Philippa Kaur

Gattuso JP, Frankignoulle M, Wollast R. Carbon and carbonate metabolism in coastal aquatic ecosystems. Annu Rev Ecol Syst. 1998;29:405–34.Article 

Google Scholar 
Arnosti C, Wietz M, Brinkhoff T, Hehemann JH, Probandt D, Zeugner L, et al. The biogeochemistry of marine polysaccharides: sources, inventories, and bacterial drivers of the carbohydrate cycle. Ann Rev Mar Sci. 2021;13:81–108.CAS 
PubMed 
Article 

Google Scholar 
Moran MA, Kujawinski EB, Stubbins A, Fatland R, Aluwihare LI, Buchan A, et al. Deciphering ocean carbon in a changing world. Proc Natl Acad Sci USA. 2016;113:3143–51.CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Poretsky RS, Sun S, Mou X, Moran MA. Transporter genes expressed by coastal bacterioplankton in response to dissolved organic carbon. Environ Microbiol. 2010;12:616–27.CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Azam F. Microbial control of oceanic carbon flux: The plot thickens. Science.1998;280:694–96.CAS 
Article 

Google Scholar 
Buchan A, LeCleir GR, Gulvik CA, González JM. Master recyclers: features and functions of bacteria associated with phytoplankton blooms. Nat Rev Microbiol. 2014;12:686–98.CAS 
PubMed 
Article 

Google Scholar 
Bunse C, Bertos-Fortis M, Sassenhagen I, Sildever S, Sjoqvist C, Godhe A, et al. Spatio-temporal interdependence of bacteria and phytoplankton during a baltic sea spring bloom. Front Microbiol. 2016;7:517.PubMed 
PubMed Central 
Article 

Google Scholar 
Cram JA, Chow CE, Sachdeva R, Needham DM, Parada AE, Steele JA, et al. Seasonal and interannual variability of the marine bacterioplankton community throughout the water column over ten years. ISME J. 2015;9:563–80.PubMed 
Article 

Google Scholar 
Seymour JR, Amin SA, Raina JB, Stocker R. Zooming in on the phycosphere: the ecological interface for phytoplankton-bacteria relationships. Nat Microbiol. 2017;2:17065.CAS 
PubMed 
Article 

Google Scholar 
Taylor JD, Cottingham SD, Billinge J, Cunliffe M. Seasonal microbial community dynamics correlate with phytoplankton-derived polysaccharides in surface coastal waters. ISME J. 2014;8:245–8.CAS 
PubMed 
Article 

Google Scholar 
Teeling H, Fuchs BM, Becher D, Klockow C, Gardebrecht A, Bennke CM, et al. Substrate-controlled succession of marine bacterioplankton populations induced by a phytoplankton bloom. Science.2012;336:608–11.CAS 
PubMed 
Article 

Google Scholar 
Hernando-Morales V, Varela MM, Needham DM, Cram J, Fuhrman JA, Teira E. Vertical and seasonal patterns control bacterioplankton communities at two horizontally coherent coastal upwelling sites off galicia (NW Spain). Microb Ecol. 2018;76:866–84.Needham DM, Fuhrman JA. Pronounced daily succession of phytoplankton, archaea and bacteria following a spring bloom. Nat Microbiol. 2016;1:16005.Tada Y, Taniguchi A, Nagao I, Miki T, Uematsu M, Tsuda A, et al. Differing growth responses of major phylogenetic groups of marine bacteria to natural phytoplankton blooms in the western North Pacific Ocean. Appl Environ Microbiol. 2011;77:4055–65.CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Williams TJ, Wilkins D, Long E, Evans F, DeMaere MZ, Raftery MJ, et al. The role of planktonic Flavobacteria in processing algal organic matter in coastal East Antarctica revealed using metagenomics and metaproteomics. Environ Microbiol. 2013;15:1302–17.CAS 
PubMed 
Article 

Google Scholar 
Ottesen EA, Young CR, Eppley JM, Ryan JP, Chavez FP, Scholin CA, et al. Pattern and synchrony of gene expression among sympatric marine microbial populations. Proc Natl Acad Sci USA. 2013;110:E488–97.CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Ottesen EA, Young CR, Gifford SM, Eppley JM, Marin R 3rd, Schuster SC, et al. Ocean microbes. Multispecies diel transcriptional oscillations in open ocean heterotrophic bacterial assemblages. Science. 2014;345:207–12.CAS 
PubMed 
Article 

Google Scholar 
Beier S, Rivers AR, Moran MA, Obernosterer I. The transcriptional response of prokaryotes to phytoplankton-derived dissolved organic matter in seawater. Environ Microbiol. 2015;17:3466–80.CAS 
PubMed 
Article 

Google Scholar 
Sarmento H, Gasol JM. Use of phytoplankton-derived dissolved organic carbon by different types of bacterioplankton. Environ Microbiol. 2012;14:2348–60.CAS 
PubMed 
Article 

Google Scholar 
Shi Y, McCarren J, DeLong EF. Transcriptional responses of surface water marine microbial assemblages to deep-sea water amendment. Environ Microbiol. 2012;14:191–206.CAS 
PubMed 
Article 

Google Scholar 
Vorobev A, Sharma S, Yu M, Lee J, Washington BJ, Whitman WB, et al. Identifying labile DOM components in a coastal ocean through depleted bacterial transcripts and chemical signals. Environ Microbiol. 2018;20:3012–30.CAS 
PubMed 
Article 

Google Scholar 
Moran MA, Belas R, Schell MA, González JM, Sun F, Sun S, et al. Ecological genomics of marine Roseobacters. Appl Environ Microbiol. 2007;73:4559–69.CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Nowinski B, Moran MA. Niche dimensions of a marine bacterium are identified using invasion studies in coastal seawater. Nat Microbiol. 2021;6:524–32.CAS 
PubMed 
Article 

Google Scholar 
Rinta-Kanto JM, Sun S, Sharma S, Kiene RP, Moran MA. Bacterial community transcription patterns during a marine phytoplankton bloom. Environ Microbiol. 2012;14:228–39.CAS 
PubMed 
Article 

Google Scholar 
Sharma AK, Becker JW, Ottesen EA, Bryant JA, Duhamel S, Karl DM, et al. Distinct dissolved organic matter sources induce rapid transcriptional responses in coexisting populations of Prochlorococcus, Pelagibacter and the OM60 clade. Environ Microbiol. 2013;16:2815–30.PubMed 
Article 
CAS 

Google Scholar 
Kieft B, Li Z, Bryson S, Hettich RL, Pan C, Mayali X, et al. Phytoplankton exudates and lysates support distinct microbial consortia with specialized metabolic and ecophysiological traits. Proc Natl Acad Sci USA. 2021;118:e2101178118.McCarren J, Becker JW, Repeta DJ, Shi Y, Young CR, Malmstrom RR, et al. Microbial community transcriptomes reveal microbes and metabolic pathways associated with dissolved organic matter turnover in the sea. Proc Natl Acad Sci USA. 2010;107:16420–7.CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Sosa OA, Gifford SM, Repeta DJ, DeLong EF. High molecular weight dissolved organic matter enrichment selects for methylotrophs in dilution to extinction cultures. ISME J. 2015;9:2725–39.CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Pontiller B, Martínez-García S, Lundin D, Pinhassi J. Labile dissolved organic matter compound characteristics select for divergence in marine bacterial activity and transcription. Front Microbiol. 2020;11:588778.PubMed 
PubMed Central 
Article 

Google Scholar 
Bryson S, Li Z, Chavez F, Weber PK, Pett-Ridge J, Hettich RL, et al. Phylogenetically conserved resource partitioning in the coastal microbial loop. ISME J. 2017;11:2781–92.PubMed 
PubMed Central 
Article 

Google Scholar 
Joglar V, Prieto A, Barber-Lluch E, Hernández-Ruiz M, Fernández E, Teira E. Spatial and temporal variability in the response of phytoplankton and prokaryotes to B-vitamin amendments in an upwelling system. Biogeosciences.2020;17:2807–23.Article 

Google Scholar 
Martínez-García S, Fernández E, Álvarez-Salgado XA, González J, Lønborg C, Marañón E, et al. Differential responses of phytoplankton and heterotrophic bacteria to organic and inorganic nutrient additions in coastal waters off the NW Iberian Peninsula. Mar Ecol Prog Ser. 2010;416:17–33.Article 
CAS 

Google Scholar 
Welschmeyer NA. Fluorometric analysis of chlorophyll a in the presence of chlorophyll b and pheopigments. Limnol Oceanogr. 1994;39:1985–92.CAS 
Article 

Google Scholar 
Parsons TR, Maita Y, Lalli CM. Fluorometric determination of chlorophylls. A manual of chemical & biological methods for seawater analysis: Oxford, UK: Pergamon Press; 1984. p. 107–09.Hansen H, Grasshoff K. Automated chemical analysis. Methods of seawater analysis. 2nd ed: Verlag Chemie, Weinheim; 1983. p. 347–95.Álvarez-Salgado XA, Miller AEJ. Simultaneous determination of dissolved organic carbon and total dissolved nitrogen in seawater by high temperature catalytic oxidation: conditions for precise shipboard measurements. Mar Chem. 1998;62:325–33.Article 

Google Scholar 
Calvo-Díaz A, Morán XAG. Seasonal dynamics of picoplankton in shelf waters of the southern Bay of Biscay. Aquat Micro Ecol. 2006;42:159–74.Article 

Google Scholar 
Smith DC, Farooq A. A simple, economical method for measuring bacterial protein synthesis rates in seawater using 3H-leucine. Mar Micro Food Webs. 1992;6:107–14.
Google Scholar 
Parada AE, Needham DM, Fuhrman JA. Every base matters: assessing small subunit rRNA primers for marine microbiomes with mock communities, time series and global field samples. Environ Microbiol. 2016;18:1403–14.CAS 
PubMed 
Article 

Google Scholar 
Logares R, Sunagawa S, Salazar G, Cornejo-Castillo FM, Ferrera I, Sarmento H, et al. Metagenomic 16S rDNA Illumina tags are a powerful alternative to amplicon sequencing to explore diversity and structure of microbial communities. Environ Microbiol. 2014;16:2659–71.CAS 
PubMed 
Article 

Google Scholar 
Straub D, Blackwell N, Langarica-Fuentes A, Peltzer A, Nahnsen S, Kleindienst S. Interpretations of environmental microbial community studies are biased by the selected 16S rRNA (Gene) amplicon sequencing pipeline. Front Microbiol. 2020;11:1–18.Article 

Google Scholar 
Quast C, Pruesse E, Yilmaz P, Gerken J, Schweer T, Yarza P, et al. The SILVA ribosomal RNA gene database project: improved data processing and web-based tools. Nucleic Acids Res. 2013;41:D590–6.CAS 
PubMed 
Article 

Google Scholar 
Poretsky RS, Gifford S, Rinta-Kanto J, Vila-Costa M, Moran MA. Analyzing gene expression from marine microbial communities using environmental transcriptomics. J Vis Exp. 2009;24:1–6.
Google Scholar 
Martin M. Cutadapt removes adapter sequences from high-throughput sequencing reads. EMBnet. 2011;17:10–12.Article 

Google Scholar 
Joshi NA, Fass JN. Sickle: a sliding-window, adaptive, quality-based trimming tool for FastQ files. 2011. https://github.com/najoshi/sickle.Del Fabbro C, Scalabrin S, Morgante M, Giorgi FM. An extensive evaluation of read trimming effects on Illumina NGS data analysis. PLoS One. 2013;8:1–13.
Google Scholar 
Li D, Liu CM, Luo R, Sadakane K, Lam TW. MEGAHIT: An ultra-fast single-node solution for large and complex metagenomics assembly via succinct de Bruijn graph. Bioinformatics.2015;31:1674–6.CAS 
PubMed 
Article 

Google Scholar 
Hyatt D, Chen GL, Locascio PF, Land ML, Larimer FW, Hauser LJ. Prodigal: prokaryotic gene recognition and translation initiation site identification. BMC Bioinform. 2010;11:119.Article 
CAS 

Google Scholar 
Rognes T, Flouri T, Nichols B, Quince C, Mahe F. VSEARCH: a versatile open source tool for metagenomics. PeerJ.2016;4:e2584.PubMed 
PubMed Central 
Article 

Google Scholar 
Buchfink B, Xie C, Huson DH. Fast and sensitive protein alignment using DIAMOND. Nat Methods. 2015;12:59–60.CAS 
PubMed 
Article 

Google Scholar 
Huson DH, Beier S, Flade I, Górska A, El-Hadidi M, Mitra S, et al. MEGAN community edition – interactive exploration and analysis of large-scale microbiome sequencing data. PLoS Comput Biol. 2016;12:1–12.Article 
CAS 

Google Scholar 
Zhang H, Yohe T, Huang L, Entwistle S, Wu P, Yang Z, et al. dbCAN2: A meta server for automated carbohydrate-active enzyme annotation. Nucleic Acids Res. 2018;46:W95–101.CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Gloor GB, Wu JR, Pawlowsky-Glahn V, Egozcue JJ. It’s all relative: analyzing microbiome data as compositions. Ann Epidemiol. 2016;26:322–9.PubMed 
Article 

Google Scholar 
R Core Team. R: A language and environment for statistical computing. 4.1.0 ed. Vienna, Austria: R Foundation for Statistical Computing; 2021.Cottrell MT, Kirchman DL. Transcriptional control in marine copiotrophic and oligotrophic bacteria with streamlined genomes. Appl Environ Microbiol. 2016;82:6010–18.CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Giovannoni SJ. SAR11 bacteria: The most abundant plankton in the oceans. Ann Rev Mar Sci. 2017;9:231–55.PubMed 
Article 

Google Scholar 
Newton RJ, Griffin LE, Bowles KM, Meile C, Gifford S, Givens CE, et al. Genome characteristics of a generalist marine bacterial lineage. ISME J. 2010;4:784–98.CAS 
PubMed 
Article 

Google Scholar 
Allers E, Gomez-Consarnau L, Pinhassi J, Gasol JM, Simek K, Pernthaler J. Response of Alteromonadaceae and Rhodobacteriaceae to glucose and phosphorus manipulation in marine mesocosms. Environ Microbiol. 2007;9:2417–29.CAS 
PubMed 
Article 

Google Scholar 
Alonso C, Pernthaler J. Roseobacter and SAR11 dominate microbial glucose uptake in coastal North Sea waters. Environ Microbiol. 2006;8:2022–30.CAS 
PubMed 
Article 

Google Scholar 
Noell SE, Giovannoni SJ. SAR11 bacteria have a high affinity and multifunctional glycine betaine transporter. Environ Microbiol. 2019;21:2559–75.CAS 
PubMed 
Article 

Google Scholar 
Sowell SM, Wilhelm LJ, Norbeck AD, Lipton MS, Nicora CD, Barofsky DF, et al. Transport functions dominate the SAR11 metaproteome at low-nutrient extremes in the Sargasso Sea. ISME J. 2009;3:93–105.CAS 
PubMed 
Article 

Google Scholar 
Chenard C, Wijaya W, Vaulot D, Lopes Dos Santos A, Martin P, Kaur A, et al. Temporal and spatial dynamics of bacteria, archaea and protists in equatorial coastal waters. Sci Rep. 2019;9:16390.PubMed 
PubMed Central 
Article 
CAS 

Google Scholar 
Pajares S, Varona-Cordero F, Hernández-Becerril DU. Spatial distribution patterns of bacterioplankton in the oxygen minimum zone of the tropical mexican pacific. Micro Ecol. 2020;80:519–36.CAS 
Article 

Google Scholar 
Signori CN, Pellizari VH, Enrich-Prast A, Sievert SM. Spatiotemporal dynamics of marine bacterial and archaeal communities in surface waters off the northern Antarctic Peninsula. Deep-Sea Res Pt Ii. 2018;149:150–60.Article 

Google Scholar 
Ling SK, Xia J, Liu Y, Chen GJ, Du ZJ. Agarilytica rhodophyticola gen. nov., sp. nov., isolated from Gracilaria blodgettii. Int J Syst Evol Microbiol. 2017;67:3778–83.CAS 
PubMed 
Article 

Google Scholar 
Cheng H, Zhang S, Huo YY, Jiang XW, Zhang XQ, Pan J, et al. Gilvimarinus polysaccharolyticus sp. nov., an agar-digesting bacterium isolated from seaweed, and emended description of the genus Gilvimarinus. Int J Syst Evol Microbiol. 2015;65:562–69.CAS 
PubMed 
Article 

Google Scholar 
Malmstrom RR, Kiene RP, Cottrell MT, Kirchman DL. Contribution of SAR11 bacteria to dissolved dimethylsulfoniopropionate and amino acid uptake in the North Atlantic ocean. Appl Environ Microbiol. 2004;70:4129–35.CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Kirchman DL. Growth rates of microbes in the oceans. Ann Rev Mar Sci. 2016;8:285–309.PubMed 
Article 

Google Scholar 
Durham BP, Dearth SP, Sharma S, Amin SA, Smith CB, Campagna SR, et al. Recognition cascade and metabolite transfer in a marine bacteria-phytoplankton model system. Environ Microbiol. 2017;19:3500–13.CAS 
PubMed 
Article 

Google Scholar 
Ferrer-González FX, Widner B, Holderman NR, Glushka J, Edison AS, Kujawinski EB, et al. Resource partitioning of phytoplankton metabolites that support bacterial heterotrophy. ISME J. 2021;15:762–73.PubMed 
Article 
CAS 

Google Scholar 
Landa M, Burns AS, Roth SJ, Moran MA. Bacterial transcriptome remodeling during sequential co-culture with a marine dinoflagellate and diatom. ISME J. 2017;11:2677–90.CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Pedler BE, Aluwihare LI, Azam F. Single bacterial strain capable of significant contribution to carbon cycling in the surface ocean. Proc Natl Acad Sci USA. 2014;111:7202–7.CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Koch H, Dürwald A, Schweder T, Noriega-Ortega B, Vidal-Melgosa S, Hehemann JH, et al. Biphasic cellular adaptations and ecological implications of Alteromonas macleodii degrading a mixture of algal polysaccharides. ISME J. 2019;13:92–103.CAS 
PubMed 
Article 

Google Scholar 
López-Pérez M, Gonzaga A, Martin-Cuadrado AB, Onyshchenko O, Ghavidel A, Ghai R, et al. Genomes of surface isolates of Alteromonas macleodii: the life of a widespread marine opportunistic copiotroph. Sci Rep. 2012;2:696.PubMed 
PubMed Central 
Article 
CAS 

Google Scholar 
Bergauer K, Fernandez-Guerra A, Garcia JAL, Sprenger RR, Stepanauskas R, Pachiadaki MG, et al. Organic matter processing by microbial communities throughout the Atlantic water column as revealed by metaproteomics. Proc Natl Acad Sci USA. 2018;115:E400–8.CAS 
PubMed 
Article 

Google Scholar 
Hou S, López-Pérez M, Pfreundt U, Belkin N, Stüber K, Huettel B, et al. Benefit from decline: the primary transcriptome of Alteromonas macleodii str. Te101 during Trichodesmium demise. ISME J. 2018;12:981–96.CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Pinhassi J, Sala MM, Havskum H, Peters F, Guadayol O, Malits A, et al. Changes in bacterioplankton composition under different phytoplankton regimens. Appl Environ Microbiol. 2004;70:6753–66.CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Riemann L, Steward GF, Azam F. Dynamics of bacterial community composition and activity during a mesocosm diatom bloom. Appl Environ Microbiol. 2000;66:578–87.CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Cottrell MT, Kirchman DL. Natural assemblages of marine Proteobacteria and members of the Cytophaga-Flavobacter cluster consuming low- and high-molecular-weight dissolved organic matter. Appl Environ Microbiol. 2000;66:1692–7.CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Krüger K, Chafee M, Ben Francis T, Glavina Del Rio T, Becher D, Schweder T, et al. In marine Bacteroidetes the bulk of glycan degradation during algae blooms is mediated by few clades using a restricted set of genes. ISME J. 2019;13:2800–16.PubMed 
PubMed Central 
Article 
CAS 

Google Scholar 
Ben Hania W, Joseph M, Bunk B, Spröer C, Klenk HP, Fardeau ML, et al. Characterization of the first cultured representative of a Bacteroidetes clade specialized on the scavenging of cyanobacteria. Environ Microbiol. 2017;19:1134–48.PubMed 
Article 
CAS 

Google Scholar 
Fernández-Gómez B, Richter M, Schüler M, Pinhassi J, Acinas SG, González JM, et al. Ecology of marine Bacteroidetes: a comparative genomics approach. ISME J. 2013;7:1026–37.PubMed 
PubMed Central 
Article 
CAS 

Google Scholar 
Orsi WD, Smith JM, Liu S, Liu Z, Sakamoto CM, Wilken S, et al. Diverse, uncultivated bacteria and archaea underlying the cycling of dissolved protein in the ocean. ISME J. 2016;10:2158–73.CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Xing P, Hahnke RL, Unfried F, Markert S, Huang S, Barbeyron T, et al. Niches of two polysaccharide-degrading Polaribacter isolates from the North Sea during a spring diatom bloom. ISME J. 2015;9:1410–22.CAS 
PubMed 
Article 

Google Scholar 
Sichert A, Corzett CH, Schechter MS, Unfried F, Markert S, Becher D, et al. Verrucomicrobia use hundreds of enzymes to digest the algal polysaccharide fucoidan. Nat Microbiol. 2020;5:1026–39.Ivanova AA, Naumoff DG, Miroshnikov KK, Liesack W, Dedysh SN. Comparative genomics of four Isosphaeraceae Planctomycetes: a common pool of plasmids and glycoside hydrolase genes shared by Paludisphaera borealis PX4(T), Isosphaera pallida IS1B(T), Singulisphaera acidiphila DSM 18658(T), and Strain SH-PL62. Front Microbiol. 2017;8:412.PubMed 
PubMed Central 
Article 

Google Scholar 
Vidal-Melgosa S, Sichert A, Francis TB, Bartosik D, Niggemann J, Wichels A, et al. Diatom fucan polysaccharide precipitates carbon during algal blooms. Nat Commun. 2021;12:1150.CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Le Costaouëc T, Unamunzaga C, Mantecon L, Helbert W. New structural insights into the cell-wall polysaccharide of the diatom Phaeodactylum tricornutum. Algal Res. 2017;26:172–9.Article 

Google Scholar 
Francis TB, Bartosik D, Sura T, Sichert A, Hehemann JH, Markert S, et al. Changing expression patterns of TonB-dependent transporters suggest shifts in polysaccharide consumption over the course of a spring phytoplankton bloom. ISME J. 2021;15:2336–50.Bode A, Estévez MG, Varela M, Vilar JA. Annual trend patterns of phytoplankton species abundance belie homogeneous taxonomical group responses to climate in the NE Atlantic upwelling. Mar Environ Res. 2015;110:81–91.CAS 
PubMed 
Article 

Google Scholar 
Cermeño P, Marañón E, Pérez V, Serret P, Fernández E, Castro CG. Phytoplankton size structure and primary production in a highly dynamic coastal ecosystem (Ría de Vigo, NW-Spain): Seasonal and short-time scale variability. Estuar Coast Shelf Sci. 2006;67:251–66.Article 

Google Scholar 
Nogueira E, Pérez FF, Rı́os AF. Modelling thermohaline properties in an estuarine upwelling ecosystem (Rı́a de Vigo: NW Spain) using box-jenkins transfer function models. Estuar Coast Shelf Sci. 1997;44:685–702.Article 

Google Scholar 
Broullón E, López-Mozos M, Reguera B, Chouciño P, Doval MD, Fernández-Castro B, et al. Thin layers of phytoplankton and harmful algae events in a coastal upwelling system. Prog Oceanogr. 2020;189:102449.Fraga F. Upwelling off the Galician Coast, Northwest Spain. In: Richards FA, editor. Coastal Upwelling. Washington: American Geophysical Union; 1981. p. 176–82.Nogueira E, Figueiras FG. The microplankton succession in the Ría de Vigo revisited: species assemblages and the role of weather-induced, hydrodynamic variability. J Mar Syst. 2005;54:139–55.Article 

Google Scholar 
Pitcher GC, Walker DR, Mitchellinnes BA, Moloney CL. Short-term variability during an anchor station study in the southern Benguela Upwelling System – phytoplankton dynamics. Prog Oceanogr. 1991;28:39–64.Article 

Google Scholar 
Smayda TJ, Trainer VL. Dinoflagellate blooms in upwelling systems: Seeding, variability, and contrasts with diatom bloom behaviour. Prog Oceanogr. 2010;85:92–107.Article 

Google Scholar 
Wilkerson FP, Lassiter AM, Dugdale RC, Marchi A, Hogue VE. The phytoplankton bloom response to wind events and upwelled nutrients during the CoOP WEST study. Deep-Sea Res Pt Ii. 2006;53:3023–48.Article 

Google Scholar 
Reintjes G, Arnosti C, Fuchs B, Amann R. Selfish, sharing and scavenging bacteria in the Atlantic Ocean: a biogeographical study of bacterial substrate utilisation. ISME J. 2019;13:1119–32.CAS 
PubMed 
Article 

Google Scholar 
Mayali X, Weber PK, Pett-Ridge J. Taxon-specific C/N relative use efficiency for amino acids in an estuarine community. FEMS Microbiol Ecol. 2013;83:402–12.CAS 
PubMed 
Article 

Google Scholar  More

Newsome, S. D., Clementz, M. T. & Koch, P. L. Using stable isotope biogeochemistry to study marine mammal ecology. Mar. Mamm. Sci. 26, 509–572. https://doi.org/10.1111/j.1748-7692.2009.00354.x (2010).CAS 
Article 

Google Scholar 
Layman, C. A. et al. Applying stable isotopes to examine food-web structure: An overview of analytical tools. Biol. Rev. Camb. Philos. Soc. 87, 545–562. https://doi.org/10.1111/j.1469-185X.2011.00208.x (2011).Article 
PubMed 

Google Scholar 
Larsen, T. et al. Tracing carbon sources through aquatic and terrestrial food webs using amino acid stable isotope fingerprinting. PLoS ONE 8, e73441. https://doi.org/10.1371/journal.pone.0073441 (2013).ADS 
CAS 
Article 
PubMed 
PubMed Central 

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

Google Scholar 
Inger, R. & Bearhop, S. Applications of stable isotope analyses to avian ecology. Ibis 150, 447–461 (2008).Article 

Google Scholar 
McCutchan, J. H., Lewis, W. M., Kendall, C. & McGrath, C. C. Variation in trophic shift for stable isotope ratios of carbon, nitrogen, and sulfur. Oikos 102, 378–390 (2003).CAS 
Article 

Google Scholar 
Olive, P. J. W., Pinnegar, J. K., Polunin, N. V. C., Richards, G. & Welch, R. Isotope trophic-step fractionation: A dynamic equilibrium model. J. Anim. Ecol. 72, 608–617 (2003).Article 

Google Scholar 
McMahon, K. W., Polito, M. J., Abel, S., McCarthy, M. D. & Thorrold, S. R. Carbon and nitrogen isotope fractionation of amino acids in an avian marine predator, the gentoo penguin (Pygoscelis papua). Ecol. Evol. 5, 1278–1290. https://doi.org/10.1002/ece3.1437 (2015).Article 
PubMed 
PubMed Central 

Google Scholar 
Webb, E. C. et al. Compound-specific amino acid isotopic proxies for distinguishing between terrestrial and aquatic resource consumption. Archaeol. Anthropol. Sci. 10, 1–18. https://doi.org/10.1007/s12520-015-0309-5 (2016).Article 

Google Scholar 
Whiteman, J. P., Kim, S. L., McMahon, K. W., Koch, P. L. & Newsome, S. D. Amino acid isotope discrimination factors for a carnivore: Physiological insights from leopard sharks and their diet. Oecologia 188, 977–989. https://doi.org/10.1007/s00442-018-4276-2 (2018).ADS 
Article 
PubMed 

Google Scholar 
Rogers, M., Bare, R., Gray, A., Scott-Moelder, T. & Heintz, R. Assessment of two feeds on survival, proximate composition, and amino acid carbon isotope discrimination in hatchery-reared Chinook salmon. Fisher. Res. https://doi.org/10.1016/j.fishres.2019.06.001 (2019).Article 

Google Scholar 
Wang, Y. V., Wan, A. H. L., Krogdahl, A., Johnson, M. & Larsen, T. (13)C values of glycolytic amino acids as indicators of carbohydrate utilization in carnivorous fish. PeerJ 7, e7701. https://doi.org/10.7717/peerj.7701 (2019).Article 
PubMed 
PubMed Central 

Google Scholar 
McMahon, K. W., Fogel, M. L., Elsdon, T. S. & Thorrold, S. R. Carbon isotope fractionation of amino acids in fish muscle reflects biosynthesis and isotopic routing from dietary protein. J. Anim. Ecol. 79, 1132–1141. https://doi.org/10.1111/j.1365-2656.2010.01722.x (2010).Article 
PubMed 

Google Scholar 
McMahon, K. W., Thorrold, S. R., Houghton, L. A. & Berumen, M. L. Tracing carbon flow through coral reef food webs using a compound-specific stable isotope approach. Oecologia 180, 809–821. https://doi.org/10.1007/s00442-015-3475-3 (2016).ADS 
Article 
PubMed 

Google Scholar 
Wang, Y. V. et al. Know your fish: A novel compound-specific isotope approach for tracing wild and farmed salmon. Food Chem 256, 380–389. https://doi.org/10.1016/j.foodchem.2018.02.095 (2018).CAS 
Article 
PubMed 

Google Scholar 
Jim, S., Jones, V., Ambrose, S. H. & Evershed, R. P. Quantifying dietary macronutrient sources of carbon for bone collagen biosynthesis using natural abundance stable carbon isotope analysis. Br J. Nutr. 95, 1055–1062. https://doi.org/10.1079/bjn20051685 (2006).CAS 
Article 
PubMed 

Google Scholar 
Newsome, S. D., Fogel, M. L., Kelly, L. & del Rio, C. M. Contributions of direct incorporation from diet and microbial amino acids to protein synthesis in Nile tilapia. Funct. Ecol. 25, 1051–1062. https://doi.org/10.1111/j.1365-2435.2011.01866.x (2011).Article 

Google Scholar 
Griffiths, H. Applications of stable isotope technology in physiological ecology. Funct. Ecol. 5, 254–269 (1991).Article 

Google Scholar 
Lorrain, A. et al. Differential δ13C and δ15N signatures among scallop tissues: Implications for ecology and physiology. J. Exp. Mar. Biol. Ecol. 275, 47–61 (2002).CAS 
Article 

Google Scholar 
Li, P., Mai, K., Trushenski, J. & Wu, G. New developments in fish amino acid nutrition: Towards functional and environmentally oriented aquafeeds. Amino Acids 37, 43–53. https://doi.org/10.1007/s00726-008-0171-1 (2009).CAS 
Article 
PubMed 

Google Scholar 
Boecklen, W. J., Yarnes, C. T., Cook, B. A. & James, A. C. On the use of stable isotopes in trophic ecology. Annu. Rev. Ecol. Evol. Syst. 42, 411–440. https://doi.org/10.1146/annurev-ecolsys-102209-144726 (2011).Article 

Google Scholar 
Perga, M. E. & Gerdeaux, D. “Are fish what they eat” all year round?. Oecologia 144, 598–606. https://doi.org/10.1007/s00442-005-0069-5 (2005).ADS 
CAS 
Article 
PubMed 

Google Scholar 
Sponheimer, M. et al. Turnover of stable carbon isotopes in the muscle, liver, and breath CO2 of alpacas (Lama pacos). Rapid Commun. Mass Spectrom. 20, 1395–1399. https://doi.org/10.1002/rcm.2454 (2006).ADS 
CAS 
Article 
PubMed 

Google Scholar 
Logan, J. M. & Lutcavage, M. E. Stable isotope dynamics in elasmobranch fishes. Hydrobiologia 644, 231–244. https://doi.org/10.1007/s10750-010-0120-3 (2010).CAS 
Article 

Google Scholar 
Madigan, D. J. et al. Tissue turnover rates and isotopic trophic discrimination factors in the endothermic teleost, pacific bluefin tuna (Thunnus orientalis). PLoS ONE 7, e49220. https://doi.org/10.1371/journal.pone.0049220 (2012).ADS 
CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
Skinner, M. M., Cross, B. K. & Moore, B. C. Estimating in situ isotopic turnover in Rainbow Trout (Oncorhynchus mykiss) muscle and liver tissue. J. Freshw. Ecol. 32, 209–217. https://doi.org/10.1080/02705060.2016.1259127 (2016).CAS 
Article 

Google Scholar 
Kaushik, S. J. & Seiliez, I. Protein and amino acid nutrition and metabolism in fish: Current knowledge and future needs. Aquac. Res. 41, 322–332. https://doi.org/10.1111/j.1365-2109.2009.02174.x (2010).CAS 
Article 

Google Scholar 
Hou, Y., Hu, S., Li, X., He, W. & Wu, G. Amino Acid Metabolism in the Liver: Nutritional and Physiological Significance. Vol. 1265 (2020).Gannes, L. Z., O’Brien, D. M. & Del Rio, C. M. Stable isotopes in animal ecology: Assumptions, caveats and a call for more laboratory experiments. Ecology 78, 1271–1276 (1997).Article 

Google Scholar 
Martinez del Rio, C. M., Wolf, N., Carleton, S. A. & Gannes, L. Z. Isotopic ecology ten years after a call for more laboratory experiments. Biol. Rev. Camb. Philos Soc. 84, 91–111. https://doi.org/10.1111/j.1469-185X.2008.00064.x (2009).Article 

Google Scholar 
Hendry, A. P., Peichel, C. L., Boughman, J. W., Matthews, B. & Nosil, P. Stickleback research: The now and the next. Evol. Ecol. Res. 15, 111–141 (2013).
Google Scholar 
Fang, B., Merila, J., Ribeiro, F., Alexandre, C. M. & Momigliano, P. Worldwide phylogeny of three-spined sticklebacks. Mol Phylogenet Evol 127, 613–625. https://doi.org/10.1016/j.ympev.2018.06.008 (2018).Article 
PubMed 

Google Scholar 
Kume, M. & Kitano, J. Genetic and stable isotope analyses of threespine stickleback from the Bering and Chukchi seas. Ichthyol. Res. 64, 478–480. https://doi.org/10.1007/s10228-017-0580-9 (2017).Article 

Google Scholar 
Reimchen, T. E., Ingram, T. & Hansen, S. C. Assessing niche differences of sex, armour and asymmetry phenotypes using stable isotope analyses in Haida Gwaii sticklebacks. Behaviour 145, 561–577 (2008).Article 

Google Scholar 
Pinnegar, J. Unusual stable isotope fractionation patterns observed for fish host–parasite trophic relationships. J. Fish Biol. 59, 494–503. https://doi.org/10.1006/jfbi.2001.1660 (2001).Article 

Google Scholar 
Power, M. & Klein, G. M. Fish host-cestode parasite stable isotope enrichment patterns in marine, estuarine and freshwater fishes from northern Canada. Isotopes Environ. Health Stud. 40, 257–266 (2004).CAS 
Article 

Google Scholar 
Li, X., Zheng, S. & Wu, G. Nutrition and metabolism of glutamate and glutamine in fish. Amino Acids 52, 671–691. https://doi.org/10.1007/s00726-020-02851-2 (2020).CAS 
Article 
PubMed 

Google Scholar 
Vander Zanden, M. J., Clayton, M. K., Moody, E. K., Solomon, C. T. & Weidel, B. C. Stable isotope turnover and half-life in animal tissues: A literature synthesis. PLoS ONE 10, e0116182. https://doi.org/10.1371/journal.pone.0116182 (2015).CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
Newsome, S. D., del Rio, C. M., Bearhop, S. & Phillips, D. L. A niche for isotopic ecology. Front. Ecol. Environ. 5, 429–436. https://doi.org/10.1890/060150.01 (2007).Article 

Google Scholar 
Voigt, C. C., Rex, K., Michener, R. H. & Speakman, J. R. Nutrient routing in omnivorous animals tracked by stable carbon isotopes in tissue and exhaled breath. Oecologia 157, 31–40. https://doi.org/10.1007/s00442-008-1057-3 (2008).ADS 
Article 
PubMed 

Google Scholar 
Tieszen, L. L., Boutton, T. W., Tesdahl, K. G. & Slade, N. A. Fractionation and turnover of stable carbon isotopes in animal tissues: Implications for δ13C analysis of diet. Oecologia 57, 21–37 (1983).Article 

Google Scholar 
Cerling, T. E. et al. Determining biological tissue turnover using stable isotopes: The reaction progress variable. Oecologia 151, 175–189. https://doi.org/10.1007/s00442-006-0571-4 (2007).ADS 
Article 
PubMed 

Google Scholar 
Martínez del Rio, C. & Carleton, S. A. How fast and how faithful: The dynamics of isotopic incorporation into animal tissues: Fig. 1. J. Mammal. 93, 353–359. https://doi.org/10.1644/11-mamm-s-165.1 (2012).Article 

Google Scholar 
McCullagh, J. S., Juchelka, D. & Hedges, R. E. Analysis of amino acid 13C abundance from human and faunal bone collagen using liquid chromatography/isotope ratio mass spectrometry. Rapid Commun. Mass Spectrom. 20, 2761–2768. https://doi.org/10.1002/rcm.2651 (2006).ADS 
CAS 
Article 
PubMed 

Google Scholar 
Raghavan, M., McCullagh, J. S., Lynnerup, N. & Hedges, R. E. Amino acid δ13C analysis of hair proteins and bone collagen using liquid chromatography/isotope ratio mass spectrometry: Paleodietary implications from intra-individual comparisons. Rapid Commun. Mass Spectrom. 24, 541–548. https://doi.org/10.1002/rcm.4398 (2010).ADS 
CAS 
Article 
PubMed 

Google Scholar 
Newsome, S. D., Wolf, N., Peters, J. & Fogel, M. L. Amino acid δ13C analysis shows flexibility in the routing of dietary protein and lipids to the tissue of an omnivore. Integr. Comp. Biol. 54, 890–902. https://doi.org/10.1093/icb/icu106 (2014).CAS 
Article 
PubMed 

Google Scholar 
Walton, M. J. & Cowey, C. B. Aspects of intermediary metabolism in salmonid fish. Comp. Biochem. Physiol. 73B, 59–79 (1982).CAS 

Google Scholar 
Fernandes, R., Nadeau, M.-J. & Grootes, P. M. Macronutrient-based model for dietary carbon routing in bone collagen and bioapatite. Archaeol. Anthropol. Sci. 4, 291–301. https://doi.org/10.1007/s12520-012-0102-7 (2012).Article 

Google Scholar 
Ohkouchi, N., Ogawa, N. O., Chikaraishi, Y., Tanaka, H. & Wada, E. Biochemical and physiological bases for the use of carbon and nitrogen isotopes in environmental and ecological studies. Prog. Earth Planet Sci. 2, 1–17. https://doi.org/10.1186/s40645-015-0032-y (2015).ADS 
Article 

Google Scholar 
Wu, G. & Morris, M. Arginine metabolism: Nitric oxide and beyond. Biochem. J. 336, 1–17 (1998).CAS 
Article 

Google Scholar 
Metges, C. C., Petzke, K. J. & Henning, U. Gas chromatography/combustion/isotope ratio mass spectrometric comparison of N-acetyl- and N-pivaloyl amino acid esters to measure 15N isotopic abundances in physiological samples : A pilot study on amino acid synthesis in the upper gastro-intestinal tract of minipigs. J. Mass Spectrom. 31, 367–376 (1996).ADS 
CAS 
Article 

Google Scholar 
Dunn, P. J., Honch, N. V. & Evershed, R. P. Comparison of liquid chromatography-isotope ratio mass spectrometry (LC/IRMS) and gas chromatography-combustion-isotope ratio mass spectrometry (GC/C/IRMS) for the determination of collagen amino acid δ13C values for palaeodietary and palaeoecological reconstruction. Rapid Commun. Mass Spectrom. 25, 2995–3011. https://doi.org/10.1002/rcm.5174 (2011).ADS 
CAS 
Article 
PubMed 

Google Scholar 
Ayayee, P. A., Jones, S. C. & Sabree, Z. L. Can (13)C stable isotope analysis uncover essential amino acid provisioning by termite-associated gut microbes?. PeerJ 3, e1218. https://doi.org/10.7717/peerj.1218 (2015).CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
Ayayee, P. A., Larsen, T. & Sabree, Z. Symbiotic essential amino acids provisioning in the American cockroach, Periplaneta americana (Linnaeus) under various dietary conditions. PeerJ 4, e2046. https://doi.org/10.7717/peerj.2046 (2016).CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
Larsen, T. et al. The dominant detritus-feeding invertebrate in Arctic peat soils derives its essential amino acids from gut symbionts. J. Anim. Ecol. 85, 1275–1285. https://doi.org/10.1111/1365-2656.12563 (2016).Article 
PubMed 

Google Scholar 
Romero-Romero, S., Miller, E. C., Black, J. A., Popp, B. N. & Drazen, J. C. Abyssal deposit feeders are secondary consumers of detritus and rely on nutrition derived from microbial communities in their guts. Sci. Rep. 11, 12594. https://doi.org/10.1038/s41598-021-91927-4 (2021).CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
McCullagh, J. S. Mixed-mode chromatography/isotope ratio mass spectrometry. Rapid Commun. Mass Spectrom. 24, 483–494. https://doi.org/10.1002/rcm.4322 (2010).ADS 
CAS 
Article 
PubMed 

Google Scholar 
Tsai, Y. et al. Histamine contents of fermented fish products in Taiwan and isolation of histamine-forming bacteria. Food Chem. 98, 64–70. https://doi.org/10.1016/j.foodchem.2005.04.036 (2006).CAS 
Article 

Google Scholar 
Landete, J. M., De Las Rivas, B., Marcobal, A. & Munoz, R. Updated molecular knowledge about histamine biosynthesis by bacteria. Crit. Rev. Food Sci. Nutr. 48, 697–714. https://doi.org/10.1080/10408390701639041 (2008).CAS 
Article 
PubMed 

Google Scholar 
Kanki, M., Yoda, T., Tsukamoto, T. & Baba, E. Histidine decarboxylases and their role in accumulation of histamine in tuna and dried saury. Appl. Environ. Microbiol. 73, 1467–1473. https://doi.org/10.1128/AEM.01907-06 (2007).ADS 
CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
Fernandez-Salguero, J. & Mackie, I. M. Histidine metabolism in mackerel (Scomber scombrus). Studies on histidine decarboxylase activity and histamine formation during storage of flesh and liver under sterile and non-sterile conditions. J. Fd Technol. 14, 131–139 (1979).CAS 
Article 

Google Scholar 
Sánchez-Muros, M.-J., Barroso, F. G. & Manzano-Agugliaro, F. Insect meal as renewable source of food for animal feeding: A review. J. Clean. Prod. 65, 16–27. https://doi.org/10.1016/j.jclepro.2013.11.068 (2014).CAS 
Article 

Google Scholar 
Khan, M. A. Histidine requirement of cultivable fish species: A review. Oceanogr Fish Open Access J. 8, 1–7. https://doi.org/10.19080/ofoaj.2018.08.555746 (2018).Article 

Google Scholar 
Hatch, K. A. in Comparative Physiology of Fasting, Starvation, and Food Limitation Ch. Chapter 20, 337–364 (2012).Bertinetto, C., Engel, J. & Jansen, J. ANOVA simultaneous component analysis: A tutorial review. Anal. Chim. Acta X 6, 100061. https://doi.org/10.1016/j.acax.2020.100061 (2020).CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
Nogales-Mérida, S. et al. Insect meals in fish nutrition. Rev. Aquac. 11, 1080–1103. https://doi.org/10.1111/raq.12281 (2018).Article 

Google Scholar 
Thongprajukaew, K., Pettawee, S., Muangthong, S., Saekhow, S. & Phromkunthong, W. Freeze-dried forms of mosquito larvae for feeding of Siamese fighting fish (Betta splendens Regan, 1910). Aquac. Res. 50, 296–303. https://doi.org/10.1111/are.13897 (2018).CAS 
Article 

Google Scholar 
Jackson, G. P., An, Y., Konstantynova, K. I. & Rashaid, A. H. Biometrics from the carbon isotope ratio analysis of amino acids in human hair. Sci. Justice 55, 43–50. https://doi.org/10.1016/j.scijus.2014.07.002 (2015).Article 
PubMed 

Google Scholar 
Werner, R. A. & Brand, W. A. Referencing strategies and techniques in stable isotope ratio analysis. Rapid. Commun. Mass Spectrom. 15, 501–519. https://doi.org/10.1002/rcm.258 (2001).ADS 
CAS 
Article 
PubMed 

Google Scholar 
Marks, R. G. H., Jochmann, M. A., Brand, W. A. & Schmidt, T. C. How to couple LC-IRMS with HRMS─a proof-of-concept study. Anal. Chem. 94, 2981–2987 (2022).CAS 
Article 

Google Scholar 
Lynch, A. H., McCullagh, J. S. & Hedges, R. E. Liquid chromatography/isotope ratio mass spectrometry measurement of δ13C of amino acids in plant proteins. Rapid Commun. Mass Spectrom. 25, 2981–2988. https://doi.org/10.1002/rcm.5142 (2011).ADS 
CAS 
Article 
PubMed 

Google Scholar 
Falco, F., Stincone, P., Cammarata, M. & Brandelli, A. Amino acids as the main energy source in fish tissues. Aquac. Fish Stud. 3, 1–11 (2020).
Google Scholar  More

Intergovernmental Science-Policy Platform on Biodiversity and Ecosystem Services (IPBES). Global assessment report on biodiversity and ecosystem services of the Intergovernmental Science-Policy Platform on Biodiversity and Ecosystem Services. 1–1148 (Intergovernmental Science-Policy Platform on Biodiversity and Ecosystem Services, Bonn, Germany, 2019).United Nations (UN). The Sustainable Development Goals report 2021, 1–68 (United Nations, United States, 2021).Whitmee, S. et al. Safeguarding human health in the Anthropocene epoch: Report of the Rockefeller Foundation-Lancet Commission on planetary health. The Lancet 386, 1973–2028. https://doi.org/10.1016/s0140-6736(15)60901-1 (2015).Article 

Google Scholar 
Convention on Biological Diversity. First draft of the post-2020 global biodiversity framework 1–12 (Convention on Biological Diversity, 2021).United Nations General Assembly. Transforming our world: The 2030 Agenda for Sustainable Development 1–35 (United Nations General Assembly, 2015).Clark, M., Hill, J. & Tilman, D. The diet, health, and environment trilemma. Annu. Rev. Environ. Resour. 43, 109–134. https://doi.org/10.1146/annurev-environ-102017-025957 (2018).Article 

Google Scholar 
Lu, N., Liu, L., Yu, D. & Fu, B. Navigating trade-offs in the social-ecological systems. Curr. Opin. Environ. Sustain. 48, 77–84. https://doi.org/10.1016/j.cosust.2020.10.014 (2021).Article 

Google Scholar 
Ellis, E. C., Pascual, U. & Mertz, O. Ecosystem services and nature’s contribution to people: Negotiating diverse values and trade-offs in land systems. Curr. Opin. Environ. Sustain. 38, 86–94. https://doi.org/10.1016/j.cosust.2019.05.001 (2019).Article 

Google Scholar 
World Health Organization (WHO). Promoting mental health: Concepts, emerging evidence, practice: Summary report 1–70 (World Health Organization, Geneva, Switzerland, 2004).Patel, V. et al. The Lancet Commission on global mental health and sustainable development. The Lancet 392, 1553–1598. https://doi.org/10.1016/s0140-6736(18)31612-x (2018).Article 

Google Scholar 
Prince, M. et al. No health without mental health. The Lancet 370, 859–877. https://doi.org/10.1016/s0140-6736(07)61238-0 (2007).Article 

Google Scholar 
GBD 2017 Disease and Injury Incidence and Prevalence Collaborators (GBD). Global, regional, and national incidence, prevalence, and years lived with disability for 354 diseases and injuries for 195 countries and territories, 1990–2017: a systematic analysis for the Global Burden of Disease Study 2017. Lancet 392, 1789–1858. https://doi.org/10.1016/s0140-6736(18)32279-7 (2018).Vigo, D., Kestel, D., Pendakur, K., Thornicroft, G. & Atun, R. Disease burden and government spending on mental, neurological, and substance use disorders, and self-harm: Cross-sectional, ecological study of health system response in the Americas. Lancet Public Health 4, e89–e96. https://doi.org/10.1016/s2468-2667(18)30203-2 (2019).Article 
PubMed 

Google Scholar 
Pienkowski, T. et al. The role of nature conservation and commercial farming in psychological distress among rural Ugandans. bioRxiv. https://doi.org/10.1101/2021.06.08.446718 (2021).Article 

Google Scholar 
Cunsolo, W. A. et al. Climate change and mental health: An exploratory case study from Rigolet, Nunatsiavut, Canada. Climatic Change 121, 255–270. https://doi.org/10.1007/s10584-013-0875-4 (2013).ADS 
Article 

Google Scholar 
Cunsolo, A. et al. “You can never replace the caribou”: Inuit experiences of ecological grief from caribou declines. Am. Imago 77, 31–59. https://doi.org/10.1353/aim.2020.0002 (2020).Article 

Google Scholar 
Ellis, N. R. & Albrecht, G. A. Climate change threats to family farmers’ sense of place and mental wellbeing: A case study from the Western Australian Wheatbelt. Soc. Sci. Med. 175, 161–168. https://doi.org/10.1016/j.socscimed.2017.01.009 (2017).Article 
PubMed 

Google Scholar 
Scyphers, S. B., Picou, J. S. & Grabowski, J. H. Chronic social disruption following a systemic fishery failure. Proc. Natl. Acad. Sci. 116, 22912. https://doi.org/10.1073/pnas.1913914116 (2019).ADS 
CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
Lund, C. et al. Social determinants of mental disorders and the sustainable development goals: A systematic review of reviews. Lancet Psychiatry 5, 357–369. https://doi.org/10.1016/s2215-0366(18)30060-9 (2018).Article 
PubMed 

Google Scholar 
Nilsson, M., Griggs, D. & Visbeck, M. Policy: Map the interactions between sustainable development goals. Nature 534, 320–322. https://doi.org/10.1038/534320a (2016).ADS 
Article 
PubMed 

Google Scholar 
Ridley, M., Rao, G., Schilbach, F. & Patel, V. Poverty, depression, and anxiety: Causal evidence and mechanisms. Science 370, eaay0214. https://doi.org/10.1126/science.aay0214 (2020).CAS 
Article 
PubMed 

Google Scholar 
Kinyanda, E. et al. Poverty, life events and the risk for depression in Uganda. Soc. Psychiatry Psychiatr. Epidemiol. 46, 35–44. https://doi.org/10.1007/s00127-009-0164-8 (2011).Article 
PubMed 

Google Scholar 
Kinyanda, E., Waswa, L., Baisley, K. & Maher, D. Prevalence of severe mental distress and its correlates in a population-based study in rural south-west Uganda. BMC Psychiatry 11, 1–9. https://doi.org/10.1186/1471-244X-11-97 (2011).Article 

Google Scholar 
United Nations (UN). The convention on biological diversity 1–30 (United Nations, Rio de Janeiro, Brazil, 1992).Díaz, S. et al. The IPBES Conceptual Framework—Connecting nature and people. Curr. Opin. Environ. Sustain. 14, 1–16. https://doi.org/10.1016/j.cosust.2014.11.002 (2015).Article 

Google Scholar 
Rasolofoson, R. A., Hanauer, M. M., Pappinen, A., Fisher, B. & Ricketts, T. H. Impacts of forests on children’s diet in rural areas across 27 developing countries. Sci. Adv. 4, eaat2853. https://doi.org/10.1126/sciadv.aat2853 (2018).ADS 
Article 
PubMed 
PubMed Central 

Google Scholar 
Ickowitz, A., Powell, B., Salim, M. A. & Sunderland, T. C. H. Dietary quality and tree cover in Africa. Glob. Environ. Chang. 24, 287–294. https://doi.org/10.1016/j.gloenvcha.2013.12.001 (2014).Article 

Google Scholar 
Ribot, J. C. & Peluso, N. L. A theory of access. Rural. Sociol. 68, 153–181. https://doi.org/10.1111/j.1549-0831.2003.tb00133.x (2003).Article 

Google Scholar 
Hicks, C. C. & Cinner, J. E. Social, institutional, and knowledge mechanisms mediate diverse ecosystem service benefits from coral reefs. Proc. Natl. Acad. Sci. 111, 17791. https://doi.org/10.1073/pnas.1413473111 (2014).ADS 
CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
Blaikie, P. Environment and access to resources in Africa. Africa 59, 18–40. https://doi.org/10.2307/1160761 (1989).Article 

Google Scholar 
Berbés-Blázquez, M., González, J. A. & Pascual, U. Towards an ecosystem services approach that addresses social power relations. Curr. Opin. Environ. Sustain. 19, 134–143. https://doi.org/10.1016/j.cosust.2016.02.003 (2016).Article 

Google Scholar 
Thoms, C. A. Community control of resources and the challenge of improving local livelihoods: A critical examination of community forestry in Nepal. Geoforum 39, 1452–1465. https://doi.org/10.1016/j.geoforum.2008.01.006 (2008).Article 

Google Scholar 
Peluso, N. L. & Lund, C. New frontiers of land control: Introduction. J. Peasant Stud. 38, 667–681. https://doi.org/10.1080/03066150.2011.607692 (2011).Article 

Google Scholar 
Ostrom, E. A diagnostic approach for going beyond panaceas. Proc. Natl. Acad. Sci. 104, 15181–15187. https://doi.org/10.1073/pnas.0702288104 (2007).ADS 
Article 
PubMed 
PubMed Central 

Google Scholar 
United Nations Environment World Conservation Monitoring Centre (UNEP-WCMC) & International Union for Conservation of Nature (IUCN). In Protected Planet: The World Database on Protected Areas (WDPA). https://www.protectedplanet.net/ (2020).Pullin, A. S. et al. Human well-being impacts of terrestrial protected areas. Environ. Evid. 2, 1–41. https://doi.org/10.1186/2047-2382-2-19 (2013).Article 

Google Scholar 
Schleicher, J. et al. Protecting half of the planet could directly affect over one billion people. Nat. Sustain. 2, 1094–1096. https://doi.org/10.1038/s41893-019-0423-y (2019).Article 

Google Scholar 
ICCA Consortium. Territories of Life: 2021 report. 1–153 (ICCA Consortium, 2021).Food and Agriculture Organization of the United Nations (FAO). FAOSTAT. https://www.fao.org/faostat/en/#data (2021).Vabi Vamuloh, V., Panwar, R., Hagerman, S. M., Gaston, C. & Kozak, R. A. Achieving Sustainable Development Goals in the global food sector: A systematic literature review to examine small farmers engagement in contract farming. Bus. Strategy Dev. 2, 276–289 (2019).Article 

Google Scholar 
Meemken, E.-M. & Bellemare, M. F. Smallholder farmers and contract farming in developing countries. Proc. Natl. Acad. Sci. 117, 259. https://doi.org/10.1073/pnas.1909501116 (2020).CAS 
Article 
PubMed 

Google Scholar 
Bellemare, M. F. & Bloem, J. R. Does contract farming improve welfare? A review. World Dev. 112, 259–271. https://doi.org/10.1016/j.worlddev.2018.08.018 (2018).Article 

Google Scholar 
Hall, R., Scoones, I. & Tsikata, D. Plantations, outgrowers and commercial farming in Africa: Agricultural commercialisation and implications for agrarian change. J. Peasant Stud. 44, 515–537. https://doi.org/10.1080/03066150.2016.1263187 (2017).Article 

Google Scholar 
Travers, H., Clements, T. & Milner-Gulland, E. J. Predicting responses to conservation interventions through scenarios: A Cambodian case study. Biol. Cons. 204, 403–410. https://doi.org/10.1016/j.biocon.2016.10.040 (2016).Article 

Google Scholar 
Booth, H. et al. Designing locally-appropriate conservation incentives for small-scale fishers. OSF Preprints. https://doi.org/10.31219/osf.io/bxzfs (2021).Martiniello, G. Bitter sugarification: Sugar frontier and contract farming in Uganda. Globalizations 18, 355–371. https://doi.org/10.1080/14747731.2020.1794564 (2021).Article 

Google Scholar 
Kyongera, D. Mapping private sector investments and their impacts on great ape habitats in Uganda’s Albertine Rift region 1–50 (International Institute for Environment and Development (IIED), London, United Kingdom, 2015).Hansen, M. C. et al. High-resolution global maps of 21st-century forest cover change. Science 342, 850–853. https://doi.org/10.1126/science.1244693 (2013).ADS 
CAS 
Article 
PubMed 

Google Scholar 
Paterson, J. D. The ecology and history of Uganda’s Budongo forest. For. Conserv. History 35, 179–187 (1991).Article 

Google Scholar 
Babweteera, F. et al. in Conservation and Development in Uganda (eds Sandbrook, C., Cavanagh, C. J. & Tumusiime, D. M.) 104–122 (Taylor and Francis Inc., 2018).National Planning Authority of Uganda. Third national development plan (NDP III) 2020/21–2024/25. 1–341 (Gobernment of Uganda, Entebbe, Uganda, 2020).Kroenke, K., Spitzer, R. L. & Williams, J. B. W. The PHQ-9: Validity of a brief depression severity measure. J. Gen. Intern. Med. 16, 606–613. https://doi.org/10.1046/j.1525-1497.2001.016009606.x (2001).CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
American Psychiatric Association. Diagnostic and Statistical Manual of Mental Disorders (DSM-5). 5th edn, 1–947 (American Psychiatric Association, 2013).Wagner, G. J. et al. The role of depression in work-related outcomes of HIV treatment in Uganda. Int. J. Behav. Med. 21, 946–955. https://doi.org/10.1007/s12529-013-9379-x (2014).Article 
PubMed 
PubMed Central 

Google Scholar 
Kinyanda, E. et al. Effectiveness and cost-effectiveness of integrating the management of depression into routine HIV Care in Uganda (the HIV + D trial): A protocol for a cluster-randomised trial. Int. J. Ment. Heal. Syst. 15, 45. https://doi.org/10.1186/s13033-021-00469-9 (2021).Article 

Google Scholar 
Wu, Y. et al. Equivalency of the diagnostic accuracy of the PHQ-8 and PHQ-9: A systematic review and individual participant data meta-analysis. Psychol. Med. 50, 1368–1380. https://doi.org/10.1017/S0033291719001314 (2020).Article 
PubMed 

Google Scholar 
Kroenke, K. et al. The PHQ-8 as a measure of current depression in the general population. J. Affect. Disord. 114, 163–173. https://doi.org/10.1016/j.jad.2008.06.026 (2009).Article 
PubMed 

Google Scholar 
Kaiser, B. N. et al. Thinking too much: A systematic review of a common idiom of distress. Soc. Sci. Med. 147, 170–183. https://doi.org/10.1016/j.socscimed.2015.10.044 (2015).Article 
PubMed 
PubMed Central 

Google Scholar 
Ballard, T., Kepple, A. & Cafiero, C. The Food Insecurity Experience Scale: Development of a global standard for monitoring hunger worldwide (Food and Agriculture Organization, Rome, Italy, 2013).Jones, A. D., Ngure, F. M., Pelto, G. & Young, S. L. What are we assessing when we measure food security? A compendium and review of current metrics. Adv. Nutr. 4, 481–505. https://doi.org/10.3945/an.113.004119 (2013).Article 
PubMed 
PubMed Central 

Google Scholar 
Travers, H. et al. Understanding complex drivers of wildlife crime to design effective conservation interventions. Conserv. Biol. 33, 1296–1306. https://doi.org/10.1111/cobi.13330 (2019).Article 
PubMed 

Google Scholar 
Zimet, G. D., Powell, S. S., Farley, G. K., Werkman, S. & Berkoff, K. A. Psychometric characteristics of the multidimensional scale of perceived social support. J. Pers. Assess. 55, 610–617. https://doi.org/10.1080/00223891.1990.9674095 (1990).CAS 
Article 
PubMed 

Google Scholar 
Bowling, A. Just one question: If one question works, why ask several?. J. Epidemiol. Community Health 59, 342–345. https://doi.org/10.1136/jech.2004.021204 (2005).Article 
PubMed 
PubMed Central 

Google Scholar 
Office for National Statistics (ONS). General Household Survey 2007: Household and individual questionnaires 1–140 (Office for National Statistics, Titchfield, United Kingdom, 2007).Coates, J. Build it back better: Deconstructing food security for improved measurement and action. Glob. Food Sec. 2, 188–194. https://doi.org/10.1016/j.gfs.2013.05.002 (2013).Article 

Google Scholar 
Sseguya, H., Mazur, R. E. & Flora, C. B. Social capital dimensions in household food security interventions: Implications for rural Uganda. Agric. Hum. Values 35, 117–129. https://doi.org/10.1007/s10460-017-9805-9 (2017).Article 

Google Scholar 
Narayan, D., Chambers, R., Shah, M. K. & Petesch, P. Voices of the poor: Crying out for change 1–332 (The World Bank, New York, United States, 2000).Harttgen, K. & Vollmer, S. Using an asset index to simulate household income. Econ. Lett. 121, 257–262. https://doi.org/10.1016/j.econlet.2013.08.014 (2013).Article 

Google Scholar 
Merkle, E. C., Fitzsimmons, E., Uanhoro, J. & Goodrich, B. Efficient Bayesian structural equation modeling in Stan. arXiv preprint arXiv:2008.07733v1 (2020).Kinyanda, E. et al. Major depressive disorder and suicidality in early HIV infection and its association with risk factors and negative outcomes as seen in semi-urban and rural Uganda. J. Affect. Disord. 212, 117–127. https://doi.org/10.1016/j.jad.2017.01.033 (2017).Article 
PubMed 

Google Scholar 
Jones, A. D. Food insecurity and mental health status: A global analysis of 149 countries. Am. J. Prev. Med. 53, 264–273. https://doi.org/10.1016/j.amepre.2017.04.008 (2017).ADS 
Article 
PubMed 

Google Scholar 
McElreath, R. Statistical Rethinking: A Bayesian Course with Examples in R and Stan, 1 edn, 1–505 (Chapman & Hall/CRC, 2016).Depaoli, S. & Van de Schoot, R. Improving transparency and replication in Bayesian statistics: The WAMBS-Checklist. Psychol. Methods 22, 240–261. https://doi.org/10.1037/met0000065 (2017).Article 
PubMed 

Google Scholar 
Naidoo, R. et al. Evaluating the impacts of protected areas on human well-being across the developing world. Sci. Adv. https://doi.org/10.1126/sciadv.aav3006 (2019).Article 
PubMed 
PubMed Central 

Google Scholar 
Golden, C. D., Fernald, L. C., Brashares, J. S., Rasolofoniaina, B. J. & Kremen, C. Benefits of wildlife consumption to child nutrition in a biodiversity hotspot. Proc. Natl. Acad. Sci. 108, 19653–19656. https://doi.org/10.1073/pnas.1112586108 (2011).ADS 
Article 
PubMed 
PubMed Central 

Google Scholar 
Mohanakumar, S. & Sharma, R. K. Analysis of farmer suicides in Kerala. Econ. Pol. Wkly 41, 1553–1558 (2006).
Google Scholar 
Bryant, L. & Garnham, B. Beyond discourses of drought: The micro-politics of the wine industry and farmer distress. J. Rural. Stud. 32, 1–9. https://doi.org/10.1016/j.jrurstud.2013.03.002 (2013).Article 

Google Scholar 
Burgman, M. A. et al. Expert status and performance. PLOS ONE 6, e22998. https://doi.org/10.1371/journal.pone.0022998 (2011).ADS 
CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
Hemming, V., Burgman, M. A., Hanea, A. M., McBride, M. F. & Wintle, B. C. A practical guide to structured expert elicitation using the IDEA protocol. Methods Ecol. Evol. 9, 169–180. https://doi.org/10.1111/2041-210X.12857 (2018).Article 

Google Scholar 
Bennett, N. J. et al. Conservation social science: Understanding and integrating human dimensions to improve conservation. Biol. Cons. 205, 93–108. https://doi.org/10.1016/j.biocon.2016.10.006 (2017).Article 

Google Scholar 
Montana, J., Elliott, L., Ryan, M. & Wyborn, C. The need for improved reflexivity in conservation science. Environ. Conserv. https://doi.org/10.1017/s0376892920000326 (2020).Article 

Google Scholar 
Kleinman, A. Global mental health: A failure of humanity. The Lancet 374, 603–604. https://doi.org/10.1016/S0140-6736(09)61510-5 (2009).Article 

Google Scholar 
Collins, P. Y. et al. Grand challenges in global mental health. Nature 475, 27–30. https://doi.org/10.1038/475027a (2011).CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
Travers, H. et al. A manifesto for predictive conservation. Biol. Cons. 237, 12–18. https://doi.org/10.1016/j.biocon.2019.05.059 (2019).Article 

Google Scholar 
Buckley, R. et al. Economic value of protected areas via visitor mental health. Nat. Commun. 10, 5005. https://doi.org/10.1038/s41467-019-12631-6 (2019).ADS 
CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
Saxena, S., Thornicroft, G., Knapp, M. & Whiteford, H. Resources for mental health: Scarcity, inequity, and inefficiency. The Lancet 370, 878–889. https://doi.org/10.1016/S0140-6736(07)61239-2 (2007).Article 

Google Scholar 
Fedele, G., Donatti, C. I., Bornacelly, I. & Hole, D. G. Nature-dependent people: mapping human direct use of nature for basic needs across the tropics. Global Environ. Change https://doi.org/10.1016/j.gloenvcha.2021.102368 (2021).Article 

Google Scholar 
Turyahabwe, N., Agea, J. G., Tweheyo, M. & Tumwebaze, S. B. In Sustainable Forest Management: Case Studies (ed J. J. Diez) Ch. 3, 51–74 (InTech, 2012).Garnett, S. T. et al. A spatial overview of the global importance of Indigenous lands for conservation. Nat. Sustain. 1, 369–374. https://doi.org/10.1038/s41893-018-0100-6 (2018).Article 

Google Scholar 
Dawson, N. M. et al. The role of Indigenous peoples and local communities in effective and equitable conservation. Ecol. Soc. https://doi.org/10.5751/ES-12625-260319 (2021).Article 

Google Scholar 
Ministry of Agriculture, Animal Industry and Fisheries. Agriculture sector strategic plan 2015/16–2019/20. 1–199 (Government of Uganda, Entebbe, Uganda, 2016). More

Renewal processes represent development under variable conditionsThe consequence of a drastic environmental change can be demonstrated by introducing a shift in development time during the process. For demonstration, we consider a scenario where a group of individuals enter into a favourable environment reducing development time from (40pm 5) time units to (20pm 5).We show, in Fig. 1, that our dynamic pseudo-stage-structured MPM yields a gradual stage completion with an average development time of approximately (30pm 5) steps (solid dark lines) when conditions shift at ({tau }=20) (each step corresponds to 1 time unit). The target Erlang-distributed development trajectories without the shift are shown as dashed gray lines. The snapshots of the population structure, represented by the development indicator q, taken at each time step, show that half of the development is complete at the time of the switch and the switch accelerates the accumulation of q (Fig. S1).Figure 1Response to change in development time. The number of developing individuals is simulated by using the cumulative development process and compared to (a) the age-dependent development process, (b) an ODE representation, (c) an LCT representation, and (d) a DDE representation. Solid dark lines show the cumulative development and thick blue lines show the alternative models. Dashed gray lines mark the two target trajectories before and after the shift in development time (marked with red crosses).Full size imageIn age-dependent development, a sharp transition, instead of a gradual one, is observed at the (20^{th}) step (Fig. 1a). The switch results in the majority of individuals reaching target development age immediately at the time of switch. Previous work, reported in Erguler et al.59 and Erguler et al.55, aimed at modelling population dynamics under variable conditions, based on this dynamic age-dependent framework. Our results suggest that cumulative development might improve the fit to the data, prediction accuracy, and applicable geospatial range of these models.We see in Fig. 1b that the canonical ODE framework represents an exponentially distributed development time and a shift in rate at (t=20). The LCT extension to the framework helps to incorporate time dependence and represent the long and short development time distributions (Fig. 1c). The resulting model accommodates change in the rate parameter (gamma ) (Eq. 8), e.g doubling of (gamma ) changes development time from (40pm 5) to (20pm 2.5). However, to accommodate the required shift, the model needs to be transformed from a 66-dimensional system to an 18-dimensional one, which is beyond the scope of this work. We argue that in cases where development time distribution is fixed a priori (excluded from model calibration), the LCT framework provides a significant advantage over canonical ODEs. Although the framework has been used in the field of infectious disease epidemiology64,65, it has recently been applied to the modelling of vector population dynamics30.The DDE framework also yields a gradual development trajectory with an intermediate duration (Fig. 1d). However, the distribution tends towards the longer development trajectory compared to the one achieved with cumulative development. The canonical DDE framework assumes a homogenous cohort, where all individuals react in the same way to variations in development rate. The assumption gives rise to sharp stage transitions within a single generation if all individuals are introduced at the same time. As a potential workaround, it has been proposed to generate a plausible population history, through variable entry times, until the required (or observed) developmental variation builds up31,32. Variation in development rates then acts upon the population and results in the modification of the existing age-structure. It is worthwhile to mention that a recent extension to the DDE framework to accommodate trait variation in population dynamics34 might also accommodate changing development rates within a single stage; however, it has not yet been employed at this scale.Cumulative development is in agreement with the widely known degree-day (DD) framework, where development time is predicted by the heat accumulating in organisms46. Although the rate of accumulation in response to environmental conditions varies considerably, the DD framework implies that the combination of two different rates yields an average development time (also seen with cumulative development in Fig. 1). Experimental evaluation of this will be the topic of future research.It is worth mentioning that our dynamically structured renewal process-based MPM follows the assumption of random population heterogeneity9,11; namely, at the individual level, the future behaviour of an organism is not affected by its historical behaviour. However, trait variation within a population is prevalent in many species, and is known to impact population dynamics and species interactions34,66,67. Future development of our framework will consider improving upon this limitation.Environmental variation transformed into development timesSeveral non-linear relationships have been proposed to represent the temperature dependence of insect development68. A common feature is the presence of low and high temperature thresholds beyond which development is prohibitively slow. Often, there exists an optimum between the thresholds where the process is most efficient. A typical relationship between temperature and development rate, reported in Briere et al.50, is seen in Fig. 2a. Mean development time, given by the reciprocal of rate in Fig. 2b, exhibits the two thresholds and the optimum.Figure 2Development under environmental variation. In (a), development rate (Eq. 9) is shown with (alpha =1.5times 10^{-5}), (T_L=0^oC), and (T_H=50^oC). In (b), mean development time is shown together with the probability densities of three temperature regimes ((rho _L), (rho _M), and (rho _H)). In (c), the number of individuals completing development at each step are shown with respect to the three temperature regimes. Solid lines indicate the median, shaded areas indicate the (90%) range of 1000 simulations, and thick lines indicate simulations with the expected values of each regime.Full size imageTo investigate how temperature variation is transformed into cumulative development time, we assumed three variation regimes at relatively low, medium, and high temperatures ((rho _L), (rho _M), and (rho _H), respectively). Densities of the corresponding Gaussian probability distributions are plotted in Fig. 2b. Accordingly, each variation is transformed by a slightly different region of the rate function (Eq. 9). Eventually, the three development time distributions emerge as shown in Fig. 2c.We found that the output of (rho _H) is skewed towards longer durations compared to what we would otherwise obtain if we simulated the process under constant conditions with the mean of (rho _H). The impact of variation in the middle range, (rho _M), is similar to that of (rho _H), but less pronounced. Conversely, the output of (rho _L) is skewed towards shorter durations. Our results suggest that, when development is already highly efficient, variation in temperature causes frequent encounters of longer (but not shorter) development durations, eventually extending the overall duration of the process. In the low efficiency range, development takes long to complete, but frequent encounters of relatively short durations—especially as the process approaches its optimum duration—triggers completion earlier than in the case of no variation.Overall, our model predictions are in agreement with the rate summation effect, which states that the different outcomes obtained under constant and varying temperatures is due to the non-linear relationship between temperature and development rate (the Kaufmann effect)16. Furthermore, acceleration of development in insects subjected to varying high temperatures, its retardation at varying low temperatures, and low variability of development time in the linear range of the rate curve have been widely discussed69. Several groups have reported evidence in support of this effect, which is also in agreement with our results. For instance, Vangansbeke et al. (2015) reported for three insect species, Phytoseiulus persimilis, Neoseiulus californicus, and Tetranychus urticae, that varying temperatures with a lower mean yields faster development compared to the yield at mean constant temperatures70. However, observations of this phenomenon might result in different responses for different species at similar temperatures due to the difference in rate curves. Identification of the optimum temperature range may facilitate comparison. For instance, Carrington et al. (2013) assumed (26^oC) as optimum based on the high dengue incidence in Thailand, and showed that large variations around (26^oC) increases development time for the dengue vector, Aedes aegypti71. Wu et al. (2015) demonstrated that development is faster at around (26^oC) compared to (23^oC) for the fly, Megaselia scalaris, and found that varying temperatures at around (23^oC) accelerates the process47. Finally, in a modelling study employing DDs, Chen et al. (2013) reported that larger diurnal temperature ranges relate to additional DD accumulation and faster development in grape berry moth, Paralobesia viteana72. Under the realistic non-optimum field conditions, where these simulations had been performed, a decrease in development time is expected in response to varying temperatures according to our results.We note that the variation in development times is due to temperature since we ignore intrinsic stochasticity to demonstrate the impact of (rho ) in isolation. The deterministic setup removes the upper limit in the number of distinct pseudo-stage indicators: a different q emerges from each k, and a different k emerges from each (rho ). Since the number of pseudo-stages quickly exhausts the computational resources, we set the precision of q to the nearest 100(^{th}) decimal point, effectively capping the number of pseudo-stages at 100 (see Accuracy of the pseudo-stage approximation). As shown in Fig. S2, the approximation has a negligible impact on accuracy.Environmental dependency extracted from life tables under constant conditionsHaving discussed the importance of environmental variability in development, in this section, we employ a well-established experimental method to unravel the relationship between temperature and development time in a common mosquito species. In contrast to invasive vectors, which effectively render new territories suitable for disease transmission, Culex species pose an imminent threat with their wide distribution and ornitophilic (Cx. pipiens biotype pipiens), mamophilic (Cx. pipiens biotype molestus), and intermixed (their hybrids) blood feeding behaviour. Here, we investigate the temperature dependencies of mortality and development of Cx. quinquefasciatus, the southern house mosquito, which is an important disease vector, widely distributed across the tropics and sub-tropics73,74.To infer the dependencies, we used a generic temperature-driven insect development model, described in Methods, and the life history observations performed at five constant temperatures (15, 20, 23, 27, and (30,^{circ })C) under laboratory conditions60,61. As a result of the inverse modelling procedure, detailed in Methods, we found that the generic model yields an overall match between the simulations and observations. In Fig. 3a, we present a comparison of observed and simulated maximum production and the stage-emergence times for pupae and adults. Here, we define the stage-emergence time as the time taken from the beginning of an experiment to the time when half of the maximum production of a stage (pupa or adult) is observed. In addition, in Fig. S3, we present the comparison of time trajectories separately for each temperature.Figure 3Inverse modelling of Cx. quinquefasciatus environmental dependency. The comparison of observed and simulated maximum pupa (P) and adult (A) production and the corresponding stage-emergence times is given in (a). Observations are represented with dots and simulations with box plots. The environmental dependency of larva and pupa development time (b) and mortality (c), derived by the posterior mode sample (Theta _q), is shown in (b,c). Solid lines represent the median and shaded areas represent the (90%) range.Full size imageWe found that the generic model faithfully replicates the observed development times of larvae and pupae. On the other hand, stage mortalities are predicted well at three temperatures, but are overestimated at 20 or (27,^{circ })C. The impact of temperature on mortality might be more complex than it is captured by the quartic equation (Eq. 11). Optimum survival seen at (27,^{circ })C suggests that the relationship might be non-symmetrical or multimodal. In addition, the observed variability in mortality suggests that the mismatch could also be due to experimental error or the intrinsic stochasticity of the biological processes.We extracted the functional forms of temperature dependence from the posterior samples, shown in Fig. 3b, c, and found that the data inform the model as expected within the temperature range of the experiments ((15{-}30,^{circ })C). Stage durations are well informed, and reflect the low variability seen in the data (the standard deviation is less than 1.5 days at all temperatures for both stages). Accordingly, pupae develop in less than 4 days, which is much shorter than the larva development time (between 10 and 20 days above (20,^{circ })C). The model predicts that the minimum temperature at which development occurs (from the larva stage) is (10.5,^{circ })C, which is close to (10.9,^{circ })C, reported in Grech et al.75.The observed variability in pupa and adult production suggests that survival is a highly stochastic process regardless of the controlled laboratory conditions. A deterministic model, such as the one used in this context, represents the mean of such processes but does not capture their variability. The simulated variability is a result of the uncertainty in parameter estimates. Model parameters contribute unequally to the output as a result of the model structure and the functional forms of temperature dependence, and the data inform certain parameters better than others76,77. For instance, daily mortality, shown in Fig. 3c, is more constrained for larva than pupa, which is likely due to the short duration of the pupa stage—changes in daily mortality have larger consequences as development time increases.We note that a well-informed model yields predictions in the form of verifiable hypotheses; however, these are not necessarily accurate predictions. Model accuracy is assessed when such hypotheses are experimentally tested as part of the cyclic process of model development78. Here, we demonstrated that our modelling framework can be used to derive biologically meaningful inferences and to help improve the understanding of the temperature dependence of Cx. quinquefasciatus.Greater information content of semi-field experimentsThe number of experiments required to test a range of conditions, including different combinations of multiple drivers, may quickly exhaust available resources. Moreover, variable conditions may have a previously unaccounted impact on development and mortality. In this section, we demonstrate that observations performed under variable conditions are valuable sources of information for our modelling framework, which is capable of representing the dynamics under such conditions.Cx. pipiens, the northern house mosquito, is a competent disease vector, widely distributed across the temperate countries in North America, Europe, Asia, and North and East Africa74,79. Unlike Cx. quinquefasciatus, Cx. pipiens biotype pipiens is known to enter a reproductive diapause phase, where adult females arrest oogenesis during harsh winter conditions80,81. When larvae are exposed to short photoperiods and low temperatures during development, they emerge as adults destined to diapause. Although Cx. pipiens biotype molestus has lost the ability to diapause, its immature stages have been reported to retain metabolic sensitivity to photoperiod82,83.To reveal the environmental dependence of the molestus biotype, we exposed its eggs to variable temperatures in semi-field conditions until adult emergence (or loss of cohort). The numbers of viable larvae, pupae, and adults observed in different experimental batches are given in Fig. S4. We employed the extended model with both temperature and photoperiod dependence (see Methods), and calibrated the model against seven of the semi-field experiments, performed in March, May, June, July, August, and September (Fig. S4(a), (b), (d), (f), (g), (i) and (j)).As a result, we found that the model replicates the patterns of abundance emerging in the observations, e.g. stage timing and maximum adult production, reasonably well in most of the experiments, regardless of the times during which they were performed (Figs. S5 and S6). Quantitative evaluation of the agreement reveals that the observed and simulated adult emergence times are less than a week apart (Table 1).Table 1 Comparison of observed and simulated adult emergence time and the total number of adults produced. Simulation output is given in terms of the median and (90%) range.Full size tableOn the other hand, we found that egg and larva mortalities, and also, pupa and adult production are highly variable in the observations (see Fig. S4(c), (f), and (g)). Spikes of larva mortality are seen in Spring and Autumn (especially in May, September, and October). Despite this variability, the difference between the predicted and observed adult production was around 11 or less, except in the case of the experiment E7, which unexpectedly yielded only one pupa and no adults.We obtain relatively large mismatches when predicting larva abundances, specifically where egg mortality is not predicted well (E5, E7, E8, E10, E11, E12). We hypothesise that the stress associated with rearing lab-grown specimens under variable conditions might elevate egg mortality, induce premature hatching, or affect the survival of the larvae produced. Since egg development starts inside gravid females, i.e. under the optimum conditions of the laboratory, the observable part of development subjected to variable conditions remains mainly the hatching behaviour. Consequently, we observed rapid and synchronous completion of the egg stage in all experiments (see Figs. S5 and S6). Being exposed to a narrow range of temperatures, relatively less information can be obtained on the environmental dependency of the egg stage. As a potential improvement, we recommend that future adaptations of the semi-field experiments consider using field-captured adult female mosquitoes as the source of eggs.In addition to egg mortality, we observed spikes of larva mortality in May (E3), July (E8), and in Autumn (E14, E15, and E16). A likely cause of such transient high mortality is brief temperature shifts towards the extremes. However, the rarity of such events prevents the inverse modelling procedure from adequately capturing their impacts on life processes. As a potential improvement, we recommend that the experiments are performed in overlapping time frames, increasing the likelihood of observing the impact of an extreme event at different times during development. We note that the early decline in larva abundance seen in Autumn could be a result of insufficient food supply due to the increased nutritional requirements. According to the proposed metabolic response to short photoperiods, larvae would require additional food to accumulate fat reserves in preparation for diapause, the state where adult females endure several months without feeding. This implies that development takes longer than it would at long photoperiods when subjected to similar temperature regimes.Using the extended model and the semi-field data, we identified the environmental dependencies shown in Fig. 4. The data informed about the temperature dependency of each life stage as well as the photoperiod dependency of larvae. As expected, the overall variability in the inferred dependencies is higher for Cx. pipiens compared to Cx. quinquefasciatus (Fig. 3). We found that the larva and pupa development times closely match the observations reported by Spanoudis et al.62 at long photoperiods (see Fig. S7). However, the development times reported in Kiarie-Makara et al.84 at short photoperiods and moderate temperatures do not suggest a significant impact of daylight, which could be due to the particular strain of Cx. pipiens used in these experiments. As expected, the temperature dependency of egg development was not well informed by the data in the current configuration of the model and the functional forms of environmental dependence.Figure 4Environmental dependency of Cx. pipiens development and mortality inferred from semi-field life table experiments. Solid lines represent the median and shaded areas represent the (90%) range.Full size imageWe found that the photoperiod dependency is significantly non-linear with an average threshold of 13.7 hours of daylight (Fig. 4c). Photoperiod-driven extension in development time (about 1.7 times more at 13:11 h L:D than at 15:9 h L:D) contributes to improving the accuracy of predictions at the end of the high season (Fig. S8). The critical photoperiod (CPP) agrees well with the ones identified for Cx. pipiens biotype pipiens85,86. For instance, Sanburg and Larsen reported that there is an exponential relationship between follicle sizes in adult females (signifying commitment to diapause) and the photoperiods they were exposed to during immature stages85. We inferred a similar (but reverse) gradient between photoperiod and the extension of larva development time from 15 to 12 hours of daylight (Fig. 4c).Risk assessment with annual development profilesWe extrapolated the development dynamics of Cx. pipiens over the calendar year by setting up a hypothetical experiment at the beginning of each week. We simulated the subsequent development dynamics and obtained the annual development profile as shown in Fig. 5. Accordingly, the immature stages begin development from late February and the first adults emerge in May (adults emerging late in May start developing in the experiments set up late in March). The profile is consistent with the regular Cx. pipiens high season in the region.Figure 5Annual development profile of Cx. pipiens in Petrovaradin, Serbia, in 2017. The outcome of each hypothetical semi-field experiment is plotted vertically along the y-axis at the date when the experiment is initiated. The maximum number of adults produced is given in blue, and the time it takes (from the date indicated on the x-axis) to produce half of the maximum is given in green. Solid lines represent the median and shaded areas represent the 90% range of model predictions. Outcomes of the semi-field experiments (dots) are plotted together with the model predictions. The time points marked with circles indicate the experiments used to calibrate the model. Estimated time of first adult emergence is given in the inset.Full size imageAs seen in Fig. 5, predicted adult emergence times agree well with the observations throughout the high season. However, there is a greater variation in the maximum number of adults than the times of emergence (extending to almost (40%) of the possible outcomes in early August). A greater variability (almost (80%) in August) is seen in the corresponding observations, which we transformed into the percentage of eggs emerging as adults (where available) to facilitate comparison. According to the model, variation in adult production is associated with the variation in both development times and mortality during immature stages. We recall that the uncertainty in the informed environmental dependencies is high around relatively less frequently encountered values—especially the lower and higher temperature extremes (Fig. 4). Specifically, egg development times cannot be identified precisely, but immediate hatching of the larvae is predicted between 20 and 25 °C. Consequently, we found that frequent exposure to temperatures outside the well-informed range have a significant impact on the variation in adult production (Fig. S9).We adopt the time of first adult emergence as a proxy of the first generation of adults in the season. According to our model, early adult emergence is a result of shorter development times and higher success rates, which indicates that the temperature conditions allow for an early first generation of adults. An early first generation greatly contributes to an early peak of adult abundance, which may increase the risk of vector-borne disease transmission in humans. For instance, an early peak of abundance may cause an early start of West Nile virus circulation and amplification in Culex pipiens and their avian hosts, which increases the likelihood of virus spillover to humans51,87. Anecdotal evidence shows that the anomalously hot April and May that occurred in 2018 in Serbia shifted the peak of Cx. pipiens abundance forward by more than one month (Petrić et al., unpublished). Similarly, 2018 was the year with the largest number of autochthonous West Nile virus infections throughout Europe (more than the total of the previous seven years together)88,89.In summary, our results showed that the semi-field experiments, when used in combination with our dynamic pseudo-stage-structured MPM, help to develop predictive models and inform over a wide range of environmental conditions. We developed a predictive model of Cx. pipiens biotype molestus development and gained insights into the specifics of temperature and photoperiod dependencies by reducing the need of extensive laboratory data. We used life history observations from 7 experiments performed under semi-field conditions and employed a generic model structure, largely uninformed on the specific environmental dependencies of the species. The cumulative development framework we introduced applies broadly to poikilotherms subjected to highly variable environmental conditions. Although the generic model structure helps to develop exploratory models and identify potential environmental dependencies, accuracy can be improved by customising the models for the known dependencies of particular species. With a straightforward extension of the development model to cover the complete life cycle (with egg laying and density dependence), it is possible to incorporate field observations of eggs or adult mosquitoes, and develop an environment-driven population dynamics model. More

Worden AZ, Follows MJ, Giovannoni SJ, Wilken S, Zimmerman AE, Keeling PJ. Rethinking the marine carbon cycle: Factoring in the multifarious lifestyles of microbes. Science. 2015;347:1257594–1257594.PubMed 
Article 
CAS 

Google Scholar 
Guillou L, Viprey M, Chambouvet A, Welsh RM, Kirkham AR, Massana R, et al. Widespread occurrence and genetic diversity of marine parasitoids belonging to Syndiniales (Alveolata). Environ Microbiol. 2008;10:3349–65.CAS 
PubMed 
Article 

Google Scholar 
Jephcott TG, Alves-de-Souza C, Gleason FH, van Ogtrop FF, Sime-Ngando T, Karpov SA, et al. Ecological impacts of parasitic chytrids, syndiniales and perkinsids on populations of marine photosynthetic dinoflagellates. Fungal Ecol. 2016;19:47–58.Article 

Google Scholar 
Alacid E, Reñé A, Garcés E. New Insights into the Parasitoid Parvilucifera sinerae Life Cycle: The Development and Kinetics of Infection of a Bloom-forming Dinoflagellate Host. Protist. 2015;166:677–99.PubMed 
Article 

Google Scholar 
Not F, Gausling R, Azam F, Heidelberg JF, Worden AZ. Vertical distribution of picoeukaryotic diversity in the Sargasso Sea. Environ Microbiol. 2007;9:1233–52.CAS 
PubMed 
Article 

Google Scholar 
Lima-Mendez G, Faust K, Henry N, Decelle J, Colin S, Carcillo F, et al. Determinants of community structure in the global plankton interactome. Science 2015;348:1262073.de Vargas C, Audic S, Henry N, Decelle J, Mahe F, Logares R, et al. Eukaryotic plankton diversity in the sunlit ocean. Science. 2015;348:1261605.PubMed 
Article 
CAS 

Google Scholar 
Siano R, Alves-De-Souza C, Foulon E, Bendif M, Simon E, Guillou NL. et al. Distribution and host diversity of Amoebophryidae parasites across oligotrophic waters of the Mediterranean Sea. Biogeosciences. 2011;8:267–78.Article 

Google Scholar 
Coats DW. Parasitic life styles of marine dinoflagellates. J Eukaryot Microbiol. 1999;46:402–9.Article 

Google Scholar 
Farhat S, Le P, Kayal E, Noel B, Bigeard E, Corre E, et al. Rapid protein evolution, organellar reductions, and invasive intronic elements in the marine aerobic parasite dinoflagellate Amoebophrya spp. BMC Biol. 2021;19:1–21.CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Gornik SG, Febrimarsa, Cassin AM, MacRae JI, Ramaprasad A, Rchiad Z, et al. Endosymbiosis undone by stepwise elimination of the plastid in a parasitic dinoflagellate. Proc Natl Acad Sci USA. 2015;112:5767–72.CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
John U, Lu Y, Wohlrab S, Groth M, Janouškovec J, Kohli GS, et al. An aerobic eukaryotic parasite with functional mitochondria that likely lacks a mitochondrial genome. Sci Adv. 2019;5:1–12.Article 
CAS 

Google Scholar 
McFadden GI, Reith ME, Munholland J, Lang-Unnasch N. Plastid in human parasites. Nature. 1996;381:482.CAS 
PubMed 
Article 

Google Scholar 
Sibbald SJ, Archibald JM. Genomic Insights into Plastid Evolution. Genome Biol Evol. 2020;12:978–90.CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Not F, Probert I, Ribeiro CG, Crenn K, Guillou L, Jeanthon C, et al. Photosymbiosis in Marine Pelagic Environments. In: M. S. Cretoiu (ed). The Marine Microbiome. 2016. New York, NY: Springer International Publishing, pp 305–32.Cai R, Kayal E, Alves-de-Souza C, Bigeard E, Corre E, Jeanthon C, et al. Cryptic species in the parasitic Amoebophrya species complex revealed by a polyphasic approach. Sci Rep. 2020;10:1–11.Article 
CAS 

Google Scholar 
Coats DW, Park MG. Parasitism of photosynthetic dinoflagellates by three strains of Amoebophrya (Dinophyta): Parasite survival, infectivity, generation time, and host specificity. J Phycol. 2002;38:520–8.Article 

Google Scholar 
Kayal E, Alves-de-Souza C, Farhat S, Velo-Suarez L, Monjol J, Szymczak J, et al. Dinoflagellate Host Chloroplasts and Mitochondria Remain Functional During Amoebophrya Infection. Front Microbiol. 2020;11:1–11.Article 

Google Scholar 
Miller JJ, Delwiche CF, Coats DW. Ultrastructure of Amoebophrya sp. and its Changes during the Course of Infection. Protist. 2012;163:720–45.PubMed 
Article 

Google Scholar 
Decelle J, Stryhanyuk H, Gallet B, Veronesi G, Schmidt M, Balzano S, et al. Algal Remodeling in a Ubiquitous Planktonic Photosymbiosis. Curr Biol. 2019;29:968–978.e4.CAS 
PubMed 
Article 

Google Scholar 
Uwizeye C, Mars Brisbin M, Gallet B, Chevalier F, LeKieffre C, Schieber NL, et al. Cytoklepty in the plankton: A host strategy to optimize the bioenergetic machinery of endosymbiotic algae. Proc Natl Acad Sci USA 2021;118.Uwizeye C, Decelle J, Jouneau P, Flori S, Gallet B, Keck J, et al. Morphological bases of phytoplankton energy management and physiological responses unveiled by 3D subcellular imaging. Nat Commun. 2021;12:1049.CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Lowe DG. Distinctive Image Features from Scale-Invariant Keypoints. Int J Comput Vis. 2004;60:91–110.Article 

Google Scholar 
Hennies J, Lleti JMS, Schieber NL, Templin RM, Steyer AM, Schwab Y. AMST: alignment to median smoothed template for focused ion beam scanning electron microscopy image stacks. Sci Rep. 2020;10:1–10.Article 
CAS 

Google Scholar 
Kikinis R, Pieper SD, Vosburgh KG. 3D Slicer: A Platform for Subject-Specific Image Analysis, Visualization, and Clinical Support. Intraoperative Imaging and Image-Guided Therapy. 2014. Springer New York, New York, NY, pp 277–89.Polerecky L, Adam B, Milucka J, Musat N, Vagner T, Kuypers MMM. Look@NanoSIMS – a tool for the analysis of nanoSIMS data in environmental microbiology. Environ Microbiol. 2012;14:1009–23.CAS 
PubMed 
Article 

Google Scholar 
Jia B, Zhu XF, Pu ZJ, Duan YX, Hao LJ, Zhang J, et al. Integrative view of the diversity and evolution of SWEET and semiSWEET sugar transporters. Front Plant Sci. 2017;8:1–18.
Google Scholar 
Katoh K, Rozewicki J, Yamada KD. MAFFT online service: multiple sequence alignment, interactive sequence choice and visualization. Brief Bioinform. 2019;20:1160–6.CAS 
PubMed 
Article 

Google Scholar 
Castresana J. Selection of Conserved Blocks from Multiple Alignments for Their Use in Phylogenetic Analysis. Mol Biol Evol. 2000;17:540–52.CAS 
PubMed 
Article 

Google Scholar 
Stamatakis A. RAxML version 8: a tool for phylogenetic analysis and post-analysis of large phylogenies. Bioinformatics. 2014;30:1312–3.CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Haas BJ, Papanicolaou A, Yassour M, Grabherr M, Blood PD, Bowden J, et al. De novo transcript sequence reconstruction from RNA-seq using the Trinity platform for reference generation and analysis. Nat Protoc. 2013;8:1494–512.CAS 
PubMed 
Article 

Google Scholar 
Farhat S, Florent I, Noel B, Kayal E, Da Silva C, Bigeard E, et al. Comparative time-scale gene expression analysis highlights the infection processes of two amoebophrya strains. Front Microbiol. 2018;9:1–19.Article 

Google Scholar 
Li B, Dewey CN. RSEM: accurate transcript quantification from RNA-Seq data with or without a reference genome. BMC Bioinforma. 2011;12:323.CAS 
Article 

Google Scholar 
Cachon J. Contribution à l’étude des péridiniens parasites. Cytologie, cycles évolutifs Ann Sci Nat Zool. 1964;6:1–158.
Google Scholar 
Van Dooren GG, Marti M, Tonkin CJ, Stimmler LM, Cowman AF, McFadden GI. Development of the endoplasmic reticulum, mitochondrion and apicoplast during the asexual life cycle of Plasmodium falciparum. Mol Microbiol. 2005;57:405–19.PubMed 
Article 
CAS 

Google Scholar 
Tyler KM, Matthews KR, Gull K. Anisomorphic cell division by African trypanosomes. Protist. 2001;152:367–78.CAS 
PubMed 
Article 

Google Scholar 
Jakob M, Hoffmann A, Amodeo S, Peitsch C, Zuber B, Ochsenreiter T. Mitochondrial growth during the cell cycle of Trypanosoma brucei bloodstream forms. Sci Rep. 2016;6:1–13.Article 
CAS 

Google Scholar 
Hughes L, Borrett S, Towers K, Starborg T, Vaughan S. Patterns of organelle ontogeny through a cell cycle revealed by whole-cell reconstructions using 3D electron microscopy. J Cell Sci. 2017;130:637–47.CAS 
PubMed 

Google Scholar 
Ovciarikova J, Lemgruber L, Stilger KL, Sullivan WJ, Sheiner L. Mitochondrial behaviour throughout the lytic cycle of Toxoplasma gondii. Sci Rep. 2017;7:1–13.Article 
CAS 

Google Scholar 
Nishi M, Hu K, Murray JM, Roos DS. Organellar dynamics during the cell cycle of Toxoplasma gondii. J Cell Sci. 2008;121:1559–68.CAS 
PubMed 
Article 

Google Scholar 
Long M, Marie D, Szymczak J, Toullec J, Bigeard E, Sourisseau M, et al. Dinophyceae can use exudates as weapons against the parasite Amoebophrya sp. (Syndiniales). ISME Commun. 2021;1:34.Article 

Google Scholar 
Harris E, Yoshida K, Cardelli J, Bush J. Rab11-like GTPase associates with and regulates the structure and function of the contractile vacuole system in. Dictyostelium J Cell Sci. 2001;114:3035–45.CAS 
PubMed 
Article 

Google Scholar 
Marshansky V, Rubinstein JL, Grüber G. Eukaryotic V-ATPase: Novel structural findings and functional insights. Biochim Biophys Acta – Bioenerg. 2014;1837:857–79.CAS 
Article 

Google Scholar 
Cox D, Lee DJ, Dale BM, Calafat J, Greenberg S. A Rab11-containing rapidly recycling compartment in macrophages that promotes phagocytosis. Proc Natl Acad Sci USA. 2000;97:680–5.CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Vines JH, King JS. The endocytic pathways of Dictyostelium discoideum. Int J Dev Biol. 2019;63:461–71.CAS 
PubMed 
Article 

Google Scholar 
Decelle J, Veronesi G, LeKieffre C, Gallet B, Chevalier F, Stryhanyuk H, et al. Subcellular architecture and metabolic connection in the planktonic photosymbiosis between Collodaria (radiolarians) and their microalgae. Environ Microbiol. 2021;23:6569–86.CAS 
PubMed 
Article 

Google Scholar 
Sigee DC, Kearns LP. Levels of dinoflagellate chromosome-bound metals in conditions of low external ion availability: An X-ray microanalytical study. Tissue Cell. 1981;13:441–51.CAS 
PubMed 
Article 

Google Scholar 
Pinchuk GE, Ammons C, Culley DE, Li SMW, McLean JS, Romine MF, et al. Utilization of DNA as a sole source of phosphorus, carbon, and energy by Shewanella spp.: Ecological and physiological implications for dissimilatory metal reduction. Appl Environ Microbiol. 2008;74:1198–208.CAS 
PubMed 
Article 

Google Scholar 
Caffaro CE, Boothroyd JC. Evidence for Host Cells as the Major Contributor of Lipids in the Intravacuolar Network of Toxoplasma-Infected Cells. Eukaryot Cell. 2011;10:1095–9.CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Lopez J, Bittame A, Massera C, Vasseur V, Effantin G, Valat A, et al. Intravacuolar membranes regulate CD8 T cell recognition of membrane-bound Toxoplasma gondii protective antigen. Cell Rep. 2015;13:2273–86.CAS 
PubMed 
Article 

Google Scholar 
Pszenny V, Ehrenman K, Romano JD, Kennard A, Schultz A, Roos DS, et al. A Lipolytic Lecithin:Cholesterol Acyltransferase Secreted by Toxoplasma Facilitates Parasite Replication and Egress. J Biol Chem. 2016;291:3725–46.CAS 
PubMed 
Article 

Google Scholar 
Nolan SJ, Romano JD, Coppens I. Host lipid droplets: An important source of lipids salvaged by the intracellular parasite Toxoplasma gondii. PLoS Pathog. 2017;13:e1006362.PubMed 
PubMed Central 
Article 
CAS 

Google Scholar 
Fox BA, Guevara RB, Rommereim LM, Falla A, Bellini V, Pètre G, et al. Toxoplasma gondii parasitophorous vacuole membrane-associated dense granule proteins orchestrate chronic infection and GRA12 underpins resistance to host gamma interferon. MBio. 2019; 10:e00589-19.Freeman Rosenzweig ES, Xu B, Kuhn Cuellar L, Martinez-Sanchez A, Schaffer M, Strauss M, et al. The eukaryotic CO2-concentrating organelle is liquid-like and exhibits dynamic reorganization. Cell. 2017;171:148–162.e19.CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Zeeman SC, Kossmann J, Smith AM. Starch: Its metabolism, evolution, and biotechnological modification in plants. Annu Rev Plant Biol. 2010;61:209–34.CAS 
PubMed 
Article 

Google Scholar 
Qureshi AA, Suades A, Matsuoka R, Brock J, McComas SE, Nji E, et al. The molecular basis for sugar import in malaria parasites. Nature. 2020;578:321–5.CAS 
PubMed 
Article 

Google Scholar 
Blume M, Rodriguez-Contreras D, Landfear S, Fleige T, Soldati-Favre D, Lucius R, et al. Host-derived glucose and its transporter in the obligate intracellular pathogen Toxoplasma gondii are dispensable by glutaminolysis. Proc Natl Acad Sci USA. 2009;106:12998–3003.CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Chen LQ, Qu XQ, Hou BH, Sosso D, Osorio S, Fernie AR, et al. Sucrose efflux mediated by SWEET proteins as a key step for phloem transport. Science (80-). 2012;335:207–11.CAS 
Article 

Google Scholar 
Latorraca NR, Fastman NM, Venkatakrishnan AJ, Frommer WB, Dror RO, Feng L. Mechanism of substrate translocation in an alternating access transporter. Cell. 2017;169:96–107.e12.CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Amiar S, Katris NJ, Berry L, Dass S, Duley S, Arnold CS, et al. Division and adaptation to host environment of apicomplexan parasites depend on apicoplast lipid metabolic plasticity and host organelle remodeling. Cell Rep. 2020;30:3778–3792.e9.CAS 
PubMed 
Article 

Google Scholar 
Hu X, Binns D, Reese ML. The coccidian parasites Toxoplasma and Neospora dysregulate mammalian lipid droplet biogenesis. J Biol Chem. 2017;292:11009–20.CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Gomes AF, Magalhães KG, Rodrigues RM, de Carvalho L, Molinaro R, Bozza PT, et al. Toxoplasma gondii-skeletal muscle cells interaction increases lipid droplet biogenesis and positively modulates the production of IL-12, IFN-g and PGE2. Parasit Vectors. 2014;7:47.PubMed 
PubMed Central 
Article 
CAS 

Google Scholar 
Jacot D, Waller RF, Soldati-Favre D, MacPherson DA, MacRae JI. Apicomplexan energy metabolism: carbon source promiscuity and the quiescence hyperbole. Trends Parasitol. 2016;32:56–70.CAS 
PubMed 
Article 

Google Scholar 
Salcedo-Sora JE, Caamano-Gutierrez E, Ward SA, Biagini GA. The proliferating cell hypothesis: a metabolic framework for Plasmodium growth and development. Trends Parasitol. 2014;30:170–5.CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Muñoz-Gómez SA, Wideman JG, Roger AJ, Slamovits CH, Agashe D. The origin of mitochondrial cristae from alphaproteobacteria. Mol Biol Evol. 2017;34:943–56.PubMed 

Google Scholar 
Evers F, Cabrera-Orefice A, Elurbe DM, Kea-te Lindert M, Boltryk SD, Voss TS, et al. Composition and stage dynamics of mitochondrial complexes in Plasmodium falciparum. Nat Commun. 2021;12:1–17.Article 
CAS 

Google Scholar 
Krungkrai J, Prapunwattana P, Krungkrai SR. Ultrastructure and function of mitochondria in gametocytic stage of Plasmodium falciparum. Parasite. 2000;7:19–26.CAS 
PubMed 
Article 

Google Scholar 
Krungkrai J. The multiple roles of the mitochondrion of the malarial parasite. Parasitology 2004;129:511–524. https://doi.org/10.1017/S0031182004005888.Lee JW. Protonic capacitor: elucidating the biological significance of mitochondrial cristae formation. Sci Rep. 2020;10:1–14.Article 
CAS 

Google Scholar 
Stephan T, Brüser C, Deckers M, Steyer AM, Balzarotti F, Barbot M, et al. MICOS assembly controls mitochondrial inner membrane remodeling and crista junction redistribution to mediate cristae formation. EMBO J. 2020;39:1–24.Article 
CAS 

Google Scholar 
Pánek T, Eliáš M, Vancová M, Lukeš J, Hashimi H. Returning to the Fold for Lessons in Mitochondrial Crista Diversity and Evolution. Curr Biol. 2020;30:R575–R588.PubMed 
Article 
CAS 

Google Scholar 
Wideman JG, Muñoz-Gómez SA. The evolution of ERMIONE in mitochondrial biogenesis and lipid homeostasis: An evolutionary view from comparative cell biology. Biochim Biophys Acta – Mol Cell Biol Lipids. 2016. Elsevier B.V., 1861: 900-12.Mühleip A, Kock Flygaard R, Ovciarikova J, Lacombe A, Fernandes P, Sheiner L, et al. ATP synthase hexamer assemblies shape cristae of Toxoplasma mitochondria. Nat Commun. 2021;12:120.PubMed 
PubMed Central 
Article 
CAS 

Google Scholar 
Flegontov P, Michálek J, Janouškovec J, Lai DH, Jirků M, Hajdušková E, et al. Divergent mitochondrial respiratory chains in phototrophic relatives of apicomplexan parasites. Mol Biol Evol. 2015;32:1115–31.CAS 
PubMed 
Article 

Google Scholar 
Painter HJ, Morrisey JM, Mather MW, Vaidya AB. Specific role of mitochondrial electron transport in blood-stage Plasmodium falciparum. Nature. 2007;446:88–91.CAS 
PubMed 
Article 

Google Scholar 
Nishida T, Hatama S, Ishikawa Y, Kadota K. Intranuclear coccidiosis in a calf. J Vet Med Sci. 2009;71:1109–13.PubMed 
Article 

Google Scholar 
Pecka Z. Life cycle and ultrastructure of Eimeria stigmosa, the intranuclear coccidian of the goose (Anser anser domesticus). Folia Parasitol (Praha). 1992;39:105–14.CAS 

Google Scholar 
Voleman L, Doležal P. Mitochondrial dynamics in parasitic protists. PLoS Pathog. 2019;15:e1008008.Bílý T, Sheikh S, Mallet A, Bastin P, Pérez‐Morga D, Lukeš J, et al. Ultrastructural changes of the mitochondrion during the life cycle of Trypanosoma brucei. J Eukaryot Microbiol. 2021;68:e12846.Elliott DA, McIntosh MT, Hosgood HD, Chen S, Zhang G, Baevova P, et al. Four distinct pathways of hemoglobin uptake in the malaria parasite Plasmodium falciparum. Proc Natl Acad Sci USA. 2008;105:2463–8.CAS 
PubMed 
PubMed Central 
Article 

Google Scholar  More

Biere, J. M. & Uetz, G. W. Web orientation in the spider Micrathena gracilis (Araneae: Araneidae). Ecology 62(2), 336–344 (1981).Article 

Google Scholar 
Korb, J. & Linsenmair, K. E. The architecture of termite mounds: a result of a trade-off between thermoregulation and gas exchange? Behav. Ecol. 10(3), 312–316 (1999).Article 

Google Scholar 
Hansell, M. H. Bird nests and construction behaviour (Cambridge University Press, 2000).Book 

Google Scholar 
Kawase, H., Okata, Y. & Ito, K. Role of huge geometric circular structures in the reproduction of a Marine Pufferfish. Sci. Rep. 3, 1–5 (2013).Article 

Google Scholar 
Dawkins, R. The extended phenotype 295 (Oxford University Press, 1982).
Google Scholar 
Odling-Smee, F. J., Laland, K. N. & Feldman, M. W. Niche construction. Am. Nat. 147(4), 641–648 (1996).Article 

Google Scholar 
Odling-Smee, F. J., Laland, K. N. & Feldman, M. W. Niche construction: the Neglected process in evolution (Princeton University Press, 2003).
Google Scholar 
Short, L. L. Burdens of the picid hole-excavating habit. Wilson Bull. 91(1), 16–28 (1979).
Google Scholar 
Wiebe, K. L., Koenig, W. D. & Martin, K. Costs and benefits of nest reuse versus excavation in cavity-nesting birds. Ann. Zool. Fenn. 44(3), 209–217 (2007).
Google Scholar 
Landler, L. et al. Global trends in woodpecker cavity orientation: latitudinal and continental effects suggest regional climate influence. Acta Ornithol. 49(2), 257–266 (2014).Article 

Google Scholar 
Ojeda, V. et al. Latitude does not influence cavity entrance orientation of South American avian excavators. Auk 138(1), ukaa064 (2021).Article 

Google Scholar 
Wiebe, K. L. Microclimate of tree cavity nests: is it important for reproductive success in Northern Flickers? Auk 118(2), 412–421 (2001).Article 

Google Scholar 
Schaaf, A. A. Effects of sun exposure and vegetation cover on Woodpecker nest orientation in subtropical forests of South America. J. Ethol. 38, 117–120 (2019).Article 

Google Scholar 
Hooge, P. N., Stanback, M. T. & Koenig, W. D. Nest-site selection in the acorn woodpecker. Auk 116(1), 45–54 (1999).Article 

Google Scholar 
Schaaf, A. A. & de la Pena, M. R. Bird nest orientation and local temperature: an analysis over three decades. Ecology 20, e03042 (2020).
Google Scholar 
Charter, M. et al. Does nest box location and orientation affect occupation rate and breeding success of barn owls Tyto alba in a semi-arid environment? Acta Ornithol. 45(1), 115–119 (2010).Article 

Google Scholar 
Butler, M. W., Whitman, B. A. & Dufty, A. M. Nest box temperature and hatching success of American kestrels varies with nest box orientation. Wilson J. Ornithol. 121(4), 778–782 (2009).Article 

Google Scholar 
Goodenough, A. E. et al. Nestbox orientation: a species-specific influence on occupation and breeding success in woodland passerines. Bird Study 55(2), 222–232 (2008).Article 

Google Scholar 
Viñuela, J. & Sunyer, C. Nest orientation and hatching success of black kites milvus migrans in Spain. Ibis 134(4), 340–345 (1992).Article 

Google Scholar 
Larson, E. R. et al. How does nest box temperature affect nestling growth rate and breeding success in a parrot?. Emu 115(3), 247–255 (2015).Article 

Google Scholar 
Austin, G. T. Nesting success of the cactus wren in relation to nest orientation. Condor 76(2), 216–217 (1974).Article 

Google Scholar 
Verbeek, N. A. Nesting success and orientation of water pipit Anthus spinoletta nests. Ornis Scand. 25, 37–39 (1981).Article 

Google Scholar 
Conner, R. N. & Rudolph, D. C. Excavation dynamics and use patterns of red-cockaded woodpecker cavities: relationships with cooperative breeding. Red cockaded Woodpecker: recovery, ecology, and management. Center for Applied Studies in Forestry, College of Forestry, Stephen F. Austin State University, Nacogdoches, TX, 1995: 343–352.Harding, S. R. & Walters, J. R. Dynamics of cavity excavation by red-cockaded woodpeckers. In Red-Cockaded Woodpecker: Road to Recovery (eds Costa, R. & Daniels, S.) 412–422 (Hancock House, 2004).
Google Scholar 
Harding, S. R. & Walters, J. R. Processes regulating the population dynamics of red-cockaded woodpecker cavities. J. Wildl. Manage. 66(4), 1083–1095 (2002).Article 

Google Scholar 
Dennis, J. V. The yellow-shafted flicker (Colaptes Auratus) on Nantucket Island, Massachusetts. Bird Banding 40(4), 290–308 (1969).Article 

Google Scholar 
Baker, W. W. Progress report on life history studies of the red-cockaded woodpecker at Tall Timbers Research Station. Ecology and Management of the Redcockaded Woodpecker 44–59 (US Bureau of Sport Fisheries and Wildlife and Tall Timbers Research Station, 1971).
Google Scholar 
Dennis, J. V. Species using red-cockaded woodpecker holes in Northeastern South Carolina. Bird-Banding 42(2), 79–87 (1971).Article 

Google Scholar 
Conner, R. N. et al. Red-cockaded woodpecker nest-cavity selection: relationships with cavity age and resin production. Auk 115(2), 447–454 (1998).Article 

Google Scholar 
Conner, R. N. Orientation of entrances to woodpecker nest cavities. Auk 92(2), 371–374 (1975).Article 

Google Scholar 
Copeyon, C. K., Walters, J. R. & Carter, J. III. Induction of red-cockaded woodpecker group formation by artificial cavity construction. J. Wildl. Manage. 55(4), 549–556 (1991).Article 

Google Scholar 
Locke, B. A. & Conner, R. N. A statistical analysis of the orientation of entrances to redcockaded woodpecker cavities. In Red-Cockaded Woodpecker Symposium II (Florida Game and Fresh Water Fish Commission, 1983).
Google Scholar 
Lay, D. W., Red-cockaded woodpecker study. Texas Parks and Wildlife Department. Project W-80-R-16. 1973. p. 33.Jones, H. K. & Ott, F. T. Some characteristics of red-cockaded woodpecker cavity trees in Georgia. Oriole 38, 33–39 (1973).
Google Scholar 
Hopkins, M. L. & Lynn, T. E. Jr. Some characteristics of red-cockaded woodpecker cavity trees and management implications in South Carolina. Ecology and Management of The Red-Cockaded Woodpecker 140–169 (US Bureau of Sport Fishing and Wildlife and Tall Timbers Research Station, 1971).
Google Scholar 
Wood, D. A. Foraging and colony habitat characteristics of the red-cockaded woodpecker in Oklahoma. In Red-Cockaded Woodpecker Symposium II 51–58 (Florida Game and Fresh Water Fish Commission, 1983).
Google Scholar 
Kalisz, P. J. & Boettcher, S. E. Active and abandoned red-cockaded woodpecker habitat in Kentucky. J. Wildl. Manage. 25, 146–154 (1991).Article 

Google Scholar 
Walters, J. R., Doerr, P. D. & J. H. Carter, III. The cooperative breeding system of the red cockaded woodpecker. Ethology 78, 275–305 (1988).Article 

Google Scholar 
Batschelet, E. Circular statistics in biology (Academic Press, 1981).MATH 

Google Scholar 
Agostinelli, C. & U. Lund, R package “circular”: circular statistics. R package version 0.4-7. https://r-forge.r-project.org/projects/circular (2013).Hijmans, R. J. & Etten, J. V. Raster: Geographic analysis and modeling with raster data. R package version 2.0-12 (2012).R Development Core Team R. A language and environment for statistical computing (R Foundation for Statistical Computing, 2012).
Google Scholar 
Goslee, S. C. & Urban, D. L. The ecodist package for dissimilarity-based analysis of ecological data. J. Stat. Softw. 22(7), 1–19 (2007).Article 

Google Scholar 
Cox, N. J. Speaking Stata: In praise of trigonometric predictors. Stand. Genomic Sci. 6(4), 561–579 (2006).
Google Scholar 
Smith, J. A. et al. How effective is the Safe Harbor program for the conservation of Red-cockaded Woodpeckers? Condor Ornithol. Appl. 120(1), 223–233 (2018).
Google Scholar 
Zuur, A. et al. Mixed effects models and extensions in ecology with R (Springer, 2009).MATH 
Book 

Google Scholar 
Bates, D., et al., lme4: Linear mixed-effects models using Eigen and S4. 2014: http://CRAN.R-project.org/package=lme4.Conner, R. N., Rudolph, D. C. & Walters, J. R. The red-cockaded woodpecker: surviving in a fire-maintained ecosystem (University of Texas Press, 2001).Book 

Google Scholar 
Rudolph, D. C., Kyle, H. & Conner, R. N. Red-cockaded woodpeckers vs rat snakes: the effectiveness of the resin barrier. Wilson Bull. 102(1), 14–22 (1990).
Google Scholar 
Conner, R. N. The effect of tree hardness on woodpecker nest entrance orientation. Auk 94(2), 369–370 (1977).Article 

Google Scholar 
Jackson, J. A. & Jackson, B. J. Ecological relationships between fungi and woodpecker cavity sites. Condor 106(1), 37–49 (2004).Article 

Google Scholar 
Jusino, M. A. et al. Experimental evidence of a symbiosis between red-cockaded woodpeckers and fungi. Proc. R. Soc. B Biol. Sci. 2016(283), 20160106 (1827).
Google Scholar 
Losin, N. et al. Relationship between aspen heartwood rot and the location of cavity excavation by a primary cavity-nester, the Red-naped Sapsucker. Condor 108(3), 706–710 (2006).Article 

Google Scholar 
Williamson, L., Garcia, V. & Walters, J. R. Life history trait differences in isolated populations of the endangered Red-cockaded Woodpecker. Ornis Hungar. 24(1), 55–68 (2016).Article 

Google Scholar 
DeMay, S. M. & Walters, J. R. Variable effects of a changing climate on lay dates and productivity across the range of the Red-cockaded Woodpecker. Condor 20, 20 (2019).
Google Scholar 
Garcia, V. Lifetime fitness and changing life history traits in red-cockaded woodpeckers (Virginia Tech, 2014).
Google Scholar 
Delmore, K. E. & Irwin, D. E. Hybrid songbirds employ intermediate routes in a migratory divide. Ecol. Lett. 17(10), 1211–1218 (2014).PubMed 
Article 

Google Scholar 
Helbig, A. J. Inheritance of migratory direction in a bird species: a cross-breeding experiment with SE-and SW-migrating blackcaps (Sylvia atricapilla). Behav. Ecol. Sociobiol. 28(1), 9–12 (1991).Article 

Google Scholar  More

Ottenburghs, J. et al. A history of hybrids? Genomic patterns of introgression in the True Geese. BMC Evol. Biol. 17, 14 (2017).Article 

Google Scholar 
Baiz, M. D., Tucker, P. K. & Cortés-Ortiz, L. Multiple forms of selection shape reproductive isolation in a primate hybrid zone. Mol. Ecol. 28, 1056–1069 (2019).CAS 
PubMed 
Article 

Google Scholar 
Slager, D. L. et al. Cryptic and extensive hybridization between ancient lineages of American crows. Mol. Ecol. 29, 956–969 (2020).CAS 
PubMed 
Article 

Google Scholar 
Grant, P. R. & Grant, B. R. Introgressive hybridization and natural selection in Darwin’s finches. Biol. J. Linnean Soc. 117, 812–822 (2016).Article 

Google Scholar 
Pauquet, G., Salzburger, W. & Egger, B. The puzzling phylogeography of the haplochromine cichlid fish Astatotilapia burtoni. Ecol. Evol. 8, 5637–5648 (2018).PubMed 
PubMed Central 
Article 

Google Scholar 
Schluter, D. Ecological character displacement in adaptive radiation. Am. Nat. 156, S4–S16 (2000).Article 

Google Scholar 
Song, Y. et al. Adaptive introgression of anticoagulant rodent poison resistance by hybridization between old world mice. Curr. Biol. 21, 1296–1301 (2011).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Anderson, R. P., Peterson, A. T. & Gómez-Laverde, M. Using niche-based GIS modeling to test geographic predictions of competitive exclusion and competitive release in South American pocket mice. Oikos 98, 3–16 (2002).Article 

Google Scholar 
Gramlich, S., Wagner, N. D. & Horandl, E. RAD-seq reveals genetic structure of the F-2-generation of natural willow hybrids (Salix L.) and a great potential for interspecific introgression. BMC Plant Biol. 18, 12 (2018).Article 

Google Scholar 
Mavárez, J. et al. Speciation by hybridization in Heliconius butterflies. Nature 441, 868–871 (2006).ADS 
PubMed 
Article 
CAS 

Google Scholar 
Cahill, J. A. et al. Genomic evidence of geographically widespread effect of gene flow from polar bears into brown bears. Mol. Ecol. 24, 1205–1217 (2015).PubMed 
PubMed Central 
Article 

Google Scholar 
Djogbénou, L. et al. Evidence of introgression of the ace-1(R) mutation and of the ace-1 duplication in West African Anopheles gambiae s. s. PLoS ONE 3, e2172 (2008).ADS 
PubMed 
PubMed Central 
Article 
CAS 

Google Scholar 
Enciso-Romero, J. et al. Evolution of novel mimicry rings facilitated by adaptive introgression in tropical butterflies. Mol. Ecol. 26, 5160–5172 (2017).PubMed 
Article 

Google Scholar 
Dasmahapatra, K. K. et al. Butterfly genome reveals promiscuous exchange of mimicry adaptations among species. Nature 487, 94–98 (2012).ADS 
CAS 
PubMed Central 
Article 

Google Scholar 
Latch, E. K., Harveson, L. A., King, J. S., Hobson, M. D. & Rhodes, J. R. Assessing hybridization in wildlife populations using molecular markers: a case study in wild turkeys. J. Wildl. Manag. 70, 485–492 (2006).Article 

Google Scholar 
Oliveira, R., Godinho, R., Randi, E. & Alves, P. C. Hybridization versus conservation: are domestic cats threatening the genetic integrity of wildcats (Felis silvestris silvestris) in Iberian Peninsula?. Philos. Trans. R. Soc. Lond. Ser. B Biol. Sci. 363, 2953–2961 (2008).Article 

Google Scholar 
Nichols, P. et al. Secondary contact seeds phenotypic novelty in cichlid fishes. Proc. R. Soc. B Biol. Sci. 282, 8 (2015).
Google Scholar 
Yang, W. Z. et al. Genomic evidence for asymmetric introgression by sexual selection in the common wall lizard. Mol. Ecol. 27, 4213–4224 (2018).CAS 
PubMed 
Article 

Google Scholar 
Boratyński, Z. et al. Introgression of mitochondrial DNA among Myodes voles: consequences for energetics?. BMC Evol. Biol. 11, 355 (2011).PubMed 
PubMed Central 
Article 
CAS 

Google Scholar 
Mondal, M. et al. Genomic analysis of Andamanese provides insights into ancient human migration into Asia and adaptation. Nat. Genet. 48, 1066–1070 (2016).CAS 
PubMed 
Article 

Google Scholar 
Melo-Ferreira, J., Seixas, F. A., Cheng, E., Mills, L. S. & Alves, P. C. The hidden history of the snowshoe hare, Lepus americanus: extensive mitochondrial DNA introgression inferred from multilocus genetic variation. Mol. Ecol. 23, 4617–4630 (2014).CAS 
PubMed 
Article 

Google Scholar 
Mims, M. C., Hulsey, C. D., Fitzpatrick, B. M. & Streelman, J. T. Geography disentangles introgression from ancestral polymorphism in Lake Malawi cichlids. Mol. Ecol. 19, 940–951 (2010).PubMed 
Article 

Google Scholar 
Salazar, C. et al. Genetic evidence for hybrid trait speciation in heliconius butterflies. PLoS Genet. 6, e1000930 (2010).PubMed 
PubMed Central 
Article 
CAS 

Google Scholar 
Naisbit, R. E., Jiggins, C. D. & Mallet, J. Mimicry: developmental genes that contribute to speciation. Evol. Dev. 5, 269–280 (2003).CAS 
PubMed 
Article 

Google Scholar 
Zhang, W., Dasmahapatra, K. K., Mallet, J., Moreira, G. R. P. & Kronforst, M. R. Genome-wide introgression among distantly related Heliconius butterfly species. Genome Biol. 17, 15 (2016).Article 
CAS 

Google Scholar 
Zhang, W., Kunte, K. & Kronforst, M. R. Genome-wide characterization of adaptation and speciation in tiger swallowtail butterflies using De Novo transcriptome assemblies. Genome Biol. Evol. 5, 1233–1245 (2013).PubMed 
PubMed Central 
Article 
CAS 

Google Scholar 
Jones, M. R. et al. Adaptive introgression underlies polymorphic seasonal camouflage in snowshoe hares. Science (New York, NY). 360, 1355–1358 (2018).ADS 
CAS 
Article 

Google Scholar 
Melville, J. Competition and character displacement in two species of scincid lizards. Ecol. Lett. 5, 386–393 (2002).Article 

Google Scholar 
Pfennig, D. W. & Pfennig, K. S. Character displacement and the origins of diversity. Am. Nat. 176, S26–S44 (2010).PubMed 
PubMed Central 
MATH 
Article 

Google Scholar 
Kooyers, N. J., James, B. & Blackman, B. K. Competition drives trait evolution and character displacement between Mimulus species along an environmental gradient. Evol. Int. J. Org. Evol. 71, 1205–1221 (2017).CAS 
Article 

Google Scholar 
Adams, D. C. & Rohlf, F. J. Ecological character displacement in Plethodon: Biomechanical differences found from a geometric morphometric study. Proc. Natl. Acad. Sci. 97, 4106–4111 (2000).ADS 
CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Grant, P. R. & Grant, B. R. Evolution of character displacement in Darwin’s finches. Science (New York, NY) 313, 224–226 (2006).ADS 
CAS 
Article 

Google Scholar 
Pfennig, D. W. & Murphy, P. J. Character displacement in polyphenic tadpoles. Evol. Int. J. Org. Evol. 54, 1738–1749 (2000).CAS 
Article 

Google Scholar 
Jones, G. Acoustic signals and speciation: the roles of natural and sexual selection in the evolution of cryptic species. Adv. Study Behav. 26, 317–354 (1997).Article 

Google Scholar 
Marsteller, S., Adams, D. C., Collyer, M. L. & Condon, M. Six cryptic species on a single species of host plant: morphometric evidence for possible reproductive character displacement. Ecol. Entomol. 34, 66–73 (2009).Article 

Google Scholar 
Tene Fossog, B. et al. Habitat segregation and ecological character displacement in cryptic African malaria mosquitoes. Evol. Appl. 8, 326–345 (2015).PubMed 
PubMed Central 
Article 

Google Scholar 
Ibáñez, C., García-Mudarra, J. L., Ruedi, M., Stadelmann, B. & Juste, J. The Iberian contribution to cryptic diversity in European bats. Acta Chiropterol. 8, 277–297 (2006).Article 

Google Scholar 
Juste, J. et al. Mitochondrial phylogeography of the long-eared bats (Plecotus) in the Mediterranean Palaearctic and Atlantic Islands. Mol. Phylogenet. Evol. 31, 1114–1126 (2004).CAS 
PubMed 
Article 

Google Scholar 
Schreber J. Die Säugthiere in Abbildungen nach der Natur, mit Beschreibungen. Erlangen – Expedition des Schreber’schen säugthier- und des Esper’schen Schmetterlingswerkes. Ernst Mayr Library of the MCZ, 1774–1855 (Harvard, 1774).Temminck, C. J. Monographies de Mammologie, ou description de quelques genres de Mammifères, dont les espèces ont été observes dans les différens Musées de l’Europe, Vol. 2, No. 302, 26–70 (G. Dufour et Ed. D’Ocagne, 1840).Centeno-Cuadros, A. et al. Comparative phylogeography and asymmetric hybridization between cryptic bat species. J. Zool. Syst. Evol. Res. 57, 1004–1018 (2019).Article 

Google Scholar 
Santos, H. et al. Shaping of bat cryptic distribution in Iberia. Biol. J. Linnean Soc. 112, 150–162 (2014).Article 

Google Scholar 
Novella-Fernandez, R. et al. Broad-scale patterns of geographic avoidance between species emerge in the absence of fine-scale mechanisms of coexistence. Divers. Distrib. 27, 1606–1618 (2021).Article 

Google Scholar 
Neubaum, M. A., Douglas, M. R., Douglas, M. E. & O’Shea, T. J. Molecular ecology of the big brown bat (Eptesicus fuscus): genetic and natural history variation in a hybrid zone. J. Mammal. 88, 1230–1238 (2007).Article 

Google Scholar 
Worthington-Wilmer, J. & Barratt, E. A non-lethal method of tissue sampling for genetic studies of chiropterans. Bat Res. News 37(1), 1–4 (1996).
Google Scholar 
Illumination, I.C.o. A colour appearance model for colour management systems: CIECAM02. Technical Report No CIE 159, 2004 (2004).Maroco, J. Análise estatística com utilização do SPSS. 3ª edição. Edições Silabo (2010).Wickham, H. et al. Welcome to the tidyverse. J. Open Source Softw. 4(43), 1686. https://doi.org/10.21105/joss.01686 (2019).ADS 
Article 

Google Scholar 
Karatzoglou, A., Smola, A., Hornik, K. & Zeileis, A. Kernlab: an S4 package for kernel methods in R. J. Stat. Softw. 11(9), 1–20 (2004).Article 

Google Scholar 
Meyer, D., Dimitriadou, E., Hornik, K., Weingessel, A. & Leisch, F. e1071: Misc Functions of the Department of Statistics, Probability Theory Group (Formerly: E1071), TU Wien. https://cran.r-project.org/web/packages/e1071/index.html (2012).Redgwell, R. D., Szewczak, J. M., Jones, G. & Parsons, S. Classification of echolocation calls from 14 species of bat by support vector machines and ensembles of neural networks. Algorithms 2, 907–924 (2009).Article 

Google Scholar 
Ochoa-López, S. et al. Ontogenetic changes in the targets of natural selection in three plant defenses. New Phytol. 226, 1480–1491 (2020).PubMed 
Article 
CAS 

Google Scholar 
Grant, P. R. & Grant, B. R. Phenotypic and genetics effects of hybridization in Darwin’s finches. Evol. Int. J. Org. Evol. 48, 297–316 (1994).Article 

Google Scholar 
Abzhanov, A., Protas, M., Grant, B. R., Grant, P. R. & Tabin, C. J. Bmp4 and morphological variation of beaks in Darwin’s finches. Science (New York, NY). 305, 1462–1465 (2004).ADS 
CAS 
Article 

Google Scholar 
von Holdt, B. M., Kays, R., Pollinger, J. P. & Wayne, R. K. Admixture mapping identifies introgressed genomic regions in North American canids. Mol. Ecol. 25, 2443–2453 (2016).Article 

Google Scholar 
Santana, S. E., Strait, S. & Dumont, E. R. The better to eat you with: functional correlates of tooth structure in bats. Funct. Ecol. 25, 839–847 (2011).Article 

Google Scholar 
Kalcounis, M. C. & Brigham, R. M. Intraspecific variation in wing loading affects habitat use by little brown bats (Myotis lucifugus). Can. J. Zool. 73, 89–95 (1995).Article 

Google Scholar 
Muijres, F. T., Johansson, L. C., Winter, Y. & Anders, H. Comparative aerodynamic performance of flapping flight in two bat species using time-resolved wake visualization. J. R. Soc. Interface 8, 1418–1428 (2011).PubMed 
PubMed Central 
Article 

Google Scholar 
Bradley, B. J. & Mundy, N. I. The primate palette: the evolution of primate coloration. Evol. Anthropolo. Issues News Rev. 17, 97–111 (2008).Article 

Google Scholar 
Müller, B. & Peichl, L. Retinal cone photoreceptors in microchiropteran bats. Investig. Ophthalmol. Vis. Sci. 46, 2259–2259 (2005).
Google Scholar 
Winter, Y., López, J. & von Helversen, O. Ultraviolet vision in a bat. Nature 425, 612–614 (2003).ADS 
CAS 
PubMed 
Article 

Google Scholar 
Caro, T. The adaptive significance of coloration in mammals. Bioscience 55, 125–136 (2005).Article 

Google Scholar 
Chaverri, G., Ancillotto, L. & Russo, D. Social communication in bats. Biol. Rev. 93, 1938–1954 (2018).PubMed 
Article 

Google Scholar 
Dietz, C., Von Helversen, O. & Nill, D. Bats of Britain, Europe and Northwest Africa 320–333 (A&C Black Publishers Ltd, 2009).
Google Scholar 
Martinoli, A., Mazzamuto, M.V. & Spada, M. Serotine Eptesicus serotinus (Schreber, 1774). In Handbook of the Mammals of Europe, 1–17 (2020).Dinger, G. Winternachweise von Breitflügelfledermaus (Eptesicus serotinus) in Kirchen. Nyctalus (N.F.) 7, 614–616 (1991).
Google Scholar 
Kowalski, K. & Rzebik-Kowalska, B. Mammals of algeria (1991).Novella-Fernandez, R. et al. Trophic resource partitioning drives fine-scale coexistence in cryptic bat species. Ecol. Evol. 10(24), 14122–14136 (2020).PubMed 
PubMed Central 
Article 

Google Scholar 
Galván, I., Vargas-Mena, J. C. & Rodríguez-Herrera, B. Tent-roosting may have driven the evolution of yellow skin coloration in Stenodermatinae bats. J. Zool. Syst. Evol. Res. 58, 519–527 (2020).Article 

Google Scholar 
Wang, Z. L., Zhang, D. Y. & Wang, G. Does spatial structure facilitate coexistence of identical competitors. Ecol. Model. 181, 17–23 (2005).Article 

Google Scholar 
Anderson, T. M. et al. Molecular and evolutionary history of melanism in North American gray wolves. Science (New York, NY). 323, 1339–1343 (2009).ADS 
CAS 
PubMed Central 
Article 

Google Scholar 
Mingo-Casas, P. et al. First cases of European bat lyssavirus type 1 in Iberian serotine bats: implications for the molecular epidemiology of bat rabies in Europe. PLoS Negl. Trop. Dis. 12(4), e0006290 (2018).PubMed 
PubMed Central 
Article 
CAS 

Google Scholar 
Vázquez-Moron, S., Juste, J., Ibáñez, C., Berciano, J. M. & Echevarria, J. E. Phylogeny of European bat Lyssavirus 1 in Eptesicus isabellinus bats, Spain. Emerg. Infect. Dis. 17, 520–523 (2011).PubMed 
PubMed Central 
Article 

Google Scholar 
Burgarella, C. et al. Detection of hybrids in nature: application to oaks (Quercus suber and Q. ilex). Heredity 102, 442–452 (2009).CAS 
PubMed 
Article 

Google Scholar 
Abrams, P. A. Character displacement and niche shift analyzed using consumer-resource models of competition. Theor. Popul. Biol. 29, 107–160 (1986).MathSciNet 
CAS 
PubMed 
MATH 
Article 

Google Scholar  More

Scott Baker, C. & Clapham, P. J. Modelling the past and future of whales and whaling. Trends Ecol. Evol. 19, 365–371. https://doi.org/10.1016/j.tree.2004.05.005 (2004).CAS 
Article 
PubMed 

Google Scholar 
Clapham, P. J. & Baker, C. S. in Encyclopedia of Marine Mammals (eds W. F. Perrin, B. Würsig, & J. G. M. Thewissen) Ch. Modern Whaling, 1328–1332 (Academic Press, 2002).International Whaling Commission. Twenty-Eighth Report of the International Whaling Commission. (International Whaling Commission, Cambridge, 1978).International Whaling Commission. Thirty-Sixth Report of the International Whaling Commission. (International Whaling Commission, Cambridge, 1986).Leaper, R. & Miller, C. Management of Antarctic baleen whales amid past exploitation, current threats and complex marine ecosystems. Antarct. Sci. 23, 503–529. https://doi.org/10.1017/s0954102011000708 (2011).ADS 
Article 

Google Scholar 
Tulloch, V. J. D., Plagányi, É. E., Matear, R., Brown, C. J. & Richardson, A. J. Ecosystem modelling to quantify the impact of historical whaling on Southern Hemisphere baleen whales. Fish Fish. 19, 117–137. https://doi.org/10.1111/faf.12241 (2018).Article 

Google Scholar 
Kemp, S. & Bennett, A. G. On the distribution and movements of whales on the South Georgia and South Shetland whaling grounds. Discov. Rep. 6, 165–190 (1932).
Google Scholar 
Branch, T. A. & Butterworth, D. S. Estimates of abundance south of 60°S for cetacean species sighted frequently on the 1978/79 to 1997/98 IWC/IDCR-SOWER sighting surveys. J. Cetac. Res. Manag. 3, 251–270 (2001).
Google Scholar 
Reilly, S. et al. Biomass and energy transfer to baleen whales in the South Atlantic sector of the Southern Ocean. Deep Sea Res. Part II 51, 1397–1409. https://doi.org/10.1016/s0967-0645(04)00087-6 (2004).ADS 
Article 

Google Scholar 
Cooke, J. G. Balaenoptera physalus. The IUCN Red List of Threatened Species 2018, e.T2478A50349982. https://doi.org/10.2305/IUCN.UK.2018-2.RLTS.T2478A50349982.en (2018).Santora, J. A., Schroeder, I. D. & Loeb, V. J. Spatial assessment of fin whale hotspots and their association with krill within an important Antarctic feeding and fishing ground. Mar. Biol. 161, 2293–2305. https://doi.org/10.1007/s00227-014-2506-7 (2014).Article 

Google Scholar 
Santora, J. A., Reiss, C. S., Loeb, V. J. & Veit, R. R. Spatial association between hotspots of baleen whales and demographic patterns of Antarctic krill Euphausia superba suggests size-dependent predation. Mar. Ecol. Prog. Ser. 405, 255–269. https://doi.org/10.3354/meps08513 (2010).ADS 
Article 

Google Scholar 
Burkhardt, E. et al. Seasonal and diel cycles of fin whale acoustic occurrence near Elephant Island, Antarctica. R Soc. Open Sci. 8, 201142. https://doi.org/10.1098/rsos.201142 (2021).ADS 
CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
Joiris, C. R. & Dochy, O. A major autumn feeding ground for fin whales, southern fulmars and grey-headed albatrosses around the South Shetland Islands, Antarctica. Polar Biol. 36, 1649–1658. https://doi.org/10.1007/s00300-013-1383-8 (2013).Article 

Google Scholar 
Herr, H. et al. Horizontal niche partitioning of humpback and fin whales around the West Antarctic Peninsula: Evidence from a concurrent whale and krill survey. Polar Biol. 39, 799–818. https://doi.org/10.1007/s00300-016-1927-9 (2016).Article 

Google Scholar 
Viquerat, S. & Herr, H. Mid-summer abundance estimates of fin whales Balaenoptera physalus around the South Orkney Islands and Elephant Island. Endanger. Spec. Res. 32, 515–524. https://doi.org/10.3354/esr00832 (2017).Article 

Google Scholar 
Meyer, B. & Wessels, W. The Expedition PS112 of the Research Vessel POLARSTERN to the Antarctic Peninsula Region in 2018. 125 p. (Alfred Wegener Institute for Polar and Marine Research, Bremerhaven, 2018). https://doi.org/10.2312/BzPM_0722_2018Knust, R. Polar research and supply vessel POLARSTERN operated by the Alfred-Wegener-Institute. J. Large-Scale Res. Facil. JLSRF 3, A119. https://doi.org/10.17815/jlsrf-3-163 (2017).Article 

Google Scholar 
Buckland, S. T. et al. Introduction to Distance Sampling: Estimating Abundance of Biological Populations (Oxford University Press, 2001).MATH 

Google Scholar 
Herr, H. et al. Aerial surveys for Antarctic minke whales (Balaenoptera bonaerensis) reveal sea ice dependent distribution patterns. Ecol Evol 9, 5664–5682. https://doi.org/10.1002/ece3.5149 (2019).Article 
PubMed 
PubMed Central 

Google Scholar 
Dorschel, B. et al. Environmental information for a marine ecosystem research approach for the northern Antarctic Peninsula (RV Polarstern expedition PS81, ANT-XXIX/3). Polar Biol. 39, 765–787. https://doi.org/10.1007/s00300-015-1861-2 (2015).Article 

Google Scholar 
Miller, D. L., Burt, M. L., Rexstad, E. A., Thomas, L. & Gimenez, O. Spatial models for distance sampling data: recent developments and future directions. Methods Ecol. Evol. 4, 1001–1010. https://doi.org/10.1111/2041-210x.12105 (2013).Article 

Google Scholar 
Hedley, S. L. & Buckland, S. T. Spatial models for line transect sampling. J. Agric. Biol. Environ. Stat. 9, 181–199. https://doi.org/10.1198/1085711043578 (2004).Article 

Google Scholar 
Hammond, P. S. et al. Estimating the abundance of marine mammal populations. Front. Mar. Sci. https://doi.org/10.3389/fmars.2021.735770 (2021).Article 

Google Scholar 
Buckland, S. T. et al. Advanced Distance Sampling (Oxford University Press, 2007).
Google Scholar 
Miller, D. L., Rexstad, E., Thomas, L., Marshall, L. & Laake, J. L. Distance sampling in R. J. Stat. Softw. 89, 1–28. https://doi.org/10.18637/jss.v089.i01 (2019).Article 

Google Scholar 
Marques, F. F. C. & Buckland, S. T. in Advanced Distance Sampling (eds S. T. Buckland et al.) 31–47 (Oxford University Press, 2004).Akaike, H. A new look at the statistical model identification. IEEE Trans. Autom. Control 19, 716–723 (1974).ADS 
MathSciNet 
Article 

Google Scholar 
Wood, S. N. Fast stable restricted maximum likelihood and marginal likelihood estimation of semparametric generalized linear models. J. R. Stat. Soc. (B) 73, 3–36. https://doi.org/10.1111/j.1467-9868.2010.00749.x (2011).Article 
MATH 

Google Scholar 
Wood, S. N., Pya, N. & Saefken, B. Smoothing parameter and model selection for general smooth models (with discussion). J. Am. Stat. Assoc. 111, 1548–1575. https://doi.org/10.1080/01621459.2016.1180986 (2016).CAS 
Article 

Google Scholar 
Dorschel, B. et al. The International Bathymetric Chart of the Southern Ocean Version 2 (IBCSO v2). Scientific Data. https://doi.org/10.1038/s41597-022-01366-7 (2022).Article 

Google Scholar 
Wilson, M. F. J., O’Connell, B., Brown, C., Guinan, J. C. & Grehan, A. J. Multiscale terrain analysis of multibeam bathymetry data for habitat mapping on the continental slope. Mar. Geod. 30, 3–35. https://doi.org/10.1080/01490410701295962 (2007).Article 

Google Scholar 
raster: Geographic Data Analysis and Modelling. R package version 3.4–5 (2020).Shelton, A. O., Thorson, J. T., Ward, E. J., Feist, B. E. & Cooper, A. Spatial semiparametric models improve estimates of species abundance and distribution. Can. J. Fish. Aquat. Sci. 71, 1655–1666. https://doi.org/10.1139/cjfas-2013-0508 (2014).CAS 
Article 

Google Scholar 
Dunn, P. K. & Smyth, G. K. Series evaluation of Tweedie exponential dispersion model densities. Statiustics Comput. 15, 267–280 (2005).MathSciNet 
Article 

Google Scholar 
Gu, C. & Wahba, G. Minimizing GCV/GML scores with multiple smoothing parameters via the newton method. SIAM J. Sci. Stat. Comput. 12, 383–398. https://doi.org/10.1137/0912021 (1991).MathSciNet 
Article 
MATH 

Google Scholar 
Hobson, E. S. Feeding behaviour in three species of sharks. Pac. Sci. 17, 171–194 (1963).
Google Scholar 
Weeks, S., Magno-Canto, M., Jaine, F., Brodie, J. & Richardson, A. Unique sequence of events triggers manta ray feeding frenzy in the Southern Great Barrier Reef, Australia. Remote Sens. 7, 3138–3152. https://doi.org/10.3390/rs70303138 (2015).ADS 
Article 

Google Scholar 
Montero-Quintana, A. N., Ocampo-Valdez, C. F., Vázquez-Haikin, J. A., Sosa-Nishizaki, O. & Osorio-Beristain, M. Whale shark (Rhincodon typus) predatory flexible feeding behaviors on schooling fish. J. Ethol. 39, 399–410. https://doi.org/10.1007/s10164-021-00717-y (2021).Article 

Google Scholar 
Findlay, K. P. et al. Humpback whale “super-groups”—A novel low-latitude feeding behaviour of Southern Hemisphere humpback whales (Megaptera novaeangliae) in the Benguela Upwelling System. PLoS ONE 12, e0172002. https://doi.org/10.1371/journal.pone.0172002 (2017).CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
Pirotta, V., Owen, K., Donnelly, D., Brasier, M. J. & Harcourt, R. First evidence of bubble-net feeding and the formation of ‘super-groups’ by the east Australian population of humpback whales during their southward migration. Aquat. Conserv. Mar. Freshwat. Ecosyst. 31, 2412–2419. https://doi.org/10.1002/aqc.3621 (2021).Article 

Google Scholar 
Baines, M., Reichelt, M. & Griffin, D. An autumn aggregation of fin (Balaenoptera physalus) and blue whales (B. musculus) in the Porcupine Seabight, southwest of Ireland. Deep Sea Res. Part II Top. Stud. Oceanogr. 141, 168–177, https://doi.org/10.1016/j.dsr2.2017.03.007 (2017).Ladrón de Guevara P., P., Lavaniegos, B. E. & Heckel, G. Fin whales (Balaenoptera physalus) foraging on daytime surface swarms of the euphausiid Nyctiphanes simplex in Ballenas Channel, Gulf of California, Mexico. J. Mammal. 89, 559–566 (2008).Bruce, W. S. Some observations on Antarctic cetacea. In: Report on the Scientific results of the voyage of S.Y. ‘Scotia’ during the years 1902, 1903, and 1904, under the leadership of William S. Bruce, 491–505 (1915).Mackintosh, N. A. & Wheeler, J. F. G. Southern blue and fin whales. Discov. Rep. I, 257–540 (1929).Jackson, J. A. et al. Have whales returned to a historical hotspot of industrial whaling? The pattern of southern right whale Eubalaena australis recovery at South Georgia. Endang. Spec. Res. 43, 323–339. https://doi.org/10.3354/esr01072 (2020).Article 

Google Scholar 
Goldbogen, J. A., Pyenson, N. D. & Shadwick, R. E. Big gulps require high drag for fin whale lunge feeding. Mar. Ecol. Prog. Ser. 349, 289–301. https://doi.org/10.3354/meps07066 (2007).ADS 
Article 

Google Scholar 
Goldbogen, J. A. et al. Scaling of lunge-feeding performance in rorqual whales: mass-specific energy expenditure increases with body size and progressively limits diving capacity. Funct. Ecol. 26, 216–226. https://doi.org/10.1111/j.1365-2435.2011.01905.x (2012).Article 

Google Scholar 
Acevedo-Gutierrez, A., Croll, D. A. & Tershy, B. R. High feeding costs limit dive time in the largest whales. J Exp Biol 205, 1747–1753. https://doi.org/10.1242/jeb.205.12.1747 (2002).CAS 
Article 
PubMed 

Google Scholar 
Keen, E. M. Aggregative and feeding thresholds of sympatric rorqual whales within a fjord system. Ecosphere 8, e01702. https://doi.org/10.1002/ecs2.1702 (2017).Article 

Google Scholar 
Veit, R. R. & Harrison, N. M. Positive interactions among foraging seabirds, marine mammals and fishes and implications for their conservation. Front. Ecol. Evol. https://doi.org/10.3389/fevo.2017.00121 (2017).Article 

Google Scholar 
Laran, S. et al. A comprehensive survey of pelagic megafauna: their distribution, densities, and taxonomic richness in the tropical Southwest Indian Ocean. Front. Mar. Sci. https://doi.org/10.3389/fmars.2017.00139 (2017).Article 

Google Scholar 
Campbell, G. S. et al. Inter-annual and seasonal trends in cetacean distribution, density and abundance off southern California. Deep Sea Res. Part II Top. Stud. Oceanogr. 112, 143–157. https://doi.org/10.1016/j.dsr2.2014.10.008 (2015).ADS 
Article 

Google Scholar 
Heide-Jørgensen, M. P. et al. Estimates of large whale abundance in West Greenland waters from an aerial survey in 2005. J. Cetac. Res. Manag. 10, 119–129 (2008).
Google Scholar 
Panigada, S. et al. Estimating cetacean density and abundance in the Central and Western Mediterranean Sea through aerial surveys: Implications for management. Deep Sea Res. Part II Top. Stud. Oceanogr. 141, 41–58. https://doi.org/10.1016/j.dsr2.2017.04.018 (2017).ADS 
Article 

Google Scholar 
Forcada, J., Aguilar, A., Hammond, P., Pastor, X. & Aguilar, R. Distribution and abundance of fin whales (Balaenoptera physalus) in the western Mediterranean sea during the summer. J. Zool. 238, 23–34. https://doi.org/10.1111/j.1469-7998.1996.tb05377.x (2009).Article 

Google Scholar 
Clapham, P. J., Aguilar, A. & Hatch, L. T. Determining spatial and temporal scales for management: lessons from whaling. Mar. Mamm. Sci. 24, 183–201. https://doi.org/10.1111/j.1748-7692.2007.00175.x (2008).Article 

Google Scholar 
Baker, C. S. et al. Strong maternal fidelity and natal philopatry shape genetic structure in North Pacific humpback whales. Mar. Ecol. Prog. Ser. 494, 291–306. https://doi.org/10.3354/meps10508 (2013).ADS 
Article 

Google Scholar 
Carroll, E. L. et al. Cultural traditions across a migratory network shape the genetic structure of southern right whales around Australia and New Zealand. Sci. Rep. 5, 16182. https://doi.org/10.1038/srep16182 (2015).ADS 
CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
Valenzuela, L. O., Sironi, M., Rowntree, V. J. & Seger, J. Isotopic and genetic evidence for culturally inherited site fidelity to feeding grounds in southern right whales (Eubalaena australis). Mol. Ecol. 18, 782–791. https://doi.org/10.1111/j.1365-294X.2008.04069.x (2009).CAS 
Article 
PubMed 

Google Scholar 
Barendse, J., Best, P. B., Carvalho, I. & Pomilla, C. Mother knows best: occurrence and associations of resighted humpback whales suggest maternally derived fidelity to a Southern Hemisphere coastal feeding ground. PLoS ONE 8, e81238. https://doi.org/10.1371/journal.pone.0081238 (2013).ADS 
CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
Richard, G. et al. Cultural transmission of fine-scale fidelity to feeding sites may shape humpback whale genetic diversity in Russian Pacific waters. J. Hered. 109, 724–734. https://doi.org/10.1093/jhered/esy033 (2018).CAS 
Article 
PubMed 

Google Scholar 
Carroll, E. L. et al. Reestablishment of former wintering grounds by New Zealand southern right whales. Mar. Mamm. Sci. 30, 206–220. https://doi.org/10.1111/mms.12031 (2014).Article 

Google Scholar 
Calderan, S. V. et al. South Georgia blue whales five decades after the end of whaling. Endang. Spec. Res. 43, 359–373. https://doi.org/10.3354/esr01077 (2020).Article 

Google Scholar 
Santora, J. A. & Veit, R. R. Spatio-temporal persistence of top predator hotspots near the Antarctic Peninsula. Mar. Ecol. Prog. Ser. 487, 287–304. https://doi.org/10.3354/meps10350 (2013).ADS 
Article 

Google Scholar 
Scheidat, M. et al. Cetacean surveys in the Southern Ocean using icebreaker-supported helicopters. Polar Biol. 34, 1513–1522. https://doi.org/10.1007/s00300-011-1010-5 (2011).Article 

Google Scholar 
Williams, R., Hedley, S. L. & Hammond, P. S. Modeling distribution and abundance of Antarctic baleen whales using ships of opportunity. Ecol. Soc. 11, [online] http://www.ecologyandsociety.org/vol11/iss11/art11/ (2006).Secchi, E. et al. Encounter rates of whales around the Antarctic peninsula with special reference to humpback whales, Megaptera novaeangliae, in the Gerlache Strait 1997–98 to 1999–2000. Mem. Qld. Mus. 47, 571–578 (2001).
Google Scholar 
Thiele, D. et al. Seasonal variability in whale encounters in the Western Antarctic Peninsula. Deep Sea Res. Part II Top. Stud. Oceanogr. 51, 2311–2325. https://doi.org/10.1016/j.dsr2.2004.07.007 (2004).ADS 
Article 

Google Scholar 
Johnston, D. W., Friedlaender, A. S., Read, A. J. & Nowacek, D. P. Initial density estimates of humpback whales Megaptera novaeangliae in the inshore waters of the western Antarctic Peninsula during the late autumn. Endang. Spec. Res. 18, 63–71. https://doi.org/10.3354/esr00395 (2012).Article 

Google Scholar 
Zerbini, A. N. et al. Assessing the recovery of an Antarctic predator from historical exploitation. R Soc Open Sci 6, 190368. https://doi.org/10.1098/rsos.190368 (2019).ADS 
Article 
PubMed 
PubMed Central 

Google Scholar 
Mackintosh, N. A. The distribution of southern blue and fin whales, in Whales, dolphins, and porpoises. (ed K. S. Norris) Ch. 8, 125–144 (University of California Press, 1966).Schmitt, N. et al. Mixed-stock analysis of humpback whales (Megaptera novaeangliae) on Antarctic feeding grounds. J. Cetac. Res. Manag. 14, 141–157 (2014).
Google Scholar 
Schall, E. et al. Humpback whale song recordings suggest common feeding ground occupation by multiple populations. Sci. Rep. 11, 18806. https://doi.org/10.1038/s41598-021-98295-z (2021).ADS 
CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
Archer, F. I. et al. Revision of fin whale Balaenoptera physalus (Linnaeus, 1758) subspecies using genetics. J. Mammal. 100, 1653–1670. https://doi.org/10.1093/jmammal/gyz121 (2019).Article 

Google Scholar 
Archer, F. I. et al. Mitogenomic phylogenetics of fin whales (Balaenoptera physalus spp.): Genetic evidence for revision of subspecies. PLoS ONE 8, e63396. https://doi.org/10.1371/journal.pone.0063396 (2013).ADS 
CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
Cabrera, A. A. et al. Fin whale (Balaenoptera physalus) mitogenomics: A cautionary tale of defining sub-species from mitochondrial sequence monophyly. Mol. Phylogenet. Evol. 135, 86–97. https://doi.org/10.1016/j.ympev.2019.02.003 (2019).Article 
PubMed 

Google Scholar 
Edwards, E. F., Hall, C., Moore, T. J., Sheredy, C. & Redfern, J. V. Global distribution of fin whales Balaenoptera physalus in the post-whaling era (1980–2012). Mammal. Rev. 45, 197–214. https://doi.org/10.1111/mam.12048 (2015).Article 

Google Scholar 
Knox, G. A. The key role of krill in the ecosystem of the Southern Ocean with special reference to the Convention on the Conservation of Antarctic Marine Living Resources. Ocean Manag. 9, 113–156 (1984).Article 

Google Scholar 
Ducklow, H. W. et al. Marine pelagic ecosystems: the west Antarctic Peninsula. Philos. Trans. R. Soc. Lond. B Biol. Sci. 362, 67–94. https://doi.org/10.1098/rstb.2006.1955 (2007).Article 
PubMed 

Google Scholar 
Atkinson, A. et al. Krill (Euphausia superba) distribution contracts southward during rapid regional warming. Nat. Clim. Change 9, 142–147. https://doi.org/10.1038/s41558-018-0370-z (2019).ADS 
Article 

Google Scholar 
Benton, M. J., Tverdokhlebov, V. P. & Surkov, M. V. Ecosystem remodelling among vertebrates at the Permian-Triassic boundary in Russia. Nature 432, 97–100. https://doi.org/10.1038/nature02950 (2004).ADS 
CAS 
Article 
PubMed 

Google Scholar 
Siegel, V., Reiss, C. S., Dietrich, K. S., Haraldsson, M. & Rohardt, G. Distribution and abundance of Antarctic krill (Euphausia superba) along the Antarctic Peninsula. Deep Sea Res. Part I Oceanogr. Res. Pap. 77, 63–74. https://doi.org/10.1016/j.dsr.2013.02.005 (2013).ADS 
Article 

Google Scholar 
Siegel, V. & Watkins, L. Distribution, Biomass and Demography of Antarctic Krill, Euphausia superba, in Biology and Ecology of Antarctic Krill Advances in Polar Ecology (ed Volker Siegel) Ch. 2, 21–101 (Springer, 2016).Roman, J. et al. Whales as marine ecosystem engineers. Front. Ecol. Environ. 12, 377–385. https://doi.org/10.1890/130220 (2014).Article 

Google Scholar 
Savoca, M. S. et al. Baleen whale prey consumption based on high-resolution foraging measurements. Nature 599, 85–90. https://doi.org/10.1038/s41586-021-03991-5 (2021).ADS 
CAS 
Article 
PubMed 

Google Scholar 
Atkinson, A., Siegel, V., Pakhomov, E. A., Jessopp, M. J. & Loeb, V. A re-appraisal of the total biomass and annual production of Antarctic krill. Deep Sea Res. Part I Oceangr. Res. Pap. 56, 727–740. https://doi.org/10.1016/j.dsr.2008.12.007 (2009).ADS 
Article 

Google Scholar 
Nicol, S. et al. Southern Ocean iron fertilization by baleen whales and Antarctic krill. Fish Fish. 11, 203–209. https://doi.org/10.1111/j.1467-2979.2010.00356.x (2010).Article 

Google Scholar 
Lavery, T. J. et al. Whales sustain fisheries: Blue whales stimulate primary production in the Southern Ocean. Mar. Mamm. Sci. 30, 888–904. https://doi.org/10.1111/mms.12108 (2014).CAS 
Article 

Google Scholar 
Smetacek, V. Are declining Antarctic krill stocks a result of global warming or of the decimation of the whales? in Effects of global warming on polar ecosystems (ed Carlos M. Duarte) (Fundación BBVA, 2008).Roman, J. & McCarthy, J. J. The whale pump: marine mammals enhance primary productivity in a coastal basin. PLoS ONE 5, e13255. https://doi.org/10.1371/journal.pone.0013255 (2010).ADS 
CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
Smetacek, V. A whale of an appetite revealed by analysis of prey consumption. Nature 599, 33–34. https://doi.org/10.1038/d41586-021-02951-3 (2021).ADS 
CAS 
Article 
PubMed 

Google Scholar 
Laws, R. M. Seals and whales of the Southern Ocean. Philios. Trans. R. Soc. Lond. B 279, 81–96 (1977).ADS 
Article 

Google Scholar 
Lavery, T. J. et al. Iron defecation by sperm whales stimulates carbon export in the Southern Ocean. Proc. Biol. Sci. 277, 3527–3531. https://doi.org/10.1098/rspb.2010.0863 (2010).Article 
PubMed 
PubMed Central 

Google Scholar 
Cunningham, S. A. Transport and variability of the Antarctic Circumpolar Current in Drake Passage. J. Geophys. Res. https://doi.org/10.1029/2001jc001147 (2003).Article 

Google Scholar 
Frölicher, T. L. et al. Dominance of the Southern Ocean in Anthropogenic Carbon and Heat Uptake in CMIP5 Models. J. Clim. 28, 862–886. https://doi.org/10.1175/jcli-d-14-00117.1 (2015).ADS 
Article 

Google Scholar 
Landschutzer, P. et al. The reinvigoration of the Southern Ocean carbon sink. Science 349, 1221–1224. https://doi.org/10.1126/science.aab2620 (2015).ADS 
CAS 
Article 
PubMed 

Google Scholar  More