Philippa Kaur

McCave IN. Vertical flux of particles in the ocean. Deep-Sea Res. 1975;22:491–502.
Google Scholar 
Ducklow HW, Steinberg DK, Buesseler KO. Upper ocean carbon export and the biological pump. Oceanography. 2001;14:50–8.
Google Scholar 
Siegenthaler U, Sarmiento JL. Atmospheric carbon dioxide and the ocean. Nature 1993;365:119–25.CAS 

Google Scholar 
Bar-On YM, Phillips R, Milo R. The biomass distribution on Earth. Proc Natl Acad Sci USA. 2018;115:6506–11.CAS 
PubMed 
PubMed Central 

Google Scholar 
Turley CM, Mackie PJ. Biogeochemical significance of attached and free-living bacteria and the flux of particles in the NE Atlantic Ocean. Mar Ecol Prog Ser. 1994;115:191–204.
Google Scholar 
Turley CM, Stutt ED. Depth-related cell-specific bacterial leucine incorporation rates on particles and its biogeochemical significance in the Northwest Mediterranean. Limnol Oceanogr. 2000;45:419–25.CAS 

Google Scholar 
Aristegui J, Gasol JM, Duarte CM, Herndl GJ. Microbial oceanography of the dark ocean’s pelagic realm. Limnol Oceanogr. 2009;54:1501–29.CAS 

Google Scholar 
Fontanez KM, Eppley JM, Samo TJ, Karl DM, DeLong EF. Microbial community structure and function on sinking particles in the North Pacific Subtropical Gyre. Front Microbiol. 2015;6:469.PubMed 
PubMed Central 

Google Scholar 
Pelve EA, Fontanez KM, DeLong EF. Bacterial succession on sinking particles in the ocean’s interior. Front Microbiol. 2017;8:2669.
Google Scholar 
Boeuf D, Edwards BR, Eppley JM, Hu SK, Poff KE, Romano AE, et al. Biological composition and microbial dynamics of sinking particulate organic matter at abyssal depths in the oligotrophic open ocean. Proc Natl Acad Sci USA. 2019;116:11824–32.CAS 
PubMed 
PubMed Central 

Google Scholar 
Preston CM, Durkin CA, Yamahara KM. DNA metabarcoding reveals organisms contributing to particulate matter flux to abyssal depths in the North East Pacific ocean. Deep-Sea Res Part II. 2020;173:104708.CAS 

Google Scholar 
Mestre M, Ruiz-González C, Logares R, Duarte CM, Gasol JM, Sala MM. Sinking particles promote vertical connectivity in the ocean microbiome. Proc Natl Acad Sci USA. 2018;115:E6799–807.CAS 
PubMed 
PubMed Central 

Google Scholar 
Jiao N, Herndl GJ, Hansell DA, Benner R, Kattner G, Wilhelm SW, et al. Microbial production of recalcitrant dissolved organic matter: Long-term carbon storage in the global ocean. Nat Rev Microbiol. 2010;8:593–9.CAS 
PubMed 

Google Scholar 
Poff KE, Leu AO, Eppley JM, Karl DM, DeLong EF. Microbial dynamics of elevated carbon flux in the open ocean’s abyss. Proc Natl Acad Sci USA. 2021;118:1–11.
Google Scholar 
DeLong EF, Franks DG, Alldredge AL. Phylogenetic diversity of aggregate‐attached vs. free‐living marine bacterial assemblages. Limnol Oceanogr. 1993;38:924–34.
Google Scholar 
Rieck A, Herlemann DPR, Jürgens K, Grossart HP. Particle-associated differ from free-living bacteria in surface waters of the Baltic Sea. Front Microbiol. 2015;6:1297.PubMed 
PubMed Central 

Google Scholar 
Crespo BG, Pommier T, Fernández-Gómez B, Pedrós-Alió C. Taxonomic composition of the particle-attached and free-living bacterial assemblages in the Northwest Mediterranean Sea analyzed by pyrosequencing of the 16S rRNA. Microbiologyopen. 2013;2:541–52.CAS 
PubMed 
PubMed Central 

Google Scholar 
Eloe EA, Shulse CN, Fadrosh DW, Williamson SJ, Allen EE, Bartlett DH. Compositional differences in particle-associated and free-living microbial assemblages from an extreme deep-ocean environment. Environ Microbiol Rep. 2011;3:449–58.PubMed 

Google Scholar 
Ghiglione JF, Mevel G, Pujo-Pay M, Mousseau L, Lebaron P, Goutx M. Diel and seasonal variations in abundance, activity, and community structure of particle-attached and free-living bacteria in NW Mediterranean Sea. Micro Ecol. 2007;54:217–31.CAS 

Google Scholar 
López-Pérez M, Kimes NE, Haro-Moreno JM, Rodriguez-Valera F. Not all particles are equal: The selective enrichment of particle-associated bacteria from the Mediterranean Sea. Front Microbiol. 2016;7:996.PubMed 
PubMed Central 

Google Scholar 
Farnelid H, Turk-Kubo K, Ploug H, Ossolinski JE, Collins JR, Van Mooy BAS, et al. Diverse diazotrophs are present on sinking particles in the North Pacific Subtropical Gyre. ISME J. 2019;13:170–82.PubMed 

Google Scholar 
Mende DR, Boeuf D, DeLong EF. Persistent core populations shape the microbiome throughout the water column in the North Pacific Subtropical Gyre. Front Microbiol. 2019;10:1–12.
Google Scholar 
Proctor LM, Fuhrman JA. Roles of viral infection in organic particle flux. Mar Ecol Prog Ser. 1991;69:133–42.
Google Scholar 
Peduzzi P, Weinbauer MG. Effect of concentrating the virus‐rich 2‐2nm size fraction of seawater on the formation of algal flocs (marine snow). Limnol Oceanogr. 1993;38:1562–5.
Google Scholar 
Weinbauer MG. Ecology of prokaryotic viruses. FEMS Microbiol Rev. 2004;28:127–81.CAS 
PubMed 

Google Scholar 
Zimmerman AE, Howard-Varona C, Needham DM, John SG, Worden AZ, Sullivan MB, et al. Metabolic and biogeochemical consequences of viral infection in aquatic ecosystems. Nat Rev Microbiol. 2019;18:21–34.PubMed 

Google Scholar 
Wilhelm SW, Suttle CA. Viruses and nutrient cycles in the sea. Bioscience. 1999;49:781–8.
Google Scholar 
Gobler CJ, Hutchins DA, Fisher NS, Cosper EM, Sañudo-Wilhelmy SA. Release and bioavailability of C, N, P, Se, and Fe following viral lysis of a marine chrysophyte. Limnol Oceanogr. 1997;42:1492–504.CAS 

Google Scholar 
Middelboe M, Jørgensen NOG, Kroer N. Effects of viruses on nutrient turnover and growth efficiency of noninfected marine bacterioplankton. Appl Environ Microbiol. 1996;62:1991–7.CAS 
PubMed 
PubMed Central 

Google Scholar 
Alldredge AL, Silver MW. Characteristics, dynamics and significance of marine snow. Prog Oceanogr. 1988;20:41–82.
Google Scholar 
Shibata A, Kogure K, Koike I, Ohwada K. Formation of submicron colloidal particles from marine bacteria by viral infection. Mar Ecol Prog Ser. 1997;155:303–7.
Google Scholar 
Yamada Y, Tomaru Y, Fukuda H, Nagata T. Aggregate formation during the viral lysis of a marine diatom. Front Mar Sci. 2018;5:1–7.
Google Scholar 
Lawrence JE, Suttle CA. Effect of viral infection on sinking rates of Heterosigma akashiwo and its implications for bloom termination. Aquat Micro Ecol. 2004;37:1–7.
Google Scholar 
Michaels A, Silver M. Primary production, sinking fluxes and the microbial food web. Deep-Sea Res. Part I 1988;35:473–90.
Google Scholar 
Richardson TL. Mechanisms and pathways of small-phytoplankton export from the surface ocean. Ann Rev Mar Sci. 2019;11:57–74.PubMed 

Google Scholar 
Richardson T, Jackson GA. Small phytoplankton and carbon export from the surface ocean. Science. 2007;315:838–40.CAS 
PubMed 

Google Scholar 
Lomas MW, Moran SB. Evidence for aggregation and export of cyanobacteria and nano-eukaryotes from the Sargasso Sea euphotic zone. Biogeosciences 2011;8:203–16.CAS 

Google Scholar 
Liu H, Nolla HA, Campbell L. Prochlorococcus growth rate and contribution to primary production in the equatorial and subtropical North Pacific Ocean. Aquat Micro Ecol. 1997;12:39–47.
Google Scholar 
Kaneko H, Blanc-Mathieu R, Endo H, Chaffron S, Delmont TO, Gaia M, et al. Eukaryotic virus composition can predict the efficiency of carbon export in the global ocean. iScience. 2021;24:102002.Guidi L, Chaffron S, Bittner L, Eveillard D, Larhlimi A, Roux S, et al. Plankton networks driving carbon export in the oligotrophic ocean. Nature 2015;532:465–70.
Google Scholar 
Laber CP, Hunter JE, Carvalho F, Collins JR, Hunter EJ, Schieler BM, et al. Coccolithovirus facilitation of carbon export in the North Atlantic. Nat Microbiol. 2018;3:537–47.CAS 
PubMed 

Google Scholar 
Sheyn U, Rosenwasser S, Lehahn Y, Barak-Gavish N, Rotkopf R, Bidle KD, et al. Expression profiling of host and virus during a coccolithophore bloom provides insights into the role of viral infection in promoting carbon export. ISME J. 2018;12:704–13.CAS 
PubMed 
PubMed Central 

Google Scholar 
Karl DM, Church MJ. Microbial oceanography and the Hawaii Ocean Time-series programme. Nat Rev Microbiol. 2014;12:1–15.
Google Scholar 
Karl DM, Church MJ, Dore JE, Letelier RM, Mahaffey C. Predictable and efficient carbon sequestration in the North Pacific Ocean supported by symbiotic nitrogen fixation. Proc Natl Acad Sci USA. 2012;109:1842–9.CAS 
PubMed 
PubMed Central 

Google Scholar 
Karl DM, Lukas R. The Hawaii Ocean Time-series (HOT) program: Background, rationale and field implementation. Deep-Sea Res Part II. 1996;43:129–56.CAS 

Google Scholar 
Roux S, Enault F, Hurwitz BL, Sullivan MB. VirSorter: mining viral signal from microbial genomic data. PeerJ. 2015;3:e985.PubMed 
PubMed Central 

Google Scholar 
Kieft K, Zhou Z, Anantharaman K. VIBRANT: automated recovery, annotation and curation of microbial viruses, and evaluation of viral community function from genomic sequences. Microbiome 2020;8:90.CAS 
PubMed 
PubMed Central 

Google Scholar 
Arumugam M, Harrington ED, Raes J, Foerstner KU, Arumugam M, Bork P. SmashCommunity: A metagenomic annotation and analysis tool. Bioinformatics. 2010;26:2977–8.CAS 
PubMed 

Google Scholar 
Bankevich A, Nurk S, Antipov D, Gurevich AA, Dvorkin M, Kulikov AS, et al. SPAdes: a new genome assembly algorithm and its applications to single-cell sequencing. J Comput Biol. 2012;19:455–77.CAS 
PubMed 
PubMed Central 

Google Scholar 
Roux S, Emerson JB, Eloe-Fadrosh EA, Sullivan MB. Benchmarking viromics: An in silico evaluation of metagenome-enabled estimates of viral community composition and diversity. PeerJ. 2017;5:e3817.PubMed 
PubMed Central 

Google Scholar 
Hyatt D, Chen G, Locascio PF, Land ML, Larimer FW, Hauser LJ. Prodigal: Prokaryotic gene recognition and translation initiation site identification. BMC Bioinformatics. 2010;11:119.Eddy SR. Accelerated Profile HMM Searches. PLoS Comput Biol. 2011;7:e1002195.CAS 
PubMed 
PubMed Central 

Google Scholar 
Finn RD, Tate J, Mistry J, Coggill PC, Sammut SJ, Hotz H, et al. The Pfam protein families database. Nucleic Acids Res. 2008;36:281–8.Li W, Godzik A. Cd-hit: A fast program for clustering and comparing large sets of protein or nucleotide sequences. Bioinformatics. 2006;22:1658–9.CAS 
PubMed 

Google Scholar 
Mizuno CM, Guyomar C, Roux S, Lavigne R, Rodriguez-Valera F, Sullivan M, et al. Numerous cultivated and uncultivated viruses encode ribosomal proteins. Nat Commun. 2019;10:752.CAS 
PubMed 
PubMed Central 

Google Scholar 
Kielbasa SM, Wan R, Sato K, Kiebasa SM, Horton P, Frith MC. Adaptive seeds tame genomic sequence comparison. Genome Res. 2011;21:487–93.CAS 
PubMed 
PubMed Central 

Google Scholar 
Nishimura Y, Watai H, Honda T, Mihara T, Omae K, Roux S, et al. Environmental viral genomes shed new light on virus-host interactions in the ocean. mSphere. 2017;2:e00359–16.CAS 
PubMed 
PubMed Central 

Google Scholar 
Imai T sprai = single pass read accuracy improver [Internet]. 2013. Available from: http://zombie.cb.k.u-tokyo.ac.jp/sprai/Kurtz S, Phillippy A, Delcher AL, Smoot M, Shumway M, Antonescu C, et al. Versatile and open software for comparing large genomes. Genome Biol. 2004;5:R12.PubMed 
PubMed Central 

Google Scholar 
Beaulaurier J, Luo E, Eppley JM, Uyl PDen, Dai X, Burger A, et al. Assembly-free single-molecule sequencing recovers complete virus genomes from natural microbial communities. Genome Res. 2020;30:437–46.CAS 
PubMed 
PubMed Central 

Google Scholar 
Parks DH, Chuvochina M, Waite DW, Rinke C, Skarshewski A, Chaumeil PA, et al. A standardized bacterial taxonomy based on genome phylogeny substantially revises the tree of life. Nat Biotechnol. 2018;36:996.CAS 
PubMed 

Google Scholar 
Skennerton CT, Imelfort M, Tyson GW. Crass: Identification and reconstruction of CRISPR from unassembled metagenomic data. Nucleic Acids Res. 2013;41:e105.CAS 
PubMed 
PubMed Central 

Google Scholar 
O’Leary NA, Wright MW, Brister JR, Ciufo S, Haddad D, Mcveigh R, et al. Reference sequence (RefSeq) database at NCBI: Current status, taxonomic expansion, and functional annotation. Nucleic Acids Res. 2016;44:733–45. (Database issue)
Google Scholar 
Luo E, Eppley JM, Romano AE, Mende DR, DeLong EF. Double-stranded DNA virioplankton dynamics and reproductive strategies in the oligotrophic open ocean water column. ISME J. 2020;14:1304–15.CAS 
PubMed 
PubMed Central 

Google Scholar 
Mizuno CM, Rodriguez-Valera F, Kimes NE, Ghai R. Expanding the marine virosphere using metagenomics. PLoS Genet. 2013;9:e1003987.PubMed 
PubMed Central 

Google Scholar 
Mizuno CM, Ghai R, Saghaï A, López-García P, Rodriguez-Valera F. Genomes of abundant and widespread viruses from the deep ocean. MBio. 2016;7:e00805–16.CAS 
PubMed 
PubMed Central 

Google Scholar 
Roux S, Brum JR, Dutilh BE, Sunagawa S, Duhaime MB, Loy A, et al. Ecogenomics and biogeochemical impacts of uncultivated globally abundant ocean viruses. Nature. 2016;537:689–93.CAS 
PubMed 

Google Scholar 
Paez-Espino D, Eloe-Fadrosh EA, Pavlopoulos GA, Thomas AD, Huntemann M, Mikhailova N, et al. Uncovering Earth’s virome. Nature. 2016;536:425–30.CAS 
PubMed 

Google Scholar 
López-Pérez M, Haro-Moreno JM, Gonzalez-Serrano R, Parras-Moltó M, Rodriguez-Valera F. Genome diversity of marine phages recovered from Mediterranean metagenomes: Size matters. PLoS Genet. 2017;13:1–23.
Google Scholar 
Coutinho FH, Silveira CB, Gregoracci GB, Thompson CC, Edwards RA, Brussaard CPD, et al. Marine viruses discovered via metagenomics shed light on viral strategies throughout the oceans. Nat Commun. 2017;8:15955.CAS 
PubMed 
PubMed Central 

Google Scholar 
Gregory AC, Zayed AA, Sunagawa S, Wincker P, Sullivan MB, Ferland J, et al. Marine DNA viral macro- and microdiversity from pole to pole. Cell. 2019;177:1–15.
Google Scholar 
Luo E, Aylward FO, Mende DR, Delong EF. Bacteriophage distributions and temporal variability in the ocean’s interior. MBio. 2017;8:e01903–17.CAS 
PubMed 
PubMed Central 

Google Scholar 
Eren AM, Esen ÖC, Quince C, Vineis JH, Morrison HG, Sogin ML, et al. Anvi’o: An advanced analysis and visualization platform for ‘omics data. PeerJ. 2015;3:e1319.PubMed 
PubMed Central 

Google Scholar 
Langfelder P, Horvath S. WGCNA: An R package for weighted correlation network analysis. BMC Bioinformatics. 2008;9:559.R Core Team. R: A Language and Environment for Statistical Computing [Internet]. Vienna, Austria; 2019. Available from: https://www.r-project.org/Lauro FM, Chastain RA, Blankenship LE, Yayanos AA, Bartlett DH. The unique 16S rRNA genes of piezophiles reflect both phylogeny and adaptation. Appl Environ Microbiol. 2007;73:838–45.CAS 
PubMed 

Google Scholar 
DeLong EF, Franks DG, Yayanos AA. Evolutionary relationships of cultivated psychrophilic and barophilic deep-sea bacteria. Appl Environ Microbiol. 1997;63:2105–8.CAS 
PubMed 
PubMed Central 

Google Scholar 
Berg KA, Lyra C, Sivonen K, Paulin L, Suomalainen S, Tuomi P, et al. High diversity of cultivable heterotrophic bacteria in association with cyanobacterial water blooms. ISME J. 2009;3:314–25.CAS 
PubMed 

Google Scholar 
Rii YM, Karl DM, Church MJ. Temporal and vertical variability in picophytoplankton primary productivity in the North Pacific Subtropical Gyre. Mar Ecol Prog Ser. 2016;562:1–18.CAS 

Google Scholar 
Martin JH, Knauer GA, Karl DM, Broenkow WW. VERTEX: Carbon cycling in the northeast Pacific. Deep-Sea Res. 1987;34:267–85.CAS 

Google Scholar 
Karl MD, Knauer AG. Detritus-microbe interactions in the marine pelagic environment: Selected results from the vertex experiment. Bull Mar Sci. 1984;35:550–65.
Google Scholar 
Scanlan DJ, Ostrowski M, Mazard S, Dufresne A, Garczarek L, Hess WR, et al. Ecological genomics of marine picocyanobacteria. Microbiol Mol Biol Rev. 2009;73:249–99.CAS 
PubMed 
PubMed Central 

Google Scholar 
McDonnell AMP, Boyd PW, Buesseler KO. Effects of sinking velocities and microbial respiration rates on the attenuation of particulate carbon fluxes through the mesopelagic zone. Glob Biogeochem Cycles. 2015;29:175–93.CAS 

Google Scholar 
Qiu B, Koh DA, Lumpkin C, Flament P. Existence and formation mechanism of the North Hawaiian Ridge Current. J Phys Oceanogr. 1997;27:431–44.
Google Scholar 
Turner JT. Zooplankton fecal pellets, marine snow, phytodetritus and the ocean’s biological pump. Prog Oceanogr. 2015;130:205–48.
Google Scholar  More

Bennett, A. F. Linkages in the Landscape The Role of Corridors and Connectivity in Wildlife Conservation. (IUCN, Gland, Switzerland and Cambridge, UK).Berger, J., Young, J. K. & Berger, K. M. Protecting migration corridors: Challenges and optimism for Mongolian Saiga. PLOS Biol. 6, e165 (2008).PubMed 
PubMed Central 

Google Scholar 
Murphy, S. M. et al. Consequences of severe habitat fragmentation on density, genetics, and spatial capture-recapture analysis of a small bear population. PLOS ONE 12, 1–20 (2017).
Google Scholar 
Kaboodvandpour, S., Almasieh, K. & Zamani, N. Habitat suitability and connectivity implications for the conservation of the Persian leopard along the Iran-Iraq border. Ecol. Evol. 11, 13464–13474 (2021).PubMed 
PubMed Central 

Google Scholar 
Hilty, J. A., Lidicker, W. Z. Jr. & Merenlender, A. M. Corridor Ecology: The Science and Practice of Linking Landscapes for Biodiversity Conservation (Island Press, Washington DC, 2012).
Google Scholar 
Noss, R. F., Quigley, H. B., Hornocker, M. G., Merrill, T. & Paquet, P. C. Conservation biology and carnivore conservation in the rocky mountains. Conserv. Biol. 10, 949–963 (1996).
Google Scholar 
Terraube, J., Van Doninck, J., Helle, P. & Cabeza, M. Assessing the effectiveness of a national protected area network for carnivore conservation. Nat. Commun. 11, 1–9 (2020).
Google Scholar 
Ashrafzadeh, M. R. et al. A multi-scale, multi-species approach for assessing effectiveness of habitat and connectivity conservation for endangered felids. Biol. Conserv. 245, 108523 (2020).
Google Scholar 
Mohammadi, A. et al. Identifying priority core habitats and corridors for effective conservation of brown bears in Iran. Sci. Rep. 11, 1–13 (2021).MathSciNet 

Google Scholar 
Beier, P., Majka, D. R. & Spencer, W. D. Forks in the road: Choices in procedures for designing wildland linkages. Conserv. Biol. 22, 836–851 (2008).PubMed 

Google Scholar 
Calvignac, S., Hughes, S. & Hänni, C. Genetic diversity of endangered brown bear (ursus arctos) populations at the crossroads of Europe, Asia and Africa. Divers. Distrib. 15, 742–750 (2009).
Google Scholar 
Khosravi, R., Hemami, M. R. & Cushman, S. A. Multi-scale niche modeling of three sympatric felids of conservation importance in central Iran. Landsc. Ecol. 34, 2451–2467 (2019).
Google Scholar 
Almasieh, K., Rouhi, H. & Kaboodvandpour, S. Habitat suitability and connectivity for the brown bear (Ursus arctos) along the Iran-Iraq border. Eur. J. Wildl. Res. 65, 1–12 (2019).
Google Scholar 
Balme, G. A., Hunter, L. T. B. & Slotow, R. Evaluating methods for counting cryptic carnivores. J. Wildl. Manage. 73, 433–441 (2009).
Google Scholar 
Guisan, A. & Zimmermann, N. E. Predictive habitat distribution models in ecology. Ecol. Modell. 135, 147–186 (2000).
Google Scholar 
McRae, B. H. & Beier, P. Circuit theory predicts gene flow in plant and animal populations. Proc. Natl. Acad. Sci. USA 104, 19885–19890 (2007).ADS 
CAS 
PubMed 
PubMed Central 

Google Scholar 
Farhadinia, M. S. et al. Leveraging trans-boundary conservation partnerships: Persistence of Persian leopard (Panthera pardus saxicolor) in the Iranian Caucasus. Biol. Conserv. 191, 770–778 (2015).
Google Scholar 
Almasieh, K., Mirghazanfari, S. M. & Mahmoodi, S. Biodiversity hotspots for modeled habitat patches and corridors of species richness and threatened species of reptiles in central Iran. Eur. J. Wildl. Res. 65, 1–13 (2019).
Google Scholar 
Mohammadi, A. et al. Road expansion: A challenge to conservation of mammals, with particular emphasis on the endangered Asiatic cheetah in Iran. J. Nat. Conserv. 43, 8–18 (2018).
Google Scholar 
Cushman, S. A., Lewis, J. S. & Landguth, E. L. Evaluating the intersection of a regional wildlife connectivity network with highways. Mov. Ecol. 1, 1–11 (2013).
Google Scholar 
Mohammadi, A. & Fatemizadeh, F. Quantifying landscape degradation following construction of a highway using landscape metrics in Southern Iran. Front. Ecol. Evol. 9, 836 (2021).
Google Scholar 
Crooks, K. R. Relative sensitivities of mammalian carnivores to habitat fragmentation. Conserv. Biol. 16, 488–502 (2002).
Google Scholar 
Moqanaki, E. M. & Cushman, S. A. All roads lead to Iran: Predicting landscape connectivity of the last stronghold for the critically endangered Asiatic cheetah. Anim. Conserv. 20, 29–41 (2017).
Google Scholar 
Neumann, W. et al. Difference in spatiotemporal patterns of wildlife road-crossings and wildlife-vehicle collisions. Biol. Conserv. 145, 70–78 (2012).
Google Scholar 
Mohammadi, A. & Kaboli, M. Evaluating wildlife-vehicle collision hotspots using kernel-based estimation: A focus on the endangered Asiatic cheetah in central Iran. Human-Wildlife Interact. 10, 103–109 (2016).
Google Scholar 
Benítez-López, A., Alkemade, R. & Verweij, P. A. The impacts of roads and other infrastructure on mammal and bird populations: A meta-analysis. Biol. Conserv. 143, 1307–1316 (2010).
Google Scholar 
Dadashi-Jourdehi, A., Shams-Esfandabad, B., Ahmadi, A., Rezaei, H. R. & Toranj-Zar, H. Predicting the potential distribution of striped hyena Hyaena hyaena in Iran. Belgian J. Zool. 150, 185–195 (2020).
Google Scholar 
Akay, A. E., Inac, S. & Yildirim, I. C. Monitoring the local distribution of striped hyenas (Hyaena hyaena L.) in the Eastern Mediterranean Region of Turkey (Hatay) by using GIS and remote sensing technologies. Environ. Monit. Assess. 181, 445–455 (2011).PubMed 

Google Scholar 
AbiSaid, M. & Dloniak, S. M. D. Hyaena hyaena. The IUCN Red List of Threatened Species 2015. (2015).Alam, M. S. & Khan, J. A. Food habits of striped hyena (Hyaena hyaena) in a semi-arid conservation area of India. J. Arid Land 7, 860–866 (2015).
Google Scholar 
Wagner, A. P. Behavioral ecology of the striped hyena (Hyaena hyaena). ProQuest Diss. Theses 195–195 (2006).Hofer, H. Species Accounts, Status Survey and Conservation Action Plan of Hyaena (Information Press, 1998).
Google Scholar 
Kruuk, H. Feeding and social behaviour of the striped hyaena (Hyaena vulgaris Desmarest). East African Wildl. J. 14, 91–111 (1976).
Google Scholar 
Tourani, M., Moqanaki, E. M. & Kiabi, B. H. Vulnerability of striped hyaenas, hyaena hyaena, in a human-dominated landscape of central Iran. Zool. Middle East 56, 133–136 (2012).
Google Scholar 
Parchizadeh, J. & Belant, J. L. Human-caused mortality of large carnivores in Iran during 1980–2021. Glob. Ecol. Conserv. 27, e01618 (2021).
Google Scholar 
Almasieh, K., Zoratipour, A., Negaresh, K. & Delfan-Hasanzadeh, K. Habitat quality modelling and effect of climate change on the distribution of Centaurea pabotii in Iran. Spanish J. Agric. Res. 16, e0304 (2018).
Google Scholar 
Ahmadi, M. et al. Species and space: A combined gap analysis to guide management planning of conservation areas. Landsc. Ecol. 35, 1505–1517 (2020).
Google Scholar 
Yusefi, G. H., Faizolahi, K., Darvish, J., Safi, K. & Brito, J. C. The species diversity, distribution, and conservation status of the terrestrial mammals of Iran. J. Mammal. 100, 55–71 (2019).
Google Scholar 
Karami, M., Ghadirian, T. & Faizolahi, K. The atlas of mammals of Iran ; Jahad daneshgahi, kharazmi Branch (Department of the Environment of Iran, 2016).Singh, P., Gopalaswamy, A. M. & Karanth, K. U. Factors influencing densities of striped hyenas (Hyaena hyaena) in arid regions of India. J. Mammal. 91, 1152–1159 (2010).
Google Scholar 
Brown, J. L. SDMtoolbox: A python-based GIS toolkit for landscape genetic, biogeographic and species distribution model analyses. Methods Ecol. Evol. 5, 694–700 (2014).
Google Scholar 
Esri. ArcGIS 10.1. Environ. Syst. Res. Institute, Redlands, CA, USA (2012).Rieger, I. A review of the biology of striped hyaenas, Hyaena hyaena (Linne, 1758). Saugetierkund. Mitt. 27, 81–95 (1979).
Google Scholar 
Jueterbock, A. ‘ MaxentVariableSelection ’ vignette (2015).Team, R. C. R: A language and environment for statistical computing. R Foundation for Statistical Computing. (2019).Naimi, B., Hamm, N. A. S., Groen, T. A., Skidmore, A. K. & Toxopeus, A. G. Where is positional uncertainty a problem for species distribution modelling?. Ecography (Cop.) 37, 191–203 (2014).
Google Scholar 
Zuur, A. F., Ieno, E. N. & Elphick, C. S. A protocol for data exploration to avoid common statistical problems. Methods Ecol. Evol. 1, 3–14 (2010).
Google Scholar 
Thuiller, W., Lafourcade, B., Engler, R. & Araújo, M. B. BIOMOD: A platform for ensemble forecasting of species distributions. Ecography (Cop.) 32, 369–373 (2009).
Google Scholar 
Shahnaseri, G. et al. Contrasting use of habitat, landscape elements, and corridors by grey wolf and golden jackal in central Iran. Landsc. Ecol. 34, 1263–1277 (2019).
Google Scholar 
Araújo, M. B. & New, M. Ensemble forecasting of species distributions. Trends Ecol. Evol. 22, 42–47 (2007).PubMed 

Google Scholar 
Eskildsen, A. et al. Testing species distribution models across space and time: high latitude butterflies and recent warming. Glob. Ecol. Biogeogr. 22, 1293–1303 (2013).
Google Scholar 
Wan, H. Y., Cushman, S. A. & Ganey, J. L. Improving habitat and connectivity model predictions with multi-scale resource selection functions from two geographic areas. Landsc. Ecol. 34, 503–519 (2019).
Google Scholar 
Mateo-Sánchez, M. C. et al. A comparative framework to infer landscape effects on population genetic structure: Are habitat suitability models effective in explaining gene flow?. Landsc. Ecol. 30, 1405–1420 (2015).
Google Scholar 
Landguth, E. L., Hand, B. K., Glassy, J., Cushman, S. A. & Sawaya, M. A. UNICOR: A species connectivity and corridor network simulator. Ecography (Cop.) 35, 9–14 (2012).
Google Scholar 
Cushman, S. A., McKelvey, K. S. & Schwartz, M. K. Use of empirically derived source-destination models to map regional conservation corridors. Conserv. Biol. 23(2), 368–376 (2009).PubMed 

Google Scholar 
Saura, S. & Torné, J. Conefor Sensinode 2.2: A software package for quantifying the importance of habitat patches for landscape connectivity. Environ. Model. Softw. 24, 135–139 (2009).
Google Scholar 
Saura, S. & Pascual-Hortal, L. A new habitat availability index to integrate connectivity in landscape conservation planning: Comparison with existing indices and application to a case study. Landsc. Urban Plan. 83, 91–103 (2007).
Google Scholar 
Saura, S. & Rubio, L. A common currency for the different ways in which patches and links can contribute to habitat availability and connectivity in the landscape. Ecography (Cop.) 33, 523–537 (2010).
Google Scholar 
Avon, C. & Bergès, L. Prioritization of habitat patches for landscape connectivity conservation differs between least-cost and resistance distances. Landsc. Ecol. 31, 1551–1565 (2016).
Google Scholar 
Cushman, S. A., Lewis, J. S. & Landguth, E. L. Why did the bear cross the road? Comparing the performance of multiple resistance surfaces and connectivity modeling methods. Diversity 6, 844–854 (2014).
Google Scholar 
Mohammadi, A. et al. Integrating spatial analysis and questionnaire survey to better understand human-onager conflict in Southern Iran. Sci. Rep. 11, 1–12 (2021).MathSciNet 

Google Scholar 
Shamoon, H. & Shapira, I. Limiting factors of Striped Hyaena, Hyaena hyaena, distribution and densities across climatic and geographical gradients (Mammalia: Carnivora). Zool. Middle East 65, 189–200 (2019).
Google Scholar 
Leakey, L. N. et al. Diet of striped hyaena in northern Kenya. Afr. J. Ecol. 37, 314–326 (1999).
Google Scholar 
Farhadinia, M. S., Johnson, P. J., Hunter, L. T. B. & Macdonald, D. W. Wolves can suppress goodwill for leopards: Patterns of human-predator coexistence in northeastern Iran. Biol. Conserv. 213, 210–217 (2017).
Google Scholar 
Bhandari, S., Bhusal, D. R., Psaralexi, M. & Sgardelis, S. Habitat preference indicators for striped hyena (Hyaena hyaena) in Nepal. Glob. Ecol. Conserv. 27, e01619 (2021).
Google Scholar 
Farashi, A. & Shariati, M. Biodiversity hotspots and conservation gaps in Iran. J. Nat. Conserv. 39, 37–57 (2017).
Google Scholar 
Farashi, A., Shariati, M. & Hosseini, M. Identifying biodiversity hotspots for threatened mammal species in Iran. Mamm. Biol. 87, 71–88 (2017).
Google Scholar 
Boitani, L., Ciucci, P., Corsi, F. & Dupre, E. Range and corridors for brown bears in the eastern potential. Ursus 11, 123–130 (1999).
Google Scholar 
Bhandari, S., Morley, C., Aryal, A. & Shrestha, U. B. The diet of the striped hyena in Nepal’s lowland regions. Ecol. Evol. 10, 7953–7962 (2020).PubMed 
PubMed Central 

Google Scholar  More

Winston, M. R. & Taylor, C. M. Upstream extirpation of four minnow species due to damming of a prairie stream. Trans. Am. Fish. Soc. 120, 8 (1991).
Google Scholar 
Graham, K. Contemporary status of the North American paddlefish, Polyodon spathula. Environ. Biol. Fishes 48, 279–289 (1997).
Google Scholar 
Kaushal, S. S. et al. Rising stream and river temperatures in the United States. Front. Ecol. Environ. 8, 461–466 (2010).
Google Scholar 
Vörösmarty, C. J. et al. Global threats to human water security and river biodiversity. Nature 467, 555–561 (2010).PubMed 
ADS 

Google Scholar 
Galbreath, P. F., Bisbee, M. A., Dompier, D. W., Kamphaus, C. M. & Newsome, T. H. Extirpation and tribal reintroduction of coho salmon to the interior columbia river basin. Fisheries 39, 77–87 (2014).
Google Scholar 
Schmidt, B. A. et al. Determining habitat limitations of Maumee River walleye production to western Lake Erie fish stocks: Documenting a spawning ground barrier. J. Gt. Lakes Res. 46, 1661–1673 (2020).
Google Scholar 
Kendall, N. W., Marston, G. W. & Klungle, M. M. Declining patterns of Pacific Northwest steelhead trout (Oncorhynchus mykiss) adult abundance and smolt survival in the ocean. Can. J. Fish. Aquat. Sci. 74, 1275–1290 (2017).
Google Scholar 
Myers, J., Bryant, G. & Lynch, J. Factors Contributing to the Decline of Chinook Salmon: An Addendum to the 1996 West Coast Steelhead Factors for Decline Report (Springer, 1998).
Google Scholar 
Molony, B. W., Lenanton, R., Jackson, G. & Norriss, J. Stock enhancement as a fisheries management tool. Rev. Fish Biol. Fish. 13, 409–432 (2005).
Google Scholar 
Merz, J. E. & Setka, J. D. Evaluation of a spawning habitat enhancement site for Chinook salmon in a regulated California river. N. Am. J. Fish. Manag. 24, 397–407 (2004).
Google Scholar 
Ostberg, C. O., Chase, D. M. & Hauser, L. Hybridization between yellowstone cutthroat trout and rainbow trout alters the expression of muscle growth-related genes and their relationships with growth patterns. PLoS ONE 10, e0141373 (2015).PubMed 
PubMed Central 

Google Scholar 
Veale, A. J. & Russello, M. A. Sockeye salmon repatriation leads to population re-establishment and rapid introgression with native kokanee. Evol. Appl. 9, 1301–1311 (2016).CAS 
PubMed 
PubMed Central 

Google Scholar 
Fraser, D. J., Cook, A. M., Eddington, J. D., Bentzen, P. & Hutchings, J. A. Mixed evidence for reduced local adaptation in wild salmon resulting from interbreeding with escaped farmed salmon: Complexities in hybrid fitness. Evol. Appl. 1, 501–512 (2008).PubMed 
PubMed Central 

Google Scholar 
Stewart, G. S. et al. The power of evolutionary rescue is constrained by genetic load. Evol. Appl. 10, 731–741 (2017).PubMed 
PubMed Central 

Google Scholar 
Weeks, A. R. et al. Genetic rescue increases fitness and aids rapid recovery of an endangered marsupial population. Nat. Commun. 8, 1071 (2017).PubMed 
PubMed Central 
ADS 

Google Scholar 
Chan, W. Y., Hoffmann, A. A. & van Oppen, M. J. H. Hybridization as a conservation management tool. Conserv. Lett. 12, e12652 (2019).
Google Scholar 
Bekkevold, D., Hansen, M. M. & Nielsen, E. E. Genetic impact of gadoid culture on wild fish populations: Predictions, lessons from salmonids, and possibilities for minimizing adverse effects. ICES J. Mar. Sci. 63, 198–208 (2006).
Google Scholar 
Muhlfeld, C. C. et al. Hybridization rapidly reduces fitness of a native trout in the wild. Biol. Lett. 5, 328–331 (2009).PubMed 
PubMed Central 

Google Scholar 
Harvey, A. C., Glover, K. A., Taylor, M. I., Creer, S. & Carvalho, G. R. A common garden design reveals population-specific variability in potential impacts of hybridization between populations of farmed and wild Atlantic salmon, Salmo salar L. Evol. Appl. 9, 435–449 (2016).PubMed 
PubMed Central 

Google Scholar 
Edmands, S. Does parental divergence predict reproductive compatibility?. Trends Ecol. Evol. 17, 520–527 (2002).
Google Scholar 
Johnson, B. M., Johnson, M. S. & Thorgaard, G. H. Salmon genetics and management in the Columbia river basin. Northwest Sci. 92, 346–363 (2019).
Google Scholar 
Hanson, A. J. & Smith, H. D. Mate selection in a population of sockeye salmon (Oncorhynchus nerka) of mixed age-groups. J. Fish. Board Can. 24, 23 (1967).
Google Scholar 
Wood, C. C. & Foote, C. J. Evidence for sympatric genetic divergence of anadromous and nonanadromous morphs of sockeye salmon (Oncorhynchus nerka). Evolution 50, 1265–1279 (1996).PubMed 

Google Scholar 
Foote, C. J. Male mate choice dependent on male size in salmon. Behaviour 106, 63–80 (1988).
Google Scholar 
Craig, J. K., Foote, C. J. & Wood, C. C. Countergradient variation in carotenoid use between sympatric morphs of sockeye salmon (Oncorhynchus nerka) exposes nonanadromous hybrids in the wild by their mismatched spawning colour. Biol. J. Linn. Soc. 84, 287–305 (2005).
Google Scholar 
Taylor, E. B. & Foote, C. J. Critical swimming velocities of juvenile sockeye salmon and kokanee, the anadromous and non-anadromous forms of Oncorhynchus nerka (Walbaum). J. Fish Biol. 38, 407–419 (1991).
Google Scholar 
Foote, C. J., Wood, C. C., Clarke, W. C. & Blackburn, J. Circannual cycle of seawater adaptability in Oncorhynchus nerka: Genetic differences between sympatric sockeye salmon and kokanee. Can. J. Fish. Aquat. Sci. 49, 99–109 (1992).
Google Scholar 
Wood, C. C. & Foote, C. J. Genetic differences in the early development and growth of sympatric sockeye salmon and kokanee (Oncorhynchus nerka), and their hybrids. Can. J. Fish. Aquat. Sci. 47, 2250–2260 (1990).
Google Scholar 
Elliott, L. D., Ward, H. G. M. & Russello, M. A. Kokanee–sockeye salmon hybridization leads to intermediate morphology and resident life history: Implications for fisheries management. Can. J. Fish. Aquat. Sci. 77, 355–364 (2020).
Google Scholar 
Hendry, A. P., Quinn, T. P. & Utter, F. M. Genetic evidence for the persistence and divergence of native and introduced sockeye salmon (Oncorhynchus nerka) within Lake Washington, Washington. Can. J. Fish. Aquat. Sci. 53, 823–832 (1996).
Google Scholar 
Praebel, K. et al. A diagnostic tool for efficient analysis of the population structure, hybridization and conservation status of European whitefish (Coregonus lavaretus (L.)) and vendace (C. albula (L.)). Adv. Limnol. 64, 247–255 (2013).
Google Scholar 
Sanz, N., Araguas, R. M., Fernández, R., Vera, M. & García-Marín, J.-L. Efficiency of markers and methods for detecting hybrids and introgression in stocked populations. Conserv. Genet. 10, 225–236 (2009).CAS 

Google Scholar 
Mcfarlane, S. & Pemberton, J. Detecting the true extent of introgression during anthropogenic hybridization. Trends Ecol. Evol. 34, 315–326 (2019).PubMed 

Google Scholar 
Vähä, J.-P. & Primmer, C. R. Efficiency of model-based Bayesian methods for detecting hybrid individuals under different hybridization scenarios and with different numbers of loci. Mol. Ecol. 15, 63–72 (2006).PubMed 

Google Scholar 
Boecklen, W. J. & Howard, D. J. Genetic analysis of hybrid zones: Numbers of markers and power of resolution. Ecology 78, 2611–2616 (1997).
Google Scholar 
Elliott, L. & Russello, M. A. SNP panels for differentiating advanced-generation hybrid classes in recently diverged stocks: A sensitivity analysis to inform monitoring of sockeye salmon re-stocking programs. Fish. Res. 208, 339–345 (2018).
Google Scholar 
Twyford, A. D. & Ennos, R. A. Next-generation hybridization and introgression. Heredity 108, 179–189 (2012).CAS 
PubMed 

Google Scholar 
Campbell, N. R., Harmon, S. A. & Narum, S. R. Genotyping-in-Thousands by sequencing (GT-seq): A cost effective SNP genotyping method based on custom amplicon sequencing. Mol. Ecol. Resour. 15, 855–867 (2015).CAS 
PubMed 

Google Scholar 
Alexander, C. A. & Pickard, D. Skaha Lake Experimental Sockeye Reintroduction: Synthesis of First 4 of 12 Years (2004–2007 Brood Years) (Springer, 2009).
Google Scholar 
McQueen, D. et al. Evaluation of the Experimental Introduction of Sockeye Salmon (Oncorhynchus nerka) into Skaha Lake and Assessment of Sockeye Rearing in Osoyoos Lake (Springer, 2013).
Google Scholar 
Hegg, J. C., Kennedy, B. P. & Chittaro, P. What did you say about my mother? The complexities of maternally derived chemical signatures in otoliths. Can. J. Fish. Aquat. Sci. 76, 81–94 (2019).CAS 

Google Scholar 
Veale, A. J. & Russello, M. A. Genomic changes associated with reproductive and migratory ecotypes in sockeye salmon (Oncorhynchus nerka). Genome Biol. Evol. 9, 2921–2939 (2017).CAS 
PubMed 
PubMed Central 

Google Scholar 
Catchen, J., Hohenlohe, P. A., Bassham, S., Amores, A. & Cresko, W. A. Stacks: An analysis tool set for population genomics. Mol. Ecol. 22, 3124–3140 (2013).PubMed 
PubMed Central 

Google Scholar 
Hohenlohe, P. A., Amish, S. J., Catchen, J. M., Allendorf, F. W. & Luikart, G. Next-generation RAD sequencing identifies thousands of SNPs for assessing hybridization between rainbow and westslope cutthroat trout. Mol. Ecol. Resour. 11, 117–122 (2011).PubMed 

Google Scholar 
Danecek, P. et al. The variant call format and VCFtools. Bioinformatics 27, 2156–2158 (2011).CAS 
PubMed 
PubMed Central 

Google Scholar 
Weir, B. S. & Cockerham, C. C. Estimating F-statistics for the analysis of population structure. Evolution 38, 1358–1370 (1984).CAS 
PubMed 

Google Scholar 
Rousset, F. genepop’007: A complete re-implementation of the genepop software for Windows and Linux. Mol. Ecol. Resour. 8, 103–106 (2008).PubMed 

Google Scholar 
Anderson, E. C. & Thompson, E. A. A model-based method for identifying species hybrids using multilocus genetic data. Genetics 160, 1217–1229 (2002).CAS 
PubMed 
PubMed Central 

Google Scholar 
Schmidt, D. A., Campbell, N. R., Govindarajulu, P., Larsen, K. W. & Russello, M. A. Genotyping-in-Thousands by sequencing (GT-seq) panel development and application to minimally invasive DNA samples to support studies in molecular ecology. Mol. Ecol. Resour. 20, 114–124 (2020).CAS 
PubMed 

Google Scholar 
Purcell, S. et al. PLINK: A tool set for whole-genome association and population-based linkage analyses. Am. J. Hum. Genet. 81, 559–575 (2007).CAS 
PubMed 
PubMed Central 

Google Scholar 
Reeves, P. A., Bowker, C. L., Fettig, C. E., Tembrock, L. R. & Richards, C. M. Effect of Error and Missing Data on Population Structure Inference Using Microsatellite Data. (2016) https://doi.org/10.1101/080630.Wringe, B. F., Stanley, R. R. E., Jeffery, N. W., Anderson, E. C. & Bradbury, I. R. hybriddetective: A workflow and package to facilitate the detection of hybridization using genomic data in r. Mol. Ecol. Resour. 17, e275–e284 (2017).CAS 
PubMed 

Google Scholar 
Walsh, P. S., Metzger, D. A. & Higuchi, R. Chelex 100 as a medium for simple extraction of DNA for PCR-based typing from forensic material. Biotechniques 10, 506–513 (1991).CAS 
PubMed 

Google Scholar 
Russell, T. et al. Development of a novel mule deer genomic assembly and species-diagnostic SNP panel for assessing introgression in mule deer, white-tailed deer, and their interspecific hybrids. Genes Genomes Genet. 9, 911–919 (2019).CAS 

Google Scholar 
Thongda, W. et al. Species-diagnostic SNP markers for the black basses (Micropterus spp.): A new tool for black bass conservation and management. Conserv. Genet. Resour. 12, 319–328 (2020).
Google Scholar 
Ricker, W. E. ‘Residual’ and kokanee salmon in Cultus lake. J. Fish. Board Can. 27, 192–218 (1938).
Google Scholar 
Crossin, G. T. et al. Exposure to high temperature influences the behaviour, physiology, and survival of sockeye salmon during spawning migration. Can. J. Zool. 86, 127–140 (2008).CAS 

Google Scholar 
Moore, M. E. et al. Early marine migration patterns of wild coastal cutthroat trout (Oncorhynchus clarkii clarkii), steelhead trout (Oncorhynchus mykiss), and their hybrids. PLoS ONE 5, e12881 (2010).PubMed 
PubMed Central 
ADS 

Google Scholar 
McCutcheon, C. S., Prentice, E. F. & Park, D. L. Passive monitoring of migrating adult steelhead with PIT tags. N. Am. J. Fish. Manag. 14, 220–223 (1994).
Google Scholar 
Scribner, K. T., Page, K. S. & Bartron, M. L. Hybridization in freshwater fishes: A review of case studies and cytonuclear methods of biological inference. Rev. Fish Biol. Fish. 10, 293–323 (2001).
Google Scholar  More

Data collection: soil organic carbonThe process of data acquisition, selection and harmonisation is illustrated in the Supplementary Fig. S15 and in the Supplementary Tables S4–6. We conducted a systematic review for peer-reviewed journal articles, published before December 2018, from Web of Science, and Google Scholar with the search terms “(tree species OR forest) AND (soil organic carbon OR soil organic matter)”. We also used studies listed in two previously published meta-analyses16,17, or cited in already retained references (including references in English, French, Spanish, Portuguese, or Russian). For inclusion in the analysis we chose studies based on the following criteria: (1) the study reported soil organic carbon (SOC) or soil organic matter (SOM) concentrations or pools, at least in the topsoil layer and under at least two single-species forest stands; (2) the stands had to be older than 10 years81; (3) the stands had not experienced a major disturbance that differed between tree species, for at least 30 years (e.g., we rejected studies that compared natural forests with planted forests that were less than 30 years old); (4) The SOC concentration was More

Both in Polistes biglumis and P. gallicus in most of the inhabited nests all types of brood were present: eggs, larvae and pupae (Table S1), with the exception of one foundress nest of P. biglumis with only one egg. The size of thermographed nests was quite variable in both species, the number of cells ranging from 18 to 99 in P. biglumis (mean: 61.6 cells), and from 19 to 381 in P. gallicus (mean: 101.7 cells) (Table S1). The mean number of wasps on the thermographed nests was higher in P. gallicus (12.6 wasps) than in P. biglumis (7.1 wasps). All nests of Polistes biglumis we observed in this study were built on stone substrate or walls (Figs. 1c, 2a). Only recently we found one nest built on a pile of wood. The choice of the nest substrate was more diverse in P. gallicus (Figs. 1d, 2b). They chose stone, concrete, walls, window grilles, and metal of fences or doorframes.Figure 2Examples of nests and fieldwork set-up in Obergail (a) and Sesto Fiorentino (b). 1 = thermocouple wire; 2 = global radiation sensor, 3 = Peltier-element IR reference source.Full size imageDaily nest temperature coursePolistes biglumisFigure 3 shows a sequence of thermograms of a P. biglumis nest taken from dawn to dusk. Before sunrise the temperatures of the nest and of the wasps on it were quite low (mean ~ 15 °C) and uniform (~ 12 to 17.5 °C; Fig. 3a). The temperature of the stone substrate where the nest was built on was considerably higher (~ 20 °C). After sunrise (Fig. 3b,c) the nest temperature began to rise quickly. It only needed 13 min of sunshine (radiation) to heat the nest from ~ 17 to ~ 25 °C. Within one hour, temperature differences of almost 20 °C were measured within the nest. At 6:50, when the highest temperature on the nest was already at 36.2 °C, fast movements of the adults with inspections of the cells were observed (Fig. 3c). Soon afterwards the increasing temperature induced the wasps to start fanning (arrow in Fig. 3d). The wasps also began to gather water and spread it on and inside cells to cool the nest by evaporation (Fig. 3d,e). Towards late morning, some parts of the nest reached temperatures as high as 46 °C (Fig. 3e)! As soon as the nest was shaded by the substrate (~ 13:00) the nest temperature decreased according to the decrease in ambient temperature (Fig. 3f,g), reaching ~ 21 °C on average after dusk (Fig. 3h). At that time the substrate temperature (~ 25 °C) was still about 4 °C higher than the nest temperature.Figure 3Thermograms of a P. biglumis nest during a whole day (19.07.2017). (a) Before sunrise at 6:20; (b) during sunrise (06:33); (c) nest temperature increasing fast in sunshine; (d) with a fanner for convective nest cooling (arrow; see also Fig. S4); (e) with water drops for evaporative cooling when sunshine increased part of the nest to temperatures  > 45 °C; (f,g) after sunset (nest now in shade) in the afternoon; (h) at dusk with wasps sitting motionless on the nest. Time = CEST = UTC + 2 h.Full size imageThe nest and body temperatures of a complete 24 h cycle of a different nest are shown in Fig. 4a. At night the nest temperature and the wasps’ thorax temperature decreased slowly according to the decrease of the air temperature. The substrate temperature was always higher than the mean nest temperature, which surely helped to keep the nest temperature higher than the temperature of the surrounding air (Tanest). Variation of within-nest temperature (max–min) was low at night. As soon as solar radiation increased in early morning, the nest temperature and the body temperature of the wasps on it increased rapidly, and the variation of nest temperature (max–min) increased (see also Fig. 3b). Though the maximum nest temperature reached values as high as 46.9 °C, cooling measures of the wasps (fanning and spreading of water drops, see below) kept the mean nest temperature always below 38.5 °C. Cooling of the nest after sunset (at the nest) was much slower than the increase in the morning, following the decrease of ambient and substrate temperature (Fig. 4a,b).Figure 4Examples of daily temperature changes of nests and wasps of P. biglumis (a,b) and P. gallicus (c,d). Tthorax = mean thorax surface temperature of up to five adult individuals per time of measurement; gray ribbon: total range of nest temperatures (Tmax:Tmin) with mean; Tsubstrate = temperature beside the nest (see Fig. S1c,d); Tanest = ambient air temperature directly at the nest. Ta = ambient air temperature in shade 1–3 m away from nest; Radiation = global radiation hitting the nest; black bars = fanning events at the time of thermographic measurements: actually, many more fanning events were observed. (c) Fanning was never observed! See also Fig. S2 for another example of a P. gallicus nest in shade. Time = CEST = UTC + 2 h.Full size imagePolistes gallicusMost P. gallicus nests were built in locations with no or only little direct sunshine (Figs. 2b, 4c, Fig. S2). In their habitats temperatures in midsummer are often already quite high in the morning, and may increase to values higher than 40 °C during the day (Fig. 4d). Mean temperatures of the nest and of the imagines on it were usually higher than the air temperature close to the nest (Tanest). In most nests variation of within-nest temperature (max–min) remained small throughout the day. On hot days (Tanest  > 40 °C), however, maximum temperatures of empty cells in the nest margin sometimes reached values as high as 49.9 °C even in shade. Body temperature of the adults was mostly similar to the mean nest temperature (Fig. 4c, Fig. S2). At night, the nest temperature decreased according to the decrease of Tanest, similar to P. biglumis but at a higher level (Fig. 4d).The situation was different in one large nest which had been built in a location exposed to the morning sun (Figs. 4d, 5). On a hot day when Tanest increased to values higher than 42 °C, the body temperature of the adults increased to values up to 5 °C higher than the mean nest temperature. Nevertheless, though the combined effects of high air temperature and intense insolation increased part of the nest to a temperature of ~ 58 °C (Fig. 4d), mean nest temperature was kept below 41 °C. This was accomplished by cooling with many water droplets in the cells (dark spots in Fig. 5), and by the occurrence of fanning during the period when the sun was shining on the nest (Fig. 4d; see arrows in Fig. 5c). Fanning, however, was quite rare in all the other observed nests, even during the hottest time of the day! Water droplets were carried onto this nest until evening (Fig. 5h), as at that time the nest temperature was still at about 35–38 °C.Figure 5Thermograms of a large P. gallicus nest during a whole day (01.08.2017). Thermograms are rotated 90° clockwise (the upper part is on the right). (a) Before sunrise (6:36); (b) during sunrise (06:46) with the first water drops visible (dark spots); (c) with two fanners for convective nest cooling (arrows, see also Fig. 4d); (d) with more cooling drops; (e) after sunset at the nest site (nest now in shade); (f–h) after sunset in the afternoon and evening. Time = CEST = UTC + 2 h. For temperature evaluation see Fig. 4d.Full size imageBody and nest temperaturesFigure 6 shows a comparison of the dependence of body and nest temperatures on ambient air temperature and insolation between the two species. In the lower ranges of air temperature, usually at night, body temperature followed Tanest closely in both species. The exposition of the P. biglumis nests to the morning sun at ESE (Fig. 7) increased the wasp body temperature to values of often more than 15 °C higher than the surrounding air. However, body temperatures remained always below 40 °C (Fig. 6a). In P. gallicus, by contrast, the body temperature of the wasps increased considerably above 40 °C, to maximum values of about 46 °C, especially (but not exclusively) during intense insolation in the nest exposed to the morning sun (Fig. 6b).Figure 6Surface temperature of the thorax of adult wasps, of different stages of brood and of water drops of P. biglumis (left) and P. gallicus (right), in dependence on ambient air temperature close to the nest (Tanest) and global radiation (color scale). Egg f.n. = single egg on a foundress nest; diagonal lines = isolines. Regressions were calculated for shaded conditions (Radiation = 0–100 W/m2; black or gray solid lines) and sunshine (Radiation  > 100 W/m2; pink broken lines); P  More

Field collectionEndophytic fungi and insects were assessed from dormant twig samples from 155 tree species at 51 locations in 32 countries. Sampled tree species belonged to genera that are native to, and occur widely across, either the northern or southern hemisphere, since very few tree genera occur naturally in both hemispheres (e.g., in our study only Podocarpus appears in both hemispheres but has a limited distribution in the northern hemisphere). We sampled largely in botanical gardens and arboreta, which allowed us to sample native and non-native, congeneric and confamiliar, tree species at each location. At each location, one native and one to three non-native congeneric or confamiliar tree species were sampled.At each location, twenty 50-cm long asymptomatic twigs were collected from 1–5 individual trees per species, from different branches and different parts of the crown (Fig. 1). The number of individual trees per species depended on the number of trees available in the specific botanical garden or arboretum, which was often low (Table 1). All twigs per tree species and location were pooled and analysed as a single sample. On some occasions two samples of the same tree species at the same location are considered. Sampling was conducted in the month with the shortest day-length in the year (end of December 2017 in the Northern hemisphere, end of June 2018 in the Southern hemisphere). Samples originating from a tropical region (eleven samples from Tanzania) were collected in June 2018. Trees were sampled in winter to align with the timing of trade, i.e. most woody plants are traded in winter or early spring, as plants will be planted in the following spring, and to reduce the risk of introducing foliar pests in deciduous trees. Evergreen gymnosperm and angiosperm tree species, which were also considered, do not lose foliage during winter, and are thus sold with leaves/needles.Table 1 Site information for sampling locations included in this study.Full size tableFungal endophytesTo assess fungal communities, a total of 352 samples from 145 native and non-native tree species, belonging to nine families of angiosperms and gymnosperms, were collected. Sampling was done at 44 locations in 28 countries on five continents (Fig. 1, Table 1).From each twig in a sample, one bud, one needle/leaf and one 1 cm long twig segment were taken (Fig. 1). Needles from gymnosperms, and leaves from evergreen angiosperms were sampled to accurately assess the risk of trading these species. Twig segments were cut from the twig bases. The selected plant parts were surface sterilized by immersion in 75% ethanol for 1 min, 4% NaOCl for 5 min, and 75% ethanol for 30 s26. After air drying on a sterile bench, the following material from each of 20 twigs per sample was pooled: half of one bud, a 0.5 cm long piece of a needle (from gymnosperms) or a 0.25 cm2 leaf (for evergreen angiosperms) and a 0.5 cm long piece of twig.DNA extraction, PCR amplification and Illumina sequencingTotal genomic DNA was extracted from 50 mg of pooled, surface sterilized, and ground tissue (Fig. 1) using DNeasy PowerPlant Pro Kit (Qiagen, Hilden, Germany), following the manufacturer’s instructions. For a total of 31 out of 352 samples, DNA was extracted from different tissues separately, and DNA extracts were then pooled. DNA concentrations were quantified using the Qubit dsDNA BR Assay Kit (Thermo Fisher Scientific, Waltham, USA) on a Qubit 3.0 Fluorometer (Thermo Fisher Scientific) and DNA was diluted to 5 ng/μl. Samples that yielded less than 5 ng/μl were not diluted. The ITS2 region was amplified with the 5.8S-Fung and ITS4-Fung primers27. PCR amplifications were carried out in 20 μl reaction volumes containing 25 ng of DNA template, 1 mg/ml BSA, 1 mM of MgCl2, 0.4 μM of each primer, and 0.76 × JumpStart REDTaq ReadyMix Reaction Mix (Sigma-Aldrich, Steinheim, Germany). PCR was performed using Veriti 96-Well Thermal Cycler (Applied Biosystems, Foster City, CA, USA) as described in Franić et al. (2019). Each sample was amplified in triplicates and successful PCR amplification confirmed by visualization of the PCR products, before and after pooling the triplicates, on 1.5% (w/v) agarose gel with ethidium bromide staining. Pooled amplicons were sent to the Génome Québec Innovation Center at McGill University (Montréal, Quebec, Canada) for barcoding using Fluidigm Access Array technology (Fluidigm, South San Francisco, CA, USA) and paired-end sequencing on the Illumina MiSeq v3 platform (Illumina Inc., San Diego, CA, USA). Raw sequences obtained in this study are deposited at the NCBI Sequence Read Archive under BioProject accession number PRJNA70814822.Bioinformatics and taxonomical classification of ASVsQuality filtering and delineation into ASVs were done with a customized pipeline28 largely based on VSEARCH29, as described by Herzog et al.30. The output data available on Figshare show the abundances of fungal ASVs in the samples24. Taxonomic classification of ASVs was conducted using Sintax31 implemented in VSEARCH against the UNITE v.7.2 database32 with a bootstrap support of 80%. The data on the taxonomic classification of fungal ASVs is deposited in Figshare24.Quality filtering, delineation into ASVs, and taxonomical assignments were done on a larger data set (total of 474 samples), which increased the confidence in the selected centroid sequences. This data set consisted of (1) sequences obtained from 352 samples of pooled tree tissues that are presented here22, (2) sequences obtained from 33 samples of pooled tree tissues which were not included in this manuscript due to violation of the common protocol, (3) sequences from 21 contaminated samples (positive DNA extraction controls), including sequences from the two control samples (not presented here), and (4) sequences obtained from 66 samples of non-pooled tree tissues of Pinus sylvestris and Quercus robur that were collected from the subset of locations considered in this study, but for a different study, and are thus not presented here.Herbivorous insectsInsects were assessed from 227 samples of 109 tree species, collected at 31 locations and in 18 countries (Fig. 1, Table 1).The collected twigs (twenty 50 cm twigs per species per location) were brought to a laboratory close to each sampling location and inspected for the presence of insects that overwinter as adults. Twigs were kept at room temperature with the cut ends immersed in water to induce budding and to allow the development of insects that overwinter as larvae, pupae or eggs. Twigs from each sample were protected with gauze bags to prevent insects moving between samples (Fig. 1). Twigs were inspected for the presence of insects daily for 4 weeks and all collected insects were stored in 95% ethanol for further examination.Morphological and molecular identificationInsects were inspected using a stereo microscope and sorted to taxonomic orders and feeding guilds (i.e. herbivores, predators, parasitoids and other). The abundance of the different feeding guilds and taxonomic orders in the samples is presented in a file deposited on Figshare24. Herbivorous insects were further sorted into morphospecies and at least one specimen per morphospecies was stored at −20 °C for molecular analysis. The abundance of the different morphospecies in each sample is presented in a file deposited on Figshare24. Specimens for molecular analysis were photographed with a Leica DVM6 digital microscope and the Leica Application Suite X (LAS X). Depending on the size of the insects, the whole individual or parts (e.g. legs, head) were used for molecular analysis. Genomic DNA was extracted with a KingFisher (Thermo Fisher Scientific) extraction protocol suitable for insects (35 min incubation at RT, 30 min wash at RT with 3 different washing buffers, 13 min elution at 60 °C) in a 96-well plate. PCR for the COI was carried out in 25 µl reaction volume with 2 µl diluted DNA (1:10), 0.5 µM of each of the primers LCO1490 and HCO219833 and 1 x REDTaq ReadyMix Reaction Mix (Sigma-Aldrich) using a Veriti 96-Well Thermal Cycler (Applied Biosystems) with the following setting: 2 min at 94 °C, five cycles of 30 s at 94 °C, 40 s at 45 °C, and 1 min at 72 °C, 35 cycles of 30 s at 94 °C, 50 s at 51 °C, and 1 min at 72 °C, and a final extension step at 72 °C for 10 min. The success of amplification was verified by electrophoresis of the PCR products in 1.5% (w/v) agarose gel at 90 V for 30 min with ethidium bromide staining. A standard Sanger sequencing of the PCR products in both directions with the same primers was done at Macrogen Europe, Amsterdam, Netherlands. Sequences were assembled and edited with CLC Workbench (Version 7.6.2, Quiagen) and compared to reference sequences in BOLD34. If no conclusive results were found, sequences were compared to reference sequences in the National Centre for Biotechnology Information (NCBI) GenBank databases35. Specimens were assigned to species if the query sequence showed less than 1% divergence from the reference sequence. If two or more taxa matched within the same range, the assignment was ranked down to the next taxonomic level (i.e., genus). When no species match was obtained based on the above criteria, a genus was assigned with a divergence of less than 3%. For lower taxonomic groups the 100 nearest sequences were inspected on the Blast Tree (Fast Minimum Evolution Method) and the taxonomic relationship was evaluated based on that tree. If none of the approaches above revealed a conclusive taxonomic assignment, the morphological identification was taken as reference. The results of morphological and molecular identification of insect specimens are presented in a file deposited on Figshare24. Insect sequences are deposited in GenBank database under accession numbers MW441337-MW44176725.Sample metadataPairwise geographic distances (Euclidean distances) between sampling locations were calculated based on the geographic coordinates of the locations, with function “dist” in the R statistical programme36.Climate data, including mean annual temperature, mean annual precipitation, and temperature seasonality were obtained from the WorldClim database37, at a resolution of 2.5 min, and represent averages between 1970 and 2000.A host-tree phylogeny was constructed with the phylomatic function from the package brranching38 in R using the “zanne2014” reference tree39. One Eucalyptus sample collected in Argentina and two Eucalyptus samples collected in Tunisia were not identified to species. To place them in the phylogeny, we assigned them to different congeneric species that were not sampled in this study and that we considered as representative samples of phylogenetic diversity from across Eucalyptus genus (E. viminalis, E. robusta and E. radiata). Pairwise phylogenetic distances between study tree species were calculated using the “cophenetic” function in R36.The described sample metadata are available in a file on Figshare24. More

Pure coordination gameIn this section we study the Pure Coordination Game (PCG) (also known as doorway game, or driving game) in which (R=1), (S=0), (T=0), and (P=1), resulting in a symmetric payoff matrix with respect to the two strategies:$$begin{gathered} begin{array}{*{20}c} {} & {quad ; {text{A}}} &; {text{B}} \ end{array}hfill \ begin{array}{*{20}c} {text{A}} \ {text{B}} \ end{array} left( {begin{array}{*{20}c} 1 & 0 \ 0 & 1 \ end{array} } right) hfill \ end{gathered}$$
(2)
There are two equivalent equilibria for both players coordinating at the strategy A or B (a third Nash equilibrium exists for players using a mix strategy of 50% A and 50% B). As the absolute values of the payoff matrix are irrelevant and the dynamics is defined by ratios between payoffs from different strategies, the payoff matrix (2) represents all games for which the relation (R=P >S=T) is fulfilled.In the PCG the dilemma of choosing between safety and benefit does not exist, because there is no distinction between risk-dominant and payoff-dominant equilibrium. Both strategies yield equal payoffs when players coordinate on them and both have the same punishment (no payoff) when players fail to coordinate. Therefore, the PCG is the simplest framework to test when coordination is possible and which factors influence it and how. It is in every player’s interest to use the same strategy as others. Two strategies, however, are present in the system at the beginning of the simulation in equal amounts. From the symmetry of the game we can expect no difference in frequency of each strategy being played, when averaged over many realisations. Still, the problem of when the system reaches full coordination in one of the strategies is not trivial. We address this question here.Figure 1Time evolution of the coordination rate (alpha) (in MC steps) in individual realisations for different values of the degree k in a random regular network of (N=1000) nodes, using (a) the replicator dynamics, (b) the best response, and (c) the unconditional imitation update rule.Full size imageFigure 2Coordination rate (alpha) and interface density (rho) vs degree k of a random regular network for (N=1000) using (a) the replicator dynamics, (b) the best response, and (c) the unconditional imitation update rule. Each green circle represents one of 500 realisations for each value of the degree k and the average value is plotted with a solid line, separately for (alpha >0.5) and (alpha le 0.5). Results are compared to the ER random network ((alpha _{ER})) with the same average degree.Full size imageFirst, we look at single trajectories as presented in Fig. 1. Some of them quickly reach (alpha =0) or 1, or stop in a frozen state without obtaining global coordination. Other trajectories take much longer and extend beyond the time scale showed in the figure. What we can already tell is that the process of reaching coordination is slower in the replicator dynamics where it usually takes more time than in the best response and unconditional imitation to reach a frozen configuration. For all update rules the qualitative effect of the connectivity is similar—for bigger degree it is more likely to obtain full coordination and it happens faster. For the UI, however, larger values of degree than for the RD and BR are required to observe coordination. For example, in the case of (k=10) or 20 the system stops in a frozen disorder when using UI, while for the RD and BR it quickly reaches a coordinated state of (alpha =0) or 1.To confirm the conclusions from observation of trajectories, we present the average outcome of the system’s evolution in the Fig. 2. The first thing to notice is that all plots are symmetrical with respect to the horizontal line of (alpha = 0.5). It indicates that the strategies are indeed equivalent as expected. In all cases there is a minimal connectivity required to obtain global coordination. For the RD and BR update rules this minimum value is (k=4), although in the case of BR the system fails to coordinate for small odd values of k due to regular character of the graph. This oscillating behaviour does not exist in Erdős–Rényi random networks. When nodes choose their strategies following the UI rule much larger values of k are required to obtain full coordination. Single realisations can result in (alpha = 0), or 1 already for (k=15). However, even for (k=60) there is still a possibility of reaching a frozen uncoordinated configuration.The important conclusion is that there is no coordination without a sufficient level of connectivity. In order to confirm that this is not a mere artefact of the random regular graphs we compare our results with those obtained for Erdős–Rényi (ER) random networks76,77 (black dashed line in Fig. 2). The level of coordination starts to increase earlier for the three update rules, but the general trend is the same. The only qualitative difference can be found in the BR. The oscillating level of coordination disappears and it doesn’t matter if the degree is odd or even. This shows that different behaviour for odd values of k is due to topological traps in random regular graphs78. Our results for the UI update rule are also consistent with previous work reporting coordination for a complete graph but failure of global coordination in sparse networks40.Figure 3Examples of frozen configuration reached under the UI update rule for small values of the average degree k in random regular networks (top row) and Erdős–Rényi networks (bottom row) with 150 nodes. Red colour indicates a player choosing the strategy A, blue colour the strategy B. Note the topological differences between random regular and ER networks when they are sparse. For (k=1) a random regular graph consists of pairs of connected nodes, while an ER network has some slightly larger components and many loose nodes. For (k=2) a random regular graph is a chain (sometimes 2–4 separate chains), while an ER network has one large component and many disconnected nodes. For (k=3) and (k=4) a random regular graph is always composed of one component, while an ER network has still a few disconnected nodes.Full size imageSince agents using the RD and BR update rule do not achieve coordination for small values of degree, one might suspect that the network is just not sufficiently connected for these values of the degree, i.e. there are separate components. This is only partially true. In Fig. 3, we can see the structures generated by random regular graph and by ER random graph algorithms. Indeed, for (k=1) and 2 the topology is trivial and a large (infinite for (k=1)) average path length23 can be the underlying feature stopping the system to reach coordination. For (k=3), however, the network is well connected with one giant component and the system still does not reach the global coordination when using RD or BR. For the UI update rule coordination arrives even for larger values of k. Looking at the strategies used by players in Fig. 3 we can see how frozen configuration without coordination can be achieved. There are various types of topological traps where nodes with different strategies are connected, but none of them is willing to change the strategy in the given update rule.We next consider the question of how the two strategies are distributed in the situations in which full coordination is not reached. Looking at the trajectories in Fig. 1 we can see that there are only few successful strategy updates in such scenario and the value of (alpha) remains close to 0.5 until arriving at a frozen state for (k=2) (also (k=7) for UI). This suggests that there is not enough time, in the sense of the number of updates, to cluster the different strategies in the network. Therefore, one might expect that they are well mixed as at the end of each simulation. However, an analysis of the density of active links in the final state of the dynamics, presented in Fig. 2, shows a slightly more complex behaviour. When the two strategies are randomly distributed (i.e. well mixed) in a network, the interface density takes the value (rho =0.5). When the two strategies are spatially clustered in the network there are only few links connecting them and therefore the interface density takes small values. Looking at the dependence of (rho) on k, we find that for the replicator dynamics the active link density starts at 0.5 for (k=1), then drops below 0.2 for (k=2) and 3 indicating good clustering between strategies, to fall to zero for (k=4) where full coordination is already obtained. When using the best response update rule the situation is quite different. For (k=1) there are no active links, (rho =0), and hardly any for (k=2). There is a slight increase of the active link density for (k=3), to drop to zero again for (k=4) due to full coordination. Because of the oscillatory level of coordination there are still active links for odd values of (kP) (otherwise we can rename the strategies and shuffle the columns and rows). What defines the outcome of a game are the greater than and smaller than relations among the payoffs. Therefore we can add/subtract any value from all payoffs, or multiply them by a factor grater than zero, without changing the game. Thus, the payoff matrix (1) can be rewritten as:$$begin{gathered} begin{array}{*{20}c} {} & {qquad {text{A}}} & {quad quad {text{B}}} \ end{array} ;; hfill \ begin{array}{*{20}c} {text{A}} \ {text{B}} \ end{array} left( {begin{array}{*{20}c} 1 & {frac{{S – P}}{{R – P}}} \ {frac{{T – P}}{{R – P}}} & 0 \ end{array} } right) hfill \ end{gathered}$$
(3)
which, after substituting (S’=frac{S-P}{R-P}) and (T’=frac{T-P}{R-P}), is equivalent to the matrix: $$begin{gathered} begin{array}{*{20}c} {} &quad ;;{text{A}} &; {text{B}} \ end{array} ;quad quad quad quad quad quad begin{array}{*{20}c} {} & quad; {text{A}} & ;{text{B}} \ end{array} hfill \ begin{array}{*{20}c} {text{A}} \ {text{B}} \ end{array} left( {begin{array}{*{20}c} 1 & {S^{prime}} \ {T^{prime}} & 0 \ end{array} } right)xrightarrow[{{text{apostrophes}}}]{{{text{skipping}}}}begin{array}{*{20}c} {text{A}} \ {text{B}} \ end{array} left( {begin{array}{*{20}c} 1 & S \ T & 0 \ end{array} } right) hfill \ end{gathered}$$
(4)
From now on we omit the apostrophes and simply refer to parameters S and T. This payoff matrix can represent many games, including e.g. the prisoner’s dilemma14,46 (for (T >1) and (S More

IPCC Climate Change 2014: Synthesis Report. In Contribution of Working Groups I, II and III to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change (eds Core Writing Team, Pachauri, R. K. & Meyer L. A.) 151 (IPCC, Geneva, Switzerland, 2014).Grizzetti, B., Bouraoui, F. & Aloe, A. Changes of nitrogen and phosphorus loads to European seas. Glob. Change Biol. 18, 769–782 (2012).
Google Scholar 
Hoegh-Guldberg, O. & Bruno, J. F. The impact of climate change on the world’s marine ecosystems. Science 328, 1523–1528 (2010).CAS 
PubMed 

Google Scholar 
Duarte, C. M. Global change and the future ocean: a grand challenge for marine sciences. Front. Mar. Sci. 1, 1–16 (2014).
Google Scholar 
Richardson, A. J. & Schoeman, D. S. Climate impact on plankton ecosystems in the Northeast Atlantic. Science 305, 1609–1612 (2004).CAS 
PubMed 

Google Scholar 
Rose, J. M. et al. Effects of increased pCO2 and temperature on the North Atlantic spring bloom. II. Microzooplankton abundance and grazing. Mar. Ecol. Prog. Ser. 388, 27–40 (2009).CAS 

Google Scholar 
Sommer, U., Paul, C. & Moustaka-Gouni, M. Warming and ocean acidification effects on phytoplankton—from species shifts to size shifts within species in a mesocosm experiment. PLoS ONE 10, 1–17 (2015).
Google Scholar 
Garzke, J., Hansen, T., Ismar, S. M. H. & Sommer, U. Combined effects of ocean warming and acidification on copepod abundance, body size and fatty acid content. PLoS ONE 11, 1–22 (2016).
Google Scholar 
Horn, H. G., Boersma, M., Garzke, J., Sommer, U. & Aberle, N. High CO2 and warming affect microzooplankton food web dynamics in a Baltic Sea summer plankton community. Mar. Biol. 167, 1–17 (2020).
Google Scholar 
Boyd, P. W. et al. Experimental strategies to assess the biological ramifications of multiple drivers of global ocean change—a review. Glob. Change Biol. 24, 2239–2261 (2018).
Google Scholar 
Stewart, R. I. A. et al. Mesocosm experiments as a tool for ecological provided for ecological climate-change research. In Advances in Ecological Research/Guy Woodward (ed. O’Gorman, E. J.) 71–181 (Academic Press, 2013).Rost, B. & Riebesell, U. Coccolithophores and the biological pump: responses to environmental changes. In Coccolithophores: From Molecular Processes to Global Impact (eds Thierstein, H. R. & Young, J. R.) 99–125 (Springer, 2004).Peter, K. H. & Sommer, U. Phytoplankton cell size reduction in response to warming mediated by nutrient limitation. PLoS ONE 8, 1–6 (2013).
Google Scholar 
Bermúdez, J. R., Riebesell, U., Larsen, A. & Winder, M. Ocean acidification reduces transfer of essential biomolecules in a natural plankton community. Sci. Rep. 6, 1–8 (2016).
Google Scholar 
Peter, K. H. & Sommer, U. Interactive effect of warming, nitrogen and phosphorus limitation on phytoplankton cell size. Ecol. Evolution 5, 1011–1024 (2015).
Google Scholar 
Alvarez-Fernandez, S. et al. Plankton responses to ocean acidification: the role of nutrient limitation. Prog. Oceanogr. 165, 11–18 (2018).
Google Scholar 
Stramski, D., Sciandra, A. & Claustre, H. Effects of temperature, nitrogen, and light limitation on the optical properties of the marine diatom Thalassiosira pseudonana. Limnol. Oceanogr. 47, 392–403 (2002).CAS 

Google Scholar 
Marañón, E. Cell size as a key determinant of phytoplankton metabolism and community structure. Annu. Rev. Mar. Sci. 7, 241–264 (2015).
Google Scholar 
Peñuelas, J., Sardans, J., Rivas‐Ubach, A. & Janssens, I. A. The human-induced imbalance between C, N and P in Earth’s life system. Glob. Change Biol. 18, 3–6 (2011).
Google Scholar 
Azam, F. et al. The ecological role of water-column microbes in the sea. Mar. Ecol. Prog. Ser. 10, 257–63. (1983).
Google Scholar 
Legendre, L. & Le Fèvre, J. Microbial food webs and the export of biogenic carbon in oceans. Aquat. Microb. Ecol. 9, 69–77 (1995).
Google Scholar 
Beaufort, L. et al. Sensitivity of coccolithophores to carbonate chemistry and ocean acidification. Nature 476, 80–83 (2011).CAS 
PubMed 

Google Scholar 
Langer, G., Nehrke, G., Probert, I., Ly, J. & Ziveri, P. Strain-specific responses of Emiliania huxleyi to changing seawater carbonate chemistry. Biogeosciences 6, 2637–2646 (2009).CAS 

Google Scholar 
Winter, A., Henderiks, J., Beaufort, L., Rickaby, R. E. M. & Brown, C. W. Poleward expansion of the coccolithophore Emiliania huxleyi. J. Plankton Res. 36, 316–325 (2014).CAS 

Google Scholar 
Hopkins, J., Henson, S. A., Painter, S. C., Tyrrell, T. & Poulton, A. J. Phenological characteristics of global coccolithophore blooms. Glob. Biogeochemical Cycles 29, 239–253 (2015).CAS 

Google Scholar 
León, P. et al. Seasonal variability of the carbonate system and coccolithophore Emiliania huxleyi at a Scottish Coastal Observatory monitoring site. Estuar., Coast. Shelf Sci. 202, 302–314 (2018).
Google Scholar 
Rivero-Calle, S., Gnanadesikan, A., Del Castillo, C. E., Balch, W. M. & Guikema, S. D. Multidecadal increase in North Atlantic coccolithophores and the potential role of rising CO2. Science 350, 1533–1537 (2015).CAS 
PubMed 

Google Scholar 
Purdie, D. A. & Finch, M. S. Impact of a coccolithophorid bloom on dissolved carbon dioxide in sea water enclosures in a Norwegian fjord. Sarsia 79, 379–387 (1994).
Google Scholar 
Nejstgaard, J. C., Gismervik, I. & Solberg, P. T. Feeding and reproduction by Calanus finmarchicus, and microzooplankton grazing during mesocosm blooms of diatoms and the coccolithophore Emiliania huxleyi. Mar. Ecol. Prog. Ser. 147, 197–217 (1997).
Google Scholar 
Leblanc, K. et al. Distribution of calcifying and silicifying phytoplankton in relation to environmental and biogeochemical parameters during the late stages of the 2005 North East Atlantic Spring Bloom. Biogeosciences 6, 2155–2179 (2009).CAS 

Google Scholar 
Sett, S. et al. Temperature modulates coccolithophorid sensitivity of growth, photosynthesis and calcification to increasing seawater pCO2. PLoS ONE 9, e88308 (2014).PubMed 
PubMed Central 

Google Scholar 
Benner, I. et al. Emiliania huxleyi increases calcification but not expression of calcification-related genes in long-term exposure to elevated temperature and pCO2. Philos. Trans. R. Soc. B 368, 20130049 (2013).
Google Scholar 
Borchard, C., Borges, A. V., Händel, N. & Engel, A. Biogeochemical response of Emiliania huxleyi (PML B92/11) to elevated CO2 and temperature under phosphorous limitation: a chemostat study. J. Exp. Mar. Biol. Ecol. 410, 61–71 (2011).CAS 

Google Scholar 
Harrison, P. J. et al. Geographical distribution of red and green Noctiluca scintillans. Chin. J. Oceanol. Limnol. 29, 807–831 (2011).
Google Scholar 
Johns, D. G., Edwards, M., Greve, W. & SJohn, A. W. G. Increasing prevelance of the marine cladoceran Penilia avirostris (Dana, 1852) in the North Sea. Helgol. Mar. Res. 59, 215–218 (2005).
Google Scholar 
O’Connor, M. I. O., Piehler, M. F., Leech, D. M., Anton, A. & Bruno, J. F. Warming and resource availability shift food web structure and metabolism. PLoS Biol. 7, 1–6 (2009).
Google Scholar 
Cross, W. F., Hood, J. M., Benstead, J. P., Huryn, A. D. & Nelson, D. Interactions between temperature and nutrients across levels of ecological organization. Glob. change Biol. 21, 1025–1040 (2015).
Google Scholar 
Boersma, M. et al. Temperature driven changes in the diet preference of omnivorous copepods: no more meat when it’s hot? Ecol. Lett. 19, 45–53 (2016).PubMed 

Google Scholar 
Anderson, T. R., Hessen, D. O., Boersma, M., Urabe, J. & Mayor, D. J. Will invertebrates require increasingly carbon-rich food in a warming world? Am. Naturalist 190, 725–742 (2017).
Google Scholar 
Kirchner, M., Sahling, G., Uhlig, G., Gunkel, W. & Klings, K.-W. Does the red tide-forming dinoflagellate Noctiluca scintillans feed on bacteria? Sarsia 81, 45–55 (2015).
Google Scholar 
Elbrächter, M. & Qi, Y. Aspects of Noctiluca (Dinophyceae) population dynamics. In Physiological Ecology of Harmful Algal Blooms (ed. Anderson, M. D.) 315–335 (Springer-Verlag, 1998).Atienza, D., Saiz, E. & Calbet, A. Feeding ecology of the marine cladoceran Penilia avirostris: natural diet, prey selectivity and daily ration. Mar. Ecol. Prog. Ser. 315, 211–220 (2006).
Google Scholar 
Zhang, S., Liu, H., Chen, B. & Chih-Jung, W. Effects of diet nutritional quality on the growth and grazing of Noctiluca scintillans. Sci. Rep. 527, 73–85 (2015).CAS 

Google Scholar 
Reid, P. C., Borges, M. F. & Svendsen, E. A regime shift in the North Sea circa 1988 linked to changes in the North Sea horse mackerel fishery. Fish. Res. 50, 163–171 (2001).
Google Scholar 
Beaugrand, G., Brander, K. M., Lindley, J. A., Souissi, S. & Reid, P. C. Plankton effect on cod recruitment in the North Sea. Nature 426, 661–664 (2003).CAS 
PubMed 

Google Scholar 
Payne, M. R. et al. Recruitment in a changing environment: the 2000s North Sea herring recruitment failure. ICES J. Mar. Sci. 66, 272–277 (2009).
Google Scholar 
Perälä, T., Olsen, E. M. & Hutchings, J. A. Disentangling conditional effects of multiple regime shifts on Atlantic cod productivity. PLoS ONE 15, e0237414 (2020).PubMed 
PubMed Central 

Google Scholar 
Behrenfeld, M. J., Boss, E. S. & Halsey, K. H. Phytoplankton community structuring and succession in a competition-neutral resource landscape. ISME COMMUN. 1, 1–8 (2021).Monteiro, F. M. et al. Why marine phytoplankton calcify. Sci. Adv. 2, 1–14 (2016).
Google Scholar 
Mayers, K. M. J. et al. The possession of coccoliths fails to deter microzooplankton grazers. Front. Mar. Sci. 7, 976 (2020).
Google Scholar 
Zhao, Y. et al. Grazing by microzooplankton and copepods on the microbial food web in spring in the southern Yellow Sea, China. Mar. Life Sci. Technol. 2, 442–455 (2020).
Google Scholar 
Aberle, N. et al. High tolerance of microzooplankton to ocean acidification in an Arctic coastal plankton community. Biogeosciences 10, 1471–1481 (2013).
Google Scholar 
Horn, H. G. et al. Low CO2 sensitivity of Microzooplankton communities in the Gullmar Fjord, Skagerrak: evidence from a long-term Mesocosm Study. PLoS ONE 11, 1–24 (2016).
Google Scholar 
Chen, B., Landry, M. R., Huang, B. & Liu, H. Does warming enhance the effect of microzooplankton grazing on marine phytoplankton in the ocean? Limnol. Oceanogr. 57, 519–526 (2012).CAS 

Google Scholar 
Vázquez-Domínguez, E., Vaqué, D. & Gasol, J. M. Temperature effects on the heterotrophic bacteria, heterotrophic nanoflagellates, and microbial top predators of the NW Mediterranean. Aquat. Microb. Ecol. 67, 107–121 (2012).
Google Scholar 
Lara, E. et al. Experimental evaluation of the warming effect on viral, bacterial and protistan communities in two contrasting Arctic systems. Aquat. Microb. Ecol. 70, 17–32 (2013).
Google Scholar 
Olson, M. B., Solem, K. & Love, B. Microzooplankton grazing responds to simulated ocean acidification indirectly through changes in prey cellular characteristics. Mar. Ecol. Prog. Ser. 604, 83–97 (2018).CAS 

Google Scholar 
Sherr, E. B. & Sherr, B. F. Bacterivory and herbivory: key roles of phagotrophic protists in pelagic food webs. Microb. Ecol. 28, 223–235 (1994).CAS 
PubMed 

Google Scholar 
Brander, K. & Kiørboe, T. Decreasing phytoplankton size adversely affects ocean food chains. Glob. Change Biol. 26, 5356–5357 (2020).
Google Scholar 
Irigoien, X. et al. A high frequency time series at weathership M, Norwegian Sea, during the 1997 spring bloom: feeding of adult female Calanus finmarchicus. Mar. Ecol. Prog. Ser. 172, 127–137 (1998).
Google Scholar 
Fenchel, T. The microbial loop—25 years later. J. Exp. Mar. Biol. Ecol. 366, 99–103 (2008).
Google Scholar 
Aberle, N., Malzahn, A. M., Lewandowska, A. M. & Sommer, U. Some like it hot: the protozooplankton— copepod link in a warming ocean. Mar. Ecol. Prog. Ser. 519, 103–113 (2015).
Google Scholar 
Berglund, J., Müren, U., Båmstedt, U. & Andersson, A. Efficiency of a phytoplankton-based and a bacteria-based food web in a pelagic marine system. Limnol. Oceanogr. 52, 121–131 (2007).CAS 

Google Scholar 
Sherr, E. B. & Sherr, B. F. Heterotrophic dinoflagellates: a significant component of microzooplankton biomass and major grazers of diatoms in the sea. Mar. Ecol. Prog. Ser. 352, 187–197 (2007).
Google Scholar 
Gifford, D. J. The protozoan-metazoan trophic link in pelagic ecosystems. J. Protozool. 38, 81–86 (1991).
Google Scholar 
Rollwagen-Bollens, G. & Gifford, S. The role of protistan microzooplankton in the upper San Francisco estuary planktonic food web: source or sink? Estuaries Coasts 34, 1026–1038 (2011).CAS 

Google Scholar 
Anjusha, A. et al. Trophic efficiency of plankton food webs: observations from the Gulf of Mannar and the Palk Bay, Southeast Coast of India. J. Mar. Syst. 115, 40–61 (2013).
Google Scholar 
IPCC. Global Warming of 1.5 °C. An IPCC Special Report on the impacts of global warming of 1.5 °C above pre-industrial levels and related global greenhouse gas emission pathways. In The Context of Strengthening the Global Response to The Threat of Climate Change, Sustainable Development, and Efforts to Eradicate Poverty (Masson-Delmotte, V. et al (eds.) 616 (IPCC, Geneva, Switzerland, 2018).Pansch, A., Winde, V., Asmus, R. & Asmus, H. Tidal benthic mesocosms simulating future climate change scenarios in the field of marine ecology. Limnol. Oceanogr.: Methods 14, 257–267 (2016).
Google Scholar 
van Leeuwen, S., Tett, P., Mills, D. & van der Molen, J. Stratified and nonstratified areas in the North Sea: long-term variability and biological and policy implications. J. Geophys. Res.: Oceans 120, 4670–4686 (2015).
Google Scholar 
Grasshoff, K., Kremling, K. & Ehrhardt, M. (eds). Methods of Seawater Analysis, 3rd edn. (Wiley-VCH, Weinheim, 1999).Dickson, A. G. An exact definition of total alkalinity and a procedure for the estimation of alkalinity and total inorganic carbon from titration data. Deep-Sea Res. 28, 609–623 (1981).CAS 

Google Scholar 
Pierrot, D. E., Lewis, E. & Wallace, D. W. R. MS Excel program developed for CO2 system calculations. ORNL/CDIAC-105a. Carbon Dioxide Information Analysis Center, Oak Ridge National Laboratory, U.S. Department of Energy, Oak Ridge, Tennessee https://doi.org/10.3334/CDIAC/otg.CO2SYS_XLS_CDIAC105a (2006).Dickson, A. G. & Millero, F. J. A comparison of the equilibrium constants for the dissociation of carbonic acid in seawater media. Deep-Sea Res. 34, 1733–1743 (1987).CAS 

Google Scholar 
Arrigo, K. R. et al. Phytoplankton community structure and the drawdown of nutrients and CO2 in the Southern Ocean. Science 283, 365–368 (1999).CAS 
PubMed 

Google Scholar 
Utermöhl, H. Zur Vervollkommnung der quantitativen Phytoplankton- Methodik. Int. Ver. für. Theoretische und Angew. Limnologie: Mitteilungen 9, 1–38 (1958).
Google Scholar 
McEwen, G. F., Johnson, M. W. & Folsom, T. R. A statistical analysis of the performance of the Folsom plankton sample splitter, based upon test observations. Archiv für. Archiv Meteorologie, Geophysik und Bioklimatologie, Ser. A 7, 502–527 (1954).
Google Scholar 
Sell, D. W. & Evans, M. S. A statistical analysis of subsampling and an evaluation of the Folsom plankton splitter. Hydrobiologia 94, 223–230 (1982).
Google Scholar 
Boersma, M., Wiltshire, K. H., Kong, S., Greve, W. & Renz, J. Long-term change in the copepod community in the southern German Bight. J. Sea Res. 101, 41–50 (2015).
Google Scholar 
Marie, D., Simon, N. & Vaulot, D. Phytoplankton cell counting by flow cytometry. Algal Culturing Tech. 1, 253–267 (2005).
Google Scholar 
Hillebrand, H., Dürselen, C., Kirschtel, D., Pollingher, U. & Zohary, T. Biovolume calculation for pelagic and benthic microalgae. J. Phycol. 35, 403–424 (1999).
Google Scholar 
Menden-Deuer, S. & Lessard, E. J. Carbon to volume relationships for dinoflagellates, diatoms, and other protist plankton. Limnol. Oceanogr. 45, 569–579 (2000).CAS 

Google Scholar 
Putt, M. & Stoecker, D. K. An experimentally determined carbon: volume ratio for marine “oligotrichous” ciliates from estuarine and coastal waters. Limnol. Oceanogr. 34, 1097–1103 (1989).
Google Scholar 
Beran, A. et al. Carbon content and biovolume of the heterotrophic dinoflagellate Noctiluca scintillans from the Northern Adriatic Sea. In Proceedings of the CESUM-BS 2003, Varna. 28 (Book of Abstracts, UNESCO, Paris, 2003).Lee, S. & Fuhrman, J. A. Relationships between biovolume and biomass of naturally derived marine bacterioplankton. Appl. Environ. Microbiol. 53, 1298–1303 (1987).CAS 
PubMed 
PubMed Central 

Google Scholar 
Kraberg, A., Baumann, M. & Dürselen, C. Coastal Phytoplankton: Photo Guide for Northern European Seas (Dr. Friedrich Pfeil, München, 2010).R Core Team. R: a language and environment for statistical computing. R Foundation for Statistical Computing, Vienna, Austria. https://www.R-project.org/ (2021). More