Philippa Kaur

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This Hubble image captures a set of galaxies that are unusual because they seem not to have dark matter.Credit: NASA/ESA/P. van Dokkum, Yale Univ.

Galaxies without dark matter baffle astronomersScientists have long thought that galaxies cannot form without the gravitational pull of the mysterious material known as dark matter. But one group of astronomers thinks it might have observed a line of 11 galaxies that don’t contain any of the substance, and could all have been created in an ancient collision (P. van Dokkum et al. Nature 605, 435–439; 2022).This kind of system could be used to learn about how galaxies form, and about the nature of dark matter itself. However, some researchers are not convinced that the claim is much more than a hypothesis.The finding centres on two galaxies, called DF2 and DF4, that were described in 2018 and 2019. Their stars moved so slowly that the pull of dark matter was not needed to explain their orbits, so the team concluded that the galaxies contained no dark matter.In the latest research, scientists identified between three and seven new candidates for dark-matter-free galaxies in a line between DF2 and DF4, as well as strange, faint galaxies at either end.“If proven right, this could certainly be exciting for galaxy formation. However, the jury is still out,” says Chervin Laporte, an astronomer at the University of Barcelona in Spain.Northern Australian tree deaths double in 35 yearsThe rate at which trees are dying in the old-growth tropical forests of northern Australia each year has doubled since the 1980s, and researchers say climate change is probably to blame.The findings, published in Nature on 18 May, come from an extraordinary record of tree deaths catalogued at 24 sites in the tropical forests of northern Queensland over the past 49 years (D. Bauman et al. Nature https://doi.org/hv67; 2022).The research team recorded that 2,305 trees across 81 key species had died since 1971. But from the mid-1980s, tree mortality risk increased from an average of 1% a year to 2% a year (see ‘Increasing death rate’). Of the 81 tree species that the team studied, 70% showed an increase in mortality risk over the study period.The study found that the rise in death rate occurred at the same time as a long-term trend of increases in the atmospheric vapour pressure deficit, which is the difference between the amount of water vapour that the atmosphere can hold and the amount of water it does hold at a given time. The higher the deficit, the more water trees lose through their leaves, which can lead to sustained stress and eventually tree death.

Europe’s first farming populations descend mostly from farmers in the Anatolian peninsula, in what is now Turkey.Credit: Fatih Kurt/Anadolu Agency/Getty

Ancient DNA maps ‘dawn of farming’Sometime before 12,000 years ago, nomadic hunter-gatherers in the Middle East made one of the most important transitions in human history: they began staying put and took to farming.Two ancient-DNA studies have now homed in on the identity of the hunter-gatherers who settled down.Researchers sequenced the genomes of 15 hunter-gatherers and early farmers who lived in southwest Asia and Europe, along a key migration routes into Europe — the Danube River (N. Marchi et al. Cell https://doi.org/gp49rr; 2022).The team found that ancient farmers in Anatolia — now Turkey — descended from repeated mixing between distinct hunter-gatherer groups from Europe and the Middle East. These groups first split at the height of the last Ice Age, some 25,000 years ago. Modelling suggests that the western groups nearly died out, before rebounding as the climate warmed.Once established in Anatolia, the researchers found, early farmers moved west into Europe in a stepping-stone-like way, beginning around 8,000 years ago. They mixed occasionally — but not extensively — with local hunter-gatherers.The findings chime with those of a similar ancient-genomics study posted on the bioRxiv preprint server this month (M. E. Allentoft. et al. Preprint at bioRxiv https://doi.org/hv7g; 2022). More

Ambitious new targets are needed to conserve nature by protecting parks and species.Credit: Tang Dehong/VCG/Getty

It will take ample time and money to slow the world’s catastrophic loss of plant and animal species — and right now, both are running dangerously low. This year, nations are due to agree to an action plan to protect global biodiversity at the 15th Conference of the Parties (COP15) to the United Nations Convention on Biological Diversity. But the meeting is already two years late because of the pandemic, and China, which will host the conference in Kunming, has yet to set a new date.Now, conflicts over financing are adding to the tension. Conservation groups and advocates suggest that rich nations must donate at least US$60 billion annually to help less-affluent ones to fund projects such as protecting areas where wildlife can thrive and tackling the illegal wildlife trade that is driving hundreds of species to extinction. This is much more than the $4 billion to $10 billion that they are estimated to be spending today, and well below the amount they are giving low- and middle-income countries (LMICs) to fight climate change, which reached around $50 billion in 2019 according to one estimate. Yet limited overseas development funds are spread ever thinner as donors deal with the pandemic and now the fallout from Russia’s invasion of Ukraine. This is where COP15 is meant to deliver: as well as agreeing to the action plan, called the Global Biodiversity Framework, nations will be encouraged to pledge more money.A mix of public and private money has started to trickle in. Currently, biodiversity funding on the table ahead of COP15 amounts to roughly $5.2 billion per year, according to estimates by a group of five leading conservation organizations. Most comes from six governments, including France, the United Kingdom and Japan, and the European Union. In April, the Global Environment Facility (GEF) — a multilateral fund to support international environmental agreements — announced that, over the next four years, around $1.9 billion will go to projects dedicated to biodiversity. However, it’s unclear how much of this will come from the coffers that donor countries have already pledged.Some cash for conservation is coming from private philanthropic donors — such as $2 billion committed by entrepreneur Jeff Bezos last year. And starting in 2020, a group of financial institutions (now 89 of them) promised to annually report their financing activities and investments that affect biodiversity, and to move away from those that do harm — a form of ecological accounting that could help to shrink the budget needed to protect biodiversity. Donors will need to reach much deeper into their pockets to meet the demands of LMICs, the custodians of much of the world’s biodiversity. In March, a group of LMICs, led by Gabon, asked for $100 billion per year in new funding when officials met in Geneva, Switzerland, to discuss progress on the Global Biodiversity Framework. The LMICs want the money placed in a new multilateral fund for biodiversity, separate from, but complementary to, the GEF.Aside from cash, the fund will need to find a new home and structure — and there are a few options. A proposal from Brazil, circulated at the Geneva meeting, suggests the fund be governed by a board of 24 members, with an equal number from rich and lower-income nations. The board would be responsible for funding decisions and would prioritize projects that help to achieve the biodiversity convention’s goals. The pitch generated interest among some countries, but also concerns that it’s an attempt by Brazil to divert attention from its failure over the past few years to protect the Amazon rainforest and prevent other environmental harm.Another option is the Kunming Biodiversity Fund, which China announced in October last year to help LMICs to safeguard their ecosystems. It allocated 1.5 billion yuan (US$223 million) to seed the fund and invited other countries to contribute, but so far none has. Sources knowledgeable about the fund say that donor countries are reluctant to pitch in because China is holding on too tightly to the reins and is not involving others in its deliberations. Details of how the fund will operate are scarce, but Nature has learnt that China is floating the idea of housing it at the Asian Infrastructure Investment Bank (AIIB), based in Beijing. Set up in 2016, the AIIB has $100 billion in total capital and 105 members, including Germany, France and the United Kingdom. The AIIB has big green plans. By 2025, it wants half of all infrastructure projects it finances to focus on climate issues. With rigorous oversight and transparency, the AIIB would make a good home for the Kunming fund.As countries prepare to meet in Nairobi on 20–26 June in a last-ditch attempt to push the biodiversity framework forwards before COP15, China, as the host, must urgently provide stronger leadership on financing, including more transparency and engagement. Progress will require quick, generous contributions from donor nations — which should prioritize grants, not loans, for biodiversity projects.Holding the COP15 meeting must be a priority, too. As China tightens restrictions in the face of a COVID-19 surge, some researchers fear that delays will stretch on, stalling conservation work and leaving less time to meet biodiversity targets. China must either commit to holding the meeting this year or let it proceed elsewhere. One option being quietly discussed is moving the meeting to Canada — home of the United Nations biodiversity convention’s secretariat — and this deserves consideration. The world needs an ambitious biodiversity plan now — nature cannot wait. More

Literature reviewWe conducted a comprehensive review of relevant literature published since 1995. Studies were extracted from the China National Knowledge Infrastructure and Web of Science using the following keywords: “N (nitrogen) loss OR NO (nitric oxide) emission OR N2O (nitrous oxide) emission OR NH3 (ammonia volatilization) emission OR NO3− (nitric leaching) OR N (nitrogen) runoff AND wheat AND China”. We excluded the following types of experiment: experiments not covering the entire wheat growing season, experiments conducted in greenhouses or laboratories, experiments without zero-N control, and experiments including manure, controlled release fertilizer, or inhibitors. In total, we extracted 941 observations from 138 articles, consisting of 121 observations of NO emission, 383 of N2O emission, 185 of NH3 emission, 188 of NO3− leaching, and 64 of Nr runoff. We also extracted data on N application rates, and climate and soil variables (Fig. 1). Missing climate data were obtained from China Meteorological Data Network (https://data.cma.cn/), miss values of soil organic carbon (SOC) and total N content were obtained from the National Scientific Fertilizer Network (http://kxsf.soilbd.com/), and missing soil silt, clay, sand content, bulk density, cation exchange capacity (CEC), and pH data were obtained from the Harmonized World Soil Database (HWSD) v. 1.2 (http://www.fao.org/soils-portal/soil-survey/soilmaps-and-databases/harmonized-world-soildatabase-v12/en). Based on this dataset, the EFs of Nr loss pathways were calculated by the following equation:$$E{F}_{i}=left({E}_{treatment}{rm{-}}{E}_{control}right){rm{/}}N;applied$$
(1)
where i = 1–5, represented NO, N2O, NH3, NO3− leaching and Nr runoff, respectively. Etreatment is the loss rate of experimental treatments with applied N fertilizer, Econtrol is the loss rate of experimental control without applied N fertilizer, and N applied is the N application rate corresponding to Etreatment. The resulting data was used to develop RF models to predict EFs of the five Nr loss pathways.Fig. 1The generate framework of the Nr loss from Chinese wheat system (Nr-Wheat) 1.0 database.Full size imageRF modelsRF models outperformed empirical models in previous studies15,18,19. We employed RF models to predict the EFs of NO, N2O, NH3, NO3− leaching, and Nr runoff. Environmental factors were selected via redundancy analysis20. Redundancy analysis, a basic ordination technique for gradients analysis, produces an ordination summarizing the variation in several response variables that can be best explained by a matrix of explanatory variables based on multiple linear regression. We conducted redundancy analysis using Canoco 5 to further analyze the effects of 10 environmental factors, including 4 soil physical factors (bulk density, silt, clay, and sand content), 4 soil chemical factors (pH, SOC, CEC and total N content), and 2 weather factors (total rainfall and mean temperature during the wheat growing period) of different EFs. Ultimately, the dataset of each pathway contained an ensemble of different environmental factors (Table 1).Table 1 Environmental factors were employed to build RF model for each pathway and total explanatory rates.Full size tableWhen establishing the RF model, the first step was to select k features from a total of m (k  More

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Mesocosm experimentsSi:Nexport measurementsBetween 2010 and 2014, we conducted five in situ mesocosm experiments to assess impacts of OA on natural plankton communities. Study sites covered a large latitudinal gradient (28 °N–79 °N) and diverse oceanic environments/ecosystems (Extended Data Fig. 1 and Extended Data Table 1). Sample collection and processing was conducted every 1 or 2 days throughout the experiments. Sinking particulate matter was obtained from sediment traps attached to the bottom of each mesocosm, thereby collecting the entire material sinking down in the enclosed water column36. Processing of sediment trap samples followed a previous protocol37. Samples for particulate matter suspended in the water column were collected with depth-integrating water samplers (HYDRO-BIOS) and filtered following standard procedures. Biogenic silica was leached from the sediment trap samples and filters by alkaline pulping (0.1 M NaOH at 85 °C). After 135 min the leaching process was terminated with 0.05 M H2SO4 and dissolved silica was measured spectrophotometrically38. Carbon and nitrogen content were determined using an elemental CN analyser (EuroEA)39.Analysis of OA impactsTo test for a systemic influence of OA on Si:Nexport, we synthesized the datasets from the different experiments and (i) conducted a meta-analysis to quantify effect sizes, and (ii) computed probability density estimates. Because the experimental design, the range of CO2 treatments, and the time periods for our analysis of Si:Nexport varied to some extent among experiments (Extended Data Table 1), we pooled mesocosms for ambient conditions and in the ({{p}}_{{{rm{CO}}}_{2}}) range of ~700–1,000 μatm (‘OA treatment’), corresponding to end-of-century values according to RCP 6.0 and 8.5 emission scenarios15. Effect sizes were calculated as log-transformed response ratios lnRR, an approach commonly used in meta-analysis40:$${rm{l}}{rm{n}}{rm{R}}{rm{R}}={rm{l}}{rm{n}}{X}_{{rm{O}}{rm{A}}}-{rm{l}}{rm{n}}{X}_{{rm{c}}{rm{o}}{rm{n}}{rm{t}}{rm{r}}{rm{o}}{rm{l}}},$$where X is the arithmetic mean of Si:Nexport ratios under OA and ambient conditions (Extended Data Table 1). Effect sizes 0 indicate that the effect was positive. Effects are considered statistically significant when 95% confidence intervals (calculated from pooled standard deviations) do not overlap with zero. The overall effect size across all studies was computed by weighing individual effect sizes according to their variance, following the common methodology for meta-analyses40. In addition, we computed probability densities of Si:Nexport based on kernel density estimation, which better accounts for data with skewed or multimodal distributions41. Another advantage of this approach is that it does not require the calculation of temporal means. Instead, the entire data timeseries can be incorporated into the analysis, thus retaining information about temporal variability. Confidence intervals of the density estimates were calculated with a bootstrapping approach using data resampling (1,000 permutations)41. The resulting probability density plots can be interpreted analogously to histograms. Differences among ambient and OA conditions are considered statistically significant when confidence intervals of the probability density distributions do not overlap. Numbers for suspended and sinking Si, C and N (and their respective ratios) for the analysis period are given in Extended Data Table 2.Analysis of pH effects on Si:N in global sediment trap dataWe analysed a recent compilation of global sediment trap data (674 locations collected between 1976 and 2012)35. The aim of this analysis was to assess the influence of pH on opal dissolution in the world ocean. In contrast to the mesocosm experiments, where export fluxes were measured only at one depth, the global dataset provides depth-resolved information, enabling us to examine the vertical change in the Si:N ratio of sinking particulate matter and how this correlates with pH. It has long been known that the silica content of sinking particles increases with depth, as opal dissolution is less efficient than organic matter remineralization25,42. The resulting accumulation of Si relative to N can be quantified as the change in Si:N with increasing depth, that is, the slope of the relationship of depth versus Si:N (ΔSi:N, in units of m−1). Our approach is analogous to previous studies, which used vertical profiles of Si:C as a proxy for differential dissolution/remineralization of opal and organic matter, and its regional variability in the ocean24,42. We extracted all data that (I) included simultaneous measurements of Si and N, and (II) contained vertical profiles with at least three depth levels (so that ΔSi:N [m−1] can be calculated). We then calculated linear regressions for individual Si:N profiles and subsequently extracted those for which Si:N displayed a statistically significant relationship with depth (p  More

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