Ecology

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

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