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51.
Huo JS Yang XG Piao JH Gao JQ Miao H Yu B Lu CQ Chen JS 《Biomedical and environmental sciences : BES》2007,20(2):126-130
Objective NaFeEDTA was considered as a promising iron fortificant for controlling iron deficiency anemia. Soy sauce is a suitable food carrier for iron fortification and is a popular condiment in China. Iron absorption rates of NaFeEDTA and FeSO4 were observed and compared in adult female subjects. Methods The stable isotope tracer method was used in Chinese females consuming a typical Chinese diet. Ten healthy young Chinese women were selected as subjects in the 15-day study. A plant-based diet was used based on the dietary pattern of adult women in the 1992 National Nutrition Survey. Six milligram of 54Fe in 54FeSO4 soy sauce and 3 mg 58Fe in Na58FeEDTA soy sauce were given to the same subjects in two days. Food samples and fecal samples were collected and analyzed. Results Iron absorption rates of NaFeEDTA and FeSO4 were 10.51%±2.83 and 4.73%±2.15 respectively. The 58Fe(NaFeEDTA) absorption was significantly higher than that of 54Fe(FeSO4)(P<0.01). The iron absorption rate from NaFeEDTA was 1.2 times higher than that from FeSO4 in Chinese adult women consuming a typical Chinese diet. Conclusion The higher absorption rate of NaFeEDTA suggested that NaFeEDTA would be a better iron fortificant used in soy sauce for the controlling of iron deficiency anemia in China. 相似文献
52.
Justin M. Mathias Richard B. Thomas 《Proceedings of the National Academy of Sciences of the United States of America》2021,118(7)
We conducted a meta-analysis of carbon and oxygen isotopes from tree ring chronologies representing 34 species across 10 biomes to better understand the environmental drivers and physiological mechanisms leading to historical changes in tree intrinsic water use efficiency (iWUE), or the ratio of net photosynthesis (Anet) to stomatal conductance (gs), over the last century. We show a ∼40% increase in tree iWUE globally since 1901, coinciding with a ∼34% increase in atmospheric CO2 (Ca), although mean iWUE, and the rates of increase, varied across biomes and leaf and wood functional types. While Ca was a dominant environmental driver of iWUE, the effects of increasing Ca were modulated either positively or negatively by climate, including vapor pressure deficit (VPD), temperature, and precipitation, and by leaf and wood functional types. A dual carbon–oxygen isotope approach revealed that increases in Anet dominated the observed increased iWUE in ∼83% of examined cases, supporting recent reports of global increases in Anet, whereas reductions in gs occurred in the remaining ∼17%. This meta-analysis provides a strong process-based framework for predicting changes in tree carbon gain and water loss across biomes and across wood and leaf functional types, and the interactions between Ca and other environmental factors have important implications for the coupled carbon–hydrologic cycles under future climate. Our results furthermore challenge the idea of widespread reductions in gs as the major driver of increasing tree iWUE and will better inform Earth system models regarding the role of trees in the global carbon and water cycles.How terrestrial plants respond to more frequent, and often prolonged, environmental stressors will have profound impacts on, and feedbacks to, the Earth-climate system at regional to continental scales (1, 2). Central to these feedbacks are plant stomata, microscopic pores on the leaves of plants that act as a control valve over the fluxes of carbon dioxide (Ca) into the leaf during photosynthesis and water vapor (H2O) out of the leaf during transpiration. Importantly, changes in stomatal aperture do not affect the fluxes of Ca and H2O equally, as the sum of resistances for the diffusion of Ca from the atmosphere to mesophyll cells where Rubisco is located are much greater than those for H2O from the surface of leaf mesophyll cells to the atmosphere (3). Indeed, as stomatal aperture changes, so does water use efficiency (WUE), or the ratio of Ca uptake to H2O released from the leaf to canopy scale (4). Consequently, understanding the environmental factors driving changes in leaf physiology is of paramount concern in the context of climate change as small changes in tree WUE can have major effects on the carbon and hydrologic cycles over large geographical areas (1, 5).Approaches using tree ring carbon isotopes (6–9), eddy-flux measurements (4, 9, 10), atmospheric carbon isotope composition analysis (11), and Earth system modeling techniques (9–13) have shown trends of recently increasing WUE. These increases can occur by stimulation of leaf photosynthetic rates (Anet) (14, 15), reduced stomatal conductance to water (gs) (14, 15), or some combination of the two. A fundamental physiological response found in numerous Ca enrichment experiments is that WUE of many plants is improved as a result of increasing Ca stimulating photosynthesis and causing partial stomatal closure (14, 16). However, environmental factors distinct from Ca, such as vapor pressure deficit (VPD), precipitation, and temperature, have independent effects on Anet and gs and, therefore, may modulate the response of WUE to rising Ca, especially across functionally distinct plant groups with differences in wood anatomy (9) and leaf morphology (17). Despite this, few studies have thoroughly examined the effects of multiple environmental factors over controls of WUE, and even fewer have considered the underlying component parts, Anet and gs (9, 13, 17–20). This has, in part, been due to the complexity of partitioning H2O gas fluxes at ecosystem scales (21), in addition to the difficulty in attributing changes in isotopically derived intrinsic water use efficiency (iWUE) (the ratio of Anet to stomatal conductance to water, gs) to Anet or gs without the accompaniment of physiological measurements (18, 19).A promising technique couples carbon isotopically derived estimates of iWUE with oxygen isotope leaf water enrichment above source water (∆18Olw; derived from tree ring δ18O and source water δ18O assumed to be ∼δ18Oprecipitation) to provide a qualitative attribution of changes in iWUE to underlying Anet and gs (9, 20, 22–29). As ∆18Olw is inversely related to gs (26–30), if increases in iWUE were due to increases in Anet, then Δ18Olw should be constant or decrease with iWUE. However, if increases in iWUE were due to decreases in gs, or a combination of a decrease in gs and an increase in Anet, Δ18Olw would increase with iWUE (9, 25, 30). As such, there currently exists a wealth of previously untapped long-term records of tree physiological responses to environmental change within numerous dendrochronological studies from around the world, providing a historical view of how iWUE has changed globally over the last century. In this analysis, we 1) synthesize published data from tree ring carbon and oxygen isotope chronologies to examine global trends in tree ring–derived iWUE, 2) identify those environmental factors, and their interactions, that best explain multidecadal to centurial trends in iWUE, and 3) investigate the potential underlying changes in Anet and gs through an analysis of coupled tree ring–derived iWUE and ∆18Olw over time (9, 20, 22, 23, 25).Using 113 unique tree ring carbon and oxygen isotope chronologies comprising 36 different species across 84 sites globally (Fig. 1A and SI Appendix, Table S1), we show tree-level iWUE increased, on average, by ∼40% (0.35% y−1, iWUE = 0.23*y − 369.16) over the last century (1901–2015) (Fig. 1B). We statistically identified a breakpoint in the combined iWUE chronology at 1963, after which iWUE increased linearly at a rate of 0.39 ± 0.01 µmol CO2⋅mol−1 H2O⋅y−1 (1.67% y−1), or ∼3.9 times faster than the previous 63 y (F = 207.14, P < 0.0001) (Fig. 1B and SI Appendix, Fig. S1). This change in the rate at which tree iWUE increased after 1963 coincided with a statistical breakpoint in Ca at 1969 where CO2 began increasing 4.1 times faster than between 1901 and 1969 (SI Appendix, Fig. S2) (31), and is similar to the trend in iWUE from a recent global synthesis by Adams et al. (32) who also showed a similar breakpoint in the 1960s. When considering individual chronologies, the increases in iWUE were widespread, with 93% (105 of 113 chronologies) of those examined having positive trends over 1901–2015 and 84% (95 of 113) of those examined having positive trends over 1963–2015 (SI Appendix, Table S1). These data for trees during the Anthropocene spanning 10 biomes in six continents that represent a spectrum of leaf and wood types reinforce reports of increasing iWUE in the United States (9), Europe (6, 8), and tropical forests (7), in addition to a recent compilation of global iWUE trends (32).Open in a separate windowFig. 1.Tree locations from which chronologies of iWUE and ∆18Olw were developed (A) and group mean-centered iWUE by species within site over the period 1901–2015 (B). The color of data points in A and B correspond to the biome from which trees were growing, while the size of the circle in A corresponds to the number of trees used in the development of each carbon and oxygen isotope-derived chronology, respectively. The vertical dashed line in B occurs at the year 1963 where the rate of change in iWUE increases. The solid lines in B denote the average trend in iWUE for the period before (1901–1963) and after (1963–2015) the identified breakpoint. Data for iWUE trends and the number of trees per chronology are listed in SI Appendix, Table S1.Globally, differences in vegetation physiognomy may have large impacts on iWUE, and despite much work examining plant physiological processes within biomes, there have been few large-scale comparisons of historical tree iWUE across biomes at a global scale (6, 8, 9, 12, 32). In this study, we found the rate of increase of iWUE from 1963 to 2015 differed among the 10 biomes represented (F = 7.21, P < 0.001) and ranged between 0.101 ± 0.07 µmol CO2⋅mol−1 H2O⋅y−1 for trees growing in deserts and xeric shrublands to 0.611 ± 0.07 µmol CO2⋅mol−1 H2O⋅y−1 for Mediterranean forests, woodlands, and scrub (SI Appendix, Fig. S3A and Table S2). Like deserts and xeric shrublands, the rate of iWUE increase since 1963 for trees growing in the tundra and in temperate grasslands, savannas, and shrublands was low, with a mean across the three biomes of 0.131 µmol CO2⋅mol−1 H2O⋅y−1. All other biomes exhibit mean rates of iWUE increase after 1963 greater than 0.409 µmol CO2⋅mol−1 H2O⋅y−1 (SI Appendix, Table S2). Furthermore, mean iWUE over 1963–2015 ranged from a low of 59.9 µmol CO2⋅mol−1 H2O in tropical and subtropical moist broadleaf forests to a high of 91.5 µmol CO2⋅mol−1 H2O in temperate conifer forests (SI Appendix, Fig. S3A).When considering changes in iWUE since 1963 with respect to wood anatomy, the rate of iWUE increase was ∼21% higher for conifers relative to diffuse porous trees (P = 0.022), but we found no difference in the rate of iWUE increase between conifer trees and ring porous trees (P = 0.61) or between diffuse porous trees and ring porous trees (P = 0.27) (SI Appendix, Fig. S4A). Our results are similar to climate-corrected iWUE trends from tree rings presented by Frank et al. (6) in European forests, but are in contrast to work by Saurer et al. (33) and Wang et al. (34) using tree rings and flux tower measurements, respectively, who showed broadleaf deciduous trees (i.e., ring porous and diffuse porous) in the Northern hemisphere having greater rates of increase in iWUE than conifer trees over the last century. It is important to note, however, that minor discrepancies among absolute iWUE values and their trends over time in these studies when compared to ours may have resulted from the formulation (i.e., our inclusion of a photorespiratory term) (35) and methodology differences (i.e., tree ring, flux tower) (36). We found no differences in the rate of iWUE increase since 1963 among trees with different leaf functional types (F = 1.05, P = 0.37) (SI Appendix, Fig. S4C). Additionally, we found mean iWUE during 1963–2015 was different among all wood types (F = 782.96, P < 0.001), being the highest in conifer species and lowest in ring porous species (SI Appendix, Fig. S4B), consistent with recent findings from 12 tree species at eight forested sites in the United States (9). Of important note, the patterns in mean iWUE for each wood type fall along a gradient of hydraulic conductivity and safety trade-offs (37). Conifer trees, which have relatively low hydraulic conductivity, but are more resistant to drought induced cavitation and embolism, had the highest mean iWUE, in contrast to ring porous trees, which have higher hydraulic conductivity, but are more vulnerable to hydraulic failure, which had lower mean iWUE (38–40). Mean iWUE was also different among trees with different leaf types, with needleleaf evergreen trees being the highest, followed by needleleaf deciduous trees, broadleaf evergreen trees, and finally broadleaf deciduous trees (SI Appendix, Fig. S4D).Changes in climate and Ca have strong effects on vegetation function from the leaf (14, 15) to global scale (13, 41, 42), yet how environmental change has influenced iWUE across large spatial scales over the last century is not fully resolved. Guerrieri et al. (9) and Frank et al. (6) recently showed increasing Ca and climate change have led to increased iWUE in tree species across the United States and Europe, but whether this response is conserved across biomes experiencing a much larger range in climate is still unknown. To address this knowledge gap, we used linear mixed effects (LME) models to examine the importance of environmental factors in driving tree iWUE. Across all species and study locations from 1963–2015, LME model results explained as much as ∼89% of the variance in iWUE and indicated a strong, positive relationship with Ca and growing season vapor pressure deficit (VPDgrw) and a negative relationship with growing season precipitation (PPTgrw), although a large proportion of the total variance was attributed to differences among sites (Table 1 and SI Appendix, Table S3). Remaining unaccounted variance may include nonclimatic factors, such as nitrogen availability or acidic air pollution, which are known to have important influences over iWUE (19, 43, 44), but these were not included in the studies from which we extracted isotopic data. When extending this analysis to changes in iWUE over the last 115 y (1901–2015), LME model main effects were remarkably consistent with data from 1963–2015, with the exception of growing season temperature (TMPgrw) having a marginally positive effect on iWUE across the 115-y chronology (SI Appendix, Table S4), which suggests TMPgrw is becoming more important in recent years.Table 1.LME model results and parameters for the best model (lowest AICc) examining the drivers of tree ring–derived iWUE for the period 1963–2015
Open in a separate windowLME model results and parameters for the best model (lowest AICc) examining the drivers of tree ring–derived iWUE for the period 1963–2015 when only considering environmental factors, climate, and CO2 (second column), including wood type as a fixed effect with no interactions in the model (third column), and when including leaf type as a fixed effect with no interactions in the model (fourth column). Intercept values for each model represent the value of iWUE when each numerical environmental factor included in the model is at its mean value during the study period. Leaf and wood type parameter estimates indicate the difference from the original intercept value for each respective variable, with comparisons made with “conifers” at the base level for wood type and “needleleaf evergreen” as the base level for leaf type. The marginal R2 describes the goodness of model fit given fixed effects only, while the conditional R2 describes the goodness of model fit including fixed and random effects (tree species nested within site). Model parameter significance is denoted by an asterisk, where *P < 0.05, **P < 0.01, and ***P < 0.001, and “ns” denotes not significant.As a complement to our analysis using LME models to examine the effects of Ca, PPTgrw, TMPgrw, and VPDgrw on tree iWUE, we used hierarchical partitioning (HP), which alleviates potential problems that arise due to multicollinearity, to estimate the individual contribution of a given environmental parameter to tree iWUE. For the period 1901–2015, HP indicated that, of the 42% variance in iWUE explained by our model, Ca accounted for 59.6% (z = 1,375.5, P < 0.05), more than any other factor considered. We found VPDgrw, TMPgrw, and PPTgrw contributed 23.6% (z = 501.3, P < 0.05), 16.2% (z = 340.1, P < 0.05), and 0.6% (z = 12.6, P < 0.05), respectively. Of the 31% variance in iWUE explained in our model over the period 1963–2015, the influence of Ca declined to 45.2% (z = 455.6, P < 0.05), consistent with Adams et al. (32), who found a diminishing rate of response of iWUE to CO2 after 1966 (SI Appendix, Fig. S5). On the other hand, the influence of TMPgrw increased to 28.0% (z = 357.2, P < 0.05), whereas the contributions of VPDgrw and PPTgrw were similar to the 1963–2015 chronology with 26.1% (z = 269.7, P < 0.05) and 0.7% (z = 6.4, P < 0.05), respectively.Increasing Ca may affect both stomatal conductance and photosynthesis (14, 15). VPD, or dryness of the atmosphere, regulates stomatal conductance (45), and therefore, both photosynthesis and transpiration (46). Air temperature drives VPD (46), but also affects leaf metabolism, including the ratio of photosynthesis to photorespiration (47). Precipitation is a proxy for soil moisture content and, thus, the amount of water available for uptake by plant roots. Thus, the individual effects of Ca, VPDgrw, PPTgrw, and TMPgrw on our observations of iWUE over the last 115 y are grounded in well-established plant physiology (6, 7, 48, 49). Here, we show that these environmental drivers interact to regulate tree iWUE and are dependent on leaf and wood functional types, demonstrating the complexity and nuance of tree responses to environmental change. In many instances, the effects of increasing Ca were modulated either positively or negatively by the other drivers of iWUE and by leaf and wood functional types. There was a Ca by VPDgrw interaction across the 115-y chronology (SI Appendix, Fig. S6A) and when considering only the chronology after the 1963 breakpoint (Fig. 2A), whereby VPDgrw had a greater effect on iWUE at lower Ca than at higher Ca and the effect of increasing Ca on iWUE was diminished by greater VPDgrw. This may, in part, explain the diminishing influence of Ca on iWUE presented by Adams et al. (32) in recent years if VPDgrw has also been increasing at the study locations. In contrast, the effect of Ca on iWUE was enhanced at greater temperatures across the 115-y chronology (SI Appendix, Fig. S6B).Open in a separate windowFig. 2.Nature of the interaction between growing season vapor pressure deficit (VPD) and atmospheric CO2 (A) and growing season VPD and growing season temperature (B) on iWUE during the period 1963–2015. The interactions shown represent a given predicted value of iWUE throughout the range experienced by each group mean-centered environmental factor since 1963. Values listed for each environmental factor are standardized with respect to the mean during the study period. Parameter estimates for each interaction are listed in Table 1.Furthermore, we found increasing Ca led to greater increases in iWUE in conifer trees, with respect to trees that had diffuse porous or ring porous wood, and the effect of Ca was diminished in broadleaf deciduous trees relative to needleleaf evergreen trees (SI Appendix, Table S3). When considering leaf type, we found that Ca interacted with PPTgrw where the effects of increasing Ca on iWUE were greatest with low PPTgrw and diminished at high PPTgrw for needleleaf evergreen (Fig. 3A) and needleleaf deciduous (Fig. 3B), in contrast to broadleaf deciduous trees where the effect of increasing Ca offset the negative effects of high PPTgrw on iWUE (Fig. 3C). These results suggest that iWUE of broadleaf deciduous trees may remain high as Ca continues to increase regardless of PPTgrw, whereas high PPTgrw offsets the Ca response of iWUE in conifers, which are adapted to drier environments (50). We found no interaction between Ca and PPTgrw on iWUE of broadleaf evergreen trees, which instead showed an interaction between Ca and VPDgrw on iWUE (Fig. 3D). In other instances, the effects of air temperature were modulated by either VPDgrw or precipitation. The response of tree iWUE to TMPgrw after 1963 was dependent upon VPDgrw (Table 1), such that the effect of TMPgrw on iWUE was the greatest at high VPDgrw (Fig. 2B). We found more complex interactions between TMPgrw and PPTgrw on iWUE when considering wood functional types, whereby the greatest iWUE for conifer tree species was at low PPTgrw and low TMPgrw (SI Appendix, Fig. S7A), but at low PPTgrw and high TMPgrw for diffuse porous trees (SI Appendix, Fig. S7B). On the other hand, ring porous trees showed an interaction between VPDgrw and PPTgrw, where iWUE was greatest at low PPTgrw and high VPDgrw (SI Appendix, Fig. S7C). Thus, these interactive effects are important to understand as they underscore the intricate interplay between Ca, temperature, precipitation, and evaporative water demand on tree physiology, highlight important differences in tree responses to environmental change across tree functional traits, and have important implications for tree carbon gain and water loss as climate changes and Ca continues to rise (46, 49).Open in a separate windowFig. 3.Nature of the interaction between atmospheric CO2 and growing season precipitation (PPTgrowing) on iWUE for needleleaf evergreen (A), needleleaf deciduous (B), and broadleaf deciduous (C) tree species, and between atmospheric CO2 and growing season VPD (VPDgrw) for broadleaf evergreen trees (D). The interactions shown represent a given predicted value of iWUE throughout the range experienced by each group mean-centered environmental factor since 1963. Values listed for each environmental factor are standardized with respect to the mean during the study period.Annually resolved, canopy-integrated iWUE chronologies reconstructed from tree ring carbon isotope signatures clearly show a positive trend over the 20th century explained, in part, by interactions between Ca, PPTgrw, VPDgrw, and TMPgrw. However, whether the increases in iWUE were due to underlying stimulated Anet, reduced gs, or some combination thereof, cannot be explained using carbon isotope signatures alone. Therefore, we combined analyses of the chronologies of iWUE with those of ∆18Olw (which is inversely related to gs) to partition the increases in iWUE between independent changes in Anet and gs. Of all chronologies showing an increase in iWUE since 1963 (Fig. 4 A–C), we found 5.3% (n = 6) of the studies showed decreasing ∆18Olw (Fig. 4D), indicating increased gs across the 53-y period; 77.9% (n = 88) showed constant ∆18Olw (Fig. 4E), indicating no significant change in gs; and 16.8% (n = 19) showed increasing ∆18Olw (Fig. 4F), reflecting a decrease in gs (22, 23, 25, 30, 51). Trends in ∆18Olw over time were not different among wood types (F = 1.39, P = 0.25) (SI Appendix, Fig. S8A), although there were differences due to leaf type (F = 4.60, P = 0.03), with broadleaf deciduous trees showing slightly increasing ∆18Olw, corresponding to a decrease in gs, but all other leaf types being no different from each other and having either a nonsignificant slope or a negative slope (SI Appendix, Fig. S8B). There were no relationships between individual trends in ∆18Olw and mean TMPgrw and mean PPTgrw across all chronologies since 1963 (SI Appendix, Fig. S9 A and B), nor was there a relationship between individual trends in ∆18Olw and individual trends in TMPgrw (SI Appendix, Fig. S9D). Mean ∆18Olw, however, did decrease with increasing mean VPDgrw, although this relationship was overwhelmingly driven by a few sites (n = 5) (SI Appendix, Fig. S9C). Finally, trends in ∆18Olw tended to become more negative, corresponding to increased gs, in sites becoming wetter since 1963 (SI Appendix, Fig. S9E) and more positive, corresponding to reduced gs, in sites where VPD was increasing (SI Appendix, Fig. S9F), although the variability in ∆18Olw trends explained by PPTgrw trends and VPDgrw trends was only 9% and 8%, respectively. Analysis of the annual variability in ∆18Olw for each of the 113 chronologies since 1963 showed a positive relationship with VPDgrw (SI Appendix, Fig. S10) and TMPgrw (SI Appendix, Fig. S11), but a negative relationship with PPTgrw (SI Appendix, Fig. S12). Our findings are similar to Guerrieri et al. (9) who showed negative or constant ∆18Olw trends in wetter sites (increased or constant gs, respectively) and align in magnitude and direction with the relationship between VPD and tree ring δ18O presented for tropical trees by Kahmen et al. (52). Our analysis of ∆18Olw assumed 1) the oxygen isotopic composition of tree source water reflects that of precipitation, and 2) 40% of oxygen atoms exchange with stem water during cellulose synthesis (pex = 0.40) (53). We performed two sensitivity analyses to explore these assumptions, first by allowing a partial decoupling of precipitation and source water oxygen isotope composition, and second by changing pex across a range from 0.20 to 0.60, or by a climate-dependent value using the equation provided by Cheesman and Cernusak (54) derived from eucalypts in Northeast Tasmania and found that our overall conclusions using pex = 0.4 are highly robust (Methods and SI Appendix, Figs. S13–S17 and Table S5).Open in a separate windowFig. 4.Standardized chronologies of iWUE (A–C) and ∆18Olw (D–F) binned by the individual ∆18Olw trend for each chronology for the period 1963–2015. Red data points (A and D) contain individual chronologies with decreasing ∆18Olw, blue data points (B and E) contain individual chronologies with constant ∆18Olw, and green data points (C and F) contain individual chronologies with increasing ∆18Olw. The respective slope for iWUE across all chronologies within each ∆18Olw category (decreasing, constant, increasing ∆18Olw) is listed within each respective panel, along with the P value with the corresponding LME model fit where species chronology is nested within site as a random factor (see SI Appendix, Table S1 for site chronologies). The mean slope across all ∆18Olw chronologies (N) within a given category is listed in the Top Left of each panel (D–F). The solid black line in each panel represents the average trend across all data points.Whether the increases in tree iWUE are caused by increases in Anet, reductions in gs, or a combination of changes in Anet or gs are consequential as changes in Anet may affect the carbon cycle possibly through tree growth and carbon sequestration, while changes in gs may affect the hydrologic cycle through changes in evapotranspiration. Our temporal analysis of trends in gs inferred from individual ∆18Olw chronologies from 1963 to 2015 indicates that gs increased in 5.3% of examined cases (Fig. 4D) and remained constant in 77.9% (Fig. 4E). Thus, it is necessary that Anet had to increase in 83.2% of examined chronologies (i.e., sum of 5.3% and 77.9%) for iWUE to increase over the 53-y period. In the remaining 16.8% of the chronologies, gs declined (Fig. 4F), and as such, increases in iWUE could have occurred because of reductions in gs alone or in combination with increasing Anet (Fig. 4 C and F). The widespread historical increase in Anet driving increases in tree iWUE was a surprising result to us since declines in gs, and a consequent increase in iWUE, are common results found in elevated Ca experiments (14, 15, 55, 56). However, elevated Ca studies often rely on large step increases in Ca, whereas tree rings record responses to long-term progressive, but small, increases in Ca. Furthermore, analyses of gs responses to increased Ca in trees indicate a large amount of variability with older trees showing less sensitivity than younger trees and conifers being less sensitive than deciduous trees (55). Indeed, ∼70% of the chronologies in this study that showed decreasing gs were broadleaf deciduous tree species. Our data using tree ring isotopes provide strong support of studies using carbonyl sulfide (13, 42, 57), satellite data (58), and seasonal Ca patterns (59) that show global increases in Anet as a result of increasing Ca (13, 42), and build upon recent observations showing widespread stimulated Anet resulting in increased iWUE in the United States (9). Moreover, the rates at which iWUE increased were highest in those chronologies with reduced gs (Fig. 4C), followed by those with constant gs (Fig. 4B), and the lowest in those chronologies with increased gs (Fig. 4A). This highlights the importance of stimulated Anet in driving increasing iWUE in all cases, and supports reductions in gs, inferred through increasing ∆18Olw, exacerbating realized increases in iWUE in a small subset of chronologies. Finally, for those tree ring chronologies where no trends in iWUE were observed since 1963 (n = 18), the overwhelming majority of associated ∆18Olw chronologies were either constant (n = 16) or decreased (n = 1), suggesting any increases in Anet that may have occurred over the 53-y time period were not sufficient to offset the increase or constant gs.Our meta-analysis using historical ∆13C and ∆18Olw from tree ring chronologies representing 34 species across 10 biomes establishes a strong process-based framework for predicting changes to tree iWUE across biomes and across wood and leaf functional types. It further provides an extensive annual record documenting a ∼40% increase in tree iWUE globally over the 20th century (Fig. 1), similar in magnitude to the ∼34% increase in Ca that occurred over the same time (31). The rate at which iWUE increased more than tripled after 1963 and occurred within years of a similar breakpoint in Ca (SI Appendix, Fig. S2). Generally, Ca stimulated the rate of increased iWUE of conifers, which comprised ∼74% of examined chronologies, to a greater degree than trees with other wood anatomy. We identified increasing Ca as a main factor in driving increases in iWUE, although both Ca and TMPgrw interacted with other environmental drivers of iWUE in ways that suggest trees in areas that experience future increases in PPTgrw or reductions VPDgrw may have lower realized iWUE than those areas experiencing drier conditions or higher evaporative demand (Table 1). Metadata on leaf area index (6), tree level photosynthesis or hydraulic conductivity with age (60, 61), height or size effects on carbon isotope discrimination (62), or the levels of air pollution (19, 24, 63) were not consistently available from the published studies used in our analyses, and thus we cannot exclude potential effects of these factors on historical tree iWUE variance. Coupling iWUE with ∆18Olw chronologies revealed increases in Anet as a consistent driver behind increasing iWUE across ∼83% of the examined chronologies, while reduced gs, or a combination of increasing Anet and reduced gs, was responsible for increases in iWUE in the remaining ∼17% (Fig. 4). The widespread patterns of stimulated Anet from this study are in line with recent findings of a 31% increase in global photosynthetic carbon gain over the 20th century (42), directly tracking increasing Ca (13, 42). Thus, this meta-analysis showing increased iWUE over the 20th century encompassing a spectrum of tree functional types across a broad geographic area highlights the complexity of tree responses to environmental change and reinforces the importance of stimulated photosynthesis, and not reductions in leaf gs, as the primary driver in global increases in iWUE. These results provide a historical baseline of the environmental drivers and physiological mechanisms that result in the uptake of ∼30% of anthropogenic carbon emissions by terrestrial ecosystems each year (64). 相似文献
Parameter | 1963–2015 | 1963–2015wood | 1963–2015leaf |
Intercept, µmol⋅mol−1 | 83.078 ± 1.401*** | 87.297 ± 1.292*** | 87.592 ± 1.326*** |
Ca, ppm | 0.238 ± 0.007*** | 0.238 ± 0.007*** | 0.238 ± 0.007*** |
PPTgrw, mm | −0.010 ± 0.001*** | −0.010 ± 0.001*** | −0.010 ± 0.001*** |
TMPgrw, °C | 0.246 ± 0.135ns | 0.245 ± 0.135ns | 0.245 ± 0.135ns |
VPDgrw, kPa | 13.141 ± 1.476*** | 13.131 ± 1.477*** | 13.131 ± 1.477*** |
CO2:TMPgrw | 0.012 ± 0.007ns | 0.012 ± 0.007ns | 0.012 ± 0.007ns |
CO2:VPDgrw | −0.175 ± 0.073* | −0.172 ± 0.073* | −0.172 ± 0.073* |
TMPgrw:VPDgrw | 1.919 ± 0.915* | — | 1.936 ± 0.915* |
Diffuse porous | — | −17.641 ± 2.851*** | — |
Ring porous | — | −18.321 ± 2.727*** | — |
Needleleaf deciduous | — | — | −3.064 ± 2.667ns |
Broadleaf evergreen | — | — | −10.720 ± 7.954ns |
Broadleaf deciduous | — | — | −18.952 ± 2.285*** |
Marginal R2 | 0.12 | 0.38 | 0.39 |
Conditional R2 | 0.88 | 0.89 | 0.89 |
53.
Thure E. Cerling Samuel A. Andanje Scott A. Blumenthal Francis H. Brown Kendra L. Chritz John M. Harris John A. Hart Francis M. Kirera Prince Kaleme Louise N. Leakey Meave G. Leakey Naomi E. Levin Fredrick Kyalo Manthi Benjamin H. Passey Kevin T. Uno 《Proceedings of the National Academy of Sciences of the United States of America》2015,112(37):11467-11472
A large stable isotope dataset from East and Central Africa from ca. 30 regional collection sites that range from forest to grassland shows that most extant East and Central African large herbivore taxa have diets dominated by C4 grazing or C3 browsing. Comparison with the fossil record shows that faunal assemblages from ca. 4.1–2.35 Ma in the Turkana Basin had a greater diversity of C3–C4 mixed feeding taxa than is presently found in modern East and Central African environments. In contrast, the period from 2.35 to 1.0 Ma had more C4-grazing taxa, especially nonruminant C4-grazing taxa, than are found in modern environments in East and Central Africa. Many nonbovid C4 grazers became extinct in Africa, notably the suid Notochoerus, the hipparion equid Eurygnathohippus, the giraffid Sivatherium, and the elephantid Elephas. Other important nonruminant C4-grazing taxa switched to browsing, including suids in the lineage Kolpochoerus-Hylochoerus and the elephant Loxodonta. Many modern herbivore taxa in Africa have diets that differ significantly from their fossil relatives. Elephants and tragelaphin bovids are two groups often used for paleoecological insight, yet their fossil diets were very different from their modern closest relatives; therefore, their taxonomic presence in a fossil assemblage does not indicate they had a similar ecological function in the past as they do at present. Overall, we find ecological assemblages of C3-browsing, C3–C4-mixed feeding, and C4-grazing taxa in the Turkana Basin fossil record that are different from any modern ecosystem in East or Central Africa.The expansion of C4 biomass beginning in the late Miocene marks a major vegetation change in the history of Earth. Today C4 plants comprise ca. 50% of net primary productivity (NPP) in the tropics (1) yet contributed less than 1% of NPP only 10 million years ago. C4 plants are primarily grasses and sedges, although C4 photosynthesis is known to be used in ∼20 plant families (2, 3). C4 photosynthesis is an adaptation to low (ca. <500 ppm by volume) concentrations of CO2 in Earth’s atmosphere along with high growing-season temperatures (4). Although genetic evidence indicates an Oligocene origin of C4 photosynthesis in the grasses (5, 6), macrofossil evidence for C4 photosynthesis in grasses is extremely sparse (7, 8).The expansion of C4 biomass has been documented through stable isotopes in paleosols (9–12), grass phytoliths (13), herbivore tooth enamel (14–16), and biomarkers in deep-sea sediments (17, 18). At 10 Ma in Africa, Asia, and North America, the δ13C values for equid tooth enamel indicate a diet dominated by C3 vegetation; by ca. 7 Ma, equids in Africa have a diet dominated (>75%) by C4 vegetation (14, 15). In East Africa today there is a distinct difference in diets of major herbivores, with most mammals either being predominantly browsing (>ca. 75% C3) or grazing (>ca. 75% C4), and there are relatively few mixed feeders (Fig. 1).Open in a separate windowFig. 1.δ13C1750 values for tooth enamel (or equivalent) for >1,900 mammals from East and Central Africa (principal localities in SI Appendix, Table S1; data from Dataset S1).A recent study of the early transition of C3 to C4 dietary change in the Turkana Basin from 10 Ma to ca. 4 Ma (15) showed that equids were the earliest mammals to fully exploit the C4 dietary resource, attaining a predominantly C4-grazing diet by 7 Ma. Other mammal groups (hippopotamids, elephantids, and bovids) changed to a C4 diet later than did the equids. In this paper we document dietary changes in the major Artiodactyla-Perissodactyla-Proboscidea (APP) taxa in the Turkana Basin between ca. 4 Ma and 1 Ma and compare those to dietary preferences of extant APP taxa in East and Central Africa. The Turkana Basin has an excellent stratigraphy (19–22) with excellent preservation of fossils from 4 to 1 Ma; this study focuses on fossils recovered from the Koobi Fora, Kanapoi, and Nachukui Formations of northern Kenya.We compare dietary changes within the major APP taxa through the past 4 Ma in the formations listed above using >900 individual fossils that represent the major taxa collected within the principal stratigraphic intervals of these formations. Fossil mammalian diets are compared with those of >1,900 extant mammal individuals sampled from >30 different regions and habitats in eastern and central Africa. We compare the ecosystem structure (C3 browsers, C3/C4 mixed diets, and C4 grazers) through the Pliocene and Pleistocene and document changes in ungulate diets over time. 相似文献
54.
Naomi E. Levin Yohannes Haile-Selassie Stephen R. Frost Beverly Z. Saylor 《Proceedings of the National Academy of Sciences of the United States of America》2015,112(40):12304-12309
The incorporation of C4 resources into hominin diet signifies increased dietary breadth within hominins and divergence from the dietary patterns of other great apes. Morphological evidence indicates that hominin diet became increasingly diverse by 4.2 million years ago but may not have included large proportions of C4 foods until 800 thousand years later, given the available isotopic evidence. Here we use carbon isotope data from early to mid Pliocene hominin and cercopithecid fossils from Woranso-Mille (central Afar, Ethiopia) to constrain the timing of this dietary change and its ecological context. We show that both hominins and some papionins expanded their diets to include C4 resources as early as 3.76 Ma. Among hominins, this dietary expansion postdates the major dentognathic morphological changes that distinguish Australopithecus from Ardipithecus, but it occurs amid a continuum of adaptations to diets of tougher, harder foods and to committed terrestrial bipedality. In contrast, carbon isotope data from cercopithecids indicate that C4-dominated diets of the earliest members of the Theropithecus oswaldi lineage preceded the dental specialization for grazing but occurred after they were fully terrestrial. The combined data indicate that the inclusion of C4 foods in hominin diet occurred as part of broader ecological changes in African primate communities.The Pliocene is a critical time in human evolution when almost all early hominins became committed terrestrial bipeds and expanded their diets to include a wider range of resources than their ancestors. Recent stable isotope studies of Australopithecus afarensis teeth indicate that hominins had increased their dietary breadth to include significant amounts of C4 or crassulacean acid metabolism (CAM) resources by 3.4 Ma (1), which is in contrast to its putative ancestor, Australopithecus anamensis, whose diet was limited to predominantly C3 foods (2). The expansion in hominin diets to include significant amounts of C4 and CAM resources indicates a transition toward more open-country foods, because C3 plants include trees, shrubs, forbs, and cool-growing season grasses, whereas C4 plants are primarily warm-growing season grasses and sedges, and CAM plants include cacti and succulents (3). This dietary expansion may have made it easier for hominins to survive in a greater range of environments or in environments that were more variable. However, we do not know when hominins started to include large proportions of C4 resources in their diets, nor do we fully understand the ecological context of these changes (1, 4).We use stable carbon isotope ratios of fossil hominin and cercopithecid teeth from Woranso-Mille in the western part of the central Afar Rift in Ethiopia (Fig. S1) to refine the timing of hominin dietary expansion to include C4 resources. We also report isotope data from other mammals and soil carbonates to evaluate the environmental context and the diagenetic integrity of the isotope data. We use stable carbon isotope ratios, or δ13C values, of fossil teeth to evaluate the dietary proportion of plants that use the C3 vs. C4 photosynthetic pathway, based on the premise that C3 and C4 plants have distinct carbon isotope signatures and that the δ13C value of tooth enamel reflects the carbon isotope composition of an animal’s diet (5). Fossil teeth from the Woranso-Mille paleontological study area are well-suited to fill the temporal gap in the isotopic record of hominin diet because they are part of a record of Pliocene mammalian fossils that spans 3.76–3.2 Ma (6–11). The hominin fossils from Woranso-Mille include those that are morphologically intermediate between Au. anamensis and Au. afarensis, some that are definitively Au. afarensis, and others that represent additional species (7–9, 12). The cercopithecids include multiple species of colobines and at least two papionins (10). Theropithecus oswaldi cf. darti is the most common cercopithecid in the assemblages (>90% of identifiable cercopithecid specimens; 40% of the total identifiable mammal specimens) (6, 10); it represents the oldest and most primitive representative of the long-lasting T. oswaldi lineage whose morphology became increasingly specialized for grazing throughout the Pliocene and into the Pleistocene (13). The carbon isotope data from cercopithecids sympatric to hominins at Woranso-Mille make it possible to evaluate the ecological context for dietary changes in hominins.Open in a separate windowFig. S1.Map with locations of sites discussed in text and listed in Datasets S2 and S5. Woranso-Mille, Middle Awash (Aramis, Asa Issie, Asa Koma), Dikika, Gona (Segala Noumou), and Hadar and are in the Afar region in Ethiopia. Lomekwi, Allia Bay, and Kanapoi are in the Omo-Turkana region of Kenya. Imagery is from NASA''s Earth Observatory from August 2004 (72). 相似文献
55.
Elonore Resongles Volker Dietze David C. Green Roy M. Harrison Raquel Ochoa-Gonzalez Anja H. Tremper Dominik J. Weiss 《Proceedings of the National Academy of Sciences of the United States of America》2021,118(26)
Although leaded gasoline was banned at the end of the last century, lead (Pb) remains significantly enriched in airborne particles in large cities. The remobilization of historical Pb deposited in soils from atmospheric removal has been suggested as an important source providing evidence for the hypothetical long-term persistency of lead, and possibly other pollutants, in the urban environment. Here, we present data on Pb isotopic composition in airborne particles collected in London (2014 to 2018), which provide strong support that lead deposited via gasoline combustion still contributes significantly to the lead burden in present-day London. Lead concentration and isotopic signature of airborne particles collected at a heavily trafficked site did not vary significantly over the last decade, suggesting that sources remained unchanged. Lead isotopic composition of airborne particles matches that of road dust and topsoils and can only be explained with a significant contribution (estimate of 32 ± 10 to 43 ± 9% based on a binary mixing model) of Pb from leaded gasoline. The lead isotopes furthermore suggest significant contributions from nonexhaust traffic emissions, even though isotopic signatures of anthropogenic sources are increasingly overlapping. Lead isotopic composition of airborne particles collected at building height shows a similar signature to that collected at street level, suggesting effective mixing of lead within the urban street canyon. Our results have important implications on the persistence of Pb in urban environments and suggest that atmospheric Pb reached a baseline in London that is difficult to decrease further with present policy measures.Risk assessments of environmental lead (Pb) exposure have drawn the conclusion that it is not possible to identify a blood Pb level below which no adverse impact is detectable (1, 2). Even very low blood Pb levels in children are associated with a loss of full-scale intelligence quotient (IQ) score, and the curve steepens at lower blood Pb levels, with a greater loss of IQ points per unit of blood Pb in the lowest exposure groups (1, 2). Although currently available research has included subjects with very low blood Pb, it is not possible for such studies to go to zero Pb, but it is prudent to treat Pb as a nonthreshold toxin (3). Bellinger estimated full-scale IQ point losses in the early 2000s in the US child population associated with six medical conditions, four neurodevelopmental disorders, two socioeconomic, nutritional, and psychosocial factors, and three environmental chemical exposures (methylmercury, organophosphate pesticides, and Pb) (1). Pb exposure ranked second among all fifteen risk factors, behind only preterm birth and first among the environmental chemical exposures. Much of the summed IQ loss occurred among the lowest-exposed groups due to their larger numbers, and even if population blood Pb levels have declined since the Bellinger study and more children have hence moved into the lower exposure groups (4), the total IQ loss across the population may remain substantial.A major policy achievement in our efforts to reduce Pb in the environment has been the global phaseout of Pb from gasoline at the end of the last century, which resulted in a drastic decrease of atmospheric Pb concentration, especially in urban and remote areas of Europe and North America. While exposure to Pb from paints, Pb pipes, and Pb-containing toys are now recognized as the main cause of elevated blood Pb levels in children in nonindustrial environments, Pb remains an environmental pollutant of great concern (5), as its ongoing existence in the environment affects environmental quality in cities and is raising particular concerns regarding long-term exposure (6). A significant association between Pb concentration in particulate matter smaller than 10 µm (PM10) and the US population’s blood was recently demonstrated (7), suggesting that inhalation and/or ingestion of coarse particles might be an important pathway for human exposure to Pb. Viewed in this context, Pb in the environment remains a significant threat to public health, and it is absolutely essential to accurately identify and monitor the sources and pathways of Pb in the urban environment.While it has been suggested that Pb sources in urban environments today include local and/or distant emissions from industries, coal burning, and traffic (exhaust and nonexhaust emissions) (8–11), recent works studying Pb contamination of airborne particles in (peri-)urban environments (10, 12) have suggested that ongoing contributions of lead released into the environment by leaded gasoline combustion during the last century remain an important but underestimated source. If confirmed more widely, then abatement strategies aimed at eliminating Pb fully from the urban environment need to be adjusted. It is very important to note that these recent findings are in line with an early study of Pb in sediments and water of the San Francisco Bay, suggesting already in the early 2000s that recycling of legacy Pb from leaded gasoline may be a continuing threat to environmental health (13, 14). Furthermore, a shift in Pb concentrations within size distribution, from the fine range (PM2.5) to the coarse range (PM10), has been highlighted after the ban of leaded gasoline in the United States and Europe and ascribed to a change in dominant Pb sources consisting of industrial emissions and road dust resuspension instead of direct vehicle exhausts (15).Alkyl Pb motor fuel additives were used in the United Kingdom from the 1930s until phaseout was completed at the end of 1999. Although added in an organometallic form, Pb emissions from vehicles were predominantly in the form of a fine aerosol of inorganic Pb salts (16). The maximum permitted level of Pb in UK motor fuel was 0.84 g ⋅ L−1. The limit fell successively to 0.40 g ⋅ L−1 in 1981 and 0.15 g ⋅ L−1 in 1986. At its peak in the early 1980s, of the order of 7,000 tons per year of Pb were emitted from road traffic exhaust in the United Kingdom, which had fallen to just 30 tons by the year 2000 (17). Until its final ban, leaded gasoline combustion remained the most important source of Pb emissions in the UK atmosphere (17). In the early 1980s, annual average airborne Pb concentrations at background sites in central London were around 500 to 600 ng ⋅ m−3, which fell to around 300 ng ⋅ m−3 in the second half of the 1980s and then dropped progressively to around 20 ng ⋅ m−3 in 2000. Airborne concentrations now are generally less than 10 ng ⋅ m−3 and have remained steady over the last decade (18).Using Pb isotope composition of atmospheric particles has been a crucial tool in tracing the origin of Pb in the environment and identifying leaded gasoline as a dominant source during the 20th century (19). Following the removal of Pb additives from gasoline in Europe, Pb isotope ratios of atmospheric particles generally changed toward more radiogenic values due to the increase in the relative contribution of other Pb sources (19). Such an evolution was recorded in London airborne particles between 1998 and 2001 during the final phasing out of leaded gasoline in the United Kingdom (20). However, this trend was no longer observed a decade later, leading to the suggestion that leaded gasoline remained an important source of atmospheric Pb (9).The aim of this study was to test if remobilization of historical gasoline-derived Pb remains today an important and persistent source of Pb in London and the urban environment. To this end, we studied the Pb isotope composition of airborne particles collected in central London between 1995 and 2018 using new and historical data and quantified the possible contribution of historical gasoline Pb. London is representative of many large cities in developed countries, where particle emissions from industries and coal combustion are now relatively low, and where traffic emissions and dust resuspension represent dominant sources of airborne particles. Therefore, it constitutes an ideal site to study the persistence of Pb in urban environments almost 20 y after the complete phaseout of leaded gasoline. The variability of Pb concentration and isotopic composition was determined in PM10 and total suspended particles (TSPpassive) during 1 mo at a heavily trafficked site in central London (Marylebone Road site [MR]), where particle emissions are dominated by traffic. The data were compared with the previously published isotopic composition of potential Pb sources and PM10 from this historically monitored site. In addition, the variability of Pb isotopic composition in TSPpassive collected between 2014 and 2018 at building height in central London (Imperial College London site [IC]) is reported to determine mixing and source contributions within the urban canyon. 相似文献
56.
Fang-Zhen Teng Yan Hu Catherine Chauvel 《Proceedings of the National Academy of Sciences of the United States of America》2016,113(26):7082-7087
Incorporation of subducted slab in arc volcanism plays an important role in producing the geochemical and isotopic variations in arc lavas. The mechanism and process by which the slab materials are incorporated, however, are still uncertain. Here, we report, to our knowledge, the first set of Mg isotopic data for a suite of arc lava samples from Martinique Island in the Lesser Antilles arc, which displays one of the most extreme geochemical and isotopic ranges, although the origin of this variability is still highly debated. We find the δ26Mg of the Martinique Island lavas varies from −0.25 to −0.10, in contrast to the narrow range that characterizes the mantle (−0.25 ± 0.04, 2 SD). These high δ26Mg values suggest the incorporation of isotopically heavy Mg from the subducted slab. The large contrast in MgO content between peridotite, basalt, and sediment makes direct mixing between sediment and peridotite, or assimilation by arc crust sediment, unlikely to be the main mechanism to modify Mg isotopes. Instead, the heavy Mg isotopic signature of the Martinique arc lavas requires that the overall composition of the mantle wedge is buffered and modified by the preferential addition of heavy Mg isotopes from fluids released from the altered subducted slab during fluid−mantle interaction. This, in turn, suggests transfer of a large amount of fluid-mobile elements from the subducting slab to the mantle wedge and makes Mg isotopes an excellent tracer of deep fluid migration.Arc volcanism records the elemental cycling between the subducting slab and subarc mantle. Of particular interest is the mechanism by which the subducted material is incorporated into the arc lava. Except for the rare case where arc lava is the direct melting product of a subducted slab (1), most scenarios suggest that mantle wedge is the major magma source that melts after being modified by fluids or melts derived from the subducted basalt and sediment (2, 3). In addition, processes such as polybaric crystallization and crustal assimilation can also modify the composition of arc magmas on their way to the surface. These different processes have different implications on subduction dynamics and elemental cycling, but, in many cases, they are difficult to distinguish. One of the best examples comes from studies of island arc lavas from the Lesser Antilles arc (Fig. 1). Geochemical and Sr, Nd, Pb, Hf, and Li isotopic studies suggest that the Lesser Antilles arc lavas incorporated a variable but to some extent significant amount of subducted sediments (4–8). However, the exact mechanism by which the sediment was incorporated into the lavas is still highly debated and involves various processes such as crustal contamination, subarc mantle metasomatism by fluids released from the slab, or melts derived by partial melting of the subducted sediments (4–17).Open in a separate windowFig. 1.(Upper) Geological map of the Lesser Antilles island arc and the two DSDP sites (sites 144 and 543). (Lower) Comparison of Martinique Island basalts with other Lesser Antilles and worldwide island arcs in 87Sr/86Sr versus 206Pb/204Pb isotopic space (data compiled by Geochemistry of Rocks of the Oceans and Continents database). Modified from ref. 4 with permission from Elsevier; www.sciencedirect.com/science/journal/0012821X.Magnesium isotopes have the potential to provide new and independent constraints on both source composition and processes operating during the formation of arc magmas, not only because Mg is a major element in all magmas but also because surficial and low-temperature processes fractionate Mg isotopes whereas high-temperature magmatic processes do not (18, 19) (Fig. 2). Subducted marine sediments and altered basalts have isotopic compositions different from those of the normal mantle as sampled by global peridotite xenoliths (Fig. 2); however, they generally have low Mg concentrations (18–25, *). In comparison, altered abyssal peridotites have Mg concentrations similar to the normal mantle whereas their Mg isotopic compositions are heavier because of the impact of hydrothermal circulation during accretion and residence in the deep ocean (Fig. 2).†,‡ Finally, although the mechanism is still not well understood, studies of a few arc peridotites show that they also have slightly heavier Mg isotopic composition than the normal mantle (Fig. 2). Given these observed ranges, Mg isotopes may help in understanding the relative contributions of crustal and mantle components to arc magmatism, but no systematic study of either continental or island arc lavas has been carried out yet.Open in a separate windowFig. 2.Magnesium isotopic composition of Martinique arc lavas and subducting forearc sediments (sites 144 and 543). (Data are reported in andS2.)S2.) Data sources for the other reservoirs are from several references (18, 22–24, 36, 42, 43,*,†,‡). The vertical solid line and gray bar represent the average δ26Mg and 2 SD of normal mantle, as sampled by global peridotite xenoliths (−0.25 ± 0.04) (18). The short bold black vertical lines represent the mean δ26Mg value of each individual reservoir.Here, we report Mg isotopic data for 27 arc lavas and 17 subducting forearc sediment samples. The lava samples are from the Martinique Island and cover most of the chemical and isotopic variations in the Lesser Antilles arc (4, 5) (Fig. 1). The sediment samples are from Deep Sea Drilling Project (DSDP) sites 543 and 144 (NE and SE of Martinique Island, respectively); they cover the whole compositional spectrum of subducting sediments and range in lithology from chalky ooze to terrigenous and pelagic deposits (6, 7).The sediments display a large range of δ26Mg (−0.76 to +0.52) with an average of −0.10 ± 0.61 (2 SD) (Fig. 2). This mineralogical control is also evident in studies of loess, shale, mudrock, and carbonates as well as leaching experiments that show preferential enrichment of light Mg isotopes in carbonates over silicates (26, 27).
Open in a separate windowThe depth, MgO, LOI, and Sr, Nd, Pb isotopic data are from Carpentier et al. (6, 7); 2 SD = 2 times the SD of the population of n (n > 20) repeated measurements of the standards during an analytical session. Duplicate refers to repeated measurement of Mg isotopic ratios on the same purified Mg cuts at different days. Replicate refers to repeated column chemistry and measurement of different aliquots of a stock solution. The average value and associated 2 SD are error-weighted values calculated by Isoplot.*Data that were measured on a Nu Plasma HR-MC-ICP-MS; all other data were measured on a Nu Plasma II MC-ICP-MS.†Rec. value is recommended value, from Teng et al. (44).The δ26Mg values of Martinique lavas define a smaller range from −0.25 to −0.10, and are, on average (−0.18 ± 0.07, 2 SD) (Fig. 2), higher than midocean ridge basalt (MORB) (δ26Mg = −0.25 ± 0.06, 2 SD) and mantle peridotite (δ26Mg = −0.25 ± 0.04 2 SD) (18, 23, 28, 29). This difference indicates that the source of Martinique lavas is different from that of MORB, which could be related to a diversity of processes that include seawater alteration for submarine lavas, melting of a mantle source with different δ26Mg, or crustal contamination during magma ascent.Chemical weathering and seawater alteration can potentially modify the Mg isotopic composition of arc basalts, and can shift their δ26Mg to higher values if clays are the dominant alteration products (24). However, the analyzed lava samples are all fresh [loss on ignition (LOI) < 2% with one exception; 4, 5). A previous Li isotopic study on the same suite of samples has shown that only the three samples that erupted as submarine lava have high δ7Li due to interaction with isotopically heavy seawater (8). These three samples, however, have Mg isotopic compositions similar to the other samples. In addition, δ26Mg of Martinique arc lavas does not correlate with their LOI. Therefore, different from Li isotopes, interaction with seawater has little effect on the δ26Mg. The different behavior between Li and Mg isotopes likely reflects the higher concentration of Mg over Li in basalts, which results in an easier isotopic fractionation of Li than Mg during weathering and alteration.
Open in a separate windowThe age, MgO, LOI, and Sr, Nd, Pb isotopic data are from Labanieh et al. (4, 5); 2 SD is 2 times the SD of the population of n (n > 20) repeated measurements of the standards during an analytical session. Duplicate refers to repeated measurement of Mg isotopic ratios on the same purified Mg cuts at different days. Replicate refers to repeated column chemistry and measurement of different aliquots of a stock solution. The average value and associated 2 SD are error-weighted values calculated by Isoplot.*Data that were measured on a Nu Plasma HR-MC-ICP-MS; all other data were measured on a Nu Plasma II MC-ICP-MS.Partial melting of a peridotite source and fractional crystallization of olivine, pyroxene, and plagioclase can be ruled out too, as these processes do not fractionate Mg isotopes (18, 28–30). Nonetheless, arc lavas could potentially be isotopically heavier than MORB if they were produced by partial melting of a subducted oceanic crust, with garnet as a residual phase, e.g., adakite (1). This is because garnet has much lower δ26Mg relative to coexisting pyroxene, as was observed in cratonic and orogenic eclogites (31–33). However, this cannot be the cause of the high δ26Mg values of Martinique lavas, because their chemical compositions are inconsistent with derivation from slab melting, i.e., adakite (4–7).The forearc sediments that enter the Lesser Antilles Trench have, on average, a heavy Mg isotopic composition (−0.10 ± 0.61, 2 SD) (Fig. 2); they could thus be a potential source for the heavy Mg isotopic compositions of the Martinique lavas. Equivalent sediments in arc crust through which the Martinique lavas erupted could provide such a source, as well, if they were assimilated into the lavas. Furthermore, due to the lack of Mg isotope fractionation during prograde metamorphism (31, 33, 34), the metamorphic counterparts of the subducting sediments should preserve their original Mg isotopic signature. Previous isotopic studies of Martinique lavas show that the sedimentary input increases with age from old to intermediate lavas whereas it is much more variable in the recent lavas (4). However, Mg isotopic compositions of the Martinique lavas do not correlate with either age or any radiogenic isotopic system (Fig. 3), suggesting that the presence of heavy Mg is not caused by sediment addition to the subarc mantle source or directly to the lavas themselves. Furthermore, neither binary mixing between subarc mantle peridotite and sediments nor assimilation and fractional crystallization of arc magma can explain the data (Fig. 3). In all modeled mixing arrays, the amount of sediments required to account for the δ26Mg measured in the lavas is unrealistically high (>50%) due to the generally much lower Mg concentration in sediment (2–3%) compared with basalt (8%) and peridotite (48%) (25). Presence of such a large amount of sediment in a source producing basalts and andesites is impossible from a major element point of view. The opposite is true for elements such as Nd, Sr, Pb, or Li, which are drastically more enriched in sediment than in peridotite. In other words, a small addition of sedimentary materials into a peridotite or basalt can change their Nd, Sr, Pb, or Li isotopic compositions significantly, whereas a very large amount of sediment is required to change their Mg isotopic composition. The fact that δ26Mg varies little in Martinique arc lavas, whereas their Nd, Sr, and Pb isotopes change significantly, implies that (i) the peridotite in the mantle wedge has an unusual Mg isotopic composition and (ii) the impact of sedimentary material, if any, is invisible from the Mg isotope perspective because of the large concentration contrast.Open in a separate windowFig. 3.Variations of Mg isotopic composition with Sr, Nd, and Pb isotopic compositions of Martinique arc lavas and subducting sediments from the Lesser Antilles arc (andS2).S2). A−C include data of both Martinique arc lavas and subducting sediments, and D−F focus on the range observed in the lavas. The yellow star represents the hypothesized composition of the normal mantle. The hexagon represents an estimate of the average composition of subducting sediments (Element/isotopes Depleted mantle Primitive magma Sediment D value (AFC) MgO, wt% 37.8 7.95 2.36 3 Sr, ppm 15.5 178 220 3.2 Nd, ppm 0.713 5.5 22.6 0.22 Pb, ppm 0.02 1.34 7.9 0.61 δ26Mg −0.25 −0.25 −0.06 87Sr/86Sr 0.70346 0.70360 0.70887 143Nd/144Sr 0.51301 0.51301 0.51181 206Pb/204Pb 18.5 18.5 19.735
Table S1.
Magnesium isotopic compositions of standards and subducting sediments from DSDP sites 144 and 543 (SE and NE of Martinique Island, respectively)Sample | Depth, m | MgO, wt% | LOI | 87Sr/86Sr | 143Nd/144Nd | 206Pb/204Pb | δ26Mg | 2 SD | δ25Mg | 2 SD |
Standards | ||||||||||
San Carlos olivine | −0.27 | 0.07 | −0.14 | 0.06 | ||||||
Duplicate | −0.25 | 0.07 | −0.11 | 0.06 | ||||||
Replicate | −0.29 | 0.09 | −0.14 | 0.06 | ||||||
Replicate | −0.24 | 0.05 | −0.12 | 0.05 | ||||||
Average | −0.26 | 0.05 | −0.13 | 0.03 | ||||||
Hawaiian seawater | −0.86 | 0.07 | −0.43 | 0.06 | ||||||
Duplicate | −0.82 | 0.07 | −0.44 | 0.05 | ||||||
Replicate | −0.86 | 0.11 | −0.45 | 0.10 | ||||||
Duplicate | −0.86 | 0.10 | −0.45 | 0.07 | ||||||
Replicate | −0.87 | 0.07 | −0.45 | 0.05 | ||||||
Duplicate | −0.84 | 0.07 | −0.43 | 0.05 | ||||||
Duplicate | −0.88 | 0.10 | −0.46 | 0.07 | ||||||
Duplicate | −0.88 | 0.09 | −0.45 | 0.06 | ||||||
Duplicate | −0.84 | 0.06 | −0.44 | 0.04 | ||||||
Average | −0.86 | 0.04 | −0.45 | 0.02 | ||||||
Rec. value† | −0.83 | 0.09 | −0.43 | 0.06 | ||||||
JB-1* | −0.27 | 0.07 | −0.14 | 0.05 | ||||||
Duplicate | −0.27 | 0.09 | −0.12 | 0.06 | ||||||
Duplicate | −0.25 | 0.06 | −0.13 | 0.04 | ||||||
Average | −0.26 | 0.04 | −0.14 | 0.03 | ||||||
Rec. value† | −0.27 | 0.10 | −0.15 | 0.04 | ||||||
SCo-1* | −0.86 | 0.10 | −0.46 | 0.07 | ||||||
Duplicate | −0.85 | 0.09 | −0.43 | 0.06 | ||||||
Duplicate | −0.87 | 0.06 | −0.46 | 0.04 | ||||||
Average | −0.86 | 0.05 | −0.45 | 0.03 | ||||||
Rec. value† | −0.89 | 0.08 | −0.47 | 0.05 | ||||||
SGR-1* | −1.03 | 0.11 | −0.54 | 0.10 | ||||||
Rec. value† | −0.98 | 0.12 | −0.50 | 0.06 | ||||||
DSDP site 144: South Antilles (9.454°N, 54.342°W) | ||||||||||
144A-2–2W-79–80.5* | −0.32 | 0.09 | −0.16 | 0.06 | ||||||
Duplicate | −0.32 | 0.06 | −0.17 | 0.04 | ||||||
Average | 41 | 0.88 | 36.7 | 0.708098 | 0.511942 | 19.3454 | −0.32 | 0.05 | −0.17 | 0.03 |
144–1-4W-98–99* | −0.78 | 0.11 | −0.40 | 0.10 | ||||||
Duplicate | −0.76 | 0.06 | −0.39 | 0.04 | ||||||
Average | 62 | 0.56 | 36.2 | 0.707944 | 0.511998 | 19.4381 | −0.76 | 0.05 | −0.39 | 0.03 |
144A-3–1W-79–80* | 0.53 | 0.11 | 0.27 | 0.10 | ||||||
Duplicate | 0.52 | 0.06 | 0.26 | 0.04 | ||||||
Average | 141 | 1.33 | 22.8 | 0.708335 | 0.511988 | 19.7053 | 0.52 | 0.05 | 0.26 | 0.03 |
144A-3–3W-125–126* | −0.35 | 0.09 | −0.17 | 0.06 | ||||||
Duplicate | −0.42 | 0.06 | −0.22 | 0.04 | ||||||
Duplicate | −0.37 | 0.09 | −0.20 | 0.06 | ||||||
Duplicate | −0.41 | 0.06 | −0.21 | 0.04 | ||||||
Duplicate | −0.42 | 0.06 | −0.22 | 0.04 | ||||||
Average | 144 | 0.52 | 36.5 | 0.708301 | 0.511780 | 19.6460 | −0.40 | 0.03 | −0.21 | 0.02 |
144–3-1W-120–121* | 0.32 | 0.11 | 0.18 | 0.10 | ||||||
Duplicate | 0.30 | 0.06 | 0.14 | 0.04 | ||||||
Average | 163 | 0.75 | 30.3 | 0.708694 | 0.511730 | 20.0424 | 0.30 | 0.05 | 0.15 | 0.03 |
144A-6–1W-125–130 | −0.23 | 0.06 | −0.12 | 0.04 | ||||||
Duplicate | −0.25 | 0.07 | −0.13 | 0.05 | ||||||
Average | 190 | 0.56 | 37.7 | 0.707904 | 0.511840 | 22.9249 | −0.24 | 0.05 | −0.13 | 0.03 |
144–6-1W-46–48* | −0.41 | 0.09 | −0.20 | 0.06 | ||||||
Duplicate | −0.38 | 0.07 | −0.19 | 0.05 | ||||||
Duplicate | −0.40 | 0.09 | −0.21 | 0.06 | ||||||
Duplicate | −0.40 | 0.06 | −0.21 | 0.04 | ||||||
Average | 295 | 1.02 | 13.5 | 0.709158 | 0.512101 | 19.1781 | −0.39 | 0.04 | −0.20 | 0.03 |
144–7-1W-80–82* | −0.04 | 0.07 | −0.03 | 0.06 | ||||||
Duplicate | −0.04 | 0.07 | −0.02 | 0.05 | ||||||
Duplicate | −0.03 | 0.07 | −0.01 | 0.05 | ||||||
Average | 299 | 2.03 | 11.5 | 0.710830 | 0.512105 | 18.8081 | −0.04 | 0.04 | −0.02 | 0.03 |
144–7-1W-125–130 | 299 | 1.63 | 12.2 | 0.709141 | 0.512105 | 18.9347 | −0.16 | 0.07 | −0.08 | 0.06 |
DSDP site 543: North Antilles (15.712°N, 58.654°W) | ||||||||||
543–23-2W-73–75 | 0.04 | 0.07 | 0.03 | 0.05 | ||||||
Duplicate | 0.03 | 0.07 | 0.02 | 0.05 | ||||||
Duplicate | 0.03 | 0.07 | 0.01 | 0.05 | ||||||
Duplicate | 0.03 | 0.05 | 0.01 | 0.05 | ||||||
Average | 220 | 1.96 | 15.1 | 0.718270 | 0.511953 | 19.4023 | 0.03 | 0.03 | 0.02 | 0.03 |
543–26-1W-120–122* | −0.19 | 0.09 | −0.08 | 0.06 | ||||||
Duplicate | −0.13 | 0.07 | −0.07 | 0.05 | ||||||
Duplicate | −0.18 | 0.07 | −0.09 | 0.05 | ||||||
Duplicate | −0.15 | 0.07 | −0.08 | 0.05 | ||||||
Duplicate | −0.17 | 0.07 | −0.09 | 0.03 | ||||||
Average | 248 | 2.17 | 12.1 | 0.717401 | 19.1260 | −0.16 | 0.03 | −0.08 | 0.02 | |
543–29-4W-45–47* | 0.11 | 0.09 | 0.06 | 0.06 | ||||||
Replicate* | 0.09 | 0.10 | 0.05 | 0.07 | ||||||
Duplicate | 0.10 | 0.07 | 0.05 | 0.05 | ||||||
Average | 280 | 1.45 | 15 | 0.719727 | 0.511947 | 19.5323 | 0.10 | 0.05 | 0.05 | 0.03 |
543–31-1W-76–78* | 0.01 | 0.09 | 0.01 | 0.06 | ||||||
Duplicate | 0.00 | 0.07 | 0.01 | 0.05 | ||||||
Duplicate | 0.00 | 0.07 | −0.01 | 0.05 | ||||||
Average | 295 | 1.67 | 15.2 | 0.718082 | 0.511880 | 19.5283 | 0.00 | 0.04 | 0.00 | 0.03 |
543–33-2W-71–73* | −0.13 | 0.07 | −0.05 | 0.06 | ||||||
Replicate* | −0.13 | 0.11 | −0.06 | 0.10 | ||||||
Duplicate | −0.15 | 0.07 | −0.08 | 0.05 | ||||||
Duplicate | −0.15 | 0.07 | −0.08 | 0.05 | ||||||
Duplicate | −0.15 | 0.07 | −0.07 | 0.03 | ||||||
Average | 315 | 2.63 | 13.7 | 0.716811 | 0.511918 | 19.1515 | −0.14 | 0.03 | −0.07 | 0.02 |
543A-5–3W-47–49* | 0.19 | 0.07 | 0.11 | 0.05 | ||||||
Duplicate | 0.25 | 0.07 | 0.13 | 0.05 | ||||||
Replicate* | 0.18 | 0.09 | 0.09 | 0.06 | ||||||
Duplicate | 0.20 | 0.07 | 0.10 | 0.05 | ||||||
Duplicate | 0.24 | 0.07 | 0.13 | 0.05 | ||||||
Duplicate | 0.23 | 0.07 | 0.13 | 0.05 | ||||||
Average | 364 | 3.07 | 14.6 | 0.722128 | 0.511936 | 19.2605 | 0.22 | 0.03 | 0.12 | 0.02 |
543A-8–1W-116–118* | −0.33 | 0.09 | −0.17 | 0.06 | ||||||
Duplicate | −0.37 | 0.07 | −0.18 | 0.05 | ||||||
Average | 390 | 4.94 | 19.7 | 0.731781 | 0.511965 | 19.3564 | −0.36 | 0.05 | −0.18 | 0.04 |
543A-10–1W-25–27* | 0.06 | 0.09 | 0.04 | 0.06 | ||||||
Duplicate | 0.05 | 0.06 | 0.02 | 0.04 | ||||||
Average | 408 | 2.38 | 28.8 | 0.709340 | 0.512077 | 19.1270 | 0.05 | 0.05 | 0.03 | 0.03 |
Table S2.
Magnesium isotopic compositions of arc lavas from Martinique Island, Lesser Antilles arcSample | Age, ka | MgO, wt% | LOI | 87Sr/86Sr | 143Nd/144Nd | 206Pb/204Pb | δ26Mg | 2 SD | δ25Mg | 2 SD |
Recent arc | ||||||||||
06MT50* | −0.21 | 0.09 | −0.12 | 0.06 | ||||||
Duplicate | −0.22 | 0.08 | −0.12 | 0.05 | ||||||
Duplicate | −0.23 | 0.07 | −0.12 | 0.03 | ||||||
Average | 1.929 | 2.27 | 0.23 | 0.704269 | 0.512744 | 19.4160 | −0.22 | 0.04 | −0.12 | 0.03 |
06MT51* | −0.19 | 0.09 | −0.10 | 0.06 | ||||||
Duplicate* | −0.19 | 0.10 | −0.09 | 0.07 | ||||||
Duplicate | −0.21 | 0.09 | −0.13 | 0.06 | ||||||
Duplicate | −0.25 | 0.06 | −0.13 | 0.04 | ||||||
Duplicate | −0.21 | 0.06 | −0.10 | 0.04 | ||||||
Average | 1.929 | 2.09 | −0 | 0.704261 | 0.512766 | 19.4235 | −0.22 | 0.03 | −0.11 | 0.02 |
06MT40* | 189 | 2.49 | 0.82 | 0.704014 | 0.512832 | 19.3014 | −0.23 | 0.07 | −0.10 | 0.07 |
06MT37 | −0.19 | 0.09 | −0.09 | 0.06 | ||||||
Duplicate | −0.19 | 0.06 | −0.10 | 0.04 | ||||||
Average | 322 | 4.36 | 1.12 | 0.704986 | 0.512426 | 19.4802 | −0.19 | 0.05 | −0.10 | 0.03 |
04MT07* | 341 | −0.15 | 0.07 | −0.06 | 0.06 | |||||
06MT18* | 346 | 2.78 | 1.03 | 0.703931 | 0.512853 | 19.2506 | −0.19 | 0.07 | −0.12 | 0.06 |
06MT28* | 543 | 2.58 | 1.2 | 0.703901 | 0.512882 | 19.2263 | −0.20 | 0.07 | −0.07 | 0.06 |
IAR* | 640 | 12.4 | −0 | 0.703821 | 0.512951 | 19.0545 | −0.19 | 0.07 | −0.09 | 0.06 |
06MT21* | −0.20 | 0.07 | −0.08 | 0.07 | ||||||
Duplicate* | −0.22 | 0.07 | −0.11 | 0.05 | ||||||
Average | 893 | 1.9 | 0.92 | 0.705718 | 0.512410 | 19.6166 | −0.21 | 0.05 | −0.10 | 0.04 |
06MT36* | −0.13 | 0.07 | −0.07 | 0.07 | ||||||
Duplicate | −0.13 | 0.07 | −0.08 | 0.05 | ||||||
Duplicate | −0.13 | 0.09 | −0.06 | 0.06 | ||||||
Duplicate | −0.12 | 0.08 | −0.04 | 0.05 | ||||||
replicate | −0.16 | 0.07 | −0.08 | 0.05 | ||||||
Duplicate | −0.15 | 0.06 | −0.07 | 0.04 | ||||||
Duplicate | −0.13 | 0.08 | −0.06 | 0.05 | ||||||
Average | 998 | 2.01 | 1.55 | 0.706307 | 0.512293 | 19.6448 | −0.14 | 0.03 | −0.07 | 0.02 |
06MT61* | 1,175 | 7.9 | 0.1 | 0.703716 | 0.512818 | 19.1318 | −0.20 | 0.07 | −0.12 | 0.06 |
06MT55* | 1,332 | 3.02 | 1.19 | 0.704157 | 0.512782 | 19.2893 | −0.18 | 0.07 | −0.07 | 0.06 |
06MT14* | 1,530 | 3.69 | 0.99 | 0.705131 | 0.512616 | 19.5857 | −0.17 | 0.07 | −0.10 | 0.06 |
06MT19* | −0.24 | 0.07 | −0.10 | 0.07 | ||||||
Duplicate* | −0.21 | 0.07 | −0.09 | 0.05 | ||||||
Average | 1,750 | 2.84 | 1.4 | 0.705120 | 0.512612 | 19.5904 | −0.22 | 0.05 | −0.10 | 0.04 |
06MT04* | −0.13 | 0.07 | −0.07 | 0.06 | ||||||
Duplicate | −0.13 | 0.09 | −0.06 | 0.06 | ||||||
Average | 1,870 | 2.02 | 1.33 | 0.704972 | 0.512629 | 19.5847 | −0.13 | 0.06 | −0.07 | 0.04 |
06MT10* | −0.13 | 0.07 | −0.04 | 0.06 | ||||||
Duplicate | −0.16 | 0.09 | −0.08 | 0.06 | ||||||
Average | 2,111 | 2.94 | 1.85 | 0.705098 | 0.512558 | 19.5970 | −0.14 | 0.05 | −0.05 | 0.04 |
06MT13* | 2,550 | 3.08 | 1.17 | 0.704960 | 0.512650 | 19.6018 | −0.10 | 0.07 | −0.06 | 0.06 |
06MT30* | 3,010 | 3.66 | 3.1 | 0.704836 | 0.512627 | 19.5997 | −0.24 | 0.07 | −0.13 | 0.06 |
06MT34* | −0.15 | 0.07 | −0.06 | 0.06 | ||||||
Duplicate | −0.15 | 0.09 | −0.08 | 0.06 | ||||||
Average | 4,100 | 2.33 | 0.64 | 0.703920 | 0.512910 | 19.2247 | −0.15 | 0.06 | −0.07 | 0.04 |
06MT23* | −0.20 | 0.07 | −0.11 | 0.05 | ||||||
Duplicate | −0.19 | 0.08 | −0.10 | 0.05 | ||||||
Replicate* | −0.19 | 0.11 | −0.12 | 0.10 | ||||||
Average | 4,863 | 3.29 | 1.16 | 0.703901 | 0.512921 | 19.2318 | −0.20 | 0.05 | −0.11 | 0.03 |
06MT32 | −0.20 | 0.07 | −0.09 | 0.05 | ||||||
Duplicate | −0.18 | 0.09 | −0.09 | 0.06 | ||||||
Duplicate | −0.20 | 0.06 | −0.10 | 0.04 | ||||||
Average | 5,130 | 3.3 | 0.7 | 0.703867 | 0.512941 | 19.1985 | −0.20 | 0.04 | −0.10 | 0.03 |
Intermediate arc | ||||||||||
06MT60* | 8,760 | 2.75 | 1.29 | 0.706689 | 0.512318 | 19.8770 | −0.18 | 0.07 | −0.09 | 0.06 |
06MT71 | −0.16 | 0.07 | −0.06 | 0.05 | ||||||
Duplicate | −0.17 | 0.09 | −0.09 | 0.06 | ||||||
Duplicate | −0.12 | 0.06 | −0.06 | 0.04 | ||||||
Duplicate | −0.12 | 0.08 | −0.04 | 0.05 | ||||||
Replicate* | −0.14 | 0.11 | −0.08 | 0.10 | ||||||
Duplicate | −0.13 | 0.06 | −0.06 | 0.04 | ||||||
Average | 10,300 | 3.8 | 0.93 | 0.704936 | 0.512806 | 19.7037 | −0.14 | 0.03 | −0.06 | 0.02 |
06MT69* | 10,640 | 5.25 | 1.95 | 0.704831 | 0.512837 | 19.6896 | −0.14 | 0.07 | −0.06 | 0.06 |
Old arc | ||||||||||
06MT54* | 20,800 | 4.76 | 1.23 | 0.704092 | 0.512973 | 19.1876 | −0.15 | 0.07 | −0.11 | 0.06 |
06MT53* | −0.13 | 0.07 | −0.05 | 0.06 | ||||||
Duplicate | −0.13 | 0.06 | −0.06 | 0.04 | ||||||
Average | 23,400 | 2.85 | 1.36 | 0.704014 | 0.512967 | 19.1900 | −0.13 | 0.05 | −0.06 | 0.03 |
06MT68* | −0.21 | 0.07 | −0.10 | 0.07 | ||||||
Duplicate* | −0.17 | 0.07 | −0.07 | 0.06 | ||||||
Average | 24,800 | 6.47 | 1.47 | 0.703701 | 0.513030 | 18.9866 | −0.19 | 0.05 | −0.08 | 0.04 |