Ecology

Present results showed that Hilsa shad had low nucleotide diversity (0.001809–0.008811) like most of the Clupeiforms, e.g., Elongate ilisha (0.001–0.010), Tapertail anchovy (0.0011–0.0029) in Yangtze river and Japanese anchovy (0.0014–0.0090)44,45,46. Sea fish population had higher genetic diversity than anadromous population within same species or among same group47. Although, Hilsa and Kelee shad belonged to the same subfamily Dorosomantinae but Hilsa shad is anadromous in nature and Kelee shad is exclusively marine48. Because of this habit, nucleotide diversity of Hilsa shad was lower than Kelee shad (Hilsa kelee) (0.010337–0.014690)49. Correspondingly, marine Pacific herring (0.020)50 also had higher nucleotide diversity than Hilsa shad. There were several researchers also reported low nucleotide diversity of Hilsa shad population in the Hoogli, the Ganges and the Brahmaputra river of India10,17,18. Low genetic diversity suggested that only small portion of the total population had the scope of successful spawning. That might be associated with their long anadromous breeding migration journey. At that time huge numbers of individuals were caught in their long migratory routes by the fishermen. Frequent changing of spawning pattern is another reason of unsuccessful spawning51. Therefore, Government of Bangladesh should place some safety and protection actions including, public conscious, restriction on fishing gear, Hilsa fisheries management activities and proper timing of the fishing ban period.Previous studies on genetic population structure of T. ilisha were mostly based on allozymes, allele frequencies, microsatellite DNA markers and mitochondrial DNA regions: Cytochrome b (CytB), ATPase 6&8 (ATPase), 12 s and 16 s rRNA10,15,16,17,18. However, genomic data is more powerful marker than previous markers to present the history, evolution, population status and phylogeny of a fish. Recently, A study discover the population genomics and structure of Hilsa shad in Bangladesh waters based on genomic data at NGS platform by NextRAD sequencing, however they mistakenly assigned samples collected from the confluent of the Meghna River as the north-eastern riverine group19,20. Our study was also based on genomic data at the NGS platform. Conversely, we collected sequence data of 4434 nuclear genes applying a cross-species gene enrichment method22, to examine the genetic diversity and population status of hilsa shad from the Bay of Bengal, its estuaries and all possible lotic and lentic waters and two migratory cohorts.. This study provided a solid estimation of the population status of Hilsa shad using genome-wide data and to infer its genetic diversity.Result of the maximum likelihood IQtree and the population structure suggested that the fresh, estuarine and marine water of Bangladesh have a single population of Hilsa shad. In-addition DAPC, dendrogram and network on SNP loci analysis also represented the same trend. In the phylogenetic tree, samples of all locations were mixed together without making any specific cluster. In the population structure analysis, a single population was present with some admixtured individuals bearing small portion of genes from other group. Pairwise FST value between most locations were poor with non-significant P value (P  > 0.05), that support the deprived local population differences and homogeneity of this fish population throughout our studied locations. The hilsa shad population in Bangladesh might retrieve from a collapsed population. Once upon a time (upto first half of 1990s), this fish was most available and cheap fish in Bangladesh. Because of overexploitation and lack of proper management, the fish population was collapsed more than one decade. After that period, because of fishing ban period and public consciousness (first imposed in 2011), the population started to increase. Hilsa fish production in Bangladesh has doubled in a decade from 2006–2007 (279,189 MT) to 2017–2018 (517,189 MT)4,64. This fact probably caused low genetic diversity and divergence among populations of hilsa shad in the Bangladesh waters.Bangladesh has diversified fresh water habitats for Hilsa shad migration including main river system, coastal and freshwater small rivers, hill stream rivers, haors etc. but anadromous migration of this shad starts from same marine water body, the Bay of Bengal, which is their living ground. Furthermore, this fish has highly migratory nature among marine, estuarine and fresh water bodies. Therefore, it is difficult to draw a conclusion that there is more than one population in this water system. Low variation among groups and among population within groups also did not support more than one population. FST value between most of the locations was poor with non-significant P value, which suggested that the population differences were not significant. Although in some cases, P value was significant but due to their poor FST value that did not provide strong support of local population differences. Here present findings of this study were supported by the findings of some previous researchers who represented the single gene pool or stock of this species in the Bay of Bengal with a substantial gene flow18,52,53.All of the spawning grounds of Hilsa shad were identified in the coastal areas of Bangladesh especially at the lower stretches of the Meghna, the Tetulia, the Ander Manik and the Shahabazpur River e.g., Hatia (Moulavir char) Sandwip (Kalir char) and Bhola (Dhal char and Monpura)6,21. However, migratory plan is mainly initiated during the spawning season, which is activated with follow of fresh water runoff from the inland rivers, and naturally it occurs with the commencement of the south-west monsoon and consequent flooding of all the major rivers draining down to the upper Bay of Bengal and there are no considerable differences in any context. Isolation of spawning ground is an important factor for population differentiation11. Due to presence of un-alienated spawning grounds, it is less feasible to draw population differences of Hilsa shad in the upper streams of different rivers and in their living ground, Bay of Bengal. Therefore, the unique spawning grounds and sole major migratory down-stream route strengthen the presence of single population in all over the Bangladesh water without any significant population clusters. Without specify exact spawning grounds for every cluster, it is unrealistic to draw several clusters in this population.Hilsa population studies in Indian part across the Hoogli, the Bhagirathi, the Ganges and the Brahmaputra Rivers also suggested single and genetically homogeneous population in Indian part10,17,18. Hilsa shad population of the Hoogli-Bhagirathi river system and Hilsa stock of Bangladesh water used same natal habitat, Bay of Bengal. Moreover, the River Ganges is the upstream of the Padma River (Bangladesh) and the Bhagirathi River (India) as well as the Brahmaputra is the upstream of the Jamuna River (Bangladesh). Most of the Hilsa shad of River Ganges comes from the Padma River and as the same way the Brahmaputra river has no other significant source of this fish except the Jamuna River. So genetic homogeneity and unique population across these rivers of Indian part also supported the Hilsa shad’s single population in the Bangladesh water.Nevertheless, Rahman and Naevdal (2000) based on allozymes and muscle proteins as well as Mazumder and Alam (2009) based on mitochondrial D-loop region figured out more than one Hilsa population in Bangladesh waters15,54. Rahman and Naevdal (2000) mentioned two populations: 1. Marine and 2. Estuary and fresh water but they processed without explaining how this highly migratory species was separated into two distinct cohorts. Mazumder and Alam (2009) divided the population into two clusters like previous study but poor pairwise FST value between two groups showed that there were no differences between fresh water and marine-estuarine locations. Recently Asaduzzaman et al. (2020) reported three clusters in the Hilsa population in Bangladesh waters, first one was in marine and estuarine waters and another two belonged to north–western riverine (turbid freshwater) and north-eastern riverine (clear freshwater) ecotypes20. Existing of a single population, the most likely assumption from the present research varied with their findings. Our result suggested that as a highly migratory species, Hilsa shad is incapable to belong to more than one population when sampled at different sections of their migration route. Our postulation is the presence of single cluster in the Bangladesh water because all water bodies are almost connected to each other, raising high rate of gene flow and created large population size. Western and eastern river systems of Bangladesh have immaterial dissimilar water quality (e.g., turbidity) but this is not enough to make population differences of Hilsa shad since they migrate and start their life from same spawning grounds and used almost same route across the lower stream and coastal estuaries during their breeding migration. Asaduzzaman et al. (2020) reported that samples of the Meghna river (MR) was included in the north-eastern riverine (clear freshwater) ecotypes by DAPC and neighbor-joining tree analysis20. However, their sample collection site (MR) was located in the common migratory route for north–western riverine (turbid freshwater) and north-eastern riverine (clear freshwater) ecotypes. Therefore, this site should be representing the samples of both ecotypes rather than specific one.If we draw several specific populations or clusters in the upper streams of Bangladesh that means we had the scope to find this shad in the freshwater all over the year round. However, in the freshwater of Bangladesh, this fish was available in the summer (June–October) and winter season (January-March) only; these were related to their summer and winter migration respectably55. If one or two groups of this fish, continue their complete lifecycle in the freshwater (Western/Eastern part of Bangladesh) that states the assurance of continuous supply of this fish almost year round. However, the original scenario does not support this hypothesis. Finally we can conclude that, only one population of this fish inhabit in the Bangladesh waters without any instance of different populations and clusters (2–4) but in some specific locations, they had some particular characteristics. The Bay of Bengal is their main living ground, at the time of their breeding they come to the freshwater upper streams, spawn in the estuaries and finally return to the sea. Therefore, using all the same ecosystems (sea, estuary and freshwater rivers) in a cyclic fashion is essential to support their life cycle, which certainly pushes all the individuals to belong a unique population.In the population structure analysis, only one population of Hilsa shad was identified with some admixtured individuals (32%) containing partial genes from other population in the water bodies of Bangladesh. The mentioned other population might not represent the Hilsa population of the Hoogly and Bhagirathi river system, India because, the Hilsa shads of both migratory routes of Bangladesh and India showed genetic homogeneity10,17. The Ganges and Brahmaputra rivers of Indian part are the upstream of the Padma and the Jamuna river of Bangladesh and might be belonged to the same population. However, Hilsa population of the Arabian Sea was genetically heterogeneous from the Bay of Bengal18 and those different population genes of admixtured individuals might come from the Arabian Sea by oceanographic dispersion. Once (almost 18,000 years ago) the Arabian Sea had a close connection with the Bay of Bengal through the Laccadive Sea, the Gulf of Mannar and the Palk Bay. Therefore, this likely was an easy way for oceanographic dispersion of Hilsa shad between these two water bodies. After that period, a bridge of limestone shoals, coral reefs and tombolo called as ‘Ram Bridge’ or ‘Adam’s Bridge’ (about 48 km) originated between Pamban Island off the south-eastern coast of Tamil Nadu, India, and Mannar Island, off the north-western coast of Sri Lanka 56,57. Sarker et al. (2020) also mentioned that type of oceanographic dispersion between these two water bodies for another Clupeid fish species, Hilsa kelee49. The Irrawaddy, the Naaf and the Sittang River of Myanmar were also regarded as another important route for Hilsa migration6,58. There is also a possibility of inflowing of these different genes of other population from such population. Still there is no population structure study was conducted in the Myanmar part. Therefore, there is no scope to compare those admixtured individuals with the Hilsa population of Myanmar. However, for completing the full scenario, the Hilsa population of Myanmar also claims research attention in population genomics field.In the present study, Samples of both migration cohorts (summer and winter) were studied. The maximum likelihood IQ tree, population structure and DAPC suggested that samples of both migration cohorts were homogenous. Similarly, Jhingran and Natarajan (1969) and Ramakrishnaiah (1972) also did not find any significant temporal population differences in their previous studies59,60. Dwivedi (2019) found morphometric variations between seasonal migrants of Hilsa shad from Hooghly estuary, India using geometric morphometrics (GM) data61. They explained that these morphotypes might be related to the food availability and temperature fluctuation of summer and winter season but they did not incorporate that to the genetic level of the population. Quddus et al. (1984) reported two seasonal migratory populations of Hilsa shad in Bangladesh water based on spawning, fecundity and sex ratio8. Based on our findings and previous studies we can conclude these mentioned seasonal cohorts might be associated with their food availability and breeding rather than genome level.Hill stream river and haor were two important and unique ecosystems for fish diversity in Bangladesh, they belong to the unique characteristics in the ecological factors as well as fish diversity62,63. Infrequently Hilsa shad use these two water bodies as their migratory routes. Samples were collected from the Shomeswari River and the Dingapota Haor, Mohanganj as the representatives of hill stream river and haor population respectively. However, Hilsa shad of these two exclusive water bodies were similar to the samples of the some other fresh water bodies (i.e., CM, CN and MG) as they were belonging to the Hilsa population without any admixtured individuals. Samples of SS do not have any significant P value with other locations whereas MO samples had significant P value with five other locations but having poor FST value with three locations (i.e., BL, PP, MG). MO samples had only mentionable FST value with MP (estuarine) and MK (marine), which might be the result of differences in water quality of these two water bodies. In DAPC, phylogenetic tree and in network, the samples of hill stream river and haor failed to make any unique cluster or monophyletic clade that represent they are also the part of single unique Hilsa population of Bangladesh waters.Main migration was occurred through the Meghna river estuary, which is connected to the Padma, Meghna and Jamuna river system. However, there are some other alternative routes through some small coastal rivers e.g., the Pashur, the Bishkhali, the Balaswar, the Kocha river, which are connected to the Padma river through the Modhumati and the Gorai river. These coastal rivers passed through or beside the world largest mangrove forest Sundarban. Thus, these two routes are ecologically different from each other. Samples of these two routes have some genetic differences, because most of the locations (MK, CF and BL with PP and KN) of these two estuarine routes had significant P value, but their FST value was not satisfactorily high to make population differences. Ecological differences of these two routes might be played an important role to create this type of slight differences among them. Therefore, these scenarios were not significant enough to describe noteworthy differences in the population level, but may make a sign of upcoming population differences. More

1.Mayaux, P. et al. Tropical forest cover change in the 1990s and options for future monitoring. Philos. Trans. R. Soc. Lond. B Biol. Sci. 60(1454), 373–384. https://doi.org/10.1098/rstb.2004.1590 (2005).Article 

Google Scholar 
2.Lovejoy, T. E. Biodiversity: What is it? In Biodiversity II: Understanding and Protecting Our Biological Resources (eds Reaka-Kudla, M. L. et al.) 7–14 (Joseph Henry Press, 1997).
Google Scholar 
3.Harris, L. D. The Fragmented Forest: Island Biogeographic Theory and the Preservation of Biological Diversity (The University of Chicago Press, 1984).Book 

Google Scholar 
4.Achard, F. et al. Determination of deforestation rates of the world’s humid tropical forests. Science 297(5583), 999–1002. https://doi.org/10.1126/science.1070656 (2002).ADS 
CAS 
Article 
PubMed 

Google Scholar 
5.NRSA – 1983. Mapping of forest cover in India from satellite imagery (1972–75 and 1980–82). Summary Report, National Remote Sensing Agency, Hyderabad, India, pp. 5–6.6.FAO – 2000. Global forest resources assessment. Chapter 23. South Asia. Food and Agriculture Organization, Rome, Italy. http://www.fao.org/3/Y1997E/y1997e0s.htm#bm28. (Accessed on 8 August 2020).7.Datta, D. & Deb, S. Analysis of coastal land use/land cover changes in the Indian Sunderbans using remotely sensed data. Geo-sp. Inf. Sci. 15, 241–250. https://doi.org/10.1080/10095020.2012.714104 (2012).Article 

Google Scholar 
8.Reddy, C. S., Jha, C. S. & Dadhwal, V. K. Assessment and monitoring of long-term forest cover changes in Odisha, India using remote sensing and GIS. Environ. Monit. Assess. 185, 4399–4415. https://doi.org/10.1007/s10661-012-2877-5 (2013).Article 
PubMed 

Google Scholar 
9.Champion, H. G. & Seth, S. K. A revised forest types of India (Manager of Publications, 1968).
Google Scholar 
10.FSI – 2019. State of forest report, Assam. Forest Survey of India, Ministry of Environment and Forests, Dehradun, pp. 23–33.11.Assam Times – 2019. Encroachment killing forest in the state. https://www.assamtimes.org/node/22026. (Accessed on 25 August 2020).12.Saikia, A., Hazarika, R. & Sahariah, D. Land-use/land-cover change and fragmentation in the Nameri Tiger Reserve India. Danish J. Geogr. 113(1), 1–10. https://doi.org/10.1080/00167223.2013.782991 (2013).Article 

Google Scholar 
13.Assam Human Development Report – 2014. Managing diversities, achieving human development. Omeo Kumar Das Institute of Social Change and Development and Institute for Human Development, Planning and Development Department, Government of Assam. https://niti.gov.in/writereaddata/files/human-development/Assam_HDR_30Sep2016.pdf. (Accessed on 26 August 2020).14.Woods, C. H. & Skole, D. Linking satellite, census, and survey data to study deforestation in the Brazilian Amazon. In People and Pixels: Linking Remote Sensing and Social Science (eds Liverman, D. et al.) 70–90 (National Academy Press, 1998). https://doi.org/10.17226/5963.
Google Scholar 
15.Srivastava, S., Singh, T. P., Singh, H., Kushwaha, S. P. S. & Roy, P. S. Assessment of large scale deforestation in Sonitpur district of Assam. Curr. Sci. 82, 1480–1484 (2002).
Google Scholar 
16.Harper, G. J., Steininger, M. K., Tucker, C. J., Juhn, D. & Hawkins, F. Fifty years of deforestation and forest fragmentation in Madagascar. Environ. Conserv. 34(4), 1–9. https://doi.org/10.1017/S0376892907004262 (2007).Article 

Google Scholar 
17.Manjula, K. R., Jyothi, S., Varma, A. K. & Kumar, S. V. Construction of spatial dataset from remote sensing using GIS for deforestation study. Int. J. Comput. Appl. 31(10), 26–32 (2011).
Google Scholar 
18.Phukan, P., Thakuriah, G. & Saikia, R. Land use land cover change detection using remote sensing and GIS techniques: A case study of Golaghat district of Assam, India. Int. Res. J. Earth Sci. 1(1), 11–15 (2013).
Google Scholar 
19.Armenta, S. A. M. et al. Determination and analysis of hot spot areas of deforestation using Remote Sensing and Geographic Information System techniques. Case study: State Sinaloa, Mexico. Open J. For. 6, 295–304. https://doi.org/10.4236/ojf.2016.64024 (2016).Article 

Google Scholar 
20.Sarma, P. K. et al. Land-use and land-cover change and future implication analysis in Manas National Park, India using multi-temporal satellite data. Curr. Sci. 95(2), 223–227 (2008).
Google Scholar 
21.Valožić, L. & Cvitanović, M. Mapping the forest change: using Landsat imagery in forest transition analysis within the Medvednica protected area. Hrvat. Geo. Glas. 73(1), 245–255. https://doi.org/10.21861/hgg.2011.73.01.16 (2011).Article 

Google Scholar 
22.Gambo, J., Mohd Shafri, H. Z., Shaharum, N. S., Abidin, F. A. & Rahman, M. T. Monitoring and predicting land use-land cover (LULC) changes within and around Krau wildlife reserve (KWR) protected area in Malaysia using multi-temporal Landsat data. Geoplanning: J. Geomatics Plan. 5(1), 17–34. https://doi.org/10.14710/geoplanning.5.1.17-34 (2018).‬23.Bapu, T. D. & Nimasow, G. Land cover change assessment of Pakke Tiger Reserve (PTR), East Kameng district of Arunachal Pradesh. J. Remote Sens. & GIS, 9(1), 26–33. http://doi.org/https://doi.org/10.37591/.v9i1.93 (2018).24.Kushwaha, S. P. S. & Hazarika, R. Assessment of habitat loss in Kameng and Sonitpur Elephant reserves. Curr. Sci. 87(10), 1447–1453 (2004).
Google Scholar 
25.Census of India – 2011. Primary Census Abstracts. Registrar General of India, Ministry of Home Affairs, Government of India, Retrieved from https://www.censusindia.gov.in/2011census/PCA/pca_highlights/pe_data.html26.Bose, A. U. Tracking the forest rights act in Nameri National Park & Sonai Rupai Wildlife Sanctuary. A report of Kalpavriksh Environmental Action Group, Pune, Maharashtra. https://kalpavriksh.org/wp-content/uploads/2020/07/Assam-Poster_August14_FINAL1.pdf. (2009). (Accessed on 25 August 2020).27.Das, N. Assessment of ecotourism resources: An applied methodology to Nameri National Park of Assam-India. J. Geogr. Reg. Plan. 6(6), 218–228. https://doi.org/10.5897/JGRP12.057 (2013).Article 

Google Scholar 
28.Dong, J. et al. Mapping deciduous rubber plantations through integration of PALSAR and multitemporal landsat imagery. Remote Sens. Environ. 134, 392–402. https://doi.org/10.3390/rs70101048 (2013).ADS 
Article 

Google Scholar 
29.USGS – 2003. Preliminary Assessment of the Value of Landsat 7 ETM+ Data Following Scan Line Corrector Malfunction. USA: EROS Data Center. United States Geological Survey. https://landsat.usgs.gov/sites/default/files/documents/SLC_off_Scientific_Usability.pdf. (Accessed on 8 August 2020).30.Settle, J. J. & Briggs, S. S. Fast maximum likelihood classification of remotely sensed imagery. Int. J. Remote Sens. 8, 723–734. https://doi.org/10.1080/01431168708948683 (1987).ADS 
Article 

Google Scholar 
31.Richards, J. A. Remote Sensing Digital Image Analysis: An introduction. https://doi.org/10.1007/978-3-642-30062-2_8 (Springer, 2013).Book 

Google Scholar 
32.Stehman, S. V. & Czaplewski, R. L. Design and analysis for thematic map accuracy assessment: Fundamental principles. Remote Sens. Environ. 64, 331–334. https://doi.org/10.1016/S0034-4257(98)00010-8 (1998).ADS 
Article 

Google Scholar 
33.Story, M. & Congalton, R. G. Accuracy assessment: A user’s perspective. Photogram. Eng. Rem. S. 52(3), 397–399 (1986).
Google Scholar 
34.Munoz, S. R. & Bangdiwala, S. I. Interpretation of kappa and B statistics measures of agreement. J. Appl. Stat. 24(1), 105–111. https://doi.org/10.1080/02664769723918 (1997).Article 

Google Scholar 
35.Sim, J. & Wright, C. C. The Kappa statistic in reliability studies: Use, interpretation, and sample size requirements. Phys. Ther. 85(3), 257–268 (2005).Article 

Google Scholar 
36.Landis, J. R. & Koch, G. G. The measurement of observer agreement for categorical data. Biometrics 33, 159–174. https://doi.org/10.2307/2529310 (1977).CAS 
Article 
PubMed 
MATH 

Google Scholar 
37.Balasubramanian, D., Arunachalam, K. & Arunachalam, A. Human-induced land use/land-cover change and bioresource management in Bura Chapori Wildlife Sanctuary in North-East India. Clim. Change Environ. Sustain. 4(1), 28–37. https://doi.org/10.5958/2320-642X.2016.00005.3 (2016).Article 

Google Scholar 
38.Sugden, A. M. Mapping global deforestation patterns. Science 361(6407), 1083. https://doi.org/10.1126/science.361.6407.1083-e (2018).Article 

Google Scholar 
39.Curtis, P. G., Slay, C. M., Harris, N. L., Tyukavina, A. & Hansen, M. C. Classifying drivers of global forest loss. Science 361(6407), 1108–1111. https://doi.org/10.1126/science.aau3445 (2018).ADS 
CAS 
Article 
PubMed 

Google Scholar 
40.Myers, N. Tropical deforestation: rates and patterns. In K. Brown & D. Pearce (Eds.), The causes of tropical deforestation. The economic and statistical analysis of factors giving rise to the loss of the tropical forest (pp. 27–40). London: UCL Press (1994).41.Barraclough, S. & Ghimire, K. B. Agricultural Expansion and Tropical Deforestation (Virginia, 2000).
Google Scholar 
42.Hansen, M. C. et al. High-resolution global maps of the 21st-century forest cover change. Science 342, 850–853. https://doi.org/10.1126/science.1244693 (2013).ADS 
CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
43.Reddy, C. S., Rao, P. R. M., Pattanaik, C. & Joshi, P. K. Assessment of large-scale deforestation in Nawarangpur district, Orissa, India: A remote sensing based study. Environ. Monit. Assess. 154, 325–335. https://doi.org/10.1007/s10661-008-0400-9 (2009).Article 

Google Scholar 
44.Saikia, A. Drivers of forest loss. In A. Saikia (Eds.), Over-exploitation of forests. Springer Briefs in Geography. Springer, Cham, Switzerland. https://doi.org/10.1007/978-3-319-01408-1_7 (2014).45.Lele, N. & Joshi, P. K. Analyzing deforestation rates, spatial forest cover changes and identifying critical areas of forest cover changes in North-East India during 1972–1999. Environ. Monit. Assess. 156, 159–170. https://doi.org/10.1007/s10661-008-0472-6 (2009).Article 
PubMed 

Google Scholar 
46.Joppa, L. N., Loarie, S. R. & Pimm, S. L. On the protection of ‘“protected areas”’. Proc. Natl. Acad. Sci. U.S.A. 105(18), 6673–6678 (2008).ADS 
CAS 
Article 

Google Scholar 
47.Bharucha, E. Textbook of Environmental Studies for Undergraduate Courses (Universities Press, 2005).
Google Scholar 
48.Talukdar, N. R. & Choudhury, P. Conserving wildlife wealth of Patharia Hills Reserve Forest, Assam, India: A critical analysis. Glob. Ecol. Conserv. 10, 126–138. https://doi.org/10.1016/j.gecco.2017.02.002 (2017).Article 

Google Scholar 
49.Sonitpur District Judiciary – 2016. In the Court of Additional Sessions Judge, Sonitpur, Tezpur. Sessions Case No. 224 of 2016, U/s. 51 of Wildlife (Protection) Act, 1972. http://sonitpurjudiciary.gov.in/Judgement/09_Sessions%20Case%20No.224%20of%202016.pdf. (Accessed on 2 August 2020.50.Assam Forest Department – 2014. A draft proposal for declaring Eco-Sensitive Zone around Sonai-Rupai Wildlife Sanctuary. Prepared by Divisional Forest Officer Western Assam Wildlife Division, Tezpur, Assam Forest Department, Government of Assam. http://103.8.249.31/assamforest/notificationsOrders/ESZ%20Sonai-Rupai_final.pdf. (Accessed on 25 August 2020.51.ESZ Expert Committee Meeting – 2020. Minutes of 41st ESZ expert committee meeting for the declaration of Eco-Sensitive Zone (ESZ) around protected areas & Zonal Master Plan through video conferencing held on 23rd to 24th June 2020. Ministry of Environment, Forests and Climate Change, Government of India. http://moef.gov.in/wp-content/uploads/2019/10/41-st-ECM_Approved-minutes_.pdf. (Accessed on 25 August 2020). More

The development of massive sequencing has provided a relatively inexpensive method to obtain the transcriptome of a species. Taking advantage of this technique, we used a previously obtained transcriptome of P. acuta to identify 18 genes related to different pathways of interest in ecotoxicology and then examined how exposure to phthalates changed the transcription of these genes. The processes of interest include DNA repair, the stress response, detoxification, apoptosis, immunity, energy reserves, and lipid transportation. There is a growing interest in combining ecologically relevant endpoints with biochemical and molecular parameters to seek a more integrative analysis. In this sense, increasing the number of described genes will allow for the design of standard arrays that could be used in combination with toxicity tests. In this way, initiatives such as the Adverse Outcome Pathway wiki24 will increase its relevance in assessing old and new compounds and provide putative mechanisms of action to explain the differences to the animals’ specific physiology. Furthermore, increasing knowledge at the molecular level in P. acuta supports its use as a representative of freshwater gastropods in toxicity analysis. There is a lack of model freshwater mollusks, which is one of the animal groups whose pollution response is currently less known.The 18 newly identified genes evaluated in this work show homology with those previously described in other species, as expected, mainly with the freshwater snail Biomphalaria glabrata, which belongs to the Planorbidae family. rad21 and rad50 are both involved in DNA repair: rad21 is an essential gene encoding a DNA double-strand break repair protein21, and rad50 is a member of the protein complex MRN (including Mre11, RAD50, and Nbs1) that functions in DNA double-strand break repair to recognize and process DNA ends as well as a signal for cell cycle arrest25. There is very little information about these genes in mollusks, with only one report in Crassostrea gigas for rad5026. The relevance of these genes is that their detection can be combined with other methodologies, such as the comet assay, to perform an integrated study to determine whether a compound is genotoxic and whether the organism has the ability to compensate for the damage.The Cat and SOD Mn genes allow us to evaluate the status of oxidative stress. Oxidative stress analysis is usually focused on biochemical parameters, such as enzyme activity. However, it should also include a transcriptional activity study because it can provide additional information about the mid- and long-term responses. Protein turnover can also be relevant in the response, especially in chronic exposure to toxicants. Detoxification mechanisms are also important to assess the response to toxicants. GST activity is one of the most used methods to assess detoxification27, but it does not differentiate between the members involved. The situation is similar regarding cytochrome P450s, which show high diversity with many roles in the cell28. Our identification of the Cyp72a15 gene increases the number of cytochromes 450 s described in P. acuta. Evaluating changes in these genes can help to elucidate how the organism can process the toxicants.The sHSP17.9 and HSC70-4 genes extend the battery of genes available to assess the stress response of P. acuta. sHSP17.9 is difficult to match with other species’ genes because while they all have an alpha-crystallin domain, there is no other sequence that presently allows for homology to be established. Additional functional studies will help to search for homology. It is worth mentioning that HIF1α offers a new aspect of stress related to hypoxia29. The stress response mainly focuses on the canonical heat shock proteins, so other mechanisms involved in specific stresses, such as hypoxia, are usually neglected. With the identification of HIF1a in P. acuta, researchers can evaluate the effect of a toxicant on oxygen intake in this species.The remaining identified genes allow for the analysis of pathways that can also be altered by toxicants, like apoptosis (AIF3), the immune system (ApA), energy reserves (PYGL), and lipid transport (ORP8). To our knowledge, in this study these genes have been analyzed for the first time concerning pollution in freshwater mollusks. The last three genes, DNMT1, KATB6, and HDAC1, are involved in epigenetic mechanisms. There is increasing evidence that epigenetic regulation is one of the long-term effects of toxicants. However, the genes involved in this process in invertebrates are still poorly represented in toxicity analysis. The description of these three genes opens the possibility of analyzing their role in the epigenetic response and its relevance in the transgenerational effects that have started to be described with different toxicants30,31,32.Plastics in the environment are a growing problem. During the degradation process, the polymers themselves and the compounds used as additives, including phthalates, are released. Hence, the presence of phthalates is increasing in the environment5,33,34. We analyzed three phthalates in this work, namely BBP, DEP, and DEHP; they showed a differential impact in P. acuta. DEP and DEHP, did not alter any of the mRNA levels. Researchers have described previously that both phthalates can alter the physiology of invertebrates16,35,36,37,38, including mollusks39,40,41. Other phthalates can also alter development and growth, which could be related to the endocrine-disrupting activity described for those chemicals. The molecular mechanisms involved are still under investigation, but some data are available. In the clam Venerupis philippinarum, DEHP alters the immune response40. In H. diversicolor, DBP affects oxidative stress, lipid and energy metabolism, and osmoregulation17. In other invertebrates, including Chironomus riparius42, Drosophila melanogaster43, and Caenorhabditis elegans15, phthalates alter endocrine pathways. The changes affect the ecdysone response as well as the expression of insulin-like peptide. Other pathways are also affected by phthalates, such as oxidative stress and detoxification routes44 and the stress response14. Finally, in C. elegans, exposure to environmentally relevant concentrations of diethylhexyl phthalate produces genomic instability by altering the expression of genes involved in DNA repair during meiosis37. It is clear then that phthalates can have a broad spectrum of actions in the cell, with a significant alteration of metabolism but primarily affecting oxidative stress and the endocrine system.The previous studies performed in mollusks have revealed alterations in several physiological processes; the analyzed molecular mechanisms mainly involved oxidative stress and immunity17,41. A recent review of the impact of phthalates on aquatic animals summarizes the effects observed, suggesting that activation of the detoxification system (cytochrome P450s) and endocrine system receptors of aquatic animals cause oxidative stress, metabolic disorders, endocrine disorders, and immunosuppression8. It would activate a cascade response that could cause genotoxicity and cell apoptosis, resulting in the disruption of growth and development. Considering this, the absence of a response observed in P. acuta exposed to DEP and DEHP is striking. The differences observed can be assigned to the type of analysis (molecular vs. physiological), the exposure time (1 week vs. a few hours or days), the concentration used (μg/L vs. mg/L), and evidently, the species used. Additional research will help elucidate the differential response in P. acuta compared with other organisms. However, it is essential to highlight that the obtained results suggest that P. acuta can manage the environmentally relevant doses of DEP and DEHP used in this work. This species may be less sensitive to these phthalates, but this eventually will require further research, including the use of other methodological approaches, to confirm it.In contrast to DEP and DEHP, BBP showed a marked effect: it increased the mRNA levels of almost all the analyzed genes. It is essential to consider that most studies on invertebrates that involve transcriptional activity analysis use arthropods and short exposure times14,44,45,46. Limited data are available on mollusks and, usually, they are marine representatives40,47. To our knowledge, this is the first study on a freshwater snail that shows that BBP can produce a substantial effect on cell metabolism. Several of the altered pathways can explain, in some way, the effects observed in other organisms, like DNA repair by the alteration of rad21 and rad50, which are related to DNA damage, or the alteration of the genes involved in histone and DNA modification (KAT6B, HDAC1, and DNMT1), which are related to epigenetic regulation. Apoptosis, which phthalates can also alter, also seems to be modulated in P. acuta by altering the AIF3 and the casp3 genes. Furthermore, the three phases of the detoxification could be acting since the genes tested (three cytochrome P450s, three GSTs, and MRP-1) were upregulated.Genes involved in oxidative stress and the stress response were also altered, as shown by the changes in the mRNA levels of Cat, SODs, stress proteins, and the hypoxia-related transcription factor genes. These changes support the alteration of oxidative stress, the stress response, and detoxification, backing previous analysis and adding new insight about the mechanisms involved in modulating these processes. In this sense, the absence of changes in GSTm1 supports a differential role for each GST family member in the response to toxicants. The altered acetylcholinesterase mRNA level also suggests effects in the nervous system, requiring additional research to elucidate the damage to the central nervous system. Finally, the alteration of PYGL, ApA, and ORP8, involved in energy metabolism, immunity, and lipid transport, respectively, shows that P. acuta responds to BBP in a way that has been observed in other organisms. In summary, the present gene profile obtained in response to BBP in P. acuta supports the proposed mechanisms and cellular processes in studies with other animals8. Immunity, oxidative stress, the stress response, detoxification, apoptosis, epigenetic modulation, DNA repair, lipid metabolism, and energy metabolism are modulated. The nervous system could also be affected. Of note, some genes showed differences in transcription based on the phthalate concentration. These findings suggest there are subtle differences, and additional kinetic analysis is required to elucidate early and late activated genes and the relevance of the damage for the population’s future.The obtained results are in line with previous studies in other organisms, which have confirmed that BBP can induce different types of damage such as apoptosis48, genotoxicity49, oxidative stress50, stress response activation45, or endocrine disruption14. Although there are studies in invertebrates showing the impact on development and other physiological processes39,51, most of them did not focus on the putative mode of action, with only a few of them trying to delve into the response mechanisms. Here we have shown that BBP can extensively affect the cell transcriptional activity in P. acuta. These results could be considered to reflect specific alterations on these pathways. This scenario would mean that BBP is the most active phthalate in P. acuta, with a broad spectrum of action and a potential effect on many pathways. However, the more probable picture is something that has been recently proposed: alterations in the oxidative stress response and the endocrine system cause a cascade of responses that affect different pathways and ultimately block growth and development8. It is relevant to keep in mind that BBP is a known endocrine disruptor47. A recent study in Daphnia magna provides some insight. Specifically, RNA-Seq revealed that genes involved in signal transduction, cell communication, and embryonic development were significantly down-regulated, while those related to biosynthesis, metabolism, cell homeostasis, and redox homeostasis were remarkably upregulated upon BBP exposure46. Although the organism and the stage analyzed are different from our study, those results support the idea that BBP can simultaneously alter multiple pathways, and it fits better with the regulatory role of the endocrine system and the extensive affection by oxidative stress.As stated before, the results obtained in this work show that DEP and DEHP had no apparent effect to P. acuta after 1 week exposure to environmentally relevant concentrations. However, BBP showed a strong effect. The difference in response could be due to several reasons that need to be explored in future work. One possibility is the structure of each compound. In this sense, BBP has two benzene rings while DEP and DEHP have only one. This factor could determine the biological activity of these compounds. Another possibility is that DEP and DEHP have effects earlier than the time studied, and the cell returned to the basal state, being able to process and remove the compounds. Finally, it cannot be dismissed that DEP and DEHP are not toxic to P. acuta, at least at environmentally relevant concentrations. In any case, BBP alters the metabolism of this species and produces a broad impact on different pathways. Additional research should be done in P. acuta and other freshwater species to determine the impact on organisms based on the freshwater ecosystem food web. More

Volcanic ash soilWe used commercially available Kanuma soil as volcanic ash soil (fine-grained pumice, Akagi Engei Co., Ltd.) for growth experiment 1. Kanuma soil is a fully weathered pyroclastic fall from the eruption of Mt. Akagi 44,000 years ago19. The soil contains 30.8% aluminum (allophane and imogolite) and 1.4% iron (ferrihydrite)20. For growth experiment 2, we used three natural volcanic ash soil types—immature soil of pumice (Kanuma soil, C horizon), as well as mature soils of andosol (A–B horizon) and topsoil (the surface of andosol, P to A horizon)—collected from a riverbed in Kanuma City (36°35′ N, 139°44′ E; 200 m a.s.l.), central Japan, where the vegetation is a cypress forest. This place is managed by the Kanuma Civil Engineering Office. The topsoil was collected at a depth of approximately 0–10 cm from the soil surface after removing the fallen leaves on the soil surface. The andosol layer, typically distributed at a depth of approximately 10–75 cm, was collected from a depth of approximately 10–30 cm. Below the andosol layer, the Akadama soil layer is distributed; further below, the pumice Kanuma soil is distributed. The pumice was collected approximately 50 cm under the Akadama layer.Temporal information on soil formation was confirmed by direct radiocarbon dating of the soil samples. After removing soil carbonate with 1.0 M HCl, the total organic fraction was analyzed using an accelerator mass spectrometer (0.5MV compact AMS system, NEC) at the laboratory of radiocarbon dating, University of Tokyo. Conventional radiocarbon age after correction of isotopic fractionation with δ13C values was calibrated to a calendar date with the calibration dataset IntCal1321.The elemental analysis of total phosphorus, nitrogen, and carbon in the soil samples was performed by Createrra Inc. (http://www.createrra.co.jp/english/top.html).Plant speciesOn the volcanic ash soil of Mt. Fuji, Japan’s highest volcano, vegetation in primary succession generally changes from herbaceous plants such as Fallopia japonica (Houtt.) Ronse Decr. var. japonica to nitrogen-fixing alder plants, and finally to non-nitrogen fixing Betula ermanii Cham22,23. Hence, we used three species—F. japonica, the alder species Alnus inokumae Murai et Kusaka, and B. ermanii—owned by and grown in our research institute, Nikko Botanical Garden, for the growth experiments. Experimental research on these plants, including the collection of plant material, comply with the relevant institutional, national, and international guidelines and legislation.Litter incubation experimentSamples (1 g) of F. japonica litter leaves—collected upon leaf fall on an autumn day, dried at 80 °C for at least 48 h, and then crushed—were placed in cultivating tubes (n = 5). Then, 5 g of wet soil from the Nikko Botanical Garden (36°45′ N, 139°35′ E; 647 m a.s.l.) in Nikko, central Japan, was added to 500 mL of water and stirred (solution I). As inoculation, 0.1 mL of the supernatant of solution I was added to the tubes24. Considering that the amounts of phosphorus and nitrogen in the solution I were approximately 0.003 mg/L and 0.3 mg/L, respectively, they were determined to have not affected the initial value (t = 0). Next, 2 mL of water was added to the tubes, which were then kept at 30 °C. The tubes were left open to maintain an aerobic environment. The efflux of phosphorus and nitrogen from the leaves was measured every week for ten weeks. For these measurements, 5 mL of water was added and the tube was centrifuged for 10 min (solution II). The supernatant of solution II was then used for phosphorus and nitrogen measurements, and the residue was continuously kept at 30 °C.Growth experimentsGrowth experiments were conducted in an open-type greenhouse in Nikko Botanical Garden. The greenhouse is only vinyl on the ceiling and good ventilation to keep the temperature constant. The mean monthly highest and lowest temperatures and the monthly precipitation observed in the botanical garden during the cultivation period are provided in Table 1. In the growth experiments, irrigation with tap water was provided to the plants and litter leaves in the morning and evening. The phosphorus and nitrogen concentration of the tap water were approximately 0.03 mg/L and 0.25 mg/L respectively.Table 1 Nikko botanical garden weather data (May–October 2019).Full size tableGrowth experiment 1: Comparative experiment on the growth of plant species with and without litterThe seedlings used for the experiment were from the species F. japonica, A. inokumae, and B. ermanii. A similar seedling size was used for each plant species. Seedlings of A. inokumae coexist with N-fixing actinomycetes.Six plants per species were collected before cultivation (t = 0) and dried in an oven at 80 °C for at least 48 h to measure the dry weight. There were four experimental groups for each species: a control (Con), a nitrogen addition (N: 10 mM NH4NO3), a phosphorus addition (P: 10 mM NaH2PO4), and a nitrogen and phosphorus addition (NP: 10 mM NH4NO3 + 10 mM NaH2PO4). Once a week, 50 mL of each nutrients was added to a 0.25-L garden pot. To verify whether the addition of litter (denoted by +) improved plant growth, litter leaves of F. japonica were placed on the soils. To verify if nutrients leached from litter sustained plant growth, we also combined nutrient and litter additions (Con+, N+, P+, NP+). When nutrients were added to the soil once a week, litter bag was removed before fertilizer application and returned after that.To reproduce how litter is deposited and supplies nutrients on volcanic ash soil in primary succession, F. japonica litter was collected in Nikko in the autumn of 2018 and dried at 80 °C or 2 days or more (the same litter was used in incubation). Approximately 9 g of litter leaves was packed in a tea mesh bag25 to prevent it from flying in the wind and placed on the soil surface of the garden pots. As indicated by the equation below, the amount of litter added to the 8 × 8 cm (0.0064 m2) garden pot used in this experiment amounts to approximately three years of litter production when converted to the amount of leaf litter in a 15-year-old alder forest, i.e., about 430 g/m2 per year26.$$frac{9,g}{{430frac{g}{{ m^{2} }} yr times 0.0064 m^{2} }} cong 3.3 yr$$Six seedlings per group of A. inokumae and B. ermanii were cultivated for approximately 2 months (June 7–August 22, 2019) and 12 seedlings per group of F. japonica were cultivated for about 1 month (September 10–October 15, 2019). The experiment was stopped after 1 month for F. japonica as it grew rapidly in 2 nutrient conditions (NP, NP+) and the roots overflowed from the garden pot. At the end of the experiment, growth was evaluated by measuring dry weight after drying seedlings at 80 °C for at least 48 h. Subsequently, the total phosphorus and nitrogen content of the dried seedlings were also measured (chemical analysis).The mass of phosphorus leached from litter during the cultivation period was calculated from the difference in the phosphorus contents of the litter before and after cultivation.Growth experiment 2: Comparative experiment on plant growth with old organic matterEight F. japonica seedlings were cultivated in three different soil-types (pumice, andosol, and topsoil, as mentioned above) under three experimental conditions (Con, N, P, same nutrition as growth experiment 1) from May 29 to July 12, 2019. These plants were then harvested and oven-dried at 80 °C for at least 48 h to measure dry weight. Subsequently, the total phosphorus and nitrogen content of the seedlings were also measured (chemical analysis).Chemical analysisPhosphorusWe used the dry destruction method to pretreat total phosphorus measurements in plant tissue27. A sample of the plant (0.05 g) was burned at 550 °C for 1 h. The plant ash was dissolved in 10 mL of 2 M H2SO4 and shaken for over 16 h; then, the solution was filtered. The filtrate was diluted at a 1:10 ratio with tris(hydroxymethyl)aminomethane (pH 8.0).The soil for available phosphorus were pretreated by Truog’ s method28. The soil (0.05 g) was dissolved in 10 mL of 0.002 M H2SO4, shaken for 30 min, and the solution was filtered. The filtrate was diluted at a 1:10 ratio with water.The amount of phosphorus in the sample solution was measured by the molybdenum blue colorimetric method29.NitrogenThe total nitrogen in plant tissue was measured using an elemental analyzer (EA; Vario Macro cube, Elementar, Germany). A few milligrams of the dried plant sample were placed in a tin capsule for EA combustion. EA carried out sample combustion and N2 separation/detection from the combusted gases and provided us with nitrogen contents.The soil sample preparation for available nitrogen measurements was based on the incubation methodology30. Half of the sampled soils were analyzed fresh, and the other half incubated for four weeks at 30 °C before analysis. 2 M KCl (20 mL) was added to 2 g of the soil sample; the solution was shaken for 1 h and filtered. The filtrate was collected, and the volume of nitrogen was measured by indophenol blue absorptiometry after reducing all to ammonia using Pack Test WAK-TNi (Kyoritsu Chemical-Check Lab., Corp, Tokyo, Japan). Available nitrogen was taken as the difference in the concentration of inorganic nitrogen (NO3-N, NO2-N and NH4-N) between incubated and fresh soil.Statistical analysisAll statistical analyses were performed with EZR31 (Saitama Medical Center, Jichi Medical University, Saitama, Japan), which is a graphical user interface for R (The R Foundation for Statistical Computing, Vienna, Austria). More precisely, it is a modified version of R commander designed to add statistical functions frequently used in biostatistics. The figure’s values are mean ± SE. Intergroup differences for nutrition conditions in soil, and soil-types were evaluated using non-parametric Kruskal–Wallis with post-hoc Steel–Dwass tests. In addition, comparisons between with or without litter were evaluated using two-tailed Mann–Whitney U-test. p values are * p  More

1.Frölicher, T. L., Fischer, E. M. & Gruber, N. Marine heatwaves under global warming. Nature 560, 360–364 (2018).ADS 
PubMed 
Article 
CAS 
PubMed Central 

Google Scholar 
2.Oliver, E. C. J. et al. Longer and more frequent marine heatwaves over the past century. Nat. Commun. 9, 1–12 (2018).CAS 
Article 

Google Scholar 
3.Smale, D. A. et al. Marine heatwaves threaten global biodiversity and the provision of ecosystem services. Nat. Clim. Change 9, 306–312 (2019).ADS 
Article 

Google Scholar 
4.Hobday, A. J. et al. A hierarchical approach to defining marine heatwaves. Prog. Oceanogr. 141, 227–238 (2016).ADS 
Article 

Google Scholar 
5.Dee, L. E. E. et al. Temperature variability alters the stability and thresholds for collapse of interacting species. Philos. Trans. R. Soc. Biol. Sci. 375, 20190457 (2020).Article 

Google Scholar 
6.Leung, J. Y. S., Russell, B. D. & Connell, S. D. Adaptive responses of marine gastropods to heatwaves. One Earth 1, 374–381 (2019).Article 

Google Scholar 
7.Whiteley, N. M. & Mackenzie, C. L. Physiological responses of marine invertebrates to thermal stress. in Stressors in the Marine Environment (eds. Solan, M. & Whiteley, N. M.) 56–72 (Oxford University Press, 2016). https://doi.org/10.1093/acprof:oso/9780198718826.003.0004.8.Lonhart, S. I., Jeppesen, R., Beas-luna, R., Crooks, J. A. & Lorda, J. Shifts in the distribution and abundance of coastal marine species along the eastern Pacific Ocean during marine heatwaves from 2013 to 2018. Mar. Biodivers. Rec. 8, 1–15 (2019).
Google Scholar 
9.Smale, D. A. & Wernberg, T. Extreme climatic event drives range contraction of a habitat-forming species. Proc. R. Soc. B Biol. Sci. 280, 20122829 (2013).Article 

Google Scholar 
10.Garrabou, J. et al. Mass mortality in Northwestern Mediterranean rocky benthic communities: Effects of the 2003 heat wave. Glob. Change Biol. 15, 1090–1103 (2009).ADS 
Article 

Google Scholar 
11.Frölicher, T. L. & Laufkötter, C. Emerging risks from marine heat waves. Nat. Commun. 9, 2015–2018 (2018).Article 
CAS 

Google Scholar 
12.Cheung, W. W. L. & Frölicher, T. L. Marine heatwaves exacerbate climate change impacts for fisheries in the northeast Pacific. Sci. Rep. 10, 1–10. https://doi.org/10.1038/s41598-020-63650-z (2020).CAS 
Article 

Google Scholar 
13.Caputi, N. et al. Management adaptation of invertebrate fisheries to an extreme marine heat wave event at a global warming hot spot. Ecol. Evolut. https://doi.org/10.1002/ece3.2137 (2016).Article 

Google Scholar 
14.Verdelhos, T., Marques, J. C. & Anastácio, P. Behavioral and mortality responses of the bivalves Scrobicularia plana and Cerastoderma edule to temperature, as indicator of climate change’s potential impacts. Ecol. Indic. 58, 95–103 (2015).Article 

Google Scholar 
15.Shanks, A. L. et al. Marine heat waves, climate change, and failed spawning by coastal invertebrates. Limnol. Oceanogr. 65, 627–636 (2020).ADS 
Article 

Google Scholar 
16.Morgan, E. A., Brown, A., Ciotti, B. J. & Panton, A. Effects of temperature stress on ecological processes. in Stressors in the Marine Environment (eds. Solan, M. & Whiteley, N. M.) 213–227 (Oxford University Press, 2016). https://doi.org/10.1093/acprof:oso/9780198718826.003.0012.17.Beukema, J. J. & Dekker, R. Winters not too cold, summers not too warm: long-term effects of climate change on the dynamics of a dominant species in the Wadden Sea: the cockle Cerastoderma edule L. Mar. Biol. 167, 1–8 (2020).Article 

Google Scholar 
18.Sousa, R. et al. Die-offs of the endangered pearl mussel Margaritifera margaritifera during an extreme drought. Aquat. Conserv. Mar. Freshw. Ecosyst. 28, 1244–1248 (2018).Article 

Google Scholar 
19.Smale, D. A., Yunnie, A. L. E., Vance, T. & Widdicombe, S. Disentangling the impacts of heat wave magnitude, duration and timing on the structure and diversity of sessile marine assemblages. PeerJ 2015, 1–23 (2015).
Google Scholar 
20.McLusky, D. S. & Elliott, M. The Estuarine Ecosystem: Ecology (Threats and Management. Oxford Press, 2004).Book 

Google Scholar 
21.Johnson, R. G. Temperature variation in the infaunal environment of a sand flat. Limnol. Oceanogr. 10, 114–120 (1965).ADS 
Article 

Google Scholar 
22.Amorim, V. E. et al. Immunological and oxidative stress responses of the bivalve Scrobicularia plana to distinct patterns of heatwaves. Fish Shellfish Immunol. 106, 1067–1077 (2020).CAS 
PubMed 
Article 

Google Scholar 
23.Grilo, T. F. F., Cardoso, P. G. G., Dolbeth, M., Bordalo, M. D. D. & Pardal, M. Â. A. Effects of extreme climate events on the macrobenthic communities’ structure and functioning of a temperate estuary. Mar. Pollut. Bull. 62, 303–311 (2011).CAS 
PubMed 
Article 

Google Scholar 
24.Dolbeth, M. et al. Long-term changes in the production by estuarine macrobenthos affected by multiple stressors. Estuar. Coast. Shelf Sci. 92, 10–18 (2011).ADS 
Article 

Google Scholar 
25.Ouellette, D. et al. Effects of temperature on in vitro sediment reworking processes by a gallery biodiffusor, the polychaete Neanthes virens. Mar. Ecol. Prog. Ser. 266, 185–193 (2004).ADS 
Article 

Google Scholar 
26.Nagelkerken, I. & Munday, P. L. Animal behaviour shapes the ecological effects of ocean acidification and warming: Moving from individual to community-level responses. Glob. Chang. Biol. 22, 974–989 (2016).ADS 
PubMed 
Article 

Google Scholar 
27.Solan, M., Bennett, E. M., Mumby, P. J., Leyland, J. & Godbold, J. A. Benthic-based contributions to climate change mitigation and adaptation. Philos. Trans. R. Soc. B Biol. Sci. 375, 20190107 (2020).Article 

Google Scholar 
28.Kristensen, E. & Kostka, J. E. Macrofaunal burrows and irrigation in marine sediment: Microbiological and biogeochemical interactions. in Interactions Between Macro‐ and Microorganisms in Marine Sediments (eds. Kristensen, E., Haese, R. R. & Kostka, J. E.), 125–157 (American Geophysical Union, 2013). https://doi.org/10.1029/CE060p0125.29.Cozzoli, F. et al. Biological and physical drivers of bio-mediated sediment resuspension: A flume study on Cerastoderma edule. Estuar. Coast. Shelf Sci. 241, 106824 (2020).Article 

Google Scholar 
30.Soissons, L. M. et al. Sandification vs. muddification of tidal flats by benthic organisms: A flume study. Estuar. Coast. Shelf Sci. 228, 106355 (2019).Article 

Google Scholar 
31.Fernandes, S., Sobral, P. & Costa, M. H. Nereis diversicolor effect on the stability of cohesive intertidal sediments. Aquat. Ecol. 40, 567–579 (2006).CAS 
Article 

Google Scholar 
32.Paramor, O. A. L. & Hughes, R. G. The effects of bioturbation and herbivory by the polychaete Nereis diversicolor on loss of saltmarsh in south-east England. J. Appl. Ecol. 41, 449–463 (2004).Article 

Google Scholar 
33.Dolbeth, M., Crespo, D., Leston, S. & Solan, M. Realistic scenarios of environmental disturbance lead to functionally important changes in benthic species-environment interactions. Mar. Environ. Res. 150, 104770 (2019).CAS 
PubMed 
Article 

Google Scholar 
34.Godbold, J. A. & Solan, M. Long-term effects of warming and ocean acidification are modified by seasonal variation in species responses and environmental conditions. Philos. Trans. R. Soc. B Biol. Sci. 368, 20130186 (2013).Article 
CAS 

Google Scholar 
35.Godbold, J. A., Hale, R., Wood, C. L. & Solan, M. Vulnerability of macronutrients to the concurrent effects of enhanced temperature and atmospheric pCO2 in representative shelf sea sediment habitats. Biogeochemistry 135, 89–102 (2017).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
36.Sorte, C. J. B., Fuller, A. & Bracken, M. E. S. Impacts of a simulated heat wave on composition of a marine community. Oikos 119, 1909–1918 (2010).Article 

Google Scholar 
37.Pansch, C. et al. Heat waves and their significance for a temperate benthic community: A near-natural experimental approach. Glob. Change Biol. 24, 4357–4367 (2018).ADS 
Article 

Google Scholar 
38.Queirós, A. M. et al. A bioturbation classification of European marine infaunal invertebrates. Ecol. Evol. 3, 3958–3985 (2013).PubMed 
PubMed Central 
Article 

Google Scholar 
39.Wrede, A., Beermann, J., Dannheim, J., Gutow, L. & Brey, T. Organism functional traits and ecosystem supporting services—A novel approach to predict bioirrigation. Ecol. Indic. 91, 737–743 (2018).Article 

Google Scholar 
40.Crespo, D. et al. New climatic targets against global warming: Will the maximum 2 °C temperature rise affect estuarine benthic communities. Sci. Rep. 7, 1–14 (2017).Article 
CAS 

Google Scholar 
41.Galasso, H. L., Richard, M., Lefebvre, S., Aliaume, C. & Callier, M. D. Body size and temperature effects on standard metabolic rate for determining metabolic scope for activity of the polychaete Hediste (Nereis) diversicolor. PeerJ 6, e5675 (2018).PubMed 
PubMed Central 
Article 
CAS 

Google Scholar 
42.Kristensen, E. Ventilation and oxygen uptake by three species of Nereis (Annelida: Polychaeta). I. Effects of hypoxia. Mar. Ecol. Prog. Ser. 12, 289–297 (1983).ADS 
Article 

Google Scholar 
43.Cozzoli, F. et al. The combined influence of body size and density on cohesive sediment resuspension by bioturbators. Sci. Rep. 8, 1–12 (2018).CAS 
Article 

Google Scholar 
44.Cozzoli, F. et al. A process based model of cohesive sediment resuspension under bioturbators’ influence. Sci. Total Environ. 670, 18–30 (2019).ADS 
CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
45.Scaps, P. A review of the biology, ecology and potential use of the common ragworm Hediste diversicolor (O.F. Müller) (Annelida: Polychaeta). Hydrobiologia 470, 203–218 (2002).Article 

Google Scholar 
46.Cassidy, C., Grange, L. J., Garcia, C., Bolam, S. G. & Godbold, J. A. Species interactions and environmental context affect intraspecific behavioural trait variation and ecosystem function. Proc. R. Soc. B Biol. Sci. 287, 20192143 (2020).Article 

Google Scholar 
47.Thomsen, M. S. et al. Compensatory responses can alter the form of the biodiversity—function relation curve. Philos. Trans. R. Soc. B 286, 20190287 (2019).CAS 

Google Scholar 
48.Hale, R. et al. Mediation of macronutrients and carbon by post-disturbance shelf sea sediment communities. Biogeochemistry 135, 121–133 (2017).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
49.Karlson, K., Bonsdorff, E. & Rosenberg, R. The impact of benthic macrofauna for nutrient fluxes from Baltic Sea sediments. Ambio 36, 161–167 (2007).CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
50.Thomsen, M. S. et al. Compensatory responses can alter the form of the biodiversity-function relation curve. Proc. R. Soc. B Biol. Sci. 286, 20190287 (2019).CAS 
Article 

Google Scholar 
51.Wohlgemuth, D., Solan, M. & Godbold, J. A. Species contributions to ecosystem process and function can be population dependent and modified by biotic and abiotic setting. Proc. R. Soc. B: Biol. Sci. 284, 20162805 (2017).Article 

Google Scholar 
52.Lillebø, A. I., Neto, J. M., Flindt, M. R., Marques, J. C. & Pardal, M. A. Phosphorous dynamics in a temperate intertidal estuary. Estuar. Coast. Shelf Sci. 61, 101–109 (2004).ADS 
Article 
CAS 

Google Scholar 
53.Lillebø, A. I. et al. Management of a shallow temperate estuary to control eutrophication: The effect of hydrodynamics on the system’s nutrient loading. Estuar. Coast. Shelf Sci. 65, 697–707 (2005).ADS 
Article 

Google Scholar 
54.Verdelhos, T., Cardoso, P. G., Dolbeth, M. & Pardal, M. A. Recovery trends of Scrobicularia plana populations after restoration measures, affected by extreme climate events. Mar. Environ. Res. 98, 39–48 (2014).CAS 
PubMed 
Article 

Google Scholar 
55.Hale, R., Mavrogordato, M. N., Tolhurst, T. J. & Solan, M. Characterizations of how species mediate ecosystem properties require more comprehensive functional effect descriptors. Sci. Rep. 4, 1–6 (2014).
Google Scholar 
56.Benton, T. G., Solan, M., Travis, J. M. J. & Sait, S. M. Microcosm experiments can inform global ecological problems. Trends Ecol. Evol. 22, 516–521 (2007).PubMed 
Article 
PubMed Central 

Google Scholar 
57.Bento, E. G. et al. Climate influence on juvenile European sea bass (Dicentrarchus labrax, L.) populations in an estuarine nursery: A decadal overview. Mar. Environ. Res. 122, 93–104 (2016).CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
58.Martinho, F. et al. The influence of an extreme drought event in the fish community of a southern Europe temperate estuary. Estuar. Coast. Shelf Sci. 75, 537–546 (2007).ADS 
Article 

Google Scholar 
59.Solan, M. et al. In situ quantification of bioturbation using time-lapse fluorescent sediment profile imaging (f-SPI), luminophore tracers and model simulation. Mar. Ecol. Prog. Ser. 271, 1–12 (2004).ADS 
Article 

Google Scholar 
60.Schiffers, K., Teal, L. R., Travis, J. M. J. & Solan, M. An open source simulation model for soil and sediment bioturbation. PLoS ONE 6, e28028 (2011).ADS 
CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
61.Grasshoff, K., Kremling, K. & Ehrhardt, M. Methods of Seawater Analysis (Verlag Chemie, 1983).
Google Scholar 
62.Jones, M. N. Nitrate reduction by shaking with cadmium. Alternative to cadmium columns. Water Res. 18, 643–646 (1984).ADS 
CAS 
Article 

Google Scholar 
63.Hayward, P. J. & Ryland, J. S. Handbook of the Marine Fauna of North-West Europe. (Oxford University Press, 2017). https://doi.org/10.1093/acprof:oso/9780199549443.001.0001.64.Anderson, M. J., Gorley, R. N. & Clarke, K. R. PERMANOVA+ for PRIMER: Guide to Software and Statistical Methods. 214 (PRIMER-E Ltd., Plymouth, UK, 2008).65.Ricotta, C. & Moretti, M. CWM and Rao’s quadratic diversity: A unified framework for functional ecology. Oecologia 167, 181–188 (2011).ADS 
PubMed 
Article 

Google Scholar 
66.Zuur, A. F. & Ieno, E. N. A protocol for conducting and presenting results of regression-type analyses. Methods Ecol. Evol. 7, 636–645 (2016).Article 

Google Scholar 
67.R Core Team. R: A language and environment for statistical computing. R Foundation for Statistical Computing, Vienna,
Austria. (2019) https://www.R-project.org/.68.Oksanen, J. et al. vegan: Community Ecology Package. R package 2.5-6. (2019). https://CRAN.Rproject.org/package=vegan.69.Laliberté, E., Legendre, P. & Shipley, B. FD: Measuring functional diversity from multiple traits, and other tools for functional ecology. R package version 1.0-12. (2014).70.Wickham, H. ggplot2: Elegant Graphics for Data Analysis (Springer-Verlag, 2016).MATH 
Book 

Google Scholar  More

CORRESPONDENCE
01 June 2021

French vote for river barriers defies biodiversity strategy

Simon Blanchet

 ORCID: http://orcid.org/0000-0002-3843-589X

0
&

Pablo A. Tedesco

 ORCID: http://orcid.org/0000-0001-5972-5928

1

Simon Blanchet

National Centre for Scientific Research (CNRS), Moulis, France.

View author publications

You can also search for this author in PubMed
 Google Scholar

Pablo A. Tedesco

French National Research Institute for Development (IRD), Toulouse, France.

View author publications

You can also search for this author in PubMed
 Google Scholar

Share on Twitter
Share on Twitter

Share on Facebook
Share on Facebook

Share via E-Mail
Share via E-Mail

Download PDF

Europe’s rivers are disrupted by more than one million artificial barriers, including small dams, weirs and fords (see, for example, B. Belleti et al. Nature 588, 436–441; 2020). There is strong scientific evidence that such obstructions can harm both hydrological and ecological systems, yet the French parliament has voted to leave them in place (see go.nature.com/3ck9mxq).By limiting the transfer of sediments and movement of organisms, these small barriers create a succession of reaches of warming, stagnant water that threatens freshwater biodiversity (M. R. Fuller et al. Ann. NY Acad. Sci. 1335, 31–51; 2015). Dismantling such small barriers is the most effective way to restore river connectivity and is now a worldwide objective (J. E. O’Connor et al. Science 348, 496–497; 2015).The French parliament’s decision flies in the face of the EU Biodiversity Strategy. It also has no economic justification. Most small barriers cannot generate hydroelectricity and those that can contribute less than 1% to France’s electricity (see go.nature.com/2rphjch).In our view, the fate of each barrier should be decided by balancing its ecological benefits and socioeconomic costs.

Nature 594, 26 (2021)
doi: https://doi.org/10.1038/d41586-021-01467-0

Competing Interests
The authors declare no competing interests.

Latest on:

Ecology

Trade resolution further threatens Brazil’s amphibians
Correspondence 25 MAY 21

Our radical changes to Earth’s greenery began long ago — with farms, not factories
Research Highlight 20 MAY 21

Controversial forestry experiment will be largest-ever in United States
News 20 MAY 21

Biodiversity

Trade resolution further threatens Brazil’s amphibians
Correspondence 25 MAY 21

Controversial forestry experiment will be largest-ever in United States
News 20 MAY 21

Nature-based solutions can help cool the planet — if we act now
Comment 12 MAY 21

Policy

Wanted: rules for pandemic data access that everyone can trust
Editorial 01 JUN 21

Elite US science academy expels astronomer Geoff Marcy following harassment complaints
News 27 MAY 21

Protect precious scientific collaboration from geopolitics
Editorial 26 MAY 21

Jobs from Nature Careers

All jobs

Research Fellow in Ancient Human Population Genetics
University of Tartu (UT)
Tartu, Estonia

JOB POST

Research Fellow in Ancient Metagenomics
University of Tartu (UT)
Tartu, Estonia

JOB POST

Post-doctoral Fellowship
The Royal Horticultural Society (RHS)
Woking, United Kingdom

JOB POST

Director, Division of Intramural Research (Scientific Director)
NIH Eunice Kennedy Shriver National Institute of Child Health and Human Development (NICHD)
Bethesda, United States

JOB POST

Nature Briefing
An essential round-up of science news, opinion and analysis, delivered to your inbox every weekday.

Email address

Yes! Sign me up to receive the daily Nature Briefing email. I agree my information will be processed in accordance with the Nature and Springer Nature Limited Privacy Policy.

Sign up More

1.Neugschwandter, R. W. & Kaul, H. P. Sowing ratio and N fertilization affect yield and yield components of oat and pea in intercrops. Field Crops Res. 155, 159–163 (2014).Article 

Google Scholar 
2.Hu, F. et al. Low N fertilizer application and intercropping increases N concentration in pea (Pisum sativum L.) grains. Front Plant Sci. 9, 1763 (2018).PubMed 
PubMed Central 
Article 

Google Scholar 
3.Jensen, E. S., Carlsson, G. & Hauggaard-Nielsen, H. Intercropping of grain legumes and cereals improves the use of soil N resources and reduces the requirement for synthetic fertilizer N: a global-scale analysis. Agron. Sustain. Dev. 40, 5 (2020).Article 

Google Scholar 
4.Jannoura, R., Joergensen, R. G. & Bruns, C. Organic fertilizer effects on growth, crop yield, and soil microbial biomass indices in sole and intercropped peas and oats under organic farming conditions. Eur. J. Agron. 52, 259–270 (2014).Article 

Google Scholar 
5.Darch, T. et al. Inter- and intra-species intercropping of barley cultivars and legume species, as affected by soil phosphorus availability. Plant Soil 427, 125–138 (2018).CAS 
PubMed 
Article 

Google Scholar 
6.Monti, M., Pellicanò, A., Santonoceto, C., Preiti, G. & Pristeri, A. Yield components and nitrogen use in cereal-pea intercrops in Mediterranean environment. Field Crops Res. 196, 379–388 (2016).Article 

Google Scholar 
7.Scalise, A., Pappa, V. A., Gelsomino, A. & Rees, R. M. Pea cultivar and wheat residues affect carbon/nitrogen dynamics in pea-triticale intercropping: a microcosms approach. Sci. Tot. Environ. 592, 436–450 (2017).CAS 
Article 

Google Scholar 
8.Bedoussac, L. et al. Ecological principles underlying the increase of productivity achieved by cereal-grain legume intercrops in organic farming. A review. Agron. Sustain. Dev. 35, 911–935 (2015).Article 

Google Scholar 
9.Garcia, K., Doidy, J., Zimmermann, S. D., Wipf, D. & Courty, P. E. Take a trip through the plant and fungal transportome of mycorrhiza. Trends Plant Sci. 21, 937–950 (2016).CAS 
PubMed 
Article 

Google Scholar 
10.Oelbermann, M., Regehr, A. & Echarte, L. Changes in soil characteristics after six seasons of cereal–legume intercropping in the Southern Pampa. Geoderma Reg. 4, 100–107 (2015).Article 

Google Scholar 
11.Wichern, F., Eberhardt, E., Mayer, J., Joergensen, R. G. & Müller, T. Nitrogen rhizodeposition in agricultural crops: methods, estimates and future prospects. Soil Biol. Biochem. 40, 30–48 (2008).CAS 
Article 

Google Scholar 
12.Pausch, J., Tian, J., Riederer, M. & Kuzyakov, Y. Estimation of rhizodeposition at field scale: upscaling of a 14C labeling study. Plant Soil 364, 273–285 (2013).CAS 
Article 

Google Scholar 
13.Fustec, J., Lesuffleur, F., Mahieu, S. & Cliquet, J. B. Nitrogen rhizodeposition of legumes. A review. Agron. Sustain. Dev. 30, 57–66 (2010).CAS 
Article 

Google Scholar 
14.Hupe, A. et al. Get on your boots: estimating root biomass and rhizodeposition of peas under field conditions reveals the necessity of field experiments. Plant Soil 443, 449–462 (2019).CAS 
Article 

Google Scholar 
15.Parniske, M. Arbuscular mycorrhiza: the mother of plant root endosymbioses. Nat. Rev. Microbiol. 6, 763–775 (2008).CAS 
PubMed 
Article 

Google Scholar 
16.Jones, D. L., Hodge, A. & Kuzyakov, Y. Plant and mycorrhizal regulation of rhizodeposition. New Phytol. 163, 459–480 (2004).CAS 
PubMed 
Article 

Google Scholar 
17.Hupe, A. et al. Even flow? Changes of carbon and nitrogen release from pea roots over time. Plant Soil 431, 143–157 (2018).CAS 
Article 

Google Scholar 
18.He, X., Xu, M., Qiu, C. Y. & Zhou, J. Use of 15N stable isotope to quantify nitrogen transfer between mycorrhizal plants. J. Plant Ecol. 2, 107–118 (2009).Article 

Google Scholar 
19.Pepe, A., Giovannetti, M. & Sbrana, C. Lifespan and functionality of mycorrhizal fungal mycelium are uncoupled from host plant lifespan. Sci. Rep. 8, 10235 (2018).ADS 
PubMed 
PubMed Central 
Article 
CAS 

Google Scholar 
20.Xiao, Y., Li, L. & Zhang, F. Effect of root contact on interspecific competition and N transfer between wheat and faba bean using direct and indirect 15N techniques. Plant Soil 262, 45–54 (2004).CAS 
Article 

Google Scholar 
21.Thilakarathna, M. S., McElroy, M. S., Chapagain, T., Papadopoulos, Y. A. & Raizada, M. N. Belowground nitrogen transfer from legumes to non-legumes under managed herbaceous cropping systems. A review. Agron. Sustain. Dev. 36, 58 (2016).Article 
CAS 

Google Scholar 
22.Meng, L. et al. Arbuscular mycorrhizal fungi and rhizobium facilitate nitrogen uptake and transfer in soybean/maize intercropping system. Front Plant Sci. 6, 339 (2015).ADS 
PubMed 
PubMed Central 

Google Scholar 
23.Shao, Z. et al. Root contact between maize and alfalfa facilitates nitrogen transfer and uptake using techniques of foliar 15N-labeling. Agronomy 10, 360 (2020).CAS 
Article 

Google Scholar 
24.Duc, G., Trouvelot, A., Gianinazzi-Pearson, V. & Gianinazzi, S. First report of non-mycorrhizal plant mutants (Myc−) obtained in pea (Pisum sativum L.) and fababean (Vicia faba L.). Plant Sci. 60, 215–222 (1989).Article 

Google Scholar 
25.Kleikamp, B. & Joergensen, R. G. Evaluation of arbuscular mycorrhiza with symbiotic and nonsymbiotic pea isolines at three sites in the Alentejo, Portugal. J. Plant Nutr. Soil Sci. 169, 661–669 (2006).CAS 
Article 

Google Scholar 
26.Jannoura, R., Kleikamp, B., Dyckmans, J. & Joergensen, R. G. Impact of pea growth and of arbuscular mycorrhizal fungi on the decomposition of 15N-labeled maize residues. Biol. Fertil. Soils 48, 547–560 (2012).Article 

Google Scholar 
27.Chalk, P. M. et al. Methodologies for estimating nitrogen transfer between legumes and companion species in agro-ecosystems: a review of 15N-enriched techniques. Soil Biol. Biochem. 73, 10–21 (2014).CAS 
Article 

Google Scholar 
28.Wahbi, S. et al. Enhanced transfer of biologically fixed N from faba bean to intercropped wheat through mycorrhizal symbiosis. Appl. Soil Ecol. 107, 91–98 (2016).Article 

Google Scholar 
29.Ingraffia, R., Amato, G., Frenda, A. S. & Giambalvo, D. Impacts of arbuscular mycorrhizal fungi on nutrient uptake, N2 fixation, N transfer, and growth in a wheat/faba bean intercropping system. PLoS ONE 14, e0213672 (2019).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
30.Fusconi, A. Regulation of root morphogenesis in arbuscular mycorrhizae, what role do fungal exudates, phosphate, sugars and hormones play in lateral root formation. Ann. Bot. 113, 19–33 (2014).CAS 
PubMed 
Article 

Google Scholar 
31.Wang, W. et al. Nutrient exchange and regulation in arbuscular mycorrhizal symbiosis. Mol. Plant 10, 1147–1158 (2017).CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
32.Xue, Y. et al. Crop acquisition of phosphorus, iron and zinc from soil in cereal/legume intercropping systems: a critical review. Ann. Bot. 117, 363–377 (2016).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
33.Abdelhalim, T., Jannoura, R. & Joergensen, R. G. Mycorrhiza response and phosphorus acquisition efficiency of sorghum cultivars differing in strigolactone composition. Plant Soil 437, 55–63 (2019).CAS 
Article 

Google Scholar 
34.Louarn, G. et al. The amounts and dynamics of nitrogen transfer to grasses differ in alfalfa and white clover-based grass-legume mixtures as a result of rooting strategies and rhizodeposit quality. Plant Soil 389, 289–305 (2015).CAS 
Article 

Google Scholar 
35.Faust, S., Kaiser, K., Wiedner, K., Glaser, B. & Joergensen, R. G. Comparison of different methods to determine lignin concentration and quality in herbaceous and woody plant residues. Plant Soil 433, 7–18 (2018).CAS 
Article 

Google Scholar 
36.Baldrian, P. et al. Production of extracellular enzymes and degradation of biopolymers by saprotrophic microfungi from the upper layers of forest soil. Plant Soil 338, 1–15 (2011).Article 
CAS 

Google Scholar 
37.Wichern, F., Andreeva, D., Joergensen, R. G. & Kuzyakov, Y. Distribution of applied 14C and 15N in legumes using two different labelling methods. J. Plant Nutr. Soil Sci. 174, 732–741 (2011).CAS 
Article 

Google Scholar 
38.Turner, T. R. et al. Comparative metatranscriptomics reveals kingdom level changes in the rhizosphere microbiome of plants. ISME J. 7, 2248–2258 (2013).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
39.Yu, L., Nicolaisen, M., Larsen, J. & Ravnskov, S. Molecular characterization of root-associated fungal communities in relation to health status of Pisum sativum using barcoded pyrosequencing. Plant Soil 357, 395–405 (2012).CAS 
Article 

Google Scholar 
40.Gunina, A. & Kuzyakov, Y. Sugars in soil and sweets for microorganisms: review of origin, content, composition and fate. Soil Biol. Biochem. 90, 87–100 (2015).CAS 
Article 

Google Scholar 
41.Allison, S. D. Cheaters, diffusion and nutrients constrain decomposition by microbial enzymes in spatially structured environments. Ecol. Lett. 8, 626–635 (2005).Article 

Google Scholar 
42.Joergensen, R. G. & Wichern, F. Alive and kicking: why dormant soil microorganisms matter. Soil Biol. Biochem. 116, 419–430 (2018).CAS 
Article 

Google Scholar 
43.IUSS Working Group. WRB World reference base for soil resources 2014 (update 2015), international soil classification system for naming soils and creating legends for soil maps. World Soil Resources Reports (2015).44.Mahieu, S., Fustec, J., Jensen, E. S. & Crozat, Y. Does labelling frequency affect N rhizodeposition assessment using the cotton-wick method?. Soil Biol. Biochem. 41, 2236–2243 (2009).CAS 
Article 

Google Scholar 
45.Russell, C. A. & Fillery, I. R. P. Estimates of lupin below-ground biomass nitrogen, drymatter, and nitrogen turnover to wheat. Crop Pasture Sci. 47, 1047–1059 (1996).CAS 
Article 

Google Scholar 
46.Wichern, F., Mayer, J., Joergensen, R. & Müller, T. Evaluation of the wick method for in situ 13C and 15N labelling of annual plants using sugar-urea mixtures. Plant Soil 329, 105–115 (2010).CAS 
Article 

Google Scholar 
47.Phillips, J. M. & Hayman, D. S. Improved procedures for clearing roots and staining parasitic and vesicular-arbuscular mycorrhizal fungi for rapid assessment of infection. Transact. Brit. Mycol. Soc. 55, 158–168 (1970).Article 

Google Scholar 
48.Brookes, P. C., Landman, A., Pruden, G. & Jenkinson, D. S. Chloroform fumigation and the release of soil nitrogen. A rapid direct extraction method to measure microbial biomass nitrogen in soil. Soil Biol. Biochem. 17, 837–842 (1985).CAS 
Article 

Google Scholar 
49.Vance, E. D., Brookes, P. C. & Jenkinson, D. S. An extraction method for measuring soil microbial biomass C. Soil Biol. Biochem. 19, 703–707 (1987).CAS 
Article 

Google Scholar 
50.Mueller, T., Joergensen, R. G. & Meyer, B. Estimation of soil microbial biomass C in the p resence of living roots by fumigation-extraction. Soil Biol. Biochem. 24, 179–181 (1992).Article 

Google Scholar 
51.Wu, J., Joergensen, R. G., Pommerening, B., Chaussod, R. & Brookes, P. C. Measurement of soil microbial biomass C by fumigation-extraction—an automated procedure. Soil Biol. Biochem. 22, 1167–1169 (1990).CAS 
Article 

Google Scholar 
52.Hupe, A., Schulz, H., Bruns, C., Joergensen, R. G. & Wichern, F. Digging in the dirt—inadequacy of below-ground plant biomass quantification. Soil Biol. Biochem. 96, 137–144 (2016).CAS 
Article 

Google Scholar  More

1.Barrett, S. C. & Hough, J. Sexual dimorphism in flowering plants. J. Exp. Bot. 64, 67–82 (2012).PubMed 
Article 
CAS 

Google Scholar 
2.Retuerto, R., Sánchez Vilas, J. & Varga, S. Sexual dimorphism in response to stress. Environ. Exp. Bot. 146, 1–4 (2018).Article 

Google Scholar 
3.Poissant, J., Wilson, A. J. & Coltman, D. W. Sex-specific genetic variance and the evolution of sexual dimorphism: a systematic review of cross-sex genetic correlations. Evolution 64, 97–107 (2010).PubMed 
Article 

Google Scholar 
4.Bonduriansky, R. & Chenoweth, S. F. Intralocus sexual conflict. Trends Ecol. Evol. 24, 280–288 (2009).PubMed 
Article 

Google Scholar 
5.Pennell, T. M., de Haas, F. J., Morrow, E. H. & van Doorn, G. S. Contrasting effects of intralocus sexual conflict on sexually antagonistic coevolution. Proc. Natl Acad. Sci. USA 113, E978–E986 (2016).CAS 
PubMed 
Article 

Google Scholar 
6.Charlesworth, B. & Charlesworth, D. A model for the evolution of dioecy and gynodioecy. Am. Nat. 112, 975–997 (1978).Article 

Google Scholar 
7.Lloyd, D. G. & Webb, C. Secondary sex characters in plants. Bot. Rev. 43, 177–216 (1977).Article 

Google Scholar 
8.Torimaru, T. & Tomaru, N. Relationships between flowering phenology, plant size, and female reproductive output in a dioecious shrub, Ilex leucoclada (Aquifoliaceae). Botany 84, 1860–1869 (2006).
Google Scholar 
9.Delph, L. F. & Meagher, T. R. Sexual dimorphism masks life history trade-offs in the dioecious plant Silene latifolia. Ecology 76, 775–785 (1995).Article 

Google Scholar 
10.Carroll, S. B. & Delph, L. F. The effects of gender and plant architecture on allocation to flowers in dioecious Silene latifolia (Caryophyllaceae). Int. J. Plant Sci. 157, 493–500 (1996).Article 

Google Scholar 
11.Delph, L. F., Gehring, J. L., Arntz, A. M., Levri, M. & Frey, F. M. Genetic correlations with floral display lead to sexual dimorphism in the cost of reproduction. Am. Nat. 166, S31–S41 (2005).PubMed 
Article 
PubMed Central 

Google Scholar 
12.Barrett, S. C., Yakimowski, S. B., Field, D. L. & Pickup, M. Ecological genetics of sex ratios in plant populations. Philos. Trans. R. Soc. Lond. B Biol. Sci. 365, 2549–2557 (2010).PubMed 
PubMed Central 
Article 

Google Scholar 
13.Muyle, A., Shearn, R. & Marais, G. A. The evolution of sex chromosomes and dosage compensation in plants. Genome Biol. Evol. 9, 627–645 (2017).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
14.Connallon, T. & Knowles, L. L. Intergenomic conflict revealed by patterns of sex-biased gene expression. Trends Genet. 21, 495–499 (2005).CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
15.Ellegren, H. & Parsch, J. The evolution of sex-biased genes and sex-biased gene expression. Nat. Rev. Genet. 8, 689–698 (2007).CAS 
PubMed 
Article 

Google Scholar 
16.Rice, W. R. Sex chromosomes and the evolution of sexual dimorphism. Evolution 38, 735–742 (1984).PubMed 
Article 

Google Scholar 
17.Charlesworth, B., Jordan, C. Y. & Charlesworth, D. The evolutionary dynamics of sexually antagonistic mutations in pseudoautosomal regions of sex chromosomes. Evolution 68, 1339–1350 (2014).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
18.Mank, J. E. The transcriptional architecture of phenotypic dimorphism. Nat. Ecol. Evol. 1, 1–7 (2017).Article 

Google Scholar 
19.Zemp, N. et al. Evolution of sex-biased gene expression in a dioecious plant. Nat. Plants 2, 16168 (2016).CAS 
PubMed 
Article 

Google Scholar 
20.Sanderson, B. J., Wang, L., Tiffin, P., Wu, Z. & Olson, M. S. Sex-biased gene expression in flowers, but not leaves, reveals secondary sexual dimorphism in Populus balsamifera. New Phytol. 221, 527–539.21.Delph, L. F. & Herlihy, C. R. Sexual, fecundity, and viability selection on flower size and number in a sexually dimorphic plant. Evolution: Int. J. Org. Evolution 66, 1154–1166 (2012).Article 

Google Scholar 
22.Golonka, A. M., Sakai, A. K. & Weller, S. G. Wind pollination, sexual dimorphism, and changes in floral traits of Schiedea (Caryophyllaceae). Am. J. Bot. 92, 1492–1502 (2005).PubMed 
Article 
PubMed Central 

Google Scholar 
23.Aloni, R., Aloni, E., Langhans, M. & Ullrich, C. I. Role of auxin in regulating Arabidopsis flower development. Planta 223, 315–328 (2006).CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
24.Rocheta, M. et al. Comparative transcriptomic analysis of male and female flowers of monoecious Quercus suber. Front. Plant Sci. 5, 599 (2014).PubMed 
PubMed Central 
Article 

Google Scholar 
25.Zhao, D. & Tao, J. Recent advances on the development and regulation of flower color in ornamental plants. Front. Plant Sci. 6, 261 (2015).PubMed 
PubMed Central 

Google Scholar 
26.Moreau, C. et al. The b gene of pea encodes a defective flavonoid 3′, 5′-hydroxylase, and confers pink flower color. Plant Physiol. 159, 759–768 (2012).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
27.Hao, Z., Liu, S., Hu, L., Shi, J. & Chen, J. Transcriptome analysis and metabolic profiling reveal the key role of carotenoids in the petal coloration of Liriodendron tulipifera. Hortic. Res. 7, 1–16 (2020).Article 
CAS 

Google Scholar 
28.Hormaza, J. & Polito, V. Pistillate and staminate flower development in dioecious Pistacia vera (Anacardiaceae). Am. J. Bot. 83, 759–766 (1996).Article 

Google Scholar 
29.Boucher, L. D., Manchester, S. R. & Judd, W. S. An extinct genus of Salicaceae based on twigs with attached flowers, fruits, and foliage from the Eocene Green River Formation of Utah and Colorado, USA. Am. J. Bot. 90, 1389–1399 (2003).PubMed 
Article 
PubMed Central 

Google Scholar 
30.Manchester, S. R., Judd, W. S. & Handley, B. Foliage and fruits of early poplars (Salicaceae: Populus) from the Eocene of Utah, Colorado, and Wyoming. Int. J. Plant Sci. 167, 897–908 (2006).Article 

Google Scholar 
31.Wu, J. et al. Phylogeny of Salix subgenus Salix sl (Salicaceae): delimitation, biogeography, and reticulate evolution. BMC Evol. Biol. 15, 31 (2015).PubMed 
PubMed Central 
Article 

Google Scholar 
32.Liao, J., Cai, Z., Song, H. & Zhang, S. Poplar males and willow females exhibit superior adaptation to nocturnal warming than the opposite sex. Sci. Total Environ. 717, 137179 (2020).CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
33.Dawson, T. E. & Bliss, L. Patterns of water use and the tissue water relations in the dioecious shrub, Salix arctica: the physiological basis for habitat partitioning between the sexes. Oecologia 79, 332–343 (1989).CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
34.Lei, Y., Chen, K., Jiang, H., Yu, L. & Duan, B. Contrasting responses in the growth and energy utilization properties of sympatric Populus and Salix to different altitudes: implications for sexual dimorphism in Salicaceae. Physiol. Plant 159, 30–41 (2017).CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
35.Ueno, N., Suyama, Y. & Seiwa, K. What makes the sex ratio female-biased in the dioecious tree Salix sachalinensis? J. Ecol. 95, 951–959 (2007).Article 

Google Scholar 
36.Jiang, H., Zhang, S., Lei, Y., Xu, G. & Zhang, D. Alternative growth and defensive strategies reveal potential and gender specific trade-offs in dioecious plants Salix paraplesia to nutrient availability. Front. Plant Sci. 7, 1064 (2016).PubMed 
PubMed Central 

Google Scholar 
37.Liao, J., Song, H., Tang, D. & Zhang, S. Sexually differential tolerance to water deficiency of Salix paraplesia-A female-biased alpine willow. Ecol. Evol. 9, 8450–8464 (2019).PubMed 
PubMed Central 
Article 

Google Scholar 
38.Saska, M. M. & Kuzovkina, Y. A. Phenological stages of willow (Salix). Ann. Appl. Biol. 156, 431–437 (2010).Article 

Google Scholar 
39.Thomas, R., Sheard, R. & Moyer, J. Comparison of conventional and automated procedures for nitrogen, phosphorus, and potassium analysis of plant material using a single digestion 1. Agron. J. 59, 240–243 (1967).CAS 
Article 

Google Scholar 
40.Arnon, D. I. Copper enzymes in isolated chloroplasts. Polyphenoloxidase in Beta vulgaris. Plant Physiol. 24, 1 (1949).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
41.R Core Team. R: A Language and Environment for Statistical Computing (R Foundation for Statistical Computing, Vienna, Austria, 2020).42.Wickham, H. ggplot2: elegant graphics for data analysis (springer, 2016).43.Love, M. I., Huber, W. & Anders, S. Moderated estimation of fold change and dispersion for RNA-seq data with DESeq2. Genome Biol. 15, 550 (2014).PubMed 
PubMed Central 
Article 
CAS 

Google Scholar 
44.Dai, X. et al. The willow genome and divergent evolution from poplar after the common genome duplication. Cell Res. 24, 1274–1277 (2014).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
45.Wei, S., Yang, Y. & Yin, T. The chromosome-scale assembly of the willow genome provides insight into Salicaceae genome evolution. Hortic. Res. 7, 1–12 (2020).Article 
CAS 

Google Scholar 
46.Yu, G., Wang, L. G., Han, Y. & He, Q. Y. clusterProfiler: an R package for comparing biological themes among gene clusters. OMICS 16, 284–287 (2012).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
47.Thimm, O. et al. MAPMAN: a user-driven tool to display genomics data sets onto diagrams of metabolic pathways and other biological processes. Plant J. 37, 914–939 (2004).CAS 
PubMed 
Article 

Google Scholar 
48.Lohse, M. et al. M ercator: a fast and simple web server for genome scale functional annotation of plant sequence data. Plant Cell Environ. 37, 1250–1258 (2014).CAS 
PubMed 
Article 

Google Scholar 
49.Oliveros, J. C. Venny. An interactive tool for comparing lists with Venn’s diagrams. 2007–2015 http://bioinfogp.cnb.csic.es/tools/venny/index.html (2016).50.Kolde, R. Pheatmap: Pretty Heatmaps. R Package Version 1.0.12. https://CRANR-project.org/package=pheatmap (2019).51.Livak, K. J. & Schmittgen, T. D. Analysis of relative gene expression data using real-time quantitative PCR and the 2(-Delta Delta C(T)) method. Methods 25, 402–408 (2001).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
52.López-Ibáñez, J., Pazos, F. & Chagoyen, M. MBROLE 2.0-functional enrichment of chemical compounds. Nucleic Acids Res. 44, W201–W204 (2016).PubMed 
PubMed Central 
Article 
CAS 

Google Scholar 
53.Peleg, Z. & Blumwald, E. Hormone balance and abiotic stress tolerance in crop plants. Curr. Opin. Plant Biol. 14, 290–295 (2011).CAS 
PubMed 
Article 

Google Scholar 
54.Kay, P., Groszmann, M., Ross, J., Parish, R. & Swain, S. Modifications of a conserved regulatory network involving INDEHISCENT controls multiple aspects of reproductive tissue development in Arabidopsis. N. Phytol. 197, 73–87 (2013).CAS 
Article 

Google Scholar 
55.Ditengou, F. A. et al. Characterization of auxin transporter PIN 6 plasma membrane targeting reveals a function for PIN 6 in plant bolting. N. Phytol. 217, 1610–1624 (2018).CAS 
Article 

Google Scholar 
56.Ogawa, M. et al. Gibberellin biosynthesis and response during Arabidopsis seed germination. Plant Cell 15, 1591–1604 (2003).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
57.Hedden, P. & Thomas, S. G. Gibberellin biosynthesis and its regulation. Biochem. J. 444, 11–25 (2012).CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
58.Tyler, L. et al. DELLA proteins and gibberellin-regulated seed germination and floral development in Arabidopsis. Plant Physiol. 135, 1008–1019 (2004).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
59.Silverstone, A. L., Ciampaglio, C. N. & Sun, T. P. The Arabidopsis RGA gene encodes a transcriptional regulator repressing the gibberellin signal transduction pathway. Plant Cell 10, 155–169 (1998).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
60.Olszewski, N., Sun, T. P. & Gubler, F. Gibberellin signaling: biosynthesis, catabolism, and response pathways. Plant Cell 14, S61–S80 (2002).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
61.Middleton, A. M. et al. Mathematical modeling elucidates the role of transcriptional feedback in gibberellin signaling. Proc. Natl Acad. Sci. USA 109, 7571–7576 (2012).CAS 
PubMed 
Article 

Google Scholar 
62.Bévort, M. & Leffers, H. Down regulation of ribosomal protein mRNAs during neuronal differentiation of human NTERA2 cells. Differentiation 66, 81–92 (2000).PubMed 
Article 

Google Scholar 
63.Brothers, M. & Rine, J. Mutations in the PCNA DNA polymerase clamp of Saccharomyces cerevisiae reveal complexities of the cell cycle and ploidy on heterochromatin assembly. Genetics 213, 449–463 (2019).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
64.Li, C., Potuschak, T., Colón-Carmona, A., Gutiérrez, R. A. & Doerner, P. Arabidopsis TCP20 links regulation of growth and cell division control pathways. Proc. Natl Acad. Sci. USA 102, 12978–12983 (2005).CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
65.Ray, S. & Pollard, J. W. KLF15 negatively regulates estrogen-induced epithelial cell proliferation by inhibition of DNA replication licensing. Proc. Natl Acad. Sci. USA 109, E1334–E1343 (2012).CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
66.Halim, V., Vess, A., Scheel, D. & Rosahl, S. The role of salicylic acid and jasmonic acid in pathogen defence. Plant Biol. 8, 307–313 (2006).CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
67.Zhu, F. et al. Salicylic acid and jasmonic acid are essential for systemic resistance against tobacco mosaic virus in Nicotiana benthamiana. Mol. Plant. Microbe Interact. 27, 567–577 (2014).CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
68.Caarls, L., Pieterse, C. M., & Van Wees, S. How salicylic acid takes transcriptional control over jasmonic acid signaling. Front. Plant Sci. 6, 170 (2015).PubMed 
PubMed Central 
Article 

Google Scholar 
69.Wang, D., Weaver, N. D., Kesarwani, M. & Dong, X. Induction of protein secretory pathway is required for systemic acquired resistance. Science 308, 1036–1040 (2005).CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
70.Checker, V. G., Kushwaha, H. R., Kumari, P. & Yadav, S. Role of phytohormones in plant defense: signaling and cross talk in Molecular aspects of plant-pathogen interaction (eds Singh, A. & Singh, I.) 159–184 (Springer, 2018).71.Niki, T., Mitsuhara, I., Seo, S., Ohtsubo, N. & Ohashi, Y. Antagonistic effect of salicylic acid and jasmonic acid on the expression of pathogenesis-related (PR) protein genes in wounded mature tobacco leaves. Plant Cell Physiol. 39, 500–507 (1998).CAS 
Article 

Google Scholar 
72.Shim, J. S. et al. AtMYB44 regulates WRKY70 expression and modulates antagonistic interaction between salicylic acid and jasmonic acid signaling. Plant J. 73, 483–495 (2013).CAS 
PubMed 
Article 

Google Scholar 
73.Romano, A. & Conway, T. Evolution of carbohydrate metabolic pathways. Res. Microbiol. 147, 448–455 (1996).CAS 
PubMed 
Article 

Google Scholar 
74.Akram, M. Citric acid cycle and role of its intermediates in metabolism. Cell Biochem. Biophys. 68, 475–478 (2014).CAS 
PubMed 
Article 

Google Scholar 
75.Plaxton, W. C. The organization and regulation of plant glycolysis. Annu. Rev. Plant Biol. 47, 185–214 (1996).CAS 
Article 

Google Scholar 
76.Montal, E. D. et al. PEPCK coordinates the regulation of central carbon metabolism to promote cancer cell growth. Mol. Cell 60, 571–583 (2015).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
77.Yang, J., Kalhan, S. C. & Hanson, R. W. What is the metabolic role of phosphoenolpyruvate carboxykinase? J. Biol. Chem. 284, 27025–27029 (2009).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
78.Huang, Y.-X. et al. Phosphoenolpyruvate carboxykinase (PEPCK) deficiency affects the germination, growth and fruit sugar content in tomato (Solanum lycopersicum L.). Plant Physiol. Biochem. 96, 417–425 (2015).CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
79.Malone, S. et al. Phospho enol pyruvate carboxykinase in Arabidopsis: changes in gene expression, protein and activity during vegetative and reproductive development. Plant Cell Physiol. 48, 441–450 (2007).CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
80.Murray, D. R. Nutritive role of seedcoats in developing legume seeds. Am. J. Bot. 74, 1122–1137 (1987).CAS 
Article 

Google Scholar 
81.Famiani, F. et al. Phosphoenolpyruvate carboxykinase and its potential role in the catabolism of organic acids in the flesh of soft fruit during ripening. J. Exp. Bot. 56, 2959–2969 (2005).CAS 
PubMed 
Article 

Google Scholar 
82.Osorio, S. et al. Alteration of the interconversion of pyruvate and malate in the plastid or cytosol of ripening tomato fruit invokes diverse consequences on sugar but similar effects on cellular organic acid, metabolism, and transitory starch accumulation. Plant Physiol. 161, 628–643 (2013).CAS 
PubMed 
Article 

Google Scholar 
83.Yuan, H., Zhang, J., Nageswaran, D. & Li, L. Carotenoid metabolism and regulation in horticultural crops. Hortic. Res. 2, 1–11 (2015).Article 
CAS 

Google Scholar 
84.Borghi, M. & Fernie, A. R. Floral metabolism of sugars and amino acids: implications for pollinators’ preferences and seed and fruit set. Plant Physiol. 175, 1510–1524 (2017).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
85.Sagawa, J. M. et al. An R2R3-MYB transcription factor regulates carotenoid pigmentation in Mimulus lewisii flowers. N. Phytol. 209, 1049–1057 (2016).CAS 
Article 

Google Scholar 
86.Tadmor, Y. et al. Genetics of flavonoid, carotenoid, and chlorophyll pigments in melon fruit rinds. J. Agric. Food Chem. 58, 10722–10728 (2010).CAS 
PubMed 
Article 

Google Scholar 
87.Chen, H. et al. A knockdown mutation of YELLOW-GREEN LEAF2 blocks chlorophyll biosynthesis in rice. Plant Cell Rep. 32, 1855–1867 (2013).CAS 
PubMed 
Article 

Google Scholar 
88.Grotewold, E. The genetics and biochemistry of floral pigments. Annu. Rev. Plant Biol. 57, 761–780 (2006).CAS 
PubMed 
Article 

Google Scholar 
89.Bennett, R. N. & Wallsgrove, R. M. Secondary metabolites in plant defence mechanisms. N. Phytol. 127, 617–633 (1994).CAS 
Article 

Google Scholar 
90.Erb, M. & Kliebenstein, D. J. Plant secondary metabolites as defenses, regulators, and primary metabolites: the blurred functional trichotomy. Plant Physiol. 184, 39–52 (2020).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
91.Falcone Ferreyra, M. L., Rius, S. & Casati, P. Flavonoids: biosynthesis, biological functions, and biotechnological applications. Front. Plant Sci. 3, 222 (2012).CAS 
PubMed 
PubMed Central 

Google Scholar 
92.Hichri, I. et al. Recent advances in the transcriptional regulation of the flavonoid biosynthetic pathway. J. Exp. Bot. 62, 2465–2483 (2011).CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
93.Jaakola, L. & Hohtola, A. Effect of latitude on flavonoid biosynthesis in plants. Plant Cell Environ. 33, 1239–1247 (2010).CAS 
PubMed 

Google Scholar 
94.Chomicki, G. et al. The velamen protects photosynthetic orchid roots against UV‐B damage, and a large dated phylogeny implies multiple gains and losses of this function during the Cenozoic. N. Phytol. 205, 1330–1341 (2015).CAS 
Article 

Google Scholar 
95.Zhang, Y., Feng, L., Jiang, H., Zhang, Y. & Zhang, S. Different proteome profiles between male and female Populus cathayana exposed to UV-B radiation. Front. Plant Sci. 8, 320 (2017).PubMed 
PubMed Central 

Google Scholar 
96.Hideg, É., Jansen, M. A. & Strid, Å. UV-B exposure, ROS, and stress: inseparable companions or loosely linked associates? Trends Plant Sci. 18, 107–115 (2013).CAS 
PubMed 
Article 

Google Scholar 
97.Kataria, S., Jajoo, A. & Guruprasad, K. N. Impact of increasing Ultraviolet-B (UV-B) radiation on photosynthetic processes. J. Photochem. Photobiol. B: Biol. 137, 55–66 (2014).CAS 
Article 

Google Scholar  More