolated globally.
•Results showed that the introduction of a new river routing configuration in the JULES model significantly improved the accuracy of river flow simulations, notably in capturing temporal flow dynamics. This led to an average increase of 0.10 in 10-day averaged Nash-Sutcliffe Efficiency across the 360 river basins. Additionally, this research has contributed to a new version of JULES, incorporating spatially varying routing parameters, marking a shift from the traditional fixed parameter approach. Our assessment revealed that with this updated configuration, about 75% of the total river area evaluated showed enhanced simulation performance compared to the default settings, marking a paradigm shift in LSMs for improved performance at the local conditions
Monitoring protected areas by integrating machine learning, remote sensing and citizen science
Full text link•1. Protected Areas (PAs) are central to addressing the world's biodiversity crisis, but their effectiveness for conservation varies. Therefore, high‐resolution habitat condition monitoring is needed to evaluate their individual impacts. Critically, monitoring must efficiently scale to cover large areas and be conducted at regular intervals.
•2. Remote sensing (RS) data and citizen‐science (CS) species data are two sources of global data available for habitat condition monitoring, and integrating these could provide high‐resolution, scalable biodiversity data required for the detailed monitoring of PAs. However, integrating these presents four data analysis challenges: RS data are large and complex, large‐scale CS data are biased, integrating RS and CS data is non‐trivial, and fine‐tuning to local priorities is required.
•3. Machine Learning (ML) methods can address these challenges: geospatial foundation models for RS data can compress large data volumes, ML de‐biasing techniques can improve CS data quality, deep learning and multimodal ML can help to integrate RS and CS data, and transfer learning can fine‐tune models to local priorities. Here, we review these techniques and discuss how they can be applied to habitat condition monitoring.
•4. Practical implication. Together, these advances in ML can deliver high‐resolution biodiversity data that can be tailored to local priorities, enabling the efficient monitoring of PAs at scale, with the potential to support spatial land use decision‐making
User priorities for hydrological monitoring infrastructures supporting research and innovation
Full text linkObservational data availability, quality, and access are major obstacles to hydrological science and innovation. To alleviate these issues, major investments are being made in hydrological monitoring infrastructures to enable data collection and sharing at unprecedented scales and resolution. These projects integrate a range of complex physical and digital components, which require careful design to prioritise the needs of end-users and optimise their value delivery. We present here the findings of multiple-methods research on end-user needs for a £38 million hydrological monitoring and research infrastructure in the UK, integrating a systematic literature review of common user-requirements with interviews of 20 national stakeholders. We find an overall trend in demand for infrastructures that complement their provision of baseline hydrological datasets, where feasible, with additional services designed specifically to enable wider and more decentralised data collection. This can unlock the capacities of user communities by addressing barriers to data collection through, for example, the provision of land access, reliable benchmark datasets, equipment rental and technical support. Similarly, value can be unlocked by providing data management services, including data access, storage, quality control, processing, visualisation and communication. Our respondents further consider digital and physical spaces where users can collaborate to be critical for incubating genuine value to science and innovation. We conclude that new hydrological monitoring infrastructures require concurrent investments to build and nurture associated research and innovation communities, where specific enabling support is provided to facilitate collaborations. Supplementing digital and monitoring services with support for data collection and collaboration among active, value-generating user communities can produce multiplier effects from initial capital investments, by attracting longer-term contributions of ideas, methods, findings, technologies, data, training and investments from their beneficiaries
Climate‐Driven Warming Disrupts the Symbiosis of Bobtail Squid Euprymna scolopes and the Luminous Bacterium Vibrio fischeri
No full textUnder the current climate crisis, marine heatwaves (MHW) are expected to intensify and become more frequent in the future, leading to adverse effects on marine life. Here, we aimed to investigate the impact of environmental warming on the symbiotic relationship between the Hawaiian bobtail squid (Euprymna scolopes) and the bioluminescent bacterium Vibrio fischeri. We exposed eggs of E. scolopes to three different temperatures during embryogenesis, namely: (i) 25°C (yearly average), (ii) 27°C (summer maximum) or (iii) 30°C (category IV MHW), followed by a colonisation assay under the same conditions. Decreased hatching success and reduced developmental time were observed across warmer conditions compared to 25°C. Moreover, exposure to the category IV MHW led to a significant decrease in survival after 48 h. With increasing temperature, bobtail squids required more bacteria in the surrounding seawater for successful colonisation. When colonised, the regression of the light organ's appendages was not dependent on temperature, but the opposite was found in non-colonised bobtail squids. Furthermore, the capacity for crypt 3 formation in the squid's light organ, which is crucial for enhancing resilience under stress, also declined with warming conditions. This study emphasises the critical need to study the dynamics of microbial symbiosis under the projected conditions for the ocean of tomorrow
Mekong River dry season changes due to hydropower dams and extractive processes: Making sense of contradictory community observations in Thailand, Laos and Cambodia
Full text linkThe Mekong is amongst the most important rivers in the world with regard to biodiversity and livelihood. Over the last few decades, however, the river has experienced dramatic hydrological changes, mainly due to the construction of large hydropower dams on the mainstream Mekong and its tributaries. Other potentially crucial factors include sand mining, erosion, embankment construction, and water extraction. In March and early April of 2024, we organised focus group interviews to discuss the changes that have occurred during the dry season with local people living in different communities along the mainstream Mekong River: 32 villages in eight provinces in northern and northeastern Thailand, 9 villages in Champassak Province, southern Laos, and 3 villages in Stung Treng Province, northeastern Cambodia. In this paper, we present some of the results of this research, particularly focusing on water level and turbidity changes, as local people along the Mekong River have varied understandings regarding whether there is more or less water in the Mekong River during the dry season. We argue that riverbed incision resulting largely from hydropower dam development and sand mining have, in particular, led many people living along the Mekong between northeastern Thailand and central Laos to incorrectly believe that there is less water in the Mekong River during the dry season compared to the past, while dry season water releases from upriver hydropower dams have led those in northern Thailand, lower northeastern Thailand, southern Laos, and northeastern Cambodia to assess that there is now more water in the Mekong River
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