Ecology

Fraser, W., Trivelpiece, W., Ainley, D. & Trivelpiece, S. Increases in Antarctic penguin populations: Reduced competition with whales or a loss of sea ice due to environmental warming?. Polar Biol. 11, 525–531 (1992).Article 

Google Scholar 
Trivelpiece, W. et al. Variability in krill biomass links harvesting and climate warming to penguin population changes in Antarctica. PNAS 108, 7625–7628 (2011).Article 
CAS 

Google Scholar 
Fraser, W. & Hofmann, E. A predator’s perspective on causal links between climate change, physical forcing and ecosystem response. Mar. Ecol. Prog. Ser. 265, 1–15 (2003).Article 

Google Scholar 
Hinke, J., Salwicka, K., Trivelpiece, S., Watters, G. & Trivelpiece, W. Divergent responses of Pygoscelis penguins reveal common environmental driver. Oecologia 153, 845–855 (2007).Article 

Google Scholar 
Poncet, S. & Poncet, J. Censuses of penguin populations of the Antarctic Peninsula, 1983–87. Br. Antarct. Surv. Bull. 77, 109–129 (1987).
Google Scholar 
Fraser, W. R. & Trivelpiece, W. Z. Factors controlling the distribution of seabirds: Winter-summer heterogeneity in the distribution of Adélie penguin populations. Found. For. Ecol. Res. West Antarct. Penins. 70, 257–272 (1996).Article 

Google Scholar 
Humphries, G. R. W. et al. Mapping application for penguin populations and projected dynamics (MAPPPD): Data and tools for dynamic management and decision support. Polar Rec. 53, 160–166 (2017).Article 

Google Scholar 
Lynch, H., Naveen, R., Trathan, P. N. & Fagan, W. F. Spatially integrated assessment reveals widespread changes in penguin populations on the Antarctic Peninsula. Ecology 93, 1367–1377 (2012).Article 

Google Scholar 
Elliot, D. H., Watts, D. R., Alley, R. B. & Gracanin, T. M. Bird and seal observations at Joinville Island and offshore islands. Antarct. J. USA 13, 154–155 (1978).
Google Scholar 
Bender, N. A., Crosbie, K. & Lynch, H. Patterns of tourism in the Antarctic Peninsula region: A twenty-year re-analysis. Antarct. Sci. 28, 194–203 (2016).Article 

Google Scholar 
Lynch, H. J. & Schwaller, M. R. Mapping the abundance and distribution of Adélie penguins using Landsat-7: First steps towards an integrated multi-sensor pipeline for tracking populations at the continental scale. PLoS ONE 9, 1–8 (2014).Article 

Google Scholar 
Lynch, H. J. & LaRue, M. A. First global census of the Adélie penguin. Auk 131, 457–466 (2014).Article 

Google Scholar 
Parkinson, C. L. & Cavalieri, D. J. Antarctic sea ice variability and trends, 1979–2010. Cryosphere 6, 871–880 (2012).Article 

Google Scholar 
Parkinson, C. L. Trends in the length of the Southern Ocean sea-ice season, 1979–99. Ann. Glaciol. 34, 435–440 (2002).Article 

Google Scholar 
Jena, B. et al. Record low sea ice extent in the Weddell Sea, Antarctica in April/May 2019 driven by intense and explosive polar cyclones. NPJ Clim. Atmos. Sci. 5, 1–15 (2022).Article 

Google Scholar 
Kumar, A., Yadav, J. & Mohan, R. Seasonal sea-ice variability and its trend in the Weddell Sea sector of West Antarctica. Environ. Res. Lett. 16, 024046 (2021).
Google Scholar 
Strass, V. H., Rohardt, G., Kanzow, T., Hoppema, M. & Boebel, O. Multidecadal warming and density loss in the deep Weddell Sea. Antarct. J. Clim. 33, 9863–9881 (2020).Article 

Google Scholar 
Morioka, Y. & Behera, S. K. Remote and local processes controlling decadal sea ice variability in the Weddell Sea. J. Geophys. Res. Ocean 126, e2020JC017036 (2021).Article 

Google Scholar 
Veytia, D. et al. Circumpolar projections of Antarctic krill growth potential. Nat. Clim. Chang. 10, 568–575 (2020).Article 

Google Scholar 
Humphries, G. R. et al. Predicting the future is hard and other lessons from a population time series data science competition. Ecol. Inf. 48, 1–11 (2018).Article 

Google Scholar 
Borowicz, A. et al. A multi-modal survey of Adèlie penguin megacolonies reveals the Danger Islands as a seabird hotspot. Sci. Rep. 8, 3926 (2018).Article 

Google Scholar 
Cimino, M., Lynch, H., Saba, V. & Oliver, M. Projected asymmetric response of Adèlie penguins to Antarctic climate change. Sci. Rep. 6, 28785 (2016).Article 
CAS 

Google Scholar 
McClintock, J., Silva-Rodriguez, P. & Fraser, W. Southerly breeding in gentoo penguins for the eastern Antarctic Peninsula: Further evidence for unprecedented climate change. Antarct. Sci. 22, 285–286 (2010).Article 

Google Scholar 
Lynch, H. J., Naveen, R. & Fagan, W. F. Censuses of penguin, blue-eyed shag Phalacrocorax atriceps and southern giant petrel Macronectes giganteus populations on the Antarctic Peninsula, 2001–2007. Mar. Ornithol. 36, 83–97 (2008).
Google Scholar 
Dunn, M. J. et al. Population size and decadal trends of three penguin species nesting at Signy Island, South Orkney Islands. PLoS ONE 11, e0164025 (2016).Article 

Google Scholar 
Delegations of Argentina and Chile. Domain 1 Marine Protected Area Preliminary Proposal Part A-1, Priority Areas for Conservation. SC-CAMLR- XXXVI/17. Retrieved from https://meetings.ccamlr.org/en/sc-camlr-xxxvi/18 (2018).Delegations of Argentina and Chile. Domain 1 Marine Protected Area Preliminary Proposal Part A-2, Priority Areas for Conservation. SC-CAMLR- XXXVI/18. Retrieved from https://meetings.ccamlr.org/en/sc-camlr-xxxvi/18 (2018).Teschke, K. et al. Planning marine protected areas under the CCAMLR regime—the case of the Weddell Sea (Antarctica). Mar. Policy 124, 104370 (2021).Article 

Google Scholar 
Herman, R. et al. Update on the global abundance and distribution of breeding Gentoo Penguins (Pygoscelis papua). Polar Biol. 43, 1947–1956 (2020).Article 

Google Scholar 
Korczak-Abshire, M., Hinke, J. T., Milinevsky, G., Juáres, M. A. & Watters, G. M. Coastal regions of the northern Antarctic Peninsula are key for gentoo populations. Biol. Lett. 17, 20200708 (2021).Article 

Google Scholar 
Miller, A. K., Karnovsky, N. J. & Trivelpiece, W. Z. Flexible foraging strategies of gentoo penguins Pygoscelis papua over 5 years in the South Shetland Islands. Antarct. Mar. Biol. 156, 2527–2537 (2009).Article 

Google Scholar 
Herman, R. W. et al. Seasonal consistency and individual variation in foraging strategies differ among and within Pygoscelis penguin species in the Antarctic Peninsula region. Mar. Biol. 164, 1–13 (2017).Article 
CAS 

Google Scholar 
Cimino, M. A., Fraser, W. R., Irwin, A. J. & Oliver, M. J. Satellite data identify decadal trends in the quality of Pygoscelis penguin chick-rearing habitat. Glob. Chang. Biol. 19, 136–148 (2013).Article 

Google Scholar 
Black, C. E. A comprehensive review of the phenology of Pygoscelis penguins. Polar Biol. 39, 405–432 (2016).Article 

Google Scholar 
Croxall, J. P. & Kirkwood, E. The Distribution of Penguins on the Antarctic Peninsula and Islands of the Scotia Sea (British Antarctic Survey, Cambridge, UK, 1979).
Google Scholar 
Naveen, R. et al. Censuses of penguin, blue-eyed shag, and southern giant petrel populations in the Antarctic Peninsula region, 1994–2000. Polar Rec. 36, 323–334 (2000).Article 

Google Scholar 
Woehler,E. J. The Distribution and Abundance of Antarctic and Subantarctic Penguins. In SCAR Comm. on Antarctic Res. Bird Biol. Subcomm. (Cambridge University Press, 1993).Naveen, R., Lynch, H. J., Forrest, S., Mueller, T. & Polito, M. First direct, site-wide penguin survey at Deception Island, Antarctica, suggests significant declines in breeding chinstrap penguins. Polar Biol. 35, 1879–1888 (2012).
Google Scholar 
Hallermann, N., Morgenthal, G. & Rodehorst, V. Unmanned aerial systems (UAS)–case studies of vision based monitoring of ageing structures. In Int. Symp. Non-Destructive Test. Civ. Eng. (NDT-CE) 15–17 (2015).Fonstad, M. A., Dietrich, J. T., Courville, B. C., Jensen, J. L. & Carbonneau, P. E. Topographic structure from motion: A new development in photogrammetric measurement. Earth Surf. Process. Landf. 38, 421–430 (2013).Article 

Google Scholar 
Cavalieri, D. J., Germain, K. M. S. & Swift, C. T. Reduction of weather effects in the calculation of sea-ice concentration with the DMSP SSM/I. J. Glaciol. 41, 455–464 (1995).Article 

Google Scholar 
Fetterer, F., Knowles, K., Meier, W., Savoie, M. & Windnagel, A. Updated daily Sea Ice Index, Version 3. In Boulder, Colo.USA. NSIDC: Natl. Snow Ice Data Cent (2017).Iles, D. T. et al. Sea ice predicts long-term trends in Adélie penguin population growth, but not annual fluctuations: Results from a range-wide multiscale analysis. Glob. Change Biol. 26, 3788–3798 (2020).Article 

Google Scholar 
Plummer, M., Stukalov, A. & Denwood, M. rjags: Bayesian graphical models using mcmc. R package version 4. https://rdrr.io/cran/rjags/ (2016).Plummer, M. et al. Jags: A program for analysis of bayesian graphical models using gibbs sampling. In Proceedings of the 3rd International Workshop on Distributed Statistical Computing, vol. 124, 1–10 (Vienna, Austria., 2003).Brooks, S. P. & Gelman, A. General methods for monitoring convergence of iterative simulations. J. Comput. Graph. Stat. 7, 434–455 (1998).MathSciNet 

Google Scholar 
Youngflesh, C. MCMCvis: Tools to visualize, manipulate, and summarize MCMC output. J. Open Sourc. Softw. 3, 640 (2018).Article 

Google Scholar 
Wickham, H. ggplot2. Wiley Interdiscipl. Rev. Comput. Stat. 3, 180–185 (2011).Article 

Google Scholar 
Kellner,K. jagsUI: A wrapper around rjags to streamline JAGS analyses. R package version 1, 2015 (2015).Herman, R. & Lynch, H. Age-structured model reveals prolonged immigration is key for colony establishment in Gentoo Penguins. Ornithol. Appl. 124, duac04 (2022).
Google Scholar 
Polito, M. J., Lynch, H. J., Naveen, R. & Emslie, S. D. Stable isotopes reveal regional heterogeneity in the pre-breeding distribution and diets of sympatrically breeding Pygoscelis spp. penguins. Mar. Ecol. Prog. Ser. 421, 265–277 (2011).Article 

Google Scholar 
Ballerini, T., Tavecchia, G., Olmastroni, S., Pezzo, F. & Focardi, S. Nonlinear effects of winter sea ice on the survival probabilities of Adélie penguins. Oecologia 161, 253–265 (2009).Article 

Google Scholar 
Wilson, P. et al. Adélie penguin population change in the pacific sector of Antarctica: Relation to sea-ice extent and the Antarctic Circumpolar Current. Mar. Ecol. Prog. Ser. 213, 301–309 (2001).Article 

Google Scholar 
Wienecke, B. et al. Adélie penguin foraging behaviour and krill abundance along the Wilkes and Adélie land coasts, Antarctica. Deep. Sea Res. Part II Top. Stud. Oceanogr. 47, 2573–2587 (2000).Article 

Google Scholar 
Ainley, D. G. The Adélie Penguin: Bellwether of Climate Change (Columbia University Press, 2002).Book 

Google Scholar 
Cherel, Y. Isotopic niches of emperor and Adélie penguins in Adélie Land. Antarct. Mar. Biol. 154, 813–821 (2008).Article 

Google Scholar 
Ainley, D. G. et al. Post-fledging survival of Adélie penguins at multiple colonies: Chicks raised on fish do well. Mar. Ecol. Prog. Ser. 601, 239–251 (2018).Article 

Google Scholar 
Ashford, J., Zane, L., Torres, J. J., La Mesa, M. & Simms, A. R. Population structure and life history connectivity of Antarctic silverfish (Pleuragramma antarctica) in the Southern Ocean ecosystem. In The Antarctic Silverfish: A Keystone Species in a Changing Ecosystem 193–234 (Springer, 2017).Pakhomov, E. & Perissinotto, R. Antarctic neritic krill Euphausia crystallorophias: Spatio-temporal distribution, growth and grazing rates. Deep. Sea Res. Part I Oceanogr. Res. Pap. 43, 59–87 (1996).Article 

Google Scholar 
La Mesa, M. & Eastman, J. T. Antarctic silverfish: Life strategies of a key species in the high-Antarctic ecosystem. Fish Fish 13, 241–266 (2012).Article 

Google Scholar 
Davis, L. B., Hofmann, E. E., Klinck, J. M., Piñones, A. & Dinniman, M. S. Distributions of krill and Antarctic silverfish and correlations with environmental variables in the western Ross Sea. Antarct. Mar. Ecol. Prog. Ser. 584, 45–65 (2017).Article 
CAS 

Google Scholar 
Chapman, E. W., Hofmann, E. E., Patterson, D. L., Ribic, C. A. & Fraser, W. R. Marine and terrestrial factors affecting Adélie penguin Pygoscelis adeliae chick growth and recruitment off the western Antarctic Peninsula. Mar. Ecol. Prog. Ser. 436, 273–289 (2011).Article 

Google Scholar 
La Mesa, M., Piñones, A., Catalano, B. & Ashford, J. Predicting early life connectivity of Antarctic silverfish, an important forage species along the Antarctic Peninsula. Fish. Oceanogr. 24, 150–161 (2015).Article 

Google Scholar 
La Mesa, M., Riginella, E., Mazzoldi, C. & Ashford, J. Reproductive resilience of ice-dependent Antarctic silverfish in a rapidly changing system along the Western Antarctic Peninsula. Mar. Ecol. 36, 235–245 (2015).Article 

Google Scholar 
Handley, J. et al. Marine important bird and biodiversity areas for penguins in Antarctica, targets for conservation action. Front. Mar. Sci. 7, 256 (2021).Article 

Google Scholar 
Brooks, C. et al. Workshop on identifying key biodiversity areas for the Southern Ocean using tracking data. In Tech.Rep. SC-CAMLR-41/BG/22, CCAMLR (2022).Lynch, H. J., Naveen, R. & Casanovas, P. Antarctic site inventory breeding bird survey data, 1994–2013: Ecological Archives E094–243. Ecology 94, 2653–2653 (2013).Article 

Google Scholar 
Myrcha, A., Tatur, A. & Valle, R. D. V. Numbers of Adélie penguins breeding at Hope Bay and Seymour Island rookeries (West Antarctica) in 1985. Pol. Polar Res. 8, 411–422 (1987).
Google Scholar 
Montalti, D. & Soave, G. E. The birds of Seymour Island, Antarctica. Ornitol. Neotrop. 13, 267–271 (2002).
Google Scholar 
Perchivale, P. J. et al. Updated estimate of the Breeding Population of Adélie penguins (Pygoscelis adeliae) at Penguin Point, Marambio/Seymour Island within the proposed Weddell Sea Marine Protected Area (2022). https://www.researchsquare.com/article/rs-2117503/v1.Che-Castaldo, C. et al. Pan-Antarctic analysis aggregating spatial estimates of Adélie penguin abundance reveals robust dynamics despite stochastic noise. Nat. Commun. 8, 832 (2017).Article 

Google Scholar 
Hinke, J. T., Trivelpiece, S. G. & Trivelpiece, W. Z. Variable vital rates and the risk of population declines in Adélie penguins from the Antarctic Peninsula region. Ecosphere 8, e01666 (2017).Article 

Google Scholar  More

Brown, A. R. et al. Assessing risks and mitigating impacts of harmful algal blooms on mariculture and marine fisheries. Rev. Aquac. 12, 1663–1688 (2020).
Google Scholar 
Bechard, A. Red tide at morning, tourists take warning? County-level economic effects of HABS on tourism dependent sectors. Harmful Algae 85, 101689–101689 (2019).Article 

Google Scholar 
Landsberg, J. H. The effects of harmful algal blooms on aquatic organisms. Rev. Fish. Sci. 10, 113–390 (2002).Article 

Google Scholar 
Flewelling, L. J. et al. Brevetoxicosis: Red tides and marine mammal mortalities. Nature 435, 755–756 (2005).Article 
CAS 

Google Scholar 
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).Article 
CAS 

Google Scholar 
Driggers, W. B. et al. Environmental conditions and catch rates of predatory fishes associated with a mass mortality on the West Florida Shelf. Estuar. Coast. Shelf Sci. 168, 40–49 (2016).Article 
CAS 

Google Scholar 
Hallett, C. S., Valesini, F. J., Clarke, K. R. & Hoeksema, S. D. Effects of a harmful algal bloom on the community ecology, movements and spatial distributions of fishes in a microtidal estuary. Hydrobiologia 763, 267–284 (2016).Article 

Google Scholar 
Anderson, D. M. et al. Marine harmful algal blooms (HABs) in the United States: History, current status and future trends. Harmful Algae 102, 101975–101975 (2021).Article 
CAS 

Google Scholar 
Steidinger, K. A. & Haddad, K. Biologic and hydrographic aspects of red tides. Bioscience 31, 814–819 (1981).Article 

Google Scholar 
Soto, I. M. et al. Advection of Karenia brevis blooms from the Florida Panhandle towards Mississippi coastal waters. Harmful Algae 72, 46–64 (2018).Article 

Google Scholar 
Steidinger, K. A. & Ingle, R. M. Observations on the 1971 summer red tide in tampa bay, Florida1. Environ. Lett. 3, 271–278 (1972).Article 
CAS 

Google Scholar 
Liu, Y. et al. Offshore forcing on the “pressure point” of the West Florida Shelf: Anomalous upwelling and its influence on harmful algal blooms. J. Geophys. Res. 121, 5501–5515 (2016).Article 

Google Scholar 
Liu, Y., Weisberg, R. H., Zheng, L., Heil, C. A. & Hubbard, K. A. Termination of the 2018 Florida red tide event: A tracer model perspective. Estuar. Coast. Shelf Sci. 272, 107901 (2022).Article 

Google Scholar 
Weisberg, R. H. & Liu, Y. Local and deep-ocean forcing effects on the West Florida continental shelf circulation and ecology. Front. Mar. Sci. https://doi.org/10.3389/fmars.2022.863227 (2022).Article 

Google Scholar 
Walsh, J. J. et al. Red tides in the Gulf of Mexico: Where, when, and why? Journal of Geophysical Research: Oceans 111, (2006).Lapointe, B. E., Herren, L. W., Debortoli, D. D. & Vogel, M. A. Evidence of sewage-driven eutrophication and harmful algal blooms in Florida’s Indian River Lagoon. Harmful Algae 43, 82–102 (2015).Article 
CAS 

Google Scholar 
Medina, M. et al. Nitrogen-enriched discharges from a highly managed watershed intensify red tide (Karenia brevis) blooms in southwest Florida. Sci. Total Environ. 827, 154149–154149 (2022).Article 
CAS 

Google Scholar 
Perkins, S. Ramping up the fight against Florida’s red tides. Proc. Natl. Acad. Sci. U.S.A. 116, 6510–6512 (2019).Article 
CAS 

Google Scholar 
Skripnikov, A. et al. Using localized Twitter activity to assess harmful algal bloom impacts of Karenia brevis in Florida, USA. Harmful Algae 110, 102118–102118 (2021).Article 
CAS 

Google Scholar 
SEDAR. SEDAR 33 Update – Gulf of Mexico gag grouper stock assessment report, 123. https://sedarweb.org/docs/suar/GagUpdateAssessReport_Final_0.pdf (2016).SEDAR. SEDAR 61 – Gulf of Mexico red grouper stock assessment report, 285. https://sedarweb.org/docs/sar/S61_Final_SAR.pdf (2019).SEDAR. SEDAR 10 Stock Assessment Report 2: Gulf of Mexico Gag Grouper, 250. www.sedarweb.org (2004).SEDAR. SEDAR 10 Update – Gulf of Mexico gag grouper stock assessment report. http://www.sedarweb.org (2009).SEDAR. SEDAR 72—Gulf of Mexico gag grouper stock assessment report, 318–318. https://sedarweb.org/docs/sar/S72_SAR_FINAL.pdf%0A (2021).Geary, W. L. et al. A guide to ecosystem models and their environmental applications. Nat. Ecol. Evol. 4, 1459–1471 (2020).Article 

Google Scholar 
Steenbeek, J. et al. Making spatial-temporal marine ecosystem modelling better—A perspective. Environ. Model. Softw. 145, 105209–105209 (2021).Article 

Google Scholar 
Gray DiLeone, A. M. & Ainsworth, C. H. Effects of Karenia brevis harmful algal blooms on fish community structure on the West Florida Shelf. Ecol. Model. 392, 250–267 (2019).Article 

Google Scholar 
Perryman, H. A. et al. A revised diet matrix to improve the parameterization of a West Florida Shelf Ecopath model for understanding harmful algal bloom impacts. Ecol. Model. 416, 108890–108890 (2020).Article 

Google Scholar 
Mayer-Pinto, M., Ledet, J., Crowe, T. P. & Johnston, E. L. Sublethal effects of contaminants on marine habitat-forming species: A review and meta-analysis. Biol. Rev. 95, 1554–1573 (2020).Article 

Google Scholar 
Reis Costa, P. Impact and effects of paralytic shellfish poisoning toxins derived from harmful algal blooms to marine fish. Fish Fish. 17, 226–248 (2016).Article 

Google Scholar 
Dahood, A., de Mutsert, K. & Watters, G. M. Evaluating Antarctic marine protected area scenarios using a dynamic food web model. Biol. Cons. 251, 108766–108766 (2020).Article 

Google Scholar 
de Mutsert, K. et al. Exploring effects of hypoxia on fish and fisheries in the northern Gulf of Mexico using a dynamic spatially explicit ecosystem model. Ecol. Model. 331, 142–150 (2016).Article 

Google Scholar 
de Mutsert, K., Lewis, K. A., White, E. D. & Buszowski, J. End-to-end modeling reveals species-specific effects of large-scale coastal restoration on living resources facing climate change. Front. Mar. Sci. 8, 104–104 (2021).Article 

Google Scholar 
Bauer, B. et al. Erratum: Reducing eutrophication increases spatial extent of communities supporting commercial fisheries: A model case study (ICES Journal of Marine Science (2018) DOI: https://doi.org/10.1093/icesjms/fsy003). ICES Journal of Marine Science, 75, 1155–1155 (2018).Sadchatheeswaran, S., Branch, G. M., Shannon, L. J., Coll, M. & Steenbeek, J. A novel approach to explicitly model the spatiotemporal impacts of structural complexity created by alien ecosystem engineers in a marine benthic environment. Ecol. Model. 459, 109731–109731 (2021).Article 

Google Scholar 
Coll, M. et al. Advancing global ecological modeling capabilities to simulate future trajectories of change in marine ecosystems. Front. Mar. Sci. 7, 741–741 (2020).Article 

Google Scholar 
Hernvann, P. Y. et al. The celtic sea through time and space: Ecosystem modeling to unravel fishing and climate change impacts on food-web structure and dynamics. Front. Mar. Sci. 7, 1018–1018 (2020).Article 

Google Scholar 
Walters, C. Ecospace: Prediction of mesoscale spatial patterns in trophic relationships of exploited ecosystems, with emphasis on the impacts of marine protected areas. Ecosystems 2, 539–554 (1999).Article 

Google Scholar 
Christensen, V., Walters, C. J., Pauly, D. & Forrest, R. Ecopath with Ecosim version 6 user guide. Fish. Cent. Univ. Br. Columbia Vanc. Can. 281, 1–235 (2008).
Google Scholar 
Okey, T. A., Mahmoudi, B., Mackinson, S., Vasconcellos, M. & Vidal-Hernandez, L. An ecosystem model of the West Florida Shelf for use in fisheries management and ecological research: Volume II. Model construction. Fish. Manag. II, 163–163 (2002).
Google Scholar 
Liu, Y. & Weisberg, R. H. Seasonal variability on the West Florida Shelf. Prog. Oceanogr. 104, 80–98 (2012).Article 

Google Scholar 
Moretzsohn, F., Chávez-Sánchez, J. A. & J.W. Tunnell, Jr. GulfBase: Resource Database for Gulf of Mexico Research. World Wide Web electronic publication (2016).Murawski, S. A., Peebles, E. B., Gracia, A., Tunnell, J. W. & Armenteros, M. Comparative abundance, species composition, and demographics of continental shelf fish assemblages throughout the Gulf of Mexico. Mar. Coast. Fish. 10, 325–346 (2018).Article 

Google Scholar 
Darnell, R. M. The American sea: A natural history of the gulf of Mexico. The American Sea: A Natural History of the Gulf of Mexico, 557, https://doi.org/10.5860/choice.193769 (2015).Brochure, I. Marine recreational information program: Implementation plan (2008).Florida Fish and Wildlife Conservation Commission. Commercial fisheries landings summaries (2021).Murawski, S. A. et al. How did the deepwater horizon oil spill affect coastal and continental shelf ecosystems of the Gulf of Mexico?. Oceanography 29, 160–173 (2016).Article 

Google Scholar 
Chagaris, D. D., Patterson, W. F. & Allen, M. S. Relative effects of multiple stressors on reef food webs in the Northern Gulf of Mexico revealed via ecosystem modeling. Front. Mar. Sci. 7, 513–513 (2020).Article 

Google Scholar 
South, A. rnaturalearth: world map data from Natural Earth. R package version 0.1.0. The R Foundation. https://CRAN.R-project.org/package=rnaturalearth (2017).Colleter, M. et al. Global overview of the applications of the Ecopath with Ecosim modeling approach using the EcoBase models repository. Ecol. Model. 302, 42–53 (2015).Article 

Google Scholar 
Ahrens, R. N., Walters, C. J. & Christensen, V. Foraging arena theory. Fish fish. 13, 41–59 (2012).Article 

Google Scholar 
Christensen, V. et al. Representing variable habitat quality in a spatial food web model. Ecosystems 17, 1397–1412 (2014).Article 
CAS 

Google Scholar 
Steenbeek, J. et al. Bridging the gap between ecosystem modeling tools and geographic information systems: Driving a food web model with external spatial–temporal data. Ecol. Model. 263, 139–151 (2013).Article 

Google Scholar 
Walters, C., Christensen, V., Walters, W. & Rose, K. Representation of multistanza life histories in Ecospace models for spatial organization of ecosystem trophic interaction patterns. Bull. Mar. Sci. 86, 439–459 (2010).
Google Scholar 
Heymans, J. J. et al. Best practice in Ecopath with Ecosim food-web models for ecosystem-based management. Ecol. Model. 331, 173–184 (2016).Article 

Google Scholar 
Okey, T. Simulating community effects of sea floor shading by plankton blooms over the West Florida Shelf. Ecol. Model. 172, 339–359 (2004).Article 

Google Scholar 
Chagaris, D. D. Ecosystem-based Evaluation of Fishery Policies and Tradeoffs on the West Florida Shelf Vol. 53, 1699 (University of Florida, 2013).
Google Scholar 
Chagaris, D. D., Mahmoudi, B., Walters, C. J. & Allen, M. S. Simulating the trophic impacts of fishery policy options on the west florida shelf using ecopath with ecosim. Mar. Coast. Fish. 7, 44–58 (2015).Article 

Google Scholar 
Chagaris, D. et al. An ecosystem-based approach to evaluating impacts and management of invasive lionfish. Fisheries 42, 421–431 (2017).Article 

Google Scholar 
Chagaris, D. West Florida Shelf Ecosystem Model. University of Florida. https://ufdc.ufl.edu/IR00011604/00001%0A West Florida Shelf Ecosystem Model (2021).Vilas, D. Spatiotemporal Ecosystem Dynamics on the West Florida Shelf: Prediction, Validation, and Application to Red Tides and Stock Assessment (University of Florida, 2022).
Google Scholar 
Chassignet, E. P. et al. The HYCOM (HYbrid Coordinate Ocean Model) data assimilative system. J. Mar. Syst. 65, 60–83 (2007).Article 

Google Scholar 
NOAA National Geophysical Data Center. U.S. Coastal Relief Model Vol. 3—Florida and East Gulf of Mexico. https://doi.org/10.7289/V5W66HP (2001).NASA. Goddard Space Flight Center, Ocean Ecology Laboratory, Ocean Biology Processing Group. Earth Data (2018).Casey, L. Nutrient and pesticide data collected from the USGS National Water Quality Network and previous networks, 1950–2020: U.S. Geological Survey, https://doi.org/10.5066/P9P2PF1N (2021).Chagaris, D. & Vilas, D. NOAA RESTORE Science Program: Ecosystem modeling to improve fisheries management in the Gulf of Mexico: model inputs and outputs for the West Florida Shelf, 1985–01–01 to 2018–12–31 (NCEI Accession 0242339), https://doi.org/10.25921/t26e-wj91. (2022).Püts, M. et al. Insights on integrating habitat preferences in process-oriented ecological models—A case study of the southern North Sea. Ecol. Model. 431, 109189–109189 (2020).Article 

Google Scholar 
Vilas, D., Fletcher, R. J. Jr., Siders, Z. A. & Chagaris, D. Understanding the temporal dynamics of estimated environmental niche hypervolumes for marine fishes. Ecol. Evol. 12, e9604 (2022).Article 

Google Scholar 
Grubbs, R. D., Musick, J. A., Conrath, C. L. & Romine, J. G. Long-term movements, migration, and temporal delineation of a summer nursery for Juvenile Sandbar Sharks in the Chesapeake Bay region. In Shark Nursery Grounds of the Gulf of Mexico and the East Coast Waters of the United States. American Fisheries Society Symposium 50 Vol. 50 (eds Grubbs, R. D. et al.) 87–107 (American Fisheries Society, 2007).
Google Scholar 
Addis, D. T., Patterson, W. F., Dance, M. A. & Ingram, G. W. Implications of reef fish movement from unreported artificial reef sites in the northern Gulf of Mexico. Fish. Res. 147, 349–358 (2013).Article 

Google Scholar 
Akins, J. L., Morris, J. A. & Green, S. J. In situ tagging technique for fishes provides insight into growth and movement of invasive lionfish. Ecol. Evol. 4, 3768–3777 (2014).Article 

Google Scholar 
Chen, Z., Xu, S., Qiu, Y., Lin, Z. & Jia, X. Modeling the effects of fishery management and marine protected areas on the Beibu Gulf using spatial ecosystem simulation. Fish. Res. 100, 222–229 (2009).Article 

Google Scholar 
Steenbeek, J. et al. Ecopath with ecosim as a model-building toolbox: Source code capabilities, extensions, and variations. Ecol. Model. 319, 178–189 (2016).Article 

Google Scholar 
Moriasi, D. N. et al. Model evaluation guidelines for systematic quantification of accuracy in watershed simulations. Trans. ASABE 50, 885–900 (2007).Article 

Google Scholar 
Hu, C. et al. Red tide detection and tracing using MODIS fluorescence data: A regional example in SW Florida coastal waters. Remote Sens. Environ. 97, 311–321 (2005).Article 

Google Scholar 
Chagaris, D., Vilas, D., Siders, Z. A. & Sinnickson, D. Monthly maps of red tide on the West Florida Shelf 2002–2021: A simple approach combining remote sensing and in situ measurements (in prep).Florida Fish and Wildlife Conservation Commission-Fish and Wildlife Research Institute. Statewide harmful algal bloom karenia brevis current status map (2022).Wickham, H. et al. ggplot2: Create elegant data visualisations using the grammar of graphics (2016).Landsberg, J. H., Flewelling, L. J. & Naar, J. Karenia brevis red tides, brevetoxins in the food web, and impacts on natural resources: Decadal advancements. Harmful Algae 8, 598–607 (2009).Article 
CAS 

Google Scholar 
Gianelli, I., Ortega, L. & Defeo, O. Modeling short-term fishing dynamics in a small-scale intertidal shellfishery. Fish. Res. 209, 242–250 (2019).Article 

Google Scholar 
Moore, S. K. et al. An index of fisheries closures due to harmful algal blooms and a framework for identifying vulnerable fishing communities on the U.S. West Coast. Mar. Policy 110, 103543–103543 (2019).Article 

Google Scholar 
GSMFC. SEAMAP: Environmental and Biological Atlas of the Gulf of Mexico. www.seamap.org (2020).Bechard, A. Harmful algal blooms and tourism: The economic impact to counties in Southwest Florida. Rev. Reg. Stud. 50, 170–188 (2020).
Google Scholar 
Foley, A. M. et al. Effects of Karenia brevis harmful algal blooms on nearshore fish communities in southwest Florida. Mar. Ecol. Prog. Ser. 378, 171–186 (2009).Article 

Google Scholar 
Karnauskas, M. et al. Timeline of severe red tide events on the West Florida Shelf: insights from oral histories. http://sedarweb.org/docs/wpapers/S61_WP_20_Karnauskasetal_red_tide.pdf (2019).Sagarese, S. R., Gruss, A., Karnauskas, M. & Walter, J. F. Ontogenetic spatial distributions of red grouper (Epinephelus morio) within the northeastern Gulf of Mexico and spatio‐ temporal overlap with red tide events, 35–35. http://sedarweb.org/docs/wpapers/S42_DW_04_Red_tide_distribution.pdf (2014).Sagarese, S. R., Vaughan, N. R., Walter, J. F. & Karnauskas, M. Enhancing single-species stock assessments with diverse ecosystem perspectives: A case study for gulf of mexico red grouper (epinephelus morio) and red tides. Can. J. Fish. Aquat. Sci. 78, 1168–1180 (2021).Article 

Google Scholar 
Sagarese, S. R. & Harford, W. J. Evaluating the risks of red tide mortality misspecification when modeling stock dynamics. Fish. Res. 250, 106271–106271 (2022).Article 

Google Scholar 
Whitehouse, G. A. & Aydin, K. Y. Assessing the sensitivity of three Alaska marine food webs to perturbations: An example of Ecosim simulations using Rpath. Ecol. Model. 429, 109074–109074 (2020).Article 

Google Scholar 
Walter, J. F. et al. Satellite derived indices of red tide severity for input for Gulf of Mexico Gag grouper stock assessment. SEDAR33-DW08 SEDAR. North Charlest. S. C. 43, 40–40 (2013).
Google Scholar 
Jackson, M. C., Pawar, S. & Woodward, G. The temporal dynamics of multiple stressor effects: From individuals to ecosystems. Trends Ecol. Evol. 36, 402–410 (2021).Article 

Google Scholar 
Walters, S., Lowerre-Barbieri, S., Bickford, J., Tustison, J. & Landsberg, J. H. Effects of Karenia brevis red tide on the spatial distribution of spawning aggregations of sand seatrout Cynoscion arenarius in Tampa Bay Florida. Mar. Ecol. Prog. Ser. 479, 191–202 (2013).Article 

Google Scholar 
Reynolds, D. A., Yoo, M. J., Dixson, D. L. & Ross, C. Exposure to the Florida red tide dinoflagellate, Karenia brevis, and its associated brevetoxins induces ecophysiological and proteomic alterations in Porites astreoides. PLoS One 15, e0228414–e0228414 (2020).Article 
CAS 

Google Scholar 
Bornman, E., Cowley, P. D., Adams, J. B. & Strydom, N. A. Daytime intra-estuary movements and harmful algal bloom avoidance by Mugil cephalus (family Mugilidae). Estuar. Coast. Shelf Sci. 260, 107492–107492 (2021).Article 
CAS 

Google Scholar 
Moreira-Santos, M., Ribeiro, R. & Araújo, C. V. M. What if aquatic animals move away from pesticide-contaminated habitats before suffering adverse physiological effects? A critical review. Crit. Rev. Environ. Sci. Technol. 49, 989–1025 (2019).Article 
CAS 

Google Scholar 
Schreck, C. B. & Tort, L. The concept of stress in fish. In Fish Physiology Vol. 35 (eds Schreck, C. B. & Tort, L.) 1–34 (Elsevier, 2016).
Google Scholar 
Madin, E. M. P., Dill, L. M., Ridlon, A. D., Heithaus, M. R. & Warner, R. R. Human activities change marine ecosystems by altering predation risk. Glob. Change Biol. 22, 44–60 (2016).Article 

Google Scholar 
Walsh, J. R., Carpenter, S. R. & Van Der Zanden, M. J. Invasive species triggers a massive loss of ecosystem services through a trophic cascade. Proc. Natl. Acad. Sci. U.S.A. 113, 4081–4085 (2016).Article 
CAS 

Google Scholar 
Short, J. W. et al. Evidence for ecosystem-level trophic cascade effects involving gulf menhaden (Brevoortia patronus) triggered by the Deepwater horizon blowout. J. Mar. Sci. Eng. 9, 1–20 (2021).Article 

Google Scholar 
Zohdi, E. & Abbaspour, M. Harmful algal blooms (red tide): A review of causes, impacts and approaches to monitoring and prediction. Int. J. Environ. Sci. Technol. 16, 1789–1806 (2019).Article 

Google Scholar 
Weisberg, R. H., Barth, A., Alvera-Azcarate, A. & Zheng, L. A coordinated coastal ocean observing and modeling system for the West Florida Continental Shelf. Harmful Algae 8, 585–597 (2009).Article 

Google Scholar 
Turley, B. D., Karnauskas, M., Campbell, M. D., Hanisko, D. S. & Kelble, C. R. Relationships between blooms of Karenia brevis and hypoxia across the West Florida Shelf. Harmful Algae 114, 102223 (2022).Article 

Google Scholar 
Fulton, E. A., Smith, A. D., Smith, D. C. & Johnson, P. An integrated approach is needed for ecosystem based fisheries management: Insights from ecosystem-level management strategy evaluation. PLoS One 9, e84242 (2014).Article 

Google Scholar 
Flynn, K. J. & McGillicuddy, D. J. Modeling marine harmful algal blooms: Current status and future prospects. Harmful Algal Blooms https://doi.org/10.1002/9781118994672.ch3 (2018).Article 

Google Scholar 
Thorson, J. T. Guidance for decisions using the Vector Autoregressive Spatio-Temporal (VAST) package in stock, ecosystem, habitat and climate assessments. Fish. Res. 210, 143–161 (2019).Article 

Google Scholar 
Fossum, T. O., Travelletti, C., Eidsvik, J., Ginsbourger, D. & Rajan, K. Learning excursion sets of vector-valued gaussian random fields for autonomous ocean sampling. Ann. Appl. Stat. 15, 597–618 (2021).Article 
MathSciNet 
MATH 

Google Scholar 
Fu, F. X., Place, A. R., Garcia, N. S. & Hutchins, D. A. CO2 and phosphate availability control the toxicity of the harmful bloom dinoflagellate Karlodinium veneficum. Aquat. Microb. Ecol. 59, 55–65 (2010).Article 

Google Scholar 
Hardison, D. R., Sunda, W. G., Shea, D. & Litaker, R. W. Increased toxicity of Karenia brevis during phosphate limited growth: Ecological and evolutionary implications. PLoS One 8, e58545–e58545 (2013).Article 
CAS 

Google Scholar 
Errera, R. M., Yvon-Lewis, S., Kessler, J. D. & Campbell, L. Reponses of the dinoflagellate Karenia brevis to climate change: PCO2 and sea surface temperatures. Harmful Algae 37, 110–116 (2014).Article 
CAS 

Google Scholar 
Wells, M. L. et al. Future HAB science: Directions and challenges in a changing climate. Harmful Algae 91, 101632–101632 (2020).Article 

Google Scholar 
Wolny, J. L. et al. Current and future remote sensing of harmful algal blooms in the chesapeake bay to support the shellfish industry. Front. Mar. Sci. 7, 337–337 (2020).Article 

Google Scholar 
Reum, J. C. P. et al. It’s not the destination, It’s the journey: Multispecies model ensembles for ecosystem approaches to fisheries management. Front. Mar. Sci. 8, 75–75 (2021).Article 

Google Scholar 
Howell, D. et al. Combining ecosystem and single-species modeling to provide ecosystem-based fisheries management advice within current management systems. Front. Mar. Sci. 7, 607831 (2021).Article 

Google Scholar 
McPherson, W. C. and J. C. and J. A. and Y. X. and J. shiny: Web application framework for R. R package version 1.4.0, 115–115 (2019). More

Publisher’s note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.This is a summary of: Uribe, M. R. et al. Net loss of biomass predicted for tropical biomes in a changing climate. Nat. Clim. Change https://doi.org/10.1038/s41558-023-01600-z (2023). More

Jerez-Cepa, I., Gorissen, M., Mancera, J. M. & Ruiz-Jarabo, I. What can we learn from glucocorticoid administration in fish? Effects of cortisol and dexamethasone on intermediary metabolism of gilthead seabream (Sparus aurata L.). Comp. Biochem. Physiol. A Mol. Integr. Physiol. 231, 1–10 (2019).Article 
CAS 

Google Scholar 
Brown, C. L., Urbinati, E. C., Zhang, W., Brown, S. B. & McComb-Kobza, M. Maternal thyroid and glucocorticoid hormone interactions in larval fish development, and their applications in aquaculture. Rev. fish. Sci. Aquac. 22, 207–220 (2014).Article 

Google Scholar 
Tort, L. Stress and immune modulation in fish. Dev. Comp. Immunol. 35, 1366–1375 (2011).Article 
CAS 

Google Scholar 
Schreck, C. B. & Tort, L. In Fish Physiology (eds Schreck, C. B. et al.) vol. 35, 1–34 (Elsevier, 2016).Sternberg, E. M., Chrousos, G. P., Wilder, R. L. & Gold, P. W. The stress response and the regulation of inflammatory disease. Ann. Intern. Med. 117, 854–866 (1992).Article 
CAS 

Google Scholar 
Staufenbiel, S. M., Penninx, B. W. J. H., Spijker, A. T., Elzinga, B. M. & van Rossum, E. F. C. Hair cortisol, stress exposure, and mental health in humans: A systematic review. Psychoneuroendocrinology 38, 1220–1235 (2013).Article 
CAS 

Google Scholar 
Pickering, A. D. & Pottinger, T. G. Cortisol can increase the susceptibility of brown trout, Salmo trutta L., to disease without reducing the white blood cell count. J. Fish Biol. 27, 611–619 (1985).Article 
CAS 

Google Scholar 
McCormick, S. D. et al. Repeated acute stress reduces growth rate of Atlantic salmon parr and alters plasma levels of growth hormone, insulin-like growth factor I and cortisol. Aquaculture 168, 221–235 (1998).Article 
CAS 

Google Scholar 
McConnachie, S. H. et al. Consequences of acute stress and cortisol manipulation on the physiology, behavior, and reproductive outcome of female Pacific salmon on spawning grounds. Horm. Behav. 62, 67–76 (2012).Article 
CAS 

Google Scholar 
Moffat, S. D., An, Y., Resnick, S. M., Diamond, M. P. & Ferrucci, L. Longitudinal change in cortisol levels across the adult life span. J. Gerontol. A Biol. Sci. Med. Sci. 75, 394–400 (2020).Article 
CAS 

Google Scholar 
Oh, H.-J. et al. Age-related decrease in stress responsiveness and proactive coping in male mice. Front. Aging Neurosci. 10, 128 (2018).Article 
PubMed Central 

Google Scholar 
Woods, H. A. 2nd. & Hellgren, E. C. Seasonal changes in the physiology of male Virginia opossums (Didelphis virginiana): Signs of the Dasyurid semelparity syndrome?. Physiol. Biochem. Zool. 76, 406–417 (2003).Article 

Google Scholar 
Barry, T. P., Unwin, M. J., Malison, J. A. & Quinn, T. P. Free and total cortisol levels in semelparous and iteroparous Chinook salmon. J. Fish Biol. 59, 1673–1676 (2001).Article 
CAS 

Google Scholar 
Petrosus, E., Silva, E. B., Lay, D. Jr. & Eicher, S. D. Effects of orally administered cortisol and norepinephrine on weanling piglet gut microbial populations and Salmonella passage. J. Anim. Sci. 96, 4543–4551 (2018).PubMed Central 

Google Scholar 
Shi, D. et al. Impact of gut microbiota structure in heat-stressed broilers. Poult. Sci. 98, 2405–2413 (2019).Article 

Google Scholar 
Uren Webster, T. M., Rodriguez-Barreto, D., Consuegra, S. & Garcia de Leaniz, C. Cortisol-related signatures of stress in the fish microbiome. Front. Microbiol. 11, 1621 (2020).Article 
PubMed Central 

Google Scholar 
Ridlon, J. M. et al. Clostridium scindens: A human gut microbe with a high potential to convert glucocorticoids into androgens. J. Lipid Res. 54, 2437–2449 (2013).Article 
CAS 
PubMed Central 

Google Scholar 
UrenWebster, T. M., Consuegra, S. & Garcia de Leaniz, C. Early life stress causes persistent impacts on the microbiome of Atlantic salmon. Comp. Biochem. Physiol. Part D Genomics Proteomics 40, 100888 (2021).Article 
CAS 

Google Scholar 
Bozzi, D. et al. Salmon gut microbiota correlates with disease infection status: Potential for monitoring health in farmed animals. Anim. Microbiome 3, 30 (2021).Article 
CAS 
PubMed Central 

Google Scholar 
Xiong, J.-B., Nie, L. & Chen, J. Current understanding on the roles of gut microbiota in fish disease and immunity. Zool. Res. 40, 70–76 (2019).
Google Scholar 
Williams, C. L., Garcia-Reyero, N., Martyniuk, C. J., Tubbs, C. W. & Bisesi, J. H. Jr. Regulation of endocrine systems by the microbiome: Perspectives from comparative animal models. Gen. Comp. Endocrinol. 292, 113437 (2020).Article 
CAS 

Google Scholar 
Schmidt, K. et al. Prebiotic intake reduces the waking cortisol response and alters emotional bias in healthy volunteers. Psychopharmacology 232, 1793–1801 (2015).Article 
CAS 

Google Scholar 
Crumeyrolle-Arias, M. et al. Absence of the gut microbiota enhances anxiety-like behavior and neuroendocrine response to acute stress in rats. Psychoneuroendocrinology 42, 207–217 (2014).Article 
CAS 

Google Scholar 
Bell, E. A., Ball, A. G., Deprey, K. L. & Uno, J. K. The impact of antibiotics on the intestinal microbiome and the gut-brain axis in zebrafish. FASEB J. 32, 765–771 (2018).Article 

Google Scholar 
Björnsson, B. T., Stefansson, S. O. & McCormick, S. D. Environmental endocrinology of salmon smoltification. Gen. Comp. Endocrinol. 170, 290–298 (2011).Article 

Google Scholar 
Carruth, L. L., Jones, R. E. & Norris, D. O. Cortisol and Pacific Salmon: A new look at the role of stress hormones in olfaction and home-stream migration. Integr. Comp. Biol. 42, 574–581 (2002).Article 
CAS 

Google Scholar 
Donaldson, E. M. & Fagerlund, U. H. M. Effect of sexual maturation and gonadectomy at sexual maturity on cortisol secretion rate in sockeye salmon (Oncorhynchus nerka). J. Fish. Res. Board Can. 27, 2287–2296 (1970).Article 

Google Scholar 
Dickhoff, W. W. Development, Maturation, and Senescence of Neuroendocrine Systems 253–266 (Elsevier, 1989).Book 

Google Scholar 
Maule, A. G., Schreck, C. B. & Kaattari, S. L. Changes in the immune system of coho salmon (Oncorhynchus kisutch) during the parr-to-smolt transformation and after implantation of cortisol. Can. J. Fish. Aquat. Sci. 44, 161–166 (1987).Article 
CAS 

Google Scholar 
Llewellyn, M. S. et al. Parasitism perturbs the mucosal microbiome of Atlantic Salmon. Sci. Rep. 7, 1–10 (2017).Article 

Google Scholar 
Vasemägi, A., Visse, M. & Kisand, V. Effect of environmental factors and an emerging parasitic disease on gut microbiome of wild Salmonid fish. mSphere 2, e00418-17 (2017).Article 
PubMed Central 

Google Scholar 
Kelly, C. & Salinas, I. Under pressure: Interactions between commensal microbiota and the teleost immune system. Front. Immunol. 8, 559 (2017).Article 
PubMed Central 

Google Scholar 
Fast, M. D., Hosoya, S., Johnson, S. C. & Afonso, L. O. B. Cortisol response and immune-related effects of Atlantic salmon (Salmo salar Linnaeus) subjected to short- and long-term stress. Fish Shellfish Immunol. 24, 194–204 (2008).Article 
CAS 

Google Scholar 
Carrizo, V. et al. Effect of cortisol on the immune-like response of rainbow trout (Oncorhynchus mykiss) myotubes challenged with Piscirickettsia salmonis. Vet. Immunol. Immunopathol. 237, 110240 (2021).Article 
CAS 

Google Scholar 
Nervino, S. Intestinal lesions and parasites associated with prespawn mortality in Chinook salmon (Oncorhynchus tshawytscha). (2022).Couch, C. E. et al. Enterocytozoon schreckii n. sp. infects the enterocytes of adult chinook salmon (Oncorhynchus tshawytscha) and may be a sentinel of immunosenescence. mSphere 7, e0090821 (2022).Article 

Google Scholar 
Redding, J. M., Schreck, C. B., Birks, E. K. & Ewing, R. D. Cortisol and its effects on plasma thyroid hormone and electrolyte concentrations in fresh water and during seawater acclimation in yearling coho salmon, Oncorhynchus kisutch. Gen. Comp. Endocrinol. 56, 146–155 (1984).Article 
CAS 

Google Scholar 
Marotz, C. et al. DNA extraction for streamlined metagenomics of diverse environmental samples. Biotechniques 62, 290–293 (2017).Article 
CAS 

Google Scholar 
Caporaso, J. G. et al. Global patterns of 16S rRNA diversity at a depth of millions of sequences per sample. Proc. Natl. Acad. Sci. USA. 108(Suppl 1), 4516–4522 (2011).Article 
CAS 

Google Scholar 
Minich, J. J. et al. High-throughput miniaturized 16S rRNA amplicon library preparation reduces costs while preserving microbiome integrity. mSystems 3, e00166-18 (2018).Article 
PubMed Central 

Google Scholar 
Caporaso, J. G. et al. Ultra-high-throughput microbial community analysis on the Illumina HiSeq and MiSeq platforms. ISME J. 6, 1621–1624 (2012).Article 
CAS 
PubMed Central 

Google Scholar 
Escalas, A. et al. Ecological specialization within a carnivorous fish family is supported by a herbivorous microbiome shaped by a combination of gut traits and specific diet. Front. Mar. Sci. 8, 622883 (2021).Article 

Google Scholar 
Parada, A. E., Needham, D. M. & Fuhrman, J. A. Every base matters: Assessing small subunit rRNA primers for marine microbiomes with mock communities, time series and global field samples. Environ. Microbiol. 18, 1403–1414 (2016).Article 
CAS 

Google Scholar 
Apprill, A., McNally, S., Parsons, R. & Weber, L. Minor revision to V4 region SSU rRNA 806R gene primer greatly increases detection of SAR11 bacterioplankton. Aquat. Microb. Ecol. 75, 129–137 (2015).Article 

Google Scholar 
R Core Team. R: A Language and Environment for Statistical Computing. (2020). https://www.R-project.org/.Callahan, B. J. et al. DADA2: High-resolution sample inference from Illumina amplicon data. Nat. Methods 13, 581–583 (2016).Article 
CAS 
PubMed Central 

Google Scholar 
Quast, C. et al. The SILVA ribosomal RNA gene database project: Improved data processing and web-based tools. Nucleic Acids Res. 41, D590–D596 (2013).Article 
CAS 

Google Scholar 
Wright, E. Using DECIPHER v2.0 to analyze big biological sequence data in R. R. J. 8, 352 (2016).Article 

Google Scholar 
Schliep, K., Potts, A. J., Morrison, D. A. & Grimm, G. W. Intertwining phylogenetic trees and networks. Methods Ecol. Evol. 8, 1212–1220 (2017).Article 

Google Scholar 
Shannon, C. E. A mathematical theory of communication. Bell Syst. Tech. J. 27, 623–656 (1948).Article 
MathSciNet 
MATH 

Google Scholar 
Shepard, R. N. The analysis of proximities: Multidimensional scaling with an unknown distance function. II. Psychometrika 27, 219–246 (1962).Article 
MathSciNet 
MATH 

Google Scholar 
Oksanen, J. et al. The vegan package. Community Ecol. Packag. 10, 631–637 (2007).
Google Scholar 
Martinez Arbizu, P. pairwiseAdonis: Pairwise Multilevel Comparison using Adonis. Preprint at (2017)Zhang, Y. Likelihood-based and Bayesian methods for Tweedie compound Poisson linear mixed models. Stat. Comput. 23, 743–757 (2013).Article 
MathSciNet 
CAS 
MATH 

Google Scholar 
Hassenrück, C., Reinwald, H., Kunzmann, A., Tiedemann, I. & Gärdes, A. Effects of thermal stress on the gut microbiome of juvenile milkfish (Chanos chanos). Microorganisms 9, 5 (2020).Article 
PubMed Central 

Google Scholar 
Liu, Y. et al. Response mechanism of gut microbiome and metabolism of European seabass (Dicentrarchus labrax) to temperature stress. Sci. Total Environ. 813, 151786 (2022).Article 
CAS 

Google Scholar 
Du, F. et al. Response of the gut microbiome of Megalobrama amblycephala to crowding stress. Aquaculture 500, 586–596 (2019).Article 
CAS 

Google Scholar 
Stothart, M. R., Palme, R. & Newman, A. E. M. It’s what’s on the inside that counts: Stress physiology and the bacterial microbiome of a wild urban mammal. Proc. Biol. Sci. 286, 20192111 (2019).PubMed Central 

Google Scholar 
Michels, N. et al. Gut microbiome patterns depending on children’s psychosocial stress: Reports versus biomarkers. Brain Behav. Immun. 80, 751–762 (2019).Article 

Google Scholar 
Zhao, H., Jiang, X. & Chu, W. Shifts in the gut microbiota of mice in response to dexamethasone administration. Int. Microbiol. 23, 565–573 (2020).Article 
CAS 

Google Scholar 
Zanuzzo, F. S., Sabioni, R. E., Marzocchi-Machado, C. M. & Urbinati, E. C. Modulation of stress and innate immune response by corticosteroids in pacu (Piaractus mesopotamicus). Comp. Biochem. Physiol. A Mol. Integr. Physiol. 231, 39–48 (2019).Article 
CAS 

Google Scholar 
Timmermans, S., Souffriau, J. & Libert, C. A general introduction to glucocorticoid biology. Front. Immunol. 10, 1545 (2019).Article 
CAS 
PubMed Central 

Google Scholar 
Kugathas, S. & Sumpter, J. P. Synthetic glucocorticoids in the environment: First results on their potential impacts on fish. Environ. Sci. Technol. 45, 2377–2383 (2011).Article 
CAS 

Google Scholar 
Schaal, P. et al. Links between host genetics, metabolism, gut microbiome and amoebic gill disease (AGD) in Atlantic salmon. Anim. Microbiome 4, 53 (2022).Article 
CAS 
PubMed Central 

Google Scholar 
Birlanga, V. B. et al. Dynamic gill and mucus microbiomes during a gill disease episode in farmed Atlantic salmon. Sci. Rep. 12, 16719 (2022).Article 
CAS 
PubMed Central 

Google Scholar 
Cipriano, R. C., Ford, L. A., Smith, D. R., Schachte, J. H. & Petrie, C. J. Differences in detection of Aeromonas salmonicida in covertly infected Salmonid fishes by the stress-inducible furunculosis test and culture-based assays. J. Aquat. Anim. Health 9, 108–113 (1997).Article 

Google Scholar 
Lovy, J., Speare, D. J., Stryhn, H. & Wright, G. M. Effects of dexamethasone on host innate and adaptive immune responses and parasite development in rainbow trout Oncorhynchus mykiss infected with Loma salmonae. Fish Shellfish Immunol. 24, 649–658 (2008).Article 
CAS 

Google Scholar 
Bakhtiyar, Y., Yousuf, T. & Arafat, M. Y. Bacterial Fish Diseases 269–278 (Elsevier, 2022).Book 

Google Scholar 
Benda, S. E., Naughton, G. P., Caudill, C. C., Kent, M. L. & Schreck, C. B. Cool, pathogen-free refuge lowers pathogen-associated prespawn mortality of Willamette River Chinook salmon. Trans. Am. Fish. Soc. 144, 1159–1172 (2015).Article 

Google Scholar 
Barton, B. A. & Iwama, G. K. Physiological changes in fish from stress in aquaculture with emphasis on the response and effects of corticosteroids. Annu. Rev. Fish Dis. 1, 3–26 (1991).Article 

Google Scholar 
Dolan, B. P. et al. Innate and adaptive immune responses in migrating spring-run adult chinook salmon, Oncorhynchus tshawytscha. Fish Shellfish Immunol. 48, 136–144 (2016).Article 
CAS 

Google Scholar 
Wedemeyer, G. A. Physiological response of juvenile coho salmon (Oncorhynchus kisutch) and rainbow trout (Salmo gairdneri) to handling and crowding stress in intensive fish culture. J. Fish. Res. Board Can. 33, 2699–2702 (1976).Article 

Google Scholar 
Suomalainen, L.-R., Tiirola, M. A. & Valtonen, E. T. Influence of rearing conditions on Flavobacterium columnare infection of rainbow trout, Oncorhynchus mykiss (Walbaum). J. Fish Dis. 28, 271–277 (2005).Article 

Google Scholar 
Schmidt-Posthaus, H., Bernet, D., Wahli, T. & Burkhardt-Holm, P. Morphological organ alterations and infectious diseases in brown trout Salmo trutta and rainbow trout Oncorhynchus mykiss exposed to polluted river water. Dis. Aquat. Organ. 44, 161–170 (2001).Article 
CAS 

Google Scholar 
Shi, N., Li, N., Duan, X. & Niu, H. Interaction between the gut microbiome and mucosal immune system. Mil. Med. Res. 4, 1–7 (2017).CAS 

Google Scholar 
Mitchell, S. O. et al. “Candidatus Branchiomonas cysticola” is a common agent of epitheliocysts in seawater-farmed Atlantic salmon Salmo salar in Norway and Ireland. Dis. Aquat. Organ. 103, 35–43 (2013).Article 
CAS 

Google Scholar 
Kormas, K. A., Meziti, A., Mente, E. & Frentzos, A. Dietary differences are reflected on the gut prokaryotic community structure of wild and commercially reared sea bream (Sparus aurata). Microbiologyopen 3, 718–728 (2014).Article 
CAS 
PubMed Central 

Google Scholar 
Engel, M. et al. Influence of lung CT changes in chronic obstructive pulmonary disease (COPD) on the human lung microbiome. PLoS ONE 12, e0180859 (2017).Article 
PubMed Central 

Google Scholar 
Lucasson, A. et al. A core of functionally complementary bacteria colonizes oysters in Pacific Oyster Mortality Syndrome. bioRxiv https://doi.org/10.1101/2020.11.16.384644 (2020).Article 

Google Scholar  More

Hillebrand, H. On the generality of the latitudinal diversity gradient. Am. Nat. 163, 192–211 (2004).
Google Scholar 
Pianka, E. R. Latitudinal gradients in species diversity: a review of concepts. Am. Nat. 100, 33–46 (1966).
Google Scholar 
Mannion, P. D., Upchurch, P., Benson, R. B. J. & Goswami, A. The latitudinal biodiversity gradient through deep time. Trends Ecol. Evol. 29, 42–50 (2014).
Google Scholar 
Jablonski, D., Roy, K. & Valentine, J. W. Out of the tropics: evolutionary dynamics of the latitudinal diversity gradient. Science 314, 102–106 (2006).ADS 
CAS 

Google Scholar 
Alexander Pyron, R. & Wiens, J. J. Large-scale phylogenetic analyses reveal the causes of high tropical amphibian diversity. Proc. R. Soc. B Biol. Sci. 280, 1–10 (2013).
Google Scholar 
Allen, A. P. & Gillooly, J. F. Assessing latitudinal gradients in speciation rates and biodiversity at the global scale. Ecol. Lett. 9, 947–954 (2006).
Google Scholar 
Wright, S., Keeling, J. & Gillman, L. The road from Santa Rosalia: a faster tempo of evolution in tropical climates. Proc. Natl Acad. Sci. USA 103, 7718–7722 (2006).ADS 
CAS 

Google Scholar 
Rolland, J., Condamine, F. L., Jiguet, F. & Morlon, H. Faster speciation and reduced extinction in the tropics contribute to the mammalian latitudinal diversity gradient. PLoS Biol. 12, e1001775 (2014).
Google Scholar 
Rabosky, D. L. et al. An inverse latitudinal gradient in speciation rate for marine fishes. Nature 559, 392–395 (2018).ADS 
CAS 

Google Scholar 
Igea, J. & Tanentzap, A. J. Angiosperm speciation speeds up near the poles. Ecol. Lett. 23, 1–40 (2020).
Google Scholar 
Weir, J. T. & Schluter, D. The latitudinal gradient in recent speciation and extinction rates of birds and mammals. Science 315, 1574–1576 (2007).ADS 
CAS 

Google Scholar 
Rabosky, D. L. & Huang, H. A robust semi-parametric test for detecting trait-dependent diversification. Syst. Biol. 65, 181–193 (2016).
Google Scholar 
Hansen, J. et al. Global temperature change. Proc. Natl Acad. Sci. USA 103, 14288–14293 (2006).ADS 
CAS 

Google Scholar 
Huey, R. B. & Kingsolver, J. G. Climate warming, resource availability, and the metabolic meltdown of ectotherms. Am. Nat. 194, E140–E150 (2019).
Google Scholar 
Gerringer, M. E., Linley, T. D., Jamieson, A. J., Goetze, E. & Drazen, J. C. Pseudoliparis swirei sp. Nov.: A newly-discovered hadal snailfish (Scorpaeniformes: Liparidae) from the Mariana Trench. Zootaxa 4358, 161–177 (2017).
Google Scholar 
Childress, J. J. Are there physiological and biochemical adaptations of metabolism in deep-sea animals? Trends Ecol. Evol. 10, 30–36 (1995).CAS 

Google Scholar 
Seibel, B. A. & Drazen, J. C. The rate of metabolism in marine animals: environmental constraints, ecological demands and energetic opportunities. Philos. Trans. R. Soc. B Biol. Sci. 362, 2061–2078 (2007).CAS 

Google Scholar 
Eme, D., Anderson, M. J., Myers, E. M. V., Roberts, C. D. & Liggins, L. Phylogenetic measures reveal eco-evolutionary drivers of biodiversity along a depth gradient. Ecography 43, 689–702 (2020).
Google Scholar 
Costello, M. J. & Chaudhary, C. Marine biodiversity, biogeography, deep-sea gradients, and conservation. Curr. Biol. 27, R511–R527 (2017).CAS 

Google Scholar 
Brown, A. & Thatje, S. Explaining bathymetric diversity patterns in marine benthic invertebrates and demersal fishes: Physiological contributions to adaptation of life at depth. Biol. Rev. 89, 406–426 (2014).
Google Scholar 
Zintzen, V., Anderson, M. J., Roberts, C. D., Harvey, E. S. & Stewart, A. L. Effects of latitude and depth on the beta diversity of New Zealand fish communities. Sci. Rep. 7, 1–10 (2017).CAS 

Google Scholar 
Coleman, R. R., Copus, J. M., Coffey, D. M., Whitton, R. K. & Bowen, B. W. Shifting reef fish assemblages along a depth gradient in Pohnpei, Micronesia. PeerJ 2018, 1–30 (2018).
Google Scholar 
Neat, F. C. & Campbell, N. Proliferation of elongate fishes in the deep sea. J. Fish. Biol. 83, 1576–1591 (2013).CAS 

Google Scholar 
Martinez, C. M. et al. The deep sea is a hot spot of fish body shape evolution. Ecol. Lett. 24, 1788–1799 (2021).
Google Scholar 
Webb, P. Introduction to Oceanography (Online OER textbook, 2017).Hanly, P. J., Mittelbach, G. G. & Schemske, D. W. Speciation and the latitudinal diversity gradient: Insights from the global distribution of endemic fish. Am. Nat. 189, 604–615 (2017).
Google Scholar 
Tedesco, P. A., Paradis, E., Lévêque, C. & Hugueny, B. Explaining global-scale diversification patterns in actinopterygian fishes. J. Biogeogr. 44, 773–783 (2017).
Google Scholar 
Cooney, C. R., Seddon, N. & Tobias, J. A. Widespread correlations between climatic niche evolution and species diversification in birds. J. Anim. Ecol. 85, 869–878 (2016).
Google Scholar 
Title, P. O. & Burns, K. J. Rates of climatic niche evolution are correlated with species richness in a large and ecologically diverse radiation of songbirds. Ecol. Lett. 18, 433–440 (2015).
Google Scholar 
Seeholzer, G. F., Claramunt, S. & Brumfield, R. T. Niche evolution and diversification in a Neotropical radiation of birds (Aves: Furnariidae). Evolution 71, 702–715 (2017).
Google Scholar 
Kozak, K. H. & Wiens, J. J. Accelerated rates of climatic-niche evolution underlie rapid species diversification. Ecol. Lett. 13, 1378–1389 (2010).
Google Scholar 
Schnitzler, J., Graham, C. H., Dormann, C. F., Schiffers, K. & Peter Linder, H. Climatic niche evolution and species diversification in the cape flora, South Africa. J. Biogeogr. 39, 2201–2211 (2012).
Google Scholar 
Ghezelayagh, A. et al. Prolonged morphological expansion of spiny-rayed fishes following the end-Cretaceous. Nat. Ecol. Evol. 1–10. https://doi.org/10.1038/s41559-022-01801-3 (2022).Polato, N. R. et al. Narrow thermal tolerance and low dispersal drive higher speciation in tropical mountains. Proc. Natl Acad. Sci. USA 115, 12471–12476 (2018).ADS 
CAS 

Google Scholar 
Rohde, K. Latitudinal gradients in species diversity: the search for the primary cause. Oikos 65, 514–527 (1992).
Google Scholar 
O’Hara, T. D., Hugall, A. F., Woolley, S. N. C., Bribiesca-Contreras, G. & Bax, N. J. Contrasting processes drive ophiuroid phylodiversity across shallow and deep seafloors. Nature 565, 636–639 (2019).ADS 

Google Scholar 
Losos, J. B. Adaptive radiation, ecological opportunity, and evolutionary determinism. Am. Nat. 175, 623–639 (2010).
Google Scholar 
Hulsey, C. D., Roberts, R. J., Loh, Y. H. E., Rupp, M. F. & Streelman, J. T. Lake Malawi cichlid evolution along a benthic/limnetic axis. Ecol. Evol. 3, 2262–2272 (2013).CAS 

Google Scholar 
Woolley, S. N. C. et al. Deep-sea diversity patterns are shaped by energy availability. Nature 533, 393–396 (2016).ADS 
CAS 

Google Scholar 
Pigot, A. L., Owens, I. P. F. & Orme, C. D. L. The environmental limits to geographic range expansion in birds. Ecol. Lett. 13, 705–715 (2010).
Google Scholar 
Gerringer, M. E., Linley, T. D. & Nielsen, J. G. Revision of the depth record of bony fishes with notes on hadal snailfishes (Liparidae, Scorpaeniformes) and cusk eels (Ophidiidae, Ophidiiformes). Mar. Biol. 168, 1–9 (2021).
Google Scholar 
Kolora, S. R. R. et al. Origins and evolution of extreme life span in Pacific Ocean rockfishes. Science 374, 842–847 (2021).ADS 
CAS 

Google Scholar 
Rutschmann, S. et al. Parallel ecological diversification in Antarctic notothenioid fishes as evidence for adaptive radiation. Mol. Ecol. 20, 4707–4721 (2011).
Google Scholar 
Wilson, L. A. B., Colombo, M., Hanel, R., Salzburger, W. & Sánchez-Villagra, M. R. Ecomorphological disparity in an adaptive radiation: opercular bone shape and stable isotopes in Antarctic icefishes. Ecol. Evol. 3, 3166–3182 (2013).
Google Scholar 
Ingram, T. Speciation along a depth gradient in a marine adaptive radiation. Proc. R. Soc. B. 278, 613–618 (2011).
Google Scholar 
Hyde, J. R., Kimbrell, C. A., Budrick, J. E., Lynn, E. A. & Vetter, R. D. Cryptic speciation in the vermilion rockfish (Sebastes miniatus) and the role of bathymetry in the speciation process. Mol. Ecol. 17, 1122–1136 (2008).CAS 

Google Scholar 
Kai, Y., Orr, J. W., Sakai, K. & Nakabo, T. Genetic and morphological evidence for cryptic diversity in the Careproctus rastrinus species complex (Liparidae) of the North Pacific. Ichthyol. Res. 58, 143–154 (2011).
Google Scholar 
Gerringer, M. E. et al. Habitat influences skeletal morphology and density in the snailfishes (family Liparidae). Front. Zool. 18, 1–22 (2021).
Google Scholar 
Saveliev, P. A. & Metelyov, E. A. Species composition and distribution of eelpouts (Zoarcidae, Perciformes, Actinopterygii) in the northwestern Sea of Okhotsk in summer. Prog. Oceanogr. 196, 102605 (2021).
Google Scholar 
Quattrini, A. M. et al. Niche divergence by deep-sea octocorals in the genus Callogorgia across the continental slope of the Gulf of Mexico. Mol. Ecol. 22, 4123–4140 (2013).
Google Scholar 
Zardus, J. D., Etter, R. J., Chase, M. R., Rex, M. A. & Boyle, E. E. Bathymetric and geographic population structure in the pan-Atlantic deep-sea bivalve Deminucula atacellana (Schenck, 1939). Mol. Ecol. 15, 639–651 (2006).CAS 

Google Scholar 
Schüller, M. Evidence for a role of bathymetry and emergence in speciation in the genus Glycera (Glyceridae, Polychaeta) from the deep Eastern Weddell Sea. Polar Biol. 34, 549–564 (2011).
Google Scholar 
Smith, W. L., Everman, E. & Richardson, C. Phylogeny and taxonomy of flatheads, scorpionfishes, sea robins, and stonefishes (Percomorpha: Scorpaeniformes) and the evolution of the lachrymal saber. Copeia 106, 94–119 (2018).
Google Scholar 
Jamon, M., Renous, S., Gasc, J. P., Bels, V. & Davenport, J. Evidence of force exchanges during the six-legged walking of the bottom-dwelling fish,Chelidonichthys lucerna. J. Exp. Zool. 307A, 542–547 (2007).
Google Scholar 
McCune, A. R. & Carlson, R. L. Twenty ways to lose your bladder: common natural mutants in zebrafish and widespread convergence of swim bladder loss among teleost fishes. Evol. Dev. 6, 246–259 (2004).
Google Scholar 
Rabosky, D. L. Speciation rate and the diversity of fishes in freshwaters and the oceans. J. Biogeogr. 47, 1207–1217 (2020).
Google Scholar 
Daane, J. M. et al. Historical contingency shapes adaptive radiation in Antarctic fishes. Nat. Ecol. Evol. 3, 1102–1109 (2019).
Google Scholar 
Mu, Y. et al. Whole genome sequencing of a snailfish from the Yap Trench (~7,000 m) clarifies the molecular mechanisms underlying adaptation to the deep sea. PLoS Genet. 17, e1009530 (2021).CAS 

Google Scholar 
Yancey, P. H., Gerringer, M. E., Drazen, J. C., Rowden, A. A. & Jamieson, A. Marine fish may be biochemically constrained from inhabiting the deepest ocean depths. Proc. Natl Acad. Sci. USA 111, 4461–4465 (2014).ADS 
CAS 

Google Scholar 
Janzen, D. Why mountain passes are higher in the tropics. Am. Nat. 101, 233–249 (1967).
Google Scholar 
Kozak, K. H. & Wiens, J. J. Climatic zonation drives latitudinal variation in speciation mechanisms. Proc. R. Soc. B: Biol. Sci. 274, 2995–3003 (2007).
Google Scholar 
Sheldon, K. S., Huey, R. B., Kaspari, M. & Sanders, N. J. Fifty years of mountain passes: a perspective on Dan Janzen’s classic article. Am. Nat. 191, 553–565 (2018).
Google Scholar 
Muñoz, M. M. & Bodensteiner, B. L. Janzen’s hypothesis meets the bogert effect: connecting climate variation, thermoregulatory behavior, and rates of physiological evolution. Integr. Organ. Biol. 1, oby002 (2019).
Google Scholar 
Santidrián Tomillo, P., Fonseca, L., Paladino, F. V., Spotila, J. R. & Oro, D. Are thermal barriers ‘higher’ in deep sea turtle nests? PLoS ONE 12, 1–14 (2017).
Google Scholar 
Brown, J. H. Why marine islands are farther apart in the tropics. Am. Nat. 183, 842–846 (2014).
Google Scholar 
Jablonski, D. et al. Out of the tropics, but how? Fossils, bridge species, and thermal ranges in the dynamics of the marine latitudinal diversity gradient. Proc. Natl Acad. Sci. USA 110, 10487–10494 (2013).ADS 
CAS 

Google Scholar 
Hattermann, T. Antarctic thermocline dynamics along a narrow shelf with easterly winds. J. Phys. Oceanogr. 48, 2419–2443 (2018).ADS 

Google Scholar 
Robison, B. H. What drives the diel vertical migrations of Antarctic midwater fish? J. Mar. Biol. Ass. 83, 639–642 (2003).
Google Scholar 
Bourgeaud, L. et al. Climatic niche change of fish is faster at high latitude and in marine environments. Preprint at bioRxiv https://doi.org/10.1101/853374 (2019).Pie, M. R. et al. The evolution of latitudinal range limits in tropical reef fishes: heritability, limits, and inverse Rapoport’s rule. J. Biogeogr. 00, 1–12 (2021).
Google Scholar 
Powell, M. G. & Glazier, D. S. Asymmetric geographic range expansion explains the latitudinal diversity gradients of four major taxa of marine plankton. Paleobiology 43, 196–208 (2017).
Google Scholar 
Lawson, A. M. & Weir, J. T. Latitudinal gradients in climatic-niche evolution accelerate trait evolution at high latitudes. Ecol. Lett. 17, 1427–1436 (2014).
Google Scholar 
Boag, T. H., Gearty, W. & Stockey, R. G. Metabolic tradeoffs control biodiversity gradients through geological time. Curr. Biol. 31, 2906–2913.e3 (2021).CAS 

Google Scholar 
Near, T. J. et al. Ancient climate change, antifreeze, and the evolutionary diversification of Antarctic fishes. Proc. Natl Acad. Sci. USA 109, 3434–3439 (2012).ADS 
CAS 

Google Scholar 
Hotaling, S., Borowiec, M. L., Lins, L. S. F., Desvignes, T. & Kelley, J. L. The biogeographic history of eelpouts and related fishes: Linking phylogeny, environmental change, and patterns of dispersal in a globally distributed fish group. Mol. Phylogenet. Evol. 162, 107211 (2021).
Google Scholar 
Thatje, S., Hillenbrand, C.-D., Mackensen, A. & Larter, R. Life hung by a thread: endurance of Antarctic fauna in glacial periods. Ecology 89, 682–692 (2008).
Google Scholar 
Keller, I. & Seehausen, O. Thermal adaptation and ecological speciation. Mol. Ecol. 21, 782–799 (2012).CAS 

Google Scholar 
Deutsch, C., Penn, J. L. & Seibel, B. Metabolic trait diversity shapes marine biogeography. Nature 585, 557–562 (2020).ADS 
CAS 

Google Scholar 
Labeyrie, L. D., Duplessy, J. C. & Blanc, P. L. Variations in mode of formation and temperature of oceanic deep waters over the past 125,000 years. Nature 327, 477–482 (1987).ADS 
CAS 

Google Scholar 
Boag, T. H., Stockey, R. G., Elder, L. E., Hull, P. M. & Sperling, E. A. Oxygen, temperature and the deep-marine stenothermal cradle of Ediacaran evolution. Proc. R. Soc. B: Biol. Sci. 285, 2011724 (2018).
Google Scholar 
Koslow, J. A. Community structure in North Atlantic deep-sea fishes. Prog. Oceanogr. 31, 321–338 (1993).ADS 

Google Scholar 
Brunn, A. The abyssal fauna: its ecology, distribution, and origin. Nature 177, 1105–1108 (1956). Fr.ADS 

Google Scholar 
Gaither, M. R. et al. Depth as a driver of evolution in the deep sea: Insights from grenadiers (Gadiformes: Macrouridae) of the genus Coryphaenoides. Mol. Phylogenet. Evol. 104, 73–82 (2016).
Google Scholar 
Eastman, J. T. Evolution and diversification of Antarctic notothenioid fishes. Am. Zool. 31, 93–110 (1991).
Google Scholar 
Quattrini, A. M., Gómez, C. E. & Cordes, E. E. Environmental filtering and neutral processes shape octocoral community assembly in the deep sea. Oecologia 183, 221–236 (2017).ADS 

Google Scholar 
Stefanoudis, P. V. et al. Depth-dependent structuring of reef fish assemblages from the shallows to the rariphotic zone. Front. Mar. Sci. 6, 1–16 (2019).
Google Scholar 
Zintzen, V., Anderson, M. J., Roberts, C. D. & Diebel, C. E. Increasing variation in taxonomic distinctness reveals clusters of specialists in the deep sea. Ecography 34, 306–317 (2011).
Google Scholar 
Price, S. A., Claverie, T., Near, T. J. & Wainwright, P. C. Phylogenetic insights into the history and diversification of fishes on reefs. Coral Reefs 34, 997–1009 (2015).ADS 

Google Scholar 
Weber, M. G., Wagner, C. E., Best, R. J., Harmon, L. J. & Matthews, B. Evolution in a Community Context: On Integrating Ecological Interactions and Macroevolution. Trends Ecol. Evol. 32, 291–304 (2017).
Google Scholar 
Linley, T. D. et al. Fishes of the hadal zone including new species, in situ observations and depth records of Liparidae. Deep Sea Res. Part I Oceanogr. Res. Pap. 114, 99–110 (2016).ADS 

Google Scholar 
Jamieson, A. J., Linley, T. D., Eigler, S. & Macdonald, T. A global assessment of fishes at lower abyssal and upper hadal depths (5000 to 8000 m). Deep Sea Res. Part I Oceanogr. Res. Pap. 103642. https://doi.org/10.1016/j.dsr.2021.103642 (2021).Boers, N. Observation-based early-warning signals for a collapse of the Atlantic meridional overturning circulation. Nat. Clim. Chang. 11, 680–688 (2021).ADS 

Google Scholar 
Paulus, E. Shedding light on deep-sea biodiversity—a highly vulnerable habitat in the face of anthropogenic change. Front. Mar. Sci. 8, 667048 (2021).Froese, R. & Pauly, D. FishBase. FishBase www.fishbase.org (2019).Boettiger, C., Lang, D. T. & Wainwright, P. C. Rfishbase: exploring, manipulating and visualizing FishBase data from R. J. Fish. Biol. 81, 2030–2039 (2012).CAS 

Google Scholar 
Revell, L. J. phytools: An R package for phylogenetic comparative biology (and other things). Methods Ecol. Evol. 3, 217–223 (2012).
Google Scholar 
Harmon, L. J., Weir, J. T., Brock, C. D., Glor, R. E. & Challenger, W. GEIGER investigating evolutionary radiations. Bioinformatics 24, 129–131 (2008).CAS 

Google Scholar 
Karstensen, J., Stramma, L. & Visbeck, M. Oxygen minimum zones in the eastern tropical Atlantic and Pacific oceans. Prog. Oceanogr. 77, 331–350 (2008).ADS 

Google Scholar 
Sutton, T. T. et al. A global biogeographic classification of the mesopelagic zone. Deep Sea Res. Part I: Oceanogr. Res. Pap. 126, 85–102 (2017).ADS 

Google Scholar 
Alfaro, M. E. et al. Explosive diversification of marine fishes at the Cretaceous–Palaeogene boundary. Nat. Ecol. Evol. 2, 688–696 (2018).
Google Scholar 
Magnuson-Ford, K. & Otto, S. P. Linking the investigations of character evolution and species diversification. Am. Nat. 180, 225–245 (2012).
Google Scholar 
Goldberg, E. E. & Igić, B. Tempo and mode in plant breeding system evolution. Evolution 66, 3701–3709 (2012).
Google Scholar 
Rabosky, D. L. & Goldberg, E. E. Model inadequacy and mistaken inferences of trait-dependent speciation. Syst. Biol. 64, 340–355 (2015).CAS 

Google Scholar 
Beaulieu, J. M. & O’Meara, B. C. Detecting hidden diversification shifts in models of trait-dependent speciation and extinction. Syst. Biol. 65, 583–601 (2016).
Google Scholar 
Adams, D. C., Collyer, M. L. & Kaliontzopoulou, A. Geomorph: Software for geometric morphometric analyses. R package version 3.1.0. (2019).Collyer, M. L. & Adams, D. C. RRPP: An r package for fitting linear models to high-dimensional data using residual randomization. Methods Ecol. Evol. 9, 1772–1779 (2018).
Google Scholar 
Title, P. O. & Rabosky, D. L. Tip rates, phylogenies and diversification: What are we estimating, and how good are the estimates? Methods Ecol. Evol. 10, 821–834 (2019).
Google Scholar 
Freckleton, R. P., Phillimore, A. B. & Pagel, M. Relating traits to diversification: a simple test. Am. Nat. 172, 102–115 (2008).
Google Scholar 
Jetz, W., Thomas, G. H., Joy, J. B., Hartmann, K. & Mooers, A. O. The global diversity of birds in space and time. Nature 491, 444–448 (2012).ADS 
CAS 

Google Scholar 
Louca, S. & Pennell, M. W. Extant timetrees are consistent with a myriad of diversification histories. Nature 580, 502–505 (2020).ADS 
CAS 

Google Scholar 
May, M. R. & Moore, B. R. A Bayesian approach for inferring the impact of a discrete character on rates of continuous-character evolution in the presence of background-rate variation. Syst. Biol. 69, 530–544 (2020).
Google Scholar 
Höhna. et al. RevBayes: Bayesian phylogenetic inference using graphical models and an interactive model-specification language. Syst. Biol. 65, 726–736 (2016).
Google Scholar 
Burress, E. D. & Muñoz, M. M. Ecological opportunity from innovation, not islands, drove the anole lizard adaptive radiation. Syst. Biol. 0, 1–12 (2021).
Google Scholar 
Rambaut, A., Drummond, A. J., Xie, D., Baele, G. & Suchard, M. A. Posterior summarization in Bayesian phylogenetics using Tracer 1.7. Syst. Biol. 67, 901–904 (2018).CAS 

Google Scholar 
Ives, A. R. & Helmus, M. R. Phylogenetic metrics of community similarity. Am. Nat. 176, E128–E142 (2010).
Google Scholar 
Costello, M. J. & Breyer, S. Ocean depths: the mesopelagic and implications for global warming. Curr. Biol. 27, R36–R38 (2017).CAS 

Google Scholar  More

As part of the streamflow data release, NEON released four relevant data products: Gauge Height26, Elevation of Surface Water29, Stage-discharge Rating Curves30, and Continuous Discharge15. Data users are able to download this full suite of information and protocols to inform decisions on data usage and applicability. We evaluated the quality of the Continuous Discharge product using all four relevant NEON data products, considering the validity of model inputs as well as the goodness-of-fit of final streamflow estimates. We analyzed 1) the fit of the regression between manual stage height readings and continuous pressure transducer data used to estimate continuous stream surface elevation, 2) the fit of rating curves transforming stream surface elevation to streamflow, and 3) the proportion of streamflow estimates over the maximum manually-measured streamflow.Stage classificationThe rating curve models predicting streamflow required continuous stream stage estimates as model inputs. NEON predicted continuous gauge height with a two step approach. First, continuous in-stream transducer readings were converted to water height by applying an offset between the transducer elevation and the staff gauge (Eq. 1). This offset is derived from the NEON geolocation database as the difference between the location of the pressure transducer and the staff gauge27. The offset changes only when the location of either the staff gauge or transducer moves.$${h}_{wc}=frac{{P}_{sw}}{p,ast ,g},ast ,1000+{h}_{stage}$$
(1)
Conversion of pressure data to water height used by NEON27 where hwc is the estimated water column height (m), Psw is calibrated surface water pressure (kPa), p is the density of water (999 kg/m3), g is the acceleration due to gravity (9.81 m/s2), and hstage is the offset between the pressure transducer and the staff gauge (m).Then, NEON uses a linear regression between manually-measured reference stage height and the calculated gauge height from Eq. 1, yielding final predictions of continuous stream gauge height27. In an ideal setting, stage and gauge height should correlate perfectly28. In the field, sensor uncertainty, manual reference measurement error, and shifting conditions in the stream can convolute the relationship. We tested the goodness of fit between continuously estimated stream gauge height values and manual stage measurements using the Nash-Sutcliffe model efficiency coefficient (Eq. 2). Nash-Sutcliffe coefficient is a commonly used metric in hydrology used to evaluate how well a model performed relative to observed values (manually measured stage and calculated gauge height). For the purposes of this discussion, manual reference measurements will be referred to as ‘stage’ and automated, sensed readings as ‘gauge height’.$$NSE=1-frac{Sigma {left({Q}_{o}-{Q}_{m}right)}^{2}}{Sigma {left({Q}_{o}-{bar{Q}}_{o}right)}^{2}}$$
(2)
Equation 2 presents Nash-Sutcliffe model efficiency coefficient, where Qo is an observed value (streamflow or stage height), Qm is a modeled value, and ({bar{Q}}_{o}) is the mean of observed values.Stage, gauge height, and regression data were sourced from the NEON Continuous Discharge product, representing what was directly applied to streamflow estimation. Up to 26 stage measurements were available per year. We examined every regression between stage and gauge height (one per site year in which data was available) and classified each as either ‘good’, ‘fair’, or ‘poor’ quality based on their goodness of fit. Regressions with a NSE (Eq. 2) of 0.90 or greater were considered good, those with a NSE of less than 0.90 but greater than or equal to 0.75 were considered fair, and those with an NSE of less than 0.75 were considered poor (Fig. 2).Drift detectionBecause electronic instruments, such as pressure transducers, can have systematic directional drift, referred to as ‘drift’, during deployment, we developed an approach to detect periods of time when NEON’s Elevation of Surface Water product drifted. We used two methods to assess and flag the potential for instrument drift at monthly time steps. First, we flagged any period the manually measured stage fell outside NEON’s uncertainty bound for gauge height made at the same time. From this, we calculated the proportion of stage measurements outside of the gauge height uncertainty bounds per month. This proved to be a relatively lenient filter that missed periods of manually identified drift. We found adding a second filter that flagged any month where the difference between the manually measured stage and gauge height exceeded 6 cm, was effective in catching the majority of periods where drift was identified. Second, we calculated the average differences between stage and gauge height for each month (Fig. 3). To determine appropriate cut-off values to classify areas of potential drift, we manually audited and flagged periods of observable directional drift. Our goal was to set a maximum cut-off difference which retained as much usable data as possible while still capturing 70% of the manually flagged directional drift periods. Applying this method, we determined a cut-off value of 6 cm average monthly deviation between observed and predicted stage values.Using these two filters in combination, we again classified data into three groups: ‘likely no drift’, ‘potential drift’, and ‘not assessed’. Site-months with no more than 50% of stage measurements outside of the gauge height time series uncertainty and an average difference between stage and gauge height less than 6 cm were considered to have ‘likely no drift’. Site-months with either more than 50% of stage readings outside of the gauge height time series uncertainty or an average difference between stage and gauge height more than 6 cm were deemed to have ‘potential drift’. Site-months with no stage measurements could not be evaluated and were considered ‘not assessed’. Although this approach to identify drift is imperfect, in that slight drift could be missed and times without manual measurements are not possible to assess, we believe this is a helpful method given the data available from NEON and the fact drift has been observed when visually inspecting data (Fig. 3).Rating curve classificationTo evaluate how well rating curves predicted streamflow, we assessed each rating curve used to convert stage to discharge. NEON prepares a new rating curve for each site’s water year (beginning on October 1st)27. In cases where NEON reported multiple rating curves for a site’s water year each curve was assessed separately across the time series which it was used. We classified rating curves into three tiers based on two metrics: the Nash-Sutcliffe coefficient (Eq. 2) between observed and predicted streamflow, and the percentage of continuous discharge values above the maximum manually measured gauging used to construct the rating curve.First, we calculated the Nash-Sutcliffe coefficient for each rating curve to estimate how well rating curves captured the variation in the stage-streamflow relationship. We used the reported values for modeled and manually measured streamflow from the ‘Y1simulated’ and ‘Y1observed’ columns in the ‘sdrc_resultsResiduals’ table of the Stage-discharge rating curves product. NEON generally conducts between 12 and 24 manual gaugings per year to build and maintain the stage-discharge relationship.Second, we calculated the percentage of continuous streamflow values outside the range of manually measured estimates of streamflow. This was useful to assess if the stage-discharge relationship is representative of observed flow conditions. The relationship between discharge and stage is often nonlinear, with inflection points around changes in channel morphology making gauging the stream at high and low flow conditions critical to building a reliable rating curve16. A rating curve based on a large number of direct field measurements all taken during a narrow range of baseflows, for example, could generate a rating curve with a high Nash-Sutcliffe coefficient that is unreliable when extrapolated to high or low flow events. Using these two metrics, we were able to classify rating curves into categories of relative quality. To calculate the percentage of values in the continuous streamflow product that fall outside the range of manually gauged streamflow values, we extracted the maximum and minimum gauging values from the ‘sdrc_resultsResiduals’ table in the Stage-discharge Rating Curve product. We then compared the predicted values derived from each rating curve (as reported in the ‘csd_continuousDischarge’ table) to the extracted range and calculated the proportion of values which fell outside of it.We used the Nash-Sutcliffe coefficient and percentage of streamflow values over the maximum observed field measurements to classify rating curves into three categories outlined in Table 1.To integrate stage-gauge regressions, drift detections, and rating curve classification, we produced a summary table with classifications for all three tests and the corresponding metrics used in each classification (Fig. 5). The table is grouped by month and site so users can query sites and determine which months have the appropriate data for their needs. More

Myxococcota and Bdellovibrionota were active constituents of activated sludge microbiotaTo explore the predating activity and diversity of predatory bacteria in activated sludge, 13C-labeled Escherichia coli and Pseudomonas putida cells (determined as 97.09 and 97.30 atom% 13C, respectively) were added to the sludge microcosms for maximumly eight days of incubation, and 13C incorporation was examined using rRNA-SIP to identify prokaryotic and eukaryotic microorganisms involved in actively consuming the 13C-labeled prey cells. Bacterial 16S rRNA gene amplicon sequencing-based analysis indicated the relative contribution of 47.9% and 42.7% of the obtained sequences by the added biomass upon amendment in the 13C-E. coli (Fig. 1A) and 13C-P. putida (Fig. 1B) microcosms, which dropped below 1.0% after 16 h and eight days of incubation, respectively. The overall bacterial community structure at the steady state was highly comparable to that of the control microcosms (Fig. 1C), indicating that the prey cell amendments did not induce too strong fluctuation in the microbiota structure during the SIP experiment that prevented disentangling the indigenous community dynamics.Fig. 1: The dynamics of the prokaryotic communities and mineralization of the added 13C-biomass during the microcosm experiment.The overall prokaryotic communities were obtained by 16S rRNA gene amplicon sequencing of the total DNA from the activated sludge microcosms amended with 13C-E. coli (A) and 13C-P. putida (B) cells, and the control group (C) without amendment. The structure of the active prokaryotic communities was inferred based on amplicon sequencing of the light rRNA fractions from the microcosms amended with 13C-E. coli (D) and 13C-P. putida (E) cells. The temporal change in the proportion of produced 13CO2 in total CO2 indicated the mineralization of the 13C-labeled cells of E. coli and P. putida in duplicate microcosms (F). Relative sequence abundance of the ten most abundant prokaryotic phyla, together with the genera Escherichia-Shigella and Pseudomonas, was shown.Full size imageThe metabolically active bacterial communities, as inferred by 16S rRNA gene transcripts of the light rRNA fractions from the microcosms, were rather consistent throughout the experiment (Fig. 1D, E), but they showed clear compositional differences compared to the overall prokaryotic communities inferred by 16S rRNA gene amplicon sequences (Fig. 1A, B). Myxococcota and Bdellovibrionota species showed an average relative abundance of 17.5 (±0.7) % and 2.7 (±0.2) % in the 16S rRNA gene transcripts, respectively, which were significantly higher than 5.4 (±0.6) % and 1.3 (±0.1) % in the 16S rRNA genes of bacterial communities (p 1% in the 13C-heavy fractions, strong 13C-labeling was found for the as-yet-uncultivated myxobacterial mle1-27 clade (average EF 2.66 across time and treatments), which contributed to 10.3% to 38.9% of the 16S rRNA gene transcripts in the 13C-heavy fractions, indicating its high metabolic activity in consuming the 13C-labeled biomass of both E. coli and P. putida. Comparatively, Haliangium spp. and uncultured Polyangiaceae belonging to Myxococcota, as well as the as-yet-uncultivated OM27 clade belonging to Bdellovibrionota, also exhibited strong 13C-labeling (maximum EF across time: 2.4–39.5), but almost exclusively in the microcosms amended with 13C-E. coli cells (Fig. 2A). The as-yet-uncultivated myxobacterial VHS-B3-70 clade exhibited the strongest enrichment (average EF 16.67 across time and treatments) but made up only 0.2% to 2.3% of 16S rRNA gene transcripts of the 13C-heavy fraction (Fig. 2A). Overall, our microcosm experiment tracking added 13C-labeled prey bacterial cells with rRNA-SIP suggested prominent predatory activity of Myxococcota and Bdellovibrionota lineages including largely as-yet-uncultivated ones (e.g., the mle1-27, VHS-B3-70, and OM27 clades) in activated sludge.Fig. 2: The enrichment of incorporators of added 13C-biomass in heavy rRNA fractions and the temporal labeling patterns.13C-labeled prokaryotic (A) and micro-eukaryotic (B) genus-level taxa were identified by SIP in the microcosms added with E. coli and P. putida after one, two, and four days of incubation. Enrichment factor (EF) was calculated for microorganisms using heavy and light rRNA gradient fractions of the 13C- and 12C-microcosms to infer 13C-labeling. Taxa with an EF  > 0.1 in at least one of the treatment groups at one sampling time point was considered labeled. The area of circles indicates the relative sequence abundance of the labeled taxa in heavy 13C-rRNA. The negative EFs and positive EFs 1% in the heavy rRNA fractions of at least one of the 13C-E. coli and 13C-P. putida microcosms at a sampling point.Full size imageMyxococcota and Bdellovibrionota predated more selectively than protistsFor the micro-eukaryotes, several taxa belonging to Ciliophora, especially Cyrtophoria spp. and Telotrochidium spp., and also Peritrichia spp., Vaginicola spp., Aspidisca spp., and Epistylis spp., were highly enriched (maximum EF across time and treatments: 0.9–6.7) in the 13C-heavy rRNA fractions (Fig. 3B), in agreement with the dominance of Ciliophora in the micro-eukaryotic rRNA gene transcripts (Fig. 2B). The Candida-Lodderomyces clade and Cyberlindnera-Candida clade within Ascomycota, Magnoliophyta spp. within Phragmoplastophyta, and Poteriospumella spp. and unclassified Chromulinales within Ochrophyta were also strongly labeled (maximum EF: 13.5–242.5, Fig. 2B). Moreover, the 13C-biomass incorporation by micro-eukaryotes was independent of whichever prey bacteria (Fig. 2B, D), revealing no detectable prey preference in the metabolically active micro-eukaryotic predators. On the contrary, differential labeling by 13C-E. coli and 13C-P. putida cells was frequently observed for the predatory bacteria (Fig. 2A, C). The most obvious example was the OM27 clade ASVs belonging to Bdellovibrionota, which were found to incorporate 13C-labeled biomass exclusively of E. coli (Fig. 2C). Comparatively, Haliangium-affiliated ASV27 and ASV63 were labeled only by 13C-E. coli, ASV57 labeled by both 13C-E. coli and 13C-P. putida, while ASV72 and ASV76 were also labeled by 13C-P. putida, but only at a later sampling point (Fig. 2C). These results on the divergent labeling patterns with the tested prey bacteria together strongly implied population-specific predating behaviors of predatory bacteria in activated sludge.Fig. 3: In situ relative abundance of Myxococcota and Bdellovibrionota in aerobic and anaerobic sludge at a local WWTP (WWTP01) based on sampling over two years.The abundance of the abundant genera belonging to Myxococcota and Bdellovibrionota in aerobic and anaerobic sludge were compared according to amplicon sequencing-based analysis of bacterial 16S rRNA gene V3-V4 region. The top 10 abundant genus-level taxa across samples collected from eight samplings are shown, with the putative predators identified by SIP in the microcosm experiment highlighted. The asterisk denotes significant difference in relative abundance between aerobic and anaerobic sludges (p 0.1% in the activated sludge of WWTP01, including the putative predators identified in the microcosm experiment, i.e., Haliangium spp. (2.8 ± 0.7%) which represented the most abundant myxobacterial lineage in the activated sludge, uncultured Polyangiaceae (0.4 ± 0.1%), and the mle1-27 clade (0.2 ± 0.0%; Fig. 3). Moreover, Pajaroellobacter (1.2 ± 0.2%), Nannocystis (0.4 ± 0.1%), Phaselicystis (0.3 ± 0.1%), and several other myxobacterial clades, although not identified as putative predators in the microcosm experiment, were among the abundant myxobacteria in situ in the activated sludge. Although the myxobacterial genera showed comparable relative abundance in the anaerobic tanks, fed by returned activated sludge, to their counterparts in the aerobic tanks, the obligately aerobic myxobacteria were presumably metabolically inactive in the anerobic sludge. Unlike Myxococcota, members of Bdellovibrionota altogether showed significantly higher relative abundance in the aerobic sludge (1.0 ± 0.2%) than in the anaerobic sludge (0.6 ± 0.1%, paired samples Wilcoxon test p  More

Orchard site selectionThe survey was conducted from 2018 to 2019 in the Circum-Bohai Bay region, which included Shandong, Hebei, and Liaoning provinces and Beijing, and the Loess Plateau region, which included Shanxi and Shaanxi provinces. Five typical production counties were selected in each province or city. Representative orchards were selected according to the production of the main varieties in each county (orchard area was greater than 1.0 ha; the pear trees were 15 to 25 years old; and the yield of orchards ranged from 40 to 60 t ha−1). A total of 225 orchards were investigated (Fig. 1), including 150 in the Circum-Bohai Bay region and 75 in the Loess Plateau region (Table 1).Fig. 1The locations of the 225 pear orchards.Full size imageTable 1 Numbers of pear orchard and main cultivated varieties investigated in Circum-Bohai Bay and Loess Plateau.Full size tableSample collection and pretreatmentSoil and leaf samples were collected at the stage in which the growth of new shoots ceased, from July 1 to July 1510. Eleven sampling sites were determined in each orchard according to an “S” shape sampling method (Fig. 2), and soil samples from the 0–20 cm, 20–40 cm and 40–60 cm layers were collected. The soil samples of the same soil layer at each sampling site were mixed into one sample. Then, the soil samples were air-dried, ground and sifted with a nylon sieve for determination of nutrient concentrations.Fig. 2The “S” shape sampling method. The red dots are the sampling locations.Full size imageTen to fifteen pear trees in each orchard of the same size and vigour and 5 to 10 mature leaves from the middle of a long shoot from the periphery of each tree were selected for leaf sampling11. Then, all the leaves from the same orchard were mixed into one leaf sample. The leaves were washed with tap water containing a detergent, with deionized water, with 0.01 M hydrochloric acid and then with deionized water again and then dried at 100 °C for 30 min and at 70 °C to a constant weight. Then, the leaf samples were crushed into a powder and sifted with a nylon sieve for nutrient determination.Fruit samples were collected at the ripening stage. Pear trees from which leaf samples were collected from each orchard were selected for fruit sample collection. Three to five peripheral fruits of the same size were collected from each tree, and fruit samples from the same orchard were mixed into one sample. The fruits were washed with tap water containing a detergent, with deionized water, with 0.01 M hydrochloric acid and then with deionized water again, cut into slices and then dried at 100 °C for 30 min and at 70 °C to a constant weight. Then, the fruit samples were crushed into a powder and sifted with a nylon sieve for nutrient determination.Sample determinationVarious indicators of soil and plant samples were determined according to the method of Cui et al.12 and Bao13.Soil pH determinationA potentiometric method was used to measure soil pH. Carbon dioxide-free water was added to soil that had been passed through a 2 mm sieve at a water-soil ratio of 2.5:1. The soil solution was stirred for 1 min and left undisturbed for 30 min. Each soil sample was measured more than three times with a pH meter (FE20K PLUS PH, Mettler-Toledo, Switzerland), and the difference in the parallel determination results was less than 0.2 pH units. The electrode was washed with deionized water and dried with filter paper after each sample measurement. A calibration solution was used to calibrate the electrode between measurements after every 10 soil samples.Soil organic matter determinationSoil organic matter was measured according to the Schollenberger method using chromic acid redox titration. Five millilitres of a 0.8 M 1/6 K2Cr2O7 solution was added to a test tube with approximately 0.5000 g of soil that had been passed through a 0.25 mm sieve. The mixture was then added to 5 mL concentrated sulfuric acid and shaken gently to disperse the soil. The tube was placed in a phosphoric acid bath, heated to 170 °C and boiled for 5 min. To condense the water vapour that escaped during the heating process, a small funnel was placed on the top of the test tube. The substances in the test tube and funnel were transferred to a conical flask after cooling. Then, the solution was added to 1,10-phenanthroline hydrate and titrated with 0.2 M FeSO4 until it turned maroon. A blank experiment was performed when each batch of samples was measured. The soil organic matter content was calculated according to the following formula:$${rm{omega }}left({rm{OM}}right)=frac{left({rm{V}}-{rm{V}}0right)times {rm{c}}times 3times 1.724times {rm{f}}}{{rm{m}}}$$
(1)
ω(OM): soil organic matter content; c: standard FeSO4 solution concentration; V: volume of the standard FeSO4 used in titration; V0: volume of standard FeSO4 used in titrating control sample; 3: molar mass of a quarter of carbon; 1.724: the conversion factor from organic carbon to organic matter; f: oxidation correction coefficient (the value was 1.1); m: mass of oven-dried soil sample.Soil total N determinationTotal N was determined by the semitrace Kjeldahl method. Approximately 1.0000 g of air-dried soil that had been passed through a 0.25 mm sieve was added to a digestion tube. Meanwhile, the soil moisture content was measured to calculate the mass of the oven-dried soil. Two grams of accelerator and 5 mL of concentrated sulfuric acid were added to the tube. The tube was then covered with a small funnel, and the sample was digested at 360 °C for 15–20 min. The mixture was digested for 1 h until the colour changed from brown to greyish green or greyish white. Two digested soilless samples were used as controls. After the digestion tube cooled, it was placed in a distiller, and a small amount of deionized water was added. Five millilitres of a 2% boric acid indicator was added to a 150 mL conical flask, and the flask was placed at the end of the condenser tube. Then, the digestion solution was distilled until the distillate volume was approximately 75 mL. The distillate was titrated with 0.01 M standard hydrochloric acid to a purplish red colour endpoint. The soil total N concentration was calculated according to the following formula:$${rm{omega }}({rm{N}})=frac{({rm{V}}-{rm{V}}0)times {rm{c}}times 14}{{rm{m}}}$$
(2)
ω(N): soil total N concentration; c: standard acid concentration; V: volume of the standard acid used in titration; V0: volume of standard acid used in titrating control sample; 14: molar mass of N; m: mass of oven-dried soil sample.Soil alkaline hydrolysable N determinationApproximately 2.00 g of air-dried soil that have been passed through a 2 mm sieve was placed in the outer chamber of a diffuser. The diffuser was gently rotated to evenly distribute the soil in the outer chamber. Two millilitres of H3BO3 indicator was placed in the inner chamber of the diffusion dish. The edge of the frosted glass surface of the diffuser was coated with alkaline glycerin and covered with frosted glass. The diffuser was covered tightly and secured with rubber bands after 10.00 mL of 1 M NaOH was injected into the diffuser through a hole in the frosted glass. The diffuser was placed in a 40 °C incubator for alkaline hydrolysis diffusion for 24 h. Then, the mixture was titrated with 0.01 M standard hydrochloric acid until it turned purplish red. A blank test was performed at the same time as the samples. The soil alkaline hydrolysable N concentration was calculated according to the following formula:$${rm{omega }}({rm{N}})=frac{({rm{V}}-{rm{V}}0)times {rm{c}}times 14}{{rm{m}}}$$
(3)
ω(N): soil alkaline hydrolysable N concentration; c: standard acid solution concentration; V: volume of the standard acid used in titration; V0: volume of standard acid used in titrating control sample; 14: molar mass of N; m: mass of air-dried soil sample.Soil available P determinationApproximately 2.50 g of air-dried soil that had been passed through a 2 mm sieve was placed in a plastic bottle and 50 mL of 0.5 M NaHCO3 was added. After the bottle was shaken for 30 min, the mixture was immediately filtered with phosphorus-free filter paper. Ten millilitres of the filtrate was accurately measured into a conical flask, and 5.00 mL of Mo-Sb-Vc colour developer and 10 mL of deionized water were added. The absorbance of the mixture was measured at approximately 700 nm after 30 min using a UV-Vis spectrophotometer (UV1900PC, AuCy Instrument, Shanghai, China). Finally, the P concentration was calculated according to a standard curve prepared with solutions of different P concentrations. A blank test was performed at the same time that the samples were determined.Soil available K determinationApproximately 5.00 g of air-dried soil that had been passed through a 2 mm sieve was placed in a plastic bottle, and 50 mL of 1.0 M NH4OAc was added. After the sample was shaken for 30 min, the mixture was immediately filtered with dry filter paper. The concentration of K in the filtrate was determined directly by a flame photometer (LM12-FP6430, Haifuda, China) according to a standard curve prepared with solutions of different K concentrations. A blank test was performed at the same time that the samples were determined.Leaf and fruit N determinationApproximately 0.3000 g of plant powder that had been passed through a 0.5 mm sieve was placed into a digestion tube and 5 mL concentrated sulfuric acid was added. Then, the digestion tube was placed onto a digestion stove at 360 °C after two doses of 2 mL H2O2, and the sample was digested until the mixture turned brown. After the tube cooled, 2 mL H2O2 was added, and the digestion was continued for 5 min. This process was repeated until the mixture turned clear. The mixture was diluted to 100 mL in a volumetric flask for testing after it cooled. Then, 5 to 10 mL of the liquid to be tested was accurately measured into a distiller for distillation. The distillation and titration processes were the same as those used for ammonium in the Soil total N determination section. A blank test was performed at the same time as sample measurement. The leaf or fruit N concentration was calculated according to the following formula:$${rm{omega }}({rm{N}})=frac{({rm{V}}-{rm{V}}0)times {rm{c}}times 14times {rm{V}}1}{{rm{m}}times {rm{V}}2}$$
(4)
ω(N): total N concentration; c: standard acid concentration; V: volume of the standard acid used in titration; V0: volume of standard acid used in titrating control sample; 14: molar mass of N; m: mass of oven-dried sample; V1: volume of the digestion solution after constant volume; V2: measured volume of digestion solution after constant volume.Leaf and fruit P, K, Ca, Fe, Mn, Cu, Zn, B determinationApproximately 0.5000 g of plant powder that had been passed through a 0.5 mm sieve was placed in a digestion tube and a 10 mL mixture of concentrated nitric acid and hypochlorous acid (4:1) was added. After the sample was left undisturbed for more than 4 h, it was placed onto a digestion stove and heated to 150 °C so that NO2 could volatilize slowly. Then, the temperature was appropriately increased to a temperature not higher than 250 °C until the digestive solution was transparent and approximately 2 mL remained. The solution was transferred into a volumetric flask after cooling and adjusted to a constant volume of 50 mL. The solution was then filtered, and the concentration of each element in the solution was determined by a plasma emission spectrometer (ICP-OES, OPTIMA 3300 DV, 75 Perkin-Elmer, USA). A blank test was performed at the same time as sample measurement. The leaf or fruit P, K, Ca, Fe, Mn, Cu, Zn, and B concentrations were calculated according to the following formula:$${rm{omega }}({rm{P}},{rm{K}},{rm{Ca}},{rm{Fe}},{rm{Mn}},{rm{Cu}},{rm{Zn}},{rm{B}})=frac{rho ({rm{P}},{rm{K}},{rm{Ca}},{rm{Fe}},{rm{Mn}},{rm{Cu}},{rm{Zn}},{rm{B}})times {rm{V}}times {rm{f}}}{{rm{m}}}$$
(5)
ω(P, K, Ca, Fe, Mn, Cu, Zn, B): P, K, Ca, Fe, Mn, Cu, Zn, B concentration in leaf or fruit; ρ(P, K, Ca, Fe, Mn, Cu, Zn, B): the concentration of P, K, Ca, Fe, Mn, Cu, Zn or B in the liquid to be measured; V: volume of the liquid to be measured after constant volume; f: dilution ratio of the liquid to be measured; m: mass of oven-dried sample. More