Philippa Kaur

1.Arora, V. Modeling vegetation as a dynamic component in soil-vegetation-atmosphere transfer schemes and hydrological models. Rev. Geophys. https://doi.org/10.1029/2001RG000103 (2002).Article 

Google Scholar 
2.Lamchin, M., Park, T., Lee, J. & Lee, W. Monitoring of vegetation dynamics in the Mongolia using MODIS NDVIs and their relationship to rainfall by Natural Zone. J. Indian Soc. Remote 43, 325–337 (2014).Article 

Google Scholar 
3.Zhang, Y., Liu, L. Y., Liu, Y., Zhang, M. & An, C. B. Response of altitudinal vegetation belts of the Tianshan Mountains in northwestern China to climate change during 1989–2015. Sci. Rep. https://doi.org/10.1038/s41598-021-84399-z (2021).Article 
PubMed 
PubMed Central 

Google Scholar 
4.Piao, S. L., Wang, X. H., Park, T. & Chen, C. Characteristics, drivers and feedbacks of global greening. Nat. Rev. Earth Environ. 1, 14–27 (2020).Article 
ADS 

Google Scholar 
5.Zhu, Z. C., Piao, S. L., Myneni, R. B. & Huang, M. T. Greening of the Earth and its drivers. Nat. Clim. Change 6, 791–795 (2016).CAS 
Article 
ADS 

Google Scholar 
6.Chen, C. et al. China and India lead in greening of the world through land-use management. Nat. Sustain. 2, 122–129 (2019).Article 

Google Scholar 
7.Fang, H. Y., Li, Q. Y. & Cai, Q. G. A study on the vegetation recovery and crop pattern adjustment on the Loess Plateau of China. Afr. J. Microbiol. Res. 5, 1414–1419 (2011).Article 

Google Scholar 
8.Jiang, C., Zhang, H. Y., Tang, Z. P. & Labzovskii, L. Evaluating the coupling effects of climate variability and vegetation restoration on ecosystems of the Loess Plateau, China. Land Use Policy 69, 134–148 (2017).Article 

Google Scholar 
9.Zhang, H. Y., Fan, J. W., Cao, W., Zhong, H. P. & Harris, W. Changes in multiple ecosystem services between 2000 and 2013 and their driving factors in the Grazing Withdrawal Program, China. Ecol. Eng. 116, 67–79 (2018).Article 

Google Scholar 
10.Liu, Y. X., Lü, Y. H., Fu, B. J., Harris, P. & Wu, L. H. Quantifying the spatio-temporal drivers of planned vegetation restoration on ecosystem services at a regional scale. Sci. Total Environ. 650, 1029–1040 (2019).CAS 
Article 
ADS 

Google Scholar 
11.Wang, J. F., Zhang, T. L. & Fu, B. J. A measure of spatial stratified heterogeneity. Ecol. Indic. 67, 250–256 (2016).Article 

Google Scholar 
12.Jiang, Y. T., Sun, Y. J., Zhang, L. P. & Wang, X. L. Influence factor analysis of soil heavy metal Cd based on the GeoDetector. Stoch. Environ. Res. Risk Assess. 34, 921–930 (2020).Article 

Google Scholar 
13.Su, Y., Li, T. X., Cheng, S. K. & Wang, X. Spatial distribution exploration and driving factor identification for soil salinisation based on geodetector models in coastal area. Ecol. Eng. https://doi.org/10.1016/j.ecoleng.2020.105961 (2020).Article 

Google Scholar 
14.Yan, S. J., Wang, H. & Jiao, K. W. Spatiotemporal dynamic of NDVI in the Beijing–Tianjin–Hebei region based on MODIS data and quantitative attribution. J. Geo-inf. Sci. 21, 767–780 (2019).
Google Scholar 
15.Evans, J. & Geerken, R. Discrimination between climate and human-induced dryland degradation. J. Arid Environ. 57, 535–554 (2007).Article 

Google Scholar 
16.Wessels, K. J. et al. Can human-induced land degradation be distinguished from the effects of rainfall variability? A case study in South Africa. J. Arid Environ. 68, 271–297 (2007).Article 
ADS 

Google Scholar 
17.Teng, M. J. et al. The impacts of climate changes and human activities on net primary productivity vary across an ecotone zone in Northwest China. Sci. Total Environ. https://doi.org/10.1016/j.scitotenv.2020.136691 (2020).Article 
PubMed 

Google Scholar 
18.Shi, S. Y. et al. Quantitative contributions of climate change and human activities to vegetation changes over multiple time scales on the Loess Plateau. Sci. Total Environ. https://doi.org/10.1016/j.scitotenv.2020.142419 (2020).Article 
PubMed 
PubMed Central 

Google Scholar 
19.Peng, J., Jiang, H., Liu, Q. H., Green, S. & Quine, T. Human activity vs. climate change, distinguishing dominant drivers on LAI dynamics in karst region of southwest China. Sci. Total Environ. https://doi.org/10.1016/j.scitotenv.2020.144297 (2020).Article 
PubMed 
PubMed Central 

Google Scholar 
20.Xu, D. Y., Li, C. L., Song, X. & Ren, H. Y. The dynamics of desertification in the farming-pastoral region of North China over the past 10 years and their relationship to climate change and human activity. CATENA 123, 11–22 (2014).Article 

Google Scholar 
21.Sun, Y. L., Yang, Y. L., Zhang, L. & Wang, Z. L. The relative roles of climate variations and human activities in vegetation change in North China. Phys. Chem. Earth 87–88, 67–78 (2015).Article 
ADS 

Google Scholar 
22.Liu, B., Sun, Y. L., Wang, Z. L. & Zhao, T. B. Analysis of the vegetation cover change and the relative role of its influencing factors in North China. J. Nat. Res. 30, 12–23 (2015).
Google Scholar 
23.Huang, L., Zheng, Y. H. & Xiao, T. Regional differentiation of ecological conservation and its zonal suitability at the county level in China. J. Geogr. Sci. 28, 46–58 (2018).Article 

Google Scholar 
24.Pan, M., Chen, T. W., Huang, L. & Cao, W. Spatial and temporal variations in ecosystem services and its driving factors analysis in Jing-Jin-Ji region. Acta Ecol. Sin. 40, 5151–5167 (2020).
Google Scholar 
25.Zhou, Q., Zhao, X. & Wu, D. H. Impact of urbanization and climate on vegetation coverage in the Beijing–Tianjin–Hebei Region of China. Remote Sens. https://doi.org/10.3390/rs11202452 (2019).Article 

Google Scholar 
26.Pantazi, M., Vasilescu, A. M., Mihai, A. & Gurau, D. Statistical-mathematical processing of anthropometric foot parameters and establishing simple and multiple correlations. Part 1, statistical analysis of foot size parameters. J. Leather Footwear 17, 199–208 (2017).Article 

Google Scholar 
27.Krishnan, S. R., Magimai-Doss, M. & Seelamantula, C. S. A Savitzky-Golay filtering perspective of dynamic feature computation. IEEE Signal Proc. Lett. 20, 281–284 (2013).Article 
ADS 

Google Scholar 
28.Li, Z., Zhang, Y., Zhu, Q. K., He, Y. M. & Yao, W. J. Assessment of bank gully development and vegetation coverage on the Chinese Loess Plateau. Geomorphology 228, 462–469 (2015).Article 
ADS 

Google Scholar 
29.Chen, J., Ban, Y. F. & Li, S. N. China, Open access to Earth land-cover map. Nature 514, 434–434 (2014).CAS 
Article 
ADS 

Google Scholar 
30.Alijani, B., Mahmoudi, P. & Chogan, A. J. A study of annual and seasonal precipitation trends in Iran using a nonparametric method (Sen’s slope estimator). For. Ecol. Manag. 121, 137–146 (2012).
Google Scholar 
31.Rahman, A. U. & Dawood, M. Spatio-statistical analysis of temperature fluctuation using Mann–Kendall and Sen’s Slope approach. Clim. Dynam. 48, 783–797 (2017).Article 
ADS 

Google Scholar 
32.Lin, X. S., Tang, J., Li, Z. Y. & Li, H. Y. Vegetation greenness modelling in response to interannual precipitation and temperature changes between 2001 and 2012 in Liao River Basin in Jilin Province, China. Springerplus https://doi.org/10.1186/s40064-016-2737-9 (2016).Article 
PubMed 
PubMed Central 

Google Scholar 
33.Lawrance, A. J. Partial and multiple correlation for time series. Am. Stat. 33, 127–130 (1979).MATH 

Google Scholar 
34.Wetzels, R. & Wagenmakers, E. J. A default Bayesian hypothesis test for correlations and partial correlations. Psychon. B Rev. 19, 1057–1064 (2012).Article 

Google Scholar 
35.Anghelache, C., Anghel, M. G., Prodan, L., Sacala, C. & Popovici, M. Multiple linear regression model used in economic analyses. Roman. Stat. Rev. Suppl. 62, 120–127 (2014).
Google Scholar 
36.Miao, L. J., Liu, Q., Fraser, R., He, B. & Cui, X. F. Shifts in vegetation growth in response to multiple factors on the Mongolian Plateau from 1982 to 2011. Phys. Chem. Earth 87–88, 50–59 (2015).Article 
ADS 

Google Scholar 
37.Tang, Y. Z., Shao, Q. Q., Liu, J. Y. & Zhang, H. Y. Did ecological restoration hit its mark? Monitoring and assessing ecological changes in the Grain for Green Program Region using multi-source satellite images. Remote Sens. https://doi.org/10.3390/rs11030358 (2019).Article 

Google Scholar 
38.Cai, D. W. et al. Contributions of ecological programs to vegetation restoration in arid and semiarid China. Environ. Res. Lett. https://doi.org/10.1088/1748-9326/abbde9 (2020).Article 

Google Scholar 
39.Yao, N., Huang, C. H., Yang, J., Bosch, C. & Jia, Z. Combined effects of impervious surface change and large-scale afforestation on the surface urban heat island intensity of Beijing, China based on remote sensing analysis. Remote Sens. https://doi.org/10.3390/rs12233906 (2020).Article 

Google Scholar 
40.Wu, Z. T., Wu, J. J., He, B., Liu, J. H. & Wang, Q. F. Drought offset ecological restoration program-induced increase in vegetation activity in the Beijing–Tianjin Sand Source Region, China. Environ. Sci. Technol. 48, 12108–12117 (2014).CAS 
Article 
ADS 

Google Scholar 
41.Yang, X. C. et al. Remote sensing monitoring of grassland vegetation growth in the Beijing–Tianjin sandstorm source project area from 2000 to 2010. Ecol. Indic. 51, 244–251 (2015).Article 

Google Scholar 
42.Li, X. S., Wang, H. Y., Zhou, S. F., Sun, B. & Gao, Z. H. Did ecological engineering projects have a significant effect on large-scale vegetation restoration in Beijing–Tianjin Sand Source Region, China? A remote sensing approach. Chin. Geogr. Sci. 26, 216–228 (2016).Article 

Google Scholar 
43.Hu, S. et al. Detecting and attributing vegetation changes in Taihang Mountain, China. J. Mt. Sci. 16, 337–350 (2019).Article 

Google Scholar 
44.Li, D. et al. Identification of the roles of climate factors, engineering construction, and agricultural practices in vegetation dynamics in the Lhasa River Basin, Tibetan Plateau. Remote Sens. https://doi.org/10.3390/rs12111883 (2020).Article 

Google Scholar 
45.Sun, H. Y. et al. Effect of precipitation change on water balance and WUE of the winter wheat-summer maize rotation in the North China Plain. Agr. Water Manag. 97, 1139–1145 (2010).Article 

Google Scholar 
46.Tao, Y., Li, F., Crittenden, J. C., Lu, Z. M. & Sun, X. Environmental impacts of China’s urbanization from 2000 to 2010 and management implications. Environ. Manag. 57, 498–507 (2016).Article 
ADS 

Google Scholar 
47.Jia, G. J., Epstein, H. E. & Balser, A. Spatial heterogeneity of tundra vegetation response to recent temperature changes. Glob. Change Biol. 12, 42–55 (2010).Article 
ADS 

Google Scholar 
48.Wen, Y. Y., Liu, X. P., Xin, Q. C. & Wu, J. Cumulative effects of climatic factors on terrestrial vegetation growth. J. Geophys. Res. Biogeosci. https://doi.org/10.1029/2018JG004751 (2019).Article 

Google Scholar 
49.Zhao, A. Z., Yu, Q. Y., Feng, L. L., Zhang, A. P. & Pei, T. Evaluating the cumulative and time-lag effects of drought on grassland vegetation, A case study in the Chinese Loess Plateau. J. Environ. Manag. https://doi.org/10.1016/j.jenvman.2020.110214 (2020).Article 

Google Scholar  More

1.Joseph, B. & Sujatha, S. Pharmacologically important natural products from marine sponges. J. Nat. Prod. 4, 5–12 (2011).CAS 

Google Scholar 
2.Bergmann, W. & Feeney, R. J. Contributions to the study of marine products XXXII The nucleosides of sponges. I. J. Org. Chem. 16, 981–987 (1951).CAS 
Article 

Google Scholar 
3.Munro, M. H. G., Luibrand, R. T. & Blunt, J. W. The search for antiviral and anticancer compounds from marine organisms. in Bioorganic Marine Chemistry (ed. Scheuer, P. J.) vol. 1 93–176 (Springer-Verlag, Berlin, Heidelberg, 1987).4.Fuerst, J. A. Diversity and biotechnological potential of microorganisms associated with marine sponges. Appl. Microbiol. Biotechnol. 98, 7331–7347 (2014).CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
5.Mehbub, M. F., Lei, J., Franco, C. & Zhang, W. Marine sponge derived natural products between 2001 and 2010: Trends and opportunities for discovery of bioactives. Mar. Drugs 12, 4539–4577 (2014).PubMed 
PubMed Central 
Article 
CAS 

Google Scholar 
6.Piel, J. Metabolites from symbiotic bacteria. Nat. Prod. Rep. 26, 338–362 (2009).CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
7.Piel, J. et al. Antitumor polyketide biosynthesis by an uncultivated bacterial symbiont of the marine sponge Theonella swinhoei. Proc. Natl. Acad. Sci. U. S. A. 101, 16222–16227 (2004).CAS 
PubMed 
PubMed Central 
Article 
ADS 

Google Scholar 
8.Noro, J. C., Kalaitzis, J. A. & Neilan, B. A. Bioactive natural products from Papua New Guinea marine sponges. Chem. Biodivers. 9, 2077–2095 (2012).CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
9.Schirmer, A. et al. Metagenomic analysis reveals diverse polyketide synthase gene clusters in microorganisms associated with the marine sponge Discodermia dissoluta. Appl. Environ. Microbiol. 71, 4840–4849 (2005).CAS 
PubMed 
PubMed Central 
Article 
ADS 

Google Scholar 
10.Siegl, A. & Hentschel, U. PKS and NRPS gene clusters from microbial symbiont cells of marine sponges by whole genome amplification. Environ. Microbiol. Rep. 2, 507–513 (2010).CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
11.Graça, A. P. et al. Antimicrobial activity of heterotrophic bacterial communities from the marine sponge Erylus discophorus (Astrophorida, Geodiidae). PLoS ONE 8, e78992 (2013).PubMed 
PubMed Central 
Article 
ADS 
CAS 

Google Scholar 
12.Santos, O. C. S. et al. Investigation of biotechnological potential of sponge-associated bacteria collected in Brazilian coast. Lett. Appl. Microbiol. 60, 140–147 (2015).CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
13.Su, P., Wang, D. X., Ding, S. X. & Zhao, J. Isolation and diversity of natural product biosynthetic genes of cultivable bacteria associated with marine sponge Mycale sp from the coast of Fujian. China. Can. J. Microbiol. 60, 217–225 (2014).CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
14.Van Soest, R. W. M. et al. World Porifera Database. http://www.marinespecies.org/porifera/. (2020).15.Bertolino, M. et al. Stability of the sponge assemblage of Mediterranean coralligenous concretions along a millennial time span. Mar. Ecol. 35, 149–158 (2014).Article 
ADS 

Google Scholar 
16.Longo, C. et al. Sponges associated with coralligenous formations along the Apulian coasts. Mar. Biodivers. 48, 2151–2163 (2018).Article 

Google Scholar 
17.Costa, G. et al. Sponge community variation along the Apulian coasts (Otranto Strait) over a pluri-decennial time span Does water warming drive a sponge diversity increasing in the Mediterranean Sea?. J. Mar. Biol. Assoc. United Kingdom 99, 1519–1534 (2019).Article 

Google Scholar 
18.Bertolino, M. et al. Changes and stability of a Mediterranean hard bottom benthic community over 25 years. J. Mar. Biol. Assoc. United Kingdom 96, 341–350 (2016).Article 

Google Scholar 
19.Bertolino, M. et al. Have climate changes driven the diversity of a Mediterranean coralligenous sponge assemblage on a millennial timescale?. Palaeogeogr. Palaeoclimatol. Palaeoecol. 487, 355–363 (2017).Article 

Google Scholar 
20.Gerovasileiou, V. et al. New Mediterranean biodiversity records. Mediterr. Mar. Sci. 18, 355–384 (2017).Article 

Google Scholar 
21.Ulman, A. et al. A massive update of non-indigenous species records in Mediterranean marinas. PeerJ 2017, e3954 (2017).Article 

Google Scholar 
22.Costantini, M. An analysis of sponge genomes. Gene 342, 321–325 (2004).CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
23.Costantini, S. et al. Anti-inflammatory effects of a methanol extract from the marine sponge Geodia cydonium on the human breast cancer MCF-7 cell line. Mediators Inflamm. https://doi.org/10.1155/2015/204975 (2015).Article 
PubMed 
PubMed Central 

Google Scholar 
24.Costantini, S. et al. Evaluating the effects of an organic extract from the mediterranean sponge Geodia cydonium on human breast cancer cell lines. Int. J. Mol. Sci. 18, 2112 (2017).PubMed Central 
Article 
CAS 

Google Scholar 
25.Coll, M. et al. The biodiversity of the Mediterranean Sea: estimates, patterns, and threats. PLoS ONE 5, e11842 (2010).PubMed 
PubMed Central 
Article 
ADS 
CAS 

Google Scholar 
26.Marra, M. V. et al. Long-term turnover of the sponge fauna in Faro Lake (North-East Sicily, Mediterranean Sea). Ital. J. Zool. 83, 579–588 (2016).CAS 
Article 

Google Scholar 
27.Cárdenas, P., Xavier, J. R., Reveillaud, J., Schander, C. & Rapp, H. T. Molecular phylogeny of the astrophorida (Porifera, Demospongiaep) reveals an unexpected high level of spicule homoplasy. PLoS ONE 6, e18318 (2011).PubMed 
PubMed Central 
Article 
ADS 
CAS 

Google Scholar 
28.Erpenbeck, D. et al. The phylogeny of halichondrid demosponges: past and present re-visited with DNA-barcoding data. Org. Divers. Evol. 12, 57–70 (2012).Article 

Google Scholar 
29.Abdul Wahab, M. A., Fromont, J., Whalan, S., Webster, N. & Andreakis, N. Combining morphometrics with molecular taxonomy: How different are similar foliose keratose sponges from the Australian tropics?. Mol. Phylogenet. Evol. 73, 23–39 (2014).CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
30.Wörheide, G. Low variation in partial cytochrome oxidase subunit I (COI) mitochondrial sequences in the coralline demosponge Astrosclera willeyana across the Indo-Pacific. Mar. Biol. 148, 907–912 (2006).Article 
CAS 

Google Scholar 
31.Carella, M., Agell, G., Cárdenas, P. & Uriz, M. J. Phylogenetic reassessment of antarctic tetillidae (Demospongiae, Tetractinellida) reveals new genera and genetic similarity among morphologically distinct species. PLoS ONE 11, 1–33 (2016).
Google Scholar 
32.Morrow, C. C. et al. Congruence between nuclear and mitochondrial genes in Demospongiae: a new hypothesis for relationships within the G4 clade (Porifera: Demospongiae). Mol. Phylogenet. Evol. 62, 174–190 (2012).CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
33.Vargas, S. et al. Diversity in a cold hot-spot: DNA-barcoding reveals patterns of evolution among Antarctic demosponges (class demospongiae, phylum Porifera). PLoS ONE 10, 1–17 (2015).
Google Scholar 
34.Yang, Q., Franco, C. M. M., Sorokin, S. J. & Zhang, W. Development of a multilocus-based approach for sponge (phylum Porifera) identification: refinement and limitations. Sci. Rep. 7, 1–14 (2017).Article 
ADS 
CAS 

Google Scholar 
35.Cosentino, A., Giacobbe, S. & Potoschi, A. The CSI of Faro coastal lake (Messina): a natural observatory for the incoming of marine alien species. Biol. Mar. Mediterr. 16, 132–133 (2009).
Google Scholar 
36.Zagami, G., Costanzo, G. & Crescenti, N. First record in Mediterranean Sea and redescription of the bentho-planktonic calanoid copepod species Pseudocyclops xiphophorus Wells, 1967. J. Mar. Syst. 55, 67–76 (2005).Article 

Google Scholar 
37.Zagami, G. et al. Biogeographical distribution and ecology of the planktonic copepod Oithona davisae: rapid invasion in lakes Faro and Ganzirri (central Meditteranean Sea). in Trends in copepod studies. Distribution, biology and ecology (ed. Uttieri, M.) 1–55 (Nova Science Publishers, 2017).38.Saccà, A. & Giuffrè, G. Biogeography and ecology of Rhizodomus tagatzi, a presumptive invasive tintinnid ciliate. J. Plankton Res. 35, 894–906 (2013).Article 
CAS 

Google Scholar 
39.Cao, S. et al. Structure and function of the Arctic and Antarctic marine microbiota as revealed by metagenomics. Microbiome 8, 47 (2020).PubMed 
PubMed Central 
Article 

Google Scholar 
40.Donnarumma, L. et al. Environmental and benthic community patterns of the shallow hydrothermal area of Secca Delle Fumose (Baia, Naples, Italy). Front. Mar. Sci. 6, 1–15 (2019).Article 

Google Scholar 
41.Poli, A., Anzelmo, G. & Nicolaus, B. Bacterial exopolysaccharides from extreme marine habitats: production, characterization and biological activities. Mar. Drugs 8, 1779–1802 (2010).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
42.Shukla, P. J., Nathani, N. M. & Dave, B. P. Marine bacterial exopolysaccharides [EPSs] from extreme environments and their biotechnological applications. Int. J. Res. Biosci. 6, 20–32 (2017).
Google Scholar 
43.Patel, A., Matsakas, L., Rova, U. & Christakopoulos, P. A perspective on biotechnological applications of thermophilic microalgae and cyanobacteria. Bioresour. Technol. 278, 424–434 (2019).CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
44.Schultz, J. & Rosado, A. S. Extreme environments: a source of biosurfactants for biotechnological applications. Extremophiles 24, 189–206 (2020).CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
45.Gloeckner, V. et al. The HMA-LMA dichotomy revisited: An electron microscopical survey of 56 sponge species. Biol. Bull. 227, 78–88 (2014).PubMed 
Article 
PubMed Central 

Google Scholar 
46.Erwin, P. M., Coma, R., López-Sendino, P., Serrano, E. & Ribes, M. Stable symbionts across the HMA-LMA dichotomy: Low seasonal and interannual variation in sponge-associated bacteria from taxonomically diverse hosts. FEMS Microbiol. Ecol. 91, 1–11 (2015).Article 
CAS 

Google Scholar 
47.Moitinho-Silva, L. et al. The sponge microbiome project. Gigascience 6, 1–13 (2017).CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
48.Hardoim, C. C. P. & Costa, R. Temporal dynamics of prokaryotic communities in the marine sponge Sarcotragus spinosulus. Mol. Ecol. 23, 3097–3112 (2014).CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
49.Karimi, E. et al. Metagenomic binning reveals versatile nutrient cycling and distinct adaptive features in alphaproteobacterial symbionts of marine sponges. FEMS Microbiol. Ecol. 94, 1–18 (2018).Article 
CAS 

Google Scholar 
50.Mohamed, N. M. et al. Diversity and quorum-sensing signal production of Proteobacteria associated with marine sponges. Environ. Microbiol. 10, 75–86 (2008).CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
51.Thiel, V. & Imhoff, J. F. Phylogenetic identification of bacteria with antimicrobial activities isolated from Mediterranean sponges. Biomol. Eng. 20, 421–423 (2003).CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
52.Bibi, F., Yasir, M., Al-Sofyani, A., Naseer, M. I. & Azhar, E. I. Antimicrobial activity of bacteria from marine sponge Suberea mollis and bioactive metabolites of Vibrio sp EA348. Saudi J. Biol. Sci. 27, 1139–1147 (2020).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
53.Thakur, A. N. et al. Antiangiogenic, antimicrobial, and cytotoxic potential of sponge-associated bacteria. Mar. Biotechnol. 7, 245–252 (2005).CAS 
Article 

Google Scholar 
54.Taylor, M. W., Radax, R., Steger, D. & Wagner, M. Sponge-associated microorganisms: Evolution, ecology, and biotechnological potential. Microbiol. Mol. Biol. Rev. 71, 295–347 (2007).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
55.Thomas, T. R. A., Kavlekar, D. P. & LokaBharathi, P. A. Marine drugs from sponge-microbe association—A review. Mar. Drugs 8, 1417–1468 (2010).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
56.Brinkmann, C. M., Marker, A. & Kurtböke, D. I. An overview on marine sponge-symbiotic bacteria as unexhausted sources for natural product discovery. Diversity 9, 40 (2017).Article 
CAS 

Google Scholar 
57.Haber, M. & Ilan, M. Diversity and antibacterial activity of bacteria cultured from Mediterranean Axinella spp sponges. J. Appl. Microbiol. 116, 519–532 (2014).CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
58.Öner, Ö. et al. Cultivable sponge-associated Actinobacteria from coastal area of eastern Mediterranean Sea. Adv. Microbiol. 04, 306–316 (2014).Article 

Google Scholar 
59.Gonçalves, A. C. S. et al. Draft genome sequence of Vibrio sp strain Vb278, an antagonistic bacterium isolated from the marine sponge Sarcotragus spinosulus. Genome Announc. 3, 2014–2015 (2015).Article 

Google Scholar 
60.Cheng, C. et al. Biodiversity, anti-trypanosomal activity screening, and metabolomic profiling of actinomycetes isolated from Mediterranean sponges. PLoS ONE 10, 1–21 (2015).
Google Scholar 
61.Graça, A. P. et al. The antimicrobial activity of heterotrophic bacteria isolated from the marine sponge Erylus deficiens (Astrophorida, Geodiidae). Front. Microbiol. 6, 389 (2015).PubMed 
PubMed Central 

Google Scholar 
62.Kuo, J. et al. Antimicrobial activity and diversity of bacteria associated with Taiwanese marine sponge Theonella swinhoei. Ann. Microbiol. 69, 253–265 (2019).CAS 
Article 

Google Scholar 
63.Liu, T. et al. Diversity and antimicrobial potential of Actinobacteria isolated from diverse marine sponges along the Beibu Gulf of the South China Sea. FEMS Microbiol. Ecol. 95, 1–10 (2019).CAS 
Article 

Google Scholar 
64.Hentschel, U. et al. Isolation and phylogenetic analysis of bacteria with antimicrobial activities from the Mediterranean sponges Aplysina aerophoba and Aplysina cavernicola. FEMS Microbiol. Ecol. 35, 305–312 (2001).CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
65.Chelossi, E., Milanese, M., Milano, A., Pronzato, R. & Riccardi, G. Characterisation and antimicrobial activity of epibiotic bacteria from Petrosia ficiformis (Porifera, Demospongiae). J. Exp. Mar. Bio. Ecol. 309, 21–33 (2004).CAS 
Article 

Google Scholar 
66.Kennedy, J. et al. Isolation and analysis of bacteria with antimicrobial activities from the marine sponge Haliclona simulans collected from irish waters. Mar. Biotechnol. 11, 384–396 (2009).CAS 
Article 

Google Scholar 
67.Penesyan, A., Marshall-Jones, Z., Holmstrom, C., Kjelleberg, S. & Egan, S. Antimicrobial activity observed among cultured marine epiphytic bacteria reflects their potential as a source of new drugs. FEMS Microbiol. Ecol. 69, 113–124 (2009).CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
68.Santos, O. C. S. et al. Isolation, characterization and phylogeny of sponge-associated bacteria with antimicrobial activities from Brazil. Res. Microbiol. 161, 604–612 (2010).CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
69.Flemer, B. et al. Diversity and antimicrobial activities of microbes from two Irish marine sponges, Suberites carnosus and Leucosolenia sp. J. Appl. Microbiol. 112, 289–301 (2012).CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
70.Margassery, L. M., Kennedy, J., O’Gara, F., Dobson, A. D. & Morrissey, J. P. Diversity and antibacterial activity of bacteria isolated from the coastal marine sponges Amphilectus fucorum and Eurypon major. Lett. Appl. Microbiol. 55, 2–8 (2012).CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
71.Abdelmohsen, U. R. et al. Actinomycetes from Red Sea sponges: sources for chemical and phylogenetic diversity. Mar. Drugs 12, 2771–2789 (2014).PubMed 
PubMed Central 
Article 

Google Scholar 
72.Montalvo, N. F. & Hill, R. T. Sponge-associated bacteria are strictly maintained in two closely related but geographically distant sponge hosts. Appl. Environ. Microbiol. 77, 7207–7216 (2011).CAS 
PubMed 
PubMed Central 
Article 
ADS 

Google Scholar 
73.Cleary, D. F. R. et al. Compositional analysis of bacterial communities in seawater, sediment, and sponges in the Misool coral reef system. Indonesia. Mar. Biodivers. 48, 1889–1901 (2018).Article 

Google Scholar 
74.Bedard, D. L., Ritalahti, K. M. & Löffler, F. E. The Dehalococcoides population in sediment-free mixed cultures metabolically dechlorinates the commercial polychlorinated biphenyl mixture aroclor 1260. Appl. Environ. Microbiol. 73, 2513–2521 (2007).CAS 
PubMed 
PubMed Central 
Article 
ADS 

Google Scholar 
75.Taş, N., Van Eekert, M. H. A., De Vos, W. M. & Smidt, H. The little bacteria that can – Diversity, genomics and ecophysiology of ‘Dehalococcoides’ spp in contaminated environments. Microb. Biotechnol. 3, 389–402 (2010).PubMed 
PubMed Central 
Article 
CAS 

Google Scholar 
76.Arnds, J., Knittel, K., Buck, U., Winkel, M. & Amann, R. Development of a 16S rRNA-targeted probe set for Verrucomicrobia and its application for fluorescence in situ hybridization in a humic lake. Syst. Appl. Microbiol. 33, 139–148 (2010).CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
77.Sizikov, S. et al. Characterization of sponge-associated Verrucomicrobia: microcompartment-based sugar utilization and enhanced toxin–antitoxin modules as features of host-associated Opitutales. Environ. Microbiol. 22, 4669–4688 (2020).CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
78.Cardman, Z. et al. Verrucomicrobia are candidates for polysaccharide-degrading bacterioplankton in an Arctic fjord of Svalbard. Appl. Environ. Microbiol. 80, 3749–3756 (2014).CAS 
PubMed 
PubMed Central 
Article 
ADS 

Google Scholar 
79.Cabello-Yeves, P. J. et al. Reconstruction of diverse verrucomicrobial genomes from metagenome datasets of freshwater reservoirs. Front. Microbiol. 8, 2131 (2017).PubMed 
PubMed Central 
Article 

Google Scholar 
80.He, S. et al. Ecophysiology of freshwater Verrucomicrobia inferred from metagenome-assembled genomes. MSphere 2, e00277 (2017).PubMed 
PubMed Central 
Article 

Google Scholar 
81.Sichert, A. et al. Verrucomicrobia use hundreds of enzymes to digest the algal polysaccharide fucoidan. Nat. Microbiol. 5, 1026–1039 (2020).CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
82.Shindo, K. et al. Diapolycopenedioic acid xylosyl esters A, B, and C, novel antioxidative glyco-C30-carotenoic acids produced by a new marine bacterium Rubritalea Squalenifaciens. J. Antibiot. (Tokyo) 61, 185–191 (2008).CAS 
Article 

Google Scholar 
83.Watson, S. W., Bock, E., Valois, F. W., Waterbury, J. B. & Schlosser, U. Nitrospira marina gen. nov sp nov: a chemolithotrophic nitrite-oxidizing bacterium. Arch. Microbiol. 144, 1–7 (1986).Article 

Google Scholar 
84.Daims, H. & Wagner, M. Nitrospira. Trends Microbiol. 26, 462–463 (2018).CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
85.Off, S., Alawi, M. & Spieck, E. Enrichment and physiological characterization of a novel nitrospira-like bacterium obtained from a marine sponge. Appl. Environ. Microbiol. 76, 4640–4646 (2010).CAS 
PubMed 
PubMed Central 
Article 
ADS 

Google Scholar 
86.Feng, G., Sun, W., Zhang, F., Karthik, L. & Li, Z. Inhabitancy of active Nitrosopumilus-like ammonia-oxidizing archaea and Nitrospira nitrite-oxidizing bacteria in the sponge Theonella swinhoei. Sci. Rep. 6, 1–11 (2016).CAS 
Article 

Google Scholar 
87.Andreo-Vidal, A., Sanchez-Amat, A. & Campillo-Brocal, J. C. The Pseudoalteromonas luteoviolacea L-amino acid oxidase with antimicrobial activity is a flavoenzyme. Mar. Drugs 16, 499 (2018).CAS 
PubMed Central 
Article 

Google Scholar 
88.Saccà, A., Guglielmo, L. & Bruni, V. Vertical and temporal microbial community patterns in a meromictic coastal lake influenced by the Straits of Messina upwelling system. Hydrobiologia 600, 89–104 (2008).Article 

Google Scholar 
89.Polese, G. et al. Meiofaunal assemblages of the bay of Nisida and the environmental status of the Phlegraean area (Naples, Southern Italy). Mar. Biodivers. 48, 127–137 (2018).Article 

Google Scholar 
90.Gambi, M. C., Tiberti, L. & Mannino, A. M. An update of marine alien species off the Ischia Island (Tyrrhenian Sea) with a closer look at neglected invasions of Lophocladia lallemandii (Rhodophyta). Not. Sibm 75, 58–65 (2019).
Google Scholar 
91.Hooper, J. N. A. ‘Sponguide’. Guide to sponge collection and identification. https://www.academia.edu/34258606/SPONGE_GUIDE_GUIDE_TO_SPONGE_COLLECTION_AND_IDENTIFICATION_Version_August_2000. (2000).92.Rützler, K. Sponges in coral reefs. in Coral reefs: Research methods, monographs on oceanographic methodology (eds. Stoddart, D. R. & Johannes, R. E.) 299–313 (Paris: Unesco, 1978).93.Medlin, L., Elwood, H. J., Stickel, S. & Sogin, M. L. The characterization of enzymatically amplified eukaryotic 16S-like rRNA-coding regions. Gene 71, 491–499 (1988).CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
94.Schmitt, S., Hentschel, U., Zea, S., Dandekar, T. & Wolf, M. ITS-2 and 18S rRNA gene phylogeny of Aplysinidae (Verongida, Demospongiae). J. Mol. Evol. 60, 327–336 (2005).CAS 
PubMed 
Article 
ADS 
PubMed Central 

Google Scholar 
95.Chombard, C., Boury-Esnault, N. & Tillier, S. Reassessment of homology of morphological characters in Tetractinellid sponges based on molecular data. Syst. Biol. 47, 351–366 (1998).CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
96.Collins, A. G. Phylogeny of medusozoa and the evolution of cnidarian life cycles. J. Evol. Biol. 15, 418–432 (2002).Article 

Google Scholar 
97.Dohrmann, M., Janussen, D., Reitner, J., Collins, A. G. & Wörheide, G. Phylogeny and evolution of glass sponges (Porifera, Hexactinellida). Syst. Biol. 57, 388–405 (2008).CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
98.Manuel, M. et al. Phylogeny and evolution of calcareous sponges: monophyly of calcinea and calcaronea, high level of morphological homoplasy, and the primitive nature of axial symmetry. Syst. Biol. 52, 311–333 (2003).PubMed 
Article 
PubMed Central 

Google Scholar 
99.Wörheide, G., Degnan, B., Hooper, J. & Reitner, J. Phylogeography and taxonomy of the Indo-Pacific reef cave dwelling coralline demosponge Astrosclera willeyana: new data from nuclear internal transcribed spacer sequences. Proc. 9th Int. Coral Reef Symp. 1, 339–346 (2002).100.Meyer, C. P., Geller, J. B. & Paulay, G. Fine scale endemism on coral reefs: Archipelagic differentiation in turbinid gastropods. Evolution 59, 113–125 (2005).PubMed 
Article 
PubMed Central 

Google Scholar 
101.Klindworth, A. et al. Evaluation of general 16S ribosomal RNA gene PCR primers for classical and next-generation sequencing-based diversity studies. Nucleic Acids Res. 41, e1 (2013).CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
102.Quast, C. et al. The SILVA ribosomal RNA gene database project: Improved data processing and web-based tools. Nucleic Acids Res. 41, 590–596 (2013).Article 
CAS 

Google Scholar 
103.Bolyen, E. et al. Reproducible, interactive, scalable and extensible microbiome data science using QIIME 2. Nat. Biotechnol. 37, 852–857 (2019).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
104.R Core Team. A language and environment for statistical computing. R Foundation for statistical computing, Vienna, Austria. (2020).105.Urbanek, S. & Horner, J. Cairo: R Graphics device using Cairo graphics library for creating high-quality bitmap (PNG, JPEG, TIFF), vector (PDF, SVG, PostScript) and display (X11 and Win32) output. R package version 1.5–12.2. https://cran.r-project.org/package=Cairo (2020).106.Chao, B. F. Interannual length-of-the-day variation with relation to the southern oscillation/El Nino. Geophys. Res. Lett. 11, 541–544 (1984).Article 
ADS 

Google Scholar 
107.Shannon, C. E. A mathematical theory of communication. Bell Syst. Tech. J. 27(379–423), 623–656 (1948).MathSciNet 
MATH 
Article 

Google Scholar 
108.Shannon, C. E. & Weaver, W. The Mathematical Theory of Communication (University of Illinois Press, 1949).MATH 

Google Scholar 
109.Simpson, E. H. Measurment of diversity. Nature 163, 688 (1949).MATH 
Article 
ADS 

Google Scholar  More

1.Sachs, J. L., Mueller, U. G., Wilcox, T. P. & Bull, J. J. The evolution of cooperation. Q. Rev. Biol. 79, 135–160 (2004).PubMed 
Article 
PubMed Central 

Google Scholar 
2.MeloClavijo, J., Donath, A., Serôdio, J. & Christa, G. Polymorphic adaptations in metazoans to establish and maintain photosymbioses. Biol. Rev. 93, 2006–2020 (2018).Article 

Google Scholar 
3.Wernegreen, J. J. Endosymbiosis. Curr. Biol. 22, 555–561 (2012).Article 
CAS 

Google Scholar 
4.Heijtz, R. D. et al. Normal gut microbiota modulates brain development and behavior. Proc. Natl. Acad. Sci. USA 108, 3047–3052 (2011).ADS 
CAS 
PubMed Central 
Article 

Google Scholar 
5.Schmidt, T. S. B., Raes, J. & Bork, P. The human gut microbiome: From association to modulation. Cell 172, 1198–1215 (2018).CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
6.Morgan, X. C. et al. Associations between host gene expression, the mucosal microbiome, and clinical outcome in the pelvic pouch of patients with inflammatory bowel disease. Genome Biol. 16, 67 (2015).PubMed 
PubMed Central 
Article 
CAS 

Google Scholar 
7.Tromas, A. et al. Heart of endosymbioses: Transcriptomics reveals a conserved genetic program among arbuscular mycorrhizal, actinorhizal and legume-rhizobial symbioses. PLoS One 7, 1–7 (2012).
Google Scholar 
8.Chun, C. K. et al. An annotated cDNA library of juvenile Euprymna scolopes with and without colonization by the symbiont Vibrio fischeri. BMC Genom. 7, 1–10 (2006).MathSciNet 
Article 
CAS 

Google Scholar 
9.Sørensen, M. E. S. et al. Comparison of independent evolutionary origins reveals both convergence and divergence in the metabolic mechanisms of symbiosis. Curr. Biol. 30, 328-334.e4 (2020).PubMed 
Article 
CAS 
PubMed Central 

Google Scholar 
10.LaJeunesse, T. C. et al. Systematic revision of Symbiodiniaceae highlights the antiquity and diversity of coral endosymbionts. Curr. Biol. 28, 2570–2580 (2018).CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
11.Muscatine, L. R., McCloskey, L. & Marian, E. R. Estimating the daily contribution of carbon from zooxanthellae to coral animal respiration. Limnol. Oceanogr. 26, 601–611 (1981).ADS 
CAS 
Article 

Google Scholar 
12.Anthony, K. R. N., Hoogenboom, M. O., Maynard, J. A., Grottoli, A. G. & Middlebrook, R. Energetics approach to predicting mortality risk from environmental stress: A case study of coral bleaching. Funct. Ecol. 23, 539–550 (2009).Article 

Google Scholar 
13.De’ath, G., Fabricius, K. E., Sweatman, H. & Puotinen, M. The 27-year decline of coral cover on the Great Barrier Reef and its causes. Proc. Natl. Acad. Sci. 109, 17995–17999 (2012).ADS 
PubMed 
PubMed Central 
Article 

Google Scholar 
14.Hughes, T. P. et al. Spatial and temporal patterns of mass bleaching of corals in the Anthropocene. Science 359, 80–83 (2018).ADS 
CAS 
Article 

Google Scholar 
15.Davies, S. W., Marchetti, A., Ries, J. B. & Castillo, K. D. Thermal and pCO2 stress elicit divergent transcriptomic responses in a resilient coral. Front. Mar. Sci. 3, 1–15 (2016).Article 

Google Scholar 
16.DeSalvo, M. K., Estrada, A., Sunagawa, S. & Medina, M. Transcriptomic responses to darkness stress point to common coral bleaching mechanisms. Coral Reefs 31, 215–228 (2011).ADS 
Article 

Google Scholar 
17.González-Pech, R. A., Vargas, S., Francis, W. R. & Wörheide, G. Transcriptomic resilience of the Montipora digitata holobiont to low pH. Front. Mar. Sci. 4, 1–9 (2017).Article 

Google Scholar 
18.Rubin, E. T. et al. Molecular mechanisms of coral persistence within highly urbanized locations in the Port of Miami, Florida. Front. Mar. Sci. 8, 8695236 (2021).Article 

Google Scholar 
19.Hawkins, T. D., Krueger, T., Wilkinson, S. P., Fisher, P. L. & Davy, S. K. Antioxidant responses to heat and light stress differ with habitat in a common reef coral. Coral Reefs 34, 1229–1241 (2015).ADS 
Article 

Google Scholar 
20.Agostini, S., Fujimura, H., Hayashi, H. & Fujita, K. Mitochondrial electron transport activity and metabolism of experimentally bleached hermatypic corals. J. Exp. Mar. Biol. Ecol. 475, 100–107 (2016).CAS 
Article 

Google Scholar 
21.Gardner, S. G. et al. Dismutase and glutathione as stress response indicators in three corals under short-term hyposalinity stress. Proc. R. Soc. B 283, 20152418 (2016).PubMed 
PubMed Central 
Article 
CAS 

Google Scholar 
22.Kenkel, C. & Matz, M. V. Gene expression plasticity as a mechanism of adaptation to a variable environment. Nat. Ecol. Evol. 1, 0014 (2016).Article 

Google Scholar 
23.Barshis, D. J. et al. Genomic basis for coral resilience to climate change. Proc. Natl. Acad. Sci. 110, 1387–1392 (2013).ADS 
CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
24.Hashimoto, K., Shibuno, T., Murayama-Kayano, E., Tanaka, H. & Kayano, T. Isolation and characterization of stress-responsive genes from the scleractinian coral Pocillopora damicornis. Coral Reefs 23, 485–491 (2004).
Google Scholar 
25.Rosic, N. N., Pernice, M., Dove, S., Dunn, S. & Hoegh-Guldberg, O. Gene expression profiles of cytosolic heat shock proteins Hsp70 and Hsp90 from symbiotic dinoflagellates in response to thermal stress: Possible implications for coral bleaching. Cell Stress Chaperones 16, 69–80 (2011).CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
26.Meyer, E., Aglyamova, G. V. & Matz, M. V. Profiling gene expression responses of coral larvae (Acropora millepora) to elevated temperature and settlement inducers using a novel RNA-Seq procedure. Mol. Ecol. 20, 3599–3616 (2011).CAS 
PubMed 
PubMed Central 

Google Scholar 
27.Mansour, T. A., Rosenthal, J. J. C., Brown, C. T. & Roberson, L. M. Transcriptome of the Caribbean stony coral Porites astreoides from three developmental stages. GigaScience 5, 33 (2016).PubMed 
PubMed Central 
Article 
CAS 

Google Scholar 
28.Polato, N. R., Altman, N. S. & Baums, I. B. Variation in the transcriptional response of threatened coral larvae to elevated temperatures. Mol. Ecol. 22, 1366–1382 (2013).CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
29.Paxton, C. W., Davy, S. K. & Weis, V. M. Stress and death of cnidarian host cells play a role in cnidarian bleaching. J. Exp. Biol. 216, 2813–2820 (2013).PubMed 
PubMed Central 

Google Scholar 
30.Matthews, J. L. et al. Optimal nutrient exchange and immune responses operate in partner specificity in the cnidarian-dinoflagellate symbiosis. Proc. Natl. Acad. Sci. USA 114, 13194–13199 (2017).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
31.Jacobovitz, M. R. et al. Dinoflagellate symbionts escape vomocytosis by host cell immune suppression. Nat. Microbiol. 6, 769–782 (2021).CAS 
PubMed 
Article 

Google Scholar 
32.Mitchelmore, C. L., Ringwood, A. H. & Weis, V. M. Differential accumulation of cadmium and changes in glutathione levels as a function of symbiotic state in the sea anemone Anthopleura elegantissima. J. Exp. Mar. Biol. Ecol. 284, 71–85 (2003).CAS 
Article 

Google Scholar 
33.Dunn, S. R., Pernice, M., Green, K., Hoegh-Guldberg, O. & Dove, S. G. Thermal stress promotes host mitochondrial degradation in symbiotic cnidarians: Are the batteries of the reef going to run out?. PLoS One 7, 25 (2012).
Google Scholar 
34.Davy, S. K., Allemand, D. & Weis, V. M. Cell biology of cnidarian-dinoflagellate symbiosis. Microbiol. Mol. Biol. Rev. 76, 229–261 (2012).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
35.Tivey, T. R., Parkinson, J. E. & Weis, V. M. Host and symbiont cell cycle coordination is mediated by symbiotic state, nutrition, and partner identity in a model cnidarian-dinoflagellate symbiosis. MBio 11, 25 (2020).Article 

Google Scholar 
36.Tivey, T. R. et al. N-linked surface glycan biosynthesis, composition, inhibition, and function in cnidarian-dinoflagellate symbiosis. Mircobial Ecol. 80, 223–236 (2020).CAS 
Article 

Google Scholar 
37.Parkinson, J. E. et al. Subtle differences in symbiont cell surface glycan profiles do not explain species-specific colonization rates in a model cnidarian-algal symbiosis. Front. Microbiol. 9, 842 (2018).PubMed 
PubMed Central 
Article 

Google Scholar 
38.Mansfield, K. M. et al. Transcription factor NF-κB is modulated by symbiotic status in a sea anemone model of cnidarian bleaching. Sci. Rep. 7, 1–14 (2017).CAS 
Article 

Google Scholar 
39.Madin, J. S. et al. The Coral Trait Database, a curated database of trait information for coral species from the global oceans. Sci. Data 3, 160017 (2016).PubMed 
PubMed Central 
Article 

Google Scholar 
40.Dimond, J. & Carrington, E. Temporal variation in the symbiosis and growth of the temperate scleractinian coral Astrangia poculata. Mar. Ecol. Prog. Ser. 348, 161–172 (2007).ADS 
CAS 
Article 

Google Scholar 
41.Sharp, K. H., Pratte, Z. A., Kerwin, A. H., Rotjan, R. D. & Stewart, F. J. Season, but not symbiont state, drives microbiome structure in the temperate coral Astrangia poculata. Microbiome 5, 120 (2017).PubMed 
PubMed Central 
Article 

Google Scholar 
42.Dimond, J. & Carrington, E. Symbiosis regulation in a facultatively symbiotic temperate coral: Zooxanthellae division and expulsion. Coral Reefs 27, 601–604 (2008).ADS 
Article 

Google Scholar 
43.Burmester, E. M., Finnerty, J. R., Kaufman, L. & Rotjan, R. D. Temperature and symbiosis affect lesion recovery in experimentally wounded, facultatively symbiotic temperate corals. Mar. Ecol. Prog. Ser. 570, 87–99 (2017).ADS 
Article 

Google Scholar 
44.Wuitchik, D. M. et al. Characterizing environmental stress responses of aposymbiotic Astrangia poculata to divergent thermal challenges. Mol. Ecol. https://doi.org/10.1111/mec.16108 (2021).Article 
PubMed 

Google Scholar 
45.Schoepf, V. et al. Coral energy reserves and calcification in a high-CO2 world at two temperatures. PloS One 8, e75049 (2013).ADS 
CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
46.Lajeunesse, T. C., Parkinson, J. E. & Reimer, J. D. A genetics-based description of Symbiodinium minutum sp. Nov. and S. psygmophilum sp. Nov. (dinophyceae), two dinoflagellates symbiotic with cnidaria. J. Phycol. 48, 1380–1391 (2012).PubMed 
Article 

Google Scholar 
47.Grabherr, M. G. et al. Full-length transcriptome assembly from RNA-Seq data without a reference genome. Nat. Biotechnol. 29, 644–652 (2011).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
48.Li, W. & Godzik, A. Cd-hit: A fast program for clustering and comparing large sets of protein or nucleotide sequences. Bioinformatics 22, 1658–1659 (2006).CAS 
PubMed 
Article 

Google Scholar 
49.Pimentel, H., Bray, N. L., Puente, S., Melsted, P. & Pachter, L. Differential analysis of RNA-seq incorporating quantification uncertainty. Nat. Methods 14, 687–690 (2017).CAS 
PubMed 
Article 

Google Scholar 
50.Harrison, P. L. Sexual reproduction of scleractinian corals. In Coral Reefs: An Ecosystem in Transition (eds Dubinsky, Z. & Stambler, N.) 59–85 (Springer, 2011).Chapter 

Google Scholar 
51.Langmead, B. & Salzberg, S. L. Fast gapped-read alignment with Bowtie 2. Nat. Methods 9, 357–359 (2012).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
52.Li, H. et al. The sequence alignment/map format and SAMtools. Bioinformatics 25, 2078–2079 (2009).PubMed 
PubMed Central 
Article 
CAS 

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

Google Scholar 
54.Jombart, T. & Ahmed, I. adegenet 1.3–1: New tools for the analysis of genome-wide SNP data. Bioinformatics 27, 3070–3071 (2011).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
55.Paradis, E., Claude, J. & Strimmer, K. APE: Analyses of phylogenetics and evolution in R language. Bioinformatics 20, 289–290 (2004).CAS 
PubMed 
Article 

Google Scholar 
56.Kauffmann, A., Gentleman, R. & Huber, W. arrayQualityMetrics—a bioconductor package for quality assessment of microarray data. Bioinformatics 25, 415–416 (2009).CAS 
PubMed 
Article 

Google Scholar 
57.Love, M. I., Huber, W. & Anders, S. Moderated estimation of fold change and dispersion for RNA-seq data with DESeq2. Genome Biol. 15, 1–21 (2014).Article 
CAS 

Google Scholar 
58.Oksanen, J. et al. vegan: Community Ecology Package. (2020).59.Kolde, R. pheatmap: Pretty Heatmaps. (2019).60.Wright, R. M., Aglyamova, G. V., Meyer, E. & Matz, M. V. Gene expression associated with white syndromes in a reef building coral, Acropora hyacinthus. BMC Genom. 16, 1–12 (2015).CAS 
Article 

Google Scholar 
61.Cui, G. et al. Host-dependent nitrogen recycling as a mechanism of symbiont control in Aiptasia. PLoS Genet. 15, 1–19 (2019).CAS 
Article 

Google Scholar 
62.Burns, J. A., Zhang, H., Hill, E., Kim, E. & Kerney, R. Transcriptome analysis illuminates the nature of the intracellular interaction in a vertebrate-algal symbiosis. Elife 6, 1–32 (2017).Article 

Google Scholar 
63.Kanehisa, M., Sato, Y., Kawashima, M., Furumichi, M. & Tanabe, M. KEGG as a reference resource for gene and protein annotation. Nucleic Acids Res. 44, D457–D462 (2016).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
64.Dixon, G. B. et al. Genomic determinants of coral heat tolerance across latitudes. Science 348, 1460–1462 (2015).ADS 
CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
65.Shinzato, C., Inoue, M. & Kusakabe, M. A snapshot of a coral “Holobiont”: A transcriptome assembly of the scleractinian coral, porites, captures a wide variety of genes from both the host and symbiotic zooxanthellae. PLoS One 9, e85182 (2014).ADS 
PubMed 
PubMed Central 
Article 
CAS 

Google Scholar 
66.Maor-Landaw, K., van Oppen, M. J. H. & McFadden, G. I. Symbiotic lifestyle triggers drastic changes in the gene expression of the algal endosymbiont Breviolum minutum (Symbiodiniaceae). Ecol. Evol. https://doi.org/10.1002/ece3.5910 (2019).Article 
PubMed 
PubMed Central 

Google Scholar 
67.Davies, S. W. Understanding Coral Dispersal. PhD Thesis, The University of Texas at Austin (2014).68.Simona, F., Zhang, H. & Voolstra, C. R. Evidence for a role of protein phosphorylation in the maintenance of the cnidarian–algal symbiosis. Mol. Ecol. 28, 5373–5386 (2019).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
69.Xiang, T. et al. Symbiont population control by host-symbiont metabolic interaction in Symbiodiniaceae-cnidarian associations. Nat. Commun. 11, 1–9 (2020).ADS 
CAS 

Google Scholar 
70.Bernard, S. M. & Habash, D. Z. The importance of cytosolic glutamine synthetase in nitrogen assimilation and recycling. New Phytol. 182, 608–620 (2009).CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
71.Konishi, N. et al. Contributions of two cytosolic glutamine synthetase isozymes to ammonium assimilation in Arabidopsis roots. J. Exp. Bot. 68, 613–625 (2017).CAS 
PubMed 
PubMed Central 

Google Scholar 
72.Lee, R. W., Robinson, J. J. & Cavanaugh, C. M. Pathways of inorganic nitrogen assimilation in chemoautotrophic bacteria-marine invertebrate symbioses: Expression of host and symbiont glutamine synthetase. J. Exp. Biol. 202, 289–300 (1999).CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
73.Kim, D., Minhas, B. F., Li-Byarlay, H. & Hansen, A. K. Key transport and ammonia recycling genes involved in aphid symbiosis respond to host–plant specialization. Genes Genomes Genet. 8, 2433–2443 (2018).CAS 

Google Scholar 
74.Lin, M. F., Takahashi, S., Forêt, S., Davy, S. K. & Miller, D. J. Transcriptomic analyses highlight the likely metabolic consequences of colonization of a cnidarian host by native or non-native Symbiodinium species. Biology Open 8, 1–11 (2019).
Google Scholar 
75.Su, Y., Zhou, Z. & Yu, X. Possible roles of glutamine synthetase in responding to environmental changes in a scleractinian coral. Mol. Biol. Rep. 45, 2115–2124 (2018).CAS 
PubMed 
Article 

Google Scholar 
76.Hamada, M. et al. Metabolic co-dependence drives the evolutionarily ancient Hydra-Chlorella symbiosis. Elife 7, 1–37 (2018).Article 

Google Scholar 
77.Hall, C. et al. Freshwater sponge hosts and their green algae symbionts: A tractable model to understand intracellular symbiosis. PeerJ 9, 1–28 (2021).
Google Scholar 
78.Mao, M. & Bennett, G. M. Symbiont replacements reset the co-evolutionary relationship between insects and their heritable bacteria. ISME J. 14, 1384–1395 (2020).PubMed 
PubMed Central 
Article 

Google Scholar 
79.Fam, R. R. S. et al. Molecular characterization of a novel algal glutamine synthetase (GS) and an algal glutamate synthase (GOGAT) from the colorful outer mantle of the giant clam, Tridacna squamosa, and the putative GS-GOGAT cycle in its symbiotic zooxanthellae. Gene 656, 40–52 (2018).CAS 
PubMed 
Article 

Google Scholar 
80.Gates, R. D., Hoegh-Guldberg, O., McFall-Ngai, M. J., Bil, K. Y. & Muscatine, L. Free amino acids exhibit anthozoan “host factor” activity: They induce the release of photosynthate from symbiotic dinoflagellates in vitro. Proc. Natl. Acad. Sci. 92, 7430–7434 (1995).ADS 
CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
81.Perland, E., Bagchi, S., Klaesson, A. & Fredriksson, R. Characteristics of 29 novel atypical solute carriers of major facilitator superfamily type: Evolutionary conservation, predicted structure and neuronal co-expression. Open Biol. 7, 25 (2017).Article 
CAS 

Google Scholar 
82.Kenkel, C. D., Meyer, E. & Matz, M. V. Gene expression under chronic heat stress in populations of the mustard hill coral (Porites astreoides) from different thermal environments. Mol. Ecol. 22, 4322–4334 (2013).CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
83.Yuyama, I., Ishikawa, M., Nozawa, M., Yoshida, M. & Ikeo, K. Transcriptomic changes with increasing algal symbiont reveal the detailed process underlying establishment of coral-algal symbiosis. Sci. Rep. 8, 1–11 (2018).CAS 
Article 

Google Scholar 
84.Rodriguez-Lanetty, M., Phillips, W. S. & Weis, V. M. Transcriptome analysis of a cnidarian-dinoflagellate mutualism reveals complex modulation of host gene expression. BMC Genom. 7, 1–11 (2006).Article 
CAS 

Google Scholar 
85.Xu, X. et al. Specific structure and unique function define the hemicentin. Cell Biosci. 3, 27 (2013).PubMed 
PubMed Central 
Article 
CAS 

Google Scholar 
86.Smith, T. E. & Moran, N. A. Coordination of host and symbiont gene expression reveals a metabolic tug-of-war between aphids and Buchnera. Proc. Natl. Acad. Sci. USA 117, 2113–2121 (2020).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
87.Sun, Y. et al. Signaling pathway of MAPK/ERK in cell proliferation, differentiation, migration, senescence and apoptosis. J. Recept. Signal Transduct. 35, 600–604 (2015).CAS 
Article 

Google Scholar 
88.Lisovsky, M., Itoh, K. & Sokol, S. Y. Frizzled receptors activate a novel JNK-dependent pathway that may lead to apoptosis. Curr. Biol. 12, 53–58 (2002).CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
89.Jiang, X. & Wang, X. Cytochrome c-mediated apoptosis. Annu. Rev. Biochem. 73, 87–106 (2004).CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
90.Detournay, O. & Weis, V. M. Role of the sphingosine rheostat in the regulation of cnidarian-dinoflagellate symbioses. Biol. Bull. 221, 261–269 (2011).CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
91.Wolfowicz, I. et al. Aiptasia sp. larvae as a model to reveal mechanisms of symbiont selection in cnidarians. Sci. Rep. 6, 1–12 (2016).Article 
CAS 

Google Scholar 
92.Weis, V. M. Cell biology of coral symbiosis: Foundational study can inform solutions to the coral reef crisis. Integr. Comp. Biol. 59, 845–855 (2019).CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
93.Mansfield, K. M. et al. Varied effects of algal symbionts on transcription factor NF-kB in a sea anemone and a coral: Possible roles in symbiosis and thermotolerance. bioRxiv 5444, 25 (2019).
Google Scholar 
94.Zuliani-Alvarez, L. et al. Mapping tenascin-C interaction with toll-like receptor 4 reveals a new subset of endogenous inflammatory triggers. Nat. Commun. 8, 25 (2017).Article 
CAS 

Google Scholar 
95.Piccinini, A. M. & Midwood, K. S. Endogenous control of immunity against infection: Tenascin-C regulates TLR4-mediated inflammation via microRNA-155. Cell Rep. 2, 914–926 (2012).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
96.Thompson, J. R., Rivera, H. E., Closek, C. J. & Medina, M. Microbes in the coral holobiont: Partners through evolution, development, and ecological interactions. Front. Cell. Infect. Microbiol. 4, 1–20 (2014).
Google Scholar 
97.Maynard, J. et al. Projections of climate conditions that increase coral disease susceptibility and pathogen abundance and virulence. Nat. Clim. Change 5, 688–694 (2015).ADS 
Article 

Google Scholar 
98.Alvarez-Filip, L., Estrada-Saldívar, N., Pérez-Cervantes, E., Molina-Hernández, A. & González-Barrios, F. J. A rapid spread of the stony coral tissue loss disease outbreak in the Mexican Caribbean. PeerJ 2019, 25 (2019).
Google Scholar 
99.Walton, C. J., Hayes, N. K. & Gilliam, D. S. Impacts of a regional, multi-year, multi-species coral disease outbreak in Southeast Florida. Front. Mar. Sci. 5, 1–14 (2018).Article 

Google Scholar 
100.Weil, E., Hernández-Delgado, E. A., Gonzalez, M., Williams, S. & Figuerola, M. Spread of the new coral disease “SCTLD” into the Caribbean: Implications for Puerto Rico. Reef Encounter 34, 38–43 (2019).
Google Scholar 
101.Rippe, J. P., Kriefall, N. G., Davies, S. W. & Castillo, K. D. Differential disease incidence and mortality of inner and outer reef corals of the upper Florida Keys in association with a white syndrome outbreak. Bull. Mar. Sci. 95, 305–316 (2019).Article 

Google Scholar 
102.DeFilippo, L., Burmester, E. M., Kaufman, L. & Rotjan, R. D. Patterns of surface lesion recovery in the Northern Star Coral, Astrangia poculata. J. Exp. Mar. Biol. Ecol. 481, 15–24 (2016).Article 

Google Scholar 
103.Leydet, K. P. & Hellberg, M. E. The invasive coral Oculina patagonica has not been recently introduced to the Mediterranean from the western Atlantic. BMC Evol. Biol. 15, 79 (2015).PubMed 
PubMed Central 
Article 

Google Scholar 
104.Leydet, K. P. & Hellberg, M. E. Discordant coral–symbiont structuring: Factors shaping geographical variation of Symbiodinium communities in a facultative zooxanthellate coral genus, Oculina. Coral Reefs 35, 583–595 (2016).ADS 
Article 

Google Scholar  More

1.IPCC, Working Group I Contribution to the IPCC Fifth Assessment Report, Climate Change 2013: The Physical Science Basis, AR5. 2013.2.IPCC, Working Group I Report ‘The Physical Science Basis,’ PCC Fourth Assessment Report. 2007.3.IPCC, “IPCC Fourth Assessment Report (AR4),” IPCC, 1, 976, 2007.4.Guerra, C. A. et al. Blind spots in global soil biodiversity and ecosystem function research. Nature Communications 11, 1–13 (2020).Article 
CAS 

Google Scholar 
5.Nagy, K., Ábrahám, Á., Keymer, J. E. & Galajda, P. Application of microfluidics in experimental ecology: the importance of being spatial. Front. Microbiol. 9, 496 (2018).6.Tecon, R. & Or, D. Biophysical processes supporting the diversity of microbial life in soil. FEMS Microbiol. Rev. 41, 599–623 (2017).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
7.Falkowski, P. G., Fenchel, T. & Delong, E. F. The microbial engines that drive earth’s biogeochemical cycles. Science. https://doi.org/10.1126/science.1153213 (2008).8.Hobbie, J. E. & Hobbie, E. A. Microbes in nature are limited by carbon and energy: the starving-survival lifestyle in soil and consequences for estimating microbial rates. Front. Microbiol. 4, 1–11 (2013). no. NOV.Article 

Google Scholar 
9.Hill, P. W., Farrar, J. F. & Jones, D. L. Decoupling of microbial glucose uptake and mineralization in soil. Soil Biol. Biochem. https://doi.org/10.1016/j.soilbio.2007.09.008 (2008).10.IPCC. Climate change 2007: the physical science basis. 2007, https://doi.org/10.1260/095830507781076194.11.Baveye, P. C. et al. Emergent properties of microbial activity in heterogeneous soil microenvironments: different research approaches are slowly converging, yet major challenges remain. Front. Microbiol. https://doi.org/10.3389/fmicb.2018.01929 (2018).12.Bruand, A. & Cousin, I. Variation of textural porosity of a clay‐loam soil during compaction. Eur. J. Soil Sci. https://doi.org/10.1111/j.1365-2389.1995.tb01334.x (1995).13.Cnudde, V. & Boone, M. N. High-resolution X-ray computed tomography in geosciences: a review of the current technology and applications. Earth-Science Rev. https://doi.org/10.1016/j.earscirev.2013.04.003 (2013).14.Pagliai, M., Vignozzi, N. & Pellegrini, S. Soil structure and the effect of management practices. https://doi.org/10.1016/j.still.2004.07.002 (2004).15.Pires, L. F., Bacchi, O. O. S., Reichardt, K. & Timm, L. C. Application of γ-ray computed tomography to analysis of soil structure before density evaluations. Appl. Radiat. Isot. https://doi.org/10.1016/j.apradiso.2005.03.019 (2005).16.Larsbo, M., Koestel, J., Kätterer, T. & Jarvis, N. Preferential transport in macropores is reduced by soil organic carbon. Vadose Zone J. https://doi.org/10.2136/vzj2016.03.0021 (2016).17.Ananyeva, K., Wang, W., Smucker, A. J. M., Rivers, M. L. & Kravchenko, A. N. Can intra-aggregate pore structures affect the aggregate’s effectiveness in protecting carbon? Soil Biol. Biochem. https://doi.org/10.1016/j.soilbio.2012.10.019 (2013).18.Toosi, E. R., Kravchenko, A. N., Mao, J., Quigley, M. Y. & Rivers, M. L. Effects of management and pore characteristics on organic matter composition of macroaggregates: evidence from characterization of organic matter and imaging. Eur. J. Soil Sci. https://doi.org/10.1111/ejss.12411 (2017).19.Katuwal, S. et al. Linking air and water transport in intact soils to macropore characteristics inferred from X-ray computed tomography. Geoderma. https://doi.org/10.1016/j.geoderma.2014.08.006 (2015).20.Negassa, W. C. et al. Properties of soil pore space regulate pathways of plant residue decomposition and community structure of associated bacteria. PLoS ONE https://doi.org/10.1371/journal.pone.0123999 (2015).21.Rabot, E., Wiesmeier, M., Schlüter, S. & Vogel, H. J. Soil structure as an indicator of soil functions: a review. Geoderma. https://doi.org/10.1016/j.geoderma.2017.11.009 (2018).22.Pronk, G. J. et al. Interaction of minerals, organic matter, and microorganisms during biogeochemical interface formation as shown by a series of artificial soil experiments. Biol. Fertil. Soils. https://doi.org/10.1007/s00374-016-1161-1 (2017).23.Downie, H. et al. Transparent Soil for Imaging the Rhizosphere. PLoS ONE. https://doi.org/10.1371/journal.pone.0044276 (2012).24.Aleklett, K. et al. Build your own soil: exploring microfluidics to create microbial habitat structures. ISME J. 12, 312–319 (2018).PubMed 
Article 
PubMed Central 

Google Scholar 
25.Beebe, D. J., Mensing, G. A. & Walker, G. M. Physics and Applications of Microfluidics in Biology. Annu. Rev. Biomed. Eng. https://doi.org/10.1146/annurev.bioeng.4.112601.125916 (2002).26.Ahmed, T., Shimizu, T. S. & Stocker, R. Microfluidics for bacterial chemotaxis. Integr. Biol. https://doi.org/10.1039/c0ib00049c (2010).27.Ahmed, T. & Stocker, R. Experimental verification of the behavioral foundation of bacterial transport parameters using microfluidics. Biophys. J. https://doi.org/10.1529/biophysj.108.134510 (2008).28.Mao, H., Cremer, P. S. & Manson, M. D. A sensitive, versatile microfluidic assay for bacterial chemotaxis. Proc. Natl Acad. Sci. https://doi.org/10.1073/pnas.0931258100 (2003).29.Saragosti, J. et al. Directional persistence of chemotactic bacteria in a traveling concentration wave. Proc. Natl Acad. Sci. https://doi.org/10.1073/pnas.1101996108 (2011).30.Deng, J. et al. Synergistic effects of soil microstructure and bacterial EPS on drying rate in emulated soil micromodels. Soil Biol. Biochem. 83, 116–124 (2015).CAS 
Article 

Google Scholar 
31.Rubinstein, R. L., Kadilak, A. L., Cousens, V. C., Gage, D. J. & Shor, L. M. Protist-facilitated particle transport using emulated soil micromodels. Environ. Sci. Technol. 49, 1384–1391 (2015).CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
32.Stanley, C. E. et al. Probing bacterial-fungal interactions at the single cell level. Integr. Biol. 6, 935–945 (2014).CAS 
Article 

Google Scholar 
33.Aleklett, K., Ohlsson, P., Bengtsson, M. & Hammer, E. C. Fungal foraging behaviour and hyphal space exploration in micro-structured Soil Chips. ISME J. https://doi.org/10.1038/s41396-020-00886-7 (2021).34.Mafla-Endara, P. M. et al. Microfluidic chips provide visual access to in situ soil ecology. Commun. Biol. 4, 889 (2021).35.Falconer, R., Houston, A., Otten, W. & Baveye, P. Emergent behavior of soil fungal dynamics: influence of soil architecture and water distribution. Soil Sci. 177, 111–119 (2012).CAS 
Article 

Google Scholar 
36.Duffy, K. J. & Ford, R. M. Turn angle and run time distributions characterize swimming behavior for Pseudomonas putida. J. Bacteriol. 179, 1428–1430 (1997).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
37.Rashid, S. et al. Adjustment in tumbling rates improves bacterial chemotaxis on obstacle-laden terrains. Proc. Natl Acad Sci USA 116, 11770–11775 (2019).38.Duffy, K. J., Cummings, P. T. & Ford, R. M. Random walk calculations for bacterial migration in porous media. Biophys. J. 68, 800–806 (1995).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
39.Shum, H. & Gaffney, E. A. Hydrodynamic analysis of flagellated bacteria swimming in corners of rectangular channels. Phys. Rev. E 92, 1–11 (2015).Article 
CAS 

Google Scholar 
40.Guadayol, Ò., Thornton, K. L. & Humphries, S. Cell morphology governs directional control in swimming bacteria. Sci. Rep. https://doi.org/10.1038/s41598-017-01565-y (2017).41.Essig, A. et al. Copsin, a novel peptide-based fungal antibiotic interfering with the peptidoglycan synthesis. J. Biol. Chem. 289, 34953–34964 (2014).PubMed 
PubMed Central 
Article 
CAS 

Google Scholar 
42.Dixon, E. F. & Hall, R. A. Noisy neighbourhoods: quorum sensing in fungal-polymicrobial infections. Cell. Microbiol. 17, 1431–1441 (2015).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
43.Banitz, T. et al. Assessing biodegradation benefits from dispersal networks. Ecol. Model. 222, 2552–2560 (2011).Article 

Google Scholar 
44.Furuno, S. et al. Fungal mycelia allow chemotactic dispersal of polycyclic aromatic hydrocarbon-degrading bacteria in water-unsaturated systems. Environ. Microbiol. 12, 1391–1398 (2010).CAS 
PubMed 
PubMed Central 

Google Scholar 
45.Kohlmeier, S. et al. Taking the fungal highway: mobilization of pollutant-degrading bacteria by fungi. Environ. Sci. Technol. 39, 4640–4646 (2005).CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
46.Held, M., Kaspar, O., Edwards, C. & Nicolau, D. V. Intracellular mechanisms of fungal space searching in microenvironments. Proc. Natl Acad. Sci. USA 116, 13543–13552 (2019).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
47.Soufan, R. et al. Pore-scale monitoring of the effect of microarchitecture on fungal growth in a two-dimensional soil-like micromodel. Front. Environ. Sci. https://doi.org/10.3389/fenvs.2018.00068 (2018).48.Hanson, K. L. et al. Fungi use efficient algorithms for the exploration of microfluidic networks. Small 2, 1212–1220 (2006).CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
49.Pajor, R., Falconer, R., Hapca, S. & Otten, W. Modelling and quantifying the effect of heterogeneity in soil physical conditions on fungal growth. Biogeosciences 7, 3731–3740 (2010).Article 

Google Scholar 
50.Varma, A., Abbott, L., Werner, D. & Hampp, R. Plant Surface Microbiology (Springer, 2008)..51.Lew, R. R. How does a hypha grow? The biophysics of pressurized growth in fungi. Nat. Rev. Microbiol. 9, 509–518 (2011).CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
52.Tayagui, A., Sun, Y., Collings, D. A., Garrill, A. & Nock, V. An elastomeric micropillar platform for the study of protrusive forces in hyphal invasion. Lab a Chip 17, 3643–3653 (2017).CAS 
Article 

Google Scholar 
53.Bardgett, R. D. & McAlister, E. The measurement of soil fungal:bacterial biomass ratios as an indicator of ecosystem self-regulation in temperate meadow grasslands. Biol. Fertil. Soils 29, 282–290 (1999).Article 

Google Scholar 
54.De Deyn, G. B., Cornelissen, J. H. C. & Bardgett, R. D. Plant functional traits and soil carbon sequestration in contrasting biomes. Ecol. Lett. 11, 516–531 (2008).PubMed 
Article 

Google Scholar 
55.Kuijper, L. D. J., Berg, M. P., Morriën, E., Kooi, B. W. & Verhoef, H. A. Global change effects on a mechanistic decomposer food web model. Glob. Change Biol. 11, 249–265 (2005).Article 

Google Scholar 
56.Falconer, R. E. et al. Microscale heterogeneity explains experimental variability and non-linearity in soil organic matter mineralisation. PLoS ONE 10, e0123774 (2015).PubMed 
PubMed Central 
Article 
CAS 

Google Scholar 
57.Deveau, A. et al. Bacterial-fungal interactions: ecology, mechanisms and challenges. FEMS Microbiol. Rev. 42, 335–352 (2018).CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
58.Postma, J. & van Veen, J.A. Habitable pore space and survival of Rhizobium leguminosarum biovar trifolii introduced into soil. Microb. Ecol. 19, 149–161 (1990).59.Grundmann, G. L. Spatial scales of soil bacterial diversity—the size of a clone. FEMS Microbiol. Ecol. 48, 119–127 (2004).CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
60.Kuzyakov, Y. & Blagodatskaya, E. Microbial hotspots and hot moments in soil: concept & review. Soil Biol. Biochem. 83, 184–199 (2015).CAS 
Article 

Google Scholar 
61.Kim, D. S. & Fogler, H. S. Biomass evolution in porous media and its effects on permeability under starvation conditions. Biotechnol. Bioeng. 69, 47–56 (2000).CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
62.Dupin, H. J. & McCarty, P. L. Mesoscale and microscale observations of biological growth in a silicon pore imaging element. Environ. Sci. Technol. 33, 1230–1236 (1999).CAS 
Article 

Google Scholar 
63.Aufrecht, J. A. et al. Pore-scale hydrodynamics influence the spatial evolution of bacterial biofilms in a microfluidic porous network. PLoS ONE 14, 1–17 (2019).Article 
CAS 

Google Scholar 
64.Vervoort, R. W. & Cattle, S. R. Linking hydraulic conductivity and tortuosity parameters to pore space geometry and pore-size distribution. J. Hydrol. 272, 36–49 (2003).Article 

Google Scholar 
65.Kögel-Knabner, I. The macromolecular organic composition of plant and microbial residues as inputs to soil organic matter. Soil Biol. Biochem. 34, 139–162 (2002).Article 

Google Scholar 
66.Hoffman, M. T. & Arnold, A. E. Diverse bacteria inhabit living hyphae of phylogenetically diverse fungal endophytes. Appl. Environ. Microbiol. 76, 4063–4075 (2010).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
67.Thompson, L. R. et al. A communal catalogue reveals Earth’s multiscale microbial diversity. Nature 551, 457–463 (2017).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
68.Mcdonald, J. C., Duffy, D. C., Anderson, J. R. & Chiu, D. T. Review general fabrication of microfluidic systems in poly (dimethylsiloxane). Electrophoresis 21, 27–40 (2000).CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
69.Cánovas, D., Cases, I. & De Lorenzo, V. Heavy metal tolerance and metal homeostasis in Pseudomonas putida as revealed by complete genome analysis. Environ. Microbiol. 5, 1242–1256 (2003).PubMed 
Article 
PubMed Central 

Google Scholar 
70.Mooney, A., Ward, P. G. & O’Connor, K. E. Microbial degradation of styrene: biochemistry, molecular genetics, and perspectives for biotechnological applications. Appl. Microbiol. Biotechnol. 72, 1–10 (2006).CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
71.Ward, P. G., Goff, M., Donner, M., Kaminsky, W. & O’Connor, K. E. A two step chemo-biotechnological conversion of polystyrene to a biodegradable thermoplastic. Environ. Sci. Technol. 40, 2433–2437 (2006).CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
72.Gomes, N. C. M., Kosheleva, I. A., Abraham, W. R. & Smalla, K. Effects of the inoculant strain Pseudomonas putida KT2442 (pNF142) and of naphthalene contamination on the soil bacterial community. FEMS Microbiol. Ecol. 54, 21–33 (2005).CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
73.Smith, M. C. M. Molecular biological methods for bacillus. FEBS Lett. https://doi.org/10.1016/0014-5793(91)80059-c. (1991).Article 
PubMed 
PubMed Central 

Google Scholar 
74.Razavi, B. S., Zhang, X., Bilyera, N., Guber, A. & Zarebanadkouki, M. Soil zymography: simple and reliable? Review of current knowledge and optimization of the method. Rhizosphere 11, 100161 (2019). no. June.Article 

Google Scholar 
75.Nicodème, M., Grill, J. P., Humbert, G. & Gaillard, J. L. Extracellular protease activity of different Pseudomonas strains: dependence of proteolytic activity on culture conditions. J. Appl. Microbiol. https://doi.org/10.1111/j.1365-2672.2005.02634.x (2005).76.Güll, I., Alves, P. M., Gabor, F. & Wirth, M. Viability of the human adenocarcinoma cell line Caco-2: influence of cryoprotectant, freezing rate, and storage temperature. Scientia Pharmaceutica https://doi.org/10.3797/scipharm.0810-07 (2009).77.Burns, C. et al. Efficient GFP expression in the mushrooms Agaricus bisporus and Coprinus cinereus requires introns. Fungal Genetics Biol. https://doi.org/10.1016/j.fgb.2004.11.005 (2005).78.Stajich, J. E. et al. Insights into evolution of multicellular fungi from the assembled chromosomes of the mushroom Coprinopsis cinerea (Coprinus cinereus). Proc. Natl Acad. Sci. USA 107, 11889–11894 (2010).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
79.Schindelin, J. et al. Fiji: an open-source platform for biological-image analysis. Nat. Methods. https://doi.org/10.1038/nmeth.2019 (2012).80.Kneen, M. A. & Annegarn, H. J. Algorithm for fitting XRF, SEM and PIXE X-ray spectra backgrounds. Nucl. Instrum. Methods Phys. Res. Section B https://doi.org/10.1016/0168-583X(95)00908-6 (1996).81.Team, R. C. R: a language and environment for statistical computing. Vienna, Austria, 2019.82.Dunn, O. J. Multiple comparisons among means. J. Am. Stat. Assoc. https://doi.org/10.2307/2282330 (1961).83.Holm, S. A simple sequentially rejective multiple test procedure. Scand. J. Stat. 1979.84.C. et al. Arellano-Caicedo, “Habitat geometry in artificial microstructure affects bacterial and fungal growth, interactions, and substrate degradation 2nd part,” Dryad, Dataset. 2021.85.C. et al. Arellano-Caicedo, “Habitat geometry in artificial microstructure affects bacterial and fungal growth, interactions, and substrate degradation. Part 1,” Dryad, Dataset, 2021. More

1.Tscharntke, T., Klein, A. M., Kruess, A., Steffan-Dewenter, I. & Thies, C. Landscape perspectives on agricultural intensification and biodiversity–ecosystem service management. Ecol. Lett. 8, 857–874 (2005).Article 

Google Scholar 
2.Van Zanten, B. T. et al. European agricultural landscapes, common agricultural policy and ecosystem services: A review. Agron. Sustain. Dev. 34, 309–325 (2014).Article 

Google Scholar 
3.Jongman, R. H. Homogenisation and fragmentation of the European landscape: Ecological consequences and solutions. Landsc. Urban Plan. 58, 211–221 (2002).Article 

Google Scholar 
4.Stoate, C. et al. Ecological impacts of arable intensification in Europe. J. Environ. Manag. 63, 337–365 (2001).CAS 
Article 

Google Scholar 
5.Storkey, J., Meyer, S., Still, K. S. & Leuschner, C. The impact of agricultural intensification and land-use change on the European arable flora. Proc. R. Soc. B 279, 1421–1429 (2012).CAS 
PubMed 
Article 

Google Scholar 
6.Donald, P. F., Sanderson, F. J., Burfield, I. J. & Van Bommel, F. P. J. Further evidence of continent-wide impacts of agricultural intensification on European farmland birds, 1990–2000. Agric. Ecosyst. Environ. 116, 189–196 (2006).Article 

Google Scholar 
7.Benton, T. G., Vickery, J. A. & Wilson, J. D. Farmland biodiversity: Is habitat heterogeneity the key?. Trends Ecol. Evol. 18, 182–188 (2003).Article 

Google Scholar 
8.Traba, J. & Morales, M. B. The decline of farmland birds in Spain is strongly associated to the loss of fallowland. Sci. Rep. 9, 1–6 (2019).Article 
CAS 

Google Scholar 
9.Mcmahon, B. J., Giralt, D., Raurell, M., Brotons, L. & Bota, G. Identifying set-aside features for bird conservation and management in northeast Iberian pseudo-steppes. Bird Study 57, 289–300 (2010).Article 

Google Scholar 
10.Tarjuelo, R. et al. Living in seasonally dynamic farmland: The role of natural and semi-natural habitats in the movements and habitat selection of a declining bird. Biol. Conserv. 251, 108794 (2020).Article 

Google Scholar 
11.Donázar, J. A., Naveso, M. A., Tella, J. L. & Campión, D. Extensive grazing and raptors in Spain. 117–149. in Farming and Birds in Europe: The Common Agricultural Policy and Its Implications for Bird Conservation (Pain, D. J. & Pienkowski, M. W. eds.). (Academic Press, 1997).12.Santos, T. & Suárez, F. Biogeography and population trends of the Iberian steppe birds. in Ecology and Conservation of Steppe-Land Birds (Bota, G., Morales, M. B., Mañosa, S. & Camprodon, J. eds.). (Lynx Edicions, 2005).13.Tarjuelo, R., Margalida, A. & Mougeot, F. Changing the fallow paradigm: A win–win strategy for the post-2020 Common Agricultural Policy to halt farmland bird declines. J. Appl. Ecol. 57, 642–649 (2020).Article 

Google Scholar 
14.Wilson, J. D., Morris, A. J., Arroyo, B. E., Clark, S. C. & Bradbury, R. B. A review of the abundance and diversity of invertebrate and plant foods of granivorous birds in northern Europe in relation to agricultural change. Agric. Ecosyst. Environ. 75, 13–30 (1999).Article 

Google Scholar 
15.Benton, T. G., Bryant, D. M., Cole, L. & Crick, H. Q. Linking agricultural practice to insect and bird populations: A historical study over three decades. J. Appl. Ecol. 39, 673–687 (2002).Article 

Google Scholar 
16.Raven, P. H. & Wagner, D. L. Agricultural intensification and climate change are rapidly decreasing insect biodiversity. Proc. Natl. Acad. Sci. U.S.A. 118, 2 (2021).Article 
CAS 

Google Scholar 
17.Andreasen, C., Jensen, H. A. & Jensen, S. M. Decreasing diversity in the soil seed bank after 50 years in Danish arable fields. Agric. Ecosyst. Environ. 259, 61–71 (2018).Article 

Google Scholar 
18.Newton, I. The recent declines of farmland bird populations in Britain: An appraisal of causal factors and conservation actions. Ibis 146, 579–600 (2004).Article 

Google Scholar 
19.Burfield, I. J. The conservation status of steppic birds in Europe. 119–140. in Ecology and Conservation of Steppe-Land Birds (Bota, G., Morales, M. B., Mañosa, S. & Camprodon, J. eds.). (Lynx Edicions, 2005).20.Del Hoyo, J., Elliott, A., Sargatal, J. & Christie D. A. Handbook of the Birds of the World. (Lynx Edicions, 1992).21.Madroño, A., González, C. & Atienza, J. C. Libro Rojo de las Aves de España. (Dirección General para la Biodiversidad-SEO/BirdLife, 2004)22.Suárez, F., Hervás, I., Levassor, C. & Casado, M. A. La alimentación de la ganga ibérica y la ganga ortega. 215–229. in La Ganga Iberica (Pterocles alchata) y la Ganga Ortega (Pterocles orientalis) en España. Distribución, Abundancia, Biología y Conservación (Herranz, J. & Suárez, F. eds.). (Ministerio de Medio Ambiente, 1999).23.Jiguet, F. Arthropods in diet of Little Bustards Tetrax tetrax during the breeding season in western France. Bird Study 49, 105–109 (2002).Article 

Google Scholar 
24.Bravo, C., Ponce, C., Palacín, C. & Alonso, J. C. Diet of young Great Bustards Otis tarda in Spain: Sexual and seasonal differences. Bird Study 59, 243–251 (2012).Article 

Google Scholar 
25.Pompanon, F. et al. Who is eating what: Diet assessment using next generation sequencing. Mol. Ecol. 21, 1931–1950 (2012).CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
26.Shokralla, S., Spall, J. L., Gibson, J. F. & Hajibabaei, M. Next-generation sequencing technologies for environmental DNA research. Mol. Ecol. 21, 1794–1805 (2012).CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
27.Mougeot, F., Fernández-Tizón, M., Tarjuelo, R., Benítez-López, A. & Jiménez, J. La Ganga Ibérica y la Ganga Ortega en España, Población Reproductora en 2019 y Método de Censo. (SEO/BirdLife, 2021).28.Martin, T. E. Food as a limit on breeding birds: A life-history perspective. Annu. Rev. Ecol. Evol. Syst. 18, 453–487 (1987).Article 

Google Scholar 
29.Martín, C. A., Casas, F., Mougeot, F., García, J. T. & Viñuela, J. Positive interactions between vulnerable species in agrarian pseudo-steppes: Habitat use by pin-tailed sandgrouse depends on its association with the little bustard. Anim. Conserv. 13, 383–389 (2010).Article 

Google Scholar 
30.Bravo, C., Cuscó, F., Morales, M. & Mañosa, S. Diet composition of a declining steppe bird the Little Bustard (Tetrax tetrax) in relation to farming practices. Avian Conserv. Ecol. 12, 1 (2017).CAS 

Google Scholar 
31.Morse, J. G. & Hoddle, M. S. Invasion biology of thrips. Annu. Rev. Entomol. 51, 67–89 (2006).CAS 
PubMed 
Article 

Google Scholar 
32.Goldarazena, A. Orden Thysanoptera. Ide@-Sea 52, 1–20 (2015).
Google Scholar 
33.Ndang’ang’a, P. K., Njoroge, J. B. & Vickery, J. Quantifying the contribution of birds to the control of arthropod pests on kale, Brassica oleracea acephala, a key crop in East African highland farmland. Int. J. Pest Manag. 59, 211–216 (2013).Article 

Google Scholar 
34.Gunnarsson, B. Bird predation on spiders: Ecological mechanisms and evolutionary consequences. J. Arachnol. 35(509), 529 (2007).
Google Scholar 
35.Lee, J. H. et al. Anticancer activity of the antimicrobial peptide scolopendrasin VII derived from the centipede, Scolopendra subspinipes mutilans. J. Microbiol. Biotechnol. 25, 1275–1280 (2015).CAS 
PubMed 
Article 

Google Scholar 
36.Lima, D. B. et al. Antiparasitic effect of Dinoponera quadriceps giant ant venom. Toxicon 120, 128–132 (2016).CAS 
PubMed 
Article 

Google Scholar 
37.Whitman, D. W. et al. Antiparasitic properties of cantharidin and the blister beetle berberomeloe majalis (Coleoptera: meloidae). Toxins 11, 234 (2019).CAS 
PubMed Central 
Article 

Google Scholar 
38.Bravo, C., Bautista, L. M., García-París, M., Blanco, G. & Alonso, J. C. Males of a strongly polygynous species consume more poisonous food than females. PLoS ONE 9, e111057 (2014).ADS 
PubMed 
PubMed Central 
Article 
CAS 

Google Scholar 
39.Bolívar, P. et al. Antiparasitic effects of plant species from the diet of great bustards. Preprint. https://doi.org/10.21203/rs.3.rs-122399/v1 (2020).Article 

Google Scholar 
40.Boyer, A. G. et al. Seasonal variation in top-down and bottom-up processes in a grassland arthropod community. Oecologia 136, 309–316 (2003).ADS 
PubMed 
Article 
PubMed Central 

Google Scholar 
41.Palacios, F., Garzón, J. & Castroviejo, J. L. alimentación de la avutarda (Otis tarda) en España, especialmente en primavera. Ardeola 21, 347–406 (1975).
Google Scholar 
42.Cabodevilla, X., Gómez-Moliner, B. J. & Madeira, M. J. Simultaneous analysis of the intestinal parasites and diet through eDNA metabarcoding. Preprint. https://doi.org/10.22541/au.158531783.33894277 (2020).Article 

Google Scholar 
43.García de la Morena, E. L., Bota, G., Mañosa, S. & Morales, M. B. El Sisón Común en España. II Censo Nacional (2016). (SEO/BirdLife, 2018).44.Cabodevilla, X., Aebischer, N. J., Mougeot, F., Morales, M. B. & Arroyo, B. Are population changes of endangered little bustards associated with releases of red-legged partridges for hunting? A large-scale study from central Spain. Eur. J. Wildl. Res. 66, 1–10 (2020).Article 

Google Scholar 
45.Cuscó, F., Cardador, L., Bota, G., Morales, M. B. & Mañosa, S. Inter-individual consistency in habitat selection patterns and spatial range constraints of female little bustards during the non-breeding season. BMC Ecol. 18, 1–12 (2018).Article 

Google Scholar 
46.González del Portillo, D., Arroyo, B., García Simón, G. & Morales, M. B. Can current farmland landscapes feed declining steppe birds? Evaluating arthropod abundance for the endangered little bustard (Tetrax tetrax) in cereal farmland during the chick‐rearing period: Variations between habitats and localities. Ecol. Evol. 11, 3219–3238 (2021).
47.Silva, J. P., Pinto, M. & Palmeirim, J. M. Managing landscapes for the little bustard Tetrax tetrax: Lessons from the study of winter habitat selection. Biol. Conserv. 117, 521–528 (2004).Article 

Google Scholar 
48.Pfiffner, L. & Luka, H. Overwintering of arthropods in soils of arable fields and adjacent semi-natural habitats. Agric. Ecosyst. Environ. 78, 215–222 (2000).Article 

Google Scholar 
49.Hendrickx, F. et al. How landscape structure, land-use intensity and habitat diversity affect components of total arthropod diversity in agricultural landscapes. J. Appl. Ecol. 44, 340–351 (2007).Article 

Google Scholar 
50.Tarjuelo, R., Morales, M. B., Arribas, L. & Traba, J. Abundance of weeds and seeds but not of arthropods differs between arable habitats in an extensive Mediterranean farming system. Ecol. Res. 34, 624–636 (2019).Article 

Google Scholar 
51.Green, R. E. The feeding ecology and survival of partridge chicks (Alectoris rufa and Perdix perdix) on arable farmland in East Anglia. J. Appl. Ecol. 1, 817–830 (1984).Article 

Google Scholar 
52.Palacín, C. La decadencia de la comunidad de aves de los cultivos cerealistas mediterráneos. in XV Congreso del Grupo Ibérico de Aguiluchos. https://xvcongresoaguiluchosgia.es/wp-content/uploads/2019/11/LA-DECADENCIA-DE-LA-COMUNIDAD-DE-AVES-DE-LOS-CULTIVOS-CEREALISTAS-MEDITERRÁNEOS-Carlos-Palac%C3%ADn.pdf (2019).53.Blanco-Aguiar, J. A., Virgós, E. & Villafuerte, R. Perdiz roja (Alectoris rufa). in Atlas de las Aves Reproductoras de España. 212–213 (2003).54.Rodríguez-Teijeiro, J. D., Puigcerver, M. & Gallego, S. Codorniz común. in Atlas de las Aves Reproductoras de España. 218–219 (2003).55.Andueza, A. et al. Evaluación del Impacto Económico y Social de la Caza en España. (Fundación Artemisan, 2018)56.Lane, S. J., Alonso, J. C., Alonso, J. A. & Naveso, M. A. Seasonal changes in diet and diet selection of great bustards (Otis tarda) in north-west Spain. J. Zool. 247, 201–214 (1999).Article 

Google Scholar 
57.QGIS Development Team. QGIS Geographic Information System. http://qgis.osgeo.org (Open Source Geospatial Foundation Project, 2018).58.Martin, M. Cutadapt removes adapter sequences from high-throughput sequencing reads. EMBnet. J. 17, 10–12 (2011).Article 

Google Scholar 
59.Edgar, R. C. Search and clustering orders of magnitude faster than BLAST. Bioinformatics 26, 2460–2461 (2010).CAS 
Article 

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

Google Scholar 
61.McKnight, D. T. et al. Methods for normalizing microbiome data: An ecological perspective. Methods Ecol. Evol. 10, 389–400 (2019).Article 

Google Scholar 
62.Lamb, P. D. et al. How quantitative is metabarcoding: A meta-analytical approach. Mol. Ecol. 28, 420–430 (2019).PubMed 
Article 
PubMed Central 

Google Scholar 
63.Piñol, J., Senar, M. A. & Symondson, W. O. The choice of universal primers and the characteristics of the species mixture determine when DNA metabarcoding can be quantitative. Mol. Ecol. 28, 407–419 (2019).PubMed 
Article 
CAS 
PubMed Central 

Google Scholar 
64.Russo, T. et al. All is fish that comes to the net: metabarcoding for rapid fisheries catch assessment. Ecol. Appl. 31, e02273 (2021).ADS 
PubMed 
Article 
PubMed Central 

Google Scholar 
65.González-Teuber, M., Vilo, C., Guevara-Araya, M. J., Salgado-Luarte, C. & Gianoli, E. Leaf resistance traits influence endophytic fungi colonization and community composition in a South American temperate rainforest. J. Ecol. 108, 1019–1029 (2020).Article 
CAS 

Google Scholar 
66.Aliche, E. B., Talsma, W., Munnik, T. & Bouwmeester, H. J. Characterization of maize root microbiome in two different soils by minimizing plant DNA contamination in metabarcoding analysis. Biol. Fertil. Soils. 57, 731–737 (2021).CAS 
Article 

Google Scholar 
67.de Groot, G. A. et al. The aerobiome uncovered: Multi-marker metabarcoding reveals potential drivers of turn-over in the full microbial community in the air. Environ. Int. 154, 106551 (2021).PubMed 
Article 
PubMed Central 

Google Scholar 
68.Tordoni, E. et al. Integrated eDNA metabarcoding and morphological analyses assess spatio-temporal patterns of airborne fungal spores. Ecol. Indic. 121, 107032 (2021).Article 

Google Scholar 
69.R Core Team. R: A Language and Environment for Statistical Computing. https://www.R-project.org/. (R Foundation for Statistical Computing, 2019).70.Russell, V. L. Least-squares means: The R Package lsmeans. J. Stat. Softw. 69, 1–33 (2016).
Google Scholar 
71.Oksanen, J. et al. Vegan: Community Ecology Package. R Package Version 2.0 (2013). More

1.Mitchell, M. S. & Powell, R. A. A mechanistic home range model for optimal use of spatially distributed resources. Ecol. Model. 177, 209–232 (2004).Article 

Google Scholar 
2.Horne, J. S., Garton, E. O. & Rachlow, J. L. A synoptic model of animal space use: Simultaneous estimation of home range, habitat selection, and inter/intra–specific relationships. Ecol. Model. 214, 338–348 (2008).Article 

Google Scholar 
3.Nathan, R. An emerging movement ecology paradigm. Proc. Natl. Acad. Sci. USA 105, 19050–19051 (2008).ADS 
CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
4.Fretwell, S. D. & Lucas, H. L. J. On territorial behavior and other factors influencing habitat distribution in birds. Part 1. Theoretical development. Acta. Biotheor. 19, 16–36 (1969).Article 

Google Scholar 
5.Powell, R. A. Animal home ranges and territories and home range estimators. Res. Tech. Animal Ecol. Controversies Consequences. 1, 476 (2000).
Google Scholar 
6.Parker, G. A. & Smith, J. M. Optimality theory in evolutionary biology. Nature 348, 27 (1990).ADS 
Article 

Google Scholar 
7.Hiller, T. L., Belant, J. L. & Beringer, J. Sexual size dimorphism mediates effects of spatial resource variability on American black bear space use. J. Zool. 296, 200–207 (2015).Article 

Google Scholar 
8.Mitchell, M. S. & Powell, R. A. Optimal use of resources structures home ranges and spatial distribution of black bears. Anim. Behav. 74, 219–230 (2007).Article 

Google Scholar 
9.McLoughlin, P. D. & Ferguson, S. H. A hierarchical pattern of limiting factors helps explain variation in home range size. Ecoscience 7, 123–130 (2000).Article 

Google Scholar 
10.Johnson, D. D., Kays, R., Blackwell, P. G. & Macdonald, D. W. Does the resource dispersion hypothesis explain group living?. Trends Ecol. Evol. 17, 563–570 (2002).Article 

Google Scholar 
11.Macdonald, D. W. The ecology of carnivore social behavior. Nature 301, 379–384 (1983).ADS 
Article 

Google Scholar 
12.Macdonald, D. W. & Johnson, D. D. P. Patchwork planet: The resource dispersion hypothesis, society, and the ecology of life. J. Zool. 295, 75–107 (2015).Article 

Google Scholar 
13.Lukacs, P. M. et al. Factors influencing elk recruitment across ecotypes in the Western United States. J. Wildl. Manag. 82, 698–710 (2018).Article 

Google Scholar 
14.Mangipane, L. S. et al. Influences of landscape heterogeneity on home-range sizes of brown bears. Mamm. Biol. 88, 1–7 (2018).Article 

Google Scholar 
15.McClintic, L. F., Taylor, J. D., Jones, J. C., Singleton, R. D. & Wang, G. Effects of spatiotemporal resource heterogeneity on home range size of American beaver. J. Zool. 293, 134–141 (2014).Article 

Google Scholar 
16.Harestad, A. S. & Bunnel, F. L. Home range and body weight—A reevaluation. Ecology 60, 389–402 (1979).Article 

Google Scholar 
17.Knick, S. T. Ecology of bobcats relative to exploitation and a prey decline in southeastern Idaho. Wildl. Monogr. 108, 3–42 (1990).
Google Scholar 
18.Kelt, D. A. & Van Vuren, D. H. The ecology and macroecology of mammalian home range area. Am. Nat. 157, 637–645 (2001).CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
19.McNab, B. K. Bioenergetics and the determination of home range size. Am. Nat. 97, 133–140 (1963).Article 

Google Scholar 
20.Dahle, B., Støen, O. G. & Swenson, J. E. Factors influencing home-range size in subadult brown bears. J. Mammal. 87, 859–865 (2006).Article 

Google Scholar 
21.Dahle, B. & Swenson, J. E. Home ranges in adult Scandinavian brown bears (Ursus arctos): Effect of mass, sex, reproductive category, population density and habitat type. J. Zool. 260, 329–335 (2003).Article 

Google Scholar 
22.Lafferty, D. J. R., Loman, Z. G., White, K. S., Morzillo, A. T. & Belant, J. L. Moose (Alces alces) hunters subsidize the scavenger community in Alaska. Polar Biol. 39, 639–647 (2016).Article 

Google Scholar 
23.Van Manen, F. T. et al. Primarily resident grizzly bears respond to late-season elk harvest. Ursus 2019, 1–15 (2019).Article 

Google Scholar 
24.Taylor, M.K. Density-dependent population regulation of black, brown and polar bears. in 9th International Conference on Bear Research and Management. International Bear Association, Missoula (1994).25.Swenson, J. E., Dahle, B. & Sandegren, F. Intraspecific predation in Scandinavian brown bears older than cubs-of-the-year. Ursus 12, 81–91 (2001).
Google Scholar 
26.Hilderbrand, G. V. et al. Body size and lean mass of brown bears across and within four diverse ecosystems. J. Zool. 305, 53–62 (2018).Article 

Google Scholar 
27.Hilderbrand, G. V. et al. The importance of meat, particularly salmon, to body size, population productivity, and conservation of North American brown bears. Can. J. Zool. 77, 132–138 (1999).Article 

Google Scholar 
28.Belant, J. L., Kielland, K., Follmann, E. H. & Adams, L. G. Interspecific resource partitioning in sympatric ursids. Ecol. Appl. 16, 2333–2343 (2006).PubMed 
Article 
PubMed Central 

Google Scholar 
29.Belant, J. L., Griffith, B., Zhang, Y., Follmann, E. H. & Adams, L. G. Population-level resource selection by sympatric brown and American black bears in Alaska. Polar Biol. 33, 31–40 (2010).Article 

Google Scholar 
30.Munro, R. H. M., Nielsen, S. E., Price, M. H., Stenhouse, G. B. & Boyce, M. S. Seasonal and diel patterns of grizzly bear diet and activity in west-central Alberta. J. Mammal. 87, 1112–1121 (2006).Article 

Google Scholar 
31.US Fish and Wildlife Service. Izembek National Wildlife Refuge Land Exchange/Road Corridor, Draft Environmental Impact Statement (US Fish and Wildlife Service, 2018).
Google Scholar 
32.Svoboda, N. J. & Crye, J. R. Roosevelt Elk Management Report and Plan, Game Management Unit 8: Report Period 1 July 2013–30 June 2018, and Plan Period 1 July 2018–30 June 2023 (Alaska Department of Fish and Game, 2020).
Google Scholar 
33.Van Daele, M. B. et al. Salmon consumption by Kodiak brown bears (Ursus arctos middendorffi) with ecosystem management implications. Can. J. Zool. 91, 164–174 (2013).Article 

Google Scholar 
34.Barnes, V. The influence of salmon availability on movements and range of brown bears on Southwest Kodiak Island. Bears Biol. Manag. 8, 305–313 (1990).
Google Scholar 
35.Deacy, W., Leacock, W., Armstrong, J. B. & Stanford, J. A. Kodiak brown bears surf the salmon red wave: Direct evidence from GPS collared individuals. Ecology 97, 1091–1098 (2016).PubMed 
Article 

Google Scholar 
36.Van Daele, L. J., Barnes, V. G. & Belant, J. L. Ecological flexibility of brown bears on Kodiak Island, Alaska. Ursus 23, 21–29 (2012).Article 

Google Scholar 
37.Stirling, I., Spencer, C. & Andriashek, D. Immobilization of polar bears (Ursus maritimus) with Telazol® in the Canadian Arctic. J. Wildl. Dis. 25, 159–168 (1989).CAS 
PubMed 
Article 

Google Scholar 
38.Woolf, A., Hays, H. R., Allen, W. B. & Swart, J. Immobilization of wild ungulates with etorphine HC1. J. Zoo Animal Med. 4, 16–19 (1973).Article 

Google Scholar 
39.Meuleman, T., Port, J. D., Stanley, T. H., Williard, K. F. & Kimball, J. Immobilization of elk and moose with carfentanil. J. Wildl. Manag. 48, 258–262 (1984).Article 

Google Scholar 
40.Lance, W.R. & Kenny, D.E. Thiafentanil oxalate (A3080) in nondomestic ungulate species. in Fowler’s Zoo and Wild Animal Medicine (ed. Miller and Fowler) 589–595 (W.B. Saunders, 2012).41.Garshelis, D. L. & McLaughlin, C. R. Review and evaluation of breakaway devices for bear radiocollars. Ursus 10, 459–465 (1998).
Google Scholar 
42.Calvert, W. & Ramsay, M. A. Evaluation of age determination of polar bears by counts of cementum growth layer groups. Ursus 10, 449–453 (1998).
Google Scholar 
43.Thiemann, G. W. et al. Effects of chemical immobilization on the movement rates of free-ranging polar bears. J. Mammal. 94, 386–397 (2013).Article 

Google Scholar 
44.Noonan, M. J. et al. A comprehensive analysis of autocorrelation and bias in home range estimation. Ecol. Monogr. 89, e01344 (2019).Article 

Google Scholar 
45.Bishop, A., Brown, C., Rehberg, M., Torres, L. & Horning, M. Juvenile Steller sea lion (Eumetopias jubatus) utilization distributions in the Gulf of Alaska. Mov. Ecol. 6, 1–15 (2018).Article 

Google Scholar 
46.Long, R. A., Muir, J. D., Rachlow, J. L. & Kie, J. G. A comparison of two modeling approaches for evaluating wildlife-habitat relationships. J. Wildl. Manag. 73, 294–302 (2009).Article 

Google Scholar 
47.Fleming, M. D. & Spencer, P. A vegetative cover map for the Kodiak Archipelago Alaska (USGS, Alaska Science Center, Anchorage, 2004).
Google Scholar 
48.Brodeur, V., Ouellet, J. P., Courtois, R. & Fortin, D. Habitat selection by black bears in an intensively logged boreal forest. Can. J. Zool. 86, 1307–1316 (2008).Article 

Google Scholar 
49.Hesselbarth, M. H., Sciaini, M., With, K. A., Wiegand, K. & Nowosad, J. landscapemetrics: An open-source R tool to calculate landscape metrics. Ecography 42, 1648–1657 (2019).Article 

Google Scholar 
50.Shannon, C. E. & Weaver, W. The Mathematical Theory of Communication (University of Illinois Press, 1963).MATH 

Google Scholar 
51.Smith, T. S. & Partridge, S. T. Dynamics of intertidal foraging by coastal brown bears in southwestern Alaska. J. Wildl. Manag. 68, 233–240 (2004).Article 

Google Scholar 
52.Zager, P. & Beecham, J. The role of American black bears and brown bears as predators on ungulates in North America. Ursus 17, 95–108 (2006).Article 

Google Scholar 
53.Boyce, M. S., Vernier, P. R., Nielsen, S. E. & Schmiegelow, F. K. Evaluating resource selection functions. Ecol. Model. 157, 281–300 (2002).Article 

Google Scholar 
54.Calabrese, J. M., Fleming, C. H. & Gurarie, E. ctmm: An R package for analyzing animal relocation data as a continuous-time stochastic process. Methods Ecol. Evol. 7, 1124–1132 (2016).Article 

Google Scholar 
55.Morris, L. R., Proffitt, K. M., Asher, V. & Blackburn, J. K. Elk resource selection and implications for anthrax management in Montana. J. Wildl. Manag. 80, 235–244 (2016).Article 

Google Scholar 
56.Pontius, R. G. & Parmentier, B. Recommendations for using the relative operating characteristic (ROC). Landsc. Ecol. 29, 367–382 (2014).Article 

Google Scholar 
57.Dormann, C. F. et al. Collinearity: A review of methods to deal with it and a simulation study evaluating their performance. Ecography 36, 27–46 (2013).Article 

Google Scholar 
58.Burnham, K. P. & Anderson, D. R. Model Selection and Multimodel Inference: A Practical Information–Theoretic Approach 2nd edn. (Springer, 2002).MATH 

Google Scholar 
59.R Core Team (2020). R: A language and environment for statistical computing. R Foundation for Statistical Computing, Vienna, Austria. URL https://www.R-project.org/.60.Lewis, T. M. & Lafferty, D. J. Brown bears and wolves scavenge humpback whale carcass in Alaska. Ursus 25, 8–13 (2014).Article 

Google Scholar 
61.Paralikidis, N. P., Papageorgiou, N. K., Kontsiotis, V. J. & Tsiompanoudis, A. C. The dietary habits of the brown bear (Ursus arctos) in western Greece. Mamm. Biol. 75, 29–35 (2010).Article 

Google Scholar 
62.Sandell, M. The mating tactics and spacing patterns of solitary carnivores In Carnivore Behavior, Ecology, and Evolution (ed. Gittleman J.L.) 164–182 (Springer, 1989).63.Hilderbrand, G. V., Jenkins, S. G., Schwartz, C. C., Hanley, T. A. & Robbins, C. T. Effect of seasonal differences in dietary meat intake on changes in body mass and composition in wild and captive brown bears. Can. J. Zool. 77, 1623–1630 (1999).Article 

Google Scholar 
64.Milakovic, B. & Parker, K. L. Quantifying carnivory by grizzly bears in a multi-ungulate system. J. Wildl. Manag. 77, 39–47 (2013).Article 

Google Scholar 
65.Nieminen, M. The impact of large carnivores on the mortality of semi-domesticated reindeer (Rangifer tarandus tarandus L.) calves in Kainuu, southeastern reindeer herding region of Finland. Rangifer. 30, 79–88 (2010).Article 

Google Scholar 
66.Mumma, M. A. et al. Intrinsic traits of woodland caribou Rangifer tarandus caribou calves depredated by black bears Ursus americanus and coyotes Canis latrans. Wildl. Biol. 2019, 1–9 (2019).Article 

Google Scholar 
67.Svoboda, N. J., Belant, J. L., Beyer, D. E., Duquette, J. F. & Lederle, P. E. Carnivore space use shifts in response to seasonal resource availability. Ecosphere. 10, e02817 (2019).Article 

Google Scholar 
68.Ruth, T. K. et al. Large-carnivore response to recreational big-game hunting along the Yellowstone National Park and Absaroka-Beartooth Wilderness boundary. Wildl. Soc. Bull. 31, 1150–1161 (2003).
Google Scholar 
69.Haroldson, M. A., Schwartz, C. C., Cherry, S. & Moody, D. S. Possible effects of elk harvest on fall distribution of grizzly bears in the Greater Yellowstone Ecosystem. J. Wildl. Manag. 68, 129–137 (2004).Article 

Google Scholar 
70.Bastille-Rousseau, G., Fortin, D., Dussault, C., Courtois, R. & Ouellet, J. P. Foraging strategies by omnivores: are black bears actively searching for ungulate neonates or are they simply opportunistic predators?. Ecography 34, 588–596 (2011).Article 

Google Scholar 
71.Gehr, B. et al. Evidence for nonconsumptive effects from a large predator in an ungulate prey?. Behav. Ecol. 29, 724–735 (2018).Article 

Google Scholar 
72.Hebblewhite, M., Merrill, E. H. & McDonald, T. L. Spatial decomposition of predation risk using resource selection functions: An example in a wolf–elk predator–prey system. Oikos 111, 101–111 (2005).Article 

Google Scholar 
73.Nielsen, S. E., Boyce, M. S. & Stenhouse, G. B. Grizzly bears and forestry: I. Selection of clearcuts by grizzly bears in west-central Alberta, Canada. For. Ecol. Manag. 199, 51–65 (2004).Article 

Google Scholar 
74.McLellan, B. N. Relationships between human industrial activity and grizzly bears. Bears Biol. Manag. 8, 57–64 (1990).
Google Scholar 
75.Sigman, M. Impacts of Clearcut Logging on the Fish and Wildlife Resources of Southeast Alaska Vol. 85 (Alaska Department of Fish and Game, 1985).
Google Scholar 
76.Linnell, J. D., Swenson, J. E., Andersen, R. & Barnes, B. How vulnerable are denning bears to disturbance?. Wildl. Soc. Bull. 28, 400–413 (2000).
Google Scholar 
77.McLellan, B. N. & Shackleton, D. M. Grizzly bears and resource-extraction industries: Effects of roads on behaviour, habitat use and demography. J. Appl. Ecol. 25, 451–460 (1988).Article 

Google Scholar 
78.Nielsen, S. E., Munro, R. H. M., Bainbridge, E. L., Stenhouse, G. B. & Boyce, M. S. Grizzly bears and forestry: II. Distribution of grizzly bear foods in clearcuts of west-central Alberta, Canada. For. Ecol. Manag. 199, 67–82 (2004).Article 

Google Scholar 
79.Nielsen, S. E., Stenhouse, G. B., Beyer, H. L., Huettmann, F. & Boyce, M. S. Can natural disturbance-based forestry rescue a declining population of grizzly bears?. Biol. Cons. 141, 2193–2207 (2008).Article 

Google Scholar 
80.Frank, S. C. et al. A “clearcut” case? Brown bear selection of coarse woody debris and carpenter ants on clearcuts. For. Ecol. Manage. 348, 164–173 (2015).PubMed 
PubMed Central 
Article 

Google Scholar 
81.Hertel, A. G. et al. Bears and berries: Species-specific selective foraging on a patchily distributed food resource in a human-altered landscape. Behav. Ecol. Sociobiol. 70, 831–842 (2016).PubMed 
PubMed Central 
Article 

Google Scholar 
82.Valeix, M., Loveridge, A. J. & Macdonald, D. W. Influence of prey dispersion on territory and group size of African lions: A test of the resource dispersion hypothesis. Ecology 93, 2490–2496 (2012).PubMed 
Article 
PubMed Central 

Google Scholar 
83.Robbins, C. T. et al. Optimizing protein intake as a foraging strategy to maximize mass gain in an omnivore. Oikos 116, 1675–1682 (2007).Article 

Google Scholar 
84.Ben-David, M., Titus, K. & Beier, L. R. Consumption of salmon by Alaskan brown bears: A trade-off between nutritional requirements and the risk of infanticide?. Oecologia 138, 465–474 (2004).ADS 
PubMed 
Article 
PubMed Central 

Google Scholar 
85.Smith, T. R. & Pelton, M. R. Home ranges and movements of black bears in bottomland hardwood forest in Arkansas. Int. Conf. Bear Res. Manag. 8, 213–218 (1990).
Google Scholar 
86.Welch, C. A., Keay, J., Kendall, K. C. & Robbins, C. T. Constraints on frugivory by bears. Ecology 78, 1105–1119 (1997).Article 

Google Scholar 
87.Gantchoff, M., Wang, G., Beyer, D. & Belant, J. Scale-dependent home range optimality for a solitary omnivore. Ecol. Evol. 8, 12271–12282 (2018).PubMed 
PubMed Central 
Article 

Google Scholar 
88.Tao, Y., Börger, L. & Hastings, A. Dynamic range size analysis of territorial animals: An optimality approach. Am. Nat. 188, 460–474 (2016).PubMed 
Article 
PubMed Central 

Google Scholar  More

Variation in microclimateMicroclimate at the tower varied across the daily sampling period with temperatures highest and relative humidity lowest around midday and the early afternoon hours, although the time of peak temperature and nadir humidity varied by height (Fig. 3a, b). Mean temperature was highest at ground level at 11:30 (30.0 °C) when it was on average 0.2 °C hotter than at 9 m, whereas at 5 m and 9 m, it was highest at 13:30 (29.7 °C and 30.3 °C, respectively). The inverse was true for mean relative humidity, which was lowest at ground level at 11:30 (83.8%) and lowest at 5 m and 9 m at 13:30 (80.1% and 76.4%, respectively). Both variables showed substantial overlap in means and standard errors across the sampled heights during the morning hours, before diverging in the afternoon. For comparison, we extracted microclimate data from the corresponding sampling period in the BG-Sentinel trap study15, which revealed clear differences in temperature and humidity at each height sampled (Fig. 3c, d). BG-Sentinel traps were often hung beneath the forest canopy where it was considerably cooler and more humid than at the treefall gap, particularly at ground level.Figure 3Variation in microclimate by height and collection method. (a) and (b) show the mean temperature (temp,°C) + / − 1 standard error (S.E.) and relative humidity (RH, %) + / − 1 standard error (S.E.) for net collections made at the tower between 10:00 and 15:00 in this study. (c) and (d) show corresponding data extracted from the BG-Sentinel trap study15.Full size imageCommunity composition of diurnally active, anthropophilic mosquitoesA total of 2146 adult mosquitoes representing seven genera and 34 species were collected using nets (Fig. 4a), of which 99.8% (2142/2146) were female and 99.7% (2140/2146) were identified to species level. Mosquito abundance was similar at ground level and 9 m but was slightly lower at 5 m, while species richness was higher at ground level (28 species), than at 5 m (18 species) and 9 m (22 species). Psorophora was the most abundant genus (1231 mosquitoes, 57.4% of the total catch), followed by Haemagogus (32.3%), and Sabethes (6.6%). The genera Limatus (1.4%), Culex (1.2%), Wyeomyia (1.0%), and Onirion ( 0.1 for both comparisons). A linear regression showed that, across all heights, lag to first approach decreased significantly as Hg. janthinomys abundance increased (DF = 1, F = 52.1, P  More

More detailed methods can be found in the supplementary material. Data from this experiment on the characterisation of the microbial community and its response to climate change has been previously published in Scanes et al.12, therefore, the present study focussed on the interaction of metabolic processes with the microbiome. We examined the links between climate change, metabolism, genotype and microbiome of the Sydney rock oyster, Saccostrea glomerata20. Nine oyster aquacultural breeding lineages (labelled as genotype-lines A–I) of S. glomerata, which are known to differ in their resilience to climate change12 were exposed to ambient and elevated temperature and PCO2 treatments. All seawater used in acclimation and experimental exposure was collected from Little Beach, Port Stephens (152°9′30.00″E, 32°42′43.03″S), filtered through canister filters to a nominal 5 µm, and stored onsite in 38,000 L polyethylene tanks as a stock of filtered seawater.Approximately 72 individual S. glomerata, from each of the nine families (A-I) were collected from intertidal leases in Cromarty Bay, Port Stephens (152° 4′0.69″E, 32°43′19.69″S). Oysters were held on private leases so a collection permit was not required. Oysters were collected in September 2019 for experiments, meaning all oysters were 22 months old when experiments began. Oysters were placed into a 2000 L fibreglass tank and maintained at 24 °C, a salinity of 35 ppt and ambient PCO2 (pH 8.18) for two weeks to acclimate to laboratory conditions. Following acclimation, oysters from each genotype-line were divided among twelve 750 L polyethylene tanks filled with 400 L of filtered seawater (5 µm) at a density of 54 oysters per tank, with each genotype-line represented by six replicate individuals. Treatments consisted of orthogonal combinations of two PCO2 concentrations (ambient [400 µatm]; elevated [1000 µatm]) and two temperature treatments (24 and 28 °C). Each combination was replicated across three tanks. Treatments were selected to represent temperatures and PCO2 concentrations predicted for 2080–2100 by the IPCC27 and reflect measured changes in estuary temperatures reported from south eastern Australia20.Once oysters were transferred to experimental tanks, the PCO2 level and temperature were steadily increased in elevated exposure tanks over one week until the experimental treatment level was reached. The elevated CO2 level was maintained using a pH negative feedback system (Aqua Medic, Aqacenta Pty Ltd, Kingsgrove, NSW, Australia; accuracy ± 0.01 pH units) bubbling food grade CO2 (BOC Australia) through a mixing chamber and into each tank, previously described in18. These PCO2 levels corresponded to a mean ambient pHNBS of (8.18 ± 0.01) and at elevated CO2 levels a mean pHNBS of (7.84 ± 0.01). Temperature was increased and then maintained using 1000 W aquarium heaters in each tank. Oysters were then exposed to their respective treatments for a further four weeks. Oysters were checked daily for mortality; no dead oysters were found in any tanks during the four-week exposure period.Haemolymph sampling for DNA extractionFollowing exposure to experimental conditions, haemolymph was taken from two replicate oysters, from each genotype-line, from each tank for microbial analysis following the methods previously described in Scanes et al.,12. This amounted to six individuals from each genotype-line, in each treatment. Each oyster was opened using an autoclave sterilised shucking knife, ensuring that the pericardial cavity was not ruptured. Excess fluid was tipped off the tissue surface and 200–300 µL of haemolymph was extracted from the pericardial cavity using a new sterile 1 mL needled syringe (Terumo Co.). Samples from two oysters were transferred to two new pre-labelled DNA/RNA free 1 mL tubes (Eppendorf Co.) and immediately frozen at − 80 °C where they were stored until DNA extraction.We used 16 s rRNA amplicon sequencing to characterise the bacterial microbiome of S. glomerata haemolymph following the methods previously described in Scanes et al.12. DNA was extracted from 216 oyster haemolymph samples (9 genotype-lines × 4 treatments × 3 replicate tanks × 2 replicate oysters per tank) using the Qiagen DNeasy Blood and Tissue Kit (Qiagen Australia, Chadstone, VIC), according to the manufacturer’s instructions. The bacterial microbiome of the oyster haemolymph was characterised with 16S rRNA amplicon sequencing, using the 341F (CCTACGGGNGGCWGCAG) and 805R (GACTACHVGGGTATCTAATCC) primer pair28 targeting the V3-V4 variable regions of the 16S rRNA gene with the following cycling conditions: 95 °C for 3 min, 25 cycles of 95 °C for 30 s, 55 °C for 30 s and 72 °C for 30 s, and a final extension at 72 °C for 5 min. Amplicons were sequenced on the Illumina Miseq platform (2 × 300 bp) following the manufacturer’s guidelines at the Ramaciotti Centre for Genomics, University of New South Wales. Raw data files in FASTQ format were deposited in NCBI Sequence Read Archive (SRA) under Bioproject number PRJNA663356.Sequence analysisRaw demultiplexed data was processed using the Quantitative Insights into Microbial Ecology (QIIME 2 version 2019.1.0) pipeline. Briefly, paired-end sequences were imported (qiime tools import), trimmed and denoised using DADA2 (version 2019.1.0). Sequences were identified at the single nucleotide threshold (Amplicon Sequence Variants; ASV) and taxonomy was assigned using the classify-sklearn QIIME 2 feature classifier against the Silva v138 database29. Sequences identified as chloroplasts or mitochondria were also removed. Cleaned data were then rarefied at 6,500 counts per sample.Physiological analysisWe measured physiological variables relating to oyster haemolymph metabolic function. These were: extracellular pH (pHe), extracellular CO2 concentrations (PCO2e) and the whole oyster metabolic rate (MR) measured as a standardised rate of oxygen consumption. Physiological measurements were taken from two oysters from each genotype-line in each tank (methods followed that of Parker et al.16,30 and Scanes et al.18). Oysters were immediately opened without rupturing the pericardial cavity. Haemolymph samples were drawn from the interstitial fluid filling the pericardial cavity chamber of an opened oyster using a sealed 1 mL needled syringe. A 0.2 mL sample was drawn carefully to avoid aeration of the haemolymph. Half of the sample was then immediately transferred to an Eppendorf tube where pHe of the sample was measured at 20 °C using a micro pH probe (Metrohm 827 biotrode). The remaining haemolymph was transferred to a gas analyser (CIBA Corning 965) to determine total CO2 (CCO2). The micro pH probe was calibrated prior to use with NBS standards at the acclimation temperature and the gas analyser was calibrated using manufacturer guidelines. Two oysters were sampled per genotype-line in each replicate tank. Partial pressure of CO2 in haemolymph (PCO2e) was calculated from the CCO2 using the modified Henderson-Hasselbalch equation according to Heisler31,32. Metabolic rate (MR) was determined using a closed respiratory system as previously described in Parker et al.16 and Scanes et al.18. Briefly, MR was measured in two oysters per genotype-line, per tank by placing oysters in a closed 500 mL glass chamber containing filtered seawater (5 µm) set at the correct treatment conditions. Oxygen concentrations were then measured within the chamber using a fibre optic dipping probe (PreSens dipping probe DP-PSt3, AS1 Ltd, Regensburg, Germany) and recorded (15 s intervals) until the oxygen concentration had been reduced by 20%, the time taken to reduce oxygen by 20% was recorded. Oysters were removed from the chambers, opened and the tissue was dried at 70 °C for 72 h. Tissue was then weighed on an electronic balance (± 0.001 g), and MR was calculated using Eq. (1):$$MR = frac{{left[ {V_{r} times Delta {text{C}}_{W} O_{2} } right]}}{{Delta t times {text{bw}}}}$$
(1)

where MR is oxygen consumption normalised to 1 g of dry tissue mass (mg O2 g−1 dry tissue mass h−1), Vr is the volume of the respiratory chamber minus the volume of the oyster (L), ΔCWO2 is the change in water oxygen concentration measured (mg O2L−1), Δt is the measuring time (h), bw is the dry tissue mass (g). Equation is modified from Parker et al.16.Data analysisIt was not possible to measure all variables in each oyster, but rather three individuals were needed to fulfil one replicate set of measurements. PCO2e and pHe could be measured in the same individual however, MR and the microbiome were measured in separate individuals. This meant that measurements were taken from 6 oysters per genotype-line, per replicate tank (each measurement replicated twice). To align physiological data with microbiome data we took a conservative approach where data from PCO2e and pHe, MR and the microbiome were randomly matched to individuals from the same genotype-line and replicate tank. This gave us the best approximation and is conservative because it increased variability compared to taking all measurements from the same individual. ANOVA was used to determine the significant (n = 210; P  More