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We used the IUCN Red List criteria34,35 and methods developed in other global status-assessment efforts36,37 to assess 10,078 reptile species for extinction risk. We additionally include recommended Red List categories for 118 turtle species38, for a total of 10,196 species covered, representing 89% of the 11,341 described reptile species as of August 202039.Data compilationWe compiled assessment data primarily through regional in-person and remote (that is, through phone and email) workshops with species experts (9,536 species) and consultation with IUCN Species Survival Commission Specialist Groups and stand-alone Red List Authorities (442 species, primarily marine turtles, terrestrial and freshwater turtles, iguanas, sea snakes, mainland African chameleons and crocodiles). We conducted 48 workshops between 2004 and 2019 (Supplementary Table 1). Workshop participants provided information to complete the required species assessment fields (geographical distribution, population abundance and trends, habitat and ecological requirements, threats, use and trade, literature) and draw a distribution map. We then applied the Red List criteria34 to this information to assign a Red List category: extinct, extinct in the wild, critically endangered, endangered, vulnerable, near threatened, least concern and data deficient. Threatened species are those categorized as critically endangered, endangered and vulnerable.TaxonomyWe used The Reptile Database39 as a taxonomic standard, diverging only to follow well-justified taxonomic standards from the IUCN Species Survival Commission40. We could not revisit new descriptions for most regions after the end of the original assessment, so the final species list is not fully consistent with any single release of The Reptile Database.Distribution mapsWhere data allowed, we developed distribution maps in Esri shapefile format using the IUCN mapping guidelines41 (1,003 species). These maps are typically broad polygons that encompass all known localities, with provisions made to show obvious discontinuity in areas of unsuitable habitat. Each polygon is coded according to species’ presence (extant, possibly extant or extinct) and origin (native, introduced or reintroduced)41. For some regions covered in workshops (Caucasus, Southeast Asia, much of Africa, Australia and western South America), we collaborated with the Global Assessment of Reptile Distributions (GARD) (http://www.gardinitiative.org/) to provide contributing experts with a baseline species distribution map for review. Although refined maps were returned to the GARD team, not all of these maps have been incorporated into the GARD.Habitat preferencesWhere known, species habitats were coded using the IUCN Habitat Classification Scheme (v.3.1) (https://www.iucnredlist.org/resources/habitat-classification-scheme). Species were assigned to all habitat classes in which they are known to occur. Where possible, habitat suitability (suitable, marginal or unknown) and major importance (yes or no) was recorded. Habitat data were available for 9,484 reptile species.ThreatsAll known historical, current and projected (within 10 years or 3 generations, whichever is the longest; generation time estimated, when not available, from related species for which it is known; generation time recorded for 76.3% of the 186 species categorized as threatened under Red List criteria A and C1, the only criteria using generation length) threats were coded using the IUCN Threats Classification Scheme v.3.2 (https://www.iucnredlist.org/resources/threat-classification-scheme), which follows a previously published study42. Where possible, the scope (whole ( >90%), majority (50–90%), minority (30%), rapid ( >20%), slow but notable ( More

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Link activationContingency tables corresponding to the three cases described in the “Methods” section are shown in Table 1. This Table is quite revealing in several ways. The most interesting aspect is that the highest probability of link establishment occurs when an agreement is activated (Operational Activation in t).Table 1 Contingency tables.Full size tableIn this case, the probability of activation of a new link is 8.8%—namely, the ratio of new activation 7.3% to the total number of links that were not active at year t-1 (82.6%)—which is significantly higher than in the case of links not covered by a commercial agreement (No Trade Agreement), amounting to 1.4%.Therefore, the findings show that operational activation is associated with creating new trade relations between two particular countries. The third set, which considers links where a trade agreement exists in both years (t-1) and t (Trade Agreement in t-1 and t), also shows a consistent activation probability of 6%. This result confirms the assumption that the coverage of a commercial agreement, and not only its implementation, encourages the genesis of new links.Moreover, Table 1 suggests some interesting considerations on trade persistence. To establish these probabilities, we focus on the row totals in which a trade relationship is present at year (t-1), i.e., 28.8% in the case Trade Agreement in t-1 and t. The presence of an agreement influences in a positive way the probability of maintaining a trade relationship. In fact, when a trade agreement is present in both years, (t-1) and t, the probability of preserving the trade relationship is 87.1% ((frac{25.1}{28.8}times {100})), while when a trade agreement is activated at year t, the probability slightly decreases to 81.6%. In cases where trade agreements are missing (No Trade Agreement in t) we observe the probability of retaining a relationship decreases to 77.3%.Another interesting aspect concerns the probability of link deactivation. Once more, the coverage of a trade agreement favors a lower likelihood of deactivation of existing links. The ratio of the percentage of links that were active at year (t-1) and are no more active at year t to the total is 22.7% ((frac{1}{4.4}times {100})) in the case of a lack of agreement. This probability decreases to 18.4% ((frac{3.2}{17.4}times {100})) if we consider only the year of activation of the agreement (Operational Activation), and drops to 12.8% ((frac{3.7}{28.8}times {100})) when looking at agreements present in both years.Together, these results provide insights into the role of trade agreements in the network topology of cereal trade. While the establishment of a trade agreement promotes the potential for new trade links, the presence of the agreement in two consecutive years allows both to maintain an existing relationship and reduce the likelihood of link shutdowns.Flow variationsIn this second part, we study the impact of trade agreements on existing trade flows, analyzing the relationship between the flows at time t and the flows at time (t-1) in each of the three cases described in the “Methods” section—i.e., No trade agreements, Operational Activation in t, and Trade agreement in t-1 and t—measured in US$, Kcal and m(^3) of virtual water.Figure 3Kernel Density scatterplot between trade flows of cereals at time t (on the y-axis) and time (t-1) (on the x-axis) for the three different sets: No trade agreements (column a), Operational Activation in t (b), and Trade agreement in (t-1) and t (c). Panels in the first, second and third row refer to flows in US$, Kcal, and virtual water (m(^3)), respectively. Flow values are shown on a logarithmic scale. The color bar indicates probability densities, and the bisector is highlighted. Notice (i) the higher volumes in the case of flows covered by trade agreement and (ii) a a less relevant increase in volume when the flows are seen in the virtual water lens.Full size imageFigure 3 shows three different scatterplots for each unit of measure (US$ and Kcal and m(^3)). The scatterplots are colored by Kernel Density Estimation (KDE), a non-parametric technique for probability density functions. KDE aims to take a finite sample of data and infer the underlying probability density function. Figure 3 relates the flows at time (t-1) with the flows at time t, both reported on a logarithmic scale since the quantities span several orders of magnitude. Let’s start focusing on flows in terms of dollars and kilocalories. What stands out from the figure is the displacement of the flows toward higher values when they are covered by trade agreements (Trade Agreement in t-1 and t), compared to the case where flows have no trade agreement.We have quantitative evidence of this result by looking at Table 2 where the average flows in both years are shown. The average values of flows in both US$ and Kcal are much higher when there is a trade agreement over time (Trade agreement in t-1 and t). Flows have an average value of (6.13times 10^{7})$, larger than the mean of (3.05times 10^{7})$ achieved by flows not covered by a trade agreement. By comparing the distributions of the two distinct sets with different dimensions by applying the non-parametric Mann-Whitney test, we stand to evaluate this result as extremely significant (p-value approximately 0).Table 2 Average values of trade flows and flow variation index (rho _{ij}) for each of the three sets, in US$ (a), Kcal (b), and Virtual water (VW, m(^3)). The bar indicates the average operator.Full size tableAlso, while operational activation plays a crucial role in creating new links in the global cereal trade, it does not appear to hold central importance in driving flow increases. The average value of flows in both years (t-1) and t are, in fact, smaller than those not covered by trade agreements.The view appears slightly different when we look at the values in terms of virtual water (VW, m(^3)), i.e., the sum of the blue and green components. Flows with a commercial agreement show higher averages values than those not covered by agreements (see panel (c) of Table 2), but the increase is significantly lower than the one recorded in the other two units (US$ and Kcal). The increase recorded in dollars is about 100%, while in terms of virtual water this increase is less than 30%. In the next subsection, we will focus on this peculiar behavior, which reveals a different water content of the goods traded along links covered or not by agreements.Another significant result that emerges from Fig. 3 is the smaller amplitude (around the bisector) of the cloud in the case of link covered by agreements in both years (t-1) and t. This is confirmed by comparing the weighted average of the absolute value of the inter-annual flow variation index (overline{rho _{ij}}_{w}) (weights are the flows traded in the year (t-1)). The index (rho _{ij}) is used to highlight cases where the activation or the presence of the agreement generates a significant flow increase.Larger (rho _{ij}) values correspond to larger average variations from year (t-1) to year t. Accordingly, we observe that in the presence of trade agreement at time (t-1) and t a smaller (rho _{ij}) value of 24.79 percentage points (p.p) is found (see panel (a) of Table 2).Considering all the units (US$, Kcal, and m(^3)), this value is about half of the average inter-annual variation that occurs when there is no trade agreement. Hence, the presence of a commercial agreement over time reduces large fluctuations, stabilizing the year-to-year variations.To shed light on the response of water flows to the occurrence of the agreement, we refer to water productivity (WP)34, both in economic and nutritional terms. Table 3 shows that the Nutritional WP for the total virtual water is, on average, 35% higher in the flows under a trade agreement than in flows that are not under any treaty, while the Economic WP is 62% higher. We also analyze the two virtual water components, blue and green, separately.Interestingly, for blue water in the presence of a trade agreement, the Nutritional WP and the Economic WP for the flows covered by trade agreement are, on average, 68% and 93% higher than for the flows not covered by agreements. In other words, for one cubic meter of water used for grain production, more kilo-calories and dollars are exchanged when an agreement is in place, and this difference is even more significant in terms of blue water.Table 3 Average of nutritional ((mathrm {kcal/m^3})) and economic ((mathrm {US$/m^3})) water productivity (WP) for the total, blue and green virtual water.Full size tableWe also investigate in detail which products contribute most to the imbalance between flows in terms of kcal or water. To this aim, Fig. 4 reports the nutritional WP for each grain item distinguishing whether or not there is a commercial agreement (similar results occur if the economic WP is considered).The figure highlights that the nutritional WP is generally higher in the case where flows are covered by trade agreements (green bars). The most noticeable cases are Maize and Wheat, which are also the most traded products: the value of nutritional WP increases from 1978 (mathrm {kcal/m^3}) (No trade agreement) to 2851 (mathrm {kcal/m^3}) in case of a trade agreement for Wheat, and from 4471 (mathrm {kcal/m^3}) to 5026 for Maize.Figure 4The bar chart shows the nutritional WP for each cereal product in the two sets of Trade agreement in t-1 and t (in green) and No trade agreement (in red). The number over the bars represents the percentage of kcal traded for each product compared to the total kcal of all cereals. Note that green bars are higher than the red ones in 80% of cases.Full size imageA few products have a higher nutritional WP value when the flows are not involved in any treaty, e.g., Rye. This behavior can be traced back to a few flows that dominate the market between countries not linked by trade agreements. For example, trade in Rye in 2014 is attributable to just two major flows in terms of caloric intake relative to water quantity (notably, one between Germany and Japan, the other between Russia and Turkey).Figure 4 clearly shows that grains characterized by greater water efficiency generally move along the links covered by agreements.Performance of trade agreements in increasing flowOur results show that links covered by agreements exhibit larger flows than links not covered by treaties. We also intend to obtain information about the possible flow increase under a specific agreement.As mentioned in the “Methods” section, we selected only those operating links when the agreement came into force to evaluate the variation index ((rho _a)) under a specific treaty. Consequently, since there are trade agreements that came into force before the time interval considered, these are excluded from this analysis. As a result, the total number of agreements selected for this analysis is 99, 61 of which show an increase (positive (rho _{a}) values), while the remaining 38 exhibits a decrease in the flux intensities compared to the overall global trend. We present in Table 5 the results for positive (rho _{a}) variations, while trade agreements with negative (rho _{a}) values are reported in Supplementary Material (5). We provide this analysis in terms of economic flows (US$), but very similar results are obtained if calories (kcal) or virtual water (m(^3)) are chosen as the unit of measure.Table 4 Flow values in millions of dollars in year t and percent changes (rho _{a}) from (t-1) to t for each trade agreement.Full size tableWhat stands out in Table 4 is that most of the positive percentage changes occur in Europe and Central Asia regions. This may be due to long-term commercial activities in Europe, which are supported by the geographical proximity of the countries, as well as the wide variety of political and economic treaties among them. Europe, in fact, is characterized by a fourfold increase in cereal production since the 1960s due to the adoption of the Common Agricultural Policy, which has intensified trade in Europe and towards external markets30.A closer inspection of Table 4 shows that among the agreements with the most significant flows that showed the greatest increases, we find EEA (European Economic Area) in Europe and Central Asia, Japan-ASEAN in East Asia and Pacific, and COMESA in Sub-Saharan Africa.With lower flow values but large increases ((rho _{a})) due to the entry into force of trade agreements, the India-Sri Lanka agreement in South Asia stands out above all others. Also, the treaty signed in 2013 between EU-Colombia and Peru shows significant variations in terms of the percentage of flow increase, but the volume of the corresponding flow is inferior when compared with other trade agreements. On the other hand, the North American Free Trade Agreement (NAFTA), which became effective in 1994, has a lower (rho _{a}) value, but the flows on which the variation is calculated are significantly higher. More

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Speciation models, conditional and intrinsic stability constants and EDH model parametersThe complete set of analytical results for the Zn(II)/ligand systems, including conditional stability constants (logβ) for the formation of hydrolysed Zn(II)–ligand complexes, of zinc hydroxide complexes and of Zn(II)–ligand complexes as well as acidity constants for citrate and DFOB at different ionic strength in NaCl and T = 298.1 K are reported in Table 1 and SI Table 2. Also shown are the values for the optimised parameter C and the intrinsic association constants (logβ0). SI Table 1 lists all the reactions included in the speciation models used to fit the potentiometric titrations and SI Fig. 2 shows single crystal X-ray structures for some of the proposed structures including ZnH2Cit2, Zn2Cit2(H2O)2 and ZnCit22− taken from the Cambridge Crystallographic Data Base. Figure 3 displays the experimentally determined conditional Zn(II)–ligand stability constants and the corresponding EDH model from this study. Also shown are logb values from the literature for [Zn(HCit)] and [Zn(Cit)]− for the Zn(II)/Cit system and [Zn(H2DFOB)]+, [Zn(HDFOB)] and [Zn(DFOB)]− for the Zn(II)/DFOB system. Examples of titration curves and manually fitted models along with the speciation model considered and the experimental conditions are included in the supporting information (see SI Figs. 3 and 4). Only models that fitted the experimental data with sigma values below 5 were considered. Examples of Hyperquad files showing titrations and model fits for Zn(II)/Cit and Zn(II)/DFOB systems and of Excel calculation files for the application of the EDH model to the Zn(II)/DFOB experimental data set, including error calculation for C and logβ0 are uploaded to the Zenodo repository (https://doi.org/10.5281/zenodo.4548162). Errors reported for measured logβ and calculated (modelled) logβ0 and C values have no detectable effect on subsequent speciation calculations. The errors reported on C are slightly larger than in comparable studies22, however, a sensitivity analysis on the two Zn(II)–ligand species with the largest relative error on C found that logβ0 remains within its error range even when logβ0 was recalculated for the maximum and minimum possible C values. The stability constant we report for specific Zn(II)–L complexes at specific ion strengths are in line with literature reports (Fig. 3). For example, the logβ for the formation of [Zn(Cit)]− in 0.15 mol dm−3 NaCl shows good agreement with the value reported by Cigala and co-workers in 0.15 mol dm−3 NaCl; 4.79 vs. 4.7126. We note, however, also significant variations within reported conditional logβ values as seen Fig. 3, with published values for the formation of [Zn(HCit)] and [Zn(Cit)]− in different 1:1 electrolytes differing over two orders of magnitudes. This highlights the analytical challenges associated with accurate and precise logβ determinations of low affinity metal–ligand complexes, in low ion strength solutions33.Figure 3Experimental Zn(II)–ligand conditional stability constants (logβ) for (a) citrate and (b) DFOB at 0.05, 0.15, 0.3, 0.5 and 1 mol dm−3 in NaCl solution (open circles) determined using potentiometric titrations. For each species, the Extended Debye-Hückel (EDH) model has been parameterised using the experimental data (see Table 1 for C and logβ0) and the corresponding model is shown as a solid line. Literature data is included in the figure for comparison (closed circles) from Cigala et al. (2015, NaNO3 and NaCl), Capone et al. (1986, KNO3), Daniele et al. (1988, KNO3), Field et al. (1975, KNO3), Matsushima et al. (1963, NaCl) and Li et al. 1959, NaCl) for the Zn–H–Cit system and from Schijf et al. (2015, NaClO4), Farkas et al. (1997, KCl) and Hernlem et al. (1996, KNO3) for the Zn-H-DFOB system. Note the large variability reported for the Zn–Cit system at 0.1 and 0.15 mol dm−3. We find good agreement with the data published by Sammartano and co-workers26,69.Full size imageThe final speciation scheme with the best statistical fits and with chemically sensible species are given in Table 1. From the eight Zn-Cit species initially considered (SI Table 1), the inclusion of five species resulted in model fits with sigma values below 5. For the Zn(II)/Cit system, the dominant species are [Zn(Cit)]−, [Zn(HCit)], and [Zn2(Cit)2(OH)2]4−. We report also the presence of a [Zn(Cit)(OH)3]4− complex above pH 9 in significant amounts ( > 20%) and we confirm the presence of [Zn(Cit)2]4− if citrate is present in large excess26,31. The presence of [Zn(Cit)]−, [Zn(HCit)] and [Zn(Cit)2]4− were confirmed in pH 6 solutions by mass spectrometry. To confirm the presence of [Zn(Cit)(OH)3]4−, further investigations are warranted. SI Fig. 5 shows the species distributions in the Zn(II)–Cit system with different Zn:L molar ratios (1:1, 1:2 and 1:10) and different concentrations (between 10–6 and 10–3 for Zn and 10–5 and 10–3 for citrate). We find that [Zn(Cit)]− dominates (i.e., formation relative to total Zn is above 50%) between pH 5 and 7.5, [Zn2(Cit)2(OH)2]4− dominates between pH 7.5 and 10 and [Zn(Cit)(OH)3]4− dominates at pH values above 10. We find the formation of [Zn(Cit)2]4− only at Zn:Cit molar ratio of 1:10 and [Zn] and [L] concentrations of 10–4 and 10–3 mol dm−3, respectively. The species [Zn(Cit)(OH)]2− and Zn(Cit)(OH))2]3− possibly form at higher pH but were excluded from the final model. We noted that for titrations of solutions with Zn:Cit molar ratios below 1:3, it was not possible to refine the stepwise stability constant (logK) for [Zn(Cit)2]4− to within ± 0.09 log units, indicating that it is an unstable species that forms at negligible concentrations. The stability constants for zinc complexation with citrate decrease with increasing ionic strength. Table 1 shows that the most significant change is seen between 0.05 and 0.15 mol dm−3 NaCl, where there is approximately a 0.5 to 1.5 log unit change. In dilute solutions, stability constants are sensitive to small increases in ionic strength because changes in the effective concentration (activity) of ions are large.For the Zn(II)–DFOB system, all the stability constants measured during this study are in good agreement with those reported in the literature50,51,53. For example, the stability constant we report for [Zn(HDFOB)] in 0.5 mol dm−3 NaCl is 19.34. This is within ~ 0.5 log units of the stability constant reported by Schijf and co-workers in 0.7 mol dm−3 NaClO4 solutions53. The speciation scheme we report differs slightly from that predicted by Schijf based on a three-step model. Our model does not include the bidentate species [Zn(H3DFOB)]2+, the weakest and least stable Zn(II)–DFOB species. In Table 1, we report stability constants for hexadentate [Zn(DFOB)]− and [Zn(HDFOB)] and tetradentate [Zn(H2DFOB)]+. We observe that as the denticity of the complex increases, so does the strength of the stability constant. The stepwise stability constant (K) differs by approximately 2 log units between the formation of the three different DFOB:Zn:H species (7.75, 9.88, 11.67, see Table 1). DFOB complexation of Zn(II) shows the same pattern of ionic strength dependence as citrate, with the greatest decrease of logβ occurring between 0.05 and 0.15 mol dm−3 NaCl, the region of most importance to the rhizosphere.The absolute decrease in [ZnL] and [Zn(HL)] stability constants between 0.05 and 0.15 mol dm−3 is approximately equal for citrate and DFOB species, average 1.58 vs. 1.73, respectively. This is explained by the effect of ionic strength primarily depending on the charge of the ions involved and free citrate and DFOB having the same electrostatic charge (−3). The ionic strength dependent parameter C shows no systematic change for neither citrate nor DFOB species. The good agreement between literature50,51,52,54,68,69,70 and our speciation models as well as the conditional logβ and pKa values validates the use of a single analytical method for the determination of the LEP. We note that the proposed formation of the trihydroxy Zn(II) citrate complex at pH above 10, needs to be investigated in greater detail using supplementary techniques. However, the formation of this species is not relevant for the pH range of interest in our study. As discussed below the main prevailing species in solution are those of 1:1:0 and 2:2:−2 stoichiometry for Zn:Cit:H.Figure 4 shows intrinsic stability constants for the formation of [Zn(Cit)]− and [Zn(HCit)] determined (i) using the Davies equation and the conditional association constants determined at different ionic strengths and (ii) fitting the parameterised EDH equation to the full ionic strength dataset. We find statistically significant (p  More