Philippa Kaur

RESEARCH HIGHLIGHT
03 March 2023

Human hunt at least 19% of bat species worldwide — especially flying foxes, which can have wingspans of 1.5 metres. More

Pianka, E. R. Niche overlap and diffuse competition. Proc. Natl. Acad. Sci. 71, 2141–2145 (1974).Article 
ADS 
CAS 
PubMed 
PubMed Central 

Google Scholar 
Chesson, P. Mechanisms of maintenance of species diversity. Annu. Rev. Ecol. Syst. 31, 343–366 (2000).Article 

Google Scholar 
Tokeshi, M. Species Coexistence: Ecological and Evolutionary Perspectives. (Wiley-Blackwell, 2009).Grinnell, J. Geography and evolution. Ecology 5, 225–229 (1924).Article 

Google Scholar 
Roughgarden, J. Resource partitioning among competing species—A coevolutionary approach. Theor. Popul. Biol. 9, 388–424 (1976).Article 
MathSciNet 
CAS 
PubMed 
MATH 

Google Scholar 
Syme, J., Kiszka, J. J. & Parra, G. J. Dynamics of cetacean mixed-species groups: A review and conceptual framework for assessing their functional significance. Front. Mar. Sci. 8, 1–19 (2021).Article 

Google Scholar 
Stensland, E., Angerbjörn, A. & Berggren, P. Mixed species groups in mammals. Mamm. Rev. 33, 205–223 (2003).Article 

Google Scholar 
Cords, M. & Würsig, B. A Mix of Species: Associations of Heterospecifics Among Primates and Dolphins. in Primates and Cetaceans: Field Research and Conservation of Complex Mammalian Societies (eds. Yamagiwa, J. & Karczmarski, L.) 409–431 (Springer, 2014). doi:https://doi.org/10.1007/978-4-431-54523-1_21.Goodale, E., Beauchamp, G. & Ruxton, G. D. Mixed-Species Groups of Animals: Behavior, Community Structure, and Conservation. (Academic Press, 2017).Krause, J. & Ruxton, G. D. Living in Groups. Oxford Series in Ecology and Evolution (Oxford University Press, 2002).Heymann, E. W. & Buchanan-Smith, H. M. The behavioural ecology of mixed-species troops of callitrichine primates. Biol. Rev. 75, 169–190 (2000).Article 
CAS 
PubMed 

Google Scholar 
Sridhar, H. & Guttal, V. Friendship across species borders: factors that facilitate and constrain heterospecific sociality. Philos. Trans. R. Soc. B Biol. Sci. 373, 1–9 (2018).Greenberg, R. Birds of many feathers: The formation and structure of mixed-species flocks of forest birds. in On the Move: How and Why Animals Travel in groups (eds. Boinski, S. & Gerber, P. A.) 521–558 (University of Chicago Press, 2000).Waser, P. M. ‘Chance’ and mixed-species associations. Behav. Ecol. Sociobiol. 15, 197–202 (1984).Article 

Google Scholar 
Whitesides, G. H. Interspecific associations of Diana monkeys, Cercopithecus diana, in Sierra Leone, West Africa: biological significance or chance?. Anim. Behav. 37, 760–776 (1989).Article 

Google Scholar 
Waser, P. M. Primate polyspecific associations: Do they occur by chance?. Anim. Behav. 30, 1–8 (1982).Article 

Google Scholar 
Alexander, R. D. The evolution of social behavior. Annu. Rev. Ecol. Syst. 5, 325–383 (1974).Article 

Google Scholar 
Kasozi, H. & Montgomery, R. A. Variability in the estimation of ungulate group sizes complicates ecological inference. Ecol. Evol. 10, 6881–6889 (2020).Article 
PubMed 
PubMed Central 

Google Scholar 
Syme, J., Kiszka, J. J. & Parra, G. J. How to define a dolphin ‘group’? Need for consistency and justification based on objective criteria. Ecol. Evol. 12, 1–18 (2022).Article 

Google Scholar 
Hutchinson, J. M. C. & Waser, P. M. Use, misuse and extensions of ‘ideal gas’ models of animal encounter. Biol. Rev. 82, 335–359 (2007).Article 
PubMed 

Google Scholar 
Gotelli, N. J. Null model analysis of species co-occurrence patterns. Ecology 81, 2606–2621 (2000).Article 

Google Scholar 
Astaras, C., Krause, S., Mattner, L., Rehse, C. & Waltert, M. Associations between the drill (Mandrillus leucophaeus) and sympatric monkeys in Korup National Park. Cameroon. Am. J. Primatol. 73, 127–134 (2011).Article 
PubMed 

Google Scholar 
Mammides, C., Chen, J., Goodale, U. M., Kotagama, S. W. & Goodale, E. Measurement of species associations in mixed-species bird flocks across environmental and human disturbance gradients. Ecosphere 9, 1–14 (2018).Article 

Google Scholar 
Ovaskainen, O., Abrego, N., Halme, P. & Dunson, D. Using latent variable models to identify large networks of species-to-species associations at different spatial scales. Methods Ecol. Evol. 7, 549–555 (2016).Article 

Google Scholar 
Pollock, L. J. et al. Understanding co-occurrence by modelling species simultaneously with a Joint Species Distribution Model (JSDM). Methods Ecol. Evol. 5, 397–406 (2014).Article 

Google Scholar 
Warton, D. I. et al. So Many variables: Joint modeling in community ecology. Trends Ecol. Evol. 30, 766–779 (2015).Article 
PubMed 

Google Scholar 
Ovaskainen, O. et al. How to make more out of community data? A conceptual framework and its implementation as models and software. Ecol. Lett. 20, 561–576 (2017).Article 
PubMed 

Google Scholar 
Ovaskainen, O. & Abrego, N. Joint Species Distribution Modelling. (Cambridge University Press, 2020). https://doi.org/10.1017/9781108591720.Blanchet, F. G., Cazelles, K. & Gravel, D. Co-occurrence is not evidence of ecological interactions. Ecol. Lett. 23, 1050–1063 (2020).Article 
PubMed 

Google Scholar 
Haak, C. R., Hui, F. K., Cowles, G. W. & Danylchuk, A. J. Positive interspecific associations consistent with social information use shape juvenile fish assemblages. Ecology 101, 1–16 (2020).Article 

Google Scholar 
Bastianelli, G., Wintle, B. A., Martin, E. H., Seoane, J. & Laiolo, P. Species partitioning in a temperate mountain chain: Segregation by habitat vs. interspecific competition. Ecol. Evol. 7, 2685–2696 (2017).Aspin, T. & House, A. Alpha and beta diversity and species co-occurrence patterns in headwaters supporting rare intermittent-stream specialists. Freshw. Biol. n/a, (2022).Astarloa, A. et al. Identifying main interactions in marine predator-prey networks of the Bay of Biscay. ICES J. Mar. Sci. 76, 2247–2259 (2019).Article 

Google Scholar 
Parra, G. J. Resource partitioning in sympatric delphinids: space use and habitat preferences of Australian snubfin and Indo-Pacific humpback dolphins. J. Anim. Ecol. 75, 862–874 (2006).Article 
PubMed 

Google Scholar 
Parra, G. J., Wojtkowiak, Z., Peters, K. J. & Cagnazzi, D. Isotopic niche overlap between sympatric Australian snubfin and humpback dolphins. Ecol. Evol. 12, 1–11 (2022).Article 

Google Scholar 
Kiszka, J. J. et al. Ecological niche segregation within a community of sympatric dolphins around a tropical island. Mar. Ecol. Prog. Ser. 433, 273–288 (2011).Article 
ADS 

Google Scholar 
Bearzi, M. Dolphin sympatric ecology. Mar. Biol. Res. 1, 165–175 (2005).Article 

Google Scholar 
Zaeschmar, J. R. et al. Occurrence of false killer whales (Pseudorca crassidens) and their association with common bottlenose dolphins (Tursiops truncatus) off northeastern New Zealand. Mar. Mammal Sci. 30, 594–608 (2014).Article 

Google Scholar 
Elliser, C. R. & Herzing, D. L. Long-term interspecies association patterns of Atlantic bottlenose dolphins, Tursiops truncatus, and Atlantic spotted dolphins, Stenella frontalis, in the Bahamas. Mar. Mammal Sci. 32, 38–56 (2016).Article 

Google Scholar 
Kiszka, J. J., Perrin, W. F., Pusineri, C. & Ridoux, V. What drives island-associated tropical dolphins to form mixed-species associations in the southwest Indian Ocean?. J. Mammal. 92, 1105–1111 (2011).Article 

Google Scholar 
Brown, A. M., Bejder, L., Cagnazzi, D., Parra, G. J. & Allen, S. J. The north west cape, Western Australia: A potential hotspot for Indo-Pacific humpback dolphins Sousa chinensis?. Pacific Conserv. Biol. 18, 240–246 (2012).Article 

Google Scholar 
Allen, S. J., Cagnazzi, D., Hodgson, A. J., Loneragan, N. R. & Bejder, L. Tropical inshore dolphins of north-western Australia: Unknown populations in a rapidly changing region. Pacific Conserv. Biol. 18, 56–63 (2012).Article 

Google Scholar 
Palmer, C., Parra, G. J., Rogers, T. & Woinarski, J. Collation and review of sightings and distribution of three coastal dolphin species in waters of the Northern Territory. Australia. Pacific Conserv. Biol. 20, 116–125 (2014).Article 

Google Scholar 
Corkeron, P. J. Aspects of the Behavioral Ecology of Inshore Dolphins Tursiops truncatus and Sousa chinensis in Moreton Bay, Australia. in The Bottlenose Dolphin (eds. Leatherwood, S. & Reeves, R.) 285–293 (Elsevier, 1990). https://doi.org/10.1016/B978-0-12-440280-5.50018-4.Haughey, R. et al. Distribution and habitat preferences of Indo-Pacific Bottlenose Dolphins (Tursiops aduncus) inhabiting coastal waters with mixed levels of protection. Front. Mar. Sci. 8, 1–20 (2021).Article 

Google Scholar 
Hanf, D., Hodgson, A. J., Kobryn, H., Bejder, L. & Smith, J. N. Dolphin distribution and habitat suitability in North Western Australia: Applications and Implications of a Broad-Scale, Non-targeted Dataset. Front. Mar. Sci. 8, 1–18 (2022).Article 

Google Scholar 
Hunt, T. N., Allen, S. J., Bejder, L. & Parra, G. J. Identifying priority habitat for conservation and management of Australian humpback dolphins within a marine protected area. Sci. Rep. 10, 1–14 (2020).Article 

Google Scholar 
Hunt, T. N. Demography, habitat use and social structure of Australian humpback dolphins (Sousa sahulensis) around the North West Cape, Western Australia: Implications for conservation and management. PhD Thesis, Flinders University, Adelaide, Australia. (Flinders University, 2018).Cassata, L. & Collins, L. B. Coral reef communities, habitats, and substrates in and near sanctuary zones of Ningaloo marine park. J. Coast. Res. 241, 139–151 (2008).Article 

Google Scholar 
CALM MPRA. Management plan for the Ningaloo Marine Park and Muiron Islands Marine Management Area 2005–2015. (2005).Hunt, T. N. et al. Demographic characteristics of Australian humpback dolphins reveal important habitat toward the southwestern limit of their range. Endanger. Species Res. 32, 71–88 (2017).Article 

Google Scholar 
Mann, J. Behavioral sampling methods for cetaceans: A review and critique. Mar. Mammal Sci. 15, 102–122 (1999).Article 

Google Scholar 
Python Software Foundation. Python Language Reference, version 3.8.0. at https://www.python.org/ (2016).QGIS Development Team. QGIS Geographic Information System, version 3.8.3 Zanzibar. at http://qgis.osgeo.org (2019).Zanardo, N., Parra, G., Passadore, C. & Möller, L. Ensemble modelling of southern Australian bottlenose dolphin Tursiops sp. distribution reveals important habitats and their potential ecological function. Mar. Ecol. Prog. Ser. 569, 253–266 (2017).Hanberry, B. B. Finer grain size increases effects of error and changes influence of environmental predictors on species distribution models. Ecol. Inform. 15, 8–13 (2013).Article 

Google Scholar 
Gottschalk, T. K., Aue, B., Hotes, S. & Ekschmitt, K. Influence of grain size on species–habitat models. Ecol. Modell. 222, 3403–3412 (2011).Article 

Google Scholar 
Zuur, A. F., Ieno, E. N. & Elphick, C. S. A protocol for data exploration to avoid common statistical problems. Methods Ecol. Evol. 1, 3–14 (2010).Article 

Google Scholar 
Passadore, C., Möller, L. M., Diaz-Aguirre, F. & Parra, G. J. Modelling dolphin distribution to inform future spatial conservation decisions in a marine protected area. Sci. Rep. 8, 1–14 (2018).Article 
CAS 

Google Scholar 
Parra, G. J., Schick, R. & Corkeron, P. J. Spatial distribution and environmental correlates of Australian snubfin and Indo-Pacific humpback dolphins. Ecography (Cop.) 29, 396–406 (2006).Article 

Google Scholar 
Conrad, O. et al. System for Automated Geoscientific Analyses (SAGA) v. 2.1.4. Geosci. Model Dev. 8, 1991–2007 (2015).R Core Team. R version 3.6.1. at https://www.r-project.org/ (2019).RStudio Team. RStudio: Integrated Develpment for R. at http://rstudio.com/ (2019).Dormann, C. F. et al. Collinearity: A review of methods to deal with it and a simulation study evaluating their performance. Ecography (Cop.) 36, 27–46 (2013).Article 

Google Scholar 
Tikhonov, G. et al. Joint species distribution modelling with the r-package Hmsc. Methods Ecol. Evol. 11, 442–447 (2020).Article 
PubMed 
PubMed Central 

Google Scholar 
Gelman, A. & Rubin, D. B. Inference from iterative simulation using multiple sequences. Stat. Sci. 7, 457–472 (1992).Article 
MATH 

Google Scholar 
Pearce, J. & Ferrier, S. Evaluating the predictive performance of habitat models developed using logistic regression. Ecol. Modell. 133, 225–245 (2000).Article 

Google Scholar 
Tjur, T. Coefficients of determination in logistic regression models—A new proposal: The coefficient of discrimination. Am. Stat. 63, 366–372 (2009).Article 
MathSciNet 
MATH 

Google Scholar 
Syme, J. The behavioural ecology of mixed-species groups of delphinids. PhD Thesis, Flinders University, Adelaide, Australia. (Flinders University, 2023).Wang, J. Y. Bottlenose Dolphin, Tursiops aduncus, Indo-Pacific Bottlenose Dolphin. in Encyclopedia of Marine Mammals (eds. Würsig, B., Thewissen, J. G. M. & Kovacs, K. M.) 125–130 (Elsevier, 2018). https://doi.org/10.1016/B978-0-12-804327-1.00073-X.Parra, G. J. & Jefferson, T. A. Humpback Dolphins. in Encyclopedia of Marine Mammals (eds. Würsig, B., Thewissen, J. G. M. & Kovacs, K. M.) 483–489 (Elsevier, 2018). https://doi.org/10.1016/B978-0-12-804327-1.00153-9.Dröge, E., Creel, S., Becker, M. S. & M’soka, J. Spatial and temporal avoidance of risk within a large carnivore guild. Ecol. Evol. 7, 189–199 (2017).Article 
PubMed 

Google Scholar 
Browning, N. E., Cockcroft, V. G. & Worthy, G. A. J. Resource partitioning among South African delphinids. J. Exp. Mar. Bio. Ecol. 457, 15–21 (2014).Article 

Google Scholar 
Kiszka, J. J., Méndez-Fernandez, P., Heithaus, M. R. & Ridoux, V. The foraging ecology of coastal bottlenose dolphins based on stable isotope mixing models and behavioural sampling. Mar. Biol. 161, 953–961 (2014).Article 
CAS 

Google Scholar 
Saayman, G. S. & Tayler, C. K. The socioecology of humpback dolphins (Sousa sp.). in Behavior of Marine Animals Current Perspectives in Research Volume 3: Cetaceans (eds. Winn, H. E. & Olla, B. L.) 165–226 (Springer, 1979).Gowans, S. & Whitehead, H. Distribution and habitat partitioning by small odontocetes in the Gully, a submarine canyon on the Scotian Shelf. Can. J. Zool. 73, 1599–1608 (1995).Article 

Google Scholar 
Clua, E. Mixed-species feeding aggregation of dolphins, large tunas and seabirds in the Azores. Aquat. Living Resour. 14, 11–18 (2001).Article 

Google Scholar 
Quérouil, S. et al. Why do dolphins form mixed-species associations in the azores?. Ethology 114, 1183–1194 (2008).Article 

Google Scholar 
Heithaus, M. R. & Dill, L. M. Food availability and tiger shark predation risk influence bottlenose dolphin habitat use. Ecology 83, 480–491 (2002).Article 

Google Scholar  More

Acropora Biological Review Team. Atlantic Acropora Status Review: Report to National Marine Fisheries Service (Acropora Biological Review Team, 2005).
Google Scholar 
Gardner, T. A., Côté, I. M., Gill, J. A., Grant, A. & Watkinson, A. R. Long-term region-wide declines in Caribbean Corals. Science 301, 958–960 (2003).ADS 
CAS 
PubMed 

Google Scholar 
Jackson, E. J., Donovan, M., Cramer, K. & Lam, V. Status and Trends of Caribbean Coral Reefs: 1970–2012 306 (International Union for the Conservation of Nature, 2012).
Google Scholar 
Schopmeyer, S. A. et al. Regional restoration benchmarks for Acropora cervicornis. Coral Reefs 36, 1047–1057 (2017).ADS 

Google Scholar 
Lirman, D. et al. Propagation of the threatened staghorn coral Acropora cervicornis: Methods to minimize the impacts of fragment collection and maximize production. Coral Reefs 29, 729–735 (2010).ADS 

Google Scholar 
Mercado-Molina, A. E., Ruiz-Diaz, C. P. & Sabat, A. M. Demographics and dynamics of two restored populations of the threatened reef-building coral Acropora cervicornis. J. Nat. Conserv. 24, 17–23 (2015).
Google Scholar 
Young, C., Schopmeyer, S. & Lirman, D. A review of reef restoration and coral propagation using the threatened genus Acropora in the Caribbean and Western Atlantic. Bull. Mar. Sci. 88, 1075–1098 (2012).
Google Scholar 
Carne, L., Kaufman, L. & Scavo, K. Measuring success for Caribbean acroporid restoration: key results from ten years of work in southern Belize. In Proc. 13th International Coral Reef Symposium, Honolulu (Abstract No. 27909) (2016).Ware, M. et al. Survivorship and growth in staghorn coral (Acropora cervicornis) outplanting projects in the Florida Keys National Marine Sanctuary. PLoS ONE 15, e0231817 (2020).CAS 
PubMed 
PubMed Central 

Google Scholar 
Shaver, E. C. et al. A roadmap to integrating resilience into the practice of coral reef restoration. Glob. Change Biol. 28, 4751–4764 (2022).CAS 

Google Scholar 
DeFilippo, L. B. et al. Assessing the potential for demographic restoration and assisted evolution to build climate resilience in coral reefs. Ecol. Appl. 32, e2650 (2022).PubMed 
PubMed Central 

Google Scholar 
Lapointe, B. E., Brewton, R. A., Herren, L. W., Porter, J. W. & Hu, C. Nitrogen enrichment, altered stoichiometry, and coral reef decline at Looe Key, Florida Keys, USA: A 3-decade study. Mar. Biol. 166, 108 (2019).
Google Scholar 
Montenero, K. A. Florida Keys Integrated Ecosystem Assessment Ecosystem Status Report. https://doi.org/10.25923/F7CE-ST38.Palacio-Castro, A. M., Dennison, C. E., Rosales, S. M. & Baker, A. C. Variation in susceptibility among three Caribbean coral species and their algal symbionts indicates the threatened staghorn coral, Acropora cervicornis, is particularly susceptible to elevated nutrients and heat stress. Coral Reefs 40, 1601–1613 (2021).
Google Scholar 
Vega Thurber, R. L. et al. Chronic nutrient enrichment increases prevalence and severity of coral disease and bleaching. Glob. Change Biol. 20, 544–554 (2014).ADS 

Google Scholar 
Zaneveld, J. R. et al. Overfishing and nutrient pollution interact with temperature to disrupt coral reefs down to microbial scales. Nat. Commun. 7, 11833 (2016).ADS 
CAS 
PubMed 
PubMed Central 

Google Scholar 
Bruno, J. F. et al. Thermal stress and coral cover as drivers of coral disease outbreaks. PLoS Biol. 5, e124 (2007).PubMed 
PubMed Central 

Google Scholar 
Wiedenmann, J. et al. Nutrient enrichment can increase the susceptibility of reef corals to bleaching. Nat. Clim. Change 3, 160–164 (2012).ADS 

Google Scholar 
Rädecker, N., Pogoreutz, C., Voolstra, C. R., Wiedenmann, J. & Wild, C. Nitrogen cycling in corals: The key to understanding holobiont functioning? Trends Microbiol. 23, 490–497 (2015).PubMed 

Google Scholar 
Shantz, A. A. & Burkepile, D. E. Context-dependent effects of nutrient loading on the coral–algal mutualism. Ecology 95, 1995–2005 (2014).PubMed 

Google Scholar 
Burkepile, D. E. et al. Nitrogen identity drives differential impacts of nutrients on coral bleaching and mortality. Ecosystems 23, 798–811 (2020).CAS 

Google Scholar 
Fabricius, K. E. Effects of terrestrial runoff on the ecology of corals and coral reefs: Review and synthesis. Mar. Pollut. Bull. 50, 125–146 (2005).CAS 
PubMed 

Google Scholar 
Ferrier-Pagès, C., Gattuso, J.-P., Dallot, S. & Jaubert, J. Effect of nutrient enrichment on growth and photosynthesis of the zooxanthellate coral Stylophora pistillata. Coral Reefs 19, 103–113 (2000).
Google Scholar 
Bourne, D. G., Morrow, K. M. & Webster, N. S. Insights into the coral microbiome: Underpinning the health and resilience of reef ecosystems. Annu. Rev. Microbiol. 70, 317–340 (2016).CAS 
PubMed 

Google Scholar 
Krediet, C. J., Ritchie, K. B., Paul, V. J. & Teplitski, M. Coral-associated micro-organisms and their roles in promoting coral health and thwarting diseases. Proc. R. Soc. B Biol. Sci. 280, 20122328 (2013).
Google Scholar 
Mao-Jones, J., Ritchie, K. B., Jones, L. E. & Ellner, S. P. How microbial community composition regulates coral disease development. PLoS Biol. 8, e1000345 (2010).PubMed 
PubMed Central 

Google Scholar 
Zilber-Rosenberg, I. & Rosenberg, E. Role of microorganisms in the evolution of animals and plants: The hologenome theory of evolution. FEMS Microbiol. Rev. 32, 723–735 (2008).CAS 
PubMed 

Google Scholar 
West, A. G. et al. The microbiome in threatened species conservation. Biol. Conserv. 229, 85–98 (2019).
Google Scholar 
Ritchie, K. Regulation of microbial populations by coral surface mucus and mucus-associated bacteria. Mar. Ecol. Prog. Ser. 322, 1–14 (2006).ADS 
CAS 

Google Scholar 
Rohwer, F., Seguritan, V., Azam, F. & Knowlton, N. Diversity and distribution of coral-associated bacteria. Mar. Ecol. Prog. Ser. 243, 1–10 (2002).ADS 

Google Scholar 
Klinges, G., Maher, R. L., Thurber, R. L. V. & Muller, E. M. Parasitic ‘Candidatus aquarickettsia rohweri’ is a marker of disease susceptibility in Acropora cervicornis but is lost during thermal stress. Environ. Microbiol. 22, 5341–5355 (2020).CAS 
PubMed 
PubMed Central 

Google Scholar 
Williams, S. D. et al. Geographically driven differences in microbiomes of Acropora cervicornis originating from different regions of Florida’s Coral Reef. PeerJ 10, e13574 (2022).PubMed 
PubMed Central 

Google Scholar 
Klinges, J. G., Patel, S. H., Duke, W. C., Muller, E. M. & Vega Thurber, R. L. Phosphate enrichment induces increased dominance of the parasite Aquarickettsia in the coral Acropora cervicornis. FEMS Microbiol. Ecol. 98, 013 (2022).
Google Scholar 
Rosales, S. M. et al. Microbiome differences in disease-resistant vs susceptible Acropora corals subjected to disease challenge assays. Sci. Rep. 9, 18279 (2019).ADS 
CAS 
PubMed 
PubMed Central 

Google Scholar 
Gignoux-Wolfsohn, S., Precht, W., Peters, E., Gintert, B. & Kaufman, L. Ecology, histopathology, and microbial ecology of a white-band disease outbreak in the threatened staghorn coral Acropora cervicornis. Dis. Aquat. Org. 137, 217–237 (2020).
Google Scholar 
Miller, N., Maneval, P., Manfrino, C., Frazer, T. K. & Meyer, J. L. Spatial distribution of microbial communities among colonies and genotypes in nursery-reared Acropora cervicornis. PeerJ 8, e9635 (2020).PubMed 
PubMed Central 

Google Scholar 
Aguirre, E. G., Million, W. C., Bartels, E., Krediet, C. J. & Kenkel, C. D. Host-specific epibiomes of distinct Acropora cervicornis genotypes persist after field transplantation. Coral Reefs. https://doi.org/10.1007/s00338-022-02218-x (2022).Article 

Google Scholar 
Shaver, E. C. et al. Effects of predation and nutrient enrichment on the success and microbiome of a foundational coral. Ecology 98, 830–839 (2017).PubMed 

Google Scholar 
Muller, E. M., Bartels, E. & Baums, I. B. Bleaching causes loss of disease resistance within the threatened coral species Acropora cervicornis. eLife 7, e35066 (2018).PubMed 
PubMed Central 

Google Scholar 
Miller, M. W. et al. Genotypic variation in disease susceptibility among cultured stocks of Elkhorn and Staghorn corals. PeerJ 7, e6751 (2019).PubMed 
PubMed Central 

Google Scholar 
Sunagawa, S., Woodley, C. M. & Medina, M. Threatened corals provide underexplored microbial habitats. PLoS ONE 5, e9554 (2010).ADS 
PubMed 
PubMed Central 

Google Scholar 
Pantos, O. et al. The bacterial ecology of a plague-like disease affecting the Caribbean coral Montastrea annularis. Environ. Microbiol. 5, 370–382 (2003).CAS 
PubMed 

Google Scholar 
Sheu, S.-Y., Liu, L.-P., Tang, S.-L. & Chen, W.-M. Thalassotalea euphylliae sp. nov., isolated from the torch coral Euphyllia glabrescens. Int. J. Syst. Evol. Microbiol. 66, 5039–5045 (2016).CAS 
PubMed 

Google Scholar 
Nakagawa, T., Iino, T., Suzuki, K.-I. & Harayama, S. Ferrimonas futtsuensis sp. nov. and Ferrimonas kyonanensis sp. nov., selenate-reducing bacteria belonging to the Gammaproteobacteria isolated from Tokyo Bay. Int. J. Syst. Evol. Microbiol. 56, 2639–2645 (2006).CAS 
PubMed 

Google Scholar 
Maher, R. L. et al. Coral microbiomes demonstrate flexibility and resilience through a reduction in community diversity following a thermal stress event. Front. Ecol. Evol. 8, 1 (2020).ADS 

Google Scholar 
Bourne, D., Iida, Y., Uthicke, S. & Smith-Keune, C. Changes in coral-associated microbial communities during a bleaching event. ISME J. 2, 350–363 (2008).CAS 
PubMed 

Google Scholar 
Ziegler, M. et al. Coral bacterial community structure responds to environmental change in a host-specific manner. Nat. Commun. 10, 3092 (2019).ADS 
PubMed 
PubMed Central 

Google Scholar 
McDevitt-Irwin, J. M. et al. Responses of coral-associated bacterial communities to local and global stressors. Front. Mar. Sci. 4, 262 (2017).
Google Scholar 
Klinges, J. G. et al. Phylogenetic, genomic, and biogeographic characterization of a novel and ubiquitous marine invertebrate-associated Rickettsiales parasite, Candidatus aquarickettsia rohweri, gen. nov., sp. nov. ISME J. 13, 2938–2953 (2019).PubMed 
PubMed Central 

Google Scholar 
Muscatine, L., Falkowski, P. G., Dubinsky, Z., Cook, P. A. & McCloskey, L. R. The effect of external nutrient resources on the population dynamics of zooxanthellae in a reef coral. Proc. R. Soc. Lond. B 236, 311–324 (1989).ADS 

Google Scholar 
Waite, D. W. et al. Comparative genomic analysis of the class Epsilonproteobacteria and proposed reclassification to Epsilonbacteraeota (phyl. Nov.). Front. Microbiol. 8, 682 (2017).PubMed 
PubMed Central 

Google Scholar 
Waite, D. W. et al. Addendum: Comparative genomic analysis of the class Epsilonproteobacteria and proposed reclassification to Epsilonbacteraeota (phyl. Nov.). Front. Microbiol. 9, 772 (2018).PubMed 
PubMed Central 

Google Scholar 
Rosales, S. M. et al. Bacterial metabolic potential and micro-eukaryotes enriched in stony coral tissue loss disease lesions. Front. Mar. Sci. 8, 776859 (2022).
Google Scholar 
Ricci, F. et al. Beneath the surface: Community assembly and functions of the coral skeleton microbiome. Microbiome 7, 159 (2019).PubMed 
PubMed Central 

Google Scholar 
Yang, S.-H. et al. Metagenomic, phylogenetic, and functional characterization of predominant endolithic green sulfur bacteria in the coral Isopora palifera. Microbiome 7, 3 (2019).PubMed 
PubMed Central 

Google Scholar 
Cai, L. et al. Metagenomic analysis reveals a green sulfur bacterium as a potential coral symbiont. Sci. Rep. 7, 9320 (2017).ADS 
PubMed 
PubMed Central 

Google Scholar 
Allgeier, J. E., Burkepile, D. E. & Layman, C. A. Animal pee in the sea: Consumer-mediated nutrient dynamics in the world’s changing oceans. Glob. Change Biol. 23, 2166–2178 (2017).ADS 

Google Scholar 
Hughes, D. J. et al. Coral reef survival under accelerating ocean deoxygenation. Nat. Clim. Change 10, 296–307 (2020).ADS 
CAS 

Google Scholar 
Miura, N. et al. Ruegeria sp. strains isolated from the reef-building coral Galaxea fascicularis inhibit growth of the temperature-dependent pathogen Vibrio coralliilyticus. Mar. Biotechnol. 21, 1–8 (2019).CAS 

Google Scholar 
Bruno, J. F., Petes, L. E., Harvell, C. D. & Hettinger, A. Nutrient enrichment can increase the severity of coral diseases. Ecol. Lett. 6, 1056–1061 (2003).
Google Scholar 
Ezzat, L. et al. Thermal stress interacts with surgeonfish feces to increase coral susceptibility to dysbiosis and reduce tissue regeneration. Front. Microbiol. 12, 620458 (2021).PubMed 
PubMed Central 

Google Scholar 
Gajigan, A. P., Diaz, L. A. & Conaco, C. Resilience of the prokaryotic microbial community of Acropora digitifera to elevated temperature. Microbiol. Open 6, e00478 (2017).
Google Scholar 
MacKnight, N. J. et al. Microbial dysbiosis reflects disease resistance in diverse coral species. Commun. Biol. 4, 679 (2021).PubMed 
PubMed Central 

Google Scholar 
Palacio-Castro, A. M., Rosales, S. M., Dennison, C. E. & Baker, A. C. Microbiome signatures in Acropora cervicornis are associated with genotypic resistance to elevated nutrients and heat stress. Coral Reefs 41, 1389–1403 (2022).
Google Scholar 
Vollmer, S. V. & Kline, D. I. Natural disease resistance in threatened staghorn corals. PLoS ONE 3, e3718 (2008).ADS 
PubMed 
PubMed Central 

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

Google Scholar 
Parkinson, J. E. et al. Extensive transcriptional variation poses a challenge to thermal stress biomarker development for endangered corals. Mol. Ecol. 27, 1103–1119 (2018).CAS 
PubMed 

Google Scholar 
Siebeck, U. E., Logan, D. & Marshall, N. J. CoralWatch—A flexible coral bleaching monitoring tool for you and your group. In Proc. 11th Int. Coral Reef Symp. Ft Lauderdale, Florida, 7–11 July, Vol. 1392, 5 (2008).Parada, A. E., Needham, D. M. & Fuhrman, J. A. Every base matters: Assessing small subunit rRNA primers for marine microbiomes with mock communities, time series and global field samples. Environ. Microbiol. 18, 1403–1414 (2016).CAS 
PubMed 

Google Scholar 
Apprill, A., McNally, S., Parsons, R. & Weber, L. Minor revision to V4 region SSU rRNA 806R gene primer greatly increases detection of SAR11 bacterioplankton. Aquat. Microb. Ecol. 75, 129–137 (2015).
Google Scholar 
Messyasz, A., Maher, R. L., Meiling, S. S. & Thurber, R. V. Nutrient enrichment predominantly affects low diversity microbiomes in a marine trophic symbiosis between algal farming fish and corals. Microorganisms 9, 1873 (2021).CAS 
PubMed 
PubMed Central 

Google Scholar 
Martin, M. Cutadapt removes adapter sequences from high-throughput sequencing reads. EMBnet. J. 17, 10–12 (2011).
Google Scholar 
Callahan, B. J. et al. DADA2: High-resolution sample inference from Illumina amplicon data. Nat. Methods 13, 581–583 (2016).CAS 
PubMed 
PubMed Central 

Google Scholar 
McMurdie, P. J. & Holmes, S. phyloseq: An R package for reproducible interactive analysis and graphics of microbiome census data. PLoS ONE 8, e61217 (2013).ADS 
CAS 
PubMed 
PubMed Central 

Google Scholar 
Magurran, A. E. Ecological Diversity and Its Measurement (Princeton University Press, 1988).
Google Scholar 
Lahti, L. & Shetty, S. Microbiome R Package.Gloor, G. B., Macklaim, J. M., Pawlowsky-Glahn, V. & Egozcue, J. J. Microbiome datasets are compositional: And this is not optional. Front. Microbiol. 8, 2224 (2017).PubMed 
PubMed Central 

Google Scholar 
Anderson, M. J. A new method for non-parametric multivariate analysis of variance. Austral Ecol. 26, 32–46 (2001).
Google Scholar 
Oksanen, J. et al. vegan: Community Ecology Package (2019).Martinez Arbizu, P. pairwiseAdonis: Pairwise multilevel comparison using adonis. R Package Version 0.0.1 (2017).Anderson, M. J. Distance-based tests for homogeneity of multivariate dispersions. Biometrics 62, 245–253 (2006).MathSciNet 
PubMed 
MATH 

Google Scholar 
Kaul, A., Mandal, S., Davidov, O. & Peddada, S. D. Analysis of microbiome data in the presence of excess zeros. Front. Microbiol. 8, 2114 (2017).PubMed 
PubMed Central 

Google Scholar  More

Ritz, K. & Young, I. M. Interactions between soil structure and fungi. Mycologist 18, 52–59 (2004).Article 

Google Scholar 
Schimel, J. P. & Schaeffer, S. M. Microbial control over carbon cycling in soil. Front. Microbiol. 3, 348 (2012).Article 
CAS 
PubMed 
PubMed Central 

Google Scholar 
Six, J., Bossuyt, H., Degryze, S. & Denef, K. A history of research on the link between (micro)aggregates, soil biota, and soil organic matter dynamics. Soil Tillage Res. 79, 7–31 (2004).Article 

Google Scholar 
van der Heijden, M. G. A. & Wagg, C. Soil microbial diversity and agro-ecosystem functioning. Plant Soil 363, 1–5 (2013).Article 
CAS 

Google Scholar 
Winter, S. et al. Effects of vegetation management intensity on biodiversity and ecosystem services in vineyards: a meta-analysis. J. Appl. Ecol. 55, 2484–2495 (2018).Article 
PubMed 
PubMed Central 

Google Scholar 
Belmonte, S. A. et al. Effect of long-term soil management on the mutual interaction among soil organic matter, microbial activity and aggregate stability in a vineyard. Pedosphere 28, 288–298 (2018).Article 
CAS 

Google Scholar 
Bronick, C. J. & Lal, R. Soil structure and management: a review. Geoderma 124, 3–22 (2005).Article 
ADS 
CAS 

Google Scholar 
Kratschmer, S. et al. Enhancing flowering plant functional richness improves wild bee diversity in vineyard inter-rows in different floral kingdoms. Ecol. Evol. 11, 7927–7945 (2021).Article 
PubMed 
PubMed Central 

Google Scholar 
Constancias, F. et al. Microscale evidence for a high decrease of soil bacterial density and diversity by cropping. Agron. Sustain. Dev. 34, 831–840 (2014).Article 
CAS 

Google Scholar 
Schmidt, R., Gravuer, K., Bossange, A. V., Mitchell, J. & Scow, K. Long-term use of cover crops and no-till shift soil microbial community life strategies in agricultural soil. PLoS One 13, e0192953 (2018).Article 
PubMed 
PubMed Central 

Google Scholar 
Vink, S. N., Chrysargyris, A., Tzortzakis, N. & Salles, J. F. Bacterial community dynamics varies with soil management and irrigation practices in grapevines (Vitis vinifera L.). Appl. Soil Ecol. 158, 103807 (2021).Article 

Google Scholar 
Pingel, M., Reineke, A. & Leyer, I. A 30-years vineyard trial: plant communities, soil microbial communities and litter decomposition respond more to soil treatment than to N fertilization. Agr. Ecosyst. Environ. 272, 114–125 (2019).Article 
CAS 

Google Scholar 
Sharma-Poudyal, D., Schlatter, D., Yin, C., Hulbert, S. & Paulitz, T. Long-term no-till: a major driver of fungal communities in dryland wheat cropping systems. PLoS One 12, e0184611 (2017).Article 
PubMed 
PubMed Central 

Google Scholar 
Hungria, M., Franchini, J. C., Brandão-Junior, O., Kaschuk, G. & Souza, R. A. Soil microbial activity and crop sustainability in a long-term experiment with three soil-tillage and two crop-rotation systems. Appl. Soil. Ecol. 42, 288–296 (2009).Article 

Google Scholar 
Pascault, N. et al. In situ dynamics of microbial communities during decomposition of wheat, rape, and alfalfa residues. Microb. Ecol. 60, 816–828 (2010).Article 
PubMed 

Google Scholar 
Tresch, S. et al. Litter decomposition driven by soil fauna, plant diversity and soil management in urban gardens. Sci. Total Environ. 658, 1614–1629 (2019).Article 
ADS 
CAS 
PubMed 

Google Scholar 
Faust, S., Koch, H.-J., Dyckmans, J. & Joergensen, R. G. Response of maize leaf decomposition in litterbags and soil bags to different tillage intensities in a long-term field trial. Appl. Soil. Ecol. 141, 38–44 (2019).Article 

Google Scholar 
Liu, Y.-R. et al. New insights into the role of microbial community composition in driving soil respiration rates. Soil Biol. Biochem. 118, 35–41 (2018).Article 
CAS 

Google Scholar 
Yang, C., Liu, N. & Zhang, Y. Soil aggregates regulate the impact of soil bacterial and fungal communities on soil respiration. Geoderma 337, 444–452 (2019).Article 
ADS 
CAS 

Google Scholar 
Delgado-Baquerizo, M. et al. Microbial diversity drives multifunctionality in terrestrial ecosystems. Nat. Commun. 7, 10541 (2016).Article 
ADS 
CAS 
PubMed 
PubMed Central 

Google Scholar 
Bruggisser, O. T., Schmidt-Entling, M. H. & Bacher, S. Effects of vineyard management on biodiversity at three trophic levels. Biol. Cons. 143, 1521–1528 (2010).Article 

Google Scholar 
Lienhard, P. et al. Pyrosequencing evidences the impact of cropping on soil bacterial and fungal diversity in Laos tropical grassland. Agron. Sustain. Dev. 34, 525–533 (2014).Article 

Google Scholar 
Schnoor, T. K., Lekberg, Y., Rosendahl, S. & Olsson, P. A. Mechanical soil disturbance as a determinant of arbuscular mycorrhizal fungal communities in semi-natural grassland. Mycorrhiza 21, 211–220 (2011).Article 
PubMed 

Google Scholar 
Kazakou, E. et al. A plant trait-based response-and-effect framework to assess vineyard inter-row soil management. Bot. Lett. 163, 373–388 (2016).Article 

Google Scholar 
Svensson, J. R., Lindegarth, M., Jonsson, P. R. & Pavia, H. Disturbance-diversity models: What do they really predict and how are they tested?. Proc. Biol. Sci. 279, 2163–2170 (2012).PubMed 
PubMed Central 

Google Scholar 
Bao, T. et al. Moderate disturbance increases the PLFA diversity and biomass of the microbial community in biocrusts in the Loess Plateau region of China. Plant Soil 451, 499–513 (2020).Article 
CAS 

Google Scholar 
Liu, J. et al. Soil carbon content drives the biogeographical distribution of fungal communities in the black soil zone of northeast China. Soil Biol. Biochem. 83, 29–39 (2015).Article 
CAS 

Google Scholar 
Cotton, J. & Acosta-Martínez, V. Intensive tillage converting grassland to cropland immediately reduces soil microbial community size and organic carbon. Agric. Environ. Lett. 3, 180047 (2018).Article 

Google Scholar 
Poeplau, C. et al. Temporal dynamics of soil organic carbon after land-use change in the temperate zone – carbon response functions as a model approach. Glob. Change Biol. 17, 2415–2427 (2011).Article 
ADS 

Google Scholar 
Burns, K. N. et al. Vineyard soil bacterial diversity and composition revealed by 16S rRNA genes: differentiation by vineyard management. Soil Biol. Biochem. 103, 337–348 (2016).Article 
CAS 

Google Scholar 
Steiner, M. et al. Local conditions matter: minimal and variable effects of soil disturbance on microbial communities and functions in European vineyards. PLoS One 18, e0280516 (2023).Article 
CAS 
PubMed 
PubMed Central 

Google Scholar 
Zeng, J. et al. Nitrogen fertilization directly affects soil bacterial diversity and indirectly affects bacterial community composition. Soil Biol. Biochem. 92, 41–49 (2016).Article 
CAS 

Google Scholar 
Fierer, N. & Jackson, R. B. The diversity and biogeography of soil bacterial communities. Proc. Natl. Acad. Sci. U.S.A. 103, 626–631 (2006).Article 
ADS 
CAS 
PubMed 
PubMed Central 

Google Scholar 
Eisenhauer, N. Plant diversity effects on soil microorganisms: spatial and temporal heterogeneity of plant inputs increase soil biodiversity. Pedobiologia 59, 175–177 (2016).Article 

Google Scholar 
Porazinska, D. L. et al. Plant diversity and density predict belowground diversity and function in an early successional alpine ecosystem. Ecology 99, 1942–1952 (2018).Article 
PubMed 

Google Scholar 
Prober, S. M. et al. Plant diversity predicts beta but not alpha diversity of soil microbes across grasslands worldwide. Ecol. Lett. 18, 85–95 (2015).Article 
PubMed 

Google Scholar 
Sun, Y.-Q., Wang, J., Shen, C., He, J.-Z. & Ge, Y. Plant evenness modulates the effect of plant richness on soil bacterial diversity. Sci. Total Environ. 662, 8–14 (2019).Article 
ADS 
CAS 
PubMed 

Google Scholar 
Kuzyakov, Y. Priming effects: interactions between living and dead organic matter. Soil Biol. Biochem. 42, 1363–1371 (2010).Article 
CAS 

Google Scholar 
Huo, C., Luo, Y. & Cheng, W. Rhizosphere priming effect: a meta-analysis. Soil Biol. Biochem. 111, 78–84 (2017).Article 
CAS 

Google Scholar 
Dimassi, B. et al. Effect of nutrients availability and long-term tillage on priming effect and soil C mineralization. Soil Biol. Biochem. 78, 332–339 (2014).Article 
CAS 

Google Scholar 
Prescott, C. E. Litter decomposition: What controls it and how can we alter it to sequester more carbon in forest soils?. Biogeochemistry 101, 133–149 (2010).Article 
CAS 

Google Scholar 
Petraglia, A. et al. Litter decomposition: effects of temperature driven by soil moisture and vegetation type. Plant Soil 435, 187–200 (2019).Article 
CAS 

Google Scholar 
Vukicevich, E., Lowery, T., Bowen, P., Úrbez-Torres, J. R. & Hart, M. Cover crops to increase soil microbial diversity and mitigate decline in perennial agriculture. A review. Agron. Sustain. Dev. (2016).Bani, A. et al. The role of microbial community in the decomposition of leaf litter and deadwood. Appl. Soil. Ecol. 126, 75–84 (2018).Article 

Google Scholar 
Bonanomi, G., Capodilupo, M., Incerti, G., Mazzoleni, S. & Scala, F. Litter quality and temperature modulate microbial diversity effects on decomposition in model experiments. Community Ecol. 16, 167–177 (2015).Article 

Google Scholar 
Daebeler, A. et al. Pairing litter decomposition with microbial community structures using the Tea Bag Index (TBI). SOIL Discuss. [preprint]; 10.5194/soil-2021-110 (2021).Keuskamp, J. A., Dingemans, B. J. J., Lehtinen, T., Sarneel, J. M. & Hefting, M. M. Tea Bag Index: a novel approach to collect uniform decomposition data across ecosystems. Methods Ecol. Evol. 4, 1070–1075 (2013).Article 

Google Scholar 
Schaller, K. Praktikum zur Bodenkunde und Pflanzenernährung. Hochschule Geisenheim, (2000).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).Article 
CAS 
PubMed 

Google Scholar 
Ihrmark, K. et al. New primers to amplify the fungal ITS2 region–evaluation by 454-sequencing of artificial and natural communities. FEMS Microbiol. Ecol. 82, 666–677 (2012).Article 
CAS 
PubMed 

Google Scholar 
Schoch, C. L. et al. SI: Nuclear ribosomal internal transcribed spacer (ITS) region as a universal DNA barcode marker for Fungi. Proc. Natl. Acad. Sci. U.S.A. 109, 6241–6246 (2012).Article 
ADS 
CAS 
PubMed 
PubMed Central 

Google Scholar 
Andrews, S. FastQC: A Quality Control Tool for High Throughput Sequence Data. Available at https://www.bioinformatics.babraham.ac.uk/projects/fastqc/ (2010).Joshi, N. A. & Fass, J. N. sickle – A Windowed Adaptive Trimming Tool for FASTQ Files Using Quality. Available at https://github.com/najoshi/sickle (2011).Schloss, P. D. et al. Introducing mothur: Open-source, platform-independent, community-supported software for describing and comparing microbial communities. Appl. Environ. Microbiol. 75, 7537–7541 (2009).Article 
ADS 
CAS 
PubMed 
PubMed Central 

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

Google Scholar 
Westcott, S. L. & Schloss, P. D. OptiClust, an improved method for assigning amplicon-based sequence data to operational taxonomic units. mSphere 2, e00073 (2017).Article 
PubMed 
PubMed Central 

Google Scholar 
Cole, J. R. et al. Ribosomal database project: data and tools for high throughput rRNA analysis. Nucleic Acids Res. 42, D633–D642 (2014).Article 
CAS 
PubMed 

Google Scholar 
Gweon, H. S. et al. PIPITS: an automated pipeline for analyses of fungal internal transcribed spacer sequences from the Illumina sequencing platform. Methods Ecol. Evol. 6, 973–980 (2015).Article 
PubMed 
PubMed Central 

Google Scholar 
Kõljalg, U. et al. Towards a unified paradigm for sequence-based identification of fungi. Mol. Ecol. 22, 5271–5277 (2013).Article 
PubMed 

Google Scholar 
R Core Team. R: A Language and Environment for Statistical Computing. Available at https://www.R-project.org/ (2019).McMurdie, P. J. & Holmes, S. phyloseq: an R package for reproducible interactive analysis and graphics of microbiome census data. PLoS One 8, e61217 (2013).Article 
ADS 
CAS 
PubMed 
PubMed Central 

Google Scholar 
Haegeman, B. et al. Robust estimation of microbial diversity in theory and in practice. ISME J. 7, 1092–1101 (2013).Article 
PubMed 
PubMed Central 

Google Scholar 
Scheu, S. Automated measurement of the respiratory response of soil microcompartments: Active microbial biomass in earthworm faeces. Soil Biol. Biochem. 24, 1113–1118 (1992).Article 

Google Scholar 
Mori, T. Validation of the Tea Bag Index as a standard approach for assessing organic matter decomposition: a laboratory incubation experiment. Ecol. Ind. 141, 109077 (2022).Article 
CAS 

Google Scholar 
Pinheiro, J., Bates, D., DebRoy, S., Sarkar, D. & R Core Team. nlme: Linear and Nonlinear Mixed Effects Models. R package version 3.1–142. Available at https://CRAN.R-project.org/package=nlme (2019).Lenth, R. Emmeans: Estimated Marginal Means, Aka Least-Squares Means. R package version 1.4.4. Available at https://CRAN.R-project.org/package=emmeans (2020).Lefcheck, J. S. piecewiseSEM: Piecewise structural equation modelling in r for ecology, evolution, and systematics. Methods Ecol. Evol. 7, 573–579 (2016).Article 

Google Scholar 
Grace, J. B., Anderson, T. M., Olff, H. & Scheiner, S. M. On the specification of structural equation models for ecological systems. Ecol. Monogr. 80, 67–87 (2010).Article 

Google Scholar 
Shipley, B. A new inferential test for path models based on directed acyclic graphs. Struct. Equ. Model. 7, 206–218 (2000).Article 
MathSciNet 

Google Scholar  More

Gillooly, J. F., Brown, J. H., West, G. B., Savage, V. M. & Charnov, E. L. Effects of size and temperature on metabolic rate. Science 293, 2248–2251 (2001).Article 
CAS 

Google Scholar 
Seebacher, F., White, C. R. & Franklin, C. E. Physiological plasticity increases resilience of ectothermic animals to climate change. Nat. Clim. Change 5, 61–66 (2015).Article 

Google Scholar 
Havird, J. C. et al. Distinguishing between active plasticity due to thermal acclimation and passive plasticity due to Q10 effects: why methodology matters. Funct. Ecol. 34, 1015–1028 (2020).Article 

Google Scholar 
Dillon, M. E., Wang, G. & Huey, R. B. Global metabolic impacts of recent climate warming. Nature 467, 704–706 (2010).Article 
CAS 

Google Scholar 
White, C. R., Alton, L. A., Bywater, C. L., Lombardi, E. J. & Marshall, D. J. Metabolic scaling is the product of life history optimization. Science 377, 834–839 (2022).Article 
CAS 

Google Scholar 
Savage, V. M., Gilloly, J. F., Brown, J. H. & Charnov, E. L. Effects of body size and temperature on population growth. Am. Nat. 163, 429–441 (2004).Article 

Google Scholar 
Bernhardt, J. R., Sunday, J. M. & O’Connor, M. I. Metabolic theory and the temperature–size rule explain the temperature dependence of population carrying capacity. Am. Nat. 192, 687–697 (2018).Article 

Google Scholar 
Damuth, J. Population density and body size in mammals. Nature 290, 699–700 (1981).Article 

Google Scholar 
Schuster, L., Cameron, H., White, C. R. & Marshall, D. J. Metabolism drives demography in an experimental field test. Proc. Natl Acad. Sci. USA 118, e2104942118 (2021).Article 
CAS 

Google Scholar 
Amarasekare, P. & Coutinho, R. M. The intrinsic growth rate as a predictor of population viability under climate warming. J. Anim. Ecol. 82, 1240–1253 (2013).Article 

Google Scholar 
Amarasekare, P. & Savage, V. A framework for elucidating the temperature dependence of fitness. Am. Nat. 179, 178–191 (2012).Article 

Google Scholar 
Lande, R. Risks of population extinction from demographic and environmental stochasticity and random catastrophes. Am. Nat. 142, 911–927 (1993).Article 

Google Scholar 
Comeault, A. A. & Matute, D. R. Temperature-dependent competitive outcomes between the fruit flies Drosophila santomea and Drosophila yakuba. Am. Nat. 197, 312–323 (2021).Article 

Google Scholar 
Davis, A. J., Jenkinson, L. S., Lawton, J. H., Shorrocks, B. & Wood, S. Making mistakes when predicting shifts in species range in response to global warming. Nature 391, 783–786 (1998).Article 
CAS 

Google Scholar 
Davis, A. J., Lawton, J. H., Shorrocks, B. & Jenkinson, L. S. Individualistic species responses invalidate simple physiological models of community dynamics under global environmental change. J. Anim. Ecol. 67, 600–612 (1998).Article 

Google Scholar 
Gilman, S. E., Urban, M. C., Tewksbury, J., Gilchrist, G. W. & Holt, R. D. A framework for community interactions under climate change. Trends Ecol. Evol. 25, 325–331 (2010).Article 

Google Scholar 
Janča, M. & Gvoždík, L. Costly neighbours: heterospecific competitive interactions increase metabolic rates in dominant species. Sci. Rep. 7, 5177 (2017).Article 

Google Scholar 
Pettersen, A. K., Hall, M. D., White, C. R. & Marshall, D. J. Metabolic rate, context-dependent selection, and the competition–colonization trade-off. Evol. Lett. 4, 333–344 (2020).Article 

Google Scholar 
DeLong, J. P., Hanley, T. C. & Vasseur, D. A. Competition and the density dependence of metabolic rates. J. Anim. Ecol. 83, 51–58 (2014).Article 

Google Scholar 
Reid, D., Armstrong, J. D. & Metcalfe, N. B. Estimated standard metabolic rate interacts with territory quality and density to determine the growth rates of juvenile Atlantic salmon. Funct. Ecol. 25, 1360–1367 (2011).Article 

Google Scholar 
Ayala, F. J. in Essays in Evolution and Genetics in Honor of Theodosius Dobzhansky (eds Hecht, M. K. & Steere, W. C.) 121–158 (Springer, 1970).Atkinson, W. D. & Shorrocks, B. Aggregation of larval Diptera over discrete and ephemeral breeding sites: the implications for coexistence. Am. Nat. 124, 336–351 (1984).Article 

Google Scholar 
McKenzie, J. A. & McKechnie, S. W. A comparative study of resource utilization in natural populations of Drosophila melanogaster and D. simulans. Oecologia 40, 299–309 (1979).Article 
CAS 

Google Scholar 
Alton, L. A. et al. Developmental nutrition modulates metabolic responses to projected climate change. Funct. Ecol. 34, 2488–2502 (2020).Article 

Google Scholar 
Mitchell, K. A. & Hoffmann, A. A. Thermal ramping rate influences evolutionary potential and species differences for upper thermal limits in Drosophila. Funct. Ecol. 24, 694–700 (2010).Article 

Google Scholar 
Overgaard, J., Kristensen, T. N., Mitchell, K. A. & Hoffmann, A. A. Thermal tolerance in widespread and tropical Drosophila species: does phenotypic plasticity increase with latitude? Am. Nat. 178, S80–S96 (2011).Article 

Google Scholar 
Kellermann, V. et al. Comparing thermal performance curves across traits: how consistent are they? J. Exp. Biol. 222, jeb193433 (2019).Article 

Google Scholar 
Terblanche, J. S., Clusella-Trullas, S. & Chown, S. L. Phenotypic plasticity of gas exchange pattern and water loss in Scarabaeus spretus (Coleoptera: Scarabaeidae): deconstructing the basis for metabolic rate variation. J. Exp. Biol. 213, 2940–2949 (2010).Article 

Google Scholar 
Tewksbury, J. J., Huey, R. B. & Deutsch, C. A. Putting the heat on tropical animals. Science 320, 1296–1297 (2008).Article 
CAS 

Google Scholar 
Bos, M., Burnet, B., Farrow, R. & Woods, R. A. Mutual facilitation between larvae of the sibling species Drosophila melanogaster and D. simulans. Evolution 31, 824–828 (1977).Article 
CAS 

Google Scholar 
Arthur, W. On the complexity of a simple environment: competition, resource partitioning and facilitation in a two-species Drosophila system. Phil. Trans. R. Soc. B 313, 471–508 (1986).
Google Scholar 
Hodge, S., Mitchell, P. & Arthur, W. Factors affecting the occurrence of facilitative effects in interspecific interactions: an experiment using two species of Drosophila and Aspergillus niger. Oikos 87, 166–174 (1999).Article 

Google Scholar 
Bath, E., Morimoto, J. & Wigby, S. The developmental environment modulates mating-induced aggression and fighting success in adult female Drosophila. Funct. Ecol. 32, 2542–2552 (2018).Article 

Google Scholar 
Thibert, J., Farine, J. P., Cortot, J. & Ferveur, J. F. Drosophila food-associated pheromones: effect of experience, genotype and antibiotics on larval behavior. PLoS ONE 11, e0151451 (2016).Article 

Google Scholar 
Chown, S. L. et al. Scaling of insect metabolic rate is inconsistent with the nutrient supply network model. Funct. Ecol. 21, 282–290 (2007).Article 

Google Scholar 
Becker, R. A., Wilks, A. R. & Brownrigg, R. mapdata: extra map databases. R version 2.3.0 https://CRAN.R-project.org/package=mapdata (2018).R Core Team R: A Language and Environment for Statistical Computing (R Foundation for Statistical Computing, 2022).Brooks, M. E. et al. glmmTMB balances speed and flexibility among packages for zero-inflated generalized linear mixed modeling. R J. 9, 378–400 (2017).Article 

Google Scholar 
Bates, D., Mächler, M., Bolker, B. & Walker, S. Fitting linear mixed-effects models using lme4. J. Stat. Softw. 67, 1–48 (2015).Article 

Google Scholar 
Bolker, B. & R Development Core Team bbmle: tools for general maximum likelihood estimation. R version 1.0.25 https://CRAN.R-project.org/package=bbmle (2022).Kuznetsova, A., Brockhoff, P. B. & Christensen, R. H. B. lmerTest package: tests in linear mixed effects models. J. Stat. Softw. 82, 1–26 (2017).Article 

Google Scholar 
Fox, J. & Weisberg, S. An R Companion to Applied Regression 3rd edn (Sage, 2019).Hartig, F. DHARMa: residual diagnostics for hierarchical (multi-level/mixed) regression models. R version 0.4.6 https://CRAN.R-project.org/package=DHARMa (2022).Messamah, B., Kellermann, V., Malte, H., Loeschcke, V. & Overgaard, J. Metabolic cold adaptation contributes little to the interspecific variation in metabolic rates of 65 species of Drosophilidae. J. Insect Physiol. 98, 309–316 (2017).Article 
CAS 

Google Scholar 
Chamberlain, S. et al. rgbif: interface to the global biodiversity information facility API. R version 3.7.3 https://CRAN.R-project.org/package=rgbif (2022).Fick, S. E. & Hijmans, R. J. WorldClim 2: new 1‐km spatial resolution climate surfaces for global land areas. Int. J. Climatol. 37, 4302–4315 (2017).Article 

Google Scholar 
Hijmans, R. J. raster: geographic data analysis and modeling. R version 3.6-3 https://CRAN.R-project.org/package=raster (2022).Alton, L. A. & Kellermann, V. Data for “Interspecific interactions alter the metabolic costs of climate warming”. Zenodo https://doi.org/10.5281/zenodo.7475922 (2023).White, C. R. et al. Geographical bias in physiological data limits predictions of global change impacts. Funct. Ecol. 35, 1572–1578 (2021).Article 

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All procedures involving animals were conducted in accordance with the guidelines and regulations from Institutional Animal Care and Use Committee (IACUC) of the University of Florida (protocol #201509019). Tis manuscript is reported in accordance with ARRIVE guidelines.Site descriptionThis study was carried out at the North Florida Research and Education Center, in Marianna, FL (30°46′35″N 85°14′17″W, 51 m.a.s.l). The trial was performed in two experimental years (2019 and 2020) in a greenhouse.The soil used was collected from a pasture of rhizoma peanut (Arachis glabrata Benth.) and Argentine bahiagrass (Paspalum notatum Flügge) as the main forages. Without plant and root material, only soil was placed into buckets, as described below in the bucket assemblage section. Soil was classified as Orangeburg loamy sand (fine-loamy-kaolinitic, thermic Typic Kandiudults), with a pHwater of 6.7, Mehlich-1-extratable P, K, Mg and Ca concentrations of 41, 59, 63, 368 mg kg−1, respectively. Average of minimum and maximum daily temperature and relative humidity in the greenhouse for September and November (September for beetle trial due seasonal appearance of beetles, and October and November to the Pear Millet trial) in 2019 and 2020 were 11 and 33 °C, 81%; 10 and 35 °C, 77%, respectively.Biological material determinationTo select the species of beetles, a previous dung beetle sampling was performed in the grazing experiment in the same area (grass and legume forage mixture) to determine the number of dung beetle species according to the functional groups as described by Conover et al.44. Beetles were pre-sampled from March 2017 to June 2018, where Tunnelers group were dominant and represented by Onthophagus taurus (Schreber), Digitonthophagus gazella (Fabricius), Phanaeus vindex (MacLeay), Onthophagus oklahomensis (Brown), and Euniticellus intermedius (Reiche). Other species were present but not abundant, including Aphodius psudolividus (Linnaeus), Aphodius carolinus (Linnaeus), and Canthon pilularius (Linnaeus) identified as Dweller and Roller groups, respectively. The pre-sampling indicated three species from the Tunneler group were more abundant, and thereby, were chosen to compose the experimental treatments (Fig. 4).Figure 4Most abundant dung beetle species in Marianna, FL used in the current study. Credits: Carlos C.V. García.Full size imageBeetles collection and experimental treatmentsThree species of common communal dung beetles were used: O. taurus (1), D. gazella (2), and P. vindex (3). Treatments included two treatments containing only soil and soil + dung without beetles were considered as Control 1 (T1) and Control 2 (T2), respectively. Isolated species T3 = 1, T4 = 2, T5 = 3 and their combinations T6 = 1 + 2 and T7 = 1 + 2 + 3. Dung beetles were trapped in the pasture with grazing animals using the standard cattle-dung-baited pitfall traps, as described by Bertone et al.41. To avoid losing samples due to cattle trampling, 18 traps were randomized in nine paddocks (two traps per paddock) and installed protected by metal cages, and after a 24-h period, beetles were collected, and the traps removed. Table 1 shows the number of dung beetles, their total mass (used to standardize treatments) per treatment, and the average mass per species. To keep uniformity across treatments we kept beetle biomass constant across species at roughly 1.7 to 1.8 g per assemblage (Table 1). Twenty-four hours after retrieving the beetles from the field traps, they were separated using an insect rearing cage, classified, and thereafter stored in small glass bottles provided with a stopper and linked to a mesh to keep the ventilation and maintaining the beetles alive.Table 1 Total number and biomass of dung beetles per treatment.Full size tableBuckets assemblageThe soil used in the buckets was collected from the grazing trial in two experimental years (August 2019 and August 2020) across nine paddocks (0.9 ha each). The 21 plastic buckets had a 23-cm diameter and 30-cm (0.034 m2) and each received 10 kg of soil (Fig. 5). At the bottom of the recipient, seven holes were made for water drainage using a metallic mesh with 1-mm diameter above the surface of the holes to prevent dung beetles from escaping. Water was added every four days to maintain the natural soil conditions at 60% of the soil (i.e., bucket) field capacity (measured with the soil weight and water holding capacity of the soil). Because soil from the three paddocks had a slightly different texture (sandy clay and sandy clay loam), we used them as the blocking factor.Figure 5Bucket plastic bucket details for dung beetle trial.Full size imageThe fresh dung amount used in the trial was determined based on the average area covered by dung and dung weight (0.05 to 0.09 m2 and 1.5 to 2.7 kg) from cattle in grazing systems, as suggested by Carpinelli et al.45. Fresh dung was collected from Angus steers grazing warm-season grass (bahiagrass) pastures and stored in fridge for 24 h, prior to start the experiment. A total of 16.2 kg of fresh dung was collected, in which 0.9 kg were used in each bucket. After the dung application, dung beetles were added to the bucket. To prevent dung beetles from escaping, a mobile plastic mesh with 0.5 mm diameter was placed covering the buckets before and after each evaluation. The experiment lasted for 24 days in each experimental year (2019 and 2020), with average temperature 28 °C and relative humidity of 79%, acquired information from the Florida Automated Weather Network (FAWN).Chamber measurementsThe gas fluxes from treatments were evaluated using the static chamber technique46. The chambers were circular, with a radius of 10.5 cm (0.034 m2). Chamber bases and lids were made of polyvinyl chloride (PVC), and the lid were lined with an acrylic sheet to avoid any reactions of gases of interest with chamber material (Fig. 6). The chamber lids were covered with reflective tape to provide insulation, and equipped with a rubber septum for sampling47. The lid was fitted with a 6-mm diameter, 10-cm length copper venting tube to ensure adequate air pressure inside the chamber during measurements, considering an average wind speed of 1.7 m s−148,49. During measurements, chamber lids and bases were kept sealed by fitting bicycle tire inner tubes tightly over the area separating the lid and the base. Bases of chambers were installed on top of the buckets to an 8-cm depth, with 5 cm extending above ground level. Bases were removed in the last evaluation day (24th) of each experimental year.Figure 6Static chamber details and instruments for GHG collection in the dung beetle trial.Full size imageGas fluxes measurementsThe gas fluxes were measured at 1000 h following sampling recommendations by Parkin & Venterea50, on seven occasions from August 28th to September 22nd in both years (2019 and 2020), being days 0, 1, 2, 3, 6, 12, and 24 after dung application. For each chamber, gas samples were taken using a 60-mL syringe at 15-min intervals (t0, t15, and t30). The gas was immediately flushed into pre-evacuated 30-mL glass vials equipped with a butyl rubber stopper sealed with an aluminium septum (this procedure was made twice per vial and per collection time). Time zero (t0) represented the gas collected out of the buckets (before closing the chamber). Immediately thereafter, the bucket lid was tightly closed by fitting the lid to the base with the bicycle inner tube, followed by the next sample deployment times.Gas sample analyses were conducted using a gas chromatograph (Trace 1310 Gas Chromatograph, Thermo Scientific, Waltham, MA). For N2O, an electron capture detector (350 °C) and a capillary column (J&W GC packed column in stainless steel tubing, length 6.56 ft (2 M), 1/8 in. OD, 2 mm ID, Hayesep D packing, mesh size 80/100, pre-conditioned, Agilent Technologies) were used. Temperature of the injector and columns were 80 and 200 °C, respectively. Daily flux of N2O-N (g ha−1 day−1) was calculated as described in Eq. (1):$${text{F}}, = ,{text{A}}*{text{dC}}/{text{dt}}$$
(1)
where F is flux of N2O (g ha−1 day−1), A is the area of the chamber, and dC/dt is the change of concentration in time calculated using a linear method of integration by Venterea et al.49.Ammonia volatilization measurementAmmonia volatilization was measured using the open chamber technique, as described by Araújo et al.51. The ammonia chamber was made of a 2-L volume polyethylene terephthalate (PET) bottle. The bottom of the bottle was removed and used as a cap above the top opening to keep the environment controlled, free of insects and other sources of contamination. An iron wire was used to support the plastic jar. A strip of polyfoam (250 mm in length, 25 mm wide, and 3 mm thick) was soaked in 20 ml of acid solution (H2SO4 1 mol dm−3 + glycerine 2% v/v) and fastened to the top, with the bottom end of the foam remaining inside the plastic jar. Inside each chamber there was a 250-mm long wire designed with a hook to support it from the top of the bottle, and wire basket at the bottom end to support a plastic jar (25 mL) that contained the acid solution to keep the foam strip moist during sampling periods (Fig. 7). The ammonia chambers were placed installed in the bucket located in the middle of each experimental block after the last gas sampling of the day and removed before the start of the next gas sampling.Figure 7Mobile ammonia chamber details for ammonia measurement in dung beetle trial. Adapted from Araújo et al.51.Full size imageNutrient cyclingPhotographs of the soil and dung portion of each bucket were taken twenty-four hours after the last day of gas flux measurement sampling to determine the dung removal from single beetle species and their combination. In the section on statistical analysis, the programming and statistical procedures are described. After this procedure, seeds of pearl millet were planted in each bucket. After 5 days of seed germination plants were thinned, maintaining four plants per bucket. Additionally, plants were clipped twice in a five-week interval, with the first cut occurring on October 23rd and the second cut occurring on November 24th, in both experimental years. Before each harvest, plant height was measured twice in the last week. In the harvest day all plants were clipped 10 cm above the ground level. Samples were dried at 55 °C in a forced-air oven until constant weight and ball-milled using a Mixer Mill MM 400 (Retsch, Newton, PA, USA) for 9 min at 25 Hz, and analyzed for total N concentration using a C, H, N, and S analyzer by the Dumas dry combustion method (Vario Micro Cube; Elementar, Hanau, Germany).Statistical analysisTreatments were distributed in a randomized complete block design (RCBD), with three replications. Data were analyzed using the Mixed Procedure from SAS (ver. 9.4., SAS Inst., Cary, NC) and LSMEANS compared using PDIFF adjusted by the t-test (P  More

Study site, tree selections, and drought-simulation experimentThis research was performed in Guangzhou (22°26′-23°56′N, 112°57′-114°03′E), which is a core city located in subtropical zones. With an area of 7434.4 km2 and a population of 18.87 million, Guangzhou’s urbanization rate has reached 86.46%. To cope with multiple environmental challenges, several urban-forest nurseries were established to cultivate and introduce various tree species. Among them, we selected the one in Tianhe District as our study site. This nursery was not only abundant with native and exotic tree species but also equipped with similar edatope in cities, which was ideal for our research.Tilia cordata Mill. (Tc) and Tilia tomentosa Moench (Tt), originating from the west of Britain and southeast of Europe, were common urban tree species planted in European cities. Based on their performance in providing ecological and landscape functions, these two tree species were considered to be introduced for urban greening. Therefore, Tilia cordata Mill. (Tc) and Tilia tomentosa Moench (Tt) were selected as our objectives, which were investigated for their growth and ecosystem services to evaluate their adaption in Guangzhou. In addition, a native tree species Tilia miqueliana Maxim (Tm) was also implemented concurrent measurement as a comparison.For each of the three surveyed tree species, ten trees with a diameter at breast height (DBH) around 5.5 cm and tree height around 2.5 m were chosen for our experiment, which were thought to possess similar initial statuses. To investigate the impact of drought on the growth and ecosystem services of the three selected tree species, a controlled experiment was launched from January to December in 2020. For each tree species, five trees were planted in the common environment as the controlled group, while the other five trees were under the precipitation-exclusion installation (PEI) as the drought-simulation group. Consisting of several water-proof tents, PEI was adequately large and could completely prevent trees from obtaining rainfalls, which created a precipitation-exclusive environment to simulate an enduring drought event within the whole research period (Fig. 1).Figure 1Schematic diagram of the drought simulation experiment for the three surveyed tree species.Full size imageEnvironmental monitoring systemsClimatic data were sampled every 10 min with a weather station (WP3103 mesoscale automatic weather station, China) located at an unshaded site in the nursery. The data were stored in the logger and copied to our laboratory to produce daily or monthly data. All the climatic variables, including photosynthetically active radiation (PAR, µmol m-2 s-1), wind speed (m s-1), precipitation (mm), and air temperature (°C) were calculated from January to December in 2020.For volumetric soil water content (%; VWC), the HOBO MX2307 system (Onetemp, Adelaide, Australia), placed in a shaded box in the nursery, was applied for all the three tree species from both the controlled and drought-simulation groups. For each individual tree, the sensing probe was inserted horizontally at the depths of 30 cm and located 20 cm in the northern direction from the tree stems. Based on the daily readings, monthly means were calculated from January to December in 2020.Measurement of above-ground growthTo investigate the above-ground growth of the three tree species from both the controlled and drought-simulation groups, their DBH (diameter at breast height, cm), tree height (m), and LAI (leaf area index) were measured at the beginning of each month in 2020. DBH was measured with the help of a caliper (Altraco Inc., Sausalito, California, USA), and their tree heights were measured using a standard tape. The crown analytical instrument CI-110 (Camas, Washington State, USA) was used to capture an accurate image of tree crowns and calculate LAI. Sufficient numbers of points were measured and recorded to describe each tree’s average crown shape. The software FV2200 (LICOR Biosciences, Lincoln, NE) helped compute each tree’s crown width and crown area.Measurement of below-ground growthFine root coring campaigns were launched for all the trees of the three tree species from both the controlled and drought treatment groups every three to four months, i.e., in February, May, September, and December. Although the coring campaign might damage part of the roots, the fine roots obtained each time were a mere portion of the whole root system, not affecting the general development of trees’ underground processes. For every individual tree, two 30-cm soil cores were applied in each direction of north, south, east, and west, of which one was located at 20 cm to the trunk (paracentral roots) and the other one was located at 40 cm (outer roots). In addition, the soil samples were evenly divided into three horizons which were 0–10 cm (shallow layer), 10–20 cm (middle layer), and 20–30 cm (deep layer). Then a sieve with 2-mm mesh size was used to filter all the fine roots. The fine roots were washed carefully to remove the adherent soils and dried in an oven at 65 ℃ for 72 h. Finally, all the samples were weighed using a balance with an accuracy of four decimal places to obtain the dry weight. The fine root biomass at different depths was calculated using the dry weight divided by the cross-sectional area of the auger20.Model’s simulation of ecosystem servicesThe process-based model City-Tree was used to predict the ecosystem services of the three tree species from both the controlled and drought-simulation groups23. The model required the data of tree growth parameters including tree height, DBH, and crown area together with environmental conditions such as edaphic and climatic data24. In this research, cooling, evapotranspiration and CO2 fixation of the three surveyed tree species in the controlled and drought-treatment groups were simulated at the end of 2020.The actual evapotranspiration eta was calculated from the potential evapotranspiration using fetp[t], Tilia’s factors fetp[t], and the reduction factor fred:$${mathrm{et}}_{mathrm{a}}={mathrm{f}}_{mathrm{red}}*{mathrm{f}}_{mathrm{etp}}left[mathrm{t}right]*{mathrm{et}}_{mathrm{p}}$$The process of tree’s evapotranspiration (etp) was calculated on the basis of SVAT algorithm together with Penman formula in the module on water balance as below:$${mathrm{et}}_{mathrm{p}}=left[mathrm{s }/ left(mathrm{s}+upgamma right)right]*left({mathrm{r}}_{mathrm{s}}-{mathrm{r}}_{mathrm{L}}right) /mathrm{ L}+left[1-mathrm{s }/ left(mathrm{s}+upgamma right)right]*{mathrm{e}}_{mathrm{s}}*mathrm{f }left({mathrm{v}}_{mathrm{u}}right)$$with γ: psychrometric constant in hPa K−1; s: the slope of the saturation vapour pressure curve in hPa K−1; rs: short wave radiation balance in W m−2; rL: long-wave radiation balance in W m−2; L: specific evaporation heat in W m−2 mm−1 d; es: saturation deficit in hPa; f (vu): ventilation function with vu being the daily average wind speed in m s−1.Within the module cooling, the energy needed for the transition of water from liquid to gaseous phase was calculated based on the crown area (CA) and the transpiration eta sum:$${mathrm{E}}_{mathrm{A}}= {mathrm{et}}_{mathrm{a}}*mathrm{CA}-left({mathrm{L}}_{mathrm{O}}* -0.00242*mathrm{temp}right) / {mathrm{f}}_{mathrm{con}}$$with EA: energy released by a tree through transpiration (kWh tree-1), LO: energy needed for the transition of the 1 kg of water from the liquid to gaseous phase = 2.498 MJ (kgH2O)-1 and temp = temperature in ℃, fcon: 0.5.The calculation of new assimilation in the module of photosynthesis and respiration was on the basis of the approach of Haxeltine and Prenticem25. The model assumed that 50% of the incoming short-wave radiation is photosynthetic active radiation (PAR). Using the LAI and a light extinction factor of 0.5, the radiation amount of 1 m2 leaf area can be estimated based on an exponential function according to the Lambert–Beer law. This way, the gross assimilation per m2 leaf area as the daily mean of the month can be derived from:$${text{A}} = {text{d}}*{{left[ {left( {{text{J}}_{{text{p}}} + {text{J}}_{{text{r}}} – {text{sqrt}} left( {left( {{text{J}}_{{text{P}}} + {text{J}}_{{text{r}}} } right)^{2} – 4*uptheta *{text{J}}_{{text{p}}} *{text{J}}_{{text{r}}} } right)} right)} right]} mathord{left/ {vphantom {{left[ {left( {{text{J}}_{{text{p}}} + {text{J}}_{{text{r}}} – {text{sqrt}} left( {left( {{text{J}}_{{text{P}}} + {text{J}}_{{text{r}}} } right)^{2} – 4*uptheta *{text{J}}_{{text{p}}} *{text{J}}_{{text{r}}} } right)} right)} right]} {left( {2*uptheta } right)}}} right. kern-0pt} {left( {2*uptheta } right)}}$$with A: gross assimilation [g C m−2 d−1]; d: mean day length of the month [h]; Jp: reaction of photosynthesis on absorbed photosynthetic radiation [g C m−2 h−1]; Jr: rubisco limited rate of photosynthesis [g C m−2 h−1]; θ: form factor = 0.7.Jp was defined as a function of the photosynthetic active radiation PAR in mol m−2 h−1 and the efficiency of carbon fixation per absorbed PAR [g C mol−1].$${text{J}}_{{text{p}}} = {text{c}}_{{text{p}}} {text{*PAR}}$$$${text{c}}_{{text{p}}} = alpha *left( {{text{p}}_{{{text{ci}}}} – {text{r}}} right){ /}left( {{text{p}}_{{{text{ci}}}} – {text{r}}} right)*gamma *{text{m}}_{{{text{co}}_{2} }} *{text{i}}left[ {text{t}} right]$$with α: intrinsic quantum efficiency for CO2 uptake = 0.08; pci: partial pressure of the internal CO2 [Pa]; r: CO2 compensation point [Pa]; ϒ: species dependent adjustment function for tree age; m CO2: molecular mass of C = 12.0 g mol−1; i[t]: influence of temperature on efficiency.Net assimilation AN [g C m−2 d−1] was then derived from the gross assimilation A and the dark respiration Rd by:$${text{A}}_{{text{N}}} = {text{A}} – {text{R}}_{{text{d}}}$$$${text{R}}_{{text{d}}} =upbeta *{text{V}}_{{text{m}}}$$where Vm was calculated as:$${text{V}}_{{text{m}}} = {1 mathord{left/ {vphantom {1 upbeta }} right. kern-0pt} upbeta } * {{{text{c}}_{{text{p}}} } mathord{left/ {vphantom {{{text{c}}_{{text{p}}} } {{text{c}}_{{text{r}}} * {text{PAR}} * left[ {left( {2uptheta – 1} right) * beta * {{text{d}} mathord{left/ {vphantom {{text{d}} {{text{d}}_{max } }}} right. kern-0pt} {{text{d}}_{max } }} – left( {2uptheta *upbeta *{{text{d}} mathord{left/ {vphantom {{text{d}} {{text{d}}_{max } }}} right. kern-0pt} {{text{d}}_{max } }} – {text{c}}_{{text{r}}} } right)*varsigma } right]}}} right. kern-0pt} {{text{c}}_{{text{r}}} * {text{PAR}} * left[ {left( {2theta – 1} right) * beta * {{text{d}} mathord{left/ {vphantom {{text{d}} {{text{d}}_{max } }}} right. kern-0pt} {{text{d}}_{max } }} – left( {2theta *upbeta *{{text{d}} mathord{left/ {vphantom {{text{d}} {{text{d}}_{max } }}} right. kern-0pt} {{text{d}}_{max } }} – {text{c}}_{{text{r}}} } right)*varsigma } right]}}$$By multiplying AN, the number of days and the total leaf area, the entire monthly net assimilation of the tree can be obtained. In this study, we assumed a fixed share of 50% as respiration based on the gross primary production that the resulting net primary production NPP was transformed in the content of fixed carbon by multiplying the value with the carbon conversion factor 0.524.$${mathrm{Carbon}}_{mathrm{fix}}=0.5*mathrm{NPP}$$Statistical analysesThe software package R was used for statistical analysis. To investigate the differences between means, two-sampled t-test and analysis of variance (ANOVA) with Tukey’s HSD (honestly significant difference) test were used. All the cases, the means were reported as significant when P  More

Knowlton, N. The future of coral reefs. Proc. Natl. Acad. Sci. 98, 5419–5425 (2001).Article 
ADS 
CAS 
PubMed 
PubMed Central 

Google Scholar 
Hughes, T. P. et al. Climate change, human impacts, and the resilience of coral reefs. Science 1979(301), 929–933 (2003).Article 
ADS 

Google Scholar 
Hoegh-Guldberg, O. et al. Coral reefs under rapid climate change and ocean acidification. Science 1979(318), 1737–1742 (2007).Article 
ADS 

Google Scholar 
Eakin, C. M. et al. Monitoring coral reefs from space. Oceanography 23, 118–133 (2010).Article 

Google Scholar 
Hughes, T. P. et al. Global warming transforms coral reef assemblages. Nature 556, 492–496 (2018).Article 
ADS 
CAS 
PubMed 

Google Scholar 
Baker, D. M., Freeman, C. J., Wong, J. C. Y., Fogel, M. L. & Knowlton, N. Climate change promotes parasitism in a coral symbiosis. ISME J. 12, 921–930 (2018).Article 
CAS 
PubMed 
PubMed Central 

Google Scholar 
Hughes, T. P. et al. Global warming and recurrent mass bleaching of corals. Nature 543, 373–377 (2017).Article 
ADS 
CAS 
PubMed 

Google Scholar 
Berkelmans, R. & van Oppen, M. J. H. The role of zooxanthellae in the thermal tolerance of corals: a ‘nugget of hope’ for coral reefs in an era of climate change. Proc. R. Soc. B Biol. Sci. 273, 2305–2312 (2006).Article 

Google Scholar 
Byler, K. A., Carmi-Veal, M., Fine, M. & Goulet, T. L. Multiple symbiont acquisition strategies as an adaptive mechanism in the coral Stylophora pistillata. PLoS One 8, e59596 (2013).Article 
ADS 
CAS 
PubMed 
PubMed Central 

Google Scholar 
Cumbo, V., van Oppen, M. & Baird, A. Temperature and Symbiodinium physiology affect the establishment and development of symbiosis in corals. Mar. Ecol. Prog. Ser. 587, 117–127 (2018).Article 
ADS 
CAS 

Google Scholar 
Mundy, C. N. & Babcock, R. C. Role of light intensity and spectral quality in coral settlement: Implications for depth-dependent settlement?. J. Exp. Mar Biol. Ecol. 223, 235–255 (1998).Article 

Google Scholar 
Gleason, D. F., Edmunds, P. J. & Gates, R. D. Ultraviolet radiation effects on the behavior and recruitment of larvae from the reef coral Porites astreoides. Mar. Biol. 148, 503–512 (2006).Article 

Google Scholar 
Yusuf, S., Zamani, N. P., Jompa, J. & Junior, M. Z. Larvae of the coral Acropora tenuis (Dana 1846) settle under controlled light intensity. IOP Conf. Ser. Earth Environ. Sci. 253, 012023 (2019).Article 

Google Scholar 
Vermeij, M. J. A., Marhaver, K. L., Huijbers, C. M., Nagelkerken, I. & Simpson, S. D. Coral larvae move toward reef sounds. PLoS One 5, e10660 (2010).Article 
ADS 
PubMed 
PubMed Central 

Google Scholar 
Doropoulos, C. et al. Characterizing the ecological trade-offs throughout the early ontogeny of coral recruitment. Ecol. Monogr. 86, 20–44 (2016).Article 

Google Scholar 
Morse, D. E., Hooker, N., Morse, A. N. C. & Jensen, R. A. Control of larval metamorphosis and recruitment in sympatric agariciid corals. J. Exp. Mar. Biol. Ecol. 116, 193–217 (1988).Article 

Google Scholar 
Price, N. Habitat selection, facilitation, and biotic settlement cues affect distribution and performance of coral recruits in French Polynesia. Oecologia 163, 747–758 (2010).Article 
ADS 
PubMed 
PubMed Central 

Google Scholar 
Ritson-Williams, R., Arnold, S. N., Paul, V. J. & Steneck, R. S. Larval settlement preferences of Acropora palmata and Montastraea faveolata in response to diverse red algae. Coral Reefs 33, 59–66 (2014).Article 
ADS 

Google Scholar 
Negri, A., Webster, N., Hill, R. & Heyward, A. Metamorphosis of broadcast spawning corals in response to bacteria isolated from crustose algae. Mar. Ecol. Prog. Ser. 223, 121–131 (2001).Article 
ADS 

Google Scholar 
Webster, N. S. et al. Metamorphosis of a scleractinian coral in response to microbial biofilms. Appl. Environ. Microbiol. 70, 1213–1221 (2004).Article 
ADS 
CAS 
PubMed 
PubMed Central 

Google Scholar 
Erwin, P. M., Song, B. & Szmant, A. M. Settlement behavior of Acropora palmata planulae: effects of biofilm age and crustose coralline algal cover. In Proceedings of 11th International Coral Reef Symposium 24, (2008).Siboni, N. et al. Crustose coralline algae that promote coral larval settlement harbor distinct surface bacterial communities. Coral Reefs 39, 1703–1713 (2020).Article 

Google Scholar 
Petersen, L.-E. et al. Mono- and multispecies biofilms from a crustose coralline alga induce settlement in the scleractinian coral Leptastrea purpurea. Coral Reefs 40, 381–394 (2021).Article 

Google Scholar 
Jorissen, H. et al. Coral larval settlement preferences linked to crustose coralline algae with distinct chemical and microbial signatures. Sci. Rep. 11, 14610 (2021).Article 
ADS 
CAS 
PubMed 
PubMed Central 

Google Scholar 
Tebben, J. et al. Induction of larval metamorphosis of the coral Acropora millepora by tetrabromopyrrole isolated from a pseudoalteromonas bacterium. PLoS One 6, e19082 (2011).Article 
ADS 
CAS 
PubMed 
PubMed Central 

Google Scholar 
Tebben, J. et al. Chemical mediation of coral larval settlement by crustose coralline algae. Sci. Rep. 5, 10803 (2015).Article 
ADS 
CAS 
PubMed 
PubMed Central 

Google Scholar 
Tran, C. & Hadfield, M. Larvae of Pocillopora damicornis (Anthozoa) settle and metamorphose in response to surface-biofilm bacteria. Mar. Ecol. Prog. Ser. 433, 85–96 (2011).Article 
ADS 

Google Scholar 
Sneed, J. M., Sharp, K. H., Ritchie, K. B. & Paul, V. J. The chemical cue tetrabromopyrrole from a biofilm bacterium induces settlement of multiple Caribbean corals. Proc. R. Soc. B Biol. Sci. 281, 20133086 (2014).Article 

Google Scholar 
Petersen, L.-E., Kellermann, M. Y., Nietzer, S. & Schupp, P. J. Photosensitivity of the bacterial pigment cycloprodigiosin enables settlement in coral larvae—light as an understudied environmental factor. Front. Mar. Sci. 8, 749070 (2021).Article 

Google Scholar 
Heyward, A. J. & Negri, A. P. Natural inducers for coral larval metamorphosis. Coral Reefs 18, 273–279 (1999).Article 

Google Scholar 
Harrington, L., Fabricius, K., Death, G. & Negri, A. Recognition and selection of settlement substrata determine post-settlement survival in corals. Ecology 85, 3428–3437 (2004).Article 

Google Scholar 
Da-Anoy, J. P., Villanueva, R. D., Cabaitan, P. C. & Conaco, C. Effects of coral extracts on survivorship, swimming behavior, and settlement of Pocillopora damicornis larvae. J. Exp. Mar. Biol. Ecol. 486, 93–97 (2017).Article 

Google Scholar 
Morse, D. E. & Morse, A. N. C. Enzymatic characterization of the morphogen recognized by Agaricia humilis (Scleractinian Coral) larvae. Biol. Bull. 181, 104–122 (1991).Article 
CAS 
PubMed 

Google Scholar 
Kitamura, M., Koyama, T., Nakano, Y. & Uemura, D. Characterization of a natural inducer of coral larval metamorphosis. J. Exp. Mar. Biol. Ecol. 340, 96–102 (2007).Article 

Google Scholar 
Kitamura, M., Schupp, P. J., Nakano, Y. & Uemura, D. Luminaolide, a novel metamorphosis-enhancing macrodiolide for scleractinian coral larvae from crustose coralline algae. Tetrahedron Lett. 50, 6606–6609 (2009).Article 
CAS 
PubMed 
PubMed Central 

Google Scholar 
Maru, N. et al. Relative configuration of luminaolide. Tetrahedron Lett. 54, 4385–4387 (2013).Article 
CAS 

Google Scholar 
Nietzer, S., Moeller, M., Kitamura, M. & Schupp, P. J. Coral larvae every day: Leptastrea purpurea, a brooding species that could accelerate coral research. Front. Mar. Sci. 5, 466 (2018).Article 

Google Scholar 
Moeller, M., Nietzer, S. & Schupp, P. J. Neuroactive compounds induce larval settlement in the scleractinian coral Leptastrea purpurea. Sci. Rep. 9, 2291 (2019).Article 
PubMed 
PubMed Central 

Google Scholar 
Petersen, L.-E., Kellermann, M. Y. & Schupp, P. J. Secondary metabolites of marine microbes: from natural products chemistry to chemical ecology. In YOUMARES 9 – The Oceans: Our Research, Our Future: Proceedings of the 2018 Conference for Young Marine Researcher in Oldenburg, Germany (eds Jungblut, S. et al.) 159–180 (Springer International Publishing, 2020). https://doi.org/10.1007/978-3-030-20389-4_8.Chapter 

Google Scholar 
Fiegel, L. J. et al. A detailed visualization of the early development stages of Leptastrea purpurea reveals distinct bio-optical features. Front. Mar. Sci. 10, 1–10 (2023).
Google Scholar 
Strader, M. E., Aglyamova, G. V. & Matz, M. V. Molecular characterization of larval development from fertilization to metamorphosis in a reef-building coral. BMC Genom. 19, 17 (2018).Article 

Google Scholar 
Puisay, A. et al. Parental bleaching susceptibility leads to differences in larval fluorescence and dispersal potential in Pocillopora acuta corals. Mar. Environ. Res. 163, 105200 (2021).Article 
CAS 
PubMed 

Google Scholar 
Perez-Tomas, R. & Vinas, M. New insights on the antitumoral properties of prodiginines. Curr. Med. Chem. 17, 2222–2231 (2010).Article 
CAS 
PubMed 

Google Scholar 
You, Z. et al. Insights into the anti-infective properties of prodiginines. Appl. Microbiol. Biotechnol. 103, 2873–2887 (2019).Article 
CAS 
PubMed 

Google Scholar 
Kellermann, M. Y., Yoshinaga, M. Y., Valentine, R. C., Wörmer, L. & Valentine, D. L. Important roles for membrane lipids in haloarchaeal bioenergetics. Biochim. Biophys. Acta (BBA) Biomembr. 1858, 2940–2956 (2016).Article 
CAS 

Google Scholar 
Hirose, M., Yamamoto, H. & Nonaka, M. Metamorphosis and acquisition of symbiotic algae in planula larvae and primary polyps of Acropora spp.. Coral Reefs 27, 247–254 (2008).Article 
ADS 

Google Scholar 
Bollati, E. et al. Green fluorescent protein-like pigments optimize the internal light environment in symbiotic reef building corals. Elife 11, e73521 (2022).Article 
PubMed 
PubMed Central 

Google Scholar 
Palmer, C. V., Modi, C. K. & Mydlarz, L. D. Coral fluorescent proteins as antioxidants. PLoS One 4, e7298 (2009).Article 
ADS 
PubMed 
PubMed Central 

Google Scholar 
Alegado, R. A. et al. A bacterial sulfonolipid triggers multicellular development in the closest living relatives of animals. Elife 1, e00013 (2012).Article 
PubMed 
PubMed Central 

Google Scholar 
Woznica, A. et al. Bacterial lipids activate, synergize, and inhibit a developmental switch in choanoflagellates. Proc. Natl. Acad. Sci. 113, 7894–7899 (2016).Article 
ADS 
CAS 
PubMed 
PubMed Central 

Google Scholar 
He, J. et al. Bacterial nucleobases synergistically induce larval settlement and metamorphosis in the invasive mussel Mytilopsis sallei. Appl. Environ. Microbiol. 85, e01039 (2019).Article 
CAS 
PubMed 
PubMed Central 

Google Scholar 
Guo, H., Rischer, M., Westermann, M. & Beemelmanns, C. Two distinct bacterial biofilm components trigger metamorphosis in the colonial hydrozoan Hydractinia echinata. MBio 12, e00401 (2021).Article 
CAS 
PubMed 
PubMed Central 

Google Scholar 
Ross, C., Fogarty, N. D., Ritson-Williams, R. & Paul, V. J. Interspecific variation in coral settlement and fertilization success in response to hydrogen peroxide exposure. Biol. Bull. 233, 206–218 (2017).Article 
CAS 
PubMed 

Google Scholar 
Boettcher, A. A., Dyer, C., Casey, J. & Targett, N. M. Hydrogen peroxide induced metamorphosis of queen conch, Strombus gigas: tests at the commercial scale. Aquaculture 148, 247–258 (1997).Article 
CAS 

Google Scholar 
Covarrubias, L., Hernández-García, D., Schnabel, D., Salas-Vidal, E. & Castro-Obregón, S. Function of reactive oxygen species during animal development: Passive or active?. Dev. Biol. 320, 1–11 (2008).Article 
CAS 
PubMed 

Google Scholar 
Gauron, C. et al. Hydrogen peroxide (H2O2) controls axon pathfinding during zebrafish development. Dev. Biol. 414, 133–141 (2016).Article 
CAS 
PubMed 

Google Scholar  More