Ecology

1.Cámara, A. & Santero-Sánchez, R. Economic, social, and environmental impact of a sustainable fishereis model in Spain. Sustainability 11, 6311 (2019).Article 

Google Scholar 
2.Department of Fisheries Malaysia. Annual Fisheries statistics 2010–2019. https://www.dof.gov.my/index.php/pages/view/82 (2020).3.Department of Fisheries, Thailand. The annual marine fisheries statistics (2010–2019) based on the sample survey. https://elibonline.fisheries.go.th/elib/cgi-bin/opacexe.exe?op=dsp&bid=10498&lang=0&db=Main&pat=&cat=sub&skin=s&lpp=20&catop=edit&scid=zzz (2020).4.Jha, S., Deepti, V., Ravali, V. & Sujatha, K. Studies on some aspects of biology of Uranoscopus cognatus Cantor, 1849 (Pisces: Uranoscopidae) off Visakhapatnam, central eastern coast of India. Indian J. Mar. Sci. 48, 85–92 (2019).
Google Scholar 
5.Clark, M. R. et al. The impacts of deep-sea fisheries on benthic communities: A review. ICES J. Mar. Sci. 73(1), 51–69 (2016).Article 

Google Scholar 
6.Van Denderen, P. D. et al. Evaluating impacts of bottom trawling and hypoxia on benthic communities at the local, habitat, and regional scale using a modelling approach. ICES J. Mar. Sci. 77(1), 578–589 (2019).
Google Scholar 
7.Erdoğan Sağlam, N. & Sağlam, C. Population parameters of stargazer (Uranoscopus scaber Linnaeus, 1758) in the southeastern Black Sea region during the 2011–2012 fishing season. J. Appl. Ichthyol. 29, 1313–1317 (2013).Article 

Google Scholar 
8.Matsunuma, M. et al. Fishes of Terengganu: East Coast of Malay Peninsula, Malaysia (National Museum of Nature and Science, 2011).
Google Scholar 
9.Vilasri, V. Family Uranoscopidae. In Fishes of Southern Taiwan (eds Koeda, K. & Ho, H. S.) 1097–1105 (National Museum of Marine Biology & Aquarium, 2019).
Google Scholar 
10.Starks, E. C. The Osteology and Relationships of the Uranoscopoid Fishes (Stanford University Press, 1923).
Google Scholar 
11.Pietsch, T. W. Phylogenetic relationships of trachinoid fishes of the family Uranoscopidae. Copeia 1989, 253–303 (1989).Article 

Google Scholar 
12.Kishimoto, H. Uranoscopidae. In FAO Species Identification Guide for Fisheries Purposes (eds Carpenter, K. E. & Niem, V. H.) 3519–3531 (FAO, 2001).
Google Scholar 
13.Randall, J. E. & Arnold, R. J. Uranoscopus rosette, a new species of stargazer (Uranoscopidae: Trachinoidei) from the Red Sea. Aqua. Int. J. Ichthyol. 18, 209–219 (2012).
Google Scholar 
14.Jung-chen, H. & Hin-Kiu, M. Stargazers (Uranoscopidae) have exceptionally more bile. Kuroshio Sci. 9–1, 17–26 (2015).
Google Scholar 
15.Vilasri, V. Comparative anatomy and phylogenetic systematics of the family Uranoscopidae (Actinopterygii: Perciformes). Mem. Fac. Fish. Hokkaido Univ. 55, 1–106 (2013).
Google Scholar 
16.Fricke, R., Eschmeyer, W. N. & Van der Laan, R. (eds). Eschmeyer’s catalog of fishes: genera, species, references. http://researcharchive.calacademy.org/research/ichthyology/catalog/fishcatmain.asp (2020).17.Froese, R. & Pauly, D. Uranoscopidae. Fishbase https://www.fishbase.se/Summary/FamilySummary.php?ID=378 (2019).18.Fricke, R. Two new species of stargazers of the genus Uranoscopus (Teleostei: Uranoscopidae) from the western Pacific Ocean. Zootaxa 4476, 157–167 (2018).PubMed 
Article 

Google Scholar 
19.Fricke, R., Jawad, L. A., Al-Kharusi, L. H. & Al-Mamry, J. M. New record and redescription of Uranoscopus crassiceps Alcock, 1890 (Uranoscopidae) From Oman, Arabian Sea, Northwestern Indian Ocean, based on adult specimens. Cybium 37, 143–147 (2013).
Google Scholar 
20.Department of Fisheries Malaysia. Valid Local Name of Malaysian Marine Fishes (Department of Fisheries Malaysia, 2009).
Google Scholar 
21.Spalding, M. D. et al. Marine ecoregions of the world: A bioregionalization of coastal and shelf areas. Bioscience 57, 573–583 (2007).Article 

Google Scholar 
22.Voris, H. K. Maps of Pleistocene sea levels in Southeast Asia: Shorelines, river systems and time durations. J. Biogeogr. 27, 1153–1167 (2000).Article 

Google Scholar 
23.Rohfritsch, A. & Borsa, P. Genetic structure of Indian scad mackerel Decapterus russelli: Pleistocene vicariance and secondary contact in the Central Indo-West Pacific Seas. Heredity 95, 315 (2005).CAS 
PubMed 
Article 

Google Scholar 
24.Lohman, D. J. et al. Biogeography of the Indo-Australian archipelago. Annu. Rev. Ecol. Evol. Syst. 42, 205–226 (2011).Article 

Google Scholar 
25.Crandall, E. D. et al. The molecular biogeography of the Indo-Pacific: Testing hypotheses with multispecies genetic patterns. Glob. Ecol. Biogeogr. 28, 943–960 (2019).Article 

Google Scholar 
26.Reece, J. S., Bowen, B. W., Joshi, K., Goz, V. & Larson, A. Phylogeography of two moray eels indicates high dispersal throughout the Indo-Pacific. J. Hered. 101, 391–402 (2010).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
27.Akib, N. A. M. et al. High connectivity in Rastrelliger kanagurta: influence of historical signatures and migratory behaviour inferred from mtDNA cytochrome b. PLoS ONE 10, e0119749 (2015).PubMed 
PubMed Central 
Article 
CAS 

Google Scholar 
28.Jamaludin, N. A. et al. Phylogeography of the Japanese scad, Decapterus maruadsi (Teleostei; Carangidae) across the Central Indo-West Pacific: evidence of strong regional structure and cryptic diversity. Mitochondrial DNA A 2, 1–13 (2020).
Google Scholar 
29.Gaither, M. R., Toonen, R. J., Robertson, D. R., Planes, S. & Bowen, B. W. Genetic evaluation of marine biogeographical barriers: perspectives from two widespread Indo-Pacific snappers (Lutjanus kasmira and Lutjanus fulvus). J. Biogeogr. 37, 133–147 (2010).Article 

Google Scholar 
30.Gaither, M. R. et al. Phylogeography of the reef fish Cephalopholis argus (Epinephelidae) indicates Pleistocene isolation across the Indo-Pacific Barrier with contemporary overlap in the Coral Triangle. BMC Evol. Biol. 11, 189 (2011).PubMed 
PubMed Central 
Article 

Google Scholar 
31.Timm, J. & Kochzius, M. Geological history and oceanography of the Indo-Malay Archipelago shape the genetic population structure in the false clown anemonefish (Amphiprion ocellaris). Mol. Ecol. 17, 3999–4014 (2008).PubMed 
Article 

Google Scholar 
32.Otwoma, L. M. & Kochzius, M. Genetic population structure of the coral reef sea star Linckia laevigata in the Western Indian Ocean and Indo-West Pacific. PLoS ONE 11, 10 (2016).Article 
CAS 

Google Scholar 
33.Williams, S. T., Jara, J., Gomez, E. & Knowlton, N. The marine Indo-West Pacific break: Contrasting the resolving power of mitochondrial and nuclear genes. Integr. Comp. Biol. 42, 941–952 (2002).CAS 
PubMed 
Article 

Google Scholar 
34.Supmee, V., Sangthong, P., Songrak, A. & Suppapan, J. Population genetic structure of Asiatic Hard Clam (Meretrix meretrix) in Thailand based on Cytochrome Oxidase subunit I gene sequence. Biodiversitas 21, 2702–2709 (2020).Article 

Google Scholar 
35.Hui, M. et al. Comparative genetic population structure of three endangered giant clams (Cardiidae: Tridacna species) throughout the Indo-West Pacific: Implications for divergence, connectivity and conservation. J. Molluscan Stud. 82, 403–414 (2016).Article 

Google Scholar 
36.Panithanarak, T., Karuwancharoen, R., Na-Nakorn, U. & Nguyen, T. T. Population genetics of the spotted seahorse (Hippocampus kuda) in Thai waters: Implications for conservation. Zool. Stud. 49, 564–576 (2010).CAS 

Google Scholar 
37.Kasim, N. S. et al. Recent population expansion of longtail tuna Thunnus tonggol (Bleeker, 1851) inferred from the mitochondrial DNA markers. PeerJ 8, 9679 (2020).Article 

Google Scholar 
38.Canales-Aguirre, C. B., Ferrada-Fuentes, S., Galleguillos, R., Oyarzun, F. X. & Hernández, C. E. Population genetic structure of Patagonian toothfish (Dissostichus eleginoides) in the Southeast Pacific and Southwest Atlantic Ocean. PeerJ 6, e4173 (2018).PubMed 
PubMed Central 
Article 

Google Scholar 
39.Sato, M. et al. Genetic structure and demographic connectivity of marbled flounder (Pseudopleuronectes yokohamae) populations of Tokyo Bay. J. Sea Res. 142, 79–90 (2018).ADS 
Article 

Google Scholar 
40.Borsa, P. Genetic structure of round scad mackerel Decapterus macrosoma (Carangidae) in the Indo-Malay archipelago. Mar. Biol. 142, 575–581 (2003).CAS 
Article 

Google Scholar 
41.Eytan, R. I. & Hellberg, M. E. Nuclear and mitochondrial sequence data reveal and conceal different demographic histories and population genetic processes in Caribbean reef fishes. Evolution 64, 3380–3397 (2010).CAS 
PubMed 
Article 

Google Scholar 
42.Tan, M. P., Jamsari, A. F. J. & Siti Azizah, M. N. Genotyping of microsatellite markers to study genetic structure of the wild striped snakehead Channa striata in Malaysia. J. Fish. Biol. 88, 1932–1948 (2016).CAS 
PubMed 
Article 

Google Scholar 
43.Tan, M. P., Jamsari, A. F. J. & Siti Azizah, M. N. Phylogeographic pattern of the striped snakehead, Channa striata in Sundaland: Ancient river connectivity, geographical and anthropogenic signatures. PLoS ONE 7, 1–11 (2012).
Google Scholar 
44.Tan, M. P., Jamsari, A. F. J., Muhlisin, Z. A. & Siti Azizah, M. N. Mitochondrial genetic variation and population structure of the striped snakehead, Channa striata in Malaysia and Sumatra. Indonesia. Biochem. Syst. Ecol. 60, 99–105 (2015).CAS 
Article 

Google Scholar 
45.Haponski, A. E. & Stepien, C. A. Phylogenetic and biogeographical relationships of the Sander pikeperches (Percidae: Perciformes): Patterns across North America and Eurasia. Biol. J. Linn. Soc. Lond. 110, 156–179 (2013).Article 

Google Scholar 
46.Milá, B., Van Tassell, J. L., Calderón, J. A., Rüber, L. & Zardoya, R. Cryptic lineage divergence in marine environments: Genetic differentiation at multiple spatial and temporal scales in the widespread intertidal goby Gobiosoma bosc. Ecol. Evol. 7, 5514–5523 (2017).PubMed 
PubMed Central 
Article 

Google Scholar 
47.Piganeau, G., Gardner, M. & Eyre-Walker, A. A broad survey of recombination in animal mitochondria. Mol. Biol. Evol. 21, 2319–2325 (2004).CAS 
PubMed 
Article 

Google Scholar 
48.Avise, J. C. Molecular Markers, Natural History, and Evolution (Sinauer Associates Inc, 2004).
Google Scholar 
49.De Mandal, S., Chhakchhuak, L., Gurusubramanian, G. & Kumar, N. S. Mitochondrial markers for identification and phylogenetic studies in insects–A Review. DNA Barcodes 2, 1–9 (2014).CAS 
Article 

Google Scholar 
50.Simon, C. et al. Evolution, weighting, and phylogenetic utility of mitochondrial gene sequences and a compilation of conserved polymerase chain reaction primers. Ann. Entomol. Soc. Am. 87, 651–701 (1994).CAS 
Article 

Google Scholar 
51.Hoofer, S. R., Reeder, S. A., Hansen, E. W. & Van Den Bussche, R. A. Molecular phylogenetics and taxonomic review of noctilionoid and vespertilionoid bats (Chiroptera: Yangochiroptera). J. Mammal. 84, 809–821 (2003).Article 

Google Scholar 
52.Hewitt, G. M. Speciation, hybrid zones and phylogeography or seeing genes in space and time. Mol. Ecol. 10, 537–549 (2001).CAS 
PubMed 
Article 

Google Scholar 
53.Surya, S. et al. Morphometry and length-weight relationship of Uranoscopus marmoratus Cuvier, 1829 (Family: Uranoscopidae) from Palk Bay, India. Int. J. Biol. Sci. 5, 1–10 (2016).ADS 

Google Scholar 
54.Narejo, N. T. Morphometric characters and their relationships in Gudusia chapra (Hamilton) from Keenjhar Lake (Distt: Thatta), Sindh, Pakistan. Pak. J. Zool. 42, 101–104 (2010).
Google Scholar 
55.Gan, H. M., Nur Ilham Syahadah, M. Y., Vilasri, V., Tun Nurul Aimi, M. J. & Tan, M. P. Four whole mitogenome sequences of yellowtail stargazers (Uranoscopus cognatus cantor 1849) from East Peninsular Malaysia and West Coast of Thailand. Mitochondrial DNA B 4, 256–258 (2019).Article 

Google Scholar 
56.Panjarat, S. Sustainable fisheries in the Andaman Sea coast of Thailand. Division for Ocean Affairs and the Law of the Sea Office of Legal Affairs. (The United Nations, 2008).57.Derrick, B., Noranarttragoon, P., Zeller, D., Teh, L. C. & Pauly, D. Thailand’s missing marine fisheries catch (1950–2014). Front. Mar. Sci. 4, 402 (2017).Article 

Google Scholar 
58.Sampantamit, T., Ho, L., Van Echelpoel, W., Lachat, C. & Goethals, P. Links and trade-offs between fisheries and environmental protection in relation to the sustainable development goals in Thailand. Water 12, 399 (2020).Article 

Google Scholar 
59.Chong, V., Lee, P. & Lau, C. Diversity, extinction risk and conservation of Malaysian fishes. J. Fish Biol. 76, 2009–2066 (2010).CAS 
PubMed 
Article 

Google Scholar 
60.Lim, H. C., Ahmad, A. T., Nuruddin, A. A. & Mohd Nor, S. A. Cytochrome b gene reveals panmixia among Japanese Threadfin Bream, Nemipterus japonicus (Bloch, 1791) populations along the coasts of Peninsular Malaysia and provides evidence of a cryptic species. Mitochondrial DNA A 27, 575–584 (2016).CAS 
Article 

Google Scholar 
61.Nabilsyafiq, M. H. et al. ND5 gene marker reveals recent population expansion of wild pearse’s mudskipper (Periophthalmus novemradiatus Hamilton) inhabits Setiu wetlands in east Peninsular Malaysia. Malays. Appl. Biol. 48, 87–93 (2019).
Google Scholar 
62.Zhou, Y. et al. Importance of incomplete lineage sorting and introgression in the origin of shared genetic variation between two closely related pines with overlapping distributions. Heredity 118, 211–220 (2017).CAS 
PubMed 
Article 

Google Scholar 
63.Lessios, H. A. The great American schism: divergence of marine organisms after the rise of the Central American Isthmus. Annu. Rev. Ecol. Evol. Syst. 39, 63–91 (2008).Article 

Google Scholar 
64.Avise, J. Molecular Markers, Natural History and Evolution (Chapman and Hall, 1994).Book 

Google Scholar 
65.Nelson, J. S., Grande, T. C. & Wilson, M. V. Fishes of the World (John Wiley and Sons, 2016).Book 

Google Scholar 
66.Young, J. Z. Memoirs: On the autonomic nervous system of the Teleostean Fish Uranoscopus scaber. J. Cell Sci. 2, 491–536 (1931).Article 

Google Scholar 
67.Day, J., Clark, J. A., Williamson, J. E., Brown, C. & Gillings, M. Population genetic analyses reveal female reproductive philopatry in the oviparous Port Jackson shark. Mar. Freshw. Res. 70, 986–994 (2019).Article 

Google Scholar 
68.Roycroft, E. J., Le Port, A. & Lavery, S. D. Population structure and male-biased dispersal in the short-tail stingray Bathytoshia brevicaudata (Myliobatoidei: Dasyatidae). Conserv. Genet. 20, 717–728 (2019).CAS 
Article 

Google Scholar 
69.King, T. L., Eackles, M. S., Spidle, A. P. & Brockmann, H. J. Regional differentiation and sex-biased dispersal among populations of the horseshoe crab Limulus polyphemus. Trans. Am. Fish. Soc. 134, 441–465 (2005).Article 

Google Scholar 
70.Lane, A. & Shine, R. Intraspecific variation in the direction and degree of sex-biased dispersal among sea-snake populations. Mol. Ecol. 20, 1870–1876 (2011).PubMed 
Article 

Google Scholar 
71.Casale, P., Laurent, L., Gerosa, G. & Argano, R. Molecular evidence of male-biased dispersal in loggerhead turtle juveniles. J. Exp. Mar. Biol. Ecol. 267, 139–145 (2002).CAS 
Article 

Google Scholar 
72.Wyrtki, K. Physical Oceanography of the Southeast Asian Waters (University of California, 1961).
Google Scholar 
73.Barber, P., Palumbi, S., Erdmann, M. & Moosa, M. Sharp genetic breaks among populations of Haptosquilla pulchella (Stomatopoda) indicate limits to larval transport: patterns, causes, and consequences. Mol. Ecol. 11, 659–674 (2002).CAS 
PubMed 
Article 

Google Scholar 
74.Kamarudin, K. R. & Esa, Y. Phylogeny and phylogeography of Barbonymus schwanenfeldii (Cyprinidae) from Malaysia inferred using partial cytochrome b mtDNA gene. J. Trop. Biol. Conserv. 5, 1–13 (2009).
Google Scholar 
75.Tan, M. P. et al. Genetic diversity of the Pearse’s Mudskipper Periophthalmus novemradiatus (Perciformes: Gobiidae) and characterization of its complete mitochondrial genome. Thalassas 36, 103–113 (2020).Article 

Google Scholar 
76.Roberts, T. R. & Khaironizam, M. Z. Trophic polymorphism in the Malaysian fish Neolissochilus soroides and other old world barbs (Teleostei, Cyprinidae). Nat. Hist. Bull. Siam Soc. 56, 25–53 (2008).
Google Scholar 
77.Forsman, A. Rethinking phenotypic plasticity and its consequences for individuals, populations and species. Heredity 115, 276 (2015).CAS 
PubMed 
Article 

Google Scholar 
78.Vasileva, E. Morphokaryological variability and divergence of stargazers (Uranoscopus, perciformes) from the Mediterranean Sea basin: I. Divergence and taxonomic state of the Black Sea Stargazer. J. Ichthyol. 52, 476–484 (2012).Article 

Google Scholar 
79.Aljanabi, S. M. & Martinez, I. Universal and rapid salt-extraction of high quality genomic DNA for PCR-based techniques. Nucleic Acids Ress 25, 4692–4693 (1997).CAS 
Article 

Google Scholar 
80.Ward, R. D., Zemlak, T. S., Innes, B. H., Last, P. R. & Hebert, P. D. DNA barcoding Australia’s fish species. Philos. Trans. R. Soc. Lond., B Biol. Sci. 360, 1847–1857 (2005).CAS 
Article 

Google Scholar 
81.López, J. A., Chen, W. J. & Ortí, G. Esociform phylogeny. Copeia 2004, 449–464 (2004).Article 

Google Scholar 
82.Mat Jaafar, T. N., Taylor, M. I., Mohd Nor, S. A., Bruyn, M. D. & Carvalho, G. R. Comparative genetic stock structure in three species of commercially exploited Indo-Malay Carangidae (Teleosteii, Perciformes). J. Fish Biol. 96, 337–349 (2020).CAS 
PubMed 
Article 

Google Scholar 
83.Tamura, K., Stecher, G., Peterson, D., Filipski, A. & Kumar, S. MEGA6: molecular evolutionary genetics analysis version 6.0. Mol. Biol. Evol. 30, 2725–2729 (2013).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
84.Farris, J. S., Källersjö, M., Kluge, A. G. & Bult, C. Testing significance of incongruence. Cladistics 10, 315–319 (1994).Article 

Google Scholar 
85.Swofford, D. L. PAUP: Phylogenetic Analysis Using Parsimony (and Other Methods), Version 4.0 Beta 10 (Sinauer Associates, 2002).
Google Scholar 
86.Librado, P. & Rozas, J. DnaSP v5: A software for comprehensive analysis of DNA polymorphism data. Bioinformatics 25, 1451–1452 (2009).CAS 
PubMed 
Article 

Google Scholar 
87.Excoffier, L. & Lischer, H. E. L. Arlequin suite ver 3.5: A new series of programs to perform population genetics analyses under Linux and Windows. Mol. Ecol. Resour. 10, 564–567 (2010).PubMed 
Article 

Google Scholar 
88.Hasegawa, M., Kishino, H. & Yano, T. A. Dating of the huma-ape splitting by a molecular clock of mitochondrial DNA. J. Mol. Evol. 22, 160–174 (1985).CAS 
PubMed 
Article 

Google Scholar 
89.Kimura, M. A simple method for estimating evolutionary rate of base substitutions through comparative studies of nucletide sequences. J. Mol. Evol. 16, 111–120 (1980).ADS 
CAS 
Article 

Google Scholar 
90.Felsenstein, J. Confidence limits on phylogenies: An approach using the bootstrap. Evolution 39, 783–791 (1985).PubMed 
Article 

Google Scholar 
91.Bandelt, H. J., Forster, P. & Röhl, A. Median-joining networks for inferring intraspecific phylogenies. Mol. Biol. Evol. 16, 37–48 (1999).CAS 
PubMed 
Article 

Google Scholar 
92.Dupanloup, I., Schneider, S. & Excoffier, L. A simulated annealing approach to define the genetic structure of populations. Mol. Ecol. 11, 2571–2581 (2002).CAS 
PubMed 
Article 

Google Scholar 
93.Benjamini, Y. & Hochberg, Y. Controlling the false discovery rate: a practical and powerful approach to multiple testing. J. R. Stat. Soc. Series B Stat. Methodol. 57, 289–300 (1995).MathSciNet 
MATH 

Google Scholar 
94.Nei, M. Analysis of gene diversity in subdivided populations. Proc. Natl. Acad. Sci. U.S.A. 70, 3321–3323 (1973).ADS 
CAS 
PubMed 
PubMed Central 
MATH 
Article 

Google Scholar 
95.Lynch, M. & Crease, T. The analysis of population survey data on DNA sequence variation. Mol. Biol. Evol. 7, 377–394 (1990).CAS 
PubMed 

Google Scholar 
96.Hudson, R. R., Slatkin, M. & Maddison, W. P. Estimation of levels of gene flow from DNA sequence data. Genetics 132, 583–589 (1992).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
97.Tajima, F. Statistical method for testing the neutral mutation hypothesis by DNA polymorphism. Genetics 123, 585–595 (1989).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
98.Fu, Y.-X. Statistical tests of neutrality of mutations against population growth, hitchhiking and background selection. Genetics 147, 915–925 (1997).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
99.Harpending, H. Infertility and forager demography. Am. J. Phys. Anthropol. 93, 385–390 (1994).CAS 
PubMed 
Article 

Google Scholar 
100.Slatkin, M. & Hudson, R. R. Pairwise comparisons of mitochondrial DNA sequences in stable and exponentially growing populations. Genetics 129, 555–562 (1991).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
101.Rogers, A. R. & Harpending, H. Population growth makes waves in the distribution of pairwise genetic differences. Mol. Biol. Evol. 9, 552–569 (1992).CAS 
PubMed 

Google Scholar 
102.Schneider, S. & Excoffier, L. Estimation of past demographic parameters from the distribution of pairwise differences when the mutation rates vary among sites: Application to human mitochondrial DNA. Genetics 152, 1079–1089 (1999).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
103.Yildirim, Y. Genetic structure of Pleurobranchaea maculata in New Zealand (Massey University, 2016).
Google Scholar 
104.Hubbs, C. & Lagler, K. The fishes of the Great Lakes region 213 (The University of Michigan Press, 1958).
Google Scholar 
105.Kishimoto, H. A new stargazer, Uranoscopus flavipinnis, from Japan and Taiwan with redescription and neotype designation of U. japonicus. Japan. J. Ichthyol. 34, 1–14 (1987).
Google Scholar 
106.Kishimoto, H. Redescription and lectotype designation of the stargazer Uranoscopus kaianus Günther. Copeia 1984, 1009–1011 (1984).Article 

Google Scholar 
107.Gomon, M. F. & Johnson, J. A new fringed stargazer (Uranoscopidae: Ichthyscopus) with descriptions of the other Australian species of the genus. Mem. Queensl. Mus. 43, 597–619 (1999).
Google Scholar 
108.Rainboth, W. J. Fishes of the Cambodian Mekong (Food and Agriculture Org, 1996).
Google Scholar 
109.Imamura, H. & Matsuura, K. Redefinition and phylogenetic relationships of the family Pinguipedidae (Teleostei: Perciformes). Ichthyol. Res. 50, 259–269 (2003).Article 

Google Scholar 
110.Hammer, Ø., Harper, D. A. & Ryan, P. D. PAST: Paleontological statistics software package for education and data analysis. Palaeontol. Electron. 4, 9 (2001).
Google Scholar 
111.Seah, Y. G., Nabilsyafiq, M. & Mazlan, A. G. Preliminary study on the morphology and biology of coexist Nemipterus furcosus and Nemipterus tambuloides from Terengganu Waters Peninsular Malaysia. J. Fish. Aquat. Sci. 11, 418–424 (2016).Article 

Google Scholar 
112.Johnson, R. A. & Wichern, D. W. Multivariate statistical analysis (Prentice Hall Upper Saddle River, 1998).MATH 

Google Scholar  More

AnimalsTissue samples for expression analysis were collected on August 24 and September 9 2016 from a population of spring-spawning herring kept in captivity at University of Bergen; the rearing of captive herring was approved by the Norwegian national animal ethics committee (Forsøksdyrutvalget FOTS ID-5072). The tissue samples used for ATAC-sequencing were collected at Hästskär on June 19 2019 from wild spring-spawning Atlantic herring in the Baltic Sea, which do not require ethical permission.Genome scan and genetic diversity at the TSHR locusWe used a 2 × 2 contingency X2 test to estimate the extent of SNP allele frequency differences between seven spring- and seven autumn-spawning herring populations from the Northeast Atlantic (Supplementary Table 2), and thus, identify the major genomic regions associated with seasonal reproduction. The SNP allele frequencies were generated in a previous study16 and were derived from Pool-seq data. For the X2 test, we formed two groups, spring and autumn spawners, and summed the reads supporting the reference and the alternative alleles for the pools included in each group.To characterize genetic diversity at the TSHR locus, we calculated nucleotide diversity (π) and Tajima’s D for the same seven spring- and seven autumn-spawning Atlantic herring populations used in the genome scan (n = ~41–100 per pool) (Supplementary Table 2). The whole-genome re-sequence data of these pools were previously reported by Han et al.16 (for details of the pools used here see Supplementary Table 2). Unbiased nucleotide diversity π and Tajima’s D were calculated for each pool using the program PoPoolation 1.2.236, which accounts for the truncated allele frequency spectrum of pooled data. In brief, a pileup file of chromosome 15 was generated from each pool BAM file using samtools v.1.1037,38. Indels and 5 bp around indels were removed to exclude spurious SNPs due to misalignments around indels. To minimize biases in the π and Tajima’s D calculations, which are sensitive to sequencing errors and coverage fluctuations39, the coverage of each pileup file was subsampled without replacement to a uniform value based on a per-pool coverage distribution (the target coverage corresponded to the 5th percentile of the coverage frequency distribution, which was used as the minimum coverage allowed for an SNP to be included in the analysis) (Supplementary Table 2). We also calculated the diversity parameters but skipping the coverage subsampling step and obtained very similar results with both approaches (Supplementary Fig. 5), thus, we decided to keep working with the subsampled datasets as coverage subsampling is recommended by the software developers36. To exclude spurious SNPs associated with repetitive sequences and copy number variants, we applied a maximum coverage filter equivalent to the per-pool 99th percentile of the coverage frequency distribution (Supplementary Table 2). A minimum base quality of 20, a minimum mapping quality of 20, and a minor allele count of 2 were required to retain high quality SNPs for further analysis. Both, π and Tajima’s D statistics were calculated using a sliding window approach with a window size of 10 kb and a step size of 2 kb (the selected window-step combination offered a good genomic resolution while reducing the noise from single SNPs, after testing windows of 5, 10, 20, 40, 50, and 100 kb for non-overlapping and overlapping windows with a step size equivalent to 20% of the window size). Only windows with a coverage fraction ≥ 0.5 were included in the computations. In addition, we estimated the effective allele frequency difference, or delta allele frequency (dAF), between spring and autumn spawning groups at the TSHR locus using the formula dAF = abs(mean(spring pools)−mean(autumn pools)). For each of the diversity parameters, we assessed whether the mean differences between sets of SNPs within chr 15: 8.85–8.95 Mb (215 SNPs) and outside (214 635 SNPs) the TSHR region were statistically significant among spring- and autumn-spawning groups using a Wilcoxon test. Data postprocessing, statistical tests, and plotting were performed in the R environment40 (for specific parameters used in PoPoolation, see the associated code to this publication).Identification of the 5.2 kb structural variantSequences spanning the entire TSHR gene plus 10 kb upstream and downstream from two reference assemblies, ASM96633v115 and Ch_v2.0.218, were aligned using BLAST41 and the output was subsequently processed with a custom R script40. Repeats were annotated by CENSOR21 for the region harboring the 5.2 kb structural variant. To validate the structural variant, long-range PCR was performed with genomic DNA from two spring- and autumn-spawning Atlantic herring in a 20 μL reaction containing 0.8 mM dNTPs, 0.3 μM each of the forward and reverse 5.2kb-confirm primers (Supplementary Table 1) and 0.75 U PrimeSTAR GXL DNA Polymerase (TaKaRa) following the program: 95 °C for 3 min, 35 cycles of 98 °C for 10 s, 58 °C for 20 s and 68 °C for 2 min 30 s, and a final extension of 10 min at 68 °C.ATAC-seq analysisBSH and brain without BSH were dissected from two spring-spawning herring caught in the Baltic Sea and transported to the lab on dry ice, then kept at –80 °C before nuclei isolation. ATAC-seq libraries were constructed according to the Omni-ATAC protocol with minor modifications42. Briefly, tissue was homogenized in 2 ml homogenization buffer (5 mM CaCl2, 3 mM Mg(Ac)2, 10 mM Tris-HCl (pH = 7.8), 0.017 mM PMSF, 0.17 mM ß-mercaptoethanol, 320 mM Sucrose, 0.1 mM EDTA and 0.1% NP-40) with a Dounce homogenizer on ice. 400 μl suspension was transferred to a 2 ml tube for the density gradient centrifugation with different concentrations of Iodixanol solution. After centrifugation, a 200 μl fraction containing the nuclei band was collected, stained with Trypan blue and counted with a Countess II Automated Cell Counter (Thermo Fisher Scientific). An aliquot of 100,000–200,000 nuclei was used as input in a 50 μl transposition reaction containing 2 X TD buffer and 100 nM assembled Tn5 transposase for a 30-min incubation at 37 °C. Tagmented DNA was purified with a Zymo clean kit (Zymo Research). Purified DNA was used for an initial pre-amplification for 5 cycles, and the additional amplification cycle was determined by qPCR based on the “R vs Cycle Number” plot43. Amplified libraries were purified with a Zymo clean kit again, and library concentrations and qualities were evaluated using the 2200 TapeStation System (Agilent Technologies).ATAC-seq was performed with a MiniSeq High Output Kit (150 cycles) on a MiniSeq instrument (Illumina) and 7–9 million reads were generated for each ATAC-seq library. Quality control, trimming, mapping, and peak calling of the sequenced reads were conducted following the ENCODE ATAC-seq pipeline (https://www.encodeproject.org/atac-seq/). The trimmed reads were aligned to the Atlantic herring reference genome (Ch_v2.0.2)18 with Bowtie244 and the mapping rate was 85–95%. Duplicate reads, reads with low mapping quality and those aligned to the mitochondria genome were removed. The remaining reads (4–5 million) were subjected to peak calling by MACS245, where 22–32 K peaks were called. Sequenced library qualities were further evaluated by calculating the TSS enrichment score and checking the library complexity with the Non-Redundant Fraction (NRF) and PCR Bottlenecking Coefficients (PBC1 and 2). Finally, conserved peaks between two biological replicates were identified by evaluating the irreproducible discovery rate (IDR).Genotyping of six differentiated variants and haplotype analysisAll six genetic variants, including the 5.2 kb structural variant, two non-coding SNPs, two missense SNPs and the copy number variant of C-terminal 22aa repeat, were genotyped in 45 spring-, 67 autumn-spawning Atlantic herring and 13 Pacific herring. TaqMan Custom SNP assays were performed to genotype the four SNPs in 5 μl reactions with a template of 20 ng genomic DNA (ThermoFisher Scientific). Copy number of the C-terminal 22aa repeat was determined by the PCR product size generated with geno22aa primers. Genotyping of the 5.2 kb structural variant was performed in a PCR reaction containing two forward primers (geno5.2kb-1F and geno5.2kb-2F) and one reverse primer (geno5.2kb-R), which generated PCR products with different sizes between spring and autumn spawners. All the primers used for genotyping are listed in Supplementary Table 1.Tissue expression profiles by quantitative PCRTotal RNA was prepared from gonad, heart, spleen, kidney, gills, intestine, hypothalamus and saccus vasculosus (BSH), and brain without BSH (brain) of six adult spring-spawning Atlantic herring using RNeasy Mini Kit (Qiagen). RNA was then reverse transcribed into cDNA with a High-Capacity cDNA Reverse Transcription Kit (ThermoFisher Scientific). TaqMan Gene Expression assay (ThermoFisher Scientific) containing 0.3 μM primers and 0.25 μM TaqMan probe (Integrated DNA Technologies) was performed to compare the relative expression levels of TSHR among different tissues. qPCR with SYBR Green chemistry was used for TSHB and DIO2 in a 10 μl reaction of SYBR Green PCR Master Mix (ThermoFisher Scientific) and 0.3 μM primers, with a program composed of an initial denaturation for 10 min at 95 °C followed by 40 cycles of 95 °C for 15 s and 60 °C for 1 min. Ct values were first normalized to the housekeeping gene ACTIN, then the average expression for each gene in the gonad was assumed to be 1 for the subsequent calculation of the relative expression in other tissues.Plasmid constructsThe coding sequence for the herring single-chain TSH (scTSH) was designed following a strategy previously used for mammalian gonadotropins46 that contained an in-frame fusion of the cDNA sequences (5′–3′) of herring TSH beta subunit (NCBI: XM_012836756.1) and alpha subunit (NCBI: XM_012822755.1) linked by six histidines and then the C-terminal peptide of the hCG beta subunit. The designed sequence should generate a protein with a size of 30.6 kDa. Both scTSH and spring herring TSHR cDNA sequences were synthesized in vitro and cloned in the expression vector pcDNA3.1 by Genscript (Leiden, Netherlands). pcDNA3.1 plasmid expressing human TSHR was kindly provided by Drs. Gilbert Vassart and Sabine Costagliola (Université libre de Bruxelles, Belgium). Then, the spring herring TSHR and human TSHR plasmids were used as templates for site-directed mutagenesis to generate constructs coding for different mutant herring or human TSHRs. Plasmids for the dual-luciferase assay, including pGL4.29[luc2P/CRE/Hygro] containing cAMP response elements (CREs) to drive the transcription of luciferase gene luc2P and pRL-TK monitoring the transfection efficiency, were purchased from Promega. Five ng of each plasmid was used to transform the XL1-Blue competent cells (Agilent), plasmid DNA was subsequently extracted from 200 ml overnight culture of a single transformant clone using an EndoFree plasmid Maxi Kit (Qiagen).Cell cultureChinese hamster ovary (CHO) (ATCC CCL-61) and human embryonic kidney 293 (HEK293) (ATCC CRL-1573) cells were maintained in DMEM supplemented with 5% (CHO) or 10% FBS (HEK293), 100 U/ml penicillin, 100 μg/ml streptomycin and 292 μg/ml l-Glutamine (ThermoFisher Scientific) at 37 °C with 5% CO2. Epithelioma Papulosum Cyprini (EPC) cells (ATCC CRL-2872) were cultured in EMEM (Sigma) supplemented with 10% FBS, 100 U/ml penicillin, 100 μg/ml streptomycin, 292 μg/ml l-Glutamine and 1 mM Sodium Pyruvate (ThermoFisher Scientific) at 26 °C with 5% CO2.Production of recombinant herring scTSHCHO cells were transfected with the scTSH expression plasmid using Lipofectamine 3000 (Invitrogen), stable clones were subsequently selected with 500 μg/ml G418 (Invitrogen) and screened for producing scTSH by western blot using a polyclonal antisera against the sea bass alpha subunit47. A positive clone was expanded in 225 cm2 cell culture flasks (Corning) in culture medium containing 5% FBS until confluence, then the cells were maintained in serum-free DMEM for hormone production for 7 days at 25 °C48. After 7 days, culture medium containing scTSH or without (negative control) was centrifuged at 15000 x g for 15 min and concentrated by ultrafiltration using Centricon Plus-70 / Ultracel PL-30 (Merck Millipore Ltd.). Then, western blotting was performed to confirm TSH production. Concentrated medium containing herring scTSH was denatured at 94 °C for 5 min in 0.1% SDS and 50 mM 2-mercaptoethanol, and then treated with 2.5 units of peptide-N-glycosidase F (Roche Diagnostics) at 37 °C for 2 h in 20 mM sodium phosphate with 0.5% Nonidet P-40, pH 7.5. All samples were run in 12% SDS-PAGE in the reducing condition and transferred to a PVDF membrane (Immobilon P; Millipore Corp.), then blocked overnight with 5% skimmed milk at 4 °C. After blocking, the membrane was incubated with polyclonal antisera against the sea bass alpha subunit (dilution 1:2000) for 90 min at room temperature, washed, and then further incubated with 1:25000 goat anti-rabbit immunoglobulin G (IgG) horseradish peroxidase conjugate (Bio-Rad Laboratories) for 60 min at room temperature. Immunodetection was performed by chemiluminescence with a Pierce ECL Plus Western Blotting Substrate kit (ThermoFisher Scientific).Cell surface expressionA Rhotag (MNGTEGPNFYVPFSNKTGVVYEE) was inserted at the N-terminus of herring TSHR for flow cytometry analysis of receptor cell surface expression. Anti-Rhotag polyclonal antibody was kindly provided by Drs. Gilbert Vassart and Sabine Costagliola (Université Libre de Bruxelles, Brussels, Belgium). PBS containing 1% BSA and 0.05% sodium azide was prepared as the flow cytometry (FCM) buffer for the washing and antibody incubation steps. 2.2 × 106 EPC cells were seeded in a 100 mm poly-d-Lysine-treated petri dish the day before transfection. Each dish was transfected with 10 μg TSHR or empty pcDNA3.1 expression plasmid using 20 μl jetPRIME transfection reagent in 500 μl jetPRIME transfection buffer (Polyplus transfection). Cells were harvested 24 h after transfection, then washed once in cold PBS and fixed in 2% PFA for 10 min at room temperature. After fixation, cells were washed three times with FCM buffer, then incubated with anti-Rhotag antibody or FCM buffer (negative control) for 1 h at room temperature. Cells were washed again with FCM buffer three times and stained with Alexa Fluor 488-labeled chicken anti-mouse IgG (H + L) antibody (1:200 dilution, ThermoFisher Scientific) or FCM buffer (negative control) for 45 min in the dark. After the fluorescent staining, cells were washed three times and resuspended in FCM buffer before analysis on a CytoFLEX instrument (Beckman Coulter). A minimum of 100,000 events was recorded for each sample, fluorescence intensities of negative control and cells transfected with empty pcDNA3.1 plasmid were used as the background for gating strategy. Cell surface expression was represented by the mean fluorescence intensity of the positively stained cell population.Dual-luciferase reporter assayEPC or HEK293 cells were plated in a 48-well plate at a density of 1 × 105 cells/well the day before transfection. A total of 250 ng plasmid mixture containing pGL4.29[luc2P/CRE/Hygro], TSHR expression plasmid (or empty pcDNA3.1) and pRL-TK with the ratio of 20:5:1 was prepared to transfect each well of cells using jetPRIME transfection reagent (Polyplus). Medium was replaced by fresh medium containing 10% FBS (TSH-induced condition) or serum-free medium (constitutive activity condition) 4 h after transfection. On day three, cells were treated with serum-free medium containing different dilutions of the concentrated scTSH medium for 4 h (TSH-induced condition) or directly subjected to the luminescence measurement without TSH induction (constitutive activity condition). Luminescence was measured using a Dual-Luciferase Reporter assay (Promega) on an Infinite M200 Microplate Reader (Tecan Group Ltd., Switzerland), and luciferase activity was represented as the ratio of firefly (pGL4.29[luc2P/CRE/Hygro]) to Renilla (pRL-TK) luminescence.5′-RACE to identify the herring DIO2 TSSTotal RNA was prepared from brain of a spring-spawning Atlantic herring using the RNeasy Mini Kit (Qiagen). Six μg of the isolated RNA was used for 5′-RACE with a FirstChoiceTM RLM-RACE Kit (ThermoFisher Scientific). One μl cDNA or Outer RACE PCR product was used as PCR template in a 20 μL reaction containing 0.8 mM dNTPs, 0.3 μM of each forward and reverse primer (Supplementary Table 1) and 0.75 U PrimeSTAR GXL DNA Polymerase (TaKaRa). Amplification was carried out with an initial denaturation of 3 min at 95 °C, followed by 35 cycles of 98 °C for 10 s, 58 °C for 20 s and 68 °C for 40 s, and a final extension of 10 min at 68 °C. The final 5′ RACE product was sequenced at Eurofins Genomics (Ebersberg, Germany).Sequence conservation analysisGenomic sequences covering the TSHR locus were extracted from Ensembl Genome Browser for Atlantic herring and 11 other fish species, including Amazon molly (Poecilia formosa), denticle herring (Denticeps clupeoides), goldfish (Carassius auratus), guppy (Poecilia reticulata), Neolamprologus brichardi, Japanese medaka (Oryzias latipes), northern pike (Esox lucius), orange clownfish (Amphiprion percula), spotted gar (Lepisosteus oculatus), three-spined stickleback (Gasterosteus aculeatus) and spotted green pufferfish (Tetraodon nigroviridis). The extracted sequences were firstly aligned using progressiveCactus49,50, and a subsequent alignment was generated using the hal2maf program from halTools51 with Atlantic herring assembly (Ch_v2.0.2)18 as the co-ordinate backbone. This alignment was used for the downstream phastCons score calculation by running phyloFit24 and phastCons25 from the PHAST package with default parameters. Peaks were called by grouping signals with a minimum phastCons score of 0.2 within 500 bp region.Structure modeling of human and herring TSHRsIn order to explore the possible interactions of the variant residues with other receptor interacting proteins and to study intramolecular interactions, we built a structural homology model for the herring TSHR (herrTSHR) complexed with herring TSH and Gs-protein. The TSHR hinge region that harbors the Q370H substitution and the C-terminus containing the 22aa repeat were excluded from the homology model due to the lack of structural templates for these regions. The homology model was constructed by using the following structural templates of evolutionarily related class A GPCRs: (i) the leucine-rich repeat domain (LRRD) complexed with hormone was modeled based on the solved FSHR LRRD – FSH complex structure (Protein Data Bank (PDB) ID: 4AY9)52,53, this part of model included herring TSHR Cys33 – Asn296 and fragments of the hinge region Gln297 – Thr312 and Ser393 – Ile421; (ii) the available structural complex of β2-adrenoreceptor with Gs-protein (PDB ID: 3SN6)54 was used as the template to model the seven-transmembrane helix domain (7TMD) of herring TSHR in the active conformation; (iii) the extracellular loop 2 (ECL2) was built by using the ECL2 of μ-opioid receptor (PDB ID: 6DDE)55. To prepare the template for herring TSHR modeling, the fused T4-lysozyme and bound ligand of β2-adrenoreceptor were deleted, the ECL1 and ECL3 loops were adjusted manually to the loop length of herring TSHR. Due to the lack of third intracellular loop (ICL3) in the β2-adrenoreceptor structure, amino acid residues of herring TSHR ICL3 were manually added to the template. Since herring TSHR does not have the TMH5 proline, which is highly conserved among all class A GPCRs and responsible for the helical kinks and bulges within this region56, we assumed a rather regular (stretched) helix conformation for the herring TSHR TMH5 and therefore replaced the kinked β2-adrenoreceptor TMH5 template with a regular α-helix. Moreover, the ECL2 template was substituted with μOR ECL2 structure because of its higher sequence similarity with herring TSHR in this region. Finally, amino acid residues of this chimeric 7TMD template and FSHR N-terminus were mutated to the corresponding spring herring TSHR residues and sequence of the heterodimeric FSH ligand was substituted by the herring TSH. All homology models were generated by using SYBYL-X 2.0 (Certara, NJ, US). The 7TMD structure was then fused with FSHR N-terminus at position 421. The assembled complex was subsequently optimized by the energy minimization under constrained backbone atoms (the AMBER F99 force field was used), followed by a 2 ns molecular dynamics simulation (MD) of the side chains. The entire TSHR complex was energetically minimized without any constraints until converging at a termination gradient of 0.05 kcal/mol*Å. Next, for autumn herrTSHR modeling, the spring TSHR sequence was substituted with autumn TSHR sequence. For humTSHR, the spring TSHR sequence was substituted with human TSHR, and the herring TSH ligand was replaced by the bovine TSH sequence. Both complex models were energetically minimized until converging at a termination gradient of 0.05 kcal/mol*Å.To investigate the microenvironment around the L471M mutation at TMH2 position 2.51, local short MD’s of 4 ns on Met4712.51 (spring herrTSHR), Leu471 (autumn herrTSHR) or Phe461 (humTSHR) and its surrounding amino acids were performed. During MD simulations, backbone atoms of the entire complexes as well as all side chains, except residues at positions 1.47, 1.51, 1.54, 2.48, 2.52, and 2.55 that form the hydrophobic patch around position 2.51, were constrained.Statistics and reproducibilityResults were presented as the mean + SD (standard deviation) calculated from at least four biological replicates for each experiment, and at least two independent experiments were conducted for each assay. Unpaired two-tailed Student’s t test was performed to calculate the P-values and means were judged as statistically significant when P ≤ 0.05.Reporting summaryFurther information on research design is available in the Nature Research Reporting Summary linked to this article. More

1.Clapham, P. J. in Encyclopedia of marine mammals 489–492 (Elsevier, 2018).2.Stevick, P. T. et al. A quarter of a world away: female humpback whale moves 10 000 km between breeding areas. Biol. Lett. 7, 299–302 (2010).PubMed 
PubMed Central 
Article 

Google Scholar 
3.Nicol, S. et al. Southern Ocean iron fertilization by baleen whales and Antarctic krill. Fish. Fish. 11, 203–209 (2010).Article 

Google Scholar 
4.Smetacek, V. & Nicol, S. Polar ocean ecosystems in a changing world. Nature 437, 362–368 (2005).CAS 
PubMed 
Article 

Google Scholar 
5.Dunlop, R. A. Potential motivational information encoded within humpback whale non-song vocal sounds. J. Acoustical Soc. Am. 141, 2204–2213 (2017).Article 

Google Scholar 
6.Stimpert, A. K., Au, W. W. L., Parks, S. E., Hurst, T. & Wiley, D. N. Common humpback whale (Megaptera novaeangliae) sound types for passive acoustic monitoring. J. Acoustical Soc. Am. 129, 476–482 (2011).Article 

Google Scholar 
7.Van Opzeeland, I., Van Parijs, S., Kindermann, L., Burkhardt, E. & Boebel, O. Calling in the cold: pervasive acoustic presence of humpback whales (Megaptera novaeangliae) in Antarctic coastal waters. PLoS ONE 8, 1–7 (2013).
Google Scholar 
8.Siegel, V. Biology and Ecology of Antarctic Krill. (Springer, 2016).9.Atkinson, A. et al. Krill (Euphausia superba) distribution contracts southward during rapid regional warming. Nat. Clim. Change 9, 142–147 (2019).Article 

Google Scholar 
10.Loeb, V. J., Hofmann, E. E., Klinck, J. M., Holm-Hansen, O. & White, W. B. ENSO and variability of the Antarctic Peninsula pelagic marine ecosystem. Antarctic Sci. https://doi.org/10.1017/s0954102008001636 (2009).11.Loeb, V. J. & Santora, J. A. Climate variability and spatiotemporal dynamics of five Southern Ocean krill species. Prog. Oceanogr. 134, 93–122 (2015).Article 

Google Scholar 
12.Bombosch, A. et al. Predictive habitat modelling of humpback (Megaptera novaeangliae) and Antarctic minke (Balaenoptera bonaerensis) whales in the Southern Ocean as a planning tool for seismic surveys. Deep-Sea Res. Part I: Oceanographic Res. Pap. 91, 101–114 (2014).Article 

Google Scholar 
13.Brierley, A. S. et al. Antarctic krill under sea ice: elevated abundance in a narrow band just south of ice edge. Science 295, 1890–1892 (2002).CAS 
PubMed 
Article 

Google Scholar 
14.Rettig, S. et al. in 1st International Conference and Exhibition on Underwater Acoustics. (eds Papadakis, J. & Bjorno, L.) 1669–1674 (2013).15.Garland, E. C. et al. Humpback whale song on the Southern Ocean feeding grounds: Implications for cultural transmission. PLoS ONE 8, e79422 (2013).PubMed 
PubMed Central 
Article 
CAS 

Google Scholar 
16.Stimpert, A. K., Peavey, L. E., Friedlaender, A. S. & Nowacek, D. P. Humpback whale song and foraging behavior on an Antarctic feeding ground. PLoS ONE 7, e51214 (2012).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
17.Filun, D. et al. Frozen verses: Antarctic minke whales (Balaenoptera bonaerensis) call predominantly during austral winter. R. Soc. Open Sci. 7, 192112 (2020).PubMed 
PubMed Central 
Article 

Google Scholar 
18.Boebel, O. The Expedition PS89 of the Research Vessel POLARSTERN to the Weddell Sea in 2014/2015. (Alfred Wegener Institute, Helmholtz Centre for Polar and Marine Research, Bremerhaven, 2015).19.Burkhardt, E. Whale sightings during Polarstern cruise PS96 (ANT-XXXI/2), https://doi.org/10.1594/PANGAEA.923113 (2020).20.Herr, H., Viquerat, S. & Siebert, U. Aerial cetacean survey Southern Ocean 2014/2015, https://doi.org/10.1594/PANGAEA.894938 (2018).21.Herr, H., Viquerat, S. & Siebert, U. Ship based cetacean survey Southern Ocean 2014/2015, https://doi.org/10.1594/PANGAEA.894873 (2018).22.National Oceanic and Atmospheric Administration & Department of Commerce. Climate Prediction Centre (CPC) Oceanic Nino Index (2019).23.Thomisch, K. et al. Spatio-temporal patterns in acoustic presence and distribution of Antarctic blue whales Balaenoptera musculus intermedia in the Weddell Sea. Endanger. Species Res. 30, 239–253 (2016).Article 

Google Scholar 
24.Širović, A. et al. Seasonality of blue and fin whale calls and the influence of sea ice in the Western Antarctic Peninsula. Deep Sea Res. Part II: Topical Stud. Oceanogr. 51, 2327–2344 (2004).Article 

Google Scholar 
25.Schall, E. et al. Large-scale spatial variabilities in the humpback whale acoustic presence in the Atlantic sector of the Southern Ocean. R. Soc. Open Sci. 7, 201347 (2020).PubMed 
PubMed Central 
Article 

Google Scholar 
26.Loeb, V., Hofmann, E. E., Klinck, J. M. & Holm-Hansen, O. Hydrographic control of the marine ecosystem in the South Shetland-Elephant Island and Bransfield Strait region. Deep Sea Res. Part II: Topical Stud. Oceanogr. 57, 519–542 (2010).Article 

Google Scholar 
27.Sallée, J.-B., Speer, K. & Rintoul, S. Zonally asymmetric response of the Southern Ocean mixed-layer depth to the Southern Annular Mode. Nat. Geosci. 3, 273–279 (2010).Article 
CAS 

Google Scholar 
28.Kim, Y. S. & Orsi, A. H. On the variability of Antarctic Circumpolar Current fronts inferred from 1992–2011 altimetry. J. Phys. Oceanogr. 44, 3054–3071 (2014).Article 

Google Scholar 
29.Yuan, X. ENSO-related impacts on Antarctic sea ice: a synthesis of phenomenon and mechanisms. Antarct. Sci. 16, 415 (2004).Article 

Google Scholar 
30.Meredith, M. P., Murphy, E. J., Hawker, E. J., King, J. C. & Wallace, M. I. On the interannual variability of ocean temperatures around South Georgia, Southern Ocean: Forcing by El Niño/Southern Oscillation and the southern annular mode. Deep Sea Res. Part II: Topical Stud. Oceanogr. 55, 2007–2022 (2008).Article 

Google Scholar 
31.Lovenduski, N. S. & Gruber, N. Impact of the Southern Annular Mode on Southern Ocean circulation and biology. Geophys. Res. Lett. 32, 1–4 (2005).Article 

Google Scholar 
32.Craig, A. S., Herman, L. M., Gabriele, C. M. & Pack, A. A. Migratory timing of humpback whales (Megaptera novaeangliae) in the central north Pacific varies with age, sex and reproductive status. Behaviour 140, 981–1001 (2003).Article 

Google Scholar 
33.Hofmann, E. E., Klinck, J. M., Locarnini, R. A., Fach, B. & Murphy, E. Krill transport in the Scotia Sea and environs. Antarct. Sci. 10, 406–415 (1998).Article 

Google Scholar 
34.Barendse, J. et al. Migration redefined? Seasonality, movements and group composition of humpback whales Megaptera novaeangliae off the west coast of South Africa. Afr. J. Mar. Sci. 32, 1–22 (2010).Article 

Google Scholar 
35.Witteveen, B. H., Foy, R. J., Wynne, K. M. & Tremblay, Y. Investigation of foraging habits and prey selection by humpback whales (Megaptera novaeangliae) using acoustic tags and concurrent fish surveys. Mar. Mammal. Sci. 24, 516–534 (2008).Article 

Google Scholar 
36.Brown, M. R., Corkeron, P. J., Hale, P. T., Schultz, K. W. & Bryden, M. M. Evidence for a sex-segregated migration in the humpback whale (Megaptera novaeangliae). Proc. R. Soc. Lond. B 259, 229–234 (1995).CAS 
Article 

Google Scholar 
37.International Whaling Commission. Report on the workshop on the comprehensive assessment of Southern Hemisphere humpback whales. J. Cetacea. Res. Manag. Spec. Issue 3, 1–50 (2011).
Google Scholar 
38.Findlay, K. P. et al. Humpback whale “super-groups” – A novel low-latitude feeding behaviour of Southern Hemisphere humpback whales (Megaptera novaeangliae) in the Benguela Upwelling System. PLOS ONE 12, e0172002 (2017).PubMed 
PubMed Central 
Article 
CAS 

Google Scholar 
39.Gridley, T., Silva, M., Wilkinson, C., Seakamela, S. & Elwen, S. H. Song recorded near a super-group of humpback whales on a mid-latitude feeding ground off South Africa. J. Acoustical Soc. Am. 143, EL298–EL304 (2018).CAS 
Article 

Google Scholar 
40.Ross-Marsh, E., Elwen, S., Prinsloo, A., James, B. & Gridley, T. Singing in South Africa: monitoring the occurrence of humpback whale (Megaptera novaeangliae) song near the Western Cape. Bioacoustics, 1–17, https://doi.org/10.1080/09524622.2019.1710254 (2020).41.Cai, W. et al. Increasing frequency of extreme El Niño events due to greenhouse warming. Nat. Clim. Change 4, 111–116 (2014).Article 

Google Scholar 
42.Bengtson Nash, S. M. et al. Signals from the south; humpback whales carry messages of Antarctic sea‐ice ecosystem variability. Glob. Change Biol. 24, 1500–1510 (2018).Article 

Google Scholar 
43.Baumgartner, M. F. & Mussoline, S. E. A generalized baleen whale call detection and classification system. J. Acoustical Soc. Am. 129, 2889–2902 (2011).Article 

Google Scholar 
44.Klinck, H. et al. Long-range underwater vocalizations of the crabeater seal (Lobodon carcinophaga). J. Acoustical Soc. Am. 128, 474–479 (2010).Article 

Google Scholar 
45.Risch, D. et al. Mysterious bio-duck sound attributed to the Antarctic minke whale (Balaenoptera bonaerensis). Biol. Lett. 10, 20140175 (2014).PubMed 
PubMed Central 
Article 

Google Scholar 
46.Schall, E. & Van Opzeeland, I. Calls produced by Ecotype C killer whales (Orcinus orca) off the Eckstrom iceshelf, Antarctica. Aquat. Mamm. 43, 117–126 (2017).Article 

Google Scholar 
47.Van Opzeeland, I. et al. Acoustic ecology of Antarctic pinnipeds. Mar. Ecol. Prog. Ser. 414, 267–291 (2010).Article 

Google Scholar 
48.Dunlop, R. A., Cato, D. H. & Noad, M. J. Non-song acoustic communication in migrating humpback whales (Megaptera novaeangliae). Mar. Mammal. Sci. 24, 613–629 (2008).Article 

Google Scholar 
49.Schall, E. & El-Gabbas, A. Humpback-whale-acoustic-detection-and-environmental-modelling, https://github.com/elenaschall/Humpback-whale-acoustic-detection-and-environmental-modelling (GitHub, GitHub, 2021).50.Bioacoustics, Research & Program. Raven Pro: Interactive Sound Analysis Software (Version 1.5) http://ravensoundsoftware.com/ (The Cornell Lab of Ornithology, Ithaca, NY, 2014).51.Cavalieri, D., Parkinson, C., Gloersen, P. & Zwally, H. Sea ice concentrations from Nimbus-7 SMMR and DMSP SSM/I-SSMIS passive microwave data, version 1. Boulder, Colorado USA, NASA National Snow and Ice Data Center Distributed Active Archive Center 10, https://doi.org/10.5067/8GQ8LZQVL0VL (1996).52.Greene, C. A. Daily Antarctic sea ice concentration (2020).53.Marshall, G. J. Trends in the southern annular mode from observations and reanalyses. J. Clim. 16, 4134–4143 (2003).Article 

Google Scholar 
54.Marshall, G. & National Center for Atmospheric Research Staff (Eds). The climate data guide: Marshall Southern Annular Mode (SAM) index (Station-based), (2019).55.R Core Team. R: A language and environment for statistical computing, https://www.R-project.org/ (R Foundation for Statistical Computing, Vienna, Austria, 2018)56.National Oceanic and Atmospheric Administration (NOAA) & Climate Prediction Centre (CPC). Oceanic Nino Index (2019).57.Wood, S. N. Generalized additive models: an introduction with R (CRC press, 2017).58.Pinheiro, J., Bates, D., DebRoy, S. & Sarkar, D. _nlme: Linear and nonlinear mixed effects models. R package version 3.1-145 (R CoreTeam, 2020).59.Hyndman, R. et al. forecast: Forecasting functions for time series and linear models_. R. package version 8.11, http://pkg.robjhyndman.com/forecast (2020)..60.Schall, E. et al. Humpback whale acoustic presence in the Atlantic sector of the Southern Ocean, https://doi.org/10.5061/dryad.ncjsxkss0 (2021).61.Wessel, P. & Smith, W. H. A global, self‐consistent, hierarchical, high‐resolution shoreline database. J. Geophys. Res.: Solid Earth 101, 8741–8743 (1996).Article 

Google Scholar 
62.Amante, C. & Eakins, B. W. ETOPO1 arc-minute global relief model: procedures, data sources and analysis, https://doi.org/10.7289/V5C8276M (2009).63.Spreen, G., Kaleschke, L. & Heygster, G. Sea ice remote sensing using AMSR-E 89-GHz channels. J. Geophys Res. Oceans 113, C02S03 (2008).Article 

Google Scholar  More

Study areaFour major river basins, located across different sub-regions of SSA, were selected for this study: Limpopo, Omo-Turkana, Volta, and Zambezi (Fig. 1). These basins were selected to (i) foster inclusion of enable different African regions and (ii) ensure focus on basins with sufficient data availability.Figure 1source malaria data23 on ArcGIS software (version 10.5. 1, Environmental Systems Research Institute Inc, Redlands, CA, USA, 2016)].Distribution of large and small dams in Limpopo, Volta, Zambezi and Omo-Turkana basins by malaria stability zone. [The figure was made using open-Full size imageThe Limpopo River basin is located in southern Africa. Draining an area of approximately 408,000 km2, the Limpopo River basin is distributed among South Africa (45%), Botswana (20%), Zimbabwe (15%) and Mozambique (20%). About 14 million people live in this basin. The climate of the Limpopo River basin varies along the path of the river from a temperate climate in the west to a subtropical climate at the river mouth in Mozambique. The hydrology of the Limpopo River basin is influenced by the highly seasonal distribution of rainfall over the catchment. About 95% of rain falls between October and April with a peak normally in February. Temperature varies from 30 to 34 °C in summer and 22–26 °C in winter15.The Volta River basin is located in West Africa with a population of over 23 million. Draining an area of 409,000 km2 the basin is spread across six countries: Benin (4%), Burkina Faso (42%), Cote d’Ivoire (3%), Ghana (41%), Mali (4%) and Togo (6%). Average annual rainfall varies across the basin from approximately 1600 mm in the southeast, to about 360 mm in the north. Annual mean temperatures in the basin vary from 27 to 30 °C16. The main rainy season is between March and October.The Zambezi River basin is located in southern Africa. Draining an area of 1.34 million km2, the basin is spread across eight countries: Angola (19%), Botswana (1%), Namibia (1%) Benin (4%), Zimbabwe (16%), Zambia (42%), Tanzania (2%), Malawi (8%) and Mozambique (12%). The population of the Zambezi basin is estimated to be about 32 million. Annual rainfall in the basin ranges from 550 mm in the south to 1800 mm in the north. The annual mean temperatures ranges from 18 °C at higher elevations in the south of the basin to 26 °C for low elevations in the delta in Mozambique17.The Omo-Turkana Basin covers approximately 131,000 km2, stretching from southern Ethiopia to northern Kenya. Hydrologically, the basin is dominated by Lake Turkana, with the Omo River, which drains the Ethiopian portion of the basin, supplying 90% of the inflow to the lake. The basin is home to approximately 15 million people, the majority of whom live in the Ethiopian highlands, in the north. The annual mean temperature ranges from 24 °C in the north to 29 °C in the south. The mean annual rainfall ranges from 250 mm in the south to 500 mm in the north18.Data sourcesDam dataSmall damsData on location and size of small dams are not readily available in either global or regional data sets. The European Commission’s Joint Research Center (JRC) Yearly Water Classification History v1.0 data set was used to identify water bodies in each of the four basins19. Water bodies less than 100 ha and greater than 2 ha were identified. All were checked with Google Earth images to distinguish between reservoirs and natural water bodies (Supplementary Fig. S1). Ultimately, a total of 4907 small dams located in the four basins were identified and included in the analyses.Large damsFor large dams, the FAO African Dams Database20, International Commission for Large dams (ICOLD)21 and the International Rivers Database22, which together contain 1286 georeferenced African large dams, were utilized. The accuracy of dam locations was first verified with Google Earth. When the location of a dam did not precisely match the coordinates stipulated in either of the two databases, manual corrections were made by adjusting the coordinates of a dam to its location as shown in Google Earth (see Supplementary Information). Dams for which precise locations could not be determined, as well as dams without reservoirs (i.e., run-of-river schemes), were removed. Ultimately, across the four basins, a total of 258 large dams with confirmed georeferenced locations were identified and included in the analyses.Perimeters of large and small dam reservoirsReservoir perimeters of both large and small dams were extracted from the European Commission’s Joint Research Center (JRC) global surface water datasets19, published through the Google Earth Engine. This dataset includes maps of the location and temporal variability in maximum perimeter records of the global surface water coverage from 1984 to 2015. In this study, the maximum perimeter records were used in each year of 2000, 2005, 2010 and 2015. The data were exported to ArcGIS.Data on anopheles mosquito distributionData for vector distribution were obtained from the Malaria Atlas Project (MAP) database23. The MAP database contains a georeferenced illustration of the major malaria vector species in different malaria-endemic areas in Africa.Malaria dataAnnual malaria incidence data were obtained from the MAP database. We acquired data for the years 2000, 2005, 2010 and 2015. These years were selected to align with updates to Worldpop population data24, which are recomputed every five years. MAP produced a 1 km resolution continuous map of annual malaria incidence for Africa based on 33,761 studies across the region. We imported these data to ArcGIS for analyses. Annual malaria incidence was determined as the number of cases per 1000 population. To ascertain the impact of dams on malaria incidence rates as a function of distance from the reservoir perimeter, we created two distance zones: 0–5 km (at risk) and 5–10 km (control). When distance zones were overlapping for two or more nearby dams, areas were assigned to the closest distance cohort. Populations residing more than 5 km from a reservoir perimeter (large or small) were considered to be free of risk from dam induced malaria transmission because the maximum mosquitoes’ flight range is considered to be  0.1 malaria cases per 1000 population), unstable (≤ 0.1 malaria cases per 1000 population) and no malaria (zero malaria incidence) based on the level of malaria incidence in each of the four years: 2000, 2005, 2010, and 2015. The number of dams in each of the three stability categories for each of the four years was determined, as well as the population at-risk of dam-related malaria (i.e.,  More

1.Lejeusne, C., Chevaldonné, P., Pergent-Martini, C., Boudouresque, C. F. & Pérez, T. Climate change effects on a miniature ocean: The highly diverse, highly impacted Mediterranean Sea. Trends Ecol. Evol. 25, 250–260 (2010).PubMed 
Article 
PubMed Central 

Google Scholar 
2.Fulton, E. A. et al. Modelling marine protected areas: Insights and hurdles. Philos. Trans. R. Soc. B Biol. Sci. 370, 20140278 (2015).Article 

Google Scholar 
3.Roff, J. & Zacharias, M. Marine Conservation Ecology (Earthscan, 2011).
Google Scholar 
4.Mitcheson, Y. S. D. et al. A global baseline for spawning aggregations of reef fishes. Conserv. Biol. 22, 1233–1244 (2008).Article 

Google Scholar 
5.Salinas-de-León, P., Rastoin, E. & Acuña-Marrero, D. First record of a spawning aggregation for the tropical eastern Pacific endemic grouper Mycteroperca olfax in the Galapagos Marine Reserve. J. Fish Biol. 87, 179–186 (2015).PubMed 
Article 
PubMed Central 

Google Scholar 
6.Bueno, L. S. et al. Evidence for spawning aggregations of the endangered Atlantic goliath grouper Epinephelus itajara in southern Brazil. J. Fish Biol. 89, 876–889 (2016).CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
7.Kumar, V. et al. Biological clocks and regulation of seasonal reproduction and migration in birds. Physiol. Biochem. Zool. 83, 827–835 (2010).ADS 
PubMed 
Article 
PubMed Central 

Google Scholar 
8.van Haren, H. & Compton, T. J. Diel vertical migration in deep sea plankton is finely tuned to latitudinal and seasonal day length. PLoS ONE 8, e64435 (2013).ADS 
PubMed 
PubMed Central 
Article 

Google Scholar 
9.Horký, P. & Slavík, O. Diel and seasonal rhythms of asp Leuciscus aspius (L.) in a riverine environment. Ethol. Ecol. Evol. 29, 449–459 (2017).Article 

Google Scholar 
10.Falcón, J., Besseau, L., Sauzet, S. & Boeuf, G. Melatonin effects on the hypothalamo–pituitary axis in fish. Trends Endocrinol. Metab. 18, 81–88 (2007).PubMed 
Article 
CAS 
PubMed Central 

Google Scholar 
11.Oliveira, C. et al. Monthly day/night changes and seasonal daily rhythms of sexual steroids in Senegal sole (Solea senegalensis) under natural fluctuating or controlled environmental conditions. Comp. Biochem. Physiol. A: Mol. Integr. Physiol. 152, 168–175 (2009).ADS 
Article 
CAS 

Google Scholar 
12.Wuitchik, D. M. et al. Seasonal temperature, the lunar cycle and diurnal rhythms interact in a combinatorial manner to modulate genomic responses to the environment in a reef-building coral. Mol. Ecol. 28, 3629–3641 (2019).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
13.Sanchez-Cardenas, C. et al. Pituitary growth hormone network responses are sexually dimorphic and regulated by gonadal steroids in adulthood. PNAS 107, 21878–21883 (2010).ADS 
CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
14.Stock, C. A. et al. Seasonal sea surface temperature anomaly prediction for coastal ecosystems. Prog. Oceanogr. 137, 219–236 (2015).ADS 
Article 

Google Scholar 
15.Bisagni, J. J. Salinity variability along the eastern continental shelf of Canada and the United States, 1973–2013. Cont. Shelf Res. 126, 89–109 (2016).ADS 
Article 

Google Scholar 
16.Brown, J. H., Gillooly, J. F., Allen, A. P., Savage, V. M. & West, G. B. Toward a metabolic theory of ecology. Ecology 85, 1771–1789 (2004).Article 

Google Scholar 
17.Dorts, J. et al. Evidence that elevated water temperature affects the reproductive physiology of the European bullhead Cottus gobio. Fish Physiol. Biochem. 38, 389–399 (2012).CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
18.Arfuso, F. et al. Water temperature influences growth and gonad differentiation in European sea bass (Dicentrarchus labrax, L. 1758). Theriogenology 88, 145–151 (2017).CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
19.Aspillaga, E. et al. Thermal stratification drives movement of a coastal apex predator. Sci. Rep. 7, 526 (2017).ADS 
PubMed 
PubMed Central 
Article 
CAS 

Google Scholar 
20.Martin, T. L. & Huey, R. B. Why, “suboptimal” is optimal: Jensen’s inequality and ectotherm thermal preferences. Am. Nat. 171, E102–E118 (2008).PubMed 
Article 
PubMed Central 

Google Scholar 
21.Freitas, C., Olsen, E. M., Moland, E., Ciannelli, L. & Knutsen, H. Behavioral responses of Atlantic cod to sea temperature changes. Ecol. Evol. 5, 2070–2083 (2015).PubMed 
PubMed Central 
Article 

Google Scholar 
22.Harmelin, J.-G. & Marinopoulos, J. Recensementde la Population de corbs (Sciaena umbra, Linneaus1758: Pisces) du Parc National de Port-Cros (Méditerrannée, France) par Inventaires Visuels 265–275 (1993).23.Coll, J., Linde, M., García-Rubies, A., Riera, F. & Grau, A. M. Spear fishing in the Balearic Islands (west central Mediterranean): Species affected and catch evolution during the period 1975–2001. Fish. Res. 70, 97–111 (2004).Article 

Google Scholar 
24.Lloret, J. et al. Spearfishing pressure on fish communities in rocky coastal habitats in a Mediterranean marine protected area. Fish. Res. 94, 84–91 (2008).Article 

Google Scholar 
25.Harmelin, J.-G. Statut du corb (Sciaena umbra) en méditerranée. In Les Espèces Marines à Protéger en Méditerranée (eds Boudouresque, C. F. et al.) 219–227 (GiS Posidonie Publ., 1991).
Google Scholar 
26.Mayol, J., Grau, A. M., Riera, F. & Oliver, J. Llista Ver-mella dels Peixos de les Balears (2000).
Google Scholar 
27.Chao, L. The IUCN Red List of Threatened Species 2020: e.T198707A130230194Sciaena umbra (2020). https://doi.org/10.2305/IUCN.UK.2020-2.RLTS.T198707A130230194.en.28.Forcada, A. et al. Effects of habitat on spillover from marine protected areas to artisanal fisheries. Mar. Ecol. Prog. Ser. 379, 197–211 (2009).ADS 
Article 

Google Scholar 
29.Franco, A. D., Bussotti, S., Navone, A., Panzalis, P. & Guidetti, P. Evaluating effects of total and partial restrictions to fishing on Mediterranean rocky-reef fish assemblages. Mar. Ecol. Prog. Ser. 387, 275–285 (2009).ADS 
Article 

Google Scholar 
30.Le Préfet de la region Provence-Alpes-Côte d’Azur, Préfet de la zone de défense et de sécurité Sud, Préfet des Bouches-du-Rhône. http://www.dirm.mediterranee.developpement-durable.gouv.fr/IMG/pdf/ap_corb_med_continentale_20_dec_2018-2.pdf (Accessed 20 December 2018).31.Harmelin-Vivien, M. et al. Effects of reserve protection level on the vulnerable fish species Sciaena umbra and implications for fishing management and policy. Glob. Ecol. Conserv. 3, 279–287 (2015).Article 

Google Scholar 
32.Chakroun-Marzouk, N. & Ktari, M.-H. Le Corb des côtes Tunisiennes, Sciaena umbra (Sciaenidae): Cycle Sexuel, Age et Croissance 15 (2003).33.Derbal, F. & Kara, M. H. Régime Alimentaire du corb Sciaena umbra (Sciaenidae) des côtes de l’est Algérien 9 (2007).34.Engin, S. & Seyhan, K. Age, growth, sexual maturity and food composition of Sciaena umbra in the south-eastern Black Sea, Turkey. J. Appl. Ichthyol. 25, 96–99 (2009).Article 

Google Scholar 
35.Botsford, L. W. et al. Connectivity, sustainability, and yield: Bridging the gap between conventional fisheries management and marine protected areas. Rev. Fish Biol. Fish. 19, 69–95 (2009).Article 

Google Scholar 
36.Alós, J. & Cabanellas-Reboredo, M. Experimental acoustic telemetry experiment reveals strong site fidelity during the sexual resting period of wild brown meagre, Sciaena umbra. J. Appl. Ichthyol. 28, 606–611 (2012).Article 

Google Scholar 
37.Jadot, C., Donnay, A., Acolas, M. L., Cornet, Y. & Bégout Anras, M. L. Activity patterns, home-range size, and habitat utilization of Sarpa salpa (Teleostei: Sparidae) in the Mediterranean Sea. ICES J. Mar. Sci. 63, 128–139 (2006).Article 

Google Scholar 
38.Jorgensen, S. J. et al. Limited movement in blue rockfish Sebastes mystinus: Internal structure of home range. Mar. Ecol. Prog. Ser. 327, 157–170 (2006).ADS 
Article 

Google Scholar 
39.Kerwath, S. E., Götz, A., Attwood, C. G., Sauer, W. H. H. & Wilke, C. G. Area utilisation and activity patterns of roman Chrysoblephus laticeps (Sparidae) in a small marine protected area. Afr. J. Mar. Sci. 29, 259–270 (2007).Article 

Google Scholar 
40.Collins, A. B., Heupel, M. R. & Motta, P. J. Residence and movement patterns of cownose rays Rhinoptera bonasus within a south-west Florida estuary. J. Fish Biol. 71, 1159–1178 (2007).Article 

Google Scholar 
41.Abecasis, D. & Erzini, K. Site fidelity and movements of gilthead sea bream (Sparus aurata) in a coastal lagoon (Ria Formosa, Portugal). Estuar. Coast. Shelf Sci. 79, 758–763 (2008).ADS 
Article 

Google Scholar 
42.Afonso, P. et al. A multi-scale study of red porgy movements and habitat use, and its application to the design of marine reserve networks. In Tagging and Tracking of Marine Animals with Electronic Devices (eds Nielsen, J. L. et al.) 423–443 (Springer, 2009).Chapter 

Google Scholar 
43.March, D., Palmer, M., Alós, J., Grau, A. & Cardona, F. Short-term residence, home range size and diel patterns of the painted comber Serranus scriba in a temperate marine reserve. Mar. Ecol. Prog. Ser. 400, 195–206 (2010).ADS 
Article 

Google Scholar 
44.Zeller, D. C. Ultrasonic telemetry: Its application to coral reef fisheries research. Fish. Bull. 97, 1058–1065 (1999).
Google Scholar 
45.Heupel, M. R., Semmens, J. M. & Hobday, A. J. Automated acoustic tracking of aquatic animals: Scales, design and deployment of listening station arrays. Mar. Freshw. Res. 57, 1–13 (2006).Article 

Google Scholar 
46.Lowe, C. G., Topping, D. T., Cartamil, D. P. & Papastamatiou, Y. P. Movement patterns, home range, and habitat utilization of adult kelp bass Paralabrax clathratus in a temperate no-take marine reserve. Mar. Ecol. Prog. Ser. 256, 205–216 (2003).ADS 
Article 

Google Scholar 
47.Kaunda-Arara, B. & Rose, G. A. Homing and site fidelity in the greasy grouper Epinephelus tauvina (Serranidae) within a marine protected area in coastal Kenya. Mar. Ecol. Prog. Ser. 277, 245–251 (2004).ADS 
Article 

Google Scholar 
48.Parsons, D. & Egli, D. Fish movement in a temperate marine reserve: New insights through application of acoustic tracking. Mar. Technol. Soc. J. 39, 56–63 (2005).Article 

Google Scholar 
49.Topping, D. T., Lowe, C. G. & Caselle, J. E. Home range and habitat utilization of adult California sheephead, Semicossyphus pulcher (Labridae), in a temperate no-take marine reserve. Mar. Biol. 147, 301–311 (2005).Article 

Google Scholar 
50.Pastor, J. et al. Acoustic telemetry survey of the dusky grouper (Epinephelus marginatus) in the Marine Reserve of Cerbère-Banyuls: Informations on the territoriality of this emblematic species. C.R. Biol. 332, 732–740 (2009).PubMed 
Article 
PubMed Central 

Google Scholar 
51.D’Anna, G., Giacalone, V. M., Pipitone, C. & Badalamenti, F. Movement pattern of white seabream, Diplodus sargus (L., 1758) (Osteichthyes, Sparidae) acoustically tracked in an artificial reef area. Ital. J. Zool. 78, 255–263 (2011).Article 

Google Scholar 
52.La Mesa, G., Consalvo, I., Annunziatellis, A. & Canese, S. Movement patterns of the parrotfish Sparisoma cretense in a Mediterranean marine protected area. Mar. Environ. Res. 82, 59–68 (2012).PubMed 
Article 
CAS 
PubMed Central 

Google Scholar 
53.Picciulin, M. et al. Passive acoustic monitoring of Sciaena umbra on rocky habitats in the Venetian littoral zone. Fish. Res. 145, 76–81 (2013).Article 

Google Scholar 
54.Lenfant, P., Louisy, P. & Licari, M.-L. Recensement des Mérous bruns (Epinephelus marginatus) de la Réserve Naturelle de Cerbère-Banyuls (France, Méditerranée) Effectué en Septembre 2001, aprés 17 Années de Protection 10 (2003).55.Koeck, B., Gudefin, A., Romans, P., Loubet, J. & Lenfant, P. Effects of intracoelomic tagging procedure on white seabream (Diplodus sargus) behavior and survival. J. Exp. Mar. Biol. Ecol. 440, 1–7 (2013).Article 

Google Scholar 
56.Garcia, J., Mourier, J. & Lenfant, P. Spatial behavior of two coral reef fishes within a Caribbean marine protected area. Mar. Environ. Res. 109, 41–51 (2015).CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
57.Percie du Sert, N. et al. The ARRIVE guidelines 2.0: Updated guidelines for reporting animal research. PLoS Biol. 18(7), e3000410. https://doi.org/10.1371/journal.pbio.3000410 (2020).CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
58.Grau, A., Linde, M. & Grau, A. M. Reproductive biology of the vulnerable species Sciaena umbra Linnaeus, 1758 (Pisces: Sciaenidae). Sci. Mar. 73, 67–81 (2009).Article 

Google Scholar 
59.McKinzie, M. K., Jarvis, E. T. & Lowe, C. G. Fine-scale horizontal and vertical movement of barred sand bass, Paralabrax nebulifer, during spawning and non-spawning seasons. Fish. Res. 150, 66–75 (2014).Article 

Google Scholar 
60.Kassambara, A. & Mundt, F. factoextra: Extract and Visualize the Results of Multivariate Data Analyses (2017).61.Zuur, A., Ieno, E. N., Walker, N., Saveliev, A. A. & Smith, G. M. Mixed Effects Models and Extensions in Ecology with R (Springer, 2009).MATH 
Book 

Google Scholar 
62.Pinheiro, J., Bates, D., DebRoy, S., Sarkar, D. & R Core Team. nlme: Linear and Nonlinear Mixed Effects Models (2018).63.Wood, S. N. Generalized Additive Models: An Introduction with R 2nd edn. (Chapman and Hall/CRC, 2017).MATH 
Book 

Google Scholar 
64.R Core Team. R: A Language and Environment for Statistical Computing. (R Foundation for Statistical Computing, 2018).65.Wearmouth, V. J. & Sims, D. W. Chapter 2 sexual segregation in marine fish, reptiles, birds and mammals: Behaviour patterns, mechanisms and conservation implications. Adv. Mar. Biol. 54, 107–170 (2008).PubMed 
Article 
PubMed Central 

Google Scholar 
66.Haraldstad, Ø. & Jonsson, B. Age and sex segregation in habitat utilization by brown trout in a Norwegian Lake. Trans. Am. Fish. Soc. 112, 27–37 (1983).Article 

Google Scholar 
67.L’Abée-Lund, J. H., Langeland, A., Jonsson, B. & Ugedal, O. Spatial segregation by age and size in Arctic Charr: A trade-off between feeding possibility and risk of predation. J. Anim. Ecol. 62, 160–168 (1993).Article 

Google Scholar 
68.Oxenford, H. A. & Hunte, W. Feeding habits of the dolphinfish (Coryphaena hippurus) in the eastern Caribbean. Sci. Mar. 63, 303–315 (1999).Article 

Google Scholar 
69.Sarà, G. et al. Effect of boat noise on the behaviour of bluefin tuna Thunnus thynnus in the Mediterranean Sea. Mar. Ecol. Prog. Ser. 331, 243–253 (2007).ADS 
Article 

Google Scholar 
70.Codarin, A., Wysocki, L. E., Ladich, F. & Picciulin, M. Effects of ambient and boat noise on hearing and communication in three fish species living in a marine protected area (Miramare, Italy). Mar. Pollut. Bull. 58, 1880–1887 (2009).CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
71.Picciulin, M., Sebastianutto, L., Codarin, A., Calcagno, G. & Ferrero, E. A. Brown meagre vocalization rate increases during repetitive boat noise exposures: A possible case of vocal compensation. J. Acoust. Soc. Am. 132, 3118–3124 (2012).ADS 
PubMed 
Article 
PubMed Central 

Google Scholar 
72.McCormick, M. I., Allan, B. J. M., Harding, H. & Simpson, S. D. Boat noise impacts risk assessment in a coral reef fish but effects depend on engine type. Sci. Rep. 8, 3847 (2018).ADS 
PubMed 
PubMed Central 
Article 
CAS 

Google Scholar 
73.Robichaud, D. & Rose, G. A. Sex differences in cod residency on a spawning ground. Fish. Res. 60, 33–43 (2003).Article 

Google Scholar 
74.Fiorentino, F. et al. On a spawning aggregation of the brown meagre Sciaena umbra L. 1758 (Sciaenidae, Osteichthyes) in the Maltese waters (Sicilian Channel—Central Mediterranean). Rapp. Commun. Int. Mer Médit. 36, 266 (2001).
Google Scholar 
75.Furukawa, S. et al. Vertical movements of Pacific bluefin tuna (Thunnus orientalis) and dolphinfish (Coryphaena hippurus) relative to the thermocline in the northern East China Sea. Fish. Res. 149, 86–91 (2014).Article 

Google Scholar 
76.Claireaux, G., Webber, D., Kerr, S. & Boutilier, R. Physiology and behaviour of free-swimming Atlantic cod (Gadus morhua) facing fluctuating temperature conditions. J. Exp. Biol. 198, 49–60 (1995).CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
77.Armstrong, J. B. et al. Diel horizontal migration in streams: Juvenile fish exploit spatial heterogeneity in thermal and trophic resources. Ecology 94, 2066–2075 (2013).PubMed 
Article 
PubMed Central 

Google Scholar 
78.Bobe, J. & Labbé, C. Egg and sperm quality in fish. Gen. Comp. Endocrinol. 165, 535–548 (2010).CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
79.Pepin, P. Effect of temperature and size on development, mortality, and survival rates of the pelagic early life history stages of marine fish. Can. J. Fish. Aquat. Sci. 48, 503–518 (1991).Article 

Google Scholar 
80.Guevara-Fletcher, C., Alvarez, P., Sanchez, J. & Iglesias, J. Effect of temperature on the development and mortality of European hake (Merluccius merluccius L.) eggs from southern stock under laboratory conditions. J. Exp. Mar. Biol. Ecol. 476, 50–57 (2016).Article 

Google Scholar 
81.Dubrovský, M. et al. Multi-GCM projections of future drought and climate variability indicators for the Mediterranean region. Reg. Environ Change 14, 1907–1919 (2014).Article 

Google Scholar 
82.Pankhurst, N. W. & Munday, P. L. Effects of climate change on fish reproduction and early life history stages. Mar. Freshw. Res. 62, 1015–1026 (2011).CAS 
Article 

Google Scholar 
83.McKenzie, D. J. et al. Conservation physiology of marine fishes: State of the art and prospects for policy. Conserv. Physiol. 4, 046 (2016).Article 

Google Scholar 
84.Fabi, G., Panfili, M. & Spagnolo, A. Note on feeding of Sciaena umbra L. (Asteichthyes:Sciaenidae) in the central Adriatic Sea. Rapp. Comm. Int. Mer Médit. 35, 426 (1998).
Google Scholar 
85.Fabi, G., Manoukian, S. & Spagnolo, A. Feeding behavior of three common fishes at an artificial reef in the northern Adriatic Sea. Bull. Mar. Sci. 78, 39–56 (2006).
Google Scholar 
86.Ramcharitar, J., Gannon, D. P. & Popper, A. N. Bioacoustics of fishes of the family Sciaenidae (croakers and drums). Trans. Am. Fish. Soc. 135, 1409–1431 (2006).Article 

Google Scholar 
87.Mesa, M. L., Colella, S., Giannetti, G. & Arneri, E. Age and growth of brown meagre Sciaena umbra (Sciaenidae) in the Adriatic Sea. Aquat. Living Resour. 21, 153–161 (2008).Article 

Google Scholar 
88.Picciulin, M. et al. Diagnostics of nocturnal calls of Sciaena umbra (L., fam. Sciaenidae) in a nearshore Mediterranean marine reserve. Bioacoustics 22, 109–120 (2013).Article 

Google Scholar 
89.Schmidt, M. B. & Gassner, H. Influence of scuba divers on the avoidance reaction of a dense vendace (Coregonus albula L.) population monitored by hydroacoustics. Fish. Res. 82, 131–139 (2006).Article 

Google Scholar 
90.Moffitt, E. A., Botsford, L. W., Kaplan, D. M. & O’Farrell, M. R. Marine reserve networks for species that move within a home range. Ecol. Appl. 19, 1835–1847 (2009).PubMed 
Article 
PubMed Central 

Google Scholar  More

1.Tilman, D. et al. The influence of functional diversity and composition on ecosystem processes. Science 277, 1300–1302 (1997).CAS 
Article 

Google Scholar 
2.Cardinale, B. J. et al. Impacts of plant diversity on biomass production increase through time because of species complementarity. Proc. Natl Acad. Sci. USA 104, 18123–18128 (2007).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
3.Van Ruijven, J. & Berendse, F. Diversity-productivity relationships: Initial effects, long-term patterns, and underlying mechanisms. Proc. Natl Acad. Sci. USA 102, 695–700 (2005).PubMed 
PubMed Central 
Article 
CAS 

Google Scholar 
4.Jochum, M. et al. The results of biodiversity–ecosystem functioning experiments are realistic. Nat. Ecol. Evol. 4, 1485–1494 (2020).PubMed 
Article 
PubMed Central 

Google Scholar 
5.Jing, J., Bezemer, T. M. & van der Putten, W. H. Complementarity and selection effects in early and mid-successional plant communities are differentially affected by plant-soil feedback. J. Ecol. 103, 641–647 (2015).Article 

Google Scholar 
6.Tilman, D., Hill, J. & Lehman, C. Carbon-negative biofuels from low-input high-diversity grassland biomass. Science 314, 1598–1600 (2006).CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
7.Mueller, K. E., Tilman, D., Fornara, D. A. & Hobbie, S. E. Root depth distribution and the diversity–productivity relationship in a long-term grassland experiment. Ecology 94, 787–793 (2013).Article 

Google Scholar 
8.Hector, A., Bazeley-White, E., Loreau, M., Otway, S. & Schmid, B. Overyielding in grassland communities: testing the sampling effect hypothesis with replicated biodiversity experiments. Ecol. Lett. 5, 502–511 (2002).Article 

Google Scholar 
9.Barry, K. E. et al. The future of complementarity: disentangling causes from consequences. Trends Ecol. Evol. 34, 167–180 (2019).PubMed 
Article 
PubMed Central 

Google Scholar 
10.Kulmatiski, A., Beard, K. H. & Heavilin, J. Plant-soil feedbacks provide an additional explanation for diversity-productivity relationships. Proc. R. Soc. B Biol. Sci. 279, 3020–3026 (2012).Article 

Google Scholar 
11.Loreau, M. & Hector, A. Partitioning selection and complementarity in biodiversity experiments. Nature 412, 72–76 (2001).CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
12.Tedersoo, L., Bahram, M. & Zobel, M. How mycorrhizal associations drive plant population and community biology. Science 367, 6480 (2020).Article 
CAS 

Google Scholar 
13.Maron, J. L., Marler, M., Klironomos, J. N. & Cleveland, C. C. Soil fungal pathogens and the relationship between plant diversity and productivity. Ecol. Lett. 14, 36–41 (2011).PubMed 
Article 
PubMed Central 

Google Scholar 
14.Wang, G. et al. Soil microbiome mediates positive plant diversity‐productivity relationships in late successional grassland species. Ecol. Lett. 22, 13273 (2019).Article 

Google Scholar 
15.Wright, A. J., Wardle, D. A., Callaway, R. & Gaxiola, A. The overlooked role of facilitation in biodiversity experiments. Trends Ecol. Evol. 32, 383–390 (2017).PubMed 
Article 
PubMed Central 

Google Scholar 
16.Bever, J. D., Platt, T. G. & Morton, E. R. Microbial population and community dynamics on plant roots and their feedbacks on plant communities. Annu. Rev. Microbiol. 66, 265–283 (2012).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
17.Bauer, J. T., Koziol, L. & Bever, J. D. Local adaptation of mycorrhizae communities changes plant community composition and increases aboveground productivity. Oecologia 192, 735–744 (2020).PubMed 
Article 
PubMed Central 

Google Scholar 
18.Bever, J. D. Feeback between plants and their soil communities in an old field community. Ecology 75, 1965–1977 (1994).Article 

Google Scholar 
19.Hendriks, M. et al. Independent variations of plant and soil mixtures reveal soil feedback effects on plant community overyielding. J. Ecol. 101, 287–297 (2013).Article 

Google Scholar 
20.Zuppinger-Dingley, D. L., Flynn, D. F. B., De Deyn, G. B., Petermann, J. S. & Schmid, B. Plant selection and soil legacy enhance long-term biodiversity effects. Ecology 97, 15–0599.1 (2015).
Google Scholar 
21.Mommer, L. et al. Lost in diversity: the interactions between soil-borne fungi, biodiversity and plant productivity. N. Phytol. 218, 542–553 (2018).Article 

Google Scholar 
22.Guerrero‐Ramírez, N. R., Reich, P. B., Wagg, C., Ciobanu, M. & Eisenhauer, N. Diversity‐dependent plant–soil feedbacks underlie long‐term plant diversity effects on primary productivity. Ecosphere 10, e02704 (2019).Article 

Google Scholar 
23.van Ruijven, J., Ampt, E., Francioli, D. & Mommer, L. Do soil-borne fungal pathogens mediate plant diversity–productivity relationships? Evidence and future opportunities. J. Ecol. 108, 1810–1821 (2020).Article 

Google Scholar 
24.Schnitzer, S. A. et al. Soil microbes drive the classic plant diversity–productivity pattern. Ecology 92, 296–303 (2011).PubMed 
Article 
PubMed Central 

Google Scholar 
25.Lekberg, Y. et al. Relative importance of competition and plant-soil feedback, their synergy, context dependency and implications for coexistence. Ecol. Lett. 21, 1268–1281 (2018).PubMed 
Article 
PubMed Central 

Google Scholar 
26.Cowles, J. Mechanisms of Coexistence: Implications for Biodiversity-Ecosystem Functioning Relationships in a Changing World. Dissertation, The University of Minnesota (2015).27.Forero, L. E., Grenzer, J., Heinze, J., Schittko, C. & Kulmatiski, A. Greenhouse- and field-measured plant-soil feedbacks are not correlated. Front. Environ. Sci. 7, 184 (2019).Article 

Google Scholar 
28.Kulmatiski, A. & Kardol, P. in Getting Plant—Soil Feedbacks out of the Greenhouse: Experimental and Conceptual Approaches 449–472 (Springer, 2008).29.Pernilla Brinkman, E., Van der Putten, W. H., Bakker, E. J. & Verhoeven, K. J. F. Plant-soil feedback: experimental approaches, statistical analyses and ecological interpretations. J. Ecol. 98, 1063–1073 (2010).Article 

Google Scholar 
30.van der Putten, W. H. et al. Plant-soil feedbacks: the past, the present and future challenges. J. Ecol. 101, 265–276 (2013).Article 

Google Scholar 
31.Rinella, M. J. & Reinhart, K. O. Toward more robust plant-soil feedback research. Ecology 99, 550–556 (2018).PubMed 
Article 
PubMed Central 

Google Scholar 
32.Crawford, K. M. et al. When and where plant‐soil feedback may promote plant coexistence: a meta‐analysis. Ecol. Lett. 22, 13278 (2019).Article 

Google Scholar 
33.Clark, A. T. et al. How to estimate complementarity and selection effects from an incomplete sample of species. Methods Ecol. Evol. 10, 2141–2152 (2019).PubMed 
PubMed Central 
Article 

Google Scholar 
34.Anacker, B. L., Klironomos, J. N., Maherali, H., Reinhart, K. O. & Strauss, S. Y. Phylogenetic conservatism in plant-soil feedback and its implications for plant abundance. Ecol. Lett. 17, 1613–1621 (2014).PubMed 
Article 
PubMed Central 

Google Scholar 
35.Mehrabi, Z. & Tuck, S. L. Relatedness is a poor predictor of negative plant–soil feedbacks. N. Phytol. 205, 1071–1075 (2015).Article 

Google Scholar 
36.Kulmatiski, A., Beard, K. H., Stevens, J. R. & Cobbold, S. M. Plant-soil feedbacks: a meta-analytical review. Ecol. Lett. 11, 980–992 (2008).PubMed 
Article 
PubMed Central 

Google Scholar 
37.Beals, K. K. et al. Predicting plant-soil feedback in the field: meta-analysis reveals that competition and environmental stress differentially influence psf. Front. Ecol. Evol. 8, 191 (2020).Article 

Google Scholar 
38.Kos, M., Tuijl, M. A. B., de Roo, J., Mulder, P. P. J. & Bezemer, T. M. Species-specific plant-soil feedback effects on above-ground plant-insect interactions. J. Ecol. 103, 904–914 (2015).CAS 
Article 

Google Scholar 
39.Bukowski, A. R. & Petermann, J. S. Intraspecific plant-soil feedback and intraspecific overyielding in Arabidopsis thaliana. Ecol. Evol. 4, 2533–2545 (2014).PubMed 
PubMed Central 
Article 

Google Scholar 
40.Tilman, D., Wedin, D. & Knops, J. Productivity and sustainability influenced by biodiversity in grassland ecosystems. Nature 379, 718–720 (1996).CAS 
Article 

Google Scholar 
41.Fornara, D. A. & Tilman, D. Ecological mechanisms associated with the positive diversity–productivity relationship in an N-limited grassland. Ecology 90, 408–418 (2009).CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
42.Laughlin, D. C. et al. The hierarchy of predictability in ecological restoration: are vegetation structure and functional diversity more predictable than community composition? J. Appl. Ecol. 54, 1058–1069 (2017).Article 

Google Scholar 
43.Metcalfe, H., Milne, A. E., Deledalle, F. & Storkey, J. Using functional traits to model annual plant community dynamics. Ecology 101, e03167 (2020).PubMed 
Article 
PubMed Central 

Google Scholar 
44.Moulin, T., Perasso, A., Calanca, P. & Gillet, F. DynaGraM: a process-based model to simulate multi-species plant community dynamics in managed grasslands. Ecol. Modell. 439, 109345 (2021).Article 

Google Scholar 
45.Putten, W. H., Bradford, M. A., Pernilla Brinkman, E., Voorde, T. F. J. & Veen, G. F. Where, when and how plant–soil feedback matters in a changing world. Funct. Ecol. 30, 1109–1121 (2016).Article 

Google Scholar 
46.Eisenhauer, N., Reich, P. B. & Scheu, S. Increasing plant diversity effects on productivity with time due to delayed soil biota effects on plants. Basic Appl. Ecol. 13, 571–578 (2012).Article 

Google Scholar 
47.Hawkes, C. V., Kivlin, S. N., Du, J. & Eviner, V. T. The temporal development and additivity of plant-soil feedback in perennial grasses. Plant Soil 369, 141–150 (2013).CAS 
Article 

Google Scholar 
48.Latz, E., Eisenhauer, N., Rall, B. C., Scheu, S. & Jousset, A. Unravelling linkages between plant community composition and the pathogen-suppressive potential of soils. Sci. Rep. 6, 1–10 (2016).Article 
CAS 

Google Scholar 
49.Chung, Y. A. & Rudgers, J. A. Plant–soil feedbacks promote negative frequency dependence in the coexistence of two aridland grasses. Proc. R. Soc. B Biol. Sci. 283 (2016).50.Mahaut, L., Fort, F., Violle, C. & Freschet, G. T. Multiple facets of diversity effects on plant productivity: species richness, functional diversity, species identity and intraspecific competition. Funct. Ecol. 34, 287–298 (2020).Article 

Google Scholar 
51.Barry, K. E. et al. Limited evidence for spatial resource partitioning across temperate grassland biodiversity experiments. Ecology 101, 2905 (2020).Article 

Google Scholar 
52.Hooper, D. U. et al. Effects of biodiversity on ecosystem functioning: a consensus of current knowledge. Ecol. Monogr. 75, 3–35 (2005).Article 

Google Scholar 
53.Pillai, P. & Gouhier, T. C. Not even wrong: the spurious measurement of biodiversity’s effects on ecosystem functioning. Ecology 100, e02645 (2019).PubMed 
Article 
PubMed Central 

Google Scholar 
54.Manning, P. et al. Transferring biodiversity-ecosystem function research to the management of ‘real-world’ ecosystems. Adv. Ecol. Res. 61, 323–356 (2019).Article 

Google Scholar 
55.Fargione, J. et al. From selection to complementarity: Shifts in the causes of biodiversity-productivity relationships in a long-term biodiversity experiment. Proc. R. Soc. B Biol. Sci. 274, 871–876 (2007).Article 

Google Scholar 
56.Helander, M. et al. Decreases mycorrhizal colonization and affects plant-soil feedback. Sci. Total Environ. 642, 285–291 (2018).CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
57.Tilman, D. et al. Diversity and productivity in a long-term grassland experiment. Science 294, 843–845 (2001).CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
58.Cadotte, M. W., Cavender-Bares, J., Tilman, D. & Oakley, T. H. Using phylogenetic, functional and trait diversity to understand patterns of plant community productivity. PLoS ONE 4, e5695 (2009).PubMed 
PubMed Central 
Article 
CAS 

Google Scholar 
59.Kulmatiski, A., Heavilin, J. & Beard, K. H. Testing predictions of a three-species plant-soil feedback model. J. Ecol. 99, 542–550 (2011).
Google Scholar 
60.Kulmatiski, A., Beard, K. H., Grenzer, J., Forero, L. & Heavilin, J. Using plant-soil feedbacks to predict plant biomass in diverse communities. Ecology 97, 2064–2073 (2016).PubMed 
Article 
PubMed Central 

Google Scholar  More

1.Bulgarelli, D., Schlaeppi, K., Spaepen, S., van Themaat, E. V. L. & Schulze-Lefert, P. Structure and functions of the bacterial microbiota of plants. Annu. Rev. Plant Biol. 64, 807–838 (2013).CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
2.Reinhold-Hurek, B. & Hurek, T. Living inside plants: bacterial endophytes. Curr. Opin. Plant Biol. 14, 435–443 (2011).PubMed 
Article 
PubMed Central 

Google Scholar 
3.Naveed, M., Mitter, B., Reichenauer, T. G., Wieczorek, K. & Sessitsch, A. Increased drought stress resilience of maize through endophytic colonization by Burkholderia phytofirmans PsJN and Enterobacter sp FD17. Environ. Exp. Bot. 97, 30–39 (2014).CAS 
Article 

Google Scholar 
4.Santoyo, G., Moreno-Hagelsieb, G., del Carmen Orozco-Mosqueda, M. & Glick, B. R. Plant growth-promoting bacterial endophytes. Microbiol. Res. 183, 92–99 (2016).CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
5.Ali, S., Charles, T. C. & Glick, B. R. Amelioration of high salinity stress damage by plant growth-promoting bacterial endophytes that contain ACC deaminase. Plant Physiol. Biochem. 80, 160–167 (2014).CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
6.Weyens, N., van der Lelie, D., Taghavi, S. & Vangronsveld, J. Phytoremediation: plant–endophyte partnerships take the challenge. Curr. Opin. Biotechnol. 20, 248–254 (2009).CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
7.Vandenkoornhuyse, P., Quaiser, A., Duhamel, M., Le Van, A. & Dufresne, A. The importance of the microbiome of the plant holobiont. New Phytol. 206, 1196–1206 (2015).PubMed 
Article 
PubMed Central 

Google Scholar 
8.Card, S. D. et al. Beneficial endophytic microorganisms of Brassica—A review. Biol. Control 90, 102–112 (2015).Article 

Google Scholar 
9.Shahzad, R., Khan, A. L., Bilal, S., Asaf, S. & Lee, I. J. What Is there in seeds? Vertically transmitted endophytic resources for sustainable improvement in plant growth. Front. Plant Sci. 9, 24. https://doi.org/10.3389/fpls.2018.00024 (2018).Article 
PubMed 
PubMed Central 

Google Scholar 
10.Truyens, S., Weyens, N., Cuypers, A. & Vangronsveld, J. Bacterial seed endophytes: genera, vertical transmission and interaction with plants. Environ. Microbiol. Rep. 7, 40–50 (2015).Article 

Google Scholar 
11.Catford, J. A., Jansson, R. & Nilsson, C. Reducing redundancy in invasion ecology by integrating hypotheses into a single theoretical framework. Divers. Distrib. 15, 22–40 (2009).Article 

Google Scholar 
12.van Kleunen, M., Dawson, W. & Maurel, N. Characteristics of successful alien plants. Mol. Ecol. 24, 1954–1968 (2015).PubMed 
Article 
PubMed Central 

Google Scholar 
13.Coats, V. C. & Rumpho, M. E. The rhizosphere microbiota of plant invaders: an overview of recent advances in the microbiomics of invasive plants. Front. Microbiol. 5, 368. https://doi.org/10.3389/fmicb.2014.00368 (2014).Article 
PubMed 
PubMed Central 

Google Scholar 
14.Richardson, D. M., Allsopp, N., D’antonio, C. M., Milton, S. J. & Rejmánek, M. Plant invasions—the role of mutualisms. Biol. Rev. 75, 65–93 (2000).CAS 
PubMed 
Article 

Google Scholar 
15.Pringle, A. et al. Mycorrhizal symbioses and plant invasions. Annu. Rev. Ecol. Evol. Syst. 40, 699–715 (2009).Article 

Google Scholar 
16.Sun, Z.-K. & He, W.-M. Evidence for enhanced mutualism hypothesis: Solidago canadensis plants from regular soils perform better. PLoS ONE 5, e15418. https://doi.org/10.1371/journal.pone.0015418 (2010).ADS 
CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
17.Kowalski, K. P. et al. Advancing the science of microbial symbiosis to support invasive species management: a case study on Phragmites in the Great Lakes. Front. Microbiol. 6, 95. https://doi.org/10.3389/fmicb.2015.00095 (2015).Article 
PubMed 
PubMed Central 

Google Scholar 
18.Dai, Z. C. et al. Different growth promoting effects of endophytic bacteria on invasive and native clonal plants. Front. Plant Sci. 7, 706. https://doi.org/10.3389/fpls.2016.00706 (2016).Article 
PubMed 
PubMed Central 

Google Scholar 
19.Rout, M. E. et al. Bacterial endophytes enhance competition by invasive plants. Am. J. Bot. 100, 1726–1737 (2013).CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
20.Soares, M. A. et al. Functional role of bacteria from invasive Phragmites australis in promotion of host growth. Microb. Ecol. 72, 407–417 (2016).CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
21.Kim, Y.-H., Kil, J.-H., Hwang, S.-M. & Lee, C.-W. Spreading and distribution of Lactuca scariola, invasive alien plant, by habitat types in Korea. Weed Turfgrass Sci. 2, 138–151 (2013).Article 

Google Scholar 
22.Moon, S.-I. et al. Isolation and characterization of bio-active materials from prickly lettuce (Lactuca serriola). J. Life Sci. 19, 206–212 (2009).Article 

Google Scholar 
23.Lebeda, A. et al. Acquisition and ecological characterization of Lactuca serriola L germplasm collected in the Czech Republic, Germany, the Netherlands and United Kingdom. Genet. Resour. Crop Evol. 54, 555–562 (2007).Article 

Google Scholar 
24.Mallory-Smith, C. A., Thill, D. C. & Dial, M. J. Identification of sulfonylurea herbicide-resistant prickly lettuce (Lactuca serriola). Weed Technol. 4, 163–168 (1990).Article 

Google Scholar 
25.Glick, B. R. Plant growth-promoting bacteria: mechanisms and applications. Scientifica 2012, 963401. https://doi.org/10.6064/2012/963401 (2012).Article 
PubMed 
PubMed Central 

Google Scholar 
26.Costa, O. Y. A., Raaijmakers, J. M. & Kuramae, E. E. Microbial extracellular polymeric substances: ecological function and impact on soil aggregation. Front. Microbiol. 9, 1636. https://doi.org/10.3389/fmicb.2018.01636 (2018).Article 
PubMed 
PubMed Central 

Google Scholar 
27.Alami, Y., Achouak, W., Marol, C. & Heulin, T. Rhizosphere soil aggregation and plant growth promotion of sunflowers by an exopolysaccharide-producing Rhizobiums strain isolated from sunflower roots. Appl. Environ. Microbiol. 66, 3393–3398 (2000).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
28.Sandhya, V., Grover, M., Reddy, G. & Venkateswarlu, B. Alleviation of drought stress effects in sunflower seedlings by the exopolysaccharides producing Pseudomonas putida strain GAP-P45. Biol. Fertility Soils 46, 17–26 (2009).CAS 
Article 

Google Scholar 
29.Vardharajula, S. Exopolysaccharide production by drought tolerant Bacillus spp and effect on soil aggregation under drought stress. J. Microbiol. Biotechnol. Food Sci. 9, 51–57 (2020).
Google Scholar 
30.Kumar, S., Stecher, G. & Tamura, K. MEGA7: molecular evolutionary genetics analysis version 70 for bigger datasets. Mol. Biol. Evol. 33, 1870–1874 (2016).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
31.Kang, S. H. et al. Two bacterial entophytes eliciting both plant growth promotion and plant defense on pepper (Capsicum annuum L). J. Microbiol. Biotechnol. 17, 96–103 (2007).CAS 
PubMed 
PubMed Central 

Google Scholar 
32.Panwar, M., Tewari, R. & Nayyar, H. Native halo-tolerant plant growth promoting rhizobacteria Enterococcus and Pantoea sp. improve seed yield of Mungbean (Vigna radiata L) under soil salinity by reducing sodium uptake and stress injury. Physiol. Mol. Biol. Plants 22, 445–459 (2016).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
33.Selvakumar, G. et al. Characterization of a cold-tolerant plant growth-promoting bacterium Pantoea dispersa 1A isolated from a sub-alpine soil in the North Western Indian Himalayas. World J. Microbiol. Biotechnol. 24, 955–960 (2008).CAS 
Article 

Google Scholar 
34.Egamberdieva, D. et al. High incidence of plant growth-stimulating bacteria associated with the rhizosphere of wheat grown on salinated soil in Uzbekistan. Environ. Microbiol. 10, 1–9 (2008).CAS 
PubMed 
PubMed Central 

Google Scholar 
35.Pereira, S., Castro, P. & Research, P. Diversity and characterization of culturable bacterial endophytes from Zea mays and their potential as plant growth-promoting agents in metal-degraded soils. Environ. Sci. Pollut. Res. 21, 14110–14123 (2014).CAS 
Article 

Google Scholar 
36.Sun, Z. et al. IAA producing Bacillus altitudinis alleviates iron stress in Triticum aestivum L seedling by both bioleaching of iron and up-regulation of genes encoding ferritins. Plant Soil 419, 1–11 (2017).CAS 
Article 

Google Scholar 
37.Pierik, R., Tholen, D., Poorter, H., Visser, E. J. W. & Voesenek, L. A. C. J. The Janus face of ethylene: growth inhibition and stimulation. Trends Plant Sci. 11, 176–183 (2006).CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
38.Glick, B. R. Bacteria with ACC deaminase can promote plant growth and help to feed the world. Microbiol. Res. 169, 30–39 (2014).CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
39.Cardinale, M., Grube, M., Erlacher, A., Quehenberger, J. & Berg, G. Bacterial networks and co-occurrence relationships in the lettuce root microbiota. Environ. Microbiol. 17, 239–252 (2015).CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
40.Yu, Y.-C., Yum, S.-J., Jeon, D.-Y. & Jeong, H.-G. Analysis of the microbiota on lettuce (Lactuca sativa L.) cultivated in South Korea to identify foodborne pathogens. J. Microbiol. Biotechnol. 28, 1318–1331 (2018).PubMed 
Article 
PubMed Central 

Google Scholar 
41.Brady, C. et al. Isolation of Enterobacter cowanii from Eucalyptus showing symptoms of bacterial blight and dieback in Uruguay. Lett. Appl. Microbiol. 49, 461–465 (2009).CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
42.Chimwamurombe, P. M., Grönemeyer, J. L. & Reinhold-Hurek, B. Isolation and characterization of culturable seed-associated bacterial endophytes from gnotobiotically grown Marama bean seedlings. FEMS Microbiol. Ecol. 92, fiw083. https://doi.org/10.1093/femsec/fiw083 (2016).CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
43.Gao, H. et al. Production exopolysaccharide from Kosakonia cowanii LT-1 through solid-state fermentation and its application as a plant growth promoter. Int. J. Biol. Macromol. 150, 955–964 (2020).CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
44.Wang, L. et al. Development of sugarcane resource for efficient fermentation of exopolysaccharide by using a novel strain of Kosakonia cowanii LT-1. Bioresour. Technol. 280, 247–254 (2019).CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
45.Borlee, B. R. et al. Pseudomonas aeruginosa uses a cyclic-di-GMP-regulated adhesin to reinforce the biofilm extracellular matrix. Mol. Microbiol. 75, 827–842 (2010).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
46.Huang, X.-F. et al. Mitsuaria sp. and Burkholderia sp. from Arabidopsis rhizosphere enhance drought tolerance in Arabidopsis thaliana and maize (Zea mays L.). Plant Soil 419, 523–539 (2017).CAS 
Article 

Google Scholar 
47.Marulanda, A., Barea, J.-M. & Azcón, R. Stimulation of plant growth and drought tolerance by native microorganisms (AM fungi and bacteria) from dry environments: mechanisms related to bacterial effectiveness. J. Plant Growth Regul. 28, 115–124 (2009).CAS 
Article 

Google Scholar 
48.Niu, X., Song, L., Xiao, Y. & Ge, W. Drought-tolerant plant growth-promoting rhizobacteria associated with foxtail millet in a semi-arid agroecosystem and their potential in alleviating drought stress. Front. Microbiol. 8, 2580. https://doi.org/10.3389/fmicb.2017.02580 (2018).Article 
PubMed 
PubMed Central 

Google Scholar 
49.Sandhya, V., Ali, S. Z., Grover, M., Reddy, G. & Venkateswarlu, B. Effect of plant growth promoting Pseudomonas spp. on compatible solutes, antioxidant status and plant growth of maize under drought stress. Plant Growth Regulat. 62, 21–30 (2010).CAS 
Article 

Google Scholar 
50.Chen, C. et al. Pantoea alhagi, a novel endophytic bacterium with ability to improve growth and drought tolerance in wheat. Sci. Rep. 7, 41564. https://doi.org/10.1038/srep41564 (2017).ADS 
CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
51.Mönchgesang, S. et al. Natural variation of root exudates in Arabidopsis thaliana-linking metabolomic and genomic data. Sci. Rep. 6, 1–11 (2016).Article 
CAS 

Google Scholar 
52.Zhang, N. et al. Effects of different plant root exudates and their organic acid components on chemotaxis, biofilm formation and colonization by beneficial rhizosphere-associated bacterial strains. Plant Soil 374, 689–700 (2014).CAS 
Article 

Google Scholar 
53.Johnston-Monje, D. & Raizada, M. N. Conservation and diversity of seed associated endophytes in Zea across boundaries of evolution, ethnography and ecology. PLoS ONE 6, e20396. https://doi.org/10.1371/journal.pone.0020396 (2011).ADS 
CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
54.Coombs, J. T. & Franco, C. M. M. Isolation and identification of Actinobacteria from surface-sterilized wheat roots. Appl. Environ. Microbiol. 69, 5603–5608. https://doi.org/10.1128/aem.69.9.5603-5608.2003 (2003).CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
55.Kimura, M. A simple method for estimating evolutionary rates of base substitutions through comparative studies of nucleotide sequences. J. Mol. Evol. 16, 111–120 (1980).ADS 
CAS 
PubMed 
Article 

Google Scholar 
56.Saitou, N. & Nei, M. The neighbor-joining method: a new method for reconstructing phylogenetic trees. Mol. Biol. Evol. 4, 406–425 (1987).CAS 
PubMed 
PubMed Central 

Google Scholar 
57.Mehta, S. & Nautiyal, C. S. An efficient method for qualitative screening of phosphate-solubilizing bacteria. Curr. Microbiol. 43, 51–56 (2001).CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
58.Milagres, A. M., Machuca, A. & Napoleao, D. Detection of siderophore production from several fungi and bacteria by a modification of chrome azurol S (CAS) agar plate assay. J. Microbiol. Methods 37, 1–6 (1999).CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
59.Dworkin, M. & Foster, J. Experiments with some microorganisms which utilize ethane and hydrogen. J. Bacteriol. 75, 592–603 (1958).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
60.Singh, J. K., Adams, F. G. & Brown, M. H. Diversity and function of capsular polysaccharide in Acinetobacter baumannii. Front. Microbiol. 9, 3301 (2019).PubMed 
PubMed Central 
Article 

Google Scholar 
61.Polak-Berecka, M., Waśko, A., Skrzypek, H. & Kreft, A. Production of exopolysaccharides by a probiotic strain of Lactobacillus rhamnosus: biosynthesis and purification methods. Acta Aliment. 42, 220–228 (2013).CAS 
Article 

Google Scholar 
62.Tschaplinski, T. J. et al. The nature of the progression of drought stress drives differential metabolomic responses in Populus deltoides. Ann. Bot. 124, 617–626 (2019).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
63.Michel, B. E. & Kaufmann, M. R. The osmotic potential of polyethylene glycol 6000. Plant Physiol. 51, 914–916 (1973).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
64.Hanna, A., Berg, M., Stout, V. & Razatos, A. Role of capsular colanic acid in adhesion of uropathogenic Escherichia coli. Appl. Environ. Microbiol. 69, 4474–4481 (2003).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
65.Liu, S.-B. et al. Structure and ecological roles of a novel exopolysaccharide from the Arctic sea ice bacterium Pseudoalteromonas sp. strain SM20310. Appl. Environ. Microbiol. 79, 224–230 (2013).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
66.Dubois, M., Gilles, K. A., Hamilton, J. K., Rebers, P. & Smith, F. Colorimetric method for determination of sugars and related substances. Analyt. Chem. 28, 350–356 (1956).CAS 
Article 

Google Scholar 
67.Yahaghi, Z., Shirvani, M., Nourbakhsh, F. & Pueyo, J. J. Uptake and effects of lead and zinc on alfalfa (Medicago sativa L.) seed germination and seedling growth: Role of plant growth promoting bacteria. S. Afr. J. Bot. 124, 573–582 (2019).CAS 
Article 

Google Scholar 
68.Zhang, Z. & Huang, R. Analysis of malondialdehyde, chlorophyll proline, soluble sugar, and glutathione content in Arabidopsis seedling. Bio-Protoc. 3, e817 (2013).
Google Scholar 
69.Türkan, I., Bor, M., Özdemir, F. & Koca, H. Differential responses of lipid peroxidation and antioxidants in the leaves of drought-tolerant P. acutifolius Gray and drought-sensitive P. vulgaris L. subjected to polyethylene glycol mediated water stress. Plant Sci. 168, 223–231 (2005).Article 
CAS 

Google Scholar  More

1.Kleijn, D. et al. On the relationship between farmland biodiversity and land-use intensity in Europe. Proc. R. Soc. Lond. B Biol. Sci. 276, 903–909 (2009).2.Ollerton, J., Erenler, H., Edwards, M. & Crockett, R. Extinctions of aculeate pollinators in Britain and the role of large-scale agricultural changes. Science 346, 1360–1362 (2014).ADS 
CAS 
PubMed 
Article 

Google Scholar 
3.Stanton, R. L., Morrissey, C. A. & Clark, R. G. Analysis of trends and agricultural drivers of farmland bird declines in North America: a review. Agric. Ecosyst. Environ. 254, 244–254 (2018).Article 

Google Scholar 
4.Beckmann, M. et al. Conventional land-use intensification reduces species richness and increases production: a global meta-analysis. Glob. Change Biol. 25, 1941–1956 (2019).ADS 
Article 

Google Scholar 
5.Allan, E. et al. Interannual variation in land-use intensity enhances grassland multidiversity. Proc. Natl Acad. Sci. USA 111, 308–313 (2014).ADS 
CAS 
PubMed 
Article 

Google Scholar 
6.Newbold, T. et al. Has land use pushed terrestrial biodiversity beyond the planetary boundary? A global assessment. Science 353, 288–291 (2016).7.Le Provost, G. et al. Land-use history impacts functional diversity across multiple trophic groups. Proc. Natl Acad. Sci. USA 117, 1573–1579 (2020).PubMed 
Article 
CAS 

Google Scholar 
8.Geiger, F. et al. Persistent negative effects of pesticides on biodiversity and biological control potential on European farmland. Basic Appl. Ecol. 11, 97–105 (2010).CAS 
Article 

Google Scholar 
9.Rajaniemi, T. K. Why does fertilization reduce plant species diversity? Testing three competition-based hypotheses. J. Ecol. 90, 316–324 (2002).Article 

Google Scholar 
10.Zeng, J. et al. Nitrogen fertilization directly affects soil bacterial diversity and indirectly affects bacterial community composition. Soil Biol. Biochem. 92, 41–49 (2016).CAS 
Article 

Google Scholar 
11.Suding, K. N. et al. Functional-and abundance-based mechanisms explain diversity loss due to N fertilization. Proc. Natl Acad. Sci. USA 102, 4387–4392 (2005).ADS 
CAS 
PubMed 
Article 

Google Scholar 
12.Perović, D. et al. Configurational landscape heterogeneity shapes functional community composition of grassland butterflies. J. Appl. Ecol. 52, 505–513 (2015).Article 

Google Scholar 
13.Redlich, S., Martin, E. A., Wende, B. & Steffan-Dewenter, I. Landscape heterogeneity rather than crop diversity mediates bird diversity in agricultural landscapes. PLoS ONE 13, e0200438 (2018).PubMed 
Article 
CAS 

Google Scholar 
14.Gámez-Virués, S. et al. Landscape simplification filters species traits and drives biotic homogenization. Nat. Commun. 6, 8568 (2015).ADS 
PubMed 
Article 
CAS 

Google Scholar 
15.Benton, T. G., Vickery, J. A. & Wilson, J. D. Farmland biodiversity: is habitat heterogeneity the key? Trends Ecol. Evol. 18, 182–188 (2003).Article 

Google Scholar 
16.Gonthier, D. J. et al. Biodiversity conservation in agriculture requires a multi-scale approach. Proc. R. Soc. Lond. B Biol. Sci. 281, 20141358 (2014).
Google Scholar 
17.Leibold, M. A. et al. The metacommunity concept: a framework for multi-scale community ecology. Ecol. Lett. 7, 601–613 (2004).Article 

Google Scholar 
18.Chase, J. M. & Myers, J. A. Disentangling the importance of ecological niches from stochastic processes across scales. Philos. Trans. R. Soc. Lond. B Biol. Sci. 366, 2351–2363 (2011).PubMed 
Article 

Google Scholar 
19.Thompson, P. L. et al. A process-based metacommunity framework linking local and regional scale community ecology. Ecol. Lett. 23, 1314–1329 (2020).PubMed 
PubMed Central 
Article 

Google Scholar 
20.Gravel, D., Canham, C. D., Beaudet, M. & Messier, C. Reconciling niche and neutrality: the continuum hypothesis. Ecol. Lett. 9, 399–409 (2006).PubMed 
Article 

Google Scholar 
21.Vellend, M. Conceptual synthesis in community ecology. Q. Rev. Biol. 85, 183–206 (2010).PubMed 
Article 

Google Scholar 
22.Tscharntke, T., Klein, A. M., Kruess, A., Steffan-Dewenter, I. & Thies, C. Landscape perspectives on agricultural intensification and biodiversity–ecosystem service management. Ecol. Lett. 8, 857–874 (2005).Article 

Google Scholar 
23.Blitzer, E. J. et al. Spillover of functionally important organisms between managed and natural habitats. Agric. Ecosyst. Environ. 146, 34–43 (2012).Article 

Google Scholar 
24.Birkhofer, K. et al. Land-use type and intensity differentially filter traits in above- and below-ground arthropod communities. J. Anim. Ecol. 86, 511–520 (2017).PubMed 
Article 

Google Scholar 
25.de Graaff, M.-A., Hornslein, N., Throop, H. L., Kardol, P. & van Diepen, L. T. A. Effects of agricultural intensification on soil biodiversity and implications for ecosystem functioning: a meta-analysis. Adv. Agron. 155, 1–44 (2019).Article 

Google Scholar 
26.De Deyn, G. B. & Van der Putten, W. H. Linking aboveground and belowground diversity. Trends Ecol. Evol. 20, 625–633 (2005).PubMed 
Article 

Google Scholar 
27.Field, R. et al. Spatial species-richness gradients across scales: a meta-analysis. J. Biogeogr. 36, 132–147 (2009).Article 

Google Scholar 
28.Cameron, E. K. et al. Global mismatches in aboveground and belowground biodiversity. Conserv. Biol. 33, 1187–1192 (2019).PubMed 
Article 

Google Scholar 
29.Gossner, M. M. et al. Land-use intensification causes multitrophic homogenization of grassland communities. Nature 540, 266–269 (2016).ADS 
CAS 
PubMed 
Article 

Google Scholar 
30.Geisen, S., Wall, D. H. & van der Putten, W. H. Challenges and opportunities for soil biodiversity in the anthropocene. Curr. Biol. 29, R1036–R1044 (2019).CAS 
PubMed 
Article 

Google Scholar 
31.Tsiafouli, M. A. et al. Intensive agriculture reduces soil biodiversity across Europe. Glob. Change Biol. 21, 973–985 (2015).ADS 
Article 

Google Scholar 
32.George, P. B. L. et al. Divergent national-scale trends of microbial and animal biodiversity revealed across diverse temperate soil ecosystems. Nat. Commun. 10, 1107 (2019).ADS 
PubMed 
Article 
CAS 

Google Scholar 
33.Sirami, C. et al. Increasing crop heterogeneity enhances multitrophic diversity across agricultural regions. Proc. Natl Acad. Sci. USA 116, 16442–16447 (2019).CAS 
PubMed 
Article 

Google Scholar 
34.Seibold, S. et al. Arthropod decline in grasslands and forests is associated with landscape-level drivers. Nature 574, 671–674 (2019).ADS 
CAS 
PubMed 
Article 

Google Scholar 
35.Dauber, J. et al. Local vs. landscape controls on diversity: a test using surface-dwelling soil macroinvertebrates of differing mobility. Glob. Ecol. Biogeogr. 14, 213–221 (2005).Article 

Google Scholar 
36.Cadotte, M. W. & Fukami, T. Dispersal, spatial scale, and species diversity in a hierarchically structured experimental landscape. Ecol. Lett. 8, 548–557 (2005).PubMed 
Article 

Google Scholar 
37.Grilli, G. et al. Fungal diversity at fragmented landscapes: synthesis and future perspectives. Curr. Opin. Microbiol. 37, 161–165 (2017).CAS 
PubMed 
Article 

Google Scholar 
38.Fenchel, T. O. M. & Finlay, B. J. The ubiquity of small species: patterns of local and global diversity. Bioscience 54, 777–784 (2004).Article 

Google Scholar 
39.Postma-Blaauw, M. B., Goede, R. G. M., de, Bloem, J., Faber, J. H. & Brussaard, L. Soil biota community structure and abundance under agricultural intensification and extensification. Ecology 91, 460–473 (2010).PubMed 
Article 
PubMed Central 

Google Scholar 
40.Boeraeve, M., Honnay, O. & Jacquemyn, H. Local abiotic conditions are more important than landscape context for structuring arbuscular mycorrhizal fungal communities in the roots of a forest herb. Oecologia 190, 149–157 (2019).ADS 
PubMed 
Article 

Google Scholar 
41.Meyer, A. et al. Different land use intensities in grassland ecosystems drive ecology of microbial communities involved in nitrogen turnover in soil. PLoS ONE 8, e73536 (2013).ADS 
CAS 
PubMed 
Article 

Google Scholar 
42.Thomson, B. C. et al. Soil conditions and land use intensification effects on soil microbial communities across a range of European field sites. Soil Biol. Biochem. 88, 403–413 (2015).CAS 
Article 

Google Scholar 
43.Fahrig, L. Effects of habitat fragmentation on biodiversity. Annu. Rev. Ecol. Evol. Syst. 34, 487–515 (2003).Article 

Google Scholar 
44.Chaudhary, V. B., Nolimal, S., Sosa-Hernández, M. A., Egan, C. & Kastens, J. Trait-based aerial dispersal of arbuscular mycorrhizal fungi. N. Phytol. 228, 238–252 (2020).CAS 
Article 

Google Scholar 
45.Vannette, R. L., Leopold, D. R. & Fukami, T. Forest area and connectivity influence root-associated fungal communities in a fragmented landscape. Ecology 97, 2374–2383 (2016).PubMed 
Article 

Google Scholar 
46.Purschke, O. et al. Interactive effects of landscape history and current management on dispersal trait diversity in grassland plant communities. J. Ecol. 102, 437–446 (2014).PubMed 
Article 

Google Scholar 
47.Thiel, N. et al. Airborne bacterial emission fluxes from manure-fertilized agricultural soil. Microb. Biotechnol. 13, 1631–1647 (2020).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
48.Adl, S. M., Coleman, D. C. & Read, F. Slow recovery of soil biodiversity in sandy loam soils of Georgia after 25 years of no-tillage management. Agric. Ecosyst. Environ. 114, 323–334 (2006).Article 

Google Scholar 
49.Fischer, M. et al. Implementing large-scale and long-term functional biodiversity research: The Biodiversity Exploratories. Basic Appl. Ecol. 11, 473–485 (2010).Article 

Google Scholar 
50.Soliveres, S. et al. Biodiversity at multiple trophic levels is needed for ecosystem multifunctionality. Nature 536, 456–459 (2016).ADS 
CAS 
PubMed 
Article 

Google Scholar 
51.Blüthgen, N. et al. A quantitative index of land-use intensity in grasslands: integrating mowing, grazing and fertilization. Basic Appl. Ecol. 13, 207–220 (2012).Article 

Google Scholar 
52.Kéfi, S. et al. More than a meal… integrating non-feeding interactions into food webs. Ecol. Lett. 15, 291–300 (2012).PubMed 
Article 

Google Scholar 
53.Birkhofer, K. et al. General relationships between abiotic soil properties and soil biota across spatial scales and different land-use types. PLoS ONE 7, e43292 (2012).ADS 
CAS 
PubMed 
Article 

Google Scholar 
54.Xue, P.-P., Carrillo, Y., Pino, V., Minasny, B. & McBratney, A. B. Soil properties drive microbial community structure in a large scale transect in South Eastern Australia. Sci. Rep. 8, 11725 (2018).ADS 
PubMed 
Article 
CAS 

Google Scholar 
55.Löbel, S., Dengler, J. & Hobohm, C. Species richness of vascular plants, bryophytes and lichens in dry grasslands: The effects of environment, landscape structure and competition. Folia Geobot. 41, 377–393 (2006).Article 

Google Scholar 
56.Myers, M. C., Mason, J. T., Hoksch, B. J., Cambardella, C. A. & Pfrimmer, J. D. Birds and butterflies respond to soil-induced habitat heterogeneity in experimental plantings of tallgrass prairie species managed as agroenergy crops in Iowa, USA. J. Appl. Ecol. 52, 1176–1187 (2015).Article 

Google Scholar 
57.Moeslund, J. E. et al. Topographically controlled soil moisture drives plant diversity patterns within grasslands. Biodivers. Conserv. 22, 2151–2166 (2013).Article 

Google Scholar 
58.Ågren, A. M., Lidberg, W., Strömgren, M., Ogilvie, J. & Arp, P. A. Evaluating digital terrain indices for soil wetness mapping–a Swedish case study. Hydrol. Earth Syst. Sci. 18, 3623–3634 (2014).ADS 
Article 

Google Scholar 
59.Vogt, J. et al. Eleven years’ data of grassland management in Germany. Biodivers. Data J. 7, e36387 (2019).PubMed 
Article 

Google Scholar 
60.Manning, P. et al. Grassland management intensification weakens the associations among the diversities of multiple plant and animal taxa. Ecology 96, 1492–1501 (2015).Article 

Google Scholar 
61.Loreau, M., Mouquet, N. & Gonzalez, A. Biodiversity as spatial insurance in heterogeneous landscapes. Proc. Natl Acad. Sci. USA 100, 12765–12770 (2003).ADS 
CAS 
PubMed 
Article 

Google Scholar 
62.Morris, M. G. The effects of structure and its dynamics on the ecology and conservation of arthropods in British grasslands. Biol. Conserv. 95, 129–142 (2000).Article 

Google Scholar 
63.Socher, S. A. et al. Direct and productivity-mediated indirect effects of fertilization, mowing and grazing on grassland species richness. J. Ecol. 100, 1391–1399 (2012).Article 

Google Scholar 
64.Simons, N. K. et al. Resource-mediated indirect effects of grassland management on arthropod diversity. PLoS ONE 9, e107033 (2014).ADS 
PubMed 
Article 
CAS 

Google Scholar 
65.Harpole, W. S. et al. Addition of multiple limiting resources reduces grassland diversity. Nature 537, 93 (2016).ADS 
CAS 
PubMed 
Article 

Google Scholar 
66.Pöyry, J. et al. Different responses of plants and herbivore insects to a gradient of vegetation height: an indicator of the vertebrate grazing intensity and successional age. Oikos 115, 401–412 (2006).Article 

Google Scholar 
67.Uchida, K. & Ushimaru, A. Biodiversity declines due to abandonment and intensification of agricultural lands: patterns and mechanisms. Ecol. Monogr. 84, 637–658 (2014).Article 

Google Scholar 
68.Shange, R. S., Ankumah, R. O., Ibekwe, A. M., Zabawa, R. & Dowd, S. E. Distinct soil bacterial communities revealed under a diversely managed agroecosystem. PLoS ONE 7, e40338 (2012).ADS 
CAS 
PubMed 
Article 

Google Scholar 
69.Poulsen, P. H. B. et al. Effects of fertilization with urban and agricultural organic wastes in a field trial—Prokaryotic diversity investigated by pyrosequencing. Soil Biol. Biochem. 57, 784–793 (2013).CAS 
Article 

Google Scholar 
70.Filazzola, A. et al. The effects of livestock grazing on biodiversity are multi-trophic: a meta-analysis. Ecol. Lett. 23, 1298–1309 (2020).PubMed 
Article 

Google Scholar 
71.Hooper, D. U. et al. Interactions between aboveground and belowground biodiversity in terrestrial ecosystems: patterns, mechanisms, and feedbacks. Bioscience 50, 1049–1061 (2000).Article 

Google Scholar 
72.López-Jamar, J., Casas, F., Díaz, M. & Morales, M. B. Local differences in habitat selection by Great Bustards Otis tarda in changing agricultural landscapes: implications for farmland bird conservation. Bird Conserv. Int. 21, 328–341 (2011).Article 

Google Scholar 
73.Boeraeve, M. et al. The impact of spatial isolation and local habitat conditions on colonization of recent forest stands by ectomycorrhizal fungi. Forest Ecol. Manag. 429, 84–92 (2018).Article 

Google Scholar 
74.Fiore-Donno, A. M., Richter-Heitmann, T. & Bonkowski, M. Contrasting responses of protistan plant parasites and phagotrophs to ecosystems, land management and soil properties. Front. Microbiol. 11, 1823 (2020).PubMed 
Article 

Google Scholar 
75.Diekötter, T., Wamser, S., Wolters, V. & Birkhofer, K. Landscape and management effects on structure and function of soil arthropod communities in winter wheat. Agric. Ecosyst. Environ. 137, 108–112 (2010).Article 

Google Scholar 
76.Decaëns, T. Macroecological patterns in soil communities. Glob. Ecol. Biogeogr. 19, 287–302 (2010).Article 

Google Scholar 
77.Hanson, C. A., Fuhrman, J. A., Horner-Devine, M. C. & Martiny, J. B. H. Beyond biogeographic patterns: processes shaping the microbial landscape. Nat. Rev. Microbiol. 10, 497–506 (2012).CAS 
PubMed 
Article 

Google Scholar 
78.Thakur, M. P. et al. Towards an integrative understanding of soil biodiversity. Biol. Rev. 95, 350–364 (2020).PubMed 
Article 

Google Scholar 
79.Peay, K., Garbelotto, M. & Bruns, T. Evidence of dispersal limitation in soil microorganisms: isolation reduces species richness on mycorrhizal tree islands. Ecology 91, 3631–3640 (2010).PubMed 
Article 

Google Scholar 
80.van der Putten, W. H. Climate change, aboveground-belowground interactions, and species’ range shifts. Annu. Rev. Ecol. Evol. Syst. 43, 365–383 (2012).Article 

Google Scholar 
81.Wubs, E. R. J., Putten, W. H., van der, Bosch, M. & Bezemer, T. M. Soil inoculation steers restoration of terrestrial ecosystems. Nat. Plants 2, 1–5 (2016).Article 

Google Scholar 
82.Bünemann, E. K., Schwenke, G. D. & Van Zwieten, L. Impact of agricultural inputs on soil organisms—a review. Soil Res. 44, 379–406 (2006).Article 

Google Scholar 
83.Cameron, E. K. et al. Global mismatches in aboveground and belowground biodiversity. Conserv. Biol. 33, 1187–1192 (2019).PubMed 
Article 

Google Scholar 
84.Guerra, C. A. et al. Tracking, targeting, and conserving soil biodiversity. Science 371, 239–241 (2021).ADS 
CAS 
PubMed 
Article 

Google Scholar 
85.Guerra, C. A. et al. Blind spots in global soil biodiversity and ecosystem function research. Nat. Commun. 11, 1–13 (2020).Article 
CAS 

Google Scholar 
86.Kleijn, D. & Sutherland, W. J. How effective are European agri-environment schemes in conserving and promoting biodiversity? J. Appl. Ecol. 40, 947–969 (2003).Article 

Google Scholar 
87.Bender, S. F., Wagg, C. & van der Heijden, M. G. An underground revolution: biodiversity and soil ecological engineering for agricultural sustainability. Trends Ecol. Evol. 31, 440–452 (2016).PubMed 
Article 

Google Scholar 
88.Gessler, P. E., Moore, I. D., McKenzie, N. J. & Ryan, P. J. Soil-landscape modelling and spatial prediction of soil attributes. Int. J. Geogr. Inf. Syst. 9, 421–432 (1995).Article 

Google Scholar 
89.Sørensen, R., Zinko, U. & Seibert, J. On the calculation of the topographic wetness index: evaluation of different methods based on field observations. Hydrol. Earth Syst. Sci. 10, 101–112 (2006).ADS 
Article 

Google Scholar 
90.Ostrowski, A., Lorenzen, K., Petzold, E. & Schindler, S. Land use intensity index (LUI) calculation tool of the Biodiversity Exploratories project for grassland survey data from three different regions in Germany since 2006, BEXIS 2 module. (Zenodo, 2020).91.Koleff, P., Gaston, K. J. & Lennon, J. J. Measuring beta diversity for presence–absence data. J. Anim. Ecol. 72, 367–382 (2003).Article 

Google Scholar 
92.Prober, S. M. et al. Plant diversity predicts beta but not alpha diversity of soil microbes across grasslands worldwide. Ecol. Lett. 18, 85–95 (2015).PubMed 
Article 

Google Scholar 
93.Ulrich, W. et al. Climate and soil attributes determine plant species turnover in global drylands. J. Biogeogr. 41, 2307–2319 (2014).PubMed 
PubMed Central 
Article 

Google Scholar 
94.Shoffner, A., Wilson, A. M., Tang, W. & Gagné, S. A. The relative effects of forest amount, forest configuration, and urban matrix quality on forest breeding birds. Sci. Rep. 8, 1–12 (2018).CAS 
Article 

Google Scholar 
95.Fahrig, L. et al. Functional landscape heterogeneity and animal biodiversity in agricultural landscapes. Ecol. Lett. 14, 101–112 (2011).PubMed 
Article 

Google Scholar 
96.R Core Team. R: A language and environment for statistical computing. (R Foundation for Statistical Computing, 2020).97.Ricci, B. et al. The influence of landscape on insect pest dynamics: a case study in southeastern France. Landsc. Ecol. 24, 337–349 (2009).Article 

Google Scholar 
98.Benjamini, Y. & Hochberg, Y. Controlling the false discovery rate: a practical and powerful approach to multiple testing. J. R. Stat. Soc. Ser. B Methodol. 57, 289–300 (1995).MathSciNet 
MATH 

Google Scholar 
99.Verhoeven, K. J. F., Simonsen, K. L. & McIntyre, L. M. Implementing false discovery rate control: increasing your power. Oikos 108, 643–647 (2005).Article 

Google Scholar 
100.Gross, N. et al. Functional trait diversity maximizes ecosystem multifunctionality. Nat. Ecol. Evol. 1, 0132 (2017).PubMed 
PubMed Central 
Article 

Google Scholar 
101.Le Bagousse-Pinguet, Y. et al. Phylogenetic, functional, and taxonomic richness have both positive and negative effects on ecosystem multifunctionality. Proc. Natl Acad. Sci. USA 116, 8419–8424 (2019).PubMed 
Article 
CAS 

Google Scholar  More