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Global vegetation physiological response to increasing atmospheric CO2 and its reduction of mitigation potentialWe calculate different effects of increasing CO2 on mean annual near-surface air temperature change over global vegetated land. We compare the direct atmospheric radiative effect (RAD)-induced climate warming to the temperature reductions caused by the BGC effect. These two global temperature changes are, in turn, then compared against our main focus of aggregated local PHY-induced temperature contributions (PHYall; Fig.1). The main finding is that the spatial aggregation of PHY feedbacks on temperature over global vegetated land (green bars) offsets a substantial amount of the cooling effect through enhanced terrestrial carbon storage because of the BGC effects (blue bars). Terrestrial carbon storage continuously increases with rising atmospheric CO2, and reaches a global total of 621 ± 260 Pg C under 4 × CO2 (Supplementary Fig. 1). This increased land carbon storage is equivalent to 293 ± 122 ppm of CO2 removal from the atmosphere and results in a temperature cooling of −1.24 ± 0.57 °C. The PHYall-induced land temperature increase is 0.83 ± 0.47 °C, from the ensemble of five ESMs we use (here we excluded bcc-csm1-1; see “Methods”), corresponding to a large offset of the cooling effect by terrestrial ecosystems through BGC. However, our estimated temperature cooling induced by the BGC effect is a transient response. This cooling effect will be larger for subsequently stabilised CO2 concentrations, since terrestrial ecosystems continue to fix carbon until reaching equilibrium. We note that BGC-induced cooling may be overestimated in the absence of land-cover change effect in the simulations, as the latter may reduce terrestrial carbon stores (Supplementary Fig. 2). However, inter-model differences, such as different parameterisation or different biogeochemistry module20, may prevent a definitive answer as to how land-cover change influence global temperature change through BGC (Supplementary Fig. 2–5). We also note that we focus our analysis on CMIP5 data mainly due to the fact that the parameters in Eqs. (1) and (2)21,22 (see “Methods”) for calculating BGC-induced cooling are only currently available for CMIP5 ESMs.Fig. 1: Climate warming mitigation potential of terrestrial ecosystems.Global mean land temperature change due to total CO2 physiological forcing (PHYall), increased terrestrial carbon storage (BGC) and CO2 radiative forcing (RAD). Note axes as coloured, and that the vertical blue axis is temperature cooling through BGC. It is straightforward to compare PHYall-, RAD- and BGC-induced temperature change when the same range and directions of bars are used. Three atmospheric CO2 horizons are selected here, for CO2 concentrations of 500 ppm, 800 ppm and 1032 ppm (4 × CO2). Each bar represents the global area-weighted average from the ensemble of five ESMs. The error bars indicate the standard error of these five models. Note, the bcc-csm1-1 model did not provide diagnostics of carbon storage data and so is not included in calculating the temperature change by carbon storage change.Full size imageThe RAD response to rising CO2 projects a global warming of 5.25 ± 0.65 °C, again under 4 × CO2, corresponding to roughly four times the magnitude of BGC-induced cooling. Hence, PHYall-induced temperature increase adds about 16 ± 8% to the RAD-induced warming globally. The relative magnitude of warming/cooling effects is similar for the lower CO2 levels of 500 ppm and 800 ppm (Fig. 1), illustrating the importance of accounting for PHYall-induced warming and how it affects the ability of terrestrial vegetation to mitigate global warming, irrespective of CO2 concentration. Our identified PHYall feedbacks are a combination of different altered land properties. The balance of changed land components will depend on location, and so spatial variations in the overall warming effect may suggest a reappraisal of some climate change adaptation measures. Hence to aid such assessments, we now consider in detail the global contributions of individual drivers of the PHY-based local warming, and then any geographical variations.In Fig. 2a, we show changes in global vegetated land air temperature associated with PHYall with increasing atmospheric CO2 from our ensemble of six ESMs (see “Methods”). PHYdir (see “Methods”; Supplementary Table 1) is based on the direct CO2 effect on vegetation physiology. PHYall represents all the vegetation physiology-related feedbacks, and captures any additional interactions between RAD and PHYdir, due to all effects not being a simple linear addition of RAD and PHY responses. The difference, PHYall minus PHYdir, is termed PHYint. Although inter-model difference exists, warming levels are projected to increase, by all the ESMs, as CO2 concentration rises, and for both PHYdir and PHYall (Fig. 2a). The interactive effects result in PHYall-induced temperature change being higher than PHYdir throughout the period. For the smaller increases in atmospheric CO2 of up to ~450 ppm, interactive effects dominate with PHYdir being almost zero up to that concentration. However, above the CO2 concentration of 450 ppm, PHYdir increases global warming reaching 0.17 ± 0.06 °C for CO2 of 500 ppm, and climbing to 0.62 ± 0.48 °C under quadrupled atmospheric CO2 (Fig. 2a). At that 4 × CO2 level, interaction term PHYint increases warming by approximately one-fifth of that induced by PHYdir. Hence, the overall physiological feedbacks described by PHYall produce a global temperature increase of 0.74 ± 0.47 °C under 4 × CO2 (again from multi-model mean of six ESMs; see “Methods”).Fig. 2: Change in global mean annual land air temperature and individual climate forcing induced by vegetation physiological response to increasing atmospheric CO2.a Global annual area-weighted temperature change of vegetated land induced by total CO2 physiological forcing (PHYall) and the direct CO2 physiological forcing (PHYdir) in response to increasing atmospheric CO2 concentration. Shaded areas are the standard errors of the six Earth System Models (ESMs) used, and the thick curves are their multi-model means. For each atmospheric CO2 concentration in panel (a), values are based on smoothing using a twenty-year running window (to match with the decomposition results in panel (b). The final temperature change induced by PHYall and PHYdir effects under 4 × CO2 are further marked on the righthand side, with the “+” markers indicating multi-model means. b PHYall-induced climate forcing associated with changes in albedo, aerodynamic resistance (ra), evapotranspiration (ET), downwelling shortwave radiation (SW) and near-surface air emissivity (ɛa). Again, the shaded areas are the standard errors of the models, and the mean is the thick continuous lines. The changes in these variables are calculated using a moving average with a 20-year window. The resulting values under 4 × CO2 are plotted on the righthand side.Full size imageTo better understand the factors influencing CO2 physiological drivers of temperature change, we decompose the global PHYall into individual biophysical components6 (see “Methods”). These five aspects are albedo, aerodynamic resistance (ra), evapotranspiration (ET), downwelling shortwave radiation (SW) and near-surface air emissivity (ɛa). These five-component changes result in climate forcings with different signs and magnitudes (Fig. 2b), and thus perturb the surface energy balance, where positive values correspond to an increase in temperature. Specifically, ET, SW, and albedo generates positive climate forcings that increase local temperature, while ra and ɛa produce negative effects and thus offset local temperature increase. The relative role of each biophysical component in influencing PHYall-induced temperature change remains largely invariant in the transition from low to high CO2 concentrations. In addition, the changes in these five quantities affecting PHYdir show similar variations with that of PHYall as atmospheric CO2 rises (Supplementary Fig. 6). Those results are generally valid for CMIP6 results but with a lower magnitude of change (Supplementary Fig. 7).The vegetation physiological response to rising CO2 causes changes in LAI and stomata closure, which adjust in parallel with more detailed attributes of the land surface. The ESM simulations of changes in LAI and transpiration compare moderately well with available field measurements23,24,25,26,27,28,29,30,31,32 (Supplementary Fig. 8). Here we focus on their effects on the near-surface thermal changes, expressed as climate forcings on near-surface energy fluxes (Fig. 2b). The LAI increase due to elevated CO2 (Supplementary Fig. 9a) leads to decreases in albedo11,33 (Supplementary Fig. 10a; Supplementary Table 2) and ra7 (Supplementary Fig. 10c). These changes are continuous with rising atmospheric CO2 and result in positive and negative effects on global land air temperature change, respectively (Fig. 2b). Specifically, the albedo reduction increases solar radiation absorption by the land surface11,33, and imposes a positive forcing of 0.30 ± 0.14 W m−2 for 500 ppm, and of 0.51 ± 0.45 W m−2 for a quadrupling of CO2 (Fig. 2b). The decreased ra favours the turbulent transport of heat from land to atmosphere7, and leads to a persistent surface cooling with increasing atmospheric CO234,35, which is −1.66 ± 0.44 W m−2 for 500 ppm and −3.15 ± 2.05 W m−2 for 4 × CO2. In contrast, ET decreases considerably (Supplementary Fig. 10e; Supplementary Table 2) due to decreased stomatal conductance responding to increasing atmospheric CO2. These reductions in ET reduce evaporative cooling15,17, and therefore result in a strong positive climate forcing on global warming (Fig. 2b), which increases with rising atmospheric CO2 and reaches 0.88 ± 0.25 W m−2 for atmospheric CO2 of 500 ppm and 2.88 ± 1.49 W m−2 with quadrupled atmospheric CO2 concentration (Fig. 2b).Moreover, ET reduction decreases the inflow of evaporative water to the atmosphere, reducing cloud fraction and water vapour content, thereby feeding back to impose indirect effects that influence temperature change. We quantify these indirect effects through their effects on the components of SW and ɛa, (Fig. 2b). The lower ET values reduce cloud fraction (Supplementary Table 2; Supplementary Fig. 9c) thereby increasing the amount of SW to the land surface6,35,36 (Supplementary Fig. 10g), and producing a moderate positive forcing of 0.67 ± 0.16 W m−2 for 500 ppm and 1.76 ± 1.26 W m−2 under 4 × CO2 (Fig. 2b). However, the reduced cloud fraction and water vapour content decreases ɛa (Supplementary Fig. 10i), which weakens the absorption of longwave radiation37 (Supplementary Fig. 9e). The ɛa changes result in a small negative forcing (−0.28 ± 0.11 W m−2 for 500 ppm and −0.57 ± 0.43 W m−2 for 4 × CO2), and thereby lowering global temperatures. Of particular note is that the direct changes in ET produce the strongest positive forcing for warming (Fig. 2b). Hence, ET changes have a dominant role in influencing the magnitude and sign of PHYall feedbacks on global temperature change. The changes in these five factors for PHYdir are very close in relative terms to those of PHYall (Supplementary Fig. 10), driving climate forcings in a similar magnitude and sign to influence PHYdir-induced global temperature change (Supplementary Fig. 6).Overall, as CO2 rises, these five biophysical factors cause vegetation to amplify warming locally, and when aggregated spatially act to raise planetary global warming. This additional warming effect is primarily driven by changes in ET, with smaller warming contributions from changes in SW and albedo, but also compensated with cooling effects from ra and ɛa changes. The substantial role of ET on biophysical climate feedbacks is consistent with a previous study6, which investigates the biophysical feedbacks because of vegetation greening. That study proposed that CO2-driven greening will enhance ET and thereby producing a net cooling effect. We build on that analysis by here additionally including the CO2-induced partial stomatal closure17,18. We find this inclusion overtakes the LAI influence on ET changes, resulting in large reductions in ET that will instead contribute to net warming as CO2 rises. Large inter-model differences in simulating ra dynamics (Supplementary Fig. 10c, d) mostly explains the substantial spread of ra-induced climate forcing (Fig. 2b). The different extents of LAI increase (Supplementary Fig. 9a, b) also contribute to such differences in simulated ra changes and effects on temperature change. Our noted large cooling through ra changes is also indicated in a recent study7, suggesting the importance of ra in affecting vegetation biophysical climate feedbacks, and the importance of constraining this factor in climate models.Spatial patterns and attributions of vegetation physiologically induced warmingWe present in Fig. 3 the area-weighted regional contributions to global temperature change of the physiological responses and BGC under 4 × CO2. PHYall-based warming and BGC-induced cooling both show larger values in East and Central North America (ENA and CNA), North and Central Europe (NEU and CEU), Amazon (AMZ) and North Asia (NAS). Whereas, the smallest changes are in the Sahel region (SAH) (Fig. 3a). PHYall-induced warming reduces large proportions of the temperature cooling through BGC in the northern mid-to-high latitudes (Fig. 3b). Furthermore, this cooling effect is fully offset by warming through vegetation physiological response in Alaska (ALA), Canada/Greenland/Iceland (CGI), NAS, NEU, SAH, Tibetan Plateau (TIB), Central (CAS) and West Asia (WAS), and West North America (WNA), resulting in slight warming in these regions. Presented as a map of the net effects of BGC and PHYall (Fig. 3b), we further illustrate this overall cooling effect from PHY and BGC for the tropical regions and South Hemisphere. Additionally, the balance of PHY and BGC to influence regional temperature change under relatively low atmospheric CO2 level of 500 ppm (Supplementary Fig. 11) is very close to that for 4 × CO2, suggesting consistency of vegetation biophysical and biogeochemical effects on climate irrespective of CO2 levels. In summary, the PHYall feedbacks offset the cooling benefits from ecosystem “draw-down” of CO2 to a large extent, in line with the global average values presented in Fig. 1. Northern mid-to-high latitudes may contribute less than expected to slow global warming (Fig. 3). However, as also noted for the global change values, the estimated temperature cooling by BGC is a transient effect that would keep increasing as the ecosystems approach their equilibrium. However, BGC-induced cooling may be overestimated without consideration of the effect of land-cover change (Supplementary Fig. 2), which is often associated with the deliberate removal of terrestrial carbon.Fig. 3: Regional contributions to temperature change by PHY and BGC under 4 × CO2.a Contribution of regional vegetation physiological responses (PHYall; green bars) and increased carbon storage (BGC; blue bars) to the overall global temperature change for each of the IPCC AR5 SREX regions. Bars represent area-weighted multi-model means, and the error bars indicate the standard errors of the models for each region. b Spatial distribution of the net effects of warming induced by PHYall and cooling through BGC.Full size imageThe regional pattern in Fig. 3b provides a motivation to investigate further the five climate forcings-driven PHYall contributions (Fig. 2a) to near-surface temperature change triggered by rising CO2. Here, we first examine the much finer spatial patterns and component contributions of PHYall, PHYdir and PHYint on warming for 4 × CO2. Focussing on PHYdir and typically for 4 × CO2, there is a larger local warming in the tropical forests (Fig. 4a), where vegetation shows higher potential to stabilise carbon than other ecosystems10 (Fig. 3). Within these tropical regions, the PHYdir-forced temperature increase is the highest in the Amazon forests (Fig. 4a), reaching approximately 30% of that induced by RAD (Supplementary Fig. 12b). Strong warming by PHYdir is also found in the northern mid-to-high latitudes ( >40°N). In contrast, smaller temperature increases due to PHYdir are seen in arid and semi-arid regions, such as Australia and Sahel (Fig. 4a). The spatial variations of PHYall-forced temperature change (Supplementary Fig. 13a) have strong similarities to those of PHYdir (Fig. 4a). These similarities again suggest a relatively small role of interactions on temperature change (Supplementary Fig. 13b) under quadrupled CO2. Additionally, the agreements across the six ESMs are reasonably high, with all the models agreeing that PHYall, PHYdir and PHYint result in local warming for 4 × CO2 across most of the global vegetated land (Supplementary Fig. 14).Fig. 4: Global patterns of local temperature change and climate forcings through vegetation physiological response to 4 × CO2.Spatial distribution of annual mean temperature change from multi-model ensemble induced by (a). direct CO2 physiological forcing (PHYdir) in response to a 4 × CO2 rise since pre-industrial level. The spatial patterns of the individual climate forcing contributing to PHYdir of panel (a) are as follows. In (b). albedo (α), c Aerodynamic resistance (ra), d Evapotranspiration (ET), e Downwelling shortwave radiation (SW) and (f). near-surface air emissivity (ɛa). In all panels, estimates use the mean of the final 20 years of the simulations (atmospheric CO2 at ~1032 ppm).Full size imageWe next analyse spatially the decomposition of PHYall- and PHYdir-induced temperature change into the five biophysical factors shown in Fig. 2b, and with findings shown in Fig. 4b–f. In response to quadrupled CO2, LAI shows increases over most global vegetated land (Supplementary Fig. 15b), leading to a positive climate forcing (and thus warming) through reducing albedo (Fig. 4b), and especially in the south Sahel and Tibetan Plateau. Moreover, a large albedo decrease occurs in the northern mid-to-high latitudes, such as for most of Siberia. This albedo decrease may be due to LAI increase combined with reduced snow cover promoted by PHY-driven warming17. The LAI increase also contributes to a strong negative forcing through ra decrease7. This ra decrease enhances the energy exchange between land and atmosphere, lowering the local warming effect in response to 4 × CO2, particularly in Australia, Sahel, South Africa and South Asia (Fig. 4c). In comparison, the projected forcing from ET reductions (Supplementary Fig. 15d) causes strongly positive warming, and especially in the tropical and boreal forests (Fig. 4d). The larger ET reductions in the tropical and boreal forests also lead to stronger cloud fraction decreases (Supplementary Fig. 15f). These feedbacks induce a positive climate forcing for additional warming by increasing SW reaching the land surface (Fig. 4e). Simultaneously, the decreased cloud cover and water vapour by the ET reductions generate a cooling effect (Fig. 4f) through decreasing net longwave radiation absorption in most locations. The PHYdir-induced warming and the associated climate forcings at an atmospheric CO2 concentration of 500 ppm (Supplementary Fig. 16) show similar spatial distributions, but smaller magnitude, compared with that adjustment for 4 × CO2 increase (Fig. 4). This result manifests that PHY continues to amplify global warming through the combined climate forcings from our identified five biophysical factors, irrespective of CO2 concentration.Substantial differences exist between PHYall- and PHYdir-induced temperature changes in northwestern Eurasia, implying pronounced PHYint-based feedbacks there under 4 × CO2 (Supplementary Fig. 13b). A relatively large change in SW, inducing a warming effect (Supplementary Fig. 13j), mainly contributes to the large PHYint-coupled changes in this region. Elsewhere, for the Amazon forests, the cooling effect in PHYint, through changes in ET (Supplementary Fig. 13h) and SW (Supplementary Fig. 13j), cancels out the warming due to changes in ra (Supplementary Fig. 13f) and ɛa (Supplementary Fig. 13l). This cancellation causes negligible PHYint feedback on temperature there (Supplementary Fig. 13b). In summary, local warming due to biophysical feedback of increasing CO2 is regulated primarily by the positive forcing from ET reductions (with smaller positive forcings from albedo and SW changes), which is partly compensated by reductions in ra (and a small contribution from ɛa).Strong physiologically induced warming in dense ecosystemsWe further investigate the finding presented in Fig. 4 that PHYdir-forced temperature increase is higher in the tropical forests, while lower in the arid and semi-arid ecosystems. This finding suggests that there may be a relationship between the forced temperature change and background baseline LAI, which we confirm in Fig. 5a (for both PHYall and PHYdir). We find widespread warming amplification with increasing LAI for lower background LAI levels. Such rates of increase in temperature per unit of LAI show evidence of flattening at higher LAI values38. That is, the CO2-fertilised LAI increase saturates in ecosystems with dense canopy cover such as tropical forests (Supplementary Fig. 17a). However, the stomatal closure-induced ET decrease varies almost linearly with baseline LAI, although the reduction breaks down when the baseline LAI approaches six (Supplementary Fig. 17b). Hence, we conclude that ET reductions caused by stomatal closure are the primary cause of PHY-induced temperature rises, although changes in ra because of LAI increases remain an important factor (Fig. 4; Supplementary Fig. 13). Taking all the components together, PHYdir-triggered temperature change increases almost linearly with baseline LAI gradients under 4 × CO2 (Fig. 5a). Of particular interest is that when expressing PHYdir-induced temperature increase as a fraction of warming induced by RAD, it also increases with the baseline LAI value (Fig. 5b). Hence, Fig. 5 provides strong evidence that background vegetation functional structure (LAI) influences the feedback of CO2 physiological forcing on warming in response to 4 × CO2. In terms of change, as the trend of global LAI increase slows down as atmospheric CO2 concentration increases to very high levels (Supplementary Fig. 9a, b), this suggests that the magnitude of the cooling effect (via ra) will decrease relative to the ET-based warming.Fig. 5: Effects of vegetation structure on CO2 physiological forcing in response to 4 × CO2.a Variations of local temperature change induced by total (PHYall) and direct (PHYdir) CO2 physiological forcing, for quadrupled CO2 and presented as a function of baseline pre-industrial leaf area index (LAI). b Variations of the ratio of PHYall- and PHYdir-induced temperature change relative to that of RAD warming, again for 4 × CO2 and as a function of baseline LAI. In both panels, the temperature changes along LAI gradients are smoothed using a five-bin running window for the 0.1 increments in LAI bin size. The shaded areas indicate the standard error among different models.Full size imageImplications and conclusionsTerrestrial ecosystems respond to increasing atmospheric CO2 not only through accumulating carbon (the “BGC” effect) but also through their physiological response (the “PHY” feedback), which can have opposing effects on local surface temperature. As such, to fully understand the net climate benefits of terrestrial ecosystems, comprehensive assessments need to account for both PHY and BGC effects39,40. We find that the warming through PHY feedback largely reduces the capacity of vegetation to slow global warming through BGC. In particular, vegetation transiently operates to amplify local warming in the northern mid-to-high latitudes, as PHY-induced warming is larger than BGC-induced cooling (Fig. 3). Tropical forests have net cooling effects, and forests and ecosystem restoration efforts there would have much higher net cooling benefit for mitigation than if they are placed in the northern mid-to-high latitudes. At the global scale, this PHY-induced warming can offset by up to 67% of the cooling gains from the transient BGC effect (Fig. 1). This is likely an upper-bound value, because the steady-state BGC effect is larger than the transient one. Thus, afforestation can still result in a net cooling effect, especially in the tropics, although the cooling achieved may be less than first expected. Of particular interest is that the PHY-based warming is generally stronger for high baseline LAI values (Fig. 5). This result further suggests the PHY produces extra warming through afforestation due to higher LAI for forests than non-forests. However, this does not contradict the finding for the tropics (where LAI is high) as possibly the most beneficial regions for afforestation, due to the compensating much larger BGC potential to lower temperature for such locations.Our analysis highlights the importance of including PHY feedbacks in any assessments of future levels of global warming. Such understanding may be especially important for the northern mid-to-high latitudes. We point out that only one-third of ESMs we use incorporate dynamic global vegetation schemes (DGVMs). Since land-cover change acts to influence PHY and BGC effects (Supplementary Fig. 2–5), future simulations with dynamic vegetation may allow refining these results. However, we note that inter-model structural differences (e.g. different ratio of transpiration to ET15,41) contribute to large uncertainties in these results. In particular, we note the potential implications of our results for climate mitigation policies with substantial reforestation as a mechanism to slow global warming through emissions offsetting. Reforestation may cause a near-surface cooling through the BGC effect. However, the PHY-based warming effect is stronger for higher LAI values as atmospheric CO2 rises (Fig. 5a). This must be accounted for in any major global reforestation plans with consideration of particular location of reforestation measures42. In general, the effects of anthropogenic land use and land cover change on vegetation biophysical and biogeochemical processes are important and complex with substantial spatial heterogeneity42,43,44,45,46,47. Given the lack of exploitable factorial simulations designed to separate land use and land-cover change effects, these are ignored in the idealised simulations, and may introduce a bias in our results. We note, however, that these idealized “4 × CO2” simulations broadly capture the sign and spatial distributions of land-atmosphere coupling effects on temperature change through comparisons to the ESM results under the RCP8.5 scenario (Supplementary Fig. 18) which include anthropogenic land use and land-cover change. We suggest that future climate projections should much more routinely account for the effect of anthropogenic land use and land-cover change together with PHY and BGC effects42,47, and so that this can be considered in reforestation climate mitigation strategies.An additional caveat of our research is that the PHY and RAD factorial ESM simulations that inform our analysis, are at present only available for the illustrative but potentially unrealistic exponential increase in atmospheric CO2 (1% per year). For this scenario, the systems can exhibit a unrealistic linear behaviour48. PHY and RAD simulations under other scenarios, such as abrupt 4 × CO2 or even historical forcing followed by a potential future scenario, would greatly help to improve projections of PHY feedbacks. We suggest this should be made a high priority of climate modelling exercises, especially if additionally including land use and land-cover change representation. We recognise that the newer CMIP6 ESMs contain improved representation of many processes (e.g. atmospheric aerosols, clouds, and land processes)49. We anticipate our findings to remain broadly valid with the CMIP6 models, although the magnitude of PHY-induced warming is lower for CMIP6 than CMIP5 ESMs18, especially in the northern high latitudes. We hope our analysis provides an incentive to others to undertake such analysis as all the calculations (BGC, PHY, and RAD) become available for that newer CMIP6 ensemble. In summary, our results illustrate that vegetation physiological response to increasing atmospheric CO2 has a substantial local warming effect, which requires consideration alongside the cooling effect vegetation offers by “drawing down” atmospheric carbon dioxide. More

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Remarks on the residual color patterns in the Kitadani Freshwater BivalvesResidual color patterns in the form of visible pigmentation on fossil molluscan shells are generally uncommon2,3. In the Paleozoic to Mesozoic fossil records, the color patterns were limited to marine species3, which are preserved as black to dark-colored bands running on the shell surface as melanin pigments20,21. The black to dark-colored stripes on the shells of the Kitadani Freshwater Bivalves resemble the color patterns in some extant freshwater bivalves, suggesting that the dark bands are residual color patterns remaining as melanin pigments. Consequently, the Kitadani Freshwater Bivalves represents the oldest and second fossil record of residual color patterns among fossil freshwater bivalves.The residual color patterns of the Kitadani Freshwater Bivalves resemble the color patterns of extant freshwater bivalves in terms of width, number, and distribution of the colored bands. Both the Kitadani Freshwater Bivalves and extant freshwater bivalves examined in this study consist of two types of color patterns: stripes along the growth lines and radial rays tapered toward the umbo. Notably, the former pattern is similar among all the species examined, as it forms in the peripheries of prominent growth lines occurring periodically. In the latter pattern, however, the morphology and distribution of the bands are slightly different between the Kitadani Freshwater Bivalves and the extant species. The Kitadani Freshwater Bivalves exhibits relatively distinct and wide radial rays running roughly parallel to the lengths of the sculpture elements (radial plications and/or wrinkles), while the extant species bear obscure and fine radial rays running diagonally to the lengths of the sculpture elements. Nonetheless, the taxa with V-shaped sculpture elements (wrinkles, ribs or arranged nodules) lack or bear ambiguous radial rays, whether extant (e.g., Triplodon spp., Indochinella spp. and Tritogonia spp.)13,15,22 or extinct (†Trigonioides tetoriensis).Hypothesis I: phylogenetic constraintsThe resemblance of the color patterns between the Kitadani Freshwater Bivalves and the extant unionids possibly resulted from the phylogenetic constrains. Each of the three species of the Kitadani Freshwater Bivalves belongs to a separate family (†Trigonioides tetoriensis: †Trigonioididae, †Plicatounio naktongensis: †Plicatounionidae, and †Matsuomtoina matsumotoi: †Pseudohyriidae) in the order Trigoniida19. Trigoniida in turn, forms the subclass Palaeoheterodonta with Unionida23. This raises a possibility that the color patterns observed in the Kitadani Freshwater Bivalves and the extant unionids is inherited from their most recent common ancestor. In other words, these color patterns, stripes along the growth lines and radial rays tapered toward the umbo, may be the apomorphy for Palaeoheterodonta. In fact, some extant trigoniid species belonging to Neotrigonia exhibit color pattern similar to those in the Kitadani Freshwater Bivalves and extant unionids in this study (e.g. Neotrigonia margaritacea)1.Interestingly, the coloration of color patterns is quite different between unioniids (green to blue colorings) and trigoniids (red to yellow colorings), and the oldest known color patterns of the Palaeoheterodonta (Myophorella nodulosa, a marine species of Trigoniida from the Oxfordian of the Early Jurassic) appears different (concentric rows of patches)10 from those of the Kitadani Freshwater Bivalves or the extant unioniids. These observations suggest that colorations evolved independently, in contrast to the color patterns, between Trigoniida and Unionida, and that Trigoniida more diverse color patterns than Unionida did in the Palaeoheterodont evolutionary history. Although further examination of the fossil record for the residual colors and color patterns in Palaeoheterodonta is essential, it is plausible that the habitat differences may have caused such discrepancy in the colorations and color patterns between Trigoniida (mainly marine) and Unionida (freshwater) in spite of the phylogenetic constrains.Hypothesis II: convergent evolutionThe other possible interpretation of the color pattern similarity between the Kitadani Freshwater Bivalves and extant Unionida, is the convergent evolution. One potential factor that may have caused this convergent evolution of the color patterns is an adaptation to their habitats. In general, much of the convergent evolution in animals occurs through the morphological evolution in response to their habitats24. Similarly in mollusks, shell colors and their patterns are generally influenced by their habitats2,6,25. Considering marine mollusks, the shell colors and their patterns have great diversity due to varying habitat environments, especially in coral reeves that exhibit various colors and complex ecosystem2,6. Conversely, in the freshwater ecosystem, the environmental colors are relatively monotonous with rocks, sand, mud, and green algae8, and such habitat conditions are likely indifferent between the Mesozoic and Cenozoic. As a result, the freshwater bivalves retained simple and monotonous color patterns for adapting to such environments throughout their evolution.Another conceivable factor to explain the convergent evolution in the color patterns of the studied freshwater bivalves is the selection pressure by visual predators. In general, the shell colors and their patterns in bivalves act as camouflages against the predators2,7,8,26,27,28. Previous studies have demonstrated that extant freshwater bivalves are preyed upon by crayfish, fish, birds, reptiles, and mammals29,30. Because shell colors in freshwater bivalves tend to be greenish, such colors may be an adaptation against visual predators for blending into the freshwater sediments on which abundant greenish phytoplanktons occur2,8. Therefore, the evolutionary conservatism in color patterns of freshwater bivalves may result from camouflages into freshwater microenvironments, which has been advantageous against visual predators since the late Early Cretaceous.The above discussion assumes that the visual predators of freshwater bivalves remained similar for at least 120 million years. Which animals could have been potential threads to the Kitadani Freshwater Bivalves, and, in turn, the Early Cretaceous freshwater bivalves? Among the extant visual predators of the freshwater bivalves, those whose lineages were present in the Early Cretaceous include crustaceans (especially brachyuran decapoda31), fish, lizards, turtles, crocodiles, birds, and mammals. Among them, the fossil record of durophagous lizards and mammals can be traced back only to the Late Cretaceous32,33. Conversely, lines of fossil evidence suggest that some fish34,35, turtles36, and crocodiles35 fed on molluscan invertebrates during the Early Cretaceous, and the Kitadani Freshwater Bivalves indeed occurs with abundant lepisosteiform scales, testudinate shells and crocodile teeth. Additionally, at least one Early Cretaceous avian species with crustacean gut contents can be attributed to the durophagous diet37, and the Kitadani Formation has yielded avialan skeletal remains38, and footprints39,40. Therefore, fish, turtles, crocodiles, and birds are likely candidates for visual predators of the Early Cretaceous freshwater bivalves, and have remained so until present. Additionally, while crustaceans have not been identified in the Kitadani Formation, they flourished in the Early Cretaceous and their remains occur with the fossil freshwater bivalves of the time elsewhere31. Thus, crustaceans may have also played a role as visual predators of the freshwater bivalves since the Early Cretaceous.In addition to the crustaceans, fishes, turtles, crocodiles and birds, the visual predators of the Early Cretaceous freshwater bivalves likely include extinct lineages. For example, some pliosauroid plesiosaurs are suggested as being durophagous34, although the freshwater members of the group are considered endemic41 and less likely to be a major thread to the Early Cretaceous freshwater bivalves. Another extinct candidate is non-avian dinosaurs. Ornithischians are suggested to have possessed a dietary flexibility including the durophagy. For instance, well-preserved hadrosaurid coprolites from the Late Cretaceous of Montana, U.S.A. include sizeable crustaceans and mollusks, possibly suggesting that the Cretaceous freshwater mollusks were consumed by these herbivorous dinosaurs42. In addition, some basal ceratopsian psittacosaurids are hypothesized for the durophagy based on the predicted large bite force in the caudal portion of the toothrow43. Among saurischians, some oviraptorosaurian theropods are indicated to consume mollusks with hard shells based on their mandibular features44. While hadrosaurids, psittacosaurids, and oviraptorosaurians have not been identified in the Kitadani Formation, psittacosaurids, and oviraptorosaurians are common elsewhere in the Early Cretaceous of East Asia45,46, and hadrosauroid Koshisaurus is present in the formation47. Because dinosaurs occupied a niche of large terrestrial predators throughout the Mesozoic, they may have acted as one of major mollusk consumers in absence of large lizards and mammals in the Early Cretaceous ecosystem. Thus, the predation pressure by visual predators to the freshwater bivalves in the Early Cretaceous is likely similar to that in the present. Consequently, one of evolutionary adaptations of the freshwater bivalves against such pressure has remained to camouflage in the phytoplankton-rich sediments, leading to the long-term evolutionary conservatism of their color patterns. More

Benchmark regression resultsParallel trend testThe premise of using DID is that the treatment group and control group meet the assumptions of parallel trend, which means that before ETS is officially implemented, the evolution trend of ecologicalization efficiency of industry of the control group and the experimental group is consistent and does not show a systematic difference. This study uses a more rigorous empirical test in parallel trend test: if the interaction coefficient is not significant and is different from zero before the implementation of ETS; and if the interaction coefficient is significant and is different from zero after the implementation of ETS, it indicates that there is no significant difference in ecologicalization efficiency of industry between the control group and the experimental group before the implementation of ETS. Results are shown in Table 4: before ETS was officially implemented, the difference coefficient was not significant; after the official implementation of ETS in 2013, the difference coefficient was significant and not equal to 0, and the ecologicalization efficiency of industry was improved significantly, which met the parallel trend of the DID. Therefore, it is scientific and reasonable to evaluate the effectiveness of ETS with DID.Table 4 Parallel trend test.Full size tableDynamic effect analysisTo compare the conditions of the experimental group and the control group before and after the implementation of ETS, dynamic graphs are drawn in this study, as shown in Fig. 1, which shows the impact of ETS on the regional ecologicalization efficiency of industry. The vertical line represents a 95% confidence interval and the broken line shows the marginal effect of regional ecologicalization efficiency, which means that the confidence interval contains is 0 before ETS’s implementation, and the result is not significant. In contrast, after 2013, the effect of ETS became apparent, the marginal effect gradually increased and the results became significant, perhaps owing to the implementation of ETS.Figure 1Dynamic analysis diagram.Full size imageThe effect of ETS on ecologicalization efficiency of industryControlling time effect and fixed effect, this study collected the data of pilot and non-pilot provinces of ETS from 2007 to 2019 to analyze the impact of ETS on the regional ecologicalization efficiency of industry and regional heterogeneity. The results are shown in Table 5. According to the results in the first column, ETS has significantly promoted the regional ecologicalization efficiency of industry, and the national implementation of ETS has achieved remarkable results. Compared with the regions that are not ETS pilot areas, the ecologicalization efficiency of industry of pilot provinces and cities has increased by 35%. Results also show that ETS has different effects on the ecologicalization efficiency of industry in different regions. Specifically, ETS significantly promoted regional ecologicalization efficiency of industry in the eastern and central regions, and the efficiency in the eastern region was more significant than that of the central region. However, the impact of ETS on the regional ecologicalization efficiency of industry in the western region was negative which may result from the fact that compared to the central and western regions, the east region has better economic development, advanced technology, and lots of talents that can respond to the implementation of ETS, accelerate the upgrade of industries, and improve the utilization level of regional resources. There are many traditional industries in the central and western regions, and the development of scientific and technological levels as well as the resource utilization efficiency there are relatively slow. Besides, it is difficult for the central and western regions to adapt to ETS in a short-term of time leading to the failure of improving the regional ecologicalization efficiency of industry in a short time.Table 5 Influence of ETS on ecologicalization efficiency of industry.Full size tableRobustness testPropensity matching score—double difference method (PSM-DID)The assumption of homogeneity and randomness between the control group and the experimental group is the premise of using the DID model. However, due to the large economic and regional differences among provinces and cities, there may be systematic differences between the experimental group and the control group, which may cause deviations in the results. Therefore, the data after propensity score matching is used in this study, making the matched individuals have no other significant differences unless they have been treated or not. The dual difference is conducted again to avoid self-selection bias, and the robustness of the above results is verified according to the measurement results. Control variables were used to match characteristic variables, the experimental group was matched with the control group, and the Logit model was adopted to delete the samples that fail to meet the matching criteria. After the matching, there are 168 observation values. The regression results of PSM-DID model show that, ETS has positive effects on the regional ecologicalization of industry (0.460***), which again proves that the conclusion that ETS improves regional ecologicalization of industry efficiency is reliable. The results are shown in Table 6.Table 6 The result of the PSM-DID.Full size tableCounterfactual testTo verify the robustness of the results again, six provinces and cities are randomly selected as experimental groups for multiple tests to construct new dummy variables of ETS, and the DID model was used again to verify the credibility of the above results. Four random samples were conducted in this study, and the results are shown in Table 7. It can be seen that the results are not significant, which also reversely proves that ETS improves the regional ecologicalization efficiency of industry.Table 7 Counterfactual test results.Full size tableActing pattern analysis of ETS on the regional ecologicalization efficiency of industryFirst, ETS may improve the regional ecologicalization efficiency of industry through industrial structure optimization and upgrading. Promoting upgrading of the industrial structure is one of the important approaches of social and economic development during the 14th Five-Year Plan formulation and is the only way to promote low-carbon and sustainable development of modern national industries. The upgrading of the industrial structure has been promoted to the national strategic level, contributing to the healthy development of the national economy system. ETS bring costs and benefits to enterprises, forcing them to transform and upgrade, increase investment in environmental protection and use clean energy, and accelerate the pace of energy conservation and emission reduction31. Second, ETS may improve the regional ecologicalization efficiency of industry through the coordinated agglomeration of resources. Marshall’s theory of scale economy, Krugman’s theory of new economic geography, Weber’s theory of agglomeration economy, Coase’s transaction cost theory, and so on reflect the importance of resource aggregation of economic activities through cost-saving, resource sharing, and other ways to improve industrial input–output efficiency, enhance industrial competitiveness, increase regional comprehensive strength and strengthen the competitive advantage of regional industrial clusters32. The benefits generated by resource aggregation far exceed the sum of benefits generated by various industries in the decentralized state. Under the pressure of ETS, enterprises may alleviate the mismatch between labor and capital through the collaborative aggregation of industrial resources, aiming to improve economic benefits and regional resource allocation efficiency and promote regional ecologicalization efficiency of industry. Third, ETS may improve the regional ecologicalization efficiency of industry by supporting ecological optimization. The sustainable development of the ecological environment is closely related to emission reduction policy. To alleviate the bad effects on the ecology, environmental protection is more and more brought to the attention of society and government. Policies for ecological protection have been introduced to reduce pollution20. All regions take effective and targeted measures to control environmental pollution and optimize the investment structure in light of their actual conditions. The purpose of ecological optimization is to improve the regional environment and strengthen pollution control which is one of the important parts of China’s fiscal spending. The government must guide the market to carry out ecological protection and environmental governance according to ETS. Studies have found that a low-carbon pilot policy helps to enhance the level of regional pollution control, promote the harmonious development of regional economy and environment, and then improve the regional ecologicalization efficiency of industry.To explore the transmission mechanism of ETS on the regional ecologicalization of industry efficiency, Baron and Kenny (1986)’s mediating effect model was referred to explore and verify whether there exists a structural optimization upgrade effect, resource synergistic agglomeration effect, ecological optimization support effect when ETC promotes regional ecologicalization efficiency of industry. Table 8 shows the regression results of the influence mechanism of ETS on the regional ecologicalization efficiency of industry. This study refers to the definition and research of industrial optimization and upgrading by Wang Qunwei, Huang Xianglan, and others, and the proportion of tertiary industry added value accounting for industrial added value is selected to measure the effectiveness of industrial optimization and upgrading. For resource synergistic agglomeration effect, this study refers to the calculation methods of Cui Shuhui, Chen Jianjun et al. and adopts the collaborative aggregation index of manufacturing and producer services to measure the collaborative aggregation effect of resources, which effectively avoids the scale difference between different regions. It can be seen from the table that the implementation of ETS has significantly influenced the three effects proposed by this study: the optimization and upgrading effect of industrial structure, the synergistic aggregation effect of resources, and the support effect of ecological optimization. In addition, ETS has a positive and significant impact on the regional ecologicalization efficiency of industry. The results in Columns 3, 5, and 7 of the table show the industrial optimization and upgrading effect, resource synergistic aggregation effect, structural upgrading effect, and resource allocation effect generated in the process of low-carbon pilot policy operation can significantly promote regional ecologicalization efficiency of industry and have an obvious intermediary effect. The mediating effect produced by industrial structure optimization and upgrading is about 0.042, the mediating effect produced by resource synergy agglomeration is about 0.148, and the mediating effect produced by ecological optimization support is about 0.166. According to the Sobal test results, all of them have passed the test, indicating that the above results are reliable.Table 8 Mediating effect test results.Full size table More

Rio Conservation and Sustainability Science Centre, Department of Geography and the Environment, Pontifical Catholic University, Rio de Janeiro, BrazilBernardo B. N. Strassburg, Alvaro Iribarrem, Carlos Leandro Cordeiro, Renato Crouzeilles, Catarina Jakovac, André Braga Junqueira, Eduardo Lacerda & Agnieszka E. LatawiecInternational Institute for Sustainability, Rio de Janeiro, BrazilBernardo B. N. Strassburg, Alvaro Iribarrem, Carlos Leandro Cordeiro, Renato Crouzeilles, Catarina Jakovac, André Braga Junqueira, Eduardo Lacerda, Agnieszka E. Latawiec, Robin L. Chazdon & Carlos Alberto de Mattos ScaramuzzaPrograma de Pós Graduacão em Ecologia, Universidade Federal do Rio de Janeiro, Rio de Janeiro, BrazilBernardo B. N. Strassburg, Renato Crouzeilles & Fabio R. ScaranoBotanical Garden Research Institute of Rio de Janeiro, Rio de Janeiro, BrazilBernardo B. N. StrassburgSchool of Biological Sciences, University of Queensland, St Lucia, Queensland, AustraliaHawthorne L. BeyerAgricultural Science Center, Federal University of Santa Catarina, Florianópolis, BrazilCatarina JakovacInstitut de Ciència i Tecnologia Ambientals, Universitat Autònoma de Barcelona, Barcelona, SpainAndré Braga JunqueiraDepartment of Geography, Fluminense Federal University, Niterói, BrazilEduardo LacerdaDepartment of Production Engineering, Logistics and Applied Computer Science, Faculty of Production and Power Engineering, University of Agriculture in Kraków, Kraków, PolandAgnieszka E. LatawiecSchool of Environmental Sciences, University of East Anglia, Norwich, UKAgnieszka E. LatawiecDepartment of Zoology, University of Cambridge, Cambridge, UKAndrew Balmford, Stuart H. M. Butchart & Paul F. DonaldInternational Union for Conservation of Nature (IUCN), Gland, SwitzerlandThomas M. BrooksWorld Agroforestry Center (ICRAF), University of The Philippines, Los Baños, The PhilippinesThomas M. BrooksInstitute for Marine & Antarctic Studies, University of Tasmania, Hobart, Tasmania, AustraliaThomas M. BrooksBirdLife International, Cambridge, UKStuart H. M. Butchart & Paul F. DonaldDepartment of Ecology and Evolutionary Biology, University of Connecticut, Storrs, CT, USARobin L. ChazdonWorld Resources Institute, Global Restoration Initiative, Washington, DC, USARobin L. ChazdonTropical Forests and People Research Centre, University of the Sunshine Coast, Sippy Downs, Queensland, AustraliaRobin L. ChazdonInstitute of Social Ecology, University of Natural Resources and Life Sciences Vienna, Vienna, AustriaKarl-Heinz Erb & Christoph PlutzarDepartment of Forest Sciences, ‘Luiz de Queiroz’ College of Agriculture, University of São Paulo, Piracicaba, BrazilPedro BrancalionRSPB Centre for Conservation Science, Royal Society for the Protection of Birds, Edinburgh, UKGraeme Buchanan & Paul F. DonaldSecretariat of the Convention on Biological Diversity (SCBD), Montreal, Quebec, CanadaDavid CooperInstituto Multidisciplinario de Biología Vegetal, CONICET and Universidad Nacional de Córdoba, Córdoba, ArgentinaSandra DíazUnited Nations Environment Programme World Conservation Monitoring Centre, Cambridge, UKValerie Kapos & Lera MilesBiodiversity and Natural Resources (BNR) program, International Institute for Applied Systems Analysis (IIASA), Laxenburg, AustriaDavid Leclère, Michael Obersteiner & Piero ViscontiDivision of Conservation Biology, Vegetation Ecology and Landscape Ecology, University of Vienna, Vienna, AustriaChristoph PlutzarB.B.N.S. wrote the first version of the paper. All authors provided input into subsequent versions of the manuscript. More