Ecology

Moberg, F. & Folke, C. Ecological goods and services of coral reef ecosystems. Ecol. Econ. 29, 215–233 (1999).
Google Scholar 
Hoegh-Guldberg, O. et al. Coral reefs under rapid climate change and ocean acidification. Science 318, 1737–1742 (2007).ADS 
CAS 
PubMed 

Google Scholar 
Muscatine, L., Pool, R. R. & Trench, R. K. Symbiosis of algae and invertebrates: Aspects of the symbiont surface and the host-symbiont interface. Trans. Am. Microsc. Soc. 94, 450–469 (1975).CAS 
PubMed 

Google Scholar 
Muscatine, L. & Porter, J. W. Reef corals: Mutualistic symbioses adapted to nutrient-poor environments. Bioscience 27, 454–460 (1977).
Google Scholar 
LaJeunesse, T. C. et al. Systematic revision of Symbiodiniaceae highlights the antiquity and diversity of coral endosymbionts. Curr. Biol. https://doi.org/10.1016/j.cub.2018.07.008 (2018).Article 
PubMed 

Google Scholar 
Colombo-Pallotta, M. F., Rodríguez-Román, A. & Iglesias-Prieto, R. Calcification in bleached and unbleached Montastraea faveolata: Evaluating the role of oxygen and glycerol. Coral Reefs 29, 899–907 (2010).ADS 

Google Scholar 
Hoegh-Guldberg, O. & Smith, G. J. The effect of sudden changes in temperature, light and salinity on the population density and export of zooxanthellae from the reef corals Stylphora pistillata Esper and Seriatopora hystrix Dana. J. Exp. Mar. Biol. Ecol. 129, 279–303 (1989).
Google Scholar 
Iglesias-Prieto, R., Matta, J. L., Robins, W. A. & Trench, R. K. Photosynthetic response to elevated temperature in the symbiotic dinoflagellate Symbiodinium microadriaticum in culture. Proc. Natl. Acad. Sci. 89, 10302–10305 (1992).ADS 
CAS 
PubMed 
PubMed Central 

Google Scholar 
Scheufen, T., Krämer, W. E., Iglesias-Prieto, R. & Enríquez, S. Seasonal variation modulates coral sensibility to heat-stress and explains annual changes in coral productivity. Sci. Rep. 7, 1–15 (2017).CAS 

Google Scholar 
Enríquez, S., Méndez, E. R. & Iglesias-Prieto, R. Multiple scattering on coral skeletons enhances light absorption by symbiotic algae. Limnol. Oceanogr. 50, 1025–1032 (2005).ADS 

Google Scholar 
Terán, E., Méndez, E. R., Enríquez, S. & Iglesias-Prieto, R. Multiple light scattering and absorption in reef-building corals. Appl. Opt. 49, 5032 (2010).ADS 
PubMed 

Google Scholar 
Swain, T. D. et al. Skeletal light-scattering accelerates bleaching response in reef-building corals. BMC Ecol. 16, 1–18 (2016).
Google Scholar 
Rodríguez-Román, A., Hernández-Pech, X., E Thome, P., Enríquez, S. & Iglesias-Prieto, R. Photosynthesis and light utilization in the Caribbean coral Montastraea faveolata recovering from a bleaching event. Limnol. Oceanogr. 51, 2702–2710 (2006).ADS 

Google Scholar 
Kemp, D. W., Hernandez-Pech, X., Iglesias-Prieto, R., Fitt, W. K. & Schmidt, G. W. Community dynamics and physiology of Symbiodinium spp. before, during, and after a coral bleaching event. Limnol. Oceanogr. 59, 788–797 (2014).ADS 
CAS 

Google Scholar 
Thornhill, D. J., LaJeunesse, T. C., Kemp, D. W., Fitt, W. K. & Schmidt, G. W. Multi-year, seasonal genotypic surveys of coral-algal symbioses reveal prevalent stability or post-bleaching reversion. Mar. Biol. 148, 711–722 (2006).
Google Scholar 
Schoepf, V. et al. Annual coral bleaching and the long-term recovery capacity of coral. Proc. R. Soc. B Biol. Sci. 282, 20151887 (2015).
Google Scholar 
Hughes, T. P. et al. Global warming and recurrent mass bleaching of corals. Nature 543, 373–377 (2017).ADS 
CAS 
PubMed 

Google Scholar 
Hoegh-Guldberg, O. Climate change, coral bleaching and the future of the world’s coral reefs. Mar. Freshw. Res. https://doi.org/10.1071/MF99078 (1999).Article 

Google Scholar 
Scheufen, T., Iglesias-Prieto, R. & Enríquez, S. Changes in the number of symbionts and Symbiodinium cell pigmentation modulate differentially coral light absorption and photosynthetic performance. Front. Mar. Sci. 4, 309 (2017).
Google Scholar 
Warner, M. E., Fitt, W. K. & Schmidt, G. W. Damage to photosystem II in symbiotic dinoflagellates: A determinant of coral bleaching. Proc. Natl. Acad. Sci. U. S. A. 96, 8007–8012 (1999).ADS 
CAS 
PubMed 
PubMed Central 

Google Scholar 
Takahashi, S., Nakamura, T., Sakamizu, M., van Woesik, R. & Yamasaki, H. Repair machinery of symbiotic photosynthesis as the primary target of heat stress for reef-building corals. Plant Cell Physiol. 45, 251–255 (2004).CAS 
PubMed 

Google Scholar 
Bollati, E. et al. Optical feedback loop involving dinoflagellate symbiont and scleractinian host drives colorful coral bleaching. Curr. Biol. https://doi.org/10.1016/j.cub.2020.04.055 (2020).Article 
PubMed 

Google Scholar 
Dove, S. G., Hoegh-Guldberg, O. & Ranganathan, S. Major colour patterns of reef-building corals are due to a family of GFP-like proteins. Coral Reefs 19, 197–204 (2001).
Google Scholar 
Salih, A., Larkum, A., Cox, G., Kühl, M. & Hoegh-Guldberg, O. Fluorescent pigments in corals are photoprotective. Nature 408, 850–853 (2000).ADS 
CAS 
PubMed 

Google Scholar 
Fine, M. & Loya, Y. Endolithic algae: An alternative source of photoassimilates during coral bleaching. Proc. Biol. Sci. 269, 1205–1210 (2002).PubMed 
PubMed Central 

Google Scholar 
Carilli, J. E., Godfrey, J., Norris, R. D., Sandin, S. A. & Smith, J. E. Periodic endolithic algal blooms in Montastraea faveolata corals may represent periods of low-level stress. Bull. Mar. Sci. 86, 10 (2010).
Google Scholar 
Le Campion-Alsumard, T., Golubic, S. & Hutchings, P. Microbial endoliths in skeletons of live and dead corals: Porites lobata (Moorea, French Polynesia). Mar. Ecol. Prog. Ser. 117, 149–157 (1995).ADS 

Google Scholar 
Schlichter, D., Kampmann, H. & Conrady, S. Trophic potential and photoecology of endolithic algae living within coral skeletons. Mar. Ecol. 18, 299–317 (1997).ADS 

Google Scholar 
Sangsawang, L. et al. 13C and 15N assimilation and organic matter translocation by the endolithic community in the massive coral Porites lutea. R. Soc. Open Sci. 4, 171201 (2017).PubMed 
PubMed Central 

Google Scholar 
Yamazaki, S. S., Nakamura, T. & Yamasaki, H. Photoprotective role of endolithic algae colonized in coral skeleton for the host photosynthesis. In Photosynthesis. Energy from the Sun (eds. Allen, J. F., et al.) 1391–1395 (Springer Netherlands, 2008). https://doi.org/10.1007/978-1-4020-6709-9_300.Halldal, P. Photosynthetic capacities and photosynthetic action spectra of endozoic algae of the massive coral Favia. Biol. Bull. 134, 411–424 (1968).CAS 

Google Scholar 
Koehne, B., Elli, G., Jennings, R. C., Wilhelm, C. & Trissl, H.-W. Spectroscopic and molecular characterization of a long wavelength absorbing antenna of Ostreobium sp. Biochim. Biophys. Acta BBA Bioenerg. 1412, 94–107 (1999).CAS 

Google Scholar 
Wangpraseurt, D. et al. In vivo microscale measurements of light and photosynthesis during coral bleaching: Evidence for the optical feedback loop?. Front. Microbiol. 8, 59 (2017).PubMed 
PubMed Central 

Google Scholar 
Lukas, K. J. Two species of the chlorophyte genus Ostreobium from skeletons of Atlantic and Caribbean reef corals. J. Phycol. 10, 331–335 (1974).
Google Scholar 
Fork, D. C. & Larkum, A. W. D. Light harvesting in the green alga Ostreobium sp., a coral symbiont adapted to extreme shade. Mar. Biol. 103, 381–385 (1989).
Google Scholar 
Massé, A., Domart-Coulon, I., Golubic, S., Duché, D. & Tribollet, A. Early skeletal colonization of the coral holobiont by the microboring Ulvophyceae Ostreobium sp. Sci. Rep. 8, 1–11 (2018).
Google Scholar 
Godinot, C., Tribollet, A., Grover, R. & Ferrier-Pagès, C. Bioerosion by euendoliths decreases in phosphate-enriched skeletons of living corals. Biogeosci. Discuss. 9, 2425–2444 (2012).ADS 

Google Scholar 
Vásquez-Elizondo, R. M. et al. Absorptance determinations on multicellular tissues. Photosynth. Res. 132, 311–324 (2017).PubMed 

Google Scholar 
Tribollet, A. The boring microflora in modern coral reef ecosystems: A review of its roles. In Current Developments in Bioerosion (eds. Wisshak, M. & Tapanila, L.) 67–94 (Springer Berlin Heidelberg, 2008). https://doi.org/10.1007/978-3-540-77598-0_4.Fine, M., Meroz-Fine, E. & Hoegh-Guldberg, O. Tolerance of endolithic algae to elevated temperature and light in the coral Montipora monasteriata from the southern Great Barrier Reef. J. Exp. Biol. 208, 75–81 (2005).PubMed 

Google Scholar 
Pernice, M. et al. Down to the bone: The role of overlooked endolithic microbiomes in reef coral health. ISME J. 14, 325–334 (2020).PubMed 

Google Scholar 
Schlichter, D., Zscharnack, B. & Krisch, H. Transfer of photoassimilates from endolithic algae to coral tissue. Naturwissenschaften 82, 564–567 (1995).ADS 

Google Scholar 
Kühl, M., Cohen, Y., Dalsgaard, T., Barker Jorgersen, B. & Revsbech, N. P. Microenvironment and photosynthesis of zooxanthellae in scleractinian corals studied with microsensors for O2, pH and light. Mar. Ecol. Prog. Ser. 117, 159–172 (1995).ADS 

Google Scholar 
Marcelino, L. A. et al. Modulation of light-enhancement to symbiotic algae by light-scattering in corals and evolutionary trends in bleaching. PLoS One 8, e61492 (2013).ADS 
CAS 
PubMed 
PubMed Central 

Google Scholar 
Wangpraseurt, D. et al. Lateral light transfer ensures efficient resource distribution in symbiont-bearing corals. J. Exp. Biol. 217, 489–498 (2014).PubMed 

Google Scholar 
Wangpraseurt, D., Jacques, S. L., Petrie, T. & Kühl, M. Monte Carlo modeling of photon propagation reveals highly scattering coral tissue. Front. Plant Sci. 7, 1404 (2016).PubMed 
PubMed Central 

Google Scholar 
Carilli, J., Donner, S. D. & Hartmann, A. C. Historical temperature variability affects coral response to heat stress. PLoS One 7, e34418 (2012).ADS 
CAS 
PubMed 
PubMed Central 

Google Scholar 
Marcelino, V. R. & Verbruggen, H. Multi-marker metabarcoding of coral skeletons reveals a rich microbiome and diverse evolutionary origins of endolithic algae. Sci. Rep. 6, 1–9 (2016).
Google Scholar 
del Campo, J., Pombert, J.-F., Šlapeta, J., Larkum, A. & Keeling, P. J. The ‘other’ coral symbiont: Ostreobium diversity and distribution. ISME J. 11, 296–299 (2017).PubMed 

Google Scholar 
Massé, A. et al. Functional diversity of microboring Ostreobium algae isolated from corals. Environ. Microbiol. 22, 4825–4846 (2020).PubMed 

Google Scholar 
Iglesias-Prieto, R., Beltran, V. H., LaJeunesse, T. C., Reyes-Bonilla, H. & Thome, P. E. Different algal symbionts explain the vertical distribution of dominant reef corals in the eastern Pacific. Proc. R. Soc. B Biol. Sci. 271, 1757–1763 (2004).CAS 

Google Scholar 
Fisher, P. L., Malme, M. K. & Dove, S. The effect of temperature stress on coral–Symbiodinium associations containing distinct symbiont types. Coral Reefs 31, 473–485 (2012).ADS 

Google Scholar 
Jeffrey, S. W. & Humphrey, G. F. New spectrophotometric equations for determining chlorophylls a, b, c1 and c2 in higher plants, algae and natural phytoplankton. BPP 167, 191–194 (1975).CAS 

Google Scholar 
Marsh, J. A. Primary productivity of reef-building calcareous red algae. Ecology 51, 255–263 (1970).
Google Scholar 
Shibata, K. Pigments and a UV-absorbing substance in corals and a blue-green alga living in the Great Barrier Reef1. Plant Cell Physiol. https://doi.org/10.1093/oxfordjournals.pcp.a074411 (1969).Article 

Google Scholar 
López-Londoño, T. et al. Physiological and ecological consequences of the water optical properties degradation on reef corals. Coral Reefs 40, 1243–1256 (2021).
Google Scholar  More

Arora, P. K. & Bae, H. Bacterial degradation of chlorophenols and their derivatives. Microb. Cell Fact. 13, 31–36 (2014).Article 

Google Scholar 
Solyanikova, I. P. & Golovleva, L. A. Bacterial degradation of chlorophenols: Pathways, biochemica, and genetic aspects. J. Environ. Sci. Health B 39, 333–351 (2004).Article 

Google Scholar 
Olaniran, A. O. & Igbinosa, E. O. Chlorophenols and other related derivatives of environmental concern: Properties, distribution and microbial degradation processes. Chemosphere 83, 1297–1306 (2011).ADS 
CAS 
Article 

Google Scholar 
Kusmierek, K. The removal of chlorophenols from aqueous solutions using activated carbon adsorption integrated with H2O2 oxidation. Reac. Kinet. Mech. Cat. 119, 19–34 (2016).CAS 
Article 

Google Scholar 
Igbinosa, E., Odjadjare, E., Vicent, N. & Ideemndia, O. Toxicological profile of chlorophenols and their derivatives in the environment: The public health perspective. Sci. World J. 2013, 11 (2013).
Google Scholar 
Hossain, G. & McLaughlan, R. Kinetic investigations of oxidation of chlorophenols by permanganate. J. Environ. Chem. Ecotoxicol 5, 81–89 (2013).
Google Scholar 
Ryan, D., Leukes, W. & Burton, S. Improving the bioremediation of phenolic wastewaters by Trametes versicolor. Bioresour. Technol 98, 579–587 (2016).Article 

Google Scholar 
Zhao, L., Wu, Q. & Ma, A. Biodegradation of phenolic contaminants: Current status and perspectives. In International Conference on Advanced Environmental Engineering IOP Publishing. Series: Earth and Environmental Science. Vol 111, 012024 (2018).Walter, M., Boul, L., Chong, R. & Ford, C. Growth substrate selection and biodegradation of PCP by New Zealand white-rot fungi. J. Environ. Qual. 24(36), 1749–1759 (2004).
Google Scholar 
Cameron, M. D., Timofeevski, S. & Aust, S. D. Enzymology of Phanerochaete chrysosporium with respect to the degradation of recalcitrant compounds and xenobiotics. Appl. Microbiol. Biotechnol. 54, 751–758 (2000).CAS 
Article 

Google Scholar 
Tuomela, M., Lyytikainen, M., Oivanena, P. & Hatakka, A. Mineralization and conversion of pentachlorophenol (PCP) in soil inoculated with the white-rot fungus Trametes versicolor. Soil Biol. Biochem. 31, 65–74 (1999).CAS 
Article 

Google Scholar 
Field, J. & Sierra-Alvarez, R. Microbial degradation of chlorinated phenols. Rev. Environ. Sci. Biotechnol 7, 211–241 (2008).CAS 
Article 

Google Scholar 
Bosso, L. & Cristinzio, G. A. A comprehensive overview of bacteria and fungi used for pentachlorophenol biodegradation. Rev. Environ. Sci. Biotechnol 13, 387–427 (2014).CAS 
Article 

Google Scholar 
Field, J. A. & Sierra-Alvarez, R. Microbial transformation and degradation of polychlorinated biphenyls. Environ. Pollut 155, 1–12 (2008).CAS 
Article 

Google Scholar 
Nikolaivits, E. et al. Degradation mechanism of 2,4-dichlorophenol by fungi isolated from marine invertebrates. Int. J. Mol. Sci 21, 3317. https://doi.org/10.3390/ijms21093317 (2020).CAS 
Article 
PubMed Central 

Google Scholar 
Cser-jesi, A. J. & Johnson, E. Methylation of entachlorophenol by Trichoderma virgatum. Can. J. Microbiol. 18, 45–49 (1972).CAS 
Article 

Google Scholar 
van Leeuwen, J., Nicholson, B., Hayes, K. & Mulcahy, D. Degradation of chlorophenolic compounds by Trichoderma harzianum isolated from Lake Bonney, South-Eastern South Australia. Environ Toxicol. Water Qual. 12, 335–342 (1997).ADS 
Article 

Google Scholar 
Carvalho, M. B. et al. Screening pentachlorophenol degradation ability by environmental fungal strains belonging to the phyla Ascomycota and Zygomycota. J. Ind. Microbiol. Biotechnol. 36, 1249–1256 (2009).CAS 
Article 

Google Scholar 
Chakroun, H., Mechichi, T., Martinez, M. J., Dhouib, A. & Sayadi, S. Purification and characterization of a novel laccase from the ascomycete Trichoderma atroviride: Application on bioremediation of phenolic compounds ‬. Process Biochem. 45, 507–513 (2010).CAS 
Article 

Google Scholar 
Abdel-Fatah, O. M. et al. Physiological studies on carboxymethyl cellulase formation by Aspergillus terreus DSM 826. Braz. J. Microbiol. 43(1), 01–11 (2012).CAS 
Article 

Google Scholar 
Sonika, P. et al. Trichoderma species cellulases produced by solid state fermentation. J. Data Min. Genom. Proteom. 6, 2 (2015).
Google Scholar 
Al-Hawash, B. A. et al. Isolation and characterization of two crude oil-degrading fungi strains from Rumaila oil field. Iraq. Biotechnol. Rep 17, 104–109. https://doi.org/10.1016/j.btre.2017.12.006 (2018).Article 

Google Scholar 
Zafra, G., Absalón, A. E. & Cortes-Espinosa, D. V. Morphological changes and growth of filamentous fungi in the presence of high concentrations of PAHs. Braz. J. Microbiol 46, 937–941. https://doi.org/10.1590/S1517-838246320140575 (2015).CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
Smit, E., Leeflang, P., Glandorf, B., van Elsas, J. D. & Wernars, K. Analysis of fungal diversity in the wheat rhizosphere by sequencing of cloned PCR-amplified genes encoding 18S rRNA and temperature gradient gel electrophoresis. Appl. Environ. Microbiol. 65(6), 2614–2621 (1999).ADS 
CAS 
Article 

Google Scholar 
White, T. J. Analysis of phylogenetic relationships by amplification and direct sequencing of ribosomal genes. In PCR Protocols: A Guide to Methods and Applications 315–22 (1990).Ryu, W. R. et al. Biodegradation of white rot fungi under ligninolytic and nonligninolytic conditions. Biotechnol Bioproc. E 5, 211–214 (2000).CAS 
Article 

Google Scholar 
Dubois, K., Gilles, J., Hamilton, P., Rebers, A. & Smith, F. Colorimetric method for determination of sugars and related substances. Anal. Chem. 28, 350–356 (1956).CAS 
Article 

Google Scholar 
Letunic, I. & Bork, P. Interactive Tree Of Life (iTOL) v5: An online tool for phylogenetic tree display and annotation. Nucleic Acids Res. 49(W1), W293–W296. https://doi.org/10.1093/nar/gkab301 (2021).CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
Kumar, S., Stecher, G. & Tamura, K. MEGA7: Molecular evolutionary genetics analysis version 7.0 for bigger datasets. Mol. Biol. Evol. 33(7), 1870–1874 (2016).CAS 
Article 

Google Scholar 
Statistical Packages for Software Sciences. Version 21.0 Armonk (New York: IBM Corporation, 2013).Lin, S.-H. & Juang, R.-S. Adsorption of phenol and its derivatives from water using synthetic resins and low-cost natural adsorbents: A review. J. Environ. Manage 90, 1336–1349. https://doi.org/10.1016/j.jenvman.2008.09.003 (2009).CAS 
Article 
PubMed 

Google Scholar 
Kumar, S. N., Subbaiah, V. M., Reddy, S. A. & Krishnaiah, A. Biosorption of phenolic compounds from aqueous solutions onto chitosan-abrus precatorius blended beads. J. Chem. Technol. Biotechnol 84, 972–981. https://doi.org/10.1002/jctb.2120 (2009).CAS 
Article 

Google Scholar 
Wang, C. C., Lee, C. M., Lu, C. J., Chuang, M. S. & Huang, C. Z. Biodegradation of 2,4,6-trichlorophenol in the presence of primary substrate by immobilized pure culture bacteria. Chemosphere 41, 1873–1879. https://doi.org/10.1016/S00456535(00)00090-4 (2000).ADS 
CAS 
Article 
PubMed 

Google Scholar 
Kavamura, V. N. & Esposito, E. Biotechnological strategies applied to the decontamination of soils polluted with heavy metals. Biotechnol. Adv 28, 61–69 (2010).CAS 
Article 

Google Scholar 
Mohsenzade, F., Chehregani, A. & Akbari, M. Evaluation of oil removal efficiency and enzymatic activity in some fungal strains for bioremediation of petroleum-polluted soils. Iran J. Environ. Health. Eng 9, 26–34 (2012).Article 

Google Scholar 
Nikolaivits, E. et al. Unraveling the detoxification mechanism of 2,4-dichlorophenol by marine-derived mesophotic symbiotic fungi isolated from marine invertebrates. Mar. Drugs. 17, 564. https://doi.org/10.3390/md17100564 (2019).CAS 
Article 
PubMed Central 

Google Scholar 
Scientific opinion on risk assessment for a selected group of pesticides from the triazole group to test possible methodologies to assess cumulative effects from exposure through food from these pesticides on human health. EFSA J. 7, 1167. https://www.efsa.europa.eu/en/efsajournal/pub/1167 (2009).Brotman, Y., Kapuganti, J. G. & Viterbo, A. Trichoderma. Curr. Biol. 20, R390–R439 (2010).CAS 
Article 

Google Scholar 
Boroujeni, N. A., Hassanshahian, M., Mohammad, S. & Khoshrou, R. Isolation and characterization of phenol degrading bacteria from Persian Gulf. IJABBR 2, 408–416 (2014).CAS 

Google Scholar 
Roostaei, N. & Tezel, F. H. Removal of phenol from aqueous solutions by adsorption. J. Environ. Manage 70, 157–164. https://doi.org/10.1016/j.jenvman.2003.11.004 (2004).Article 
PubMed 

Google Scholar 
Demnerova, K. et al. Two approaches to biological decontamination of groundwater and soil polluted by aromatics-characterization of microbial populations. Int. Microbiol 8, 205–211 (2005).CAS 
PubMed 

Google Scholar 
Reddy, G. V. B. & Gold, M. H. Degradation of pentachlorophenol by Phanerochaete chrysosporium: Intermediates and reactions involved. Microbiology 146, 405–413 (2000).CAS 
Article 

Google Scholar 
Cortés, D. V., Bernal, R. & Tomasini, A. Efecto de las condiciones de cultivo sumergido en la degradación de pentaclorofenol. Información Tecnológica 12, 75–80 (2001).
Google Scholar 
Crawford, R. L., Jung, C. M. & Strap, J. L. The recent evolution of pentachlorophenol (PCP)-4-monooxygenase (PcpB) and associated pathways for bacterial degradation of PCP. Biodegradation 18, 525–539 (2007).CAS 
Article 

Google Scholar 
Bergauer, P., Fonteyne, P. A., Nolard, N., Schinner, F. & Margesin, R. Biodegradation of phenol and phenol-related compounds by psychrophilic and cold-tolerant alpine yeasts. Hemosphere 59, 909–918 (2005).ADS 
CAS 
Article 

Google Scholar 
Bovio, E. et al. The culturable mycobiota of a Mediterranean marine site after an oil spill: Isolation, identification and potential application in bioremediation. Sci. Total Environ. 576, 310–318 (2017).ADS 
CAS 
Article 

Google Scholar  More

Wehner, R., Michel, B. & Antonsen, P. Visual navigation in insects: Coupling of egocentric and geocentric information. J. Exp. Biol. 199(1), 129–140 (1996).CAS 
PubMed 

Google Scholar 
Collett, M., Chittka, L. & Collett, T. S. Spatial memory in insect navigation. Curr. Biol. 23(17), R789–R800 (2013).CAS 
PubMed 

Google Scholar 
Cheng, K., Schultheiss, P., Schwarz, S., Wystrach, A. & Wehner, R. Beginnings of a synthetic approach to desert ant navigation. Behav. Proc. 102, 51–61 (2014).
Google Scholar 
Freas, C. A. & Schultheiss, P. How to navigate in different environments and situations: Lessons from ants. Front. Psych. 9, 841 (2018).
Google Scholar 
Wehner, R. Desert ant navigation: how miniature brains solve complex tasks. J. Comp. Physiol. A 189(8), 579–588 (2003).ADS 
CAS 

Google Scholar 
Wehner, R. The desert ant’s navigational toolkit: Procedural rather than positional knowledge. Navigation 55(2), 101–114 (2008).
Google Scholar 
Wehner, R. Desert Navigator (The Belknap Press of Harvard University Press, 2020).
Google Scholar 
Kohler, M. & Wehner, R. Idiosyncratic route-based memories in desert ants, Melophorus bagoti: How do they interact with path-integration vectors?. Neurobiol. Learn. Mem. 83(1), 1–12 (2005).PubMed 

Google Scholar 
Müller, M. & Wehner, R. Path integration provides a scaffold for landmark learning in desert ants. Curr. Biol. 20(15), 1368–1371 (2010).PubMed 

Google Scholar 
Mangan, M. & Webb, B. Spontaneous formation of multiple routes in individual desert ants (Cataglyphis velox). Behav. Ecol. 23(5), 944–954 (2012).
Google Scholar 
Schwarz, S., Wystrach, A. & Cheng, K. Ants’ navigation in an unfamiliar environment is influenced by their experience of a familiar route. Sci. Rep. 7(1), 1–10 (2017).
Google Scholar 
Graham, P. & Cheng, K. Ants use the panoramic skyline as a visual cue during navigation. Curr. Biol. 19, R935–R937 (2009).CAS 
PubMed 

Google Scholar 
Wystrach, A., Beugnon, G. & Cheng, K. Landmarks or panoramas: What do navigating ants attend to for guidance?. Front. Zool. 8(1), 21 (2011).PubMed 
PubMed Central 

Google Scholar 
Wehner, R., Meier, C. & Zollikofer, C. The ontogeny of foraging behaviour in desertants, Cataglyphis bicolor. Ecol. Entom. 29, 240–250 (2004).
Google Scholar 
Zeil, J. & Fleischmann, P. N. The learning walks of ants (Hymenoptera: Formicidae). Myrmecol. News. 29, 93–110 (2019).
Google Scholar 
Schultheiss, P. et al. Crucial role of ultraviolet light for desert ants in determining direction from the terrestrial panorama. Anim. Behav. 115, 19–28 (2016).
Google Scholar 
Freas, C. A., Wystrach, A., Narendra, A. & Cheng, K. The view from the trees: Nocturnal bull ants, Myrmecia midas, use the surrounding panorama while descending from trees. Front. Psych. 9, 1–10 (2018).
Google Scholar 
Freas, C. A. & Cheng, K. Landmark learning, cue conflict, and outbound view sequence in navigating desert ants. J. Exp. Psych. Anim. Learn. Cogn. 44(4), 409–421 (2018).
Google Scholar 
Freas, C. A. & Spetch, M. L. Terrestrial cue learning and retention during the outbound and inbound foraging trip in the desert ant, Cataglyphis bicolor. J. Comp. Physiol. A. 205(2), 177–189 (2019).
Google Scholar 
Narendra, A., Si, A., Sulikowski, D. & Cheng, K. Learning, retention and coding of nest-associated visual cues by the Australian desert ant, Myrmecia midas. Behav. Ecol. Sociobiol. 61(10), 1543–1553 (2007).
Google Scholar 
Zeil, J. Visual homing: an insect perspective. Curr. Opin. Neurobiol. 22(2), 285–293 (2012).CAS 
PubMed 

Google Scholar 
Zeil, J., Hofmann, M. I. & Chahl, J. S. Catchment areas of panoramic snapshots in outdoor scenes. J. Optic. Soc. Am. A. 20(3), 450 (2003).ADS 

Google Scholar 
Wystrach, A., Cheng, K., Sosa, S. & Beugnon, G. Geometry, features, and panoramic views: Ants in rectangular arenas. J. Exp. Psychol. 37(4), 420–435 (2011).
Google Scholar 
Baddeley, B., Graham, P., Husbands, P. & Philippides, A. A model of ant route navigation driven by scene familiarity. PLoS Comp. Biol. 8(1), e1002336 (2012).ADS 
CAS 

Google Scholar 
Kodzhabashev, A. & Mangan, M. Route Following Without Scanning In Biomimetic and Biohybrid Systems 199–210 (Springer, 2015).
Google Scholar 
Möller, R. A model of ant navigation based on visual prediction. J. Theo. Biol. 305, 118–130 (2012).ADS 
MathSciNet 
MATH 

Google Scholar 
Le Möel, F. & Wystrach, A. Opponent processes in visual memories: A model of attraction and repulsion in navigating insects’ mushroom bodies. PLoS Comp. Biol. 16, e1007631 (2020).
Google Scholar 
Murray, T. et al. The role of attractive and repellent scene memories in ant homing (Myrmecia croslandi). J. Exp. Biol. 223, 21002 (2020).
Google Scholar 
Jayatilaka, P., Murray, T., Narendra, A. & Zeil, J. The choreography of learning walks in the Australian jack jumper ant Myrmecia croslandi. J. Exp. Biol. 221(20), 185306 (2018).
Google Scholar 
Schwarz, S., Mangan, M., Webb, B. & Wystrach, A. Route-following ants respond to alterations of the view sequence. J. Exp. Biol. 223, 218701 (2020).
Google Scholar 
Wystrach, A., Buehlmann, C., Schwarz, S., Cheng, K. & Graham, P. Rapid aversive and memory trace learning during route navigation in desert ants. Curr. Biol. 30(100), 1927–1933 (2020).CAS 
PubMed 

Google Scholar 
Wystrach, A., Philippides, A., Aurejac, A., Cheng, K. & Graham, P. Visual scanning behaviours and their role in the navigation of the Australian desert ant Melophorus bagoti. J. Comp. Physiol. A 200(7), 615–626 (2014).
Google Scholar 
Wystrach, A., Schwarz, S., Graham, P. & Cheng, K. Running paths to nowhere: Repetition of routes shows how navigating ants modulate online the weights accorded to cues. Anim. Cogn. 2, 213–222 (2019).
Google Scholar 
MacArthur, R. H. & Pianka, E. R. On optimal use of a patchy environment. Am. Nat. 100(916), 603–609 (1966).
Google Scholar 
Krebs, J. R. Foraging Theory (Princeton University Press, 1986).
Google Scholar 
Kacelnik, A. & Bateson, M. Risky theories: The effects of variance on foraging decisions. Am. Zool. 36(4), 402–434 (1996).
Google Scholar 
Kacelnik, A. & Abreu, F. B. Risky choice and Weber’s law. J. Theor. Biol. 194(2), 289–298 (1998).ADS 
CAS 
PubMed 

Google Scholar 
Fechner, G. T. Elemente der Psychophysik Vol. 2 (Breitkopf u Härtel, 1860).
Google Scholar 
Bruce, A. C. & Johnson, J. E. V. Decision-making under risk: Effect of complexity on performance. Psychol. Rep. 79(1), 67–76 (1996).
Google Scholar 
Stevens, S. S. & Marks, L. E. Psychophysics: Introduction to its Perceptual, Neural, and Social Prospects (Routledge, 2017).
Google Scholar 
Kacelnik, A. & El Mouden, C. Triumphs and trials of the risk paradigm. Anim. Behav. 86(6), 1117–1129 (2013).
Google Scholar 
Hübner, C. & Czaczkes, T. J. Risk preference during collective decision making: Ant colonies make risk-indifferent collective choices. Anim. Behav. 132, 21–28 (2017).
Google Scholar 
De Agrò, M., Grimwade, D., Bach, R. & Czaczkes, T. J. Irrational risk aversion in an ant. Anim. Cogn. 1, 1–9 (2021).
Google Scholar 
Waddington, K. D., Allen, T. & Heinrich, B. Floral preferences of bumblebees (Bombus edwardsii) in relation to intermittent versus continuous rewards. Anim. Behav. 29(3), 779–784 (1981).
Google Scholar 
Cartar, R. V. A test of risk-sensitive foraging in wild bumble bees. Ecology 72(3), 888–895 (1991).
Google Scholar 
Perez, S. M. & Waddington, K. D. Carpenter bee (Xylocopa micans) risk indifference and a review of nectarivore risk-sensitivity studies. Am. Zool. 36(4), 435–446 (1996).
Google Scholar 
Fülöp, A. & Menzel, R. Risk-indifferent foraging behaviour in honeybees. Anim. Behav. 60(5), 657–666 (2000).PubMed 

Google Scholar 
Burns, D. D., Sendova-Franks, A. B. & Franks, N. R. The effect of social information on the collective choices of ant colonies. Behav. Ecol. 27(4), 1033–1040 (2016).
Google Scholar 
Sasaki, T., Pratt, S. C. & Kacelnik, A. Parallel vs. comparative evaluation of alternative options by colonies and individuals of the ant Temnothorax rugatulus. Sci. Rep. 8(1), 1–8 (2018).
Google Scholar 
Sasaki, T., Stott, B. & Pratt, S. C. Rational time investment during collective decision making in Temnothorax ants. Biol. Lett. 15(10), 20190542 (2019).PubMed 
PubMed Central 

Google Scholar 
Freas, C. A., Fleischmann, P. N. & Cheng, K. Experimental ethology of learning in desert ants: Becoming expert navigators. Behav. Proc. 158, 181–191 (2019).
Google Scholar 
Le Moël, F. & Wystrach, A. Towards a multi-level understanding in insect navigation. Curr. Opin. Inst. Sci. 42, 110–117 (2020).
Google Scholar 
Heinze, S. Visual navigation: Ants lose track without mushroom bodies. Curr. Biol. 30(17), R984–R986 (2020).CAS 
PubMed 

Google Scholar 
Ardin, P., Peng, F., Mangan, M., Lagogiannis, K. & Webb, B. Using an insect mushroom body circuit to encode route memory in complex natural environments. PLOS Comp. Biol. 12(2), e1004683 (2016).ADS 

Google Scholar 
Buehlmann, C. et al. Mushroom bodies are required for learned visual navigation, but not for innate visual behavior, in ants. Curr. Biol. 30(17), 3438–3443 (2020).CAS 
PubMed 

Google Scholar 
Kamhi, J. F., Barron, A. B. & Narendra, A. Vertical lobes of the mushroom bodies are essential for view-based navigation in Australian Myrmecia ants. Curr. Biol. 30(17), 3432–3437 (2020).CAS 
PubMed 

Google Scholar 
Heisenberg, M. Mushroom body memoir: From maps to models. Nat. Rev. Neurosci. 4(4), 266–275 (2003).CAS 
PubMed 

Google Scholar 
Webb, B. & Wystrach, A. Neural mechanisms of insect navigation. Curr. Opin. Inst. Sci. 15, 27–39 (2016).
Google Scholar 
Habenstein, J., Amini, E., Grübel, K., El Jundi, B. & Rössler, W. The brain of Cataglyphis ants: Neuronal organization and visual projections. J. Comp. Neurol. 528(18), 3479–3506 (2020).PubMed 

Google Scholar 
Cohn, R., Morantte, I. & Ruta, V. Coordinated and compartmentalized neuromodulation shapes sensory processing in Drosophila. Cell 163(7), 1742–1755 (2015).CAS 
PubMed 
PubMed Central 

Google Scholar 
Aso, Y. & Rubin, G. M. Dopaminergic neurons write and update memories with cell-type-specific rules. Elife 5, e16135 (2015).
Google Scholar 
Beck, C. D. O., Schroeder, B. & Davis, R. L. Learning performance of normal and mutant Drosophila after repeated conditioning trials with discrete stimuli. J. Neurosci. 20(8), 2944–2953 (2000).CAS 
PubMed 
PubMed Central 

Google Scholar 
Boto, T. & Ramaswami, M. Learning and memory: Clashing engrams in the fly brain. Curr. Biol. 31(16), R1009–R1011 (2021).CAS 
PubMed 

Google Scholar 
Bennett, J. E. M., Philippides, A. & Nowotny, T. Learning with reinforcement prediction errors in a model of the Drosophila mushroom body. Nat. Commun. 12, 22595 (2021).
Google Scholar 
Rescorla, R. A. & Wagner, A. R. A theory of Pavlovian conditioning: Variations in the effectiveness of reinforcement and nonreinforcement. In Classical Conditioning Ii: Current Theory and Research (eds Black, A. & Prokasy, W.) (Appleton-Century-Crofts, 1972).
Google Scholar  More

Study islands and bird dataSystematic ringing in spring on Mediterranean islands has been promoted by the Piccole Isole project since 198826. Standard methods of the project involve ringing between 16th April and 15th May attempting to include the peak of the spring passage of long-distance migrants. Ringing is performed from dawn to nightfall using a constant number of nets within ringing stations placed at stable sites located at representative habitats in each island (Supplementary Table S1). The use of tape-lures is not allowed. We have compiled ringing data for all the Spanish Mediterranean islands that have been applying this methodology, with the exception of Mallorca and Menorca where the ringing stations were located in wetlands and captured a large percentage of local birds (Fig. 2, Table 1). The nine study islands are spread along a south-west to north-east gradient and, with the exception of Columbrets, they are distributed in pairs of similar longitude but different latitudes (Fig. 2). Ringing stations have been operating over a variable number of years (5–27 years), with the maximum number of ringing stations operating at the same time occurring between 2003 and 2010. To include between-year variation on islands that started ringing campaigns more recently we used data from the years 2003–2018.Figure 2Image source: Google Earth. Data SIO, NOAA, US Navy, NGA, GEBCO. Image Landsat/Copernicus.Geographical location of studied islands in the western Mediterranean.Full size imageTable 1 Period of activity of the ringing stations located on each island between the years 1992 and 2018.Full size tableThe ringing period within each spring also varied in most islands, owing to funding or logistic limitations; thus, to reduce the possible effects on migrant composition we only used data from the standard period of the Piccole Isole project and from years that included at least one week of ringing in the fortnight of each month within this interval. This procedure excluded the use of some years for several islands, and the final number of data years for islands ranged between 5 and 16 (Table 1).We used only data for trans-Saharan nocturnal migrant passerines, which form the bulk of species ringed on Mediterranean islands during the standard period. The standard ringing period only covers the tail end of the short-distance migrants’ passage; thus, these species were excluded as their contribution to composition of migrants could vary mainly due to between-year variation in migration phenology. Diurnal migrants, like hirundinids and fringillids, also represent a small fraction of birds ringed and may use different cues to select stopover islands. In addition, some of these species nest in some of the islands studied and birds ringed could include breeding birds. To avoid the distorting effect of species that are captured accidentally in very small numbers, we considered only the species that were ringed in at least five separate years, or on five different islands, which limited the species considered to 35 (Supplementary Table S2). This led to the exclusion of just two species (Ficedula semitorquata with three individuals ringed in two islands and Locustella luscinioides with one individual ringed in Aire island). In addition, we only considered the number of ringed birds, since the proportion of recaptures varies among islands, likely reflecting variation in the duration of stopovers21, which could bias the comparison of the patterns of migrant species composition.Island descriptorsWe obtained two groups of variables describing the characteristics of the study islands (Tables 2, 3): (1) Variables related to geographical location: latitude, longitude, straight distance and minimum distance to the North African coast, minimum distance to the closest large body of land (continent or large island) in any direction and to the closest large body of land situated in a southerly angle between SW and SE. (2) Variables related to the habitat characteristics of the islands: area, maximum altitude and Normalized Difference Vegetation Index (NDVI). We estimated NDVI from Landsat 8 Images taken during the standard ringing period in the years 2015 and 2016. Pixels containing shoreline were excluded and the average NDVI was calculated for the rest of the pixels.Table 2 Variables describing the characteristics of the islands that included the ringing stations studied.Full size tableTable 3 Values of the island descriptors (see Table 2) and two measures of temporal variability of migrant composition in each island: average local contribution of each island to beta diversity (LCBD) and beta diversity for each island (BDTi).Full size tableContinental abundance dataAbundance estimates for western Europe were obtained from the European Red List of Birds27. We used the mean of the minimum and maximum number of pairs estimated for the 27 EU Member States as a measure of continental abundance (Supplementary Table S2).Data analysisAll analyses were done using R 3.6.128. We built a matrix of island-year x species containing the number of individuals of each selected species ringed in the study period in each island and year. Average number of individuals of each species ringed at each island was calculated and was used (log-transformed) as a dependent variable in a linear model with continental abundance (log-transformed), island and their interaction as predictors. This model was simplified using AICc as criteria to identify the best model.To analyze variation of species composition, the matrix of island-year x species was transformed using the chord transformation29 with the function decostand in the vegan package30.Using the function beta.div of the adespatial package31 we calculated beta diversity, including temporal and between-island variability (BDI,T), as the total variance of the aforementioned transformed matrix (BDTotal in29), which varies between 0 and 1 when chord distance is used. Considering that yijk is the chord transformed abundance of the species j in the island i and year k and (overline{{y }_{j}}) is the mean for species j in all islands and years altogether, then:$${SS}_{Total}=sum_{i=1}^{n}sum_{j=1}^{p}{sum_{k=1}^{q}{({y}_{ijk}-{overline{y} }_{j})}^{2}}$$$$BD_{I,T} = , SS_{Total} /left( {N – 1} right)$$where N is the total number of samples. The function beta.div also provides an estimation of contribution of localities (LCBD) and species (SCBD) to beta diversity (Table 3). Yearly LCBD (log transformed because of skewed distribution) of each island were averaged and compared between islands using ANOVA and a post-hoc Tukey test.We partitioned the above sum of squares in several ways. First, we calculated a beta diversity that considered only between-island variability, excluding temporal variability (BDI), by averaging the chord transformed abundances of each species j in each island along study years (({overline{y} }_{ij})) and applying the same procedure, but using the number of studied islands (n):$${SS}_{I}=sum_{i=1}^{n}sum_{j=1}^{p}{{({overline{y} }_{ij}-{overline{y} }_{j})}^{2}}$$$$BD_{I} = SS_{I} /left( {n – 1} right)$$Second, we calculated a beta diversity due to inter-annual variation of migrant composition within islands (BDT) as:$${SS}_{Temp}=sum_{i=1}^{n}sum_{j=1}^{p}{sum_{k=1}^{q}{({y}_{ijk}-{overline{y} }_{ij})}^{2}}$$$$BD_{T} = SS_{Temp} /left( {Y – n} right)$$where Y is the total number of study years and n is the number of studied islands (9). We also calculated a temporal beta diversity for each island i (BDTi) as the sum of squares due to variation within the island divided by the number of the island study years (Yi) minus 1:$${SS}_{Temp,i}=sum_{j=1}^{p}sum_{k=1}^{q}{({y}_{ijk}-{overline{y} }_{ij})}^{2}$$$$BD_{Ti} = SS_{Temp,i} /left( {Y_{i} – 1} right)$$Differences in temporal variability between islands could be due to different predominance of species that are more or less variable between years. To check this, we calculated Spearman’s rank correlation between the percentage of captures of each species in the total ringed on each island and BDTi and LCDB indices, for species present on all islands.We tested for the existence of differences between islands in migrant species composition using Permutational Multivariate Analysis of Variance (PERMANOVA) using the function adonis2 in the vegan package. We performed a multivariate test of homogeneity of variances using the betadisper function (vegan package) with the adjustment for small sample bias, to test if temporal variability in species composition differed between islands. We made post-hoc comparisons between islands with False Discovery Rate (FDR) correction using the function pairwise.perm.manova of the package RVAideMemoire32.To identify gradients in migrant species composition and the island characteristics that were associated with them, we employed Redundancy Analysis using the rda function (vegan package). We used the chord transformed matrix of species x island-year as a response matrix. We used two explanatory matrices, one including variables of geographical location and the other the variables related to habitat characteristics of the islands. We evaluated the relative importance of each group of variables to explain migrant species composition by performing a variation partitioning analysis, using the varpart function (vegan package). For that analysis, we followed the steps and R scripts recommended in33.Variables describing island characteristics were transformed using natural logarithms and collinearity within each group was evaluated with variance inflation factor (VIF)34. All the habitat variables presented VIF  More

Eddy-covariance observationsWe used half-hourly or hourly GPP, air temperature, VPD, SWC and incoming shortwave radiation from the recently released ICOS (Integrated Carbon Observation System)44 and the FLUXNET2015 dataset of energy, water, and carbon fluxes and meteorological data, both of which have undergone a standardized set of quality control and gap filling19. Data were already processed following a consistent and uniform processing pipeline19. This data processing pipeline mainly included: (1) thorough data quality control checks; (2) calculation of a range of friction velocity thresholds; (3) gap-filling of meteorological and flux measurements; (4) partitioning of CO2 fluxes into respiration and photosynthesis components; and (5) calculation of a correction factor for energy fluxes19. All the corrections listed were already applied to the available product19. We used incoming shortwave radiation, temperature, VPD, and SWC that were gap-filled using the marginal distribution method21. The GPP estimates from the night-time partitioning method were used for the analysis (GPP_NT_VUT_REF). SWC was measured as volumetric SWC (percentage) at different depths, varying across sites. We mainly used the surface SWC observations but deeper SWC measurements were also used when available. Data were quality controlled so that only measured and good-quality gap filled data (QC = 0 or 1) were used.Analysis of the extreme summer drought in 2018 in Europe to prove nonlinearityTo analyze the effect of summer drought in 2018 on GPP in Europe, we selected 15 sites with measurements during 2014–2018 from the ICOS dataset, representing the major ecosystems across Europe (Supplementary Table 1). Croplands were excluded due to the effect of management on the seasonal timing of ecosystem fluxes, both from crop rotation that change from year to year and from the variable timing of planting and harvesting. In croplands, the changes of GPP anomalies across different growing season could be mainly depend on crop varieties and management activities. Information of crop varieties, growing times yearly and other management data for each cropland site should be collected in future in order to fully consider and disentangle the impacts of SWC and VPD on its photosynthesis. Wetland sites were also removed because they are influenced by upstream organic matter and nutrient input, as well as fluctuating water tables. Daytime half-hourly data (7 am to 19 pm) were aggregated to daily values. At each site, the relative changes ((triangle {{{{{rm{X}}}}}})) of summer (June–July–August) GPP, SWC and VPD during 2014–2018 refer to the summer average of 2014–2018 were calculated for each year. For example, the calculation of the relative change in 2018 is shown in Eq. (1):$$triangle {{{{{rm{X}}}}}}=frac{{X}_{2018}-,{X}_{{average};{of};2014-2018}}{{X}_{{average};{of};2014-2018}}times 100 %$$
(1)
where X2018 is the mean of the daily values of (X) (GPP, SWC, or VPD) during the summer of 2018, and Xaverage of 2014–2018 is the mean of the daily values of (X) over all the summers of the 2014–2018 period. The average (triangle {{{{{rm{X}}}}}}) across a certain number of sites at each bin were used for the results in Fig. 1a.Daily time series of GPP, SWC and VPD during summer for each site were normalized (z-scores) to derive the standardized sensitivity of GPP to SWC and VPD. For each variable, the mean value across the summer of 2014–2018 was subtracted for each day at each site and then normalized by its standard deviation. At each site, we used a multiple linear regression (Eq. 2) to estimate daily GPP anomalies sensitivities to SWC and VPD anomalies across 2014–2018 and 2014–2017, respectively:$${GPP}={beta }_{1},{SWC}+{beta }_{2},{VPD}+{beta }_{3},{SWC},times {VPD}+{beta }_{4},{T}_{a}+{beta }_{5}{RAD}+b+varepsilon$$
(2)
where ({beta }_{i}) is the standardized sensitivity of GPP to each variable; ({T}_{a}) represents the air temperature; ({RAD}) represents the incoming shortwave radiation;(,b) represents the intercept; and (varepsilon) is the random error term. We compared estimated sensitivities with and without 2018 data to quantify the impacts of extreme drought in 2018 on GPP sensitivity to SWC (Fig. 1d) and VPD (Fig. 1e). The slope was calculated at each site and then the distribution of slopes across sites were plotted in Fig. 1d, e.Global analysis of the sensitivities of GPP to SWC and VPDFor the global analysis, instead of summer, we focused on the growing season and days when the SWC and VPD effects were most likely to control ecosystem fluxes and screen out days when other meteorological drivers were likely to have a larger influence on fluxes. Following previous studies5,8,45, for each site, we restrict our analyses to the days in which: (i) the daily average temperature >15 °C; (ii) sufficient evaporative demand existed to drive water fluxes, constrained as daily average VPD  > 0.5 kPa; (iii) high solar radiation, constrained as daily average incoming shortwave radiation >250 Wm−2.By combining ICOS and FLUXNET2015 data, at the global scale, we evaluated 67 sites with at least 300 days observations over the growing seasons for the years available (Supplementary Table 2). We excluded cropland and wetland sites for the above-mentioned reasons. These 67 sites were used to calculate the relative effects of low SWC and high VPD on GPP following the approach of ref. 5 (see below sections). For 8 sites, the ANN results failed performance criteria (the correlation between predicted GPP and observed GPP is {{VPD}}_{0}\ {beta }_{0},,{VPD}le {{VPD}}_{0}end{array}right.$$
(7)
where β0 and k are fitted parameters and VPD0 is 1 kPa48. Following Luo and Keenan48, we applied this method to a short time window (2–14 days) of Fc depending on the availability of flux measurements and assumed that every day in the same time window has the same daily Amax. We retrieved the daily Amax by implementing Eqs. (6) and (7) using the REddyProc R package (https://github.com/bgctw/REddyProc)20.Vcmax represents the activity of the primary carboxylating enzyme ribulose 1,5-bisphosphate carboxylase–oxygenase (Rubisco) as measured under light-saturated conditions. To evaluate the responses of Vcmax to SWC and VPD, we first calculated the daily internal leaf CO2 partial pressure (ci) in the middle of the day (11:00–14:00) via Fick’s Law (Eq. 8), excluding periods with low incoming shortwave radiation (0.7 at most sites. During the training process, weight and bias values were optimized using the Levenberg–Marquardt optimization58,59. The maximum number of epochs to train is 1000. An example to demonstrate the ANN training at one site was shown in Supplementary Fig. 3.At each site, ANN was run and sensitivities were calculated for all data within each SWC and VPD bin and the median value was used. For each of the five trained ANNs, one of the predictor variables was perturbed by one standard deviation (a value of 1 due to the initial input data normalization), and GPP was predicted again using the existing ANN with the predictors including the perturbed variable; this process was repeated for each predictor variable. The predicted values of GPP obtained with and without perturbation were then compared to determine the sensitivity values. The sample equation showing the calculation of the GPP sensitivity to VPD is shown in Eq. (10).$${{{{{{rm{Sensitivity}}}}}}}_{{VPD}}={median}left(,frac{{{GPP}}_{left({ANN};{VPD}+{stdev}left({VPD}right)right)}-{{GPP}}_{left({ANN};{all};{VAR}right)}}{{stdev}left({VPD}right)}right)$$
(10)
We repeated the ANN and sensitivity analyses five times and the median of these were used at each site. Across all sites, significances of the sensitivities for each bin were tested using t-tests (p  More

Mack, R. N. et al. Biotic invasions: causes, epidemiology, global consequences, and control. Ecol. Appl. 10, 689–710. https://doi.org/10.1890/1051-0761 (2000).Article 

Google Scholar 
Dukes, J. S. & Mooney, H. A. Disruption of ecosystem processes in western North America by invasive species. Rev. Chil. Hist. Nat. 77, 411–437 (2004).Article 

Google Scholar 
Vitousek, P. M. Biological invasions and ecosystem processes: towards an integration of population biology and ecosystem studies. Oikos 57, 7–13. https://doi.org/10.2307/3565731 (1990).Article 

Google Scholar 
Richardson, D. M. et al. Naturalization and invasion of alien plants: concepts and definitions. Diver. Distrib. 6, 93–107 (2000).Article 

Google Scholar 
Theoharides, K. A. & Dukes, J. S. Plant invasion across space and time: factors affecting nonindigenous species success during four stages of invasion. New Phytol. 176, 256–273 (2007).Article 

Google Scholar 
Pyšek, P. et al. Naturalization of central European plants in North America: species traits, habitats, propagule pressure, residence time. Ecology 96, 762–774. https://doi.org/10.1890/14-1005.1 (2015).Article 
PubMed 

Google Scholar 
Estrada, J. A., Wilson, C. H. & Flory, S. L. Clonal integration enhances performance of an invasive grass. Oikos https://doi.org/10.1111/oik.07016 (2020).Article 

Google Scholar 
Otfinowski, R. & Kenkel, N. C. Clonal integration facilitates the proliferation of smooth brome clones invading northern fescue prairies. Plant Ecol. 199, 235–242. https://doi.org/10.1007/s11258-008-9428-8 (2008).Article 

Google Scholar 
Pyšek, P. & Richardson, D. M. in Biological Invasions (ed N. Nentwig) pp. 97–125 (Springer, New York, 2007).Klimešová, J. & Klimeš, L. Clonal growth diversity and bud banks of plants in the Czech flora: an evaluation using the CLO-PLA3 database. Preslia 80, 255–275 (2008).
Google Scholar 
Klimešová, J. et al. Handbook of standardized protocols for collecting plant modularity traits. Persp. Plant Ecol. https://doi.org/10.1016/j.ppees.2019.125485 (2019).Article 

Google Scholar 
Wang, Y. J. et al. Invasive alien plants benefit more from clonal integration in heterogeneous environments than natives. New Phytol. 216, 1072–1078 (2017).Article 

Google Scholar 
Klimešová, J. in Encyclopedia of Invasive Introduced Species (eds D. Simberloff & M. Reimanek) pp. 678–679 (University of California Press, California, 2011).Ott, J. P., Klimešová, J. & Hartnett, D. C. The ecology and significance of below-ground bud banks in plants. Ann. Bot. Lond. 123, 1099–1118. https://doi.org/10.1093/aob/mcz051 (2019).Article 

Google Scholar 
Sanchez, J. M., Sanchez, C. & Navarro, L. Can asexual reproduction by plant fragments help to understand the invasion of the NW Iberian coast by Spartina patens? Flora 257, 151410. https://doi.org/10.1016/j.flora.2019.05.009 (2019).Speek, T. A. A. et al. Factors relating to regional and local success of exotic plant species in their new range. Diver. Distrib. 17, 542–551 (2011).Article 

Google Scholar 
Wang, J. Y. et al. A meta-analysis of effects of physiological integration in clonal plants under homogeneous vs heterogeneous environments. Funct. Ecol. https://doi.org/10.1111/1365-2435.13732 (2020).Article 

Google Scholar 
Maurer, D. A. & Zedler, J. B. Differential invasion of a wetland grass explained by tests of nutrients and light availability on establishment and clonal growth. Oecologia 131, 279–288. https://doi.org/10.1007/s00442-002-0886-8 (2002).ADS 
Article 
PubMed 

Google Scholar 
Mueller, I. M. & Weaver, J. E. Relative drought resistance of seedlings of dominant prairie grasses. Ecology 23, 387–398 (1942).Article 

Google Scholar 
Vetter, V. M. S. et al. Invasion windows for a global legume invader are revealed after joint examination of abiotic and biotic filters. Plant Biol. 21, 832–843. https://doi.org/10.1111/plb.12987 (2019).CAS 
Article 
PubMed 

Google Scholar 
Ibanez, I. et al. Integrated assessment of biological invasions. Ecol. Appl. 24, 25–37. https://doi.org/10.1890/13-0776.1 (2014).Article 
PubMed 

Google Scholar 
Diez, J. M. et al. Will extreme climatic events facilitate biological invasions?. Front. Ecol. Environ. 10, 249–257. https://doi.org/10.1890/110137 (2012).Article 

Google Scholar 
Davis, M. A., Grime, J. P. & Thompson, K. Fluctuating resources in plant communities: a general theory of invasibility. J. Ecol. 88, 528–534. https://doi.org/10.1046/j.1365-2745.2000.00473.x (2000).Article 

Google Scholar 
Li, W. & Stevens, M. H. H. Fluctuating resource availability increases invasibility in microbial microcosms. Oikos 121, 435–441. https://doi.org/10.1111/j.1600-0706.2011.19762.x (2012).Article 

Google Scholar 
Koerner, S. E. et al. Invasibility of a mesic grassland depends on the time-scale of fluctuating resources. J. Ecol. 103, 1538–1546. https://doi.org/10.1111/1365-2745.12479 (2015).Article 

Google Scholar 
Hendrickson, J. R. & Lund, C. Plant community and target species affect responses to restoration strategies. Rangel. Ecol. Manag. 63, 435–442 (2010).Article 

Google Scholar 
Bennett, J., Smart, A. & Perkins, L. Using phenological niche separation to improve management in a Northern Glaciated Plains grassland. Restor. Ecol. 27, 745–749. https://doi.org/10.1111/rec.12932 (2019).Article 

Google Scholar 
Jordan, N. R., Larson, D. L. & Huerd, S. C. Soil modification by invasive plants: effects on native and invasive species of mixed-grass prairies. Biol. Invas. 10, 177–190. https://doi.org/10.1007/s10530-007-9121-1 (2008).Article 

Google Scholar 
Piper, C. L., Lamb, E. G. & Siciliano, S. D. Smooth brome changes gross soil nitrogen cycling processes during invasion of a rough fescue grassland. Plant Ecol. 216, 235–246. https://doi.org/10.1007/s11258-014-0431-y (2015).Article 

Google Scholar 
Stotz, G. C., Gianoli, E. & Cahill, J. F. Biotic homogenization within and across eight widely distributed grasslands following invasion by Bromus inermis. Ecology https://doi.org/10.1002/ecy.2717 (2019).Article 
PubMed 

Google Scholar 
Dillemuth, F. P., Rietschier, E. A. & Cronin, J. T. Patch dynamics of a native grass in relation to the spread of invasive smooth brome (Bromus inermis). Biol. Invas. 11, 1381–1391. https://doi.org/10.1007/s10530-008-9346-7 (2009).Article 

Google Scholar 
Trammell, M. A. & Butler, J. L. Effects of exotic plants on native ungulate use of habitat. J. Wildlife Manag. 59, 808–816. https://doi.org/10.2307/3801961 (1995).Article 

Google Scholar 
Gibson, D. J. Grasses and Grassland Ecology (Oxford Univ. Press, 2009).
Google Scholar 
Knapp, A. K. & Smith, M. D. Variation among biomes in temporal dynamics of aboveground primary production. Science 291, 481–484. https://doi.org/10.1126/science.291.5503.481 (2001).ADS 
CAS 
Article 
PubMed 

Google Scholar 
Easterling, D. R. et al. Precipitation change in the United States. pp. 207–230 (Washington, D.C. USA, 2017).Gutschick, V. P. & BassiriRad, H. Extreme events as shaping physiology, ecology, and evolution of plants: toward a unified definition and evaluation of their consequences. New Phytol. 160, 21–42. https://doi.org/10.1046/j.1469-8137.2003.00866.x (2003).Article 
PubMed 

Google Scholar 
Briske, D. D. in Grazing management: An ecological perspective (eds R.K. Heitschmidt & J.W. Stuth) pp. 85–108 (Timber Press, Inc., 1991).Liu, F., Liu, J. & Dong, M. Ecological consequences of clonal integration in plants. Front. Plant Sci. 217, 277–287 (2016).
Google Scholar 
Hoover, D. L., Knapp, A. K. & Smith, M. D. Resistance and resilience of a grassland ecosystem to climate extremes. Ecology 95, 2646–2656. https://doi.org/10.1890/13-2186.1 (2014).Article 

Google Scholar 
VanderWeide, B. L., Hartnett, D. C. & Carter, D. L. Belowground bud banks of tallgrass prairie are insensitive to multi-year, growing-season drought. Ecosphere. https://doi.org/10.1890/Es14-00058.1 (2014).Article 

Google Scholar 
VanderWeide, B. L. & Hartnett, D. C. Belowground bud bank response to grazing under severe, short-term drought. Oecologia 178, 795–806. https://doi.org/10.1007/s00442-015-3249-y (2015).ADS 
Article 
PubMed 

Google Scholar 
Ott, J. P., Butler, J. L., Rong, Y. P. & Xu, L. Greater bud outgrowth of Bromus inermis than Pascopyrum smithii under multiple environmental conditions. J. Plant Ecol. 10, 518–527. https://doi.org/10.1093/jpe/rtw045 (2017).Article 

Google Scholar 
Oesterheld, M., Loreti, J., Semmartin, M. & Sala, O. E. Inter-annual variation in primary production of a semi-arid grassland related to previous-year production. J. Veg. Sci. 12, 137–142. https://doi.org/10.1111/j.1654-1103.2001.tb02624.x (2001).Article 

Google Scholar 
Ott, J. P. & Hartnett, D. C. Bud bank dynamics and clonal growth strategy in the rhizomatous grass, Pascopyrum smithii. Plant Ecol. 216, 395–405. https://doi.org/10.1007/s11258-014-0444-6 (2015).Article 

Google Scholar 
Carlsson, B. A. & Callaghan, T. V. Programmed tiller differentiation, intraclonal density regulation and nutrient dynamics in Carex bigelowii. Oikos 58, 219–230. https://doi.org/10.2307/3545429 (1990).Article 

Google Scholar 
Ye, X. H., Yu, F. H. & Dong, M. A trade-off between guerrilla and phalanx growth forms in Leymus secalinus under different nutrient supplies. Ann. Bot. Lond. 98, 187–191. https://doi.org/10.1093/aob/mcl086 (2006).Article 

Google Scholar 
Dibbern, J. C. Vegetative responses of Bromus inermis to certain variations in environment. Bot. Gazette 109, 44–58 (1947).Article 

Google Scholar 
Dong, X., Patton, J., Wang, G., Nyren, P. & Peterson, P. Effect of drought on biomass allocation in two invasive and two native grass species dominating the mixed-grass prairie. Grass Forage Sci. 69, 160–166. https://doi.org/10.1111/gfs.12020 (2014).Article 

Google Scholar 
Saeidnia, F., Majidi, M. M., Mirlohi, A. & Soltan, S. Physiological and tolerance indices useful for drought tolerance selection in smooth bromegrass. Crop Sci. 57, 282–289. https://doi.org/10.2135/cropsci2016.07.0636 (2017).CAS 
Article 

Google Scholar 
Vinton, M. A. & Hartnett, D. C. Effects of bison grazing on Andropogon gerardii and Panicum virgatum in burned and unbruned tallgrass prairie. Oecologia 90, 374–382. https://doi.org/10.1007/bf00317694 (1992).ADS 
CAS 
Article 
PubMed 

Google Scholar 
Eneboe, E. J., Sowell, B. F., Heitschmidt, R. K., Karl, M. G. & Haferkamp, M. R. Drought and grazing: IV. Blue grama and western wheatgrass. J. Range Manag. 55, 197–203. https://doi.org/10.2307/4003357 (2002).Article 

Google Scholar 
Broadbent, T. S., Bork, E. W. & Willms, W. D. Divergent effects of defoliation intensity and frequency on tiller growth and production dynamics of Pascopyrum smithii and Hesperostipa comata. Grass Forage Sci. 73, 532–543. https://doi.org/10.1111/gfs.12318 (2018).Article 

Google Scholar 
Donkor, N. T., Bork, E. W. & Hudson, R. J. Bromus-Poa response to defoliation intensity and frequency under three soil moisture levels. Can. J. Plant Sci. 82, 365–370. https://doi.org/10.4141/p01-040 (2002).Article 

Google Scholar 
Reynolds, J. H. & Smith, D. Trend of carbohydrate reserves in alfalfa, smooth bromegrass, and timothy grown under various cutting schedules. Crop Sci. 2, 333–336 (1962).CAS 
Article 

Google Scholar 
Lamp, H. F. Reproductive activity in Bromus inermis in relation to phases of tiller development. Bot. Gazette 113, 413–438 (1952).Article 

Google Scholar 
Paulsen, G. M. & Smith, D. Organic reserves, axillary bud activity, and herbage yields of smooth bromegrass as influenced by time of cutting, nitrogen fertilization, and shading. Crop Sci. 9, 529–534 (1969).Article 

Google Scholar 
Ott, J. P. & Hartnett, D. C. Contrasting bud bank dynamics of two co-occurring grasses in tallgrass prairie: implications for grassland dynamics. Plant Ecol. 213, 1437–1448. https://doi.org/10.1007/s11258-012-0102-9 (2012).Article 

Google Scholar 
Busso, C. A., Mueller, R. J. & Richards, J. H. Effects of drought and defoliation on bud viability in 2 caespitose grasses. Ann. Bot. Lond. 63, 477–485. https://doi.org/10.1093/oxfordjournals.aob.a087768 (1989).Article 

Google Scholar 
Tuomi, J., Nilsson, P. & Astrom, M. Plant compensatory responses-bud dormancy as an adaptation to herbivory. Ecology 75, 1429–1436. https://doi.org/10.2307/1937466 (1994).Article 

Google Scholar 
US Department of Agriculture. The PLANTS Database, (2006).Gong, K. et al. Analysis on the distribution, breeding and utilization of Bromus inermis germplasm resource in China. Heilongjiang Anim. Sci. Vet. Med. 21, 33–36 (2019).
Google Scholar 
Coupland, R. T. & Johnson, R. E. Rooting characteristics of native grassland species in Saskatchewan. J. Ecol. 53, 475–507 (1965).Article 

Google Scholar 
Gist, G. R. & Smith, R. M. Root development of several common forage grasses to a depth of eighteen inches. Agron. J. 1036–1042 (1948).Okamoto, H., Ishii, K. & An, P. Effects of soil moisture deficit and subsequent watering on the growth of four temperate grasses. Grassl. Sci. 57, 192–197. https://doi.org/10.1111/j.1744-697X.2011.00232.x (2011).Article 

Google Scholar 
Morrow, L. A. & Power, J. F. Effect of soil temperature on development of perennial forage grasses. Agron. J. 71, 7–10 (1979).Article 

Google Scholar 
Duell, E. B., Wilson, G. W. T. & Hickman, K. R. Above- and below-ground responses of native and invasive prairie grasses to future climate scenarios. Botany 94, 471–479. https://doi.org/10.1139/cjb-2015-0238 (2016).Article 

Google Scholar 
Duell, E. B., Londe, D. W., Hickman, K. R., Greer, M. J. & Wilson, G. W. T. Superior performance of invasive grasses over native counterparts will remain problematic under warmer and drier conditions. Plant Ecol. 222, 993–1006 (2021).Article 

Google Scholar 
Cully, A. C., Cully, J. F. & Hiebert, R. D. Invasion of exotic plant species in tallgrass prairie fragments. Conser. Biol. 17, 990–998. https://doi.org/10.1046/j.1523-1739.2003.02107.x (2003).Article 

Google Scholar 
DeKeyser, E. S., Meehan, M., Clambey, G. & Krabbenhoft, K. Cool season invasive grasses in northern great plains natural areas. Nat. Areas J. 33, 81–90. https://doi.org/10.3375/043.033.0110 (2013).Article 

Google Scholar 
Grant, T. A., Shaffer, T. L. & Flanders, B. Resiliency of native prairies to invasion by kentucky bluegrass, smooth brome, and woody vegetation. Rangeland Ecol. Manag. 73, 321–328. https://doi.org/10.1016/j.rama.2019.10.013 (2020).Article 

Google Scholar 
Otfinowski, R., Kenkel, N. C. & Catling, P. M. The biology of Canadian weeds. 134. Bromus inermis Leyss. Can. J. Plant Sci. 87, 183–198. https://doi.org/10.4141/p06-071 (2007).Article 

Google Scholar 
Moore, K. J. et al. Describing and quantifying growth stages of perennial forage grasses. Agron. J. 83, 1073–1077 (1991).Article 

Google Scholar 
SAS Institute. SAS 9.4. (SAS Institute Inc, 2017). More

Laurance, W. F. et al. A global strategy for road building. Nature 513, 229–232 (2014).ADS 
CAS 
PubMed 

Google Scholar 
Weng, L. et al. Mineral industries, growth corridors and agricultural development in Africa. Glob. Food Sec. 2, 195–202 (2013).
Google Scholar 
Laurance, W. F., Goosem, M. & Laurance, S. G. W. Impacts of roads and linear clearings on tropical forests. Trends Ecol. Evol. 24, 659–669 (2009).PubMed 

Google Scholar 
Trombulak, S. C. & Frissell, C. A. Review of ecological effects of roads on terrestrial and aquatic communities. Conserv. Biol. 14, 18–30 (2000).
Google Scholar 
van der Ree, R., Smith, D. J. & Grilo, C. The ecological effects of linear infrastructure and traffic. in Handbook of road ecology 1–9 (John Wiley and Sons, Ltd., 2015). https://doi.org/10.1002/9781118568170.ch1.Grilo, C., Smith, D. J. & Klar, N. Carnivores: Struggling for survival in roaded landscapes. in Handbook of road ecology 300–312 (John Wiley and Sons, Ltd., 2015). doi:https://doi.org/10.1002/9781118568170.ch35.Wallach, A. D., Izhaki, I., Toms, J. D., Ripple, W. J. & Shanas, U. What is an apex predator?. Oikos 124, 1453–1461 (2015).
Google Scholar 
Ripple, W. J. et al. Status and ecological effects of the world’s largest carnivores. Science 343, (2014).Estes, J. A. et al. Trophic downgrading of planet earth. Science 333, 301–306 (2011).ADS 
CAS 
PubMed 

Google Scholar 
Stolton, S. & Dudley, N. The New Lion Economy. Unlocking the value of lions and their landscapes. http://lionrecoveryfund.org/newlioneconomy (2019).Meijer, J. R., Huijbregts, M. A. J., Schotten, K. C. G. J. & Schipper, A. M. Global patterns of current and future road infrastructure. Environ. Res. Lett. 13, 064006 (2018). Data is available at http://www.globio.infoAscensão, F. et al. Environmental challenges for the belt and road initiative. Nat. Sustain. 1, 206–209 (2018).
Google Scholar 
Dulac, J. Global land transport infrastructure requirements – Estimating road and railway infrastructure capacity and costs to 2050. (International Energy Agency, 2013).Laurance, W. F. et al. Reducing the global environmental impacts of rapid infrastructure expansion. Curr. Biol. 25, R259–R262 (2015).CAS 
PubMed 

Google Scholar 
Vilela, T. et al. A better Amazon road network for people and the environment. Proc. Natl. Acad. Sci. 117, 7095–7102 (2020).ADS 
CAS 
PubMed 
PubMed Central 

Google Scholar 
Laurance, W. F., Sloan, S., Weng, L. & Sayer, J. A. Estimating the environmental costs of Africa’s massive “development corridors”. Curr. Biol. 25, 3202–3208 (2015).CAS 
PubMed 

Google Scholar 
Sharma, R., Rimal, B., Stork, N., Baral, H. & Dhakal, M. Spatial assessment of the potential impact of infrastructure development on biodiversity conservation in lowland Nepal. ISPRS Int. J. Geo Inf. 7, 365 (2018).
Google Scholar 
IUCN. The IUCN red list of threatened species. Version 2018-1. http://www.iucnredlist.org (2018).Garrote, G. et al. Prediction of Iberian lynx road-mortality in southern Spain: A new approach using the MaxEnt algorithm. Anim. Biodivers. Conserv. 41, 217–225 (2018).
Google Scholar 
Parchizadeh, J. et al. Roads threaten Asiatic cheetahs in Iran. Curr. Biol. 28, R1141–R1142 (2018).CAS 
PubMed 

Google Scholar 
Crooks, K. R., Burdett, C. L., Theobald, D. M., Rondinini, C. & Boitani, L. Global patterns of fragmentation and connectivity of mammalian carnivore habitat. Philos. Trans. R. Soc. B Biol. Sci. 366, 2642–2651 (2011).
Google Scholar 
Kattan, G. et al. Range fragmentation in the spectacled bear Tremarctos ornatus in the northern Andes. Oryx 38, 155–163 (2004).
Google Scholar 
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 

Google Scholar 
Holderegger, R. & Giulio, M. D. The genetic effects of roads: a review of empirical evidence. Basic Appl. Ecol. 11, 522–531 (2010).
Google Scholar 
Riley, S. P. D. et al. A southern California freeway is a physical and social barrier to gene flow in carnivores. Mol. Ecol. 15, 1733–1741 (2006).CAS 
PubMed 

Google Scholar 
Proctor, M. F., McLellan, B. N., Strobeck, C. & Barclay, R. M. R. Genetic analysis reveals demographic fragmentation of grizzly bears yielding vulnerably small populations. Proc. R. Soc. B Biol. Sci. 272, 2409–2416 (2005).
Google Scholar 
Riley, S. P. D. et al. Individual behaviors dominate the dynamics of an urban mountain lion population isolated by roads. Curr. Biol. 24, 1989–1994 (2014).CAS 
PubMed 

Google Scholar 
Janečka, J. E. et al. Reduced genetic diversity and isolation of remnant ocelot populations occupying a severely fragmented landscape in southern Texas. Anim. Conserv. 14, 608–619 (2011).
Google Scholar 
Thatte, P., Joshi, A., Vaidyanathan, S., Landguth, E. & Ramakrishnan, U. Maintaining tiger connectivity and minimizing extinction into the next century: Insights from landscape genetics and spatially-explicit simulations. Biol. Cons. 218, 181–191 (2018).
Google Scholar 
Vaeokhaw, S. et al. Effects of a highway on the genetic diversity of Asiatic black bears. Ursus 2020, 1–15 (2020).
Google Scholar 
Benı́tez-López, A. et al. The impact of hunting on tropical mammal and bird populations. Science 356, 180–183 (2017).ADS 
PubMed 

Google Scholar 
Clements, G. R. et al. Where and how are roads endangering mammals in Southeast Asia’s forests?. PLoS ONE 9, e115376 (2014).ADS 
PubMed 
PubMed Central 

Google Scholar 
Sharma, K., Wright, B., Joseph, T. & Desai, N. Tiger poaching and trafficking in India: Estimating rates of occurrence and detection over four decades. Biol. Cons. 179, 33–39 (2014).
Google Scholar 
Espinosa, S., Branch, L. C. & Cueva, R. Road development and the geography of hunting by an Amazonian indigenous group: Consequences for wildlife conservation. PLoS ONE 9, e114916 (2014).ADS 
PubMed 
PubMed Central 

Google Scholar 
Wato, Y. A., Wahungu, G. M. & Okello, M. M. Correlates of wildlife snaring patterns in Tsavo west national park Kenya. Biol. Conserv. 132, 500–509 (2006).
Google Scholar 
Watson, F., Becker, M. S., McRobb, R. & Kanyembo, B. Spatial patterns of wire-snare poaching: Implications for community conservation in buffer zones around national parks. Biol. Cons. 168, 1–9 (2013).
Google Scholar 
Henschel, P., Hunter, L. T. B., Coad, L., Abernethy, K. A. & Mühlenberg, M. Leopard prey choice in the Congo Basin rainforest suggests exploitative competition with human bushmeat hunters. J. Zool. 285, 11–20 (2011).
Google Scholar 
Espinosa, S., Celis, G. & Branch, L. C. When roads appear jaguars decline: Increased access to an Amazonian wilderness area reduces potential for jaguar conservation. PLoS ONE 13, e0189740 (2018).PubMed 
PubMed Central 

Google Scholar 
Parsons, M. A., Newsome, T. M. & Young, J. K. The consequences of predators without prey. Front. Ecol. Environ. https://doi.org/10.1002/fee.2419 (2021).Article 

Google Scholar 
Caro, T., Dobson, A., Marshall, A. J. & Peres, C. A. Compromise solutions between conservation and road building in the tropics. Curr. Biol. 24, R722–R725 (2014).CAS 
PubMed 

Google Scholar 
Grilo, C. et al. Conservation threats from roadkill in the global road network. Glob. Ecol. Biogeogr. 30, 2200–2210 (2021).
Google Scholar 
Carter, N., Killion, A., Easter, T., Brandt, J. & Ford, A. Road development in Asia: assessing the range-wide risks to tigers. Sci. Adv. 6(18), eaaz9619 (2020).ADS 
PubMed 
PubMed Central 

Google Scholar 
Ceia-Hasse, A., Borda-de-Água, L., Grilo, C. & Pereira, H. M. Global exposure of carnivores to roads. Glob. Ecol. Biogeogr. 26, 592–600 (2017).
Google Scholar 
Gaveau, D. L. A. et al. Four decades of forest persistence, clearance and logging on Borneo. PLoS ONE 9, e101654 (2014).ADS 
PubMed 
PubMed Central 

Google Scholar 
Gerber, B. D., Karpanty, S. M. & Randrianantenaina, J. The impact of forest logging and fragmentation on carnivore species composition, density and occupancy in Madagascar’s rainforests. Oryx 46, 414–422 (2012).
Google Scholar 
Cullen, L. et al. Implications of fine-grained habitat fragmentation and road mortality for jaguar conservation in the Atlantic forest, Brazil. PLoS ONE 11, e0167372 (2016).PubMed 
PubMed Central 

Google Scholar 
Kirby, K. R. et al. The future of deforestation in the Brazilian Amazon. Futures 38, 432–453 (2006).
Google Scholar 
UNEP-WCMC & IUCN. Protected planet: The world database on protected areas. http://www.protectedplanet.net (2019).de la Torre, J. A., Gonzalez-Maya, J. F., Zarza, H., Ceballos, G. & Medellin, R. A. The jaguars spots are darker than they appear: assessing the global conservation status of the jaguar Panthera onca. Oryx 52, 300–315 (2017).
Google Scholar 
Coelho, L., Romero, D., Queirolo, D. & Guerrero, J. C. Understanding factors affecting the distribution of the maned wolf (Chrysocyon brachyurus) in South America: spatial dynamics and environmental drivers. Mamm. Biol. 92, 54–61 (2018).
Google Scholar 
Fearnside, P. M. Brazil’s Cuiabá- Santarém (BR-163) highway: The environmental cost of paving a soybean corridor through the Amazon. Environ. Manage. 39, 601–614 (2007).ADS 
PubMed 

Google Scholar 
Vetter, D., Hansbauer, M. M., Végvári, Z. & Storch, I. Predictors of forest fragmentation sensitivity in neotropical vertebrates: A quantitative review. Ecography 34, 1–8 (2011).
Google Scholar 
Morcatty, T. Q. et al. Illegal trade in wild cats and its link to Chinese-led development in central and South America. Conserv. Biol. 34, 1525–1535 (2020).PubMed 

Google Scholar 
Ramsar. Ngiri-Tumba-Maindombe. Ramsar Sites Information Service https://rsis.ramsar.org/ris/1784 (2017).Dobson, A. P. et al. Road will ruin Serengeti. Nature 467, 272–273 (2010).ADS 
CAS 
PubMed 

Google Scholar 
Riggio, J. et al. The size of savannah Africa: A lion’s (Panthera leo) view. Biodivers. Conserv. 22, 17–35 (2012).
Google Scholar 
Government of Nepal. Economic survey 2019/20. (Ministry of Finance, 2020).Jnawali, S. R. et al. The status of Nepal mammals: The national red list series. (Department of National Parks and Wildlife Conservation, 2011).Joshi, A. R. Nepal court blocks road construction in rhino stronghold of Chitwan Park. https://news.mongabay.com/2019/02/nepal-court-blocks-road-construction-in-rhino-stronghold-of-chitwan-park/ (2019).Government of Nepal. Conservation Landscapes of Nepal. (Ministry of Forest and Soil Conservation, 2016).Poudel, A. et al. Biological and socio-economic study in corridors of Terai Arc Landscape, Nepal. (Center for Policy Analysis; Development, 2013).Arlidge, W. N. S. et al. A Global Mitigation Hierarchy for Nature Conservation. Bioscience 68, 336–347 (2018).PubMed 
PubMed Central 

Google Scholar 
Ekstrom, J., Bennun, L. & Mitchell, R. A Cross-Sector Guide for Implementing the Mitigation Hierarchy. (The Biodiversity Consultancy, 2015).Malo, J. E., Suárez, F. & Díez, A. Can we mitigate animal-vehicle accidents using predictive models?. J. Appl. Ecol. 41, 701–710 (2004).
Google Scholar 
R Core Team. R: A language and environment for statistical computing (Version 4.0.3). https://www.R-project.org/ (R Foundation for Statistical Computing, 2020).Tucker, M. A., Ord, T. J. & Rogers, T. L. Evolutionary predictors of mammalian home range size: body mass, diet and the environment. Glob. Ecol. Biogeogr. 23, 1105–1114 (2014).
Google Scholar 
Santini, L., Boitani, L., Maiorano, L. & Rondinini, C. Effectiveness of protected areas in conserving large carnivores in Europe. in Protected areas 122–133 (John Wiley and Sons, Ltd., 2016). https://doi.org/10.1002/9781118338117.ch7.Rodrigues, A. S. L., Pilgrim, J. D., Hoffmann, M. & Lamoreux, J. F. The value of the IUCN red list for conservation. Trends Ecol. Evol. 21, 71–76 (2006).PubMed 

Google Scholar 
Government of Brazil. Mapas multimodais. Ministério da Infraestrutura http://www.infraestrutura.gov.br/ (2018).Assis, L. F. F. G. et al. TerraBrasilis: A spatial data analytics infrastructure for large-scale thematic mapping. ISPRS Int. J. Geo Inf. 8, 513 (2019).
Google Scholar  More

We showed that the new metabolic rate relation13 can be directly linked to the total energy consumed in a lifespan if a constant number ({mathrm{N}}_{mathrm{r}}) of respiration cycles per lifespan is conjectured, and a corrected relation for the total energy consumed in a lifespan was found [Eq. (1)] that can explain the origin of variations in the ‘rate of living’ theory2,5 and unify them into a single formulation. It is important to note that Eq. (1) is a direct consequence of combining the two empirical relations mentioned (the new metabolic rate relation and the relation of the total number ({mathrm{N}}_{mathrm{r}}) of respiration cycles per lifespan) and is not an assumption (based on the lifespan energy expenditure per gram) as in the traditional ‘rate of living’ theory2,5. We test the validity and accuracy of the predicted relation [Eq. (1)] for the total energy consumed in a lifespan with (sim 300) species representing different classes of living organisms, and we find that the relation has an average scatter of only 0.3 dex, with 95% of the organisms having departures of less than a factor of (pi) from the relation, despite the difference of (sim 20) orders of magnitude in body mass.Successful testing of predictions is crucial in any proposed theory according to Popper’s deductive method of falsification (27), which is the criterion for identifying a successful scientific theory. Therefore, the success of the predicted Eq. (1) that is displayed in Fig. 1 implies that the corrected metabolic rate relation13 has passed an initial test. This prediction also reduces any possible interclass variation in the relation, which has been considered the most persuasive evidence against the ‘rate of living’ theory, to only a geometrical factor and strongly supports the conjectured invariant number ({mathrm{N}}_{mathrm{r}} sim 10^8) of respiration cycles per lifespan in all living organisms.Invariant quantities in physics traditionally reflect fundamental underlying constraints, a principle that has also been applied recently to life sciences such as ecology21,22. Figure 2 indicates the fact that, for a given temperature, the total lifespan energy consumption per gram per ‘generalized beat’ (({mathrm{N}}_{{mathrm{b}}}^{mathrm{G}} equiv mathrm{a} {mathrm{N}}_{{mathrm{r}}} = {mathrm{a}} ,1.62 times 10^8)) is remarkably constant (around ({mathrm{E}}_{2019})), a result that is also in agreement with previous expectations based on (lifespan) basal oxygen consumption at the molecular level38. This supports the idea that the overall energetics during the lifespan are the same for all the organisms studied, as it is predetermined by the basic energetics of respiration, and therefore, Rubner’s original picture is shown to be valid without systematic exceptions but in a more general form. Moreover, since the value determined from Fig. 2 is remarkably similar to ({mathrm{E}}_{2019} {mathrm{N}}_{mathrm{r}}), it can be considered an independent determination of ({mathrm{E}}_{2019}), suggesting that ({mathrm{E}}_{2019}) is a candidate for being a universal constant and not just a fitting parameter from the corrected metabolic relation13.In addition, we showed here that the invariant total lifespan energy consumption per gram per ‘generalized beat’ comes directly from the existence of another invariant, the approximately constant total number ({mathrm{N}}_{mathrm{r}} sim 10^8) of respiration cycles per lifetime, effectively converting the ‘generalized beat’ into the characteristic clock during the lifespan. Therefore, the exact physical relation between (oxidative) free radical damage and the origin of aging is most likely related to the striking existence of a constant total number of respiration cycles ({mathrm{N}}_{{mathrm{r}}}) over the lifetime of all organisms, which predetermines the extension of life. Moreover, the relation ({mathrm{t}}_{{mathrm{life}}} = mathrm{N}_{mathrm{r}}/mathrm{f}_{{{mathrm{resp}}}}) quantifies the ideas of oxidative damage by the respiratory metabolism, which are motivated mainly by biomedical considerations, into a simple mathematical form that could be included in a broader life-history framework; this is needed to produce testable predictions for the ‘free-radical’ hypothesis in the life-history context28. Future theoretical and experimental studies that investigate the exact link between the constant number ({mathrm{N}}_{mathrm{r}} sim 10^8) of respiration cycles per lifespan and the production rates of free radicals (or alternatively, other byproducts of metabolism) should shed light on the origin of aging and the physical cause of natural mortality.Although this relation ({mathrm{t}}_{{mathrm{life}}} = mathrm{N}_{mathrm{r}}/mathrm{f}_{{mathrm{resp}}}) has only been empirically examined for mammalian vertebrates, in terms of heartbeats per lifetime, there is evidence to believe that the relative constancy of the number of respiration cycles per lifetime is more widely distributed in the animal kingdom. For example, a reptile such as the Galapagos tortoise with a life expectancy of 177 years and a respiration rate of 3 breaths/min has (2.8 times 10^8) breaths per lifetime29, which is within a factor of 2 of the value determined for mammals. A more different case is that of birds, which have more heartbeats/lifetime by a factor of 330; this difference is reduced to a factor of 1.5 in terms of breaths/lifetime ((mathrm{N}_{mathrm{r}} = mathrm{N}_{mathrm{b}}/{mathrm{a}}), with (hbox {a}=9) for birds and 4.5 for mammals; 17). Among fish, the average number of heartbeats/lifetime tends to be an order of magnitude less than that in mammals ((mathrm{N}_{mathrm{b}} = 7.3 times 10^8);16), for example, (mathrm{N}_{mathrm{b}} = 6.7 times 10^7) for trout31, but in such cases, the parameter a can be as low as 0.5 (i.e., a heart frequency lower than the respiratory frequency; 32), again implying a similar ({mathrm{N}}_{mathrm{r}} ,(= mathrm{N}_{mathrm{b}}/{mathrm{a}} = 1.3 times 10^8)). A more extreme difference in terms of heartbeats is the tiny Daphnia, which uses up to (1.7 times 10^7) heartbeats (at 25 C) in a short lifespan of 30 days33. Simple invertebrates, such as Daphnia, do not have a complex respiratory system with lungs and obtain oxygen for respiration through diffusion, but a “breath frequency” can be estimated from its respiration rate ((sim mu {mathrm{l}} {mathrm{O}}_2 hbox {hr}^{-1});34) divided by ({mathrm{E}}_{2019} M) (with ({mathrm{M}} sim 100 mu {mathrm{g}});35), giving ({mathrm{N}}_{mathrm{r}} = 1.5 times 10^8) respiration cycles per lifetime. In summary, a difference of two orders of magnitude in total heartbeats (between Daphnia and birds) is reduced to less than a factor of 2 in breaths per lifetime, further supporting that all organisms seem to live for the same span in units of respiration cycles (({mathrm{N}}_{mathrm{r}} sim 10^8)).It has also been suggested that an analogous invariant originates at the molecular level23, the number of ATP turnovers of the molecular respiratory complexes per cell in a lifetime, which, from an energy conservation model that extends metabolism to intracellular levels, is estimated to be (sim 1.5 times 10^{16})23. A similar number can be determined by taking into account that human cells require the synthase of approximately 100 moles of ATP daily, equivalent to (7 times 10^{20}) molecules per second. For (sim 3 times 10^{13}) cells in the human body and for a respiration rate of 15 breaths per minute, this gives (sim 9 times 10^{7}) ATP molecules synthesized per cell per breath, which for the invariant total number ({mathrm{N}}_{mathrm{r}}) of respiration cycles per lifetime found in this work, rises to the same number of (sim 1.5 times 10^{16}) ATP turnovers in a lifetime per cell, showing the equivalence between both invariants and linking ({mathrm{N}}_{mathrm{r}}) to the energetics of respiratory complexes at the cellular level.The excellent agreement between the predicted relation [Eq. (1)] and the data across all types of organisms emphasizes the fact that lifespan indeed depends on multiple factors (B, a, M, T & (mathrm{T}_{mathrm{a}})) and strongly supports the methodology presented in this work of multifactorial testing, as shown in Fig. 1, since quantities in life sciences generally suffer from a confounding variable problem. An example of this problem, illustrated by individually testing each of the relevant factors, is given in24, which for a large (and noisy) sample test for ({mathrm{t}}_{{{mathrm{life}}}} propto 1/B) shows no clear correlation. From Eq. (1), it is clear that in an uncontrolled experiment, the dependence on the rest of the parameters (M, a, T, & ({mathrm{T}}_{mathrm{a}})) might eliminate the dependence on the metabolic rate B (in fact, this may be for the same reason that Rubner’s work7 focused on the mass-specific metabolic rate B/M instead of B). This work24 finds only a residual inverse dependence of ({mathrm{t}}_{{mathrm{life}}}) on the ambient temperature ({mathrm{T}}_{{mathrm{a}}}) for ectotherms, which is expected according to Eq. (1) (Big (mathrm{t}_{{mathrm{life}}} propto {mathrm{exp}}Big ({small frac{mathrm{E}_{mathrm{a}}}{mathrm{k} {mathrm{T}}_{mathrm{a}}}}Big ) Big )).Finally, the empirical support in favor of Eq. (1) allows us to perform several estimations regarding how much the energy consumption will vary with changing physical conditions on Earth. For example, computing by how much the energy consumption will vary in biomass performing aerobic respiration as the Earth’s temperature increases is relevant in the current context of possible global warming. This is given by the factor ({mathrm{exp}}Big [{small frac{mathrm{E}_{mathrm{a}}}{{mathrm{k}}} Big (frac{1}{ {mathrm{T}}}} – {small frac{1}{ {mathrm{T}}+1}}Big ) Big ]), which for an activation energy of ({mathrm{E}}_{mathrm{a}} = 0.63 ,hbox {eV}) and a temperature of (30^{circ }hbox {C}) implies an increase of 8.3% in energy consumption per 1 degree increase in the average Earth temperature. This result can be straightforwardly applied in ectotherms since their body temperatures adapt to the environmental temperature (({mathrm{T}}={mathrm{T}}_{mathrm{a}})), but its implications for endothermic organisms are less clear. Another relevant estimation is to compute by how much B({mathrm{t}}_{{mathrm{life}}})/M would vary from Eq. (1) (i.e., the difference between Figs. 2 and 3) as a function of body temperature (T) and the ratio of heart rate to respiratory rate ((mathrm{a}= mathrm{f}_{mathrm{H}}/ {mathrm{f}}_{{mathrm{resp}}})). Variations in B({mathrm{t}}_{{mathrm{life}}})/M are relevant since this is a key quantity in the estimation of the energy allocation to fitness, which aims to explain in terms of trade offs the so-called ‘Equal Fitness Paradigm’39 that concerns why most organisms in the biosphere are more or less equally fit, other than the diversity seen in the size, form and function of living organisms on Earth.In the near future, our plan is to generate a (metabolic) theory starting from the new metabolic rate relation13 by assuming that it is the controlling rate in ecology in order to explain a variety of ecological phenomena in a similar fashion as the metabolic theory of ecology18 does using Kleiber’s law. A first step in this direction looks very promising40, as it can show that ontogenetic growth can be described by a universal growth curve without the aid of fitting parameters, can explain the origin of several ‘Life History Invariants’21 and can show how the heart rate may actually set several biological times (i.e., lifespan and generation time) and even some ecological rates (i.e., The Malthusian parameter). More