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Countries have yet to agree to protect at least 30% of land, a crucial target proposed in the global biodiversity deal.Credit: Roberto Schmidt/AFP via Getty

A beleaguered global deal to save the environment got a financial boost last week when Germany announced that it was upping its funding for international biodiversity conservation to €1.5 billion (US$1.49 billion) a year — an increase of €0.87 billion — making it the largest national financial pledge yet to save nature. The announcement came at a 20 September meeting in New York City, where political leaders, businesses and conservation and Indigenous-rights groups came together to rally momentum and support ahead of the United Nations biodiversity summit in Montreal, Canada, in December.Conservationists welcomed the extra funding, but warned that other wealthy countries must also reach deeper into their pockets to ensure that nations agree on a new biodiversity agreement, called the Post-2020 Global Biodiversity Framework. Estimates suggest that an additional US$700 billion annually is needed to protect the environment.Concerns over insufficient financing for global biodiversity conservation have stalled negotiations and threaten to derail attempts to finalize a deal in Montreal. The forthcoming summit will be the 15th meeting of the Conference of the Parties (COP15) to the UN’s Convention on Biological Diversity.Announcing the new funds, German Chancellor Olaf Scholz said: “With this contribution, we want to send a strong signal for an ambitious outcome of the biodiversity COP-15.”Claire Blanchard, head of global advocacy at WWF, a conservation group, told Nature that the extra funding “is highly significant” and sends an important signal that rich countries are prepared to step up.But she adds: “More signals of this kind will be needed to create the environment conducive to constructive dialogue in the negotiation room.”Andrew Deutz, a specialist in biodiversity law and finance at the Nature Conservancy, a conservation group in Arlington, Virginia, says he expects further funding announcements to come in the run up to and at the COP15.Other pledgesSeveral key political leaders, including Justin Trudeau, Canada’s prime minister, echoed calls for rich nations to make urgent progress to secure the biodiversity deal. Trudeau urged countries to agree on two crucial targets proposed in the biodiversity framework, both to be met by 2030: to halt and reverse biodiversity loss, and to protect at least 30% of land and seas.The new funding was bolstered by other pledges and developments, including a promise from a partnership of some of the world’s wealthiest private philanthropic foundations and charities to add to the $5 billion they have already committed to conservation, if other countries promise more funds.The partnership — which includes the Bezos Earth Fund, an environmental fund financed by entrepreneur Jeff Bezos — has already spent around $1 billion of its promised financing over the past two years, says Cristián Samper, head of the Wildlife Conservation Society, a not-for-profit group. Samper was speaking on behalf of the partnership at the meeting in New York City.Frans Timmermans, vice-president of the European Commission, reaffirmed that Europe would double its international biodiversity funding to $1.13 billion annually — a promise originally announced in September last year. Timmermans told the meeting that the European Union would set out more details about the funding soon.Funding shortfallAlso at the meeting, a group of four countries comprising Ecuador, Gabon, the Maldives and the United Kingdom launched a joint 10-point plan to bridge the biodiversity finance gap, which is estimated at $700 billion annually.The plan sets out the financial commitments and policy reforms needed to finance biodiversity on the required scale. For example, it encourages wealthy and lower-income nations to allocate new funds for biodiversity and to quickly deliver on their existing financial pledges. It requires donor countries to ensure that funds for overseas development do no harm to biodiversity. And it asks countries to dedicate a portion of their national funding for climate change to activities that also protect and conserve nature.The plan also commits countries to ensuring that public finance is invested in ways that benefit biodiversity, and to reviewing national subsidies and redirecting those that are harmful to nature. It calls on businesses to assess and disclose commercial risks associated with biodiversity decline, and to set quantitative targets to reduce their impact on the natural world. And it encourages multilateral development banks — such as the World Bank in Washington DC — and international financial institutions to ensure that their investments benefit biodiversity, and asks that they report on their biodiversity funding in time for COP-15.So far, 15 countries, including Canada, Germany and Norway, as well as the EU have endorsed the plan.“The plan provides a clear pathway for bridging the global biodiversity finance gap. Its significance lies in the political signal it sends,” says Blanchard.António Guterres, secretary-general of the UN, urged political leaders to “act now and at scale” to secure biodiversity financing and ensure agreement on the framework. “If negotiations continue at their slow pace, we are headed to failure,” he told the meeting. More

Structural characterization of slag samplesThe FTIR spectra of granulated blast furnace slag (Sample 1), waste slag dumped in landfill (Sample 2) and combination of both 50% granulated blast furnace slag + 50% waste slag dumped in landfill (Sample 3) are presented in Fig. 1.Figure 1FTIR spectra of slag samples.Full size imageBy analysing the spectrum (detailed figure) in the range of 700–1100 cm−1, it can be found that there are obvious absorption peaks in the spectrum of all the slag samples. The granulated blast furnace slag shows the characteristic absorption bands at 3640, 1418, 980, 944, 861, 753 and 710 cm−1. The band at 3640 cm−1 is assigned to the stretching vibration of the hydroxyl group originated from the weakly absorbed water molecules on the slag surface24. The characteristic absorption bands at 1418, 861 and 710 cm−1 are ascribed to the asymmetric stretching mode and bending mode of carbonate group, respectively and the band at 980 cm−1 are attributable to the stretching vibrations of Si–O25. The band at 944 and 752 cm−1 represent the internal vibration of [SiO4]4− and [AlO4]5− tetrahedral and comes from Si (Al)–O-antisymmetric stretching vibration26.The different vibration modes for the sample of waste slag can be observed in the FTIR spectrum. The absorption bands shown are at 1418, 873, 712, 667 and 419 cm−1. The peak at 1418 cm−1 is assigned to the asymmetric stretching mode and bending mode of carbonate group. Calcite phase is confirmed by characteristic peaks at 712 cm−1 (ʋ2 out of plane bending vibration of the CO3−2 ion) and 873 cm−1 peak (ʋ2 split in-plane bending vibrations of the CO3−2 ion27. Calcium aluminate phase is identified by characteristic peak at 419 cm−128. Peak around 667 cm−1 is described as absorption band for different M–O (metal oxide) such as Al–O, Fe–O, Mg–O etc.29.In the case of combination of both 50% granulated blast furnace slag and 50% waste slag dumped in landfill the intensity of absorption peaks is smaller in comparison with Sample 1 and Sample 2 of slag. The characteristic absorption peaks (978 and 753 cm−1) which correspond with characteristic peaks of Sample 1 are shifted compared to the Sample 1, assigned to the stretching vibrations of Si–O and to the Si (Al)–O-antisymmetric stretching vibration, respectively, can provide important evidence of chemical interaction between Sample 1 and Sample 2. The decrease of the intensity of the bands appearing at 875 and 709 cm−1 cans be attributed to overlapping the vibrations of the CO3−2 ion from calcite phase.Figure 2 presents the SEM micrographs of the slag samples (Sample 1–3). One can see the characteristic morphology- the sizes and the forms of the slag samples.Figure 2SEM images of slag samples.Full size imageAt larger magnifications it can be observed that the surface is rough and uneven, and one can notice rounded grain-like rugged formations. The slag samples display aggregated particles with average diameter of a few microns. Also, in these rounded formations it can be seen different morphologies like spheres, rods, boards specific each compound/phase from metallurgical slags.Figure 3 illustrates the EDX elemental analysis of granulated blast furnace slag (Sample 1), waste slag dumped in landfill (Sample 2) and combination of both 50% granulated blast furnace slag + 50% waste slag dumped in landfill (Sample 3).Figure 3EDX elemental map of slag samples.Full size imageOne can observe that the predominant elements in the examined area are constated in carbon, oxygen, calcium, and iron, confirming the FTIR spectra.Figure 4 shows EDX spectra of slag samples recorded on different selected punctual area, to obtain more information about the elemental composition of specific areas. For all the tested slag samples have similar elements content.Figure 4EDX spectra analysis of slag samples.Full size imageThe selected punctual areas are highlighted thus: the spheric structure are with yellow line and the structure like boards are with green line for all the analysed slag samples. In the case of Sample 1 for both structures the values of chemical elements present are similar and the silicon has a higher value at spheric structure which can be correlated with the presence of silica (SiO2). The higher content of calcium reveals that the Sample 1 is blast furnace slag dominated by calcium and silicon compositions. In the case of slag dumped in landfill (Sample 2) the content of carbon increase for both structures and some chemical elements like titanium, barium, manganese doesn`t appear in EDX spectra and the explanation for this phenomenon is that the slag was dumped in landfill for more than 30 years. One can observe for combination of both 50% granulated blast furnace slag + 50% waste slag dumped in landfill (Sample 3) that the values of all the chemical elements for both spheric and board-like structure are between the first two samples, confirming the FTIR spectra regarding chemical interaction between Sample 1.XRD patterns of the slag samples with the phases identified are shown in Fig. 5. Sample 1 show minor peaks of free CaO and MgO, which may be deleterious and cause reduction in strength. The phases and amorphous contents of the Sample 1 granulated blast furnace slag are broadly consistent with literature30. Sample 3 of slag consists of crystalline phase – Ca2Mg2SiO7, Ca2Fe2AlO5, CaCO3 and CaO as observed by the XRD analysis. In terms of the relations of phase thermal equilibrium, the compounds identified form an isomorphic series of melilites that is specific to basic metallurgical slags.Figure 5X-ray diffraction patterns of slag samples.Full size imageIn Table 1 are presented the values expressed as ppm of chemical element detected in slag samples (Sample 1, 2 and 3).Table 1 XRF analysis of the slag samples.Full size tableThe results show a large quantity of calcium in all three samples of slag. Also, the elements detected such as Fe, Al, Mg and Si are in accordance with XRD spectra.Physical–chemical characterization of soil-slag mixturesThe chemical composition of the major elements that compound the soil, soil- slag and slag samples was determined by XRF. The values expressed as ppm of chemical elements are presented in Table 2. In the case of soil sample the content of the main constituents is iron, titanium, manganese, and potentially toxic elements (PTE) such as arsenic, zinc, copper, and cobalt. For soil-slag 1 with weight ratio soil: slag (1:1) it can be observed the disappearance of the potentially toxic elements (PTE) founded in soil sample and the decrease of concentration value of zinc. When the weight ratio of slag increases at 3 (soil-slag 2 sample) the values of main component increased in accordance with values of slag sample, but in the case of soil-slag 3 sample where the weight ratio of soil is bigger (3) it can be observed the cobalt presence. Based on these XRF results we can say that take place an elimination of potentially toxic elements in contaminated soil by applying slag in a bigger proportion.Table 2 XRF analysis of the soil-slag samples.Full size tableWith the aid of a pH meter, CONSORT C 533 the important parameters of soil and slag solutions were measured as: the pH, conductivity, and the salinity, as shown in Table 3. The data presented in Table 3 suggest that the soil sampled has the pH = 5.2 corresponding to a medium acid soil, which does not sustain a high fertility and is not able to offer proper conditions for crops. Also, the pH of soil has important influence on soil fertility, decreases the availability of essential elements and the activity of soil microorganisms which can determine calcium and magnesium deficiency in plants and decreases phosphorous availability. The pH value of slag solution (12.5) corresponds as strongly basic character which is beneficial in amelioration process of acidic soils and the presence of this type of slag sustain the improving of soil characteristics, too. For the soil-slag samples the pH value increase with the increasing of the weight ratio of slag and the mixtures soil-slag obtained can be framed into the category of weakly alkaline soils.Table 3 The physical–chemical characteristics of soil and slag solutions.Full size tableThe data given in Table 4 show that the humidity of soil is bigger and decreases in soil-slag samples with adding of slag content. The values of total soil-slag porosity are between 40 and 50% and depends on the density and apparent density of the soil being influenced by the mineralogical composition, the content of organic matter and the degree of compaction and loosening of the soil, the crystalline structure of soil minerals.Table 4 The physical–chemical characteristics of soil-slag samples.Full size tableConsidering the structural and morphological characterization of the investigated slag samples we propose a recipe of blast furnace slag and of waste slag dumped in landfill in accordance with the waste directive 2008/98/EC regarding the strategic goal of EU to a complete elimination of the disposal of wastes. The slag dump of Steel Plant of Galati has an enormous quantity of unused waste slag which may be mixed with granulated blast furnace slag, to save the natural resources used as raw materials in the metallurgical technological process.The presence of Ca2+ in the composition of the slag can maintain high alkalinity in the soil for a long time in the natural environment. The alkaline pH of the soil may contribute to a decrease the available concentration of heavy metals by reducing metal mobility and bonding metals into more stable fractions. One of the objectives of this research is improving the quality of the environment by using the mixture between two different slags on agricultural lands and reintroducing them in the agricultural centre, especially in acid soils. Acidic soils are characterized by an acidic pH that has spread in recent years due to excessive fertilizers or far too aggressive work31. The production is significantly influenced, and the treatment of acid soils is usually done using a series of natural materials (lime, dolomite), the consumption being approx. 20 t/hectare depending on the acidity of the soil and the nature of the plants grown on the respective surfaces.Our research consists in improving the characteristics and qualities of the acidic soils and helping to reintroduce it into the agricultural circuit by transforming a waste into a new material friendly-environmental, the mixture of blast furnace slag and waste slag dumped in landfill. More

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Glass eels collection (Anguilla anguilla, L.)Three hundred European glass eels ( More

External datasetsWoody biomass carbon dataThe dataset by ref. 16 maps annual global woody biomass carbon densities for 2000–2019 at a spatial resolution of ~10 km. The annual estimates represent averages for the tropical regions and growing-season (April–October) averages for the extra-tropical regions. Ref. 16 analyse global trends of gains and losses in woody biomass carbon for 2000–2019. Overall, they find that grid cells with (significant) net gains of vegetation carbon are by a factor of 1.4 more abundant than grid cells with net losses of vegetation carbon, indicating that there is a global greening trend when only considering the areal extent of biomass gains and not the magnitude of carbon gains. Their regionally distinct analysis of trends shows that almost all regions, except for the tropical moist forests in South America and parts of Southeast Asia, experienced net gains in biomass carbon. On the country scale, the largest net increase in biomass carbon is shown in China, which is mainly attributed to the large-scale afforestation programs in the southern part of the country and increased carbon uptake of established forests. On the other hand, the largest vegetation carbon losses are shown for Brazil and Indonesia, which is partly attributed to deforestation, degradation, and drought events. All of the mentioned trends have been found to be significant16. The decreasing carbon sink in Brazil is in line with ref. 44, who, considering both natural and anthropogenic fluxes, show that the southeastern Amazon has even turned from a carbon sink to a carbon source, mainly owing to fire emissions from forest clearing. Isolating carbon fluxes in intact, old-growth Amazonian rainforests (i.e., SLAND,B), ref. 45 also find evidence for a significantly decreasing carbon sink due to the negative effects of increasing temperatures and droughts on carbon uptake since the 1990s.The dataset was remapped to the BLUE resolution of 0.25∘ through conservative remapping (i.e., area-weighted averaging).ERA-5 dataThe ERA-5 variables were downloaded from the Copernicus Climate Data Store (https://cds.climate.copernicus.eu/cdsapp#!/home). Monthly air temperature (Ta) at 2 m height was averaged over each year, and annual precipitation was calculated by taking the sum of the monthly total precipitation (P). Both variables were regridded from the original resolution of ~0.1° to 0.25° resp. to the TRENDY resolution of 0.5° through conservative remapping.TRENDY dataWe used the TRENDY model ensemble version 8 (conducted for the 2019 GCB; ref. 8). We used net biome production (NBP) and annual vegetation carbon stocks (cVeg) for 2000–2018 from four different model setups (S2, S3, S5, and S6) and eight resp. 13 DGVMs (depending on the data available). The selection of DGVMs is done as in ref. 19 (Supplementary Tab. 3), but we included one additional model (ISAM) for the S2 simulations. The terrestrial biomass carbon sink (SLAND,B) was calculated for 13 DGVMs following the GCB 2020 approach, i.e., from the S2 simulation, which is the simulation without LULCC (i.e., fixed pre-industrial land cover) under transient environmental conditions (climate, nitrogen deposition, CO2 evolution). SLAND,B is the annual difference of cVeg and makes no statements about the further fate of biomass if cVeg decreases. SLAND,B, therefore, should not be interpreted as equivalent to the flux to/from the atmosphere, since parts of cVeg may be transferred to litter, dead wood, or soil. The same applies to our BLUE estimates of SLAND,B, ensuring comparability between our BLUE estimates and the TRENDY estimates. Increases (decreases) of cVeg between two years are a net uptake (release) of carbon from the terrestrial biosphere. The global sums of biomass carbon stocks under transient climate and CO2 were calculated from the S3 setup (LULCC under historical environmental conditions), whereas the S5 setup provides biomass carbon under constant present-day environmental forcing (closest to the classical bookkeeping approach). In line with the GCB, ELUC was calculated under historical environmental conditions as the difference in NBP between the S2 and S3 simulations (ELUC = NBP_S2 – NBP_S3). ELUC under constant present-day environmental forcing was calculated as the difference in NBP between the S6 (fixed pre-industrial land cover under present-day environmental forcing) and S5 simulations (ELUC = NBP_S6 – NBP_S5)19. All datasets were remapped to a common resolution of 0.5∘ through conservative remapping (area-weighted average) for the data analysis.Assimilation of observed woody biomass carbon in BLUEThe observed woody biomass carbon densities by ref. 16 are assimilated in BLUE in several steps.Carbon transfer in the default setup of BLUEThe BLUE simulation is started in AD 850. Biomass and soil vegetation carbon densities are based on ref. 17, which are converted to exponential time constants. A detailed explanation of the exponential model can be found in ref. 5.While in the default setup, changes are only due to LULCC, our assimilation approach now introduces environmental effects on woody vegetation carbon by assimilating the observed woody biomass carbon densities in BLUE from 2000 onward according to the methodological considerations explained below.Calculation of woody biomass carbon densities for different land cover types and PFTsWithin each 0.25° cell of the global grid, the (remapped) woody biomass carbon density from ref. 16 must be the sum of woody biomass carbon stored in all woody PFTs of all woody land cover types. The distribution of the woody biomass carbon across PFTs and land cover types is achieved by distributing the observed (i.e., actual) woody biomass carbon densities (ρBa) from ref. 16 across the two land cover types (j) and the eight PFTs (l) that can be woody vegetation (primary land, called virgin, “v” in BLUE and secondary, “s”, land) according to the fraction of total woody biomass carbon (fB) contained in each land cover type and each PFT (fB,j,l) as estimated by BLUE. fB,j,l varies for different PFTs and land cover types, depending on their history of LULCC and their potential for carbon uptake (i.e., the potential carbon densities).fB,j,l is extracted from the default simulations for the first year of the time series (i.e., 2000) and calculated for subsequent years from the BLUE simulations using the assimilated woody vegetation carbon densities for that year:$${f}_{B,j,l}(t)=frac{{C}_{B,j,l}(t)}{{C}_{B}(t)}$$
(1)
where CB is the woody biomass carbon stock.Consequently, the assimilated woody biomass carbon stock per cover type and PFT (CB_as,j,l) at each time step can be calculated as:$${C}_{B_as,j,l}(t)={rho }_{Ba}(t);*;A;*;{f}_{B,j,l}(t)$$
(2)
with j{v, s}; l{1. . 8}; t{2000. . 2019}. A is the area per grid cell.Thresholds for excluding inconsistent woody biomass carbon densitiesWe eliminate unrealistically large values for woody biomass carbon densities that our assimilation framework produces. Woody biomass carbon densities in BLUE that exceed the highest value (~374 t ha−1) of the original dataset indicate inconsistencies between the observed woody biomass carbon estimates and the fractional grid cell areas per PFT and land cover types that BLUE simulates. To account for uncertainties related to the criteria for exclusion of grid cells, multiple threshold approaches are applied and the results are compared. To maintain a temporally and spatially consistent time series of woody biomass carbon, grid cells that are excluded according to the chosen threshold approach are interpolated through linear barycentric interpolation. A first approach relies on a uniform upper threshold of More