Philippa Kaur

Humpenöder, F. et al. Projected environmental benefits of replacing beef with microbial protein. Nature 605, 90–96 (2022).Article 

Google Scholar 
Tilman, D. & Clark, M. Global diets link environmental sustainability and human health. Nature 515, 518–522 (2014).Article 
CAS 

Google Scholar 
Clark, M. A. et al. Global food system emissions could preclude achieving the 1.5° and 2°C climate change targets. Science 370, 705–708 (2020).Article 
CAS 

Google Scholar 
Eisler, M. C. et al. Agriculture: steps to sustainable livestock. Nature 507, 32–34 (2014).Article 

Google Scholar 
Kamke, J. et al. Rumen metagenome and metatranscriptome analyses of low methane yield sheep reveals a Sharpea-enriched microbiome characterised by lactic acid formation and utilisation. Microbiome 4, 56 (2016).Article 

Google Scholar 
Kruger Ben Shabat, S. et al. Specific microbiome-dependent mechanisms underlie the energy harvest efficiency of ruminants. ISME J. 10, 2958–2972 (2016).Article 

Google Scholar 
Janssen, P. H. Influence of hydrogen on rumen methane formation and fermentation balances through microbial growth kinetics and fermentation thermodynamics. Anim. Feed Sci. Technol. 160, 1–22 (2010).Article 
CAS 

Google Scholar 
Wallace, R. J. et al. A heritable subset of the core rumen microbiome dictates dairy cow productivity and emissions. Sci. Adv. 5, eaav8391 (2019).Article 
CAS 

Google Scholar 
Urrutia, N. L. & Harvatine, K. J. Acetate dose-dependently stimulates milk fat synthesis in lactating dairy cows. J. Nutr. 147, 763–769 (2017).Article 
CAS 

Google Scholar 
Seshadri, R. et al. Cultivation and sequencing of rumen microbiome members from the Hungate1000 Collection. Nat. Biotechnol. 36, 359–367 (2018).Article 
CAS 

Google Scholar 
Anderson, C. J., Koester, L. R. & Schmitz-Esser, S. Rumen epithelial communities share a core bacterial microbiota: a meta-analysis of 16S rRNA Gene Illumina MiSeq sequencing datasets. Front. Microbiol. 12, 625400 (2021).Wallace, R. J., Cheng, K.-J., Dinsdale, D. & Ørskov, E. R. An independent microbial flora of the epithelium and its role in the ecomicrobiology of the rumen. Nature 279, 424–426 (1979).Article 
CAS 

Google Scholar 
Mann, E., Wetzels, S. U., Wagner, M., Zebeli, Q. & Schmitz-Esser, S. Metatranscriptome sequencing reveals insights into the gene expression and functional potential of rumen wall bacteria. Front. Microbiol. 9, 43 (2018).Pacífico, C. et al. Unveiling the bovine epimural microbiota composition and putative function. Microorganisms 9, 342 (2021).Article 

Google Scholar 
VanInsberghe, D., Arevalo, P., Chien, D. & Polz, M. F. How can microbial population genomics inform community ecology?. Phil. Trans. R. Soc. B 375, 20190253 (2020).Article 

Google Scholar 
Hunt, D. E. et al. Resource partitioning and sympatric differentiation among closely related bacterioplankton. Science 320, 1081–1085 (2008).Article 
CAS 

Google Scholar 
Fraser, C., Hanage, W. P. & Spratt, B. G. Recombination and the nature of bacterial speciation. Science 315, 476–480 (2007).Article 
CAS 

Google Scholar 
Shapiro, B. J. et al. Population genomics of early events in the ecological differentiation of bacteria. Science 335, 48–51 (2012).Article 

Google Scholar 
Cadillo-Quiroz, H. et al. Patterns of gene flow define species of thermophilic Archaea. PLoS Biol. 10, e1001265 (2012).Article 
CAS 

Google Scholar 
Koeppel, A. et al. Identifying the fundamental units of bacterial diversity: a paradigm shift to incorporate ecology into bacterial systematics. Proc. Natl Acad. Sci. USA 105, 2504–2509 (2008).Article 
CAS 

Google Scholar 
Arevalo, P., VanInsberghe, D., Elsherbini, J., Gore, J. & Polz, M. F. A reverse ecology approach based on a biological definition of microbial populations. Cell 178, 820–834.e14 (2019).Article 
CAS 

Google Scholar 
Wetzels, S. U. et al. Epimural bacterial community structure in the rumen of Holstein cows with different responses to a long-term subacute ruminal acidosis diet challenge. J. Dairy Sci. 100, 1829–1844 (2017).Article 
CAS 

Google Scholar 
Neubauer, V. et al. Effects of clay mineral supplementation on particle-associated and epimural microbiota, and gene expression in the rumen of cows fed high-concentrate diet. Anaerobe 59, 38–48 (2019).Article 
CAS 

Google Scholar 
Stewart, R. D. et al. Compendium of 4,941 rumen metagenome-assembled genomes for rumen microbiome biology and enzyme discovery. Nat. Biotechnol. 37, 953–961 (2019).Article 
CAS 

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

Google Scholar 
Rodriguez-R, L. M. & Konstantinidis, K. T. Bypassing cultivation to identify bacterial species. Microbe Mag. 9, 111–118 (2014).Article 

Google Scholar 
Bendall, M. L. et al. Genome-wide selective sweeps and gene-specific sweeps in natural bacterial populations. ISME J. 10, 1589–1601 (2016).Article 

Google Scholar 
Birky, C. W., Adams, J., Gemmel, M. & Perry, J. Using population genetic theory and DNA sequences for species detection and identification in asexual organisms. PLoS ONE 5, e10609 (2010).Article 

Google Scholar 
Li, W.-H. Unbiased estimation of the rates of synonymous and nonsynonymous substitution. J. Mol. Evol. 36, 96–99 (1993).Article 
CAS 

Google Scholar 
Novichkov, P. S., Wolf, Y. I., Dubchak, I. & Koonin, E. V. Trends in prokaryotic evolution revealed by comparison of closely related bacterial and archaeal genomes. J. Bacteriol. 191, 65–73 (2009).Article 
CAS 

Google Scholar 
Tilman, D. Resource competition between plankton algae: an experimental and theoretical approach. Ecology 58, 338–348 (1977).Article 
CAS 

Google Scholar 
Yawata, Y. et al. Competition–dispersal tradeoff ecologically differentiates recently speciated marine bacterioplankton populations. Proc. Natl Acad. Sci. USA 111, 5622–5627 (2014).Article 
CAS 

Google Scholar 
Basan, M. et al. A universal trade-off between growth and lag in fluctuating environments. Nature 584, 470–474 (2020).Article 
CAS 

Google Scholar 
Flamholz, A., Noor, E., Bar-Even, A., Liebermeister, W. & Milo, R. Glycolytic strategy as a tradeoff between energy yield and protein cost. Proc. Natl Acad. Sci. USA 110, 10039–10044 (2013).Article 
CAS 

Google Scholar 
Szymanski, C. M., Yao, R., Ewing, C. P., Trust, T. J. & Guerry, P. Evidence for a system of general protein glycosylation in Campylobacter jejuni. Mol. Microbiol. 32, 1022–1030 (1999).Article 
CAS 

Google Scholar 
Roux, D. et al. Identification of poly-N-acetylglucosamine as a major polysaccharide component of the Bacillus subtilis biofilm matrix. J. Biol. Chem. 290, 19261–19272 (2015).Article 
CAS 

Google Scholar 
Troutman, J. M. & Imperiali, B. Campylobacter jejuni PglH is a single active site processive polymerase that utilizes product inhibition to limit sequential glycosyl transfer reactions. Biochemistry 48, 2807–2816 (2009).Article 
CAS 

Google Scholar 
Hehemann, J. H. et al. Adaptive radiation by waves of gene transfer leads to fine-scale resource partitioning in marine microbes. Nat. Commun. 7, 12860 (2016).Article 
CAS 

Google Scholar 
Treangen, T. J. & Rocha, E. P. C. Horizontal transfer, not duplication, drives the expansion of protein families in prokaryotes. PLoS Genet. 7, e1001284 (2011).Article 
CAS 

Google Scholar 
Castric, P. pilO, a gene required for glycosylation of Pseudomonas aeruginosa 1244 pilin. Microbiology 141, 1247–1254 (1995).Article 
CAS 

Google Scholar 
Mourkas, E. et al. Host ecology regulates interspecies recombination in bacteria of the genus Campylobacter. eLife 11, e73552 (2022).Article 
CAS 

Google Scholar 
Sheppard, S. K. et al. Genome-wide association study identifies vitamin B 5 biosynthesis as a host specificity factor in Campylobacter. Proc. Natl Acad. Sci. USA 110, 11923–11927 (2013).Article 
CAS 

Google Scholar 
Bobay, L.-M. & Ochman, H. Biological species are universal across life’s domains. Genome Biol. Evol. https://doi.org/10.1093/gbe/evx026 (2017).Dieho, K. et al. Morphological adaptation of rumen papillae during the dry period and early lactation as affected by rate of increase of concentrate allowance. J. Dairy Sci. 99, 2339–2352 (2016).Article 
CAS 

Google Scholar 
Lawson, C. E. et al. Autotrophic and mixotrophic metabolism of an anammox bacterium revealed by in vivo 13C and 2H metabolic network mapping. ISME J. 15, 673–687 (2021).Article 
CAS 

Google Scholar 
Kwong, W. K., Zheng, H. & Moran, N. A. Convergent evolution of a modified, acetate-driven TCA cycle in bacteria. Nat. Microbiol. 2, 17067 (2017).Article 
CAS 

Google Scholar 
Kather, B., Stingl, K., van der Rest, M. E., Altendorf, K. & Molenaar, D. Another unusual type of citric acid cycle enzyme in Helicobacter pylori: the malate:quinone oxidoreductase. J. Bacteriol. 182, 3204–3209 (2000).Article 
CAS 

Google Scholar 
Mullins, E. A. & Kappock, T. J. Crystal structures of Acetobacter aceti succinyl-coenzyme A (CoA):acetate CoA-transferase reveal specificity determinants and illustrate the mechanism used by class I CoA-transferases. Biochemistry 51, 8422–8434 (2012).Article 
CAS 

Google Scholar 
Letten, A. D., Hall, A. R. & Levine, J. M. Using ecological coexistence theory to understand antibiotic resistance and microbial competition. Nat. Ecol. Evol. 5, 431–441 (2021).Article 

Google Scholar 
Park, S. Y. et al. Strain-level fitness in the gut microbiome is an emergent property of glycans and a single metabolite. Cell 185, 513–529.e21 (2022).Article 
CAS 

Google Scholar 
Kim, C. H. Control of lymphocyte functions by gut microbiota-derived short-chain fatty acids. Cell Mol. Immunol. 18, 1161–1171 (2021).Article 
CAS 

Google Scholar 
Morrison, D. J. & Preston, T. Formation of short chain fatty acids by the gut microbiota and their impact on human metabolism. Gut Microbes 7, 189–200 (2016).Article 

Google Scholar 
Frampton, J., Murphy, K. G., Frost, G. & Chambers, E. S. Short-chain fatty acids as potential regulators of skeletal muscle metabolism and function. Nat. Metab. 2, 840–848 (2020).Article 
CAS 

Google Scholar 
Good, B. H., McDonald, M. J., Barrick, J. E., Lenski, R. E. & Desai, M. M. The dynamics of molecular evolution over 60,000 generations. Nature 551, 45–50 (2017).Article 

Google Scholar 
Lang, G. I. et al. Pervasive genetic hitchhiking and clonal interference in forty evolving yeast populations. Nature 500, 571–574 (2013).Article 
CAS 

Google Scholar 
Shapiro, B. J. & Polz, M. F. Microbial speciation. Cold Spring Harb. Perspect. Biol. 7, a018143 (2015).Article 

Google Scholar 
Sheppard, S. K. et al. Evolution of an agriculture-associated disease causing Campylobacter coli clade: evidence from national surveillance data in Scotland. PLoS ONE 5, e15708 (2010).Article 
CAS 

Google Scholar 
Callahan, B. J. et al. DADA2: high-resolution sample inference from Illumina amplicon data. Nat. Methods 13, 581–583 (2016).Article 
CAS 

Google Scholar 
Wang, Q., Garrity, G. M., Tiedje, J. M. & Cole, J. R. Naive Bayesian classifier for rapid assignment of rRNA sequences into the new bacterial taxonomy. Appl. Environ. Microbiol. 73, 5261–5267 (2007).Article 
CAS 

Google Scholar 
Pacífico, C. et al. Bovine rumen epithelial miRNA–mRNA dynamics reveals post-transcriptional regulation of gene expression upon transition to high-grain feeding and phytogenic supplementation. Genomics 114, 110333 (2022).Article 

Google Scholar 
Rivera-Chacon, R. et al. Supplementing a phytogenic feed additive modulates the risk of subacute rumen acidosis, rumen fermentation and systemic inflammation in cattle fed acidogenic diets. Animals 12, 1201 (2022).Article 

Google Scholar 
Bolger, A. M., Lohse, M. & Usadel, B. Trimmomatic: a flexible trimmer for Illumina sequence data. Bioinformatics 30, 2114–2120 (2014).Article 
CAS 

Google Scholar 
Bankevich, A. et al. SPAdes: a new genome assembly algorithm and its applications to single-cell sequencing. J. Comput. Biol. 19, 455–477 (2012).Article 
CAS 

Google Scholar 
Li, H. Aligning sequence reads, clone sequences and assembly contigs with BWA-MEM. Preprint at https://arxiv.org/abs/1303.3997 (2013).Parks, D. H., Imelfort, M., Skennerton, C. T., Hugenholtz, P. & Tyson, G. W. CheckM: assessing the quality of microbial genomes recovered from isolates, single cells, and metagenomes. Genome Res. 25, 1043–1055 (2015).Article 
CAS 

Google Scholar 
Seemann, T. Prokka: rapid prokaryotic genome annotation. Bioinformatics 30, 2068–2069 (2014).Article 
CAS 

Google Scholar 
Putri, G. H., Anders, S., Pyl, P. T., Pimanda, J. E. & Zanini, F. Analysing high-throughput sequencing data in Python with HTSeq 2.0. Bioinformatics 38, 2943–2945 (2022).Article 
CAS 

Google Scholar 
O’doherty, A. et al. Development of nalidixic acid amphotericin B vancomycin (NAV) medium for the isolation of Campylobacter ureolyticus from the stools of patients presenting with acute gastroenteritis. Br. J. Biomed. Sci. 71, 6–12 (2014).Article 

Google Scholar 
Schmieder, R. & Edwards, R. Quality control and preprocessing of metagenomic datasets. Bioinformatics 27, 863–864 (2011).Article 
CAS 

Google Scholar 
Karst, S. M., Kirkegaard, R. H. & Albertsen, M. mmgenome: a toolbox for reproducible genome extraction from metagenomes. Preprint at bioRxiv https://doi.org/10.1101/059121 (2014).Edgar, R. C. MUSCLE: multiple sequence alignment with high accuracy and high throughput. Nucleic Acids Res. 32, 1792–1797 (2004).Article 
CAS 

Google Scholar 
Jain, C., Rodriguez-R, L. M., Phillippy, A. M., Konstantinidis, K. T. & Aluru, S. High throughput ANI analysis of 90K prokaryotic genomes reveals clear species boundaries. Nat. Commun. 9, 5114 (2018).Article 

Google Scholar 
Jukes, T. H. & Cantor, C. R. in Mammalian Protein Metabolism (ed. Munro, H. N.) 21–132 (Elsevier, 1969).Guindon, S. et al. New algorithms and methods to estimate maximum-likelihood phylogenies: assessing the performance of PhyML 3.0. Syst. Biol. 59, 307–321 (2010).Article 
CAS 

Google Scholar 
Nguyen, L.-T., Schmidt, H. A., von Haeseler, A. & Minh, B. Q. IQ-TREE: a fast and effective stochastic algorithm for estimating maximum-likelihood phylogenies. Mol. Biol. Evol. 32, 268–274 (2015).Article 
CAS 

Google Scholar 
Kalyaanamoorthy, S., Minh, B. Q., Wong, T. K. F., von Haeseler, A. & Jermiin, L. S. ModelFinder: fast model selection for accurate phylogenetic estimates. Nat. Methods 14, 587–589 (2017).Article 
CAS 

Google Scholar 
Hoang, D. T., Chernomor, O., von Haeseler, A., Minh, B. Q. & Vinh, L. S. UFBoot2: improving the ultrafast bootstrap approximation. Mol. Biol. Evol. 35, 518–522 (2018).Article 
CAS 

Google Scholar 
Altschul, S. F., Gish, W., Miller, W., Myers, E. W. & Lipman, D. J. Basic local alignment search tool. J. Mol. Biol. 215, 403–410 (1990).Article 
CAS 

Google Scholar 
Le, S. Q. & Gascuel, O. An improved general amino acid replacement matrix. Mol. Biol. Evol. 25, 1307–1320 (2008).Article 
CAS 

Google Scholar 
Danecek, P. et al. Twelve years of SAMtools and BCFtools. Gigascience 10, giab008 (2021).Article 

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

Google Scholar 
Love, M. I., Huber, W. & Anders, S. Moderated estimation of fold change and dispersion for RNA-seq data with DESeq2. Genome Biol. 15, 550 (2014).Article 

Google Scholar 
Tan, R. S. G., Zhou, M., Li, F. & Guan, L. L. Identifying active rumen epithelial associated bacteria and archaea in beef cattle divergent in feed efficiency using total RNA-seq. Curr. Res. Microbial Sci. 2, 100064 (2021).Article 
CAS 

Google Scholar 
Chaumeil, P.-A., Mussig, A. J., Hugenholtz, P. & Parks, D. H. GTDB-Tk: a toolkit to classify genomes with the Genome Taxonomy Database. Bioinformatics https://doi.org/10.1093/bioinformatics/btz848 (2019).Brewer, M. T., Anderson, K. L., Yoon, I., Scott, M. F. & Carlson, S. A. Amelioration of salmonellosis in pre-weaned dairy calves fed Saccharomyces cerevisiae fermentation products in feed and milk replacer. Vet. Microbiol. 172, 248–255 (2014).Article 

Google Scholar  More

Peaucelle, M. et al. Spatial variance of spring phenology in temperate deciduous forests is constrained by background climatic conditions. Nat. Commun. 10, 5388 (2019).Article 

Google Scholar 
Hopkins, A. D. The bioclimatic law. Mon. Weather Rev. 48, 355–355 (1920).Article 

Google Scholar 
Piao, S. et al. Plant phenology and global climate change: current progresses and challenges. Glob. Change Biol. 25, 1922–1940 (2019).Article 

Google Scholar 
Ge, Q., Wang, H., Rutishauser, T. & Dai, J. Phenological response to climate change in China: a meta-analysis. Glob. Change Biol. 21, 265–274 (2015).Article 

Google Scholar 
Templ, B. et al. Pan European Phenological database (PEP725): a single point of access for European data. Int. J. Biometeorol. 62, 1109–1113 (2018).Article 

Google Scholar 
Richardson, A. D. et al. Climate change, phenology, and phenological control of vegetation feedbacks to the climate system. Agric. For. Meteorol. 169, 156–173 (2013).Article 

Google Scholar 
Morisette, J. T. et al. Tracking the rhythm of the seasons in the face of global change: phenological research in the 21st century. Front. Ecol. Environ. 7, 253–260 (2009).Article 

Google Scholar 
Flynn, D. F. B. & Wolkovich, E. M. Temperature and photoperiod drive spring phenology across all species in a temperate forest community. New Phytol. 219, 1353–1362 (2018).Article 
CAS 

Google Scholar 
Peñuelas, J., Rutishauser, T. & Filella, I. Phenology feedbacks on climate change. Science 324, 887–888 (2009).Article 

Google Scholar 
Körner, C. & Basler, D. Plant science. Phenol. Glob. Warm. Sci. 327, 1461–1462 (2010).
Google Scholar 
Delpierre, N. et al. Temperate and boreal forest tree phenology: from organ-scale processes to terrestrial ecosystem models. Ann. For. Sci. 73, 5–25 (2016).Article 

Google Scholar 
Klosterman, S. T. et al. Evaluating remote sensing of deciduous forest phenology at multiple spatial scales using PhenoCam imagery. Biogeosciences 11, 4305–4320 (2014).Article 

Google Scholar 
Hufkens, K. et al. Linking near-surface and satellite remote sensing measurements of deciduous broadleaf forest phenology. Remote Sens. Environ. 117, 307–321 (2012).Article 

Google Scholar 
Garrity, S. R. et al. A comparison of multiple phenology data sources for estimating seasonal transitions in deciduous forest carbon exchange. Agric. For. Meteorol. 151, 1741–1752 (2011).Article 

Google Scholar 
Fracheboud, Y. et al. The control of autumn senescence in European aspen. Plant Physiol. 149, 1982–1991 (2009).Article 
CAS 

Google Scholar 
Mariën, B. et al. Does drought advance the onset of autumn leaf senescence in temperate deciduous forest trees? Biogeosciences 18, 3309–3330 (2021).Article 

Google Scholar 
Fu, Y. H. et al. Larger temperature response of autumn leaf senescence than spring leaf-out phenology. Glob. Change Biol. 24, 2159–2168 (2018).Article 

Google Scholar 
Menzel, A., Sparks, T. H., Estrella, N. & Roy, D. B. Altered geographic and temporal variability in phenology in response to climate change. Glob. Ecol. Biogeogr. 15, 498–504 (2006).Article 

Google Scholar 
Gordo, O. & Sanz, J. J. Long-term temporal changes of plant phenology in the Western Mediterranean. Glob. Change Biol. 15, 1930–1948 (2009).Article 

Google Scholar 
Meier, M., Vitasse, Y., Bugmann, H. & Bigler, C. Phenological shifts induced by climate change amplify drought for broad-leaved trees at low elevations in Switzerland. Agric. For. Meteorol. 307, 108485 (2021).Basler, D. Evaluating phenological models for the prediction of leaf-out dates in six temperate tree species across central Europe. Agric. For. Meteorol. 217, 10–21 (2016).Article 

Google Scholar 
Keenan, T. F. et al. Terrestrial biosphere model performance for inter-annual variability of land–atmosphere CO2 exchange. Glob. Change Biol. 18, 1971–1987 (2012).Article 

Google Scholar 
Liu, G., Chen, X., Fu, Y. & Delpierre, N. Modelling leaf coloration dates over temperate China by considering effects of leafy season climate. Ecol. Modell. 394, 34–43 (2019).Article 

Google Scholar 
Keenan, T. F. & Richardson, A. D. The timing of autumn senescence is affected by the timing of spring phenology: implications for predictive models. Glob. Change Biol. 21, 2634–2641 (2015).Article 

Google Scholar 
Wu, C., Hou, X., Peng, D., Gonsamo, A. & Xu, S. Land surface phenology of China’s temperate ecosystems over 1999–2013: spatial–temporal patterns, interaction effects, covariation with climate and implications for productivity. Agric. For. Meteorol. 216, 177–187 (2016).Article 

Google Scholar 
Fu, Y. S. H. et al. Variation in leaf flushing date influences autumnal senescence and next year’s flushing date in two temperate tree species. Proc. Natl Acad. Sci. USA 111, 7355–7360 (2014).Article 
CAS 

Google Scholar 
Zani, D., Crowther, T. W., Mo, L., Renner, S. S. & Zohner, C. M. Increased growing-season productivity drives earlier autumn leaf senescence in temperate trees. Science 370, 1066–1071 (2020).Article 
CAS 

Google Scholar 
Paul, M. J. & Foyer, C. H. Sink regulation of photosynthesis. J. Exp. Bot. 52, 1383–1400 (2001).Article 
CAS 

Google Scholar 
Herold, A. Regulation of photosynthesis by sink activity—the missing link. New Phytol. 86, 131–144 (1980).Article 
CAS 

Google Scholar 
Keenan, T. F. et al. Recent pause in the growth rate of atmospheric CO2 due to enhanced terrestrial carbon uptake. Nat. Commun. 7, 13428 (2016).Campbell, J. E. et al. Large historical growth in global terrestrial gross primary production. Nature 544, 84–87 (2017).Article 
CAS 

Google Scholar 
Schimel, D., Stephens, B. B. & Fisher, J. B. Effect of increasing CO2 on the terrestrial carbon cycle. Proc. Natl Acad. Sci.USA 112, 436–441 (2015).Article 
CAS 

Google Scholar 
Walker, A. P. et al. Integrating the evidence for a terrestrial carbon sink caused by increasing atmospheric CO. New Phytol. 229, 2413–2445 (2021).Article 
CAS 

Google Scholar 
Liu, Q. et al. Modeling leaf senescence of deciduous tree species in Europe. Glob. Change Biol. 26, 4104–4118 (2020).Article 

Google Scholar 
Friedl, M., Gray, J. & Sulla-Menashe, D. MCD12Q2 MODIS/Terra+Aqua Land Cover Dynamics Yearly L3 Global 500m SIN Grid V006 (NASA, 2019).Zhang, X. et al. Monitoring vegetation phenology using MODIS. Remote Sens. Environ. 84, 471–475 (2003).Article 

Google Scholar 
Stocker, B. D. et al. P-model v1.0: an optimality-based light use efficiency model for simulating ecosystem gross primary production. Geosci. Model Dev. 13, 1545–1581 (2020).Article 

Google Scholar 
Pastorello, G. et al. The FLUXNET2015 dataset and the ONEFlux processing pipeline for eddy covariance data. Sci. Data 7, 225 (2020).Article 

Google Scholar 
Sitch, S. et al. Evaluation of ecosystem dynamics, plant geography and terrestrial carbon cycling in the LPJ dynamic global vegetation model. Glob. Change Biol. 9, 161–185 (2003).Article 

Google Scholar 
Hänninen, H. & Tanino, K. Tree seasonality in a warming climate. Trends Plant Sci. 16, 412–416 (2011).Article 

Google Scholar 
Kikuzawa, K. & Lechowicz, M. J. Ecology of Leaf Longevity (Springer, 2011).Fu, Y. H. et al. Nutrient availability alters the correlation between spring leaf-out and autumn leaf senescence dates. Tree Physiol. 39, 1277–1284 (2019).Article 
CAS 

Google Scholar 
Lim, P. O., Kim, H. J. & Nam, H. G. Leaf senescence. Annu. Rev. Plant Biol. 58, 115–136 (2007).Article 
CAS 

Google Scholar 
Piao, S., Friedlingstein, P., Ciais, P., Viovy, N. & Demarty, J. Growing season extension and its impact on terrestrial carbon cycle in the Northern Hemisphere over the past 2 decades. Glob. Biogeochem. Cycles 21, GB3018 (2007).Jeong, S.-J., Ho, C.-H., Gim, H.-J. & Brown, M. E. Phenology shifts at start vs. end of growing season in temperate vegetation over the Northern Hemisphere for the period 1982–2008. Glob. Change Biol. 17, 2385–2399 (2011).Article 

Google Scholar 
Cong, N. et al. Changes in satellite-derived spring vegetation green-up date and its linkage to climate in China from 1982 to 2010: a multimethod analysis. Glob. Change Biol. 19, 881–891 (2013).Article 

Google Scholar 
Keenan, T. F. et al. Net carbon uptake has increased through warming-induced changes in temperate forest phenology. Nat. Clim. Change 4, 598–604 (2014).Article 
CAS 

Google Scholar 
Garonna, I., de Jong, R. & Schaepman, M. E. Variability and evolution of global land surface phenology over the past three decades (1982–2012). Glob. Change Biol. 22, 1456–1468 (2016).Article 

Google Scholar 
Smith, N. G. & Dukes, J. S. Plant respiration and photosynthesis in global-scale models: incorporating acclimation to temperature and CO2. Glob. Change Biol. 19, 45–63 (2013).Article 

Google Scholar 
Estiarte, M. & Peñuelas, J. Alteration of the phenology of leaf senescence and fall in winter deciduous species by climate change: effects on nutrient proficiency. Glob. Change Biol. 21, 1005–1017 (2015).Article 

Google Scholar 
Delpierre, N. et al. Modelling interannual and spatial variability of leaf senescence for three deciduous tree species in France. Agric. For. Meteorol. 149, 938–948 (2009).Article 

Google Scholar 
Chung, H. et al. Experimental warming studies on tree species and forest ecosystems: a literature review. J. Plant Res. 126, 447–460 (2013).Article 

Google Scholar 
Schaaf, C. B. et al. First operational BRDF, albedo nadir reflectance products from MODIS. Remote Sens. Environ. 83, 135–148 (2002).Article 

Google Scholar 
Tuck, S. L. et al. MODISTools—downloading and processing MODIS remotely sensed data in R. Ecol. Evol. 4, 4658–4668 (2014).Article 

Google Scholar 
Farquhar, G. D., von Caemmerer, S. & Berry, J. A. A biochemical model of photosynthetic CO2 assimilation in leaves of C3 species. Planta 149, 78–90 (1980).Article 
CAS 

Google Scholar 
Medlyn, B. E. et al. Reconciling the optimal and empirical approaches to modelling stomatal conductance. Glob. Change Biol. 17, 2134–2144 (2011).Article 

Google Scholar 
Stocker, B. rsofun: A modelling framework that implements the P-model for leaf-level acclimation of photosynthesis. R package version 4.3 https://github.com/computationales/rsofun (2020).Weedon, G. P. et al. The WFDEI meteorological forcing data set: WATCH Forcing Data methodology applied to ERA-Interim reanalysis data. Water Resour. Res. 50, 7505–7514 (2014).Article 

Google Scholar 
Meek, D. W., Hatfield, J. L., Howell, T. A., Idso, S. B. & Reginato, R. J. A generalized relationship between photosynthetically active radiation and solar radiation 1. Agron. J. 76, 939–945 (1984).Article 

Google Scholar 
Fick, S. E. & Hijmans, R. J. WorldClim 2: new 1‐km spatial resolution climate surfaces for global land areas. Int. J. Climatol. 37, 4302–4315 (2017).Article 

Google Scholar 
Stocker, B. ingestr: A tool to extract environmental point data from large global files or remote data servers. R package version 1.4 https://github.com/computationales/ingestr (2020).Wang, H. et al. Towards a universal model for carbon dioxide uptake by plants. Nat. Plants 3, 734–741 (2017).Article 
CAS 

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

Google Scholar 
R Core Team. R: A Language and Environment for Statistical Computing (R Foundation for Statistical Computing, 2021).Myneni, R., Knyazikhin, Y. & Park, T. MCD15A3H MODIS/Terra+Aqua Leaf Area Index/FPAR 4-day L4 Global 500m SIN Grid V006 (NASA EOSDIS Land Processes DAAC, 2015). More

Mace, G. M. et al. Aiming higher to bend the curve of biodiversity loss. Nat. Sustain. 1, 448–451 (2018).Article 

Google Scholar 
Díaz, S., et al. Pervasive human-driven decline of life on Earth points to the need for transformative change. Science 366, eaax3100 (2019).Díaz, S. et al. Set ambitious goals for biodiversity and sustainability. Science 370, 411 (2020).Article 

Google Scholar 
Locke, H., et al. A Nature-Positive World: The Global Goal for Nature (Wildlife Conservation Society, 2020); https://library.wcs.org/doi/ctl/view/mid/33065/pubid/DMX3974900000.aspxOpen-ended Working Group on the Post-2020 Global Biodiversity Framework. First Draft of the Post-2020 Global Biodiversity Framework CBD/WG2020/3/3 (Convention on Biological Diversity, 2021).Open-Ended Working Group on the Post-2020 Global Biodiversity Framework. Draft Recommendation Submitted by the Co-Chairs CBD/WG2020/4/L.2-ANNEX (Convention on Biological Diversity, 2022).Environment Act 2021 (UK) (HM Government, 2021); https://www.legislation.gov.uk/ukpga/2021/30/contents/enactedBull, J. W. & Strange, N. The global extent of biodiversity offset implementation under no net loss policies. Nat. Sustain. 1, 790–798 (2018).Article 

Google Scholar 
Prendeville, S., Cherim, E. & Bocken, N. Circular cities: mapping six cities in transition. Environ. Innov. Soc. Transit. 26, 171–194 (2018).de Silva, G. C., Regan, E. C., Pollard, E. H. B. & Addison, P. F. E. The evolution of corporate no net loss and net positive impact biodiversity commitments: understanding appetite and addressing challenges. Bus. Strategy Environ. 28, 1481–1495 (2019).Article 

Google Scholar 
zu Ermgassen, S. O. S. E. et al. Exploring the ecological outcomes of mandatory biodiversity net gain using evidence from early‐adopter jurisdictions in England. Conserv. Lett. 14, e12820 (2021).Article 

Google Scholar 
McGlyn, J., et al. Science-Based Targets for Nature: Initial Guidance for Business (Science Based Targets Network, 2020); https://sciencebasedtargetsnetwork.org/resource-repository/zu Ermgassen, S. O. S. E. et al. Are corporate biodiversity commitments consistent with delivering ‘nature-positive’ outcomes? A review of ‘nature-positive’ definitions, company progress and challenges. J. Clean. Prod. 379, 134798 (2022).Article 

Google Scholar 
Addison, P. F. E., Bull, J. W. & Milner‐Gulland, E. J. Using conservation science to advance corporate biodiversity accountability. Conserv. Biol. 33, 307–318 (2019).Article 

Google Scholar 
Smith, T. et al. Biodiversity means business: reframing global biodiversity goals for the private sector. Conserv. Lett. 13, e12690 (2020).Article 

Google Scholar 
Maron, M. et al. Setting robust biodiversity goals. Conserv. Lett. https://doi.org/10.1111/conl.12816 (2021).Newing, H. & Perram, A. What do you know about conservation and human rights? Oryx 53, 595–596 (2019).Article 

Google Scholar 
Standard on Biodiversity Offsets (The Business and Biodiversity Offsets Programme, 2012).Arlidge, W. N. S., et al. A mitigation hierarchy approach for managing sea turtle captures in small-scale fisheries. Front. Mar. Sci. 7, 49 (2020).Squires, D. & Garcia, S. The least-cost biodiversity impact mitigation hierarchy with a focus on marine fisheries and bycatch issues. Conserv. Biol. 32, 989–997 (2018).Article 

Google Scholar 
Booth, H., Squires, D. & Milner-Gulland, E. J. The mitigation hierarchy for sharks: a risk-based framework for reconciling trade-offs between shark conservation and fisheries objectives. Fish Fish. 21, 269–289 (2020).Article 

Google Scholar 
Gupta, T. et al. Mitigation of elasmobranch bycatch in trawlers: a case study in Indian fisheries. Front. Mari. Sci. 7, 571 (2020).Budiharta, S. et al. Restoration to offset the impacts of developments at a landscape scale reveals opportunities, challenges and tough choices. Global Environ. Change 52, 152–161 (2018).Article 

Google Scholar 
Bull, J. W. et al. Net positive outcomes for nature. Nat. Ecol. Evol. 4, 4–7 (2020).Article 

Google Scholar 
Arlidge, W. N. S. et al. A global mitigation hierarchy for nature conservation. BioScience 68, 336–347 (2018).Article 

Google Scholar 
Milner-Gulland, E. J. et al. Four steps for the Earth: mainstreaming the post-2020 global biodiversity framework. One Earth 4, 75–87 (2021).Article 
ADS 

Google Scholar 
Wolff, A., Gondran, N. & Brodhag, C. Detecting unsustainable pressures exerted on biodiversity by a company. Application to the food portfolio of a retailer. J. Clean. Prod. 166, 784–797 (2017).Article 

Google Scholar 
FAOSTAT Analytical Brief 15 Land Use and Land Cover Statistics: Global, Regional and Country Trends, 1990–2018 (FAO, 2020).Williams, D. R. et al. Proactive conservation to prevent habitat losses to agricultural expansion. Nat. Sustain. 4, 314–322 (2021).Article 

Google Scholar 
Leclère, D. et al. Bending the curve of terrestrial biodiversity needs an integrated strategy. Nature 585, 551–556 (2020).Article 
ADS 

Google Scholar 
Springmann, M. et al. Health and nutritional aspects of sustainable diet strategies and their association with environmental impacts: a global modelling analysis with country-level detail. Lancet Planet. Health 2, e451–e461 (2018).Article 

Google Scholar 
Clark, M. A., Springmann, M., Hill, J. & Tilman, D. Multiple health and environmental impacts of foods. Proc. Natl Acad. Sci. USA 116, 23357 (2019).Article 
ADS 
CAS 

Google Scholar 
Willett, W. et al. Food in the Anthropocene: the EAT–Lancet Commission on healthy diets from sustainable food systems. Lancet 393, 447–492 (2019).Article 

Google Scholar 
Poore, J. & Nemecek, T. Reducing food’s environmental impacts through producers and consumers. Science 360, 987 (2018).Article 
ADS 
CAS 

Google Scholar 
Wiedmann, T., Lenzen, M., Keyßer, L. T. & Steinberger, J. K. Scientists’ warning on affluence. Nat. Commun. 11, 3107 (2020).Article 
ADS 
CAS 

Google Scholar 
Benton, T. G. et al. A ‘net zero’ equivalent target is needed to transform food systems. Nat. Food 2, 905–906 (2021). 2021.Article 

Google Scholar 
Crenna, E., Sinkko, T. & Sala, S. Biodiversity impacts due to food consumption in Europe. J. Clean. Prod. 227, 378–391 (2019).Article 
CAS 

Google Scholar 
Bull, J. W., et al. Analysis: the biodiversity footprint of the University of Oxford. Nature 604, 420–424 (2022).Harrington, R. A., Adhikari, V., Rayner, M. & Scarborough, P. Nutrient composition databases in the age of big data: foodDB, a comprehensive, real-time database infrastructure. BMJ Open 9, e026652 (2019).Article 

Google Scholar 
Chaudhary, A., Verones, F., De Baan, L. & Hellweg, S. Quantifying land use impacts on biodiversity: combining species–area models and vulnerability indicators. Environ. Sci. Technol. 49, 9987–9995 (2015).Article 
ADS 
CAS 

Google Scholar 
Winter, L., Lehmann, A., Finogenova, N. & Finkbeiner, M. Including biodiversity in life cycle assessment—state of the art, gaps and research needs. Environ. Impact Assess. Rev. 67, 88–100 (2017).Article 

Google Scholar 
Chaudhary, A. & Kastner, T. Land use biodiversity impacts embodied in international food trade. Global Environ. Change 38, 195–204 (2016).Article 

Google Scholar 
Lenzen, M. et al. International trade drives biodiversity threats in developing nations. Nature 486, 109–112 (2012).Article 
ADS 
CAS 

Google Scholar 
Bates, B., et al. National Diet and Nutrition Survey Years 1 to 9 of the Rolling Programme (2008/2009–2016/2017): Time Trend and Income Analyses (Public Health England & Food Standards Agency, 2019).Stewart, C., Piernas, C., Cook, B. & Jebb, S. A. Trends in UK meat consumption: analysis of data from years 1–11 (2008–09 to 2018–19) of the National Diet and Nutrition Survey rolling programme. Lancet Planet. Health 5, e699–e708 (2021).Article 

Google Scholar 
Nielsen, K. S. et al. Improving climate change mitigation analysis: a framework for examining feasibility. One Earth 3, 325–336 (2020).Article 
ADS 

Google Scholar 
Selinske, M. J. et al. We have a steak in it: eliciting interventions to reduce beef consumption and its impact on biodiversity. Conserv. Lett. 13, e12721 (2020).Article 

Google Scholar 
Hollands, G. J. et al. The TIPPME intervention typology for changing environments to change behaviour. Nat. Hum. Behav. 1, 1–9 (2017).Article 

Google Scholar 
Marteau, T. M., Hollands, G. J. & Fletcher, P. C. Changing human behavior to prevent disease: the importance of targeting automatic processes. Science 337, 1492–1495 (2012).Article 
ADS 
CAS 

Google Scholar 
Michie, S., van Stralen, M. M. & West, R. The behaviour change wheel: a new method for characterising and designing behaviour change interventions. Implement. Sci. 6, 42 (2011).Article 

Google Scholar 
Moran, D., Giljum, S., Kanemoto, K. & Godar, J. From satellite to supply chain: new approaches connect earth observation to economic decisions. One Earth 3, 5–8 (2020).Article 
ADS 

Google Scholar 
Godar, J., Suavet, C., Gardner, T. A., Dawkins, E. & Meyfroidt, P. Balancing detail and scale in assessing transparency to improve the governance of agricultural commodity supply chains. Environ. Res. Lett. 11, 035015 (2016).Article 
ADS 

Google Scholar 
DeFries, R. S., Fanzo, J., Mondal, P., Remans, R. & Wood, S. A. Is voluntary certification of tropical agricultural commodities achieving sustainability goals for small-scale producers? A review of the evidence. Environ. Res. Lett. 12, 033001 (2017).Article 
ADS 

Google Scholar 
Bull, J. W., Suttle, K. B., Gordon, A., Singh, N. J. & Milner-Gulland, E. J. Biodiversity offsets in theory and practice. Oryx 47, 369–380 (2013).Article 

Google Scholar 
zu Ermgassen, S. O. S. E. et al. The ecological outcomes of biodiversity offsets under “no net loss” policies: a global review. Conserv. Lett. 12, e12664 (2019).Article 

Google Scholar 
Waddock, S. Achieving sustainability requires systemic business transformation. Glob. Sustain. 3, e12 (2020).Travers, H., Walsh, J., Vogt, S., Clements, T. & Milner-Gulland, E. J. Delivering behavioural change at scale: what conservation can learn from other fields. Biol. Conserv. 257, 109092 (2021).Article 

Google Scholar 
Gaupp, F. et al. Food system development pathways for healthy, nature-positive and inclusive food systems. Nat. Food 2, 928–934 (2021).Article 

Google Scholar 
Astill, J. et al. Transparency in food supply chains: a review of enabling technology solutions. Trends Food Sci. Technol. 91, 240–247 (2019).Article 
CAS 

Google Scholar 
Poore, J & Nemecek, T. Full Excel model: life-cycle environmental impacts of food drink products. Oxford University Research Archive https://ora.ox.ac.uk/objects/uuid:a63fb28c-98f8-4313-add6-e9eca99320a5 (2018).Clark, M., et al. Estimating the environmental impacts of 57,000 food products. Proc. Natl Acad. Sci. USA 119, e2120584119 (2022).Clark, M., et al. Supplemental Data for ‘Estimating the environmental impacts of 57,000 food products’. Oxford University Research Archive https://ora.ox.ac.uk/objects/uuid:4ad0b594-3e81-4e61-aefc-5d869c799a87 (2022).Bianchi, F., Dorsel, C., Garnett, E., Aveyard, P. & Jebb, S. A. Interventions targeting conscious determinants of human behaviour to reduce the demand for meat: a systematic review with qualitative comparative analysis. IJBNPA 15, 102 (2018).
Google Scholar 
Bianchi, F., Garnett, E., Dorsel, C., Aveyard, P. & Jebb, S. A. Restructuring physical micro-environments to reduce the demand for meat: a systematic review and qualitative comparative analysis. Lancet Planet. Health 2, e384–e397 (2018).Article 

Google Scholar 
Hillier-Brown, F. C. et al. The impact of interventions to promote healthier ready-to-eat meals (to eat in, to take away or to be delivered) sold by specific food outlets open to the general public: a systematic review. Obes. Rev. 18, 227–246 (2017).Article 
CAS 

Google Scholar 
von Philipsborn, P. et al. Environmental interventions to reduce the consumption of sugar-sweetened beverages and their effects on health. Cochrane Database Syst. Rev. 6, Cd012292 (2019).
Google Scholar 
Attwood, S., Voorheis, P., Mercer, C., Davies, K. & Vennard, D. Playbook for Guiding Diners toward Plant-Rich Dishes in Food Service (World Resources Institute, 2020); https://www.wri.org/research/playbook-guiding-diners-toward-plant-rich-dishes-food-serviceGarnett, E. E., Balmford, A., Sandbrook, C., Pilling, M. A. & Marteau, T. M. Impact of increasing vegetarian availability on meal selection and sales in cafeterias. Proc. Natl Acad. Sci. USA 116, 20923 (2019).Article 
ADS 
CAS 

Google Scholar 
Reinders, M. J., Huitink, M., Dijkstra, S. C., Maaskant, A. J. & Heijnen, J. Menu-engineering in restaurants—adapting portion sizes on plates to enhance vegetable consumption: a real-life experiment. IJBNPA 14, 41 (2017).
Google Scholar 
Brunner, F., Kurz, V., Bryngelsson, D. & Hedenus, F. Carbon label at a university restaurant—label implementation and evaluation. Ecol. Econ. 146, 658–667 (2018).Article 

Google Scholar 
McClain, A. D., Hekler, E. B. & Gardner, C. D. Incorporating prototyping and iteration into intervention development: a case study of a dining hall-based intervention. J. Am. Coll. Health 61, 122–131 (2013).Article 

Google Scholar 
de Vaan, J. Eating Less Meat: How to Stimulate the Choice for a Vegetarian Option without Inducing Reactance. MSc thesis, Radboud Univ. (2018). More

Reeburgh, W. S. Oceanic methane biogeochemistry. Chem. Rev. 107, 486–513 (2007).Article 
CAS 

Google Scholar 
Chadwick, G. L. et al. Comparative genomics reveals electron transfer and syntrophic mechanisms differentiating methanotrophic and methanogenic archaea. PLoS Biol. 20, e3001508 (2022).Article 

Google Scholar 
Haroon, M. F. et al. Anaerobic oxidation of methane coupled to nitrate reduction in a novel archaeal lineage. Nature 500, 567–570 (2013).Article 
CAS 

Google Scholar 
Hallam, S. J. et al. Reverse methanogenesis: testing the hypothesis with environmental genomics. Science 305, 1457–1462 (2004).Article 
CAS 

Google Scholar 
McGlynn, S. E. Energy metabolism during anaerobic methane oxidation in ANME Archaea. Microbes Environ. 32, 5–13 (2017).Article 

Google Scholar 
Beal, E. J., House, C. H. & Orphan, V. J. Manganese- and iron-dependent marine methane oxidation. Science 325, 184–187 (2009).Article 
CAS 

Google Scholar 
McGlynn, S. E., Chadwick, G. L., Kempes, C. P. & Orphan, V. J. Single cell activity reveals direct electron transfer in methanotrophic consortia. Nature 526, 531–535 (2015).Article 
CAS 

Google Scholar 
Wegener, G., Krukenberg, V., Riedel, D., Tegetmeyer, H. E. & Boetius, A. Intercellular wiring enables electron transfer between methanotrophic archaea and bacteria. Nature 526, 587–590 (2015).Article 
CAS 

Google Scholar 
Cai, C. et al. A methanotrophic archaeon couples anaerobic oxidation of methane to Fe(III) reduction. ISME J. 12, 1929–1939 (2018).Article 
CAS 

Google Scholar 
Ettwig, K. F. et al. Archaea catalyze iron-dependent anaerobic oxidation of methane. Proc. Natl Acad. Sci. USA 113, 12792–12796 (2016).Article 
CAS 

Google Scholar 
Leu, A. O. et al. Anaerobic methane oxidation coupled to manganese reduction by members of the Methanoperedenaceae. ISME J. 14, 1030–1041 (2020).Article 
CAS 

Google Scholar 
Leu, A. O. et al. Lateral gene transfer drives metabolic flexibility in the anaerobic methane-oxidizing archaeal family Methanoperedenaceae. mBio 11, e01325-20 (2020).Cai, C. et al. Response of the anaerobic methanotrophic archaeon Candidatus ‘Methanoperedens nitroreducens’ to the long-term ferrihydrite amendment. Front. Microbiol. 13, 799859 (2022).Arshad, A. et al. A metagenomics-based metabolic model of nitrate-dependent anaerobic oxidation of methane by Methanoperedens-like Archaea. Front. Microbiol. 6, 1423 (2015).Article 

Google Scholar 
Raghoebarsing, A. A. et al. A microbial consortium couples anaerobic methane oxidation to denitrification. Nature 440, 918–921 (2006).Article 
CAS 

Google Scholar 
Walker, D. J. F. et al. The archaellum of Methanospirillum hungatei is electrically conductive. mBio 10, e00579-19 (2019).Article 
CAS 

Google Scholar 
Krukenberg, V. et al. Gene expression and ultrastructure of meso- and thermophilic methanotrophic consortia. Environ. Microbiol. 20, 1651–1666 (2018).Article 
CAS 

Google Scholar 
Schubert, C. J. et al. Evidence for anaerobic oxidation of methane in sediments of a freshwater system (Lago di Cadagno). FEMS Microbiol. Ecol. 76, 26–38 (2011).Article 
CAS 

Google Scholar 
Stahl, D. A. & Amann, R. in Nucleic Acid Techniques in Bacterial Systematics (eds Stackebrandt, E. & Goodfellow, M.) 205–248 (Wiley, 1991).Wallner, G., Amann, R. & Beisker, W. Optimizing fluorescent in situ hybridization with rRNA-targeted oligonucleotide probes for flow cytometric identification of microorganisms. Cytometry 14, 136–143 (1993).Article 
CAS 

Google Scholar 
Bowers, R. M. et al. Minimum information about a single amplified genome (MISAG) and a metagenome- assembled genome (MIMAG) of Bacteria and Archaea. Nat. Biotechnol. 36, 660 (2018).Article 
CAS 

Google Scholar 
Vo, C. H., Goyal, N., Karimi, I. A. & Kraft, M. First observation of an acetate switch in a methanogenic autotroph (Methanococcus maripaludis S2). Microbiol. Insights 13, 1178636120945300 (2020).Article 

Google Scholar 
Cai, C. et al. Acetate production from anaerobic oxidation of methane via intracellular storage compounds. Environ. Sci. Technol. 53, 7371–7379 (2019).Article 
CAS 

Google Scholar 
Ratcliff, W. C. & Denison, R. F. Bacterial persistence and bet hedging in Sinorhizobium meliloti. Commun. Integr. Biol. 4, 98–100 (2011).Article 
CAS 

Google Scholar 
Ma, K., Schicho, R. N., Kelly, R. M. & Adams, M. W. Hydrogenase of the hyperthermophile Pyrococcus furiosus is an elemental sulfur reductase or sulfhydrogenase: evidence for a sulfur-reducing hydrogenase ancestor. Proc. Natl Acad. Sci. USA 90, 5341–5344 (1993).Article 
CAS 

Google Scholar 
Simon, G.-C. et al. Response of the anaerobic methanotroph “Candidatus Methanoperedens nitroreducens” to oxygen stress. Appl. Environ. Microbiol. 84, e01832-18 (2018).
Google Scholar 
van der Star, W. R. L. et al. The membrane bioreactor: a novel tool to grow anammox bacteria as free cells. Biotechnol. Bioeng. 101, 286–294 (2008).Article 

Google Scholar 
Duggin, I. G. et al. CetZ tubulin-like proteins control archaeal cell shape. Nature 519, 362–365 (2015).Article 
CAS 

Google Scholar 
Schwarzer, S., Rodriguez-Franco, M., Oksanen, H. M. & Quax, T. E. F. Growth phase dependent cell shape of Haloarcula. Microorganisms 9, 231 (2021).Article 
CAS 

Google Scholar 
Dang, H. Y. & Lovell, C. R. Microbial surface colonization and biofilm development in marine environments. Microbiol. Mol. Biol. Rev. 80, 91–138 (2016).Article 
CAS 

Google Scholar 
Howard-Varona, C., Hargreaves, K. R., Abedon, S. T. & Sullivan, M. B. Lysogeny in nature: mechanisms, impact and ecology of temperate phages. ISME J. 11, 1511–1520 (2017).Article 

Google Scholar 
Pires, D. P., Melo, L. D. R. & Azeredo, J. Understanding the complex phage–host interactions in biofilm communities. Annu. Rev. Virol. 8, 73–94 (2021).Canchaya, C., Proux, C., Fournous, G., Bruttin, A. & Brüssow, H. Prophage genomics. Microbiol. Mol. Biol. Rev. 67, 238–276 (2003).Article 
CAS 

Google Scholar 
Zhang, X. et al. Polyhydroxyalkanoate-driven current generation via acetate by an anaerobic methanotrophic consortium. Water Res. 221, 118743 (2022).Article 
CAS 

Google Scholar 
Knittel, K., Lösekann, T., Boetius, A., Kort, R. & Amann, R. Diversity and distribution of methanotrophic Archaea at cold seeps. Appl. Environ. Microbiol. 71, 467–479 (2005).Article 
CAS 

Google Scholar 
Orphan, V. J., House, C. H., Hinrichs, K.-U., McKeegan, K. D. & DeLong, E. F. Multiple archaeal groups mediate methane oxidation in anoxic cold seep sediments. Proc. Natl Acad. Sci. USA 99, 7663–7668 (2002).Article 
CAS 

Google Scholar 
Orphan, V. J. et al. Geological, geochemical, and microbiological heterogeneity of the seafloor around methane vents in the Eel River Basin, offshore California. Chem. Geol. 205, 265–289 (2004).Article 
CAS 

Google Scholar 
Ackermann, M. A functional perspective on phenotypic heterogeneity in microorganisms. Nat. Rev. Microbiol. 13, 497–508 (2015).Article 
CAS 

Google Scholar 
Robinson, R. W. Life cycles in the methanogenic archaebacterium Methanosarcina mazei. Appl. Environ. Microbiol. 52, 17–27 (1986).Article 
CAS 

Google Scholar 
Daims, H., Stoecker, K. & Wagner, M. in Molecular Microbial Ecology (eds Osborn, A. M. & Smith, C. J.) 213–239 (Taylor & Francis, 2005).Ludwig, W. et al. ARB: a software environment for sequence data. Nucleic Acids Res. 32, 1363–1371 (2004).Article 
CAS 

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

Google Scholar 
Yilmaz, L. S., Parnerkar, S. & Noguera, D. R. mathFISH, a web tool that uses thermodynamics-based mathematical models for in silico evaluation of oligonucleotide probes for fluorescence in situ hybridization. Appl. Environ. Microbiol. 77, 1118–1122 (2011).Article 
CAS 

Google Scholar 
Stoecker, K., Dorninger, C., Daims, H. & Wagner, M. Double labeling of oligonucleotide probes for fluorescence in situ hybridization (DOPE-FISH) improves signal intensity and increases rRNA accessibility. Appl. Environ. Microbiol. 76, 922–926 (2010).Article 
CAS 

Google Scholar 
Fuchs, B. M., Glockner, F. O., Wulf, J. & Amann, R. Unlabeled helper oligonucleotides increase the in situ accessibility to 16S rRNA of fluorescently labeled oligonucleotide probes. Appl. Environ. Microbiol. 66, 3603–3607 (2000).Article 
CAS 

Google Scholar 
Manz, W., Amann, R., Ludwig, W., Wagner, M. & Schleifer, K.-H. Phylogenetic oligodeoxynucleotide probes for the major subclasses of Proteobacteria: problems and solutions. Syst. Appl. Microbiol. 15, 593–600 (1992).Article 

Google Scholar 
Ostle, A. G. & Holt, J. G. Nile blue A as a fluorescent stain for poly-beta-hydroxybutyrate. Appl. Environ. Microbiol. 44, 238–241 (1982).Article 
CAS 

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

Google Scholar 
Martin, M. Cutadapt removes adapter sequences from high-throughput sequencing reads. EMBnet J. 17, 10–12 (2011).Bolger, A. M., Lohse, M. & Usadel, B. Trimmomatic: a flexible trimmer for Illumina sequence data. Bioinformatics 30, 2114–2120 (2014).Article 
CAS 

Google Scholar 
Bolyen, E. et al. Reproducible, interactive, scalable and extensible microbiome data science using QIIME 2. Nat. Biotechnol. 37, 1091 (2019).Article 
CAS 

Google Scholar 
Callahan, B. J. et al. DADA2: high-resolution sample inference from Illumina amplicon data. Nat. Methods 13, 581–583 (2016).Article 
CAS 

Google Scholar 
Eren, A. M., Vineis, J. H., Morrison, H. G. & Sogin, M. L. A filtering method to generate high quality short reads using Illumina paired-end technology. PLoS ONE 8, e66643 (2013).Article 

Google Scholar 
Minoche, A. E., Dohm, J. C. & Himmelbauer, H. Evaluation of genomic high-throughput sequencing data generated on Illumina HiSeq and genome analyzer systems. Genome Biol. 12, R112 (2011).Parks, D. H., Imelfort, M., Skennerton, C. T., Hugenholtz, P. & Tyson, G. W. CheckM: assessing the quality of microbial genomes recovered from isolates, single cells, and metagenomes. Genome Res. 25, 1043–1055 (2015).Article 
CAS 

Google Scholar 
Warren, R. L. et al. LINKS: scalable, alignment-free scaffolding of draft genomes with long reads. GigaScience 4, 35 (2015).Article 

Google Scholar 
Walker, B. J. et al. Pilon: an integrated tool for comprehensive microbial variant detection and genome assembly improvement. PLoS ONE 9, e112963 (2014).Article 

Google Scholar 
Wick, R. R., Schultz, M. B., Zobel, J. & Holt, K. E. Bandage: interactive visualization of de novo genome assemblies. Bioinformatics 31, 3350–3352 (2015).Article 
CAS 

Google Scholar 
Wick, R. R. et al. Trycycler: consensus long-read assemblies for bacterial genomes. Genome Biol. 22, 266 (2021).Article 
CAS 

Google Scholar 
Kolmogorov, M., Yuan, J., Lin, Y. & Pevzner, P. A. Assembly of long, error-prone reads using repeat graphs. Nat. Biotechnol. 37, 540–546 (2019).Article 
CAS 

Google Scholar 
Wick, R. R. & Holt, K. E. Benchmarking of long-read assemblers for prokaryote whole genome sequencing. F1000Res. 8, 2138 (2021).Vaser, R. & Šikić, M. Time- and memory-efficient genome assembly with Raven. Nat. Comput. Sci. 1, 332–336 (2021).Article 

Google Scholar 
Wick, R. R. & Holt, K. E. Polypolish: short-read polishing of long-read bacterial genome assemblies. PLoS Comput. Biol. 18, e1009802 (2022).Article 
CAS 

Google Scholar 
Chaumeil, P.-A., Mussig, A. J., Hugenholtz, P. & Parks, D. H. GTDB-Tk: a toolkit to classify genomes with the Genome Taxonomy Database. Bioinformatics 36, 1925–1927 (2019).
Google Scholar 
Li, H. Minimap2: pairwise alignment for nucleotide sequences. Bioinformatics 34, 3094–3100 (2018).Article 
CAS 

Google Scholar 
Poplin, R. et al. Scaling accurate genetic variant discovery to tens of thousands of samples. Preprint at bioRxiv https://doi.org/10.1101/201178 (2017).Article 

Google Scholar 
Heller, D. & Vingron, M. SVIM: structural variant identification using mapped long reads. Bioinformatics 35, 2907–2915 (2019).Article 
CAS 

Google Scholar 
Seemann, T. Prokka: rapid prokaryotic genome annotation. Bioinformatics 30, 2068–2069 (2014).Article 
CAS 

Google Scholar 
Buchfink, B., Xie, C. & Huson, D. H. Fast and sensitive protein alignment using DIAMOND. Nat. Methods 12, 59–60 (2015).Article 
CAS 

Google Scholar 
Suzek, B. E., Huang, H., McGarvey, P., Mazumder, R. & Wu, C. H. UniRef: comprehensive and non-redundant UniProt reference clusters. Bioinformatics 23, 1282–1288 (2007).Article 
CAS 

Google Scholar 
Tatusov, R. L. et al. The COG database: an updated version includes eukaryotes. BMC Bioinformatics 4, 41 (2003).Article 

Google Scholar 
Finn, R. D. et al. The Pfam protein families database: towards a more sustainable future. Nucleic Acids Res. 44, D279–D285 (2016).Article 
CAS 

Google Scholar 
Haft, D. H. et al. TIGRFAMs and genome properties in 2013. Nucleic Acids Res. 41, D387–D395 (2013).Article 
CAS 

Google Scholar 
Zhou, Z. et al. METABOLIC: high-throughput profiling of microbial genomes for functional traits, metabolism, biogeochemistry, and community-scale functional networks. Microbiome 10, 33 (2022).Article 
CAS 

Google Scholar 
Eddy, S. R. Accelerated profile HMM searches. PLoS Comput. Biol. 7, e1002195 (2011).Article 
CAS 

Google Scholar 
Bateman, A. et al. The Pfam protein families database. Nucleic Acids Res. 32, D138–D141 (2004).Article 
CAS 

Google Scholar 
Amann, R. I. et al. Combination of 16S rRNA-targeted oligonucleotide probes with flow cytometry for analyzing mixed microbial populations. Appl. Environ. Microbiol. 56, 1919–1925 (1990).Article 
CAS 

Google Scholar 
Daims, H., Brühl, A., Amann, R., Schleifer, K. H. & Wagner, M. The domain-specific probe EUB338 is insufficient for the detection of all Bacteria: development and evaluation of a more comprehensive probe set. Syst. Appl. Microbiol. 22, 434–444 (1999).Article 
CAS 

Google Scholar 
Schmid, M. C. et al. Biomarkers for in situ detection of anaerobic ammonium-oxidizing (anammox) bacteria. Appl. Environ. Microbiol. 71, 1677–1684 (2005).Article 
CAS 

Google Scholar 
Yu, N. Y. et al. PSORTb 3.0: improved protein subcellular localization prediction with refined localization subcategories and predictive capabilities for all prokaryotes. Bioinformatics 26, 1608–1615 (2010).Article 
CAS 

Google Scholar 
Bendtsen, J. D., Nielsen, H., von Heijne, G. & Brunak, S. Improved prediction of signal peptides: SignalP 3.0. J. Mol. Biol. 340, 783–795 (2004).Article 

Google Scholar  More

Galperin, M. Y. Environ. Microbiol. 6, 552–567 (2004).Article 
CAS 

Google Scholar 
Higgins, D. & Dworkin, J. FEMS Microbiol. Rev. 36, 131–148 (2012).Article 
CAS 

Google Scholar 
Maamar, H., Raj, A. & Dubnau, D. Science 317, 526–529 (2007).Article 
CAS 

Google Scholar 
Ackermann, M. Nat. Rev. Microbiol. 13, 497–508 (2015).Article 
CAS 

Google Scholar 
Robinson, R. W. Appl. Environ. Microbiol. 52, 17–27 (1986).Article 
CAS 

Google Scholar 
McIlroy, S. J. et al. Nat. Microbiol. https://doi.org/10.1038/s41564-022-01292-9 (2023).Article 

Google Scholar 
Leu, A. O. et al. ISME J. 14, 1030–1041 (2020).Article 
CAS 

Google Scholar 
Cui, M., Ma, A., Qi, H., Zhuang, X. & Zhuang, G. Microbiologyopen 4, 1–11 (2015).Article 

Google Scholar 
Haroon, M. F. et al. Nature 500, 567–570 (2013).Article 
CAS 

Google Scholar 
Fritts, R. K., McCully, A. L. & McKinlay, J. B. Microbiol. Molec. Biol. Rev. 85, e00135-20 (2021).Article 

Google Scholar  More

Edwards, R. B., Naylor, R. L., Higgins, M. M. & Falcon, W. P. Causes of Indonesia’s forest fires. World Dev. 127, 104717 (2020).Article 

Google Scholar 
Page, S. E., Rieley, J. O. & Banks, C. J. Global and regional importance of the tropical peatland carbon pool. Glob. Chang. Biol. 17, 798–818 (2011).Article 
ADS 

Google Scholar 
Page, S., et al. Tropical Fire Ecology Ch. 9 (Springer, 2009).Page, S. E. & Hooijer, A. In the line of fire: the peatlands of Southeast Asia. Philos. Trans. R. Soc. Lond., B, Biol. Sci. 371, 20150176 (2016).Huijnen, V. et al. Fire carbon emissions over maritime southeast Asia in 2015 largest since 1997. Sci. Rep. 6, 1–8 (2016).Article 

Google Scholar 
Kusumaningtyas, S. D. A. & Aldrian, E. Impact of the June 2013 Riau province Sumatera smoke haze event on regional air pollution. Environ. Res. Lett. 11, 075007 (2016).Article 
ADS 

Google Scholar 
Gaveau, D. L. et al. Major atmospheric emissions from peat fires in Southeast Asia during non-drought years: Evidence from the 2013 Sumatran fires. Sci. Rep. 4, 1–7 (2014).Article 

Google Scholar 
Tacconi, L. Preventing fires and haze in Southeast Asia. Nat. Clim. Chang. 6, 640–643 (2016).Article 
ADS 

Google Scholar 
Posa, M. R. C., Wijedasa, L. S. & Corlett, R. T. Biodiversity and conservation of tropical peat swamp forests. Bioscience 61, 49–57 (2011).Article 

Google Scholar 
Harrison, M. E. & Rieley, J. O. Tropical peatland biodiversity and conservation in Southeast Asia. Mires Peat 22, 1–7 (2018).
Google Scholar 
Purnomo, H. et al. Fire economy and actor network of forest and land fires in Indonesia. For. Policy Econ. 78, 21–31 (2017).Article 

Google Scholar 
Wösten, J. H. M., Clymans, E., Page, S. E., Rieley, J. O. & Limin, S. H. Peat–water interrelationships in a tropical peatland ecosystem in Southeast Asia. CATENA 73, 212–224 (2008).Article 

Google Scholar 
Taufik, M., Setiawan, B. I. & Van Lanen, H. A. Increased fire hazard in human-modified wetlands in Southeast Asia. Ambio 48, 363–373 (2019).Article 

Google Scholar 
Taufik, M. et al. Amplification of wildfire area burnt by hydrological drought in the humid tropics. Nat. Clim. Chang. 7, 428–431 (2017).Article 
ADS 

Google Scholar 
Fanin, T. & Werf, G. R. Precipitation–fire linkages in Indonesia (1997–2015). Biogeosciences 14, 3995–4008 (2017).Article 
ADS 

Google Scholar 
Field, R. D. et al. Indonesian fire activity and smoke pollution in 2015 show persistent nonlinear sensitivity to El Niño-induced drought. Proc. Natl. Acad. Sci. U.S.A. 113, 9204–9209 (2016).Article 
ADS 
CAS 

Google Scholar 
Hirano, T. et al. Effects of disturbances on the carbon balance of tropical peat swamp forests. Glob. Chang. Biol. 18, 3410–3422 (2012).Article 
ADS 

Google Scholar 
Ohkubo, S., Hirano, T. & Kusin, K. Influence of fire and drainage on evapotranspiration in a degraded peat swamp forest in Central Kalimantan Indonesia. J. Hydrol. 603, 126906 (2021).Article 

Google Scholar 
Nikonovas, T., Spessa, A., Doerr, S. H., Clay, G. D. & Mezbahuddin, S. Near-complete loss of fire-resistant primary tropical forest cover in Sumatra and Kalimantan. Commun. Earth Environ. 1, 1–8 (2020).Article 

Google Scholar 
Lin, Y., Wijedasa, L. S. & Chisholm, R. A. Singapore’s willingness to pay for mitigation of transboundary forest-fire haze from Indonesia. Environ. Res. Lett. 12, 024017 (2017).Article 
ADS 

Google Scholar 
Nikonovas, T., Spessa, A., Doerr, S. H., Clay, G. & Mezbahuddin, S. ProbFire: A probabilistic fire early warning system for Indonesia. Nat. Hazards Earth Syst. Sci. 22, 303–322 (2022).Article 
ADS 

Google Scholar 
Taufik, M., Veldhuizen, A. A., Wösten, J. H. M. & van Lanen, H. A. J. Exploration of the importance of physical properties of Indonesian peatlands to assess critical groundwater table depths, associated drought and fire hazard. Geoderma 347, 160–169 (2019).Article 
ADS 

Google Scholar 
Sloan, S., Tacconi, L. & Cattau, M. E. Fire prevention in managed landscapes: Recent success and challenges in Indonesia. Mitig. Adapt. Strateg. Glob. Chang. 26, 1–30 (2021).Article 

Google Scholar 
Lestari, I., Murdiyarso, D. & Taufik, M. Rewetting tropical peatlands reduced net greenhouse gas emissions in Riau Province Indonesia. Forests 13, 505 (2022).Article 

Google Scholar 
Spessa, A. C. et al. Seasonal forecasting of fire over Kalimantan Indonesia. Nat. Hazards Earth Syst. Sci. 15, 429–442 (2015).Article 
ADS 

Google Scholar 
Mezbahuddin, M., Grant, R. F. & Hirano, T. How hydrology determines seasonal and interannual variations in water table depth, surface energy exchange, and water stress in a tropical peatland: Modeling versus measurements. J. Geophys. Res. Biogeosci. 120, 2132–2157 (2015).Article 

Google Scholar 
Mezbahuddin, M., Grant, R. F. & Hirano, T. Modelling effects of seasonal variation in water table depth on net ecosystem CO2 exchange of a tropical peatland. Biogeosciences 11, 577–599 (2014).Article 
ADS 

Google Scholar 
Cobb, A. R. & Harvey, C. F. Scalar simulation and parameterization of water table dynamics in tropical peatlands. Water Resour. Res. 55, 9351–9377 (2019).Article 
ADS 

Google Scholar 
Dadap, N. C., Cobb, A. R., Hoyt, A. M., Harvey, C. F. & Konings, A. G. Satellite soil moisture observations predict burned area in Southeast Asian peatlands. Environ. Res. Lett. 14, 094014 (2019).Article 
ADS 

Google Scholar 
Evans, C. D. et al. Rates and spatial variability of peat subsidence in Acacia plantation and forest landscapes in Sumatra Indonesia. Geoderma 338, 410–421 (2019).Article 
ADS 

Google Scholar 
Hooijer, A. et al. Subsidence and carbon loss in drained tropical peatlands. Biogeosciences 9, 1053–1071 (2012).Article 
ADS 
CAS 

Google Scholar 
Couwenberg, J. & Hooijer, A. Towards robust subsidence-based soil carbon emission factors for peat soils in south-east Asia, with special reference to oil palm plantations. Mires Peat 12, 1–13 (2013).
Google Scholar 
Khasanah, N. M. & van Noordwijk, M. Subsidence and carbon dioxide emissions in a smallholder peatland mosaic in Sumatra Indonesia. Mitig. Adapt. Strateg. Glob. Chang. 24, 147 (2019).Article 

Google Scholar 
Marwanto, S., Watanabe, T., Iskandar, W., Sabiham, S. & Funakawa, S. Effects of seasonal rainfall and water table movement on the soil solution composition of tropical peatland. Soil Sci. Plant Nutr. 64, 386–395 (2018).Article 
CAS 

Google Scholar 
Lubis, M. E. S. et al. Changes in water table depth in an oil palm plantation and its surrounding regions in Sumatra Indonesia. J. Agron. 13, 140–146 (2014).Article 

Google Scholar 
Page, S. E., Rieley, J. O. & Wüst, R. Developments in Earth Surface Processes (Volume 9) Ch. 3 (Elsevier, 2006).Haffiez, N. et al. Exploration of machine learning algorithms for predicting the changes in abundance of antibiotic resistance genes in anaerobic digestion. Sci. Total Environ. 839, 156211 (2022).Article 
ADS 
CAS 

Google Scholar 
Grant, R. F., Desai, A. R. & Sulman, B. N. Modelling contrasting responses of wetland productivity to changes in water table depth. Biogeosciences 9, 4215–4231 (2012).Article 
ADS 
CAS 

Google Scholar 
Mezbahuddin, M., Grant, R. F. & Flanagan, L. B. Modeling hydrological controls on variations in peat water content, water table depth, and surface energy exchange of a boreal western Canadian fen peatland. J. Geophys. Res. Biogeosci. 121, 2216–2242 (2016).Article 

Google Scholar 
Dimitrov, D. D., Grant, R. F., Lafleur, P. M. & Humphreys, E. R. Modeling the effects of hydrology on gross primary productivity and net ecosystem productivity at Mer Bleue bog. J. Geophys. Res. Biogeosci. 116, G04010 (2011).Article 
ADS 

Google Scholar 
Dimitrov, D. D., Bhatti, J. S. & Grant, R. F. The transition zones (ecotone) between boreal forests and peatlands: Modelling water table along a transition zone between upland black spruce forest and poor forested fen in central Saskatchewan. Ecol. Modell. 274, 57–70 (2014).Article 

Google Scholar 
Hengl, T. et al. SoilGrids250m: Global gridded soil information based on machine learning. PLoS ONE 12, e0169748 (2017).Article 

Google Scholar 
Hodnett, M. G. & Tomasella, J. Marked differences between van Genuchten soil water-retention parameters for temperate and tropical soils: A new water-retention pedo-transfer functions developed for tropical soils. Geoderma 108, 155–180 (2002).Article 
ADS 
CAS 

Google Scholar 
Funk, C. et al. The climate hazards infrared precipitation with stations-a new environmental record for monitoring extremes. Sci. Data 2, 1–21 (2015).Article 

Google Scholar 
Osaki, M., Hirose, K., Segah, H. & Helmy, F. Tropical Peatland Ecosystems Ch. 9 (Springer, 2016).Razavi, S. Deep learning, explained: Fundamentals, explainability, and bridgeability to process-based modelling. Environ. Modell. Softw. 144, 105159 (2021).Article 

Google Scholar  More

Newmark, W. D. A land-bridge island perspective on mammalian extinctions in western North American parks. Nature 325, 430–432 (1987).Article 
ADS 
CAS 

Google Scholar 
Newmark, W. D. Isolation of African protected areas. Front. Ecol. Environ. 6, 321–328 (2008).Article 

Google Scholar 
Radeloff, V. C. et al. Housing growth in and near United States protected areas limits their conservation value. Proc. Natl. Acad. Sci. U. S. A. 107, 940–945 (2010).Article 
ADS 
CAS 

Google Scholar 
Jones, K. R. et al. One-third of global protected land is under intense human pressure. Science 360, 788–791 (2018).Article 
CAS 

Google Scholar 
Elsen, P. R., Monahan, W. B., Dougherty, E. R. & Merenlender, A. M. Keeping pace with climate change in global terrestrial protected areas. Sci. Adv. https://doi.org/10.1126/sciadv.aay0814 (2020).Article 

Google Scholar 
Wasser, S. K. et al. Genetic assignment of large seizures of elephant ivory reveals Africa’s major poaching hotspots. Science 349, 84–87 (2015).Article 
ADS 
CAS 

Google Scholar 
Davis, C. R. & Hansen, A. J. Trajectories in land use change around U,S. national parks and challenges and opportunities for management. Ecol. Appl. 21, 3299–3316 (2011).Article 

Google Scholar 
Newmark, W. D. Extinction of mammal populations in western North American national parks. Conserv. Biol. 9, 512–526 (1995).Article 

Google Scholar 
Newmark, W. D. Insularization of Tanzanian parks and the local extinction of large mammals. Conserv. Biol. 10, 1549–1556 (1996).Article 

Google Scholar 
Brashares, J. S., Arcese, P. & Sam, M. K. Human demography and reserve size predict wildlife extinction in West Africa. Proc. R. Soc. B Biol. Sci. 268, 2473–2478 (2001).Article 
CAS 

Google Scholar 
Woodroffe, R. & Ginsberg, J. R. Edge effects and the extinction of populations inside protected areas. Science 280, 2126–2128 (1998).Article 
ADS 
CAS 

Google Scholar 
Turner, M. G. & Dale, V. H. Comparing large, infrequent disturbances: What have we learned?. Ecosystems 1, 493–496 (1998).Article 

Google Scholar 
Berger, J. The last mile: How to sustain long-distance migration in mammals. Conserv. Biol. 18, 320–331 (2004).Article 

Google Scholar 
Bolger, D. T., Newmark, W. D., Morrison, T. A. & Doak, D. F. The need for integrative approaches to understand and conserve migratory ungulates. Ecol. Lett. 11, 63–77 (2008).
Google Scholar 
Sawyer, H., Kauffman, M. J., Nielson, R. M. & Horne, J. S. Identifying and prioritizing ungulate migration routes for landscape-level conservation. Ecol. Appl. 19, 2016–2025 (2009).Article 

Google Scholar 
Tucker, M. A. et al. Moving in the anthropocene: Global reductions in terrestrial mammalian movements. Science 469, 466–469 (2018).Article 
ADS 

Google Scholar 
Soulé, M. E. & Terborgh, J. Conserving nature at regional and continental scales-a scientific program for North America. Bioscience 49, 809–817 (1999).Article 

Google Scholar 
Hilty, J. et al. Guidelines for conserving connectivity through ecological networks and corridors. Best Pract. Prot. Area Guidel. Ser. 30, 122 (2020).
Google Scholar 
Haddad, N. & Tewksbury, J. Impacts of corridors on populations and communities. in Connectivity Conservation (eds. Crooks, K. R. & Sanjayan, M.) 390–415 (Cambridge University Press, 2010).
Google Scholar 
Ramiadantsoa, T., Ovaskainen, O., Rybicki, J. & Hanski, I. Large-scale habitat corridors for biodiversity conservation: A forest corridor in Madagascar. PLoS One 10, 1–18 (2015).Article 
CAS 

Google Scholar 
Newmark, W. D., Jenkins, C. N., Pimm, S. L., McNeally, P. B. & Halley, J. M. Targeted habitat restoration can reduce extinction rates in fragmented forests. Proc. Natl. Acad. Sci. USA. 114, 9635–9640 (2017).Article 
ADS 
CAS 

Google Scholar 
Diamond, J. M. Biogeographic kinetics: Estimation of relaxation times for avifaunas of southwest Pacific islands. Proc. Natl. Acad. Sci. 69, 3199–3203 (1972).Article 
ADS 
CAS 

Google Scholar 
Terborgh, J. Preservation of natural diversity: The problem of extinction prone species. Bioscience 24, 715–722 (1974).Article 

Google Scholar 
Tilman, D., May, R. M., Lehman, C. L. & Nowak, M. A. Habitat destruction and the extinction debt revisited. Nature 371, 65–66 (1994).Article 
ADS 

Google Scholar 
Halley, J. M., Monokrousos, N., Mazaris, A. D., Newmark, W. D. & Vokou, D. Dynamics of extinction debt across five taxonomic groups. Nat. Commun. 7, 1–6 (2016).Article 

Google Scholar 
Wearn, O. R., Reuman, D. C. & Ewers, R. M. Extinction debt and windows of conservation opportunity in the Brazilian amazon. Science 337, 228–232 (2012).Article 
ADS 
CAS 

Google Scholar 
Hanski, I. Extinction debt and species credit in boreal forests: Modelling the consequences of different approaches to conservation. Ann. Zool. Fennici 37, 271–280 (2000).
Google Scholar 
LaBarbera, M. Analyzing body size as a factor in ecology and evolution. Annu. Rev. Ecol. Syst. 20, 97–117 (1989).Article 

Google Scholar 
Oakleaf, J. K. et al. Habitat selection by recolonizing wolves in the northern Rocky mountains of the United States. J. Wildl. Manage. 70, 554–563 (2006).Article 

Google Scholar 
Cushman, S. A., McKelvey, K. S. & Schwartz, M. K. Use of empirically derived source-destination models to map regional conservation corridors. Conserv. Biol. 23, 368–376 (2009).Article 

Google Scholar 
Schwartz, M. K. et al. Wolverine gene flow across a narrow climatic niche. Ecology 90, 3222–3232 (2014).Article 

Google Scholar 
McKelvey, K. S. et al. Climate change predicted to shift wolverine distributions, connectivity, and dispersal corridors. Ecol. Appl. 21, 2882–2897 (2011).Article 

Google Scholar 
Carroll, C., Mcrae, B. H. & Brookes, A. Use of linkage mapping and centrality analysis across habitat gradients to conserve connectivity of gray wolf populations in western North America. Conserv. Biol. 26, 78–87 (2012).Article 

Google Scholar 
Parks, S. A., McKelvey, K. S. & Schwartz, M. K. Effects of weighting schemes on the identification of wildlife corridors generated with least-cost methods. Conserv. Biol. 27, 145–154 (2013).Article 

Google Scholar 
Peck, C. P. et al. Potential paths for male-mediated gene flow to and from an isolated grizzly bear population. Ecosphere 8, e01969 (2017).Article 

Google Scholar 
Wild Migrations: Atlas of Wyoming’s Ungulates. (Oregon State University, 2018).Singleton, P. H., Gaines, W. L. & Lehmkuhl, J. F. Landscape permeability for large carnivores in Washington: A geographic information system weighted-distance and least-cost corridor assessment. (2002).Long, R. A. et al. The Cascades carnivore connectivity project: A landscape genetic assessment of connectivity in Washington’s north Cascades ecosystem. Final report for the Seattle City Light Wildlife Research Program (2013).Diamond, J. M. The island dilemma: Lessons of modern biogeographic studies for the design of natural reserves. Biol. Conserv. 7, 129–146 (1975).Article 

Google Scholar 
Wilson, E. O. & Willis, E. O. Applied biogeography. In Ecological structure of ecological communities (eds. Cody, M. L, & Diamond, J. M.) 522–534 (Harvard University Press, 1975)
Google Scholar 
Halley, J. M. & Iwasa, Y. Neutral theory as a predictor of avifaunal extinctions after habitat loss. Proc. Natl. Acad. Sci. USA 108, 2316–2321 (2011).Article 
ADS 
CAS 

Google Scholar 
Cushman, S. A., Lewis, J. S. & Landguth, E. L. Evaluating the intersection of a regional wildlife connectivity network with highways. Mov. Ecol. 1, 1–11 (2013).Article 

Google Scholar 
Singleton, P. H. & Lehmkuhl, J. F. I-90 Snoqualmie pass wildlife habitat linkage assessment. Final Report. USDA, Pacific Northwest Research Station. (2000).Craighead, L., Craighead, A., Oeschslia, L. & Kociolek, A. Bozeman pass post-fencing wildlife monitoring. Final Report. FHWA/MT-10-006/8173 (2011).Andis, A. Z., Huijser, M. P. & Broberg, L. Performance of arch-style road crossing structures from relative movement rates of large mammals. Front. Ecol. Evol. 5, 1–13 (2017).Article 

Google Scholar 
Millward, L. Small mammal microhabitat use and species composition at a wildlife crossing structure compared with nearby forest (Central Washington University, 2018).
Google Scholar 
Bischof, R., Steyaert, S. M. J. G. & Kindberg, J. Caught in the mesh: Roads and their network-scale impediment to animal movement. Ecography 40, 1369–1380 (2017).Article 

Google Scholar 
Balkenhol, N. & Waits, L. P. Molecular road ecology: Exploring the potential of genetics for investigating transportation impacts on wildlife. Mol. Ecol. 18, 4151–4164 (2009).Article 

Google Scholar 
Clevenger, A. P. & Wierzchowski, J. Maintaining and restoring connectivity in landscapes fragmented by roads. In Connectivity Conservation, (eds. Crooks, K. R. & Sanjayan, M.) 502–535 (Cambridge University Press, 2010.)
Google Scholar 
Sawaya, M. A., Kalinowski, S. T. & Clevenger, A. P. Genetic connectivity for two bear species at wildlife crossing structures in Banff National Park. Proc. R. Soc. B Biol. Sci. 281, 20131705 (2014).Article 

Google Scholar 
Sawaya, M. A., Clevenger, A. P. & Schwartz, M. K. Demographic fragmentation of a protected wolverine population bisected by a major transportation corridor. Biol. Conserv. 236, 616–625 (2019).Article 

Google Scholar 
Kamal, S., Grodzińska-Jurczak, M. & Brown, G. Conservation on private land: A review of global strategies with a proposed classification system. J. Environ. Plan. Manag. 58, 576–597 (2015).Article 

Google Scholar 
Wasserman, T. N., Cushman, S. A., Littell, J. S., Shirk, A. J. & Landguth, E. L. Population connectivity and genetic diversity of American marten (Martes americana) in the United States northern Rocky Mountains in a climate change context. Conserv. Genet. 14, 529–541 (2013).Article 

Google Scholar 
Wasserman, T. N., Cushman, S. A., Shirk, A. S., Landguth, E. L. & Littell, J. S. Simulating the effects of climate change on population connectivity of American marten (Martes americana) in the northern Rocky Mountains, USA. Landsc. Ecol. 27, 211–225 (2012).Article 

Google Scholar 
Cushman, S. A., Landguth, E. L. & Flather, C. H. Evaluating the sufficiency of protected lands for maintaining wildlife population connectivity in the U.S. northern Rocky Mountains. Divers. Distrib. 18, 873–884 (2012).Article 

Google Scholar 
Beier, P., Spencer, W., Baldwin, R. F. & Mcrae, B. H. Toward best practices for developing regional connectivity maps. Conserv. Biol. 25, 879–892 (2011).Article 

Google Scholar 
R Core Team. R: A language and environment for statistical computing. (2020). More

Ethics statementAnimals’ care was in accordance with institutional guidelines. Ethical permit was issued by Malmö-Lund djurförsöksetiska nämnd 5.8.18-00848/2018.Field workWe carried out the field work in Sweden during four breeding seasons (2018–2021). Adult male willow warblers were captured in their breeding territories using mist nets and playback of a song. From each bird, we collected the innermost primary feather from the right wing. From the birds that returned with a logger we also collected ~20 μl of blood from the brachial wing vein. The blood was stored in SET buffer (0.015 M NaCl, 0.05 M Tris, 0.001 M of EDTA, pH 8.0) at room temperature until deposited for permanent storage at −20 °C. We deployed Migrate Technology Ltd geolocators (Intigeo-W30Z11-DIP 12 × 5 × 4 mm, 0.32 g) and used a nylon string to mount them on birds with the “leg-loop” harness method as outlined in our previous work24. The mass of the logger relative to that of the bird was on average 3.3% (range 2.7–3.8%).The tagged birds were ringed with a numbered aluminum ring, and two, colored plastic rings for later identification in the field. In total, we tagged 466 males (349 in 2018 and 117 in 2020) at breeding territories. During the first tagging season (2018), birds were trapped at 17 locations (average 22 birds per site; range 7–30) distributed across Sweden (Fig. S1). Three of the sites were in southern Sweden to document migration routes of allopatric trochilus and three sites were located above the Arctic circle to record migratory routes of allopatric acredula, whereas the remaining (239) loggers were spread over 11 sites located in the migratory divide. Given the observed densities and distribution of hybrids after analyzing returning birds in 2019, we deployed 117 more loggers at one single site (63.439°N, 14.831°E) in 2020. We successfully retrieved tracks from 57 birds tagged in 2019 and 16 from birds tagged in 2021. In search for birds with loggers, we checked circa 3000 willow warbler males and covered an area of at least 0.5 km radius around each site the year after tagging.Geolocator data treatmentThe R package GeoLight (version 2.0)25 was used to extract and analyze locations from raw geolocator data. All twilight events were obtained with light threshold of 3 lux. The most extreme outliers were trimmed with “loessFilter” function and a K value of 3. We used GeoLight’s function “getElevation” for estimating the sun elevation angle for the breeding period: these sets of locations were used to infer the positions for autumn departure direction. In addition, we carried out a “Hill-Ekström” calibration for the longest stationary winter site during the period before the spring equinox. Winter calibration produced location sets that better reflected the winter coordinates of the main winter site in sub-Saharan Africa26. We reduced some of the inherent geolocation “noise” by applying cantered 5-day rolling means to the coordinates. The equinox periods were visually identified by inspecting standard deviations in latitude. Latitudes from equinox periods were omitted (on average autumn equinox obscured data for 45 days (range 25–68). For the main winter site, we used the longest period at which bird stayed stationary and from which in all cases begun the spring migration (mean = 118, SD = 23 days). Timing of autumn departure was estimated by manual inspection of longitudes and latitudes plotted in time series. To estimate at which longitude the birds crossed the Mediterranean, we extracted the longitude when birds crossed latitude 35 N° (Mediterranean crossing longitude). For 29 birds, it was possible to directly extract the longitude at crossing latitude 35 N°. For the rest of the cases, the birds had not reached latitude 35 N° before the latitude was obscured by the equinox, we calculated the mean longitude of 10 days from the onset of fall equinox as a measure of the Mediterranean crossing. This measurement correlated highly with the winter longitude (r = 0.78, p = 2.8 × 10−16). To control for the birds relative breeding site longitude, we extracted the departure direction (1°–360°) relative from the tagging site to the location where the birds crossed the Mediterranean (departure direction). The departure data was of circular type (measured in 360°), however the variance did not span more than 180° degrees (range 151°–224°). Therefore, we proceeded with analyses using linear statistics. Geographic distances and departure direction were calculated using R package “geosphere” (version 1.5-10). Complete set of positions of each individual bird with equinoxes excluded is presented in Supplementary Data 1.Laboratory work and molecular data extractionWe extracted DNA from blood samples following the ammonium acetate protocol16. Genotyping for divergent regions on chromosome 1 (InvP-Ch1) and chromosome 5 (InvP-Ch5) was done using a qPCR SNP assay16, which is based on one informative SNP per region (SNP 65 for chromosome 1 and SNP 285 for chromosome 5). Probes and primers were produced by Thermo Fisher Scientific and were designed using the online Custom TaqMan® Assay Design tool (Table S4). We used Bio-Rad CFX96™ Real-time PCR system (Bio-Rad Laboratories, CA, USA) and the universal Fast-two-steps protocol: 95 °C, 15 min—40*(95 °C, 10 s–60 °C, 30 s, plate read. Both regions contain inversion polymorphisms that restrict recombination between subspecies-specific haplotypes and contain nearly all the SNPs separating the two subspecies13. For each region, we scored genotypes as either “Tro” (homozygous for trochilus haplotypes), “Acr” (homozygous for acredula haplotypes) or “Het” (heterozygous). The method that we used to assess the presence of MARB-a is based on a qPCR assay that quantifies the copy number of a novel TE (previously known as AFLP-WW212) that has expanded in acredula. The quantification of repeats by this method has been shown to be highly repeatable (R2 = 0.88) when comparing estimates obtained from DNA in blood and feathers15. We used the forward (5′-CCTTGCATACTTCTATTTCTCCC-3′) and reverse (5′-CATAGGACAGACATTGTTGAGG-3′) primers developed by Caballero-López et al.15 to amplify the TE motif. For reference of a single copy region we used the primers SFRS3F and SFRS3R27. We diluted DNA to 1 ng/μl−1 and used a Bio-Rad CFX96™ Real-time PCR system (Bio-Rad Laboratories, CA, USA) with SYBR-green-based detection. Total reaction volume was 25 μl of which 4 μl of DNA, 12.5 μl of SuperMix, 0.1 μl ROX, 1 μl of primer (forward and reverse), and 6.4 μl of double distilled H2O. We ran quantifications of the single copy gene and the TE variant found on MARB-a on separate plates with the following settings: 50 °C for 2 min as initial incubation, 95 °C for 2 min X 43 (94 °C for 30 s [55.3 °C SFRS3 and 55.5 °C for TE, 30 s] and 72 °C for 45 s). Each sample was run in duplicate and together with a two-fold serial standard dilution (2.5–7.8 × 10−2 ng). Allopatric trochilus have 0–6 copies whereas allopatric acredula have 8–45 copies15; a bimodal distribution was also confirmed in this new data set (Fig. S2). Accordingly, for the present analyses, we split the data in two groups: birds with ≤6 TE copies and birds with >7, translating into absence or presence of MARB-a, with the former assumed to be homozygous for the absence of MARB-a and the latter heterozygous or homozygous for the presence of MARB-a. Data from two investigated willow warbler families suggest a Mendelian inheritance pattern and provide support for our interpretation of how TE copy numbers reflect the three genotypes (Table S5). Moreover, the TE copy numbers within the hybrid swarm have a distribution similar to a combination of allopatric trochilus and acredula, further supporting that the copies are inherited as intact blocks (haplotypes). However, a precise distinction between heterozygotes and homozygotes on MARB-a is still not possible15.Statistical analysisWe used linear models with departure direction, winter longitude, migration distance and departure timing as response variables and the three genetic markers: MARB-a (a factor with two levels), InvP-Ch1 (a factor with three levels) and InvP-Ch5 (a factor with three levels) as explanatory variables. Models were constructed with R base package “stats”. We reported Type II ANOVA for models with more than one explanatory variable and no interactions and type III ANOVA results for models with interaction term by using R package “Car” (version 3.0-12)28. We initially constructed mixed effect models with timing of departure and tagging year as random factors however, this delivered singular fits due to insufficient sample sizes across categories. Normality of residuals was checked with a Shapiro–Wilk test. For carrying out circular statistics on autumn migration direction we used the R package “circular” (version 0.4-93). Watson’s U2 pairwise comparisons of different groups delivered the same results as linear models (Table S2 and Fig. S5). Circular means were identical to conventional linear means in our data set, which we take as another evidence that linear models are appropriate for the analysis of our data (Table S3 and Fig. S5). Maps in Figs. 1 and 2b and S1, S3 and S4 were created with R package “ggplot2” (version 3.3.6) using continent contours from Natural Earth, naturalearthdata.com/. Heat gradient over the maps in Fig. 1a–d were created with R package “gstat” (version 2.0-8) and the inverse distance weighting power of 3.0. Circular plots were created with ORIANA (version 4.02). All analyses were carried out with R version 4.1.1 (R Core Team 2021).Reporting summaryFurther information on research design is available in the Nature Portfolio Reporting Summary linked to this article. More