Philippa Kaur

To our knowledge, this is the only work done on the terrestrial biodiversity status in the direct vicinity of a limited duration volcanic eruption. In this contribution, we document and assess the impact on the main plant and animal groups within the ecosystems during a volcanic eruption (Table 1). While some groups were clearly disadvantaged: ferns and herbaceous plants as well as invertebrates and saurians (lizards and geckos); other groups such as conifers and woody shrubs showed better resilience, as did the birds.This study is particularly important because of its location in a Mediterranean biodiversity hotspot13,14, harbouring a unique ecosystem of oceanic island organisms (38% of the Canary archipelago endemicity). Islands indeed exhibit a disproportionate amount of the world’s biodiversity but unfortunately a high number of extinctions have also occurred there14. The biodiversity in the south of the island is poorer than in the north. This is probably explained in part by the relatively frequent volcanic activity featuring seven major eruptions since 1585, including this one in 2021 (see15), which led to alternating destruction and neo-colonization processes.Concerning the flora, the Canary pine forest was the most affected ecosystem and vegetation type, as it is dominant in the vicinity of the new volcanic vents. The southern slopes of this forest were the most disturbed area due to the location of the volcano, combined with the prevailing northeasterly trade winds (Fig. 1). Tephra fallout and sulphurous gases were the main factors that affected the pine forest, over a vast surface area. Furthermore, the local xerophytic and thermophilous habitats also lost much of their surface area. In contrast to the pine forest, this drastic reduction was caused by the progressive downslope expansion of the lava flows.The Canary Island pine was thus notably affected by tephra fall, sulphuric acid aerosol12, and short episodes of acid rain. However, this conifer shows high resistance to temperature, confirming its great adaptation to volcanic events16, which is probably also one of the keys to its resistance to the more frequent present-day wildfires17. This pine species has evolved among volcanoes for the last 13 My16 and has adapted successfully to high temperatures. Moreover, thunderstorms with lightning occur in the Canaries together with abundant rainfall; consequently, wild forest fires should presumably not have been so frequent in the island’s past, before human colonization. In this habitat it is also remarkable that epiphytic lichens (U. articulata) apparently resisted on the pines until the 12th week, considering their high sensitivity to anthropogenic pollution18.The life cycle of flowering plants was drastically disrupted due to all the above factors, with great impact on foliage, photosynthesis, and growth. However, soil changes due to the deposition of tephra and its lixiviation by rain is one of the most dramatic factors affecting plants and a long-term impact of volcanic eruptions19. The nearest individuals to the crater were most directly affected by intense tephra falls and concentrated volcanic gases (SO2, HCl, HF, CO2). However, plants located in the nearest 200 m to the lava flows but at more than 2 km from the crater were presumably more disturbed by the high temperature of the slow-cooling lava and its lesser gas emissions.Large woody plants exhibited a better frequency of survival than smaller ones in the face of this extreme stress (Table S1 and19). In the Hekla area (Iceland), most trees have thickened trunks, indicating that those trees that survive have had a long life subjected to frequent volcanic damage19. Secondary woodiness of island plants (sensu20) has been traditionally related to drought20,21, ecological shift22 or a counter-selection of inbreeding depression in founding island populations23. However, this adaptation also favours the resistance of many shrubby plants to high temperatures close to craters and lava flows but primarily their resistance to the intense tephra falls that affect a much larger area. In addition, plant and stem height plays a fundamental role in overcoming the deep layers of deposits. This latter effect was particularly important up to 2.5 km from the crater (tephra thickness  > 30 cm) (Figs. 1 and 2), as the herbaceous plants were completely buried, sometimes to more than 1.5 m depth. Therefore, the seed bank has also probably been rendered largely non-functional. However, deposits were recorded over almost the whole island, indicating that longer lasting or more intense eruptions would severely affect an even larger area. Such events have been hitherto ignored in the intensely discussed “island woodiness” debate21,23,24,25,26,27. We found surviving populations of endemic woody taxa heavily impacted by tephra deposits close to lava flows, across a wide range of genera such as Rumex (R. lunaria), Echium (E. brevirame), Euphorbia (E. lamarckii, E. canariensis and E. balsamifera), Aeonium (A. davidbramwellii), Rubia (R. fruticosa), Schizogyne (S. sericea), Carlina (C. falcata) or Sonchus (S. hierrensis) (Table S2), which coincide with the general list of woody Canary plants20. Most members of these genera in other ecosystems on continents are mainly herbaceous. As such eruptions and their impacts due to ash depositions are frequent events on volcanic islands, e.g. several times within a century on La Palma, this is a “frequent” selective process at evolutionary time scales.With regard to the fauna, the invertebrate community collapsed during the first two weeks (Table S2), probably due to rapid deterioration of the growth state of plants. These changes in the invertebrates were caused by the tephra contacting the cuticular lipid layer28 and water loss due to tegument abrasion29. In this period, many insect pests (especially whitefly pupae) in banana plantations (farmers’ observations) were drastically reduced. This sudden decrease in insect populations affected the whole food web and probably caused part of the ecological collapse of saurian and some passerine communities30. In the case of lizards, smaller individuals seem to resist the adverse conditions better than large ones, as observed in other eruptions3. This could be linked to their lower food requirements and greater ease in finding refuges. Loss of body condition in lizards post-eruption has been recorded and negatively affects reproduction quality31. However, some lizards have shown a good ability to find food in the tephra substrate32. We found abundant tephra particles in some vertebrate droppings (lizards, birds, and mammals) during the eruption, probably involuntarily ingested. At least in bats, ingestion during feeding produces physiological stress that is likely related to baldness, high ectoparasite loads or possible mineral deficiencies33.As described in the Canary Islands, some passerines show high fidelity to their territories (see34). During the eruption, Sardinian warblers (Curruca melanocephala) maintained their territories until the imminent arrival of lava flows. Larger birds (kestrels F. tinnunculus, ravens C. corax and buzzards B. buteo) were well able to continue flying in the areas surrounding the crater. Furthermore, some cases like F. tinnunculus showed great feeding plasticity in the first couple of weeks. At least six times, kestrels tried to catch birds (especially small passerines and doves), contrary to their usual diet based on abundant lizards and insects35. Widening of trophic niches in island organisms has traditionally been interpreted as linked to disharmony in island ecosystems36,37,38. However, this plasticity is tremendously beneficial in ecological catastrophes, where food becomes exceptionally scarce. In the case of bats, their flight is limited by the delicate structure of their patagium, which can be damaged by the frequent pyroclastic tephra fall. Furthermore, scarcity of insects in the first few kilometres from the crater probably led to their displacement to other more distant and richer food resource zones.As we learned from the movement capacity of the vertebrate animals that still inhabited the affected area, those with greater mobility, birds and bats, resisted the eruptive process much better than those with less mobility, e.g. saurians.Lastly, during this destructive event on La Palma, we had the opportunity to increase our knowledge of how ecological-evolutionary adaptations have favoured the survival of insular organisms. Such responses are traditionally mentioned in the context of island biology. As already mentioned, one of the most interesting findings verifies the remarkable adaptation of Canary Island pine trees (P. canariensis) to volcanism (see16), including extremely harsh ecological conditions. Other insular trends related to the prevalence of woodiness in insular flowering plants20,21, or the high trophic plasticity of some vertebrates on oceanic islands36, have not previously been associated with their potential evolution along with volcanic processes. However, such evolutionary adaptations most likely played an important role in the survival of plants and animals affected by the volcano. For this reason, it is worth considering and debating whether these previously mentioned evolutionary processes are in fact also linked to repeated volcanic episodes on oceanic islands. More

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

Publisher’s note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.This is a summary of: Sauterey, B. et al. Early Mars habitability and global cooling by H2-based methanogens. Nat. Astron. https://doi.org/10.1038/s41550-022-01786-w (2022). More

Lavelle, P. et al. Soil invertebrates and ecosystem services. Eur. J. Soil Sci. 42, S3–S15 (2006).
Google Scholar 
Nielsen, U. N. et al. Response of belowground communities to short-term phosphorus addition in a phosphorus-limited woodland. Plant Soil 391, 321–331 (2015).
Google Scholar 
Nielsen, U. N., Ayres, E., Wall, D. H. & Bardgett, R. D. Soil biodiversity and carbon cycling: A review and synthesis of studies examining diversity function relationships. Eur. J. Soil Sci. 62, 105–116 (2011).
Google Scholar 
Lu, P. et al. Composition and structure of soil fauna communities and their relationships with environmental factors in copper mine waste rock after re-vegetation. Glob. Ecol. Conserv. 32, e01889 (2021).
Google Scholar 
Lin, D. et al. Soil fauna promote litter decomposition but do not alter the relationship between leaf economics spectrum and litter decomposability. Soil Biol. Biochem. 136, 107519 (2019).
Google Scholar 
Shao, Y., Zhang, W., Liu, S., Wang, X. & Fu, S. Diversity and function of soil fauna. Acta Ecol. Sin. (in Chinese) 35, 6614–6625 (2015).
Google Scholar 
Voronin, A. N. & Kotyak, P. A. Influence of different agricultural practices on the number of soil fauna and productivity of agricultural crops. Taurida Herald Agrar. Sci. 3, 49–56 (2019).
Google Scholar 
Zhu, X. & Zhu, B. Effect of different fertilization regimes on the main groups of soil fauna in cropland of purple soil. Sci. Agric. Sin. (in Chinese) 45, 911–920 (2015).
Google Scholar 
Islam, M. U., Guo, Z., Jiang, F. & Peng, X. Does straw return increase crop yield in the wheat-maize cropping system in China? A meta-analysis. Field Crop Res. 279, 108447 (2022).
Google Scholar 
Cui, H. et al. Straw return strategies to improve soil properties and crop productivity in a winter wheat-summer maize cropping system. Eur. J. Agron. 133, 126436 (2022).
Google Scholar 
Wang, X. et al. Changes in soil characteristics and maize yield under straw returning system in dryland farming. Field Crop Res. 218, 11–17 (2018).
Google Scholar 
Gai, X. et al. Contrasting impacts of long-term application of manure and crop straw on residual nitrate-N along the soil profile in the North China Plain. Sci. Total Environ. 650, 2251–2259 (2019).ADS 
PubMed 

Google Scholar 
Wang, W. et al. Effects of different fertility-building practices on the meso-micro soil fauna communities in a black soil area. Chin. J. Appl. Environ. Biol. (in Chinese) 25, 1344–1351 (2019).
Google Scholar 
Kautz, T., López-Fando, C. & Ellmer, F. Abundance and biodiversity of soil microarthropods as influenced by different types of organic manure in a long-term field experiment in Central Spain. Appl. Soil Ecol. 33, 278–285 (2006).
Google Scholar 
Zhang, T. et al. Effects of straw returning on soil meso-and micro-arthropod community diversity in wheat-maize fields in North China. Chin. J. Appl. Environ. Biol. (in Chinese) 25, 70–75 (2019).
Google Scholar 
Yang, P., Wang, H. & Yue, J. Ecological distribution of middle-small-size soil faunas under conservation tillage and straw mulch conditions. Res. Soil Water Conserv. (in Chinese) 20, 145–150 (2013).
Google Scholar 
Zhu, Q., Zhu, A., Zhang, J., Zhang, H. & Zhang, C. Effect of conservation tillage on soil fauna in wheat field of Huang-huai-hai Plain. J. Agro Environ. Sci. (in Chinese) 28, 1766–1772 (2009).
Google Scholar 
Cao, Z. et al. Changes in the abundance and structure of a soil mite (Acari) community under long-term organic and chemical fertilizer treatments. Appl. Soil Ecol. 49, 131–138 (2011).
Google Scholar 
Li, Y., Xu, Z., Xu, H., Chen, Y. & Ruan, H. Review of the effect of fertilizer application on the soil fauna in soil ecosystems. J. Nanjing For. Univ. Nat. Sci. Ed. (in Chinese) 42, 179–184 (2018).
Google Scholar 
McGee, K. M. & Eaton, W. D. A comparison of the wet and dry season DNA-based soil invertebrate community characteristics in large patches of the bromeliad Bromelia pinguin in a primary forest in Costa Rica. Appl. Soil Ecol. 87, 99–107 (2015).
Google Scholar 
Zhu, B., Wang, T., You, X. & Gao, M. Nutrient release from weathering of purplish rocks in the Sichuan Basin, China. Pedosphere 18, 257–264 (2008).
Google Scholar 
Zhu, B. et al. Measurements of nitrate leaching from a hillslope cropland in the Central Sichuan Basin, China. Soil Sci. Soc. Am. J. 73, 1419–1426 (2009).ADS 

Google Scholar 
He, Y. Purple Soil of China Part (II) (Science Press, 2003).
Google Scholar 
Huang, R. et al. Responses of soil carbon pool and soil aggregates associated organic carbon to straw and straw-derived biochar addition in a dryland cropping mesocosm system. Agric. Ecosyst. Environ. 265, 576–586 (2018).
Google Scholar 
Zhu, X., Dong, Z., Kuang, F. & Zhu, B. Effects of fertilization regimes on soil faunal communities in cropland of purple soil. Acta Ecol. Sin. (in Chinese) 33, 464–474 (2013).
Google Scholar 
Querner, P. & Bruckner, A. Combining pitfall traps and soil samples to collect Collembola for site scale biodiversity assessments. Appl. Soil. Ecol. 45, 293–297 (2010).
Google Scholar 
Smith, M. A. et al. Extreme diversity of tropical parasitoid wasps exposed by iterative integration of natural history, DNA barcoding, morphology, and collections. PNAS 105, 12359–12364 (2008).ADS 
PubMed 
PubMed Central 

Google Scholar 
Müller, C. A. et al. Meiofaunal diversity in the Atlantic Forest soil: A quest for nematodes in a native reserve using eukaryotic metabarcoding analysis. For. Ecol. Manag. 453, 117591 (2019).
Google Scholar 
Ding, J. et al. Effects of long-term fertilization on the associated microbiota of soil collembolan. Soil Biol. Biochem. 130, 141–149 (2019).
Google Scholar 
Oliverio, A. M., Gan, H., Wickings, K. & Fierer, N. A DNA metabarcoding approach to characterize soil arthropod communities. Soil Biol. Biochem. 125, 37–43 (2018).
Google Scholar 
McGee, K. M., Porter, T. M., Wright, M. & Hajibabaei, M. Drivers of tropical soil invertebrate community composition and richness across tropical secondary forests using DNA metasystematics. Sci. Rep. 10, 18429 (2020).PubMed 
PubMed Central 

Google Scholar 
Porter, T. M. et al. Variations in terrestrial arthropod DNA metabarcoding methods recovers robust beta diversity but variable richness and site indicators. Sci. Rep. 9, 18218 (2019).ADS 
PubMed 
PubMed Central 

Google Scholar 
Morise, H., Miyazaki, E., Yoshimitsu, S. & Eki, T. Profiling nematode communities in unmanaged flowerbed and agricultural field soils in Japan by DNA barcode sequencing. PLoS One 7, e51785 (2012).ADS 
PubMed 
PubMed Central 

Google Scholar 
Drummond, A. J. et al. Evaluating a multigene environmental DNA approach for biodiversity assessment. Gigascience 4, 46 (2015).PubMed 
PubMed Central 

Google Scholar 
Dopheide, A. et al. Estimating the biodiversity of terrestrial invertebrates on a forested island using DNA barcodes and metabarcoding data. Ecol. Appl. 29, e01877 (2019).PubMed 

Google Scholar 
Watts, C. et al. DNA metabarcoding as a tool for invertebrate community monitoring: A case study comparison with conventional techniques. Austral Entomol. 58, 675–686 (2019).
Google Scholar 
Kvist, S. Barcoding in the dark? A critical view of the sufficiency of zoological DNA barcoding databases and a plea for broader integration of taxonomic knowledge. Mol. Phylogenet. Evol. 69, 39–45 (2013).PubMed 

Google Scholar 
Shao, Y. et al. Nematodes as indicators of soil recovery in tailings of a lead/zinc mine. Soil Biol. Biochem. 40, 2040–2046 (2008).
Google Scholar 
Neher, D. A., Wu, J., Barbercheck, M. E. & Anas, O. Ecosystem type affects interpretation of soil nematode community measures. Appl. Soil Ecol. 30, 47–64 (2005).
Google Scholar 
Yang, C., Ji, Y., Wang, X., Yang, C. & Yu, D. W. Testing three pipelines for 18S rDNA-based metabarcoding of soil faunal diversity. Sci. China Life Sci. 56, 73–81 (2013).ADS 
PubMed 

Google Scholar 
Horton, D. J., Kershner, M. W. & Blackwood, C. B. Suitability of PCR primers for characterizing invertebrate communities from soil and leaf litter targeting metazoan 18S ribosomal or cytochrome oxidase I (COI) genes. Eur. J. Soil Biol. 80, 43–48 (2017).
Google Scholar 
Geisen, S., Laros, I., Vizcaino, A., Bonkowski, M. & de Groot, G. A. Not all are free-living: High-throughput DNA metabarcoding reveals a diverse community of protists parasitizing soil metazoa. Mol. Ecol. 24, 4556–4569 (2015).PubMed 

Google Scholar 
Clarke, L. J., Soubrier, J., Weyrich, L. S. & Cooper, A. Environmental metabarcodes for insects: In silico PCR reveals potential for taxonomic bias. Mol. Ecol. Resour. 14, 1160–1170 (2014).PubMed 

Google Scholar 
Kitagami, Y. & Matsuda, Y. High-throughput sequencing covers greater nematode diversity than conventional morphotyping on natural cedar forests in Yakushima Island, Japan. Eur. J. Soil Biol. 112, 103432 (2022).
Google Scholar 
Juliet, W. K., Lisa, B. F., Lamers, J. P. A., Till, S. & Christian, B. Soil fertility and biodiversity on organic and conventional smallholder farms in Kenya. Appl. Soil Ecol. 134, 85–97 (2019).
Google Scholar 
Li, Q., Zhou, D. & Chen, X. The accumulation decomposition and ecological effects of above-ground litter in terrestrial ecosystem. Acta Ecol. Sin. (in Chinese) 34, 3807–3819 (2014).
Google Scholar 
Tie, L. et al. Phosphorus addition reverses the negative effect of nitrogen addition on soil arthropods during litter decomposition in a subtropical forest. Sci. Total. Environ. 781, 146786 (2021).ADS 

Google Scholar 
Nottingham, A. T., Turner, B. L., Stott, A. W. & Tanner, E. V. J. Nitrogen and phosphorus constrain labile and stable carbon turnover in lowland tropical forest soils. Soil Biol. Biochem. 80, 26–33 (2015).
Google Scholar 
Xiao, Q. et al. Impact of soil thickness on productivity and nitrate leaching from sloping cropland in the upper Yangtze River Basin. Agric. Ecosyst. Environ. 311, 107266 (2021).
Google Scholar 
Zhu, X. & Zhu, B. Diversity and abundance of soil fauna as influenced by long-term fertilization in cropland of purple soil, China. Soil Till. Res. 146, 39–46 (2015).
Google Scholar 
Wei, K., Wang, J., Dong, Z., Tang, J. & Zhu, B. The combined application of organic materials and chemical fertilizer mitigates the deterioration of the trophic structure of nematode community by increasing soil N concentration. J. Soil Sci. Plant Nutr. 21, 2530–2537 (2021).
Google Scholar 
Kuo, S. Phosphorus. In Methods of Soil Analysis (ed. Sparks, D. L.) 869–919 (Soil Science Society of America, 1996).
Google Scholar 
Nelson, D. W. & Sommers, L. E. Total carbon, organic carbon and organic matter. In Methods of Soil Analysis (ed. Sparks, D. L.) 960–1010 (ASA and SSSA, 1996).
Google Scholar 
Lu, R. Analysis of Soil Agro-Chemistry (Chinese Agricultural Science and Technology Press, 2000).
Google Scholar 
Page, A. L., Miller, R. H. & Keeney, D. R. Chemical and microbiological properties. In Methods of Soil Analysis (ASA and SSSA, 1982).
Google Scholar 
Olsen, S. R., Cole, C. U., Watanabe, F. S. & Deen, L. A. Estimation of Available Phosphorus in Soil by Extracting with Sodium Bicarbonate (USDA Circular 939, 1954).
Google Scholar 
Townshend, J. L. A modification and evaluation of the apparatus for the Oostenbrink direct cottonwool filter extraction method. Nematologica 9, 106–110 (1963).
Google Scholar 
Geller, J., Meyer, C., Parker, M. & Hawk, H. Redesign of PCR primers for mitochondrial cytochrome c oxidase subunit I for marine invertebrates and application in all-taxa biotic surveys. Mol. Ecol. Resour. 13, 851–861 (2013).PubMed 

Google Scholar 
Yang, T., Song, X., Xu, X., Zhou, C. & Shi, A. A comparative analysis of spider prey spectra analyzed through the next-generation sequencing of individual and mixed DNA samples. Ecol. Evol. 11, 15444–15454 (2021).PubMed 
PubMed Central 

Google Scholar 
Chen, H. & Jiang, W. Application of high-throughput sequencing in understanding human oral microbiome related with health and disease. Front. Microbiol. 5, 508 (2014).PubMed 
PubMed Central 

Google Scholar 
Magoc, T. & Salzberg, S. L. FLASH: Fast length adjustment of short reads to improve genome assemblies. Bioinformatics 27, 2957–2963 (2011).PubMed 
PubMed Central 

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

Google Scholar 
Altschul, S. F. et al. Gapped BLAST and PSI-BLAST a new generation of protein database search programs. Nucleic Acids Res. 25, 3389–3402 (1997).PubMed 
PubMed Central 

Google Scholar 
R Core Team. R: A language and environment for statistical computing. http://www.r-project.org (2020).Shannon, C. E. A mathematical theory of communication. Bell Syst. Tech. J. 27, 379–423 (1948).MathSciNet 
MATH 

Google Scholar 
Margalef, R. Perspectives in Ecological Theory 111–119 (The University of Chicago Press, 1970).
Google Scholar 
Pielou, E. C. The measurement of diversity in different types of biological collections. J. Theor. Biol. 88, 131–144 (1966).ADS 

Google Scholar 
Zhou, Y. et al. Species richness and phylogenetic diversity of seed plants across vegetation zones of Mount Kenya, East Africa. Ecol. Evol. 8, 8930–8939 (2018).PubMed 
PubMed Central 

Google Scholar 
Wang, H. et al. Nitrogen addition reduces soil bacterial richness, while phosphorus addition alters community composition in an old-growth N-rich tropical forest in southern China. Soil Biol. Biochem. 127, 22–30 (2018).
Google Scholar 
Yang, K. et al. Responses of soil ammonia-oxidizing bacteria and archaea diversity to N, P and NP fertilization: Relationships with soil environmental variables and plant community diversity. Soil Biol. Biochem. 145, 107795 (2020).
Google Scholar 
Zhang, S., Li, Q., Lü, Y., Zhang, X. & Liang, W. Contributions of soil biota to C sequestration varied with aggregate fractions under different tillage systems. Soil Biol. Biochem. 62, 147–156 (2013).
Google Scholar  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

Data processingIn 2019, the CETUS data spanning between 2012 and 2017 was published open access through the Flanders Marine Institute (VLIZ) IPT portal and distributed by EMODnet and OBIS, in a first version of the CETUS dataset9. The data collected between 2018 and 2019 was prepared as the 2012–2017 data9. Methods for photographic verification/validation and to evaluate the MMOs experience were applied (see below), in order to include new variables on data quality in an updated version of the dataset. Currently, the CETUS dataset is updated, with a 2nd version available10. It comprises data from 2012 to 2017, with the following two new columns on the observers’ experience: “most experienced observer” and “least experienced observer”; and a new column associated with validation of the sightings’ identifications: “photographic validation”. The results here presented correspond to the analysis of the data from 2012 to 2019, and the open-access dataset will soon be further updated with the 2018–2019 data.Photographic verificationAll the former MMOs who have integrated the CETUS Project, between 2012 and 2019, were contacted and asked to provide any available photographic or video records of cetaceans collected during their on board periods. The collection of sighting’s images was not a requirement of the CETUS protocol, and so these records were obtained opportunistically, with availability and quality depending on several factors: observers on board having personal cameras, camera quality, intention of the observer taking the photograph (e.g., for aesthetic or identification purposes).The images obtained were organized in a folder hierarchy from the year to the day of recording. However, not all the images had metadata up to the day of recording, so these were inserted into the most appropriate hierarchy-level of the folder organization. For each set of records corresponding to a single-taxon sighting, the photos/videos with the better quality or framing (i.e., that allowed for an easier species identification) were selected for that sighting. The remaining photos/videos were only consulted in case of doubt (e.g., to look for additional details that could help with the identification).Verification consisted of the process of matching the photographic/video records with the dataset sighting registers. Whenever possible and ideally, the file metadata was used for the process. However, often, the date and/or time of the file metadata were wrong, non-existent, or in different time zones. In these cases, a conservative methodology was applied using all available information to match as many sightings as possible. An estimation of time lag was attempted (based on, at least, two obvious matches between photographs/videos and dataset registers, e.g., unique sighting of the day, close to the boat, easy/obvious identification). When not possible, further evaluation consisted in assessing whether the sighting and image record was too obvious, and accounting for unique complementary information on the sighting (e.g., the number of animals or the side of the sighting were unique for that day and/or for that species/group).Photographic validationAfter the verification process, the validation of the matched records was carried out, to confirm or correct the species identification of sightings in the 1st version of the CETUS dataset (i.e., reported by the MMOs on board). The validation approach involved, for more dubious identification through the photo/video records, the discussion between four experienced observers of the CETUS team. In cases where no consensual agreement was achieved, an external expert on cetacean identification was also consulted. Identifications made through the photographic/video records required 100% certainty, and these were then compared with the cetacean identifications provided in the 1st version of the CETUS dataset. Then, the occurrence records with originally misidentifications of cetaceans, as well as those records where validation allowed to achieve an identification to a lower taxon, were corrected in the 2nd version of the dataset (i.e., a delphinid sighting validated as common dolphin, will now appear as common dolphin). A new column “photographic validation” was added to the dataset with the following categories: “yes” (i.e., validated with photograph/video), “no” (i.e., not validated with photograph/video), and “to the family” (i.e., validation only to the family taxon).For further analysis, specifically for the model process on the identification success (see below), registers were considered “completely validated” if it was possible to complete the photographic/video identification process up to the species level (then, differentiating if the original identification from the MMOs was or not correct). For Globicephala sp. and Kogia sp., validation to the genus was considered complete, since the species from both genera are visually hardly differentiated, especially at sea.Creating a data quality criteria: evaluating MMOs experienceQuality criteria were created to evaluate the MMOs experience based on the information collected from their curricula vitae (CVs) (alumni MMOs provided as many CVs as the years of their participation in CETUS). The following quality criteria were considered: (i) the experience at sea, (ii) the experience with cetaceans’ ID, (iii) the number of species they have worked with, and (iv) the experience working with the CETUS Project protocol. Each of these quality criteria was ranked from 0 to 5, and then these were summed to generate an evaluation score, on a scale of 0 to 20, attributed to each MMO (Table 4).Table 4 Quality criteria for MMOs evaluation.Full size tableThe MMOs evaluations were computed for each cruise (i.e., the trip from one port to another), considering the experience of the MMOs based on the CV obtained for that year, plus the experience acquired during CETUS participation in previous cruises that year. Since most of the times, the team of observers on board each cruise was constituted by two MMOs, two final evaluation scores were attributed to each cruise in the 2nd version of the CETUS dataset, into two new columns: “most experienced observer” and “least experienced observer”. On rare occasions where there is only one observer on board that cruise, only the evaluation of the single observer was included under the column “most experienced observer”, leaving the column “least experienced observer” as “NULL”. To investigate the experience of MMOs on board, both individually and cumulative (LEO + MEO), the combination of the score values was computed by cruise. These were then trimmed to unique combinations of evaluation scores.The names of observers, previously presented in the online dataset for each cruise, were removed for anonymity purposes, as there is now ancillary information regarding their experience.Model fittingTwo Generalized Additive Models (GAM) were fitted to assess bias on the number of sightings recorded per survey and on the identification success of cetacean species. Details for each model are presented below. Both models were fitted in R (Version 4.1.0). Prior to modelling, Pearson correlations were calculated between all pairs of explanatory variables, considered for each model (see below), to exclude highly correlated variables, considering a threshold of 0.7524,25,28. Since the variables regarding MMOs’ experience were correlated (LEO or MEO correlated with cumulative and mean experience; and cumulative experience correlated with mean experience – Supplementary Fig. S3), these variables were not included in the first fitting stage (backward selection) but included later through forward selection (see below). Multicollinearity among explanatory variables was measured through the Variance Inflation Factor (VIF), with a threshold of 3 (Supplementary Tables S4)24,25,29. After removing the MMOs evaluation scores, no multicollinearity was observed, so all the other variables were kept for the first fitting stage.For model selection, a backward selection was applied to oversaturated models18,24,25,30,31. The Akaike Information Criterion (AIC) was used as a measure of adequation of fitness, choosing the model with the lowest AIC value at each step of the model fitting process, i.e., comparing nested models (larger model incorporating one more explanatory variable compared with the smaller model). If the AIC-difference between the two models was less than 2, an Analysis of Variance (ANOVA), through chi-square test, was used to check if the AIC-difference was significant24,25,32. If this difference was not statistically significant (p  > 0.05), the simplest model (smaller model) was kept. Through a forward selection process, the variables regarding the MMOs evaluation scores were added, one at a time, to the best model obtained in the previous backward selection. After comparing the models with each other (separate variables for LEO + MEO vs. Cumulative Evaluation vs Mean Evaluation), the best model, considering the AIC value, was kept. A final backward selection process was then applied.All GAMs were fitted with the “mgcv” package (https://cran.r-project.org/web/packages/mgcv) and a maximum of four splines (k = 4) was chosen to limit the complexity of smoothers describing the effects of the explanatory variables25,31. If a spline was close to linear (with estimated degrees of freedom of ~1), the smooth term was removed, and a linear function was fitted. To check for model quality, the “gam.check” function was used to verify the diagnostic plots and the adequacy of the number of splines (Supplementary Figs. S5 and S6). Existence of influential data points was assessed (with the threshold of 0.25 for the Hat values), as well as the correlation between model residuals and explanatory variables. In both final models, number of splines was adequate and there were no influential data points or clear correlation between residuals and explanatory variables (Supplementary Figs. S7 and S8)24,32.Bias modelling of number of sightingsTo assess the bias parameters on the number of sightings recorded per survey (i.e., a full day monitoring, from sunrise to sunset), the following detectability factors were considered as explanatory variables: weather conditions (i.e., the minimums and maximums of the sea state, wind state, and visibility), the experience of MMOs (i.e., the evaluation scores of the least and the most experienced observers, as well as the mean and cumulative evaluations of the MMOs experience) and kilometres sampled “on-effort” (i.e., periods of active survey). Sampling periods were divided into “On-effort” and “Off-effort” conditions, based on four meteorological variables: sea state (Douglas scale), wind state (Beaufort scale), visibility (measured in a categorical scale ranging from 0–10 and estimated from the distance to the horizon line and possible reference points at a known range, e.g., ships with an automatic identification system,  > 1000 km), and the occurrence of rain (see Supplementary Table S9)10. For the model fitting, only “on-effort” periods of sampling were considered. Given that the response variable was count data, a Poisson distribution was tested (with a log link function). Then, the resulting first oversaturated model was checked for overdispersion, through a Pearson estimator. Since it tested positive for overdispersion (φ = 1.99), a negative binomial distribution (with a log link function) was fitted.Bias modelling of identification successA binary response variable, based on the success in the species identification for each sighting, was generated, and a model with binomial distribution (with a logit link function) was fitted. As in the previous model, only “on-effort” records were used. The total number of non-successful identifications across the dataset (the 0 s of the model) was extrapolated from the proportion of wrong identifications obtained in the validation process. To calculate this proportion, only complete validated sightings registered “on-effort” were used. Proportions were computed and extrapolated to Odontoceti and Mysticeti, separately. This resulted in 78 non-successful identifications in delphinids, plus 17 misidentifications in baleen whales, i.e., a total of 95 “on-effort” sightings randomly selected from the dataset were defined as unsuccessful identifications (0 s in the response variable for model fitting). The remaining records were considered successful identifications (1 s in the response variable for model fitting). To assess the bias parameters on the identification success, the following independent variables were considered in the analysis: the group (i.e., Group A: Odontoceti sightings, excluding sperm whale (Physeter macrocephalus); and Group B: Mysticeti sightings, plus sperm whale), the size of the group (i.e., the best estimate of the number of animals in a sighting, from the observer’s perspective), sighting distance (i.e., a relative measure according to the scale of the binoculars), weather conditions (i.e., the sea state, wind state, and visibility at the time of each sighting), the experience of MMOs (i.e., the evaluation scores of least and most experienced observers, as well as the mean and cumulative scores of the MMOs teams). Group A and B were settled according to cetacean morphology. However, since sperm whales have closer similarities with Mysticeti species, they were also included in Group B21,33. This categorization was mostly based on body size, as this is likely the main factor, regarding the species morphology, influencing the identification. Group A is constituted by species with a medium length of less than 10 meters, while Group B includes the larger species over 10 meters (Mysticeti plus P. macrocephalus)33. Since in the CETUS Project, different binoculars have been used – with two different reticle scales – it was necessary to standardize binocular distances to the same scale. More

Theurillat, J.-P. & Guisan, A. Potential impact of climate change on vegetation in the European Alps: A review. Clim. Change 50, 77–109 (2001).CAS 

Google Scholar 
Diaz, H. F. & Eischeid, J. K. Disappearing “alpine tundra” Köppen climatic type in the western United States. Geophys. Res. Lett. 34, L18707 (2007).ADS 

Google Scholar 
Dirnböck, T., Essl, F. & Rabitsch, W. Disproportional risk for habitat loss of high-altitude endemic species under climate change. Glob. Change Biol. 17, 990–996 (2011).ADS 

Google Scholar 
Myers, N., Mittermeier, R. A., Mittermeier, C. G., Da Fonseca, G. A. & Kent, J. Biodiversity hotspots for conservation priorities. Nature 403, 853–858 (2000).ADS 
CAS 
PubMed 

Google Scholar 
Pauli, H., Gottfried, M., Dirnböck, T., Dullinger, S. & Grabherr, G. Assessing the long-term dynamics of endemic plants at summit habitats. In Alpine Biodiversity in Europe (eds. Nagy, L., Grabherr, G., Körner, C., & Thompson, D. B.) 195–207 (Springer, 2003).Cogoni, D., Sulis, E., Bacchetta, G. & Fenu, G. The unpredictable fate of the single population of a threatened narrow endemic Mediterranean plant. Biodivers. Conserv. 28, 1799–1813 (2019).
Google Scholar 
Cursach, J., Besnard, A., Rita, J. & Fréville, H. Demographic variation and conservation of the narrow endemic plant Ranunculus weyleri. Acta Oecol. 53, 102–109 (2013).ADS 

Google Scholar 
Dibner, R. R., DeMarche, M. L., Louthan, A. M. & Doak, D. F. Multiple mechanisms confer stability to isolated populations of a rare endemic plant. Ecol. Monogr. 89, e01360 (2019).
Google Scholar 
Boyce, M. S., Haridas, C. V., Lee, C. T., NCEAS Stochastic Demography Working Group. Demography in an increasingly variable world. Trends Ecol. Evol. 21, 141–148 (2006).PubMed 

Google Scholar 
Buckley, Y. M. et al. Causes and consequences of variation in plant population growth rate: A synthesis of matrix population models in a phylogenetic context. Ecol. Lett. 13, 1182–1197 (2010).PubMed 

Google Scholar 
Abbott, R. E., Doak, D. F. & DeMarche, M. L. Portfolio effects, climate change, and the persistence of small populations: Analyses on the rare plant Saussurea weberi. Ecology 98, 1071–1081 (2017).PubMed 

Google Scholar 
Villellas, J., Doak, D. F., García, M. B. & Morris, W. F. Demographic compensation among populations: What is it, how does it arise and what are its implications?. Ecol. Lett. 18, 1139–1152 (2015).PubMed 

Google Scholar 
Doak, D. F. & Morris, W. F. Demographic compensation and tipping points in climate-induced range shifts. Nature 467, 959–962 (2010).ADS 
CAS 
PubMed 

Google Scholar 
García-Camacho, R., Albert, M. J. & Escudero, A. Small-scale demographic compensation in a high-mountain endemic: The low edge stands still. Plant Ecol. Divers. 5, 37–44 (2012).
Google Scholar 
Andrello, M. et al. Accounting for stochasticity in demographic compensation along the elevational range of an alpine plant. Ecol. Lett. 23, 870–880 (2020).PubMed 

Google Scholar 
Valladares, F. et al. The effects of phenotypic plasticity and local adaptation on forecasts of species range shifts under climate change. Ecol. Lett. 17, 1351–1364 (2014).PubMed 

Google Scholar 
Ægisdóttir, H. H., Kuss, P. & Stöcklin, J. Isolated populations of a rare alpine plant show high genetic diversity and considerable population differentiation. Ann. Bot. 104, 1313–1322 (2009).PubMed 
PubMed Central 

Google Scholar 
Morente-López, J. et al. Geography and environment shape landscape genetics of Mediterranean alpine species Silene ciliata Poiret. (Caryophyllaceae). Front. Plant Sci. 9, 1698 (2018).PubMed 
PubMed Central 

Google Scholar 
Franks, S. J., Weber, J. J. & Aitken, S. N. Evolutionary and plastic responses to climate change in terrestrial plant populations. Evol. Appl. 7, 123–139 (2014).PubMed 

Google Scholar 
Jeong, H., Cho, Y.-C. & Kim, E. Differential plastic responses to temperature and nitrogen deposition in the subalpine plant species, Primula farinosa subsp. modesta. AoB Plants 13, plab061 (2021).ADS 
PubMed 
PubMed Central 

Google Scholar 
Sulis, E., Bacchetta, G., Cogoni, D. & Fenu, G. From global to local scale: Where is the best for conservation purpose?. Biodivers. Conserv. 30, 183–200 (2021).
Google Scholar 
Hambler, D. & Dixon, J. Primula farinosa L. J. Ecol. 91, 694–705 (2003).
Google Scholar 
Arnold, E. & Richards, A. On the occurrence of unilateral incompatibility in Primula section Aleuritia Duby and the origin of Primula scotica Hook. Bot. J. Linn. Soc. 128, 359–368 (1998).
Google Scholar 
Tribsch, A. Areas of endemism of vascular plants in the eastern Alps in relation to Pleistocene glaciation. J. Biogeogr. 31, 747–760 (2004).
Google Scholar 
Chung, J.-M., Son, S.-W., Kim, S.-Y., Park, G.-W. & Kim, S.-S. Genetic diversity and geographic differentiation in the endangered Primula farinosa subsp. modesta, a subalpine endemic to Korea. Korean J. Plant. Taxon. 43, 236–243 (2013).
Google Scholar 
Lindborg, R. & Ehrlén, J. Evaluating the extinction risk of a perennial herb: Demographic data versus historical records. Conserv. Biol. 16, 683–690 (2002).
Google Scholar 
Caswell, H. Matrix Population Models, 2nd ed (Sinauer Associates Inc, 2000).Salguero-Gómez, R. & De Kroon, H. Matrix projection models meet variation in the real world. J. Ecol. 98, 250–254 (2010).
Google Scholar 
Jongejans, E. et al. Region versus site variation in the population dynamics of three short-lived perennials. J. Ecol. 98, 279–289 (2010).
Google Scholar 
Jongejans, E. & De Kroon, H. Space versus time variation in the population dynamics of three co-occurring perennial herbs. J. Ecol. 93, 681–692 (2005).
Google Scholar 
Suggitt, A. J. et al. Habitat microclimates drive fine-scale variation in extreme temperatures. Oikos 120, 1–8 (2011).
Google Scholar 
Tomimatsu, H. & Ohara, M. Demographic response of plant populations to habitat fragmentation and temporal environmental variability. Oecologia 162, 903–911 (2010).ADS 
PubMed 

Google Scholar 
Kudernatsch, T., Fischer, A., Bernhardt-Römermann, M. & Abs, C. Short-term effects of temperature enhancement on growth and reproduction of alpine grassland species. Basic Appl. Ecol. 9, 263–274 (2008).
Google Scholar 
Kim, E. & Donohue, K. Local adaptation and plasticity of Erysimum capitatum to altitude: Its implications for responses to climate change. J. Ecol. 101, 796–805 (2013).
Google Scholar 
Forbis, T. A. Seedling demography in an alpine ecosystem. Am. J. Bot. 90, 1197–1206 (2003).PubMed 

Google Scholar 
Yenni, G., Adler, P. B. & Ernest, S. M. Strong self-limitation promotes the persistence of rare species. Ecology 93, 456–461 (2012).PubMed 

Google Scholar 
Doak, D. F. Source-sink models and the problem of habitat degradation: General models and applications to the Yellowstone grizzly. Conserv. Biol. 9, 1370–1379 (1995).
Google Scholar 
Lesica, P. & Crone, E. E. Arctic and boreal plant species decline at their southern range limits in the Rocky Mountains. Ecol. Lett. 20, 166–174 (2017).PubMed 

Google Scholar 
Oldfather, M. F. & Ackerly, D. D. Microclimate and demography interact to shape stable population dynamics across the range of an alpine plant. New Phytol. 222, 193–205 (2019).PubMed 

Google Scholar 
Ågren, J., Fortunel, C. & Ehrlén, J. Selection on floral display in insect-pollinated Primula farinosa: Effects of vegetation height and litter accumulation. Oecologia 150, 225–232 (2006).ADS 
PubMed 

Google Scholar 
Ehrlén, J., Syrjänen, K., Leimu, R., Begona Garcia, M. & Lehtilä, K. Land use and population growth of Primula veris: An experimental demographic approach. J. Appl. Ecol. 42, 317–326 (2005).
Google Scholar 
Ehrlén, J. & Morris, W. F. Predicting changes in the distribution and abundance of species under environmental change. Ecol. Lett. 18, 303–314 (2015).PubMed 
PubMed Central 

Google Scholar 
Stubben, C. & Milligan, B. Estimating and analyzing demographic models using the popbio package in R. J. Stat. Softw. 22, 1–23 (2007).
Google Scholar 
Weiss, N. Package ‘wPerm’. https://cran.r-project.org/web/packages/wPerm/wPerm.pdf. (2015).Frossard, J. & Renaud, O. Permutation tests for regression, ANOVA, and comparison of signals: The permuco package. J. Stat. Softw. 99, 1–32 (2021).
Google Scholar  More

Williams, R. et al. Prioritizing global marine mammal habitats using density maps in place of range maps. Ecography 37, 212–220 (2014).
Google Scholar 
Tyack, P. L. & Clark, C. W. Communication and acoustic behavior of dolphins and whales in Hearing by whales and dolphins 156–224 (Springer, 2000).Davis, G. E. et al. Exploring movement patterns and changing distributions of baleen whales in the western North Atlantic using a decade of passive acoustic data. Glob. Change Biol. 26, 4812 (2020).ADS 

Google Scholar 
Lomac-MacNair, K. S. et al. Marine mammal visual and acoustic surveys near the Alaskan Colville River Delta. Polar Biol. 42, 441–448 (2018).
Google Scholar 
Keen, E., Hendricks, B., Wray, J., Alidina, H. & Picard, C. Integrating passive acoustic and visual surveys for marine mammals in coastal habitats in 176th Meeting of Acoustical Society of America. 1 edn.Gregr, E. J., Baumgartner, M. F., Laidre, K. L. & Palacios, D. M. Marine mammal habitat models come of age: The emergence of ecological and management relevance. Endang. Species Res. 22, 205–212 (2013).
Google Scholar 
Hastie, G. D., Wilson, B., Wilson, L., Parsons, K. M. & Thompson, P. M. Functional mechanisms underlying cetacean distribution patterns: Hotspots for bottlenose dolphins are linked to foraging. Mar. Biol. 144, 397–403 (2004).
Google Scholar 
Lambert, C., Mannocci, L., Lehodey, P. & Ridoux, V. Predicting cetacean habitats from their energetic needs and the distribution of their prey in two contrasted tropical regions. PLoS ONE 9, e105958 (2014).ADS 
PubMed 
PubMed Central 

Google Scholar 
Huot, Y. et al. Does chlorophyll a provide the best index of phytoplankton biomass for primary productivity studies?. Biogeosci. Discuss. 4, 707–745 (2007).ADS 

Google Scholar 
Etnoyer, P. et al. Sea-surface temperature gradients across blue whale and sea turtle foraging trajectories off the Baja California Peninsula, Mexico. Deep Sea Res. II 53, 340–358 (2006).ADS 

Google Scholar 
Shabangu, F. W. et al. Seasonal occurrence and diel calling behaviour of Antarctic blue whales and fin whales in relation to environmental conditions off the west coast of South Africa. J. Mar. Syst. 190, 25–39 (2019).
Google Scholar 
Haida Nation & Parks Canada Agency. Gwaii Haanas Gina ’Waadluxan Kilguhlga. Land-Sea-People Management Plan. 33 (© Council of the Haida Nation and Her Majesty the Queen in Right of Canada, represented by the Chief Executive Officer of Parks Canada, 2018).Ford, J. K. B. Marine Mammals of British Columbia. (Royal BC Museum, 2014).Allen, A. S., Yurk, H., Vagle, S., Pilkington, J. & Canessa, R. The underwater acoustic environment at SGaan Kinghlas-Bowie Seamount Marine Protected Area: Characterizing vessel traffic and associated noise using satellite AIS and acoustic datasets. Mar. Pollut. Bull. 128, 82–88 (2018).CAS 
PubMed 

Google Scholar 
Ainslie, M. A. Principles of Sonar Performance Modeling. (Springer, 2010).Collins, M. D. A split-step Padé solution for the parabolic equation method. J. Acoust. Soc. Am. 93, 1736–1742 (1993).ADS 

Google Scholar 
Porter, M. B. & Bucker, H. P. Gaussian beam tracing for computing ocean acoustic fields. J. Acoust. Soc. Am. 82, 1349–1359 (1987).ADS 

Google Scholar 
Mouy, X., MacGillivray, A. O., Vallarta, J. H., Martin, B. & Delarue, J. J.-Y. Ambient Noise and Killer Whale Monitoring near Port Metro Vancouver’s Proposed Terminal 2 Expansion Site: July–September 2012. (Technical report by JASCO Applied Sciences for Hemmera, 2012).Ford, J. et al. Distribution and relative abundance of cetaceans in western Canadian waters from ship surveys, 2002–2008. Can. Tech. Rep. Fish. Aquat. Sci. 2913, 51 (2010).
Google Scholar 
Wright, B. M., Nichol, L. M. & Doniol-Valcroze, T. Spatial density models of cetaceans in the Canadian Pacific estimated from 2018 ship-based surveys. DFO Can. Sci. Advis. Sec. Res. Doc. 2021, 49 (2021).
Google Scholar 
Devred, E., Hardy, M. & Hannah, C. Satellite observations of the Northeast Pacific Ocean. Can. Tech. Rep. Hydrogr. Ocean Sci. 335, 46 (2021).
Google Scholar 
Saha, K. et al. NOAA National centers for environmental information. Dataset https://doi.org/10.7289/v52j68xx (2018).Article 

Google Scholar 
NASA Goddard Space Flight Center, Ocean Ecology Laboratory & Ocean Biology Processing Group. (NASA OB.DAAC, Greenbelt, MD, USA. https://doi.org/10.5067/AQUA/MODIS/L3B/CHL/2018. Accessed 3 Feb 2021.Wood, S. N. Fast stable restricted maximum likelihood and marginal likelihood estimation of semiparametric generalized linear models. J. R. Stat. Soc. B Stat. Methodol. 73, 3–36 (2011).MathSciNet 
MATH 

Google Scholar 
R Core Team. R: A language and environment for statistical computing. R foundation for Statistical Computing, Vienna, Austria. https://www.R-project.org/ (2021).Ogle, D. H., Wheeler, P. & Dinno, A. FSA: Fisheries Stock Analysis. R package version 0.8.32. https://github.com/droglenc/FSA (2021).Payne, R. S. & McVay, S. Songs of humpback whales. Science 173, 585–597 (1971).ADS 
CAS 
PubMed 

Google Scholar 
Rekdahl, M. L. et al. Non-song social call bouts of migrating humpback whales. J. Acoust. Soc. Am. 137, 3042–3053 (2015).ADS 
PubMed 
PubMed Central 

Google Scholar 
Oswald, J. N., Rankin, S. & Barlow, J. To whistle or not to whistle? Geographic variation in the whistling behavior of small odontocetes. Aquat. Mamm. 34, 288–302 (2008).
Google Scholar 
Rankin, S., Oswald, J., Barlow, J. P. & Lammers, M. Patterned burst-pulse vocalizations of the northern right whale dolphin, Lissodelphis borealis. J. Acoust. Soc. Am. 121, 1213–1218. https://doi.org/10.1121/1.2404919 (2007).Article 
ADS 
PubMed 

Google Scholar 
Arranz, P. et al. Discrimination of fast click-series produced by tagged Risso’s dolphins (Grampus griseus) for echolocation or communication. J. Exp. Biol. 219, 2898–2907. https://doi.org/10.1242/jeb.144295 (2016).Article 
CAS 
PubMed 

Google Scholar 
Halpin, L. R., Towers, J. R. & Ford, J. K. First record of common bottlenose dolphin (Tursiops truncatus) in Canadian Pacific waters. Mar. Biodivers. Rec. 11, 1–5 (2018).
Google Scholar 
Nikolich, K. & Towers, J. R. Vocalizations of common minke whales (Balaenoptera acutorostrata) in an eastern North Pacific feeding ground. Bioacoustics 29, 97–108 (2020).
Google Scholar 
Money, J. H. & Trites, A. W. A preliminary assessment of the status of marine mammal populations and associated research needs for the west coast of Canada. Report No. Final Report, 80 (Fisheries and Oceans Canada, 1998).Gregr, E. J. & Trites, A. W. Predictions of critical habitat for five whale species in the waters of coastal British Columbia. Can. J. Fish. Aquat. Sci. 58, 1265–1285 (2001).
Google Scholar 
Ou, H., Au, W. W. L., 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 
Mellinger, D. K., Stafford, K. M., Moore, S. E., Dziak, R. P. & Matsumoto, H. An overview of fixed passive acoustic observation methods for cetaceans. Oceanography 20, 36–45 (2007).
Google Scholar 
Stafford, K. M., Citta, J. J., Moore, S. E., Daher, M. A. & George, J. E. Environmental correlates of blue and fin whale call detections in the North Pacific Ocean from 1997 to 2002. Mar. Ecol. Prog. Ser. 395, 37–53 (2009).ADS 

Google Scholar 
Burnham, R., Duffus, D. & Mouy, X. The presence of large whale species in Clayoquot Sound and its offshore waters. Cont. Shelf Res. 177, 15–23 (2019).ADS 

Google Scholar 
Burtenshaw, J. C. et al. Acoustic and satellite remote sensing of blue whale seasonality and habitat in the Northeast Pacific. Deep Sea Res. II 51, 967–986 (2004).ADS 

Google Scholar 
Calambokidis, J., Barlow, J., Ford, J. K. B., Chandler, T. E. & Douglas, A. B. Insights into the population structure of blue whales in the Eastern North Pacific from recent sightings and photographic identification. Mar. Mamm. Sci. 25, 816–832 (2009).
Google Scholar 
Jackson, J. M., Thomson, R. E., Brown, L. N., Willis, P. G. & Borstad, G. A. Satellite chlorophyll off the British Columbia Coast, 1997–2010. J. Geophys. Res. Oceans 120, 4709–4728 (2015).ADS 

Google Scholar 
Evans, R., English, P. A., Anderson, S. C., Gauthier, S. & Robinson, C. L. Factors affecting the seasonal distribution and biomass of E. pacifica and T. spinifera along the Pacific coast of Canada: A spatiotemporal modelling approach. PLoS ONE 16, e0249818 (2021).CAS 
PubMed 
PubMed Central 

Google Scholar 
Moore, S. E., Watkins, W. A., Daher, M. A., Davies, J. R. & Dahlheim, M. E. Blue whale habitat associations in the Northwest Pacific: Analysis of remotely-sensed data using a Geographic Information System. Oceanography 15, 1–10 (2002).
Google Scholar 
Lockyer, C. Review of Baleen Whale (Mysticeti) reproduction and implications for management. Rep. Int. Whal. Commn Spec. Issue 6, 27–50 (1984).
Google Scholar 
Ohsumi, S. M. N. Growth of fin whale in the Northern Pacific Ocean. Sci. Rep. Whale Res. Inst. 13, 97–133 (1958).
Google Scholar 
Watkins, W. A. et al. Seasonality and distribution of whale calls in the North Pacific. Oceanography 13, 62–67 (2000).
Google Scholar 
Watkins, W. A., Tyack, P., Moore, K. E. & Bird, J. E. The 20-Hz signals of finback whales (Balaenoptera physalus). J. Acoust. Soc. Am. 82, 1901–1912 (1987).ADS 
CAS 
PubMed 

Google Scholar 
Stafford, K. M., Mellinger, D. K., Moore, S. E. & Fox, C. G. Seasonal variability and detection range modeling of baleen whale calls in the Gulf of Alaska, 1999–2002. J. Acoust. Soc. Am. 122, 3378–3390 (2007).ADS 
PubMed 

Google Scholar 
Koot, B. Winter Behaviour and Population Structure of Fin Whales (Balaenoptera physalus) in British Columbia inferred from passive acoustic data (University of British Columbia, 2015).
Google Scholar 
Pilkington, J. F., Stredulinsky, E. H., Abernethy, R. M. & Ford, J. K. B. Patterns of Fin whale (Balaenoptera physalus) Seasonality and Relative Distribution in Canadian Pacific Waters Inferred from Passive Acoustic Monitoring. DFO Can. Sci. Advis. Sec. Res. Doc. (2018).Best, B. D., Fox, C. H., Williams, R., Halpin, P. H. & Paquet, P. C. Updated Marine Mammal Distribution and Abundance Estimates in British Columbia (Springer, 2015).
Google Scholar 
Clarke, C. & Jamieson, G. Identification of ecologically and biologically significant areas in the Pacific North Coast integrated management area: Phase II: Final report. Can. Tech. Rep. Fish. Aquat. Sci. 2678, 59 (2006).
Google Scholar 
Nichol, L. M. et al. Distribution, movements and habitat fidelity patterns of Fin Whales (Balaenoptera physalus) in Canadian Pacific Waters. DFO Can. Sci. Advis. Sec. Res. Doc. (2018).Nichol, L. M. & Ford, J. K. B. Information in Support of the Identification of Habitat of Special Importance to Fin Whales (Balaenoptera physalus) in Canadian Pacific Waters. DFO Can. Sci. Advis. Sec. Res. Doc. (2018).Mizroch, S. A., Rice, D. W., Zwiefelhofer, D., Waite, J. & Perryman, W. L. Distribution and movements of fin whales in the North Pacific Ocean. Mammal Rev. 39, 193–227 (2009).
Google Scholar 
Širović, A., Williams, L. N., Kerosky, S. M., Wiggins, S. M. & Hildebrand, J. A. Temporal separation of two fin whale call types across the eastern North Pacific. Mar. Biol. 160, 47–57 (2013).PubMed 

Google Scholar 
Flinn, R. D., Trites, A. W., Gregr, E. J. & Perry, R. I. Diets of fin, sei, and sperm whales in British Columbia: an analysis of commercial whaling records, 1963–1967. Mar. Mamm. Sci. 18, 663–679 (2002).
Google Scholar 
Barnes, R. S. K. & Hughes, R. N. An Introduction to Marine Ecology (Wiley, 1999).
Google Scholar 
Romagosa, M. et al. Food talks: 40-hz fin whale calls are associated with prey biomass. Proc. R. Soc. B 288, 20211156 (2021).PubMed 
PubMed Central 

Google Scholar 
Gregr, E. J., Nichol, L., Ford, J. K., Ellis, G. & Trites, A. W. Migration and population structure of northeastern Pacific whales off coastal British Columbia: An analysis of commercial whaling records from 1908–1967. Mar. Mamm. Sci. 16, 699–727 (2000).
Google Scholar 
Williams, R. & Thomas, L. Distribution and abundance of marine mammals in the coastal waters of British Columbia, Canada. J. Cetac. Res. Manage. 9, 15 (2007).
Google Scholar 
Dalla Rosa, L., Ford, J. K. & Trites, A. W. Distribution and relative abundance of humpback whales in relation to environmental variables in coastal British Columbia and adjacent waters. Contin. Shelf Res. 36, 89–104 (2012).ADS 

Google Scholar 
Winn, H. E. & Winn, L. K. The song of the humpback whale Megaptera novaeangliae in the West Indies. Mar. Biol. 47, 97–114. https://doi.org/10.1007/BF00395631 (1978).Article 

Google Scholar 
Baker, C. S. et al. Population characteristics and migration of summer and late-season humpback whales (Megaptera novaeangliae) in southeastern Alaska. Mar. Mamm. Sci. 1, 304–323 (1985).ADS 

Google Scholar 
McSweeney, D., Chu, K., Dolphin, W. & Guinee, L. North Pacific humpback whale songs: A comparison of southeast Alaskan feeding ground songs with Hawaiian wintering ground songs. Mar. Mamm. Sci. 5, 139–148 (1989).
Google Scholar 
Norris, T. F., McDonald, M. & Barlow, J. Acoustic detections of singing humpback whales (Megaptera novaeangliae) in the eastern North Pacific during their northbound migration. J. Acoust. Soc. Am. 106, 506–514 (1999).ADS 
CAS 
PubMed 

Google Scholar 
Clark, C. W. & Clapham, P. J. Acoustic monitoring on a humpback whale (Megaptera novaeangliae) feeding ground shows continual singing into late spring. Proc. R. Soc. Lond. B 271, 1051–1057 (2004).
Google Scholar 
Stimpert, A. K., Peavey, L. E., Friedlaender, A. S. & Nowacek, D. P. Humpback whale song and foraging behavior on an Antarctic feeding ground. PLoS ONE 7, e51214 (2012).ADS 
CAS 
PubMed 
PubMed Central 

Google Scholar 
Kowarski, K., Evers, C., Moors-Murphy, H., Martin, B. & Denes, S. L. Singing through winter nights: Seasonal and diel occurrence of humpback whale (Megaptera novaeangliae) calls in and around the Gully MPA, offshore eastern Canada. Mar. Mamm. Sci. 34, 169–189 (2018).
Google Scholar 
Nichol, L. M., Abernethy, R., Flostrand, L., Lee, T. S. & Ford, J. K. B. Information relevant for the identification of critical habitats of north pacific humpback whales (Megaptera novaeangliae) in British Columbia. DFO Can. Sci. Advis. Sec. Res. Doc. (2010).Williams, R., Erbe, C., Ashe, E. & Clark, C. W. Quiet (er) marine protected areas. Mar. Pollut. Bull. 100, 154–161 (2015).CAS 
PubMed 

Google Scholar 
Gaston, A. J., Pilgrim, N. G. & Pattison, V. Humpback Whale (Megaptera novaeangliae) observations in Laskeek Bay, western Hecate Strait, in spring and early summer, 1990–2018. Can. Field Nat. 133, 263–269 (2019).
Google Scholar 
Robinson, C. L., Gower, J. F. & Borstad, G. Twenty years of satellite observations describing phytoplankton blooms in seas adjacent to Gwaii Haanas National Park Reserve, Canada. Can. J. Remote Sens. 30, 36–43 (2004).ADS 

Google Scholar 
Swartz, S. L., Taylor, B. L. & Rugh, D. J. Gray whale Eschrichtius robustus population and stock identity. Mamm. Rev. 36, 66–84 (2006).
Google Scholar 
Gaston, A. J. & Heise, K. Results of cetacean observations in Laskeek Bay, 1990–2003. Laskeek Bay Res. 55, 1–10 (2004).
Google Scholar 
Ford, J. K. et al. New insights into the northward migration route of gray whales between Vancouver Island, British Columbia, and southeastern Alaska. Mar. Mamm. Sci. 29, 325–337 (2013).
Google Scholar 
Burnham, R. E. & Duffus, D. A. The use of passive acoustic monitoring as a census tool of gray whale (Eschrichtius robustus) migration. Ocean Coast. Manag. 188, 105070 (2020).
Google Scholar 
Best, P. B. Social organization in sperm whales. In Physeter macrocephalus in Behavior of Marine Animals (eds Winn, H. E. & Olla, B. L.) 227–289 (Springer, 1979).
Google Scholar 
Jaquet, N. & Gendron, D. Distribution and relative abundance of sperm whales in relation to key environmental features, squid landings and the distribution of other cetacean species in the Gulf of California, Mexico. Mar. Biol. 141, 591–601 (2002).
Google Scholar 
Rice, D. W. Sperm whale Physeter macrocephalus Linnaeus, 1758. Handb. Mar. Mamm. 4, 177–233 (1989).
Google Scholar 
Whitehead, H. & Arnbom, T. Social organization of sperm whales off the Galapagos Islands, February–April 1985. Can. J. Zool. 65, 913–919 (1987).
Google Scholar 
Whitehead, H. Sperm whale: Physeter macrocephalus. In Encyclopedia of Marine Mammals 3rd edn (eds Würsig, B. et al.) 919–925 (Academic Press, 2018).
Google Scholar 
Mizroch, S. A. & Rice, D. W. Ocean nomads: Distribution and movements of sperm whales in the North Pacific shown by whaling data and Discovery marks. Mar. Mamm. Sci. 29, E136–E165 (2013).
Google Scholar 
Diogou, N. et al. Sperm whale (Physeter macrocephalus) acoustic ecology at Ocean Station PAPA in the Gulf of Alaska-Part 2: Oceanographic drivers of interannual variability. Deep Sea Res. I 150, 103044 (2019).
Google Scholar 
Ford, J. K. & Ellis, G. M. You are what you eat: Foraging specializations and their influence on the social organization and behavior of killer whales. in Primates and Cetaceans 75–98 (Springer, 2014).Ford, J. K. B. et al. Habitats of special importance to resident killer whales (Orcinus orca) off the West Coast of Canada. DFO Can. Sci. Advis. Sec. Res. Doc. (2017).Ford, J. K. B., Stredulinsky, E. H., Ellis, G. M., Durban, J. W. & Pilkington, J. F. Offshore Killer whales in Canadian pacific waters: Distribution, seasonality, foraging ecology, population status and potential for recovery. DFO Can. Sci. Advis. Sec. Res. Doc. (2014).Nichol, L. M. & Shackleton, D. M. Seasonal movements and foraging behaviour of northern resident killer whales (Orcinus orca) in relation to the inshore distribution of salmon (Oncorhynchus spp.) in British Columbia. Can. J. Zool. 74, 983–991 (1996).
Google Scholar 
Olesiuk, P. F., Ellis, G. M. & Ford, J. K. Life History and Population Dynamics of Northern Resident Killer Whales (Orcinus orca) in British Columbia (Canadian Science Advisory Secretariat Ottawa, 2005).
Google Scholar 
Newman, K. & Springer, A. Nocturnal activity by mammal-eating killer whales at a predation hot spot in the Bering Sea. Mar. Mamm. Sci. 24, 990 (2008).
Google Scholar 
Ford, J. K. B. et al. Dietary specialization in two sympatric populations of killer whales (Orcinus orca) in coastal British Columbia and adjacent waters. Can. J. Zool. 76, 1456–1471 (1998).
Google Scholar 
Barrett-Lennard, L. G., Ford, J. K. B. & Heise, K. A. The mixed blessing of echolocation: Differences in sonar use by fish-eating and mammal-eating killer whales. Anim. Behav. 51, 553–565 (1996).
Google Scholar 
Deecke, V. B., Ford, J. K. B. & Slater, P. J. B. The vocal behaviour of mammal-eating killer whales: Communicating with costly calls. Anim. Behav. 69, 395–405 (2005).
Google Scholar 
Ford, J. K. B. Call traditions and vocal dialects of killer whales (Orcinus orca) in British Columbia Ph.D. thesis, University of British Columbia (1984).Baird, R. W. Status of killer whales, Orcinus orca, Canada. Can. Field. Nat. 115, 676–701 (2001).
Google Scholar 
Ford, J. K. B., Stredulinsky, E. H., Towers, J. R. & Ellis, G. M. Information in Support of the Identification of Critical Habitat for Transient Killer Whales (Orcinus orca) off the West Coast of Canada. DFO Can. Sci. Advis. Sec. Res. Doc. (2013).Tyack, P. L., Johnson, M., Soto, N. A., Sturlese, A. & Madsen, P. T. Extreme diving of beaked whales. J. Exp. Biol. 209, 4238–4253 (2006).PubMed 

Google Scholar 
Baumann-Pickering, S. et al. Species-specific beaked whale echolocation signals. J. Acoust. Soc. Am. 134, 2293–2301 (2013).ADS 
PubMed 

Google Scholar 
Pike, G. C. Two records of Berardius bairdi from the coast of British Columbia. J. Mammal. 34, 98–104 (1953).
Google Scholar 
Pike, G. C. & MacAskie, I. Marine mammals of British Columbia. Fish. Res. Board Can. Bull. 171, 1–10 (1969).
Google Scholar 
Willis, P. M. & Baird, R. W. Sightings and strandings of beaked whales on the west coast of. Aquat. Mamm. 24, 21–25 (1998).
Google Scholar 
Jefferson, T. A. Phocoenoides dalli. Mamm. Spec. https://doi.org/10.2307/3504170 (1988).Article 

Google Scholar 
Boyd, C. et al. Estimation of population size and trends for highly mobile species with dynamic spatial distributions. Divers. Distrib. 24, 1–12 (2018).
Google Scholar 
Carretta, J. V., Taylor, B. L. & Chivers, S. J. Abundance and depth distribution of harbor porpoise (Phocoena phocoena) in northern California determined from a 1995 ship survey. Fish. Bull. 99, 29–29 (2001).
Google Scholar 
Willis, P. M. & Baird, R. W. Status of the dwarf sperm whale, Kogia simus, with special reference to Canada. Can. Field Nat. 112, 114–125 (1998).
Google Scholar 
Kyhn, L. A. et al. Clicking in a killer whale habitat: Narrow-band, high-frequency biosonar cliks of harbour porpoise (Phocoena phocoena) and Dall’s porpoise (Phocoenoides dalli). PLoS ONE 8, e63763 (2013).ADS 
CAS 
PubMed 
PubMed Central 

Google Scholar 
Madsen, P., Carder, D., Bedholm, K. & Ridgway, S. Porpoise clicks from a sperm whale nose—Convergent evolution of 130 kHz pulses in toothed whale sonars?. Bioacoustics 15, 195–206 (2005).
Google Scholar 
Merkens, K. et al. Clicks of dwarf sperm whales (Kogia sima). Mar. Mamm. Sci. 34, 963–978 (2018).
Google Scholar 
Griffiths, E. T. et al. Detection and classification of narrow-band high frequency echolocation clicks from drifting recorders. J. Acoust. Soc. Am. 147, 3511–3522 (2020).ADS 
PubMed 

Google Scholar 
Baird, R. W. & Stacey, P. J. Status of Risso’s Dolphin, Grampus griseus, in Canada. Naturalist 5, 233142 (1991).
Google Scholar 
Benoit-Bird, K. J. & Au, W. W. Prey dynamics affect foraging by a pelagic predator (Stenella longirostris) over a range of spatial and temporal scales. Behav. Ecol. Sociobiol. 53, 364–373 (2003).
Google Scholar 
Benoit-Bird, K. J., Würsig, B. & Mfadden, C. J. Dusky dolphin (Lagenorhynchus obscurus) foraging in two different habitats: active acoustic detection of dolphins and their prey. Mar. Mamm. Sci. 20, 215–231 (2004).
Google Scholar 
Soldevilla, M. S., Wiggins, S. M. & Hildebrand, J. A. Spatial and temporal patterns of Risso’s dolphin echolocation in the Southern California Bight. J. Acoust. Soc. Am. 127, 124–132 (2010).ADS 
PubMed 

Google Scholar 
Soldevilla, M. S., Wiggins, S. M. & Hildebrand, J. A. Spatio-temporal comparison of Pacific white-sided dolphin echolocation click types. Aquat. Biol. 9, 49–62 (2010).
Google Scholar 
Taylor, F. The relationship of midwater trawl catches to sound scattering layers off the coast of northern British Columbia. J. Fish. Board Can. 25, 457–472 (1968).
Google Scholar 
Curtis, K. R., Howe, B. M. & Mercer, J. A. Low-frequency ambient sound in the North Pacific: Long time series observations. J. Acoust. Soc. Am. 106, 3189–3200 (1999).ADS 

Google Scholar 
Aroyan, J. L. et al. Acoustic models of sound production and propagation in Hearing by whales and dolphins 409–469 (Springer, 2000).
Google Scholar 
Cummings, W. C. & Thompson, P. O. Underwater sounds from the blue whale, Balaenoptera musculus. J. Acoust. Soc. Am. 50, 1193–1198 (1971).ADS 

Google Scholar 
McDonald, M. A., Calambokidis, J., Teranishi, A. M. & Hildebrand, J. A. The acoustic calls of blue whales off California with gender data. J. Acoust. Soc. Am. 109, 1728–1735 (2001).ADS 
CAS 
PubMed 

Google Scholar 
Weirathmueller, M. J., Wilcock, W. S. D. & Soule, D. C. Source levels of fin whale 20 Hz pulses measured in the Northeast Pacific Ocean. J. Acoust. Soc. Am. 133, 741–749 (2013).ADS 
PubMed 

Google Scholar 
Vihtakari, M. ggOceanMaps: Plot Data on Oceanographic Maps using ‘ggplot2’. R package version 1.2.14. https://mikkovihtakari.github.io/ggOceanMaps/ (2022). More