Ecology

Bioactive composition analysisThe main bioactive components in the three products are listed in Table 1. The main chemical constitutes of DL and LT were quite similar; although significant differences were noted in indicators such as protein (DL  > LT, difference = 8.22, P  DL, difference = 1.79, P  LT, difference = 4.07, P  0.05) of the samples. Among the bioactive constitutes, only POL contents in LT and DL were significantly lower (P  DR (1.90%). Compared with DL and LT, the DR exhibited significantly higher POL, increased by 50.21% (P  More

1.Raup, D. M. Size of the Permo-Triassic bottleneck and its evolutionary implications. Science 206, 217–218 (1979).ADS 
CAS 
PubMed 
Article 

Google Scholar 
2.Stanley, S. M. Estimates of the magnitudes of major marine mass extinctions in earth history. Proc. Natl. Acad. Sci. U. S. A. 113, E6325–E6334 (2016).ADS 
CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
3.Sepkoski, J. J. A factor analytic description of the Phanerozoic marine fossil record. Paleobiology 7, 36–53 (1981).Article 

Google Scholar 
4.Tozer, E. T. Marine Triassic faunas of North America: Their significance for assessing plate and terrane movements. Geol. Rundschau 71, 1077–1104 (1982).ADS 
Article 

Google Scholar 
5.Hallam, A. Major bio-events in the Triassic and Jurassic. In Global Events and Event Stratigraphy in the Phanerozoic (ed. Walliser O.H.) 265–283 (Springer, 1996).6.Brayard, A. et al. Good genes and good luck: Ammonoid diversity and the end-Permian mass extinction. Science 325, 1118–1121 (2009).ADS 
CAS 
PubMed 
Article 

Google Scholar 
7.Stanley, S. M. Evidence from ammonoids and conodonts for multiple Early Triassic mass extinctions. Proc. Natl. Acad. Sci. U. S. A. 106, 15264–15267 (2009).ADS 
CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
8.Chen, Z.-Q. & Benton, M. J. The timing and pattern of biotic recovery following the end-Permian mass extinction. Nat. Geosci. 5, 375–383 (2012).ADS 
CAS 
Article 

Google Scholar 
9.Friesenbichler, E., Hautmann, M., Nützel, A., Urlichs, M. & Bucher, H. Palaeoecology of Late Ladinian (Middle Triassic) benthic faunas from the Schlern/Sciliar and Seiser Alm/Alpe di Siusi area (South Tyrol, Italy). Pal. Z. 93, 1–29 (2019).
Google Scholar 
10.Zhao, X. et al. Recovery of lacustrine ecosystems after the end-Permian mass extinction. Geology 48, 609–613 (2020).ADS 
Article 

Google Scholar 
11.Friesenbichler, E., Hautmann, M. & Bucher, H. The main stage of recovery after the end-Permian mass extinction: Taxonomic rediversification and ecologic reorganization of marine level-bottom communities during the Middle Triassic. PeerJ 9, e11654 (2021).PubMed 
PubMed Central 
Article 

Google Scholar 
12.Twitchett, R. J. Incompleteness of the Permian-Triassic fossil record: A consequence of productivity decline?. Geol. J. 36, 341–353 (2001).Article 

Google Scholar 
13.Foster, W. J. & Twitchett, R. J. Functional diversity of marine ecosystems after the Late Permian mass extinction event. Nat. Geosci. 7, 233–238 (2014).ADS 
CAS 
Article 

Google Scholar 
14.Hu, S. et al. The Luoping biota: exceptional preservation, and new evidence on the Triassic recovery from end-Permian mass extinction. Proc. R. Soc. London B 278, 2274–2282 (2011).
Google Scholar 
15.Brayard, A. et al. Unexpected Early Triassic marine ecosystem and the rise of the modern evolutionary fauna. Sci. Adv. 3, e1602159 (2017).ADS 
PubMed 
PubMed Central 
Article 

Google Scholar 
16.Widmann, P. et al. Dynamics of the largest carbon isotope excursion during the Early Triassic biotic recovery. Front. Earth Sci. 8, 196 (2020).ADS 
Article 

Google Scholar 
17.Brayard, A. et al. The Early Triassic ammonoid recovery: Paleoclimatic significance of diversity gradients. Palaeogeogr. Palaeoclimatol. Palaeoecol. 239, 374–395 (2006).Article 

Google Scholar 
18.Jattiot, R. et al. Palaeobiogeographical distribution of Smithian (Early Triassic) ammonoid faunas within the western USA basin and its controlling parameters. Palaeontology 61, 881–904 (2018).Article 

Google Scholar 
19.Orchard, M. J. Conodont diversity and evolution through the latest Permian and Early Triassic upheavals. Palaeogeogr. Palaeoclimatol. Palaeoecol. 252, 93–117 (2007).Article 

Google Scholar 
20.Zhang, L. et al. The Smithian/Spathian boundary (late Early Triassic): A review of ammonoid, conodont, and carbon-isotopic criteria. Earth Sci. Rev. 195, 7–36 (2019).ADS 
CAS 
Article 

Google Scholar 
21.Goudemand, N. et al. Dynamic interplay between climate and marine biodiversity upheavals during the Early Triassic Smithian -Spathian biotic crisis. Earth Sci. Rev. 195, 169–178 (2019).ADS 
CAS 
Article 

Google Scholar 
22.Kashiyama, Y. & Oji, T. Low-diversity shallow marine benthic fauna from the Smithian of northeast Japan: Paleoecologic and paleobiogeographic implications. Pal. Res. 8, 199–218 (2004).Article 

Google Scholar 
23.Hautmann, M. et al. An unusually diverse mollusc fauna from the earliest Triassic of South China and its implications for benthic recovery after the end-Permian biotic crisis. Geobios 44, 71–85 (2011).Article 

Google Scholar 
24.Hofmann, R. et al. Recovery of benthic marine communities from the end-Permian mass extinction at the low latitudes of eastern Panthalassa. Palaeontology 57, 547–589 (2014).Article 

Google Scholar 
25.Foster, W. J. et al. Early Triassic benthic invertebrates from the Great Bank of Guizhou, South China: Systematic palaeontology and palaeobiology. Pap. Pal. 5, 613–656 (2019).Article 

Google Scholar 
26.Hautmann, M. et al. Competition in slow motion: The unusual case of benthic marine communities in the wake of the end-Permian mass extinction. Palaeontology 58, 871–901 (2015).Article 

Google Scholar 
27.Schaeffer, B., Mangus, M. & Laudon, L. R. An Early Triassic fish assemblage from British Columbia. Bull. AMNH. 156, article 5. (1976).28.Tintori, A., Hitij, T., Jiang, D., Lombardo, C. & Sun, Z. Triassic actinopterygian fishes: the recovery after the end-Permian crisis. Integr. Zool. 9, 394–411 (2014).PubMed 
Article 

Google Scholar 
29.Neuman, A. G. Fishes from the Lower Triassic portion of the Sulphur Mountain Formation in Alberta, Canada: Geological context and taxonomic composition. Can. J. Earth Sci. 52, 557–568 (2015).ADS 
Article 

Google Scholar 
30.Romano, C. et al. Permian-Triassic Osteichthyes (bony fishes): Diversity dynamics and body size evolution. Biol. Rev. 91, 106–147 (2016).PubMed 
Article 
PubMed Central 

Google Scholar 
31.Qiu, X. et al. The Early Triassic Jurong fish fauna, South China: Age, anatomy, taphonomy, and global correlation. Glob. Planet. Change 180, 33–50 (2019).ADS 
Article 

Google Scholar 
32.Li, Q. & Liu, J. An Early Triassic sauropterygian and associated fauna from South China provide insights into Triassic ecosystem health. Commun. Biol. 3, 63 (2020).PubMed 
PubMed Central 
Article 

Google Scholar 
33.Song, H., Wignall, P. B. & Dunhill, A. M. Decoupled taxonomic and ecological recoveries from the Permo-Triassic extinction. Sci. Adv. 4, eaat5091 (2018).ADS 
PubMed 
PubMed Central 
Article 

Google Scholar 
34.Muscente, A. D. et al. Exceptionally preserved fossil assemblages through geologic time and space. Gondwana Res. 48, 164–188 (2017).ADS 
CAS 
Article 

Google Scholar 
35.Lucas, S. G., Krainer, K. & Milner, A. R. C. The type section and age of the Timpoweap Member and stratigraphic nomenclature of the Triassic Moenkopi Group in Southwestern Utah. New Mexico Mus. Nat. Hist. Sci. Bull. 40, 109–117 (2007).
Google Scholar 
36.Caravaca, G. et al. Controlling factors for differential subsidence in the Sonoma Foreland Basin (Early Triassic, western USA). Geol. Mag. 155, 1305–1329 (2018).ADS 
Article 

Google Scholar 
37.Brayard, A., Jenks, J. F., Bylund, K. G. & the Paris Biota team. Ammonoids and nautiloids from the earliest Spathian Paris Biota and other early Spathian localities in southeastern Idaho, USA. Geobios 54, 13–36 (2019).Article 

Google Scholar 
38.Lucas, S. G. & Orchard, M. J. Triassic lithostratigraphiy and biostratigraphy North of Currie, Elko County, Nevada. New Mexico Mus. Nat. Hist. Sci. Bull. 40, 119–126 (2007).
Google Scholar 
39.Guex, J. et al. Spathian (Lower Triassic) ammonoids from western USA (Idaho, California, Utah and Nevada). Mémoires de Géologie (Lausanne) 49, (2010).40.Doguzhaeva, L. et al. An Early Triassic gladius associated with soft tissue remains from Idaho, USA: A squid-like coleoid cephalopod at the onset of Mesozoic Era. Acta Pal. Pol. 63, 341–355 (2018).
Google Scholar 
41.Laville, T., Smith, C. P. A., Forel, M.-B., Brayard, A. & Charbonnier, S. Review of Early Triassic Thylacocephala. Riv. Italiana Pal. Sed. 127, 73–101 (2021).
Google Scholar 
42.Charbonnier, S., Brayard, A. & the Paris Biota team. New thylacocephalans from the Early Triassic Paris Biota (Bear Lake County, Idaho, USA). Geobios 54, 37–43 (2019).Article 

Google Scholar 
43.Roopnarine, P. Graphs, networks, extinction and paleocommunity food webs. Nat. Prec. https://doi.org/10.1038/npre.2010.4433.1 (2010).Article 

Google Scholar 
44.Baselga, A. Partitioning the turnover and nestedness components of beta diversity. Glob. Ecol. Biogeogr. 19, 134–143 (2010).Article 

Google Scholar 
45.Shi, G. R. & Zwan, L.-P. A mixed mid-Permian marine fauna from the Yanji area, northeastern China: A paleobiogeographical reinterpretation. Isl. Arc. 5, 386–395 (1996).Article 

Google Scholar 
46.Chen, Z.-Q., Tong, J., Liao, Z.-T. & Chen, J. Structural changes of marine communities over the Permian-Triassic transition: Ecologically assessing the end-Permian mass extinction and its aftermath. Glob. Planet. Change 73, 123–140 (2010).ADS 
Article 

Google Scholar 
47.Massare, J. A. & Callaway, J. M. Cymbospondylus (Ichthyosauria: Shastasauridae) from the Lower Triassic Thaynes Formation of southeastern Idaho. J. Vertebr. Paleontol. 14, 139–141 (1994).Article 

Google Scholar 
48.Scheyer, T. M., Romano, C., Jenks, J. & Bucher, H. Early Triassic marine biotic recovery: The predators’ perspective. PLoS ONE 9, e88987 (2014).ADS 
PubMed 
PubMed Central 
Article 
CAS 

Google Scholar 
49.Song, H. et al. Recovery tempo and pattern of marine ecosystems after the end-Permian mass extinction. Geology 39, 739–742 (2011).ADS 
Article 

Google Scholar 
50.Brayard, A., Gueriau, P., Thoury, M., Escarguel, G. & the Paris Biota team. Glow in the dark: Use of synchrotron μXRF trace elemental mapping and multispectral macro-imaging on fossils from the Paris Biota (Bear Lake County, Idaho, USA). Geobios 54, 71–79 (2019).Article 

Google Scholar 
51.Iniesto, M., Thomazo, C. & Fara, E. Deciphering the exceptional preservation of the Early Triassic Paris Biota (Bear Lake County, Idaho, USA). Geobios 54, 81–93 (2019).Article 

Google Scholar 
52.Sørensen, T. A method of establishing groups of equal amplitude in plant sociology based on similarity of species content and its application to analyses of the vegetation on Danish commons. Biol. Skrif. 5, 3–34 (1948).
Google Scholar 
53.Koleff, P., Gaston, K. J. & Lennon, J. J. Measuring beta diversity for presence-absence data. J. An. Ecol. 72, 367–382 (2003).Article 

Google Scholar 
54.Romano, C., Kogan, I., Jenks, J., Jerjen, I. & Brinkmann, W. Saurichthys and other fossil fishes from the late Smithian (Early Triassic) of Bear Lake County (Idaho, USA), with a discussion of saurichthyid palaeogeography and evolution. Bull. Geosci. 3, 543–570. https://doi.org/10.3140/bull.geosci.1337 (2012).Article 

Google Scholar 
55.Horton, J. D. The State Geologic Map Compilation (SGMC) Geodatabase of the conterminous United States: US Geological Survey data release. US Geol. Surv. https://doi.org/10.5066/F7WH2N65 (2017).Article 

Google Scholar 
56.Kummel, B. The Thaynes Formation, Bear Lake Valley, Idaho. Am. J. Sci. 241, 316–332 (1943).ADS 
Article 

Google Scholar 
57.Kummel, B. Triassic stratigraphy of Southeastern Idaho and adjacent areas. U. S. Geol. Surv. Prof. Pap. 254H, 165–194 (1954).
Google Scholar 
58.Brayard, A., Brühwiler, T., Bucher, H. & Jenks, J. Guodunites, a low-palaeolatitude and trans-Panthalassic Smithian (Early Triassic) ammonoid genus. Palaeontology 52, 471–481 (2009).Article 

Google Scholar 
59.Brayard, A. et al. Smithian ammonoid faunas from Utah: implications for Early Triassic biostratigraphy, correlation and basinal paleogeography. Swiss J. Palaeontol. 132, 141–219 (2013).Article 

Google Scholar 
60.Jenks, J. et al. Ammonoid biostratigraphy of the Early Spathian Columbites parisianus zone (Early Triassic) at Bear Lake Hot Springs Idaho. New Mexico Mus. Natl. Hist. Sci. Bull. 61, 268–283 (2013).
Google Scholar  More

1.Rezende, E. L., Albert, E. M., Fortuna, M. A. & Bascompte, J. Compartments in a marine food web associated with phylogeny, body mass, and habitat structure. Ecol. Lett. 12, 779–788 (2009).PubMed 
Article 
PubMed Central 

Google Scholar 
2.Rosenblatt, A. E. & Heithaus, M. R. Does variation in movement tactics and trophic interactions among American alligators create habitat linkages?. J. Anim. Ecol. 80, 786–798 (2011).PubMed 
Article 
PubMed Central 

Google Scholar 
3.Pringle, R. M. & Fox-Dobbs, K. Coupling of canopy and understory food webs by ground-dwelling predators. Ecol. Lett. 11, 1328–1337 (2008).PubMed 
Article 
PubMed Central 

Google Scholar 
4.Pimm, S. L. & Lawton, J. H. Are food webs divided into compartments? J. Anim. Ecol. 49, 879–898 (1980).Article 

Google Scholar 
5.Stouffer, D. B. & Bascompte, J. Compartmentalization increases food-web persistence. Proc. Natl. Acad. Sci. 108, 3648–3652 (2011).ADS 
CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
6.Krause, A. E., Frank, K. A., Mason, D. M., Ulanowicz, R. E. & Taylor, W. W. Compartments revealed in food-web structure. Nature 426, 282–285 (2003).ADS 
CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
7.Rooney, N., McCann, K., Gellner, G. & Moore, J. C. Structural asymmetry and the stability of diverse food webs. Nature 442, 265–269. https://doi.org/10.1038/nature04887 (2006).ADS 
CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
8.Schindler, D. E. & Scheuerell, M. D. Habitat coupling in lake ecosystems. Oikos 98, 177–189 (2002).Article 

Google Scholar 
9.Matich, P., Heithaus, M. R. & Layman, C. A. Contrasting patterns of individual specialization and trophic coupling in two marine apex predators. J. Anim. Ecol. 80, 294–305 (2011).PubMed 
Article 
PubMed Central 

Google Scholar 
10.Conway-Cranos, L. et al. Stable isotopes and oceanographic modeling reveal spatial and trophic connectivity among terrestrial, estuarine, and marine environments. Mar. Ecol. Prog. Ser. 533, 15–28 (2015).ADS 
CAS 
Article 

Google Scholar 
11.Dias, E., Morais, P., Cotter, A. M., Antunes, C. & Hoffman, J. C. Estuarine consumers utilize marine, estuarine and terrestrial organic matter and provide connectivity among these food webs. Mar. Ecol. Prog. Ser. 554, 21–34 (2016).ADS 
Article 

Google Scholar 
12.Hobson, K. A., Ambrose, W. G. Jr. & Renaud, P. E. Sources of primary production, benthic-pelagic coupling, and trophic relationships within the Northeast Water Polynya: Insights from δ13C and δ15N analysis. Mar. Ecol. Prog. Ser. 128, 1–10 (1995).ADS 
Article 

Google Scholar 
13.Quevedo, M., Svanbäck, R. & Eklöv, P. Intrapopulation niche partitioning in a generalist predator limits food web connectivity. Ecology 90, 2263–2274 (2009).PubMed 
Article 
PubMed Central 

Google Scholar 
14.Allgeier, J. E. et al. Individual behavior drives ecosystem function and the impacts of harvest. Sci. Adv. 6, eaax8329 (2020).ADS 
CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
15.McPeek, M. A. Trade-offs, food web structure, and the coexistence of habitat specialists and generalists. Am. Nat. 148, S124–S138 (1996).Article 

Google Scholar 
16.Bolnick, D. I. et al. The ecology of individuals: Incidence and implications of individual specialization. Am. Nat. 161, 1–28 (2003).MathSciNet 
PubMed 
Article 
PubMed Central 

Google Scholar 
17.Araújo, M. S., Bolnick, D. I. & Layman, C. A. The ecological causes of individual specialisation. Ecol. Lett. 14, 948–958 (2011).PubMed 
Article 
PubMed Central 

Google Scholar 
18.Rossman, S. et al. Individual specialization in the foraging habits of female bottlenose dolphins living in a trophically diverse and habitat rich estuary. Oecologia 178, 415–425 (2015).ADS 
PubMed 
Article 
PubMed Central 

Google Scholar 
19.Bolnick, D. I. et al. Why intraspecific trait variation matters in community ecology. Trends Ecol. Evol. 26, 183–192 (2011).PubMed 
PubMed Central 
Article 

Google Scholar 
20.Rossman, S. et al. Foraging habits in a generalist predator: Sex and age influence habitat selection and resource use among bottlenose dolphins (Tursiops truncatus). Mar. Mamm. Sci. 31, 155–168 (2015).CAS 
Article 

Google Scholar 
21.Sargeant, B. L. & Mann, J. Developmental evidence for foraging traditions in wild bottlenose dolphins. Anim. Behav. 78, 715–721 (2009).Article 

Google Scholar 
22.Araújo, M. S. & Gonzaga, M. O. Individual specialization in the hunting wasp Trypoxylon (Trypargilum) albonigrum (Hymenoptera, Crabronidae). Behav. Ecol. Sociobiol. 61, 1855–1863 (2007).Article 

Google Scholar 
23.Silva, M. A. et al. Ranging patterns of bottlenose dolphins living in oceanic waters: Implications for population structure. Mar. Biol. 156, 179–192 (2008).Article 

Google Scholar 
24.Tobeña, M. et al. Inter-island movements of common bottlenose dolphins Tursiops truncatus among the Canary Islands: Online catalogues and implications for conservation and management. Afr. J. Mar. Sci. 36, 137–141 (2014).Article 

Google Scholar 
25.Wells, R. S. & Scott, M. D. Encyclopedia of Marine Mammals 249–255 (Elsevier, 2009).Book 

Google Scholar 
26.Wells, R. S. et al. Ranging patterns of common bottlenose dolphins Tursiops truncatus in Barataria Bay, Louisiana, following the Deepwater Horizon oil spill. Endanger. Species Res. 33, 159–180 (2017).Article 

Google Scholar 
27.Zolman, E. S. Residence patterns of bottlenose dolphins (Tursiops truncatus) in the Stono River estuary, Charleston County, South Carolina, USA. Mar. Mamm. Sci. 18, 879–892 (2002).Article 

Google Scholar 
28.Wilson, R. M. et al. Niche differentiation and prey selectivity among common bottlenose dolphins (Tursiops truncatus) sighted in St. George Sound, Gulf of Mexico. Front. Mar. Sci. 4, 235 (2017).Article 

Google Scholar 
29.Wells, R. S. Primates and Cetaceans 149–172 (Springer, 2014).Book 

Google Scholar 
30.Urian, K. W., Hofmann, S., Wells, R. S. & Read, A. J. Fine-scale population structure of bottlenose dolphins (Tursiops truncatus) in Tampa Bay, Florida. Mar. Mamm. Sci. 25, 619–638 (2009).Article 

Google Scholar 
31.Wilson, R., Nelson, J., Balmer, B., Nowacek, D. & Chanton, J. Stable isotope variation in the northern Gulf of Mexico constrains bottlenose dolphin (Tursiops truncatus) foraging ranges. Mar. Biol. 160, 2967–2980 (2013).Article 

Google Scholar 
32.Mullin, K. D. et al. Density, abundance, survival, and ranging patterns of common bottlenose dolphins (Tursiops truncatus) in Mississippi Sound following the Deepwater Horizon oil spill. PLoS ONE 12, e0186265 (2017).PubMed 
PubMed Central 
Article 
CAS 

Google Scholar 
33.Di Giacomo, A. B. & Ott, P. H. Long-term site fidelity and residency patterns of bottlenose dolphins (Tursiops truncatus) in the Tramandaí Estuary, southern Brazil. Latin Am. J. Aquat. Mamm. 11, 155–161 (2017).Article 

Google Scholar 
34.Bailey, H. & Thompson, P. Quantitative analysis of bottlenose dolphin movement patterns and their relationship with foraging. J. Anim. Ecol. 75, 456–465 (2006).PubMed 
Article 
PubMed Central 

Google Scholar 
35.Torres, L. G. & Read, A. J. Where to catch a fish? The influence of foraging tactics on the ecology of bottlenose dolphins (Tursiops truncatus) in Florida Bay, Florida. Mar. Mamm. Sci. 25, 797–815 (2009).Article 

Google Scholar 
36.Berens McCabe, E. J., Gannon, D. P., Barros, N. B. & Wells, R. S. Prey selection by resident common bottlenose dolphins (Tursiops truncatus) in Sarasota Bay, Florida. Mar. Biol. 157, 931–942 (2010).Article 

Google Scholar 
37.Jaureguizar, A. J., Ruarte, C. & Guerrero, R. A. Distribution of age-classes of striped weakfish (Cynoscion guatucupa) along an estuarine–marine gradient: Correlations with the environmental parameters. Estuar. Coast. Shelf Sci. 67, 82–92 (2006).ADS 
Article 

Google Scholar 
38.Antonio, E. S. et al. Spatial-temporal feeding dynamics of benthic communities in an estuary-marine gradient. Estuar. Coast. Shelf Sci. 112, 86–97 (2012).ADS 
CAS 
Article 

Google Scholar 
39.Cloyed, C. S. & Eason, P. K. Different ecological conditions support individual specialization in closely related, ecologically similar species. Evol. Ecol. 30, 379–400 (2016).Article 

Google Scholar 
40.Araújo, M. S., Bolnick, D. I., Machado, G., Giaretta, A. A. & Dos Reis, S. F. Using δ13C stable isotopes to quantify individual-level diet variation. Oecologia 152, 643–654 (2007).ADS 
PubMed 
Article 
PubMed Central 

Google Scholar 
41.Wissel, B., Gaçe, A. & Fry, B. Tracing river influences on phytoplankton dynamics in two Louisiana estuaries. Ecology 86, 2751–2762 (2005).Article 

Google Scholar 
42.Post, D. M. Using stable isotopes to estimate trophic position: Models, methods, and assumptions. Ecology 83, 703–718 (2002).Article 

Google Scholar 
43.Fry, B. Conservative mixing of stable isotopes across estuarine salinity gradients: A conceptual framework for monitoring watershed influences on downstream fisheries production. Estuaries 25, 264–271 (2002).Article 

Google Scholar 
44.Barratclough, A. et al. Health assessments of common bottlenose dolphins (Tursiops truncatus): Past, present, and potential conservation applications. Front. Vet. Sci. 6, 444 (2019).PubMed 
PubMed Central 
Article 

Google Scholar 
45.Wells, R. S. et al. Bottlenose dolphins as marine ecosystem sentinels: Developing a health monitoring system. EcoHealth 1, 246–254 (2004).Article 

Google Scholar 
46.Hohn, A. et al. Assigning stranded bottlenose dolphins to source stocks using stable isotope ratios following the Deepwater Horizon oil spill. Endanger. Species Res. 33, 235–252 (2017).Article 

Google Scholar 
47.Sinclair, C. et al. Remote biopsy field sampling procedures for cetaceans used during the Natural Resource Damage Assessment of the MSC252 Deepwater Horizon Oil Spill. (2015).48.Hansen, L. J. et al. Geographic variation in polychorinated biphenyl and organochlorine pesticide concentrations in the blubber of bottlenose dolphins from the US Atlantic coast. Sci. Total Environ. 319, 147–172 (2004).ADS 
CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
49.Giménez, J., Ramírez, F., Almunia, J., Forero, M. G. & de Stephanis, R. From the pool to the sea: Applicable isotope turnover rates and diet to skin discrimination factors for bottlenose dolphins (Tursiops truncatus). J. Exp. Mar. Biol. Ecol. 475, 54–61 (2016).Article 
CAS 

Google Scholar 
50.Thomas, S. M. & Crowther, T. W. Predicting rates of isotopic turnover across the animal kingdom: A synthesis of existing data. J. Anim. Ecol. 84, 861–870 (2015).PubMed 
Article 
PubMed Central 

Google Scholar 
51.Vander Zanden, M. J., Clayton, M. K., Moody, E. K., Solomon, C. T. & Weidel, B. C. Stable isotope turnover and half-life in animal tissues: A literature synthesis. PLoS ONE 10, e0116182 (2015).PubMed 
PubMed Central 
Article 
CAS 

Google Scholar 
52.Cloyed, C. et al. Interaction of dietary and habitat niche breadth influences cetacean vulnerability to environmental disturbance. Ecosphere 12, e03759 (2021).Article 

Google Scholar 
53.Sweeting, C., Polunin, N. & Jennings, S. Effects of chemical lipid extraction and arithmetic lipid correction on stable isotope ratios of fish tissues. Rapid Commun. Mass Spectrom. 20, 595–601 (2006).ADS 
CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
54.Cloyed, C. S., DaCosta, K. P., Hodanbosi, M. R. & Carmichael, R. H. The effects of lipid extraction on δ13C and δ15N values and use of lipid-correction models across tissues, taxa and trophic groups. Methods Ecol. Evol. 11, 751–762 (2020).Article 

Google Scholar 
55.Jackson, A. L., Inger, R., Parnell, A. C. & Bearhop, S. Comparing isotopic niche widths among and within communities: SIBER–Stable Isotope Bayesian Ellipses in R. J. Anim. Ecol. 80, 595–602 (2011).PubMed 
Article 
PubMed Central 

Google Scholar 
56.Roughgarden, J. Evolution of niche width. Am. Nat. 106, 683–718 (1972).Article 

Google Scholar 
57.Bolnick, D. I., Yang, L. H., Fordyce, J. A., Davis, J. M. & Svanbäck, R. Measuring individual-level resource specialization. Ecology 83, 2936–2941 (2002).Article 

Google Scholar 
58.Team, R. C. R: A language and environment for statistical computing. (2013).59.Lusseau, D. et al. Quantifying the influence of sociality on population structure in bottlenose dolphins. J. Anim. Ecol. 75, 14–24 (2006).PubMed 
Article 
PubMed Central 

Google Scholar 
60.Ingram, S. N. & Rogan, E. Identifying critical areas and habitat preferences of bottlenose dolphins Tursiops truncatus. Mar. Ecol. Prog. Ser. 244, 247–255 (2002).ADS 
Article 

Google Scholar 
61.Balmer, B. et al. Extended movements of common bottlenose dolphins (Tursiops truncatus) along the northern Gulf of Mexico’s central coast. Gulf Mexico Sci. 33, 8 (2016).Article 

Google Scholar 
62.Bearzi, G., Bonizzoni, S. & Gonzalvo, J. Mid-distance movements of common bottlenose dolphins in the coastal waters of Greece. J. Ethol. 29, 369–374 (2011).Article 

Google Scholar 
63.Balmer, B. et al. Ranging patterns, spatial overlap, and association with dolphin morbillivirus exposure in common bottlenose dolphins (Tursiops truncatus) along the Georgia, USA coast. Ecol. Evol. 8, 12890–12904. https://doi.org/10.1002/ece3.4727 (2018).Article 
PubMed 
PubMed Central 

Google Scholar 
64.Rossi-Santos, M. R., Wedekin, L. L. & Monteiro-Filho, E. L. Residence and site fidelity of Sotalia guianensis in the Caravelas River Estuary, eastern Brazil. J. Mar. Biol. Assoc. UK 87, 207 (2007).Article 

Google Scholar 
65.Simcharoen, A. et al. Female tiger Panthera tigris home range size and prey abundance: Important metrics for management. Oryx 48, 370–377 (2014).Article 

Google Scholar 
66.Kouba, M. et al. Home range size of Tengmalm’s owl during breeding in Central Europe is determined by prey abundance. PLoS ONE 12, e0177314 (2017).PubMed 
PubMed Central 
Article 
CAS 

Google Scholar 
67.Humphries, N. E., Weimerskirch, H., Queiroz, N., Southall, E. J. & Sims, D. W. Foraging success of biological Lévy flights recorded in situ. Proc. Natl. Acad. Sci. USA. 109, 7169–7174 (2012).ADS 
CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
68.Newsome, S. D., Martinez del Rio, C., Bearhop, S. & Phillips, D. L. A niche for isotopic ecology. Front. Ecol. Environ. 5, 429–436 (2007).Article 

Google Scholar 
69.Bearhop, S., Adams, C. E., Waldron, S., Fuller, R. A. & MaCleod, H. Determining trophic niche width: A novel approach using stable isotope analysis. J. Anim. Ecol. 73, 1007–1012 (2004).Article 

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

Google Scholar 
71.Ward-Paige, C. A., Britten, G. L., Bethea, D. M. & Carlson, J. K. Characterizing and predicting essential habitat features for juvenile coastal sharks. Mar. Ecol. 36, 419–431 (2015).ADS 
Article 

Google Scholar 
72.Rogers, K. M. Stable carbon and nitrogen isotope signatures indicate recovery of marine biota from sewage pollution at Moa Point, New Zealand. Mar. Pollut. Bull. 46, 821–827 (2003).CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
73.Lee, S. Carbon dynamics of Deep Bay, eastern Pearl River estuary, China. II: Trophic relationship based on carbon-and nitrogen-stable isotopes. Mar. Ecol. Progress Ser. 205, 1–10 (2000).ADS 
Article 

Google Scholar 
74.Grady, J. R. Properties of sea grass and sand flat sediments from the intertidal zone of St. Andrew Bay, Florida. Estuaries 4, 335 (1981).Article 

Google Scholar 
75.Poulakis, G. R., Blewett, D. A. & Mitchell, M. E. The effects of season and proximity to fringing mangroves on seagrass-associated fish communities in Charlotte Harbor, Florida. Gulf Mexico Sci. 21, 3 (2003).Article 

Google Scholar 
76.Borrell, A., Vighi, M., Genov, T., Giovos, I. & Gonzalvo, J. Feeding ecology of the highly threatened common bottlenose dolphin of the Gulf of Ambracia, Greece, through stable isotope analysis. Mar. Mamm. Sci. 37, 98–110 (2021).Article 

Google Scholar 
77.Gibbs, S. E., Harcourt, R. G. & Kemper, C. M. Niche differentiation of bottlenose dolphin species in South Australia revealed by stable isotopes and stomach contents. Wildl. Res. 38, 261–270 (2011).CAS 
Article 

Google Scholar 
78.Lenes, J. M. & Heil, C. A. A historical analysis of the potential nutrient supply from the N2 fixing marine cyanobacterium Trichodesmium spp. to Karenia brevis blooms in the eastern Gulf of Mexico. J. Plankton Res. 32, 1421–1431 (2010).CAS 
Article 

Google Scholar 
79.Bergman, B., Sandh, G., Lin, S., Larsson, J. & Carpenter, E. J. Trichodesmium–a widespread marine cyanobacterium with unusual nitrogen fixation properties. FEMS Microbiol. Rev. 37, 286–302 (2013).CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
80.Barros, N. B. & Odell, D. K. In The Bottlenose Dolphin (eds Leatherwood, S. & Reeves, R. R.) Ch. 16, 309–328 (Academic Press, 1990).81.Lane, S. M. et al. Reproductive outcome and survival of common bottlenose dolphins sampled in Barataria Bay, Louisiana, USA, following the Deepwater Horizon oil spill. Proc. R. Soc. B Biol. Sci. 282, 20151944 (2015).Article 
CAS 

Google Scholar 
82.Smith, C. R. et al. Slow recovery of Barataria Bay dolphin health following the Deepwater Horizon oil spill (2013–2014), with evidence of persistent lung disease and impaired stress response. Endanger. Species Res. 33, 127–142 (2017).Article 

Google Scholar 
83.McDonald, T. L. et al. Survival, density, and abundance of common bottlenose dolphins in Barataria Bay (USA) following the Deepwater Horizon oil spill. Endanger. Species Res. 33, 193–209 (2017).ADS 
Article 

Google Scholar 
84.Trustees, D. N. Deepwater Horizon oil spill: final programmatic damage assessment and restoration plant (PDARP) and final programmatic environmental impact statement (PEIS). (2016).85.Carmichael, R. H., Graham, W. M., Aven, A., Worthy, G. & Howden, S. Were multiple stressors a ‘perfect storm’ for northern Gulf of Mexico bottlenose dolphins (Tursiops truncatus) in 2011?. PLoS ONE 7, e41155 (2012).ADS 
CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
86.Booth, C. & Thomas, L. In Oceans. 179–192 (Multidisciplinary Digital Publishing Institute).87.Gannon, D. P. et al. Effects of Karenia brevis harmful algal blooms on nearshore fish communities in southwest Florida. Mar. Ecol. Prog. Ser. 378, 171–186 (2009).ADS 
CAS 
Article 

Google Scholar 
88.Rossman, S. et al. Retrospective analysis of bottlenose dolphin foraging: A legacy of anthropogenic ecosystem disturbance. Mar. Mamm. Sci. 29, 705–718 (2013).CAS 

Google Scholar 
89.Schwacke, L. H. et al. Quantifying injury to common bottlenose dolphins from the Deepwater Horizon oil spill using an age-, sex-and class-structured population model. Endanger. Species Res. 33, 265–279 (2017).Article 

Google Scholar 
90.McCann, K. S., Rasmussen, J. & Umbanhowar, J. The dynamics of spatially coupled food webs. Ecol. Lett. 8, 513–523 (2005).CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
91.Schwacke, L. H. et al. Health of common bottlenose dolphins (Tursiops truncatus) in Barataria Bay, Louisiana, following the deepwater horizon oil spill. Environ. Sci. Technol. 48, 93–103 (2013).ADS 
PubMed 
Article 
CAS 
PubMed Central 

Google Scholar 
92.Sheaves, M., Baker, R., Nagelkerken, I. & Connolly, R. M. True value of estuarine and coastal nurseries for fish: Incorporating complexity and dynamics. Estuar. Coasts 38, 401–414 (2015).Article 

Google Scholar 
93.Nagelkerken, I., Sheaves, M., Baker, R. & Connolly, R. M. The seascape nursery: A novel spatial approach to identify and manage nurseries for coastal marine fauna. Fish Fish. 16, 362–371 (2015).Article 

Google Scholar 
94.Kenworthy, M. D. et al. Movement ecology of a mobile predatory fish reveals limited habitat linkages within a temperate estuarine seascape. Can. J. Fish. Aquat. Sci. 75, 1990–1998 (2018).CAS 
Article 

Google Scholar 
95.Fitzgerald, D. M., Kulp, M., Penland, S., Flocks, J. & Kindinger, J. Morphologic and stratigraphic evolution of muddy ebb-tidal deltas along a subsiding coast: Barataria Bay, Mississippi River Delta. Sedimentology 51, 1157–1178 (2004).ADS 
Article 

Google Scholar 
96.Habib, E. et al. Assessing effects of data limitations on salinity forecasting in Barataria basin, Louisiana, with a Bayesian analysis. J. Coast. Res. 2007, 749–763 (2007).Article 

Google Scholar 
97.Eleuterius, C. K. Geographical definition of Mississippi Sound. Gulf Caribb. Res. 6, 179–181 (1978).
Google Scholar 
98.Lucas, K. L. & Carter, G. A. Decadal changes in habitat-type coverage on Horn Island, Mississippi, USA. J. Coast. Res. 26, 1142–1148 (2010).Article 

Google Scholar 
99.Ichiye, T. & Jones, M. L. On the hydrography of the St. Andrew Bay system, Florida 1. Limnol. Oceanogr. 6, 302–311 (1961).ADS 
Article 

Google Scholar 
100.Morgan, S. G. Plasticity in reproductive timing by crabs in adjacent tidal regimes. Mar. Ecol. Prog. Ser. 139, 105–118 (1996).ADS 
Article 

Google Scholar 
101.Livingston, R. et al. Modelling oyster population response to variation in freshwater input. Estuar. Coast. Shelf Sci. 50, 655–672 (2000).ADS 
Article 

Google Scholar 
102.Twichell, D. et al. Geologic controls on the recent evolution of oyster reefs in Apalachicola Bay and St. George Sound, Florida. Estuar. Coast. Shelf Sci. 88, 385–394 (2010).ADS 
Article 

Google Scholar 
103.Chen, Z., Hu, C., Conmy, R. N., Muller-Karger, F. & Swarzenski, P. Colored dissolved organic matter in Tampa Bay, Florida. Mar. Chem. 104, 98–109 (2007).CAS 
Article 

Google Scholar 
104.Julian, P. & Estevez, E. D. In Proceedings of the Tampa Bay Area Scientific Information Symposium, BASIS 5: Using Our Knowledge to Shape Our Future. 27–33.105.Adams, A. J. & Blewett, D. A. Spatial patterns of estuarine habitat type use and temporal patterns in abundance of juvenile permit, Trachinotus falcatus, in Charlotte Harbor, Florida. Gulf Caribb. Res. 16, 129–139 (2004).Article 

Google Scholar 
106.Kahle, D., Wickham, H. & Kahle, M. D. Package ‘ggmap’. (2019). More

1.Gallo RL, Hooper LV. Epithelial antimicrobial defence of the skin and intestine. Nat Rev Immunol. 2012;12:503–16.PubMed 
PubMed Central 
CAS 

Google Scholar 
2.Erin Chen Y, Fischbach MA, Belkaid Y. Skin microbiota-host interactions. Nature. 2018;553:427–36.PubMed 
PubMed Central 

Google Scholar 
3.Toledo RC, Jared C. Cutaneous granular glands and amphibian venoms. Comp Biochem Physiol A Physiol. 1995;111:1–29.4.Walke JB, Becker MH, Loftus SC, House LL, Cormier G, Jensen RV, et al. Amphibian skin may select for rare environmental microbes. ISME J. 2014;8:2207–17.PubMed 
PubMed Central 
CAS 

Google Scholar 
5.Jani AJ, Briggs CJ. Host and aquatic environment shape the amphibian skin microbiome but effects on downstream resistance to the pathogen Batrachochytrium dendrobatidis are variable. Front Microbiol. 2018;9:487.PubMed 
PubMed Central 

Google Scholar 
6.Bletz MC, Archer H, Harris RN, McKenzie VJ, Rabemananjara FCE, Rakotoarison A, et al. Host ecology rather than host phylogeny drives amphibian skin microbial community structure in the biodiversity hotspot of Madagascar. Front Microbiol. 2017;8:1–14.
Google Scholar 
7.Becker MH, Walke JB, Murrill L, Woodhams DC, Reinert LK, Rollins‐Smith LA, et al. Phylogenetic distribution of symbiotic bacteria from Panamanian amphibians that inhibit growth of the lethal fungal pathogen Batrachochytrium dendrobatidis. Mol Ecol. 2015;24:1628–41.PubMed 

Google Scholar 
8.Flechas SV, Acosta-González A, Escobar LA, Kueneman JG, Sánchez-Quitian ZA, Parra-Giraldo CM, et al. Microbiota and skin defense peptides may facilitate coexistence of two sympatric Andean frog species with a lethal pathogen. ISME J. 2019;13:361–73.PubMed 
CAS 

Google Scholar 
9.Brunetti AE, Lyra ML, Melo WGP, Andrade LE, Palacios-Rodríguez P, Prado BM, et al. Symbiotic skin bacteria as a source for sex-specific scents in frogs. Proc Natl Acad Sci USA. 2019;116:2124–9.PubMed 
PubMed Central 
CAS 

Google Scholar 
10.Vaelli PM, Theis KR, Williams JE, O’Connell LA, Foster JA, Eisthen HL. The skin microbiome facilitates adaptive tetrodotoxin production in poisonous newts. Elife. 2020;9:e53898.PubMed 
PubMed Central 

Google Scholar 
11.Pukala TL, Bowie JH, Maselli VM, Musgrave IF, Tyler MJ. Host-defence peptides from the glandular secretions of amphibians: Structure and activity. Nat Prod Rep. 2006;23:368–93.PubMed 
CAS 

Google Scholar 
12.Bevins CL, Zasloff M. Peptides from frog skin. Annu Rev Biochem. 1990;59:395–414.PubMed 
CAS 

Google Scholar 
13.Woodhams DC, Rollins-Smith LA, Reinert LK, Lam BA, Harris RN, Briggs CJ, et al. Probiotics modulate a novel amphibian skin defense peptide that is antifungal and facilitates growth of antifungal bacteria. Micro Ecol. 2020;79:192–202.CAS 

Google Scholar 
14.Mergaert P. Role of antimicrobial peptides in controlling symbiotic bacterial populations. Nat Prod Rep. 2018;35:336–56.PubMed 
CAS 

Google Scholar 
15.Pontes MH, Smith KL, de Vooght L, van den Abbeele J, Dale C. Attenuation of the sensing capabilities of PhoQ in transition to obligate insect-bacterial association. PLoS Genet. 2011;7:e1002349.16.Bosch TCG. Cnidarian-microbe interactions and the origin of innate immunity in metazoans. Annu Rev Microbiol. 2013;67:499–518.PubMed 
CAS 

Google Scholar 
17.Foster KR, Schluter J, Coyte KZ, Rakoff-Nahoum S. The evolution of the host microbiome as an ecosystem on a leash. Nature. 2017;548:43–51.PubMed 
PubMed Central 
CAS 

Google Scholar 
18.Joo H-S, Fu C-I, Otto M. Bacterial strategies of resistance to antimicrobial peptides. Philos Trans R Soc B Biol Sci. 2016;371:20150292.
Google Scholar 
19.Stock AM, Robinson VL, Goudreau PN. Two-component signal transduction. Annu Rev Biochem. 2000;69:183–215.PubMed 
CAS 

Google Scholar 
20.Jacob-Dubuisson F, Mechaly A, Betton J-M, Antoine R. Structural insights into the signalling mechanisms of two-component systems. Nat Rev Microbiol. 2018;16:585–93.PubMed 
CAS 

Google Scholar 
21.Gao R, Stock AM. Biological insights from structures of two-component proteins. Annu Rev Microbiol. 2009;63:133–54.22.Piddock LJV. Multidrug-resistance efflux pumps? not just for resistance. Nat Rev Microbiol. 2006;4:629–36.PubMed 
CAS 

Google Scholar 
23.Jeannot K, Bolard A, Plésiat P. Resistance to polymyxins in Gram-negative organisms. Int J Antimicrob Agents. 2017;49:526–35.PubMed 
CAS 

Google Scholar 
24.Fernández L, Jenssen H, Bains M, Wiegand I, Gooderham WJ, Hancock REW. The two-component system CprRS senses cationic peptides and triggers adaptive resistance in Pseudomonas aeruginosa independently of ParRS. Antimicrob Agents Chemother. 2012;56:6212–22.PubMed 
PubMed Central 

Google Scholar 
25.Hong J, Jiang H, Hu J, Wang L, Liu R. Transcriptome analysis reveals the resistance mechanism of Pseudomonas aeruginosa to tachyplesin I. Infect Drug Resist. 2020;13:155.PubMed 
PubMed Central 
CAS 

Google Scholar 
26.Brunetti AE, Neto FC, Vera MC, Taboada C, Pavarini DP, Bauermeister A, et al. An integrative omics perspective for the analysis of chemical signals in ecological interactions. Chem Soc Rev. 2018;47:1574–91.PubMed 
CAS 

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

Google Scholar 
28.Tatusova T, DiCuccio M, Badretdin A, Chetvernin V, Nawrocki EP, Zaslavsky L, et al. NCBI prokaryotic genome annotation pipeline. Nucleic Acids Res. 2016;44:6614–24.PubMed 
PubMed Central 
CAS 

Google Scholar 
29.Huerta-Cepas J, Forslund K, Coelho LP, Szklarczyk D, Jensen LJ, Von Mering C, et al. Fast genome-wide functional annotation through orthology assignment by eggNOG-mapper. Mol Biol Evol. 2017;34:2115–22.PubMed 
PubMed Central 
CAS 

Google Scholar 
30.Blin K, Shaw S, Steinke K, Villebro R, Ziemert N, Lee SY, et al. antiSMASH 5.0: updates to the secondary metabolite genome mining pipeline. Nucleic Acids Res. 2019;47:W81–W87.PubMed 
PubMed Central 
CAS 

Google Scholar 
31.Hesse C, Schulz F, Bull CT, Shaffer BT, Yan Q, Shapiro N, et al. Genome-based evolutionary history of Pseudomonas spp. Environ Microbiol. 2018;20:2142–59.PubMed 
CAS 

Google Scholar 
32.Lechner M, Findeiß S, Steiner L, Marz M, Stadler PF, Prohaska SJ. Proteinortho: detection of (co-)orthologs in large-scale analysis. BMC Bioinforma. 2011;12:124.
Google Scholar 
33.Stamatakis A. RAxML version 8: a tool for phylogenetic analysis and post-analysis of large phylogenies. Bioinformatics. 2014;30:1312–3.PubMed 
PubMed Central 
CAS 

Google Scholar 
34.Letunic I, Bork P. Interactive tree of life (iTOL) v3: an online tool for the display and annotation of phylogenetic and other trees. Nucleic Acids Res. 2016;44:W242–W245.PubMed 
PubMed Central 
CAS 

Google Scholar 
35.Meier-Kolthoff JP, Göker M. TYGS is an automated high-throughput platform for state-of-the-art genome-based taxonomy. Nat Commun. 2019;10:1–10.CAS 

Google Scholar 
36.Jain C, Rodriguez-R LM, Phillippy AM, Konstantinidis KT, Aluru S. High-throughput ANI analysis of 90k prokaryotic genomes reveals clear species boundaries. Nat Commun. 2018;9:5114.37.Goloboff PA, Catalano SA. TNT version 1.5, including a full implementation of phylogenetic morphometrics. Cladistics. 2016;32:221–38.
Google Scholar 
38.Garber M, Grabherr MG, Guttman M, Trapnell C. Computational methods for transcriptome annotation and quantification using RNA-seq. Nat Methods. 2011;8:469–77.PubMed 
CAS 

Google Scholar 
39.Min XJ, Butler G, Storms R, Tsang A. OrfPredictor: predicting protein-coding regions in EST-derived sequences. Nucleic Acids Res. 2005;33:W677–W680.PubMed 
PubMed Central 
CAS 

Google Scholar 
40.Bray NL, Pimentel H, Melsted P, Pachter L. Near-optimal probabilistic RNA-seq quantification. Nat Biotechnol. 2016;34:525–7.PubMed 
CAS 

Google Scholar 
41.Nacif-Marçal L, Pereira GR, Abranches MV, Costa NCS, Cardoso SA, Honda ER, et al. Identification and characterization of an antimicrobial peptide of Hypsiboas semilineatus (Spix, 1824) (Amphibia, Hylidae). Toxicon. 2015;99:16–22.PubMed 

Google Scholar 
42.Magalhães BS, Melo JAT, Leite JRSA, Silva LP, Prates MV, Vinecky F, et al. Post-secretory events alter the peptide content of the skin secretion of Hypsiboas raniceps. Biochem Biophys Res Commun. 2008;377:1057–61.PubMed 

Google Scholar 
43.Brunetti AE, Marani MM, Soldi RA, Mendonça JN, Faivovich J, Cabrera GM, et al. Cleavage of peptides from amphibian skin revealed by combining analysis of gland secretion and in situ MALDI imaging mass spectrometry. ACS Omega. 2018;3:5426–34.PubMed 
PubMed Central 
CAS 

Google Scholar 
44.Van Zundert GCP, Rodrigues J, Trellet M, Schmitz C, Kastritis PL, Karaca E, et al. The HADDOCK2. 2 web server: user-friendly integrative modeling of biomolecular complexes. J Mol Biol. 2016;428:720–5.PubMed 

Google Scholar 
45.Cheung J, Bingman CA, Reyngold M, Hendrickson WA, Waldburger CD. Crystal structure of a functional dimer of the PhoQ sensor domain. J Biol Chem. 2008;283:13762–70.PubMed 
PubMed Central 
CAS 

Google Scholar 
46.Rebollar EA, Hughey MC, Medina D, Harris RN, Ibáñez R, Belden LK. Skin bacterial diversity of Panamanian frogs is associated with host susceptibility and presence of Batrachochytrium dendrobatidis. ISME J. 2016;10:1682–95.47.Bletz MC, Bunk B, Spröer C, Biwer P, Reiter S, Rabemananjara FCE, et al. Amphibian skin-associated Pigmentiphaga: genome sequence and occurrence across geography and hosts. PLoS One. 2019;14:1–14.
Google Scholar 
48.Reimer LC, Vetcininova A, Carbasse JS, Söhngen C, Gleim D, Ebeling C, et al. Bac Dive in 2019: bacterial phenotypic data for high-throughput biodiversity analysis. Nucleic Acids Res. 2019;47:D631–D636.PubMed 
CAS 

Google Scholar 
49.Ramette A, Frapolli M, Saux MF-L, Gruffaz C, Meyer JM, Défago G, et al. Pseudomonas protegens sp. nov., widespread plant-protecting bacteria producing the biocontrol compounds 2,4-diacetylphloroglucinol and pyoluteorin. Syst Appl Microbiol. 2011;34:180–8.PubMed 
CAS 

Google Scholar 
50.Flury P, Aellen N, Ruffner B, Péchy-Tarr M, Fataar S, Metla Z, et al. Insect pathogenicity in plant-beneficial pseudomonads: phylogenetic distribution and comparative genomics. ISME J. 2016;10:2527–42.PubMed 
PubMed Central 

Google Scholar 
51.Flury P, Vesga P, Dominguez-Ferreras A, Tinguely C, Ullrich CI, Kleespies RG, et al. Persistence of root-colonizing Pseudomonas protegens in herbivorous insects throughout different developmental stages and dispersal to new host plants. ISME J. 2019;13:860–72.PubMed 
CAS 

Google Scholar 
52.Pupin M, Esmaeel Q, Flissi A, Dufresne Y, Jacques P, Leclère V. Norine: a powerful resource for novel nonribosomal peptide discovery. Synth Syst Biotechnol. 2016;1:89–94.PubMed 
CAS 

Google Scholar 
53.Wilson DJ, Shi C, Teitelbaum AM, Gulick AM, Aldrich CC. Characterization of AusA: a dimodular nonribosomal peptide synthetase responsible for the production of aureusimine pyrazinones. Biochemistry. 2013;52:926–37.PubMed 
CAS 

Google Scholar 
54.Ryona I, Leclerc S, Sacks GL. Correlation of 3-isobutyl-2-methoxypyrazine to 3-Isobutyl-2-hydroxypyrazine during maturation of bell pepper (Capsicum annuum) and wine grapes (Vitis vinifera). J Agric Food Chem. 2010;58:9723–30.PubMed 
CAS 

Google Scholar 
55.Brucker RM, Baylor CM, Walters RL, Lauer A, Harris RN, Minbiole KPC. The identification of 2, 4-diacetylphloroglucinol as an antifungal metabolite produced by cutaneous bacteria of the salamander Plethodon cinereus. J Chem Ecol. 2008;34:39–43.PubMed 
CAS 

Google Scholar 
56.König E, Bininda-Emonds ORP, Shaw C. The diversity and evolution of anuran skin peptides. Peptides. 2015;63:96–117.PubMed 

Google Scholar 
57.Yeaman MR, Yount NY. Mechanisms of antimicrobial peptide action and resistance. Pharm Rev. 2003;55:27–55.PubMed 
CAS 

Google Scholar 
58.Kumar P, Kizhakkedathu JN, Straus SK. Antimicrobial peptides: diversity, mechanism of action and strategies to improve the activity and biocompatibility in vivo. Biomolecules. 2018;8:4.PubMed Central 

Google Scholar 
59.Castro MS, Ferreira TCG, Cilli EM, Crusca E, Mendes-Giannini MJS, Sebben A, et al. Hylin a1, the first cytolytic peptide isolated from the arboreal South American frog Hypsiboas albopunctatus (‘spotted treefrog’). Peptides. 2009;30:291–6.PubMed 
CAS 

Google Scholar 
60.Resnick NM, Maloy WL, Guy HR, Zasloff M. A novel endopeptidase from Xenopus that recognizes α-helical secondary structure. Cell. 1991;66:541–54.PubMed 
CAS 

Google Scholar 
61.Gutu AD, Sgambati N, Strasbourger P, Brannon MK, Jacobs MA, Haugen E, et al. Polymyxin resistance of Pseudomonas aeruginosa phoQ mutants is dependent on additional two-component regulatory systems. Antimicrob Agents Chemother. 2013;57:2204–15.PubMed 
PubMed Central 
CAS 

Google Scholar 
62.Möglich A, Ayers RA, Moffat K. Structure and signaling mechanism of Per-ARNT-Sim domains. Structure. 2009;17:1282–94.PubMed 
PubMed Central 

Google Scholar 
63.Zschiedrich CP, Keidel V, Szurmant H. Molecular mechanisms of two-component signal transduction. J Mol Biol. 2016;428:3752–75.PubMed 
PubMed Central 
CAS 

Google Scholar 
64.Shah N, Gaupp R, Moriyama H, Eskridge KM, Moriyama EN, Somerville GA. Reductive evolution and the loss of PDC/PAS domains from the genus Staphylococcus. BMC Genomics. 2013;14:524.65.Bader MW, Sanowar S, Daley ME, Schneider AR, Cho U, Xu W, et al. Recognition of antimicrobial peptides by a bacterial sensor kinase. Cell. 2005;122:461–72.PubMed 
CAS 

Google Scholar 
66.Chang C, Tesar C, Gu M, Babnigg G, Joachimiak A, Raj Pokkuluri P, et al. Extracytoplasmic PAS-like domains are common in signal transduction proteins. J Bacteriol. 2010;192:1156–9.PubMed 
CAS 

Google Scholar  More

1.Gibbs, A. G. & Rajpurohit, S. Cuticular lipids and water balance. in Insect hydrocarbons: biology, biochemistry, and chemical ecology 100–120 (Cambridge University Press Cambridge, UK, 2010). https://doi.org/10.1017/CBO9780511711909.0072.Pedrini, N., Ortiz-Urquiza, A., Zhang, S. & Keyhani, N. O. Targeting of insect epicuticular lipids by the entomopathogenic fungus Beauveria bassiana: hydrocarbon oxidation within the context of a host-pathogen interaction. Front. Microbiol. 4, 24 (2013).PubMed 
PubMed Central 
Article 

Google Scholar 
3.Howard, R. W. & Blomquist, G. J. Ecological, behavioral, and biochemical aspects of insect hydrocarbons. Annu. Rev. Entomol. 50, 371–393 (2005).CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
4.Lang, C. & Menzel, F. Lasius niger ants discriminate aphids based on their cuticular hydrocarbons. Anim. Behav. 82, 1245–1254 (2011).Article 

Google Scholar 
5.Sakata, I., Hayashi, M. & Nakamuta, K. Tetramorium tsushimae ants use methyl branched hydrocarbons of aphids for partner recognition. J. Chem. Ecol. 43, 966–970 (2017).CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
6.Salazar, A. et al. Aggressive mimicry coexists with mutualism in an aphid. Proc. Natl. Acad. Sci. 112, 1101–1106 (2015).ADS 
CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
7.Endo, S. & Itino, T. The aphid-tending ant Lasius fuji exhibits reduced aggression toward aphids marked with ant cuticular hydrocarbons. Popul. Ecol. 54, 405–410 (2012).Article 

Google Scholar 
8.Endo, S. & Itino, T. Myrmecophilous aphids produce cuticular hydrocarbons that resemble those of their tending ants. Popul. Ecol. 55, 27–34 (2013).Article 

Google Scholar 
9.Stadler, B. & Dixon, A. F. G. Ecology and evolution of aphid-ant interactions. Annu. Rev. Ecol. Evol. Syst. 36, 345–372 (2005).Article 

Google Scholar 
10.Schillewaert, S. et al. The influence of facultative endosymbionts on honeydew carbohydrate and amino acid composition of the black bean aphid Aphis fabae. Physiol. Entomol. 42, 125–133 (2017).CAS 
Article 

Google Scholar 
11.Oliver, K. M., Degnan, P. H., Burke, G. R. & Moran, N. A. Facultative symbionts in aphids and the horizontal transfer of ecologically important traits. Annu. Rev. Entomol. 55, 247–266 (2010).CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
12.Douglas, A. E. Nutritional interactions in insect-microbial symbioses: aphids and their symbiotic bacteria Buchnera. Annu. Rev. Entomol. 43, 17–37 (1998).CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
13.Montllor, C. B., Maxmen, A. & Purcell, A. H. Facultative bacterial endosymbionts benefit pea aphids Acyrthosiphon pisum under heat stress. Ecol. Entomol. 27, 189–195 (2002).Article 

Google Scholar 
14.Russell, J. A. & Moran, N. A. Costs and benefits of symbiont infection in aphids: variation among symbionts and across temperatures. Proc. R. Soc. B Biol. Sci. 273, 603–610 (2005).Article 

Google Scholar 
15.Wagner, S. M. et al. Facultative endosymbionts mediate dietary breadth in a polyphagous herbivore. Funct. Ecol. 29, 1402–1410 (2015).Article 

Google Scholar 
16.Scarborough, C. L., Ferrari, J. & Godfray, H. C. J. Aphid protected from pathogen by endosymbiont. Science 310, 1781 (2005).CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
17.Łukasik, P., van Asch, M., Guo, H., Ferrari, J. & Godfray, H. C. J. Unrelated facultative endosymbionts protect aphids against a fungal pathogen. Ecol. Lett. 16, 214–218 (2013).PubMed 
Article 
PubMed Central 

Google Scholar 
18.Oliver, K. M., Russell, J. A., Moran, N. A. & Hunter, M. S. Facultative bacterial symbionts in aphids confer resistance to parasitic wasps. Proc. Natl. Acad. Sci. 100, 1803–1807 (2003).ADS 
CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
19.Vorburger, C., Gehrer, L. & Rodriguez, P. A strain of the bacterial symbiont Regiella insecticola protects aphids against parasitoids. Biol. Lett. 6, 109–111 (2010).PubMed 
Article 
PubMed Central 

Google Scholar 
20.Vorburger, C. & Gouskov, A. Only helpful when required: a longevity cost of harbouring defensive symbionts. J. Evol. Biol. 24, 1611–1617 (2011).CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
21.Vorburger, C., Ganesanandamoorthy, P. & Kwiatkowski, M. Comparing constitutive and induced costs of symbiont-conferred resistance to parasitoids in aphids. Ecol. Evol. 3, 706–713 (2013).PubMed 
PubMed Central 
Article 

Google Scholar 
22.Gwynn, D. M., Callaghan, A., Gorham, J., Walters, K. F. A. & Fellowes, M. D. E. Resistance is costly: trade-offs between immunity, fecundity and survival in the pea aphid. Proc. R. Soc. B Biol. Sci. 272, 1803–1808 (2005).CAS 
Article 

Google Scholar 
23.Oliver, K. M., Campos, J., Moran, N. A. & Hunter, M. S. Population dynamics of defensive symbionts in aphids. Proc. R. Soc. B Biol. Sci. 275, 293–299 (2008).Article 

Google Scholar 
24.Wernegreen, J. J. Genome evolution in bacterial endosymbionts of insects. Nat. Rev. Genet. 3, 850–861 (2002).CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
25.Degnan, P. H., Yu, Y., Sisneros, N., Wing, R. A. & Moran, N. A. Hamiltonella defensa, genome evolution of protective bacterial endosymbiont from pathogenic ancestors. Proc. Natl. Acad. Sci. 106, 9063–9068 (2009).ADS 
CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
26.Ankrah, N. Y. D., Luan, J. & Douglas, A. E. Cooperative metabolism in a three-partner insect-bacterial symbiosis revealed by metabolic modeling. J. Bacteriol. 199, e00872-e916 (2017).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
27.Herren, J. K. et al. Insect endosymbiont proliferation is limited by lipid availability. Elife 3, e02964 (2014).PubMed 
PubMed Central 
Article 
CAS 

Google Scholar 
28.Hamilton, R. J. Waxes: Chemistry, Molecular Biology and Functions (Insect Waxes. Oily Press, 1995).
Google Scholar 
29.Blailock, T. T., Blomquist, G. J. & Jackson, L. L. Biosynthesis of 2-methylalkanes in the crickets: Nemobiusfasciatus and Grylluspennsylvanicus. Biochem. Biophys. Res. Commun. 68, 841–849 (1976).CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
30.Engl, T. et al. Effect of antibiotic treatment and gamma-irradiation on cuticular hydrocarbon profiles and mate choice in tsetse flies (Glossina m. morsitans). BMC Microbiol. 18, 145 (2018).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
31.Engl, T. et al. Ancient symbiosis confers desiccation resistance to stored grain pest beetles. Mol. Ecol. 27, 2095–2108 (2018).CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
32.Schneider, D. I. et al. Symbiont-driven male mating success in the Neotropical Drosophila paulistorum superspecies. Behav. Genet. 49, 83–98 (2019).PubMed 
Article 
PubMed Central 

Google Scholar 
33.de Souza, D. J., Devers, S. & Lenoir, A. Blochmannia endosymbionts and their host, the ant Camponotus fellah: cuticular hydrocarbons and melanization. C. R. Biol. 334, 737–741 (2011).Article 
CAS 

Google Scholar 
34.Richard, F.-J. Symbiotic bacteria influence the odor and mating preference of their hosts. Front. Ecol. Evol. 5, 143 (2017).Article 

Google Scholar 
35.Fischer, M. K. & Shingleton, A. W. Host plant and ants influence the honeydew sugar composition of aphids. Funct. Ecol. 15, 544–550 (2001).Article 

Google Scholar 
36.Yao, I. & Akimoto, S. Ant attendance changes the sugar composition of the honeydew of the drepanosiphid aphid Tuberculatus quercicola. Oecologia 128, 36–43 (2001).ADS 
PubMed 
Article 
PubMed Central 

Google Scholar 
37.Yao, I. & Akimoto, S. Flexibility in the composition and concentration of amino acids in honeydew of the drepanosiphid aphid Tuberculatus quercicola. Ecol. Entomol. 27, 745–752 (2002).Article 

Google Scholar 
38.Offenberg, J. Balancing between mutualism and exploitation: the symbiotic interaction between Lasius ants and aphids. Behav. Ecol. Sociobiol. 49, 304–310 (2001).Article 

Google Scholar 
39.Stadler, B. & Dixon, A. F. G. Ant attendance in aphids: why different degrees of myrmecophily?. Ecol. Entomol. 24, 363–369 (1999).Article 

Google Scholar 
40.Vantaux, A., Van den Ende, W., Billen, J. & Wenseleers, T. Large interclone differences in melezitose secretion in the facultatively ant-tended black bean aphid Aphis fabae. J. Insect. Physiol. 57, 1614–1621 (2011).CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
41.Moran, N. A., Russell, J. A., Koga, R. & Fukatsu, T. Evolutionary relationships of three new species of Enterobacteriaceae living as symbionts of aphids and other insects. Appl. Environ. Microbiol. 71, 3302–3310 (2005).ADS 
CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
42.Molloy, J. C., Sommer, U., Viant, M. R. & Sinkins, S. P. Wolbachia modulates lipid metabolism in Aedes albopictus mosquito cells. Appl. Environ. Microbiol. 82, 3109–3120 (2016).ADS 
CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
43.Paredes, J. C., Herren, J. K., Schüpfer, F. & Lemaitre, B. The role of lipid competition for endosymbiont-mediated protection against parasitoid wasps in Drosophila. MBio 7, e01006-e1016 (2016).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
44.Chung, H. & Carroll, S. B. Wax, sex and the origin of species: dual roles of insect cuticular hydrocarbons in adaptation and mating. BioEssays 37, 822–830 (2015).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
45.Bos, N. et al. Learning and perceptual similarity among cuticular hydrocarbons in ants. J. Insect Physiol. 58, 138–146 (2012).ADS 
CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
46.van Wilgenburg, E. et al. Learning and discrimination of cuticular hydrocarbons in a social insect. Biol. Lett. 8, 17–20 (2012).PubMed 
Article 
PubMed Central 

Google Scholar 
47.Oberhauser, F. B., Koch, A. & Czaczkes, T. J. Small differences in learning speed for different food qualities can drive efficient collective foraging in ant colonies. Behav. Ecol. Sociobiol. 72, 164 (2018).Article 

Google Scholar 
48.Erickson, D. M., Wood, E. A., Oliver, K. M., Billick, I. & Abbot, P. The effect of ants on the population dynamics of a protective symbiont of aphids, Hamiltonella defensa. Ann. Entomol. Soc. Am. 105, 447–453 (2012).Article 

Google Scholar 
49.Schmidt, M. H. et al. Relative importance of predators and parasitoids for cereal aphid control. Proc. R. Soc. Lond. Ser. B. Biol. Sci. 270, 1905–1909 (2003).Article 

Google Scholar 
50.Łukasik, P., Dawid, M. A., Ferrari, J. & Godfray, H. C. J. The diversity and fitness effects of infection with facultative endosymbionts in the grain aphid, Sitobion avenae. Oecologia 173, 985–996 (2013).ADS 
PubMed 
Article 
PubMed Central 

Google Scholar 
51.Oliver, K. M. et al. Parasitic wasp responses to symbiont-based defense in aphids. BMC Biol. 10, 11 (2012).PubMed 
PubMed Central 
Article 

Google Scholar 
52.Dennis, A. B., Patel, V., Oliver, K. M. & Vorburger, C. Parasitoid gene expression changes after adaptation to symbiont-protected hosts. Evolution 71, 2599–2617 (2017).PubMed 
Article 
PubMed Central 

Google Scholar 
53.Guo, J. et al. Nine facultative endosymbionts in aphids, a review. J. Asia. Pac. Entomol. 20, 794–801 (2017).Article 

Google Scholar 
54.Vorburger, C., Sandrock, C., Gouskov, A., Castañeda, L. E. & Ferrari, J. Genotypic variation and the role of defensive endosymbionts in an all-parthenogenetic host–parasitoid interaction. Evol. Int. J. Org. Evol. 63, 1439–1450 (2009).Article 

Google Scholar 
55.Carlson, D. A., Bernier, U. R. & Sutton, B. D. Elution patterns from capillary GC for methyl-branched alkanes. J. Chem. Ecol. 24, 1845–1865 (1998).CAS 
Article 

Google Scholar 
56.Katritzky, A. R., Chen, K., Maran, U. & Carlson, D. A. QSPR correlation and predictions of GC retention indexes for methyl-branched hydrocarbons produced by insects. Anal. Chem. 72, 101–109 (2000).CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
57.R Core Team. R: A Language and Environment for Statistical Computing. (2019).58.Jombart, T., Devillard, S. & Balloux, F. Discriminant analysis of principal components: a new method for the analysis of genetically structured populations. BMC Genet. 11, 94 (2010).PubMed 
PubMed Central 
Article 

Google Scholar 
59.Jombart, T. adegenet: a R package for the multivariate analysis of genetic markers. Bioinformatics 24, 1403–1405 (2008).CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
60.Anderson, M. J. Permutational multivariate analysis of variance (PERMANOVA). Wiley Statsref. Stat. Ref. https://doi.org/10.1002/9781118445112.stat07841 (2014).Article 

Google Scholar 
61.Anderson, M. J. & Walsh, D. C. I. PERMANOVA, ANOSIM, and the Mantel test in the face of heterogeneous dispersions: what null hypothesis are you testing?. Ecol. Monogr. 83, 557–574 (2013).Article 

Google Scholar 
62.Arbizu, P. M. pairwiseAdonis: Pairwise Multilevel Comparison using Adonis (2017).63.Ritchie, M. E. et al. limma powers differential expression analyses for RNA-sequencing and microarray studies. Nucleic Acids Res. 43, e47–e47 (2015).PubMed 
PubMed Central 
Article 
CAS 

Google Scholar  More

1.Urban, M. C. & Skelly, D. K. Evolving metacommunities: Toward an evolutionary perspective on metacommunities. Ecology 87, 1616–1626 (2006).Article 

Google Scholar 
2.Cortez, M. H. & Ellner, S. P. Understanding rapid evolution in predator-prey interactions using the theory of fast-slow dynamical systems. Am. Nat. 176, E109–E127. https://doi.org/10.1086/656485 (2010).Article 
PubMed 

Google Scholar 
3.Ellner, S. P., Geber, M. A. & Hairston, N. G. Does rapid evolution matter? Measuring the rate of contemporary evolution and its impacts on ecological dynamics. Ecol. Lett. 14, 603–614. https://doi.org/10.1111/j.1461-0248.2011.01616.x (2011).Article 
PubMed 

Google Scholar 
4.Yoder, J. B. et al. Ecological opportunity and the origin of adaptive radiations. J. Evol. Biol. 23, 1581–1596. https://doi.org/10.1111/j.1420-9101.2010.02029.x (2010).CAS 
Article 
PubMed 

Google Scholar 
5.Losos, J. B. Adaptive radiation, ecological opportunity, and evolutionary determinism. Am. Nat. 175, 623–639. https://doi.org/10.1086/652433 (2010).Article 
PubMed 

Google Scholar 
6.Meyer, J. R. & Kassen, R. The effects of competition and predation on diversification in a model adaptive radiation. Nature 446, 432–435. https://doi.org/10.1038/nature05599 (2007).ADS 
CAS 
Article 
PubMed 

Google Scholar 
7.Stroud, J. T. & Losos, J. B. Ecological opportunity and adaptive radiation. Annu. Rev. Ecol. Evol. Syst. 47(47), 507–532. https://doi.org/10.1146/annurev-ecolsys-121415-032254 (2016).Article 

Google Scholar 
8.Keller, I. & Seehausen, O. Thermal adaptation and ecological speciation. Mol. Ecol. 21, 782–799. https://doi.org/10.1111/j.1365-294X.2011.05397.x (2012).CAS 
Article 
PubMed 

Google Scholar 
9.Schluter, D., Price, T. D. & Grant, P. R. ecological character displacement in Darwin Finches. Science 227, 1056–1059. https://doi.org/10.1126/science.227.4690.1056 (1985).ADS 
CAS 
Article 
PubMed 

Google Scholar 
10.Munkemuller, T. & Gallien, L. VirtualCom: A simulation model for eco-evolutionary community assembly and invasion. Methods Ecol. Evol. 6, 735–743. https://doi.org/10.1111/2041-210x.12364 (2015).Article 

Google Scholar 
11.Munoz, F. et al. ecolottery: Simulating and assessing community assembly with environmental filtering and neutral dynamics in R. Methods Ecol. Evol. 9, 693–703. https://doi.org/10.1111/2041-210x.12918 (2018).Article 

Google Scholar 
12.Ruffley, M., Peterson, K., Week, B., Tank, D. C. & Harmon, L. J. Identifying models of trait-mediated community assembly using random forests and approximate Bayesian computation. Ecol. Evol. 9, 13218–13230. https://doi.org/10.1002/ece3.5773 (2019).Article 
PubMed 
PubMed Central 

Google Scholar 
13.van der Plas, F. et al. A new modeling approach estimates the relative importance of different community assembly processes. Ecology 96, 1502–1515. https://doi.org/10.1890/14-0454.1 (2015).Article 

Google Scholar 
14.Stegen, J. C. et al. Quantifying community assembly processes and identifying features that impose them. ISME J. 7, 2069–2079. https://doi.org/10.1038/ismej.2013.93 (2013).Article 
PubMed 
PubMed Central 

Google Scholar 
15.Dieckmann, U. & Doebeli, M. On the origin of species by sympatric speciation. Nature 400, 354–357 (1999).ADS 
CAS 
Article 

Google Scholar 
16.Geritz, S. A. H., Kisdi, E., Meszena, G. & Metz, J. A. J. Evolutionarily singular strategies and the adaptive growth and branching of the evolutionary tree. Evol. Ecol. 12, 35–57 (1998).Article 

Google Scholar 
17.Vellend, M. Conceptual synthesis in community ecology. Q. R. Biol. 85, 183–206 (2010).Article 

Google Scholar 
18.Urban, M. C. et al. The evolutionary ecology of metacommunities. Trends Ecol. Evol. 23, 311–317 (2008).Article 

Google Scholar 
19.Pausas, J. G. & Verdu, M. The jungle of methods for evaluating phenotypic and phylogenetic structure of communities. Bioscience 60, 614–625. https://doi.org/10.1525/bio.2010.60.8.7 (2010).Article 

Google Scholar 
20.Mouquet, N. et al. Ecophylogenetics: Advances and perspectives. Biol. Rev. 87, 769–785. https://doi.org/10.1111/j.1469-185X.2012.00224.x (2012).Article 
PubMed 

Google Scholar 
21.Kraft, N. J. B., Cornwell, W. K., Webb, C. O. & Ackerly, D. D. Trait evolution, community assembly, and the phylogenetic structure of ecological communities. Am. Nat. 170, 271–283. https://doi.org/10.1086/519400 (2007).Article 
PubMed 

Google Scholar 
22.Wilson, J. B., Weiher, E. & Keddy, P. Assembly Rules in Plant Communities (Cambridge University Press, 1999).Book 

Google Scholar 
23.MacArthur, R. H. & Levins, R. Limiting similarity convergence and divergence of coexisting species. Am. Nat. 101, 377–385 (1967).Article 

Google Scholar 
24.Webb, C. O., Ackerly, D. D., McPeek, M. A. & Donoghue, M. J. Phylogenies and community ecology. Annu. Rev. Ecol. Syst. 33, 475–505. https://doi.org/10.1146/annurev.ecolysis.33.010802.150448 (2002).Article 

Google Scholar 
25.Pontarp, M., Brännström, A. & Petchey, O. L. Inferring community assembly processes from macroscopic patterns using dynamic eco-evolutionary models and Approximate Bayesian Computation (ABC). Methods Ecol. Evol. 10, 450–460. https://doi.org/10.1111/2041-210x.13129 (2019).Article 

Google Scholar 
26.Mittelbach, G. G. & Schemske, D. W. Ecological and evolutionary perspectives on community assembly. Trends Ecol. Evol. 30, 241–247. https://doi.org/10.1016/j.tree.2015.02.008 (2015).Article 
PubMed 

Google Scholar 
27.Cavender-Bares, J., Kozak, K. H., Fine, P. V. A. & Kembel, S. W. The merging of community ecology and phylogenetic biology. Ecol. Lett. 12, 693–715. https://doi.org/10.1111/j.1461-0248.2009.01314.x (2009).Article 
PubMed 

Google Scholar 
28.Pontarp, M. & Petchey, O. L. Ecological opportunity and predator–prey interactions: Linking eco-evolutionary processes and diversification in adaptive radiations. Proc. R. Soc. B Biol. Sci. https://doi.org/10.1098/rspb.2017.2550 (2018).Article 

Google Scholar 
29.Seehausen, O. African cichlid fish: A model system in adaptive radiation research. Proc. R. Soc. B Biol. Sci. 273, 1987–1998. https://doi.org/10.1098/rspb.2006.3539 (2006).Article 

Google Scholar 
30.Schluter, D. The Ecology of Adaptive Radiation (Columbia University Press, 2000).
Google Scholar 
31.Nosil, P. Ecological Speciation (Oxford University Press, 2012).Book 

Google Scholar 
32.Christiansen, F. B. & Loeschcke, V. Evolution and intraspecific exploitative competition I. One-locus theory for small additive gene effects. Theor. Popul. Biol. 18, 297–313 (1980).MathSciNet 
Article 

Google Scholar 
33.Brown, J. S. & Vincent, T. L. A theory for the evolutionary game. Theor. Popul. Biol. 31, 140–166 (1987).MathSciNet 
Article 

Google Scholar 
34.Doebeli, M. & Dieckmann, U. Speciation along environmental gradients. Nature 421, 259–264. https://doi.org/10.1038/Nature01274 (2003).ADS 
CAS 
Article 
PubMed 

Google Scholar 
35.Brose, U., Williams, R. J. & Martinez, N. D. Allometric scaling enhances stability in complex food webs. Ecol. Lett. 9, 1228–1236. https://doi.org/10.1111/j.1461-0248.2006.00978.x (2006).Article 
PubMed 

Google Scholar 
36.Leyequien, E., de Boer, W. F. & Cleef, A. Influence of body size on coexistence of bird species. Ecol. Res. 22, 735–741. https://doi.org/10.1007/s11284-006-0311-6 (2007).Article 

Google Scholar 
37.Yvon-Durocher, G. et al. Across ecosystem comparisons of size structure: Methods, approaches and prospects. Oikos 120, 550–563. https://doi.org/10.1111/j.1600-0706.2010.18863.x (2011).Article 

Google Scholar 
38.Rudolf, V. H. W. Seasonal shifts in predator body size diversity and trophic interactions in size-structured predator-prey systems. J. Anim. Ecol. 81, 524–532. https://doi.org/10.1111/j.1365-2656.2011.01935.x (2012).Article 
PubMed 

Google Scholar 
39.DeLong, J. P. & Vasseur, D. A. A dynamic explanation of size-density scaling in carnivores. Ecology 93, 470–476 (2012).Article 

Google Scholar 
40.DeLong, J. P. & Vasseur, D. A. Size-density scaling in protists and the links between consumer-resource interaction parameters. J. Anim. Ecol. 81, 1193–1201. https://doi.org/10.1111/j.1365-2656.2012.02013.x (2012).Article 
PubMed 

Google Scholar 
41.Violle, C. et al. Let the concept of trait be functional!. Oikos 116, 882–892. https://doi.org/10.1111/j.2007.0030-1299.15559.x (2007).Article 

Google Scholar 
42.Pontarp, M., Ripa, J. & Lundberg, P. On the origin of phylogenetic structure in competitive metacommunities. Evol. Ecol. Res. 14, 269–284 (2012).
Google Scholar 
43.Pontarp, M., Ripa, J. & Lundberg, P. The biogeography of adaptive radiations and the geographic overlap of sister species. Am. Nat. 186, 565–581 (2015).Article 

Google Scholar 
44.Barabás, G., Pigolotti, S., Gyllenberg, M., Dieckmann, U. & Meszéna, G. Continuous coexistence or discrete species? A new review of an old question. (2012).45.Brännström, A. et al. Modelling the ecology and evolution of communities: A review of past achievements, current efforts, and future promises. Evol. Ecol. Res. 14, 601–625 (2012).
Google Scholar 
46.Emerson, B. C. & Gillespie, R. G. Phylogenetic analysis of community assembly and structure over space and time. Trends Ecol. Evol. 23, 619–630 (2008).Article 

Google Scholar 
47.Vamosi, S. M., Heard, S. B., Vamosi, J. C. & Webb, C. O. Emerging patterns in the comparative analysis of phylogenetic community structure. Mol. Ecol. 18, 572–592. https://doi.org/10.1111/j.1365-294X.2008.04001.x (2009).CAS 
Article 
PubMed 

Google Scholar 
48.Sjödin, H., Ripa, J. & Lundberg, P. Principles of niche expansion. Proc. R. Soc. B Biol. Sci. https://doi.org/10.1098/rspb.2018.2603 (2018).Article 

Google Scholar 
49.Ackermann, M. & Doebeli, M. Evolution of niche width and adaptive diversification. Evolution 58, 2599–2612 (2004).Article 

Google Scholar 
50.Urban, M. C. & De Meester, L. Community monopolization: Local adaptation enhances priority effects in an evolving metacommunity. Proc. R. Soc. B. Biol. Sci. 276, 4129–4138 (2009).Article 

Google Scholar 
51.Urban, M. C., De Meester, L., Vellend, M., Stoks, R. & Vanoverbeke, J. A crucial step toward realism: responses to climate change from an evolving metacommunity perspective. Evol. Appl. 5, 154–167. https://doi.org/10.1111/j.1752-4571.2011.00208.x (2012).Article 
PubMed 

Google Scholar 
52.Pontarp, M. & Wiens, J. J. The origin of species richness patterns along environmental gradients: Uniting explanations based on time, diversification rate and carrying capacity. J. Biogeogr. 44, 722–735. https://doi.org/10.1111/jbi.12896 (2017).Article 

Google Scholar 
53.Pontarp, M. & Petchey, O. L. Community trait overdispersion due to trophic interactions: Concerns for assembly process inference. Proc. R. Soc. B Biol. Sci. https://doi.org/10.1098/rspb.2016.1602 (2016).Article 

Google Scholar 
54.Pontarp, M. et al. The latitudinal diversity gradient: Novel understanding through mechanistic eco-evolutionary. Trends Ecol. Evol. 34, 211–223. https://doi.org/10.1016/j.tree.2018.11.009 (2019).Article 
PubMed 

Google Scholar 
55.Case, T. J. An Illustrated Guide to Theoretical Ecology (Oxford University Press, Inc, 2000).
Google Scholar 
56.Barabas, G., Michalska-Smith, M. J. & Allesina, S. The effect of intra- and interspecific competition on coexistence in multispecies communities. Am. Nat. 188, E1–E12. https://doi.org/10.1086/686901 (2016).Article 
PubMed 

Google Scholar 
57.Heinz, S. K., Mazzucco, R. & Dieckmann, U. Speciation and the evolution of dispersal along environmental gradients. Evol. Ecol. 23, 53–70. https://doi.org/10.1007/s10682-008-9251-7 (2009).Article 

Google Scholar 
58.Metz, J. A. J., Nisbet, R. M. & Geritz, S. A. H. How should we define fitness for general ecolgical scenarios. Trends Ecol. Evol. 7, 198–202. https://doi.org/10.1016/0169-5347(92)90073-k (1992).CAS 
Article 
PubMed 

Google Scholar 
59.Doebeli, M. & Dieckmann, U. Evolutionary branching and sympatric speciation caused by different types of ecological interactions. Am. Nat. 156, S77–S101. https://doi.org/10.1086/303417 (2000).Article 
PubMed 

Google Scholar 
60.Ito, H. C. & Dieckmann, U. A new mechanism for recurrent adaptive Radiations. Am. Nat. 170, E96–E111. https://doi.org/10.1086/521229 (2007).Article 
PubMed 

Google Scholar 
61.Cressman, R. et al. Unlimited niche packing in a Lotka-Volterra competition game. Theor. Popul. Biol. 116, 1–17 (2017).Article 

Google Scholar 
62.Webb, C. O., Ackerly, D. D. & Kembel, S. W. Phylocom: software for the analysis of phylogenetic community structure and trait evolution. Bioinformatics 24, 2098–2100. https://doi.org/10.1093/bioinformatics/btn358 (2008).CAS 
Article 
PubMed 

Google Scholar 
63.Harmon-Threatt, A. N. & Ackerly, D. D. Filtering across spatial scales: Phylogeny, biogeography and community structure in bumble bees. PLoS ONE https://doi.org/10.1371/journal.pone.0060446 (2013).Article 
PubMed 
PubMed Central 

Google Scholar 
64.Pybus, O. G. & Harvey, P. H. Testing macro-evolutionary models using incomplete molecular phylogenies. Proc. R. Soc. B Biol. Sci. 267, 2267–2272. https://doi.org/10.1098/rspb.2000.1278 (2000).CAS 
Article 

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Figure 1 presents the track of the eight-month cruise, and Table 1 provides the detail of the legs and dates. On a routine basis R/V Hesperides sailed at an average speed of 11 knots from around 3 pm to 4 am (local time). The vessel arrived on station at around 4 am daily to carry out sampling operations at a fixed point for about 11 hours.Fig. 1Cruise track and integrated backscatter at different stations (NASC, daytime 200 to 1000 m).Full size imageTable 1 Dates and starting points of the 7 legs of the Malaspina cruise.Full size tableAcoustic measurements were carried out continuously using a Simrad EK60 echosounder), operating at 38 and 120 kHz (7° beamwidth transducers) with a ping rate of 0.5 Hz. Unfortunately, the 120 kHz failed during the first leg of the cruise and only 38 kHz data were collected. Echosounder observations were recorded down to 1000 m depth. The echosounder files are in the proprietary Simrad raw format and can be read by various softwares (e.g., LSSS, Echoview, Sonar5, MATECHO, ESP3, echopype, pyEcholab). GPS locations and calibration constants are imbedded in each file.Additionally, daytime data integrated over 2 m vertical bins from 200 to 1000 m depth are provided as Nautical Area Scattering Coefficient (NASC). Each “voxel” is the average of all cleaned and validated data recorded over that depth range, in a time period starting 8 hours before the start of the station (defined as start of the CTD cast) and ending 8 hours after the start of the station, with only data recorded in the period between 1 hour after local sunrise and 1 hour prior to local sunset accepted (i.e., during local daytime hours, but removing crepuscular periods when vertical migration of biota is strong). The relatively long interval over which data were accepted around each station was chosen since the station sampling resulted in noisy acoustic data,, a long interval was therefore chosen to ensure valid data on all stations.Finally, summaries of per station daytime and nighttime acoustic data (omitting data recorded within 1 hour of sunrise and sunset) are provided. The data fields in this file are station date, latitude and longitude, and per day and night average NASC 200–1000 m, average NASC 0–1000, weighted mean depth (WMD) of NASC 200–1000 m, migration amplitude, NASC day-to-night ratio and migration ratio. More

We analyse weekly reported counts of suspected and confirmed human cases and deaths attributed to LF (as defined in Supplementary Table 1), between 1 January 2012 and 30 December 2019, from across the entire of Nigeria. The weekly counts were reported from 774 LGAs in 36 Federal states and the Federal Capital Territory, under Integrated Disease Surveillance and Response (IDSR) protocols, and collated by the NCDC. All suspected cases, confirmed cases and deaths from notifiable infectious diseases (including viral haemorrhagic fevers; VHFs) are reported weekly to the LGA Disease Surveillance and Notification Officer (DSNO) and State Epidemiologist (SE). IDSR routine data on priority diseases are collected from inpatient and outpatient registers in health facilities, and forwarded to each LGA’s DSNO using SMS or paper form. Subsequently, individual LGA DSNOs collate and forward the data to their respective SE, also by SMS and paper form, for weekly and monthly reporting respectively to NCDC. From mid-2017 onwards, data entry in 18 states has been conducted using a mobile phone-based electronic reporting system called mSERS, with the data entered using a customised Excel spreadsheet that is used to manually key into NCDC-compatible spreadsheets. Data from this surveillance regime (WERs) were collated by epidemiologists at NCDC throughout the period 2012 to March 2018 (Supplementary Fig. 1).Throughout the study period, within-country LF surveillance and response has been strengthened under NCDC coordination2,20,33. LGAs are now required to notify immediately any suspected case to the state-level, which in turn reports to NCDC within 24 h, and also sends a cumulative weekly report of all reported cases. A dedicated, multi-sectoral NCDC LF TWG was set up in 2016 with the responsibility of coordinating all LF preparedness and response activities across states. Further capacity building occurred in 2017 to 2019, with the opening of three additional LF diagnostic laboratories in Abuja (Federal Capital Territory), Abakaliki (Ebonyi state) and Owo (Ondo state) (to a total of five; Fig. 2) and the rollout of intensive country-wide training on surveillance, clinical case management and diagnosis. We note that, due to the rapid expansion in a test capacity, the definition of a suspected case in our data has subtly changed over the surveillance period: from 2012 to 2016, suspected cases include probable cases that were not lab-tested, whereas from 2017 to 2019, all suspected cases were tested and confirmed to be negative.In addition to the WERs data, since 2017 LF case reporting data has also been collated by the LF TWG and used to inform the weekly NCDC LF Situation Reports (SitRep data; https://ncdc.gov.ng/diseases/sitreps). This regime includes post hoc follow-ups to ensure more accurate case counts, so our analyses use WER-derived case data from 2012 to 2016, and SitRep-derived case data from 2017 to 2019 (see Fig. 1 for full time series). A visual comparison of the data from each separate time series, including the overlap period (2017 to March 2018) is provided in Supplementary Fig. 1, and all statistical models considered random intercepts for the different surveillance regimes. Where other studies of recent Nigeria LF incidence have been more spatially and temporally restricted34,35, the extended monitoring period and fine spatial granularity of these data provide the opportunity for a detailed empirical perspective on the local drivers of LF at a country-wide scale and their relationship to changes in reporting effort.Recent trends in LF surveillance in NigeriaWe visualised temporal and seasonal trends in suspected and confirmed LF cases within and between years, for both surveillance datasets. Weekly case counts were aggregated to country-level and visualised as both annual case accumulation curves, and aggregated weekly case totals (Fig. 1 and Supplementary Fig. 1). We also mapped annual counts of suspected and confirmed cases across Nigeria at the LGA-level to examine spatial changes in reporting over the surveillance period (Fig. 2). State and LGA shapefiles used for modelling and mapping were obtained from Humanitarian Data Exchange under a CC-BY-IGO license (https://data.humdata.org/dataset/nga-administrative-boundaries).Analyses of aggregated district data are sensitive to differences in scale and shape of aggregation (the modifiable areal unit problem; MAUP36), and LGA geographical areas in Nigeria are highly skewed and vary over >3 orders of magnitude (median 713 km2, mean 1175 km2, range 4–11,255 km2). We therefore also aggregated all LGAs across Nigeria into 130 composite districts with a more even distribution of geographical areas, using distance-based hierarchical clustering on LGA centroids (implemented using hclust in R), with the constraint that each new cluster must contain only LGAs from within the same state (to preserve potentially important state-level differences in surveillance regime). Weekly and annual suspected and confirmed LF case totals were then calculated for each aggregated district. We used these spatially aggregated districts to test for the effects of scale on spatial drivers of LF occurrence and incidence.Statistical analysisWe analysed the full case time series (Fig. 1) to characterise the spatiotemporal incidence and drivers of LF in Nigeria, while controlling for year-on-year increases and expansions of surveillance effort. We firstly modelled annual LF occurrence and incidence at a country-wide scale, to identify the spatial, climatic and socio-ecological correlates of disease risk across Nigeria. Secondly, we modelled seasonal and temporal trends in weekly LF incidence within hyperendemic areas in the north and south of Nigeria, to identify the seasonal climatic conditions associated with LF risk dynamics and evaluate the scope for forecasting. All data processing and modelling was conducted in R v.3.4.1 with the packages R-INLA v.20.03.1737, raster v.3.4.1338 and velox v0.2.039. Statistical modelling was conducted using hierarchical regression in a Bayesian inference framework (integrated nested Laplace approximation (INLA)), which provides fast, stable and accurate posterior approximation for complex, spatially and temporally-structured regression models37,40, and has been shown to outperform alternative methods for modelling environmental phenomena with evidence of spatially biased reporting41.Processing climatic and socio-ecological covariatesWe collated geospatial data on socio-ecological and climatic factors that are hypothesised to influence either M. natalensis distribution and population ecology (rainfall, temperature and vegetation patterns), frequency and mode of human–rodent contact (poverty and improved housing prevalence), both of the above (agricultural and urban land cover) or likelihood of LF reporting (travel time to nearest laboratory with LF diagnostic capacity and travel time to nearest hospital). For each LGA we extracted the mean value for each covariate across the LGA polygon. The full suite of covariates tested across all analyses, data sources and associated hypotheses are described in Supplementary Table 5.We collated climate data spanning the full monitoring period and up until the date of analysis (July 2011 to January 2021). We obtained daily precipitation rasters for Africa42 from the Climate Hazards Infrared Precipitation with Stations (CHIRPS) project; this dataset is based on combining sparse weather station data with satellite observations and interpolation techniques, and is designed to support hydrologic forecasts in areas with poor weather station coverage (such as tropical West Africa)42. A recent study ground-truthing against weather station data showed that CHIRPS provides greater overall accuracy than other gridded precipitation products in Nigeria43. Air temperature daily minimum and maximum rasters were obtained from NOAA and were also averaged to calculate daily mean temperature. EVI, a measure of vegetation quality, was obtained from processing 16-day composite layers from NASA (National Aeronautics and Space Administration) (excluding all grid cells with unreliable observations due to cloud cover and linearly interpolating between observations to give daily values; Supplementary Table 5).We derived several spatial bioclimatic variables to capture conditions across the full monitoring period (Jan 2012 to Dec 2019): mean precipitation of the driest annual month, mean precipitation of the wettest annual month, precipitation seasonality (coefficient of variation), annual mean air temperature, air temperature seasonality, annual mean EVI and EVI seasonality. We also calculated monthly total precipitation, 3-month SPI44, average daily mean (Tmean), minimum (Tmin) and maximum (Tmax) temperature and EVI variables at sequential time lags prior to reporting week for seasonal modelling (described below in Temporal drivers). SPI is a standardised measure of drought or wetness conditions relative to the historical average conditions for a given period of the year. SPI was calculated within a rolling 3-month window across the full 40-year historical CHIRPS rainfall time series (1981–2020) using the R package SPEI v.1.744.We accessed annual human population rasters at 100 m resolution from WorldPop. We accessed the proportion of the population living in poverty in 2010 ( More