Philippa Kaur

1.Bartz, R. & Kowarik, I. Assessing the environmental impacts of invasive alien plants: a review of assessment approaches. Neobiota https://doi.org/10.3897/neobiota.43.30122 (2019).Article 

Google Scholar 
2.Chen, C. et al. Historical introduction, geographical distribution, and biological characteristics of alien plants in China. Biodivers. Conserv. 26, 353–381. https://doi.org/10.1007/s10531-016-1246-z (2017).Article 

Google Scholar 
3.Feng, J. & Zhu, Y. Alien invasive plants in China: risk assessment and spatial patterns. Biodivers. Conserv. 19, 3489–3497. https://doi.org/10.1007/s10531-010-9909-7 (2010).Article 

Google Scholar 
4.Thapa, S., Chitale, V., Rijal, S. J., Bisht, N. & Shrestha, B. B. Understanding the dynamics in distribution of invasive alien plant species under predicted climate change in Western Himalaya. Plos One https://doi.org/10.1371/journal.pone.0195752 (2018).Article 
PubMed 
PubMed Central 

Google Scholar 
5.Majewska, M. L. et al. Do the impacts of alien invasive plants differ from expansive native ones? An experimental study on arbuscular mycorrhizal fungi communities. Biol. Fertil. Soils 54, 631–643. https://doi.org/10.1007/s00374-018-1283-8 (2018).Article 

Google Scholar 
6.Shi, J., Luo, Y.-Q., Zhou, F. & He, P. The relationship between invasive alien species and main climatic zones. Biodivers. Conserv. 19, 2485–2500. https://doi.org/10.1007/s10531-010-9855-4 (2010).Article 

Google Scholar 
7.Hulme, P. E. Trade, transport and trouble: managing invasive species pathways in an era of globalization. J. Appl. Ecol. 46, 10–18. https://doi.org/10.1111/j.1365-2664.2008.01600.x (2009).Article 

Google Scholar 
8.Hulme, P. E. et al. Grasping at the routes of biological invasions: a framework for integrating pathways into policy. J. Appl. Ecol. 45, 403–414. https://doi.org/10.1111/j.1365-2664.2007.01442.x (2008).Article 

Google Scholar 
9.Jara-Guerrero, A., De la Cruz, M. & Mendez, M. Seed dispersal spectrum of woody species in south ecuadorian dry forests: environmental correlates and the effect of considering species abundance. Biotropica 43, 722–730. https://doi.org/10.1111/j.1744-7429.2011.00754.x (2011).Article 

Google Scholar 
10.van Oudtshoorn, K. v. R. & van Rooyen, M. W. Dispersal biology of desert plants. (Springer 1999).11.Liu, J., Liang, S. C., Liu, F. H., Wang, R. Q. & Dong, M. Invasive alien plant species in China: regional distribution patterns. Divers. Distrib. 11, 341–347. https://doi.org/10.1111/j.1366-9516.2005.00162.x (2005).Article 

Google Scholar 
12.Caughlin, T. T., Ferguson, J. M., Lichstein, J. W., Bunyavejchewin, S. & Levey, D. J. The importance of long-distance seed dispersal for the demography and distribution of a canopy tree species. Ecology 95, 952–962. https://doi.org/10.1890/13-0580.1 (2014).Article 
PubMed 

Google Scholar 
13.Nathan, R. et al. Mechanisms of long-distance seed dispersal. Trends Ecol. Evol. 23, 638–647. https://doi.org/10.1016/j.tree.2008.08.003 (2008).Article 
PubMed 

Google Scholar 
14.Wang, R. et al. Multiple mechanisms underlie rapid expansion of an invasive alien plant. New Phytol. 191, 828–839. https://doi.org/10.1111/j.1469-8137.2011.03720.x (2011).Article 
PubMed 

Google Scholar 
15.Vittoz, P. & Engler, R. Seed dispersal distances: a typology based on dispersal modes and plant traits. Bot. Helv. 117, 109–124. https://doi.org/10.1007/s00035-007-0797-8 (2007).Article 

Google Scholar 
16.Willson, M. F., Rice, B. L. & Westoby, M. Seed dispersal spectra – a comparison of temperate plant-communities. J. Veg. Sci. 1, 547–562. https://doi.org/10.2307/3235789 (1990).Article 

Google Scholar 
17.Nilsson, C., Brown, R. L., Jansson, R. & Merritt, D. M. The role of hydrochory in structuring riparian and wetland vegetation. Biol. Rev. 85, 837–858. https://doi.org/10.1111/j.1469-185X.2010.00129.x (2010).Article 
PubMed 

Google Scholar 
18.Eminniyaz, A. et al. Dispersal Mechanisms of the Invasive Alien Plant Species Buffalobur (Solanum rostratum) in Cold Desert Sites of Northwest China. Weed Sci. 61, 557–563. https://doi.org/10.1614/ws-d-13-00011.1 (2013).CAS 
Article 

Google Scholar 
19.Soons, M. B., Heil, G. W., Nathan, R. & Katul, G. G. Determinants of long-distance seed dispersal by wind in grasslands. Ecology 85, 3056–3068. https://doi.org/10.1890/03-0522 (2004).Article 

Google Scholar 
20.Tackenberg, O. Modeling long-distance dispersal of plant diaspores by wind. Ecol. Monogr. 73, 173–189. https://doi.org/10.1890/0012-9615(2003)073[0173:mldopd]2.0.co;2 (2003).Article 

Google Scholar 
21.Wallace, H. M., Howell, M. G. & Lee, D. J. Standard yet unusual mechanisms of long-distance dispersal: seed dispersal of Corymbia torelliana by bees. Divers. Distrib. 14, 87–94. https://doi.org/10.1111/j.1472-4642.2007.00427.x (2008).Article 

Google Scholar 
22.Soons, M. B., Nathan, R. & Katul, G. G. Human effects on long-distance wind dispersal and colonization by grassland plants. Ecology 85, 3069–3079. https://doi.org/10.1890/03-0398 (2004).Article 

Google Scholar 
23.Taylor, K., Brummer, T., Taper, M. L., Wing, A. & Rew, L. J. Human-mediated long-distance dispersal: an empirical evaluation of seed dispersal by vehicles. Divers. Distrib. 18, 942–951. https://doi.org/10.1111/j.1472-4642.2012.00926.x (2012).Article 

Google Scholar 
24.Cain, M. L., Milligan, B. G. & Strand, A. E. Long-distance seed dispersal in plant populations. Am. J. Bot. 87, 1217–1227. https://doi.org/10.2307/2656714 (2000).CAS 
Article 
PubMed 

Google Scholar 
25.Thomson, F. J., Moles, A. T., Auld, T. D. & Kingsford, R. T. Seed dispersal distance is more strongly correlated with plant height than with seed mass. J. Ecol. 99, 1299–1307. https://doi.org/10.1111/j.1365-2745.2011.01867.x (2011).Article 

Google Scholar 
26.Zhu, J., Liu, M., Xin, Z., Zhao, Y. & Liu, Z. Which factors have stronger explanatory power for primary wind dispersal distance of winged diaspores: the case of Zygophyllum xanthoxylon (Zygophyllaceae)?. J Plant Ecol 9, 346–356. https://doi.org/10.1093/jpe/rtv051 (2016).Article 

Google Scholar 
27.Jones, F. A. & Muller-Landau, H. C. Measuring long-distance seed dispersal in complex natural environments: an evaluation and integration of classical and genetic methods. J. Ecol. 96, 642–652. https://doi.org/10.1111/j.1365-2745.2008.01400.x (2008).Article 

Google Scholar 
28.Snell, R. S. Simulating long-distance seed dispersal in a dynamic vegetation model. Glob. Ecol. Biogeogr. 23, 89–98. https://doi.org/10.1111/geb.12106 (2014).Article 

Google Scholar 
29.Jongejans, E. & Telenius, A. Field experiments on seed dispersal by wind in ten umbelliferous species (Apiaceae). Plant Ecol. 152, 67–78. https://doi.org/10.1023/a:1011467604469 (2001).Article 

Google Scholar 
30.Guitian, J. & Sanchez, J. M. Seed dispersal spectra of plant-communities in the Iberian Peninsula. Vegetatio 98, 157–164. https://doi.org/10.1007/bf00045553 (1992).Article 

Google Scholar 
31.Ou, H., Lu, C. & O’Toole, D. K. A risk assessment system for alien plant bio-invasion in Xiamen China. J. Environ. Sci. 20, 989–997. https://doi.org/10.1016/s1001-0742(08)62198-1 (2008).Article 

Google Scholar 
32.Huang, Q. Q., Wu, J. M., Bai, Y. Y., Zhou, L. & Wang, G. X. Identifying the most noxious invasive plants in China: role of geographical origin, life form and means of introduction. Biodivers. Conserv. 18, 305–316. https://doi.org/10.1007/s10531-008-9485-2 (2009).Article 

Google Scholar 
33.Liu, J. et al. Invasive alien plants in China: role of clonality and geographical origin. Biol. Invasions 8, 1461–1470. https://doi.org/10.1007/s10530-005-5838-x (2006).Article 

Google Scholar 
34.Xu, H., Wang, J., Qiang, S. & Wang, C. Study of key issues under the convention on biological diversity: alien species invasion, biosafety, genetic resources. (Science Press, 2004).35.Ma, J. & Li, H. The checklist of the alien invasive plants in China. (Higher Education Press, 2018).36.Wang, C., Liu, J., Xiao, H., Zhou, J. & Du, D. Floristic characteristics of alien invasive seed plant species in China. Anais Da Academia Brasileira De Ciencias 88, 1791–1797. https://doi.org/10.1590/0001-3765201620150687 (2016).Article 
PubMed 

Google Scholar 
37.Xie, Y., Li, Z. Y., Gregg, W. P. & Dianmo, L. Invasive species in China – an overview. Biodivers. Conserv. 10, 1317–1341 (2001).Article 

Google Scholar 
38.Qi, W., Liu, S. H., Zhao, M. F. & Liu, Z. China’s different spatial patterns of population growth based on the “Hu Line”. J. Geog. Sci. 26, 1611–1625. https://doi.org/10.1007/s11442-016-1347-3 (2016).Article 

Google Scholar 
39.Chen, M. X., Gong, Y. H., Li, Y., Lu, D. D. & Zhang, H. Population distribution and urbanization on both sides of the Hu Huanyong Line: answering the Premier’s question. J. Geog. Sci. 26, 1593–1610. https://doi.org/10.1007/s11442-016-1346-4 (2016).Article 

Google Scholar 
40.Pan, X. B. et al. Spatial similarity in the distribution of invasive alien plants and animals in China. Nat. Hazards 77, 1751–1764. https://doi.org/10.1007/s11069-015-1672-3 (2015).Article 

Google Scholar 
41.Yan, X. et al. The categorization and analysis on the geographic distribution patterns of Chinese alien invasive plants. Biodiv. Sci. 22, 667–676 (2014).Article 

Google Scholar 
42.Wang, G., Bai, F. & Sang, W. Spatial distribution of invasive alien animal and plant species and its influencing factors in China. Plant Sci. J. 35, 513–524 (2017).
Google Scholar 
43.Weber, E., Sun, S. G. & Li, B. Invasive alien plants in China: diversity and ecological insights. Biol. Invasions 10, 1411–1429. https://doi.org/10.1007/s10530-008-9216-3 (2008).Article 

Google Scholar 
44.Zhou, Q. et al. Geographical distribution and determining factors of different invasive ranks of alien species across China. Sci. Total Environ. 722, 137929. https://doi.org/10.1016/j.scitotenv.2020.137929 (2020).ADS 
CAS 
Article 
PubMed 

Google Scholar 
45.Ding, J., Mack, R. N., Lu, P., Ren, M. & Huang, H. China’s booming economy is sparking and accelerating biological invasions. Bioscience 58, 317–324. https://doi.org/10.1641/b580407 (2008).Article 

Google Scholar 
46.Pysek, P. et al. Alien plants in checklists and floras: towards better communication between taxonomists and ecologists. Taxon 53, 131–143. https://doi.org/10.2307/4135498 (2004).Article 

Google Scholar 
47.Zeng, C. & Chen, W. A forecasting model of urban underground space development intensity. Chin. J. Undergr. Space Eng. 14, 1154–1160 (2018).
Google Scholar 
48.Shao, M. N. et al. Outbreak of a new alien invasive plant Salvia reflexa in north-east China. Weed Res. 59, 201–208. https://doi.org/10.1111/wre.12357 (2019).Article 

Google Scholar 
49.Wan, F. H. et al. Invasive mechanism and control strategy of Ageratina adenophora (Sprengel). Sci. China-Life Sci. 53, 1291–1298. https://doi.org/10.1007/s11427-010-4080-7 (2010).Article 
PubMed 

Google Scholar 
50.Poudel, A. S., Jha, P. K., Shrestha, B. B. & Muniappan, R. Biology and management of the invasive weed Ageratina adenophora (Asteraceae): current state of knowledge and future research needs. Weed Res. 59, 79–92. https://doi.org/10.1111/wre.12351 (2019).Article 

Google Scholar 
51.Datta, A., Schweiger, O. & Kuehn, I. Niche expansion of the invasive plant species Ageratina adenophora despite evolutionary constraints. J. Biogeogr. 46, 1306–1315. https://doi.org/10.1111/jbi.13579 (2019).Article 

Google Scholar 
52.Guo, X., Ren, M. & Ding, J. Do the introductions by botanical gardens facilitate the invasion of Solidago canadensis (Asterceae) in China?. Weed Res. 56, 442–451. https://doi.org/10.1111/wre.12227 (2016).Article 

Google Scholar 
53.Ganneru, S., Shaik, H., Peddi, K. & Mudiam, M. K. R. Evaluating the metabolic perturbations in Mangifera indica (mango) ripened with various ripening agents/practices through gas chromatography – mass spectrometry based metabolomics. J. Sep. Sci. 42, 3086–3094. https://doi.org/10.1002/jssc.201900291 (2019).CAS 
Article 
PubMed 

Google Scholar 
54.Mahandran, V., Murugan, C. M., Marimuthu, G. & Nathan, P. T. Seed dispersal of a tropical deciduous Mahua tree, Madhuca latifolia (Sapotaceae) exhibiting bat-fruit syndrome by pteropodid bats. Glob. Ecol. Conserv. 14, e00396. https://doi.org/10.1016/j.gecco.2018.e00396 (2018).Article 

Google Scholar 
55.Weber, E. & Li, B. Plant invasions in China: What is to be expected in the wake of economic development?. Bioscience 58, 437–444. https://doi.org/10.1641/b580511 (2008).Article 

Google Scholar 
56.Jian, L., Hua, C., Kowarik, I., Zhang, Y. & Wang, R. Plant invasions in China: an emerging hot topic in invasion science. Neobiota 15, 27–41 (2012).Article 

Google Scholar  More

1.Leakey, L. S. B., Tobias, P. V. & Napier, J. R. A new species of the genus Homo from Olduvai Gorge. Nature 202, 7–9 (1964).ADS 
CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
2.Bromage, T. G. & Schrenk, F. Biogeographic and climatic basis for a narrative of early hominid evolution. J. Hum. Evol. 28, 109–114 (1995).Article 

Google Scholar 
3.Klein, R. The causes of “robust” australopithecine extinction in Evolutionary history of the “robust” australopithecines (ed. Grine, F.E.) 499–505 (Aldine de Gruyter, 1988).4.McPherron, S.P. et al. Evidence for stone-tool-assisted consumption of animal tissues before 3.39 million years ago at Dikika, Ethiopia. Nature 466, 857–860 (2010).5.Harmand, S. et al. Before the Oldowan: 3.3 Ma Stone Tools from Lomekwi 3, West Turkana, Kenya. Nature 521, 310–315 (2015).6.Cerling, T. E. et al. Diet of Panthropus boisei in the early Pleistocene of East Africa. Proc. Natl. Acad. Sci. USA 108, 9337–9341 (2011).ADS 
CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
7.Ungar, P. S. & Sponheiner, M. The diets of early hominins. Science 334, 190–193 (2011).ADS 
CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
8.Cerling, T. E. et al. Stable isotope-based diet reconstructions of Turkana Basin hominins. Proc. Natl. Acad. Sci. USA 110, 10501–10506 (2013).ADS 
CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
9.Cerling, T. E. et al. Diet of Theropithecus from 4 to 1 Ma in Kenya. Proc. Natl. Acad. Sci. 110, 10507–10512 (2013).ADS 
CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
10.Ungar, P. S., Grine, F. E. & Teaford, M. F. Dental microwear and diet of the Plio-Pleistocene hominin Paranthropus boisei. PLoS ONE 3, e2044 (2008).ADS 
PubMed 
PubMed Central 
Article 
CAS 

Google Scholar 
11.Ludecke, T. et al. Dietary versatility of early Pleistocene hominins. Proc. Natl. Acad. Sci. USA 115, 13330–13335 (2018).PubMed 
Article 
CAS 
PubMed Central 

Google Scholar 
12.Wynn, J. G. et al. Isotopic evidence for the timing of the dietary shift toward C4 foods in eastern African Paranthropus. Proc. Natl. Acad. Sci. USA https://doi.org/10.1073/pnas.2006221117 (2020).Article 
PubMed 
PubMed Central 

Google Scholar 
13.Martin, J. E., Tacail, T., Braga, J., Cerling, T. E. & Balter, V. Calcium isotopic ecology of Turkana Basin hominins. Nat. Commun. 11, 3587 (2020).ADS 
PubMed 
PubMed Central 
Article 
CAS 

Google Scholar 
14.Dominguez-Rodrigo, M. et al. First partial skeleton of a 1.34-million-year-old Paranthropus boisei from Bed II, Oluvai Gorge, Tanzania. PLoS ONE 8, e80347 (2013).15.Wood, B., Wood, C. & Konigsberg, L. Paranthropus boisei: An example of evolutionary stasis?. Am. J. Phys. Anthropol. 95, 117–136 (1994).CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
16.Wood, B. A. & Patterson, B. A. Paranthropus through the looking glass. Proc. Natl. Acad. Sci. USA https://doi.org/10.1073/pnas.2016445117 (2020).Article 
PubMed 
PubMed Central 

Google Scholar 
17.Antón, S. C., Potts, R. & Aiello, L. C. Evolution of early Homo: an integrated biological perspective. Science 345, 1236828 (2014).PubMed 
Article 
CAS 
PubMed Central 

Google Scholar 
18.Wood, B. & Constantino, P. Paranthropus boisei: Fifty years of evidence and analysis. Yrbk. Phys. Anthropol. 50, 106–132 (2007).Article 

Google Scholar 
19.Muttoni, G., Scardia, G. & Kent, D. V. Early hominins in Europe: The Galerian migration hypothesis. Quat. Sci. Rev. 180, 1–29 (2018).ADS 
Article 

Google Scholar 
20.Shultz, S., Nelson, E. & Dunbar, R. I. M. Hominin cognitive evolution: identifying patterns and processes in the fossil and archaeological record. Phil. Trans. R. Soc. B 367, 2130–2140 (2012).PubMed 
Article 
PubMed Central 

Google Scholar 
21.Clark, P. U. et al. The middle Pleistocene transition: characteristics, mechanisms, and implications for long-term changes in atmospheric pCO2. Quat. Sci. Rev. 25, 3150–3184 (2006).ADS 
Article 

Google Scholar 
22.Raymo, M. E., Oppo, D. W. & Curry, W. The mid-Pleistocene climate transition: a deep sea carbon isotopic perspective. Paleoceanogr. 12, 546–559 (1997).ADS 
Article 

Google Scholar 
23.Levin, N. E. Environment and climate of early human evolution. Ann. Rev. Earth Planet. Sci. 43, 405–429 (2015).ADS 
CAS 
Article 

Google Scholar 
24.Cerling, T. E. et al. Woody cover and hominin environments in the past 6 million years. Nature 476, 51–56 (2011).ADS 
CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
25.Potts, R. & Faith, J. T. Alternating high and low climate variability: the context of natural selection and speciation in Plio-Pleistocene hominin evolution. J. Hum. Evol. 87, 5–20 (2015).PubMed 
Article 
PubMed Central 

Google Scholar 
26.Cerling, T. E. et al. Dietary changes of large herbivores in the Turkana Basin, Kenya from 4 to 1 Ma. Proc. Natl. Acad. Sci. USA 112, 11467–11472 (2015).ADS 
CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
27.Negash, E. W. et al. Dietary trends in herbivores from the Shungura Formation, southwestern Ethiopia. Proc. Natl. Acad. Sci. USA https://doi.org/10.1073/pnas.2006982117 (2020).Article 
PubMed 
PubMed Central 

Google Scholar 
28.Pasquette, J. & Drapeau, M. S. M. Environmental comparisons of the Awash Valley, Turkana Basin and lower Omo Valley from upper Miocene to Holocene as assessed from stable carbon and oxygen isotopes of mammalian enamel. Palaeogeogr. Palaeoclimatol. Palaeoecol. 562, 110099 (2021).Article 

Google Scholar 
29.Bobe, R. & Behrensmeyer, A. K. The expansion of grassland ecosystems in Africa in relation to mammalian evolution and the origins of the genus Homo. Palaeogeogr. Palaeoclimatol. Palaeoecol. 207, 399–420 (2004).Article 

Google Scholar 
30.Nutz, A. et al. Plio-Pleistocene sedimentation in West Turkana (Turkana Depression, Kenya, East African Rift System): paleolake fluctuations, paleolandscapes and controlling factors. Earth-Sci. Rev. 211, 103415 (2020).CAS 
Article 

Google Scholar 
31.Sankaran, M. et al. Determinants of woody cover in African savannas. Nature 438, 846–849 (2005).ADS 
CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
32.Saji, N. H., Goswami, B. N., Vinayachandran, P. N. & Yamagata, T. A dipole in the tropical Indian Ocean. Nature 401, 360–363 (1999).ADS 
CAS 
PubMed 
PubMed Central 

Google Scholar 
33.Peterson, L. C., Haug, G. H., Hughen, K. A. & Rohl, U. Rapid changes in the hydrologic cycle of the tropical Atlantic during the Last Glacial. Science 290, 1947–1951 (2000).ADS 
CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
34.Schefuß, E., Schouten, S., Jansen, J.H.F., Sinninghe Damste, J.S. African vegetation controlled sea surface temperatures in the mid-Pleistocene period. Nature 422, 418–421 (2003).35.deMenocal, P.B. African climate change and faunal evolution during the Pliocene-Pleistocene. Earth Planet. Sci. Lett. 220, 3–24 (2004).36.Trauth, M. H., Maslin, M. A., Deino, A. & Strecker, M. R. Late Cenozoic moisture history of East Africa. Science 309, 2051–2053 (2005).ADS 
CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
37.Donges, J. F. et al. Nonlinear detection of paleoclimate-variability transitions possibly related to human evolution. Proc. Natl. Acad. Sci. USA 108, 20422–20427 (2011).ADS 
CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
38.Polissar, P. J., Rose, C., Uno, K. T., Phelps, S. R. & deMenocal, P. Synchronous rise of African C4 ecosystems 10 million years ago in the absence of aridification. Nat. Geosci. 12, 657–660 (2019).ADS 
CAS 
Article 

Google Scholar 
39.Gathogo, P. N. & Brown, F. H. Stratigraphy of the Koobi Fora Formation (Pliocene and Pleistocene) in the Ileret region of northern Kenya. J. Afr. Earth Sci. 45, 369–390 (2006).ADS 
Article 

Google Scholar 
40.Feibel, C. S. A geological history of the Turkana Basin. Evol. Anthropol. 20, 206–216 (2011).PubMed 
Article 
PubMed Central 

Google Scholar 
41.Faith, T. J., Rowan, J., Du, A. & Koch, P. L. Plio-Pleistocene decline of African megaherbivores: No evidence for ancient hominin impacts. Science 362, 938–941 (2018).ADS 
CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
42.Blumenthal, S. A. et al. Aridity and hominin environments. Proc. Natl. Acad. Sci. USA 114, 7331–7336 (2017).ADS 
CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
43.Lepre, C. J. Constraints on Fe-oxide formation in monsoonal Vertisols of Pliocene Kenya using rock magnetism and spectroscopy. Geochem. Geophys. Geosyst. 20, 4998–5013 (2019).ADS 
CAS 
Article 

Google Scholar 
44.Faurby, S., Silvestro, D., Werdelin, L. & Antonelli, A. Brain expansion in early hominins predicts carnivore extinctions in East Africa. Ecol. Lett. 23, 537–544 (2020).PubMed 
PubMed Central 
Article 

Google Scholar 
45.Faith, T. J., Rowan, J. & Du, A. Early hominins evolved within non-analog ecosystems. Proc. Natl. Acad. Sci. USA 116, 21478–21483 (2019).ADS 
CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
46.Bond, W. J., Midgley, G. F. & Woodward, F. I. The importance of low atmospheric CO2 and fire in promoting the spread of grasslands and savannas. Glob. Change Biol. 9, 973–982 (2003).ADS 
Article 

Google Scholar 
47.Bragg, F. J. et al. Stable isotope and modeling evidence for CO2 as a driver of glacial-interglacial vegetation shifts in southern Africa. Biogeosci. 10, 2001–2010 (2013).ADS 
CAS 
Article 

Google Scholar 
48.Ehleringer, J. R., Cerling, T. E. & Helliker, B. R. C4 photosynthesis, atmospheric CO2, and climate. Oecologia 112, 285–299 (1997).ADS 
PubMed 
Article 
PubMed Central 

Google Scholar 
49.Da, J., Zhang, Y., Li, G., Meng, X. & Ji, J. Low CO2 levels of the entire Pleistocene epoch. Nat. Commun. 10, 4342 (2019).ADS 
PubMed 
PubMed Central 
Article 
CAS 

Google Scholar 
50.Stap, L. B. et al. CO2 over the past 5 million years: continuous simulation and new δ11B-based proxy data. Earth Planet. Sci. Lett. 439, 1–10 (2016).ADS 
CAS 
Article 

Google Scholar 
51.van de Wal, R.S.W., de Boer, B., Lourens, LJ.., Kohler, P., Bintanja, R. Reconstruction of a continuous high-resolution CO2 record over the past 20 million years. Clim. Past 7, 1459–69 (2011).52.Passey, B. H., Levin, N. E., Cerling, T. E., Brown, F. H. & Eiler, J. M. High-temperature environments of human evolution in East Africa based on bond ordering in paleosol carbonates. Proc. Nat. Acad. Sci. USA 107, 11245–11249 (2010).ADS 
CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
53.Petit, J.R. et al. Climate and atmospheric history of the past 420,000 years from the Vostok ice core, Antarctica: Nature 399, 429–436 (1999).54.Schefuß, E. & Dupont, L. M. Multiple drivers of Miocene C4 ecosystem expansions. Nat. Geosci. 13, 463–464 (2020).ADS 
Article 
CAS 

Google Scholar 
55.Johnson, T.C. et al. A progressively wetter climate in southern East Africa over the past 1.3 million years. Nature 537, 220–224 (2016).56.Skonieczny, C. et al. Monsoon-driven Saharan dust variability over the past 240,000 years. Sci. Adv. 5, eaav1887 (2019).57.Caley, T. et al. A two-million-year-long hydroclimatic context for hominin evolution in southeastern Africa. Nature 560, 76–79.58.Kim, S.-J. et al. High-resolution climate simulation of the last glacial maximum. Clim Dyn 31, 1–16 (2008).Article 

Google Scholar 
59.Tierney, J. E., Russell, J. M., Sinninghe Damsté, J. S., Huang, Y. & Verschuren, D. Late quaternary behavior of the East African monsoon and the importance of the Congo Air Boundary. Quatern. Sci. Rev. 30, 798–807 (2011).ADS 
Article 

Google Scholar 
60.Kingston, J. D. & Harrison, T. Isotopic dietary reconstructions of Pliocene herbivores at Laetoli: implications for early hominin paleoecology. Palaeogeogr. Palaeoclimatol. Palaeoecol. 243, 272–306 (2007).Article 

Google Scholar 
61.Quinn, R. L. Isotopic equifinality and rethinking the diet of Australopithecus anamensis. Am. J. Phys. Anthropol. 169, 403–421 (2019).PubMed 
Article 
PubMed Central 

Google Scholar 
62.Wood, D. Strait, Patterns of resource use in early Homo and Paranthropus. J. Hum. Evol. 46, 119–162 (2004).PubMed 
Article 
PubMed Central 

Google Scholar 
63.Patterson, D. B. et al. Comparative isotopic evidence from East Turkana supports a dietary shift within the genus Homo. Nat. Ecol. Evol. https://doi.org/10.1038/s41559-019-0916-0 (2019).Article 
PubMed 
PubMed Central 

Google Scholar 
64.Lepre, C. J. et al. An earlier origin for the Acheulian. Nature 477, 82–85 (2011).ADS 
CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
65.Braun, D.R. et al. Earliest known Oldowan artifacts at >2.58 Ma from Ledi-Geraru, Ethiopia, highlight technological diversity. Proc. Natl. Acad. Sci. USA 116, 11712–11717 (2019).66.Mana, S., Hemming, S., Kent, D. V. & Lepre, C. J. Temporal and stratigraphic framework for paleoanthropology site within East-Central Area 130, Koobi Fora Kenya. Front. Earth Sci. 7, 230 (2019).ADS 
Article 

Google Scholar 
67.Shea, J. J. Occasional, obligatory, and habitual stone tool use in hominin evolution. Evol. Anthropol. 26, 200–217 (2017).PubMed 
Article 
PubMed Central 

Google Scholar 
68.de la Torre, I. The origins of the Acheulean: past and present perspectives on a major transition in human evolution. Philos. Trans. R. Soc. B 371, 20150245 (2016).Article 

Google Scholar 
69.Harris, J. M., Brown, F. H. & Leakey, M. G. Geology and paleontology of Plio-Pleistocene localities west of Lake Turkana Kenya. Contrib. Sci. 399, 1–128 (1988).
Google Scholar 
70.McDougall, I. et al. New single crystal 40Ar/39Ar ages improve time scale for deposition of the Omo Group, Omo-Turkana Basin East Africa. J. Geol. Soc. Lond. 169, 213–226 (2012).CAS 
Article 

Google Scholar 
71.Quinn, R. L. et al. Pedogenic carbonate stable isotopic evidence for wooded habitat preference of early Pleistocene tool makers in the Turkana Basin. J. Hum. Evol. 65, 65–78 (2013).PubMed 
Article 
PubMed Central 

Google Scholar 
72.Potts, R. et al. Environmental dynamics during the onset of the Middle Stone Age in eastern Africa. Science 360, 86–90 (2018).ADS 
CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
73.Levin, N. E., Zipser, E. J. & Cerling, T. E. isotopic compositions of waters from Ethiopia and Kenya: insights into moisture sources for eastern Africa. J. Geophys. Res. 114, D23306 (2009).ADS 
Article 
CAS 

Google Scholar  More

1.Ford, A. K. et al. Reefs under siege: the rise, putative drivers, and consequences of benthic cyanobacterial mats. Front. Mar. Sci. 5, 18 (2018).Article 

Google Scholar 
2.Brocke, H. J. et al. Organic matter degradation drives benthic cyanobacterial mat abundance on Caribbean coral reefs. PLoS ONE 10, e0125445 (2015).PubMed 
PubMed Central 
Article 
CAS 

Google Scholar 
3.Charpy, L., Casareto, B. E., Langlade, M. J. & Suzuki, Y. Cyanobacteria in coral reef ecosystems: a review. J. Mar. Biol. 2012, e259571 (2012).Article 

Google Scholar 
4.Mangubhai, S. & Obura, D. O. Silent killer: black reefs in the Phoenix Islands Protected Area. Pac. Conserv. Biol. 25, 213 (2019).Article 

Google Scholar 
5.de Bakker, D. M. et al. 40 years of benthic community change on the Caribbean reefs of Curaçao and Bonaire: the rise of slimy cyanobacterial mats. Coral Reefs 36, 355–367 (2017).ADS 
Article 

Google Scholar 
6.Albert, S., Dunbabin, M., Skinner, M., Moore, B. & Grinham, A. Benthic shift in a Solomon Islands’ lagoon: corals to cyanobacteria. In Proceedings of the 12th International Coral Reef Symposium, Cairns, Australia, 9–13 July 2012 1–5 (2012).7.Puyana, M., Acosta, A., Bernal-Sotelo, K., Velásquez-Rodríguez, T. & Ramos, F. Spatial scale of cyanobacterial blooms in Old Providence Island Colombian Caribbean. Universitas Scientiarum 20, 83–105 (2015).Article 

Google Scholar 
8.Ford, A. K. et al. High sedimentary oxygen consumption indicates that sewage input from small islands drives benthic community shifts on overfished reefs. Environ. Conserv. 44, 405–411 (2017).Article 

Google Scholar 
9.Chapra, S. C. et al. Climate change impacts on harmful algal blooms in US freshwaters: a screening-level assessment. Environ. Sci. Technol. 51, 8933–8943 (2017).ADS 
CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
10.Huisman, J. et al. Cyanobacterial blooms. Nat. Rev. Microbiol. 16, 471–483 (2018).CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
11.Gobler, C. J. Climate change and harmful algal blooms: insights and perspective. Harmful Algae 91, 101731 (2020).PubMed 
PubMed Central 
Article 

Google Scholar 
12.Wood, S. A. et al. Toxic benthic freshwater cyanobacterial proliferations: challenges and solutions for enhancing knowledge and improving monitoring and mitigation. Freshw. Biol. 65, 1824–1842 (2020).Article 

Google Scholar 
13.Brown, K. T., Bender-Champ, D., Bryant, D. E. P., Dove, S. & Hoegh-Guldberg, O. Human activities influence benthic community structure and the composition of the coral-algal interactions in the central Maldives. J. Exp. Mar. Biol. Ecol. 497, 33–40 (2017).Article 

Google Scholar 
14.Titlyanov, E. A., Yakovleva, I. M. & Titlyanova, T. V. Interaction between benthic algae (Lyngbya bouillonii, Dictyota dichotoma) and scleractinian coral Porites lutea in direct contact. J. Exp. Mar. Biol. Ecol. 342, 282–291 (2007).Article 

Google Scholar 
15.Carmichael, W. W. Cyanobacteria secondary metabolites—the cyanotoxins. J. Appl. Bacteriol. 72, 445–459 (1992).CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
16.Ritson-Williams, R., Paul, V. J. & Bonito, V. Marine benthic cyanobacteria overgrow coral reef organisms. Coral Reefs 24, 629–629 (2005).ADS 
Article 

Google Scholar 
17.Kuffner, I. et al. Inhibition of coral recruitment by macroalgae and cyanobacteria. Mar. Ecol. Prog. Ser. 323, 107–117 (2006).ADS 
Article 

Google Scholar 
18.Kuffner, I. B. & Paul, V. J. Effects of the benthic cyanobacterium Lyngbya majuscula on larval recruitment of the reef corals Acropora surculosa and Pocillopora damicornis. Coral Reefs 23, 455–458 (2004).Article 

Google Scholar 
19.Ritson-Williams, R., Arnold, S. N. & Paul, V. J. The impact of macroalgae and cyanobacteria on larval survival and settlement of the scleractinian corals Acropora palmata, A cervicornis and Pseudodiploria strigosa. Mar. Biol. 167, 31 (2020).Article 

Google Scholar 
20.McClanahan, T. R. et al. Prioritizing key resilience indicators to support coral reef management in a changing climate. PLoS ONE 7, e42884 (2012).ADS 
CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
21.Cardini, U., Bednarz, V. N., Foster, R. A. & Wild, C. Benthic N2 fixation in coral reefs and the potential effects of human-induced environmental change. Ecol. Evol. 4, 1706–1727 (2014).PubMed 
PubMed Central 
Article 

Google Scholar 
22.Brocke, H. J. et al. Nitrogen fixation and diversity of benthic cyanobacterial mats on coral reefs in Curaçao. Coral Reefs 37, 861–874 (2018).ADS 
Article 

Google Scholar 
23.Brocke, H. J. et al. High dissolved organic carbon release by benthic cyanobacterial mats in a Caribbean reef ecosystem. Sci. Rep. 5, 8852 (2015).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
24.Haas, A. F. et al. Global microbialization of coral reefs. Nat. Microbiol. 1, 1–7 (2016).Article 
CAS 

Google Scholar 
25.Box, S. J. & Mumby, P. J. Effect of macroalgal competition on growth and survival of juvenile Caribbean corals. Mar. Ecol. Prog. Ser. 342, 139–149 (2007).ADS 
Article 

Google Scholar 
26.Webster, F. J., Babcock, R. C., Keulen, M. V. & Loneragan, N. R. Macroalgae inhibits larval settlement and increases recruit mortality at Ningaloo Reef, Western Australia. PLoS ONE 10, e0124162 (2015).PubMed 
PubMed Central 
Article 
CAS 

Google Scholar 
27.Barott, K. et al. Natural history of coral−algae competition across a gradient of human activity in the Line Islands. Mar. Ecol. Prog. Ser. 460, 1–12 (2012).ADS 
Article 

Google Scholar 
28.Bonaldo, R. M. & Hay, M. E. Seaweed-coral interactions: variance in seaweed allelopathy, coral susceptibility, and potential effects on coral resilience. PLoS ONE 9, e85786 (2014).ADS 
PubMed 
PubMed Central 
Article 
CAS 

Google Scholar 
29.Rasher, D. B., Hoey, A. S. & Hay, M. E. Consumer diversity interacts with prey defenses to drive ecosystem function. Ecology 94, 1347–1358 (2013).PubMed 
PubMed Central 
Article 

Google Scholar 
30.Capper, A., Cruz-Rivera, E., Paul, V. J. & Tibbetts, I. R. Chemical deterrence of a marine cyanobacterium against sympatric and non-sympatric consumers. Hydrobiologia 553, 319 (2006).CAS 
Article 

Google Scholar 
31.Clements, K. D., German, D. P., Piché, J., Tribollet, A. & Choat, J. H. Integrating ecological roles and trophic diversification on coral reefs: multiple lines of evidence identify parrotfishes as microphages. Biol. J. Linn. Soc. https://doi.org/10.1111/bij.12914 (2016).Article 

Google Scholar 
32.Cissell, E. C., Manning, J. C. & McCoy, S. J. Consumption of benthic cyanobacterial mats on a Caribbean coral reef. Sci. Rep. 9, 12693 (2019).ADS 
PubMed 
PubMed Central 
Article 
CAS 

Google Scholar 
33.Edwards, C. B. et al. Global assessment of the status of coral reef herbivorous fishes: evidence for fishing effects. Proc. Biol. Sci. 281, 20131835 (2014).CAS 
PubMed 
PubMed Central 

Google Scholar 
34.Goatley, C., Bonaldo, R., Fox, R. & Bellwood, D. Sediments and herbivory as sensitive indicators of coral reef degradation. Ecol. Soc. 21, 29 (2016).35.Robinson, J. P. W. et al. Habitat and fishing control grazing potential on coral reefs. Funct. Ecol. 34, 240–251 (2020).Article 

Google Scholar 
36.Mouillot, D. et al. Functional over-redundancy and high functional vulnerability in global fish faunas on tropical reefs. PNAS 111, 13757–13762 (2014).ADS 
CAS 
PubMed 
Article 

Google Scholar 
37.Elmqvist, T. et al. Response diversity, ecosystem change, and resilience. Front. Ecol. Environ. 1, 488–494 (2003).Article 

Google Scholar 
38.Duperron, S. et al. New benthic cyanobacteria from Guadeloupe mangroves as producers of antimicrobials. Mar. Drugs https://doi.org/10.3390/md18010016 (2020).Article 

Google Scholar 
39.Bonaldo, R. M., Pires, M. M., Junior, P. R. G., Hoey, A. S. & Hay, M. E. Small marine protected areas in Fiji provide refuge for reef fish assemblages, feeding groups, and corals. PLoS ONE 12, e0170638 (2017).PubMed 
PubMed Central 
Article 
CAS 

Google Scholar 
40.Ford, A. K. et al. Evaluation of coral reef management effectiveness using conventional versus resilience-based metrics. Ecol. Ind. 85, 308–317 (2018).Article 

Google Scholar 
41.Robinson, J. P. W. et al. Environmental conditions and herbivore biomass determine coral reef benthic community composition: implications for quantitative baselines. Coral Reefs 37, 1157–1168 (2018).ADS 
PubMed 
PubMed Central 
Article 

Google Scholar 
42.Capper, A. et al. Palatability and chemical defences of benthic cyanobacteria to a suite of herbivores. J. Exp. Mar. Biol. Ecol. 474, 100–108 (2016).CAS 
Article 

Google Scholar 
43.Cruz-Rivera, E. & Paul, V. J. Chemical deterrence of a cyanobacterial metabolite against generalized and specialized grazers. J. Chem. Ecol. 33, 213–217 (2007).CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
44.Bejarano, S. et al. The shape of success in a turbulent world: wave exposure filtering of coral reef herbivory. Funct. Ecol. 31, 1312–1324 (2017).Article 

Google Scholar 
45.Lefcheck, J. S. et al. Tropical fish diversity enhances coral reef functioning across multiple scales. Sci. Adv. 5, eaav6420 (2019).ADS 
PubMed 
PubMed Central 
Article 

Google Scholar 
46.Nagle, D. G. & Paul, V. J. Chemical defense of a marine cyanobacterial bloom. J. Exp. Mar. Biol. Ecol. 225, 29–38 (1998).CAS 
Article 

Google Scholar 
47.Wilson, S. K., Graham, N. J., Pratchett, M. S., Jones, G. P. & Polunin, N. V. C. Multiple disturbances and the global degradation of coral reefs: are reef fishes at risk or resilient? Glob. Change Biol. 12, 2220–2234 (2006).ADS 
Article 

Google Scholar 
48.Pratchett, M. S. et al. Effects of climate-induced coral bleaching on coral-reef fishes: ecological and economic consequences. Oceanogr. Mar. Biol. Ann. Rev. 46, 251–296 (2006).
Google Scholar 
49.Pratchett, M. S., Hoey, A. S., Wilson, S. K., Messmer, V. & Graham, N. A. J. Changes in biodiversity and functioning of reef fish assemblages following coral bleaching and coral loss. Diversity 3, 424–452 (2011).Article 

Google Scholar 
50.Potts, D. C. Suppression of coral populations by filamentous algae within damselfish territories. J. Exp. Mar. Biol. Ecol. 28, 207–216 (1977).Article 

Google Scholar 
51.Mumby, P. J. et al. Empirical relationships among resilience indicators on Micronesian reefs. Coral Reefs https://doi.org/10.1007/s00338-012-0966-0 (2012).Article 

Google Scholar 
52.Birrell, C. L., McCook, L. J. & Willis, B. L. Effects of algal turfs and sediment on coral settlement. Mar. Pollut. Bull. 51, 408–414 (2005).CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
53.Wismer, S., Tebbett, S. B., Streit, R. P. & Bellwood, D. R. Spatial mismatch in fish and coral loss following 2016 mass coral bleaching. Sci. Total Environ. 650, 1487–1498 (2019).ADS 
CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
54.de la Morinière, E. C. et al. Ontogenetic dietary changes of coral reef fishes in the mangrove-seagrass-reef continuum: stable isotopes and gut-content analysis. Mar. Ecol. Prog. Ser. 246, 279–289 (2003).ADS 
Article 

Google Scholar 
55.Komárek, J. A polyphasic approach for the taxonomy of cyanobacteria: principles and applications. Eur. J. Phycol. 51, 346–353 (2016).Article 
CAS 

Google Scholar 
56.Xiao, X. et al. Use of high throughput sequencing and light microscopy show contrasting results in a study of phytoplankton occurrence in a freshwater environment. PLoS ONE 9, e106510 (2014).ADS 
PubMed 
PubMed Central 
Article 

Google Scholar 
57.Palinska, K. A. & Surosz, W. Taxonomy of cyanobacteria: a contribution to consensus approach. Hydrobiologia 740, 1–11 (2014).Article 

Google Scholar 
58.Li, X. et al. Factors related to aggravated Cylindrospermopsis (cyanobacteria) bloom following sediment dredging in an eutrophic shallow lake. Environ. Sci. Ecotechnol. 2, 100014 (2020).Article 

Google Scholar 
59.Taton, A., Grubisic, S., Brambilla, E., De Wit, R. & Wilmotte, A. Cyanobacterial diversity in natural and artificial microbial mats of Lake Fryxell (McMurdo Dry Valleys, Antarctica): a morphological and molecular approach. Appl. Environ. Microbiol. 69, 5157–5169 (2003).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
60.Knight, R. et al. Best practices for analysing microbiomes. Nat. Rev. Microbiol. 16, 410–422 (2018).CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
61.Kim, M., Oh, H.-S., Park, S.-C. & Chun, J. Towards a taxonomic coherence between average nucleotide identity and 16S rRNA gene sequence similarity for species demarcation of prokaryotes. Int. J. Syst. Evol. Microbiol. 64, 346–351 (2014).CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
62.Hoffmann, L. & Demoulin, V. Marine Cyanophyceae of Papua New Guinea. III. The genera Borzia and Oscillatoria. Bot. Mar. 36, 451–459 (1993).Article 

Google Scholar 
63.Engene, N. et al. Moorea producens gen. nov., sp. Nov. and Moorea bouillonii comb. nov., tropical marine cyanobacteria rich in bioactive secondary metabolites. Int. J. Syst. Evol. Microbiol. 62, 1171–1178 (2012).PubMed 
PubMed Central 
Article 

Google Scholar 
64.Engene, N. et al. Five chemically rich species of tropical marine cyanobacteria of the genus Okeania gen. nov. (Oscillatoriales, Cyanoprokaryota). J. Phycol. 49, 1095–1106 (2013).PubMed 
Article 
PubMed Central 

Google Scholar 
65.Komarek, J., Kaštovský, J., Mares, J. & Johansen, J. R. Taxonomic classification of cyanoprokaryotes (cyanobacterial genera) 2014, using a polyphasic approach. Preslia 86, 295–335 (2014).
Google Scholar 
66.Wilmotte, A., Laughinghouse, H. D. I., Capelli, C., Rippka, R. & Salmaso, N. Taxonomic Identification of Cyanobacteria by a Polyphasic Approach. Molecular Tools for the Detection and Quantification of Toxigenic Cyanobacteria (Wiley, 2017).
Google Scholar 
67.Salmaso, N. et al. Diversity and cyclical seasonal transitions in the bacterial community in a large and deep perialpine lake. Microb. Ecol. 76, 125–143 (2018).CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
68.Zubia, M. et al. Benthic cyanobacteria on coral reefs of Moorea Island (French Polynesia): diversity response to habitat quality. Hydrobiologia 843, 61–78 (2019).Article 

Google Scholar 
69.Bernard, C. et al. Appendix 2: Cyanobacteria Associated with the Production of Cyanotoxins. Handbook of Cyanobacterial Monitoring and Cyanotoxin Analysis 501–525 (Wiley, 2017). https://doi.org/10.1002/9781119068761.app2.
Google Scholar 
70.Moritz, C. et al. Status and Trends of Coral Reefs in the Pacific (Global Coral Reef Monitoring Network, 2018).
Google Scholar 
71.Smith, J. E. et al. Re-evaluating the health of coral reef communities: baselines and evidence for human impacts across the central Pacific. Proc. R. Soc. B Biol. Sci. 283, 20151985 (2016).Article 
CAS 

Google Scholar 
72.Kelly, L. W. et al. Black reefs: iron-induced phase shifts on coral reefs. ISME J. 6, 638–649 (2012).CAS 
PubMed 
Article 

Google Scholar 
73.Bohnsack, J. A. & Bannerot, S. P. A stationary visual census technique for quantitatively assessing community structure of coral reef fishes. NOAA Technical Report NMFS 41, 21 (1986).74.Froese, R. & Pauly, D. FishBase. World Wide Web electronic publication. www.fishbase.orghttps://www.fishbase.org/.75.Heenan, A., Hoey, A. S., Williams, G. J. & Williams, I. D. Natural bounds on herbivorous coral reef fishes. Proc. R. Soc. B Biol. Sci. 283, 20161716 (2016).Article 

Google Scholar 
76.R Development Core Team. R: A Language and Environment for Statistical Computing. (R Foundation for Statistical Computing, 2019).77.Brooks, M. E. et al. glmmTMB balances speed and flexibility among packages for zero-inflated generalized linear mixed modeling. R J. 9, 378–400 (2017).Article 

Google Scholar 
78.Hartig, F. DHARMa: Residual Diagnostics for Hierarchical (Multi-Level/Mixed) Regression Models. R package version 0.3.3.0. (2020). http://florianhartig.github.io/DHARMa/79.Wickham, H. ggplot2: Elegant Graphics for Data Analysis (Springer, 2016).
Google Scholar 
80.Komárek, J. & Anagnostidis, K. Cyanoprokaryota 2.Teil: Oscillatoriales (Elsevier, 2005).
Google Scholar 
81.Quince, C., Lanzen, A., Davenport, R. J. & Turnbaugh, P. J. Removing noise from pyrosequenced amplicons. BMC Bioinform. 12, 38 (2011).Article 

Google Scholar 
82.Bolyen, E. et al. Reproducible, interactive, scalable and extensible microbiome data science using QIIME 2. Nat. Biotechnol. 37, 852–857 (2019).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
83.Ramos, V., Morais, J. & Vasconcelos, V. M. A curated database of cyanobacterial strains relevant for modern taxonomy and phylogenetic studies. Sci. Data 4, 170054 (2017).PubMed 
PubMed Central 
Article 

Google Scholar 
84.Katoh, K., Rozewicki, J. & Yamada, K. D. MAFFT online service: multiple sequence alignment, interactive sequence choice and visualization. Brief. Bioinform. 20, 1160–1166 (2019).CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
85.Price, M. N., Dehal, P. S. & Arkin, A. P. FastTree 2: approximately maximum-likelihood trees for large alignments. PLoS ONE 5, e9490 (2010).ADS 
PubMed 
PubMed Central 
Article 
CAS 

Google Scholar 
86.Letunic, I. & Bork, P. Interactive tree of life (iTOL) v4: recent updates and new developments. Nucl. Acids Res. 47, W256–W259 (2019).CAS 
PubMed 
Article 
PubMed Central 

Google Scholar  More

Confirmation of species, using DNA analysisBecause the DNA of our sphere samples matches that of adult squid identified as I. coindetii from Norwegian waters we infer that the spheres are from I. coindetii. Much has been written about taxonomic difficulties in Illex. The COI tree comprises four clades of Illex, one of which clearly pertains to Illex argentinus (Castellanos, 1960). There are three other described species: Illex coindetii, Illex illecebrosus (Lesueur, 1821), and I. oxygonius Roper, Lu & Mangold, 1969. We labelled our clades A, B, and C, to indicate their correspondence with the findings of Carlini et al.32, and assume that each pertains to one of the described species of Illex. Carlini was unable to match species to clades, but Clade A not only contains the adults identified in this project as I. coindetii, but also contains specimens from the Mediterranean (DQ373941). Since I. coindetii is the only species of Illex known from the Mediterranean, this is further confirmation of the identity of Clade A, and thus our spheres, as Illex coindetii.Using citizen science from roughly 200 divers secured observations of 90 spheres, including rare tissue samples of four of them, thus enabling a molecular approach towards the first confirmation of egg masses in situ as those of the broadtail shortfin squid, Illex coindetii. Illex coindetii was named in honour of Dr. Coindet from Geneva in 185137. It took 180 years from the description of the adult to identification of its egg mass in the wild. To our knowledge no whole egg mass of Illex spp. has previously been reported from the wild, except by Adolf Naef, who reported on live ommastrephid embryos and paralarvae from Naples, Italy2. The embryos were pulled out of a floating spawn or floating egg mass, or as he describes «Fig. 1 und 2 sind aus einem flottierenden Laich gezogene Larven von Ommatostrephiden». These illustrations were later identified as Illex coindetii by Boletzky et al.26, studying egg development of I. coindetii in the laboratory, claiming «The general characteristics of the embryonic developement observed by us match the figures given by Naef (1923 : plts 9–12) of an unidentified egg mass of a member of the Ommastrephidae (Naef 1921)». However, no drawing of the «laich» was provided.Challenges collecting in situ materialHuge gelatinous spheres from squid are difficult to study in situ. They are rarely reported, and hard to sample. We have collected 90 sphere observations from ~ 35 years back (~ 1985 to 2019), from an area stretching from the Mediterranean Sea north to the Norwegian Sea, which gives a good illustration on sphere findings of ~ 2.6 sphere observations per year. In addition, the spheres most likely have a short-life span. Life span of spheres spawned and reared in aquaria (between 40 and 120 cm in diameter) of Todarodes pacificus (Steenstrup, 1880) is 5–7 days, with the smallest disintegrating first38.Sphere shape and sizeGelatinous egg masses of cephalopods vary in size and form among species. Some egg masses are spherical, but there are also examples of oblong structures39,40,41. Sphere size may be up to 4 m in diameter1,5,42. Ringvold and Taite (op. cit.) collected information on a total of 27 spheres recorded in European waters varying from 0.3 to 2 m in diameter, as also for the additional spheres from this study. The four spheres in our study, confirmed to belong to I. coindetii, measured between 0.5 and 1 m in diameter.Egg mass of another ommastrephid squid, Todarodes sagittatus, has yet to be found in situ. The species is known to be larger than Illex species, and egg mass is also most probably larger. The largest spheres recorded in our study measured up to 2 m in diameter, but none of these were sampled for molecular analysis, nor were pictures taken. It is uncertain whether they could belong to other species e.g., Todaropsis eblanae (Ball, 1841), Todarodes sagittatus or Ommastrephes sp..Dark streak through coreAlmost 60% of the spheres had a dark streak through the center. This feature might be ink, one important characteristic of cephalopods, produced by most cephalopod orders. The ink sac with its ink glands produces black ink containing melanin43. During fertilization, sperm are released—as well as possibly some ink. Spheres with or without ink may be a result of spheres beeing at different maturity stages1, where spheres with ink are freshly spawned. After a while, when embryos starts developing, the whole sphere, including the streak, will start to disintegrate.Some of us speculate that one function of the streak through the center might serve as visual mimics e.g. of a large fish in order to scare off predators. Other possible functions discussed are also if the streak/structure can be caused by a sphere strengthening structure which is denser or having a higher optical density than the sourrounding structure. A disadvantage with the streak is that it might reveal the whole transparent sphere in the water, visible to e.g. scuba divers.Function of the gelatinous matrixObservations in captivity3,44 showed that species within the genus Illex produce gelatinous egg masses while swimming in open water. Gel functions as a buoyancy mechanism that prevents eggs from sinking, and complete density equilibration requires many days under most conditions44. Such a buoyancy mechanism keeps pelagically spawned eggs of Illex in areas where temperatures are most optimal for embryonic development. Optimal environmental conditions will likely have a positive effect on survival of both hatchlings and paralarvae. Despite consistency in where spawning areas are found, interannual variability has been recorded in the main recruitment areas, which could be related to e.g. mesoscale eddies and/or affecting post-hatching dispersal45.Huge spheres are formed of mucus produced by the nidamental glands, situated inside the mantle cavity of the female46,47. When fully developed, hatchlings emit an enzyme which starts to dissolve the mucus. Eggs and embryos from our four spheres were covered in sticky gelatinous mass, except for a few specimens (from Arendal, collected 7 August, and Søgne) laying in the petri dish outside the sticky gel, in the surrounding sea water following the tissue sample, and might have been old enough to start producing such enzymes.When at hatching, Illex coindetii eggs are about 2 mm long26,48, in line with other ommastrephids12. The longest of our embryos (from Arendal, collected 7 August) measured ~ 2 mm, a developed embryo with long proboscis, mantle about ½ of total body length, as well as chromatophores, large eyes and funnel visible (Fig. 3). It could possibly be a hatchling.Abiotic factors and locationsThe success and duration of embryonic development is related to water temperature. All observations available to date indicate that successful embryonic development for I. coindetii takes ca. 10–14 days at 15 °C; this temperature corresponds to the median temperature value reported for Mediterranean Sea midwater48. Boletzky et al.26 reports on a temperature minimum above 10 °C. Spheres in the Mediterranean were observed in temperatures ranging between 14 and 24 °C. Watermass temperature for one sphere with recently fertilized eggs (Ålesund sample, embryos stage ~ 3) from Norway was 8 °C. It was also observed north of the existing known distribution range for I. coindetii, in the Norwegian Sea, at 43 m depth. Most spheres from Norway were observed from July and August, in water mass 10–14 °C, with maximum temperature at 18 °C.It is unknown whether some of the observed spheres had drifted to water layers unsuitable for the development of the eggs, and, eventually, would have died due to unfavourable abiotic conditions (e.g. transport outside the optimal temperature- or depth range for that particular species), but most likely they were in an area where they would survive. Higher occurrence of sphere sightings from 2017 to 2019, could be a combination of higher abundance of these squid in the area as well as increased knowledge regarding our Citizen Science Sphere Project, and thereby increased reports of observations.Illex coindetii may be considered as an intermittent spawner with a spawning season extending throughout the year, reaching a peak in July–August18.Our sphere observations from all areas were made from March to October: The earliest sphere which can be documented (to month) in the North Sea to date was observed 27 May (2001), and the last sphere was reported on 20 October (2019), coinciding with a study on adult Illex condetii from the North Sea where the spawning season has been suggested to be between spring and autumn49. However, our data show a peak of sphere observations from July to September (all areas combined), from July to August in Norway and from August to September in the Mediterranean Sea. The two recordings from Galicia in Spain, and Seiano in Italy, were the earliest recordings of the year, observed 24 March (in 2017 and 2019, respectively). For all areas combined, no observations during wintertime (November to February) have been recorded.Embryonic development and consistencies of spheresWe collected tissue mass of four different spheres of I. coindetii, and embryos in each sphere were at different developmental stages, ~ 3 to 30, according to Sakai et al.36 based on I. argentinus. The sphere walls of the four spheres were also of different consistencies (Table 2); from Ålesund sphere with recently fertilized eggs and firm, transparent sphere wall to Søgne and Arendal spheres (the latter collected 7 August) with developed embryos and disintegrating sphere walls. The remains of the Arendal sphere was hanging as a long «scarf» in the water. Experienced divers, who previously had seen a few spherical spheres, recognized this disintegrating sphere.Function of spheresOmmastrephidae fecundity is extremely high, and a single sphere may contain thousands to several hundred thousands of eggs41,50,51,52. The function of the spheres is protection and transport of the offspring by sea currents for paralarval dispersal. Inside these gelatinous structures, the eggs and newly hatched paralarvae are protected from predation by e.g. fish, parasite infection and infestation by crustaceans and protozoans during a first relative short period of their lives5,51. Bottom trawlers operate in spawning areas of squids, exposing them to a risk of egg loss, as also for our fisherman at Askøy, Norway, who caught a sphere in his trawl1,5.Scientific cruises and fisheryThe Institute for Marine Research in Norway started identification of cephalopods on their regular scientific cruises in 2013, but no Illex coindetii was recorded that year. However, data show increasing catches from 2014 to 2019 (unpublished). No spheres are reported from Norway in 2013, but between 1995 to 2010, and from 2015 to 2019, observations were made. Most observations are between 2017 and 2019, indicating more frequent squid visits/spawnings. This coincides with more frequent sphere observations from 2017 to 2019.The broadtail shortfin squid, Illex coindetii, is probably the most widespread species found on both sides of the Atlantic and throughout the Mediterranean Sea12. In the NE Atlantic, it has been reported from Oslofjorden, Norway (59°N);53 and the Firth of Forth, east Scotland54, southwards along the European and African coasts to Namibia, including Hollam’s Bird Island (24°S) and Cape Frio (18°S)55. For example, I. coindetii is periodically very abundant in coastal waters of the eastern North Atlantic off Scotland, Ireland and Spain, where it supports opportunistic fisheries. However, the oceanographic and biological factors that drive this phenomenon, are still unknown12.Illex coindetii is widely distributed throughout the Mediterranean Sea11, where it is caught commercially mostly by Italian trawlers, usually as a by-catch, but also by recreational fishing, by means of squid jigging. Annual Italian landings during the last five years have varied between two and three thousand tonnes, but with historical landings reaching numbers of more than eight thousand tonnes during the 1980s and 1990s (FAO 2019)15.In the North Sea, studies show that inshore squids (Alloteuthis subulata (Lamarck, 1798) and Loligo forbesii Steenstrup, 1856) are more abundant than short-finned squid (Illex coindetii, Todaropsis eblanae and Todarodes sagittatus), and I. coindetii is among the rarest ommastrephid species caught49,56. However, two recent studies (1) on summer spawning stock of Illex coindetii in the North Sea57 and (2) I. coindetii recorded from the brackish Baltic Sea58 suggest more frequent visits to this area. Reports on Illex coindetii from Norwegian waters are scarce, but it has been reported from Oslofjorden53, and recently as by-catch from Stavanger area, and by divers from Oslofjorden and Bergen. More

Most problematic fishing methods based on ALDFG relative risksThis study presents the first quantitative assessment of gear-specific relative risks from ALDFG. Findings accounted for the: (a) derelict gear leakage rate; (b) fishing gear quantity indicators of catch and area of fishing grounds; and (c) adverse consequences from ALDFG. Maximum global conservation gains can be achieved through focusing ALDFG mitigation efforts on the fishing gears with the highest overall relative risk. Set and fixed gillnets and trammel nets, drift gillnets, gears using drifting and anchored FADs (tuna purse seines and pole-and-lines), and bottom trawls were the five most problematic gears on a global scale. This was followed by traps (fyke nets, pots, barriers, fences, weirs, corrals and pound nets).The overall RR score indicates a fishing gear’s relative degree of total adverse effects from ALDFG, accounting for the quantity of ALDFG produced by that gear (estimated from the ALDFG leakage rate and indices of fishing gear quantity of catch and area of fishing grounds), and the adverse consequences that result from ALDFG from that gear type relative to other gears. Globally, gillnets have the highest risks from ALDFG, while hand dredges and harpoons were least problematic.The focus of local management interventions to address problematic derelict fishing gear will be dictated by the specific context. Locally, adopting ALDFG controls following a sequential mitigation hierarchy and implementing effective monitoring, surveillance and enforcement systems are needed to curb derelict gear from these most problematic fisheries. This includes accounting for which fishing gears are predominant and the existing fisheries management framework. For example, a site may have pot and tuna purse seine anchored FAD fisheries. The purse seine fishery has a higher relative risk globally. However, a fisheries management system may have effective ALDFG preventive methods in place for this fishery, such as a high rate of detection and recovery of anchored FADs when they break from moorings, and minimization methods, such as prescribing the use of only non-entangling and biodegradable FAD designs to minimize adverse effects from derelict FADs35, 36. But there may be minimal measures in place to monitor and manage ALDFG from pots. In this hypothetical example, it would be a higher priority locally to improve ALDFG management for the pot fishery.Priority data quality improvementsThere are several priorities for data quality improvement to increase the certainty of future assessments. Given substantial deficits both in estimates of gear-specific quantity/effort and ALDFG rates, it is not yet possible to produce a robust contemporary estimate to replace the ca. five decades-old crude estimate of the magnitude of the annual quantity of leaked ALDFG4, 30. More robust estimates of ALDFG rates are needed for all gear types. Gear-specific estimates have low certainty due to small numbers of studies and sample sizes. Many compiled records estimate only one ALDFG component, typically only loss rates, and therefore may substantially underestimate total ALDFG rates. Most records are dated and may not accurately characterize contemporary rates. There is geographical sampling bias with estimates being primarily derived from the northern hemisphere. Furthermore, many estimates were derived from expert surveys (Supplementary Material Table S1), which have a higher risk of error and bias than approaches higher on the evidence hierarchy37. Substantially more primary studies with robust designs are needed.An expanded meta-analysis on gear-specific ALDFG rates is an additional priority, once sufficient sample sizes of robust studies accumulate. The statistical modeling approach used by Richardson et al.34 could be readily improved by using (1) a random-effects instead of a fixed effects structure to account for study-specific heterogeneity, and (2) a more appropriate model likelihood, such as zero-inflated Beta likelihood, to account for the zero values in the dataset38. Due to larger sample sizes and the number of independent studies, meta-analyses can produce estimates with increased accuracy, with increased statistical power to detect real effects. By synthesizing estimates from an assortment of independent, small and context-specific studies, pooled estimates from random-effects meta-analyses are generalizable and therefore relevant over diverse settings39. The strength of conclusions of hypotheses based on a single study can vary. This is because a single study can be context-specific, where true results may be affected by conditions specific to that single study, such as the species involved and environmental conditions, that cause the results from the single study to not be applicable under different conditions. A single study may also fail to find a meaningful result due to small sample sizes and low power. However, robust synthesis research, including meta-analysis, is more precise and powerful once a sufficient number of similar studies have accumulated, and therefore investing in more primary ALDFG studies is a high priority.For some gear types and fisheries, estimated ALDFG rates may overestimate adverse effects when gear that is abandoned, lost or even discarded does not become derelict because another fishing vessel continues to use the gear. For example, gear that is lost by theft remains in use. Macfadyen et al.4 explained that theft was likely a minor contribution to ALDFG, occurring, for instance, in inshore fishing grounds where static commercial fishing gear and recreational marine activities conflict. However, fishing gear theft may be prevalent in some developing country fisheries (e.g., Cambodian crab traps40). And, there is one gear type where theft has become a globally prevalent, routine and largely accepted practice: Tuna purse seine vessels routinely exchange satellite buoys attached to drifting FADs that they encounter at sea. The stolen FAD, lost by the previous vessel that had been tracking its position, remains in-use and not derelict, although it may eventually become derelict41, 42. Furthermore, because ALDFG leakage rates may be highest in illegal and unregulated fisheries4, if only legal fisheries are sampled, then this may produce underestimates. Thus, accounting for theft and illegal and unregulated fishing would increase the certainty of estimates of ALDFG leakage rates for some fishing gear types.The 20% ALDFG global production rate value used for anchored FADs by pole-and-line fisheries was likely an underestimate. We relied on a single value from the contemporary Maldives pole-and-line fishery’s government-owned and -managed network of anchored FADs. This fishery underwent a substantial reduction in anchored FAD loss rate, from 82 to 20%, by improving designs and a government incentive program that pays fishers to retrieve FADs when they break from their moorings35, 43. For comparison, describing Indonesia’s pole-and-line fishery’s anchored FADs, Widodo et al.44 stated: “Inaccuracy of number and position of FADs in the fishing ground are the outstanding issue facing by fisheries manager…This was largely the result of the current lack of effective systems of FAD registration and monitoring, and also because of the desire of fishing companies and vessel skippers to keep FADs position information confidential. [sic]”.Proctor et al.45, who estimated that between 5000 and 10,000 anchored FADs are used in Indonesian tuna fisheries, also reported a lack of accurate estimates of the numbers and locations of anchored FADs due to ineffective implementation of the government registration system and to high loss rates, including from storms, strong currents, vandalism, vessel collisions and wear and degradation of the FADs. Using the estimated rates of (1) Shainee and Leira43 that 82% of anchored FADs were lost per year prior to the Maldivian government’s incentives program, which might accurately characterize the Indonesian and other anchored FAD networks used by pole-and-line fisheries, and (2) the 20% loss rate value from Adam et al.35, the posterior mean = 0.506 (95% HDI: 0.15–0.84). Thus, 51% might have been a more appropriate estimate for a global ALDFG production rate for pole-and-line anchored FADs. The Maldivian and Indonesian pole-and-line fisheries, which combined supply over half of global pole-and-line catch, rely heavily on anchored FADs, as do several other smaller pole-and-line fisheries (e.g., Solomon Islands, segments of the Japanese pole-and-line fleet)35, 45,46,47,48.Units for ALDFG rates are highly variable. Records using different rates cannot be pooled for synthesis research29, 34. For example, some records reported rates of the percent of number of panels (sheets) or fleets (strings) of gillnets that were lost, while others reported the percent of the length or area of gillnets that were lost29. Similarly, for longline gear, some studies reported the percent of the length of the mainline, while others reported the percent of the number of branchlines/snoods that were lost34. Employment of agreed harmonized units for ALDFG rates are needed.Future assessments could use a ratio of ALDFG risk-to-seafood production to assess gear-specific relative risks locally and globally, similar to assessments of vulnerable fisheries bycatch by using bycatch-to-target catch ratios49. This would enable the assessment of risk from ALDFG to be balanced against meeting objectives of food security and nutritional health.Relationship between alternative indices with the quantity of fishing gearWe used gear-specific annual catch and area of fishing grounds as indicators of the relative global amount of each gear that is used annually as two terms in the model to assess gear-specific relative risks from ALDFG. However, the assumption of a linear relationship between these indices and gear quantity is questionable for similar reasons that have been raised with the relationship between various indices of effort (number of fishing hours, number of vessels, engine power, vessel length, gross tonnage, gear size, hold capacity, as well as kWh) and catch. For example, the ratio of catch from one set by an anchoveta purse seiner to the volume or weight of the gear is likely substantially different than for pots or driftnets. Not only is the relationship between catch and amount of gear variable by gear type and target species, there is also high variability within gear types—by fishery and within fisheries—due to the broad range of factors that significantly explain fishing efficiency per unit of nominal effort50, 51. Similarly, the relationship between catch weight and number of fishing operations varies substantially across gear types. For example, an industrial tropical tuna purse seine vessel might have a total catch of about 37 t per set on a drifting FAD27 while a tuna pole-and-line vessel catches about 1 t per fishing day52.Similarly, the relationship between the area of fishing grounds and amount of gear may vary substantially between gear types. A small number of vessels using a relatively small magnitude of active, mobile gear may have a much larger area of fishing grounds than a large number of vessels and shore-based fishers using a large amount of passive and static gears. For example, about 686 large-scale tuna purse seine vessels fish across the tropics53, while gillnets, which may be the most globally prevalent gear type, are used predominantly within 20 nm (37 km) of shore, most intensively in southeast Asia and the northwest Pacific54.Fishing effort has also been estimated using engine power as well as by using energy expended, such as in kilowatt-hours (kWh), the product of the fishing time and engine power of a fishing vessel, including non-motorized vessels55,56,57. We did not use these metrics for effort because the correlation between rate of production of ALDFG and vessel engine power or kWh, including of non-motorized vessels (1.70 million of the estimated global 4.56 million fishing vessels21), has not been explored. In general, vessel power and power per unit of fishing period largely distinguishes between mobile and passive gears, where the former (e.g., trawls, dredges), use substantially more vessel power per weight of catch than passive gears (traps, gillnets). Also, estimates using these fishing effort metrics used a small number of aggregated gear categories and extrapolated estimates primarily from sampled developed world fisheries (however, see56). These effort indices would also prevent inclusion of shore-based fishing methods.There have been recent gear specific estimates of effort, in units of time spent fishing and the estimated energy expended (fishing power * fishing time), using Automated Identification Systems (AIS) data, which are available for industrial fishing vessels, primarily using longlines, trawls and pelagic purse seines6, 58. AIS data provide coverage of the majority of large fishing vessels ≥ 24 m in overall length58. However, this accounts for only about 2% of the number of global fishing vessels (of an estimated 4.56 million global fishing vessels, about 67,800 are ≥ 24 m in length21).ALDFG monitoring, management and performance assessmentsA sequential mitigation hierarchy of avoidance, minimization, remediation and offsets can be applied to manage ALDFG29, 59. Referring to the three components of relative risk assessed by this study, avoidance and minimization of risks from ALDFG is achieved by reducing the ALDFG leakage rate, fishing effort, and/or adverse consequences from derelict gear. Remedial methods reduce adverse effects, such as reducing ghost fishing by reducing the duration that ALDFG remains in the marine environment1, 29, 60. In general, preventative methods are more cost effective than remedial methods—it is less expensive to prevent gear abandonment, loss and discarding than it is, for example, to detect and then disable or remove derelict gear61. Methods to prevent ALDFG include, for instance, spatially and temporally separating passive and mobile fishing gears, having bottom trawlers avoid features that could snag the net such as by using high-resolution seabed maps, tracking the real-time position of unattended fishing gears using various electronic technologies, and using gear marking to identify the owner and increase the visibility of passive gears. Furthermore, because some remedial methods, such as using less durable materials for fishing gear components, can reduce economic viability and practicality, preventative methods and remediation through quick recovery of ALDFG may be more effective as well as elicit broader stakeholder support29, 62.To assess the performance of global ALDFG management interventions against this study’s quantitative benchmark, substantial deficits in monitoring and surveillance of fisheries’ waste management practices must first be addressed1. Of 68 fisheries that catch marine resources managed by regional fisheries management organizations, 47 lack any observer coverage, half do not collect monitoring data on ALDFG, and surveillance and enforcement systems are rudimentary or nonexistent in many fisheries1, 63.Findings from this quantitative, global assessment of ALDFG risks guide the allocation of resources to achieve the largest improvements from preventing and remediating derelict gear from the world’s 4.6 million fishing vessels. With improved data quality and governance frameworks for fishing vessel waste management, including ALDFG, we can expect reductions in ecological and socioeconomic risks from derelict gear. More

pH distributions within sediment microcosms showed distinct spatial and temporal patterns. For the January 2019 experiment series, the strong pH maximum bands developed in the oxic surface sediment after 20 ~ 22 days of incubation. Development was not spatially uniform. Sediment surface pH maxima started to develop from isolated points covering horizontal lengths of 0.6–1 cm at the times of imaging (Fig. 1). Within a week, the pH maximum bands expanded laterally, covering the entire 6 cm long monitoring panel, and were sustained until the end of the experiment (106 days, data not shown). The pH maximum bands were about 2 mm in vertical extent with average pH ~ 8.5. Even after the electrogenic colonization was laterally complete (day 27), the activity intensities of cable bacteria, or the impacts of their activity as reflected by the magnitudes of pH elevations and associated reactions, were still spatially heterogeneous (Fig. 1C). pH values in the underlying anoxic sediment decreased from 7.0 to 6.5 gradually as surface pH maxima formed. However, in the experiment with the sediment collected in August 2019, sediment surface pH maxima started appearing on day 39 and expanded much more slowly, with only a 2.4 cm-long lateral coverage after a week of growth in one of the duplicate microcosms and no development at all in the other (Fig. 2A,B). At the same time, where pH maxima were present, a pH minimum band developed in the anodic zone ( > 2 mm depth) just below sediment surface pH maxima and expanded over time (Fig. 2A).Figure 12D pH distribution dynamics of duplicate microcosms starting from day 20 (January 2019 sediment). (A,B) For duplicate microcosms, sediment surface pH maximum bands started from isolated hotspots and quickly spread across the whole surface area with spreading speed ~ 1.2 cm/day. Anoxic sediment pH decreased from day 20–26. (C) The horizontal pH variations within the surficial pH maximum band on day 27 (microcosm B; vertical width ~ 1 mm). The pH ± standard deviations within the band are indicated by the blue dots.Full size imageFigure 22D pH and H2S distribution dynamics within microcosms during colonization. (A) 2D pH distribution dynamics starting from day 39 (August 2019 sediment) and corresponding 1D pH profiles. The sediment surface pH maximum band started from isolated hotspots and spread across sediment surface with an average rate of 0.3 cm/day, which is much slower compared with January 2019 sediment (1.2 cm/day). The pH in the cable bacteria anodic zone is lower (blue line, A2) compared with un-colonized sediment side (black line, A1) in the pH profile panel insert. (B) The duplicate microcosm (August 2019 sediment) did not show sediment surface pH maxima during the same time window. (C) 2D pH distribution dynamics starting from day 46 (October 2020 sediment). Both surface pH maxima and deep minima expand during electrogenic colonization. The pH minima (white arrows) are evident first on day 46 and 64. (D) Sediment 2D H2S distribution on day 71 (October 2020 sediment) with upper boundary showing the sediment water interface. The H2S distributions are consistent with pH patterns, but the sediment H2S concentrations are generally lower compared with other experiment series (Fig. 4).Full size imageThe October 2020 results (Fig. 2C) further resolved the cathodic and anodic zone development patterns. During colonization, the anoxic zone pH minima were evident earlier (day 46 and 64) and were distinctly wider than the sediment surface pH maxima (day 46–71). These differences in cathode and anode detection sensitivity might be caused by more rapid diffusion at the sediment–water boundary (free solution) and rapid neutralization by seawater CO2. They may also be related to the mode of colonization as discussed below. In addition to pH heterogeneity, the electrogenic activity also resulted in complex topographies of H2S distributions (Fig. 2D). In the locations where pH hotspots were found, the depths of detectable H2S are deeper compared with anywhere else, consistent with ongoing electrogenic sulfide oxidation metabolic activity. These data suggest that cable bacteria dynamics can be distinctly different in otherwise similar sediment (e.g. similar concentrations of dissolved H2S at depth), with variable development controlled by unknown factors.High resolution cable bacteria abundance data are not available in this study because of the design of the experiment. However, the vertical cable bacteria abundance dynamics, which were retrieved from random locations in the January 2019 microcosms during incubation from day 20 to 43, show that cable bacteria cell abundance in the oxic zone of the sediment did not vary significantly during the primary colonization period. In contrast, one of the microcosm series (Fig. 3B) showed a distinct trend of subsurface, anodic region (0.5–1.5 cm depth) enrichment of filament cells, and importantly, both microcosm series had similar terminal distributions when electrogenic activity had expanded across the entire surface (day 43) (Fig. 3). The first time sample in series A (day 20) (Fig. 3A) is similar in abundance distribution as at day 43. It is possible that the first sample taken in series A was located in an electrogenic colonization patch, that is, locally comparable to what would become the pattern across the entire surface at day 43. These abundance data together with the high resolution pH patterns allow inference of the colonization strategy of cable bacteria, as outlined subsequently.Figure 3Cable bacteria cell abundance dynamics in the duplicate January 2019 sediment microcosms. (A) and (B) represent duplication microcosms. From day 20 to 43, cable bacteria can be detected throughout the top 5 cm sediment with heterogeneous abundance patterns. There were depth intervals (e.g. B 2.5–3 cm) with cable bacteria cell abundance below the detection limit of the enumeration method. The sediment surface ( More

1.Spalding, M., Kainuma, M. & Collins, L. World Atlas of Mangroves. (Earthscan, 2010).2.Duke, N. et al. A world without mangroves?. Science 317, 41–42 (2007).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
3.Friess, D. et al. The state of the world’s mangrove forests: past, present, and future. Annu. Rev. Env. Resour. 44, 89–115 (2019).Article 

Google Scholar 
4.Wee, et al. The integration and application of genomic information in mangrove conservation. Conserv. Biol. 33, 206–209 (2019).PubMed 
Article 
PubMed Central 

Google Scholar 
5.Duke, N., Lo, E. & Sun, M. Global distribution and genetic discontinuities of mangroves—emerging patterns in the evolution of Rhizophora. Trees-Struct. Funct. 16, 65–79 (2002).Article 

Google Scholar 
6.Ellison, A. M., Farnsworth, E. J. & Merkt, R. E. Origins of mangrove ecosystems and the mangrove biodiversity anomaly. Global Ecol. Biogeogr. 8, 95–115 (1999).
Google Scholar 
7.Plaziat, J.-C., Cavagnetto, C., Koeniguer, J.-C. & Baltzer, F. History and biogeography of the mangrove ecosystem, based on a critical reassessment of the paleontological record. Wetl. Ecol. Manag. 9, 161–180 (2001).Article 

Google Scholar 
8.Duke, N., Ball, M. & Ellison, J. Factors influencing biodiversity and distributional gradients in mangroves. Global Ecol. Biogeogr. Lett. 7, 27–47 (1998).Article 

Google Scholar 
9.Duke, N. Genetic diversity, distributional barriers and rafting continents—more thoughts on the evolution of mangroves. Hydrobiologia 295, 167–181 (1995).Article 

Google Scholar 
10.Tomlinson, P. B. The botany of mangroves. (Cambridge University press, 1986).11.Schwarzbach, A. E. & Ricklefs, R. E. Systematic affinities of Rhizophoraceae and Anisophylleaceae, and intergeneric relationships within Rhizophoraceae, based on chloroplast DNA, nuclear ribosomal DNA, and morphology. Am. J. Bot. 87, 547–564 (2000).CAS 
PubMed 
Article 

Google Scholar 
12.Lo, E. Y. Y. Testing hybridization hypotheses and evaluating the evolutionary potential of hybrids in mangrove plant species. J. Evol. Biol. 23, 2249–2261 (2010).CAS 
PubMed 
Article 

Google Scholar 
13.Takayama, K., Tamura, M., Tateishi, Y., Webb, E. L. & Kajita, T. Strong genetic structure over the American continents and transoceanic dispersal in the mangrove genus Rhizophora (Rhizophoraceae) revealed by broad-scale nuclear and chloroplast DNA analysis. Am. J. Bot. 100, 1191–1201 (2013).CAS 
PubMed 
Article 

Google Scholar 
14.Lo, E., Duke, N. & Sun, M. Phylogeographic pattern of Rhizophora (Rhizophoraceae) reveals the importance of both vicariance and long-distance oceanic dispersal to modern mangrove distribution. BMC Evol. Biol. 14, 83 (2014).PubMed 
PubMed Central 
Article 

Google Scholar 
15.Chen, Y. et al. Applications of multiple nuclear genes to the molecular phylogeny, population genetics and hybrid identification in the mangrove genus Rhizophora. PLoS ONE 10, e0145058 (2015).PubMed 
PubMed Central 
Article 
CAS 

Google Scholar 
16.Xu, S. H. et al. The origin, diversification and adaptation of a major mangrove clade (Rhizophoreae) revealed by whole-genome sequencing. Natl. Sci. Rev. 4, 721–734 (2017).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
17.Tyagi, A. P. Cytogenetics and reproductive biology of mangroves in Rhizophoraceae. Aust. J. Bot. 50, 601–605 (2002).Article 

Google Scholar 
18.Tyagi, A. P. Chromosomal Pairing and Pollen Viability in Rhizophora mangle and Rhizophora stylosa Hybrids. S. Pac. J. Nat. Sci. 20, 1–3 (2002).Article 

Google Scholar 
19.Tyagi, A. P. & Singh, E. V. V. Pollen fertility and intraspecific and interspecific compatibility in mangroves of Fiji. Sex. Plant Reprod. 11, 60–63 (1998).Article 

Google Scholar 
20.Steininger, F. F. & Rögl, F. Paleogeography and palinspastic reconstruction of the Neogene of the Mediterranean and Paratethys. Geol. Soc. Spec. Publ. 17, 659–668 (1984).ADS 
Article 

Google Scholar 
21.Harzhauser, M. et al. Biogeographic responses to geodynamics: a key study all around the Oligo-Miocene Tethyan Seaway. Zoo. Anz. 246, 241–256 (2007).Article 

Google Scholar 
22.Vrielynck, B., Odin, G. & Dercourt, J. Miocene palaeogeography of the Tethys Ocean; potential global correlations in the Mediterranean. Miocene stratigraphy: an integrated approach. Elsevier Science, (1997).23.Harzhauser, M., Piller, W. E. & Steininger, F. F. Circum-Mediterranean Oligo-Miocene biogeographic evolution—the gastropods’ point of view. Palaeogeogr. Palaeoclimatol. Palaeoecol. 183, 103–133 (2002).Article 

Google Scholar 
24.Dercourt, J. et al. Geological evolution of the Tethys belt from the Atlantic to the Pamirs since the LIAS. Tectonophysics 123, 241–315 (1986).ADS 
Article 

Google Scholar 
25.Marko, P. B. Fossil calibration of molecular clocks and the divergence times of geminate species pairs separated by the Isthmus of Panama. Mol. Biol. Evol. 19, 2005–2021 (2002).CAS 
PubMed 
Article 

Google Scholar 
26.Saenger, P. Mangrove vegetation: an evolutionary perspective. Mar. Freshw. Res. 49, 277–286 (1998).CAS 
Article 

Google Scholar 
27.Muller, J. & Caratini, C. Pollen of Rhizophora (Rhizophoraceae) as a guide fossil. Pollen Spores 19, 361–390 (1977).
Google Scholar 
28.Muller, J. Fossil pollen records of extant angiosperms. Bot. Rev. 47, 1–142 (1981).Article 

Google Scholar 
29.Germeraad, J. H., Hopping, C. A. & Muller, J. Palynology of tertiary sediments from tropical areas. Rev. Palaeobot. Palyno. 6, 189–348 (1968).Article 

Google Scholar 
30.Zachos, J., Pagani, H., Sloan, L., Thomas, E. & Billups, K. Trends, rhythms, and aberrations in global climate 65 Ma to present. Science 292, 686–693 (2001).ADS 
CAS 
Article 

Google Scholar 
31.Pole, M. S. & Macphail, M. K. Eocene Nypa from Regatta Point, Tasmania. Rev. Palaeobot. Palyno. 92, 55–67 (1996).Article 

Google Scholar 
32.Hornibrook, N. D. B. New Zealand Cenozoic marine paleoclimates: a review based on the distribution of some shallow water and terrestrial biota. Pacific Neogene: environment, evolution, and events, 83–106 University of Tokyo Press, (1992).33.Hou, Z. & Li, S. Tethyan changes shaped aquatic diversification. Biol. Rev. 93, 874–896 (2018).PubMed 
Article 
PubMed Central 

Google Scholar 
34.Wee, A. K. S. et al. Genetic differentiation and phylogeography of partially sympatric species complex Rhizophora mucronata Lam. and R. stylosa Griff. using SSR markers. BMC Evol. Biol. 15, 57 (2015).PubMed 
PubMed Central 
Article 

Google Scholar 
35.Ng, W. L. et al. Closely related and sympatric but not all the same: genetic variation of Indo-West Pacific Rhizophora mangroves across the Malay Peninsula. Conserv. Genet. 16, 137–150 (2015).Article 

Google Scholar 
36.Doyle, J. & Doyle, J. A rapid DNA isolation procedure for small quantities of fresh leaf tissue. Phytochem. Bull. 9, 11–15 (1987).
Google Scholar 
37.Strand, A. E., Leebens-Mack, J. & Milligan, B. G. Nuclear DNA-based markers for plant evolutionary biology. Mol. Ecol. 6, 113–118 (1997).CAS 
PubMed 
Article 

Google Scholar 
38.Cronn, R. C., Small, R. L. & Wendel, J. F. Duplicated genes evolve independently after polyploid formation in cotton. Proc. Natl. Acad. Sci. USA 96, 14406–14411 (1999).ADS 
CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
39.Hayashi, K. PCR-SSCP: a simple and sensitive method for detection of mutations in the genomic DNA. Genome Res. 1, 34–38 (1991).CAS 
Article 

Google Scholar 
40.Rozas, J., Sanchez-DelBarrio, J. C., Messeguer, X. & Rozas, R. DnaSP, DNA polymorphism analyses by the coalescent and other methods. Bioinformatics 19, 2496–2497 (2003).CAS 
Article 

Google Scholar 
41.Swofford, D.L. PAUP*. Phylogenetic Analysis Using Parsimony (*and Other Methods). Sinauer Associates, Sunderland, Massachusetts, (2002).42.Stamatakis, A. RAxML version 8: a tool for phylogenetic analysis and post-analysis of large phylogenies. Bioinformatics 30, 1312–1313 (2014).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
43.Leigh, J. W. & Bryant, D. PopART: Full-feature software for haplotype network construction. Methods Ecol. Evol. 6, 1110–1116 (2015).Article 

Google Scholar 
44.Bandelt, H. J., Forster, P. & Röhl, A. Median-joining networks for inferring intraspecific phylogenies. Mol. Biol. Evol. 16, I37-48 (1999).Article 

Google Scholar 
45.Drummond, A. J. & Rambaut, A. BEAST: Bayesian evolutionary analysis by sampling trees. BMC Evol. Biol. 7, 214 (2007).PubMed 
PubMed Central 
Article 
CAS 

Google Scholar 
46.Heled, J. & Drummond, A. J. Bayesian inference of species trees from multilocus data. Mol. Biol. Evol. 27, 570–580 (2009).PubMed 
PubMed Central 
Article 
CAS 

Google Scholar 
47.Graham, A. Paleobotanical evidence and molecular data in reconstructing the historical phytogeography of Rhizophoraceae. Ann. Mo. Bot. Gard. 93, 325–334 (2006).Article 

Google Scholar 
48.Rambaut, A. Fig Tree v1.4. (2012). Available at http://tree.bio.ed.ac.uk/software/figtree/49.Matzke, N. J. Probabilistic historical biogeography: new models for founder-event speciation, imperfect detection, and fossils allow improved accuracy and model-testing. Front. Biogeogr. 5, 242–248 (2013).Article 

Google Scholar 
50.Blair, C. & He, X. J. RASP 4: ancestral state reconstruction tool for multiple genes and characters. Mol. Biol. Evol. 37, 604–606 (2020).PubMed 
Article 
CAS 

Google Scholar 
51.Takayama, K., Tamura, M., Tateishi, Y. & Kajita, T. Isolation and characterization of microsatellite loci in a mangrove species, Rhizophora stylosa (Rhizophoraceae). Conserv. Genet. Resour. 1, 175–178 (2009).Article 

Google Scholar 
52.Takayama, K., Tamura, M., Tateishi, Y. & Kajita, T. Isolation and characterization of microsatellite loci in the red mangrove Rhizophora mangle (Rhizophoraceae) and its related species. Conserv. Genet. 9, 1323–1325 (2008).CAS 
Article 

Google Scholar 
53.Pritchard, J. K., Stephens, M. & Donnelly, P. Inference of population structure using multilocus genotype data. Genetics 155, 945–959 (2000).CAS 
PubMed 
PubMed Central 

Google Scholar 
54.Falush, D., Stephens, M. & Pritchard, J. K. Inference of population structure using multilocus genotype data: dominant markers and null alleles. Mol. Ecol. Notes. 7, 574–578 (2007).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
55.Hubisz, M. J., Falush, D., Stephens, M. & Pritchard, J. K. Inferring weak population structure with the assistance of sample group information. Mol. Ecol. Resour. 9, 1322–1332 (2009).PubMed 
PubMed Central 
Article 

Google Scholar 
56.Evanno, G., Regnaut, S. & Goudet, J. Detecting the number of clusters of individuals using the software structure: a simulation study. Mol. Ecol. 14, 2611–2620 (2005).CAS 
Article 

Google Scholar  More

1.Voss, N. A., Nesis, K. N. & Rodhouse, P. G. The cephalopod family Histioteuthidae (Oegopsida): systematics, biology, and biogeography in Systematics and Biogeography of Cephalopods (eds. Voss, N. A., Vecchione, M., Toll, R. B. & Sweeney M. J.) 277–291 (Smithsonian Contributions to Zoology, 1998).2.Crocetta, F. et al. Biogeographical homogeneity in the eastern Mediterranean Sea – III: new records and a state of the art of Polyplacophora, Scaphopoda and Cephalopoda (Mollusca) from Lebanon. Spixiana 37(2), 183–206 (2014).
Google Scholar 
3.Guerra, A. Mollusca, cephalopoda in Fauna Iberica (eds. Ramos, M. A. et al.) 327 (Museo Nacional de Ciencias Naturales, 1992).4.Cuccu, D., Mereu, M., Loi, B., Sanna, I. & Cau, A. The squid family Histioteuthidae in the Sardinian waters. Biol. Mar. Mediterr. 13, 262–263 (2007).
Google Scholar 
5.Jereb, P. & Roper, C. F. E. Cephalopods of the world. An annotated and illustrated catalogue of cephalopod species known to date. Myopsid and Oegopsid Squids in FAO species catalogue for fishery purposes (ed. FAO) 649 (FAO, 2010).6.Quetglas, A., de Mesa, A., Ordines, F. & Grau, A. Life history of the deep-sea cephalopod family Histioteuthidae in the western Mediterranean. Deep Sea Res. Part I 57, 999–1008. https://doi.org/10.1016/j.dsr.2010.04.008 (2010).Article 

Google Scholar 
7.Oshima, T., Shimazu, T., Koyama, H. & Akahane, H. J. J. On the larvae of the genus Anisakis (Nematoda: Anisakidaae) from euphausiids. Jpn. J. Parasitol. 18, 241–248 (1969).
Google Scholar 
8.Hochberg, F. G. The parasites of cephalopods: a review. Mem. Nat. Mus. Vict. 44, 109–145. https://doi.org/10.24199/j.mmv.1983.44.10 (1983).Article 

Google Scholar 
9.Bello, G. Role of cephalopods in the diet of the swordfish, Xiphias gladius, from the eastern Mediterranean Sea. Bull. Mar. Sci. 49, 312–324 (1991).
Google Scholar 
10.Bello, G. Teuthophagous predators as collectors of oceanic cephalopods: the case of the Adriatic Sea. Boll. Malacol. 32, 71–78 (1996).
Google Scholar 
11.Santos, M. et al. Stomach contents of sperm whales Physeter macrocephalus stranded in the North Sea 1990–1996. Mar. Ecol. Prog. Ser. 183, 281–294 (1999).ADS 
Article 

Google Scholar 
12.Xavier, J. et al. Current status of using beaks to identify cephalopods: III International Workshop and training course on Cephalopod beaks, Faial island, Azores, April 2007. Arquipélago-Life Mar. Sci. 24, 41–48 (2007).
Google Scholar 
13.Marcogliese, D. J. & Cone, D. K. Food webs: a plea for parasites. Trends Ecol. Evol. 12, 320–325. https://doi.org/10.1016/S0169-5347(97)01080-X (1997).CAS 
Article 
PubMed 

Google Scholar 
14.Abollo, E. et al. Squid as trophic bridges for parasite flow within marine ecosystems: the case of Anisakis simplex (Nematoda: Anisakidae), or when the wrong way can be right. Afr. J. Mar. Sci. 20, 223–232. https://doi.org/10.2989/025776198784126575 (1998).Article 

Google Scholar 
15.Klimpel, S., Seehagen, A., Palm, H. W. & Rosenthal, H. Deep-water metazoan fish parasites of the world. (eds. Klimpel, S., Seehagen, A., Palm, H. W. & Rosenthal, H.) (Logos Verlag, 2001).16.Parker, G. A., Chubb, J. C., Ball, M. A. & Roberts, G. N. Evolution of complex life cycles in helminth parasites. Nature 425, 480–484 (2003).ADS 
CAS 
Article 

Google Scholar 
17.Santoro, M., Iaccarino, D. & Bellisario, B. Host biological factors and geographic locality influence predictors of parasite communities in sympatric sparid fishes off the southern Italian coast. Sci. Rep. 10(1), 13283. https://doi.org/10.1038/s41598-020-69628-1 (2020).ADS 
CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
18.Pascual, S., González, A., Arias, C. & Guerra, A. Helminth infection in the short-finned squid Illex coindetii (Cephalopoda, Ommastrephidae) off NW Spain. Dis. Aquat. Org. 23, 71–75. https://doi.org/10.3354/dao023071 (1995).Article 

Google Scholar 
19.Petrić, M., Mladineo, I. & Šifner, S. Insight into the short-finned squid Illex coindetii (Cephalopoda: Ommastrephidae) feeding ecology: is there a link between helminth parasites and food composition? J. Parasitol. 97, 55–62. https://doi.org/10.1645/GE-2562.1 (2011).Article 
PubMed 

Google Scholar 
20.Klimpel, S. & Rückert, S. Life cycle strategy of Hysterothylacium aduncum to become the most abundant anisakid fish nematode in the North Sea. Parasitol. Res. 97, 141–149. https://doi.org/10.1007/s00436-005-1407-6 (2005).Article 
PubMed 

Google Scholar 
21.Tursi, A., D’Onghia, A., Matarrese, A., Panetta, P. & Maiorano, P. Finding of uncommon cephalopods (Ancistroteuthis lichtensteinii, Histioteuthis bonnellii, Histioteuthis reversa) and first record of Chiroteuthis veranyi in the Ionian Sea. Cah. Biol. Mar. 35, 339–346 (1994).
Google Scholar 
22.Koutsoubas, D. & Boyle, P. Histioteuthis bonnelli (Férussac, 1835) (Cephalopoda) in the Eastern Mediterranean: new record and biological considerations. J. Mollus. Stud. 65, 380–383. https://doi.org/10.1093/mollus/65.3.380 (1999).Article 

Google Scholar 
23.Bello, G. How rare is Histioeuthis bonnellii (Cephalopoda: Histioteuthidae) in the eastern Mediterranean Sea? J. Mollus. Stud. 66, 575–576. https://doi.org/10.1093/mollus/66.4.575 (2000).Article 

Google Scholar 
24.Belcari, P. & Sartor, P. Bottom trawling teuthofauna of the northern Tyrrhenian Sea. Sci. Mar. 57, 145–152 (1993).
Google Scholar 
25.Quetglas, A., Carbonell, A. & Sánchez, P. Demersal continental shelf and upper slope cephalopod assemblages from the Balearic Sea (North-Western Mediterranean). Biological aspects of some deep-sea species. Estuar. Coast. Shelf Sci. 50, 739–749. https://doi.org/10.1006/ecss.1999.0603 (2000).ADS 
Article 

Google Scholar 
26.Culurgioni, J., Cuccu, D., Mereu, M. & Figus, V. Larval anisakid nematodes of Histioteuthis reversa (Verril, 1880) and H. bonnellii (Férussac, 1835) (Cephalopoda: Teuthoidea) from Sardinian Channel (western Mediterranean). Bull. Eur. Ass. Fish Pathol. 30, 217 (2010).
Google Scholar 
27.Capua, D. I cefalopodi delle coste e dell’Arcipelago Toscano: sistematica, anatomia, fisiologia e sfruttamento delle specie presenti nel Mediterraneo. 446 (Evolver, 2004).28.Crocetta, F. et al. Bottom-trawl catch composition in a highly polluted coastal area reveals multifaceted native biodiversity and complex communities of fouling organisms on litter discharge. Mar. Environ. Res. 155, 104875. https://doi.org/10.1016/j.marenvres.2020.104875 (2020).CAS 
Article 
PubMed 

Google Scholar 
29.Folmer, O., Black, M., Hoeh, W., Lutz, R. & Vrijenhoek, R. DNA primers for amplification of mitochondrial cytochrome c oxidase subunit I from diverse metazoan invertebrates. Mol. Mar. Biol. Biotechnol. 3, 294–299 (1994).CAS 
PubMed 

Google Scholar 
30.Meyer, C. P. Molecular systematics of cowries (Gastropoda: Cypraeidae) and diversification patterns in the tropics. Biol. J. Linn. Soc. 79, 401–459. https://doi.org/10.1046/j.1095-8312.2003.00197.x (2003).Article 

Google Scholar 
31.Morgulis, A. et al. Database indexing for production MegaBLAST searches. Bioinformatics 24, 1757–1764. https://doi.org/10.1093/bioinformatics/btn322 (2008).CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
32.Berland, B. Nematodes from some Norwegian marine fishes. Sarsia 2, 1–50. https://doi.org/10.1080/00364827.1961.10410245 (1961).Article 

Google Scholar 
33.Nagasawa, K. & Moravec, F. Larval anisakid nematodes of Japanese common squid (Todarodes pacificus) from the Sea of Japan. J. Parasitol. 81, 69–75. https://doi.org/10.2307/3284008 (1995).CAS 
Article 
PubMed 

Google Scholar 
34.Nagasawa, K. & Moravec, F. Larval anisakid nematodes from four species of squid (Cephalopoda: Teuthoidea) from the central and western North Pacific Ocean. J. Nat. Hist. 36, 8. https://doi.org/10.1080/00222930110051752 (2002).Article 

Google Scholar 
35.Bush, A. O., Lafferty, K. D., Lotz, J. M. & Shostak, A. W. Parasitology meets ecology on its own terms: Margolis et al. revisited. J. Parasitol. 83(4), 575–583 (1997).CAS 
Article 

Google Scholar 
36.Zhu, X., Gasser, R. B., Podolska, M. & Chilton, N. Characterisation of anisakid nematodes with zoonotic potential by nuclear ribosomal DNA sequences. Int. J. Parasitol. 28, 1911–1921. https://doi.org/10.1016/S0020-7519(98)00150-7 (1998).CAS 
Article 
PubMed 

Google Scholar 
37.Nadler, S. A. & Hudspeth, D. S. Phylogeny of the Ascaridoidea (Nematoda: Ascaridida) based on three genes and morphology: hypotheses of structural and sequence evolution. J. Parasitol. 86, 380–393. https://doi.org/10.1645/0022-3395(2000)086[0380:POTANA]2.0.CO;2 (2000).CAS 
Article 
PubMed 

Google Scholar 
38.Valentini, A. et al. Genetic relationships among Anisakis species (Nematoda: Anisakidae) inferred from mitochondrial cox2 sequences, and comparison with allozyme data. J. Parasitol. 92, 156–166. https://doi.org/10.1645/GE-3504.1 (2006).CAS 
Article 
PubMed 

Google Scholar 
39.Vaidya, G., Lohman, D. J. & Meier, R. SequenceMatrix: concatenation software for the fast assembly of multi-gene datasets with character set and codon information. Cladistics 27, 171–180. https://doi.org/10.1111/j.1096-0031.2010.00329.x (2011).Article 

Google Scholar 
40.Darriba, D., Taboada, G. L., Doallo, R. & Posada, D. jModelTest 2: more models, new heuristics and parallel computing. Nat. Methods 9(8), 772. https://doi.org/10.1038/nmeth.2109 (2012).CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
41.Guindon, S. & Gascuel, O. A simple, fast and accurate method to estimate large phylogenies by maximum-likelihood. Syst. Biol. 52, 696–704. https://doi.org/10.1080/10635150390235520 (2003).Article 
PubMed 

Google Scholar 
42.Akaike, H. Information theory and an extension of the maximum likelihood principle in Proceeding of the second international symposium on information theory (eds. Petrov, T. & Caski, F.) 267–281 (Akademiai Kiado, 1973).43.Posada, D. jModelTest: phylogenetic model averaging. Mol. Biol. Evol. 25, 1253–1256. https://doi.org/10.1093/molbev/msn083 (2008).CAS 
Article 
PubMed 

Google Scholar 
44.Posada, D. & Buckley, T. R. Model selection and model averaging in phylogenetics: advantages of Akaike Information Criterion and Bayesian approaches over likelihood ratio tests. Syst. Biol. 53, 793–808. https://doi.org/10.1080/10635150490522304 (2004).Article 
PubMed 
PubMed Central 

Google Scholar 
45.Ronquist, F. & Huelsenbeck, J. MrBayes 3: Bayesian phylogenetic inference under mixed models. Bioinformatics 19, 1572–1574. https://doi.org/10.1093/bioinformatics/btg180 (2003).CAS 
Article 

Google Scholar 
46.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 
Article 

Google Scholar 
47.Kumar, S. et al. MEGA X: molecular evolutionary genetics analysis across computing platforms. Mol. Biol. Evol. 35, 1547–1549. https://doi.org/10.1093/molbev/msy096 (2018).CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
48.Lindgren, A. R. Molecular inference of phylogenetic relationships among Decapodiformes (Mollusca: Cephalopoda) with special focus on the squid Order Oegopsida. Mol. Phylogenet. 56(1), 77–90. https://doi.org/10.1016/j.ympev.2010.03.025 (2010).Article 

Google Scholar 
49.Taite, M., Vecchione, M., Fennell, S. & Allcock, L. A. Paralarval and juvenile cephalopods within warm-core eddies in the North Atlantic. Bul. Mar. Sci. 96(2), 235–262. https://doi.org/10.5343/bms.2019.0042 (2020).Article 

Google Scholar 
50.Guardone, L. et al. Larval ascaridoid nematodes in horned and musky octopus (Eledone cirrhosa and E. moschata) and longfin inshore squid (Doryteuthis pealeii): safety and quality implications for cephalopod products sold as fresh on the Italian market. Int. J. Food Microbiol. 333, 108812 (2020).CAS 
Article 

Google Scholar 
51.Pascual, S., Abollo, E., Mladineo, I. & Gestal, C. Metazoa and Related Diseases in Handbook of Pathogens and Diseases in Cephalopods (eds. Gestal, C., Pascual S., Guerra A., Fiorito G. & Vieites, J. M.) 169–179 (2019).52.Mattiucci, S. & Nascetti, G. Advances and trends in the molecular systematics of anisakid nematodes, with implications for their evolutionary ecology and host—parasite co-evolutionary processes. Adv. Parasitol. 66, 47–148. https://doi.org/10.1016/S0065-308X(08)00202-9 (2008).Article 
PubMed 

Google Scholar 
53.Mattiucci, S., Cipriani, P., Levsen, A., Paoletti, M. & Nascetti, G. Molecular epidemiology of Anisakis and Anisakiasis: an ecological and evolutionary road map. Adv. Parasitol. 99, 93–263. https://doi.org/10.1016/bs.apar.2017.12.001 (2018).Article 
PubMed 

Google Scholar 
54.Kie, M. Aspects of the life cycle and morphology of Hysterothylacium aduncum (Rudolphi, 1802) (Nematoda, Ascaridoidea, Anisakidae). Can. J. Zool. 71, 1289–1296. https://doi.org/10.1139/z93-178 (1993).Article 

Google Scholar 
55.Santoro, M. et al. Helminth parasites of the dwarf sperm whale Kogia sima (Cetacea: Kogiidae) from the Mediterranean Sea, with implications on host ecology. Dis. Aquat. Organ. 14, 175–182. https://doi.org/10.3354/dao03251 (2018).CAS 
Article 

Google Scholar 
56.Kawakami, T. A review of sperm whale food. Sci. Rep. Whales Res. Inst. 32, 199–218 (1980).
Google Scholar 
57.Garibaldi, F. & Podestà, M. Stomach contents of a sperm whale (Physeter macrocephalus) stranded in Italy (Ligurian Sea, northwestern Mediterranean). JMBA 94(6), 1087–1091. https://doi.org/10.1017/S0025315413000428 (2014).Article 

Google Scholar 
58.Mattiucci, S., Nascetti, G., Bullini, L., Orecchia, P. & Paggi, L. Genetic structure of Anisakis physeteris and its differentiation from the Anisakis simplex complex (Ascaridida: Anisakidae). Parasitology 93, 383–387. https://doi.org/10.1017/S0031182000051544 (1986).CAS 
Article 
PubMed 

Google Scholar 
59.Mattiucci, S. et al. Genetic divergence and reproductive isolation between Anisakis brevispiculata and Anisakis physeteris (Nematoda: Anisakidae). Int. J. Parasitol. 31, 9–14. https://doi.org/10.1016/S0020-7519(00)00125-9 (2001).CAS 
Article 
PubMed 

Google Scholar 
60.Gupta, P. C. & Masoodi, B. A. Three new and one known nematode (Family: Anisakidae) from marine fishes of India. Indian J. Parasitol. 14(2), 157–164 (1990).
Google Scholar 
61.Vicente, J. J., Mincarone, M. M. & Pint, R. M. First report of Lappetascaris lutjani Rasheed, 1965 (Nematoda, Ascaridoidea, Anisakidae) parasitizing Trachipterus arawatae (Pisces, Lampridiformes) on the Atlantic coast of Brazil. Mem. Inst. Oswaldo Cruz. 97, 93–94. https://doi.org/10.1590/s0074-02762002000100015(2002) (2002).Article 
PubMed 

Google Scholar 
62.Bruce, N. L. & Cannon, L. R. G. Hysterothylacium, Iheringascaris and Maricostula new genus, nematodes (Ascaridoidea) from Australian pelagic marine fishes. J. Nat. Hist. 23(6), 1397–1441. https://doi.org/10.1080/00222938900770771 (1989).Article 

Google Scholar 
63.Shamsi, S. Morphometric and molecular descriptions of three new species of Hysterothylacium (Nematoda: Raphidascarididae) from Australian marine fish. J. Helminthol. 91, 1–12. https://doi.org/10.1017/S0022149X16000596 (2016).CAS 
Article 

Google Scholar 
64.Li, L. et al. Molecular phylogeny and dating reveal a terrestrial origin in the early carboniferous for ascaridoid nematodes. Syst. Biol. 67(5), 888–900. https://doi.org/10.1093/sysbio/syy018 (2018).Article 
PubMed 

Google Scholar 
65.Garcia, A., Mattiucci, S., Santos, M. N., Damiano, S. & Nascetti, G. Metazoan parasites of Xiphias gladius (L. 1758) (Pisces: Xiphiidae) from the Atlantic Ocean: implications for host stock identification. ICES J. Mar. Sci. 68, 175–182 (2010).Article 

Google Scholar 
66.Klimpel, S. & Palm, H. W. Anisakid Nematode (Ascaridoidea) Life Cycles and Distribution: Increasing Zoonotic Potential in the Time of Climate Change? in Progress in Parasitology. Parasitology Research Monographs (ed. Mehlhorn, H.) https://doi.org/10.1007/978-3-642-21396-0_11 (Springer, 2011).67.Kuhn, T., Cunze, S., Kochmann, J. & Klimpel, S. Environmental variables and definitive host distribution: a habitat suitability modelling for endohelminth parasites in the marine realm. Sci. Rep. 6, 30246. https://doi.org/10.1038/srep30246 (2016).ADS 
CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
68.Cipriani, P. et al. Occurrence of larval ascaridoid nematodes in the Argentinean short-finned squid Illex argentinus from the Southwest Atlantic Ocean (off Falkland Islands). Int. J. Food Microbiol. 297, 27–31. https://doi.org/10.1016/j.ijfoodmicro.2019.02.019 (2019).Article 
PubMed 

Google Scholar 
69.Cipriani, P. et al. Anisakis simplex (s.s.) larvae (Nematoda: Anisakidae) hidden in the mantle of European flying squid Todarodes sagittatus (Cephalopoda: Ommastrephidae) in NE Atlantic Ocean: food safety implications. Int. J. Food Microbiol. 339, 109021 (2021).CAS 
Article 

Google Scholar 
70.Klimpel, S., Kellermanns, E. & Palm, H. W. The role of pelagic swarm fish (Myctophidae: Teleostei) in the oceanic life cycle of Anisakis sibling species at the Mid-Atlantic Ridge, Central Atlantic. Parasitol. Res. 104, 43–53. https://doi.org/10.1007/s00436-008-1157-3 (2008).Article 
PubMed 

Google Scholar 
71.Mattiucci, S., Paoletti, M. & Webb, S. C. Anisakis nascettii n. sp. (Nematoda: Anisakidae) from beaked whales of the southern hemisphere: morphological description, genetic relationships between congeners and ecological data. Syst. Parasitol. 74, 199–217. https://doi.org/10.1007/s11230-009-9212-8 (2009).Article 
PubMed 

Google Scholar 
72.Pico-Duran, G., Pulleiro-Potel, L., Abollo, E., Pascual, S. & Munoz, P. Molecular identification of Anisakis and Hysterothylacium larvae in commercial cephalopods from the Spanish Mediterranean coast. Vet. Parasitol. 220, 47–53. https://doi.org/10.1016/j.vetpar.2016.02.020 (2016).Article 
PubMed 

Google Scholar 
73.Menconi, V. et al. Occurrence of ascaridoid nematodes in Illex coindetii, a commercially relevant cephalopod species from the Ligurian Sea (Northwest Mediterranean Sea). Food Control https://doi.org/10.1016/j.foodcont.2020.107311 (2020).Article 

Google Scholar 
74.Blazekovic, K. et al. Three Anisakis spp. isolated from toothed whales stranded along the eastern Adriatic Sea coast. Int. J. Parasitol. 45(1), 17–31. https://doi.org/10.1016/j.ijpara.2014.07.012 (2015).Article 
PubMed 

Google Scholar 
75.Santoro, M. et al. Epidemiology of Sulcascaris sulcata (Nematoda: Anisakidae) ulcerous gastritis in the Mediterranean loggerhead sea turtle (Caretta caretta). Parasitol. Res. 118, 1457–1463. https://doi.org/10.1007/s00436-019-06283-0 (2019).Article 
PubMed 

Google Scholar 
76.Bao, M., Cipriani, P., Giulietti, L., Drivenes, N. & Levsen, A. Quality issues related to the presence of the fish parasitic nematode Hysterothylacium aduncum in export shipments of fresh Northeast Arctic cod (Gadus morhua). Food Control 121, 107724. https://doi.org/10.1016/j.foodcont.2020.107724 (2020).CAS 
Article 

Google Scholar 
77.Zhang, K., Xu, Z., Chen, H. X., Guo, N. & Li, L. Anisakid and raphidascaridid nematodes (Ascaridoidea) infection in the important marine food-fish Lophius litulon (Jordan) (Lophiiformes: Lophiidae). Int. J. Food Microbiol. 284, 105–111. https://doi.org/10.1016/j.ijfoodmicro.2018.08.002 (2018).Article 
PubMed 

Google Scholar 
78.Szostakowska, B., Myjak, P., Kur, J. & Sywula, T. Molecular evaluation of Hysterothylacium auctum (Nematoda, Ascaridida, Raphidascarididae) taxonomy from fish of the southern Baltic. Acta Parasitol. 46(3), 194–201 (2001).CAS 

Google Scholar 
79.Andres, M. J., Peterson, M. S. & Overstreet, R. M. Endohelminth parasites of some midwater and benthopelagic stomiiform fishes from the northern Gulf of Mexico. Gulf Caribb. Res. 27, 11–19. https://doi.org/10.18785/gcr.2701.02 (2016).Article 

Google Scholar 
80.Li, L., Liu, Y. Y. & Zhang, L. P. Morphological and molecular identification of Hysterothylacium longilabrum sp. Nov. (Nematoda: Anisakidae) and larvae of different stages from marine fishes in the South China Sea. Parasitol. Res. 111(2), 767–777 (2012).Article 

Google Scholar 
81.Shamsi, S. et al. Occurrence of ascaridoid nematodes in selected edible fish from the Persian Gulf and description of Hysterothylacium larval type XV and Hysterothylacium persicum n. sp. (Nematoda: Raphidascarididae). Int. J. Food Microbiol. 236, 65–67. https://doi.org/10.1016/j.ijfoodmicro.2016.07.006 (2016).Article 
PubMed 

Google Scholar 
82.Chen, H. X. et al. Detection of ascaridoid nematode parasites in the important marine food-fish Conger myriaster (Brevoort) (Anguilliformes: Congridae) from the Zhoushan fishery, China. Parasit. Vectors 11, 274. https://doi.org/10.1186/s13071-018-2850-4 (2018).CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
83.Liu, Y. Y., Xu, Z., Zhang, L. P. & Li, L. Redescription and genetic characterization of Hysterothylacium thalassini Bruce, 1990 (Nematoda: Anisakidae) from marine fishes in the South China Sea. J. Parasitol. 99, 655–661. https://doi.org/10.1645/12-136.1 (2013).Article 
PubMed 

Google Scholar 
84.Shamsi, S., Gasser, R. & Beveridge, I. Description and genetic characterisation of Hysterothylacium (Nematoda: Raphidascarididae) larvae parasitic in Australian marine fishes. Parasitol. Int. 62, 320–328. https://doi.org/10.1016/j.parint.2012.10.001 (2013).CAS 
Article 
PubMed 

Google Scholar 
85.Li, L., Zhao, W. T., Guo, Y. N. & Zhang, L. P. Nematode parasites infecting the starry batfish Halieutaea stellata (Vahl) (Lophiiformes: Ogcocephalidae) from the East and South China Sea. J. Fish. Dis. 39(5), 515–529. https://doi.org/10.1111/jfd.12374 (2016).CAS 
Article 
PubMed 

Google Scholar 
86.Zhao, W. T. et al. Ascaridoid parasites infecting in the frequently consumed marine fishes in the coastal area of China: a preliminary investigation. Parasitol. Int. 65(2), 87–98. https://doi.org/10.1016/j.parint.2015.11.002 (2016).Article 
PubMed 

Google Scholar 
87.Hossen, M. S. & Shamsi, S. Zoonotic nematode parasites infecting selected edible fish in New South Wales, Australia. Int. J. Food Microbiol. 308, 108306. https://doi.org/10.1016/j.ijfoodmicro.2019.108306 (2019).CAS 
Article 
PubMed 

Google Scholar 
88.Jabbar, A. et al. Mutation scanning-based analysis of anisakid larvae from Sillago flindersi from Bass Strait, Australia. Electrophoresis 33, 499–505. https://doi.org/10.1002/elps.201100438 (2012).CAS 
Article 
PubMed 

Google Scholar 
89.Jabbar, A. et al. Molecular characterization of anisakid nematode larvae from 13 species of fish from Western Australia. Int. J. Food Microbiol. 161(3), 247–253. https://doi.org/10.1016/j.ijfoodmicro.2012.12.012 (2013).CAS 
Article 
PubMed 

Google Scholar 
90.Shamsi, S., Stellar, E. & Chen, Y. New and known zoonotic nematode larvae within selected fish species from Queensland waters in Australia. Int. J. Food Microbiol. 272, 73–82. https://doi.org/10.1016/j.ijfoodmicro.2018.03.007 (2018).Article 
PubMed 

Google Scholar 
91.Arizono, N. et al. Ascariasis in Japan: is pig-derived ascaris infecting humans? Jpn. J. Infect. Dis. 63(6), 447–448 (2010).PubMed 

Google Scholar 
92.Mattiucci, S. et al. Metazoan parasitic infections of swordfish (Xiphias gladius) from the Mediterranean Sea and Atlantic Gibraltar waters: implications for stock assessment. Col. Vol. Sci. Pap. ICCAT 58(4), 1470–1482 (2005).
Google Scholar 
93.Di Azevedo, M. I. N. & Iñiguez, A. M. Nematode parasites of commercially important fish from the southeast coast of Brazil: morphological and genetic insight. Int. J. Food Microbiol. 267, 29–41. https://doi.org/10.1016/j.ijfoodmicro.2017.12.014 (2018).Article 
PubMed 

Google Scholar 
94.Pekmezci, G. Z., Yardimci, B., Onuk, E. E. & Umur, S. Molecular characterization of Hysterothylacium fabri (Nematoda: Anisakidae) from Zeus faber (Pisces: Zeidae) caught off the Mediterranean coasts of Turkey based on nuclear ribosomal and mitochondrial DNA sequences. Parasitol. Int. 63(1), 127–131. https://doi.org/10.1016/j.parint.2013.10.006 (2014).CAS 
Article 
PubMed 

Google Scholar 
95.Zhao, J. Y., Zhao, W. T., Ali, A. H., Chen, H. X. & Li, L. Morphological variability, ultrastructure and molecular characterisation of Hysterothylacium reliquens (Norris & Overstreet, 1975) (Nematoda: Raphidascarididae) from the oriental sole Brachirus orientalis (Bloch & Schneider) (Pleuronectiformes: Soleidae). Parasitol. Int. 66(1), 831–838. https://doi.org/10.1016/j.parint.2016.09.012 (2016).Article 
PubMed 

Google Scholar  More