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Reducing material use and their greenhouse gas emissions in Greater Oslo
Get PDFResource efficiency strategies are key to reduce material use and help limit global warming to below 2 degrees C in 2100. Understanding the role of such strategies at the municipal level requires a localized approach. Here we evaluate a ramp-up of resource efficiency strategies and their associated effects on car use and climate benefits toward 2050 for 19 individual subregions within the Greater Oslo region in Norway. In our scenarios, material stocks increase from 356 megatonnes (Mt) in 2022 to 361-381 Mt in 2050 driven by population growth, with low-end estimate relying on a sufficiency (SUF) scenario limiting floor area per capita and banning new single-family houses. The SUF scenario reduces total material consumption until 2050 (50.5 Mt) with 28% relative to a business-as-usual (BAU) scenario (70.8 Mt) with continuation of ongoing trends, thereby reducing greenhouse gas (GHG) emissions from material production by 21% (BAU: 11.8 MtCO2-eq, SUF: 9.4 MtCO2-eq). If resource efficiency strategies are combined with material production decarbonization in-line with a 2 degrees C scenario, a 35% reduction in emissions is achievable (7.7 MtCO2-eq). Car ownership rates and traveled distance per capita decrease in the SUF scenario compared to 2022 with 11%. Assuming the current relationship between settlement characteristics and transport demand, total driving distance fails to decline due to population growth. Limiting the floor-area per capita in residential buildings significantly decreases material demand. Resource efficiency strategies including densification need to be complemented with a rapid decarbonization of material supply and stronger incentives to move away from car driving to maximize climate change mitigation. This article met the requirements for a gold-gold JIE data openness badge described at
Navigating climate threats in forestry across five European regions: Stakeholder's adaptive management and policy strategies to resilience
Get PDFThis study explores the perspectives and adaptive strategies of forest stakeholders across five regions of Europe, North to South-Finland, Lithuania, Romania, Serbia, and Greece-regarding climate change challenges in forestry. 129 stakeholders were surveyed, including forest owners, professionals, environmental NGOs, government representatives, and recreationists, who pointed at soil quality, biodiversity, carbon sequestration, and timber production as the main concerns. Regional threats varied, with storms and pests prevailing in Finland, illegal logging in Lithuania, Romania and Serbia, and fires and unsustainable grazing in Greece. Proposed solutions emphasise active forest management, stakeholder engagement and policy reforms. While Finland and Serbia are optimistic about future forest resilience, Lithuania and Romania are neutral. Greece shows mixed reactions, mainly due to concerns about the political will to implement effective forest policy. The study highlights nuanced regional responses to climate-related forest challenges and the need for region-specific approaches to forest management and policy, with broader implications for environmental governance strategies
Living labs som samverkansinsats för markhälsa: exempel från Sverige inom jordbruk, skogsbruk och urbana miljöer
Get PDFMarkförstöring är en utmaning i hela Europa, vilket hotar miljömässig hållbarhet, jordbruksproduktivitet och människors välbefinnande. Som repsons på detta syftar Europeiska kommissionens Mission Soil1 -initiativ till att etablera 100 Living Labs (LL) och Lighthouses fram till 2030 för att främja innovativ och hållbar markhälsa. Living Labs definieras av tre grundprinciper2 : samverkan mellan olika aktörer, användarcentrerad innovation och verkliga miljöer, vilket möjliggör att olika intressenter tillsammans kan utveckla, testa och skala upp lösningar som hanterar markrelaterade utmaningar och markhälsa. Denna rapport undersöker hur principerna för Living Labs återspeglas i tre befintliga svenska initiativ inom jordbruk, skog och urbana miljöer med samarbete med flera aktörer. Syftet är att förstå hur dessa samarbeten överensstämmer med Living Lab-konceptet samt att identifiera deras potentiella bidrag till Mission Soil:s mål för markhälsa. Med hjälp av en kriterie-baserad kvalitativ analys utvärderar rapporten hur dessa initiativ fungerar som plattformar för innovation och samverkan för att stärka markhälsa. Fallstudierna har utvecklats under olika tidsperioder, från etablerade initiativ till nyare projekt, och representerar utvecklingen av de tre fallen. Resultaten visar att samtliga tre fall fungerar redan i linje med Living Lab-principer, såsom att främja tvärsektoriellt samarbete, engagemang av användare, och aktivt främjande av innovation i en geografisk kontext. Fallen visade på bidrag till miljömässiga och sociala effekter, inklusive förbättrad biologisk mångfald och markhälsa samt ökad allmän medvetenhet. Varje initiativ uppvisade utveckling av praktiska verktyg, samverkan för kunskapsutveckling och pilotutveckling av lösningar anpassade till lokala behov som stödjer regional utveckling. Även om angreppssätt och kontext varierar, visar vår analys att utmaning för dessa fallstudier betsår i långsiktig finansiell stabilitet. Sammanfattningsvis visar rapporten hur befintliga samverkansinitiativ i Sverige kan bidra till Mission Soils agenda för Living Labs, som bidrar till hållbar markhälsa och innovations-system. För forskare understryker dessa tre fallstudier betydelsen av tillämpad, tvärvetenskaplig forskning för att främja markhälsa i olika miljöer. Beslutsfattare kan dra lärdomar om hur man mobiliserar ramverk som stödjer regionalt samarbete, deltagande metoder, innovationsprocesser och uppskaling av pilotverksamhet
Fungal and arbuscular mycorrhizal communities unique to old grasslands in a Swedish agricultural landscape
Get PDFFungal and arbuscular mycorrhizal communities in semi-natural grassland soils depend on management regime, but we lack knowledge about these communities in a north European agricultural landscape context. Several species inhabiting semi-natural grasslands are of a high conservational interest, but their presence in nearby short-term grasslands such as leys is unknown. We investigated fungi and arbuscular mycorrhizal fungi (AMF) using DNA metabarcoding and quantitative PCR in soils and roots in nine semi-natural grasslands and adjacent leys and assessed unique and overlapping community members and their guilds. We observed a generally higher abundance and alpha diversity of total fungi and AMF in grasslands than in leys, but only in the uppermost soil layer. At the landscape level, leys also had more similar fungal and AMF communities than grasslands. Both fungal and AMF community composition differed between grasslands and leys, with higher relative abundance of root-associated ascomycetes, saprotrophic basidiomycetes and AMF Glomeraceae in grasslands, and more pathogens and dung saprotrophs in leys. The fraction of species shared between soils and roots was higher in grasslands than in leys. We identified distinct fungal and AMF communities associated with semi-natural grasslands that could be of interest for conservation purposes. Assessing how these communities respond to management will be important for proposing conservational measures. The higher abundances of saprotrophic basidiomycetes and root-associated ascomycetes in grasslands than in leys, together with a greater overlap of species between soils and roots, suggests that processes in soils may be more interconnected with roots via fungi in semi-natural grasslands
Harnessing novel genetic markers for scald resistance from gene bank spring barley genotypes
Get PDFBackground Scald caused by Rhynchosporium graminicola is a common foliar disease affecting barley production worldwide. Identifying and utilizing scald resistance genes and quantitative trait loci (QTL) to develop barley cultivars with durable and effective resistance to scald is crucial. Results In the present study, we evaluated 275 spring barley genotypes together with 4 commercial check cultivars under controlled conditions and examined the underlying genetics of scald resistance in these genotypes. A significant genetic variation (P value < 0.0001) for scald resistance was observed among the tested barley germplasms. A genome-wide association study (GWAS) identified eight markers-trait associations (MTAs) forming seven QTL located on chromosomes 3H, 6H, and 7H, of which three are novel. The allelic effects of these MTAs were further examined, and favorable alleles associated with scald resistance were identified. Conclusions The identification of QTL for scald resistance, along with favorable allele identification, will be crucial for marker-assisted breeding programs. These findings will facilitate the development of new scald-resistant cultivars and contribute to the sustainability of barley production. Further studies, such as fine-mapping of candidate genes within these identified QTL regions, will help to narrow down the potential causative genetic variants and understand their functional effects on scald resistance
WetCH4: a machine-learning-based upscaling of methane fluxes of northern wetlands during 2016-2022
Get PDFWetlands are the largest natural source of methane (CH4) emissions globally. Northern wetlands ( > 45 degrees N), accounting for 42 % of global wetland area, are increasingly vulnerable to carbon loss, especially as CH4 emissions may accelerate under intensified high-latitude warming. However, the magnitude and spatial patterns of high-latitude CH4 emissions remain relatively uncertain. Here, we present estimates of daily CH4 fluxes obtained using a new machine learning-based wetland CH4 upscaling framework (WetCH(4)) that combines the most complete database of eddy-covariance (EC) observations available to date with satellite remote-sensing-informed observations of environmental conditions at 10 km resolution. The most important predictor variables included near-surface soil temperatures (top 40 cm), vegetation spectral reflectance, and soil moisture. Our results, modeled from 138 site years across 26 sites, had relatively strong predictive skill, with a mean R 2 of 0.51 and 0.70 and a mean absolute error (MAE) of 30 and 27 nmol m(-2) s(-1) for daily and monthly fluxes, respectively. Based on the model results, we estimated an annual average of 22.8 +/- 2.4 Tg CH4 yr(-1) for the northern wetland region (2016-2022), and total budgets ranged from 15.7 to 51.6 Tg CH4 yr(-1), depending on wetland map extents. Although 88 % of the estimated CH4 budget occurred during the May-October period, a considerable amount ( 2.6 +/- 0.3 Tg CH4) occurred during winter. Regionally, the Western Siberian wetlands accounted for a majority (51 %) of the interannual variation in domain CH4 emissions. Overall, our results provide valuable new high-spatiotemporal-resolution information on the wetland emissions in the high-latitude carbon cycle. However, many key uncertainties remain, including those driven by wetland extent maps and soil moisture products and the incomplete spatial and temporal representativeness in the existing CH4 flux database; e.g., only 23 % of the sites operate outside of summer months, and flux towers do not exist or are greatly limited in many wetland regions. These uncertainties will need to be addressed by the science community to remove the bottlenecks currently limiting progress in CH4 detection and monitoring. The dataset can be found at 10.5281/zenodo.10802153 (Ying et al., 2024)
Assessing CO2 Fluxes for European Peatlands in ORCHIDEE-PEAT With Multiple Plant Functional Types
Get PDFPeatlands are significant carbon reservoirs vulnerable to climate change and land use change such as drainage for cultivation or forestry. We modified the ORCHIDEE-PEAT global land surface model, which has a detailed description of peat processes, by incorporating three new peatland-specific plant functional types (PFTs), namely deciduous broadleaf shrub, moss and lichen, as well as evergreen needleleaf tree in addition to previously peatland graminoid PFT to simulate peatland vegetation dynamic and soil CO2 fluxes. Model parameters controlling photosynthesis, autotrophic respiration, and carbon decomposition have been optimized using eddy-covariance observations from 14 European peatlands and a Bayesian optimization approach. Optimization was conducted for each individual site (single-site calibration) or all sites simultaneously (multi-site calibration). Single-site calibration performed better, particularly for gross primary production (GPP), with root mean square deviation (RMSD) reduced by 53%. While multi-site calibration showed limited improvement (e.g., RMSD of GPP reduced by 22%) due to the model's inability to account for spatial parameter variations under different climatic contexts (trait-climate correlations). Site-optimized parameters, such as Q10, the temperature sensitivity of heterotrophic respiration, revealed strong empirical relationships with environmental factors, such as air temperature. For instance, Q10 decreased significantly at warmer sites, consistent with independent field data. To improve the model by using the lessons from single-site optimization, we incorporated two key trait-climate relationships for Q10 and Vcmax (maximum carboxylation rate) into a new version of the ORCHIDEE-PEAT models. Using this description of spatial variability of parameters holds significant promise for improving the accuracy of carbon cycle simulations in peatlands
A population Monte Carlo model for underwater acoustic telemetry positioning in reflective environments
Get PDFUnderwater acoustic telemetry positioning is widely used to track the fine-scale movements of aquatic animals. In study areas near acoustically reflective surfaces, reflected transmissions may cause large detection outliers that can severely reduce the accuracy of positioning models. A novel time-of-arrival model for telemetry positioning is presented that utilizes a population Monte Carlo algorithm to solve positions (termed PMC-TOA). Telemetry detection error is modelled as a mixture distribution, allowing reflected detections to be identified and positions to be estimated despite their presence. Importantly, the PMC-TOA model provides good measures of positioning uncertainty, facilitating the use of post-processing state-space models to further refine position estimates. A simulated telemetry study is used to validate the PMC-TOA model and compare its performance to a conventional time-difference-of-arrival positioning model. A real case study on Atlantic salmon (Salmo salar) smolt passage behaviour is further used to demonstrate how PMC-TOA can be combined with post-processing models to produce high-resolution tracks. The resulting tracks are compared against those resulting from YAPS and TDOA positioning. The PMC-TOA model was shown to work well as either (i) a pre-processing step to remove reflected transmissions from time-of-arrival datasets, or (ii) a fast and accurate positioning method when paired with a post-processing state-space model. Positions returned by the model can be further used for animal movement statistics, allowing researchers to test the effects of experimental or environmental factors on the fine-scaled movement behaviours of aquatic animals in acoustically challenging environments
Genome-wide association analysis revealed genetic markers linked to grain yield and yield related traits in finger millet grown in acidic soils
Get PDFAimSoil acidity has a major impact on the finger millet yield and productivity as tolerant cultivars that perform well in acidic soils are limited. This study aimed at evaluating major finger millet phenotypic traits under acidic soils followed by identifying associated markers.MethodA total of 288 finger millet genotypes were field evaluated for 8 major phenotypic traits including grain yield under acid soil conditions at two independent locations (Bako and Gute) in Ethiopia. In parallel, the same genotypes were subjected to genotyping-by-sequencing to generate single nucleotide polymorphism markers to be used in the association panel.ResultsPhenotypic data analysis revealed significant phenotypic variation in all the targeted traits among the studied genotypes. Genotypes Ec-100093, Ec-215803, and Ec-203322 were relatively high-yielding, whereas genotypes Ec-229721 and Ec-242110 had the lowest grain yield across the two locations. The broad-sense heritability of the traits ranged from 0.04 for the number of effective tillers (NET) to 0.78 for days to emergence (DE). The marker-trait association analysis revealed 23 SNP markers significantly associated with one or more traits. Among the 23 significant markers, one marker associated with DE, seven with days to heading (DH), four with days to maturity (DM), one with plant height (PH), two with number of fingers, two with ear length (EL), three with the number of effective tillers (NET) and three with grain yield (GY).ConclusionsThe identified novel markers associated with the targeted traits will potentially be useful for genomics-driven finger millet improvement in acidic soils
Genomic insights into antimicrobial resistance and virulence of E. coli in central Ethiopia: a one health approach
Get PDFAntimicrobial resistance is a global threat causing millions of deaths annually. The study aimed to identify antibiotic resistance genes (ARGs), mobile genetic elements (MGEs), and virulence genes (VGs) and track their dissemination among E. coli isolates. Seventy-seven isolates from calves, environments, and human sources were studied. The study involved WGS sequencing, bacterial strains characterized; pan genome, multi-locus sequence typing, and serotyping using O-, and H-typing. The ARGs, VGs, and MGEs were identified using ABRicate against selected respective databases. A maximum likelihood SNP (single nucleotide polymorphism) tree was constructed and visualized with an interactive tree of life (IToL). Descriptive statistics were used to analyze the data. Seventy-seven of the isolates were identified as E. coli, later grouped into 5 clades and four known phylogroups. ST10 and O16:H48 were most prevalent in 12 and 42 isolates, respectively. There were about 106 unique ARGs detected between 1.3% and 91.9%, with 57 detected in 40% of isolates. In terms of ARGs, the most common were bla-ampH (90.9%), bla-AmpC1 (89.6%), tet(A) (84.4%), mdf(A) (81.8%), aph(3")-Ib (79%), sul2 (79%), aph(6)-Id (75%), and bla-PBP (70%). It was found that 95 percent (96/106) of ARGs came from at least two sources. The majority of detected ARGs exhibited high concordance between phenotypic resistance and ARGs profiles (JSI >= 0.5). In eight isolates, mutations in the gyrA (3) and par-C/E (5) genes led to ciprofloxacin and nalidixic acid resistance. The most common co-occurrences of ARG and MGE were Tn3 with bla-TEM-105 (34), Int1 with sul1 (13), and dhfr7 (11). Meanwhile, the most frequently detected VGs (n >= 71 isolates) included elfA-G, fimB-I, hcpA-C, espL, ibeC, entA, fepA-C, ompA, ecpA-E, fepD, fes, and ibeB. Nearly, 88.3% (128/1450) VGs were shared in isolates from at least two sources. ETEC (53.2%), EAEC (22.1%), and STEC (14.3%) were the three most frequently predicted pathotypes. Despite significant ST diversity, ARGs and VGs showed an extensive distribution among the study groups. These findings suggest limited clonal transmission of isolates. In comparison, the wide distribution of ARGs and VGs may be attributed to horizontal gene transfer driven by similar antibiotic selection pressures in the study area