Philippa Kaur

Ackerly, D. D. et al. The geography of climate change: Implications for conservation biogeography. Divers. Distrib. 16, 476–487 (2010).
Google Scholar 
Lawton, J. H. Are there general laws in ecology?. Oikos 84, 177–192 (1999).
Google Scholar 
Simberloff, D. Community ecology: Is it time to move on?. Am. Nat. 163, 787–799 (2004).PubMed 

Google Scholar 
Ricklefs, R. E. Disintegration of the ecological community. Am. Nat. 172, 741–750 (2008).PubMed 

Google Scholar 
McGill, B. J. et al. Species abundance distributions: Moving beyond single prediction theories to integration within an ecological framework. Ecol. Lett. 10, 995–1015 (2007).PubMed 

Google Scholar 
Paine, R. T. The Pisaster-Tegula interaction: Prey patches, predator food preference, and intertidal community structure. Ecology 50, 950–961 (1969).
Google Scholar 
Dayton, P. K. Competition, disturbance, and community organization: The provision and subsequent utilization of space in a rocky intertidal community. Ecol. Monogr. 41, 351–389 (1971).
Google Scholar 
Hairston, N. G., Smith, F. E. & Slobodkin, L. B. Community structure, population control, and competition. Am. Nat. 94, 421–425 (1960).
Google Scholar 
Brose, U., Berlow, E. L. & Martinez, N. D. Scaling up keystone effects from simple to complex ecological networks. Ecol. Lett. 8, 1317–1325 (2005).
Google Scholar 
Stouffer, D. B. & Bascompte, J. Understanding food-web persistence from local to global scales. Ecol. Lett. 13, 154–161 (2010).PubMed 

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

Google Scholar 
Leibold, M. A. et al. The metacommunity concept: A framework for multi-scale community ecology. Ecol. Lett. 7, 601–613 (2004).
Google Scholar 
Holyoak, M., Leibold, M. A. & Holt, R. D. Metacommunities: Spatial Dynamics and Ecological Communities (University of Chicago Press, 2005).
Google Scholar 
Gotelli, N. J. Macroecological signals of species interactions in the Danish avifauna. Proc. Natl. Acad. Sci. USA. 107, 5030–5035 (2010).ADS 
CAS 
PubMed 
PubMed Central 

Google Scholar 
Gouhier, T. C., Guichard, F. & Menge, B. A. Ecological processes can synchronize marine population dynamics over continental scales. Proc. Natl. Acad. Sci. 107, 8281–8286 (2010).ADS 
CAS 
PubMed 
PubMed Central 

Google Scholar 
Salois, S. L., Gouhier, T. C. & Menge, B. A. The multifactorial effects of dispersal on biodiversity in environmentally forced metacommunities. Ecosphere 9, e02357 (2018).
Google Scholar 
Helmuth, B. et al. Beyond long-term averages: Making biological sense of a rapidly changing world. Clim. Change Responses 1, 6 (2014).
Google Scholar 
Pacifici, M. et al. Assessing species vulnerability to climate change. Nat. Clim. Change 5, 215 (2015).ADS 

Google Scholar 
Gunderson, A. R., Armstrong, E. J. & Stillman, J. H. Multiple stressors in a changing world: The need for an improved perspective on physiological responses to the dynamic marine environment. Annu. Rev. Mar. Sci. 8, 357–378 (2016).ADS 

Google Scholar 
Rilov, G. et al. Adaptive marine conservation planning in the face of climate change: What can we learn from physiological, ecological and genetic studies?. Glob. Ecol. Conserv. 17, e00566 (2019).
Google Scholar 
Hampe, A. Bioclimate envelope models: What they detect and what they hide. Glob. Ecol. Biogeogr. 13, 469–471 (2004).
Google Scholar 
Pearson, R. G. & Dawson, T. P. Predicting the impacts of climate change on the distribution of species: Are bioclimate envelope models useful?. Glob. Ecol. Biogeogr. 12, 361–371 (2003).
Google Scholar 
Gilman, S. E., Urban, M. C., Tewksbury, J., Gilchrist, G. W. & Holt, R. D. A framework for community interactions under climate change. Trends Ecol. Evol. 25, 325–331 (2010).PubMed 

Google Scholar 
Davis, A. J., Jenkinson, L. S., Lawton, J. H., Shorrocks, B. & Wood, S. Making mistakes when predicting shifts in species range in response to global warming. Nature 391, 783–786 (1998).ADS 
CAS 
PubMed 

Google Scholar 
Araújo, M. B. & Peterson, A. T. Uses and misuses of bioclimatic envelope modeling. Ecology 93, 1527–1539 (2012).PubMed 

Google Scholar 
Helmuth, B. et al. Mosaic patterns of thermal stress in the rocky intertidal zone: Implications for climate change. Ecol. Monogr. 76, 461–479 (2006).
Google Scholar 
Helmuth, B., Mieszkowska, N., Moore, P. & Hawkins, S. J. Living on the edge of two changing worlds: Forecasting the responses of rocky intertidal ecosystems to climate change. Annu. Rev. Ecol. Evol. Syst. 37, 373–404 (2006).
Google Scholar 
Vasseur, D. A. et al. Synchronous dynamics of zooplankton competitors prevail in temperate lake ecosystems. Proc. R. Soc. B Biol. Sci. 281, 20140633 (2014).
Google Scholar 
Dillon, M. E. et al. Life in the frequency domain: The biological impacts of changes in climate variability at multiple time scales. Integr. Comp. Biol. icw024 (2016).Kroeker, K. J. et al. Interacting environmental mosaics drive geographic variation in mussel performance and predation vulnerability. Ecol. Lett. 19, 771–779 (2016).PubMed 

Google Scholar 
Seabra, R., Wethey, D. S., Santos, A. M. & Lima, F. P. Understanding complex biogeographic responses to climate change. Sci. Rep. 5, (2015).Di Cecco, G. J. & Gouhier, T. C. Increased spatial and temporal autocorrelation of temperature under climate change. Sci. Rep. 8, 14850 (2018).ADS 
PubMed 
PubMed Central 

Google Scholar 
Keppel, G. et al. Refugia: identifying and understanding safe havens for biodiversity under climate change. Glob. Ecol. Biogeogr. 21, 393–404 (2012).
Google Scholar 
Morelli, T. L. et al. Climate change refugia and habitat connectivity promote species persistence. Clim. Change Responses 4, 8 (2017).
Google Scholar 
Bates, A. E. et al. Biologists ignore ocean weather at their peril. Nature 560, 299–301 (2018).ADS 
CAS 
PubMed 

Google Scholar 
Molinos, J. G. et al. Climate velocity and the future global redistribution of marine biodiversity. Nat. Clim. Change (2015).Levins, R. Some demographic and genetic consequences of environmental heterogeneity for biological control. Bull. Entomol. Soc. Am. 15, 237–240 (1969).
Google Scholar 
Brown, J. H. & Kodric-Brown, A. Turnover rates in insular biogeography: Effect of immigration on extinction. Ecology 58, 445–449 (1977).
Google Scholar 
Pulliam, H. R. Sources, sinks, and population regulation. Am. Nat. 132, 652–661 (1988).
Google Scholar 
Hannah, L. et al. Fine-grain modeling of species’ response to climate change: Holdouts, stepping-stones, and microrefugia. Trends Ecol. Evol. 29, 390–397 (2014).PubMed 

Google Scholar 
Barceló, C., Ciannelli, L. & Brodeur, R. D. Pelagic marine refugia and climatically sensitive areas in an eastern boundary current upwelling system. Glob. Change Biol. 24, 668–680 (2018).ADS 

Google Scholar 
Dong, Y. et al. Untangling the roles of microclimate, behaviour and physiological polymorphism in governing vulnerability of intertidal snails to heat stress. Proc. R. Soc. B Biol. Sci. 284, 20162367 (2017).
Google Scholar 
Smit, A. J. et al. A coastal seawater temperature dataset for biogeographical studies: large biases between in situ and remotely-sensed data sets around the Coast of South Africa. PLoS ONE 8, e81944 (2013).ADS 
PubMed 
PubMed Central 

Google Scholar 
Castro, S. L., Monzon, L. A., Wick, G. A., Lewis, R. D. & Beylkin, G. Subpixel variability and quality assessment of satellite sea surface temperature data using a novel High Resolution Multistage Spectral Interpolation (HRMSI) technique. Remote Sens. Environ. 217, 292–308 (2018).ADS 

Google Scholar 
Rahaghi, A. I., Lemmin, U. & Barry, D. A. Surface water temperature heterogeneity at subpixel satellite scales and its effect on the surface cooling estimates of a large lake: Airborne remote sensing results from Lake Geneva. J. Geophys. Res. Oceans 124, 635–651 (2019).ADS 

Google Scholar 
Pfister, C. A., Wootton, J. T. & Neufeld, C. J. The relative roles of coastal and oceanic processes in determining physical and chemical characteristics of an intensively sampled nearshore system. Limnol. Oceanogr. 52, 1767–1775 (2007).ADS 
CAS 

Google Scholar 
Meneghesso, C. et al. Remotely-sensed L4 SST underestimates the thermal fingerprint of coastal upwelling. Remote Sens. Environ. 237, 111588 (2020).ADS 

Google Scholar 
Leichter, J. J., Helmuth, B. & Fischer, A. M. Variation beneath the surface: Quantifying complex thermal environments on coral reefs in the Caribbean, Bahamas and Florida. J. Mar. Res. 64, 563–588 (2006).
Google Scholar 
Castillo, K. D. & Lima, F. P. Comparison of in situ and satellite-derived (MODIS-Aqua/Terra) methods for assessing temperatures on coral reefs. Limnol. Oceanogr. Methods 8, 107–117 (2010).
Google Scholar 
Wyatt, A. S. J. et al. Heat accumulation on coral reefs mitigated by internal waves. Nat. Geosci. 13, 28–34 (2020).ADS 
CAS 

Google Scholar 
Lourenço, C. R. et al. Upwelling areas as climate change refugia for the distribution and genetic diversity of a marine macroalga. J. Biogeogr. 43, 1595–1607 (2016).
Google Scholar 
Seabra, R. et al. Reduced nearshore warming associated with eastern boundary upwelling systems. Front. Mar. Sci. 6, (2019).Randall, C. J., Toth, L. T., Leichter, J. J., Maté, J. L. & Aronson, R. B. Upwelling buffers climate change impacts on coral reefs of the eastern tropical Pacific. Ecology 101, (2020).Varela, R., Lima, F. P., Seabra, R., Meneghesso, C. & Gómez-Gesteira, M. Coastal warming and wind-driven upwelling: A global analysis. Sci. Total Environ. 639, 1501–1511 (2018).ADS 
CAS 
PubMed 

Google Scholar 
Schulz, K. G., Hartley, S. & Eyre, B. Upwelling amplifies ocean acidification on the east Australian shelf: Implications for marine ecosystems. Front. Mar. Sci. 6, (2019).Connell, J. H. The influence of interspecific competition and other factors on the distribution of the barnacle Chthamalus stellatus. Ecology 42, 710–723 (1961).
Google Scholar 
Somero, G. N. Linking biogeography to physiology: Evolutionary and acclimatory adjustments of thermal limits. Front. Zool. 2, 1 (2005).PubMed 
PubMed Central 

Google Scholar 
Sydeman, W. J. et al. Climate change and wind intensification in coastal upwelling ecosystems. Science 345, 77–80 (2014).ADS 
CAS 
PubMed 

Google Scholar 
Sweijd, N. A. & Smit, A. J. Trends in sea surface temperature and chlorophyll-a in the seven African Large Marine Ecosystems. Environ. Dev. 36, 100585 (2020).
Google Scholar 
Wang, D., Gouhier, T. C., Menge, B. A. & Ganguly, A. R. Intensification and spatial homogenization of coastal upwelling under climate change. Nature 518, 390–394 (2015).ADS 
CAS 
PubMed 

Google Scholar 
Lima, F. P. & Wethey, D. S. Robolimpets: measuring intertidal body temperatures using biomimetic loggers: Biomimetic loggers for intertidal temperatures. Limnol. Oceanogr. Methods 7, 347–353 (2009).
Google Scholar 
Judge, R., Choi, F. & Helmuth, B. Recent advances in data logging for intertidal ecology. Front. Ecol. Evol. 6, (2018).Harley, C. D. G. & Helmuth, B. S. T. Local- and regional-scale effects of wave exposure, thermal stress, and absolute versus effective shore level on patterns of intertidal zonation. Limnol. Oceanogr. 48, 1498–1508 (2003).ADS 

Google Scholar 
Seabra, R., Wethey, D. S., Santos, A. M., Gomes, F. & Lima, F. P. Equatorial range limits of an intertidal ectotherm are more linked to water than air temperature. Glob. Change Biol. 22, 3320–3331 (2016).ADS 

Google Scholar 
Lima, F. P. et al. Loss of thermal refugia near equatorial range limits. Glob. Change Biol. 22, 254–263 (2016).ADS 

Google Scholar 
Tapia, F. J. et al. Thermal indices of upwelling effects on inner-shelf habitats. Prog. Oceanogr. 83, 278–287 (2009).ADS 

Google Scholar 
Freeman, E. et al. ICOADS release 3.0: A major update to the historical marine climate record. Int. J. Climatol. 37, 2211–2232 (2017).
Google Scholar 
Lemos, R. T. & Pires, H. O. The upwelling regime off the West Portuguese Coast, 1941–2000. Int. J. Climatol. 24, 511–524 (2004).
Google Scholar 
Seabra, R., Wethey, D. S., Santos, A. M. & Lima, F. P. Side matters: Microhabitat influence on intertidal heat stress over a large geographical scale. J. Exp. Mar. Biol. Ecol. 400, 200–208 (2011).
Google Scholar 
Legendre, P. Species associations: The Kendall coefficient of concordance revisited. J. Agric. Biol. Environ. Stat. 10, 226–245 (2005).
Google Scholar 
Gouhier, T. C. & Guichard, F. Synchrony: Quantifying variability in space and time. Methods Ecol. Evol. 5, 524–533 (2014).
Google Scholar 
Torrence, C. & Compo, G. P. A practical guide to wavelet analysis. Bull. Am. Meteorol. Soc. 79, 61–78 (1998).ADS 

Google Scholar 
Cazelles, B. et al. Wavelet analysis of ecological time series. Oecologia 156, 287–304 (2008).ADS 
PubMed 

Google Scholar 
Recknagel, F., Ostrovsky, I., Cao, H., Zohary, T. & Zhang, X. Ecological relationships, thresholds and time-lags determining phytoplankton community dynamics of Lake Kinneret, Israel elucidated by evolutionary computation and wavelets. Ecol. Model. 255, 70–86 (2013).CAS 

Google Scholar 
Mislan, K. A. S., Helmuth, B. & Wethey, D. S. Geographical variation in climatic sensitivity of intertidal mussel zonation: Biogeography of climatic sensitivity. Glob. Ecol. Biogeogr. 23, 744–756 (2014).
Google Scholar 
Grinsted, A., Moore, J. C. & Jevrejeva, S. Application of the cross wavelet transform and wavelet coherence to geophysical time series. Nonlinear Process. Geophys. 11, 561–566 (2004).ADS 

Google Scholar 
Cazelles, B. & Stone, L. Detection of imperfect population synchrony in an uncertain world. J. Anim. Ecol. 72, 953–968 (2003).
Google Scholar 
Keppel, G. et al. The capacity of refugia for conservation planning under climate change. Front. Ecol. Environ. 13, 106–112 (2015).
Google Scholar 
Vasseur, D. A. et al. Increased temperature variation poses a greater risk to species than climate warming. Proc. R. Soc. B Biol. Sci. 281, 20132612–20132612 (2014).
Google Scholar 
Potter, K. A., Woods, H. A. & Pincebourde, S. Microclimatic challenges in global change biology. Glob. Change Biol. 19, 2932–2939 (2013).ADS 

Google Scholar 
Sandel, B. et al. The influence of late quaternary climate-change velocity on species endemism. Science 334, 660–664 (2011).ADS 
CAS 
PubMed 

Google Scholar 
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).ADS 
CAS 
PubMed 

Google Scholar 
Araújo, M. B. & Luoto, M. The importance of biotic interactions for modelling species distributions under climate change. Glob. Ecol. Biogeogr. 16, 743–753 (2007).
Google Scholar 
Morelli, T. L. et al. Managing climate change refugia for climate adaptation. PLoS ONE 11, e0159909 (2016).PubMed 
PubMed Central 

Google Scholar 
Stenseth, N. Ecological effects of climate fluctuations. Science 297, 1292–1296 (2002).ADS 
CAS 
PubMed 

Google Scholar 
Zellweger, F., De Frenne, P., Lenoir, J., Rocchini, D. & Coomes, D. Advances in microclimate ecology arising from remote sensing. Trends Ecol. Evol. 34, 327–341 (2019).PubMed 

Google Scholar 
Helmuth, B. et al. Long-term, high frequency in situ measurements of intertidal mussel bed temperatures using biomimetic sensors. Sci. Data 3, 160087 (2016).MathSciNet 
PubMed 
PubMed Central 

Google Scholar 
Wikelski, M. & Cooke, S. J. Conservation physiology. Trends Ecol. Evol. 21, 38–46 (2006).PubMed 

Google Scholar 
Helmuth, B. S. T. & Hofmann, G. E. Microhabitats, thermal heterogeneity, and patterns of physiological stress in the rocky intertidal zone. Biol. Bull. 201, 374–384 (2001).CAS 
PubMed 

Google Scholar 
Kearney, M. Habitat, environment and niche: What are we modelling?. Oikos 115, 186–191 (2006).
Google Scholar 
Ashcroft, M. B. Identifying refugia from climate change. J. Biogeogr. 37, 1407–1413 (2010).
Google Scholar 
Maggs, C. A. et al. Evaluating signatures of glacial refugia for North Atlantic Benthic Marine Taxa. Ecology 89, S108–S122 (2008).PubMed 

Google Scholar 
Bennett, K. & Provan, J. What do we mean by ‘refugia’?. Quat. Sci. Rev. 27, 2449–2455 (2008).ADS 

Google Scholar 
Ashcroft, M. B., Chisholm, L. A. & French, K. O. Climate change at the landscape scale: predicting fine-grained spatial heterogeneity in warming and potential refugia for vegetation. Glob. Change Biol. 15, 656–667 (2009).ADS 

Google Scholar 
Hofmann, G. E. et al. High-frequency dynamics of ocean pH: A multi-ecosystem comparison. PLoS ONE 6, e28983 (2011).ADS 
CAS 
PubMed 
PubMed Central 

Google Scholar 
Bakun, A. et al. Anticipated Effects of Climate Change on Coastal Upwelling Ecosystems. Curr. Clim. Change Rep. 1, 85–93 (2015).
Google Scholar 
Iles, A. C. et al. Climate-driven trends and ecological implications of event-scale upwelling in the California Current System. Glob. Change Biol. 18, 783–796 (2012).ADS 

Google Scholar 
García-Reyes, M. et al. Under pressure: Climate change, upwelling, and eastern boundary upwelling ecosystems. Front. Mar. Sci. 2, (2015).Liebhold, A., Koenig, W. D. & Bjørnstad, O. N. Spatial synchrony in population dynamics. Annu. Rev. Ecol. Evol. Syst. 467–490 (2004).Amarasekare, P. & Nisbet, R. M. Spatial heterogeneity, source-sink dynamics, and the local coexistence of competing species. Am. Nat. 158, 572–584 (2001).CAS 
PubMed 

Google Scholar 
Adler, F. R. & Nuernberger, B. Persistence in patchy irregular landscapes. Theor. Popul. Biol. 45, 41–75 (1994).MATH 

Google Scholar 
Rykaczewski, R. R. et al. Poleward displacement of coastal upwelling-favorable winds in the ocean’s eastern boundary currents through the 21st century. Geophys. Res. Lett. 42, 6424–6431 (2015).ADS 

Google Scholar 
Varela, R., Rodríguez-Díaz, L., de Castro, M. & Gómez-Gesteira, M. Influence of Canary upwelling system on coastal SST warming along the 21st century using CMIP6 GCMs. Glob. Planet. Change 208, 103692 (2022).
Google Scholar 
Ocean deoxygenation: everyone’s problem. Causes, impacts, consequences and solutions. (IUCN, International Union for Conservation of Nature, 2019). https://doi.org/10.2305/IUCN.CH.2019.13.en.Howard, E. M. et al. Climate-driven aerobic habitat loss in the California Current System. Sci. Adv. 6, eaay3188 (2020).ADS 
PubMed 
PubMed Central 

Google Scholar 
Iles, A. C. Toward predicting community-level effects of climate: Relative temperature scaling of metabolic and ingestion rates. Ecology 95, 2657–2668 (2014).
Google Scholar 
Harris, R. M. B. et al. Biological responses to the press and pulse of climate trends and extreme events. Nat. Clim. Change 8, 579 (2018).ADS 

Google Scholar 
Salinas, S., Irvine, S. E., Schertzing, C. L., Golden, S. Q. & Munch, S. B. Trait variation in extreme thermal environments under constant and fluctuating temperatures. Philos. Trans. R. Soc. B Biol. Sci. 374, 20180177 (2019).
Google Scholar 
Fischer, E. M. & Knutti, R. Anthropogenic contribution to global occurrence of heavy-precipitation and high-temperature extremes. Nat. Clim. Change 5, 560–564 (2015).ADS 

Google Scholar 
Buckley, L. B. & Huey, R. B. Temperature extremes: geographic patterns, recent changes, and implications for organismal vulnerabilities. Glob. Change Biol. 22, 3829–3842 (2016).ADS 

Google Scholar  More

Bascompte, J. & Jordano, P. Mutualistic Networks (Princeton Univ. Press, Princeton, NJ, 2013).Cordeiro, N. J. & Howe, H. F. Forest fragmentation severs mutualism between seed dispersers and an endemic African tree. Proc. Natl Acad. Sci. USA 100, 14052–14056 (2003).CAS 
PubMed 
PubMed Central 

Google Scholar 
Wandrag, E. M., Dunham, A. E., Duncan, R. P. & Rogers, H. S. Seed dispersal increases local species richness and reduces spatial turnover of tropical tree seedlings. Proc. Natl Acad. Sci. USA 114, 10689–10694 (2017).CAS 
PubMed 
PubMed Central 

Google Scholar 
Fahrig, L. Effects of habitat fragmentation on biodiversity. Annu. Rev. Ecol. Evol. Syst. 34, 487–515 (2003).
Google Scholar 
Fahrig, L. Ecological responses to habitat fragmentation per se. Annu. Rev. Ecol. Evol. Syst. 48, 1–23 (2017).
Google Scholar 
Haddad, N. M. et al. Habitat fragmentation and its lasting impact on Earth’s ecosystems. Sci. Adv. 1, e1500052 (2015).PubMed 
PubMed Central 

Google Scholar 
Fricke, E. C. & Svenning, J. C. Accelerating homogenization of the global plant-frugivore meta-network. Nature 585, 74–78 (2020).CAS 
PubMed 

Google Scholar 
Fontúrbel, F. E. et al. Meta-analysis of anthropogenic habitat disturbance effects on animal-mediated seed dispersal. Glob. Change Biol. 21, 3951–3960 (2015).
Google Scholar 
Poisot, T. et al. Global knowledge gaps in species interaction networks data. J. Biogeogr. 48, 1552–1563 (2021).
Google Scholar 
Hortal, J. et al. Seven shortfalls that beset large-scale knowledge of biodiversity. Annu. Rev. Ecol. Evol. Syst. 46, 523–549 (2015).
Google Scholar 
Magrach, A., Laurance, W. F., Larrinaga, A. R. & Santamaria, L. Meta-analysis of the effects of forest fragmentation on interspecific interactions. Conserv. Biol. 28, 1342–1348 (2014).PubMed 

Google Scholar 
Pocock, M. J. O., Evans, D. M. & Memmott, J. The robustness and restoration of a network of ecological networks. Science 335, 973–977 (2012).CAS 
PubMed 

Google Scholar 
Tylianakis, J. M., Didham, R. K., Bascompte, J. & Wardle, D. A. Global change and species interactions in terrestrial ecosystems. Ecol. Lett. 11, 1351–1363 (2008).PubMed 

Google Scholar 
de Assis Bomfim, J., Guimarães, P. R. Jr., Peres, C. A., Carvalho, G. & Cazetta, E. Local extinctions of obligate frugivores and patch size reduction disrupt the structure of seed dispersal networks. Ecography 41, 1899–1909 (2018).
Google Scholar 
Emer, C. et al. Seed dispersal networks in tropical forest fragments: Area effects, remnant species, and interaction diversity. Biotropica 52, 81–89 (2020).
Google Scholar 
Evans, D. M., Pocock, M. J. O. & Memmott, J. The robustness of a network of ecological networks to habitat loss. Ecol. Lett. 16, 844–852 (2013).PubMed 

Google Scholar 
Grass, I., Jauker, B., Steffan-Dewenter, I., Tscharntke, T. & Jauker, F. Past and potential future effects of habitat fragmentation on structure and stability of plant-pollinator and host-parasitoid networks. Nat. Ecol. Evol. 2, 1408–1417 (2018).PubMed 

Google Scholar 
Neff, F. M. et al. Changes in plant-herbivore network structure and robustness along land-use intensity gradients in grasslands and forests. Sci. Adv. 7, eabf3985 (2021).PubMed 
PubMed Central 

Google Scholar 
Dunne, J. A., Williams, R. J. & Martinez, N. D. Network structure and biodiversity loss in food webs: robustness increases with connectance. Ecol. Lett. 5, 558–567 (2002).
Google Scholar 
James, A., Pitchford, J. W. & Plank, M. J. Disentangling nestedness from models of ecological complexity. Nature 487, 227–230 (2012).CAS 
PubMed 

Google Scholar 
Jordano, P. Patterns of mutualistic interactions in pollination and seed dispersal: connectance, dependence asymmetries, and coevolution. Am. Nat. 129, 657–677 (1987).
Google Scholar 
Vieira, M. C. & Almeida-Neto, M. A simple stochastic model for complex coextinctions in mutualistic networks: robustness decreases with connectance. Ecol. Lett. 18, 144–152 (2015).PubMed 

Google Scholar 
Olesen, J. M., Bascompte, J., Dupont, Y. L. & Jordano, P. The modularity of pollination networks. Proc. Natl Acad. Sci. USA 104, 19891–19896 (2007).CAS 
PubMed 
PubMed Central 

Google Scholar 
Gilarranz, L. J., Rayfield, B., Liñán-Cembrano, G., Bascompte, J. & Gonzalez, A. Effects of network modularity on the spread of perturbation impact in experimental metapopulations. Science 357, 199–201 (2017).CAS 
PubMed 

Google Scholar 
Liu, H. et al. Geographic variation in the robustness of pollination networks is mediated by modularity. Glob. Ecol. Biogeogr. 30, 1447–1460 (2021).
Google Scholar 
Bascompte, J., Jordano, P., Melián, C. J. & Olesen, J. M. The nested assembly of plant-animal mutualistic networks. Proc. Natl Acad. Sci. USA 100, 9383–9387 (2003).CAS 
PubMed 
PubMed Central 

Google Scholar 
Bastolla, U. et al. The architecture of mutualistic networks minimizes competition and increases biodiversity. Nature 458, 1018–1020 (2009).CAS 
PubMed 

Google Scholar 
Memmott, J., Waser, N. M. & Price, M. V. Tolerance of pollination networks to species extinctions. Proc. R. Soc. B. 271, 2605–2611 (2004).PubMed 
PubMed Central 

Google Scholar 
Delmas, E. et al. Analysing ecological networks of species interactions. Biol. Rev. 9, 16–36 (2019).
Google Scholar 
Fortuna, M. A. et al. Nestedness versus modularity in ecological networks: two sides of the same coin? J. Anim. Ecol. 79, 811–817 (2010).PubMed 

Google Scholar 
Song, C., Rohr, R. P. & Saavedra, S. Why are some plant-pollinator networks more nested than others? J. Anim. Ecol. 86, 1417–1424 (2017).PubMed 

Google Scholar 
Schleuning, M., Böhning-Gaese, K., Dehling, D. M. & Burns, K. C. At a loss for birds: insularity increases asymmetry in seed-dispersal networks. Glob. Ecol. Biogeogr. 23, 385–394 (2014).
Google Scholar 
Aizen, M. A., Sabatino, M. & Tylianakis, J. M. Specialization and rarity predict nonrandom loss of interactions from mutualist networks. Science 335, 1486–1489 (2012).CAS 
PubMed 

Google Scholar 
Fortuna, M. A. & Bascompte, J. Habitat loss and the structure of plant-animal mutualistic networks. Ecol. Lett. 9, 278–283 (2006).
Google Scholar 
Spiesman, B. J. & Inouye, B. D. Habitat loss alters the architecture of plant-pollinator interaction networks. Ecology 94, 2688–2696 (2013).PubMed 

Google Scholar 
Traveset, A. et al. Bird-flower visitation networks in the Galápagos unveil a widespread interaction release. Nat. Commun. 6, 6376 (2015).CAS 
PubMed 

Google Scholar 
Thébault, E. & Fontaine, C. Stability of ecological communities and the architecture of mutualistic and trophic networks. Science 329, 853–856 (2010).PubMed 

Google Scholar 
Monteiro, E. C. S., Pizo, M. A., Vancine, M. H. & Ribeiro, M. C. Forest cover and connectivity have pervasive effects on the maintenance of evolutionary distinct interactions in seed dispersal networks. Oikos 2022, e08240 (2022).
Google Scholar 
Whittaker, R. J., Fernández-Palacios, J. M., Matthews, T. J., Borregaard, M. K. & Triantis, K. A. Island biogeography: taking the long view of nature’s laboratories. Science 357, eaam8326 (2017).PubMed 

Google Scholar 
Vizentin-Bugoni, J. et al. Structure, spatial dynamics, and stability of novel seed dispersal mutualistic networks in Hawai’i. Science 364, 78–82 (2019).CAS 
PubMed 

Google Scholar 
Diamond, J. Dammed experiments! Science 294, 1847–1848 (2001).CAS 
PubMed 

Google Scholar 
Jones, I. L., Bunnefeld, N., Jump, A. S., Peres, C. A. & Dent, D. H. Extinction debt on reservoir land-bridge islands. Biol. Conserv. 199, 75–83 (2016).
Google Scholar 
Wu, J., Huang, J., Han, X., Xie, Z. & Gao, X. Three-Gorges dam–experiment in habitat Fragmentation? Science 300, 1239–1240 (2003).CAS 
PubMed 

Google Scholar 
Wilson, M. C. et al. Habitat fragmentation and biodiversity conservation: key findings and future challenges. Landsc. Ecol. 31, 219–227 (2016).
Google Scholar 
Trøjelsgaard, K. et al. Island biogeography of mutualistic interaction networks. J. Biogeogr. 40, 2020–2031 (2013).
Google Scholar 
Emer, C., Venticinque, E. M. & Fonseca, C. R. Effects of dam-induced landscape fragmentation on amazonian ant-plant mutualistic networks. Conserv. Biol. 27, 763–773 (2013).PubMed 

Google Scholar 
Zhu, C. et al. Arboreal camera trapping: a reliable tool to monitor plant-frugivore interactions in the trees on large scales. Remote Sens. Ecol. Conserv. 8, 92–104 (2022).
Google Scholar 
Zhu, C., Li, W., Wang, D., Ding, P. & Si, X. Plant-frugivore interactions revealed by arboreal camera trapping. Front. Ecol. Environ. 19, 149–151 (2021).
Google Scholar 
Galiana, N. et al. The spatial scaling of species interaction networks. Nat. Ecol. Evol. 2, 782–790 (2018).PubMed 

Google Scholar 
Hanski, I., Zurita, G. A., Bellocq, M. I. & Rybicki, J. Species-fragmented area relationship. Proc. Natl Acad. Sci. USA 110, 12715–12720 (2013).CAS 
PubMed 
PubMed Central 

Google Scholar 
Sugiura, S. Species interactions-area relationships: biological invasions and network structure in relation to island area. Proc. R. Soc. B. 277, 1807–1815 (2010).PubMed 
PubMed Central 

Google Scholar 
Galiana, N. et al. Ecological network complexity scales with area. Nat. Ecol. Evol. 6, 307–314 (2022).PubMed 

Google Scholar 
Santos, M., Cagnolo, L., Roslin, T., Marrero, H. J. & Vázquez, D. P. Landscape connectivity explains interaction network patterns at multiple scales. Ecology 100, e02883 (2019).PubMed 

Google Scholar 
Si, X., Pimm, S. L., Russell, G. J. & Ding, P. Turnover of breeding bird communities on islands in an inundated lake. J. Biogeogr. 41, 2283–2292 (2014).
Google Scholar 
Si, X. et al. Functional and phylogenetic structure of island bird communities. J. Anim. Ecol. 86, 532–542 (2017).PubMed 

Google Scholar 
Rosenfeld, J. S. Functional redundancy in ecology and conservation. Oikos 98, 156–162 (2002).
Google Scholar 
Sebastián-González, E. Drivers of species’ role in avian seed-dispersal mutualistic networks. J. Anim. Ecol. 86, 878–887 (2017).PubMed 

Google Scholar 
Donoso, I. et al. Downsizing of animal communities triggers stronger functional than structural decay in seed-dispersal networks. Nat. Commun. 11, 1582 (2020).CAS 
PubMed 
PubMed Central 

Google Scholar 
Kaiser-Bunbury, C. N., Muff, S., Memmott, J., Müller, C. B. & Caflisch, A. The robustness of pollination networks to the loss of species and interactions: a quantitative approach incorporating pollinator behaviour. Ecol. Lett. 13, 442–452 (2010).PubMed 

Google Scholar 
Dalsgaard, B. et al. Opposed latitudinal patterns of network-derived and dietary specialization in avian plant-frugivore interaction systems. Ecography 40, 1395–1401 (2017).
Google Scholar 
Borrvall, C., Ebenman, B. & Jonsson, T. Biodiversity lessens the risk of cascading extinction in model food webs. Ecol. Lett. 3, 131–136 (2000).
Google Scholar 
Liao, J. et al. Robustness of metacommunities with omnivory to habitat destruction: disentangling patch fragmentation from patch loss. Ecology 98, 1631–1639 (2017).PubMed 

Google Scholar 
Rumeu, B. et al. Predicting the consequences of disperser extinction: richness matters the most when abundance is low. Funct. Ecol. 31, 1910–1920 (2017).
Google Scholar 
Wong, B. B. M. & Candolin, U. Behavioral responses to changing environments. Behav. Ecol. 26, 665–673 (2015).
Google Scholar 
Betts, M. G. et al. Extinction filters mediate the global effects of habitat fragmentation on animals. Science 366, 1236–1239 (2019).CAS 
PubMed 

Google Scholar 
Menke, S., Böhning-Gaese, K. & Schleuning, M. Plant-frugivore networks are less specialized and more robust at forest–farmland edges than in the interior of a tropical forest. Oikos 121, 1553–1566 (2012).
Google Scholar 
Redhead, J. W. et al. Potential landscape-scale pollinator networks across Great Britain: structure, stability and influence of agricultural land cover. Ecol. Lett. 21, 1821–1832 (2018).PubMed 

Google Scholar 
Si, X. et al. The importance of accounting for imperfect detection when estimating functional and phylogenetic community structure. Ecology 99, 2103–2112 (2018).PubMed 

Google Scholar 
Schoereder, J. H. et al. Should we use proportional sampling for species-area studies? J. Biogeogr. 31, 1219–1226 (2004).
Google Scholar 
Liu, J. et al. The distribution of plants and seed dispersers in response to habitat fragmentation in an artificial island archipelago. J. Biogeogr. 46, 1152–1162 (2019).
Google Scholar 
Olson, E. R. et al. Arboreal camera trapping for the Critically Endangered greater bamboo lemur Prolemur simus. Oryx 46, 593–597 (2012).
Google Scholar 
Li, H.-D. et al. The functional roles of species in metacommunities, as revealed by metanetwork analyses of bird-plant frugivory networks. Ecol. Lett. 23, 1252–1262 (2020).PubMed 

Google Scholar 
Snow, B. & Snow, D. Birds and berries: a study of an ecological interaction (T & AD Poyser, Calton, 1988).Si, X., Kays, R. & Ding, P. How long is enough to detect terrestrial animals? Estimating the minimum trapping effort on camera traps. PeerJ 2, e374 (2014).PubMed 
PubMed Central 

Google Scholar 
Vázquez, D. P. et al. Species abundance and asymmetric interaction strength in ecological networks. Oikos 116, 1120–1127 (2007).
Google Scholar 
Chao, A. & Jost, L. Coverage-based rarefaction and extrapolation: standardizing samples by completeness rather than size. Ecology 93, 2533–2547 (2012).PubMed 

Google Scholar 
Hsieh, T. C., Ma, K. H. & Chao, A. iNEXT: an R package for rarefaction and extrapolation of species diversity (Hill numbers). Methods Ecol. Evol. 7, 1451–1456 (2016).
Google Scholar 
Beckett, S. J. Improved community detection in weighted bipartite networks. R. Soc. Open. Sci. 3, 140536 (2016).PubMed 
PubMed Central 

Google Scholar 
Almeida-Neto, M. & Ulrich, W. A straightforward computational approach for measuring nestedness using quantitative matrices. Environ. Modell. Softw. 26, 173–178 (2011).
Google Scholar 
Scherber, C. et al. Bottom-up effects of plant diversity on multitrophic interactions in a biodiversity experiment. Nature 468, 553–556 (2010).CAS 
PubMed 

Google Scholar 
Schleuning, M. et al. Ecological networks are more sensitive to plant than to animal extinction under climate change. Nat. Commun. 7, 13965 (2016).CAS 
PubMed 
PubMed Central 

Google Scholar 
Humphreys, A. M., Govaerts, R., Ficinski, S. Z., Nic Lughadha, E. & Vorontsova, M. S. Global dataset shows geography and life form predict modern plant extinction and rediscovery. Nat. Ecol. Evol. 3, 1043–1047 (2019).PubMed 

Google Scholar 
Dirzo, R. et al. Defaunation in the Anthropocene. Science 345, 401–406 (2014).CAS 
PubMed 

Google Scholar 
Rogers, H. S., Donoso, I., Traveset, A. & Fricke, E. C. Cascading impacts of seed disperser loss on plant communities and ecosystems. Annu. Rev. Ecol. Evol. Syst. 52, 641–666 (2021).
Google Scholar 
Dormann, C. F., Gruber, B. & Fründ, J. Introducing the bipartite package: analysing ecological networks. R News 8, 8–11 (2008).
Google Scholar 
Patefield, W. M. Algorithm AS 159: An efficient method of generating random R × C tables with given row and column totals. Appl. Stat. 30, 91–97 (1981).
Google Scholar 
Lefcheck, J. S. piecewiseSEM: piecewise structural equation modelling in R for ecology, evolution, and systematics. Methods Ecol. Evol. 7, 573–579 (2016).
Google Scholar 
Kabacoff, R. R in Action: Data Analysis and Graphics with R (Manning Publications Co, 2015).R Core Team. R: A Language And Environment For Statistical Computing (R Foundation for Statistical Computing, 2021). More

Meghana, M. & Shastri, Y. Sustainable valorization of sugar industry waste: Status, opportunities, and challenges. Biores. Technol. 303, 122929 (2020).CAS 

Google Scholar 
Petrescu, D. C., Vermeir, I. & Petrescu-Mag, R. M. Consumer understanding of food quality, healthiness, and environmental impact: a cross-national perspective. IJERPH 17, 169 (2019).PubMed Central 

Google Scholar 
Kassam, A., Friedrich, T., Shaxson, F. & Pretty, J. The spread of conservation agriculture: justification, sustainability and uptake. Int. J. Agric. Sustain. 7, 292–320 (2009).
Google Scholar 
Malviya, M. K. et al. Sugarcane microbiome: role in sustainable production. In Microbiomes and Plant Health 225–242 (Elsevier, 2021). https://doi.org/10.1016/B978-0-12-819715-8.00007-0.Chapter 

Google Scholar 
Sandhu, H. S., Wratten, S. D. & Cullen, R. Organic agriculture and ecosystem services. Environ. Sci. Policy 13, 1–7 (2010).CAS 

Google Scholar 
Schipanski, M. E. et al. Balancing multiple objectives in organic feed and forage cropping systems. Agr. Ecosyst. Environ. 239, 219–227 (2017).
Google Scholar 
Knapp, S. & van der Heijden, M. G. A. A global meta-analysis of yield stability in organic and conservation agriculture. Nat. Commun. 9, 3632 (2018).ADS 
PubMed 
PubMed Central 

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

Google Scholar 
Berendsen, R. L., Pieterse, C. M. J. & Bakker, P. A. H. M. The rhizosphere microbiome and plant health. Trends Plant Sci. 17, 478–486 (2012).CAS 
PubMed 

Google Scholar 
Chialva, M., Lanfranco, L. & Bonfante, P. The plant microbiota: composition, functions, and engineering. Curr. Opin. Biotechnol. 73, 135–142 (2022).CAS 
PubMed 

Google Scholar 
Dastogeer, K. M. G., Tumpa, F. H., Sultana, A., Akter, M. A. & Chakraborty, A. Plant microbiome–an account of the factors that shape community composition and diversity. Curr. Plant Biol. 23, 100161 (2020).
Google Scholar 
Yang, B., Wang, Y. & Qian, P.-Y. Sensitivity and correlation of hypervariable regions in 16S rRNA genes in phylogenetic analysis. BMC Bioinformat. 17, 135 (2016).
Google Scholar 
Nilsson, R. H. et al. The UNITE database for molecular identification of fungi: handling dark taxa and parallel taxonomic classifications. Nucleic Acids Res. 47, D259–D264 (2019).CAS 
PubMed 

Google Scholar 
Wright, R. J., Gibson, M. I. & Christie-Oleza, J. A. Understanding microbial community dynamics to improve optimal microbiome selection. Microbiome 7, 85 (2019).PubMed 
PubMed Central 

Google Scholar 
Praeg, N. & Illmer, P. Microbial community composition in the rhizosphere of Larix decidua under different light regimes with additional focus on methane cycling microorganisms. Sci. Rep. 10, 22324 (2020).ADS 
CAS 
PubMed 
PubMed Central 

Google Scholar 
de Souza, R. S. C. et al. Unlocking the bacterial and fungal communities assemblages of sugarcane microbiome. Sci. Rep. 6, 28774 (2016).ADS 
PubMed 
PubMed Central 

Google Scholar 
Tayyab, M. et al. Sugarcane cultivars manipulate rhizosphere bacterial communities’ structure and composition of agriculturally important keystone taxa. 3 Biotech. 12, 32 (2022).PubMed 

Google Scholar 
Tayyab, M. et al. Sugarcane cultivar-dependent changes in assemblage of soil rhizosphere fungal communities in subtropical ecosystem. Environ. Sci. Pollut. Res. 29, 20795–20807 (2022).
Google Scholar 
Dakora, F. D., Matiru, V. N. & Kanu, A. S. Rhizosphere ecology of lumichrome and riboflavin, two bacterial signal molecules eliciting developmental changes in plants. Front. Plant Sci. 6, 700 (2015).PubMed 
PubMed Central 

Google Scholar 
Chapelle, E., Mendes, R., Bakker, P. A. H. & Raaijmakers, J. M. Fungal invasion of the rhizosphere microbiome. ISME J. 10, 265–268 (2016).CAS 
PubMed 

Google Scholar 
Teheran-Sierra, L. G. et al. Bacterial communities associated with sugarcane under different agricultural management exhibit a diversity of plant growth-promoting traits and evidence of synergistic effect. Microbiol. Res. 247, 126729 (2021).CAS 
PubMed 

Google Scholar 
de Carvalho, L. A. L. et al. Farming systems influence the compositional, structural, and functional characteristics of the sugarcane-associated microbiome. Microbiol. Res. 252, 126866 (2021).PubMed 

Google Scholar 
Henneron, L. et al. Fourteen years of evidence for positive effects of conservation agriculture and organic farming on soil life. Agron. Sustain. Dev. 35, 169–181 (2015).
Google Scholar 
Hartmann, M., Frey, B., Mayer, J., Mäder, P. & Widmer, F. Distinct soil microbial diversity under long-term organic and conventional farming. ISME J. 9, 1177–1194 (2015).PubMed 

Google Scholar 
Tayyab, M. et al. Sugarcane monoculture drives microbial community composition, activity and abundance of agricultural-related microorganisms. Environ. Sci. Pollut. Res. 28, 48080–48096 (2021).CAS 

Google Scholar 
Pang, Z. et al. Soil Metagenomics reveals effects of continuous sugarcane cropping on the structure and functional pathway of rhizospheric microbial community. Front. Microbiol. 12, 627569 (2021).PubMed 
PubMed Central 

Google Scholar 
Orr, C. H., Stewart, C. J., Leifert, C., Cooper, J. M. & Cummings, S. P. Effect of crop management and sample year on abundance of soil bacterial communities in organic and conventional cropping systems. J. Appl. Microbiol. 119, 208–214 (2015).CAS 
PubMed 

Google Scholar 
Brasil. Lei no 10.831, de 23 de dezembro de 2003. Dispõe sobre a agricultura orgânica e dá outras providências. In Publicado no Diário Oficial da União de 24/12/2003 (2003).Europea, C. Reglamento (CE) no 834/2007 del Consejo, de 28 de junio de 2007, sobre producción y etiquetado de los productos ecológicos y por el que se deroga el Reglamento (CEE) no 2092/91. D. Of. Unión Eur. 20, 1–23 (2007).
Google Scholar 
Council of the European Union. 889/2008, “Commission Regulation 889/2008/EC of 5 September 2008 laying down detailed rules for the implementation of Council Regulation (EC) No 834/2007 on organic production and labelling of organic products with regard to organic production, labelling and control”. Off. J. Eur. Union (L) 250, 18–19 (2007).
Google Scholar 
de Andrade, J. C., Cantarella, H. & Quaggio, J. A. Análise química para avaliação da fertilidade de solos tropicais. (2001).Donagema, G. K., de Campos, D. B., Calderano, S. B., Teixeira, W. G. & Viana, J. M. Manual de métodos de análise de solo. In Embrapa Solos-Documentos (INFOTECA-E) (2011).Kassambara, A. ggpubr: ‘ggplot2’ Based Publication Ready Plots. (2020). at R Core Team. R: A Language and Environment for Statistical Computing. (R Foundation for Statistical Computing, 2020). At Lundberg, D. S., Yourstone, S., Mieczkowski, P., Jones, C. D. & Dangl, J. L. Practical innovations for high-throughput amplicon sequencing. Nat. Methods 10, 999–1002 (2013).CAS 
PubMed 

Google Scholar 
Fadrosh, D. W. et al. An improved dual-indexing approach for multiplexed 16S rRNA gene sequencing on the Illumina MiSeq platform. Microbiome 2, 6 (2014).PubMed 
PubMed Central 

Google Scholar 
Renaud, G., Stenzel, U., Maricic, T., Wiebe, V. & Kelso, J. deML: robust demultiplexing of Illumina sequences using a likelihood-based approach. Bioinformatics 31, 770–772 (2015).CAS 
PubMed 

Google Scholar 
Zhang, J., Kobert, K., Flouri, T. & Stamatakis, A. PEAR: a fast and accurate Illumina Paired-End reAd mergeR. Bioinformatics 30, 614–620 (2014).CAS 
PubMed 

Google Scholar 
Edgar, R. C. Search and clustering orders of magnitude faster than BLAST. Bioinformatics 26, 2460–2461 (2010).CAS 
PubMed 

Google Scholar 
Callahan, B. J. et al. DADA2: high-resolution sample inference from Illumina amplicon data. Nat. Methods 13, 581 (2016).CAS 
PubMed 
PubMed Central 

Google Scholar 
Cole, J. R. et al. Ribosomal database project: data and tools for high throughput rRNA analysis. Nucleic Acids Res. 42, D633–D642 (2014).CAS 
PubMed 

Google Scholar 
McMurdie, P. J. & Holmes, S. phyloseq: an R package for reproducible interactive analysis and graphics of microbiome census data. PLoS One 8, e61217 (2013).ADS 
CAS 
PubMed 
PubMed Central 

Google Scholar 
Lahti, L. & Shetty, S. Microbiome R package. (2012).Oksanen, J. et al. vegan: Community Ecology Package. (2019). At Love, M. I., Huber, W. & Anders, S. Moderated estimation of fold change and dispersion for RNA-seq data with DESeq2. Genome Biol. 15, 550 (2014).PubMed 
PubMed Central 

Google Scholar 
Dhariwal, A. et al. MicrobiomeAnalyst: a web-based tool for comprehensive statistical, visual and meta-analysis of microbiome data. Nucleic Acids Res. 45, W180–W188 (2017).CAS 
PubMed 
PubMed Central 

Google Scholar 
Douglas, G. M. et al. PICRUSt2: an improved and extensible approach for metagenome inference. Bioinformatics https://doi.org/10.1101/672295 (2019).Article 
PubMed 
PubMed Central 

Google Scholar 
Parks, D. H., Tyson, G. W., Hugenholtz, P. & Beiko, R. G. STAMP: statistical analysis of taxonomic and functional profiles. Bioinformatics 30, 3123–3124 (2014).CAS 
PubMed 
PubMed Central 

Google Scholar 
Kohl, M., Wiese, S. & Warscheid, B. Cytoscape: software for visualization and analysis of biological networks. In Data Mining in Proteomics (eds Hamacher, M. et al.) 291–303 (Humana Press, Totowa, NJ, 2011). https://doi.org/10.1007/978-1-60761-987-1_18.Chapter 

Google Scholar 
Assenov, Y., Ramírez, F., Schelhorn, S.-E., Lengauer, T. & Albrecht, M. Computing topological parameters of biological networks. Bioinformatics 24, 282–284 (2008).CAS 
PubMed 

Google Scholar 
Shen, Z. et al. Deep 16S rRNA pyrosequencing reveals a bacterial community associated with banana fusarium wilt disease suppression induced by bio-organic fertilizer application. PLoS One 9, e98420 (2014).ADS 
PubMed 
PubMed Central 

Google Scholar 
Yun, Y. et al. The relationship between pH and bacterial communities in a single karst ecosystem and its implication for soil acidification. Front. Microbiol. 7, 1955 (2016).PubMed 
PubMed Central 

Google Scholar 
Wu, Y., Zeng, J., Zhu, Q., Zhang, Z. & Lin, X. pH is the primary determinant of the bacterial community structure in agricultural soils impacted by polycyclic aromatic hydrocarbon pollution. Sci. Rep. 7, 40093 (2017).ADS 
CAS 
PubMed 
PubMed Central 

Google Scholar 
Li, R. et al. Pyrosequencing reveals the influence of organic and conventional farming systems on bacterial communities. PLoS One 7, e51897 (2012).ADS 
CAS 
PubMed 
PubMed Central 

Google Scholar 
Bill, M., Chidamba, L., Gokul, J. K., Labuschagne, N. & Korsten, L. Bacterial community dynamics and functional profiling of soils from conventional and organic cropping systems. Appl. Soil. Ecol. 157, 103734 (2021).
Google Scholar 
Xun, W., Shao, J., Shen, Q. & Zhang, R. Rhizosphere microbiome: Functional compensatory assembly for plant fitness. Comput. Struct. Biotechnol. J. 19, 5487–5493 (2021).CAS 
PubMed 
PubMed Central 

Google Scholar 
Semenov, M. V., Krasnov, G. S., Semenov, V. M. & van Bruggen, A. Mineral and organic fertilizers distinctly affect fungal communities in the crop rhizosphere. JoF 8, 251 (2022).CAS 
PubMed 
PubMed Central 

Google Scholar 
Wang, Z., Li, Y., Li, T., Zhao, D. & Liao, Y. Tillage practices with different soil disturbance shape the rhizosphere bacterial community throughout crop growth. Soil Tillage Res. 197, 104501 (2020).
Google Scholar 
Gdanetz, K. & Trail, F. The wheat microbiome under four management strategies, and potential for endophytes in disease protection. Phytobiom. J. 1, 158–168 (2017).
Google Scholar 
Lazcano, C. et al. The rhizosphere microbiome plays a role in the resistance to soil-borne pathogens and nutrient uptake of strawberry cultivars under field conditions. Sci. Rep. 11, 3188 (2021).ADS 
CAS 
PubMed 
PubMed Central 

Google Scholar 
Leys, N. M. E. J. et al. Occurrence and phylogenetic diversity of Sphingomonas strains in soils contaminated with polycyclic aromatic hydrocarbons. Appl. Environ. Microbiol. 70, 1944–1955 (2004).ADS 
CAS 
PubMed 
PubMed Central 

Google Scholar 
Yin, C. et al. Role of bacterial communities in the natural suppression of rhizoctonia solani bare patch disease of wheat (Triticum aestivum L.). Appl. Environ. Microbiol. 79, 7428–7438 (2013).ADS 
CAS 
PubMed 
PubMed Central 

Google Scholar 
Stewart, A. & Hill, R. Applications of trichoderma in plant growth promotion. In Biotechnology and Biology of Trichoderma 415–428 (Elsevier, 2014). https://doi.org/10.1016/B978-0-444-59576-8.00031-X.Chapter 

Google Scholar 
Banerjee, S. et al. Network analysis reveals functional redundancy and keystone taxa amongst bacterial and fungal communities during organic matter decomposition in an arable soil. Soil Biol. Biochem. 97, 188–198 (2016).CAS 

Google Scholar 
Andargie, M., Congyi, Z., Yun, Y. & Li, J. Identification and evaluation of potential bio-control fungal endophytes against Ustilagonoidea virens on rice plants. World J. Microbiol. Biotechnol. 33, 120 (2017).PubMed 

Google Scholar 
Orrù, L. et al. How tillage and crop rotation change the distribution pattern of fungi. Front. Microbiol. 12, 634325 (2021).PubMed 
PubMed Central 

Google Scholar 
van der Heijden, M. G. A. & Hartmann, M. Networking in the plant microbiome. PLoS Biol. 14, e1002378 (2016).PubMed 
PubMed Central 

Google Scholar 
Wang, W. et al. Consistent responses of the microbial community structure to organic farming along the middle and lower reaches of the Yangtze River. Sci. Rep. 6, 35046 (2016).ADS 
CAS 
PubMed 
PubMed Central 

Google Scholar 
Silva, T. M. et al. Degradation of 2,4-D herbicide by microorganisms isolated from Brazilian contaminated soil. Braz. J. Microbiol. 38, 522–525 (2007).
Google Scholar 
Laura, M., Snchez-Salinas, E., Gonzlez, E. D. & Luisa, M. Pesticide biodegradation: mechanisms, genetics and strategies to enhance the process. In Biodegradation – Life of Science (ed. Chamy, R.) (InTech, 2013). https://doi.org/10.5772/56098.Chapter 

Google Scholar 
Upadhyay, L. S. B. & Dutt, A. Microbial detoxification of residual organophosphate pesticides in agricultural practices. In Microbial Biotechnology (eds Patra, J. K. et al.) 225–242 (Springer Singapore, Singapore, 2017). https://doi.org/10.1007/978-981-10-6847-8_10.Chapter 

Google Scholar 
Hassan, Y. I., Lepp, D., He, J. & Zhou, T. Draft genome sequences of Devosia sp. strain 17-2-E-8 and Devosia riboflavina strain IFO13584. Genome Announ. https://doi.org/10.1128/genomeA.00994-14 (2014).Article 

Google Scholar 
Talwar, C. et al. Defining the environmental adaptations of genus Devosia: insights into its expansive short peptide transport system and positively selected genes. Sci. Rep. 10, 1151 (2020).ADS 
CAS 
PubMed 
PubMed Central 

Google Scholar 
Li, F., Chen, L., Zhang, J., Yin, J. & Huang, S. Bacterial community structure after long-term organic and inorganic fertilization reveals important associations between soil nutrients and specific taxa involved in nutrient transformations. Front. Microbiol. 8, 187 (2017).PubMed 
PubMed Central 

Google Scholar 
Ho, A., Lonardo, D. P. D. & Bodelier, P. L. E. Revisiting life strategy concepts in environmental microbial ecology. Microbiol. Ecol. https://doi.org/10.1093/femsec/fix006 (2017).Article 

Google Scholar 
Lupatini, M., Korthals, G. W., de Hollander, M., Janssens, T. K. S. & Kuramae, E. E. Soil microbiome is more heterogeneous in organic than in conventional farming system. Front. Microbiol. 7, 2064 (2017).PubMed 
PubMed Central 

Google Scholar 
Wang, H. et al. Eight years of manure fertilization favor copiotrophic traits in paddy soil microbiomes. Eur. J. Soil Biol. 106, 103352 (2021).CAS 

Google Scholar 
Fließbach, A., Oberholzer, H.-R., Gunst, L. & Mäder, P. Soil organic matter and biological soil quality indicators after 21 years of organic and conventional farming. Agric. Ecosyst. Environ. 118, 273–284 (2007).
Google Scholar 
Lewin, G. R. et al. Evolution and ecology of Actinobacteria and their bioenergy applications. Annu. Rev. Microbiol. 70, 235–254 (2016).CAS 
PubMed 
PubMed Central 

Google Scholar 
Karanja, E. N. et al. Diversity and structure of prokaryotic communities within organic and conventional farming systems in central highlands of Kenya. PLoS One 15, e0236574 (2020).CAS 
PubMed 
PubMed Central 

Google Scholar 
Francioli, D. et al. Mineral versus organic amendments: microbial community structure, activity and abundance of agriculturally relevant microbes are driven by long-term fertilization strategies. Front. Microbiol. 7, 1446 (2016).PubMed 
PubMed Central 

Google Scholar 
Paungfoo-Lonhienne, C. et al. Nitrogen fertilizer dose alters fungal communities in sugarcane soil and rhizosphere. Sci. Rep. 5, 8678 (2015).CAS 
PubMed 
PubMed Central 

Google Scholar 
Pang, Z. et al. Liming positively modulates microbial community composition and function of sugarcane fields. Agronomy 9, 808 (2019).CAS 

Google Scholar 
Aira, M., Gómez-Brandón, M., Lazcano, C., Bååth, E. & Domínguez, J. Plant genotype strongly modifies the structure and growth of maize rhizosphere microbial communities. Soil Biol. Biochem. 42, 2276–2281 (2010).CAS 

Google Scholar 
Ma, M. et al. Responses of fungal community composition to long-term chemical and organic fertilization strategies in Chinese Mollisols. MicrobiologyOpen 7, e00597 (2018).PubMed 
PubMed Central 

Google Scholar 
Bellenger, J. P., Darnajoux, R., Zhang, X. & Kraepiel, A. M. L. Biological nitrogen fixation by alternative nitrogenases in terrestrial ecosystems: a review. Biogeochemistry 149, 53–73 (2020).
Google Scholar 
Schmidt, J. E. et al. Effects of agricultural management on rhizosphere microbial structure and function in processing tomato plants. Appl. Environ. Microbiol. https://doi.org/10.1128/AEM.01064-19 (2019).Article 
PubMed 
PubMed Central 

Google Scholar 
Agler, M. T. et al. Microbial hub taxa link host and abiotic factors to plant microbiome variation. PLoS Biol. 14, e1002352 (2016).PubMed 
PubMed Central 

Google Scholar 
Lin, Y. et al. Nitrosospira cluster 8a plays a predominant role in the nitrification process of a subtropical Ultisol under long-term inorganic and organic fertilization. Appl. Environ. Microbiol. 84, e01031-e1118 (2018).CAS 
PubMed 
PubMed Central 

Google Scholar 
Chu, H. et al. Community structure of ammonia-oxidizing bacteria under long-term application of mineral fertilizer and organic manure in a sandy loam soil. Appl. Environ. Microbiol. 73, 485–491 (2007).ADS 
CAS 
PubMed 

Google Scholar 
Xun, W. et al. Specialized metabolic functions of keystone taxa sustain soil microbiome stability. Microbiome 9, 35 (2021).CAS 
PubMed 
PubMed Central 

Google Scholar  More

Suttle CA. Marine viruses-major players in the global ecosystem. Nat Rev Microbiol. 2007;5:801–12.CAS 
PubMed 

Google Scholar 
Fuhrman JA. Marine viruses and their biogeochemical and ecological effects. Nature 1999;399:541–8.CAS 
PubMed 

Google Scholar 
Rohwer F, Thurber RV. Viruses manipulate the marine environment. Nature 2009;459:207–12.CAS 
PubMed 

Google Scholar 
Breitbart M, Bonnain C, Malki K, Sawaya NA. Phage puppet masters of the marine microbial realm. Nat Microbiol. 2018;3:754–66.CAS 
PubMed 

Google Scholar 
Zimmerman AE, Howard-Varona C, Needham DM, John SG, Worden AZ, Sullivan MB, et al. Metabolic and biogeochemical consequences of viral infection in aquatic ecosystems. Nat Rev Microbiol. 2020;18:21–34.CAS 
PubMed 

Google Scholar 
Rosenwasser S, Ziv C, Creveld SGV, Vardi A. Virocell metabolism: metabolic innovations during host-virus interactions in the ocean. Trends Microbiol. 2016;24:821–32.CAS 
PubMed 

Google Scholar 
Fuchsman CA, Carlson MCG, Garcia Prieto D, Hays MD, Rocap G. Cyanophage host-derived genes reflect contrasting selective pressures with depth in the oxic and anoxic water column of the Eastern Tropical North Pacific. Environ Microbiol. 2021;23:2782–2800.CAS 
PubMed 

Google Scholar 
Roux S, Brum JR, Dutilh BE, Sunagawa S, Duhaime MB, Loy A, et al. Ecogenomics and potential biogeochemical impacts of globally abundant ocean viruses. Nature 2016;537:689–93.CAS 
PubMed 

Google Scholar 
Gregory AC, Zayed AA, Conceição-Neto N, Temperton B, Bolduc B, Alberti A, et al. Marine DNA viral macro-and microdiversity from pole to pole. Cell 2019;177:1109–23.CAS 
PubMed 
PubMed Central 

Google Scholar 
Brum JR, Ignacio-Espinoza JC, Roux S, Doulcier G, Acinas SG, Alberti A, et al. Patterns and ecological drivers of ocean viral communities. Science 2015;348:1261498.PubMed 

Google Scholar 
Dion MB, Oechslin F, Moineau S. Phage diversity, genomics and phylogeny. Nat Rev Microbiol. 2020;18:125–38.CAS 
PubMed 

Google Scholar 
Sullivan MB, Waterbury JB, Chisholm SW. Cyanophages infecting the oceanic cyanobacterium Prochlorococcus. Nature 2003;424:1047–51.CAS 
PubMed 

Google Scholar 
Mann NH. Phages of the marine cyanobacterial picophytoplankton. FEMS Microbiol Rev. 2003;27:17–34.CAS 
PubMed 

Google Scholar 
Ni T, Zeng Q. Diel infection of cyanobacteria by cyanophages. Front Mar Sci. 2016;2:123.
Google Scholar 
Flombaum P, Gallegos JL, Gordillo RA, Rincon J, Zabala LL, Jiao N, et al. Present and future global distributions of the marine Cyanobacteria Prochlorococcus and Synechococcus. Proc Natl Acad Sci USA 2013;110:9824–9.CAS 
PubMed 
PubMed Central 

Google Scholar 
Biller SJ, Berube PM, Lindell D, Chisholm SW. Prochlorococcus: the structure and function of collective diversity. Nat Rev Microbiol 2015;13:13–27.CAS 
PubMed 

Google Scholar 
Proctor LM, Fuhrman JA. Viral mortality of marine-bacteria and cyanobacteria. Nature 1990;343:60–62.
Google Scholar 
Carlson MCG, Ribalet F, Maidanik I, Durham BP, Hulata Y, Ferron S, et al. Viruses affect picocyanobacterial abundance and biogeography in the North Pacific Ocean. Nat Microbiol 2022;7:570–80.CAS 
PubMed 
PubMed Central 

Google Scholar 
Matteson AR, Loar SN, Pickmere S, DeBruyn JM, Ellwood MJ, Boyd PW, et al. Production of viruses during a spring phytoplankton bloom in the South Pacific Ocean near of New Zealand. FEMS Microbiol Ecol 2012;79:709–19.CAS 
PubMed 

Google Scholar 
Ribalet F, Swalwell J, Clayton S, Jimenez V, Sudek S, Lin Y, et al. Light-driven synchrony of Prochlorococcus growth and mortality in the subtropical Pacific gyre. Proc Natl Acad Sci USA. 2015;112:8008–12.CAS 
PubMed 
PubMed Central 

Google Scholar 
Demory D, Liu R, Chen Y, Zhao F, Coenen AR, Zeng Q, et al. Linking light-dependent life history traits with population dynamics for Prochlorococcus and cyanophage. mSystems 2020;5:e00586–19.CAS 
PubMed 
PubMed Central 

Google Scholar 
Avrani S, Wurtzel O, Sharon I, Sorek R, Lindell D. Genomic island variability facilitates Prochlorococcus-virus coexistence. Nature 2011;474:604–8.CAS 
PubMed 

Google Scholar 
Marston MF, Pierciey FJ Jr, Shepard A, Gearin G, Qi J, Yandava C, et al. Rapid diversification of coevolving marine Synechococcus and a virus. Proc Natl Acad Sci USA 2012;109:4544–9.CAS 
PubMed 
PubMed Central 

Google Scholar 
Xiao X, Guo W, Li X, Wang C, Chen X, Lin X, et al. Viral lysis alters the optical properties and biological availability of dissolved organic matter derived from Prochlorococcus picocyanobacteria. Appl Environ Microbiol. 2021;87:e02271–20.CAS 
PubMed 
PubMed Central 

Google Scholar 
Xiao X, Zeng Q, Zhang R, Jiao N. Prochlorococcus viruses—From biodiversity to biogeochemical cycles. Sci China Earth Sci. 2018;61:1728–36.
Google Scholar 
Jover LF, Effler TC, Buchan A, Wilhelm SW, Weitz JS. The elemental composition of virus particles: implications for marine biogeochemical cycles. Nat Rev Microbiol. 2014;12:519–28.CAS 
PubMed 

Google Scholar 
Puxty RJ, Millard AD, Evans DJ, Scanlan DJ. Viruses inhibit CO2 fixation in the most abundant phototrophs on earth. Curr Biol 2016;26:1585–9.CAS 
PubMed 

Google Scholar 
Weitz JS, Stock CA, Wilhelm SW, Bourouiba L, Coleman ML, Buchan A, et al. A multitrophic model to quantify the effects of marine viruses on microbial food webs and ecosystem processes. ISME J. 2015;9:1352–64.PubMed 
PubMed Central 

Google Scholar 
Sullivan MB, Coleman ML, Weigele P, Rohwer F, Chisholm SW. Three Prochlorococcus cyanophage genomes: signature features and ecological interpretations. PLoS Biol. 2005;3:e144.PubMed 
PubMed Central 

Google Scholar 
Sullivan MB, Krastins B, Hughes JL, Kelly L, Chase M, Sarracino D, et al. The genome and structural proteome of an ocean siphovirus: a new window into the cyanobacterial ‘mobilome’. Environ Microbiol. 2009;11:2935–51.CAS 
PubMed 
PubMed Central 

Google Scholar 
Sullivan MB, Huang KH, Ignacio-Espinoza JC, Berlin AM, Kelly L, Weigele PR, et al. Genomic analysis of oceanic cyanobacterial myoviruses compared with T4-like myoviruses from diverse hosts and environments. Environ Microbiol. 2010;12:3035–56.CAS 
PubMed 
PubMed Central 

Google Scholar 
Sabehi G, Shaulov L, Silver DH, Yanai I, Harel A, Lindell D. A novel lineage of myoviruses infecting cyanobacteria is widespread in the oceans. Proc Natl Acad Sci USA 2012;109:2037–42.CAS 
PubMed 
PubMed Central 

Google Scholar 
Huang S, Wang K, Jiao N, Chen F. Genome sequences of siphoviruses infecting marine Synechococcus unveil a diverse cyanophage group and extensive phage-host genetic exchanges. Environ Microbiol. 2012;14:540–58.CAS 
PubMed 

Google Scholar 
Labrie SJ, Frois-Moniz K, Osburne MS, Kelly L, Roggensack SE, Sullivan MB, et al. Genomes of marine cyanopodoviruses reveal multiple origins of diversity. Environ Microbiol. 2013;15:1356–76.CAS 
PubMed 

Google Scholar 
Dekel-Bird NP, Avrani S, Sabehi G, Pekarsky I, Marston MF, Kirzner S, et al. Diversity and evolutionary relationships of T7-like podoviruses infecting marine cyanobacteria. Environ Microbiol. 2013;15:1476–91.CAS 
PubMed 

Google Scholar 
Huang S, Wilhelm SW, Jiao N, Chen F. Ubiquitous cyanobacterial podoviruses in the global oceans unveiled through viral DNA polymerase gene sequences. ISME J. 2010;4:1243–51.PubMed 

Google Scholar 
Baran N, Goldin S, Maidanik I, Lindell D. Quantification of diverse virus populations in the environment using the polony method. Nat Microbiol. 2018;3:62–72.CAS 
PubMed 

Google Scholar 
Chow C-ET, Suttle CA. Biogeography of viruses in the sea. Annu Rev Virol. 2015;2:41–66.CAS 
PubMed 

Google Scholar 
Chen F, Lu JR. Genomic sequence and evolution of marine cyanophage P60: a new insight on lytic and lysogenic phages. Appl Environ Microbiol. 2002;68:2589–94.CAS 
PubMed 
PubMed Central 

Google Scholar 
Huang S, Zhang S, Jiao N, Chen F. Comparative genomic and phylogenomic analyses reveal a conserved core genome shared by estuarine and oceanic cyanopodoviruses. PLoS One. 2015;10:e0142962.PubMed 
PubMed Central 

Google Scholar 
Pope WH, Weigele PR, Chang J, Pedulla ML, Ford ME, Houtz JM, et al. Genome sequence, structural proteins, and capsid organization of the cyanophage Syn5: A “horned’ bacteriophage of marine Synechococcus. J Mol Biol. 2007;368:966–81.CAS 
PubMed 
PubMed Central 

Google Scholar 
Huang S, Sun Y, Zhang S, Long L. Temporal transcriptomes of a marine cyanopodovirus and its Synechococcus host during infection. Microbiologyopen 2021;10:e1150.CAS 
PubMed 

Google Scholar 
Wang K, Chen F. Prevalence of highly host-specific cyanophages in the estuarine environment. Environ Microbiol. 2008;10:300–12.CAS 
PubMed 

Google Scholar 
Chen F, Wang K, Huang S, Cai H, Zhao M, Jiao N, et al. Diverse and dynamic populations of cyanobacterial podoviruses in the Chesapeake Bay unveiled through DNA polymerase gene sequences. Environ Microbiol. 2009;11:2884–92.PubMed 

Google Scholar 
Goldin S, Hulata Y, Baran N, Lindell D. Quantification of T4-like and T7-like cyanophages using the polony method show they are significant members of the virioplankton in the North Pacific Subtropical Gyre. Front Microbiol. 2020;11:1210.PubMed 
PubMed Central 

Google Scholar 
Nasko DJ, Chopyk J, Sakowski EG, Ferrell BD, Polson SW, Wommack KE. Family A DNA polymerase phylogeny uncovers diversity and replication gene organization in the virioplankton. Front Microbiol. 2018;9:3053.PubMed 
PubMed Central 

Google Scholar 
Dekel-Bird NP, Sabehi G, Mosevitzky B, Lindell D. Host-dependent differences in abundance, composition and host range of cyanophages from the Red Sea. Environ Microbiol. 2015;17:1286–99.CAS 
PubMed 

Google Scholar 
Hanson CA, Marston MF, Martiny JBH. Biogeographic variation in host range phenotypes and taxonomic composition of marine cyanophage isolates. Front Microbiol. 2016;7:983.PubMed 
PubMed Central 

Google Scholar 
Rocap G, Larimer FW, Lamerdin J, Malfatti S, Chain P, Ahlgren NA, et al. Genome divergence in two Prochlorococcus ecotypes reflects oceanic niche differentiation. Nature 2003;424:1042–7.CAS 
PubMed 

Google Scholar 
Chen B, Wang L, Song S, Huang B, Sun J, Liu H. Comparisons of picophytoplankton abundance, size, and fluorescence between summer and winter in northern South China Sea. Cont Shelf Res. 2011;31:1527–40.
Google Scholar 
Lindell D, Jaffe JD, Coleman ML, Futschik ME, Axmann IM, Rector T, et al. Genome-wide expression dynamics of a marine virus and host reveal features of co-evolution. Nature 2007;449:83–86.CAS 
PubMed 

Google Scholar 
Zhao Y, Qin F, Zhang R, Giovannoni SJ, Zhang Z, Sun J, et al. Pelagiphages in the Podoviridae family integrate into host genomes. Environ Microbiol. 2019;21:1989–2001.CAS 
PubMed 

Google Scholar 
Leptihn S, Gottschalk J, Kuhn A. T7 ejectosome assembly: A story unfolds. Bacteriophage 2016;6:e1128513.CAS 
PubMed 
PubMed Central 

Google Scholar 
Thompson LR, Zeng Q, Kelly L, Huang KH, Singer AU, Stubbe J, et al. Phage auxiliary metabolic genes and the redirection of cyanobacterial host carbon metabolism. Proc Natl Acad Sci USA 2011;108:E757–64.CAS 
PubMed 
PubMed Central 

Google Scholar 
Zeng Q, Chisholm SW. Marine viruses exploit their host’s two-component regulatory system in response to resource limitation. Curr Biol 2012;22:124–8.CAS 
PubMed 

Google Scholar 
Zeng Q, Bonocora RP, Shub DA. A free-standing homing endonuclease targets an intron insertion site in the psbA gene of cyanophages. Curr Biol. 2009;19:218–22.CAS 
PubMed 

Google Scholar 
Lindell D, Jaffe JD, Johnson ZI, Church GM, Chisholm SW. Photosynthesis genes in marine viruses yield proteins during host infection. Nature 2005;438:86–89.CAS 
PubMed 

Google Scholar 
Breitbart M, Thompson LR, Suttle CA, Sullivan MB. Exploring the vast diversity of marine viruses. Oceanography. 2007;20:135–9.
Google Scholar 
Kazlauskas D, Venclovas C. Computational analysis of DNA replicases in double-stranded DNA viruses: relationship with the genome size. Nucleic Acids Res. 2011;39:8291–305.CAS 
PubMed 
PubMed Central 

Google Scholar 
Liu X, Zhang Q, Murata K, Baker ML, Sullivan MB, Fu C, et al. Structural changes in a marine podovirus associated with release of its genome into Prochlorococcus. Nat Struct Mol Biol. 2010;17:830–6.CAS 
PubMed 
PubMed Central 

Google Scholar 
Dai W, Fu C, Raytcheva D, Flanagan J, Khant HA, Liu XG, et al. Visualizing virus assembly intermediates inside marine cyanobacteria. Nature 2013;502:707–10.CAS 
PubMed 
PubMed Central 

Google Scholar 
Liu R, Liu Y, Chen Y, Zhan Y, Zeng Q. Cyanobacterial viruses exhibit diurnal rhythms during infection. Proc Natl Acad Sci USA 2019;116:14077–82.CAS 
PubMed 
PubMed Central 

Google Scholar 
Maidanik I, Kirzner S, Pekarski I, Arsenieff L, Tahan R, Carlson MCG, et al. Cyanophages from a less virulent clade dominate over their sister clade in global oceans. ISME J. 2022;16:2169–80.CAS 
PubMed 
PubMed Central 

Google Scholar 
Shitrit D, Hackl T, Laurenceau R, Raho N, Carlson MCG, Sabehi G, et al. Genetic engineering of marine cyanophages reveals integration but not lysogeny in T7-like cyanophages. ISME J. 2022;16:488–99.CAS 
PubMed 

Google Scholar 
Liang Y, Wang L, Wang Z, Zhao J, Yang Q, Wang M, et al. Metagenomic analysis of the diversity of DNA viruses in the surface and deep sea of the South China Sea. Front Microbiol. 2019;10:1951.PubMed 
PubMed Central 

Google Scholar 
Pedrós-Alió C, Potvin M, Lovejoy C. Diversity of planktonic microorganisms in the Arctic Ocean. Prog Oceanogr. 2015;139:233–43.
Google Scholar 
Luo E, Eppley JM, Romano AE, Mende DR, DeLong EF. Double-stranded DNA virioplankton dynamics and reproductive strategies in the oligotrophic open ocean water column. ISME J. 2020;14:1304–15.CAS 
PubMed 
PubMed Central 

Google Scholar 
Steidinger BS, Crowther TW, Liang J, Van Nuland ME, Werner GDA, Reich PB, et al. Climatic controls of decomposition drive the global biogeography of forest-tree symbioses. Nature 2019;569:404–8.CAS 
PubMed 

Google Scholar 
Xie X, Wu T, Zhu M, Jiang G, Xu Y, Wang X, et al. Comparison of random forest and multiple linear regression models for estimation of soil extracellular enzyme activities in agricultural reclaimed coastal saline land. Ecol Indic. 2021;120:106925.CAS 

Google Scholar 
Lee SJ, Richardson CC. Choreography of bacteriophage T7 DNA replication. Curr Opin Chem Biol. 2011;15:580–6.CAS 
PubMed 
PubMed Central 

Google Scholar 
Kulczyk AW, Richardson CC. The replication system of bacteriophage T7. Enzymes. 2016;39:89–136.CAS 
PubMed 

Google Scholar 
Benkovic SJ, Valentine AM, Salinas F. Replisome-mediated DNA replication. Annu Rev Biochem. 2001;70:181–208.CAS 
PubMed 

Google Scholar 
Johnson A, O’Donnell M. Cellular DNA replicases: components and dynamics at the replication fork. Annu Rev Biochem. 2005;74:283–315.CAS 
PubMed 

Google Scholar 
Seco EM, Zinder JC, Manhart CM, Lo Piano A, McHenry CS, Ayora S. Bacteriophage SPP1 DNA replication strategies promote viral and disable host replication in vitro. Nucleic Acids Res. 2013;41:1711–21.CAS 
PubMed 

Google Scholar 
Mruwat N, Carlson MCG, Goldin S, Ribalet F, Kirzner S, Hulata Y, et al. A single-cell polony method reveals low levels of infected Prochlorococcus in oligotrophic waters despite high cyanophage abundances. ISME J. 2021;15:41–54.CAS 
PubMed 

Google Scholar 
Moore LR, Rocap G, Chisholm SW. Physiology and molecular phylogeny of coexisting Prochlorococcus ecotypes. Nature 1998;393:464–7.CAS 
PubMed 

Google Scholar 
Puxty RJ, Millard AD, Evans DJ, Scanlan DJ. Shedding new light on viral photosynthesis. Photosynth Res. 2015;126:71–97.CAS 
PubMed 

Google Scholar 
Edwards KF, Steward GF, Schvarcz CR. Making sense of virus size and the tradeoffs shaping viral fitness. Ecol Lett. 2021;24:363–73.PubMed 

Google Scholar 
Moore LR, Coe A, Zinser ER, Saito MA, Sullivan MB, Lindell D, et al. Culturing the marine cyanobacterium Prochlorococcus. Limnol Oceanogr Methods. 2007;5:353–62.CAS 

Google Scholar 
Hyman P, Abedon ST. Bacteriophage host range and bacterial resistance. Adv Appl Microbiol. 2010;70:217–48.CAS 
PubMed 

Google Scholar 
Fridman S, Flores-Uribe J, Larom S, Alalouf O, Liran O, Yacoby I, et al. A myovirus encoding both photosystem I and II proteins enhances cyclic electron flow in infected Prochlorococcus cells. Nat Microbiol. 2017;2:1350–7.CAS 
PubMed 

Google Scholar 
Fang X, Liu Y, Zhao Y, Chen Y, Liu R, Qin QL, et al. Transcriptomic responses of the marine cyanobacterium Prochlorococcus to viral lysis products. Environ Microbiol. 2019;21:2015–28.CAS 
PubMed 

Google Scholar 
John SG, Mendez CB, Deng L, Poulos B, Kauffman AK, Kern S, et al. A simple and efficient method for concentration of ocean viruses by chemical flocculation. Environ Microbiol Rep. 2011;3:195–202.CAS 
PubMed 
PubMed Central 

Google Scholar 
Bolger AM, Lohse M, Usadel B. Trimmomatic: a flexible trimmer for Illumina sequence data. Bioinformatics 2014;30:2114–20.CAS 
PubMed 
PubMed Central 

Google Scholar 
Schmieder R, Edwards R. Quality control and preprocessing of metagenomic datasets. Bioinformatics 2011;27:863–4.CAS 
PubMed 
PubMed Central 

Google Scholar 
Langmead B, Trapnell C, Pop M, Salzberg SL. Ultrafast and memory-efficient alignment of short DNA sequences to the human genome. Genome Biol. 2009;10:1–10.
Google Scholar 
Peng Y, Leung HC, Yiu SM, Chin FY. IDBA-UD: a de novo assembler for single-cell and metagenomic sequencing data with highly uneven depth. Bioinformatics 2012;28:1420–8.CAS 
PubMed 

Google Scholar 
Seemann T. Prokka: rapid prokaryotic genome annotation. Bioinformatics 2014;30:2068–9.CAS 
PubMed 

Google Scholar 
Edgar RC. MUSCLE: multiple sequence alignment with high accuracy and high throughput. Nucleic Acids Res. 2004;32:1792–7.CAS 
PubMed 
PubMed Central 

Google Scholar 
Minh BQ, Schmidt HA, Chernomor O, Schrempf D, Woodhams MD, von Haeseler A, et al. IQ-TREE 2: new models and efficient methods for phylogenetic Inference in the genomic era. Mol Biol Evol. 2020;37:2461–2461.PubMed 
PubMed Central 

Google Scholar 
Hoang DT, Chernomor O, von Haeseler A, Minh BQ, Vinh LS. UFBoot2: improving the ultrafast bootstrap approximation. Mol Biol Evol. 2018;35:518–22.CAS 
PubMed 

Google Scholar 
Martinez-Hernandez F, Fornas O, Lluesma Gomez M, Bolduc B, de la Cruz Pena MJ, Martinez JM, et al. Single-virus genomics reveals hidden cosmopolitan and abundant viruses. Nat Commun. 2017;8:15892.CAS 
PubMed 
PubMed Central 

Google Scholar 
Zhang Z, Qin F, Chen F, Chu X, Luo H, Zhang R, et al. Culturing novel and abundant pelagiphages in the ocean. Environ Microbiol 2021;23:1145–61.CAS 
PubMed 

Google Scholar 
Buchholz HH, Michelsen ML, Bolanos LM, Browne E, Allen MJ, Temperton B. Efficient dilution-to-extinction isolation of novel virus-host model systems for fastidious heterotrophic bacteria. ISME J. 2021;15:1585–98.CAS 
PubMed 
PubMed Central 

Google Scholar 
Qin F, Du S, Zhang Z, Ying H, Wu Y, Zhao G, et al. Newly identified HMO-2011-type phages reveal genomic diversity and biogeographic distributions of this marine viral group. ISME J. 2022;16:1363–75.CAS 
PubMed 
PubMed Central 

Google Scholar  More

Howe, B. M. et al. Observing the oceans acoustically. Front. Mar. Sci. 6, 426. https://doi.org/10.3389/fmars.2019.00426 (2019).Article 

Google Scholar 
Molenaar, M. M., Hill, D., Webster, P., Fidan, E. & Birch, B. First downhole application of distributed acoustic sensing for hydraulic-fracturing monitoring and diagnostics. SPE Drill. Complet. 27, 32–38. https://doi.org/10.2118/140561-PA (2012).Article 

Google Scholar 
Lindsey, N. J. et al. Fiber-optic network observations of earthquake wavefields. Geophys. Res. Lett. 44, 11792–11799. https://doi.org/10.1002/2017GLO75722 (2017).Article 
ADS 

Google Scholar 
Jousset, P. et al. Dynamic strain determination using fibre-optic cables allows imaging of seismological and structural features. Nat. Commun. 9, 1–11. https://doi.org/10.1038/s41467-018-04860-y (2018).Article 
CAS 

Google Scholar 
Ajo-Franklin, J. B. et al. Distributed acoustic sensing using dark fiber for near-surface characterization and broadband seismic event detection. Sci. Rep. 9, 1–14. https://doi.org/10.1038/s41598-018-36675-8 (2019).Article 
CAS 

Google Scholar 
Williams, E. F. et al. Distributed sensing of microseisms and teleseisms with submarine dark fibers. Nat. Commun. 10, 1–11. https://doi.org/10.1038/s41467-019-13262-7 (2019).Article 
CAS 

Google Scholar 
Lindsey, N. J., Dawe, T. C. & Ajo-Franklin, J. B. Illuminating seafloor faults and ocean dynamics with dark fiber distributed acoustic sensing. Science 366, 1103–1107. https://doi.org/10.1126/science.aay5881 (2019).Article 
ADS 
CAS 
PubMed 

Google Scholar 
Sladen, A. et al. Distributed sensing of earthquakes and ocean-solid Earth interactions on seafloor telecom cables. Nat. Commun. 10, 5777. https://doi.org/10.1038/s41467-019-13793-z (2019).Article 
ADS 
CAS 
PubMed 
PubMed Central 

Google Scholar 
Williams, E. F. et al. Surface gravity wave interferometry and ocean current monitoring with ocean-bottom DAS. J. Geophys. Res. Oceans 127, e2021JC018375. https://doi.org/10.1029/2021JC018375 (2022).Article 
ADS 

Google Scholar 
Zhan, Z. et al. Optical polarization-based seismic and water wave sensing on transoceanic cables. Science 371, 931–936. https://doi.org/10.1126/science.abe6648 (2021).Article 
ADS 
CAS 
PubMed 

Google Scholar 
Waagaard, O. H. et al. Real-time low noise distributed acoustic sensing in 171 km low loss fiber. OSA Contin. 4, 688–701. https://doi.org/10.1364/OSAC.408761 (2021).Article 
CAS 

Google Scholar 
Rivet, D., de Cacqueray, B., Sladen, A., Roques, A. & Calbris, G. Preliminary assessment of ship detection and trajectory evaluation using distributed acoustic sensing on an optical fiber telecom cable. J. Acoust. Soc. Am. 149, 2615–2627. https://doi.org/10.1121/10.0004129 (2021).Article 
ADS 
PubMed 

Google Scholar 
Taweesintananon, K., Landrø, M., Brenne, J. K. & Haukanes, A. Distributed acoustic sensing for near-surface imaging using submarine telecommunication cable: a case study in the Trondheimsfjord, Norway. Geophysics 86, B303–B320. https://doi.org/10.1190/geo2020-0834.1 (2021).Article 

Google Scholar 
Matsumoto, H. et al. Detection of hydroacoustic signals on a fiber-optic submarine cable. Sci. Rep. 11, 2797. https://doi.org/10.1038/s41598-021-82093-8 (2021).Article 
ADS 
CAS 
PubMed 
PubMed Central 

Google Scholar 
Bouffaut, L. et al. Eavesdropping at the speed of light: Distributed acoustic sensing of baleen whales in the Arctic. Front. Mar. Sci. 9, 901348. https://doi.org/10.3389/fmars.2022.901348 (2022).Article 

Google Scholar 
Jones, N. The quest for quieter seas. Nature 568, 158–161. https://doi.org/10.1038/d41586-019-01098-6 (2019).Article 
ADS 
CAS 
PubMed 

Google Scholar 
Williams, R. et al. Chronic ocean noise and cetacean population models. J. Cetacean Res. Manag. 21, 85–94. https://doi.org/10.47536/jcrm.v21i1.202 (2020).Article 

Google Scholar 
Roman, J. et al. Whales as marine ecosystem engineers. Front. Ecol. Environ. 12, 377–385. https://doi.org/10.1890/130220 (2014).Article 

Google Scholar 
Pershing, A. J., Christensen, L. B., Record, N. R., Sherwood, G. D. & Stetson, P. B. The impact of whaling on the ocean carbon cycle: Why bigger was better. PLoS ONE 5, 1–9. https://doi.org/10.1371/journal.pone.0012444 (2010).Article 
CAS 

Google Scholar 
IUCN – SSC Cetacean Specialist Group. Status of the World’s cetaceans (2021). https://iucn-csg.org/status-of-the-worlds-cetaceans/.Bailey, H. et al. Behavioural estimation of blue whale movements in the Northeast Pacific from state-space model analysis of satellite tracks. Endanger. Species Res. 10, 93–106. https://doi.org/10.3354/esr00239 (2010).Article 

Google Scholar 
Thomas, P. O., Reeves, R. R. & Brownell, R. L. Jr. Status of the world’s baleen whales. Mar. Mamm. Sci. 32, 682–734. https://doi.org/10.1111/mms.12281 (2016).Article 

Google Scholar 
Grigoli, F. et al. Current challenges in monitoring, discrimination, and management of induced seismicity related to underground industrial activities: A European perspective. Rev. Geophys. 55, 310–340. https://doi.org/10.1002/2016RG000542 (2017).Article 
ADS 

Google Scholar 
Bigg, G. R. & Hanna, E. Impacts and effects of ocean warming on the weather. In: Laffoley, D. & Baxter, J. M. (eds.) Explaining ocean warming: Causes, scale, effects and consequences, 359–372, https://doi.org/10.2305/IUCN.CH.2016.08.en (International Union for Conservation of Nature and Natural Resources (IUCN), Gland, Switzerland, 2016).Hartog, A. H. An Introduction to Distributed Optical Fibre Sensors 1st edn. (CRC Press, 2017). https://doi.org/10.1201/9781315119014.Book 

Google Scholar 
Lin, J., Fang, S., Li, X., Wu, R. & Zheng, H. Seismological observations of ocean swells induced by Typhoon Megi using dispersive microseisms recorded in coastal areas. Remote Sens.https://doi.org/10.3390/rs10091437 (2018).Article 

Google Scholar 
Munk, W. H., Miller, G. R., Snodgrass, F. E., Barber, N. F. & Deacon, G. E. R. Directional recording of swell from distant storms. Philos. Trans. R. Soc. Lond. Ser. A Math. Phys. Sci. 255, 505–584. https://doi.org/10.1098/rsta.1963.0011 (1963).Article 
ADS 

Google Scholar 
Mellinger, D. K. & Clark, C. W. Blue whale (balaenoptera musculus) sounds from the North Atlantic. J. Acoust. Soc. Am. 114, 1108–1119. https://doi.org/10.1121/1.1593066 (2003).Article 
ADS 
PubMed 

Google Scholar 
Ou, H., Au, W. W., Van Parijs, S., Oleson, E. M. & Rankin, S. Discrimination of frequency-modulated baleen whale downsweep calls with overlapping frequencies. J. Acoust. Soc. Am. 137, 3024–3032. https://doi.org/10.1121/1.4919304 (2015).Article 
ADS 
PubMed 

Google Scholar 
Saito, T. & Tsushima, H. Synthesizing ocean bottom pressure records including seismic wave and tsunami contributions: Toward realistic tests of monitoring systems. J. Geophys. Res. Solid Earth 121, 8175–8195. https://doi.org/10.1002/2016JB013195 (2016).Article 
ADS 

Google Scholar 
Rørstadbotnen, R. A. et al. Analysis of a local earthquake in the Arctic using a 120 km long fibre-optic cable. In 83rd EAGE Annual Conference & Exhibition, vol. 2022 of Conference Proceedings, 1–5, https://doi.org/10.3997/2214-4609.202210404 (European Association of Geoscientists & Engineers, 2022).Bromirski, P. D. & Duennebier, F. K. The near-coastal microseism spectrum: Spatial and temporal wave climate relationships. J. Geophys. Res. Solid Earth 107, ESE 5-1-20. https://doi.org/10.1029/2001JB000265 (2002).Article 

Google Scholar 
Pasch, R. J. National hurricane center tropical cyclone report: Tropical storm Edouard (AL052020). Technical report, National Oceanic and Atmospheric Administration (2021). https://www.nhc.noaa.gov/data/tcr/AL052020_Edouard.pdf.Gobato, R. & Heidari, A. Cyclone Bomb hits Southern Brazil in 2020. J. Atmos. Sci. Res. 3, 8–12. https://doi.org/10.30564/jasr.v3i3.2163 (2020).Article 

Google Scholar 
Khalid, A., de Lima, Ad. S., Cassalho, F., Miesse, T. & Ferreira, C. Hydrodynamic and wave responses during storm surges on the Southern Brazilian Coast: A real-time forecast system. Water 12, 3397. https://doi.org/10.3390/w12123397 (2020).Article 

Google Scholar 
Ćirić, J. Weather warning for Central Highland, Northwest Iceland (2020). https://www.icelandreview.com/travel/weather-warning-for-central-highland-northwest-iceland/.Schoeman, R. P., Patterson-Abrolat, C. & Plön, S. A global review of vessel collisions with marine animals. Front. Mar. Sci. 7, 292. https://doi.org/10.3389/fmars.2020.00292 (2020).Article 

Google Scholar 
Ringrose, P. S. et al. Storage of carbon dioxide in saline aquifers: Physicochemical processes, key constraints, and scale-up potential. Annu. Rev. Chem. Biomol. Eng. 12, 471–494. https://doi.org/10.1146/annurev-chembioeng-093020-091447 (2021).Article 
CAS 
PubMed 

Google Scholar 
Nishimura, T. et al. Source location of volcanic earthquakes and subsurface characterization using fiber-optic cable and distributed acoustic sensing system. Sci. Rep. 11, 6319. https://doi.org/10.1038/s41598-021-85621-8 (2021).Article 
CAS 
PubMed 
PubMed Central 

Google Scholar 
Ardhuin, F. & Herbers, T. H. C. Noise generation in the solid Earth, oceans and atmosphere, from nonlinear interacting surface gravity waves in finite depth. J. Fluid Mech. 716, 316–348. https://doi.org/10.1017/jfm.2012.548 (2013).Article 
ADS 
MATH 

Google Scholar 
Airy, G. B. Encyclopaedia Metropolitana (1817–1845), vol. 3 of Mixed Sciences, chap. Tides and waves (London, 1841).Craik, A. D. The origins of water wave theory. Annu. Rev. Fluid Mech. 36, 1–28. https://doi.org/10.1146/annurev.fluid.36.050802.122118 (2004).Article 
ADS 
MathSciNet 
MATH 

Google Scholar 
Matsumoto, H., Inoue, S. & Ohmachi, T. Dynamic response of bottom water pressure due to the 2011 Tohoku earthquake. J. Disaster Res. 7, 468–475. https://doi.org/10.20965/jdr.2012.p0468 (2012).Article 

Google Scholar 
Landrø, M. & Hatchell, P. Normal modes in seismic data: Revisited. Geophysics 77, W27–W40. https://doi.org/10.1190/geo2011-0094.1 (2012).Article 
ADS 

Google Scholar  More

Study areaSituated in western India’s southwestern part of the Gujarat state, the Saurashtra region typically represents the semi-arid Gujarat-Rajputana province 4B23, which covers 11 out of 33 districts of the state. The region forms a rocky tableland (altitude 300–600 m) fringed by coastal plains with an undulating central plain broken by hills and dissected by various rivers that flow in all directions24. With the longest coastline (~ 1600 km) in India, Gujarat is endowed with rich coastal biodiversity25,26. The Saurashtra coast in Gujarat is encircled by the open sea between two Gulfs (68° 58′–71° 30′ N and 22° 15′–20° 50′ E) and divided into two segments, viz. the southwestern coast from Dwarka to Diu (~ 300 km stretch) and south-eastern coast from Diu to Bhavnagar (~ 250 km stretch)26.The Asiatic Lion Landscape covers an area of ~ 30,000 km2 (permanent lion distribution range: ~ 16,000 km2; visitation record range: ~ 14,000 km2) of varied habitat types within Saurashtra. The landscape includes five protected areas (Gir National Park, Gir Wildlife Sanctuary, Paniya Wildlife Sanctuary, Mitiyala Wildlife Sanctuary, and Girnar Wildlife Sanctuary) and other forest classes (reserved forests, protected forests, and unclassed forests).The coastal habitats extend across the districts of Bhavnagar, Amreli, Gir-Somnath, and Junagadh (Fig. 1). Within these districts (Fig. 1), the tehsils (sub-divisions/taluka) of Mangrol, Malia, Patan-Veraval, Sutrapada, Kodinar and Una are categorized under the southwestern coast (hereafter western coastal habitat), Jafrabad, Rajula, form the south-eastern coast and Mahuva and Talaja constitute the Bhavnagar coast and represent distinct lion range units (Fig. 1). The total area covered in the study is 2843 km2 on the eastern coast and 1413 km2 on the western coast (Fig. 1).The Saurashtra region is bestowed with three distinct seasons, viz. dry and hot summer (March–June), monsoon (July–October), and primarily dry winter (November–February). It receives a mean annual rainfall of ~ 600 mm, with most rainfall during the southwest monsoon27. The mean maximum and minimum temperatures are 34 °C and 19 °C, respectively28. There is a 110 km2 stretch of forests along the coast. The rest of the areas are multi-use consisting of private, industrial, pastoral and wastelands of varied ownerships. The natural vegetation primarily consists of Prosopis juliflora and Casuarina equistsetifolia. On the beach and dune areas, vegetation such as Ipomea pescaprae, Sporobolus trinules, Fimrystylis sp., Crotalaria sp., and Euphorbia nivuleria29. The mudflats along the coast are restricted to Talaja, Mahuva, Pipavav Port, Jafrabad creek, and Porbandar, sparsely covered by the Avicennia marina29. Fisheries, agriculture, horticulture, livestock rearing, and some large- and small-scale industries are the leading economies in the coastal belt.Coastal segments are characterized by the variety of vegetation, sandy beaches, small cliffs, wave-cut platforms, open and submerged dunes, minor estuaries, embankments, and transition from the open sea to gulf environment with tidal mud26,29 and also support a diverse assemblage of biodiversity25. This biodiversity is further enriched by several perennial/ephemeral rivers originating from the Gir PA (Shetrunji, Machundari, Raval, Ardak, Bhuvatirth, Shinghoda, Hiran, Saraswati, etc.)12. These rivers meet the sea at different sections of the coast, forming prominent coastal ecosystems25. The riverine tracts act as important corridors for wildlife movement9,12,30. Dispersing through these corridors, lions have started inhabiting these coastal habitats30,31.MethodsAll the research activities involved in this study on Asiatic lions were carried out after taking due permission from the Ministry of Environment, Forests & Climate Change (MoEF&CC), Government of India (Letter No.: F. No. 1-50/2018 WL) and Principal Chief Conservator of Forests (Wildlife) & Chief Wildlife Warden, Gujarat State, Gandhinagar (Letter No.: WLP 26B 781-83/2019-20). Procedures and protocols were followed as per the Standard Operating Procedures of the Gujarat Forest Department, Government of Gujarat, concerning the handling of wild animals. Qualified and experienced veterinarians and their team carried out all procedures related to radio-collaring. Moreover, the study is reported in accordance with ‘Animal Research: Reporting of In Vivo Experiments’ (ARRIVE) guidelines as applicable.A long-term lion monitoring project was initiated in 2019 by the Gujarat Forest Department to understand the movement patterns and ecology of lions in the Asiatic Lion Landscape. Looking at the heterogeneity and vastness of the coastal areas, ten individuals were carefully selected for satellite radio-collaring based on their frequent movement in different coastal habitats and monitored from 2019 to 2021.The lions were deployed with Vertex Plus GPS Collars (Vectronics Aerospace GmbH, Berlin, Germany) that weighed less than three per cent of the individual’s body weight, irrespective of age and sex. The lions were immobilized using a combination of Ketamine hydrochloride (2.2 mg per kg body weight; Ketamine, Biowet, Pulawy) and Xylazine hydrochloride (1.1 mg per kg body weight; Xylaxil, Brilliant Bio Pharma Pvt. Ltd., Telangana)32 administered intramuscularly using a gas-powered Telinject™ G.U.T 50 (Telinject Inc., Dudenhofen, Germany) dart delivery system. A blindfold was placed to protect the eyes and decrease visual stimuli33,34. Each sedated individual was sexed, aged, and measured as per the standard operating procedure (SOP) of the Gujarat Forest Department, Government of Gujarat, and recorded the data in the trapping datasheet. The radio-collars were deployed considering the neck girth of the individual, ensuring free movement of it so as not to hamper the individual’s routine activities. After deploying the radio-collar, we used the specific antidote for Xylazine, i.e., Yohimbine hydrochloride (0.1–0.15 mg per kg body weight; Yohimbe, Equimed, USA) intravenously, resulting in the total recovery of immobilized individuals32 within 5–10 min. The individuals were intensively monitored for 72 h and, after that, regularly monitored throughout the functional period of the radio-collars. The entire radio-collaring exercise was carried out by trained and experienced veterinary officers and their teams that constituted wildlife health care personnel and field staff.Each collar had a unique VHF and UHF frequency. The radio-collars were equipped with a programmable GPS schedule and configured to record the location fixes at every hour and provided the data through the constellation of low-earth-orbit Iridium satellite data service (Iridium Communications Inc., Virginia, USA) at four-hour intervals after getting activated. The data logs included location fixes in degree decimal format (latitude/longitude), speed (km/hour), altitude (meters above mean sea level), UTC timestamp (dd-mm-yyyy h:m:s), direction (degrees), and temperature (Celsius). Radio-collars were equipped with mortality sensors and a programmable drop-off activation system. Gir Hi-Tech Monitoring Unit, Sasan-Gir, Gujarat, monitored and coordinated these activities. The location data from each radio-collar was downloaded using the GPS Plus X software (Vectronics Aerospace GmbH, Berlin, Germany) in the Gir Hi-Tech Monitoring Unit (a technology-driven scientific monitoring initiative in the landscape established in 2019 at Sasan-Gir, Gujarat).Data analysisIn this study, we calculated the home range of lions resident in the coastal region using the Fixed Kernel method. We expressed them as 90% and 50% Fixed Kernel (FK) to summarize the overall home range and core area, respectively35,36,37. Additionally, the home range of lions categorized as “link lions” and lions of the protected area was summarized for comparison (Table 1).MaxEnt (version 3.4.1) stand-alone software38 was applied for fine-scaled lion distribution modelling39,40. The logistic output format was set for the MaxEnt output. 30% random lion occurrence points were used as test data to evaluate model performance. The area under the receiver operating characteristic curve (AUC) was used to evaluate the discriminative ability of the model based on the values of sensitivity (correct discrimination of true positive location points) and specificity (correct discrimination of true negative absence points)41. The Jackknife regularised training gain for the species was used to understand the effect of each variable in model building. The logical output by the MaxEnt was presented in a table format as “percent contribution” and “permutation importance” values (from 0 to 100%). Spatial inputs were prepared on the GIS platform using ArcMap (version 10.8.1, ESRI, Redlands, USA)42. Input data for MaxEnt were categorized as (i) lion occurrence data, (ii) model variables were prepared as described below:

i.

Occurrence data
At the first level, inconsistent location fixes (records with missing coordinates, time stamps, and elevation) and outliers were filtered out. Next, each lion’s hourly GPS location fixes obtained from remotely monitored radio-telemetry data were randomized to overcome spatial and temporal biases. The data was reduced by taking every three-hour location fix43,44. The data was further categorized season-wise, viz. summer, monsoon and winter. This consolidated data was then subject to spatial thinning of one kilometre using SDMtoolbox (version 2.0)45,46.

ii.

Model variables

The variables used for distribution modelling broadly included different categories of land use, including both natural habitats and anthropogenic factors, namely, roads and human settlement areas. All variables were rasterized at 10 m spatial resolution.Land Use Land Cover (LULC) data of Saurashtra was obtained from Bhaskaracharya National Institute for Space Applications and Geo-informatics (BISAG-N), Gandhinagar, Gujarat. The data was then further classified into 18 sub-classes—Forest, Sandy areas, Salt-affected, Saltpan, open scrub, dense scrub (Wastelands), Waterlogged, River/Stream/Drain, Lakes and Ponds, Mining/Industrial areas, Reservoir/Tanks, Mangrove/Swamp Area, Crop Land, Agriculture Plantation (horticulture and agro-forestry), Core urban, Mixed settlement, Peri-urban, Village (Fig. 2).Roads and highways were also analyzed as separate variables in the model. Roads were classified as village roads, major district roads, and state and national highways and digitized individually to estimate Euclidean distance further (Table 2). Euclidean distance from the human settlement (Core-urban, Peri-urban, villages and mixed settlement) was analyzed and taken as a separate input variable for the model. More

Urbanization is fast progressing in the Global South, requiring new solutions for infrastructure, services, industrial development and land and energy use for these regions. In this context, fast-growing cities in Africa can take on a leadership role in driving climate change mitigation and adaptation, disaster risk reduction and sustainable development.
Credit: Stefan Rotter / Alamy Stock PhotoCities in Africa and elsewhere in the Global South continue to grapple with the challenge of delivering equitable services, infrastructure, housing and action to respond to climate change extremes and disasters. One well-known problem is a mismatch between the pace of urban growth and the slower development of basic services and critical infrastructure. This results in, for example, deficient sanitation, water supply systems and localized waste management for large parts of the population, which in turn contribute substantially to heightened poverty and inequality. For inclusive, equitable, prosperous and climate-resilient cities, urban management needs to integrate low-income communities into the urban economy by ensuring access to water, sanitation, energy transition, waste management, poverty reduction and by improving resilience through innovative solutions.
Credit: Patrick J. Endres/Corbis Documentary/GettySuch an equitable urban transition requires changes in the urban infrastructure, and land and energy use, as well as water and ecosystem management. The key research question in this field is to find ways to ensure city-wide access to infrastructure and services, while minimizing emissions and resource use, and building resilience to climate change impacts. In this regard, cities in the Global South and Africa in particular can serve as examples for other parts of the world as they have the potential to adopt disruptive, innovative yet practical solutions to low emissions, resource minimization and resilience building.
Credit: Nature Picture Library / Alamy Stock PhotoFor example, rapid urbanization creates the opportunity to develop economic structures in African cities that strongly integrate waste by promoting recovery, recycling, re-use and repair for lengthening lifecycles. Such a circular economy can create business opportunities, while also reducing resource use, thus creating a pathway for sustainable development. Another potential solution is hybrid systems for urban water management that are off-grid and utilize multiple water sources and treatment but that can also connect to centralized water systems. Business models for micro-to-medium enterprises have the potential to integrate some of the low-income groups through these kinds of technology and building social resilience.
Credit: Images of Africa Photobank / Alamy Stock PhotoThese examples are part of a broader assessment of urban infrastructure innovations, their disruption of centralized systems and rethinking of urban form for more compact, walkable, co-located land use for low carbon intensity towards net-zero cities. However, to translate research on these new solutions into action, a shift is necessary in the planning, governing and managing of cities so as to allow for opportunities for leapfrogging to emerge and expand the possibilities of urban development for inclusive and resilient African cities. More

Precht, W. F., Gintert, B. E., Robbart, M. L., Fura, R. & van Woesik, R. Unprecedented disease-related coral mortality in Southeastern Florida. Sci. Rep. 6, 31374 (2016).ADS 
CAS 
PubMed 
PubMed Central 

Google Scholar 
NOAA. Stony Coral Tissue Loss Disease Case Definition. NOAA, Silver Spring, MD 10 (2018).Aeby, G. S. et al. Pathogenesis of a tissue loss disease affecting multiple species of corals along the Florida Reef Tract. Front Mar. Sci. 6, 00678 (2019).
Google Scholar 
Landsberg, J. H. et al. Stony coral tissue loss disease in Florida is associated with disruption of host–zooxanthellae physiology. Front Mar. Sci. 7, 576013 (2020).
Google Scholar 
Neely, K. L., Macaulay, K. A., Hower, E. K. & Dobler, M. A. Effectiveness of topical antibiotics in treating corals affected by Stony Coral Tissue Loss Disease. PeerJ 8, 9289 (2020).
Google Scholar 
Shilling, E. N., Combs, I. R. & Voss, J. D. Assessing the effectiveness of two intervention methods for stony coral tissue loss disease on Montastraea cavernosa. Sci. Rep. 11, 8566 (2021).CAS 
PubMed 
PubMed Central 

Google Scholar 
Walker, B. K., Turner, N. R., Noren, H. K. G., Buckley, S. F. & Pitts, K. A. Optimizing stony coral tissue loss disease (SCTLD) intervention treatments on Montastraea cavernosa in an endemic zone. Front Mar. Sci. 8, 666224 (2021).
Google Scholar 
Work, T. M. et al. Viral-like particles are associated with endosymbiont pathology in Florida corals affected by stony coral tissue loss disease. Front Mar. Sci. 8, 750658 (2021).
Google Scholar 
Veglia, A. J. et al. Alphaflexivirus genomes in stony coral tissue loss disease-affected, disease-exposed, and disease-unexposed coral colonies in the U.S. Virgin Islands. Microbiol. Resource Announc. 11, e01199-e1221 (2022).CAS 

Google Scholar 
Rosales, S. M. et al. Bacterial metabolic potential and micro-eukaryotes enriched in stony coral tissue loss disease lesions. Front Mar. Sci. 8, 776859 (2022).
Google Scholar 
Rosales, S. M., Clark, A. S., Huebner, L. K., Ruzicka, R. R. & Muller, E. M. Rhodobacterales and Rhizobiales are associated with stony coral tissue loss disease and its suspected sources of transmission. Front. Microbiol. 11, 681 (2020).PubMed 
PubMed Central 

Google Scholar 
Studivan, M. S. et al. Reef sediments can act as a stony coral tissue loss disease vector. Front Mar. Sci. 8, 815698 (2022).
Google Scholar 
Meyer, J. L. et al. Microbial community shifts associated with the ongoing stony coral tissue loss disease outbreak on the Florida Reef Tract. Front. Microbiol. 10, 2244 (2019).PubMed 
PubMed Central 

Google Scholar 
Ushijima, B. et al. Disease diagnostics and potential coinfections by Vibrio coralliilyticus during an ongoing coral disease outbreak in Florida. Front. Microbiol. 11, 2682 (2020).
Google Scholar 
Meiling, S. S. et al. Variable species responses to experimental stony coral tissue loss disease (SCTLD) exposure. Front Mar. Sci. 8, 670829 (2021).
Google Scholar 
Becker, C. C., Brandt, M., Miller, C. A. & Apprill, A. Microbial bioindicators of stony coral tissue loss disease identified in corals and overlying waters using a rapid field-based sequencing approach. Environ. Microbiol. 24, 1166–1182 (2021).PubMed 

Google Scholar 
Dobbelaere, T., Muller, E. M., Gramer, L. J., Holstein, D. M. & Hanert, E. Coupled epidemio-hydrodynamic modeling to understand the spread of a deadly coral disease in Florida. Front Mar. Sci. 7, 591881 (2020).
Google Scholar 
Dobbelaere, T. et al. Connecting the dots: Transmission of stony coral tissue loss disease from the Marquesas to the Dry Tortugas. Front Mar. Sci. 9, 778938 (2022).
Google Scholar 
Muller, E. M., Sartor, C., Alcaraz, N. I. & van Woesik, R. Spatial epidemiology of the stony-coral-tissue-loss disease in Florida. Front Mar. Sci. 7, 00163 (2020).
Google Scholar 
Sharp, W. C., Shea, C. P., Maxwell, K. E., Muller, E. M. & Hunt, J. H. Evaluating the small-scale epidemiology of the stony-coral-tissue-loss-disease in the middle Florida Keys. PLoS ONE 15, e0241871 (2020).CAS 
PubMed 
PubMed Central 

Google Scholar 
Williamson, O. M., Dennison, C. E., O’Neil, K. L. & Baker, A. C. Susceptibility of Caribbean brain coral recruits to stony coral tissue loss disease (SCTLD). Front Mar. Sci. 9, 821165 (2022).
Google Scholar 
Noonan, K. R. & Childress, M. J. Association of butterflyfishes and stony coral tissue loss disease in the Florida Keys. Coral Reefs 39, 1581–1590 (2020).
Google Scholar 
Dahlgren, C., Pizarro, V., Sherman, K., Greene, W. & Oliver, J. Spatial and temporal patterns of stony coral tissue loss disease outbreaks in the Bahamas. Front Mar. Sci. 8, 682114 (2021).
Google Scholar 
Rosenau, N. A. et al. Considering commercial vessels as potential vectors of stony coral tissue loss disease. Front Mar. Sci. 8, 709764 (2021).
Google Scholar 
Roth, L., Kramer, P., Doyle, E. & O’Sullivan, C. Caribbean SCTLD Dashboard. Available www.agrra.org. Accessed 06 Mar 2021. (2020).Brandt, M. E. et al. The emergence and initial impact of stony coral tissue loss disease (SCTLD) in the United States Virgin Islands. Front Mar. Sci. 8, 715329 (2021).
Google Scholar 
Bailey, S. A. et al. Trends in the detection of aquatic non-indigenous species across global marine, estuarine and freshwater ecosystems: A 50-year perspective. Divers. Distrib. 26, 1780–1797 (2020).MathSciNet 

Google Scholar 
Hewitt, C. L., Gollasch, S. & Minchin, D. The vessel as a vector: Biofouling, ballast water and sediments. In Biological Invasions in Marine Ecosystems Vol. 204 (eds Rilov, G. & Crooks, J. A.) 117–131 (Springer, 2009).
Google Scholar 
Zabin, C. J. et al. Small boats provide connectivity for nonindigenous marine species between a highly invaded international port and nearby coastal harbors. Manag. Biol. Invas. 5, 97–112 (2014).
Google Scholar 
Ashton, G. V., Zabin, C. J., Davidson, I. C. & Ruiz, G. M. Recreational boats routinely transfer organisms and promote marine bioinvasions. Biol. Invas. 24, 1083–1096 (2022).
Google Scholar 
Drake, L. A., Doblin, M. A. & Dobbs, F. C. Potential microbial bioinvasions via ships’ ballast water, sediment, and biofilm. Mar. Pollut. Bull. 55, 333–341 (2007).CAS 
PubMed 

Google Scholar 
Pagenkopp Lohan, K. M., Fleischer, R. C., Carney, K. J., Holzer, K. K. & Ruiz, G. M. Amplicon-based pyrosequencing reveals high diversity of protistan parasites in ships’ ballast water: Implications for biogeography and infectious diseases. Microb. Ecol. 71, 530–542 (2015).PubMed 

Google Scholar 
Ruiz, G. M. et al. Global spread of microorganisms by ships. Nature 408, 49–50 (2000).ADS 
CAS 
PubMed 

Google Scholar 
Hwang, J., Park, S. Y., Lee, S. & Lee, T. K. High diversity and potential translocation of DNA viruses in ballast water. Mar. Pollut. Bull. 137, 449–455 (2018).CAS 
PubMed 

Google Scholar 
Shikuma, N. J. & Hadfield, M. G. Marine biofilms on submerged surfaces are a reservoir for Escherichia coli and Vibrio cholerae. Biofouling 26, 39–46 (2009).
Google Scholar 
Aguirre-Macedo, M. L. et al. Ballast water as a vector of coral pathogens in the Gulf of Mexico: The case of the Cayo Arcas coral reef. Mar. Pollut. Bull. 56, 1570–1577 (2008).CAS 
PubMed 

Google Scholar 
Bruno, J. F. The coral disease triangle. Nat. Clim. Chang. 5, 302–303 (2015).ADS 

Google Scholar 
Lakshmi, E., Priya, M. & Achari, V. S. An overview on the treatment of ballast water in ships. Ocean Coast. Manag. 199, 105296 (2021).
Google Scholar 
Petersen, N. B., Madsen, T., Glaring, M. A., Dobbs, F. C. & Jørgensen, N. O. G. Ballast water treatment and bacteria: Analysis of bacterial activity and diversity after treatment of simulated ballast water by electrochlorination and UV exposure. Sci. Total Environ. 648, 408–421 (2019).ADS 
CAS 
PubMed 

Google Scholar 
Romero-Martínez, L., Moreno-Andrés, J., Acevedo-Merino, A. & Nebot, E. Evaluation of ultraviolet disinfection of microalgae by growth modeling: Application to ballast water treatment. J. Appl. Phycol. 28, 2831–2842 (2016).
Google Scholar 
First, M. R. et al. Stratification of living organisms in ballast tanks: How do organism concentrations vary as ballast water is discharged?. Environ. Sci. Technol. 47, 4442–4448 (2013).ADS 
CAS 
PubMed 

Google Scholar 
Drake, L. A. et al. Microbial ecology of ballast water during a transoceanic voyage and the effects of open-ocean exchange. Mar. Ecol. Prog. Ser. 233, 13–20 (2002).ADS 

Google Scholar 
Khandeparker, L., Kuchi, N., Desai, D. V. & Anil, A. C. Changes in the ballast water tank bacterial community during a trans-sea voyage: Elucidation through next generation DNA sequencing. J. Environ. Manag. 273, 111018 (2020).
Google Scholar 
Ruiz, G. M., Lorda, J., Arnwine, A. & Lion, K. Shipping patterns associated with the Panama Canal: Effects on biotic exchange? In Bridging Divides Vol. 83 (eds Gollasch, S. et al.) 113–126 (Springer, 2006).
Google Scholar 
Pagano, A., Wang, G., Sánchez, O., Ungo, R. & Tapiero, E. The impact of the Panama Canal expansion on Panama’s maritime cluster. Marit. Policy Manag. 43, 164–178 (2016).
Google Scholar 
Muirhead, J. R., Minton, M. S., Miller, W. A. & Ruiz, G. M. Projected effects of the Panama Canal expansion on shipping traffic and biological invasions. Divers. Distrib. 21, 75–87 (2015).
Google Scholar 
Ros, M. et al. The Panama Canal and the transoceanic dispersal of marine invertebrates: Evaluation of the introduced amphipod Paracaprella pusilla Mayer, 1890 in the Pacific Ocean. Mar. Environ. Res. 99, 204–211 (2014).ADS 
CAS 
PubMed 

Google Scholar 
Stehouwer, P. P., Buma, A. & Peperzak, L. A comparison of six different ballast water treatment systems based on UV radiation, electrochlorination and chlorine dioxide. Environ. Technol. 36, 2094–2104 (2015).CAS 
PubMed 

Google Scholar 
Wu, Y., Li, Z., Du, W. & Gao, K. Physiological response of marine centric diatoms to ultraviolet radiation, with special reference to cell size. J. Photochem. Photobiol., B 153, 1–6 (2015).CAS 

Google Scholar 
Aguirre, L. E. et al. Diatom frustules protect DNA from ultraviolet light. Sci. Rep. 8, 5138 (2018).ADS 
PubMed 
PubMed Central 

Google Scholar 
First, M. R. & Drake, L. A. Life after treatment: Detecting living microorganisms following exposure to UV light and chlorine dioxide. J. Appl. Phycol. 26, 227–235 (2014).CAS 

Google Scholar 
Liebich, V., Stehouwer, P. P. & Veldhuis, M. Re-growth of potential invasive phytoplankton following UV-based ballast water treatment. Aquat. Invas. 7, 29–36 (2012).
Google Scholar 
Hess-Erga, O. K., Moreno-Andrés, J., Enger, Ø. & Vadstein, O. Microorganisms in ballast water: Disinfection, community dynamics, and implications for management. Sci. Total Environ. 657, 704–716 (2019).ADS 
CAS 
PubMed 

Google Scholar 
Endresen, Ø., Lee Behrens, H., Brynestad, S., Bjørn Andersen, A. & Skjong, R. Challenges in global ballast water management. Mar. Pollut. Bull. 48, 615–623 (2004).CAS 
PubMed 

Google Scholar 
Vorkapić, A., Radonja, R. & Zec, D. Cost efficiency of ballast water treatment systems based on ultraviolet irradiation and electrochlorination. Promet Traffic Transp. 30, 343–348 (2018).
Google Scholar 
King, D., Hagan, P., Riggio, M. & Wright, D. Preview of global ballast water treatment markets. J. Mar. Eng. Technol. 11, 3–15 (2012).
Google Scholar 
Wang, Z., Saebi, M., Corbett, J. J., Grey, E. K. & Curasi, S. R. Integrated biological risk and cost model analysis supports a geopolitical shift in ballast water management. Environ. Sci. Technol. 55, 12791–12800 (2021).CAS 
PubMed 

Google Scholar 
Moreno-Andrés, J. & Peperzak, L. Operational and environmental factors affecting disinfection byproducts formation in ballast water treatment systems. Chemosphere 232, 496–505 (2019).ADS 
PubMed 

Google Scholar 
David, M., Linders, J., Gollasch, S. & David, J. Is the aquatic environment sufficiently protected from chemicals discharged with treated ballast water from vessels worldwide? A decadal environmental perspective and risk assessment. Chemosphere 207, 590–600 (2018).ADS 
CAS 
PubMed 

Google Scholar 
U.S. Environmental Protection Agency. Generic protocol for the verification of ballast water treatment technology, version 5.1. Report number EPA/600/R-10/146. Washington, D.C. 157 (2010).Evans, J. S., Paul, V. J., Ushijima, B. & Kellogg, C. A. Combining tangential flow filtration and size fractionation of mesocosm water as a method for the investigation of waterborne coral diseases. Biol. Methods Protocols 7, bpac007 (2022).
Google Scholar 
Fujimoto, M. et al. Application of Ion Torrent sequencing to the assessment of the effect of alkali ballast water treatment on microbial community diversity. PLoS ONE 9, e107534 (2014).ADS 
PubMed 
PubMed Central 

Google Scholar 
United States Coast Guard. Ballast Water Best Management Practices to Reduce the Likelihood of Transporting Pathogens That May Spread Stony Coral Tissue Loss Disease. Marine Safety Information Bulletin 07–19. Washington, D.C. 2 (2019).Bolton, J. R. & Linden, K. G. Standardization of methods for fluence (UV dose) determination in bench-scale UV experiments. J. Environ. Eng. 129, 209–215 (2003).CAS 

Google Scholar 
Enochs, I. C. et al. The influence of diel carbonate chemistry fluctuations on the calcification rate of Acropora cervicornis under present day and future acidification conditions. J. Exp. Mar. Biol. Ecol. 506, 135–143 (2018).CAS 

Google Scholar 
R Core Team. R: A language and environment for statistical computing. R Foundation for Statistical Computing, Vienna, Austria. URL https://www.R-project.org/. Preprint at https://www.r-project.org/ (2019).Therneau, T. M. survival: A package for survival analysis in R. R package version 3.2–13. (2021).Kassambara, A., Kosinski, M. & Biecek, P. survminer: Drawing survival curves using “ggplot2”. R package version 0.4.9. (2021).Bakalar, G. Review of interdisciplinary devices for detecting the quality of ship ballast water. Springerplus 3, 468 (2014).PubMed 
PubMed Central 

Google Scholar 
Water Environmental Federation & American Public Health Association. Standard methods for the examination of water and wastewater. Washington, D.C. 21 (2005).Steinberg, M. K., Lemieux, E. J. & Drake, L. A. Determining the viability of marine protists using a combination of vital, fluorescent stains. Mar. Biol. 158, 1431–1437 (2011).
Google Scholar 
Oksanen, J. et al. vegan: Community ecology package. R package version 2.0–10. (2015).Martinez Arbizu, P. pairwiseAdonis: Pairwise multilevel comparison using adonis. R package version 0.4. (2020).Studivan, MS. Mstudiva/SCTLD-ballast-transmission: Stony coral tissue loss disease ballast transmission and treatment (Version 1.0), Zenodo, https://doi.org/10.5281/zenodo.6561517 (2022). More