Ecology

Cho, I. & Blaser, M. J. The human microbiome: at the interface of health and disease. Nat. Rev. Genet. 13, 260–270 (2012).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Shi, Z. Gut microbiota: an important link between Western diet and chronic diseases. Nutrients 11, 2287 (2019).CAS 
PubMed Central 
Article 

Google Scholar 
Lynch, S. V. & Pedersen, O. The human intestinal microbiome in health and disease. N. Engl. J. Med. 375, 2369–2379 (2016).CAS 
PubMed 
Article 

Google Scholar 
Glowacki, R. W. P. & Martens, E. C. In sickness and health: effects of gut microbial metabolites on human physiology. PLoS Pathog. 16, e1008370 (2020).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Nazaries, L. et al. Evidence of microbial regulation of biogeochemical cycles from a study on methane flux and land use change. Appl. Environ. Microbiol. 79, 4031–4040 (2013).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Konopka, A. What is microbial community ecology? ISME J. 3, 1223–1230 (2009).PubMed 
Article 

Google Scholar 
Flemming, H.-C. et al. Biofilms: an emergent form of bacterial life. Nat. Rev. Microbiol. 14, 563–575 (2016).CAS 
PubMed 
Article 

Google Scholar 
Bauer, E., Zimmermann, J., Baldini, F., Thiele, I. & Kaleta, C. BacArena: individual-based metabolic modeling of heterogeneous microbes in complex communities. PLoS Comput. Biol. 13, e1005544 (2017).PubMed 
PubMed Central 
Article 
CAS 

Google Scholar 
van Hoek, M. J. A. & Merks, R. M. H. Emergence of microbial diversity due to cross-feeding interactions in a spatial model of gut microbial metabolism. BMC Syst. Biol. 11, 56 (2017).Article 
CAS 

Google Scholar 
Gorter, F. A., Manhart, M. & Ackermann, M. Understanding the evolution of interspecies interactions in microbial communities. Philos. Trans. R. Soc. Lond. B Biol. Sci. 375, 20190256 (2020).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Wintermute, E. H. & Silver, P. A. Emergent cooperation in microbial metabolism. Mol. Syst. Biol. 6, 407 (2010).PubMed 
PubMed Central 
Article 

Google Scholar 
Chen, J., Yoshinaga, M. & Rosen, B. P. The antibiotic action of methylarsenite is an emergent property of microbial communities. Mol. Microbiol. 111, 487–494 (2019).CAS 
PubMed 
Article 

Google Scholar 
Konstantinidis, D. et al. Adaptive laboratory evolution of microbial co-cultures for improved metabolite secretion. Mol. Syst. Biol. 17, e10189 (2021).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Park, H. et al. Artificial consortium demonstrates emergent properties of enhanced cellulosic-sugar degradation and biofuel synthesis. NPJ Biofilms Microbiomes 6, 59 (2020).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Schwartzman, J. A. et al. Bacterial growth in multicellular aggregates leads to the emergence of complex lifecycles. Preprint at bioRxiv https://doi.org/10.1101/2021.11.01.466752 (2021).Levins, R. & Lewontin, R. The Dialectical Biologist (Harvard Univ. Press, 1985).Diaz, P. I. & Valm, A. M. Microbial interactions in oral communities mediate emergent biofilm properties. J. Dent. Res. 99, 18–25 (2020).CAS 
PubMed 
Article 

Google Scholar 
Buerger, A. N. et al. Gastrointestinal dysbiosis following diethylhexyl phthalate exposure in zebrafish (Danio rerio): altered microbial diversity, functionality, and network connectivity. Environ. Pollut. 265, 114496 (2020).CAS 
PubMed 
Article 

Google Scholar 
Kim, M. K., Ingremeau, F., Zhao, A., Bassler, B. L. & Stone, H. A. Local and global consequences of flow on bacterial quorum sensing. Nat. Microbiol. 1, 15005 (2016).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Ebrahimi, A. & Or, D. Hydration and diffusion processes shape microbial community organization and function in model soil aggregates. Water Resour. Res. 51, 9804–9827 (2015).Article 

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

Google Scholar 
Fredrickson, J. K. Ecological communities by design. Science 348, 1425–1427 (2015).CAS 
PubMed 
Article 

Google Scholar 
Singer, E. et al. Next generation sequencing data of a defined microbial mock community. Sci. Data 3, 160081 (2016).PubMed 
PubMed Central 
Article 

Google Scholar 
Marino, S., Baxter, N. T., Huffnagle, G. B., Petrosino, J. F. & Schloss, P. D. Mathematical modeling of primary succession of murine intestinal microbiota. Proc. Natl Acad. Sci. USA 111, 439–444 (2014).CAS 
PubMed 
Article 

Google Scholar 
Cariboni, J., Gatelli, D., Liska, R. & Saltelli, A. The role of sensitivity analysis in ecological modelling. Ecol. Modell. 203, 167–182 (2007).Article 

Google Scholar 
Oreskes, N., Shrader-Frechette, K. & Belitz, K. Verification, validation, and confirmation of numerical models in the Earth sciences. Science 263, 641–646 (1994).CAS 
PubMed 
Article 

Google Scholar 
Machado, D., Andrejev, S., Tramontano, M. & Patil, K. R. Fast automated reconstruction of genome-scale metabolic models for microbial species and communities. Nucleic Acids Res. 46, 7542–7553 (2018).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Buffie, C. G. et al. Precision microbiome reconstitution restores bile acid mediated resistance to Clostridium difficile. Nature 517, 205–208 (2015).CAS 
PubMed 
Article 

Google Scholar 
Hammarlund, S. P., Chacón, J. M. & Harcombe, W. R. A shared limiting resource leads to competitive exclusion in a cross-feeding system. Environ. Microbiol. 21, 759–771 (2019).PubMed 
PubMed Central 
Article 

Google Scholar 
Coyte, K. Z., Schluter, J. & Foster, K. R. The ecology of the microbiome: networks, competition, and stability. Science 350, 663–666 (2015).CAS 
PubMed 
Article 

Google Scholar 
Machado, D. et al. Polarization of microbial communities between competitive and cooperative metabolism. Nat. Ecol. Evol. 5, 195–203 (2021).PubMed 
PubMed Central 
Article 

Google Scholar 
Gu, C., Kim, G. B., Kim, W. J., Kim, H. U. & Lee, S. Y. Current status and applications of genome-scale metabolic models. Genome Biol. 20, 121 (2019).PubMed 
PubMed Central 
Article 

Google Scholar 
O’Brien, E. J., Monk, J. M. & Palsson, B. O. Using genome-scale models to predict biological capabilities. Cell 161, 971–987 (2015).PubMed 
PubMed Central 
Article 
CAS 

Google Scholar 
Fang, X., Lloyd, C. J. & Palsson, B. O. Reconstructing organisms in silico: genome-scale models and their emerging applications. Nat. Rev. Microbiol. 18, 731–743 (2020).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Colarusso, A. V., Goodchild-Michelman, I., Rayle, M. & Zomorrodi, A. R. Computational modeling of metabolism in microbial communities on a genome-scale. Curr. Opin. Syst. Biol. 26, 46–57 (2021).Article 

Google Scholar 
García-Jiménez, B., Torres-Bacete, J. & Nogales, J. Metabolic modelling approaches for describing and engineering microbial communities. Comput. Struct. Biotechnol. J. 19, 226–246 (2020).PubMed 
PubMed Central 
Article 
CAS 

Google Scholar 
Frioux, C., Singh, D., Korcsmaros, T. & Hildebrand, F. From bag-of-genes to bag-of-genomes: metabolic modelling of communities in the era of metagenome-assembled genomes. Comput. Struct. Biotechnol. J. 18, 1722–1734 (2020).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Chaffron, S., Rehrauer, H., Pernthaler, J. & von Mering, C. A global network of coexisting microbes from environmental and whole-genome sequence data. Genome Res. 20, 947–959 (2010).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Faust, K. & Raes, J. Microbial interactions: from networks to models. Nat. Rev. Microbiol. 10, 538–550 (2012).CAS 
PubMed 
Article 

Google Scholar 
Li, J. et al. Distinct mechanisms shape soil bacterial and fungal co-occurrence networks in a mountain ecosystem. FEMS Microbiol. Ecol. 96, fiaa030 (2020).CAS 
PubMed 
Article 

Google Scholar 
Berry, D. & Widder, S. Deciphering microbial interactions and detecting keystone species with co-occurrence networks. Front. Microbiol. 5, 219 (2014).PubMed 
PubMed Central 
Article 

Google Scholar 
Stein, R. R. et al. Ecological modeling from time-series inference: insight into dynamics and stability of intestinal microbiota. PLoS Comput. Biol. 9, e1003388 (2013).PubMed 
PubMed Central 
Article 
CAS 

Google Scholar 
Barbier, M., Arnoldi, J.-F., Bunin, G. & Loreau, M. Generic assembly patterns in complex ecological communities. Proc. Natl Acad. Sci. USA 115, 2156–2161 (2018).Gralka, M., Szabo, R., Stocker, R. & Cordero, O. X. Trophic interactions and the drivers of microbial community assembly. Curr. Biol. 30, R1176–R1188 (2020).CAS 
PubMed 
Article 

Google Scholar 
Madeo, D., Comolli, L. R. & Mocenni, C. Emergence of microbial networks as response to hostile environments. Front. Microbiol. 5, 407 (2014).PubMed 
PubMed Central 
Article 

Google Scholar 
Ratzke, C. & Gore, J. Modifying and reacting to the environmental pH can drive bacterial interactions. PLoS Biol. 16, e2004248 (2018).PubMed 
PubMed Central 
Article 
CAS 

Google Scholar 
Wang, B. & Allison, S. D. Emergent properties of organic matter decomposition by soil enzymes. Soil Biol. Biochem. 136, 107522 (2019).CAS 
Article 

Google Scholar 
Walsh, A. M. et al. Microbial succession and flavor production in the fermented dairy beverage kefir. mSystems 1, e00052-16 (2016).PubMed 
PubMed Central 
Article 

Google Scholar 
Oliveira, N. M., Niehus, R. & Foster, K. R. Evolutionary limits to cooperation in microbial communities. Proc. Natl Acad. Sci. USA 111, 17941–17946 (2014).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Leigh, E. R. in Some Mathematical Problems in Biology (ed. Gerstenhaber, M.) 1–61 (American Mathematical Society, 1968).Nedorezov, L. The dynamics of the lynx–hare system: an application of the Lotka–Volterra model. Biophys. 61, 149–154 (2016).CAS 
Article 

Google Scholar 
Mühlbauer, L. K., Schulze, M., Harpole, W. S. & Clark, A. T. gauseR: simple methods for fitting Lotka–Volterra models describing Gause’s “struggle for existence”. Ecol. Evol. 10, 13275–13283 (2020).PubMed 
PubMed Central 
Article 

Google Scholar 
Belovsky, G. E. Moose and snowshoe hare competition and a mechanistic explanation from foraging theory. Oecologia 61, 150–159 (1984).CAS 
PubMed 
Article 

Google Scholar 
May, R. M. Limit cycles in predator–prey communities. Science 177, 900–902 (1972).CAS 
PubMed 
Article 

Google Scholar 
Friedman, J., Higgins, L. M. & Gore, J. Community structure follows simple assembly rules in microbial microcosms. Nat. Ecol. Evol. 1, 109 (2017).PubMed 
Article 

Google Scholar 
Voit, E. O., Davis, J. D. & Olivença, D. V. Inference and validation of the structure of Lotka–Volterra models. Preprint at bioXriv https://doi.org/10.1101/2021.08.14.456346 (2021).Bucci, V. & Xavier, J. B. Towards predictive models of the human gut microbiome. J. Mol. Biol. 426, 3907–3916 (2014).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Fisher, C. K. & Mehta, P. Identifying keystone species in the human gut microbiome from metagenomic timeseries using sparse linear regression. PLoS ONE 9, e102451 (2014).PubMed 
PubMed Central 
Article 
CAS 

Google Scholar 
Bucci, V. et al. MDSINE: Microbial Dynamical Systems INference Engine for microbiome time-series analyses. Genome Biol. 17, 121 (2016).PubMed 
PubMed Central 
Article 
CAS 

Google Scholar 
Gao, X., Huynh, B.-T., Guillemot, D., Glaser, P. & Opatowski, L. Inference of significant microbial interactions from longitudinal metagenomics data. Front. Microbiol. 9, 2319 (2018).PubMed 
PubMed Central 
Article 

Google Scholar 
Li, C. et al. An expectation-maximization algorithm enables accurate ecological modeling using longitudinal microbiome sequencing data. Microbiome 7, 118 (2019).PubMed 
PubMed Central 
Article 

Google Scholar 
Joseph, T. A., Shenhav, L., Xavier, J. B., Halperin, E. & Pe’er, I. Compositional Lotka–Volterra describes microbial dynamics in the simplex. PLoS Comput. Biol. 16, e1007917 (2020).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Hosoda, S., Fukunaga, T. & Hamada, M. Umibato: estimation of time-varying microbial interaction using continuous-time regression hidden Markov model. Bioinformatics 37, i16–i24 (2021).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Remien, C. H., Eckwright, M. J. & Ridenhour, B. J. Structural identifiability of the generalized Lotka–Volterra model for microbiome studies. R. Soc. Open Sci. 8, 201378 (2021).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
White, J. R. Novel Methods for Metagenomic Analysis. PhD thesis, Univ. of Maryland (2010).Sousa, A., Frazão, N., Ramiro, R. S. & Gordo, I. Evolution of commensal bacteria in the intestinal tract of mice. Curr. Opin. Microbiol. 38, 114–121 (2017).PubMed 
Article 

Google Scholar 
Mounier, J. et al. Microbial interactions within a cheese microbial community. Appl. Environ. Microbiol. 74, 172–181 (2008).CAS 
PubMed 
Article 

Google Scholar 
Momeni, B., Xie, L. & Shou, W. Lotka–Volterra pairwise modeling fails to capture diverse pairwise microbial interactions. eLife 6, e25051 (2017).PubMed 
PubMed Central 
Article 

Google Scholar 
Hoek, T. A. et al. Resource availability modulates the cooperative and competitive nature of a microbial cross-feeding mutualism. PLoS Biol. 14, e1002540 (2016).PubMed 
PubMed Central 
Article 
CAS 

Google Scholar 
Piccardi, P., Vessman, B. & Mitri, S. Toxicity drives facilitation between 4 bacterial species. Proc. Natl Acad. Sci. USA 116, 15979–15984 (2019).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Mai, T. S. N. Impact of Metabolic Plasticity on Microbial Community Diversity and Stability. MSc thesis, Univ. of Groningen (2021).Sanchez-Gorostiaga, A., Bajić, D., Osborne, M. L., Poyatos, J. F. & Sanchez, A. High-order interactions distort the functional landscape of microbial consortia. PLoS Biol. 17, e3000550 (2019).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Mickalide, H. & Kuehn, S. Higher-order interaction between species inhibits bacterial invasion of a phototroph-predator microbial community. Cell Syst. 9, 521–533.e10 (2019).CAS 
PubMed 
Article 

Google Scholar 
Guo, X. & Boedicker, J. Q. The contribution of high-order metabolic interactions to the global activity of a four-species microbial community. PLoS Comput. Biol. 12, e1005079 (2016).PubMed 
PubMed Central 
Article 
CAS 

Google Scholar 
Meroz, N., Tovi, N., Sorokin, Y. & Friedman, J. Community composition of microbial microcosms follows simple assembly rules at evolutionary timescales. Nat. Commun. 12, 2891 (2021).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Zomorrodi, A. R. & Segrè, D. Synthetic ecology of microbes: mathematical models and applications. J. Mol. Biol. 428, 837–861 (2016).CAS 
PubMed 
Article 

Google Scholar 
Song, H. S., Cannon, W. R., Beliaev, A. S. & Konopka, A. Mathematical modeling of microbial community dynamics: a methodological review. Processes 2, 711–752 (2014).Article 

Google Scholar 
Descheemaeker, L., Grilli, J. & de Buyl, S. Heavy-tailed abundance distributions from stochastic Lotka–Volterra models. Phys. Rev. E 104, 034404 (2021).CAS 
PubMed 
Article 

Google Scholar 
Bairey, E., Kelsic, E. D. & Kishony, R. High-order species interactions shape ecosystem diversity. Nat. Commun. 7, 12285 (2016).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Ji, B., Herrgård, M. J. & Nielsen, J. Microbial community dynamics revisited. Nat. Comput. Sci. 1, 640–641 (2021).Article 

Google Scholar 
Abreu, C. I., Anderen Woltz, V. L., Friedman, J. & Gore, J. Microbial communities display alternative stable states in a fluctuating environment. PLoS Comput. Biol. 16, e1007934 (2020).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Xu, L., Xu, X., Kong, D., Gu, H. & Kenney T. Stochastic generalized Lotka–Volterra model with an application to learning microbial community structures. Preprint at arXiv https://doi.org/10.48550/arXiv.2009.10922 (2020).Brunner, J. D. & Chia, N. Metabolite-mediated modelling of microbial community dynamics captures emergent behaviour more effectively than species–species modelling. J. R. Soc. Interface 16, 20190423 (2019).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
MacArthur, R. Species packing and competitive equilibrium for many species. Theor. Popul. Biol. 1, 1–11 (1970).CAS 
PubMed 
Article 

Google Scholar 
Tilman, D. Resource competition and community structure. Monogr. Popul. Biol. 17, 1–296 (1982).CAS 
PubMed 

Google Scholar 
Chesson, P. MacArthur’s consumer-resource model. Theor. Popul. Biol. 37, 26–38 (1990).Article 

Google Scholar 
Goldford, J. E. et al. Emergent simplicity in microbial community assembly. Science 361, 469–474 (2018).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Marsland, R.3rd et al. Available energy fluxes drive a transition in the diversity, stability, and functional structure of microbial communities. PLoS Comput. Biol. 15, e1006793 (2019).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Marsland, R.3rd, Cui, W. & Mehta, P. A minimal model for microbial biodiversity can reproduce experimentally observed ecological patterns. Sci. Rep. 10, 3308 (2020).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Estrela, S., Sanchez-Gorostiaga, A., Vila, J. C. & Sanchez, A. Nutrient dominance governs the assembly of microbial communities in mixed nutrient environments. eLife 10, e65948 (2021).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Cui, W., Marsland, R. & Mehta, P. Diverse communities behave like typical random ecosystems. Phys. Rev. E 104, 034416 (2021).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Haygood, R. Coexistence in MacArthur-style consumer–resource models. Theor. Popul. Biol. 61, 215–223 (2002).PubMed 
Article 

Google Scholar 
Dubinkina, V., Fridman, Y., Pandey, P. P. & Maslov, S. Multistability and regime shifts in microbial communities explained by competition for essential nutrients. eLife 8, e49720 (2019).PubMed 
PubMed Central 
Article 

Google Scholar 
Pacheco, A. R., Osborne, M. L. & Segrè, D. Non-additive microbial community responses to environmental complexity. Nat. Commun. 12, 2365 (2021).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Zelezniak, A. et al. Metabolic dependencies drive species co-occurrence in diverse microbial communities. Proc. Natl Acad. Sci. USA 112, 6449–6454 (2015).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Crowther, T. W. et al. Untangling the fungal niche: the trait-based approach. Front. Microbiol. 5, 579 (2014).PubMed 
PubMed Central 
Article 

Google Scholar 
Pacciani-Mori, L., Suweis, S., Maritan, A. & Giometto, A. Constrained proteome allocation affects coexistence in models of competitive microbial communities. ISME J. 15, 1458–1477 (2021).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Marsland, R. et al. The Community Simulator: a Python package for microbial ecology. PLoS ONE 15, e0230430 (2020).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Obadia, B. et al. Probabilistic invasion underlies natural gut microbiome stability. Curr. Biol. 27, 1999–2006.e8 (2017).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
D’Andrea, R., Gibbs, T. & O’Dwyer, J. P. Emergent neutrality in consumer-resource dynamics. PLoS Comput. Biol. 16, e1008102 (2020).PubMed 
PubMed Central 
Article 
CAS 

Google Scholar 
Mancuso, C. P., Lee, H., Abreu, C. I., Gore, J. & Khalil, A. S. Environmental fluctuations reshape an unexpected diversity-disturbance relationship in a microbial community. eLife 10, e67175 (2021).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Lajoie, G. & Kembel, S. W. Making the most of trait-based approaches for microbial ecology. Trends Microbiol. 27, 814–823 (2019).CAS 
PubMed 
Article 

Google Scholar 
Zakharova, L., Meyer, K. M. & Seifan, M. Trait-based modelling in ecology: a review of two decades of research. Ecol. Modell. 407, 108703 (2019).Article 

Google Scholar 
Merico, A., Brandt, G., Lan Smith, S. L. & Oliver, M. Sustaining diversity in trait-based models of phytoplankton communities. Front. Ecol. Evol. 2, 59 (2014).Grigoratou, M. et al. A trait-based modelling approach to planktonic foraminifera ecology. Biogeosciences 16, 1469–1492 (2019).Article 

Google Scholar 
Muscarella, M. E., Howey, X. M. & Lennon, J. T. Trait-based approach to bacterial growth efficiency. Environ. Microbiol. 22, 3494–3504 (2020).CAS 
PubMed 
Article 

Google Scholar 
Shao, P., Lynch, L., Xie, H., Bao, X. & Liang, C. Tradeoffs among microbial life history strategies influence the fate of microbial residues in subtropical forest soils. Soil Biol. Biochem. 153, 108112 (2021).CAS 
Article 

Google Scholar 
Malik, A. A. et al. Defining trait-based microbial strategies with consequences for soil carbon cycling under climate change. ISME J. 14, 1–9 (2020).CAS 
PubMed 
Article 

Google Scholar 
Le Roux, X. et al. Predicting the responses of soil nitrite-oxidizers to multi-factorial global change: a trait-based approach. Front. Microbiol. 7, 628 (2016).PubMed 
PubMed Central 
Article 

Google Scholar 
Bouskill, N. J., Tang, J., Riley, W. J. & Brodie, E. L. Trait-based representation of biological nitrification: model development, testing, and predicted community composition. Front. Microbiol. 3, 364 (2012).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Kyker-Snowman, E., Wieder, W. R., Frey, S. D. & Grandy, A. S. Stoichiometrically coupled carbon and nitrogen cycling in the MIcrobial-MIneral Carbon Stabilization model version 1.0 (MIMICS-CN v1.0). Geosci. Model Dev. 13, 4413–4434 (2020).CAS 
Article 

Google Scholar 
Kruk, C. et al. A trait-based approach predicting community assembly and dominance of microbial invasive species. Oikos 130, 571–586 (2021).Article 

Google Scholar 
Litchman, E., Ohman, M. D. & Kiørboe, T. Trait-based approaches to zooplankton communities. J. Plankton Res. 35, 473–484 (2013).Article 

Google Scholar 
Garcia, C. A. et al. Linking regional shifts in microbial genome adaptation with surface ocean biogeochemistry. Philos. Trans. R. Soc. Lond. B Biol. Sci. 375, 20190254 (2020).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Moreno, A. R., Hagstrom, G. I., Primeau, F. W., Levin, S. A. & Martiny, A. C. Marine phytoplankton stoichiometry mediates nonlinear interactions between nutrient supply, temperature, and atmospheric CO2. Biogeosciences 15, 2761–2779 (2018).CAS 
Article 

Google Scholar 
Follows, M. J., Dutkiewicz, S., Grant, S. & Chisholm, S. W. Emergent biogeography of microbial communities in a model ocean. Science 315, 1843–1846 (2007).CAS 
PubMed 
Article 

Google Scholar 
Coles, V. J. et al. Ocean biogeochemistry modeled with emergent trait-based genomics. Science 358, 1149–1154 (2017).CAS 
PubMed 
Article 

Google Scholar 
Ratzke, C., Barrere, J. & Gore, J. Strength of species interactions determines biodiversity and stability in microbial communities. Nat. Ecol. Evol. 4, 376–383 (2020).PubMed 
Article 

Google Scholar 
Bradford, M. A. et al. Quantifying microbial control of soil organic matter dynamics at macrosystem scales. Biogeochemistry 156, 19–40 (2021).Article 

Google Scholar 
Ward, B. A., Dutkiewicz, S., Moore, C. M. & Follows, M. J. Iron, phosphorus, and nitrogen supply ratios define the biogeography of nitrogen fixation. Limnol. Oceanogr. 58, 2059–2075 (2013).CAS 
Article 

Google Scholar 
Zwart, J. A., Solomon, C. T. & Jones, S. E. Phytoplankton traits predict ecosystem function in a global set of lakes. Ecology 96, 2257–2264 (2015).PubMed 
Article 

Google Scholar 
Nemergut, D. R., Shade, A. & Violle, C. When, where and how does microbial community composition matter. Front. Microbiol. 5, 497 (2014).PubMed 
PubMed Central 
Article 

Google Scholar 
Severin, I., Östman, Ö. & Lindström, E. S. Variable effects of dispersal on productivity of bacterial communities due to changes in functional trait composition. PLoS ONE 8, e80825 (2013).PubMed 
PubMed Central 
Article 
CAS 

Google Scholar 
Staley, C. et al. Core functional traits of bacterial communities in the Upper Mississippi River show limited variation in response to land cover. Front. Microbiol. 5, 414 (2014).PubMed 
PubMed Central 

Google Scholar 
Worden, L. Conservation of community functional structure across changes in composition in consumer-resource models. J. Theor. Biol. 493, 110239 (2020).PubMed 
Article 

Google Scholar 
van der Plas, F. et al. Plant traits alone are poor predictors of ecosystem properties and long-term ecosystem functioning. Nat. Ecol. Evol. 4, 1602–1611 (2020).PubMed 
Article 

Google Scholar 
Song, H.-S. et al. Regulation-structured dynamic metabolic model provides a potential mechanism for delayed enzyme response in denitrification process. Front. Microbiol. 8, 1866 (2017).PubMed 
PubMed Central 
Article 

Google Scholar 
Hemelrijk, C. K. & Hildenbrandt, H. Schools of fish and flocks of birds: their shape and internal structure by self-organization. Interface Focus 2, 726–737 (2012).PubMed 
PubMed Central 
Article 

Google Scholar 
Hellweger, F. L., Clegg, R. J., Clark, J. R., Plugge, C. M. & Kreft, J.-U. Advancing microbial sciences by individual-based modelling. Nat. Rev. Microbiol. 14, 461–471 (2016).CAS 
PubMed 
Article 

Google Scholar 
Griesemer, M. & Sindi, S. S. Rules of engagement: a guide to developing agent-based models. Methods Mol. Biol. 2349, 367–380 (2022).PubMed 
Article 

Google Scholar 
Jayathilake, P. G. et al. A mechanistic individual-based model of microbial communities. PLoS ONE 12, e0181965 (2017).PubMed 
PubMed Central 
Article 
CAS 

Google Scholar 
Clark, J. R., Daines, S. J., Lenton, T. M., Watson, A. J. & Williams, H. T. P. Individual-based modelling of adaptation in marine microbial populations using genetically defined physiological parameters. Ecol. Modell. 222, 3823–3837 (2011).Article 

Google Scholar 
Nadell, C. D. et al. Cutting through the complexity of cell collectives. Proc. Biol. Sci. 280, 20122770 (2013).PubMed 
PubMed Central 

Google Scholar 
Allen, B., Gore, J. & Nowak, M. A. Spatial dilemmas of diffusible public goods. eLife 2, e01169 (2013).PubMed 
PubMed Central 
Article 

Google Scholar 
Abs, E., Leman, H. & Ferrière, R. A multi-scale eco-evolutionary model of cooperation reveals how microbial adaptation influences soil decomposition. Commun. Biol. 3, 520 (2020).PubMed 
PubMed Central 
Article 

Google Scholar 
Kreft, J.-U. et al. Mighty small: observing and modeling individual microbes becomes big science. Proc. Natl Acad. Sci. USA 110, 18027–18028 (2013).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Parise, F., Lygeros, J. & Ruess, J. Bayesian inference for stochastic individual-based models of ecological systems: a pest control simulation study. Front. Environ. Sci. 3, https://doi.org/10.3389/fenvs.2015.00042 (2015).Allison, S. D. & Goulden, M. L. Consequences of drought tolerance traits for microbial decomposition in the DEMENT model. Soil Biol. Biochem. 107, 104–113 (2017).CAS 
Article 

Google Scholar 
Allison, S. D. A trait-based approach for modelling microbial litter decomposition. Ecol. Lett. 15, 1058–1070 (2012).CAS 
PubMed 
Article 

Google Scholar 
Doloman, A., Varghese, H., Miller, C. D. & Flann, N. S. Modeling de novo granulation of anaerobic sludge. BMC Syst. Biol. 11, 69 (2017).PubMed 
PubMed Central 
Article 
CAS 

Google Scholar 
Gogulancea, V. et al. Individual based model links thermodynamics, chemical speciation and environmental conditions to microbial growth. Front. Microbiol. 10, 1871 (2019).PubMed 
PubMed Central 
Article 

Google Scholar 
Gutierrez, M. & Rodriguez-Paton, A. Simulating multicell populations with an accelerated gro simulator. In Proc. ECAL 2017, Fourteenth European Conf. on Artificial Life, 186–188 (2017).Gutiérrez, M. et al. A new improved and extended version of the multicell bacterial simulator gro. ACS Synth. Biol. 6, 1496–1508 (2017).PubMed 
Article 
CAS 

Google Scholar 
Momeni, B., Waite, A. J. & Shou, W. Spatial self-organization favors heterotypic cooperation over cheating. eLife 2, e00960 (2013).PubMed 
PubMed Central 
Article 
CAS 

Google Scholar 
Kreft, J.-U., Booth, G. & Wimpenny, J. W. T. BacSim, a simulator for individual-based modelling of bacterial colony growth. Microbiology 144, 3275–3287 (1998).CAS 
PubMed 
Article 

Google Scholar 
Picioreanu, C., Van Loosdrecht, M. C. & Heijnen, J. J. Effect of diffusive and convective substrate transport on biofilm structure formation: a two-dimensional modeling study. Biotechnol. Bioeng. 69, 504–515 (2000).CAS 
PubMed 
Article 

Google Scholar 
Lardon, L. A. et al. iDynoMiCS: next-generation individual-based modelling of biofilms. Environ. Microbiol. 13, 2416–2434 (2011).CAS 
PubMed 
Article 

Google Scholar 
Chacón, J. M., Möbius, W. & Harcombe, W. R. The spatial and metabolic basis of colony size variation. ISME J. 12, 669–680 (2018).PubMed 
PubMed Central 
Article 
CAS 

Google Scholar 
Oyebamiji, O. K. et al. Gaussian process emulation of an individual-based model simulation of microbial communities. J. Comput. Sci. 22, 69–84 (2017).Article 

Google Scholar 
Menon, R. & Korolev, K. S. Public good diffusion limits microbial mutualism. Phys. Rev. Lett. 114, 168102 (2015).PubMed 
Article 
CAS 

Google Scholar 
Dobay, A., Bagheri, H. C., Messina, A., Kümmerli, R. & Rankin, D. J. Interaction effects of cell diffusion, cell density and public goods properties on the evolution of cooperation in digital microbes. J. Evol. Biol. 27, 1869–1877 (2014).CAS 
PubMed 
Article 

Google Scholar 
Canzian, L., Zhao, K., Wong, G. C. L. & van der Schaar, M. A dynamic network formation model for understanding bacterial self-organization into micro-colonies. IEEE Trans. Mol. Biol. Multiscale Commun. 1, 76–89 (2015).Article 

Google Scholar 
Nadell, C. D., Foster, K. R. & Xavier, J. B. Emergence of spatial structure in cell groups and the evolution of cooperation. PLoS Comput. Biol. 6, e1000716 (2010).PubMed 
PubMed Central 
Article 
CAS 

Google Scholar 
Mendoza, S. N., Olivier, B. G., Molenaar, D. & Teusink, B. A systematic assessment of current genome-scale metabolic reconstruction tools. Genome Biol. 20, 158 (2019).PubMed 
PubMed Central 
Article 

Google Scholar 
Orth, J. D., Thiele, I. & Palsson, B. Ø. What is flux balance analysis? Nat. Biotechnol. 28, 245–248 (2010).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Feist, A. M. & Palsson, B. O. The biomass objective function. Curr. Opin. Microbiol. 13, 344–349 (2010).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Blasche, S. et al. Metabolic cooperation and spatiotemporal niche partitioning in a kefir microbial community. Nat. Microbiol. 6, 196–208 (2021).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Dukovski, I. et al. A metabolic modeling platform for the computation of microbial ecosystems in time and space (COMETS). Nat. Protoc. 16, 5030–5082 (2021).CAS 
PubMed 
Article 

Google Scholar 
Varahan, S., Sinha, V., Walvekar, A., Krishna, S. & Laxman, S. Resource plasticity-driven carbon-nitrogen budgeting enables specialization and division of labor in a clonal community. eLife 9, e57609 (2020).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Angeles-Martinez, L. & Hatzimanikatis, V. Spatio-temporal modeling of the crowding conditions and metabolic variability in microbial communities. PLoS Comput. Biol. 17, e1009140 (2021).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Zimmermann, J., Kaleta, C. & Waschina, S. gapseq: informed prediction of bacterial metabolic pathways and reconstruction of accurate metabolic models. Genome Biol. 22, 81 (2021).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Zorrilla, F., Buric, F., Patil, K. R. & Zelezniak, A. metaGEM: reconstruction of genome scale metabolic models directly from metagenomes. Nucleic Acids Res. 49, e126 (2021).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Ponomarova, O. et al. Yeast creates a niche for symbiotic lactic acid bacteria through nitrogen overflow. Cell Syst. 5, 345–357.e6 (2017).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Foster, K. R. & Bell, T. Competition, not cooperation, dominates interactions among culturable microbial species. Curr. Biol. 22, 1845–1850 (2012).CAS 
PubMed 
Article 

Google Scholar 
Borer, B., Ataman, M., Hatzimanikatis, V. & Or, D. Modeling metabolic networks of individual bacterial agents in heterogeneous and dynamic soil habitats (IndiMeSH). PLoS Comput. Biol. 15, e1007127 (2019).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Labhsetwar, P., Cole, J. A., Roberts, E., Price, N. D. & Luthey-Schulten, Z. A. Heterogeneity in protein expression induces metabolic variability in a modeled Escherichia coli population. Proc. Natl Acad. Sci. USA 110, 14006–14011 (2013).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Kehe, J. et al. Positive interactions are common among culturable bacteria. Sci. Adv. 7, eabi7159 (2021).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Zmora, N. et al. Personalized gut mucosal colonization resistance to empiric probiotics is associated with unique host and microbiome features. Cell 174, 1388–1405.e21 (2018).CAS 
PubMed 
Article 

Google Scholar 
Korem, T. et al. Growth dynamics of gut microbiota in health and disease inferred from single metagenomic samples. Science 349, 1101–1106 (2015).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Hamilton, J. J. et al. Metabolic network analysis and metatranscriptomics reveal auxotrophies and nutrient sources of the cosmopolitan freshwater microbial lineage acI. mSystems 2, e00091-17 (2017).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Guerra, C. A. et al. Tracking, targeting, and conserving soil biodiversity. Science 371, 239–241 (2021).CAS 
PubMed 
Article 

Google Scholar 
IPCC. Climate Change and Land: an IPCC Special Report on Climate Change, Desertification, Land Degradation, Sustainable Land Management, Food Security, and Greenhouse Gas Fluxes in Terrestrial Ecosystems (IPCC, 2020).Pacciani-Mori, L., Giometto, A., Suweis, S. & Maritan, A. Dynamic metabolic adaptation can promote species coexistence in competitive microbial communities. PLoS Comput. Biol. 16, e1007896 (2020).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar  More

Wheat production and yield vis-à-vis climate trendsWheat is currently grown in all six continents except Antarctica. The leading producers include China, the Russian Federation, Ukraine, Kazakhstan (RUK), India, USA, France, Canada, Pakistan, Germany, Argentina, Turkey, Australia, and United Kingdom (Fig. 1 and Supplementary Table 1). The total grain production of these twelve countries is estimated at 600 megatons (2019 data), which accounts for over 78% of the global wheat production. The top three producers are China with 133.6 megatons per year (Mt y−1), RUK with 114.1 Mt y−1, and India with 103.6 Mt y−1. RUK contains the largest harvested area of 45.8 million hectares, followed by India with 29.3 million hectares and China with 23.7 million hectares (Fig. 1A). Despite a relatively small harvested area of 10.1 million hectares (only 22% of RUK’s harvested area), the United Kingdom, France, and Germany account for the world’s highest yields per hectare, with 8.93 tons ha−1, 7.74 tons ha−1, and 7.40 tons ha−1, respectively (compared with the world’s average yield of only 3.2 tons ha−1), accounting for a total yearly production of 79.9 Mt y−1.Figure 1Global wheat area and trends in wheat yield and climate in top-twelve global wheat producers (1961–2019). (A) Worldwide wheat cropping area (%)29, total harvested area (106 hectares in 2019), and wheat production (megatons for 2019) of the top 12 global wheat producers (China, RUK—Russia, Ukraine, and Kazakhstan, India, USA—hard red winter (HRW) and hard red spring (HRS), France, Canada, Pakistan, Germany, Argentina, Turkey, Australia, and United Kingdom) (Map was generated in Python 3.8.5; http://www.python.org). (B) Changes in wheat yield (tons per hectare) and (C) climate—mean daily temperature (red dashed line; °C) and the seasonal water balance represented as potential evaporation minus precipitation (blue line; PET—P in millimeters of H2O). A positive trend in PET-P indicates an increase in water deficit. The seasonal atmospheric [CO2] in μmol CO2 per mol−1 air is also shown in the insert of C (black line). Temperature, PET-P, and [CO2] shown in C are averaged values over the wheat-growing period and the shared area of the wheat-growing areas of the top 12 global wheat producers. Decadal trends in temperature (red) and PET-P (blue) as well as the significance levels of these trends are presented in C.Full size imageWhile all these twelve major wheat producers saw an increase in yield during the last six decades (Fig. 1B), China displayed the most noteworthy increase with a nearly sevenfold higher yield in 2019 than in 1961 and a mean total increase of 5.19 tons ha−1 for the period of 1961–2019. Germany, the UK, and France reported comparable yield increases of 5.20 tons ha−1, 5.19 tons ha−1, and 4.81 tons ha−1, respectively, during this period, suggesting an approximately 1.6-fold improvement since 1961 (Fig. 1B). Australia, RUK, and Turkey reported the lowest gains with only 0.87 tons ha−1, 1.26 tons ha−1, and 1.71 tons ha−1, respectively, representing improvements of 67%, 150%, and 175% in yield per hectare since 1961.Yield increase occurred despite the steep rise in temperature (nearly 1.2 °C) in the twelve countries during the last six decades (Fig. 1C). Water deficit—calculated as the difference between potential evaporative demand and precipitation (PET—P; mm H2O y−1)—also increased by an average of (sim) 29 mm of H2O for the same period. Increases in yield since the early 1960s were likely due to breeding and agrotechnological advances, improved management, and a steep rise in atmospheric [CO2] of (sim) 98 μmol mol−1, from 315.9 μmol mol−1 in 1961 to 413.4 μmol mol−1 in 2019 (insert in Fig. 1C).Unraveling the impacts of climate and [CO2] on yieldBased on previous studies30,31, we used a log-linear model to quantify the impact of [CO2] and daily minimum (Tmin), maximum (Tmax), and mean (Tmean) temperatures, as well as seasonal water deficit (PET-P), and rainfall distribution on wheat yield. Climate variables were obtained from the TerraClimate data set32, while monthly records of [CO2] from the Mauna Loa station were used to model the effects of CO2 (see “Methods”). To quantify wheat yield as a function of climate variables and [CO2], we included all 12 countries in the regression analysis. Supplementary Table 2 presents summary statistics of all variables, while Supplementary Fig. 1 depicts trends in Tmean and PET-P per country.Since climate variables tend to be correlated over time (Supplementary Table 3), controlling for all of these variables in the model facilitates the estimation of their distinct effect on yield. We used country-specific trends to distinguish changes in wheat yield related to climate and [CO2] from those attributed to agrotechnological advancements, changes in country-specific policies, and other local-changing factors (e.g., economic and population growth; more information on how this was done can be found in “Methods”). We also included country-specific effects across all models to account for unobserved time-invariant heterogeneity at the country level, such as geographical properties, edaphic characteristics, and other local-specific features (see “Methods”).Table 1 reports the estimated regression coefficients of four models, (1) using only temperature variables (T), (2) temperature and water-related (i.e., seasonal rainfall distribution and water deficit as PET-P) variables (T + W), (3) including [CO2] (T + W + C), and (4) the interaction between [CO2] and climate variables (T + W + C + interactions).Table 1 Effects of climate variables and [CO2] on log wheat yields of the world’s major wheat producers.Full size tableAmong the temperature measures, only Tmean had a consistently significant effect on yield (p  More

IPCC: IPCC Special Report on the Ocean and Cryosphere in a Changing Climate [Pörtner, H.-O. et al.] In press (2019).Oliver, E. C. J. et al. Longer and more frequent marine heatwaves over the past century. Nat. Commun. 9, 1324 (2018).ADS 
PubMed 
PubMed Central 
Article 
CAS 

Google Scholar 
Gibble, C. et al. Investigation of a largescale Common Murre (Uria aalge) mortality event in California, USA, in 2015. J. Wildl. Dis. 54, 569–574 (2018).PubMed 
Article 

Google Scholar 
Brodeur, R. D., Auth, T. D. & Phillips, A. J. Major shifts in pelagic micronekton and macrozooplankton community structure in an upwelling ecosystem related to an unprecedented marine heatwave. Front. Mar. Sci. 6, 212 (2019).Article 

Google Scholar 
Le Nohaïc, M. et al. Marine heatwave causes unprecedented regional mass bleaching of thermally resistant corals in northwestern Australia. Sci. Rep. 7, 1–11 (2017).ADS 
Article 
CAS 

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

Google Scholar 
Genevier, L. G., Jamil, T., Raitsos, D. E., Krokos, G. & Hoteit, I. Marine heatwaves reveal coral reef zones susceptible to bleaching in the Red Sea. Glob. Change Biol. 25, 2338–2351 (2019).ADS 
Article 

Google Scholar 
Leggat, W. P. et al. Rapid coral decay is associated with marine heatwave mortality events on reefs. Curr. Biol. 29, 2723–2730 (2019).CAS 
PubMed 
Article 

Google Scholar 
Green, E. P. & Short, F. T. World Atlas of Seagrasses (University of California Press, 2003).Duarte, C. M. The future of seagrass meadows. Environ. Conserv. 29, 192–206 (2002).Article 

Google Scholar 
Alongi, D. M. Blue Carbon: Coastal Sequestration for Climate Change Mitigation (Springer, Berlin, 2018).Book 

Google Scholar 
Blandon, A. & ZuErmgassen, P. S. Quantitative estimate of commercial fish enhancement by seagrass habitat in southern Australia. Estuarine Coast. Shelf Sci. 141, 1–8 (2014).ADS 
Article 

Google Scholar 
Boudouresque, C. F., Mayot, N. & Pergent, G. The outstanding traits of the functioning of the Posidonia oceanica seagrass ecosystem. Biol. Mar. Medit. 13, 109–113 (2006).
Google Scholar 
Carr, J., D’odorico, P., McGlathery, K. & Wiberg, P. L. Stability and bistability of seagrass ecosystems in shallow coastal lagoons: Role of feedbacks with sediment resuspension and light attenuation. J. Geophys. Res. Biogeosci. https://doi.org/10.1029/2009JG001103 (2010).Article 

Google Scholar 
Welsh, D. T. Nitrogen fixation in seagrass meadows: regulation, plant–bacteria interactions and significance to primary productivity. Ecol. Lett. 3, 58–71. https://doi.org/10.1046/j.1461-0248.2000.00111.x (2000).Article 

Google Scholar 
Duarte, C. M. et al. Seagrass community metabolism: Assessing the carbon sink capacity of seagrass meadows. Glob. Biogeochem. Cycles. https://doi.org/10.1029/2010GB003793 (2010).Article 

Google Scholar 
Cabaço, S. & Santos, R. Human-induced changes of the seagrass Cymodocea nodosa in Ria Formosa lagoon (Southern Portugal) after a decade. Cah. Biol. Mar. 55, 101–108 (2014).
Google Scholar 
Marbà, N., Krause-Jensen, D., Masqué, P. & Duarte, C. M. Expanding Greenland seagrass meadows contribute new sediment carbon sinks. Sci. Rep. 8, 1–8 (2018).Article 
CAS 

Google Scholar 
Bañolas, G., Fernández, S., Espino, F., Haroun, R. & Tuya, F. Evaluation of carbon sinks by the seagrass Cymodocea nodosa at an oceanic island: Spatial variation and economic valuation. Ocean Coast. Manag. 187, 105112 (2020).Article 

Google Scholar 
Duarte, C. M. & Krause-Jensen, D. Export from seagrass meadows contributes to marine carbon sequestration. Front. Mar. Sci. 4, 13 (2017).
Google Scholar 
Duarte, C. M., Middelburg, J. J. & Caraco, N. Major role of marine vegetation on the oceanic carbon cycle. Biogeosci. 2, 1–8 (2005).ADS 
CAS 
Article 

Google Scholar 
Kennedy, H. et al. Seagrass sediments as a global carbon sink: Isotopic constraints. Glob. Biogeochem. Cycles https://doi.org/10.1029/2010GB003848 (2010).Article 

Google Scholar 
Orth, R. J. et al. A global crisis for seagrass ecosystems. Bioscience 56, 987–996 (2006).Article 

Google Scholar 
Waycott, M. et al. Accelerating loss of seagrasses across the globe threatens coastal ecosystems. Proc. Natl. Acad. Sci. 106, 12377–12381 (2009).ADS 
CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Arias-Ortiz, A. et al. A marine heatwave drives massive losses from the world’s largest seagrass carbon stocks. Nat. Clim. Change 8, 338 (2018).ADS 
CAS 
Article 

Google Scholar 
Collier, C. J. et al. Optimum temperatures for net primary productivity of three tropical seagrass species. Front. Plant Sci. 8, 1446 (2017).PubMed 
PubMed Central 
Article 

Google Scholar 
George, R., Gullström, M., Mangora, M. M., Mtolera, M. S. & Björk, M. High midday temperature stress has stronger effects on biomass than on photosynthesis: a mesocosm experiment on four tropical seagrass species. Ecol. Evol. 8, 4508–4517 (2018).PubMed 
PubMed Central 
Article 

Google Scholar 
Savva, I., Bennett, S., Roca, G., Jordà, G. & Marbà, N. Thermal tolerance of Mediterranean marine macrophytes: Vulnerability to global warming. Ecol. Evol. 8, 12032–12043 (2018).PubMed 
PubMed Central 
Article 

Google Scholar 
Massa, S. I., Arnaud-Haond, S., Pearson, G. A. & Serrão, E. A. Temperature tolerance and survival of intertidal populations of the seagrass Zostera noltii (Hornemann) in Southern Europe (Ria Formosa, Portugal). Hydrobiologia 619, 195–201 (2009).Article 

Google Scholar 
Bergmann, N. et al. Population-specificity of heat stress gene induction in northern and southern eelgrass Zostera marina populations under simulated global warming. Mol. Ecol. 19, 2870–2883 (2010).PubMed 
Article 

Google Scholar 
Franssen, S. U. et al. Genome-wide transcriptomic responses of the seagrasses Zostera marina and Nanozostera noltii under a simulated heatwave confirm functional types. Mar. Genomics 15, 65–73 (2014).PubMed 
Article 

Google Scholar 
Qin, L. Z. et al. Influence of regional water temperature variability on the flowering phenology and sexual reproduction of the seagrass Zostera marina in Korean coastal waters. Estuaries Coasts 43, 449–462 (2020).CAS 
Article 

Google Scholar 
Gao, Y. et al. Photosynthetic and metabolic responses of eelgrass Zostera marina L. to short-term high-temperature exposure. J. Oceanol. Limnol. 37, 199–209 (2019).ADS 
CAS 
Article 

Google Scholar 
Marín-Guirao, L. et al. Carbon economy of Mediterranean seagrasses in response to thermal stress. Mar. Pollut. Bull. 135, 617–629 (2018).PubMed 
Article 
CAS 

Google Scholar 
Costa, M. M., Silva, J., Barrote, I. & Santos, R. Heatwave effects on the photosynthesis and antioxidant activity of the seagrass Cymodocea nodosa under contrasting light regimes. Oceans 2, 448–460 (2021).Article 

Google Scholar 
de los Santos, C. et al. Recent trend reversal for declining European seagrass meadows. Nat. Commun. 10, 3356 (2019).Cunha, A. H., Assis, J. F. & Serrão, E. A. Reprint of “Seagrasses in Portugal: A most endangered marine habitat”. Aquat. Bot. 115, 3–13 (2014).Article 

Google Scholar 
Olsen, Y. S., Sánchez-Camacho, M., Marbà, N. & Duarte, C. M. Mediterranean seagrass growth and demography responses to experimental warming. Estuaries Coasts 35, 1205–1213 (2012).Article 

Google Scholar 
Marín-Guirao, L., Ruiz, J. M., Dattolo, E., Garcia-Munoz, R. & Procaccini, G. Physiological and molecular evidence of differential short-term heat tolerance in Mediterranean seagrasses. Sci. Rep. 6, 1–13 (2016).Article 
CAS 

Google Scholar 
Lüning, K. Seaweeds. Their Environment, Biogeography, and Ecophysiology (Wiley-Interscience, New York, 1990).Lee, K. S., Park, S. R. & Kim, Y. K. Effects of irradiance, temperature, and nutrients on growth dynamics of seagrasses: a review. J. Exp. Mar. Biol. Ecol. 350, 144–175 (2007).Article 

Google Scholar 
Franssen, S. U. et al. Transcriptomic resilience to global warming in the seagrass Zostera marina, a marine foundation species. Proc. Natl. Acad. Sci. 108, 19276–19281 (2011).ADS 
CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Winters, G., Nelle, P., Fricke, B., Rauch, G. & Reusch, T. B. H. Effects of a simulated heat wave on photophysiology and gene expression of high- and low-latitude populations of Zostera marina. Mar. Ecol. Prog. Ser. 435, 83–95 (2011).ADS 
Article 

Google Scholar 
Maxwell, K. & Johnson, G. N. Chlorophyll fluorescence—A practical guide. J. Exp. Bot. 51, 659–668 (2000).CAS 
PubMed 
Article 

Google Scholar 
Schubert, N. et al. Photoacclimation strategies in northeastern Atlantic seagrasses: Integrating responses across plant organizational levels. Sci. Rep. 8, 1–14 (2018).CAS 
Article 

Google Scholar 
Miyake, C., Yonekura, K., Kobayashi, Y. & Yokota, A. Cyclic electron flow within PSII functions in intact chloroplasts from spinach leaves. Plant Cell Physiol. 43, 951–957 (2002).CAS 
PubMed 
Article 

Google Scholar 
Rasmusson, L. M., Gullström, M., Gunnarsson, P. C. B., George, R. & Björk, M. Estimation of a whole plant Q10 to assess seagrass productivity during temperature shifts. Sci. Rep. 9, 1–9 (2019).CAS 
Article 

Google Scholar 
Buapet, P. & Björk, M. The role of O2 as an electron acceptor alternative to CO2 in photosynthesis of the common marine angiosperm Zostera marina L. Photosynth. Res. 129, 59–69 (2016).CAS 
PubMed 
Article 

Google Scholar 
Mehler, A. H. Studies on reactions of illuminated chloroplasts. II Stimulation and inhibition of the reaction with molecular oxygen. Arch. Biochem. Biophys. 34, 339–51 (1951).CAS 
PubMed 
Article 

Google Scholar 
Apel, K. & Hirt, H. Reactive oxygen species: metabolism, oxidative stress, and signal transduction. Annu. Rev. Plant Biol. 55, 373–399 (2004).CAS 
PubMed 
Article 

Google Scholar 
Chalanika De Silva, H. C. & Asaeda, T. Effects of heat stress on growth, photosynthetic pigments, oxidative damage and competitive capacity of three submerged macrophytes. J. Plant Interact. 12, 228–236 (2017).Article 
CAS 

Google Scholar 
Beer, S., Björk, M., Gademann, R. & Ralph, P. Measurements of photosynthetic rates in seagrasses. In Global Seagrass Research Methods pp. 183–198 (Elsevier Science, 2001).Brodersen, K. E., Kühl, M., Nielsen, D. A., Pedersen, O. & Larkum, A. W. Rhizome, root/sediment interactions, aerenchyma and internal pressure changes in seagrasses. In Seagrasses of Australia pp. 393–418; https://doi.org/10.1007/978-3-319-71354-0_13 (Springer, Cham, 2018).Purnama, P. R., Purnama, E. R., Manuhara, Y. S. W., Hariyanto, S. & Purnobasuki, H. Effect of high temperature stress on changes in morphology, anatomy and chlorophyll content in tropical seagrass Thalassia hemprichii. AACL Bioflux 11, 1825–1833 (2018).
Google Scholar 
Rosalina, D., Herawati, E. Y., Musa, M., Sofarini, D. & Risjani, Y. Anatomical changes in the roots, rhizomes and leaves of seagrass (Cymodocea serrulata) in response to lead. Biodiversitas 20, 2583–2588; https://doi.org/10.13057/biodiv/d200921 (2019).Beca-Carretero, P., Olesen, B., Marbà, N. & Krause-Jensen, D. Response to experimental warming in northern eelgrass populations: comparison across a range of temperature adaptations. Mar. Ecol. Progr. Ser. 589, 59–72; https://doi.org/10.3354/meps12439 (2018).Beca-Carretero, P., Guihéneuf, F., Krause-Jensen, D. & Stengel, D. B. Seagrass fatty acid profiles as a sensitive indicator of climate settings across seasons and latitudes. Mar. Env. Res. 161, 105075; https://doi.org/10.1016/j.marenvres.2020.105075 (2020).Pérez, M. & Romero, J. Photosynthetic response to light and temperature of the seagrass Cymodocea nodosa and the prediction of its seasonality. Aquat. Bot. 43, 51–62; https://doi.org/10.1016/0304-3770(92)90013-9 (1992).Saha, M. et al. Response of foundation macrophytes to near‐natural simulated marine heatwaves. Global Change Biol. 26, 417–430; https://doi.org/10.1111/gcb.14801 (2020).Tutar, O., Marín-Guirao, L., Ruiz, J. M. & Procaccini, G. Antioxidant response to heat stress in seagrasses. A gene expression study. Mar. Environ. Res. 132, 94–102; https://doi.org/10.1016/j.marenvres.2017.10.011 (2017).Moreno‐Marín, F., Brun, F. G. & Pedersen, M. F. Additive response to multiple environmental stressors in the seagrass Zostera marina L. Limnol. Oceanogr. 63, 1528–1544; https://doi.org/10.1002/lno.10789 (2018).Kim, M. et al. Influence of water temperature anomalies on the growth of Zostera marina plants held under high and low irradiance levels. Estuaries Coasts 43, 463–476; https://doi.org/10.1007/s12237-019-00578-2 (2020).Egea, L. G., Jiménez-Ramos, R., Vergara, J. J., Hernández, I. & Brun, F. G. Interactive effect of temperature, acidification and ammonium enrichment on the seagrass Cymodocea nodosa. Mar. Pollut. Bull. 134, 14–26; https://doi.org/10.1016/j.marpolbul.2018.02.029 (2018).Newton, A. & Mudge, S. M. Temperature and salinity regimes in a shallow, mesotidal lagoon, the Ria Formosa, Portugal. Estuarine Coastal Shelf Sci. 57, 73–85; https://doi.org/10.1016/S0272-7714(02)00332-3 (2003).Instituto Hidrográfico. Marés 81/82 Ria de Faro. Estudo das marés de oito estacões da Ria de Faro pp. 13 (Lisbon: Instituto Hidrográfico, 1986).Andrade, J. P. Aspectos Geomorfológicos, Ecológicos e Socioeconómicos da Ria Formosa pp. 91 (Faro: Universidade do Algarve, 1985).Hobday, A.J. et al. A hierarchical approach to defining marine heatwaves. Prog. Oceanogr. 141, 227–238; https://doi.org/10.1016/j.pocean.2015.12.014 (2016).Hobday, A. J. et al. Categorizing and naming marine heatwaves. Oceanogr. 31, 162–173; https://doi.org/10.5670/oceanog.2018.205 (2018).Cunha, A. H., Paulo, D. S., Sousa, I. & Serrão, E. The rediscovery of Caulerpa prolifera in Ria Formosa, Portugal, 60 years after the previous record. Cah. Biol. Mar. 54, 359–364 (2013).
Google Scholar 
Huang, B. et al. Improvements of the daily optimum interpolation sea surface temperature (DOISST) Version 2.1. J. Clim. 34, 2923–2939 (2020).ADS 
Article 

Google Scholar 
Reynolds, R. W. et al. Daily high-resolution-blended analyses for sea surface temperature. J. Clim. 20, 5473–5496 (2007).ADS 
Article 

Google Scholar 
Banzon, V., Smith, T. M., Chin, T. M., Liu, C. & Hankins, W. A long-term record of blended satellite and in situ sea-surface temperature for climate monitoring, modelling and environmental studies. Earth Syst. Sci. Data 8, 165–176 (2016).ADS 
Article 

Google Scholar 
Schlegel, R. W. Marine Heatwave Tracker. http://www.marineheatwaves.org/tracker; 10.5281/zenodo.3787872 (2020).Field, C. B., Barros, V., Stocker, T. F. & Dahe, Q. (Eds.). Managing the risks of extreme events and disasters to advance climate change adaptation: special report of the intergovernmental panel on climate change (IPCC) (Cambridge University Press, 2012).Silva, J., Barrote, I., Costa, M. M., Albano, S. & Santos, R. Physiological responses of Zostera marina and Cymodocea nodosa to light-limitation stress. PLoS One 8, e81058 (2013).ADS 
PubMed 
PubMed Central 
Article 
CAS 

Google Scholar 
Silva, J. & Santos, R. Can chlorophyll fluorescence be used to estimate photosynthetic production in the seagrass Zostera noltii?. J. Exp. Mar. Biol. Ecol. 307, 207–216 (2004).CAS 
Article 

Google Scholar 
Jassby, A. D. & Platt, T. Mathematical formulation of the relationship between photosynthesis and light for phytoplankton. Limnol. Oceanogr. 21, 540–547 (1976).ADS 
CAS 
Article 

Google Scholar 
Henley, W. J. Measurement and interpretation of photosynthetic light-response curves in algae in the context of photoinhibition and diel changes. J. Phycol. 29, 729–739 (1993).Article 

Google Scholar 
Genty, B., Briantais, J. M. & Baker, N. R. The relationship between the quantum yield of photosynthetic electron transport and quenching of chlorophyll fluorescence. Biochim. Biophys. Acta 990, 87–92 (1989).CAS 
Article 

Google Scholar 
Folin, O. & Ciocalteu, V. On tyrosine and tryptophane determinations in proteins. J. Biol. Chem. 73, 627–650 (1927).CAS 
Article 

Google Scholar 
Booker, F. L. & Miller, J. E. Phenylpropanoid metabolism and phenolic composition of soybean [Glycine max (L) Merr] leaves following exposure to ozone. J. Exp. Bot. 49, 1191–1202 (1998).CAS 
Article 

Google Scholar 
Re, R. et al. Antioxidant activity applying an improved ABTS radical cation decolorization assay. Free Radical Biol. Med. 26, 1231–1237 (1999).CAS 
Article 

Google Scholar 
Gillespie, K. M., Chae, J. M. & Ainsworth, E. A. Rapid measurement of total antioxidant capacity in plants. Nat. Protoc. 2, 867–870 (2007).CAS 
PubMed 
Article 

Google Scholar 
Huang, D., Ou, B., Hampsch-Woodill, M., Flanagan, J. A. & Prior, R. L. High-Throughput Assay of Oxygen Radical Absorbance Capacity (ORAC) Using a Multichannel Liquid Handling System Coupled with a Microplate Fluorescence Reader in 96-Well Format. J. Agric. Food Chem. 50, 4437–4444 (2002).CAS 
PubMed 
Article 

Google Scholar 
Hodges, D. M., DeLong, J. M., Forney, C. F. & Prange, R. K. Improving the thiobarbituric acid-reactive-substances assay for estimating lipid peroxidation in plant tissues containing anthocyanin and other interfering compounds. Planta 207, 604–611 (1999).CAS 
Article 

Google Scholar 
Rasband, W.S. ImageJ, U. S. National Institutes of Health, Bethesda, Maryland, USA, 1997–2018. https://imagej.nih.gov/ij/ (1997).R Core Team. R: A language and environment for statistical computing. R Foundation for Statistical Computing, Vienna, Austria. http://www.R-project.org/ (2014).Devore, J. & Farnum, N. Applied Statistics for Engineers and Scientists (ed. Brooks/Cole) pp. 656 (Pacific Grove, CA, USA, 1999). More

Literature studyLiterature considering the effect of pasture degradation on SOC, N, and clay content, as well as bulk density (BD), was assembled by searching (i) Web of Science V.5.22.1, (ii) ScienceDirect (Elsevier B.V.) (iii) Google Scholar, and (iv) the China Knowledge Resource Integrated Database (CNKI). Search terms were “degradation gradient”, “degradation stages”, “alpine meadow”, “Tibetan Plateau”, “soil”, “soil organic carbon”, and “soil organic matter” in different combinations. The criteria for including a study in the analysis were: (i) a clear and comprehensible classification of degradation stages was presented, (ii) data on SOC, N, and/or BD were reported, (iii) a non-degraded pasture site was included as a reference to enable an effect size analysis and the calculation of SOC and N losses, (iv) sampling depths and study location were clearly presented. (v) Studies were only considered that took samples in 10 cm depth intervals, to maintain comparability to the analyses from our own study site. The degradation stages in the literature studies were regrouped into the six successive stages (S0–S5) according to the respective degradation descriptions. In total, we compiled the results of 49 publications published between 2002 and 2020.When SOM content was presented, this was converted to SOC content using a conversion factor of 2.032. SOC and N stocks were calculated using the following equation:$${{{{{rm{Elemental; stock}}}}}}=100* {{{{{rm{content}}}}}}* {{{{{rm{BD}}}}}}* {{{{{rm{depth}}}}}}$$
(1)
where elemental stock is SOC or N stock [kg ha−1]; content is SOC or N content [g kg−1]; BD is soil bulk density [g cm−3] and depth is the soil sampling depth [cm].The effect sizes of individual variables (i.e., SOC and N stocks as well as BD) were quantified as follows:$${{{{{rm{ES}}}}}}=,frac{(D-R)}{R* 100 % }$$
(2)
where ES is the effect size in %, D is the value of the corresponding variable in the relevant degradation stage and R is the value of each variable in the non-degraded stage (reference site). When ES is positive, zero, or negative, this indicates an increase, no change, or decrease, respectively, of the parameter compared to the non-degraded stage.Experimental design of the field studyLarge areas in the study region are impacted by grassland degradation. In total, 45% of the surface area of the Kobresia pasture ecosystem on the TP is already degraded2. The experiment was designed to differentiate and quantify SOC losses by erosion vs. net decomposition and identify underlying shifts in microbial community composition and link these to changes in key microbial functions in the soil C cycle. We categorized the range of Kobresia root-mat degradation from non-degraded to bare soils into six successive degradation stages (S0–S5). Stage S0 represented non-degraded root mats, while stages S1–S4 represented increasing degrees of surface cracks, and bare soil patches without root mats defined stage S5 (Supplementary Fig. 1). All six degradation stages were selected within an area of about 4 ha to ensure equal environmental conditions and each stage was sampled in four field replicates. However, the studied degradation patterns are common for the entire Kobresia ecosystem (Supplementary Fig. 1).Site descriptionThe field study was conducted near Nagqu (Tibet, China) in the late summer 2013 and 2015. The study site of about 4 ha (NW: 31.274748°N, 92.108963°E; NE: 31.274995°N, 92.111482°E; SW: 31.273488°N, 92.108906°E; SE: 31.273421°N, 92.112025°E) was located on gentle slopes (2–5%) at 4,484 m a.s.l. in the core area of the Kobresia pygmaea ecosystem according to Miehe et al.8. The vegetation consists mainly of K. pygmaea, which covers up to 61% of the surface. Other grasses, sedges, or dwarf rosette plants (Carex ivanoviae, Carex spp., Festuca spp., Kobresia pusilla, Poa spp., Stipa purpurea, Trisetum spp.) rarely cover more than 40%. The growing season is strongly restricted by temperature and water availability. At most, it lasts from mid-May to mid-September, but varies strongly depending on the onset and duration of the summer monsoon. Mean annual precipitation is 431 mm, with roughly 80% falling as summer rains. The mean annual temperature is −1.2 °C, while the mean maximum temperature of the warmest month (July) is +9.0 °C2.A characteristic feature of Kobresia pastures is their very compact root mats, with an average thickness of 15 cm at the study site. These consist mainly of living and dead K. pygmaea roots and rhizomes, leaf bases, large amounts of plant residue, and mineral particles. Intact soil is a Stagnic Eutric Cambisol (Humic), developed on a loess layer overlying glacial sediments and containing 50% sand, 33% silt, and 17% clay in the topsoil (0–25 cm). The topsoil is free of carbonates and is of neutral pH (pH in H2O: 6.8)5. Total soil depth was on average 35 cm.The site is used as a winter pasture for yaks, sheep, and goats from January to April. Besides livestock, large numbers of plateau pikas (Ochotona) are found on the sites. These animals have a considerable impact on the plant cover through their burrowing activity, in particular the soil thrown out of their burrows, which can cover and destroy the Kobresia turf.Sampling designThe vertical and horizontal extent of the surface cracks was measured for each plot (Supplementary Table 2). Vegetation cover was measured and the aboveground biomass was collected in the cracks (Supplementary Table 2). In general, intact Kobresia turf (S0) provided high resistance to penetration as measured by a penetrologger (Eijkelkamp Soil and Water, Giesbeek, NL) in 1 cm increments and four replicates per plot.Soil sampling was conducted using soil pits (30 cm length × 30 cm width × 40 cm depth). Horizons were classified and then soil and roots were sampled for each horizon directly below the cracks. Bulk density and root biomass were determined in undisturbed soil samples, using soil cores (10 cm height and 10 cm diameter). Living roots were separated from dead roots and root debris by their bright color and soft texture using tweezers under magnification, and the roots were subsequently washed with distilled water to remove the remaining soil. Because over 95% of the roots occurred in the upper 25 cm5, we did not sample for root biomass below this depth.Additional soil samples were taken from each horizon for further analysis. Microbial community and functional characterization were performed on samples from the same pits but with a fixed depth classification (0–5 cm, 5–15 cm, 15–35 cm) to reduce the number of samples.Plant and soil analysesSoil and roots were separated by sieving (2 mm) and the roots subsequently washed with distilled water. Bulk density and root density were determined by dividing the dry soil mass (dried at 105 °C for 24 h) and the dry root biomass (60 °C) by the volume of the sampling core. To reflect the root biomass, root density was expressed per soil volume (mg cm−3). Soil and root samples were milled for subsequent analysis.Elemental concentrations and SOC characteristicsTotal SOC and total N contents and stable isotope signatures (δ13C and δ15N) were analyzed using an isotope ratio mass spectrometer (Delta plus, Conflo III, Thermo Electron Cooperation, Bremen, Germany) coupled to an elemental analyzer (NA 1500, Fisons Instruments, Milano, Italy). Measurements were conducted at the Centre for Stable Isotope Research and Analysis (KOSI) of the University of Göttingen. The δ13C and δ15N values were calculated by relating the isotope ratio of each sample (Rsample = 13C/12C or 15N/14N) to the international standards (Pee Dee Belemnite 13C/12C ratio for δ13C; the atmospheric 15N/14N composition for δ15N).Soil pH of air-dried soil was measured potentiometrically at a ratio (v/v) of 1.0:2.5 in distilled water.Lignin phenols were depolymerized using the CuO oxidation method25 and analyzed with a gas chromatography-mass spectrometry (GC–MS) system (GC 7820 A, MS 5977B, Agilent Technologies, Waldbronn, Germany). Vanillyl and syringyl units were calculated from the corresponding aldehydes, ketones, and carboxylic acids. Cinnamyl units were derived from the sum of p-coumaric acid and ferulic acid. The sum of the three structural units (VSC = V + S + C) was considered to reflect the lignin phenol content in a sample.DNA extraction and PCRSamples were directly frozen on site at −20 °C and transported to Germany for analysis of microbial community structure. Total DNA was extracted from the soil samples with the PowerSoil DNA isolation kit (MoBio Laboratories Inc., Carlsbad, CA, USA) according to the manufacturer’s instructions, and DNA concentration was determined using a NanoDrop 1000 spectrophotometer (Thermo Fisher Scientific, Wilmington, DE, USA). The extracted DNA was amplified with forward and reverse primer sets suitable for either t-RFLP (fluorescence marked, FAM) or Illumina MiSeq sequencing (Illumina Inc., San Diego, USA): V3 (5’-CCT ACG GGN GGC WGC AG-3’) and V4 (5’-GAC TAC HVG GGT ATC TAA TCC-3’) primers were used for bacterial 16 S rRNA genes whereas ITS1 (5’-CTT GGT CAT TTA GAG GAA GTA A-3’), ITS1-F_KYO1 (5’-CTH GGT CAT TTA GAG GAA STA A-3’), ITS2 (5’-GCT GCG TTC TTC ATC GAT GC-3’) and ITS4 (5’-TCC TCC GCT TAT TGA TAT GC-3’) were used for fungi33,34. Primers for Illumina MiSeq sequencing included adaptor sequences (forward: 5’-TCG TCG GCA GCG TCA GAT GTG TAT AAG AGA CAG-3’; reverse: 5’-GTC TCG TGG GCT CGG AGA TGT GTA TAA GAG ACA G-3’)33. PCR was performed with the Phusion High-Fidelity PCR kit (New England Biolabs Inc., Ipswich, MA, USA) creating a 50 µl master mix with 28.8 µl H2Omolec, 2.5 µl DMSO, 10 µl Phusion GC buffer, 1 µl of forward and reverse primer, 0.2 µl MgCl2, 1 µl dNTPs, 0.5 µl Phusion HF DNA Polymerase, and 5 µl template DNA. PCR temperatures started with initial denaturation at 98 °C for 1 min, followed by denaturation (98 °C, 45 s), annealing (48/60 °C, 45 s), and extension (72 °C, 30 s). These steps were repeated 25 times, finalized again with a final extension (72 °C, 5 min), and cooling to 10 °C. Agarose gel electrophoresis was used to assess the success of the PCR and the amount of amplified DNA (0.8% gel:1.0 g Rotigarose, 5 µl Roti-Safe Gelstain, Carl Roth GmbH & Co. KG, Karlsruhe, Germany; and 100 ml 1× TAE-buffer). PCR product was purified after initial PCR and restriction digestion (t-RFLP) with either NucleoMag 96 PCR (16 S rRNA gene amplicons, Macherey-Nagel GmbH & Co. KG, Düren, Germany) or a modified clean-up protocol after Moreau (t-RFLP)35: 3× the volume of the reaction solution as 100% ethanol and ¼x vol. 125 mM EDTA was added and mixed by inversion or vortex. After incubation at room temperature for 15 min, the product was centrifuged at 25,000 × g for 30 min at 4 °C. Afterwards the supernatant was removed, and the inverted 96-well plate was centrifuged shortly for 2 min. Seventy microliters ethanol (70%) were added and centrifuged at 25,000 × g for 30 min at 4 °C. Again, the supernatant was removed, and the pallet was dried at room temperature for 30 min. Finally, the ethanol-free pallet was resuspended in H2Omolec.T-RFLP fingerprintingThe purified fluorescence-labeled PCR products were digested with three different restriction enzymes (MspI and BstUI, HaeIII) according to the manufacturer’s guidelines (New England Biolabs Inc., Ipswich, MA, USA) with a 20 µl master mix: 16.75 µl H2Omolec, 2 µl CutSmart buffer, 0.25 or 0.5 µl restriction enzyme, and 1 µl PCR product for 15 min at 37 °C (MspI) and 60 °C (BstUI, HaeIII), respectively. The digested PCR product was purified a second time35, dissolved in Super-DI Formamide (MCLAB, San Francisco, CA, USA) and, along with Red DNA size standard (MCLAB, San Francisco, USA), analyzed in an ABI Prism 3130 Genetic Analyzer (Applied Biosystems, Carlsbad, CA, USA). Terminal restriction fragments shorter than 50 bp and longer than 800 bp were removed from the t-RFLP fingerprints.16 S rRNA gene and internal transcribed spacer (ITS) sequencing and sequence processingThe 16 S rRNA gene and ITS paired-end raw reads for the bacterial and fungal community analyses were deposited in the National Center for Biotechnology Information (NCBI) Sequence Read Archive (SRA) and can be found under the BioProject accession number PRJNA626504. This BioProject contains 70 samples and 139 SRA experiments (SRR11570615–SRR11570753) which were processed using CASAVA software (Illumina, San Diego, CA, USA) for demultiplexing of MiSeq raw sequences (2 × 300 bp, MiSeq Reagent Kit v3).Paired-end sequences were quality-filtered with fastp (version 0.19.4)36 using default settings with the addition of an increased per base phred score of 20, base-pair corrections by overlap (-c), as well as 5′- and 3′-end read trimming with a sliding window of 4, a mean quality of 20 and minimum sequence size of 50 bp. Paired-end sequences were merged using PEAR v0.9.1137 with default parameters. Subsequently, unclipped reverse and forward primer sequences were removed with cutadapt v1.1838 with default settings. Sequences were then processed using VSEARCH (v2.9.1)39. This included sorting and size-filtering (—sortbylength,—minseqlength) of the paired reads to ≥300 bp for bacteria and ≥140 bp for ITS1, dereplication (—derep_fulllength). Dereplicated sequences were denoised with UNOISE340 using default settings (—cluster_unoise—minsize 8) and chimeras were removed (—uchime3_denovo). An additional reference-based chimera removal was performed (—uchime_ref) against the SILVA41 SSU NR database (v132) and UNITE42 database (v7.2) resulting in the final set of amplicon sequence variants (ASVs)43. Quality-filtered and merged reads were mapped to ASVs (—usearch_global–id 0.97). Classification of ASVs was performed with BLAST 2.7.1+ against the SILVA SSU NR (v132) and UNITE (v7.2) database with an identity of at least 90%. The ITS sequences contained unidentified fungal ASVs after UNITE classification, these sequences were checked (blastn)44 against the “nt” database (Nov 2018) to remove non-fungal ASVs and only as fungi classified reads were kept. Sample comparisons were performed at the same surveying effort, utilizing the lowest number of sequences by random selection (total 15,800 bacteria, 20,500 fungi). Species richness, alpha and beta diversity estimates, and rarefaction curves were determined using the QIIME 1.9.145 script alpha_rarefaction.py.The final ASV tables were used to compute heatmaps showing the effect of degradation on the community using R (Version 3.6.1, R Foundation for Statistical Computing, Vienna, Austria) and R packages “gplots”, “vegan”, “permute” and “RColorBrewer”. Fungal community functions were obtained from the FunGuild database46. Plant mycorrhizal association types were compiled from the literature38,39,40,41,47,48,49,50. If no direct species match was available, the mycorrhizal association was assumed to remain constant within the same genus.Enzyme activityEnzyme activity was measured to characterize the functional activity of the soil microorganisms. The following extracellular enzymes, involved in C, N, and P transformations, were considered: two hydrolases (β-glucosidase and xylanase), phenoloxidase, urease, and alkaline phosphatase. Enzyme activities were measured directly at the sampling site according to protocols after Schinner et al.51. Beta-glucosidase was incubated with saligenin for 3 h at 37 °C, xylanase with glucose for 24 h at 50 °C, phenoloxidase with L-3,4-dihydroxy phenylalanine (DOPA) for 1 h at 25 °C, urease with urea for 2 h at 37 °C and alkaline phosphatase on P-nitrophenyl phosphate for 1 h at 37 °C. Reaction products were measured photometrically at recommended wavelengths (578, 690, 475, 660, and 400 nm, respectively).SOC stocks and SOC lossThe SOC stocks (in kg C m−2) for the upper 30 cm were determined by multiplying the SOC content (g C kg−1) by the BD (g cm−3) and the thickness of the soil horizons (m). SOC losses (%) were calculated for each degradation stage and horizon and were related to the mean C stock of the reference stage (S0). The erosion-induced SOC loss of the upper horizon was estimated by considering the topsoil removal (extent of vertical soil cracks) of all degraded soil profiles (S1–S5) and the SOC content and BD of the reference (S0). To calculate the mineralization-derived SOC loss, we accounted for the effects of SOC and root mineralization on both SOC content and BD. Thus, we used the SOC content and BD from each degradation stage (S1–S5) and multiplied it by the mean thickness of each horizon (down to 30 cm) from the reference site (S0). The disentanglement of erosion-derived SOC loss from mineralization-derived SOC loss was based on explicit assumptions that (i) erosion-derived SOC losses are mainly associated with losses from the topsoil, and (ii) the decreasing SOC contents in the erosion-unaffected horizons were mainly driven by mineralization and decreasing root C input.Statistical analysesStatistical analyses were performed using PASW Statistics (IBM SPSS Statistics) and R software (Version 3.6.1). Soil and plant characteristics are presented as means and standard errors (means ± SE). The significance of treatment effects (S0–S5) and depth was tested by one-way ANOVA at p  More

Kronfeld-Schor, N. & Dayan, T. Partitioning of time as an ecological resource. Annu. Rev. Ecol. Evol. Syst. 34, 153–181 (2003).Article 

Google Scholar 
Tauber, E. & Kyriacou, C. P. Review: Genomic approaches for studying biological clocks. Funct. Ecol. 22, 19–29 (2008).
Google Scholar 
White, E. R. & Hastings, A. Seasonality in ecology: Progress and prospects in theory. Ecol. Complex. 44, 100867 (2020).Article 

Google Scholar 
Ko, C. H. & Takahashi, J. S. Molecular components of the mammalian circadian clock. Hum. Mol. Genet. 15, R271–R277 (2006).CAS 
PubMed 
Article 

Google Scholar 
Cassone, V. M. Avian circadian organization: A chorus of clocks. Front. Neuroendocrinol. 35, 76–88 (2014).PubMed 
Article 

Google Scholar 
Kyriacou, C. P., Peixoto, A. A., Sandrelli, F., Costa, R. & Tauber, E. Clines in clock genes: Fine-tuning circadian rhythms to the environment. Trends Genet. 24, 124–132 (2008).CAS 
PubMed 
Article 

Google Scholar 
Partch, C. L., Green, C. B. & Takahashi, J. S. Molecular architecture of the mammalian circadian clock. Trends Cell Biol. 24, 90–99 (2014).CAS 
PubMed 
Article 

Google Scholar 
Helm, B. et al. Two sides of a coin: ecological and chronobiological perspectives of timing in the wild. Philos. Trans. R. Soc. B Biol. Sci. 372, 20160246 (2017).Article 

Google Scholar 
Kalmbach, D. A. et al. Genetic basis of chronotype in humans: Insights from three landmark GWAS. Sleep https://doi.org/10.1093/sleep/zsw048 (2017).Article 
PubMed 
PubMed Central 

Google Scholar 
Takahashi, J. S., Shimomura, K. & Kumar, V. Searching for genes underlying behavior: Lessons from circadian rhythms. Science 322, 909–912 (2008).CAS 
PubMed 
PubMed Central 
Article 
ADS 

Google Scholar 
Yoshimura, T. et al. Molecular analysis of avian circadian clock genes11Published on the World Wide Web on 23 May 2000. Mol. Brain Res. 78, 207–215 (2000).CAS 
PubMed 
Article 

Google Scholar 
Gekakis, N. et al. Role of the CLOCK Protein in the Mammalian circadian mechanism. Science 280, 1564–1569 (1998).CAS 
PubMed 
Article 
ADS 

Google Scholar 
Saleem, Q., Anand, A., Jain, S. & Brahmachari, S. K. The polyglutamine motif is highly conserved at the Clock locus in various organisms and is not polymorphic in humans. Hum. Genet. 109, 136–142 (2001).CAS 
PubMed 
Article 

Google Scholar 
Darlington, T. K. et al. Closing the circadian loop: CLOCK-induced transcription of its own inhibitors per and tim. Science 280, 1599–1603 (1998).CAS 
PubMed 
Article 
ADS 

Google Scholar 
King, D. P. et al. Positional cloning of the mouse circadian clock gene. Cell 89, 641–653 (1997).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Follett, B. Rhythms and photoperiodism in birds. Biological rhythms and photoperiodism in plants (1998).Hazlerigg, D. G. & Wagner, G. C. Seasonal photoperiodism in vertebrates: from coincidence to amplitude. Trends Endocrinol. Metab. 17, 83–91 (2006).CAS 
PubMed 
Article 

Google Scholar 
Gwinner, E. Circadian and circannual programmes in avian migration. J. Exp. Biol. 199, 39–48 (1996).CAS 
PubMed 
Article 

Google Scholar 
Stirland, J. A., Mohammad, Y. N. & Loudon, A. S. I. A mutation of the circadian timing system (tau gene) in the seasonally breeding Syrian hamster alters the reproductive response to photoperiod change. Proc. R Soc. London Ser. B Biol. Sci. 263, 345–350 (1996).CAS 
Article 
ADS 

Google Scholar 
Bradshaw, W. E. & Holzapfel, C. M. Evolution of animal photoperiodism. Annu. Rev. Ecol. Evol. Syst. 38, 1–25 (2007).Article 

Google Scholar 
Graham, J. L., Cook, N. J., Needham, K. B., Hau, M. & Greives, T. J. Early to rise, early to breed: A role for daily rhythms in seasonal reproduction. Behav. Ecol. 28, 1266–1271 (2017).Article 

Google Scholar 
Rittenhouse, J. L., Robart, A. R. & Watts, H. E. Variation in chronotype is associated with migratory timing in a songbird. Biol. Lett. 15, 20190453 (2019).PubMed 
PubMed Central 
Article 

Google Scholar 
O’Malley, K. G., Ford, M. J. & Hard, J. J. Clock polymorphism in Pacific salmon: Evidence for variable selection along a latitudinal gradient. Proc. R. Soc. B Biol. Sci. 277, 3703–3714 (2010).Article 
CAS 

Google Scholar 
O’Malley, K. G. & Banks, M. A. A latitudinal cline in the Chinook salmon (Oncorhynchus tshawytscha) Clock gene: Evidence for selection on PolyQ length variants. Proc. R. Soc. B Biol. Sci. 275, 2813–2821 (2008).Article 
CAS 

Google Scholar 
Peterson, M. P. et al. Variation in candidate genes CLOCK and ADCYAP1 does not consistently predict differences in migratory behavior in the songbird genus Junco. F1000Research 2, 115 (2013).PubMed 
PubMed Central 
Article 

Google Scholar 
Saino, N. et al. Polymorphism at the Clock gene predicts phenology of long-distance migration in birds. Mol. Ecol. 24, 1758–1773 (2015).CAS 
PubMed 
Article 

Google Scholar 
Saino, N. et al. Timing of molt of barn swallows is delayed in a rare Clock genotype. PeerJ 1, e17 (2013).PubMed 
PubMed Central 
Article 
CAS 

Google Scholar 
Johnsen, A. et al. Avian Clock gene polymorphism: Evidence for a latitudinal cline in allele frequencies. Mol. Ecol. 16, 4867–4880 (2007).CAS 
PubMed 
Article 

Google Scholar 
Liedvogel, M., Szulkin, M., Knowles, S. C. L., Wood, M. & Sheldon, B. C. Phenotypic correlates of Clock gene variation in a wild blue tit population: Evidence for a role in seasonal timing of reproduction. Mol. Ecol. 18, 2444–2456 (2009).PubMed 
Article 

Google Scholar 
Caprioli, M. et al. Clock gene variation is associated with breeding phenology and maybe under directional selection in the migratory barn swallow. PLoS ONE 7, e35140 (2012).CAS 
PubMed 
PubMed Central 
Article 
ADS 

Google Scholar 
Dor, R. et al. Clock gene variation in Tachycineta swallows. Ecol. Evol. 2, 95–105 (2012).PubMed 
PubMed Central 
Article 

Google Scholar 
Dor, R. et al. Low variation in the polymorphic Clock gene poly-Q region despite population genetic structure across barn swallow (Hirundo rustica) populations. PLoS ONE 6, e28843 (2011).CAS 
PubMed 
PubMed Central 
Article 
ADS 

Google Scholar 
O’Brien, C. et al. Geography of the circadian gene clock and photoperiodic response in western North American populations of the three-spined stickleback Gasterosteus aculeatus. J. Fish Biol. 82, 827–839 (2013).PubMed 
PubMed Central 
Article 

Google Scholar 
Mueller, J. C., Pulido, F. & Kempenaers, B. Identification of a gene associated with avian migratory behaviour. Proc. R. Soc. B Biol. Sci. 278, 2848–2856 (2011).CAS 
Article 

Google Scholar 
Liedvogel, M. & Sheldon, B. C. Low variability and absence of phenotypic correlates of Clock gene variation in a great tit Parus major population. J. Avian Biol. 41, 543–550 (2010).Article 

Google Scholar 
Lugo-Ramos, J. S., Delmore, K. E. & Liedvogel, M. Candidate genes for migration do not distinguish migratory and non-migratory birds. J. Comp. Physiol. A 203, 383–397 (2017).CAS 
Article 

Google Scholar 
Majoy, S. B. & Heideman, P. D. Tau differences between short-day responsive and short-day nonresponsive white-footed mice (Peromyscus leucopus) do not affect reproductive photoresponsiveness. J. Biol. Rhythms 15, 501–513 (2000).CAS 
PubMed 
Article 

Google Scholar 
O’Brien, C. et al. Geography of the circadian gene clock and photoperiodic response in western North American populations of the threespine stickleback Gasterosteus aculeatus. J. Fish Biol. 82, 827–839 (2013).PubMed 
PubMed Central 
Article 

Google Scholar 
Contina, A., Bridge, E. S., Ross, J. D., Shipley, J. R. & Kelly, J. F. Examination of clock and Adcyap1 gene variation in a neotropical migratory passerine. PLoS ONE 13, e0190859 (2018).PubMed 
PubMed Central 
Article 
CAS 

Google Scholar 
Herzog, E. D. Neurons and networks in daily rhythms. Nat. Rev. Neurosci. 8, 790–802 (2007).CAS 
PubMed 
Article 

Google Scholar 
Chahad-Ehlers, S. et al. Expanding the view of clock and cycle gene evolution in Diptera. Insect Mol. Biol. 26, 317–331 (2017).CAS 
PubMed 
Article 

Google Scholar 
Denlinger, D. L., Hahn, D. A., Merlin, C., Holzapfel, C. M. & Bradshaw, W. E. Keeping time without a spine: What can the insect clock teach us about seasonal adaptation?. Philos. Trans. R. Soc. B Biol. Sci. 372, 20160257 (2017).Article 

Google Scholar 
van Noordwijk, A. J. et al. A framework for the study of genetic variation in migratory behaviour. J .Ornithol. 147, 221–233 (2006).Article 

Google Scholar 
Newton, I. The Migration Ecology of Birds (Academic Press, 2008).
Google Scholar 
Gohli, J., Lifjeld, J. T. & Albrecht, T. Migration distance is positively associated with sex-linked genetic diversity in passerine birds. Ethol. Ecol. Evol. 28, 42–52 (2016).Article 

Google Scholar 
Bazzi, G. et al. Clock gene polymorphism, migratory behaviour and geographic distribution: A comparative study of trans-Saharan migratory birds. Mol. Ecol. 25, 6077–6091 (2016).CAS 
PubMed 
Article 

Google Scholar 
Doren, B. M. V., Liedvogel, M. & Helm, B. Programmed and flexible: Long-term Zugunruhe data highlight the many axes of variation in avian migratory behaviour. J. Avian Biol. 48, 155–172 (2017).Article 

Google Scholar 
Helm, B., Gwinner, E. & Trost, L. Flexible seasonal timing and migratory behavior: Results from stonechat breeding programs. Ann. N. Y. Acad. Sci. 1046, 216–227 (2005).PubMed 
Article 
ADS 

Google Scholar 
Helm, B. & Gwinner, E. Migratory restlessness in an equatorial nonmigratory bird. PLoS Biol. 4, e110 (2006).PubMed 
PubMed Central 
Article 
CAS 

Google Scholar 
Helm, B. Geographically distinct reproductive schedules in a changing world: Costly implications in captive Stonechats. Integr Comp Biol 49, 563–579 (2009).CAS 
PubMed 
Article 

Google Scholar 
Dhondt, A. A. Variations in the number of overwintering stonechats possibly caused by natural selection. Ringing Migr. 4, 155–158 (1983).Article 

Google Scholar 
Brown, C. R. & Brown, M. B. Weather-mediated natural selection on arrival time in cliff swallows (Petrochelidon pyrrhonota). Behav. Ecol. Sociobiol. 47, 339–345 (2000).Article 

Google Scholar 
GOUDET, J. FSTAT, a program to estimate and test gene diversities and fixation indices, version 2.9.3. http://www2.unil.ch/popgen/softwares/fstat.htm (2001).Van Doren, B. M. et al. Correlated patterns of genetic diversity and differentiation across an avian family. Mol. Ecol. 26, 3982–3997 (2017).PubMed 
Article 

Google Scholar 
Illera, J. C., Richardson, D. S., Helm, B., Atienza, J. C. & Emerson, B. C. Phylogenetic relationships, biogeography and speciation in the avian genus Saxicola. Mol. Phylogenet. Evol. 48, 1145–1154 (2008).PubMed 
Article 

Google Scholar 
Illera, J. C. & Díaz, M. Reproduction in an endemic bird of a semiarid island: A food-mediated process. J. Avian Biol. 37, 447–456 (2006).Article 

Google Scholar 
Illera, J. C. & Díaz, M. Site fidelity in the Canary Islands stonechat Saxicola dacotiae in relation to spatial and temporal patterns of habitat suitability. Acta Oecol. 34, 1–8 (2008).Article 
ADS 

Google Scholar 
Gwinner, E. & Dittami, J. Endogenous reproductive rhythms in a tropical bird. Science 249, 906–908 (1990).CAS 
PubMed 
Article 
ADS 

Google Scholar 
Dittami, J. & Gwinner, E. Annual cycles in the African stonechat Saxicola torquata axillaris and their relationship to environmental factors. J. Zool. 207, 357–370 (1985).Article 

Google Scholar 
Gwinner, E. Circannual rhythms in tropical and temperate-zone stonechats: A comparison of properties under constant conditions. Ökologie der Vögel 13, 5–14 (1991).
Google Scholar 
Gwinner, E. Circannual Rhythms: Endogenous Annual Clocks in the Organization of Seasonal Processes (Springer, 2012).
Google Scholar 
Helm, B., Fiedler, W. & Callion, J. Movements of European stonechats Saxicola torquata according to ringing recoveries. ARDEA-WAGENINGEN- 94, 33 (2006).
Google Scholar 
Opaev, A., Red’kin, Y., Kalinin, E. & Golovina, M. Species limits in Northern Eurasian taxa of the common stonechats, Saxicola torquatus complex (Aves: Passeriformes, Muscicapidae). Vertebr.ate Zool. 68, 199 (2018).
Google Scholar 
Gwinner, E. & Czeschlik, D. On the significance of spring migratory restlessness in caged birds. Oikos 30, 364–372 (1978).Article 

Google Scholar 
Krist, M., Munclinger, P., Briedis, M. & Adamík, P. The genetic regulation of avian migration timing: combining candidate genes and quantitative genetic approaches in a long-distance migrant. Oecologia https://doi.org/10.1007/s00442-021-04930-x (2021).Article 
PubMed 

Google Scholar 
Berthold, P. & Pulido, F. Heritability of migratory activity in a natural bird population. Proc. R. Soc. London Ser. B Biol. Sci. 257, 311–315 (1994).Article 
ADS 

Google Scholar 
Pulido, F. & Berthold, P. Current selection for lower migratory activity will drive the evolution of residency in a migratory bird population. Proc. Natl. Acad. Sci. 107, 7341–7346 (2010).CAS 
PubMed 
PubMed Central 
Article 
ADS 

Google Scholar 
Liedvogel, M. & Lundberg, M. The Genetics of Migration. In Animal Movement Across Scales (eds Hansson, L.-A. & Åkesson, S.) 219–231 (Oxford University Press, 2014). https://doi.org/10.1093/acprof:oso/9780199677184.003.0012.Chapter 

Google Scholar 
Åkesson, S. & Helm, B. Endogenous programs and flexibility in bird migration. Front. Ecol. Evol. 8, 78 (2020).Article 

Google Scholar 
Stevenson, T. J. & Kumar, V. Neural control of daily and seasonal timing of songbird migration. J. Comp. Physiol. A 203, 399–409 (2017).Article 

Google Scholar 
Verhagen, I. et al. Genetic and phenotypic responses to genomic selection for timing of breeding in a wild songbird. Funct. Ecol. 33, 1708–1721 (2019).Article 

Google Scholar 
Helm, B. & Gwinner, E. Timing of Postjuvenal molt in African (Saxicola Torquata Axillaris) and European (Saxicola Torquata Rubicola) stonechats: Effects of genetic and environmental factors. Auk 116, 589–603 (1999).Article 

Google Scholar 
Zink, R. M., Pavlova, A., Drovetski, S., Wink, M. & Rohwer, S. Taxonomic status and evolutionary history of the Saxicola torquata complex. Mol. Phylogenet. Evol. 52, 769–773 (2009).CAS 
PubMed 
Article 

Google Scholar 
Flinks, H. & Pfeifer, F. Brutzeit, Gelegegröße und Bruterfolg beim Schwarzkehlchen (Saxicola torquata). Charadrius 23, 128–140 (1987).
Google Scholar 
Urquhart, E. Stonechats (Christopher Helm, 2002).
Google Scholar 
Glutz von Blotzheim, U. Bauer Handbuch der Vögel Mitteleuropas KM: Bd. 11. Aula, Wiesbaden (1988).Yamaura, Y. et al. Tracking the Stejneger’s stonechat Saxicola stejnegeri along the East Asian-Australian Flyway from Japan via China to southeast Asia. J. Avian Biol. 48, 197–202 (2017).Article 

Google Scholar 
Gwinner, E., Neusser, V., Engl, D., Schmidl, D. & Bals, L. Haltung, Zucht und Eiaufzucht afrikanischer und europäischer Schwarzkehlchen Saxicola torquata. Gefiederte Welt 111, 118–120 (1987).
Google Scholar 
Flinks, H., Helm, B. & Rothery, P. Plasticity of moult and breeding schedules in migratory European Stonechats Saxicola rubicola. Ibis 150, 687–697 (2008).Article 

Google Scholar 
Humphrey, P. S. & Parkes, K. C. An approach to the study of molts and plumages. Auk 76, 1–31 (1959).Article 

Google Scholar 
Berthold, P. Bird Migration: A General Survey (Oxford University Press, 2001).
Google Scholar 
RStudio | Open source & professional software for data science teams. https://rstudio.com/.R Core Team. R: A language and environment for statistical computing. (R Foundation for Statistical Computing, 2013).Bates, D., Mächler, M., Bolker, B. & Walker, S. Fitting linear mixed-effects models using lme4. http://arxiv.org/abs/1406.5823 (2014).Lüdecke, D. & Lüdecke, M. D. Package ‘sjPlot’. (2015).del Hoyo, J., Elliott, A., Sargatal, J., Christie, D. A. & de Juana, E. Handbook of the Birds of the World Alive (Lynx Edicions, 2018).
Google Scholar  More

Springer, A. M. et al. Sequential megafaunal collapse in the North Pacific Ocean: an ongoing legacy of industrial whaling?. Proc. Natl. Acad. Sci. 100, 12223–12228. https://doi.org/10.1073/pnas.1635156100 (2003).ADS 
CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
Estes, J. A., Heithaus, M., McCauley, D. J., Rasher, D. B. & Worm, B. Megafaunal impacts on structure and function of ocean ecosystems. Annu. Rev. Environ. Resour. 41, 83–116. https://doi.org/10.1146/annurev-environ-110615-085622 (2016).Article 

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

Google Scholar 
Bowen, W. D. & Iverson, S. J. Methods of estimating marine mammal diets: a review of validation experiments and sources of bias and uncertainty. Mar. Mamm. Sci. 29, 719–754. https://doi.org/10.1111/j.1748-7692.2012.00604.x (2013).Article 

Google Scholar 
Krahn, M. M. et al. Use of chemical tracers in assessing the diet and foraging regions of eastern North Pacific killer whales. Mar. Environ. Res. 63, 91–114. https://doi.org/10.1016/j.marenvres.2006.07.002 (2007).ADS 
CAS 
Article 
PubMed 

Google Scholar 
Remili, A. et al. Individual prey specialization drives PCBs in Icelandic killer whales. Environ. Sci. Technol. 55, 4923–4931. https://doi.org/10.1021/acs.est.0c08563 (2021).ADS 
CAS 
Article 
PubMed 

Google Scholar 
Foote, A. D., Vester, H., Vikingsson, G. A. & Newton, J. Dietary variation within and between populations of northeast Atlantic killer whales, Orcinus orca, inferred from d13C and d15N analyses. Mar. Mamm. Sci. 28, E472–E485. https://doi.org/10.1111/j.1748-7692.2012.00563.x (2012).CAS 
Article 

Google Scholar 
Remili, A. et al. Humpback whales (Megaptera novaeangliae) breeding off Mozambique and Ecuador show geographic variation of persistent organic pollutants and isotopic niches. Environ. Pollut. 267, 115575. https://doi.org/10.1016/j.envpol.2020.115575 (2020).CAS 
Article 
PubMed 

Google Scholar 
Pinzone, M., Damseaux, F., Michel, L. N. & Das, K. Stable isotope ratios of carbon, nitrogen and sulphur and mercury concentrations as descriptors of trophic ecology and contamination sources of Mediterranean whales. Chemosphere 237, 124448. https://doi.org/10.1016/j.chemosphere.2019.124448 (2019).ADS 
CAS 
Article 
PubMed 

Google Scholar 
Bourque, J. et al. Feeding habits of a new Arctic predator: insight from full-depth blubber fatty acid signatures of Greenland, Faroe Islands, Denmark, and managed-care killer whales Orcinus orca. Mar. Ecol. Prog. Ser. 603, 1–12. https://doi.org/10.3354/meps12723 (2018).ADS 
CAS 
Article 

Google Scholar 
Krahn, M. M., Pitman, R. L., Burrows, D. G., Herman, D. P. & Pearce, R. W. Use of chemical tracers to assess diet and persistent organic pollutants in Antarctic Type C killer whales. Mar. Mamm. Sci. 24, 643–663. https://doi.org/10.1111/j.1748-7692.2008.00213.x (2008).CAS 
Article 

Google Scholar 
Groß, J. et al. Interannual variability in the lipid and fatty acid profiles of east Australia-migrating humpback whales (Megaptera novaeangliae) across a 10-year timeline. Sci. Rep. 10, 18274. https://doi.org/10.1038/s41598-020-75370-5 (2020).CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
Jory, C. et al. Individual and population dietary specialization decline in fin whales during a period of ecosystem shift. Sci. Rep. 11, 17181. https://doi.org/10.1038/s41598-021-96283-x (2021).ADS 
CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
Iverson, S. J., Field, C., Bowen, W. D. & Blanchard, W. Quantitative fatty acid signature analysis: a new method of estimating predator diets. Ecol. Monogr. 74, 211–235. https://doi.org/10.1890/02-4105 (2004).Article 

Google Scholar 
McKinney, M. A. et al. Global change effects on the long-term feeding ecology and contaminant exposures of East Greenland polar bears. Glob. Change Biol. 19, 2360–2372. https://doi.org/10.1111/gcb.12241 (2013).ADS 
Article 

Google Scholar 
Nordstrom, C. A., Wilson, L. J., Iverson, S. J. & Tollit, D. J. Evaluating quantitative fatty acid signature analysis (QFASA) using harbour seals Phoca vitulina richardsi in captive feeding studies. Mar. Ecol. Prog. Ser. 360, 245–263. https://doi.org/10.3354/meps07378 (2008).ADS 
Article 

Google Scholar 
Bourque, J., Atwood, T. C., Divoky, G. J., Stewart, C. & McKinney, M. A. Fatty acid-based diet estimates suggest ringed seal remain the main prey of southern Beaufort Sea polar bears despite recent use of onshore food resources. Ecol. Evol. https://doi.org/10.1002/ece3.6043 (2020).Article 
PubMed 
PubMed Central 

Google Scholar 
Thiemann, G. W., Derocher, A. E. & Stirling, I. Polar bear Ursus maritimus conservation in Canada: an ecological basis for identifying designatable units. Oryx 42, 504–515. https://doi.org/10.1017/S0030605308001877 (2008).Article 

Google Scholar 
Choy, E. S. et al. A comparison of diet estimates of captive beluga whales using fatty acid mixing models with their true diets. J. Exp. Mar. Biol. Ecol. 516, 132–139. https://doi.org/10.1016/j.jembe.2019.05.005 (2019).ADS 
Article 

Google Scholar 
Kirsch, P. E., Iverson, S. J. & Bowen, W. D. Effect of a low-fat diet on body composition and blubber fatty acids of captive Juvenile Harp Seals (Phoca groenlandica). Physiol. Biochem. Zool. 73, 45–59. https://doi.org/10.1086/316723 (2000).CAS 
Article 
PubMed 

Google Scholar 
Koopman, H. N. Phylogenetic, ecological, and ontogenetic factors influencing the biochemical structure of the blubber of odontocetes. Mar. Biol. 151, 277–291. https://doi.org/10.1007/s00227-006-0489-8 (2007).Article 

Google Scholar 
Strandberg, U. et al. Stratification, composition, and function of marine mammal blubber: the ecology of fatty acids in marine mammals. Physiol. Biochem. Zool 81, 473–485. https://doi.org/10.1086/589108 (2008).CAS 
Article 
PubMed 

Google Scholar 
Choy, E. S. et al. Variation in the diet of beluga whales in response to changes in prey availability: insights on changes in the Beaufort Sea ecosystem. Mar. Ecol. Prog. Ser. 647, 195–210 (2020).ADS 
CAS 
Article 

Google Scholar 
Koopman, H. N., Iverson, S. J. & Gaskin, D. E. Stratification and age-related differences in blubber fatty acids of the male harbour porpoise (Phocoena phocoena). J. Comp. Physiol. B. 165, 628–639. https://doi.org/10.1007/BF00301131 (1996).CAS 
Article 
PubMed 

Google Scholar 
Budge, S. M., Iverson, S. J. & Koopman, H. N. Studying trophic ecology in marine ecosystems using fatty acids: a primer on analysis and interpretation. Mar. Mamm. Sci. 22, 759–801. https://doi.org/10.1111/j.1748-7692.2006.00079.x (2006).Article 

Google Scholar 
Krahn, M. M. et al. Stratification of lipids, fatty acids and organochlorine contaminants in blubber of white whales and killer whales. J. Cetacean Res. Manag. 6, 175–189 (2004).
Google Scholar 
Loseto, L. L. et al. Summer diet of beluga whales inferred by fatty acid analysis of the eastern Beaufort Sea food web. J. Exp. Mar. Biol. Ecol. 374, 12–18. https://doi.org/10.1016/j.jembe.2009.03.015 (2009).CAS 
Article 

Google Scholar 
Heide-Jørgensen, M.-P. Occurrence and hunting of killer whales in Greenland. Rit Fiskedeildar 11, 115–135 (1988).
Google Scholar 
Nøttestad, L. et al. Prey selection of offshore killer whales Orcinus orca in the Northeast Atlantic in late summer: spatial associations with mackerel. Mar. Ecol. Prog. Ser. 499, 275–283 (2014).ADS 
Article 

Google Scholar 
Nikolioudakis, N. et al. Drivers of the summer-distribution of Northeast Atlantic mackerel (Scomber scombrus) in the Nordic Seas from 2011 to 2017; a Bayesian hierarchical modelling approach. ICES J. Mar. Sci. 76, 530–548. https://doi.org/10.1093/icesjms/fsy085 (2019).Article 

Google Scholar 
Olafsdottir, A. H. et al. Geographical expansion of Northeast Atlantic mackerel (Scomber scombrus) in the Nordic Seas from 2007 to 2016 was primarily driven by stock size and constrained by low temperatures. Deep Sea Res. Part II 159, 152–168. https://doi.org/10.1016/j.dsr2.2018.05.023 (2019).Article 

Google Scholar 
Jansen, T. et al. Ocean warming expands habitat of a rich natural resource and benefits a national economy. Ecol. Appl. 26, 2021–2032. https://doi.org/10.1002/eap.1384 (2016).Article 
PubMed 

Google Scholar 
Ferguson, S. H., Higdon, J. W. & Westdal, K. H. Prey items and predation behavior of killer whales (Orcinus orca) in Nunavut, Canada based on Inuit hunter interviews. Aquat. Biosyst. 8, 3–3. https://doi.org/10.1186/2046-9063-8-3 (2012).Article 
PubMed 
PubMed Central 

Google Scholar 
Laidre, K. L., Heide-Jørgensen, M. P. & Orr, J. R. Reactions of narwhals, Monodon monoceros, to killer whale, Orcinus orca, attacks in the eastern Canadian Arctic. Can. Field-Naturalist 120, 457–465 (2006).Article 

Google Scholar 
Willoughby, A. L., Ferguson, M. C., Stimmelmayr, R., Clarke, J. T. & Brower, A. A. Bowhead whale (Balaena mysticetus) and killer whale (Orcinus orca) co-occurrence in the U.S. Pacific Arctic, 2009–2018: evidence from bowhead whale carcasses. Polar Biol. 43, 1669–1679. https://doi.org/10.1007/s00300-020-02734-y (2020).Article 

Google Scholar 
Bloch, D. & Lockyer, C. Killer whales (Orcinus orca) in Faroese waters. Rit Fiskideildar 11, 55–64 (1988).
Google Scholar 
Pedro, S. et al. Blubber-depth distribution and bioaccumulation of PCBs and organochlorine pesticides in Arctic-invading killer whales. Sci. Total Environ. 601, 237–246. https://doi.org/10.1016/j.scitotenv.2017.05.193 (2017).ADS 
CAS 
Article 
PubMed 

Google Scholar 
Samarra, F. I. P. et al. Prey of killer whales (Orcinus orca) in Iceland. PLoS ONE 13, 20. https://doi.org/10.1371/journal.pone.0207287 (2018).CAS 
Article 

Google Scholar 
Jourdain, E. et al. Isotopic niche differs between seal and fish-eating killer whales (Orcinus orca) in northern Norway. Ecol. Evol. 10, 4115–4127. https://doi.org/10.1002/ece3.6182 (2020).Article 
PubMed 
PubMed Central 

Google Scholar 
Bromaghin, J. F., Budge, S. M., Thiemann, G. W. & Rode, K. D. Assessing the robustness of quantitative fatty acid signature analysis to assumption violations. Methods Ecol. Evol. 7, 51–59. https://doi.org/10.1111/2041-210X.12456 (2016).Article 

Google Scholar 
Jefferson, T. A., Stacey, P. J. & Baird, R. W. A review of Killer Whale interactions with other marine mammals: predation to co-existence. Mamm. Rev. 21, 151–180. https://doi.org/10.1111/j.1365-2907.1991.tb00291.x (1991).Article 

Google Scholar 
Bromaghin, J. F. QFASAR: quantitative fatty acid signature analysis with R. Methods Ecol. Evol. 8, 1158–1162. https://doi.org/10.1111/2041-210x.12740 (2017).Article 

Google Scholar 
Stewart, C., Iverson, S. & Field, C. Testing for a change in diet using fatty acid signatures. Environ. Ecol. Stat. 21, 775–792. https://doi.org/10.1007/s10651-014-0280-9 (2014).MathSciNet 
CAS 
Article 

Google Scholar 
Zhang, J. et al. Review of estimating trophic relationships by quantitative fatty acid signature analysis. J. Marine Sci. Eng. 8, 1030 (2020).Article 

Google Scholar 
Budge, S. M., Penney, S. N., Lall, S. P. & Trudel, M. Estimating diets of Atlantic salmon (Salmo salar) using fatty acid signature analyses; validation with controlled feeding studies. Can. J. Fish. Aquat. Sci. 69, 1033–1046. https://doi.org/10.1139/f2012-039 (2012).CAS 
Article 

Google Scholar 
Happel, A. et al. Evaluating quantitative fatty acid signature analysis (QFASA) in fish using controlled feeding experiments. Can. J. Fish. Aquat. Sci. 73, 1222–1229. https://doi.org/10.1139/cjfas-2015-0328 (2016).CAS 
Article 

Google Scholar 
Bromaghin, J. F. Simulating realistic predator signatures in quantitative fatty acid signature analysis. Eco. Inform. 30, 68–71. https://doi.org/10.1016/j.ecoinf.2015.09.011 (2015).Article 

Google Scholar 
Bromaghin, J. F., Budge, S. M., Thiemann, G. W. & Rode, K. D. Simultaneous estimation of diet composition and calibration coefficients with fatty acid signature data. Ecol. Evol. 7, 6103–6113. https://doi.org/10.1002/ece3.3179 (2017).Article 
PubMed 
PubMed Central 

Google Scholar 
Burns, J. M., Costa, D. P., Frost, K. & Harvey, J. T. Development of body oxygen stores in harbor seals: effects of age, mass, and body composition. Physiol. Biochem. Zool. 78, 1057–1068. https://doi.org/10.1086/432922 (2005).CAS 
Article 
PubMed 

Google Scholar 
Noren, D. P. & Mocklin, J. A. Review of cetacean biopsy techniques: Factors contributing to successful sample collection and physiological and behavioral impacts. Mar. Mamm. Sci. 28, 154–199. https://doi.org/10.1111/j.1748-7692.2011.00469.x (2012).Article 

Google Scholar  More

Study siteWe conducted this research at the Uluwatu temple site in Bali, Indonesia. Uluwatu is located on the Island’s southern coast, in the Badung Regency. The temple at Uluwatu is a Pura Luhur, which is a significant temple for Balinese Hindus across the island and is therefore visited regularly for significant regional, community, family, and household rituals by Balinese people from different regions throughout the year18. During the period of data collection hundreds of tourists also visit the Uluwatu temple each day. The temple sits on top of a promontory cliff edge, with walking paths in front of it that continue in loops to the North and South. These looping pathways surround scrub forests, which the macaques frequently inhabit but the humans rarely enter.In 2017–2018 there were five macaque groups at Uluwatu, which ranged throughout the temple complex area, and beyond. All groups are provisioned daily with a mixed diet of corn, cucumbers, and bananas by temple staff members. The two groups included in this research are the Celagi and Riting groups. We selected these groups because they previously exhibited significant differences in robbing frequencies whereby Riting was observed exhibiting robbing and bartering more frequently than Celagi1. Furthermore, both groups include the same highly trafficked tourist areas in their overlapping home ranges relative to the other groups at Uluwatu, theoretically minimizing between group differences in the contexts of human interaction1,19.Data collectionJVP collected data from May, 2017 to March, 2018 totaling 197 focal observation hours on all 13 subadult males in Celagi and Riting that were identified in May–June 2017. Subadult male long-tailed macaques exhibit characteristic patterns of incomplete canine eruption, sex organ development, and body size growth, which achieves a maximum of 80% of total adult size18. Mean sampling effort per individual was 15.2 hours (h), with a range of 1.75 h, totaling 102.75 h for Riting and 94.75 h for Celagi. The data collection protocol consisted of focal-animal sampling and instantaneous scan sampling20 on all six subadult males in the Celagi group, and all seven subadult males in the Riting group. Focal follows were 15 minutes in length. Sampling effort per individual is presented in Table 1. A random number generator determined the order of focal follows each morning. In the event a target focal animal could not be located within 10 minutes of locating the group, the next in line was located and observed. Data presented here come from focal animal sampling records of state and event behaviors. Relevant event behaviors consist of agonistic gestures used for calculating dominance relationships, including the target, or interaction partner, of all communicative event behaviors and the time of its occurrence. All changes in the focal animal’s state behavior were noted, recording the time of the change to the minute.Table 1 Focal Subadult male long-tailed macaques in Celagi and Riting at Uluwatu, Bali, Indonesia.Full size tableDuring focal samples we recorded robbing and bartering as a sequence of mixed event and state behaviors. We scored both the robbery and exchange phases as event behaviors, and the interim phase of item possession as a state behavior. We record a robbery as successful if the focal animal took an object from a human and established control of the object with their hands or teeth, and as unsuccessful if the focal animal touched the object but was not able to establish control of it. For each successful robbery we recorded the object taken. Unsuccessful robberies end the sequence, whereas successful robberies are typically followed by various forms of manipulating the object.The robbing and bartering sequence ends with one of several event behavior exchange outcomes: (1) “Successful exchanges” consist of the focal animal receiving a food reward from a human and releasing the stolen object; (2) “forced exchanges” are when a human takes the object back without a bartering event; (3) “dropped objects” describe when the macaque loses control of the object while carrying it or otherwise locomoting, and is akin to an “accidental drop”; (4) “no exchange” includes instances of the macaque releasing the object for no reward after manipulating it; and (5) “expired observation” consists of instances in which the final result of the robbing and bartering event was unobserved in the sample period (i.e., the sample period ended while the macaque still had possession of the object). A 6th exchange outcome is “rejected exchange,” which occurs when the focal animal does not drop the stolen object after being offered, or in some cases even accepting, a food reward. The “rejected exchange” outcome is unique in that it does not end the robbing and bartering sequence because a human may have one or more exchange attempts rejected before eventually facilitating a successful exchange, or before one of the other outcomes (2–5) occurs. For each successful exchange we recorded the food item the macaques received. Food items are grouped into four categories: fruits, peanuts, eggs, and human snacks. Snacks include packaged and processed food items such as candy or chips.Data analysisWe grouped the broad range of stolen items into classes of general types. “Eyewear” combines eyeglasses and sunglasses, while “footwear” combines sandals and shoes. “Ornaments” includes objects attached to and/or hanging from backpacks, such as keychains, while “accessories” includes decorative objects attached to an individual’s body or clothing like bracelets and hair ties. “Electronics” covers cellular phones and tablets. “Hats” encompasses removable forms of headwear, most typically represented by baseball-style hats or sun hats. “Plastics” is an item class consisting of lighters and bottles, which may be filled with water, soda, or juice. The “unidentified” category is used for stolen items which could not be clearly observed during or after the robbing and bartering sequence.“Robbery attempts” refers to the combined total number of successful and unsuccessful robberies. “Robbery efficiency” is a novel metric referring to the number of successful robberies divided by the total number of robbery attempts. The “Exchange Outcome Index” is calculated by dividing the number of successful exchanges by the total number of robbery attempts. We make this calculation using robbery attempts instead of successful robberies to account for total robbery effort because failed robberies still factor into an individual’s total energy expenditure toward receiving a bartered food reward and their total exposure to the risks (e.g., physical retaliation) of stealing from humans relative to achieving the desired end result of a food reward.Social rank was measured with David’s Score, calculated using dyadic agonistic interactions. We coded “winners” of contests as those who exhibited the agonistic behavior, while “losers” were the recipients of those agonistic behaviors21,22. We excluded intergroup agonistic interactions in our calculations of David’s Score.To account for potential variation in the overall patterns of interaction with humans between groups we calculated a Human Interaction Rate, which is the sum of human-directed interactions from focal animals in each group divided by the total number of observation hours on focal animals in that group.Statistical analysisWe ran statistical tests in SYSTAT software with a significance level set at 0.05. We used chi-square goodness-of-fit tests to assess the significance of differences in successful robberies between individuals for each group. To avoid having cells with values of zero, two focal subjects, Minion and Spot from Celagi, are excluded from this test because neither were observed making a successful robbery during the observation period. We also used chi-square goodness-of-fit tests to assess exchange outcome occurrences within each group, as well as a Fisher’s exact to test for significant differences in robbery outcomes between groups due to low expected counts in 40% of the cells. “Rejected exchange” events were not included in the analysis of robbery outcomes because they do not end the sequence and are therefore not mutually exclusive with the other robbery outcomes.We further tested for the effect of dominance position on robbery outcomes. Due to our small sample size and the preliminary nature of this investigation, we used Spearman correlations to assess the relationship between subadult male dominance position via David’s Score and (1) robbing efficiency and (2) the Exchange Outcome Index.Compliance with ethical standardsThis research complied with the standards and protocols for observational fieldwork with nonhuman primates and was approved by the University of Notre Dame Compliance IACUC board (protocol ID: 16-02-2932), where JVP and AF were affiliated at the time of this research. This study did not involve human subjects. This research further received a research permit from RISTEK in Indonesia (permit number: 2C21EB0881-R), and complied with local laws and customary practices in Bali. More

Gordon, D. M. From division of labor to the collective behavior of social insects. Behav. Ecol. Sociobiol. 70, 1101–1108 (2016).PubMed 
Article 

Google Scholar 
Gordon, D. M. The organization of work in social insect colonies. Nature 380, 121–124 (1996).CAS 
Article 

Google Scholar 
Bonabeau, E., Theraulaz, G. & Deneubourg, J.-L. Quantitative study of the fixed threshold model for the regulation of division of labour in insect societies. Proc. R. Soc. Lond. Ser. B Biol. Sci. 263, 1565–1569 (1996).Article 

Google Scholar 
Pankiw, T. & Page, R. E. Jr. The effect of genotype, age, sex, and caste on response thresholds to sucrose and foraging behavior of honey bees (Apis mellifera L.). J. Comp. Physiol. A 185, 207–213 (1999).CAS 
PubMed 
Article 

Google Scholar 
Bonabeau, E., Sobkowski, A., Theraulaz, G. & Deneubourg, J.-L. Adaptive task allocation inspired by a model of division of labor in social insects. In BCEC 36–45 (1997).Robinson, G. E. & Page, R. E. J. Genetic basis for division of labor in an insect society. In The Genetics of Social Evolution (ed. Breed, R. P.) 61–80 (Westview Press, 1989).
Google Scholar 
Hogeweg, P. & Hesper, B. The ontogeny of the interaction structure in bumble bee colonies: A MIRROR model. Behav. Ecol. Sociobiol. 12, 271–283 (1983).Article 

Google Scholar 
Theraulaz, G., Bonabeau, E. & Denuebourg, J. N. Response threshold reinforcements and division of labour in insect societies. Proc. R. Soc. Lond. Ser. B Biol. Sci. 265, 327–332 (1998).Article 

Google Scholar 
Robinson, G. E. Labor in insect societies. Annu. Rev. Entomol. 37, 637–665 (1992).CAS 
PubMed 
Article 

Google Scholar 
Hölldobler, B. & Wilson, E. O. The Superorganism: The Beauty, Elegance, and Strangeness of Insect Societies (WW Norton & Company, 2009).
Google Scholar 
Gordon, D. M. The organization of work in social insect colonies. Complexity 8, 43–46 (2002).Article 

Google Scholar 
Bourke, A. F. G. & Franks, N. R. Social Evolution in Ants (Princeton University Press, 1995).
Google Scholar 
Robinson, E. J. H., Feinerman, O. & Franks, N. R. Flexible task allocation and the organization of work in ants. Proc. R. Soc. B Biol. Sci. 276, 4373–4380 (2009).Article 

Google Scholar 
Pinter-Wollman, N., Hubler, J., Holley, J.-A., Franks, N. R. & Dornhaus, A. How is activity distributed among and within tasks in Temnothorax ants?. Behav. Ecol. Sociobiol. 66, 1407–1420 (2012).Article 

Google Scholar 
Ishii, Y. & Hasgeawa, E. The mechanism underlying the regulation of work-related behaviors in the monomorphic ant, Myrmica kotokui. J. Ethol. 31, 61–69 (2013).Article 

Google Scholar 
Baudier, K. M. et al. Changing of the guard: Mixed specialization and flexibility in nest defense (Tetragonisca angustula). Behav. Ecol. 30, 1041–1049 (2019).Article 

Google Scholar 
Schmid-Hempel, P. & Schmid-Hempel, R. Life duration and turnover of foragers in the antcataglyphis bicolor (hymenoptera, formicidae). Insectes Soc. 31, 345–360 (1984).Article 

Google Scholar 
O’Donnell, S. & Jeanne, R. L. Lifelong patterns of forager behaviour in a tropical swarm-founding wasp: Effects of specialization and activity level on longevity. Anim. Behav. 44, 1021–1027 (1992).Article 

Google Scholar 
Calabi, P. Behavioral flexibility in Hymenoptera: a re-examination of the concept of caste. In Advances in Myrmecology (ed. J. C. Trager) 237–258 (Leiden,1988).Gordon, D. M. Dynamics of task switching in harvester ants. Anim. Behav. 38, 194–204 (1989).Article 

Google Scholar 
Giray, T. & Robinson, G. E. Effects of intracolony variability in behavioral development on plasticity of division of labor in honey bee colonies. Behav. Ecol. Sociobiol. 35, 13–20 (1994).Article 

Google Scholar 
Cartar, R. V. Adjustment of foraging effort and task switching in energy-manipulated wild bumblebee colonies. Anim. Behav. 44, 75–87 (1992).Article 

Google Scholar 
Huang, Z. Y. & Robinson, G. E. Regulation of honey bee division of labor by colony age demography. Behav. Ecol. Sociobiol. 39, 147–158 (1996).Article 

Google Scholar 
Gordon, D. M. The dynamics of the daily round of the harvester ant colony (Pogonomyrmex barbatus). Anim. Behav. 34, 1402–1419 (1986).Article 

Google Scholar 
Wilson, E. O. Caste and division of labor in leaf-cutter ants (Hymenoptera: Formicidae: Atta): III. Ergonomic resiliency in foraging by A. cephalotes. Behav. Ecol. Sociobiol. 14, 47–54 (1983).Article 

Google Scholar 
Middleton, E. J. T. & Latty, T. Resilience in social insect infrastructure systems. J. R. Soc. Interface 13, 20151022 (2016).PubMed 
PubMed Central 
Article 

Google Scholar 
Nalepa, C. A. Origin of termite eusociality: Trophallaxis integrates the social, nutritional, and microbial environments. Ecol. Entomol. 40, 323–335 (2015).Article 

Google Scholar 
McMahan, E. A. Mound repair and foraging polyethism in workers of Nasutitermes exitiosus (Hill):(Isoptera: Termitidae). Insectes Soc. 24, 225–232 (1977).Article 

Google Scholar 
Watson, J. A. L. & McMahan, E. A. Polyethism in the Australian harvester Termite Drepanotermes (Isoptera, Termitinae). Insectes Soc. 25, 53–62 (1978).Article 

Google Scholar 
Du, H., Chouvenc, T. & Su, N.-Y. Development of age polyethism with colony maturity in Coptotermes formosanus (Isoptera: Rhinotermitidae). Environ. Entomol. 46, 311–318 (2017).PubMed 

Google Scholar 
Gerber, C., Badertscher, S. & Leuthold, R. H. Polyethism in Macrotermes bellicosus (Isoptera). Insectes Soc. 35, 226–240 (1988).Article 

Google Scholar 
Rosengaus, R. B. & Traniello, J. F. A. Temporal polyethism in incipient colonies of the primitive termite Zootermopsis angusticollis: A single multiage caste. J. Insect Behav. 6, 237–252 (1993).Article 

Google Scholar 
Crosland, M. W. J., Lok, C. M., Wong, T. C., Shakarad, M. & Traniello, J. F. A. Division of labour in a lower termite: The majority of tasks are performed by older workers. Anim. Behav. 54, 999–1012 (1997).CAS 
PubMed 
Article 

Google Scholar 
Miura, T. & Matsumoto, T. Foraging organization of the open-air processional lichen-feeding termite Hospitalitermes (Isoptera, Termitidae) in Borneo. Insectes Soc. 45, 17–32 (1998).Article 

Google Scholar 
Hinze, B. & Leuthold, R. H. Age related polyethism and activity rhythms in the nest of the termite Macrotermes bellicosus (Isoptera, Termitidae). Insectes Soc. 46, 392–397 (1999).Article 

Google Scholar 
Konate, S., Leuthold, R., Hari, M. & Veivers, P. Colour variation and polyethism of the soldier caste in the termite Macrotermes bellicosus. Entomol. Exp. Appl. 94, 51–55 (2000).Article 

Google Scholar 
Yang, R.-L., Su, N.-Y. & Bardunias, P. Individual task load in tunnel excavation by the Formosan subterranean termite (Isoptera: Rhinotermitidae). Ann. Entomol. Soc. Am. 102, 906–910 (2009).Article 

Google Scholar 
Yanagihara, S., Suehiro, W., Mitaka, Y. & Matsuura, K. Age-based soldier polyethism: Old termite soldiers take more risks than young soldiers. Biol. Lett. 14, 20180025 (2018).PubMed 
PubMed Central 
Article 

Google Scholar 
Su, N. & Scheffrahn, R. H. Foraging population and territory of the Formosan subterranean termite (Isoptera, Rhinotermitidae) in an urban-environment. Sociobiology 14, 353–360 (1988).
Google Scholar 
King, E. G. & Spink, W. T. Foraging galleries of the Formosan subterranean termite, Coptotermes formosanus, in Louisiana. Ann. Entomol. Soc. Am. 62, 536–542 (1969).Article 

Google Scholar 
Abe, T. Evolution of life types in termites. In Evolution and Coadaptation in Biotic Communities (eds. J.H. Connell and J. Hidaka) 125-148 (University of Tokyo Press, 1987)Shellman-Reeve, J. S. The Spectrum of Eusociality in Termites. The Evolution of Social Behavior in Insects and Arachnids (Cambridge University Press, 1997).
Google Scholar 
Legendre, F. et al. The phylogeny of termites (Dictyoptera: Isoptera) based on mitochondrial and nuclear markers: Implications for the evolution of the worker and pseudergate castes, and foraging behaviors. Mol. Phylogenet. Evol. 48, 615–627 (2008).CAS 
PubMed 
Article 

Google Scholar 
Du, H., Chouvenc, T., Osbrink, W. L. A. & Su, N. Y. Heterogeneous distribution of castes/instars and behaviors in the nest of Coptotermes formosanus Shiraki. Insectes Soc. 64, 103–112 (2017).Article 

Google Scholar 
Su, N. Y., Osbrink, W., Kakkar, G., Mullins, A. & Chouvenc, T. Foraging distance and population size of juvenile colonies of the Formosan subterranean termite (Isoptera: Rhinotermitidae) in laboratory extended arenas. J. Econ. Entomol. 110, 1728–1735 (2017).PubMed 
Article 

Google Scholar 
Osbrink, W. L. A., Cornelius, M. L. & Lax, A. R. Effects of flooding on field populations of Formosan subterranean termites (Isoptera: Rhinotermitidae) in New Orleans, Louisiana. J. Econ. Entomol. 101, 1367–1372 (2008).PubMed 
Article 

Google Scholar 
Tuma, J., Eggleton, P. & Fayle, T. M. Ant-termite interactions: An important but under-explored ecological linkage. Biol. Rev. 95, 555–572 (2020).PubMed 
Article 

Google Scholar 
Rust, M. K. & Su, N.-Y. Managing social insects of urban importance. Annu. Rev. Entomol. 57, 355–375 (2012).CAS 
PubMed 
Article 

Google Scholar 
Evans, T. A., Forschler, B. T. & Grace, J. K. Biology of invasive termites: A worldwide review. Annu. Rev. Entomol. 58, 455–474 (2013).CAS 
PubMed 
Article 

Google Scholar 
Beverly, B. D., McLendon, H., Nacu, S., Holmes, S. & Gordon, D. M. How site fidelity leads to individual differences in the foraging activity of harvester ants. Behav. Ecol. 20, 633–638 (2009).Article 

Google Scholar 
Tenczar, P., Lutz, C. C., Rao, V. D., Goldenfeld, N. & Robinson, G. E. Automated monitoring reveals extreme interindividual variation and plasticity in honeybee foraging activity levels. Anim. Behav. 95, 41–48 (2014).Article 

Google Scholar 
O’Donnell, S. Effects of experimental forager removals on division of labour in the primitively eusocial wasp Polistes instabilis (Hymenoptera: Vespidae). Behaviour 135, 173–193 (1998).Article 

Google Scholar 
Crall, J. D. et al. Spatial fidelity of workers predicts collective response to disturbance in a social insect. Nat. Commun. 9, 1–13 (2018).Article 

Google Scholar 
Charbonneau, D. & Dornhaus, A. When doing nothing is something. How task allocation strategies compromise between flexibility, efficiency, and inactive agents. J. Bioeconomics 17, 217–242 (2015).Article 

Google Scholar 
Gordon, D. M. The regulation of foraging activity in red harvester ant colonies. Am. Nat. 159, 509–518 (2002).PubMed 
Article 

Google Scholar 
O’Donnell, S. Polybia wasp biting interactions recruit foragers following experimental worker removals. Anim. Behav. 71, 709–715 (2006).Article 

Google Scholar 
Gentry, J. B. Response to predation by colonies of the Florida harvester ant, Pogonomyrmex badius. Ecology 55, 1328–1338 (1974).Article 

Google Scholar 
Schafer, R. J., Holmes, S. & Gordon, D. M. Forager activation and food availability in harvester ants. Anim. Behav. 71, 815–822 (2006).Article 

Google Scholar 
Tschinkel, W. R. Biomantling and bioturbation by colonies of the Florida harvester ant, Pogonomyrmex badius. PLoS ONE 10, e0120407 (2015).PubMed 
PubMed Central 
Article 

Google Scholar 
Kwapich, C. L. & Tschinkel, W. R. Demography, demand, death, and the seasonal allocation of labor in the Florida harvester ant (Pogonomyrmex badius). Behav. Ecol. Sociobiol. 67, 2011–2027 (2013).Article 

Google Scholar 
Perry, C. J., Søvik, E., Myerscough, M. R. & Barron, A. B. Rapid behavioral maturation accelerates failure of stressed honey bee colonies. Proc. Natl. Acad. Sci. 112, 3427–3432 (2015).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Vance, J. T., Williams, J. B., Elekonich, M. M. & Roberts, S. P. The effects of age and behavioral development on honey bee (Apis mellifera) flight performance. J. Exp. Biol. 212, 2604–2611 (2009).PubMed 
PubMed Central 
Article 

Google Scholar 
Nalepa, C. A. Body size and termite evolution. Evol. Biol. 38, 243–257 (2011).Article 

Google Scholar 
Chouvenc, T. & Su, N. Y. Colony age-dependent pathway in caste development of Coptotermes formosanus Shiraki. Insectes Soc. 61, 171–182 (2014).Article 

Google Scholar 
Robinson, G. E., Page, R. E. Jr. & Huang, Z. Y. Temporal polyethism in social insects is a developmental process. Anim. Behav. 48, 467–469 (1994).Article 

Google Scholar 
Kakkar, G., Chouvenc, T., Osbrink, W. & Su, N. Y. Temporal assessment of molting in workers of Formosan subterranean termites (Isoptera: Rhinotermitidae). J. Econ. Entomol. 109, 2175–2181 (2016).CAS 
PubMed 
Article 

Google Scholar 
Kakkar, G., Osbrink, W., Mullins, A. & Su, N. Y. Molting site fidelity in workers of Formosan subterranean termites (Isoptera: Rhinotermitidae). J. Econ. Entomol. https://doi.org/10.1093/jee/tox246 (2017).Article 
PubMed 

Google Scholar 
Raina, A., Park, Y. I. & Gelman, D. Molting in workers of the Formosan subterranean termite Coptotermes formosanus. J. Insect Physiol. 54, 155–161 (2008).CAS 
PubMed 
Article 

Google Scholar 
Lee, S.-B., Chouvenc, T. & Su, N.-Y. Differential time allocation of foraging workers in the subterranean termite. Front. Zool. 18, 1–8 (2021).Article 

Google Scholar 
Lee, S.-B., Chouvenc, T. & Su, N.-Y. A reproductives excluder for subterranean termites in laboratory experiments. J. Econ. Entomol. 112, 2882–2887 (2019).PubMed 
Article 

Google Scholar 
Team, R. C. R: A language and environment for statistical computing. (2022). More