Ecology

1.Holling, C. S. & Meffe, G. K. Command and control and the pathology of natural resource management. Conserv. Biol. 10, 328–337 (1996).Article 

Google Scholar 
2.Peterson, G., Allen, C. R. & Holling, C. S. Ecological resilience, biodiversity, and scale. Ecosystems 1, 6–18 (1998).Article 

Google Scholar 
3.Gunderson, L. H. Ecological resilience—In theory and application. Annu. Rev. Ecol. Syst. 31, 425–439 (2000).Article 

Google Scholar 
4.Thrush, S. F. et al. Forecasting the limits of resilience: Integrating empirical research with theory. Proc. R. Soc. B Biol. Sci. 276, 3209–3217 (2009).Article 

Google Scholar 
5.Bagchi, S. et al. Quantifying long-term plant community dynamics with movement models: Implications for ecological resilience. Ecol. Appl. 27, 1514–1528 (2017).PubMed 
Article 
PubMed Central 

Google Scholar 
6.Tilman, D., Reich, P. B. & Knops, J. M. Biodiversity and ecosystem stability in a decade-long grassland experiment. Nature 441, 629–632 (2006).ADS 
CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
7.Hillebrand, H. et al. Decomposing multiple dimensions of stability in global change experiments. Ecol. Lett. 21, 21–30 (2018).PubMed 
Article 
PubMed Central 

Google Scholar 
8.Radchuk, V. et al. The dimensionality of stability depends on disturbance type. Ecol. Lett. 22, 674–684 (2019).PubMed 
Article 
PubMed Central 

Google Scholar 
9.Donohue, I. et al. Navigating the complexity of ecological stability. Ecol. Lett. 19, 1172–1185 (2016).PubMed 
Article 
PubMed Central 

Google Scholar 
10.Pimm, S. L. The complexity and stability of ecosystems. Nature 307, 321–326 (1984).ADS 
Article 

Google Scholar 
11.Donohue, I. et al. On the dimensionality of ecological stability. Ecol. Lett. 16, 421–429 (2013).PubMed 
Article 
PubMed Central 

Google Scholar 
12.Pennekamp, F. et al. Biodiversity increases and decreases ecosystem stability. Nature 563, 109–112 (2018).ADS 
CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
13.Kéfi, S. et al. Advancing our understanding of ecological stability. Ecol. Lett. 22, 1349–1356 (2019).PubMed 
Article 
PubMed Central 

Google Scholar 
14.Raffaelli, D. & Hawkins, S. J. Intertidal Ecology (Chapman & Hall, 1996).Book 

Google Scholar 
15.Tsujino, M. et al. Distance decay of community dynamics in rocky intertidal sessile assemblages evaluated by transition matrix models. Popul. Ecol. 52, 171–180 (2010).Article 

Google Scholar 
16.Kanamori, Y., Fukaya, K. & Noda, T. Seasonal changes in community structure along a vertical gradient: Patterns and processes in rocky intertidal sessile assemblages. Popul. Ecol. 59, 301–313 (2017).Article 

Google Scholar 
17.Menge, B. A. et al. Benthic–pelagic links and rocky intertidal communities: Bottom-up effects on top-down control?. Proc. Natl. Acad. Sci. 94, 14530–14535 (1997).ADS 
CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
18.Sanford, E. Regulation of keystone predation by small changes in ocean temperature. Science 283, 2095–2097 (1999).ADS 
CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
19.Menge, B. A. Top-down and bottom-up community regulation in marine rocky intertidal habitats. J. Exp. Mar. Biol. Ecol. 250, 257–289 (2000).CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
20.Connolly, S. R., Menge, B. A. & Roughgarden, J. A. Latitudinal gradient in recruitment of intertidal invertebrates in the northeast Pacific Ocean. Ecology 82, 1799–1813 (2001).Article 

Google Scholar 
21.Menge, B. A. et al. Coastal oceanography sets the pace of rocky intertidal community dynamics. Proc. Natl. Acad. Sci. 100, 12229–12234 (2003).ADS 
CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
22.Nielsen, K. J. & Navarrete, S. A. Mesoscale regulation comes from the bottom-up: Intertidal interactions between consumers and upwelling. Ecol. Lett. 7, 31–41 (2004).Article 

Google Scholar 
23.Schoch, G. C. et al. Fifteen degrees of separation: Latitudinal gradients of rocky intertidal biota along the California Current. Limnol. Oceanogr. 51, 2564–2585 (2006).ADS 
Article 

Google Scholar 
24.Vinueza, L. R., Menge, B. A., Ruiz, D. & Palacios, D. M. Oceanographic and climatic variation drive top-down/bottom-up coupling in the Galápagos intertidal meta-ecosystem. Ecol. Monogr. 84, 411–434 (2014).Article 

Google Scholar 
25.Menge, B. A., Gouhier, T. C., Hacker, S. D., Chan, F. & Nielsen, K. J. Are meta-ecosystems organized hierarchically? A model and test in rocky intertidal habitats. Ecol. Monogr. 85, 213–233 (2015).Article 

Google Scholar 
26.Hacker, S. D., Menge, B. A., Nielsen, K. J., Chan, F. & Gouhier, T. C. Regional processes are stronger determinants of rocky intertidal community dynamics than local biotic interactions. Ecology https://doi.org/10.1002/ecy.2763 (2019).Article 
PubMed 
PubMed Central 

Google Scholar 
27.Qiu, B. Kuroshio and Oyashio currents. In Ocean Currents: A Derivative of the Encyclopedia of Ocean Sciences (eds Steele, J. H. et al.) 1413–1425 (Academic Press, 2001).Chapter 

Google Scholar 
28.Qiu, B. Large-scale variability in the midlatitude subtropical and subpolar North Pacific Ocean: Observations and causes. J. Phys. Oceanogr. 32, 353–375 (2002).ADS 
Article 

Google Scholar 
29.Sakurai, Y. An overview of the Oyashio ecosystem. Deep Sea Res. Pt. II 54, 2526–2542 (2007).ADS 
Article 

Google Scholar 
30.Yatsu, A. et al. Climate forcing and the Kuroshio/Oyashio ecosystem. ICES J. Mar. Sci. 70, 922–933 (2013).Article 

Google Scholar 
31.Kawabe, M. Variations of the Kuroshio in the southern region of Japan: Conditions for large meander of the Kuroshio. J. Oceanogr. 61, 529–537 (2005).Article 

Google Scholar 
32.Okunishi, T. et al. Characteristics of oceanographic condition of Tohoku prefecture in 2018. in Bulletin of Liaison Conference of Tohoku Marine Surveys and Technology , Vol. 68, 4–5 (2018) (in Japanese).33.Japan Meteorological Agency. Fluctuations in the Kuroshio Current on a Scale of Months to Decades (Paths). http://www.data.jma.go.jp/gmd/kaiyou/data/shindan/b_2/kuroshio_stream/kuroshio_stream.html (in Japanese, accessed 11 March 2021).34.Taniguchi, K., Sato, M. & Owada, K. On the characteristics of the structural variation in the Eisenia bicyclis population on Joban coast, Japan. Bull Tohoku Natl. Fish. Res. Inst. 48, 49–57 (1986) (in Japanese with English abstract).
Google Scholar 
35.Nomura, K., & Hirabayashi, I. Mass mortality of coral communities caused by abnormality low water temperature observed at Kii peninsula west coast for winter season in 2018. Marine Pavilion. Supplement 7 (2018) (in Japanese).36.Yamaguchi, M. Acanthaster planci infestations of reefs and coral assemblages in Japan: A retrospective analysis of control efforts. Coral Reefs 5, 23–30 (1986).ADS 
Article 

Google Scholar 
37.Ohgaki, S. I. et al. Effects of temperature and red tides on sea urchin abundance and species richness over 45 years in southern Japan. Ecol. Indic. 96, 684–693 (2019).Article 

Google Scholar 
38.Kawajiri, M., Sasaki, T. & Kageyama, Y. Extensive deterioration of Ecklonia kelp stands and death of the plants, and fluctuations in abundance of the abalone off Toji, southern Izu peninsula. Bull. Shizuoka Pref. Fish. Exp. Stn. 15, 19–30 (1981) (in Japanese).
Google Scholar 
39.Takami, H. et al. Overwinter mortality of young-of-the-year Ezo abalone in relation to seawater temperature on the North Pacific coast of Japan. Mar. Ecol. Prog. Ser. 367, 203–212 (2008).ADS 
Article 

Google Scholar 
40.Okuda, T., Noda, T., Yamamoto, T., Ito, N. & Nakaoka, M. Latitudinal gradient of species diversity: Multi-scale variability in rocky intertidal sessile assemblages along the Northwestern Pacific coast. Popul. Ecol. 46, 159–170 (2004).Article 

Google Scholar 
41.Nakaoka, M., Ito, N., Yamamoto, T., Okuda, T. & Noda, T. Similarity of rocky intertidal assemblages along the Pacific coast of Japan: Effects of spatial scales and geographic distance. Ecol. Res. 21, 425–435 (2006).Article 

Google Scholar 
42.Gotelli, N. J. et al. Community-level regulation of temporal trends in biodiversity. Sci. Adv. 3, e1700315. https://doi.org/10.1126/sciadv.1700315 (2017).ADS 
Article 
PubMed 
PubMed Central 

Google Scholar 
43.Hillebrand, H. et al. Thresholds for ecological responses to global change do not emerge from empirical data. Nat. Ecol. Evol. 4, 1502–1509 (2020).PubMed 
Article 
PubMed Central 

Google Scholar 
44.Iwasaki, A., Fukaya, K. & Noda, T. Quantitative evaluation of the impact of the Great East Japan Earthquake and tsunami on the rocky intertidal community. In Ecological Impacts of Tsunamis on Coastal Ecosystems (eds Urabe, J. & Nakashizuka, T.) 35–46 (Springer Japan, 2016).Chapter 

Google Scholar 
45.Noda, T., Iwasaki, A. & Fukaya, K. Recovery of rocky intertidal zonation: Two years after the 2011 Great East Japan Earthquake. J. Mar. Biol. Assoc. UK 96, 1549–1555 (2016).Article 

Google Scholar 
46.Noda, T., Sakaguchi, M., Iwasaki, A. & Fukaya, K. Influence of the 2011 Tohoku Earthquake on population dynamics of a rocky intertidal barnacle: Cause and consequence of alternation in larval recruitment. Coast. Mar. Sci. 40, 35–43 (2017).
Google Scholar 
47.Nuvoloni, F. M., Feres, R. J. F. & Gilbert, B. Species turnover through time: Colonization and extinction dynamics across metacommunities. Am. Nat. 187, 786–796 (2016).PubMed 
Article 
PubMed Central 

Google Scholar 
48.Clarke, A. Life in cold water: The physiological ecology of polar marine ectotherms. Oceanogr. Mar. Biol. A Rev. 21, 341–453 (1983).
Google Scholar 
49.Moss, D. K. et al. Lifespan, growth rate, and body size across latitude in marine Bivalvia, with implications for Phanerozoic evolution. Proc. R. Soc. B Biol. Sci. 283, 20161364. https://doi.org/10.1098/rspb.2016.1364 (2016).Article 

Google Scholar 
50.Bulleri, F. et al. Temporal stability of European rocky shore assemblages: Variation across a latitudinal gradient and the role of habitat-formers. Oikos 121, 1801–1809 (2012).Article 

Google Scholar 
51.Noda, T. Spatial hierarchical approach in community ecology: A way beyond high context-dependency and low predictability in local phenomena. Popul. Ecol. 46, 105–117 (2004).Article 

Google Scholar 
52.Sahara, R. et al. Larval dispersal dampens population fluctuation and shapes the interspecific spatial distribution patterns of rocky intertidal gastropods. Ecography 39, 487–495 (2015).Article 

Google Scholar 
53.Hanawa, K. & Mitsudera, H. Variation of water system distribution in the Sanriku coastal area. J. Oceanogr. 42, 435–446 (1987).Article 

Google Scholar 
54.Ohtani, K. Westward inflow of the coastal Oyashio Water into the Tsugaru Strait. Bull. Fac. Fish Hokkaido Univ. 38, 209–220 (1987) (in Japanese with English abstract).
Google Scholar 
55.Takasugi, S. Distribution of Tsugaru Warm Current water in the Iwate coastal area and their influence to sea surface temperature at coastal hydrographic station. Bull. Jpn. Soc. Fish. Oceanogr. 56, 434–448 (1992) (in Japanese with English abstract).
Google Scholar 
56.Takasugi, S. & Yasuda, I. Variation of the Oyashio water in the Iwate coastal region and in the vicinity of east coast of Japan. Bull. Jpn. Soc. Fish. Oceanogr. 58, 253–259 (1994) (in Japanese with English abstract).
Google Scholar 
57.Conlon, D. M. On the outflow modes of the Tsugaru Warm Current. La Mer. 20, 60–64 (1982).
Google Scholar 
58.Isoda, Y. & Suzuki, K. Interannual variations of the Tsugaru gyre. Bull. Fac. Fish. Hokkaido Univ. 55, 71–74 (2004) (in Japanese with English abstract).
Google Scholar 
59.Mrowicki, R. J., O’Connor, N. E. & Donohue, I. Temporal variability of a single population can determine the vulnerability of communities to perturbations. J. Ecol. 104, 887–897 (2016).Article 

Google Scholar 
60.R Core Team. R: A Language and Environment for Statistical Computing. R Foundation for Statistical Computing, Vienna, Austria. https://www.R-project.org/ (2018). More

1.Parmesan, C. Ecological and evolutionary responses to recent climate change. Annu. Rev. Ecol. Evol. Syst. 37, 637–669 (2006).
Google Scholar 
2.Sheridan, J. A. & Bickford, D. Shrinking body size as an ecological response to climate change. Nat. Clim. Change 1, 401–406 (2011).Article 

Google Scholar 
3.Gardner, J. L., Peters, A., Kearney, M. R., Joseph, L. & Heinsohn, R. Declining body size: a third universal response to warming? Trends Ecol. Evol. 26, 285–291 (2011).Article 

Google Scholar 
4.McCauley, S. J. & Mabry, K. E. Climate change, body size, and phenotype dependent dispersal. Trends Ecol. Evol. 26, 554–555 (2011).Article 

Google Scholar 
5.Norberg, J., Urban, M. C., Vellend, M., Klausmeier, C. A. & Loeuille, N. Eco-evolutionary responses of biodiversity to climate change. Nat. Clim. Change 2, 747–751 (2012).Article 

Google Scholar 
6.Amigo, I. The Amazon’s fragile future. Nature 578, 505–507 (2020).CAS 
Article 

Google Scholar 
7.Reddin, C. J., Nätscher, P. S., Kocsis, Á. T., Pörtner, H. O. & Kiessling, W. Marine clade sensitivities to climate change conform across timescales. Nat. Clim. Change 10, 249–253 (2020).Article 

Google Scholar 
8.Comte, L. & Olden, J. D. Climatic vulnerability of the world’s freshwater and marine fishes. Nat. Clim. Change 7, 718–722 (2017).Article 

Google Scholar 
9.Skelly, D. K. et al. Evolutionary responses to climate change. Conserv. Biol. 21, 1353–1355 (2007).Article 

Google Scholar 
10.Chen, I., Hill, J. K., Ohlemûller, R., Roy, D. B. & Thomas, C. D. Rapid range shifts of species associated with high levels of climate warming. Science 333, 1024–1026 (2011).CAS 
Article 

Google Scholar 
11.Cheung, W. W. L. et al. Projecting global marine biodiversity impacts under climate change scenarios. Fish Fish. 10, 235–251 (2009).Article 

Google Scholar 
12.Cheung, W. W. L. et al. Shrinking of fishes exacerbates impacts of global ocean changes on marine ecosystems. Nat. Clim. Change 3, 254–258 (2013).Article 

Google Scholar 
13.Crozier, L. G. & Hutchings, J. A. Plastic and evolutionary responses to climate change in fish. Evol. Appl. 7, 68–87 (2014).Article 

Google Scholar 
14.Travis, J. M. J. et al. Dispersal and species’ responses to climate change. Oikos 122, 1532–1540 (2013).Article 

Google Scholar 
15.Pauly, D. & Cheung, W. W. L. Sound physiological knowledge and principles in modeling shrinking of fishes under climate change. Glob. Change Biol. 24, e15–e26 (2018).Article 

Google Scholar 
16.Tamario, C., Sunde, J., Petersson, E., Tibblin, P. & Forsman, A. Ecological and evolutionary consequences of environmental change and management actions for migrating fish. Front. Ecol. Evol. 7, 271 (2019).Article 

Google Scholar 
17.Ljungström, G., Claireaux, M., Fiksen, Ø. & Jørgensen, C. Body size adaptions under climate change: zooplankton community more important than temperature or food abundance in model of a zooplanktivorous fish. Mar. Ecol. Prog. Ser. 636, 1–18 (2020).Article 
CAS 

Google Scholar 
18.Daufresne, M., Lengfellner, K. & Sommer, U. Global warming benefits the small in aquatic ecosystems. Proc. Natl Acad. Sci. USA 106, 12788–12793 (2009).CAS 
Article 

Google Scholar 
19.Lenoir, J. et al. Species better track climate warming in the oceans than on land. Nat. Ecol. Evol. 4, 1044–1059 (2020).Article 

Google Scholar 
20.Audzijonyte, A. et al. Fish body sizes change with temperature but not all species shrink with warming. Nat. Ecol. Evol. 4, 809–814 (2020).Article 

Google Scholar 
21.Rosenzweig, M. L. Loss of speciation rate will impoverish future diversity. Proc. Natl Acad. Sci. USA 98, 5404–5410 (2001).CAS 
Article 

Google Scholar 
22.Burns, M. D. & Bloom, D. D. Migratory lineages rapidly evolve larger body sizes than non-migratory relatives in ray-finned fishes. Proc. Biol. Sci. 287, 20192615 (2020).
Google Scholar 
23.Comte, L. & Olden, J. D. Evidence for dispersal syndromes in freshwater fishes. Proc. R. Soc. B 285, 20172214 (2018).Article 

Google Scholar 
24.Bohonak, A. J. Dispersal, gene flow, and population structure. Q. Rev. Biol. 74, 21–45 (1999).CAS 
Article 

Google Scholar 
25.Perry, A. L., Low, P. J., Ellis, J. R. & Reynolds, J. D. Climate change and distribution shifts in marine fishes. Science 308, 1912–1915 (2005).CAS 
Article 

Google Scholar 
26.Pinsky, M. L., Worm, B., Fogarty, M. J., Sarmiento, J. L. & Levin, S. A. Marine taxa track local climate velocities. Science 341, 1239–1242 (2013).CAS 
Article 

Google Scholar 
27.Stevens, V. M. et al. A comparative analysis of dispersal syndromes in terrestrial and semi-terrestrial animals. Ecol. Lett. 17, 1039–1052 (2014).Article 

Google Scholar 
28.Dieckmann, U., O’Hara, B. & Weisser, W. The evolutionary ecology of dispersal. Trends Ecol. Evol. 14, 88–90 (1999).Article 

Google Scholar 
29.Kokko, H. & López-Sepulcre, A. From individual dispersal to species ranges: perspectives for a changing world. Science 313, 789–791 (2006).CAS 
Article 

Google Scholar 
30.Lavoué, S., Miya, M., Musikasinthorn, P., Chen, W. J. & Nishida, M. Mitogenomic evidence for an indo-west Pacific origin of the Clupeoidei (Teleostei: Clupeiformes). PLoS ONE 8, e56485 (2013).Article 
CAS 

Google Scholar 
31.Bloom, D. D., Burns, M. D. & Schriever, T. A. Evolution of body size and trophic position in migratory fishes: a phylogenetic comparative analysis of Clupeiformes (anchovies, herring, shad and allies). Biol. J. Linn. Soc. 125, 302–314 (2018).Article 

Google Scholar 
32.O’Donovan, C., Meade, A. & Venditti, C. Dinosaurs reveal the geographical signature of an evolutionary radiation. Nat. Ecol. Evol. 2, 452–458 (2018).Article 

Google Scholar 
33.Cheng, L. et al. Record-setting ocean warmth continued in 2019. Adv. Atmos. Sci. 37, 137–142 (2020).Article 

Google Scholar 
34.Pinek, L., Mansour, I., Lakovic, M., Ryo, M. & Rillig, M. C. Rate of environmental change across scales in ecology. Biol. Rev. 95, 1798–1811 (2020).Article 

Google Scholar 
35.Avaria-Llautureo, J., Hernández, C. E., Rodríguez-Serrano, E. & Venditti, C. The decoupled nature of basal metabolic rate and body temperature in endotherm evolution. Nature 572, 651–654 (2019).CAS 
Article 

Google Scholar 
36.Gaston, K. J. Species-range size distributions: products of speciation, extinction and transformation. Philos. Trans. R. Soc. B 353, 219–230 (1998).Article 

Google Scholar 
37.Baker, J., Meade, A., Pagel, M. & Venditti, C. Positive phenotypic selection inferred from phylogenies. Biol. J. Linn. Soc. 118, 95–115 (2016).Article 

Google Scholar 
38.Angilletta, M. J. & Dunham, A. E. The temperature–size rule in ectotherms: simple evolutionary explanations may not be general. Am. Nat. 162, 332–342 (2003).Article 

Google Scholar 
39.Quintero, I. & Wiens, J. J. Rates of projected climate change dramatically exceed past rates of climatic niche evolution among vertebrate species. Ecol. Lett. 16, 1095–1103 (2013).Article 

Google Scholar 
40.Pauly, D., Christensen, V., Dalsgaard, J., Froese, R. & Torres, F. Jr Fishing down marine food webs. Science 279, 860–863 (1998).CAS 
Article 

Google Scholar 
41.Rabosky, D. L. et al. An inverse latitudinal gradient in speciation rate for marine fishes. Nature 559, 392–395 (2018).CAS 
Article 

Google Scholar 
42.Whitehead, P. J. P. FAO species catalogue: vol. 7 Clupeoid fishes of the world. FAO Fish. Synop. 7, 303 (1985).
Google Scholar 
43.Charnov, E. L. & Berrigan, D. Evolution of life history parameters in animals with indeterminate growth, particularly fish. Evol. Ecol. 5, 63–68 (1991).Article 

Google Scholar 
44.Önsoy, B., Tarkan, A. S., Filiz, H. & Bilge, G. Determination of the best length measurement of fish. North. West. J. Zool. 7, 178–180 (2011).
Google Scholar 
45.Mohseni, O. & Stefan, H. G. Stream temperature/air temperature relationship: a physical interpretation. J. Hydrol. 218, 128–141 (1999).Article 

Google Scholar 
46.Morrill, J. C., Bales, R. C. & Conklin, M. H. Estimating stream temperature from air temperature: implications for future water quality. J. Environ. Eng. 131, 139–146 (2005).CAS 
Article 

Google Scholar 
47.Sharma, S., Jackson, D. A., Minns, C. K. & Shuter, B. J. Will northern fish populations be in hot water because of climate change? Glob. Change Biol. 13, 2052–2064 (2007).Article 

Google Scholar 
48.Avaria-Llautureo, J. et al. Data for: Historical Warming Consistently Decreased Size, Dispersal and Speciation Rate of Fish (Dryad, 2021); https://doi.org/10.5061/dryad.cfxpnvx5g49.Pagel, M., Meade, A. & Barker, D. Bayesian estimation of ancestral character states on phylogenies. Syst. Biol. 53, 673–684 (2004).Article 

Google Scholar 
50.Venditti, C., Meade, A. & Pagel, M. Multiple routes to mammalian diversity. Nature 479, 393–396 (2011).CAS 
Article 

Google Scholar 
51.Kocsis, Á. T. & Raja, N. B. chronosphere (Zenodo, 2020); https://doi.org/10.5281/zenodo.353070352.Raftery, A. E. in Markov Chain Monte Carlo in Practice (eds Gilks, W. et al.) 163–187 (Chapman & Hall, 1996).53.Hijmans, R. J. geosphere: spherical trigonometry. R package version 1.5-10 https://CRAN.R-project.org/package=geosphere (2019).54.Harvey, M. G. & Rabosky, D. L. Continuous traits and speciation rates: alternatives to state-dependent diversification models. Methods Ecol. Evol. 9, 984–993 (2018).Article 

Google Scholar 
55.Title, P. O. & Rabosky, D. L. Tip rates, phylogenies and diversification: what are we estimating, and how good are the estimates? Methods Ecol. Evol. 10, 821–834 (2019).Article 

Google Scholar 
56.Louca, S. & Pennell, M. W. Extant timetrees are consistent with a myriad of diversification histories. Nature 580, 502–505 (2020).CAS 
Article 

Google Scholar 
57.Shafir, A., Azouri, D., Goldberg, E. E. & Mayrose, I. Heterogeneity in the rate of molecular sequence evolution substantially impacts the accuracy of detecting shifts in diversification rates. Evolution 74, 1620–1639 (2020).Article 

Google Scholar 
58.Ganzach, Y. Misleading interaction and curvilinear terms. Psychol. Methods 2, 235–247 (1997).Article 

Google Scholar 
59.Revell, L. J. phytools: an R package for phylogenetic comparative biology (and other things). Methods Ecol. Evol. 3, 217–223 (2012).Article 

Google Scholar 
60.Lunt, D. J. et al. Palaeogeographic controls on climate and proxy interpretation. Clim. Past 12, 1181–1198 (2016).Article 

Google Scholar  More

1.Foley, J. A. et al. Global consequences of land use. Science 309, 570–574 (2005).ADS 
CAS 
Article 

Google Scholar 
2.Chapin, F. S., Sala, O. E. & Huber-Sannwald, E. Global Biodiversity in a Changing Environment Vol 152 (Springer, 2001).Book 

Google Scholar 
3.Hobohm, C. Endemism in Vascular Plants (Springer, 2014). https://doi.org/10.1007/978-94-007-6913-7.Book 

Google Scholar 
4.Al-Rowaily, S. L. et al. Effects of open grazing and livestock exclusion on floristic composition and diversity in natural ecosystem of Western Saudi Arabia. Saudi J. Biol. Sci. 22, 430–437 (2015).Article 

Google Scholar 
5.Alonso, I., Hartley, S. E. & Thurlow, M. Competition between heather and grasses on Scottish moorlands: Interacting effects of nutrient enrichment and grazing regime. J. Veg. Sci. 12, 249–260 (2001).Article 

Google Scholar 
6.Diamond, J. Guns, Germs and Steel: The Fate of Human Societies (W.W. Norton & Company, 1997). https://doi.org/10.4324/9781912128273.Book 

Google Scholar 
7.Hayward, M. W. & Kerley, G. I. H. Fencing for conservation: Restriction of evolutionary potential or a riposte to threatening processes?. Biol. Conserv. 142, 1–13 (2009).Article 

Google Scholar 
8.Hayward, M. W. et al. Fencing for conservation: Restriction of evolutionary potential or a riposte to threatening processes?. Biol. Conserv. 142, 1–13 (2009).Article 

Google Scholar 
9.Santoro, R. et al. Effects of trampling limitation on coastal dune plant communities. Environ. Manag. 49, 534–542 (2012).ADS 
Article 

Google Scholar 
10.Fenu, G. et al. A common approach to the conservation of threatened island vascular plants: First results in the Mediterranean Basin. Diversity 12, 157 (2020).Article 

Google Scholar 
11.Fenu, G., Cogoni, D. & Bacchetta, G. The role of fencing in the success of threatened plant species translocation. Plant Ecol. 217, 207–217 (2016).Article 

Google Scholar 
12.Fazan, L. et al. Free behind bars: Effects of browsing exclusion on the growth and regeneration of Zelkova abelicea. For. Ecol. Manag. 488, 118967 (2021).Article 

Google Scholar 
13.Aschero, V. & García, D. The fencing paradigm in woodland conservation: Consequences for recruitment of a semi-arid tree. Appl. Veg. Sci. 15, 307–317 (2012).Article 

Google Scholar 
14.Bessega, C., Pometti, C., Campos, C., Saidman, B. O. & Vilardi, J. C. Implications of mating system and pollen dispersal indices for management and conservation of the semi-arid species Prosopis flexuosa (Leguminosae). For. Ecol. Manag. 400, 218–227 (2017).Article 

Google Scholar 
15.Scofield, R. P., Cullen, R. & Wang, M. Are predator-proof fences the answer to New Zealand’s terrestrial faunal biodiversity crisis?. N. Z. J. Ecol. 35, 312–317 (2011).
Google Scholar 
16.Tanentzap, A. J. & Lloyd, K. M. Fencing in nature? Predator exclusion restores habitat for native fauna and leads biodiversity to spill over into the wider landscape. Biol. Conserv. 214, 119–126 (2017).Article 

Google Scholar 
17.Valderrábano, E. M., Gil, T., Heywood, V. & Montmollin, B. D. Conserving Wild Plants in the South and East Mediterranean Region (IUCN, International Union for Conservation of Nature, 2018). https://doi.org/10.2305/IUCN.CH.2018.21.en.Book 

Google Scholar 
18.Bañares, A., Blanca, G., Guemes, J., Moreno, J. & Ortiz, S. Atlas y Libro Rojo de la Flora Vascular Amenazada de España (Ministerio de Medio Ambiente Medio Rural y Marino, 2004).
Google Scholar 
19.Médail, F. & Diadema, K. Glacial refugia influence plant diversity patterns in the Mediterranean Basin. J. Biogeogr. 36, 1333–1345 (2009).Article 

Google Scholar 
20.Gutiérrez, L., García, S., Cuerda, D. & Marchal, F. Aportaciones al conocimiento de la distribución y el estado de conservación del endemismo amenazado Solenanthus reverchonii Debeaux ex Degen (Boraginaceae ) en Andalucía ( España ). An. Biol. 36, 135–140 (2014).
Google Scholar 
21.Spooner, P., Lunt, I. & Robinson, W. Is fencing enough? The short-term effects of stock exclusion in remnant grassy woodlands in southern NSW. Ecol. Manag. Restor. 3, 117–126 (2002).Article 

Google Scholar 
22.Prober, S. M., Standish, R. J. & Wiehl, G. After the fence: Vegetation and topsoil condition in grazed, fenced and benchmark eucalypt woodlands of fragmented agricultural landscapes. Aust. J. Bot. 59, 369–381 (2011).Article 

Google Scholar 
23.Newman, M., Mitchell, F. J. G. & Kelly, D. L. Exclusion of large herbivores: Long-term changes within the plant community. For. Ecol. Manag. 321, 136–144 (2014).Article 

Google Scholar 
24.Cogoni, D., Fenu, G., Concas, E. & Bacchetta, G. The effectiveness of plant conservation measures: The Dianthus morisianus reintroduction. Oryx 47, 203–206 (2013).Article 

Google Scholar 
25.Schowalter, T. D. Herbivory. Insect Ecology 347–382 (Elsevier, 2006). https://doi.org/10.1016/B978-012088772-9/50038-8.Book 

Google Scholar 
26.van der Waal, C. et al. Large herbivores may alter vegetation structure of semi-arid savannas through soil nutrient mediation. Oecologia 165, 1095–1107 (2011).ADS 
Article 

Google Scholar 
27.Körner, C. The use of ‘altitude’ in ecological research. Trends Ecol. Evol. 22, 569–574 (2007).Article 

Google Scholar 
28.Körner, C. Alpine Treelines (Springer, 2012). https://doi.org/10.1007/978-3-0348-0396-0.Book 

Google Scholar 
29.Anderson, P. M. L. & Hoffman, M. T. Grazing response in the vegetation communities of the Kamiesberg, South Africa: Adopting a plant functional type approach. J. Arid Environ. 75, 255–264 (2011).ADS 
Article 

Google Scholar 
30.Hardin, G. The tragedy of the commons. Science (80–) 162, 1243–1248 (1968).ADS 
CAS 
Article 

Google Scholar 
31.Vera, J. A. Geología de Andalucía. Enseñanza Cien. Tierra 2, 306–317 (1994).
Google Scholar 
32.Gómez-Mercado, F. Vegetación y flora de la Sierra de Cazorla. Guineana 17, 1–481 (2011).
Google Scholar 
33.Benavente, A. Flora y vegetacion: Parque Natural de las Sierras de Cazorla, Segura y Las Villas. Anu. Adelantamiento Cazorla 50, 149–153 (2008).
Google Scholar 
34.Soriguer, R., Fandos, P., Granados, J., Castillo, A. & Serrano, E. Herbivoría por ungulados silvestres en el piso mesomediterráneo de las Sierras de Cazorla, Segura y Las Villas. In “In memoriam” al profesor Dr Isidoro Ruiz Martínez 479–504 (Universidad de Jaén, 2003).
Google Scholar 
35.García-González, R. & Cuartas, P. A comparison of the diets of the wild goat (Capra pyrenaica), Domesc Goat (Capra hircus), Mouflon (Ovis musimon) and the domestic sheep (Ovis aries) in the Cazorla Mountain range. Acta Biol. 9, 123–132 (1989).
Google Scholar 
36.Araque, E. Territorio y patrimonio rural en las sierras de Cazorla, Segura y las Villas. Nuevas perspectivas de investigación. Rev. PH 84, 28–47 (2013).
Google Scholar 
37.Tíscar Oliver, P. Patterns of shrub diversity and tree regeneration across topographic and stand-structural gradients in a Mediterranean forest. For. Syst. 24, 1–11 (2015).
Google Scholar 
38.Lorite, J., Navarro, F. B. & Valle, F. Estimation of threatened orophytic flora and priority of its conservation in the Baetic range (S. Spain). Plant Biosyst. 141, 1–14 (2007).Article 

Google Scholar 
39.Blanca, G. et al. Flora Vascular de Andalucía Oriental. vol. 4 (Servicios de Publicaciones de las universidades de Granada, Almería, Jaén y Málaga, 2011).
Google Scholar 
40.Mateos, M. et al. FAME. Aplicación Web de apoyo al seguimiento, localización e integración de la información sobre flora amenazada y de interés generada en Andalucía. In Tecnologías de la Información Geográfica: La Información Geográfica al servicio de los ciudadanos 222–229 (2010).41.Valle, F. et al. Mapa de Series de Vegetación de Andalucía (Editorial Rueda, 2003).
Google Scholar 
42.Sutherland, W. J. Ecological Census Techniques: A Handbook 2nd edn. (Cambridge University Press, 2006).Book 

Google Scholar 
43.Lorite, J., Peñas, J., Benito, B., Cañadas, E. & Valle, F. Conservation status of the first known population of Polygala balansae in Europe. Ann. Bot. Fenn. 47, 45–50 (2010).Article 

Google Scholar 
44.Blanca, G., Cabezudo, B., Cueto, M., Morales, C. & Salazar, C. Flora vascular de Andalucía oriental (2a Edición Corregida y Aumentada) (Universidad de Granada, 2011).
Google Scholar 
45.R Core Development Team. R: A Language and Environment for Statistical Computing (R Core Development Team, 2019).
Google Scholar 
46.Oksanen, J. et al. vegan: Community Ecology Package. R package version 2.5-2. (2018).47.Pinheiro, J. et al. nlme: Linear and Nonlinear Mixed Effects Models. (2020).48.Zuur, A. F., Ieno, E. N., Walker, N., Saveliev, A. A. & Smith, G. M. Mixed Effects Models and Extensions in Ecology with R Statistics for Biology and Health (Springer, 2009). https://doi.org/10.1007/978-0-387-87458-6.Book 
MATH 

Google Scholar 
49.Wheeler, R. E. R. et al. lmPerm: Permutation tests for linear models. 24 (2016).50.Wickham, H. Elegant Graphics for Data Analysis (Springer, 2009).MATH 

Google Scholar  More

1.Patrick, R., Binetti, V. P. & Halterman, S. G. Acid lakes from natural and anthropogenic causes. Science 211, 446–448 (1981).ADS 
CAS 
PubMed 
Article 

Google Scholar 
2.Dokulil, M., Chen, W. & Cai, Q. Anthropogenic impacts to large lakes in China: The Tai Hu example. Aquat. Ecosyst. Health Manage. 3, 81–94 (2000).Article 

Google Scholar 
3.Woolway, R. I. et al. Global lake responses to climate change. Nat. Rev. Earth Environ. 1, 388–403 (2020).ADS 
Article 

Google Scholar 
4.Søndergaard, M. & Jeppesen, E. Anthropogenic impacts on lake and stream ecosystems, and approaches to restoration. J. Appl. Ecol. 44, 1089–1094 (2007).Article 

Google Scholar 
5.Zhupankhan, A., Tussupova, K. & Berndtsson, R. Water in Kazakhstan, a key in Central Asian water management. Hydrol. Sci. J. 63, 752–762 (2018).Article 

Google Scholar 
6.Corell, D. L. The role of phosphorus in the euthrophication of receiving waters: A review. J. Environ. Qual. 27, 261–266 (1998).Article 

Google Scholar 
7.Hansson, L.-A. & Tranvik, L. A. Algal species composition and phosphorus recycling at contrasting grazing pressure: An experimental study in sub-Antarctic lakes with two trophic levels. Freshw. Biol. 37, 45–53 (1997).Article 

Google Scholar 
8.Gozlan, R., Karimov, B., Zadereev, E., Kuznetsova, D. & Brucet, S. Status, trends, and future dynamics of freshwater ecosystems in Europe and Central Asia. Inland Waters 9, 78–94 (2019).CAS 
Article 

Google Scholar 
9.WBGU (Wissenschaftliche Beirat der Bundesregierung Globale Umweltveränderungen; German Advisory Council on Global Change). Climate Change as a Security Risk (Earthscan, 2007).
Google Scholar 
10.Campbell, L. et al. Response of microbial community structure to environmental forcing in the Arabian Sea. Deep Sea Res II Top. Stud. Oceanogr. 45, 2301–2325 (1998).ADS 
Article 

Google Scholar 
11.Winder, M. & Sommer, U. Phytoplankton response to a changing climate. Hydrobiologia 698, 5–16 (2012).Article 

Google Scholar 
12.Bellinger, E. G. & Sigee, D. C. Freshwater Algae: Identification and Use as Bioindicators (Wiley, 2010).Book 

Google Scholar 
13.Zohary, T., Flaim, G. & Sommer, U. Temperature and the size of freshwater phytoplankton. Hydrobiologia 848, 143–155 (2021).Article 

Google Scholar 
14.Reynolds, C. S., Padisák, J. & Sommer, U. Intermediate disturbance in the ecology of phytoplankton and the maintenance of species diversity: A synthesis. Hydrobiologia 249, 183–188 (1993).Article 

Google Scholar 
15.Likens, G. E. Plankton of Inland Waters (Academic Press, 2010).
Google Scholar 
16.Hutchinson, G. E. A Treatise on Limnology. Volume 2. Introduction to Lake Biology and the Limnoplankton (Wiley, 1967).
Google Scholar 
17.Reynolds, C. S. The concept of ecological succession applied to seasonal periodicity of freshwater phytoplankton. Int. Ver. Limnol. 23, 683–691 (1988).
Google Scholar 
18.Bartram, J. & Ballance, R. (eds) Water Quality Monitoring—A Practical Guide to the Design and Implementation of Freshwater Quality Studies and Monitoring Programs (UNEP/WHO, 1996).
Google Scholar 
19.Lepistő, L., Holopainen, A.-L. & Vuorosto, H. Type-specific and indicator taxa of phytoplankton as a quality criterion for assessing the ecological status of Finnish boreal lakes. Limnologica 34, 236–248 (2004).Article 

Google Scholar 
20.Järvinen, M. et al. Phytoplankton indicator taxa for reference conditions in Northern and Central European lowland lakes. Hydrobiologia 704, 97–113 (2013).Article 

Google Scholar 
21.Soares, M. C. S. et al. Light microscopy in aquatic ecology: Methods for plankton communities studies. In Light Microscopy: Methods and Protocols (eds Chiarini-Garcia, H. & Melo, R. C. N.) 215–227 (Springer, 2011).Chapter 

Google Scholar 
22.Findlay, D. L. & Kling, H. J. Protocols for Measuring Biodiversity: Phytoplankton in Fresh Water Lakes (Department of Fisheries and Oceans, 1998).
Google Scholar 
23.Maurer, D. The dark side of taxonomic sufficiency. Mar. Pollut. Bull. 40, 98–101 (2000).CAS 
Article 

Google Scholar 
24.Bourlat, S. J. et al. Genomics in marine monitoring: New opportunities for assessing marine health status. Mar. Pollut. Bull. 74, 19–31 (2013).CAS 
PubMed 
Article 

Google Scholar 
25.Hering, D. et al. Implementation options for DNA-based identification into ecological status assessment under the European water framework directive. Water Res. 138, 192–205 (2018).CAS 
PubMed 
Article 

Google Scholar 
26.Ayaglas, E. et al. Translational molecular ecology in practice: Linking DNA-based methods to actionable marine environmental management. Sci. Total Environ. 744, 140780 (2020).ADS 
Article 
CAS 

Google Scholar 
27.Peperzak, L., Vrieling, E. G., Sandee, B. & Rutten, T. Immuno flow cytometry in marine phytoplankton research. Sci. Mar. 64, 165–181 (2000).Article 

Google Scholar 
28.Dashkova, V., Malashenkov, D., Poulton, N., Vorobjev, I. & Barteneva, N. S. Imaging flow cytometry for phytoplankton analysis. Methods 112, 188–200 (2017).CAS 
PubMed 
Article 

Google Scholar 
29.Dubelaar, G. & Jonker, R. R. Flow cytometry as a tool for the study of phytoplankton. Sci. Mar. 64, 135–156 (2000).Article 

Google Scholar 
30.Stockner, J. G. Phototrophic picoplankton: An overview from marine and freshwater ecosystems. Limnol. Oceanogr. 33, 765–775 (1988).ADS 
CAS 

Google Scholar 
31.Schmidt, T. M., DeLong, E. F. & Pace, N. R. Analysis of a marine picoplankton community by 16S rRNA gene cloning and sequencing. J. Bacteriol. 173, 4371–4378 (1991).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
32.Diez, B., Pedros-Aliŏ, C. & Massana, R. Study of genetic diversity of eukaryotic picoplankton in different oceanic regions by small-subunit rRNA gene cloning and sequencing. Appl. Environ. Microbiol. 67, 2932–2941 (2001).ADS 
CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
33.Stoeck, T., Hayward, B., Taylor, G. T., Varela, R. & Epstein, S. S. A multiple PCR-primer approach to access the microeukaryotic diversity in the anoxic Cariaco Basin (Caribbean Sea). Protist 157, 31–43 (2006).CAS 
PubMed 
Article 

Google Scholar 
34.Stoeck, T. et al. Multiple marker parallel tag environmental DNA sequencing reveals a highly complex eukaryotic community in marine anoxic water. Mol. Ecol. 19, 21–31 (2010).CAS 
PubMed 
Article 

Google Scholar 
35.Szabó, A. et al. Soda pans of the Pannonian steppe harbor unique bacterial communities adapted to multiple extreme conditions. Extremophiles 21, 639–649 (2017).PubMed 
Article 

Google Scholar 
36.Bott, N. J. et al. Toward routine, DNA-based detection methods for marine pests. Biotechnol. Adv. 28, 706–714 (2010).CAS 
PubMed 
Article 

Google Scholar 
37.Tan, S. et al. An association network analysis among microeukaryotes and bacterioplankton reveals algal bloom dynamics. J. Phycol. 51, 120–132 (2015).CAS 
PubMed 
Article 

Google Scholar 
38.Medlin, L. K. & Orozco, J. Molecular techniques for the detection of organisms in aquatic environments, with emphasis on harmful algal bloom species. Sensors 17, 1184 (2017).ADS 
PubMed Central 
Article 
CAS 
PubMed 

Google Scholar 
39.de Bruin, A., Ibelings, B. W. & Van Donk, E. Molecular techniques in phytoplankton research: From allozyme electrophoresis to genomics. Hydrobiologia 491, 47–63 (2003).Article 

Google Scholar 
40.Ebenezer, V., Medlin, L. K. & Ki, J. S. Molecular detection, quantification, and diversity evaluation of microalgae. Mar. Biotechnol. 14, 129–142 (2012).CAS 
Article 

Google Scholar 
41.Kim, J. et al. Microfluidic high-throughput selection of microalgal strains with superior photosynthetic productivity using competitive phototaxis. Sci. Rep. 6, 21155 (2016).ADS 
CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
42.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 
43.Sogin, M. L. et al. Microbial diversity in the deep sea and the underexplored “rare biosphere”. Proc. Natl. Acad. Sci. U.S.A. 103, 12115–12120 (2006).ADS 
CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
44.Medinger, R. et al. Diversity in a hidden world: Potential and limitation of next-generation sequencing for surveys of molecular diversity of eukaryotic microorganisms. Mol. Ecol. 19, 32–40 (2010).PubMed 
PubMed Central 
Article 

Google Scholar 
45.Andersson, A. F., Riemann, L. & Bertilsson, S. Pyrosequencing reveals contrasting seasonal dynamics of taxa within Baltic Sea bacterioplankton communities. ISME J. 4, 171–181 (2010).PubMed 
Article 

Google Scholar 
46.Filker, S., Gimmler, A., Dunthorn, M., Mahe, F. & Stoeck, T. Deep sequencing uncovers protistan plankton diversity in the Portuguese Ria Formosa solar saltern ponds. Extremophiles 19, 283–295 (2015).CAS 
PubMed 
Article 

Google Scholar 
47.Eiler, A. et al. Unveiling distribution patterns of freshwater phytoplankton by a next generation sequencing based approach. PLoS ONE 8, e53516 (2013).ADS 
CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
48.Visco, J. A. et al. Environmental monitoring: Inferring the diatom index from next generation sequencing data. Environ. Sci. Technol. 49, 7597–7605 (2015).ADS 
CAS 
PubMed 
Article 

Google Scholar 
49.Abad, D. et al. Is metabarcoding suitable for estuarine plankton monitoring? A comparative study with microscopy. Mar. Biol. 163, 149 (2016).Article 

Google Scholar 
50.Gao, W. et al. Bioassessment of a drinking water reservoir using plankton: High throughput sequencing vs. traditional morphological method. Water 10, 82 (2018).Article 
CAS 

Google Scholar 
51.Rimet, F., Vasselon, V., Barabar, A. & Bouchez, A. Do we similarly assess diversity with microscopy and high-throughput sequencing? Case of microalgae in lakes. Org. Divers. Evol. 18, 51–62 (2018).Article 

Google Scholar 
52.Kazhydromet. Environmental Monitoring Bulletin of Republic of Kazakhstan for 2007 (Kazhydromet, 2007).
Google Scholar 
53.Lewis, W. M. Jr. A revised classification of lakes based on mixing. Can. J. Fish. Aquat. Sci. 40, 1779–1787 (1983).Article 

Google Scholar 
54.Welch, E. B. & Cooke, G. D. Internal phosphorus loading in shallow lakes: Importance and control. Lake Reserv. Manage. 11, 273–281 (1995).Article 

Google Scholar 
55.Kabiyeva, M. & Zubairov, B. Bathymetric measurements of Lake Shortandy, Burabay National Nature Park. In Proc. Central Asia GIS Conference—GISCA “Geospatial Management of Land, Water and Resources” ( May 14–16, Tashkent) 44–48 (2015).56.Plokhikh, R. V. Ecological state of regions: Northern Kazakhstan. In Republic of Kazakhstan: Environment and Ecology Vol. 3 (eds Budnikova, T. I. et al.) (Institute of Geography, 2010).
Google Scholar 
57.Kumanbayeva, A. S., Khusainov, A. T. & Zhumaj, E. Ecological state of Lake Burabay, National State Park Burabay. Sci. News Kazakhstan 3, 171–178 (2019).
Google Scholar 
58.Sadchikov, A. P. Methods of Studying Freshwater Phytoplankton: A Manual (Universitet i shkola, 2003).
Google Scholar 
59.Sukhanova, I. N. Settling without the inverted microscope. In Phytoplankton Manual (ed. Sourina, A.) 97 (UNESCO, 1978).
Google Scholar 
60.Schwoerbel, J. Methods of Hydrobiology (Freshwater Biology) (Elsevier, 1970).
Google Scholar 
61.Xia, S., Cheng, Y. Y., Zhu, H., Liu, G. X. & Hu, Z. Y. Improved methodology for identification of Cryptomonads: Combining light microscopy and PCR amplification. J. Microbiol. Biotechnol. 23, 289–296 (2013).CAS 
PubMed 
Article 

Google Scholar 
62.LeGresley, M. & McDermott, G. Counting chamber methods for quantitative phytoplankton—Haemocytometer, Palmer-Maloney cell and Sedgewick-Rafter cell. In Microscopic and Molecular Methods for Quantitative Phytoplankton Analysis (eds Karlson, B. et al.) 25–30 (UNESCO, 2010).
Google Scholar 
63.Hillebrand, H., Dürselen, C. D., Kirschtel, D., Pollingher, U. & Zohary, T. Biovolume calculation for pelagic and benthic microalgae. J. Phycol. 35, 403–424 (1999).Article 

Google Scholar 
64.Sun, J. & Liu, D. Geometric models for calculating cell biovolume and surface area for phytoplankton. J. Plankton Res. 25, 1331–1346 (2003).Article 

Google Scholar 
65.Konoplya, B. I. & Soares, F. S. New geometric models for calculation of microalgal biovolume. Braz. Arch. Biol. Technol. 54, 527–534 (2011).Article 

Google Scholar 
66.Vadrucci, M. R., Mazziotti, C. & Fiocca, A. Cell biovolume and surface area in phytoplankton of Mediterranean transitional water ecosystems: Methodological aspects. Transit. Water. Bull. 7, 100–123 (2013).
Google Scholar 
67.Saccà, A. A simple yet accurate method for the estimation of the biovolume of planktonic microorganisms. PLoS ONE 11, e0151955 (2016).PubMed 
PubMed Central 
Article 
CAS 

Google Scholar 
68.Mirasbekov, Y. et al. Semi-automated classification of colonial Microcystis by FlowCam imaging flow cytometry in mesocosm experiment reveals high heterogeneity during a seasonal bloom. Sci. Rep. 11, 9377 (2021).ADS 
CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
69.Bolger, A. M., Lohse, M. & Usadel, B. Trimmomatic: A flexible trimmer for Illumina sequence data. Bioinformatics 30, 2114–2120 (2014).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
70.Aronesty, E. Comparison of sequencing utility programs. Open Bionforma J. 7, 1–8. https://doi.org/10.2174/1875036201307010001 (2013). (Accessed 6 May 2021)71.Li, H. & Durbin, R. Fast and accurate long-read alignment with Burrows-Wheeler transform. Bioinformatics 26, 589–595 (2010).PubMed 
PubMed Central 
Article 
CAS 

Google Scholar 
72.Padisák, J., Crossetti, L. O. & Naselli-Flores, L. Use and misuse in the application of the phytoplankton functional classification: A critical review with updates. Hydrobiologia 621, 1–19 (2009).Article 

Google Scholar 
73.Lee, M. S. Y. A worrying systematic decline. Trends Ecol. Evol. 15, 346 (2000).CAS 
PubMed 
Article 

Google Scholar 
74.Kermarrec, L. et al. Next-generation sequencing to inventory taxonomic diversity in eukaryotic communities: A test for freshwater diatoms. Mol. Ecol. Resour. 13, 607–619 (2013).CAS 
PubMed 
Article 

Google Scholar 
75.Aylagas, E., Borja, Á., Irigoien, X. & Rodríguez-Ezpeleta, N. Benchmarking DNA metabarcoding for biodiversity-based monitoring and assessment. Front. Mar. Sci. 3, 96 (2016).
Google Scholar 
76.Bazin, P. et al. Phytoplankton diversity and community composition along the estuarine gradient of a temperate macrotidal ecosystem: Combined morphological and molecular approaches. PLoS ONE 9, e94110 (2014).ADS 
PubMed 
PubMed Central 
Article 
CAS 

Google Scholar 
77.Edwards, D. L. & Knowles, L. L. Species detection and individual assignment in species delimitation: Can integrative data increase efficacy? Proc. R. Soc. B 281, 20132765 (2014).PubMed 
PubMed Central 
Article 

Google Scholar 
78.Guillot, G., Renaud, S., Ledevin, R., Michaux, J. & Claude, J. A unifying model for the analysis of phenotypic, genetic, and geographic data. Syst. Biol. 61, 897–911 (2012).PubMed 
Article 

Google Scholar 
79.Padial, J. M., Miralles, A., De la Riva, I. & Vences, M. The integrative future of taxonomy. Front. Zool. 7, 16 (2010).PubMed 
PubMed Central 
Article 

Google Scholar 
80.Bickford, D. et al. Cryptic species as a window on diversity and conservation. Trends Ecol. Evol. 22, 148–155 (2007).PubMed 
Article 

Google Scholar 
81.Boopathi, T. & Ki, J.-S. Unresolved diversity and monthly dynamics of eukaryotic phytoplankton in a temperate freshwater reservoir explored by pyrosequencing. Mar. Freshw. Res. 67, 1680–1691 (2015).Article 

Google Scholar 
82.Kurmayer, R., Deng, L. & Entfellner, E. Role of toxic and bioactive secondary metabolites in colonization and bloom formation by filamentous cyanobacteria Planktothrix. Harmful Algae 54, 69–86 (2016).CAS 
PubMed 
PubMed Central 
Article 

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

Google Scholar 
84.Cellamare, M., Rolland, A. & Jacquet, S. Flow cytometry sorting of freshwater phytoplankton. J. Appl. Phycol. 22, 87–100 (2010).Article 

Google Scholar 
85.Reynolds, C. S., Huszar, V., Kruk, C., Naselli-Flores, L. & Melo, S. Towards a functional classification of the freshwater phytoplankton. J. Plankton Res. 24, 417–428 (2002).Article 

Google Scholar 
86.Adl, S. M. et al. The new higher level classification of eukaryotes with emphasis on the taxonomy of protists. J. Eukaryot. Microbiol. 52, 399–451 (2005).PubMed 
Article 

Google Scholar 
87.Adl, S. M. et al. The revised classification of eukaryotes. J. Eukaryot. Microbiol. 59, 429–493 (2012).PubMed 
PubMed Central 
Article 

Google Scholar 
88.Komárek, J., Kaštovský, J., Mareš, J. & Johansen, J. R. Taxonomic classification of cyanoprokaryotes (cyanobacterial genera) 2014, using a polyphasic approach. Preslia 86, 295–335 (2014).
Google Scholar 
89.Guiry, M. D. & Guiry, G. M. AlgaeBase (World-Wide Electronic Publication, National University of Ireland, 2019).
Google Scholar  More

Seed sourceSeeds of a segregating GR B. scoparia population identified from a wheat field (45°54′54.76″N; 108°14′44.15″W) in 2013 in Hill County, Montana, USA (designated as MT009) were used. The field was under a continuous no-till wheat-fallow rotation for  > 8 years and had a history of repeated glyphosate use (at least 3 applications per year) for weed control during the summer fallow phase prior to winter wheat planting. The permission of land owner was obtained prior to B. scoparia seed collection. All experimental research and field studies on plants, including the collection of plant material complied with the Montana State University guidelines and state/US legislation. Seeds of the field-collected population were used to generate GS and GR B. scoparia subpopulations through recurrent group selection procedure as described below.Development of GS and GR subpopulationsField collected seeds of MT009 population were sown on the surface of plastic trays (53 by 35 by 10 cm) filled with commercial potting soil (VERMISOIL, Vermicrop Organics, 4265 Duluth Avenue, Rocklin, CA, USA) in a greenhouse in the fall of 2013 at the Montana State University Southern Agricultural Research Center (MSU-SARC) near Huntley, MT, USA. Growth conditions in greenhouse were maintained at 25/22 ± 2 °C day/night temperatures and 16/8 h day/night photoperiods supplemented with metal halide lamps (450 μmol m-2 s-1). After emergence, approximately 200 uniform seedlings were individually transplanted in plastic pots (10-cm diam) containing the same potting mixture and grown for 6 weeks. A set of three clones (3 shoot cuttings) from each plant were then prepared and transplanted in plastic pots (10-cm diam) as described by Kumar and Jha22. At the 8- to 10-cm height, all cloned seedlings were separately treated with 435 (0.5×), 870 (1×), and 1740 (2×) g ae ha−1 of glyphosate (Roundup Powermax, Bayer Crop Science, Saint Louis, MO, USA) where 1× = field-use rate of glyphosate. All three glyphosate treatments included ammonium sulfate (2% w/v). Glyphosate applications were made using a cabinet spray chamber (Research Track Sprayer, De Vries Manufacturing, RR 1 Box 184, Hollandale, MN, USA) equipped with an even flat-fan nozzle tip (Teejet 8001EXR, Spraying System Co., Wheaton, IL, USA), calibrated to deliver 140 L ha−1 of spray solution at 276 kPa. Treated seedlings were returned to the greenhouse, watered as needed, and fertilized [Miracle-Gro water soluble fertilizer (24-8-16), Scotts Miracle-Gro Products Inc., 14111 Scottslawn Road, Marysville, OH, USA] bi-weekly to maintain good plant growth. At 21 days after treatment, clones surviving the 2× rate of glyphosate were considered as ‘glyphosate-resistant (GR)’ and the clones that did not survive 1× rate of glyphosate were considered as ‘glyphosate-susceptible (GS)’. The parent B. scoparia plants corresponding to survived (resistant) or not-survived (susceptible) clones were transplanted separately in 20-L plastic pots (group of 3 to 4 plants pot−1) containing same potting soil for seed production. All 3- to 4 plants in each pot were collectively covered with a single pollination bag (DelStar Technologies, Inc., 601 Industrial drive, Middletown, DE, USA) prior to flower initiation to restrict cross-pollination between GR and GS plants. At maturity, seeds from the respective GR and GS parent plants were collected and cleaned separately using an air column blower. The collected seeds from GR plants were subjected to three generations of recurrent group selection with the 2× rate of glyphosate in each generation. Seeds of GS plants were also subjected to recurrent group selection for three generations without glyphosate. Progenies of the GS plants were grown and sprayed with 1× rate of glyphosate to confirm the susceptibility to glyphosate in each generation23. This procedure allowed the development of relatively genetically homogenous GR and GS subpopulations from within a single B. scoparia population.Determination of EPSPS gene copy numberPreviously established protocols were adopted to estimate the relative EPSPS gene copy number in seedlings of GR and GS subpopulations through quantitative real-time polymerase chain reaction (qPCR)16,17,18. The ALS gene was used as reference since the relative ALS gene copy number and transcript abundance did not vary across B. scoparia samples17,18,29. Relative EPSPS:ALS gene copy number is a ratio of EPSPS to ALS PCR product fluorescence. Due to small differences in amplicon size, qPCR run conditions, and fluorescence detection, the values presented were estimates of relative gene copy number29.A total of 600 seedlings from the GR (450 seedlings) and GS (150 seedlings) B. scoparia subpopulations (developed by recurrent group selection) were grown in a greenhouse at MSU-SARC near Huntley, MT, USA in 2015 and 2016 to select enough plants for the field study each year. At 4-to 6-cm height, young leaf tissues (100 mg) from each seedling were sampled, frozen with liquid nitrogen and ground into powder using mortar and pestle. Genomic DNA were extracted from the tissue samples using the protocol from Qiagen Dneasy plant mini kit (Qiagen Inc., Valencia, CA, USA). Genomic DNA quantity and quality were determined using a Smartspec Plus spectrophotometer (Bio-Rad Company, CA, USA) and gel electrophoresis with 1% agarose, respectively. High quality genomic DNA (260/280 ratio of ≥ 1.8) were used to determine the relative EPSPS gene copy number. Two sets of primers to amplify the EPSPS and ALS genes, the final reaction volume and reagents used for each qPCR reaction, and the qPCR conditions used in this study were the same as previously described by Kumar and Jha22. Each qPCR reaction was performed on a Bio-Rad 96-well PCR plate in triplicates and fluorescence was detected using CFX Connect Real-Time PCR detection system. A negative control consisting of 250 nM of each forward and reverse primer, 1× Perfecta SYBR Green supermix, and deionized water with no DNA template was included. The EPSPS genomic copy number relative to ALS gene was estimated by ΔCT method (ΔCT = CT, ALS-CT, EPSPS)18,29. The relative increase in the EPSPS gene copy number was calculated as 2ΔCT.Survival and fecundity traits of GR and GS B. scoparia subpopulationsSeedlings (4- to 6-cm tall) of GR and GS B. scoparia subpopulations with known EPSPS gene copy numbers were transplanted into a fallow field in the summer of 2015 and 2016 at the MSU-SARC near Huntley, MT, USA. All transplanted B. scoparia seedlings were equally spaced at 1.5 m apart from each other and all plants were fertilized biweekly [2 to 3 g of MIRACLE-GRO water soluble fertilizer (24-8-16)] and irrigated as and when needed to avoid moisture stress. Experiments were conducted with a factorial arrangement of treatments (Factor A and Factor B) in a randomized complete block design, with 6 replications. Each transplanted B. scoparia seedling was an experimental unit. The factor A (4 levels) was comprised of B. scoparia plants with 1, 2–4, 5–6, and ≥ 8 EPSPS gene copy numbers, which were categorized as susceptible, low, moderate, and highly resistant plants, respectively based on their percent visible injury response to glyphosate. The factor B (ten levels) was comprised of increasing rates of glyphosate applied as single or sequential applications. Current labels of glyphosate allow a total of 3954 g ae ha−1 in split POST applications in GR sugar beet. As per the label, the maximum glyphosate rate of 2214 g ae ha−1 is allowed from crop emergence to 8-leaf stage of sugar beet and 1740 g ae ha−1 of glyphosate from 8-leaf stage to canopy closure or 30 days prior to sugar beet harvest. Hence, the tested total glyphosate rates were 0, 108, 217, 435, 870, 1265, 1740 [870 followed by ( +) 870], 2214 [1265 + 949], 3084 [1265 + 949 + 870], and 3954 [1265 + 949 + 870 + 870] g ae ha−1 along with ammonium sulfate (2% w/v). Sequential applications were made at 7- to 14-day intervals, with first application at 8- to 10-cm tall B. scoparia seedlings using a CO2-operated backpack sprayer fitted with a single AIXR 8001 flat-fan nozzle calibrated to deliver 94 L ha−1. Glyphosate rates and applications timings were selected to simulate the 2-leaf, 6-leaf, 8–10 leaf, and the canopy closure stage of GR sugar beet.Data collectionPercent visible control (relative to the non-treated) on a scale of 0 to 100 (0 means no control and 100 means complete plant death30) for each individual plant (240 plants total each year) were assessed at 7, 14, and 21 days after glyphosate treatment. Data on number of days from transplanting to 50% flowering (half of the inflorescences from each plant were covered with visible flowers) and seed set (seeds on half of the inflorescences from each plant were turned brown) were recorded for an individual plant. Each plant was covered with a pollination bag (DelStar Technologies, Inc., 601 Industrial drive, Middletown, DE, USA) prior to flowering to prevent any cross-pollination. At the time of flowering, pollens from each survived plant were collected in early morning hours (between 8 to 10 am). At maturity, each individual plant was harvested and threshed to determine 1000-seed weight and seeds plant−1.Pollen and progeny seed viabilityPollens and seeds collected from individual B. scoparia plants (240 plants total each year) were tested for viability using a tetrazolium test. Pollens were collected in petri dishes by shaking the whole plant at the time of flowering. Four sub-samples of pollens from each petri dish were transferred into glass slides. The pollens in the glass slides were soaked with a tetrazolium chloride solution (10 g L−1), sealed with a cover slip using a nail polish and were incubated at room temperature for an hour. Viable (red) and non-viable pollens (yellow/white) were counted using a simple microscope. The physical structure of viable and non-viable pollens was also checked for any deformity using a compound microscope. Pollen viability for individual plants (240 plants total each year) was calculated as percent viable pollens of the total number of pollens counted.For seed viability test, twenty-five intact seeds collected from each individual plant (240 plants total each year) from the field were evenly placed in between two layers of filter papers (WHATMAN Grade 2, SigmaAldrich, St Louis, MO, USA) inside a 10-cm-diameter petri dish. Seeds were soaked with a 5-ml of distilled water and the filter papers were kept moist for the entire duration of the germination test. Light is not required for B. scoparia seed germination31, so the petri dishes were wrapped with a thin aluminum foil and placed inside an incubator (VMR International, Sheldon Manufacturing, Cornelius, OR, USA) with alternating day/night temperatures set to 20/25 °C23. Seeds with a visible uncoiled radicle tip longer than the seed diameter was considered germinated32,33. Radicle length was measured from three randomly selected germinated seeds 24 h after incubation to test the seedling vigor. The number of germinated seeds in each petri dish were counted daily until no further germination was observed for 10 consecutive days. Non-germinated seeds were tested for viability by soaking the seeds with tetrazolium chloride solution (10 g L−1) for 24 h23,34. Seeds with a red-stained embryo examined under a dissecting microscope (tenfold magnification) were considered viable35. Seed viability was expressed as the percentage of total viable seeds.Relative fitness (w)Fitness is the evolutionary potential for success of a genotype based on survival, competitive ability, and reproduction. Individuals with the greatest number of offspring and with the most genes contributing to the gene pool of a population are considered most fit genotypes36. Fitness of a genotype is determined by comparison of its vigor, productivity or competitiveness relative to the other genotype by quantifying specific traits such as seed dormancy, flowering date, seedling vigor, seed production, and other factors that can possibly influence the survival and reproductive success of a genotype36,37. In this study, relative fitness (w) of GR B. scoparia was calculated as the reproductive rate (seed production plant−1) of a resistant genotype (B. scoparia plants with 2–4, 5–6, and ≥ 8 EPSPS gene copies) relative to the maximum reproductive rate of the susceptible genotype (B. scoparia plants with 1 EPSPS gene copy) in the population. The relative fitness (w) of susceptible plants was assumed to be one.Statistical analysesA natural logarithm transformation was performed on data for time to 50% flowering, time to seed set, seeds plant−1. An arcsine square root transformation was performed on data for pollen viability, visible control, seed viability, and relative fitness (w) before subjecting to analysis of variance, however all data were presented in their back-transformed values. No transformation was needed for 1000-seed weight and radicle length data. Experimental year, B. scoparia plants with different EPSPS copy number groups, glyphosate rate, and their interactions were considered fixed effects and replication nested within a year was considered as a random effect in the model. Data on percent visible control, time to 50% flowering, pollen viability, time to seed set, 1000-seed weight, and seeds plant−1, seed viability and radicle length were subjected to ANOVA using Proc Mixed in SAS (SAS version 9.4, SAS Institute, Cary, NC, USA) to test the significance of experimental run, treatment factors, and interactions. The ANOVA assumptions for normality of residuals and homogeneity of variance were tested using Proc Univariate and PROC GLM in SAS. Means were separated using Tukey–Kramer’s HSD with α = 0.05. Furthermore, data on percent visible control and seeds plant-1 for each group of B. scoparia plants with different EPSPS gene copy number were regressed against total glyphosate rates using a four-parameter log-logistic model Eq. (1)38,39:$$Y=c+{d-c/{1+mathrm{exp}[bleft(mathrm{log}left(xright)-mathrm{log}left(ED50right)right)]}$$
(1)
where Y is the percent visible control or seed production plant−1 (% of nontreated); d is the upper asymptote (the highest estimated % control or % seed reduction); c is the lower asymptote (the lowest estimated % control or % seed reduction); ED50 is the effective rate of glyphosate needed to achieve 50% control or 50% reduction in seed production; and b denotes the slope around the inflection point “ED50.” Slope parameter (b) indicates the response rate of each group of B. scoparia plants with different EPSPS gene copy number to glyphosate rates (i.e., a slope with a large negative value suggests a rapid response of selected B. scoparia group). The Akaike Information Criterion (AIC) was used to select the nonlinear four-parameter model. A lack-of-fit test (P  > 0.10) was used to confirm that the nonlinear regression model Eq. (1) described the response data for each B. scoparia group38. Parameter estimates, ED90, and SR99 values (i.e. effective rate required for 90% control or effective rate required for 99% reduction in seed production) for each group of B. scoparia plants with different EPSPS gene copy number were determined using the ‘drc’ package in R software37,39. Parameter estimates of B. scoparia groups were compared using the approximate t-test with the ‘compParm’ and ‘EDcomp’ functions in the ‘drc’ package of the R software39,40. More

1.Ghosh, S. K., Podder, D., Panja, S., & Mukherjee, S. In target areas where human mosquito-borne diseases are diagnosed, the inclusion of the pre-adult mosquito aquatic niches parameters will improve the integrated mosquito control program. PLos Neg. Trop. Dis. 14(8), e0008605 (2020).Article 

Google Scholar 
2.Becker, B. N. et al. Mosquitoes and Their Control 499 (Springer, 2010).Book 

Google Scholar 
3.Hyde, K. D. et al. The amazing potential of fungi: 50 ways we can exploit fungi industrially. Fungal Divers. 97, 1–136 (2019).Article 

Google Scholar 
4.Clark, T. B., Kellen, W. R., Fukuda, T. & Lindegren, J. E. Field and laboratory studies on the pathogenicity of the fungus Beauveria bassiana to three genera of mosquitoes. J. Invertebr. Pathol. 11(1), 1–7 (1968).PubMed 
Article 
PubMed Central 

Google Scholar 
5.Scholte, E. J., Knols, B. G. & Takken, W. Infection of the malaria mosquito Anopheles gambiae with the entomopathogenic fungus Metarhizium anisopliae reduces blood feeding and fecundity. J. Invertebr. Pathol. 91(1), 43–49 (2006).PubMed 
Article 
PubMed Central 

Google Scholar 
6.Bukhari, T., Takken, W. & Koenraadt, C. J. Development of Metarhizium anisopliae and Beauveria bassiana formulations for control of malaria mosquito larvae. Parasit. Vectors 4(1), 23 (2011).PubMed 
PubMed Central 
Article 

Google Scholar 
7.Mukherjee, A., Debnath, P., Ghosh, S. K. & Medda, P. K. Biological control of papaya aphid (Aphis gossypii Glover) using entomopathogenic fungi. Vegetos 33, 1–10 (2020).Article 

Google Scholar 
8.Fernández-Grandon, G. M., Harte, S. J., Ewany, J., Bray, D. & Stevenson, P. C. Additive effect of botanical insecticide and entomopathogenic fungi on pest mortality and the behavioral response of its natural enemy. Plants 9, 173 (2020).PubMed Central 
Article 
CAS 

Google Scholar 
9.Sobczak, J. F. et al. Manipulation of wasp (Hymenoptera: Vespidae) behavior by the entomopathogenic fungus Ophiocordyceps humbertii in the Atlantic forest in Ceará, Brazil. Entomol. News 129, 98–104 (2020).Article 

Google Scholar 
10.Ghosh, S. K. & Pal, S. Entomopathogenic potential of Trichoderma longibrachiatum and its comparative evaluation with malathion against the insect pest Leucinodes orbonalis. Environ. Monit. Assess. 188(1), 37 (2016).PubMed 
Article 
CAS 
PubMed Central 

Google Scholar 
11.Podder, D. & Ghosh, S. K. A new application of Trichoderma asperellum as an anopheline larvicide for eco friendly management in medical science. Sci. Reps. 9(1), 1108 (2019).ADS 
Article 
CAS 

Google Scholar 
12.Jones, E. B. G. Fungal adhesion. Mycol. Res. 98(9), 961–981 (1994).Article 

Google Scholar 
13.Shah, P. A. & Pell, J. K. Entomopathogenic fungi as biological control agents. Appl. Microbiol. Biotechnol. 61, 413–423 (2003).CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
14.Rudall, K. M. The chitin/protein complexes of insect cuticles. Adv. Insect Physiol. 1, 257–313 (1963).ADS 
CAS 
Article 

Google Scholar 
15.Shah, F. A., Wang, C. S. & Butt, T. M. Nutrition influences growth and virulence of the insect-pathogenic fungus Metarhizium anisopliae. FEMS Microbiol. Lett. 251(2), 259–266 (2005).CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
16.Jackson, M. A., Dunlap, C. A. & Jaronski, S. T. Ecological considerations in producing and formulating fungal entomopathogens for use in insect biocontrol. Biocontrol 55(1), 129–145 (2010).Article 

Google Scholar 
17.Vega, F.E.; Meyling, N., Luangsa-ard, J.& Blackwell, M. Fungal entomopathogens. In: edit Vega, F. and Kaya, H. A. Insect pathology, 2nd edn , San Diego, CA, Academic Press, pp 171–220 (2012).18.Gaugler, R. Entomopathogenic nematodes in biological control. CRC press (2018).19.McKinnon, A. C. et al. Detection of the entomopathogenic fungus Beauveria bassiana in the rhizosphere of wound-stressed zea mays plants. Front. Microbiol. 9, 1161 (2018).PubMed 
PubMed Central 
Article 

Google Scholar 
20.Zimmermann, G. Review on safety of the entomopathogenic fungus Metarhizium anisopliae. Biocontrol Sci. Technol. 17(9), 879–920 (2007).Article 

Google Scholar 
21.Hamer, J. E., Howard, R. J., Chumley, F. G. & Valent, B. A mechanism for surface attachment in spores of a plant pathogenic fungus. Science 239(4837), 288–290 (1988).ADS 
CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
22.Dhawan, M. & Joshi, N. (Enzymatic comparison and mortality of Beauveria bassiana against cabbage caterpillar Pieris brassicae LINN. Braz. J. Microbiol. 48(3), 522–529 (2017).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
23.Mora, M. A. E., Castilho, A. M. C. & Fraga, M. E. Classification and infection mechanism of entomopathogenic fungi. Arq. Inst. Biol. 84, 0552015 (2017).
Google Scholar 
24.Li, J., Tracy, J. W. & Christensen, B. M. Phenol oxidase activity in hemolymph compartments of Aedes aegypti during melanotic encapsulation reactions against microfilariae. Dev. Comp. Immunol. 16(1), 41–48 (1992).CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
25.Hillyer, J. F. & Strand, M. R. Mosquito hemocyte-mediated immune responses. Curr. Opin. Insect Sci. 3, 14–21 (2014).PubMed 
PubMed Central 
Article 

Google Scholar 
26.Nanda, K. P. Chronic lead (Pb) exposure results in diminished hemocyte count and increased susceptibility to bacterial infection in Drosophila melanogaster. Chemosphere 236, 124349 (2019).ADS 
CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
27.Ghosh, S. K., Chatterjee, T., Chakravarty, A. & Basak, A. K. Sodium and potassium nitrite-induced developmental genotoxicity in Drosophila melanogaster—effects in larval immune and brain stem cells. Interdiscip. Toxicol. 13(4), 101–105 (2020).
Google Scholar 
28.Chatterjee, T., Ghosh, S. K., Paik, S., Chakravarty, A. & Basak, A. K. Benzoic acid treated Drosophila melanogaster the genetic disruption of larval brain stem cells and non-neural cells during metamorphosis. Toxicol. Environ. Health Sci. https://doi.org/10.1007/s13530-021-00082-w (2021).Article 

Google Scholar 
29.Campos, R. A. Boophilus microplus infection by Beauveria amorpha and Beauveria bassiana: SEM analysis and regulation of subtilisin-like proteases and chitinases. Curr. Microbiol. 50(5), 257–261 (2005).CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
30.McFarlane, H. E., Gendre, D. & Western, T. L. Seed coat ruthenium red staining assay. Bio-Protoc. 4, 1096 (2014).Article 

Google Scholar 
31.Bhosale, R. R., Osmani, R. A. M. & Moin, A. Natural gums and mucilages: A review on multifaceted excipients in pharmaceutical science and research. Int. J. Res. Phytochem. Pharmacol 6(4), 901–912 (2014).
Google Scholar 
32.Shah, F. A., Allen, N., Wright, C. J. & Butt, T. M. Repeated in vitro subculturing alters spore surface properties and virulence of Metarhizium anisopliae. FEMS Microbiol. Lett. 276(1), 60–66 (2007).CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
33.Hsu, S. C. & Lockwood, J. L. Powdered chitin agar as a selective medium for enumeration of actinomycetes in water and soil. Appl. Environ. Microbiol. 29(3), 422–426 (1975).CAS 
Article 

Google Scholar 
34.Parida, D., Jena, S. K. & Rath, C. C. Enzyme activities of bacterial isolates from iron mine areas of Barbil, Keonjhar district, Odisha, India. Int. J. Pure Appl. Biosci. 2(3), 265–271 (2014).
Google Scholar 
35.Kasana, R. C., Salwan, R., Dhar, H., Dutt, S. & Gulati, A. A rapid and easy method for the detection of microbial cellulases on agar plates using Gram’s iodine. Curr. Microbiol. 57(5), 503–507 (2008).CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
36.Medina, P. & Baresi, L. Rapid identification of gelatin and casein hydrolysis using TCA. J. Microbiol. Methods 69(2), 391–393 (2007).CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
37.Al-Nahdi, H. S. Isolation and screening of extracellular proteases produced by new isolated Bacillus sp. J. Appl. Pharm. Sci. 2(9), 71–74 (2012).CAS 

Google Scholar 
38.Murthy, N. K. & Bleakley, B. H. Simplified method of preparing colloidal chitin used for screening of chitinase-producing microorganisms. Int. J. Microbiol. 10(2), 1937–8289 (2012).
Google Scholar 
39.Park, S. H., Lee, J. H. & Lee, H. K. Purification and characterization of chitinase from a marine bacterium, Vibrio sp. 98CJ11027. J. Microbiol 38, 224–229 (2000).CAS 

Google Scholar 
40.Roberts, W. K. & Selitrennikoff, C. P. Plant and bacterial chitinases differ in antifungal activity. Microbiology 134(1), 169–176 (1986).Article 

Google Scholar 
41.Tsuchida, O. et al. An alkaline proteinase of an alkalophilic Bacillus sp. Curr. Microbiol. 14(1), 7–12 (1986).CAS 
Article 

Google Scholar 
42.Crowell, A. M., Wall, M. J. & Doucette, A. A Maximizing recovery of water-soluble proteins through acetone precipitation. Anal. Chim. Acta. 796, 48–54 (2013).CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
43.He, F. BCA (Bicinchoninic Acid) protein assay. Bio Protocol 1(5), 44 (2011).Article 

Google Scholar 
44.Sierra, L.M., Carmona, E.R., Aguado, L. & Marcos, R. The comet assay in Drosophila: neuroblast and hemocyte cells. In Genotoxicity and DNA Repair. Methods in Pharmacology and Toxicology. Humana Press, New York, NY. 269–82 (2014).45.Xu, T. et al. (2012) HMGB in mollusk Crassostrea ariakensis Gould: structure, pro-inflammatory cytokine function characterization and anti-infection role of its antibody. PLoS ONE 7(11), e50789 (2012).ADS 
CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
46.Basak, A. K., Chatterjee, T., Chakravarty, A. & Ghosh, S. K. Silver nanoparticle-induced developmental inhibition of Drosophila melanogaster accompanies disruption of genetic material of larval neural stem cells and non-neuronal cells. Environ. Monit. Assess. 191(8), 497 (2019).PubMed 
Article 
CAS 
PubMed Central 

Google Scholar  More

1.McFall-Ngai, M. et al. Animals in a bacterial world: a new imperative for the life sciences. Proc. Natl Acad. Sci. 110, 3229–3236 (2013).CAS 
Article 

Google Scholar 
2.Pita, L., Rix, L., Slaby, B. M., Franke, A. & Hentschel, U. The sponge holobiont in a changing ocean: from microbes to ecosystems. Microbiome 6, 46 (2018).CAS 
Article 

Google Scholar 
3.Caruso, R., Lo, B. C. & Núñez, G. Host–microbiota interactions in inflammatory bowel disease. Nat. Rev. Immunol. 20, 411–426 (2020).CAS 
Article 

Google Scholar 
4.Hughes, T. P. et al. Spatial and temporal patterns of mass bleaching of corals in the Anthropocene. Science 359, 80–83 (2018).CAS 
Article 

Google Scholar 
5.Scheele, B. C. et al. Amphibian fungal panzootic causes catastrophic and ongoing loss of biodiversity. Science 363, 1459–1463 (2019).CAS 
Article 

Google Scholar 
6.Bell, J. J., Bennett, H. M., Rovellini, A. & Webster, N. S. Sponges to be winners under near-future climate scenarios. BioScience 68, 955–968 (2018).Article 

Google Scholar 
7.Bosch, T. C. G., Guillemin, K. & McFall-Ngai, M. Evolutionary “experiments” in symbiosis: the study of model animals provides insights into the mechanisms underlying the diversity of host–microbe interactions. Bioessays 41, e1800256 (2019).8.Nyholm, S. V. & McFall-Ngai, M. J. A lasting symbiosis: how the Hawaiian bobtail squid finds and keeps its bioluminescent bacterial partner. Nat. Rev. Microbiol. https://doi.org/10.1038/s41579-021-00567-y (2021).Article 
PubMed 

Google Scholar 
9.Visick, K. L., Stabb, E. V. & Ruby, E. G. A lasting symbiosis: how Vibrio fischeri finds a squid partner and persists within its natural host. Nat. Rev. Microbiol. https://doi.org/10.1038/s41579-021-00557-0 (2021).Article 
PubMed 

Google Scholar 
10.Peixoto, R. S. et al. Coral probiotics: premise, promise, prospects. Annu. Rev. Anim. Biosci. 9, 265–288 (2021).Article 

Google Scholar  More

1.Serebrovsky, A. S. On the possibility of a new method for the control of insect pests. Zool. Zh. 19, 618–630 (1940).
Google Scholar 
2.Curtis, C. F. Possible use of translocations to fix desirable genes in insect pest populations. Nature 218, 368–369 (1968). This paper is one of the first to describe how reciprocal chromosomal translocations could be used to drive a favoured linked trait in a threshold-dependent fashion.CAS 
PubMed 
Article 

Google Scholar 
3.Dawkins, R. The Selfish Gene Vol. 345 (Oxford University Press, 1976).4.Bastide, H. et al. Rapid rise and fall of selfish sex-ratio X chromosomes in Drosophila simulans: spatiotemporal analysis of phenotypic and molecular data. Mol. Biol. Evol. 28, 2461–2470 (2011).CAS 
PubMed 
Article 

Google Scholar 
5.Corbett-Detig, R., Medina, P., Frerot, H., Blassiau, C. & Castric, V. Bulk pollen sequencing reveals rapid evolution of segregation distortion in the male germline of Arabidopsis hybrids. Evol. Lett. 3, 93–103 (2019).PubMed 
PubMed Central 
Article 

Google Scholar 
6.Kingan, S. B., Garrigan, D. & Hartl, D. L. Recurrent selection on the Winters sex-ratio genes in Drosophila simulans. Genetics 184, 253–265 (2010).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
7.McLaughlin, R. N. Jr. & Malik, H. S. Genetic conflicts: the usual suspects and beyond. J. Exp. Biol. 220, 6–17 (2017).PubMed 
PubMed Central 
Article 

Google Scholar 
8.Presgraves, D. C., Gerard, P. R., Cherukuri, A. & Lyttle, T. W. Large-scale selective sweep among segregation distorter chromosomes in African populations of Drosophila melanogaster. PLoS Genet. 5, e1000463 (2009).PubMed 
PubMed Central 
Article 
CAS 

Google Scholar 
9.Seymour, D. K., Chae, E., Arioz, B. I., Koenig, D. & Weigel, D. Transmission ratio distortion is frequent in Arabidopsis thaliana controlled crosses. Heredity 122, 294–304 (2019).CAS 
PubMed 
Article 

Google Scholar 
10.Courret, C., Chang, C. H., Wei, K. H., Montchamp-Moreau, C. & Larracuente, A. M. Meiotic drive mechanisms: lessons from Drosophila. Proc. Biol. Sci. 286, 20191430 (2019).CAS 
PubMed 
PubMed Central 

Google Scholar 
11.Kusano, A., Staber, C., Chan, H. Y. & Ganetzky, B. Closing the (Ran)GAP on segregation distortion in Drosophila. Bioessays 25, 108–115 (2003).CAS 
PubMed 
Article 

Google Scholar 
12.Merel, V., Boulesteix, M., Fablet, M. & Vieira, C. Transposable elements in Drosophila. Mob. DNA 11, 23 (2020).PubMed 
PubMed Central 
Article 

Google Scholar 
13.Boulesteix, M. & Biemont, C. Transposable elements in mosquitoes. Cytogenet. Genome Res. 110, 500–509 (2005).CAS 
PubMed 
Article 

Google Scholar 
14.Lee, Y. C. & Langley, C. H. Transposable elements in natural populations of Drosophila melanogaster. Philos. Trans. R. Soc. Lond. B Biol. Sci. 365, 1219–1228 (2010).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
15.Kelleher, E. S. Reexamining the P-element invasion of Drosophila melanogaster through the lens of piRNA silencing. Genetics 203, 1513–1531 (2016).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
16.Majumdar, S. & Rio, D. C. P transposable elements in drosophila and other eukaryotic organisms. Microbiol. Spectr. 3, MDNA3–0004-2014 (2015).PubMed 
PubMed Central 

Google Scholar 
17.Burns, K. H. & Boeke, J. D. Human transposon tectonics. Cell 149, 740–752 (2012).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
18.Doring, H. P., Tillmann, E. & Starlinger, P. DNA sequence of the maize transposable element Dissociation. Nature 307, 127–130 (1984).CAS 
PubMed 
Article 

Google Scholar 
19.Wallau, G. L., Capy, P., Loreto, E. & Hua-Van, A. Genomic landscape and evolutionary dynamics of mariner transposable elements within the Drosophila genus. BMC Genomics 15, 727 (2014).PubMed 
PubMed Central 
Article 
CAS 

Google Scholar 
20.Hawkins, J. S., Hu, G., Rapp, R. A., Grafenberg, J. L. & Wendel, J. F. Phylogenetic determination of the pace of transposable element proliferation in plants: copia and LINE-like elements in Gossypium. Genome 51, 11–18 (2008).CAS 
PubMed 
Article 

Google Scholar 
21.Biemont, C., Vieira, C., Borie, N. & Lepetit, D. Transposable elements and genome evolution: the case of Drosophila simulans. Genetica 107, 113–120 (1999).CAS 
PubMed 
Article 

Google Scholar 
22.Buchman, A. B., Ivy, T., Marshall, J. M., Akbari, O. S. & Hay, B. A. Engineered reciprocal chromosome translocations drive high threshold, reversible population replacement in drosophila. ACS Synth. Biol. 7, 1359–1370 (2018).CAS 
PubMed 
Article 

Google Scholar 
23.Akbari, O. S. et al. Novel synthetic Medea selfish genetic elements drive population replacement in Drosophila; a theoretical exploration of Medea-dependent population suppression. ACS Synth. Biol. 3, 915–928 (2014).CAS 
PubMed 
Article 

Google Scholar 
24.Buchman, A., Marshall, J. M., Ostrovski, D., Yang, T. & Akbari, O. S. Synthetically engineered Medea gene drive system in the worldwide crop pest Drosophila suzukii. Proc. Natl Acad. Sci. USA 115, 4725–4730 (2018).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
25.Champer, J., Zhao, J., Champer, S. E., Liu, J. & Messer, P. W. Population dynamics of underdominance gene drive systems in continuous space. ACS Synth. Biol. 9, 779–792 (2020).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
26.Chen, C. C. et al. EXO1 suppresses double-strand break induced homologous recombination between diverged sequences in mammalian cells. DNA Repair. 57, 98–106 (2017).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
27.Leftwich, P. T. et al. Recent advances in threshold-dependent gene drives for mosquitoes. Biochem. Soc. Trans. 46, 1203–1212 (2018).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
28.Raban, R. R., Marshall, J. M. & Akbari, O. S. Progress towards engineering gene drives for population control. J. Exp. Biol. 223 (Suppl. 1), jeb208181 (2020).PubMed 
PubMed Central 
Article 

Google Scholar 
29.Ward, C. M. et al. Medea selfish genetic elements as tools for altering traits of wild populations: a theoretical analysis. Evolution 65, 1149–1162 (2011).PubMed 
Article 

Google Scholar 
30.Oberhofer, G., Ivy, T. & Hay, B. A. Gene drive and resilience through renewal with next generation Cleave and Rescue selfish genetic elements. Proc. Natl Acad. Sci. USA 117, 9013–9021 (2020).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
31.Oberhofer, G., Ivy, T. & Hay, B. A. Cleave and Rescue, a novel selfish genetic element and general strategy for gene drive. Proc. Natl Acad. Sci. USA 116, 6250–6259 (2019).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
32.Champer, J. et al. A toxin-antidote CRISPR gene drive system for regional population modification. Nat. Commun. 11, 1082 (2020).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
33.Yen, P. S. & Failloux, A. B. A review: Wolbachia-based population replacement for mosquito control shares common points with genetically modified control approaches. Pathogens 9, 404 (2020).PubMed Central 
Article 
PubMed 

Google Scholar 
34.O’Neill, S. L. The use of wolbachia by the world mosquito program to interrupt transmission of aedes aegypti transmitted viruses. Adv. Exp. Med. Biol. 1062, 355–360 (2018).PubMed 
Article 
CAS 

Google Scholar 
35.Niang, E. H. A., Bassene, H., Fenollar, F. & Mediannikov, O. Biological control of mosquito-borne diseases: the potential of wolbachia-based interventions in an IVM framework. J. Trop. Med. 2018, 1470459 (2018).PubMed 
PubMed Central 
Article 

Google Scholar 
36.Chevalier, B. S. & Stoddard, B. L. Homing endonucleases: structural and functional insight into the catalysts of intron/intein mobility. Nucleic Acids Res. 29, 3757–3774 (2001).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
37.Macreadie, I. G., Scott, R. M., Zinn, A. R. & Butow, R. A. Transposition of an intron in yeast mitochondria requires a protein encoded by that intron. Cell 41, 395–402 (1985).CAS 
PubMed 
Article 

Google Scholar 
38.Rong, Y. S. & Golic, K. G. The homologous chromosome is an effective template for the repair of mitotic DNA double-strand breaks in Drosophila. Genetics 165, 1831–1842 (2003).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
39.Chan, Y. S., Huen, D. S., Glauert, R., Whiteway, E. & Russell, S. Optimising homing endonuclease gene drive performance in a semi-refractory species: the Drosophila melanogaster experience. PLoS ONE 8, e54130 (2013).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
40.Windbichler, N. et al. A synthetic homing endonuclease-based gene drive system in the human malaria mosquito. Nature 473, 212–215 (2011). This study is the first demonstration of nuclease-mediated gene drive in mosquitoes based on the homing endonuclease gene I-SceI.CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
41.Carroll, D. Genome engineering with targetable nucleases. Annu. Rev. Biochem. 83, 409–439 (2014).CAS 
PubMed 
Article 

Google Scholar 
42.Barrangou, R. et al. CRISPR provides acquired resistance against viruses in prokaryotes. Science 315, 1709–1712 (2007).CAS 
Article 
PubMed 

Google Scholar 
43.Jinek, M. et al. A programmable dual-RNA-guided DNA endonuclease in adaptive bacterial immunity. Science 337, 816–821 (2012). This foundational study developed the most widely used dual synthetic CRISPR system consisting of Cas9 endonuclease and gRNA components.CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
44.Doudna, J. A., Sternberg, S. H. A Crack in Creation: Gene Editing and the Unthinkable Power to Control Evolution 281 (Houghton Mifflin Harcourt, 2017).45.Gantz, V. M. & Bier, E. The mutagenic chain reaction: a method for converting heterozygous to homozygous mutations. Science 348, 442–444 (2015). This study reported the first CRISPR-based gene drive in a metazoan organism (D. melanogaster) with a specialized germline.CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
46.Gantz, V. M. et al. Highly efficient Cas9-mediated gene drive for population modification of the malaria vector mosquito Anopheles stephensi. Proc. Natl Acad. Sci. USA 112, E6736–E6743 (2015). This study describes the first efficient CRISPR-based gene drive system in mosquitoes, which carried a dual anti-malarial effector cassette.CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
47.Hammond, A. et al. A CRISPR-Cas9 gene drive system targeting female reproduction in the malaria mosquito vector Anopheles gambiae. Nat. Biotechnol. 34, 78–83 (2016). This study describes the first efficient CRISPR-based suppression gene drive system in mosquitoes.CAS 
PubMed 
Article 

Google Scholar 
48.Kyrou, K. et al. A CRISPR-Cas9 gene drive targeting doublesex causes complete population suppression in caged Anopheles gambiae mosquitoes. Nat. Biotechnol. 36, 1062–1066 (2018). This study describes a highly efficient suppression gene drive system in mosquitoes targeting an invariant genome target site in the doublesex locus.CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
49.Li, M. et al. Development of a confinable gene drive system in the human disease vector Aedes aegypti. eLife 9, e51701 (2020).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
50.Grunwald, H. A. et al. Super-Mendelian inheritance mediated by CRISPR-Cas9 in the female mouse germline. Nature 566, 105–109 (2019). This study provided the first proof-of-principle gene drive system in mammals, which selectively sustained drive via the female germline.CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
51.DiCarlo, J. E., Chavez, A., Dietz, S. L., Esvelt, K. M. & Church, G. M. Safeguarding CRISPR-Cas9 gene drives in yeast. Nat. Biotechnol. 33, 1250–1255 (2015). This study demonstrated CRISPR-based gene conversion in diploid yeast, which could then be transmitted meiotically.CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
52.Valderrama, J. A., Kulkarni, S. S., Nizet, V. & Bier, E. A bacterial gene-drive system efficiently edits and inactivates a high copy number antibiotic resistance locus. Nat. Commun. 10, 5726 (2019). This study generalizes the concept of gene drive to bacteria, where it is applied to efficiently reduce the frequency of antibiotic reistance.CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
53.Esvelt, K. M., Smidler, A. L., Catteruccia, F. & Church, G. M. Concerning RNA-guided gene drives for the alteration of wild populations. eLife 3, e03401 (2014).PubMed 
PubMed Central 
Article 

Google Scholar 
54.Adolfi, A. et al. Efficient population modification gene-drive rescue system in the malaria mosquito Anopheles stephensi. Nat. Commun. 11, 5553 (2020). This study reports on the first recoded gene drive in mosquitoes that drove efficiently through both males and females based on the process of lethal/sterile mosaicism.CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
55.Champer, J. et al. A CRISPR homing gene drive targeting a haplolethal gene removes resistance alleles and successfully spreads through a cage population. Proc. Natl Acad. Sci. USA 117, 24377–24383 (2020).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
56.Kandul, N. P., Liu, J., Bennett, J. B., Marshall, J. M. & Akbari, O. S. A confinable home-and-rescue gene drive for population modification. eLife 10, e65939 (2021).PubMed 
PubMed Central 
Article 

Google Scholar 
57.Terradas, G. et al. Inherently confinable split-drive systems in Drosophila. Nat. Commun. 12, 1480 (2021). This study further develops the strategy of inserting a recoded gene drive in genes essential for viability or reproduction in the context of split drive systems.CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
58.Xu, X. S., Gantz, V. M., Siomava, N. & Bier, E. CRISPR/Cas9 and active genetics-based trans-species replacement of the endogenous Drosophila kni-L2 CRM reveals unexpected complexity. eLife 6, e30281 (2017).PubMed 
PubMed Central 
Article 

Google Scholar 
59.Lopez Del Amo, V. et al. A transcomplementing gene drive provides a flexible platform for laboratory investigation and potential field deployment. Nat. Commun. 11, 352 (2020). This study reports on the reconstitution of a full gene drive from split constituent parts.CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
60.Guichard, A. et al. Efficient allelic-drive in Drosophila. Nat. Commun. 10, 1640 (2019). The study develops two allelic drive systems, copy-cutting and copy-grafting, to propagate favoured alleles of an essential gene.PubMed 
PubMed Central 
Article 
CAS 

Google Scholar 
61.Kandul, N. P. et al. Assessment of a split homing based gene drive for efficient knockout of multiple genes. G3 10, 827–837 (2020).CAS 
PubMed 
Article 

Google Scholar 
62.Xu, X.-R. S. et al. Active-genetic neutralizing elements for halting or deleting gene-drives. Mol. Cell 80, 246–262 (2020). This study reports on two drive-neutralizing systems that either inactivate (e-CHACR) or delete and replace (ERACR) a gene drive.CAS 
PubMed 
Article 

Google Scholar 
63.Burt, A. Site-specific selfish genes as tools for the control and genetic engineering of natural populations. Proc. Biol. Sci. 270, 921–928 (2003). This seminal modelling study provides the theoretical underpinnings for the modern gene-drive field.CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
64.North, A. R., Burt, A. & Godfray, H. C. J. Modelling the potential of genetic control of malaria mosquitoes at national scale. BMC Biol. 17, 26 (2019). This study provides a comprehensive analysis of the perfomance of suppressive gene drives following iterative releases across various topographies.PubMed 
PubMed Central 
Article 

Google Scholar 
65.North, A. R., Burt, A. & Godfray, H. C. J. Modelling the suppression of a malaria vector using a CRISPR-Cas9 gene drive to reduce female fertility. BMC Biol. 18, 98 (2020).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
66.Collins, C. M., Bonds, J. A. S., Quinlan, M. M. & Mumford, J. D. Effects of the removal or reduction in density of the malaria mosquito, Anopheles gambiae s.l., on interacting predators and competitors in local ecosystems. Med. Vet. Entomol. 33, 1–15 (2019).CAS 
PubMed 
Article 

Google Scholar 
67.James, A. A. Gene drive systems in mosquitoes: rules of the road. Trends Parasitol. 21, 64–67 (2005).CAS 
PubMed 
Article 

Google Scholar 
68.Gantz, V. M. & Bier, E. The dawn of active genetics. Bioessays 38, 50–63 (2016).PubMed 
Article 

Google Scholar 
69.Macias, V. M. & James, A. A. in Genetic Control of Malaria and Dengue (ed. Adelman, Z. N.) 423–444 (Elsevier Academic Press, 2015).70.Eckhoff, P. A., Wenger, E. A., Godfray, H. C. & Burt, A. Impact of mosquito gene drive on malaria elimination in a computational model with explicit spatial and temporal dynamics. Proc. Natl Acad. Sci. USA 114, E255–E264 (2017). This study provides a detailed analysis of drive parameters relevant to both suppression-based and modification-based drives and is the first to model a drive in the context of a two-dimensional environment.CAS 
PubMed 
Article 

Google Scholar 
71.Hammond, A. M. et al. The creation and selection of mutations resistant to a gene drive over multiple generations in the malaria mosquito. PLoS Genet. 13, e1007039 (2017).PubMed 
PubMed Central 
Article 
CAS 

Google Scholar 
72.Joyce, E. F., Paul, A., Chen, K. E., Tanneti, N. & McKim, K. S. Multiple barriers to nonhomologous DNA end joining during meiosis in Drosophila. Genetics 191, 739–746 (2012).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
73.Bozas, A., Beumer, K. J., Trautman, J. K. & Carroll, D. Genetic analysis of zinc-finger nuclease-induced gene targeting in Drosophila. Genetics 182, 641–651 (2009).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
74.Do, A. T., Brooks, J. T., Le Neveu, M. K. & LaRocque, J. R. Double-strand break repair assays determine pathway choice and structure of gene conversion events in Drosophila melanogaster. G3 4, 425–432 (2014).PubMed 
Article 
CAS 

Google Scholar 
75.Wei, D. S. & Rong, Y. S. A genetic screen for DNA double-strand break repair mutations in Drosophila. Genetics 177, 63–77 (2007).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
76.Lin, C. C. & Potter, C. J. Non-Mendelian dominant maternal effects caused by CRISPR/Cas9 transgenic components in Drosophila melanogaster. G3 6, 3685–3691 (2016).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
77.Champer, J. et al. Novel CRISPR/Cas9 gene drive constructs reveal insights into mechanisms of resistance allele formation and drive efficiency in genetically diverse populations. PLoS Genet. 13, e1006796 (2017).PubMed 
PubMed Central 
Article 
CAS 

Google Scholar 
78.Anopheles gambiae 1000 Genomes Consortiumet al. Genetic diversity of the African malaria vector Anopheles gambiae. Nature 552, 96–100 (2017).Article 
CAS 

Google Scholar 
79.Deredec, A., Burt, A. & Godfray, H. C. The population genetics of using homing endonuclease genes in vector and pest management. Genetics 179, 2013–2026 (2008).PubMed 
PubMed Central 
Article 

Google Scholar 
80.Fasulo, B. et al. A fly model establishes distinct mechanisms for synthetic CRISPR/Cas9 sex distorters. PLoS Genet. 16, e1008647 (2020).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
81.Galizi, R. et al. A synthetic sex ratio distortion system for the control of the human malaria mosquito. Nat. Commun. 5, 3977 (2014).CAS 
PubMed 
Article 

Google Scholar 
82.Galizi, R. et al. A CRISPR-Cas9 sex-ratio distortion system for genetic control. Sci. Rep. 6, 31139 (2016).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
83.Turner, J. M. Meiotic sex chromosome inactivation. Development 134, 1823–1831 (2007).CAS 
PubMed 
Article 

Google Scholar 
84.Simoni, A. et al. A male-biased sex-distorter gene drive for the human malaria vector Anopheles gambiae. Nat. Biotechnol. 38, 1054–1060 (2020).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
85.Carballar-Lejarazu, R. & et al. Next-generation gene drive for population modification of the malaria vector mosquito, Anopheles gambiae.Proc. Natl Acad. Sci. USA 117, 22805–22814 (2020). This study describes a modification gene drive that propagates with high efficiency through both males and females.CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
86.Pham, T. B. et al. Experimental population modification of the malaria vector mosquito, Anopheles stephensi. PLoS Genet. 15, e1008440 (2019).PubMed 
PubMed Central 
Article 
CAS 

Google Scholar 
87.Dong, Y., Simoes, M. L. & Dimopoulos, G. Versatile transgenic multistage effector-gene combinations for Plasmodium falciparum suppression in Anopheles. Sci. Adv. 6, eaay5898 (2020).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
88.Dong, Y. et al. Engineered anopheles immunity to Plasmodium infection. PLoS Pathog. 7, e1002458 (2011).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
89.Isaacs, A. T. et al. Transgenic Anopheles stephensi coexpressing single-chain antibodies resist Plasmodium falciparum development. Proc. Natl Acad. Sci. USA 109, E1922–E1930 (2012). This study demonstrates 100% protection against parasite transmission in transgenic mosquitoes carrying a dual anti-parasite effector cassette.CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
90.Haber, J. E. TOPping off meiosis. Mol. Cell 57, 577–581 (2015).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
91.Hammond, A. et al. Regulating the expression of gene drives is key to increasing their invasive potential and the mitigation of resistance. PLoS Genet. 17, e1009321 (2021).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
92.Lee, Y. et al. Genome-wide divergence among invasive populations of Aedes aegypti in California. BMC Genomics 20, 204 (2019).PubMed 
PubMed Central 
Article 

Google Scholar 
93.Callaway, E. Gene drives thwarted by emergence of resistant organisms. Nature 542, 15 (2017).CAS 
PubMed 
Article 

Google Scholar 
94.Unckless, R. L., Clark, A. G. & Messer, P. W. Evolution of resistance against CRISPR/Cas9 gene drive. Genetics 205, 827–841 (2017).PubMed 
Article 

Google Scholar 
95.Drury, D. W., Dapper, A. L., Siniard, D. J., Zentner, G. E. & Wade, M. J. CRISPR/Cas9 gene drives in genetically variable and nonrandomly mating wild populations. Sci. Adv. 3, e1601910 (2017).PubMed 
PubMed Central 
Article 
CAS 

Google Scholar 
96.Schmidt, H. et al. Abundance of conserved CRISPR-Cas9 target sites within the highly polymorphic genomes of Anopheles and Aedes mosquitoes. Nat. Commun. 11, 1425 (2020). This study provides computational evidence that conserved CRISPR cleavage sites are abundant in the genome.CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
97.Akbari, O. S. et al. Safeguarding gene drive experiments in the laboratory. Science 349, 927–929 (2015).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
98.Li, J. et al. Genome-block expression-assisted association studies discover malaria resistance genes in Anopheles gambiae. Proc. Natl Acad. Sci. USA 110, 20675–20680 (2013).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
99.Niu, G. et al. The fibrinogen-like domain of FREP1 protein is a broad-spectrum malaria transmission-blocking vaccine antigen. J. Biol. Chem. 292, 11960–11969 (2017).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
100.Zhang, G. et al. Anopheles midgut FREP1 mediates plasmodium invasion. J. Biol. Chem. 290, 16490–16501 (2015).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
101.Dong, Y., Simoes, M. L., Marois, E. & Dimopoulos, G. CRISPR/Cas9 -mediated gene knockout of Anopheles gambiae FREP1 suppresses malaria parasite infection. PLoS Pathog. 14, e1006898 (2018).PubMed 
PubMed Central 
Article 
CAS 

Google Scholar 
102.Simoes, M. L., Caragata, E. P. & Dimopoulos, G. Diverse host and restriction factors regulate mosquito-pathogen interactions. Trends Parasitol. 34, 603–616 (2018).PubMed 
Article 

Google Scholar 
103.Nash, A. et al. Integral gene drives for population replacement. Biol. Open 8, bio037762 (2019). This study describes a bipartite drive system that can enable testing of anti-parasite effector cassettes under standard mosquito confinement protocols.CAS 
PubMed 

Google Scholar 
104.Enayati, A., Hanafi-Bojd, A. A., Sedaghat, M. M., Zaim, M. & Hemingway, J. Evolution of insecticide resistance and its mechanisms in Anopheles stephensi in the WHO Eastern Mediterranean Region. Malar. J. 19, 258 (2020).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
105.Ffrench-Constant, R. H., Williamson, M. S., Davies, T. G. & Bass, C. Ion channels as insecticide targets. J. Neurogenet. 30, 163–177 (2016).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
106.Silva, J. J. & Scott, J. G. Conservation of the voltage-sensitive sodium channel protein within the Insecta. Insect Mol. Biol. 29, 9–18 (2020).CAS 
PubMed 
Article 

Google Scholar 
107.Casida, J. E. & Durkin, K. A. Novel GABA receptor pesticide targets. Pestic. Biochem. Physiol. 121, 22–30 (2015).CAS 
PubMed 
Article 

Google Scholar 
108.Ihara, M., Buckingham, S. D., Matsuda, K. & Sattelle, D. B. Modes of action, resistance and toxicity of insecticides targeting nicotinic acetylcholine receptors. Curr. Med. Chem. 24, 2925–2934 (2017).CAS 
PubMed 
Article 

Google Scholar 
109.Thapa, S., Lv, M. & Xu, H. Acetylcholinesterase: a primary target for drugs and insecticides. Mini Rev. Med. Chem. 17, 1665–1676 (2017).CAS 
PubMed 
Article 

Google Scholar 
110.Kleinstiver, B. P. et al. Engineered CRISPR-Cas12a variants with increased activities and improved targeting ranges for gene, epigenetic and base editing. Nat. Biotechnol. 37, 276–282 (2019).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
111.Walton, R. T., Christie, K. A., Whittaker, M. N. & Kleinstiver, B. P. Unconstrained genome targeting with near-PAMless engineered CRISPR-Cas9 variants. Science 368, 290–296 (2020).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
112.Committee on Gene Drive Research in Non-Human Organisms: Recommendations for Responsible Conduct; Board on Life Sciences; Division on Earth and Life Studies; National Academies of Sciences, Engineering, and Medicine. Gene Drives on the Horizon: Advancing Science, Navigating Uncertainty, and Aligning Research with Public Values (The National Academies Press, 2016). This comprehensive advisory and historical review document summarizes consensus views for how to safely rear and study gene-drive systems in the laboratory.113.Adelman, Z. et al. Rules of the road for insect gene drive research and testing. Nat. Biotechnol. 35, 716–718 (2017).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
114.James, S. et al. Pathway to deployment of gene drive mosquitoes as a potential biocontrol tool for elimination of malaria in Sub-Saharan Africa: recommendations of a scientific working group(dagger). Am. J. Trop. Med. Hyg. 98, 1–49 (2018).PubMed 
PubMed Central 
Article 

Google Scholar 
115.James, S. L., Marshall, J. M., Christophides, G. K., Okumu, F. O. & Nolan, T. Toward the definition of efficacy and safety criteria for advancing gene drive-modified mosquitoes to field testing. Vector Borne Zoonotic Dis. 20, 237–251 (2020).PubMed 
PubMed Central 
Article 

Google Scholar 
116.Warmbrod, K. L. et al. Gene Drives: Pursuing Opportunities, Minimizing Risk – A Johns Hopkins University Report on Responsible Governance (Johns Hopkins Bloomberg School of Public Health, Center for Health Security, Johns Hopkins University, 2020).117.Vella, M. R., Gunning, C. E., Lloyd, A. L. & Gould, F. Evaluating strategies for reversing CRISPR-Cas9 gene drives. Sci. Rep. 7, 11038 (2017).PubMed 
PubMed Central 
Article 
CAS 

Google Scholar 
118.Rode, N. O., Courtier-Orgogozo, V. & Debarre, F. Can a population targeted by a CRISPR-based homing gene drive be rescued? G3 10, 3403–3415 (2020).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
119.Fedoroff, N., Wessler, S. & Shure, M. Isolation of the transposable maize controlling elements Ac and Ds. Cell 35, 235–242 (1983).CAS 
PubMed 
Article 

Google Scholar 
120.Paix, A. et al. Precision genome editing using synthesis-dependent repair of Cas9-induced DNA breaks. Proc. Natl Acad. Sci. USA 114, E10745–E10754 (2017).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
121.Wu, B., Luo, L. & Gao, X. J. Cas9-triggered chain ablation of cas9 as a gene drive brake. Nat. Biotechnol. 34, 137–138 (2016).PubMed 
PubMed Central 
Article 
CAS 

Google Scholar 
122.Taxiarchi, C. et al. A genetically encoded anti-CRISPR protein constrains gene drive spread and prevents population suppression. Nat. Commun. 12, 3977 (2021).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
123.Conklin, B. R. On the road to a gene drive in mammals. Nature 566, 43–45 (2019).CAS 
PubMed 
Article 

Google Scholar 
124.Salkeld, D. J. Vaccines for conservation: plague, prairie dogs & black-footed ferrets as a case study. Ecohealth 14, 432–437 (2017).PubMed 
Article 

Google Scholar 
125.Teem, J. L. et al. Genetic biocontrol for invasive species. Front. Bioeng. Biotechnol. 8, 452 (2020).PubMed 
PubMed Central 
Article 

Google Scholar 
126.Godwin, J. et al. Rodent gene drives for conservation: opportunities and data needs. Proc. Biol. Sci. 286, 20191606 (2019).PubMed 
PubMed Central 

Google Scholar 
127.McFarlane, G. R., Whitelaw, C. B. A. & Lillico, S. G. CRISPR-based gene drives for pest control. Trends Biotechnol. 36, 130–133 (2018).CAS 
PubMed 
Article 

Google Scholar 
128.Klompe, S. E., Vo, P. L. H., Halpin-Healy, T. S. & Sternberg, S. H. Transposon-encoded CRISPR-Cas systems direct RNA-guided DNA integration. Nature 571, 219–225 (2019).CAS 
PubMed 
Article 

Google Scholar 
129.Koonin, E. V., Makarova, K. S., Wolf, Y. I. & Krupovic, M. Evolutionary entanglement of mobile genetic elements and host defence systems: guns for hire. Nat. Rev. Genet. 21, 119–131 (2020).CAS 
PubMed 
Article 

Google Scholar 
130.Strecker, J. et al. RNA-guided DNA insertion with CRISPR-associated transposases. Science 365, 48–53 (2019).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
131.Peters, J. E., Makarova, K. S., Shmakov, S. & Koonin, E. V. Recruitment of CRISPR-Cas systems by Tn7-like transposons. Proc. Natl Acad. Sci. USA 114, E7358–E7366 (2017).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
132.Wiegand, T. & Wiedenheft, B. CRISPR Surveillance Turns Transposon Taxi. CRISPR J. 3, 10–12 (2020).PubMed 
PubMed Central 
Article 

Google Scholar 
133.Hamilton, T. A. et al. Efficient inter-species conjugative transfer of a CRISPR nuclease for targeted bacterial killing. Nat. Commun. 10, 4544 (2019).PubMed 
PubMed Central 
Article 
CAS 

Google Scholar 
134.Price, V. J. et al. Enterococcus faecalis CRISPR-cas is a robust barrier to conjugative antibiotic resistance dissemination in the murine intestine. mSphere 4, e00464-19 (2019).PubMed 
PubMed Central 
Article 

Google Scholar 
135.Rodrigues, M., McBride, S. W., Hullahalli, K., Palmer, K. L. & Duerkop, B. A. Conjugative delivery of CRISPR-Cas9 for the selective depletion of antibiotic-resistant enterococci. Antimicrob. Agents Chemother. 63, e01454-19 (2019).PubMed 
PubMed Central 
Article 

Google Scholar 
136.Carraro, N. et al. Plasmid-like replication of a minimal streptococcal integrative and conjugative element. Microbiology 162, 622–632 (2016).CAS 
PubMed 
Article 

Google Scholar 
137.Brophy, J. A. N. et al. Engineered integrative and conjugative elements for efficient and inducible DNA transfer to undomesticated bacteria. Nat. Microbiol. 3, 1043–1053 (2018).CAS 
PubMed 
Article 

Google Scholar 
138.Bikard, D. et al. Exploiting CRISPR-Cas nucleases to produce sequence-specific antimicrobials. Nat. Biotechnol. 32, 1146–1150 (2014).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
139.Citorik, R. J., Mimee, M. & Lu, T. K. Sequence-specific antimicrobials using efficiently delivered RNA-guided nucleases. Nat. Biotechnol. 32, 1141–1145 (2014).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
140.Yosef, I., Manor, M., Kiro, R. & Qimron, U. Temperate and lytic bacteriophages programmed to sensitize and kill antibiotic-resistant bacteria. Proc. Natl Acad. Sci. USA 112, 7267–7272 (2015).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
141.Park, J. Y. et al. Genetic engineering of a temperate phage-based delivery system for CRISPR/Cas9 antimicrobials against Staphylococcus aureus. Sci. Rep. 7, 44929 (2017).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
142.Pazda, M., Kumirska, J., Stepnowski, P. & Mulkiewicz, E. Antibiotic resistance genes identified in wastewater treatment plant systems – a review. Sci. Total. Env. 697, 134023 (2019).CAS 
Article 

Google Scholar 
143.Kraemer, S. A., Ramachandran, A. & Perron, G. G. Antibiotic pollution in the environment: from microbial ecology to public policy. Microorganisms 7, 180 (2019).CAS 
PubMed Central 
Article 
PubMed 

Google Scholar 
144.Ram, G., Ross, H. F., Novick, R. P., Rodriguez-Pagan, I. & Jiang, D. Conversion of staphylococcal pathogenicity islands to CRISPR-carrying antibacterial agents that cure infections in mice. Nat. Biotechnol. 36, 971–976 (2018).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
145.Bier, E. & Nizet, V. Driving to safety: CRISPR-based genetic approaches to reducing antibiotic resistance. Trends Genet. https://doi.org/10.1016/j.tig.2021.02.007 (2021).Article 
PubMed 

Google Scholar 
146.Rossati, A. et al. Climate, environment and transmission of malaria. Infez. Med. 24, 93–104 (2016).PubMed 

Google Scholar 
147.Fontenille, D. & Powell, J. R. From anonymous to public enemy: how does a mosquito become a feared arbovirus vector? Pathogens 9, 265 (2020).PubMed Central 
Article 
PubMed 

Google Scholar 
148.Lidani, K. C. F. et al. Chagas disease: from discovery to a worldwide health problem. Front. Public Health 7, 166 (2019).PubMed 
PubMed Central 
Article 

Google Scholar 
149.Buscher, P., Cecchi, G., Jamonneau, V. & Priotto, G. Human African trypanosomiasis. Lancet 390, 2397–2409 (2017).PubMed 
Article 

Google Scholar 
150.Desjeux, P. Leishmaniasis: current situation and new perspectives. Comp. Immunol. Microbiol. Infect. Dis. 27, 305–318 (2004).CAS 
PubMed 
Article 

Google Scholar 
151.Saxena, V., Bolling, B. G. & Wang, T. West nile virus. Clin. Lab. Med. 37, 243–252 (2017).PubMed 
Article 

Google Scholar 
152.Simon, L. V., Kong, E. L. & Graham, C. in St. Louis Encephalitis (StatPearls, 2020).153.Feng, X. et al. Optimized CRISPR tools and site-directed transgenesis towards gene drive development in Culex quinquefasciatus mosquitoes. Nat. Commun. 12, 2960 (2021).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
154.Nepomichene, T. N., Andrianaivolambo, L., Boyer, S. & Bourgouin, C. Efficient method for establishing F1 progeny from wild populations of Anopheles mosquitoes. Malar. J. 16, 21 (2017).PubMed 
PubMed Central 
Article 

Google Scholar 
155.Marchand, R. P. A new cage for observing mating behavior of wild Anopheles gambiae in the laboratory. J. Am. Mosq. Control. Assoc. 1, 234–236 (1985).CAS 
PubMed 

Google Scholar 
156.Nunes-da-Fonseca, R., Berni, M., Tobias-Santos, V., Pane, A. & Araujo, H. M. Rhodnius prolixus: from classical physiology to modern developmental biology. Genesis https://doi.org/10.1002/dvg.22995 (2017).Article 
PubMed 

Google Scholar 
157.Chaverra-Rodriguez, D. et al. Targeted delivery of CRISPR-Cas9 ribonucleoprotein into arthropod ovaries for heritable germline gene editing. Nat. Commun. 9, 3008 (2018).PubMed 
PubMed Central 
Article 
CAS 

Google Scholar 
158.Macias, V. M. et al. Cas9-mediated gene-editing in the malaria mosquito anopheles stephensi by ReMOT Control. G3 10, 1353–1360 (2020).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
159.Chaverra-Rodriguez, D. et al. Germline mutagenesis of Nasonia vitripennis through ovarian delivery of CRISPR-Cas9 ribonucleoprotein. Insect Mol. Biol. 29, 569–577 (2020).CAS 
PubMed 
Article 

Google Scholar 
160.Heu, C. C., McCullough, F. M., Luan, J. & Rasgon, J. L. CRISPR-Cas9-based genome editing in the silverleaf whitefly (Bemisia tabaci). CRISPR J. 3, 89–96 (2020).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
161.Prowse, T. A., Adikusuma, F., Cassey, P., Thomas, P. & Ross, J. V. A Y-chromosome shredding gene drive for controlling pest vertebrate populations. eLife 8, e41873 (2019).PubMed 
PubMed Central 
Article 

Google Scholar 
162.Carballar-Lejarazu, R. & James, A. A. Population modification of Anopheline species to control malaria transmission. Pathog. Glob. Health 111, 424–435 (2017).PubMed 
Article 
PubMed Central 

Google Scholar 
163.Annas, G. J. et al. A code of ethics for gene drive research. CRISPR J. 4, 19–24 (2021).PubMed 
PubMed Central 
Article 

Google Scholar 
164.Bier, E. & Sober, E. Gene editing and the war against malaria. Am. Sci. 108, 162–169 (2020).Article 

Google Scholar 
165.Long, K. C. et al. Core commitments for field trials of gene drive organisms. Science 370, 1417–1419 (2020).CAS 
PubMed 
Article 

Google Scholar 
166.Kormos, A. et al. Application of the relationship-based model to engagement for field trials of genetically engineered malaria vectors.Am. J. Trop. Med. Hyg. 104, 805–811 (2020).PubMed Central 
PubMed 

Google Scholar 
167.World Health Organization. Guidance framework for testing of genetically modified mosquitoes. WHO http://apps.who.int/iris/bitstream/10665/127889/1/9789241507486_eng.pdf (2014).168.Smith, D. L., McKenzie, F. E., Snow, R. W. & Hay, S. I. Revisiting the basic reproductive number for malaria and its implications for malaria control. PLoS Biol. 5, e42 (2007).PubMed 
PubMed Central 
Article 
CAS 

Google Scholar 
169.Brauer, F., Castillo-Chavez, C., Mubayi, A. & Towers, S. Some models for epidemics of vector-transmitted diseases. Infect. Dis. Model. 1, 79–87 (2016).PubMed 
PubMed Central 

Google Scholar 
170.Deredec, A., Godfray, H. C. & Burt, A. Requirements for effective malaria control with homing endonuclease genes. Proc. Natl Acad. Sci. USA 108, E874–E880 (2011).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
171.Escalante, A. A. & Pacheco, M. A. Malaria molecular epidemiology: an evolutionary genetics perspective. Microbiol. Spectr. https://doi.org/10.1128/microbiolspec.AME-0010-2019 (2019).Article 
PubMed 
PubMed Central 

Google Scholar 
172.Selvaraj, P. et al. Vector genetics, insecticide resistance and gene drives: An agent-based modeling approach to evaluate malaria transmission and elimination. PLoS Comput. Biol. 16, e1008121 (2020).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar  More