Chaigneau, A., Gizolme, A. & Grados, C. Mesoscale eddies off Peru in altimeter records: identification algorithms and eddy spatio-temporal patterns. Prog. Oceanogr. 79, 106–119 (2008).
McGillicuddy, D. J. Jr et al. Influence of mesoscale eddies on new production in the Sargasso Sea. Nature 394, 263–266 (1998).
Dufois, F. et al. Anticyclonic eddies are more productive than cyclonic eddies in subtropical gyres because of winter mixing. Sci. Adv. 2, 1–7 (2016).
Godø, O. R. et al. Mesoscale eddies are oases for higher trophic marine life. PLoS ONE 7, e30161 (2012).
Chelton, D. B., Gaube, P., Schlax, M. G., Early, J. J. & Samelson, R. M. The influence of nonlinear mesoscale eddies on near-surface oceanic chlorophyll. Science 334, 328–333 (2011).
Sarmiento, J. L. et al. Response of ocean ecosystems to climate warming. Global Biogeochem. Cycles 18, GB3003 (2004).
Bell, J. D. et al. Diversifying the use of tuna to improve food security and public health in Pacific Island countries and territories. Mar. Policy 51, 584–591 (2015).
Della Penna, A. & Gaube, P. Mesoscale eddies structure mesopelagic communities. Front. Mar. Sci. 7, 454 (2020).
Braun, C. D. et al. The functional and ecological significance of deep diving by large marine predators. Ann. Rev. Mar. Sci. 14, 129–159 (2022).
McGillicuddy, D. J. Jr Mechanisms of physical-biological-biogeochemical interaction at the oceanic mesoscale. Ann. Rev. Mar. Sci. 8, 125–159 (2016).
Fennell, S. & Rose, G. Oceanographic influences on deep scattering layers across the North Atlantic. Deep-Sea Res. Part I Oceanogr. Res. Pap. 105, 132–141 (2015).
Duffy, L. M. et al. Global trophic ecology of yellowfin, bigeye, and albacore tunas: understanding predation on micronekton communities at ocean-basin scales. Deep-Sea Res. Part II Topical Stud. Oceanogr. 140, 55–73 (2017).
Gaube, P. et al. Mesoscale eddies influence the movements of mature female white sharks in the Gulf Stream and Sargasso Sea. Sci. Rep. 8, 7363 (2018).
Braun, C. D., Gaube, P., Sinclair-Taylor, T. H., Skomal, G. B. & Thorrold, S. R. Mesoscale eddies release pelagic sharks from thermal constraints to foraging in the ocean twilight zone. Proc. Natl Acad. Sci. USA 116, 17187–17192 (2019).
Doyle, T. K. et al. Leatherback turtles satellite-tagged in European waters. Endanger. Species Res. 4, 23–31 (2008).
Pauly, D. & Christensen, V. Primary production required to sustain global fisheries. Nature 374, 255–257 (1995).
Lynham, J., Nikolaev, A., Raynor, J., Vilela, T. & Villaseñor-Derbez, J. C. Impact of two of the world’s largest protected areas on longline fishery catch rates. Nat. Commun. 11, 979 (2020).
Polovina, J. J., Abecassis, M., Howell, E. A. & Woodworth, P. Increases in the relative abundance of mid-trophic level fishes concurrent with declines in apex predators in the subtropical North Pacific, 1996-2006. Fish. Bull. 107, 523–531 (2009).
Royer, T. C. Ocean eddies generated by seamounts in the North Pacific. Science 199, 1063–1064 (1978).
Liu, Y. et al. Eddy analysis in the subtropical zonal band of the North Pacific Ocean. Deep-Sea Res. Part I Oceanogr. Res. Pap. 68, 54–67 (2012).
Bernstein, R. L. & White, W. B. Time and length scales of baroclinic eddies in the central North Pacific Ocean. J. Phys. Oceanogr. 4, 613–624 (1974).
Maunder, M. N. & Punt, A. E. Standardizing catch and effort data: a review of recent approaches. Fish. Res. 70, 141–159 (2004).
Woodworth, P. A. et al. Eddies as offshore foraging grounds for melon-headed whales (Peponocephala electra). Mar. Mammal Sci. 28, 638–647 (2012).
Gaube, P. et al. The use of mesoscale eddies by juvenile loggerhead sea turtles (Caretta caretta) in the southwestern Atlantic. PLoS ONE 12, e0172839 (2017).
Chambault, P. et al. Swirling in the ocean: immature loggerhead turtles seasonally target old anticyclonic eddies at the fringe of the North Atlantic Gyre. Prog. Oceanogr. 175, 345–358 (2019).
Gaube, P., McGillicuddy Jr, D., Chelton, D., Behrenfeld, M. & Strutton, P. Regional variations in the influence of mesoscale eddies on near-surface chlorophyll. J. Geophys. Res. Oceans 119, 8195–8220 (2014).
Waga, H., Kirawake, T. & Ueno, H. Impacts of mesoscale eddies on phytoplankton size structure. Geophys. Res. Lett. 46, 13191–13198 (2019).
Irigoien, X. et al. Large mesopelagic fishes biomass and trophic efficiency in the open ocean. Nat. Commun. 5, 3271 (2014).
Chen, Y.-lL. et al. Biologically active warm-core anticyclonic eddies in the marginal seas of the western Pacific Ocean. Deep Sea Res. Part I 106, 68–84 (2015).
Harke, M. J. et al. Microbial community transcriptional patterns vary in response to mesoscale forcing in the North Pacific Subtropical Gyre. Environ. Microbiol. 23, 4807–4822 (2021).
Hawco, N. J. et al. Iron depletion in the deep chlorophyll maximum: mesoscale eddies as natural iron fertilization experiments. Global Biogeochem. Cycles 35, e2021GB007112 (2021).
Klevjer, T. A. et al. Large scale patterns in vertical distribution and behaviour of mesopelagic scattering layers. Sci. Rep. 6, 19873 (2016).
Behrenfeld, M. J. et al. Global satellite-observed daily vertical migrations of ocean animals. Nature 576, 257–261 (2019).
Madigan, D. J. et al. Water column structure defines vertical habitat of twelve pelagic predators in the South Atlantic. ICES J. Mar. Sci. 78, 867–883 (2021).
Arostegui, M., Gaube, P. & Braun, C. Movement ecology and stenothermy of satellite-tagged shortbill spearfish (Tetrapturus angustirostris). Fish. Res. 215, 21–26 (2019).
Lehodey, P., Senina, I. & Murtugudde, R. A spatial ecosystem and populations dynamics model (SEAPODYM)—modeling of tuna and tuna-like populations. Prog. Oceanogr. 78, 304–318 (2008).
Varghese, S. P., Somvanshi, V. S. & Dalvi, R. S. Diet composition, feeding niche partitioning and trophic organisation of large pelagic predatory fishes in the eastern Arabian Sea. Hydrobiologia 736, 99–114 (2014).
Ward, P. & Myers, R. A. Inferring the depth distribution of catchability for pelagic fishes and correcting for variations in the depth of longline fishing gear. Can. J. Fish. Aquat.Sci. 62, 1130–1142 (2005).
Kai, E. T. et al. Top marine predators track Lagrangian coherent structures. Proc. Natl Acad. Sci. USA 106, 8245–8250 (2009).
Lima, I. D., Olson, D. B. & Doney, S. C. Biological response to frontal dynamics and mesoscale variability in oligotrophic environments: biological production and community structure. J. Geophys. Res. Oceans 107, 25-1–25-21 (2002).
Spall, S. A. & Richards, K. J. A numerical model of mesoscale frontal instabilities and plankton dynamics—I. model formulation and initial experiments. Deep-Sea Res. Part I Oceanogr. Res. Pap. 47, 1261–1301 (2000).
Siegelman, L., O’Toole, M., Flexas, M., Rivière, P. & Klein, P. Submesoscale ocean fronts act as biological hotspot for southern elephant seal. Sci. Rep. 9, 5588 (2019).
Lévy, M., Ferrari, R., Franks, P. J., Martin, A. P. & Rivière, P. Bringing physics to life at the submesoscale. Geophys. Res. Lett. https://doi.org/10.1029/2012GL052756 (2012).
Guidi, L. et al. Does eddy-eddy interaction control surface phytoplankton distribution and carbon export in the North Pacific Subtropical Gyre? J. Geophys. Res. Biogeosciences https://doi.org/10.1029/2012JG001984 (2012).
Chow, C. H., Cheah, W., Tai, J. H. & Liu, S. F. Anomalous wind triggered the largest phytoplankton bloom in the oligotrophic North Pacific Subtropical Gyre. Sci. Rep. 9, 15550 (2019).
Guo, M., Xiu, P., Chai, F. & Xue, H. Mesoscale and submesoscale contributions to high sea surface chlorophyll in subtropical gyres. Geophys. Res. Lett. 46, 13217–13226 (2019).
Klein, P. et al. Ocean-scale interactions from space. Earth Space Sci. 6, 795–817 (2019).
Martin, A. et al. The oceans’ twilight zone must be studied now, before it is too late. Nature 580, 26–28 (2020).
St. John, M. A. et al. A dark hole in our understanding of marine ecosystems and their services: perspectives from the mesopelagic community. Front. Marine Sci. 3, 31 (2016).
Bigelow, K., Musyl, M. K., Poisson, F. & Kleiber, P. Pelagic longline gear depth and shoaling. Fish. Res. 77, 173–183 (2006).
Brodziak, J. & Walsh, W. A. Model selection and multimodel inference for standardizing catch rates of bycatch species: a case study of oceanic whitetip shark in the Hawaii-based longline fishery. Can. J. Fish. Aquat.Sci. 70, 1723–1740 (2013).
Woodworth-Jefcoats, P. A., Polovina, J. & Drazen, J. Synergy among oceanographic variability, fishery expansion, and longline catch composition in the central North Pacific Ocean. Fish. Bull. 116, 228–239 (2018).
Boggs, C. H. Depth, capture time, and hooked longevity of longline-caught pelagic fish: timing bites of fish with chips. Fish. Bull. 90, 642–658 (1992).
Walsh, W. A. & Brodziak, J. Applications of Hawaii longline fishery observer and logbook data for stock assessment and fishery research. NOAA Tech. Memo. 57, 62 (2016).
Walsh, W. A. & Brodziak, J. Billfish CPUE standardization in the Hawaii longline fishery: model selection and multimodel inference. Fish. Res. 166, 151–162 (2015).
Gilman, E., Chaloupka, M., Fitchett, M., Cantrell, D. L. & Merrifield, M. Ecological responses to blue water MPAs. PLoS ONE 15, e0235129 (2020).
Portner, E. J., Polovina, J. J. & Choy, C. A. Patterns in micronekton diversity across the North Pacific Subtropical Gyre observed from the diet of longnose lancetfish (Alepisaurus ferox). Deep-Sea Research Part I 125, 40–51 (2017).
Brooks, M. E. et al. glmmTMB balances speed and flexibility among packages for zero-inflated generalized linear mixed modeling. R J. 9, 378–400 (2017).
Hartig, F. DHARMa: Residual diagnostics for hierarchical (multi-level/mixed) regression models. R package version 0.3.3.0 http://florianhartig.github.io/DHARMa/ (2020).
Jackson, C. H. Multi-state models for panel data: the msm package for R. J. Stat. Softw. https://doi.org/10.18637/jss.v038.i08 (2011).
Bates, D. et al. lme4: Linear mixed-effects models using ’Eigen’ and S4. R package version 1.1-25 https://github.com/lme4/lme4/ (2020).
Lenth, R. et al. emmeans: Estimated marginal means, aka least-squares mean. R package version 1.7.2 https://github.com/rvlenth/emmeans (2022).
R Core Team. R: A Language and Environment for Statistical Computing (R Foundation for Statistical Computing, 2020); http://www.r-project.org/
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