Philippa Kaur

Delavaux, C. S., Smith‐Ramesh, L. M. & Kuebbing, S. E. Beyond nutrients: a meta‐analysis of the diverse effects of arbuscular mycorrhizal fungi on plants and soils. Ecology 98, 2111–2119 (2017).Lugtenberg, B. & Kamilova, F. Plant-growth-promoting rhizobacteria. Annu. Rev. Microbiol. 63, 541–556 (2009).Article 
CAS 
PubMed 

Google Scholar 
Franche, C., Lindström, K. & Elmerich, C. Nitrogen-fixing bacteria associated with leguminous and non-leguminous plants. Plant Soil 321, 35–59 (2009).Article 
CAS 

Google Scholar 
Razanajatovo, M. et al. Autofertility and self‐compatibility moderately benefit island colonization of plants. Glob. Ecol. Biogeogr. 28, 341–352 (2019).Article 

Google Scholar 
Schrader, J., Wright, I. J., Kreft, H. & Westoby, M. A roadmap to plant functional island biogeography. Biol. Rev. (2021).Herridge, D. F., Peoples, M. B. & Boddey, R. M. Global inputs of biological nitrogen fixation in agricultural systems. Plant Soil 311, 1–18 (2008).Article 
CAS 

Google Scholar 
Vitousek, P. Nutrient cycling and limitation: Hawai’i as a model ecosystem. (Princeton Univ. Press, Princeton, NJ, 2004). Nutrient cycling and limitation: Hawai’i as a model ecosystem. Princeton Univ. Press, Princeton, NJ.Book 

Google Scholar 
Becking, L. G. M. B. Geobiologie of inleiding tot de milieukunde. (WP Van Stockum & Zoon, 1934).Peay, K. G. & Bruns, T. D. Spore dispersal of basidiomycete fungi at the landscape scale is driven by stochastic and deterministic processes and generates variability in plant–fungal interactions. N. Phytol. 204, 180–191 (2014).Article 

Google Scholar 
Delavaux, C. S. et al. Mycorrhizal fungi influence global plant biogeography. Nat. Ecol. Evol. 3, 424 (2019).Article 
PubMed 

Google Scholar 
Duchicela, J., Bever, J. D. & Schultz, P. A. Symbionts as Filters of Plant Colonization of Islands: Tests of Expected Patterns and Environmental Consequences in the Galapagos. Plants 9, 74 (2020).Article 
CAS 
PubMed Central 

Google Scholar 
Delavaux, C. S. et al. Mycorrhizal types influence island biogeography of plants. Commun. Biol. 4, 1–8 (2021).Article 

Google Scholar 
Simonsen, A. K., Dinnage, R., Barrett, L. G., Prober, S. M. & Thrall, P. H. Symbiosis limits establishment of legumes outside their native range at a global scale. Nat. Commun. 8, 1–9 (2017).Article 

Google Scholar 
Poole, P., Ramachandran, V. & Terpolilli, J. Rhizobia: from saprophytes to endosymbionts. Nat. Rev. Microbiol. 16, 291–303 (2018).Article 
CAS 
PubMed 

Google Scholar 
Sprent, J. I., Ardley, J. & James, E. K. Biogeography of nodulated legumes and their nitrogen‐fixing symbionts. N. Phytol. 215, 40–56 (2017).Article 
CAS 

Google Scholar 
Menge, D. N. Hedin, L. O. & Pacala, S. W. Nitrogen and phosphorus limitation over long-term ecosystem development in terrestrial ecosystems. (2012).Lambers, H., Raven, J. A., Shaver, G. R. & Smith, S. E. Plant nutrient-acquisition strategies change with soil age. Trends Ecol. evolution 23, 95–103 (2008).Article 

Google Scholar 
Walker, T. & Syers, J. K. The fate of phosphorus during pedogenesis. Geoderma 15, 1–19 (1976).Article 
CAS 

Google Scholar 
Jin, L. et al. Synergistic interactions of arbuscular mycorrhizal fungi and rhizobia promoted the growth of Lathyrus sativus under sulphate salt stress. Symbiosis 50, 157–164 (2010).Article 
CAS 

Google Scholar 
Afkhami, M. E. & Stinchcombe, J. R. Multiple mutualist effects on genomewide expression in the tripartite association between Medicago truncatula, nitrogen‐fixing bacteria and mycorrhizal fungi. Mol. Ecol. 25, 4946–4962 (2016).Article 
CAS 
PubMed 

Google Scholar 
Larimer, A. L., Clay, K. & Bever, J. D. Synergism and context dependency of interactions between arbuscular mycorrhizal fungi and rhizobia with a prairie legume. Ecology 95, 1045–1054 (2014).Article 
PubMed 

Google Scholar 
Primieri, S., Magnoli, S. M., Koffel, T. S., Stürmer, S. L. & Bever, J. D. Perennial, but not annual legumes synergistically benefit from infection with arbuscular mycorrhizal fungi and rhizobia: a meta‐analysis. N. Phytol. 233, 505-514 (2021).Larimer, A. L., Bever, J. D. & Clay, K. The interactive effects of plant microbial symbionts: a review and meta-analysis. Symbiosis 51, 139–148 (2010).Article 

Google Scholar 
Werner, G. D., Cornwell, W. K., Sprent, J. I., Kattge, J. & Kiers, E. T. A single evolutionary innovation drives the deep evolution of symbiotic N 2-fixation in angiosperms. Nat. Commun. 5, 1–9 (2014).Article 

Google Scholar 
Weigelt, P., König, C. & Kreft, H. GIFT- A global inventory of floras and traits for macroecology and biogeography. J. Biogeogr. 47, 16–43 (2020).Article 

Google Scholar 
Werner, G. D. et al. Symbiont switching and alternative resource acquisition strategies drive mutualism breakdown. Proc. Natl Acad. Sci. 115, 5229–5234 (2018).Article 
CAS 
PubMed 
PubMed Central 

Google Scholar 
Bamba, M. et al. Wide distribution range of rhizobial symbionts associated with pantropical sea-dispersed legumes. Antonie van. Leeuwenhoek 109, 1605–1614 (2016).Article 
PubMed 

Google Scholar 
Chen, W.-M., Lee, T.-M., Lan, C.-C. & Cheng, C.-P. Characterization of halotolerant rhizobia isolated from root nodules of Canavalia rosea from seaside areas. FEMS Microbiol. Ecol. 34, 9–16 (2000).Article 
CAS 
PubMed 

Google Scholar 
Toma, M. A. et al. Tripartite symbiosis of Sophora tomentosa, rhizobia and arbuscular mycorhizal fungi. Braz. J. Microbiol. 48, 680–688 (2017).Article 
CAS 
PubMed 
PubMed Central 

Google Scholar 
Elanchezhian, R., Rajalakshmi, S. & Jayakumar, V. Salt tolerance characteristics of rhizobium species associated with Vigna marina. Indian J. Agric. Sci. 79, 980–985 (2009).CAS 

Google Scholar 
Chapin, F. S., Matson, P. A., Mooney, H. A. & Vitousek, P. M. Principles of Terrestrial Ecosystem Ecology (Springer, 2002).Vitousek, P. M., Walker, L. R., Whiteaker, L. D. & Matson, P. A. Nutrient limitations to plant growth during primary succession in Hawaii Volcanoes National Park. Biogeochemistry 23, 197–215 (1993).Article 

Google Scholar 
Liao, C. et al. Altered ecosystem carbon and nitrogen cycles by plant invasion: a meta-analysis. N. Phytologist 177, 706–714 (2008).Article 
CAS 

Google Scholar 
Woodward, S. A. et al. Use of the Exotic Tree Myrica Faya by Native and Exotic Birds in Hawai’i Volcanoes National Park (University of Hawaii Press, 1990).Vitousek, P. M., Walker, L. R., Whiteaker, L. D., Mueller-Dombois, D. & Matson, P. A. Biological invasion by Myrica faya alters ecosystem development in Hawaii. Science 238, 802–804 (1987).Article 
CAS 
PubMed 

Google Scholar 
Theoharides, K. A. & Dukes, J. S. Plant invasion across space and time: factors affecting nonindigenous species success during four stages of invasion. N. phytologist 176, 256–273 (2007).Article 

Google Scholar 
Kalwij, J. M. Review of ‘The Plant List, a working list of all plant species’. J. Vegetation Sci. 23, 998–1002 (2012).Article 

Google Scholar 
Byng, J. W. et al. An update of the Angiosperm Phylogeny Group classification for the orders and families of flowering plants: APG IV. Botanical J. Linn. Soc. 181, 1–20 (2016).Article 

Google Scholar 
Soudzilovskaia, N. A. et al. FungalRoot: Global online database of plant mycorrhizal associations. N. Phytol. 227, 955–966 (2020).Article 

Google Scholar 
Weigelt, P., König, C. & Kreft, H. GIFT–A global inventory of floras and traits for macroecology and biogeography. J. Biogeogr. 47, 16–43 (2020).Article 

Google Scholar 
Karger, D. N. et al. Climatologies at high resolution for the earth’s land surface areas. Sci. Data 4, 170122 (2017).Article 
PubMed 
PubMed Central 

Google Scholar 
Danielson, J. J. & Gesch, D. B. “Global multi-resolution terrain elevation data 2010 (GMTED2010),” (US Geological Survey, 2011).Weigelt, P. & Kreft, H. Quantifying island isolation–insights from global patterns of insular plant species richness. Ecography 36, 417–429 (2013).Article 

Google Scholar 
Kreft, H., Jetz, W., Mutke, J., Kier, G. & Barthlott, W. Global diversity of island floras from a macroecological perspective. Ecol. Lett. 11, 116–127 (2008).PubMed 

Google Scholar 
Triantis, K. A., Economo, E. P., Guilhaumon, F. & Ricklefs, R. E. Diversity regulation at macro‐scales: species richness on oceanic archipelagos. Glob. Ecol. Biogeogr. 24, 594–605 (2015).Article 

Google Scholar 
Crase, B., Liedloff, A. C. & Wintle, B. A. A new method for dealing with residual spatial autocorrelation in species distribution models. Ecography 35, 879–888 (2012).Article 

Google Scholar 
Bivand, R. R packages for analyzing spatial data: a comparative case study with areal data. Geogr. Anal. 54, 488–518 (2022).Article 

Google Scholar 
R. C. Team, R: A language and environment for statistical computing. (R Foundation for Statistical Computing, 2019).Bates, D., Maechler, M., Bolker, B. & Walker, S. Fitting Linear Mixed-Effects Models Using lme4. J. Stat. Softw. 67, 1–48 (2015).Article 

Google Scholar  More

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

i.

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

ii.

Model variables

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

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

Wright, S. Evolution in Mendelian Populations. Genetics 16, 97–159 (1931).CAS 
PubMed 
PubMed Central 

Google Scholar 
DiBattista, J. D. Patterns of genetic variation in anthropogenically impacted populations. Conserv. Genet. 9, 141–156 (2008).
Google Scholar 
Ellegren, H. & Galtier, N. Determinants of genetic diversity. Nat. Rev. Genet. 17, 422–433 (2016).CAS 
PubMed 

Google Scholar 
Nei, M., Maruyama, T. & Chakraborty, R. The Bottleneck Effect and Genetic Variability in Populations. Evolution 29, 1–10 (1975).PubMed 

Google Scholar 
Frankham, R. Stress and adaptation in conservation genetics. J. Evol. Biol. 18, 750–755 (2005).CAS 
PubMed 

Google Scholar 
Mimura, M. et al. Understanding and monitoring the consequences of human impacts on intraspecific variation. Evol. Appl. 10, 121–139 (2017).PubMed 

Google Scholar 
Whitham, T. G. et al. A framework for community and ecosystem genetics: from genes to ecosystems. Nat. Rev. Genet. 7, 510–523 (2006).CAS 
PubMed 

Google Scholar 
Hughes, A., Inouye, B., Johnson, M., Underwood, N. & Vellend, M. Ecological consequences of genetic diversity. Ecol. Lett. 11, 609–623 (2008).PubMed 

Google Scholar 
Hughes, A. R., Stachowicz, J. J. & Williams, S. L. Morphological and physiological variation among seagrass (Zostera marina) genotypes. Oecologia 159, 725–733 (2009).PubMed 

Google Scholar 
Schweitzer, J. A. et al. Genetically based trait in a dominant tree affects ecosystem processes: Plant genetics impact ecosystems. Ecol. Lett. 7, 127–134 (2004).
Google Scholar 
Hughes, A. R. & Stachowicz, J. J. Genetic diversity enhances the resistance of a seagrass ecosystem to disturbance. Proc. Natl Acad. Sci. USA 101, 8998–9002 (2004).CAS 
PubMed 
PubMed Central 

Google Scholar 
Wimp, G. M. et al. Conserving plant genetic diversity for dependent animal communities. Ecol. Lett. 7, 776–780 (2004).
Google Scholar 
Reusch, T. B. H., Ehlers, A., Hämmerli, A. & Worm, B. Ecosystem recovery after climatic extremes enhanced by genotypic diversity. Proc. Natl Acad. Sci. 102, 2826–2831 (2005).CAS 
PubMed 
PubMed Central 

Google Scholar 
Cardinale, B. J. et al. Biodiversity loss and its impact on humanity. Nature 486, 59–67 (2012).CAS 
PubMed 

Google Scholar 
Salo, T. & Gustafsson, C. The Effect of Genetic Diversity on Ecosystem Functioning in Vegetated Coastal Ecosystems. Ecosystems 19, 1429–1444 (2016).
Google Scholar 
Zettlemoyer, M. A. & Peterson, M. L. Does Phenological Plasticity Help or Hinder Range Shifts Under Climate Change? Front. Ecol. Evol. 9, 392 (2021).
Google Scholar 
Fei, S. et al. Divergence of species responses to climate change. Sci. Adv. 3, e1603055 (2017).PubMed 
PubMed Central 

Google Scholar 
Yiming, L. et al. Latitudinal gradients in genetic diversity and natural selection at a highly adaptive gene in terrestrial mammals. Ecography 44, 206–218 (2021).
Google Scholar 
Excoffier, L., Foll, M. & Petit, R. J. Genetic Consequences of Range Expansions. Annu. Rev. Ecol. Evol. Syst. 40, 481–501 (2009).
Google Scholar 
Alsos, I. G. et al. Genetic consequences of climate change for northern plants. Proc. R. Soc. B Biol. Sci. 279, 2042–2051 (2012).
Google Scholar 
Stahl, U., Reu, B. & Wirth, C. Predicting species’ range limits from functional traits for the tree flora of North America. Proc. Natl Acad. Sci. 111, 13739–13744 (2014).CAS 
PubMed 
PubMed Central 

Google Scholar 
Van Nuland, M. E. et al. Intraspecific trait variation across elevation predicts a widespread tree species’ climate niche and range limits. Ecol. Evol. 10, 3856–3867 (2020).PubMed 
PubMed Central 

Google Scholar 
Peterson, M. L., Angert, A. L. & Kay, K. M. Experimental migration upward in elevation is associated with strong selection on life history traits. Ecol. Evol. 10, 612–625 (2019).PubMed 
PubMed Central 

Google Scholar 
Vitasse, Y., Signarbieux, C. & Fu, Y. H. Global warming leads to more uniform spring phenology across elevations. Proc. Natl Acad. Sci. 115, 1004–1008 (2018).CAS 
PubMed 

Google Scholar 
Piao, S. et al. Plant phenology and global climate change: Current progresses and challenges. Glob. Change Biol. 25, 1922–1940 (2019).
Google Scholar 
Chen, I.-C., Hill, J., Ohlemüller, R., Roy, D. B. & Thomas, C. Rapid Range Shifts of Species Associated with High Levels of Climate Warming. Science 333, 1024–6 (2011).CAS 
PubMed 

Google Scholar 
Pauls, S. U., Nowak, C., Bálint, M. & Pfenninger, M. The impact of global climate change on genetic diversity within populations and species. Mol. Ecol. 22, 925–946 (2013).PubMed 

Google Scholar 
De Kort, H. et al. Life history, climate and biogeography interactively affect worldwide genetic diversity of plant and animal populations. Nat. Commun. 12, 516 (2021).PubMed 
PubMed Central 

Google Scholar 
Hampe, A. & Petit, R. J. Conserving biodiversity under climate change: the rear edge matters. Ecol. Lett. 8, 461–467 (2005).PubMed 

Google Scholar 
DeMarche, M. L., Doak, D. F. & Morris, W. F. Incorporating local adaptation into forecasts of species’ distribution and abundance under climate change. Glob. Change Biol. 25, 775–793 (2019).
Google Scholar 
Bothwell, H. M. et al. Genetic data improves niche model discrimination and alters the direction and magnitude of climate change forecasts. Ecol. Appl. 31, e02254 (2021).Syfert, M. M., Brummitt, N. A., Coomes, D. A., Bystriakova, N. & Smith, M. J. Inferring diversity patterns along an elevation gradient from stacked SDMs: A case study on Mesoamerican ferns. Glob. Ecol. Conserv. 16, e00433 (2018).
Google Scholar 
Mateo, R. G., Felicísimo, Á. M., Pottier, J., Guisan, A. & Muñoz, J. Do Stacked Species Distribution Models Reflect Altitudinal Diversity Patterns? PLOS ONE 7, e32586 (2012).CAS 
PubMed 
PubMed Central 

Google Scholar 
Ferrier, S. & Guisan, A. Spatial modelling of biodiversity at the community level. J. Appl. Ecol. 43, 393–404 (2006).
Google Scholar 
Ware, I. M. et al. Climate-driven reduction of genetic variation in plant phenology alters soil communities and nutrient pools. Glob. Change Biol. 25, 1514–1528 (2019).
Google Scholar 
Endler, J. A. Geographic variation, speciation, and clines (Princeton University Press, 1977).May, R. M. & Godfrey, J. Biological Diversity: Differences between Land and Sea [and Discussion]. Philos. Trans. Biol. Sci. 343, 105–111 (1994).
Google Scholar 
Des Roches, S. et al. The ecological importance of intraspecific variation. Nat. Ecol. Evol. 2, 57–64 (2018).PubMed 

Google Scholar 
Van Nuland, M. E., Bailey, J. K. & Schweitzer, J. A. Divergent plant–soil feedbacks could alter future elevation ranges and ecosystem dynamics. Nat. Ecol. Evol. 1, 0150 (2017).
Google Scholar 
Richardson, A. D. et al. Influence of spring and autumn phenological transitions on forest ecosystem productivity. Philos. Trans. R. Soc. B Biol. Sci. 365, 3227–3246 (2010).
Google Scholar 
Richardson, A. D. et al. Influence of spring phenology on seasonal and annual carbon balance in two contrasting New England forests. Tree Physiol. 29, 321–321 (2009).CAS 
PubMed 

Google Scholar 
Huntington, T. G. CO2-induced suppression of transpiration cannot explain increasing runoff. Hydrol. Process. 22, 311–314 (2008).
Google Scholar 
Kim, J. H. et al. Warming-Induced Earlier Greenup Leads to Reduced Stream Discharge in a Temperate Mixed Forest Catchment. J. Geophys. Res. Biogeosciences 123, 1960–1975 (2018).
Google Scholar 
Ware, I. M. et al. Climate-driven divergence in plant-microbiome interactions generates range-wide variation in bud break phenology. Commun. Biol. 4, 748 (2021).PubMed 
PubMed Central 

Google Scholar 
Mori, A. S. et al. Biodiversity–productivity relationships are key to nature-based climate solutions. Nat. Clim. Change 11, 543–550 (2021).
Google Scholar 
Woolbright, S. A., Whitham, T. G., Gehring, C. A., Allan, G. J. & Bailey, J. K. Climate relicts and their associated communities as natural ecology and evolution laboratories. Trends Ecol. Evol. 29, 406–416 (2014).PubMed 

Google Scholar 
Naiman, R. J., Décamps, H. & McClain, M. E. Riparia: ecology, conservation, and management of streamside communities (Elsevier Academic Press, 2005).Bayliss, S. L. J., Mueller, L. O., Ware, I. M., Schweitzer, J. A. & Bailey, J. K. Plant genetic variation drives geographic differences in atmosphere–plant–ecosystem feedbacks. Plant-Environ. Interact. 1, 166–180 (2020).
Google Scholar 
Cooke, J. E. K. & Rood, S. B. Trees of the people: the growing science of poplars in Canada and worldwide. This commentary is one of a selection of papers published in the Special Issue on Poplar Research in Canada. Can. J. Bot. 85, 1103–1110 (2007).
Google Scholar 
Evans, L. M., Allan, G. J., Meneses, N., Max, T. L. & Whitham, T. G. Herbivore host- associated genetic differentiation depends on the scale of plant genetic variation examined. Evol. Ecol. 27, 65–81 (2013).
Google Scholar 
Evans, L. M. et al. Geographical barriers and climate influence demographic history in narrowleaf cottonwoods. Heredity 114, 387–396 (2015).CAS 
PubMed 
PubMed Central 

Google Scholar 
Hargreaves, A. L., Samis, K. E., Eckert, C. G., Schmitz, A. E. O. J. & Bronstein, E. J. L. Are Species’ Range Limits Simply Niche Limits Writ Large? A Review of Transplant Experiments beyond the Range. Am. Nat. 183, 157–173 (2014).PubMed 

Google Scholar 
Gotelli, N. J. & Stanton-Geddes, J. Climate change, genetic markers and species distribution modelling. J. Biogeogr. 42, 1577–1585 (2015).
Google Scholar 
Cushman, S. A. et al. Landscape genetic connectivity in a riparian foundation tree is jointly driven by climatic gradients and river networks. Ecol. Appl. 24, 1000–1014 (2014).PubMed 

Google Scholar 
Bothwell, H. M. et al. Conserving threatened riparian ecosystems in the American West: Precipitation gradients and river networks drive genetic connectivity and diversity in a foundation riparian tree (Populus angustifolia). Mol. Ecol. 26, 5114–5132 (2017).PubMed 

Google Scholar 
Jimenez-Valverde, A. Sample Size for the evaluation of presence-absence models. Ecol. Indic. 114, 106289 (2020).
Google Scholar 
Hamann, A., Wang, T., Spittlehouse, D. L. & Murdock, T. Q. A Comprehensive, High-Resolution Database of Historical and Projected Climate Surfaces for Western North America. Bull. Am. Meteorol. Soc. 94, 1307–1309 (2013).
Google Scholar 
Lucinda. M. et al. NHDPlus version 2: user guide (Horizon Systems Corporation, 2012).ESRI. ArcMap (ESRI, 2018).Bayliss, S. L. J., Papeş, M., Schweitzer, J. A. & Bailey, J. K. Aggregate population-level models informed by genetics predict more suitable habitat than traditional species-level model across the range of a widespread riparian tree. PLoS One. 17, e0274892 (2022).CAS 
PubMed 
PubMed Central 

Google Scholar 
Elith, J. & Leathwick, J. R. Species Distribution Models: Ecological Explanation and Prediction Across Space and Time. Annu. Rev. Ecol. Evol. Syst. 40, 677–697 (2009).
Google Scholar 
Franklin, J. Mapping species distributions: spatial inference and prediction (Cambridge University Press, 2009).Bates, D., Maechler, M., Bolker, B. & Walker, S. Fitting Linear Mixed-Effects Models Using lme4. J. Stat. Softw. 67, 1–48 (2015). (1).
Google Scholar 
Elith, J. et al. A statistical explanation of MaxEnt for ecologists. Divers. Distrib. 17, 43–57 (2011).
Google Scholar 
Elith, J. et al. Novel methods improve prediction of species’ distributions from occurrence data. Ecography 29, 129–151 (2006).
Google Scholar 
Pearson, R. G., Raxworthy, C. J., Nakamura, M. & Townsend Peterson, A. Predicting species distributions from small numbers of occurrence records: a test case using cryptic geckos in Madagascar. J. Biogeogr. 34, 102–117 (2007).
Google Scholar 
Swets, J. A. Measuring the Accuracy of Diagnostic Systems. Science 240, 1285–1293 (1988).CAS 
PubMed 

Google Scholar 
Engler, R. et al. 21st century climate change threatens mountain flora unequally across Europe. Glob. Change Biol. 17, 2330–2341 (2011).
Google Scholar 
Randin, C. F. et al. Climate change and plant distribution: local models predict high-elevation persistence. Glob. Change Biol. 15, 1557–1569 (2009).
Google Scholar 
Knutti, R., Masson, D. & Gettelman, A. Climate model genealogy: Generation CMIP5 and how we got there. Geophys. Res. Lett. 40, 1194–1199 (2013).
Google Scholar 
Mateo, R. G., Mokany, K. & Guisan, A. Biodiversity Models: What If Unsaturation Is the Rule? Trends Ecol. Evol. 32, 556–566 (2017).PubMed 
PubMed Central 

Google Scholar 
R. Core Team. R: a language and environment for statistical computing (R Foundation for Statistical Computing, 2020).Oksanen, J. et al. vegan: community ecology package (2020) http://CRAN.R-project.org/package=vegan. More

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

Google Scholar  More

Ahmed MZ, Li SJ, Xue X, Yin XJ, Ren SX, Jiggins FM et al. (2015) The Intracellular bacterium Wolbachia uses parasitoid wasps as phoretic vectors for efficient horizontal transmission. PLoS Pathog 11:1–19
Google Scholar 
Arai H, Hirano T, Akizuki N, Abe A, Nakai M, Kunimi Y et al. (2019) Multiple infection and reproductive manipulations of Wolbachia in Homona magnanima (Lepidoptera: Tortricidae). Microb Ecol 77:257–266PubMed 

Google Scholar 
Arai H, Lin SR, Nakai M, Kunimi Y, Inoue MN (2020) Closely related male-killing and nonmale-killing Wolbachia strains in the oriental tea tortrix Homona magnanima. Microb Ecol 79:1011–1020CAS 
PubMed 

Google Scholar 
Bailey NW, Zuk M (2008) Changes in immune effort of male field crickets infested with mobile parasitoid larvae. J Insect Physiol 54:96–104CAS 
PubMed 

Google Scholar 
Ballad JWO, Hatzidakis J, Karr TL, Kreitman M (1996) Reduced variation in Drosophila simulans mitochondrial DNA. Genetics 144:1519–1528
Google Scholar 
Birch LC (1948) The intrinsic rate of natural increase of an insect population. J Anim Ecol 17:15–26
Google Scholar 
Capobianco IIIF, Nandkumar S, Parker JD (2018) Wolbachia affects survival to different oxidative stressors dependent upon the genetic background in Drosophila melanogaster. Physiol Entomol 43:239–244
Google Scholar 
Danthanarayana W (1975) Factors determining variation in fecundity of the light brown apple moth, Epiphyas postvittana (Walker) (Tortricidae). Aust J Zool 23:309–319
Google Scholar 
Dean MD (2006) A Wolbachia-associated fitness benefit depends on genetic background in Drosophila simulans. Proc R Soc B 273:1415–1420CAS 
PubMed 
PubMed Central 

Google Scholar 
Deseo KV (1971) Study of factors influencing the fecundity and fertility of codling moth (Laspeyresia pomonella L., Lepidoptera, Tortricidae). Acta Phytopathol Hun 6:243–252
Google Scholar 
Dobson SL, Rattanadechakul W, Marsland EJ (2004) Fitness advantage and cytoplasmic incompatibility in Wolbachia single-and superinfected Aedes albopictus. Heredity 93:135–142CAS 
PubMed 

Google Scholar 
Duron O, Bouchon D, Boutin S, Bellamy L, Zhou L, Engelstädter J, Hurst GD (2008) The diversity of reproductive parasites among arthropods: Wolbachia do not walk alone. BMC Biol 6:1–12
Google Scholar 
Engelstädter J, Telschow A, Hammerstein P (2004) Infection dynamics of different Wolbachia-types within one host population. J Theor Biol 231:345–55PubMed 

Google Scholar 
Fleury F, Vavre F, Ris N, Fouillet P, Boulétreau M (2000) Physiological cost induced by the maternally-transmitted endosymbiont Wolbachia in the Drosophila parasitoid Leptopilina heterotoma. Parasitology 121:493–500PubMed 

Google Scholar 
Frank SA (1998) Dynamics of cytoplasmic incompatibility with multiple Wolbachia infections. J Theor Biol 192:213–18CAS 
PubMed 

Google Scholar 
Frank SA, Hurst LD (1996) Mitochondria and male disease. Nature 383:224–224CAS 
PubMed 

Google Scholar 
Fry AJ, Palmer MR, Rand DM (2004) Variable fitness effects of Wolbachia infection in Drosophila melanogaster. Heredity 93:379–389CAS 
PubMed 

Google Scholar 
Gómez-Valero L, Soriano-Navarro M, Pérez-Brocal V, Heddi A, Moya A, García-Verdugo JM, Latorre A (2004) Coexistence of Wolbachia with Buchnera Aphidicola and a secondary symbiont in the aphid Cinara cedri. J Bacteriol 186:6626–33PubMed 
PubMed Central 

Google Scholar 
Hoffmann AA, Turelli M, Harshman LG (1990) Factors affecting the distribution of cytoplasmic incompatibility in Drosophila simulans. Genetics 126:933–948CAS 
PubMed 
PubMed Central 

Google Scholar 
Hornett EA, Charlat S, Duplouy AMR, Davies N, Roderick GK, Wedell N et al. (2006) Evolution of male-killer suppression in a natural population. PLoS Biol 4:1643–1648CAS 

Google Scholar 
Hough JA, Pimentel D (1978) Influence of host foliage on development, survival, and fecundity of the gypsy moth. Environ Entomol 7:97–102
Google Scholar 
Ikeda T, Ishikawa H, Sasaki T (2003) Infection density of Wolbachia and level of cytoplasmic incompatibility in the Mediterranean flour moth, Ephestia kuehniella. J Invertebr Pathol 84:1–5PubMed 

Google Scholar 
Ishii T, Nakai M, Okuno S, Takatsuka J, Kunimi Y (2003) Characterization of Adoxophyes honmai single-nucleocapsid nucleopolyhedrovirus: morphology, structure, and effects on larvae. J Invertebr Pathol 83:206–214CAS 
PubMed 

Google Scholar 
Kondo N, Shimada M, Fukatsu T (2005) Infection density of Wolbachia endosymbiont affected by coinfection and host genotype. Biol Lett 1:488–491PubMed 
PubMed Central 

Google Scholar 
Lu P, Bian G, Pan X, Xi Z (2012) Wolbachia induces density-dependent inhibition to dengue virus in mosquito cells. PLoS Negl Trop D 6:1–8CAS 

Google Scholar 
Maia AHN, Luiz AJB, Campanhola C (2000) Statistical inference on associated fertility life table parameters using jackknife technique: computational aspects. J Econ Entomol 93:511–518
Google Scholar 
Mazzetto F, Gonella E, Alma A (2015) Wolbachia infection affects female fecundity in Drosophila suzukii. Bull Insectol 68:153–157
Google Scholar 
Meyer JS, Ingersoll CG, McDonald LL, Boyce MS (1986) Estimating uncertainty in population growth rates: jackknife vs. bootstrap techniques. Ecology 67:1156–1166
Google Scholar 
Moreira LA, Iturbe-Ormaetxe I, Jeffery JA, Lu G, Pyke AT, Hedges LM et al. (2009) A Wolbachia symbiont in Aedes aegypti limits infection with dengue, Chikungunya, and Plasmodium. Cell 139:1268–1278PubMed 

Google Scholar 
Mouton L, Henri H, Bouletreau M, Vavre F (2006) Effect of temperature on Wolbachia density and impact on cytoplasmic incompatibility. Parasitology 132:49–56CAS 
PubMed 

Google Scholar 
Narita S, Nomura M, Kageyama D (2007) Naturally occurring single and double infection with Wolbachia strains in the butterfly Eurema hecabe: transmission efficiencies and population density dynamics of each Wolbachia strain. FEMS Microb Ecol 61:235–245CAS 

Google Scholar 
Pigeault R, Braquart-Varnier C, Marcadé I, Mappa G, Mottin E, Sicard M (2014) Modulation of host immunity and reproduction by horizontally acquired Wolbachia. J Insect Physiol 70:125–133CAS 
PubMed 

Google Scholar 
Rancès E, Ye YH, Woolfit M, McGraw EA, O´Neill SL (2012) The relative importance of innate immune priming in Wolbachia-mediated dengue interference. PLoS Pathog 8:e1002548. https://doi.org/10.1371/journal.ppat.1002548Article 
CAS 
PubMed 
PubMed Central 

Google Scholar 
R Core Team (2021) R: A language and environment for statistical computing. R Foundation for Statistical Computing, Vienna, Austria. https://www.R-project.org/Stevanovic AL, Arnold PA, Johnson KN (2015) Wolbachia -mediated antiviral protection in Drosophila larvae and adults following oral infection. Appl Environ Micro 81:8215–8223CAS 

Google Scholar 
Takamatsu T, Arai H, Abe N, Nakai M, Kunimi Y, Inoue MN (2021) Coexistence of two male-killers and their impact on the development of oriental tea tortrix Homona magnanima. Microb Ecol 81:193–202CAS 
PubMed 

Google Scholar 
Takehana A, Katsuyama T, Yano T, Oshima Y, Takada H, Aigaki T et al. (2002) Overexpression of a pattern-recognition receptor, peptidoglycan-recognition protein-LE, activates imd/relish-mediated antibacterial defense and the prophenoloxidase cascade in Drosophila larvae. Proc Natl Acad Sci USA 99:13705–13710CAS 
PubMed 
PubMed Central 

Google Scholar 
Takatsuka J, Okuno S, Ishii T, Nakai M, Kunimi Y (2010) Fitness-related traits of entomopoxviruses isolated from Adoxophyes honmai (Lepidoptera: Tortricidae) at three localities in Japan. J Invertebr Pathol 105:121–131PubMed 

Google Scholar 
Teixeira L, Ferreira Á, Ashburner M (2008) The Bacterial symbiont Wolbachia induces resistance to RNA viral infections in Drosophila melanogaster. PLoS Biol 6:e1000002. https://doi.org/10.1371/journal.pbio.1000002Article 
CAS 
PubMed Central 

Google Scholar 
Thomas P, Kenny N, Eyles D, Moreira LA, O´Neill SL, Asgari S (2011) Infection with the wMel and wMelPop strains of Wolbachia leads to higher levels of melanization in the hemolymph of Drosophila melanogaster, Drosophila simulans and Aedes aegypti. Dev Comp Immunol 35:360–365CAS 
PubMed 

Google Scholar 
Tsuruta K, Wennmann JT, Kunimi Y, Inoue MN, Nakai M (2018) Morphological properties of the occlusion body of Adoxophyes orana granulovirus. J Invertebr Pathol 154:58–64CAS 
PubMed 

Google Scholar 
Turelli M, Hoffmann AA (1991) Rapid spread of an inherited incompatibility factor in California Drosophila. Nature 353:440–442CAS 
PubMed 

Google Scholar 
Vautrin E, Vavre F (2009) Interactions between vertically transmitted symbionts: cooperation or conflict? Trends Microbiol 17:95–99CAS 
PubMed 

Google Scholar 
Vavre F, Fleury F, Lepetit D, Fouillet P, Boulétreau M (1999) Phylogenetic evidence for horizontal transmission of Wolbachia in host- parasitoid associations. Mol Biol Evol 16:1711–1723CAS 
PubMed 

Google Scholar 
Vollmer J, Schiefer A, Schneider T, Jülicher K, Johnston KL, Taylor MJ et al. (2013) Requirement of lipid II biosynthesis for cell division in cell wall-less Wolbachia, endobacteria of arthropods and filarial nematodes. Int J Med Microbiol 303:140–149CAS 
PubMed 

Google Scholar 
Voronin D, Guimarães AF, Molyneux GR, Johnston KL, Ford L, Taylor MJ (2014) Wolbachia lipoproteins: abundance, localization and serology of Wolbachia peptidoglycan associated lipoprotein and the Type IV Secretion System component, VirB6 from Brugia malayi and Aedes albopictus. Parasite Vector 7:462
Google Scholar 
Watanabe M, Miura K, Hunter MS, Wajnberg E (2011) Superinfection of cytoplasmic incompatibility-inducing Wolbachia is not additive in Orius strigicollis (Hemiptera: Anthocoridae). Heredity 106:642–648CAS 
PubMed 

Google Scholar 
Weeks AR, Turelli M, Harcombe WR, Reynolds KT, Hoffmann AA (2007) From parasite to mutualist: rapid evolution of Wolbachia in natural populations of Drosophila. PLoS Biol 5:0997–1005CAS 

Google Scholar 
Werren JH, Baldo L, Clark ME (2008) Wolbachia: Master manipulators of invertebrate biology. Nat Rev Microbiol 6:741–751CAS 
PubMed 

Google Scholar 
Xue X, Li S, Ahmed MZ, Barro PJ, Ren S, Qiu B (2012) Inactivation of Wolbachia reveals its biological roles in whitefly host. PLoS One 7:e48148. https://doi.org/10.1371/journal.pone.0048148Article 
CAS 
PubMed 
PubMed Central 

Google Scholar 
Zug R, Hammerstein P (2012) Still a host of hosts for Wolbachia: analysis of recent data suggests that 40% of terrestrial arthropod species are infected. PLoS One 7:e38544. https://doi.org/10.1371/journal.pone.0038544Article 
CAS 
PubMed 
PubMed Central 

Google Scholar 
Zug R, Hammerstein P (2015) Wolbachia and the insect immune system: what reactive oxygen species can tell us about the mechanisms of Wolbachia-host interactions. Front Microbiol 6:1–16
Google Scholar  More