The Arctic Ocean (AO) is being rapidly transformed by global warming, but its biodiversity remains understudied for many planktonic organisms, in particular for unicellular eukaryotes that play pivotal roles in marine food webs and biogeochemical cycles. The aim of this study was to characterize the biogeographic ranges of species that comprise the contemporary pool of unicellular eukaryotes in the AO as a first step toward understanding mechanisms that structure these communities and identifying potential target species for monitoring. Leveraging the Tara Oceans DNA metabarcoding data, we mapped the global distributions of operational taxonomic units (OTUs) found on Arctic shelves into five biogeographic categories, identified biogeographic indicators, and inferred the degree to which AO communities of unicellular eukaryotes share members with assemblages from lower latitudes. Arctic/Polar indicator OTUs, as well as some globally ubiquitous OTUs, dominated the detection and abundance of DNA reads in the Arctic samples. OTUs detected only in Arctic samples (Arctic-exclusives) showed restricted distribution with relatively low abundances, accounting for 10–16% of the total Arctic OTU pool. OTUs with high abundances in tropical and/or temperate latitudes (non-Polar indicators) were also found in the AO but mainly at its periphery. We observed a large change in community taxonomic composition across the Atlantic-Arctic continuum, supporting the idea that advection and environmental filtering are important processes that shape plankton assemblages in the AO. Altogether, this study highlights the connectivity between the AO and other oceans, and provides a framework for monitoring and assessing future changes in this vulnerable ecosystem.

This study investigates, in the context of a weak institution, the single and interactive effects of firms’ nonmarket strategies in China’s government procurement market. Based on transaction cost and signal theory, using data pertaining to Chinese government procurement contracts from 2016 to 2018, this study also provides evidence that superior corporate social responsibility (CSR) performance enables firms to obtain more government procurement contracts. However, the effect is only significant in non-state-owned enterprises. Considering the interaction effect of corporate political activity (CPA) and CSR in business to government (B2G) markets, this study finds that CPA, characterized by the political attributes of executives, enhances the signal effect of CSR in B2G markets. Further heterogeneity analysis indicates that CSR’s signal effect diminishes over time and is enhanced with high industry competition. Our findings provide new insights on nonmarket mechanisms (such as CSR), through which firms competing in China’s B2G market can compensate for the weak institution.

The ROMEO campaign (ROmanian Methane Emissions from Oil and gas) focused on measurements of methane (CH4) emission rates from oil and natural gas (O&G) production in Romania. The campaign took place in October 2019 and covered the southern part of Romania around the cities Bucharest, Ploiesti, Pitesti, and Craiova. This study presents emission rates calculated from mobile in situ measurement of CH4 and wind measurements using the Other Test Method 33a from U.S. Environmental Protection Agency and the Gaussian Plume Method. These methods were used to determine emission rates from 112 O&G well sites and other production-related facilities. Estimated mean CH4 emission rate with a 95% confidence interval equals 0.49 [0.35, 0.71] kg CH4 h−1 per site; 10% of all quantified sites account for 56% of the estimated emission rates. In addition, more than 1,000 O&G sites were visited for a qualitative “screening” (CH4 detection without quantification). Analysis of the screening data shows that 65% of the sites emitted methane at detectable rates. The CH4 emission rates obtained during the ROMEO campaign are comparable to the methane emission rates in study carried out in other Romanian regions.

Since committing to a 40%–45% reduction in methane emissions from the oil and gas industry in Canada by 2025, relative to 2012 levels, the federal government has reported significant emission reductions from the industry in its largest producing province, Alberta. At the same time, multiple measurement studies have shown that the industry’s emissions in Canada’s national greenhouse gas inventory are underreported, generally by a factor of 1.5 to 2. To better understand the source and reliability of claimed emission reductions, we developed an upstream oil and gas (UOG) methane emissions inventory model for the province of Alberta, 2011–2021, following government methodologies. The model revealed that historically only approximately 28% of Alberta’s UOG methane emissions are based on reported data, and although more comprehensive reporting was enforced in 2020, further analysis suggests that this reporting shift could represent a significant fraction of the apparent emission reductions since 2012. Reviewing the data and modeling assumptions underlying the inventory estimate revealed significant uncertainty in not only modeled emission sources but also in the operator-reported data. These findings imply that the reported emission trends since 2012 are highly uncertain, and even future emission factor updates might not improve the reliability in reported trends of emission reduction. This poses a significant problem for the validation of the stated 40%–45% reduction from 2012 levels. To improve the representativeness of both annual inventory magnitudes and the emission trends for the upstream sector in Alberta, we make recommendations to the Canadian federal and Alberta provincial governments.

Growing season drought can be devastating to crop yields. Soil microbial communities have the potential to buffer yield loss under drought through increasing plant drought tolerance and soil water retention. Microbial inoculation on agricultural fields has been shown to increase plant growth, but few studies have examined the impact of microbial inoculation on plant and soil microbial drought tolerance. We conducted a rainout shelter experiment and subsequent greenhouse experiment to explore 3 objectives. First, we evaluated the performance of a large rainout shelter design for studying drought in agricultural fields. Second, we tested how crop (corn vs. soybean) and microbial inoculation alter the response of soil microbial composition, diversity, and biomass to drought. Third, we tested whether field inoculation treatments and drought exposure altered microbial communities in ways that promote plant drought tolerance in future generations. In our field experiment, the effects of drought on soil bacterial composition depended on crop type, while drought decreased bacterial diversity in corn plots and drought decreased microbial biomass carbon in soybean plots. Microbial inoculation did not alter overall microbial community composition, plant growth, or drought tolerance despite our efforts to address common barriers to inoculation success. Still, a history of inoculation affected growth of future plant generations in the greenhouse. Our study demonstrates the importance of plant species in shaping microbial community responses to drought and the importance of legacy effects of microbial inoculation.

Marine species are widely threatened by anthropogenic activities, including fishing and human-induced climate change. However, geographically broad and spatially explicit assessments of the simultaneous impacts of these major threats at regional scales are mostly lacking due to the practical challenges of surveying vast geographical areas and obtaining adequately resolved data. Yet, these assessments are key for identifying highly and cumulatively impacted areas and species that should be prioritized for conservation through knowledge-based management strategies. Here, we analysed a 26-year (1993–2018) time series of highly resolved remotely sensed environmental data to evaluate changes in optimal habitat availability (i.e., extent of marine areas encompassing optimal environmental conditions) for 15 species representative of small, medium and large pelagic fish inhabiting the Mediterranean Sea Large Marine Ecosystem. We then combined spatial and temporal data on fishing pressure and changes in optimal habitats to identify areas of high risk of cumulative impacts. Overall, results show how most of the studied Mediterranean pelagic species experienced a reduction in optimal habitat availability over the past decades. The few species that showed positive trends in optimal habitat availability expanded only to a small degree and hence were unlikely to compensate for the loss of key functional roles at the group level. Habitat loss concentrated in the western and central regions. Similarly, fishing pressure was found to be higher in these regions, thus overlapping with the areas experiencing a higher reduction of optimal habitat. Small and large pelagic fish were the most impacted groups, having a larger proportion of their distributions in highly, cumulative impacted areas. Redistributing fishing pressure and reducing it in highly impacted areas may alleviate the overall cumulative pressure on pelagic stocks, contributing to the necessary shift to sustainable and resilient fisheries that would ensure food security and a healthy ecosystem in this highly impacted basin.

Arctic sea ice is changing rapidly. Its retreat significantly impacts Arctic heat fluxes, ocean currents, and ecology, warranting the continuous monitoring and tracking of changes to sea ice extent and thickness. L-band (1.4 GHz) microwave radiometry can measure sea ice thickness for thin ice ≤1 m, depending on salinity and temperature. The sensitivity to thin ice makes L-band measurements complementary to radar altimetry which can measure the thickness of thick ice with reasonable accuracy. During the Multidisciplinary drifting Observatory for the Study of Arctic Climate (MOSAiC) expedition, we deployed the mobile ARIEL L-band radiometer on the sea ice floe next to research vessel Polarstern to study the sensitivity of the L-band to different sea ice parameters (e.g., snow and ice thickness, ice salinity, ice and snow temperature), with the aim to help improve/validate current microwave emission models. Our results show that ARIEL is sensitive to different types of surfaces (ice, leads, and melt ponds) and to ice thickness up to 70 cm when the salinity of the sea ice is low. The measurements can be reproduced with the Burke emission model when in situ snow and ice measurements for the autumn transects were used as model input. The correlation coefficient for modeled Burke brightness temperature (BT) versus ARIEL measurements was approximately 0.8. The discrepancy between the measurements and the model is about 5%, depending on the transects analyzed. No explicit dependence on snow depth was detected. We present a qualitative analysis for thin ice observations on leads. We have demonstrated that the ARIEL radiometer is an excellent field instrument for quantifying the sensitivity of L-band radiometry to ice and snow parameters, leading to insights that can enhance sea ice thickness retrievals from L-band radiometer satellites (such as Soil Moisture Ocean Salinity (SMOS) and Soil Moisture Active Passive (SMAP)) and improve estimates of Arctic sea-ice thickness changes on a larger scale.

In the Arctic Ocean, sea ice algae are responsible for a small but seasonally important pulse of primary production. Their persistence is threatened by the rapid loss of sea ice from the Arctic Ocean due to climate change, but this threat will be at least partially offset by the replacement of multiyear ice (MYI) with first-year ice (FYI). FYI is thinner and usually features a thinner snow cover than MYI, thus transmitting more light to support ice algal growth. We combined remote sensing, reanalysis data, and modeling products with a radiative transfer model to assess how the changing physical conditions in the Arctic altered the extent and duration of the bottom ice algal habitat over a 34-year period. Habitat was defined as areas where enough light penetrates to the bottom ice to support net positive photosynthesis. The Arctic shifted from 37% FYI in 1985 to 63% in 2018, as the 2.0 × 106 km2 increase in FYI extent outpaced the 0.6 × 106 km2 decrease in overall sea ice extent above the Arctic Circle. The proliferation of younger ice corresponded with a 0.08 m decade–1 decrease in average sea ice thickness and a 0.003 m decade–1 decrease in average snow depth. The end of the ice algal season, marked by the onset of warm summer air temperatures, moved slightly earlier, by 1.4 days decade–1. Our analysis indicates that ice algal habitat extent increased by 0.4 × 106 km2 decade–1, or from 48% to 66% of total sea ice extent. The average ice algal growing season also lengthened by 2.4 days and shifted earlier in the year. Together, these trends suggest that net primary production in Arctic sea ice increased during 1985–2018. The most dramatic changes were localized in the Central Basin and the Chukchi Sea and were driven primarily by the declining snow cover and the shift from MYI to FYI. Although the Arctic recently became more favorable to ice algae, we expect that this trend will not continue indefinitely, as a limited amount of MYI remains.

Vertical heat conduction through young ice is a major source of wintertime sea ice growth in the Arctic. However, field observations indicate that young ice preferentially accumulates wind-blown snow, resulting in greater snow thickness on young ice than would be expected from precipitation alone, and hence greater snow thickness on young ice than climate models represent. As snow has a low thermal conductivity, this additional snow thickness due to redistribution will reduce the actual heat conduction. We present new observations from the Multidisciplinary drifting Observatory for the Study of Arctic Climate Expedition which show that young ice rapidly accumulates a snow thickness of 2.5–8 cm, when wind-blown snow is available from the nearby mature ice. By applying a simple redistribution scheme and heat flux model to simulated conditions from the Community Earth System Model 2.0, we suggest that neglecting this snow redistribution onto young ice could result in the potential overestimation of conductive heat flux—and hence ice growth rates—by 3–8% on average in the Arctic in the winter in the absence of climate feedbacks. The impacts of snow redistribution are highest in the springtime and in coastal regions.

We present a holistic examination of tropospheric OH reactivity (OHR) in South Korea using comprehensive NASA DC-8 airborne measurements collected during the Korea–United States Air Quality field study and chemical transport models. The observed total OHR (tOHR) averaged in the planetary boundary layer (PBL, <2.0 km) and free troposphere was 5.2 s−1 and 2.0 s−1 during the campaign, respectively. These values were higher than the calculated OHR (cOHR, 3.4 s−1, 1.0 s−1) derived from trace-gas observations, indicating missing OHR fractions in the PBL and free troposphere of 35% and 50%, respectively. Incorporating nonobserved secondary species from the observationally constrained box model increased cOHR to 4.0 s−1 in the PBL and 1.3 s−1 in the free troposphere. Simulated OHR (sOHR, 2.7 s−1, 0.8 s−1) was substantially lower than both tOHR and cOHR by as much as 60%. This underestimate was substantial in the free troposphere and marine boundary layer of the marginal sea (Yellow Sea). We then discuss the potential causes of unaccounted OHR. First, we suggest improving the accuracy of tropospheric reaction kinetics, which vary significantly in the available literature. Second, underestimated emissions of anthropogenic CO and oxygenated volatile organic compounds in East Asia contributed to the discrepancy between tOHR and sOHR. In addition, oxygenated and biogenic volatile organic compounds emitted from the marginal sea may contribute substantially to the regional OHR. Typical chemical transport models underestimate these sources, leading to a large missing OHR fraction. Despite this discrepancy, we found that simulated OH concentrations were comparable with those observed during the campaign because of slow OH recycling rates in the models; therefore, the models predicted less formation of photochemical oxidation products such as ozone.