Ecology

The full-length sequences of PacBio SMRT sequencingBased on PacBio SMRT sequencing, 3,751,089, 3,434,452, 3,900,180, 8,535,019, and 4,435,846 subreads were generated for root, stem, leaf, flower, and seed, with a N50 of 3040, 3367, 2611, 2198, and 4584 bp, respectively (Table S1; Fig. S1). Subreads were processed to generate circular consensus sequences (CCSs). By detecting the primers and poly(A) tail, 238,196, 232,290, 211,535, 257,905, and 231,877 full-length non-chimeric (FLNC) reads were identified for root, stem, leaf, flower, and seed, with a mean length of 2633, 3070, 2561, 1746, and 3762 bp, respectively (Table S2; Fig. S2). After Iterative Clustering for Error Correction (ICE) clustering, polishing, base correction, de-redundancy, and non-plant sequences filtering, 37,789, 34,034, 38,100, 54,937, and 53,906 unigenes were retained for root, stem, leaf, flower, and seed, respectively, with an average unigene length of 1802–3786 bp and N50 of 2238–4707 bp (Table S2). The length of most unigenes from five organs exceeded 2000 bp, accounting for 68.88% of the total number (Table S3; Fig. 1A). Based on Benchmarking Universal Single-Copy Orthologs (BUSCO) assessment, about 88.1% (single-copy: 353; duplicated: 916) of the 1440 core embryophyte genes were found to be complete (90.6% were present when counting fragmented genes), suggesting the high integrity of the M. micrantha transcriptome (Fig. S3).Figure 1Length distribution of unigenes from PacBio SMRT sequencing (A) and Illumina RNA-Seq (B) across five organs.Full size imageDe novo assembly of Illumina RNA-Seq dataBased on Illumina RNA-Seq, 43.23, 40.27, 41.01, 65.85, and 41.09 million clean reads were obtained for root, stem, leaf, flower, and seed, respectively, with Q20 exceeding 96.72%. Using Trinity software, clean reads were de novo assembled into 124,238, 60,232, 63,370, 93,229, and 66,411 unigenes for root, stem, leaf, flower, and seed. After filtering non-plant sequences, 124,233, 60,232, 63,370, 93,228, and 66,410 unigenes were finally retained for the five organs, respectively (Table S4). The length of most unigenes (84.70%) was shorter than 2000 bp (Table S3). In addition, the average length and N50 of unigenes generated by Illumina RNA-Seq were 1067–1312 bp and 1336–1685 bp, respectively, which were shorter than that from PacBio SMRT sequencing (Table S4; Fig. 1B).Functional annotationTo obtain a comprehensive functional annotation of M. micrantha transcriptome, unigenes generated by PacBio SMRT sequencing were annotated in seven public databases, including NCBI non-redundant nucleotide sequences (NT), NCBI non-redundant protein sequences (NR), Gene Ontology (GO), Eukaryotic Orthologous Groups (KOG), Kyoto Encyclopedia of Genes and Genomes (KEGG), Swiss-Prot, and Pfam protein families. For root, stem, leaf, flower, and seed, 35,714 (94.51%), 32,614 (95.83%), 36,134 (94.84%), 49,197 (89.55%), and 50,962 (94.54%) unigenes were annotated to at least one database, respectively, suggesting that our transcriptome is well annotated and that most of unigenes may be functional (Table 1).Table 1 Statistics of annotation of full-length transcripts from five M. micrantha organs in seven databases.Full size tableBased on NR database annotation, the top three homologous species for the five organs were Cynara cardunculus, Vitis vinifera, and Daucus carota (Fig. S4). The top homologous species was a plant of the Asteraceae family. For the GO function annotation, “binding”, “catalytic activities”, “metabolic process”, “cellular process”, “cell”, and “cell part” were functional categories with the most abundant unigenes (Fig. S5). In addition, numerous unigenes were assigned to “response to stimulus”, “response to biotic stimulus”, and “response to oxidative stress” category (Table S5). Positive response to stress stimuli is an important strategy for invasive plants to adapt to the environment. In the KEGG annotation, the top two pathways with the most abundant unigenes were “carbohydrate metabolism” and “translation”. Furthermore, “energy metabolism” and “environmental adaptation” were also worthy of attention, which are important pathways responsible for energy supply and stress responses (Fig. S6).TFs identification and AS analysisUsing the iTAK pipeline, 1776 (root), 1293 (stem), 1627 (leaf), 2529 (flower), and 1733 (seed) unigenes were identified as TFs, which were classified into 68 families (Table S6). C3H (884), C2H2 (525), and bHLH (501) were the most abundant TF families (Fig. S7A). In addition, MYB (333) TFs were also found in the five organs. The differential expression levels of the top 15 TF families were further characterized. We found that the top 15 TF families had a certain amount of expression in the five organs of M. micrantha (Fig. S7B).For root, stem, leaf, flower, and seed, 3300, 2324, 3219, 4730, and 3740 unique transcript models (UniTransModels) were constructed, among which the UniTransModels containing two isoforms were the most common (Fig. S8A). There were 329, 270, 358, 336, and 537 AS events identified in root, stem, leaf, flower, and seed, respectively. Retained introns (RIs) were detected as the most abundant AS event in all five organs, followed by alternative 3′ splice sites (A3) and alternative 5′ splice sites (A5). Mutually exclusive exons (MX) were the least frequent event (Fig. S8B).Gene expression analysisThe number of unigenes in different expression level intervals was similar across the five organs (Fig. 2A). Using FPKM  > 0.3 as the threshold for unigene expression, the total number of unigenes expressed in the five organs was 102,464 (Fig. 2B). Among them, 39,227 unigenes were co-expressed in all five organs. The information of differentially expressed genes (DEGs) identified in pairwise comparisons among the five organs is listed in Table S7. In total, 21,161 DEGs were identified among the five organs (Fig. S9). The number of DEGs between the five organs varied from 3469 (root vs stem) to 10,716 (leaf vs seed) (Fig. 2C). Notably, 933, 428, 1410, 1018, and 1292 DEGs showed significant higher expression in root, stem, leaf, flower, and seed, respectively (Figs. S10 and S11).Figure 2Gene expression patterns in five M. micrantha organs. (A) The FPKM interval distribution in the five organs. (B) Venn diagram of the number of unigenes expressed in five organs. (C) Number of differentially expressed genes in each pairwise comparison of five organs.Full size imageKEGG enrichment of unigenes with higher expression in each organAccording to the KEGG enrichment analysis results, there were obvious differences in enriched pathways in the five organs (Table S8; Fig. 3). The unigenes with higher expression in root were mainly enriched to defense response and protein processing pathways, such as “plant-pathogen interaction” and “protein processing in endoplasmic reticulum”. In stem, unigenes with higher expression were predominantly enriched to pathways related to the secondary metabolite, sugar, and terpenoid biosynthesis, such as “phenylpropanoid biosynthesis”, “starch and sucrose metabolism”, and “diterpenoid biosynthesis”. In flower, unigenes with higher expression were mainly related to “starch and sucrose metabolism”, “phenylpropanoid biosynthesis”, and “cutin, suberine, and wax biosynthesis”. The unigenes with higher expression in seed were mainly enriched in three fatty acid and sugar metabolism pathways, namely “biosynthesis of unsaturated fatty acids”, “galactose metabolism”, and “amino sugar and nucleotide sugar metabolism”. The unigenes with higher expression in leaf were significantly enriched in photosynthesis pathways, including “photosynthesis-antenna proteins”, “photosynthesis”, “porphyrin and chlorophyll metabolism”, and “carbon fixation in photosynthetic organisms”, which are important for the photosynthesis of M. micrantha.Figure 3The Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analysis of unigenes with higher expression in each organ. The significantly enriched pathways with corrected p-value (q value)  More

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

Google Scholar 
Orgiazzi, A. et al. Global Soil Biodiversity Atlas (European Commission, Publications Office of the European Union, 2016).Tecon, R. & Or, D. Biophysical processes supporting the diversity of microbial life in soil. FEMS Microbiol. Rev. 41, 599–623 (2017).CAS 
PubMed 
PubMed Central 

Google Scholar 
Williamson, K. E., Fuhrmann, J. J., Wommack, K. E. & Radosevich, M. Viruses in soil ecosystems: an unknown quantity within an unexplored territory. Annu. Rev. Virol. 4, 201–219 (2017). This Review provides a comprehensive overview of methods and technologies used to study soil viruses alongside a guide of metrics describing soil viruses across diverse soil ecosystems.CAS 
PubMed 

Google Scholar 
Stefan, G., Cornelia, B., Jörg, R. & Michael, B. Soil water availability strongly alters the community composition of soil protists. Pedobiologia 57, 205–213 (2014).
Google Scholar 
Leake, J. et al. Networks of power and influence: the role of mycorrhizal mycelium in controlling plant communities and agroecosystem functioning. Can. J. Bot. 82, 1016–1045 (2004).
Google Scholar 
Bahram, M. et al. Structure and function of the global topsoil microbiome. Nature 560, 233–237 (2018). This study compiled metagenomic and metabarcoding data from 189 sites to demonstrate global patterns in the structure and function of soil microbial communities as well as the widespread prevalence of bacterial–fungal antagonism as an important structuring force of microbial communities.CAS 
PubMed 

Google Scholar 
He, L. et al. Global biogeography of fungal and bacterial biomass carbon in topsoil. Soil Biol. Biochem. 151, 108024 (2020).CAS 

Google Scholar 
Bach, E. M., Williams, R. J., Hargreaves, S. K., Yang, F. & Hofmockel, K. S. Greatest soil microbial diversity found in micro-habitats. Soil Biol. Biochem. 118, 217–226 (2018).CAS 

Google Scholar 
Bardgett, R. D. & van der Putten, W. H. Belowground biodiversity and ecosystem functioning. Nature 515, 505–511 (2014).CAS 
PubMed 

Google Scholar 
Fierer, N. Embracing the unknown: disentangling the complexities of the soil microbiome. Nat. Rev. Microbiol. 15, 579–590 (2017).CAS 
PubMed 

Google Scholar 
Delgado-Baquerizo, M. et al. Multiple elements of soil biodiversity drive ecosystem functions across biomes. Nat. Ecol. Evol. 4, 210–220 (2020).PubMed 

Google Scholar 
Crowther, T. W. et al. The global soil community and its influence on biogeochemistry. Science 365, eaav0550 (2019).CAS 
PubMed 

Google Scholar 
Liang, C., Amelung, W., Lehmann, J. & Kästner, M. Quantitative assessment of microbial necromass contribution to soil organic matter. Glob. Change Biol. 25, 3578–3590 (2019). This article estimates that more than 50% of SOM may be derived from microbial necromass in grassland and agricultural ecosystems based on extrapolations from amino sugar biomarker data.
Google Scholar 
Angst, G., Mueller, K. E., Nierop, K. G. J. & Simpson, M. J. Plant- or microbial-derived? A review on the molecular composition of stabilized soil organic matter. Soil Biol. Biochem. 156, 108189 (2021).CAS 

Google Scholar 
Ludwig, M. et al. Microbial contribution to SOM quantity and quality in density fractions of temperate arable soils. Soil Biol. Biochem. 81, 311–322 (2015). This study uses lipid biomarkers to estimate that at least 50% of SOM may be derived from microbial necromass.CAS 

Google Scholar 
Simpson, A. J., Simpson, M. J., Smith, E. & Kelleher, B. P. Microbially derived inputs to soil organic matter: are current estimates too low? Environ. Sci. Technol. 41, 8070–8076 (2007).CAS 
PubMed 

Google Scholar 
Blazewicz, S. J. et al. Taxon-specific microbial growth and mortality patterns reveal distinct temporal population responses to rewetting in a California grassland soil. ISME J. 14, 1520–1532 (2020). This study used quantitative stable isotope probing to calculate growth and mortality rates of bacteria following the rewetting of a dry Mediterranean soil, and demonstrated that bacterial growth was density independent whereas bacterial mortality was density dependent.PubMed 
PubMed Central 

Google Scholar 
Vieira, S. et al. Drivers of the composition of active rhizosphere bacterial communities in temperate grasslands. ISME J. 14, 463–475 (2020).CAS 
PubMed 
PubMed Central 

Google Scholar 
Nuccio, E. E. et al. Niche differentiation is spatially and temporally regulated in the rhizosphere. ISME J. 14, 999–1014 (2020).CAS 
PubMed 
PubMed Central 

Google Scholar 
Shi, S. et al. Successional trajectories of rhizosphere bacterial communities over consecutive seasons. mBio 6, e00746 (2015).PubMed 
PubMed Central 

Google Scholar 
Bastian, F., Bouziri, L., Nicolardot, B. & Ranjard, L. Impact of wheat straw decomposition on successional patterns of soil microbial community structure. Soil Biol. Biochem. 41, 262–275 (2009).CAS 

Google Scholar 
Whitman, T. et al. Microbial community assembly differs across minerals in a rhizosphere microcosm. Environ. Microbiol. 20, 4444–4460 (2018).CAS 
PubMed 

Google Scholar 
Maynard, D. S., Crowther, T. W. & Bradford, M. A. Fungal interactions reduce carbon use efficiency. Ecol. Lett. 20, 1034–1042 (2017). This study demonstrated that antagonistic interactions between wood-decay fungi can reduce CUE of the fungal community.PubMed 

Google Scholar 
Crowther, T. W. et al. Environmental stress response limits microbial necromass contributions to soil organic carbon. Soil Biol. Biochem. 85, 153–161 (2015).CAS 

Google Scholar 
Hu, Y., Zheng, Q., Noll, L., Zhang, S. & Wanek, W. Direct measurement of the in situ decomposition of microbial-derived soil organic matter. Soil Biol. Biochem. 141, 107660 (2020).CAS 

Google Scholar 
Fernandez, C. W., Langley, J. A., Chapman, S., McCormack, M. L. & Koide, R. T. The decomposition of ectomycorrhizal fungal necromass. Soil Biol. Biochem. 93, 38–49 (2016). This review article summarizes how the stoichiometry, morphology and chemistry of microbial necromass affects its decomposition rate in soil.CAS 

Google Scholar 
Buckeridge, K. M. et al. Sticky dead microbes: rapid abiotic retention of microbial necromass in soil. Soil Biol. Biochem. 149, 107929 (2020).CAS 

Google Scholar 
Creamer, C. A. et al. Mineralogy dictates the initial mechanism of microbial necromass association. Geochim. Cosmochim. Acta 260, 161–176 (2019). This study used Raman microspectroscopy and 13C-labelled necromass to demonstrate that different mineral types retained microbial necromass through different mechanisms and with different strengths.CAS 

Google Scholar 
Schurig, C. et al. Microbial cell-envelope fragments and the formation of soil organic matter: a case study from a glacier forefield. Biogeochemistry 113, 595–612 (2013).CAS 

Google Scholar 
Kopittke, P. M. et al. Nitrogen-rich microbial products provide new organo-mineral associations for the stabilization of soil organic matter. Glob. Change Biol. 24, 1762–1770 (2018).
Google Scholar 
Miltner, A., Bombach, P., Schmidt-Brücken, B. & Kästner, M. SOM genesis: microbial biomass as a significant source. Biogeochemistry 111, 41–55 (2012).CAS 

Google Scholar 
Kleber, M. et al. Dynamic interactions at the mineral–organic matter interface. Nat. Rev. Earth Environ. 2, 402–421 (2021).
Google Scholar 
Blagodatskaya, E. & Kuzyakov, Y. Active microorganisms in soil: critical review of estimation criteria and approaches. Soil Biol. Biochem. 67, 192–211 (2013).CAS 

Google Scholar 
Or, D., Smets, B. F., Wraith, J. M., Dechesne, A. & Friedman, S. P. Physical constraints affecting bacterial habitats and activity in unsaturated porous media–a review. Adv. Water Resour. 30, 1505–1527 (2007).
Google Scholar 
Kuzyakov, Y. & Blagodatskaya, E. Microbial hotspots and hot moments in soil: concept & review. Soil Biol. Biochem. 83, 184–199 (2015).CAS 

Google Scholar 
Finzi, A. C. et al. Rhizosphere processes are quantitatively important components of terrestrial carbon and nutrient cycles. Glob. Change Biol. 21, 2082–2094 (2015).
Google Scholar 
Yuan, M. M. et al. Fungal-bacterial cooccurrence patterns differ between arbuscular mycorrhizal fungi and nonmycorrhizal fungi across soil niches. mBio 12, e03509-20 (2015).
Google Scholar 
Zhang, L. & Lueders, T. Micropredator niche differentiation between bulk soil and rhizosphere of an agricultural soil depends on bacterial prey. FEMS Microbiol. Ecol. 93, fix103 (2017).
Google Scholar 
Sokol, N. W. & Bradford, M. A. Microbial formation of stable soil carbon is more efficient from belowground than aboveground input. Nat. Geosci. 12, 46–53 (2019).CAS 

Google Scholar 
Kallenbach, C. M., Frey, S. D. & Grandy, A. S. Direct evidence for microbial-derived soil organic matter formation and its ecophysiological controls. Nat. Commun. 7, 13630 (2016). This study used artificial soils to provide empirical evidence that SOM can be entirely microbially derived, and also demonstrated a positive relationship between CUE and SOM formation.CAS 
PubMed 
PubMed Central 

Google Scholar 
Wood, J. L., Tang, C. & Franks, A. E. Competitive traits are more important than stress-tolerance traits in a cadmium-contaminated rhizosphere: a role for trait theory in microbial ecology. Front. Microbiol. 9, 121 (2018).PubMed 
PubMed Central 

Google Scholar 
Violle, C. et al. Let the concept of trait be functional! Oikos 116, 882–892 (2007).
Google Scholar 
Madin, J. S. et al. A synthesis of bacterial and archaeal phenotypic trait data. Sci. Data 7, 170 (2020).CAS 
PubMed 
PubMed Central 

Google Scholar 
Shaffer, M. et al. DRAM for distilling microbial metabolism to automate the curation of microbiome function. Nucleic Acids Res. 48, 8883–8900 (2020).CAS 
PubMed 
PubMed Central 

Google Scholar 
Brown, C. T., Olm, M. R., Thomas, B. C. & Banfield, J. F. Measurement of bacterial replication rates in microbial communities. Nat. Biotechnol. 34, 1256–1263 (2016). This study developed an algorithm, iRep, that uses draft-quality genome sequences and single time-point metagenome sequencing to infer microbial population replication rates.CAS 
PubMed 
PubMed Central 

Google Scholar 
Nayfach, S. & Pollard, K. S. Average genome size estimation improves comparative metagenomics and sheds light on the functional ecology of the human microbiome. Genome Biol. 16, 51 (2015).PubMed 
PubMed Central 

Google Scholar 
Leff, J. W. et al. Consistent responses of soil microbial communities to elevated nutrient inputs in grasslands across the globe. Proc. Natl Acad. Sci. USA 112, 10967–10972 (2015).CAS 
PubMed 
PubMed Central 

Google Scholar 
Vieira-Silva, S. & Rocha, E. P. C. The systemic imprint of growth and its uses in ecological (meta)genomics. PLoS Genet. 6, e1000808 (2010).PubMed 
PubMed Central 

Google Scholar 
Hasby, F. A., Barbi, F., Manzoni, S. & Lindahl, B. D. Transcriptomic markers of fungal growth, respiration and carbon-use efficiency. FEMS Microbiol. Lett. 368, fnab100 (2021).PubMed 
PubMed Central 

Google Scholar 
Maillard, F., Schilling, J., Andrews, E., Schreiner, K. M. & Kennedy, P. Functional convergence in the decomposition of fungal necromass in soil and wood. FEMS Microbiol. Ecol. 96, fiz209 (2020).CAS 
PubMed 

Google Scholar 
Clemmensen, K. E. et al. Carbon sequestration is related to mycorrhizal fungal community shifts during long-term succession in boreal forests. N. Phytol. 205, 1525–1536 (2015).CAS 

Google Scholar 
Olivelli, M. S. et al. Unraveling mechanisms behind biomass–clay interactions using comprehensive multiphase nuclear magnetic resonance (NMR) Spectroscopy. ACS Earth Space Chem. 4, 2061–2072 (2020).CAS 

Google Scholar 
Achtenhagen, J., Goebel, M.-O., Miltner, A., Woche, S. K. & Kästner, M. Bacterial impact on the wetting properties of soil minerals. Biogeochemistry 122, 269–280 (2015).CAS 

Google Scholar 
Lehmann, J. et al. Persistence of soil organic carbon caused by functional complexity. Nat. Geosci. 13, 529–534 (2020).CAS 

Google Scholar 
Ahmed, E. & Holmström, S. J. M. Microbe–mineral interactions: The impact of surface attachment on mineral weathering and element selectivity by microorganisms. Chem. Geol. 403, 13–23 (2015).CAS 

Google Scholar 
Chenu, C. Clay- or sand-polysaccharide associations as models for the interface between micro-organisms and soil: water related properties and microstructure. Geoderma 56, 143–156 (1993).CAS 

Google Scholar 
Sher, Y. et al. Microbial extracellular polysaccharide production and aggregate stability controlled by switchgrass (Panicum virgatum) root biomass and soil water potential. Soil Biol. Biochem. 143, 107742 (2020).CAS 

Google Scholar 
Lybrand, R. A. et al. A coupled microscopy approach to assess the nano-landscape of weathering. Sci. Rep. 9, 5377 (2019).PubMed 
PubMed Central 

Google Scholar 
Prommer, J. et al. Increased microbial growth, biomass, and turnover drive soil organic carbon accumulation at higher plant diversity. Glob. Change Biol. 26, 669–681 (2020).
Google Scholar 
Cotrufo, M. F., Wallenstein, M. D., Boot, C. M., Denef, K. & Paul, E. The Microbial Efficiency-Matrix Stabilization (MEMS) framework integrates plant litter decomposition with soil organic matter stabilization: do labile plant inputs form stable soil organic matter? Glob. Change Biol. 19, 988–995 (2013).
Google Scholar 
Liang, C., Schimel, J. P. & Jastrow, J. D. The importance of anabolism in microbial control over soil carbon storage. Nat. Microbiol. 2, 17105 (2017).CAS 
PubMed 

Google Scholar 
Geyer, K. M., Kyker-Snowman, E., Grandy, A. S. & Frey, S. D. Microbial carbon use efficiency: accounting for population, community, and ecosystem-scale controls over the fate of metabolized organic matter. Biogeochemistry 127, 173–188 (2016).CAS 

Google Scholar 
Kallenbach, C. M., Grandy, A. S., Frey, S. D. & Diefendorf, A. F. Microbial physiology and necromass regulate agricultural soil carbon accumulation. Soil Biol. Biochem. 91, 279–290 (2015).CAS 

Google Scholar 
Buckeridge, K. M. et al. Environmental and microbial controls on microbial necromass recycling, an important precursor for soil carbon stabilization. Commun. Earth Env. 1, 36 (2020).
Google Scholar 
Saifuddin, M., Bhatnagar, J. M., Segrè, D. & Finzi, A. C. Microbial carbon use efficiency predicted from genome-scale metabolic models. Nat. Commun. 10, 3568 (2019).PubMed 
PubMed Central 

Google Scholar 
Schimel, J., Balser, T. C. & Wallenstein, M. Microbial stress-response physiology and its implications for ecosystem function. Ecology 88, 1386–1394 (2007).PubMed 

Google Scholar 
Mason‐Jones, K., Banfield, C. C. & Dippold, M. A. Compound-specific 13C stable isotope probing confirms synthesis of polyhydroxybutyrate by soil bacteria. Rapid Commun. Mass. Spectrom. 33, 795–802 (2019).PubMed 

Google Scholar 
Bååth, E. The use of neutral lipid fatty acids to indicate the physiological conditions of soil fungi. Microb. Ecol. 45, 373–383 (2003).PubMed 

Google Scholar 
Slessarev, E. W. et al. Cellular and extracellular C contributions to respiration after wetting dry soil. Biogeochemistry 147, 307–324 (2020).CAS 

Google Scholar 
Slessarev, E. W. & Schimel, J. P. Partitioning sources of CO2 emission after soil wetting using high-resolution observations and minimal models. Soil Biol. Biochem. 143, 107753 (2020).CAS 

Google Scholar 
Lennon, J. T. & Jones, S. E. Microbial seed banks: the ecological and evolutionary implications of dormancy. Nat. Rev. Microbiol. 9, 119–130 (2011).CAS 
PubMed 

Google Scholar 
Brangarí, A. C., Manzoni, S. & Rousk, J. A soil microbial model to analyze decoupled microbial growth and respiration during soil drying and rewetting. Soil Biol. Biochem. 148, 107871 (2020).
Google Scholar 
Zha, J. & Zhuang, Q. Microbial dormancy and its impacts on northern temperate and boreal terrestrial ecosystem carbon budget. Biogeosciences 17, 4591–4610 (2020).CAS 

Google Scholar 
Anderson, T.-H. Microbial eco-physiological indicators to asses soil quality. Agric. Ecosyst. Environ. 98, 285–293 (2003).
Google Scholar 
Geyer, K., Schnecker, J., Grandy, A. S., Richter, A. & Frey, S. Assessing microbial residues in soil as a potential carbon sink and moderator of carbon use efficiency. Biogeochemistry 151, 237–249 (2020).CAS 

Google Scholar 
Sepehrnia, N. et al. Transport, retention, and release of Escherichia coli and Rhodococcus erythropolis through dry natural soils as affected by water repellency. Sci. Total Environ. 694, 133666 (2019).CAS 
PubMed 

Google Scholar 
Boeddinghaus, R. S. et al. The mineralosphere — interactive zone of microbial colonization and carbon use in grassland soils. Biol. Fertil. Soils 57, 587–601 (2021).CAS 

Google Scholar 
Vieira, S. et al. Bacterial colonization of minerals in grassland soils is selective and highly dynamic. Environ. Microbiol. 22, 917–933 (2020).CAS 
PubMed 

Google Scholar 
Ma, T. et al. Divergent accumulation of microbial necromass and plant lignin components in grassland soils. Nat. Commun. 9, 3480 (2018).PubMed 
PubMed Central 

Google Scholar 
Blazewicz, S. J., Schwartz, E. & Firestone, M. K. Growth and death of bacteria and fungi underlie rainfall-induced carbon dioxide pulses from seasonally dried soil. Ecology 95, 1162–1172 (2014).PubMed 

Google Scholar 
Ceja-Navarro, J. A. et al. Protist diversity and community complexity in the rhizosphere of switchgrass are dynamic as plants develop. Microbiome 9, 96 (2021).CAS 
PubMed 
PubMed Central 

Google Scholar 
Starr, E. P., Nuccio, E. E., Pett-Ridge, J., Banfield, J. F. & Firestone, M. K. Metatranscriptomic reconstruction reveals RNA viruses with the potential to shape carbon cycling in soil. Proc. Natl Acad. Sci. USA 116, 25900–25908 (2019). This comprehensive study of RNA viruses detectable in a grassland soil showed how these viruses are shaped by the presence of plant roots and litter.CAS 
PubMed 
PubMed Central 

Google Scholar 
Shi, S. et al. The interconnected rhizosphere: high network complexity dominates rhizosphere assemblages. Ecol. Lett. 19, 926–936 (2016).PubMed 

Google Scholar 
Yan, Y., Kuramae, E. E., de Hollander, M., Klinkhamer, P. G. L. & van Veen, J. A. Functional traits dominate the diversity-related selection of bacterial communities in the rhizosphere. ISME J. 11, 56–66 (2017).PubMed 

Google Scholar 
Zhalnina, K. et al. Dynamic root exudate chemistry and microbial substrate preferences drive patterns in rhizosphere microbial community assembly. Nat. Microbiol. 3, 470 (2018).CAS 
PubMed 

Google Scholar 
Pett-Ridge, J. et al. in Rhizosphere Biology: Interactions Between Microbes and Plants (eds Gupta, V. V. S. R. & Sharma, A. K.) 51–73 (Springer, 2021).Poll, C., Marhan, S., Ingwersen, J. & Kandeler, E. Dynamics of litter carbon turnover and microbial abundance in a rye detritusphere. Soil Biol. Biochem. 40, 1306–1321 (2008).CAS 

Google Scholar 
Buchkowski, R. W., Bradford, M. A., Grandy, A. S., Schmitz, O. J. & Wieder, W. R. Applying population and community ecology theory to advance understanding of belowground biogeochemistry. Ecol. Lett. 20, 231–245 (2017).PubMed 

Google Scholar 
Erktan, A., Or, D. & Scheu, S. The physical structure of soil: determinant and consequence of trophic interactions. Soil Biol. Biochem. 148, 107876 (2020).CAS 

Google Scholar 
Roesch, L. F. W. et al. Pyrosequencing enumerates and contrasts soil microbial diversity. ISME J. 1, 283–290 (2007).CAS 
PubMed 

Google Scholar 
Carson, J. K. et al. Low pore connectivity increases bacterial diversity in soil. Appl. Environ. Microbiol. 76, 3936–3942 (2010).CAS 
PubMed 
PubMed Central 

Google Scholar 
Raynaud, X. & Nunan, N. Spatial ecology of bacteria at the microscale in soil. PLoS ONE 9, e87217 (2014).PubMed 
PubMed Central 

Google Scholar 
Ekelund, F., Rønn, R. & Christensen, S. Distribution with depth of protozoa, bacteria and fungi in soil profiles from three Danish forest sites. Soil Biol. Biochem. 33, 475–481 (2001).CAS 

Google Scholar 
Sharrar, A. M. et al. Bacterial secondary metabolite biosynthetic potential in soil varies with phylum, depth, and vegetation type. mBio 11, e00416-20 (2020).PubMed 
PubMed Central 

Google Scholar 
Georgiou, K., Abramoff, R. Z., Harte, J., Riley, W. J. & Torn, M. S. Microbial community-level regulation explains soil carbon responses to long-term litter manipulations. Nat. Commun. 8, 1223 (2017). This modelling study demonstrated that including a density-dependent microbial mortality term can reduce the oscillatory behaviour of soil carbon models.PubMed 
PubMed Central 

Google Scholar 
Thakur, M. P. & Geisen, S. Trophic regulations of the soil microbiome. Trends Microbiol. 27, 771–780 (2019).CAS 
PubMed 

Google Scholar 
Fanin, N. et al. The ratio of Gram-positive to Gram-negative bacterial PLFA markers as an indicator of carbon availability in organic soils. Soil Biol. Biochem. 128, 111–114 (2019).CAS 

Google Scholar 
Wang, W. et al. Predatory Myxococcales are widely distributed in and closely correlated with the bacterial community structure of agricultural land. Appl. Soil Ecol. 146, 103365 (2020).
Google Scholar 
Hungate, B. A. et al. The functional significance of bacterial predators. mBio 12, e00466-21 (2021).PubMed 
PubMed Central 

Google Scholar 
Jover, L. F., Effler, T. C., Buchan, A., Wilhelm, S. W. & Weitz, J. S. The elemental composition of virus particles: implications for marine biogeochemical cycles. Nat. Rev. Microbiol. 12, 519–528 (2014).CAS 
PubMed 

Google Scholar 
Emerson, J. B. et al. Host-linked soil viral ecology along a permafrost thaw gradient. Nat. Microbiol. 3, 870–880 (2018). This study identified novel viral genomes from metagenomes and linked many of these viruses in silico to bacterial hosts and carbon metabolisms across the spatial gradient of permafrost thaw.CAS 
PubMed 
PubMed Central 

Google Scholar 
Ren, D., Madsen, J. S., Sørensen, S. J. & Burmølle, M. High prevalence of biofilm synergy among bacterial soil isolates in cocultures indicates bacterial interspecific cooperation. ISME J. 9, 81–89 (2015).CAS 
PubMed 

Google Scholar 
Lee, K. W. K. et al. Biofilm development and enhanced stress resistance of a model, mixed-species community biofilm. ISME J. 8, 894–907 (2014).CAS 
PubMed 

Google Scholar 
Witzgall, K. et al. Particulate organic matter as a functional soil component for persistent soil organic carbon. Nat. Commun. 12, 4115 (2021).CAS 
PubMed 
PubMed Central 

Google Scholar 
Frey, S. D. Mycorrhizal fungi as mediators of soil organic matter dynamics. Annu. Rev. Ecol. Evol. Syst. 50, 237–259 (2019).
Google Scholar 
Drigo, B. et al. Shifting carbon flow from roots into associated microbial communities in response to elevated atmospheric CO2. Proc. Natl Acad. Sci. USA 107, 10938–10942 (2010).CAS 
PubMed 
PubMed Central 

Google Scholar 
Kaiser, C. et al. Exploring the transfer of recent plant photosynthates to soil microbes: mycorrhizal pathway vs direct root exudation. N. Phytol. 205, 1537–1551 (2015).CAS 

Google Scholar 
Shah, F. et al. Ectomycorrhizal fungi decompose soil organic matter using oxidative mechanisms adapted from saprotrophic ancestors. N. Phytol. 209, 1705–1719 (2016).CAS 

Google Scholar 
Tisserant, E. et al. Genome of an arbuscular mycorrhizal fungus provides insight into the oldest plant symbiosis. Proc. Natl Acad. Sci. USA 110, 20117–20122 (2013).CAS 
PubMed 
PubMed Central 

Google Scholar 
Hestrin, R., Hammer, E. C., Mueller, C. W. & Lehmann, J. Synergies between mycorrhizal fungi and soil microbial communities increase plant nitrogen acquisition. Commun. Biol. 2, 233 (2019).PubMed 
PubMed Central 

Google Scholar 
Averill, C., Turner, B. L. & Finzi, A. C. Mycorrhiza-mediated competition between plants and decomposers drives soil carbon storage. Nature 505, 543–545 (2014).CAS 
PubMed 

Google Scholar 
Averill, C. & Hawkes, C. V. Ectomycorrhizal fungi slow soil carbon cycling. Ecol. Lett. 19, 937–947 (2016).PubMed 

Google Scholar 
Craig, M. E. et al. Tree mycorrhizal type predicts within-site variability in the storage and distribution of soil organic matter. Glob. Change Biol. 24, 3317–3330 (2018).
Google Scholar 
See, C. R. et al. Hyphae move matter and microbes to mineral microsites: Integrating the hyphosphere into conceptual models of soil organic matter stabilization. Glob. Change Biol. https://doi.org/10.1111/gcb.16073 (2022).Article 

Google Scholar 
Adamczyk, B., Sietiö, O.-M., Biasi, C. & Heinonsalo, J. Interaction between tannins and fungal necromass stabilizes fungal residues in boreal forest soils. N. Phytol. 223, 16–21 (2019).
Google Scholar 
Vidal, A. et al. Visualizing the transfer of organic matter from decaying plant residues to soil mineral surfaces controlled by microorganisms. Soil Biol. Biochem. 160, 108347 (2021).CAS 

Google Scholar 
Kallenbach, C. M., Wallenstein, M. D., Schipanksi, M. E. & Grandy, A. S. Managing agroecosystems for soil microbial carbon use efficiency: ecological unknowns, potential outcomes, and a path forward. Front. Microbiol. 10, 1146 (2019).PubMed 
PubMed Central 

Google Scholar 
Blagodatskaya, E., Blagodatsky, S., Anderson, T.-H. & Kuzyakov, Y. microbial growth and carbon use efficiency in the rhizosphere and root-free soil. PLoS ONE 9, e93282 (2014).PubMed 
PubMed Central 

Google Scholar 
Domeignoz-Horta, L. A. et al. Microbial diversity drives carbon use efficiency in a model soil. Nat. Commun. 11, 3684 (2020).CAS 
PubMed 
PubMed Central 

Google Scholar 
Fernandez, C. W. & Kennedy, P. G. Revisiting the ‘Gadgil effect’: do interguild fungal interactions control carbon cycling in forest soils? N. Phytol. 209, 1382–1394 (2016).CAS 

Google Scholar 
Nicolas, A. M. et al. Soil candidate phyla radiation bacteria encode components of aerobic metabolism and co-occur with nanoarchaea in the rare biosphere of rhizosphere grassland communities. mSystems 6, e0120520 (2021).PubMed 

Google Scholar 
Starr, E. P. et al. Stable isotope informed genome-resolved metagenomics reveals that Saccharibacteria utilize microbially-processed plant-derived carbon. Microbiome 6, 122 (2018).PubMed 
PubMed Central 

Google Scholar 
Pace, M. L. Bacterial mortality and the fate of bacterial production. Hydrobiologia 159, 41–49 (1988).
Google Scholar 
Cram, J. A., Parada, A. E. & Fuhrman, J. A. Dilution reveals how viral lysis and grazing shape microbial communities. Limnol. Oceanogr. 61, 889–905 (2016).
Google Scholar 
Ankrah, N. Y. D. et al. Phage infection of an environmentally relevant marine bacterium alters host metabolism and lysate composition. ISME J. 8, 1089–1100 (2014). This study demonstrated that in a marine environment, the mechanism of death (that is, phage infection) altered the biochemistry of microbial necromass relative to uninfected cells.CAS 
PubMed 

Google Scholar 
Lindeman, R. L. The trophic-dynamic aspect of ecology. Ecology 23, 399–417 (1942).
Google Scholar 
Clarholm, M. Interactions of bacteria, protozoa and plants leading to mineralization of soil nitrogen. Soil Biol. Biochem. 17, 181–187 (1985).CAS 

Google Scholar 
Pasternak, Z. et al. In and out: an analysis of epibiotic vs periplasmic bacterial predators. ISME J. 8, 625–635 (2014).CAS 
PubMed 

Google Scholar 
Lee, X., Wu, H.-J., Sigler, J., Oishi, C. & Siccama, T. Rapid and transient response of soil respiration to rain. Glob. Change Biol. 10, 1017–1026 (2004).
Google Scholar 
Schimel, J. P. Life in dry soils: effects of drought on soil microbial communities and processes. Annu. Rev. Ecol. Evol. Syst. 49, 409–432 (2018).
Google Scholar 
Granato, E. T., Meiller-Legrand, T. A. & Foster, K. R. The evolution and ecology of bacterial warfare. Curr. Biol. 29, R521–R537 (2019).CAS 
PubMed 

Google Scholar 
Bradford, M. A. et al. Managing uncertainty in soil carbon feedbacks to climate change. Nat. Clim. Change 6, 751–758 (2016).
Google Scholar 
Sierra, C. A. & Müller, M. A general mathematical framework for representing soil organic matter dynamics. Ecol. Monogr. 85, 505–524 (2015).
Google Scholar 
Wang, G. et al. Microbial dormancy improves development and experimental validation of ecosystem model. ISME J. 9, 226–237 (2015).CAS 
PubMed 

Google Scholar 
Wieder, W., Grandy, S., Kallenbach, M. & Bonan, B. Integrating microbial physiology and physio-chemical principles in soils with the MIcrobial-MIneral Carbon Stabilization (MIMICS) model. Biogeosciences 11, 3899–3917 (2014).
Google Scholar 
Allison, S. D. A trait-based approach for modelling microbial litter decomposition. Ecol. Lett. 15, 1058–1070 (2012). This paper described one of the first trait-based modelling approaches to link microbial community composition with physiological and enzymatic traits to predict litter decomposition in soil.CAS 
PubMed 

Google Scholar 
Kaiser, C., Franklin, O., Dieckmann, U. & Richter, A. Microbial community dynamics alleviate stoichiometric constraints during litter decay. Ecol. Lett. 17, 680–690 (2014).PubMed 
PubMed Central 

Google Scholar 
Ebrahimi, A. & Or, D. Microbial community dynamics in soil aggregates shape biogeochemical gas fluxes from soil profiles – upscaling an aggregate biophysical model. Glob. Change Biol. 22, 3141–3156 (2016). This paper presented a demonstration of how to upscale results from a mechanistic model of microbial activity in soil aggregates to scales of practical interest for hydrological and climate models.
Google Scholar 
Lajoie, G. & Kembel, S. W. Making the most of trait-based approaches for microbial ecology. Trends Microbiol. 27, 814–823 (2019). This opinion article discussed trait-based approaches in microbial ecology with a focus on utilization of large-scale datasets for improved ecological understanding.CAS 
PubMed 

Google Scholar 
Wang, G., Post, W. M. & Mayes, M. A. Development of microbial-enzyme-mediated decomposition model parameters through steady-state and dynamic analyses. Ecol. Appl. 23, 255–272 (2013).PubMed 

Google Scholar 
Moorhead, D. L. & Sinsabaugh, R. L. A theoretical model of litter decay and microbial interaction. Ecol. Monogr. 76, 151–174 (2006).
Google Scholar 
Kooijman, S. A. L. M., Muller, E. B. & Stouthamer, A. H. Microbial growth dynamics on the basis of individual budgets. Antonie Van Leeuwenhoek 60, 159–174 (1991).CAS 
PubMed 

Google Scholar 
Evans, S., Dieckmann, U., Franklin, O. & Kaiser, C. Synergistic effects of diffusion and microbial physiology reproduce the Birch effect in a micro-scale model. Soil Biol. Biochem. 93, 28–37 (2016).CAS 

Google Scholar 
Allison, S. D. Modeling adaptation of carbon use efficiency in microbial communities. Front. Microbiol. 5, 571 (2014).PubMed 
PubMed Central 

Google Scholar 
Hawkes, C. V. & Keitt, T. H. Resilience vs. historical contingency in microbial responses to environmental change. Ecol. Lett. 18, 612–625 (2015).PubMed 

Google Scholar 
Tang, J. & Riley, W. J. Weaker soil carbon–climate feedbacks resulting from microbial and abiotic interactions. Nat. Clim. Change 5, 56–60 (2015).CAS 

Google Scholar 
Zhang, Y. et al. Simulating measurable ecosystem carbon and nitrogen dynamics with the mechanistically-defined MEMS 2.0 model. Biogeosciences 18, 3147–3171 (2021).CAS 

Google Scholar 
Blankinship, J. C. et al. Improving understanding of soil organic matter dynamics by triangulating theories, measurements, and models. Biogeochemistry 140, 1–13 (2018).CAS 

Google Scholar 
Ebrahimi, A. N. & Or, D. Microbial dispersal in unsaturated porous media: Characteristics of motile bacterial cell motions in unsaturated angular pore networks. Water Resour. Res. 50, 7406–7429 (2014).
Google Scholar 
Tang, J. & Riley, W. J. A theory of effective microbial substrate affinity parameters in variably saturated soils and an example application to aerobic soil heterotrophic respiration. J. Geophys. Res. Biogeosci. 124, 918–940 (2019).
Google Scholar 
Manzoni, S., Schaeffer, S. M., Katul, G., Porporato, A. & Schimel, J. P. A theoretical analysis of microbial eco-physiological and diffusion limitations to carbon cycling in drying soils. Soil Biol. Biochem. 73, 69–83 (2014).CAS 

Google Scholar 
Brangarí, A. C., Fernàndez-Garcia, D., Sanchez-Vila, X. & Manzoni, S. Ecological and soil hydraulic implications of microbial responses to stress – a modeling analysis. Adv. Water Resour. 116, 178–194 (2018).
Google Scholar 
Alster, C. J., Weller, Z. D. & von Fischer, J. C. A meta-analysis of temperature sensitivity as a microbial trait. Glob. Change Biol. 24, 4211–4224 (2018).
Google Scholar 
Wang, G., Li, W., Wang, K. & Huang, W. Uncertainty quantification of the soil moisture response functions for microbial dormancy and resuscitation. Soil Biol. Biochem. 160, 108337 (2021).CAS 

Google Scholar 
Sierra, C. A., Trumbore, S. E., Davidson, E. A., Vicca, S. & Janssens, I. Sensitivity of decomposition rates of soil organic matter with respect to simultaneous changes in temperature and moisture. J. Adv. Model. Earth Syst. 7, 335–356 (2015).
Google Scholar 
Nunan, N., Schmidt, H. & Raynaud, X. The ecology of heterogeneity: soil bacterial communities and C dynamics. Philos. Trans. R. Soc. B Biol. Sci. 375, 20190249 (2020).CAS 

Google Scholar 
Kaiser, C., Franklin, O., Richter, A. & Dieckmann, U. Social dynamics within decomposer communities lead to nitrogen retention and organic matter build-up in soils. Nat. Commun. 6, 8960 (2015).CAS 
PubMed 

Google Scholar 
Craig, M. E., Mayes, M. A., Sulman, B. N. & Walker, A. P. Biological mechanisms may contribute to soil carbon saturation patterns. Glob. Change Biol. 27, 2633–2644 (2021).
Google Scholar 
Fan, X. et al. Improved model simulation of soil carbon cycling by representing the microbially derived organic carbon pool. ISME J. 15, 2248–2263 (2021).CAS 
PubMed 

Google Scholar 
Sulman, B. N. et al. Multiple models and experiments underscore large uncertainty in soil carbon dynamics. Biogeochemistry 141, 109–123 (2018). This paper addressed key uncertainties in the representation of microbial degradation and mineral stabilization in five microbially explicit soil carbon models.CAS 

Google Scholar 
Marschmann, G. L., Pagel, H., Kügler, P. & Streck, T. Equifinality, sloppiness, and emergent structures of mechanistic soil biogeochemical models. Environ. Model. Softw. 122, 104518 (2019).
Google Scholar 
Martiny, J. B. H., Jones, S. E., Lennon, J. T. & Martiny, A. C. Microbiomes in light of traits: a phylogenetic perspective. Science 350, aac9323 (2015).PubMed 

Google Scholar 
Malik, A. A., Thomson, B. C., Whiteley, A. S., Bailey, M. & Griffiths, R. I. Bacterial physiological adaptations to contrasting edaphic conditions identified using landscape scale metagenomics. mBio 8, e00799-17 (2017).PubMed 
PubMed Central 

Google Scholar 
Westoby, M. et al. Trait dimensions in bacteria and archaea compared to vascular plants. Ecol. Lett. 24, 1487–1504 (2021).PubMed 

Google Scholar 
Jung, M.-Y. et al. Ammonia-oxidizing archaea possess a wide range of cellular ammonia affinities. ISME J. 16, 272–283 (2022).CAS 
PubMed 

Google Scholar 
Kempes, C. P., Wang, L., Amend, J. P., Doyle, J. & Hoehler, T. Evolutionary tradeoffs in cellular composition across diverse bacteria. ISME J. 10, 2145–2157 (2016).CAS 
PubMed 
PubMed Central 

Google Scholar 
Dethlefsen, L. & Schmidt, T. M. Performance of the translational apparatus varies with the ecological strategies of bacteria. J. Bacteriol. 189, 3237–3245 (2007).CAS 
PubMed 
PubMed Central 

Google Scholar 
Andersen, K. H. et al. Characteristic sizes of life in the oceans, from bacteria to whales. Annu. Rev. Mar. Sci. 8, 217–241 (2016).CAS 

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 

Google Scholar 
Weissman, J. L., Hou, S. & Fuhrman, J. A. Estimating maximal microbial growth rates from cultures, metagenomes, and single cells via codon usage patterns. Proc. Natl Acad. Sci. USA 118, e2016810118 (2021).CAS 
PubMed 
PubMed Central 

Google Scholar 
Li, G., Rabe, K. S., Nielsen, J. & Engqvist, M. K. M. Machine learning applied to predicting microorganism growth temperatures and enzyme catalytic optima. ACS Synth. Biol. 8, 1411–1420 (2019).CAS 
PubMed 

Google Scholar 
Hungate, B. A. et al. Quantitative microbial ecology through stable isotope probing. Appl. Environ. Microbiol. 81, 7570–7581 (2015).CAS 
PubMed 
PubMed Central 

Google Scholar 
Couradeau, E. et al. Probing the active fraction of soil microbiomes using BONCAT-FACS. Nat. Commun. 10, 2770 (2019).PubMed 
PubMed Central 

Google Scholar 
Starr, E. P. et al. Stable-isotope-informed, genome-resolved metagenomics uncovers potential cross-kingdom interactions in rhizosphere soil. mSphere 6, e0008521 (2021).PubMed 

Google Scholar 
Rousk, J. & Bååth, E. Fungal and bacterial growth in soil with plant materials of different C/N ratios. FEMS Microbiol. Ecol. 62, 258–267 (2007).CAS 
PubMed 

Google Scholar 
Koechli, C., Campbell, A. N., Pepe-Ranney, C. & Buckley, D. H. Assessing fungal contributions to cellulose degradation in soil by using high-throughput stable isotope probing. Soil Biol. Biochem. 130, 150–158 (2019).CAS 

Google Scholar 
Wilhelm, R. C., Singh, R., Eltis, L. D. & Mohn, W. W. Bacterial contributions to delignification and lignocellulose degradation in forest soils with metagenomic and quantitative stable isotope probing. ISME J. 13, 413–429 (2019).CAS 
PubMed 

Google Scholar 
Neurath, R. A. et al. Root carbon interaction with soil minerals is dynamic, leaving a legacy of microbially derived residues. Environ. Sci. Technol. 55, 13345–13355 (2021).CAS 
PubMed 

Google Scholar 
Luo, Y. et al. Rice rhizodeposition promotes the build-up of organic carbon in soil via fungal necromass. Soil Biol. Biochem. 160, 108345 (2021).CAS 

Google Scholar 
Carini, P. et al. Relic DNA is abundant in soil and obscures estimates of soil microbial diversity. Nat. Microbiol. 2, 16242 (2016).PubMed 

Google Scholar 
Sharma, K., Palatinszky, M., Nikolov, G., Berry, D. & Shank, E. A. Transparent soil microcosms for live-cell imaging and non-destructive stable isotope probing of soil microorganisms. eLife 9, e56275 (2020).CAS 
PubMed 
PubMed Central 

Google Scholar 
Arellano-Caicedo, C., Ohlsson, P., Bengtsson, M., Beech, J. P. & Hammer, E. C. Habitat geometry in artificial microstructure affects bacterial and fungal growth, interactions, and substrate degradation. Commun. Biol. 4, 1226 (2021).PubMed 
PubMed Central 

Google Scholar 
Jansson, J. K. & Hofmockel, K. S. Soil microbiomes and climate change. Nat. Rev. Microbiol. 18, 35–46 (2020).CAS 
PubMed 

Google Scholar 
García-Palacios, P. et al. Evidence for large microbial-mediated losses of soil carbon under anthropogenic warming. Nat. Rev. Earth Env. 2, 507–517 (2021).
Google Scholar 
Schulz, F. et al. Hidden diversity of soil giant viruses. Nat. Commun. 9, 4881 (2018).PubMed 
PubMed Central 

Google Scholar 
Trubl, G. et al. Towards optimized viral metagenomes for double-stranded and single-stranded DNA viruses from challenging soils. PeerJ 7, e7265 (2019).PubMed 
PubMed Central 

Google Scholar 
Guo, J. et al. VirSorter2: a multi-classifier, expert-guided approach to detect diverse DNA and RNA viruses. Microbiome 9, 37 (2021).PubMed 
PubMed Central 

Google Scholar 
Sommers, P., Chatterjee, A., Varsani, A. & Trubl, G. Integrating viral metagenomics into an ecological framework. Annu. Rev. Virol. 8, 133–158 (2021).PubMed 

Google Scholar 
Pratama, A. A. & van Elsas, J. D. The ‘neglected’ soil virome–potential role and impact. Trends Microbiol. 26, 649–662 (2018).CAS 
PubMed 

Google Scholar 
Ghosh, D. et al. Prevalence of lysogeny among soil bacteria and presence of 16S rRNA and trzN genes in viral-community DNA. Appl. Environ. Microbiol. 74, 495–502 (2008).CAS 
PubMed 

Google Scholar 
Roux, S. et al. Ecogenomics and potential biogeochemical impacts of globally abundant ocean viruses. Nature 537, 689–693 (2016).CAS 
PubMed 

Google Scholar 
Howard-Varona, C. et al. Phage-specific metabolic reprogramming of virocells. ISME J. 14, 881–895 (2020).PubMed 
PubMed Central 

Google Scholar 
Howard-Varona, C. et al. Multiple mechanisms drive phage infection efficiency in nearly identical hosts. ISME J. 12, 1605–1618 (2018).PubMed 
PubMed Central 

Google Scholar 
Van Goethem, M. Characteristics of wetting-induced bacteriophage blooms in biological soil crust. mBio 10, e02287-19 (2019).PubMed 
PubMed Central 

Google Scholar 
Trubl, G. et al. Active virus-host interactions at sub-freezing temperatures in Arctic peat soil. Microbiome 9, 208 (2021).CAS 
PubMed 
PubMed Central 

Google Scholar 
Lee, S. et al. Methane-derived carbon flows into host–virus networks at different trophic levels in soil. Proc. Natl Acad. Sci. USA 118, e2105124118 (2021). This study used stable isotope probing metagenomics to connect, in situ, active virus–host infections with the biogeochemical process of methane oxidation in soil.CAS 
PubMed 
PubMed Central 

Google Scholar 
Bolduc, B., Youens-Clark, K., Roux, S., Hurwitz, B. L. & Sullivan, M. B. iVirus: facilitating new insights in viral ecology with software and community data sets imbedded in a cyberinfrastructure. ISME J. 11, 7–14 (2017).PubMed 

Google Scholar  More

Hutchinson, G. E. An Introduction to Population Ecology (Yale University, 1978).MATH 

Google Scholar 
Erb, J., Boyce, M. S. & Stenseth, N. C. Population dynamics of large and small mammals. Oikos 92, 3–12 (2001).
Google Scholar 
Gaillard, J. M., Festa-Bianchet, M. & Yoccoz, N. G. Population dynamics of large herbivores: Variable recruitment with constant adult survival. Trends Ecol. Evol. 13, 58–63 (1998).CAS 
PubMed 

Google Scholar 
Dennis, B., Ponciano, J. M., Lele, S. R., Taper, M. L. & Staples, D. F. Estimating density dependence, process noise, and observation error. Ecol. Monogr. 76, 323–341 (2006).
Google Scholar 
Rockwood, L. L. Introduction to Population Ecology (Wiley, 2015).
Google Scholar 
Caughley, G. Directions in conservation biology. J. Anim. Ecol. 63, 215–244 (1994).
Google Scholar 
Hoffmann, M. et al. The impact of conservation on the status of the world’s vertebrates. Science 330, 1503–1509 (2010).ADS 
CAS 
PubMed 

Google Scholar 
Ripple, W. J. et al. Collapse of the world’s largest herbivores. Sci. Adv. 1, e1400103 (2015).ADS 
PubMed 
PubMed Central 

Google Scholar 
Dickinson, A. B. A History of Sealing in the Falkland Island and Dependencies, 1764–1972. Doctoral dissertation, Scott Polar Research Institute, University of Cambridge. (1987).Clapham, P. J. & Baker, C. S. Whaling, modern. In Encyclopedia of Marine Mammals (eds Perrin, W. F. et al.) 1070–1074 (Academic Press, 2018).
Google Scholar 
Reeves, R. R. The origins and character of ‘aboriginal subsistence’ whaling: a global review. Mamm. Rev. 32, 71–106 (2002).
Google Scholar 
Reeves, R. R. Hunting of marine mammals. In Encyclopedia of Marine Mammals (eds Perrin, W. F. et al.) 585–588 (Academic Press, 2009).
Google Scholar 
Magera, A. M., Flemming, J. E. M., Kaschner, K., Christensen, L. B. & Lotze, H. K. Recovery trends in marine mammal populations. PLoS One 8, e77908 (2013).ADS 
CAS 
PubMed 
PubMed Central 

Google Scholar 
Tulloch, V. J., Plagányi, É. E., Matear, R., Brown, C. J. & Richardson, A. J. Ecosystem modelling to quantify the impact of historical whaling on Southern Hemisphere baleen whales. Fish Fish. 19, 117–137 (2017).
Google Scholar 
Best, P. B. Increase rates in severely depleted stocks of baleen whales. ICES J. Mar. Sci. 50, 169–186 (1993).
Google Scholar 
Townsend, C. H. The distribution of certain whales as shown by logbook records of American whaleships. Zool. Sci. Contr. New York Zool. Soc. 19, 1–50 (1935).MathSciNet 

Google Scholar 
Du Pasquier, J. T. Les baleiniers français au XIXe siècle, 1814–1868 (Terre et mer, 1982).Richards, R. Into the South Seas: The Southern Whale Fishery Comes of Age on the Brazil Banks 1765 to 1812 (The Paramatta Press, 1993).
Google Scholar 
International Whaling Commission. Report of the workshop on the comprehensive assessment of right whales: A worldwide comparison. J. Cetacean Res. Manag. (Special Issue) 2, 1–60 (2001).
Google Scholar 
Jackson, J., Patenaude, N., Carroll, E. & Baker, C. S. How few whales were there after whaling? Inference from contemporary mtDNA diversity. Mol. Ecol. 17, 236–251 (2008).CAS 
PubMed 

Google Scholar 
Tormosov, D. D. et al. Soviet catches of southern right whales Eubalaena australis, 1951–1971. Biological data and conservation implications. Biol. Conserv. 86, 185–197 (1998).
Google Scholar 
International Whaling Commission. Report of the IWC workshop on the assessment of Southern Right whales. J. Cetacean Res. Manag. (Supp) 14, 439–462 (2013).
Google Scholar 
Carroll, E. L. et al. Accounting for female reproductive cycles in a superpopulation capture–recapture framework. Ecol. Appl. 23, 1677–1690 (2013).CAS 
PubMed 

Google Scholar 
Brandão, A., Vermeulen, E., Ross-Gillespie, A., Findlay, K. & Butterworth, D. Updated application of a photo-identification based assessment model to southern right whales in South African waters, focussing on inferences to be drawn from a series of appreciably lower counts of calving females over 2015 to 2017. IWC SC/67b/SH/22 (2018).Bannister, J. L. Project A7- Monitoring Population Dynamics of ‘Western’ Right Whales off Southern Australia 2015–2018. Final report to National Environment Science Program, Australian Commonwealth Government. (2017).Stamation, K., Watson, M., Moloney, P., Charlton, C. & Bannister, J. L. Population estimate and rate of increase of Southern Right whales Eubalaena australis in Southeastern Australia. Endanger. Species Res. 41, 373–383 (2020).
Google Scholar 
Baker, C. S., Patenaude, N. J., Bannister, J. L., Robins, J. & Kato, H. Distribution and diversity of mtDNA lineages among southern right whales (Eubalaena australis) from Australia and New Zealand. Mar. Biol. 134, 1–7 (1999).CAS 

Google Scholar 
Patenaude, N. J. et al. Mitochondrial DNA diversity and population structure among southern right whales (Eubalaena australis). J. Hered. 98, 147–157 (2007).CAS 
PubMed 

Google Scholar 
Carroll, E. L. et al. Population structure and individual movement of southern right whales around New Zealand and Australia. Mar. Ecol. Prog. Ser. 432, 257–268 (2011).ADS 
CAS 

Google Scholar 
Cooke, J. G., Rowntree, V. J. & Payne, R. Estimates of demographic parameters for southern right whales (Eubalaena australis) observed off Península Valdés, Argentina. J. Cetacean Res. Manag. (Special Issue) 2, 125–132 (2001).
Google Scholar 
Cooke, J., Rowntree, V. & Sironi, M. Southwest Atlantic right whales: interim updated population assessment from photo-id collected at Península Valdéz, Argentina. IWC SC/66a/BRG/23 (2015).Crespo, E. A. et al. The southwestern Atlantic southern right whale, Eubalaena australis, population is growing but at a decelerated rate. Mar. Mamm. Sci. 35, 93–107 (2019).
Google Scholar 
Groch, K. R., Palazzo, J. T. Jr., Flores, P. A. C., Adler, F. R. & Fabian, M. E. Recent rapid increases in the right whale (Eubalaena australis) population off southern Brazil. LAJAM 4, 41–47 (2005).
Google Scholar 
Danilewicz, D., Moreno, I. B., Tavares, M. & Sucunza, F. Southern right whales (Eubalaena australis) off Torres, Brazil: group characteristics, movements, and insights into the role of the Brazilian-Uruguayan wintering ground. Mammalia 81, 225–234 (2017).
Google Scholar 
Costa, P., Praderi, R., Piedra, M. & Franco-Fraguas, P. Sightings of southern right whales, Eubalaena australis, off Uruguay. LAJAM 4, 157–161 (2005).
Google Scholar 
Lodi, L. & Tardelli, R. M. Southern right whale on the coast of Rio de Janeiro State, Brazil: Conflict between conservation and human activity. J. Mar. Biol. Ass. UK 87, 105–107 (2007).
Google Scholar 
Belgrano, J., Iñíguez, M., Gibbons, J., García, C. & Olavarría, C. South-west Atlantic right whales Eubalaena australis (Desmoulins, 1822) distribution nearby the Magellan Strait. Anales Instituto Patagonia (Chile) 36, 69–74 (2008).
Google Scholar 
Belgrano, J., Kröhling, F., Arcucci, D., Melcón, M. & Iñíguez, M. First Southern right whale aerial surveys in Golfo San Jorge, Santa Cruz, Argentina. IWC SC/63/BRG11 (2011).Arias, M. et al. Southern right whale Eubalaena australis in Golfo San Matías (Patagonia, Argentina): Evidence of recolonisation. PloS One 13(12), e0207524 (2018).CAS 
PubMed 
PubMed Central 

Google Scholar 
Mandiola, M. A. et al. Half a century of sightings data of southern right whales in Mar del Plata (Buenos Aires, Argentina). J. Mar. Biol. Ass. UK 100, 165–171 (2020).
Google Scholar 
Weir, C. R. & Stanworth, A. The Falkland Islands (Malvinas) as sub-Antarctic foraging, migratory and wintering habitat for southern right whales. J. Mar. Biol. Ass. UK 100, 153–163 (2020).
Google Scholar 
Du Pasquier, T. Catch history of French right whaling mainly in the South Atlantic. Right whales: Past and present status. Rep. Int. Whaling Commission (Special Issue) 10, 269–274 (1986).
Google Scholar 
Best, P. B. Estimates of the landed catch of right (and other whalebone) whales in the American fishery, 1805–1909. Fish. Bull. 85, 403–418 (1987).
Google Scholar 
Rowntree, V., Groch, K. R., Vilches, F. & Sironi, M. Sighting Histories of 124 Southern Right Whales Recorded off Both Southern Brazil and Península Valdés, Argentina, between 1971 and 2017. IWC SC/68B/CMP/20 (2020).Zerbini, A. N., et al. Satellite tracking of Southern right whales (Eubalaena australis) from Golfo San Matias, Rio Negro Province, Argentina. IWC SC/67b/CMP/17 (2018).Rowntree, V. J., Valenzuela, L. O., Fraguas, P. F. & Seger, J. Foraging behaviour of Southern Right Whales (Eubalaena australis) inferred from variation of carbon stable isotope ratios in their baleen. IWC SC/60/BRG23 (2008).Vighi, M. A. et al. Stable isotopes indicate population structuring in the Southwest Atlantic population of right whales (Eubalaena australis). PLoS One 9, e90489 (2014).ADS 
PubMed 
PubMed Central 

Google Scholar 
Valenzuela, L. O., Rowntree, V. J., Sironi, M. & Seger, J. Stable isotopes (d15N, d13C, d34S) in skin reveal diverse food sources used by southern right whales Eubalaena australis. Mar. Ecol. Prog. Ser. 603, 243–255 (2018).ADS 
CAS 

Google Scholar 
Ott, P. H., Flores, P. A. C., Freitas, T. R. O. & White, B. N. Genetic diversity and population structure of southern right whales, Eubalaena australis, from the Atlantic coast of South America. IWC SC/S11/RW25 (2011).Carroll, E. L. et al. Genetic diversity and connectivity of southern right whales (Eubalaena australis) found in the Brazil and Chile-Peru wintering grounds and the South Georgia (Islas Georgias del Sur) feeding ground. J. Hered. 111, 263–276 (2020).PubMed 
PubMed Central 

Google Scholar 
Smith, T. D., Reeves, R. R., Josephson, E. A. & Lund, J. N. Spatial and seasonal distribution of American whaling and whales in the age of sail. PLoS One 7, e34905 (2012).ADS 
CAS 
PubMed 
PubMed Central 

Google Scholar 
International Whaling Commission. Report of the Scientific Committee of the International Whaling Commission. J. Cetacean Res. Manage. 20 (Suppl.), 675 (2019).Peterson, B. W. South Atlantic whaling: 1603–1830. Ph.D. thesis, University of California. (1948).Palazzo, J. T. Jr., Groch, K. R. & Silveira, H. A. Projeto Baleia Franca: 25 anos de pesquisa e conservação, 1982–2007 (2007).Reeves, R. R. & Smith, T. D. A taxonomy of world whaling. In Whales, Whaling, and Ocean Ecosystems (eds Estes, J. A. et al.) 82–101 (University of California Press, 2006).
Google Scholar 
Richards, R. Past and present distributions of southern right whales (Eubalaena australis). N. Z. J. Zool. 36, 447–459 (2009).
Google Scholar 
Harcourt, R., Van Der Hoop, J., Kraus, S. & Carroll, E. L. Future directions in Eubalaena spp.: comparative research to inform conservation. Front. Mar. Sci. 5, 530 (2019).Cooke, J. G. & Zerbini, A. N. Eubalaena australis. IUCN Red List of Threatened Species 2018: e. T8153A50354147 (2018).Ellis, M. Aspectos da pesca da baleia no Brasil colonial. Rev. Hist. Sao Paulo 14, 1–126 (1958).
Google Scholar 
Ellis, M. A baleia no Brasil colonial (Melhoramentos, 1969).Palazzo, J. T., Jr. & Carter, L. A. A caça de baleias no Brasil (AGAPAN, 1983).Furniss, H. W. Whaling in Brazil. Bull. Int. Union Am. Republ. 2, 1048–1054 (1909).
Google Scholar 
Acosta y Lara, E. F. Un ballenero inglés en la Cisplatina. Hoy es Historia 24, 82–88 (1987).Moore, M. J. et al. Relative abundance of large whales around South Georgia (1979–1998). Mar. Mamm. Sci. 15, 1287–1302 (1999).
Google Scholar 
Comerlato, F. A baleia como recurso energético no Brasil. Simpósio Internacional de História Ambiental e Migrações 1119–1138 (2010).Comerlato, F. As armações baleeiras na configuração da costa catarinense em tempos coloniais. Tempos Históricos 15, 481–501 (2011).
Google Scholar 
de Morais, I. O. B. et al. From the southern right whale hunting decline to the humpback whaling expansion: A review of whale catch records in the tropical western South Atlantic Ocean. Mammal Rev. 47, 11–23 (2017).
Google Scholar 
American Whaling Logbook Data: A Database, by New Bedford Whaling Museum and Mystic Seaport Museum, Inc. Compilers Judith Lund and Tim Smith. Hosted at whalinghistory.org. (2020).BSWF Databases. 2020. Compilers–A. G. E. Jones; Dale Chatwin; Rhys Richards. Contributors–Jane Clayton; Mark Howard. Hosted at whalinghistory.orgFrench Offshore Whaling Voyages: A Database, https://whalinghistory.org/fv/, Mystic Seaport Museum, Inc. and New Bedford Whaling Museum.Allison, C. IWC summary catch database Version 6.1. (2016).Vighi, M. et al. The missing whales: relevance of “struck and lost” rates for the impact assessment of historical whaling in the southwestern Atlantic Ocean. ICES J. Mar. Sci. 78, 14–24 (2021).
Google Scholar 
McCullagh, P. & Nelder, J. Generalized Linear Models Monographs on Statistics and Applied Probability (Chapman and Hall, 1989).MATH 

Google Scholar 
Zuur, A. F., Ieno, E. N., Walker, N. J., Saveliev, A. A. & Smith, G. M. Mixed Effects Model and Extension in Ecology with R (Springer, 2009).MATH 

Google Scholar 
Ward, E., Zerbini, A., Kinas, P. G., Engel, M. H. & Adriolo, A. Estimates of growth rates of humpback whales (Megaptera novaeangliae) in the wintering grounds off the coast of Brazil (Breeding Stock A). J. Cetacean Res. Manag. (Special Issue) 3, 145–149 (2011).
Google Scholar 
R Core Team. R: A language and environment for statistical computing. R Foundation for Statistical Computing, Vienna, Austria (2019).Rowntree, V., Payne, R. & Schell, D. M. Changing patterns of habitat use by southern right whales (Eubalaena australis) on their nursery ground at Península Valdés, Argentina, and in their long-range movements. J. Cetacean Res. Manag. (Special Issue) 2, 133–143 (2001).
Google Scholar 
de Valpine, P. & Hastings, A. Fitting population models incorporating process noise and observation error. Ecol. Monogr. 72, 57–76 (2002).
Google Scholar 
Pella, J. J. & Tomlinson, P. K. A generalised stock production model. Int.-Am. Trop. Tuna Commun. Bull. 13, 421–496 (1969).
Google Scholar 
Zerbini, A. N. et al. Assessing the recovery of an Antarctic predator from historical exploitation. R. Soc. Open Sci. 6, 190368 (2019).ADS 
PubMed 
PubMed Central 

Google Scholar 
Halley, J. & Inchausti, P. Lognormality in ecological time series. Oikos 99, 518–530 (2002).
Google Scholar 
Best, J. K. & Punt, A. E. Parameterizations for Bayesian state-space surplus production models. Fish. Res. 222, 105411 (2020).
Google Scholar 
Walters, C. & Ludwig, D. Calculation of Bayes posterior probability distributions for key population parameters. Can. J. Fish. Aquat. Sci. 51, 713–722 (1994).
Google Scholar 
International Whaling Commission. Annex I: report of the sub-committee on stock definition. J. Cetacean Res. Manag. 13(Supp.), 233–241 (2012).Butterworth, D. S. & Punt, A. E. On the Bayesian approach suggested for the assessment of the Bering-Chukchi-Beaufort Seas stock of bowhead whales. Rep. Int. Whaling Commun. 45, 303–311 (1995).
Google Scholar 
McAllister, M. K., Pikitch, E. K., Punt, A. E. & Hilborn, R. Bayesian approach to stock assessment and harvest decisions using the sampling/importance resampling algorithm. Can. J. Fish. Aquat. Sci. 12, 2673–2687 (1999).
Google Scholar 
Best, P., Brandao, A. & Butterworth, D. Demographic parameters of southern right whales off South Africa. J. Cetacean Res. Manag. (Special Issue) 2, 161–169 (2001).
Google Scholar 
Punt, A. E. et al. Robustness of potential biological removal to monitoring, environmental, and management uncertainties. ICES J. Mar. Sci. 77, 2491–2507 (2020).
Google Scholar 
Pace, R. M., Corkeron, P. J. & Kraus, S. D. State-space mark-recapture estimates reveal a recent decline in abundance of North Atlantic right whales. Ecol. Evol. 7, 8730–8741 (2017).PubMed 
PubMed Central 

Google Scholar 
Sueyro, N., Crespo, E. A., Arias, M. & Coscarella, M. A. Density-dependent changes in the distribution of Southern right whales (Eubalaena australis) in the breeding ground Peninsula Valdés. PeerJ 6, e5957 (2018).PubMed 
PubMed Central 

Google Scholar 
Wilberg, M. J., Thorson, J. T., Linton, B. C. & Berkson, J. Incorporating time-varying catchability into population dynamic stock assessment models. Rev. Fish. Sci. 18, 7–24 (2010).
Google Scholar 
Kass, R. E. & Raftery, A. E. Bayes factors. J. Am. Stat. Assoc. 90, 773–795 (1995).MathSciNet 
MATH 

Google Scholar 
Chang, Y. J. et al. Model selection and multi-model inference for Bayesian surplus production models: a case study for Pacific blue and striped marlin. Fish. Res. 166, 129–139 (2015).CAS 

Google Scholar 
Barker, D. & Sibly, R. M. The effects of environmental perturbation and measurement error on estimates of the shape parameter in the theta-logistic model of population regulation. Ecol. Model. 219, 170–177 (2008).
Google Scholar 
Dennis, B., Ponciano, J. M. & Taper, M. L. Replicated sampling increases efficiency in monitoring biological populations. Ecology 91, 610–620 (2010).PubMed 

Google Scholar 
Punt, A. E. Review of contemporary cetacean stock assessment models. J. Cetacean Res. Manag. 17, 35–56 (2017).
Google Scholar 
Punt, A. E., Butterworth, D. S., de Moor, C. L., De Oliveira, J. A. & Haddon, M. Management strategy evaluation: best practices. Fish Fish. 17, 303–334 (2016).
Google Scholar 
Baker, C. S. & Clapham, P. J. Modelling the past and future of whales and whaling. Trends Ecol. Evol. 19, 365–371 (2004).
Google Scholar 
Lotze, H. K. & Worm, B. Historical baselines for large marine animals. Trends Ecol. Evol. 24, 254–262 (2009).PubMed 

Google Scholar 
Foley, C. M. & Lynch, H. J. A method to estimate pre-exploitation population size. Conserv. Biol. 34, 256–265 (2020).PubMed 

Google Scholar 
Collins, A. C., Böhm, M. & Collen, B. Choice of baseline affects historical population trends in hunted mammals of North America. Biol. Conserv. 242, 108421 (2020).
Google Scholar 
Jackson, J. A. et al. An integrated approach to historical population assessment of the great whales: Case of the New Zealand southern right whale. R. Soc. Open Sci. 3, 150669 (2016).ADS 
PubMed 
PubMed Central 

Google Scholar 
Richards, R. & Du Pasquier, T. Bay whaling off southern Africa, c. 1785–1805. S. Afr. J. Mar. Sci. 8, 231–250 (1989).
Google Scholar 
Brandão, A., Best, P. B. & Butterworth, D. S. Estimates of demographic parameters for southern right whales off South Africa from survey data from 1979 to 2006. IWC SC/62/BRG30 (2010).Rowntree, V. J. et al. Unexplained recurring high mortality of southern right whale Eubalaena australis calves at Península Valdés, Argentina. Mar. Ecol. Progr. Ser. 493, 275–289 (2013).ADS 

Google Scholar 
Valenzuela, L. O., Sironi, M., Rowntree, V. J. & Seger, J. Isotopic and genetic evidence for culturally inherited site fidelity to feeding grounds in southern right whales (Eubalaena australis). Mol. Ecol. 18, 782–791 (2009).CAS 
PubMed 

Google Scholar 
Clapham, P. J., Aguilar, A. & Hatch, L. T. Determining spatial and temporal scales for management: lessons from whaling. Mar. Mamm. Sci. 24, 183–201 (2008).
Google Scholar 
Baker, C. S. et al. Strong maternal fidelity and natal philopatry shape genetic structure in North Pacific humpback whales. Mar. Ecol. Prog. Ser. 494, 291–306 (2013).ADS 

Google Scholar 
González, C. V., Piola, A., O’Brien, T. D., Tormosov, D. D. & Acha, E. M. Circumpolar frontal systems as potential feeding grounds of Southern Right whales. Prog. Oceanogr. 176, 102123 (2019).
Google Scholar 
Leaper, R. et al. Global climate drives southern right whale (Eubalaena australis) population dynamics. Biol. Lett. 2, 289–292 (2006).PubMed 
PubMed Central 

Google Scholar 
Seyboth, E. et al. Southern right whale (Eubalaena australis) reproductive success is influenced by krill (Euphausia superba) density and climate. Sci. Rep. 6, 28205 (2016).ADS 
CAS 
PubMed 
PubMed Central 

Google Scholar 
Punt, A. E. Extending production models to include process error in the population dynamics. Can. J. Fish. Aquat. Sci. 60, 1217–1228 (2003).
Google Scholar 
Tulloch, V. J., Plagányi, É. E., Brown, C., Richardson, A. J. & Matear, R. Future recovery of baleen whales is imperiled by climate change. Glob. Change Biol. 25, 1263–1281 (2019).ADS 

Google Scholar 
Witting, L. Selection-delayed population dynamics in baleen whales and beyond. Popul. Ecol. 55, 377–401 (2013).
Google Scholar 
Pante, E. & Benoit, S.-B. marmap: A package for importing, plotting and analyzing bathymetric and topographic data in R. PLoS ONE 8, e73051 (2013).ADS 
CAS 
PubMed 
PubMed Central 

Google Scholar 
Wickham, H. ggplot2: Elegant Graphics for Data Analysis (Springer, 2016).MATH 

Google Scholar  More

Müller, K. Investigations on the organic drift in north Swedish streams. Rep. Inst. Freshw. Res. Drottningholm 35, 133–148 (1954).
Google Scholar 
Müller, K. Stream drift as a chronobiological phenomenon in running water ecosystems. Annu. Rev. Ecol. Syst. 5, 309–323 (1974).Article 

Google Scholar 
Waters, T. F. Interpretation of invertebrate drift in streams. Ecology 46, 327–334. https://doi.org/10.2307/1936336 (1965).Article 

Google Scholar 
Waters, T. F. The drift of stream insects. Annu. Rev. Entomol. 17, 253–272 (1972).Article 

Google Scholar 
Thiesmeier, B. Der Feuersalamander (Laurenti, 2004).
Google Scholar 
Hughes, D. A. Some factors affecting drift and upstream movements of Gammarus pulex. Ecology 51, 301–305. https://doi.org/10.2307/1933668 (1970).Article 

Google Scholar 
Humphries, S. & Ruxton, G. D. Is there really a drift paradox?. J. Anim. Ecol. 71, 151–154 (2002).Article 

Google Scholar 
Altig, R. & McDiarmid, R. W. In Tadpoles: The Biology of Anuran Larvae (eds McDiarmid, R. W. & Altig, R.) 24–51 (University of Chicago Press, 2000).
Google Scholar 
Sherratt, E., Vidal-García, M., Anstis, M. & Keogh, J. S. Adult frogs and tadpoles have different macroevolutionary patterns across the Australian continent. Nat. Ecol. Evol. 1, 1385–1391. https://doi.org/10.1038/s41559-017-0268-6 (2017).Article 
PubMed 

Google Scholar 
Griffiths, R. A. Newts and Salamanders of Europe (Poyser Natural History, 1996).
Google Scholar 
Cecala, K. K., Price, S. J. & Dorcas, M. E. Evaluating existing movement hypotheses in linear systems using larval stream salamanders. Can. J. Zool. 87, 292–298. https://doi.org/10.1139/z09-013 (2009).Article 

Google Scholar 
Grant, E. H. C., Nichols, J. D., Lowe, W. H. & Fagan, W. F. Use of multiple dispersal pathways facilitates amphibian persistence in stream networks. Proc. Natl. Acad. Sci. USA 107, 6936–6940. https://doi.org/10.1073/pnas.1000266107 (2010).ADS 
Article 

Google Scholar 
Lowe, W. H. Linking dispersal to local population dynamics: A case study using a headwater salamander system. Ecology 84, 2145–2154. https://doi.org/10.1890/0012-9658(2003)084[2145:LDTLPD]2.0.CO;2 (2003).Article 

Google Scholar 
Bruce, R. C. Upstream and downstream movements of Eurycea bislineata and other salamanders in a southern appalachian stream. Herpetologica 42, 149–155 (1986).
Google Scholar 
Thiesmeier, B. Untersuchungen zur Phänologie und Populationsdynamik des Feuersalamanders (Salamandra salamandra terrestris Lacépède, 1788) im Niederbergische Land (BRD). Zool. Jahrbücher Abteilung für Systematik Ökologie Geographie der Tiere 117, 331–353 (1990).
Google Scholar 
Thiesmeier, B. & Schuhmacher, H. Causes of larval drift of the fire salamander, Salamandra salamandra terrestris, and its effects on population dynamics. Oecologia 82, 259–263. https://doi.org/10.1007/BF00323543 (1990).ADS 
Article 
PubMed 

Google Scholar 
Reinhardt, T., Baldauf, L., Ilic, M. & Fink, P. Cast away: Drift as the main determinant for larval survival in western fire salamanders (Salamandra salamandra) in headwater streams. J. Zool. 306, 171–179 (2018).Article 

Google Scholar 
Baumgartner, N., Waringer, A. & Waringer, J. Hydraulic microdistribution patterns of larval fire salamanders (Salamandra salamandra salamandra) in the Weidlingbach near Vienna, Austria. Freshw. Biol. 41, 31–41. https://doi.org/10.1046/j.1365-2427.1999.00378.x (1999).Article 

Google Scholar 
Krause, E. T., Steinfartz, S. & Caspers, B. A. Poor nutritional conditions during the early larval stage reduce risk-taking activities of fire salamander larvae (Salamandra salamandra). Ethology 117, 416–421. https://doi.org/10.1111/j.1439-0310.2011.01886.x (2011).Article 

Google Scholar 
Veith, M. et al. Drift compensation in larval European fire salamanders, Salamandra salamandra (Amphibia: Urodela)?. Hydrobiologia 828, 315–325. https://doi.org/10.1007/s10750-018-3820-8 (2019).Article 

Google Scholar 
Arnold, A. Zur Verbreitung des Feuersalamanders im Tal der Zwickauer Mulde. Veröffentlichungen aus dem Museum für Naturkunde Karl-Marx-Stadt 71–79 (1983).Thiesmeier, B. Ökologie des Feuersalamanders (Westarp Wissenschaften, 1992).
Google Scholar 
Thiesmeier-Hornberg, B. Zur Ökologie und Populationsdynamik des Feuersalamanders (Salamandra salamandra terrestris Lacépède, 1788) im niederbergischen Land unter besonderer Berücksichtigung der Larvalphase. PhD thesis, Universität-Gesamthochschule Essen (1988).Thiesmeier, B. & Grossenbacher, K. Salamandra salamandra (Linnaeus, 1758)—Feuersalamander. In Die Amphibien und Reptilien Europas. Schwanzlurche IIB (eds Thiesmeier, B. & Grossenbacher, K.) 1059–1132 (Aula, 2004).
Google Scholar 
Reques, R. & Tejedo, M. Intraspecific aggressive behaviour in fire salamander larvae (Salamandra salamandra): The effects of density and body size. Herpetol. J. 6, 15–19 (1996).
Google Scholar 
Thiesmeier, B. & Günther, R. Feuersalamander: Salamandra salamandra (Linnaeus, 1758). In Die Amphibien und Reptilien Deutschlands (ed. Günther, R.) 82–104 (Fischer, 1996).
Google Scholar 
Wagner, N., Pfrommer, J. & Veith, M. Comparison of different methods to estimate abundances of larval fire salamanders (Salamandra salamandra) in first-order creeks. Salamandra 56, 265–274 (2020).
Google Scholar 
Peig, J. & Green, A. J. New perspectives for estimating body condition from mass/length data: The scaled mass index as an alternative method. Oikos 118, 1883–1891. https://doi.org/10.1111/j.1600-0706.2009.17643.x (2009).Article 

Google Scholar 
White, G. C. & Burnham, K. P. Program MARK: Survival estimation from populations of marked animals. Bird Study 46, S120–S139 (1999).Article 

Google Scholar 
Otis, D. L., Burnham, K. P., White, G. C. & Anderson, D. R. Statistical inference from capture data on closed animal populations. Wildl. Monogr. 62, 3–135 (1978).MATH 

Google Scholar 
Schwarz, C. J. & Arnason, A. N. A general methodology for the analysis of capture-recapture experiments in open populations. Biometrics 52, 860–873 (1996).MathSciNet 
Article 

Google Scholar 
Seber, G. A. A note on the multiple-recapture census. Biometrika 52, 249–259 (1965).MathSciNet 
CAS 
Article 

Google Scholar 
Jolly, G. M. Explicit estimates from capture-recapture data with both death and immigration-stochastic model. Biometrika 52, 225–247 (1965).MathSciNet 
CAS 
Article 

Google Scholar 
Cormack, R. Estimates of survival from the sighting of marked animals. Biometrika 51, 429–438 (1964).Article 

Google Scholar 
Razali, N. M. & Wah, Y. B. Power comparisons of Shapiro-Wilk, Kolmogorov-Smirnov, Lilliefors and Anderson-Darling tests. J. Stat. Model. Anal. 2, 21–33 (2011).
Google Scholar 
Bates, D., Mächler, M., Bolker, B. & Walker, S. Fitting linear mixed-effects models using lme4. J. Stat. Softw. 67, 1–48. https://doi.org/10.18637/jss.v067.i01 (2015).Article 

Google Scholar 
R Core Team. R: A Language and Environment for Statistical Computing (R Foundation for Statistical Computing, 2017).
Google Scholar 
Segev, O. & Blaustein, L. Influence of water velocity and predation risk on fire salamander (Salamandra infraimmaculata) larval drift among temporary pools in ephemeral streams. Freshw. Sci. 33, 950–957. https://doi.org/10.1086/676634 (2014).Article 

Google Scholar 
Montori, A., Llorente, G. & Richter-Boix, À. Habitat features affecting the small-scale distribution and longitudinal migration patterns of Calotriton asper in a Pre-Pyrenean population. Amphibia-Reptilia 29, 371–381. https://doi.org/10.1163/156853808785112048 (2008).Article 

Google Scholar 
Zakrzewski, M. Effect of definite temperature ranges on development metamorphosis and procreation of the spotted salamander larvae, Salamandra salamandra (L.). Acta Biol. Crac. Ser. Zool. 29, 77–83 (1987).
Google Scholar 
Degani, G., Goldenberg, S. & Warburg, M. R. Cannibalistic phenomena in Salamandra salamandra larvae in certain water bodies and under experimental conditions. Hydrobiologia 75, 123–128. https://doi.org/10.1007/BF00007425 (1980).Article 

Google Scholar 
Manenti, R., Ficetola, G. F. & De Bernardi, F. Water, stream morphology and landscape: Complex habitat determinants for the fire salamander Salamandra salamandra. Amphibia-Reptilia 30, 7–15. https://doi.org/10.1163/156853809787392766 (2009).Article 

Google Scholar 
Klewen, R. Landsalamander Europa: Teil1. Die Gattungen Salamandra und Mertensiella 2nd edn. (Ziemsen-Verlag, 1991).
Google Scholar 
Orth, R., Zscheischler, J. & Seneviratne, S. I. Record dry summer in 2015 challenges precipitation projections in Central Europe. Sci. Rep. 6, 1–8 (2016).Article 

Google Scholar 
Degani, G. Temperature selection in Salamandra salamandra (L.) larvae and juveniles from different habitats. Biol. Behav. 9, 175–183 (1984).
Google Scholar  More

Wilson, A. D. M. et al. Social networks in changing environments. Behav. Ecol. Sociobiol. 69, 1617–1629 (2015).
Google Scholar 
Ward, A. J. W. et al. Association patterns and shoal fidelity in the three–spined stickleback. Proc. R. Soc. Lond. Ser. B Biol. Sci. 269, 2451–2455 (2002).
Google Scholar 
Croft, D. P. et al. Assortative interactions and social networks in fish. Oecologia 143, 211–219 (2005).CAS 
PubMed 

Google Scholar 
Helfman, G. S. & Schultz, E. T. Social transmission of behavioural traditions in a coral reef fish. Anim. Behav. 32, 379–384 (1984).
Google Scholar 
Wong, M. Y. L., Buston, P. M., Munday, P. L. & Jones, G. P. The threat of punishment enforces peaceful cooperation and stabilizes queues in a coral-reef fish. Proc. R. Soc. B Biol. Sci. 274, 1093–1099 (2007).
Google Scholar 
King, A. J., Fehlmann, G., Biro, D., Ward, A. J. & Fürtbauer, I. Re-wilding collective behaviour: an ecological perspective. Trends Ecol. Evol. 33, 347–357 (2018).PubMed 

Google Scholar 
Bro-Jørgensen, J., Franks, D. W. & Meise, K. Linking behaviour to dynamics of populations and communities: application of novel approaches in behavioural ecology to conservation. Philos. Trans. R. Soc. B Biol. Sci. 374, 20190008 (2019).
Google Scholar 
Rose, G. A. Cod spawning on a migration highway in the north-west Atlantic. Nature 366, 458 (1993).
Google Scholar 
Wilson, A. D. M., Croft, D. P. & Krause, J. Social networks in elasmobranchs and teleost fishes. Fish Fish. 15, 676–689 (2014). This study reviewed the state of knowledge of the mechanisms and functions underpinning social network structure in fishes, including a discussion on methodological issues and developments in this area of research.Taborsky, M. & Wong, M. In Comparative Social Evolution (eds. Rubenstein, D. R., Abbot, P.) 354–389 (Cambridge University Press, 2017).Lusseau, D. Evidence for social role in a dolphin social network. Evol. Ecol. 21, 357–366 (2007).
Google Scholar 
Krause, J., James, R., Franks, D. W. & Croft, D. P. Animal social networks. (Oxford University Press, 2015).Smith, J. E. & Pinter‐Wollman, N. Observing the unwatchable: Integrating automated sensing, naturalistic observations and animal social network analysis in the age of big data. J. Anim. Ecol. 90, 62–75 (2021).PubMed 

Google Scholar 
Webber, Q. M. R. & Vander Wal, E. Trends and perspectives on the use of animal social network analysis in behavioural ecology: a bibliometric approach. Anim. Behav. 149, 77–87 (2019).
Google Scholar 
Aspillaga, E., Arlinghaus, R., Martorell-Barceló, M., Barcelo-Serra, M. & Alós, J. High-throughput tracking of social networks in marine fish populations. Front. Mar. Sci. 8, 794 (2021). This original and pioneering study demonstrated the use of high-resolution tracking to infer social behaviour and social structure in the marine environment.Silk, M. J., Jackson, A. L., Croft, D. P., Colhoun, K. & Bearhop, S. The consequences of unidentifiable individuals for the analysis of an animal social network. Anim. Behav. 104, 1–11 (2015).
Google Scholar 
Hughey, L. F., Hein, A. M., Strandburg-Peshkin, A. & Jensen, F. H. Challenges and solutions for studying collective animal behaviour in the wild. Philos. Trans. R. Soc. B Biol. Sci. 373, 20170005 (2018).
Google Scholar 
Hussey, N. E. et al. Aquatic animal telemetry: a panoramic window into the underwater world. Science 348, 1255642 (2015).PubMed 

Google Scholar 
Barkley, A. N. et al. A framework to estimate the likelihood of species interactions and behavioural responses using animal-borne acoustic telemetry transceivers and accelerometers. J. Anim. Ecol. 89, 146–160 (2020).PubMed 

Google Scholar 
Baktoft, H., Gjelland, K. Ø., Økland, F. & Thygesen, U. H. Positioning of aquatic animals based on time-of-arrival and random walk models using YAPS (Yet Another Positioning Solver). Sci. Rep. 7, 14294 (2017).PubMed 
PubMed Central 

Google Scholar 
Aspillaga, E. et al. Performance of a novel system for high-resolution tracking of marine fish societies. Anim. Biotelemetry 9, 1 (2021).
Google Scholar 
Jacoby, D. M. P., Papastamatiou, Y. P. & Freeman, R. Inferring animal social networks and leadership: applications for passive monitoring arrays. J. R. Soc. Interface 13, 20160676 (2016).PubMed 
PubMed Central 

Google Scholar 
Papastamatiou, Y. P., Meyer, C. G., Watanabe, Y. & Heithaus, M. in Shark Research: Emerging Technologies and Applications for the Field and Laboratory, (eds. Carrier, J. C., Heithaus, M. R., Simpfendorfer, C. A.) 83–119 (C. R. C. Press, 2018).Butcher, P. A. et al. The drone revolution of shark. Sci. A Rev. Drones 5, 8 (2021).
Google Scholar 
Hamede, R. K., Bashford, J., McCallum, H. & Jones, M. Contact networks in a wild Tasmanian devil (Sarcophilus harrisii) population: using social network analysis to reveal seasonal variability in social behaviour and its implications for transmission of devil facial tumour disease. Ecol. Lett. 12, 1147–1157 (2009).PubMed 

Google Scholar 
Sih, A., Spiegel, O., Godfrey, S., Leu, S. & Bull, C. M. Integrating social networks, animal personalities, movement ecology and parasites: a framework with examples from a lizard. Anim. Behav. 136, 195–205 (2018).
Google Scholar 
Carne, C., Semple, S., Morrogh-Bernard, H., Zuberbühler, K. & Lehmann, J. Predicting the vulnerability of great apes to disease: the role of superspreaders and their potential vaccination. PLoS ONE 8, e84642 (2013).PubMed 
PubMed Central 

Google Scholar 
Fielding, H. R. et al. Spatial and temporal variation in proximity networks of commercial dairy cattle in Great Britain. Prev. Vet. Med. 194, 105443 (2021).PubMed 
PubMed Central 

Google Scholar 
Haulsee, D. E. et al. Social network analysis reveals potential fission-fusion behavior in a shark. Sci. Rep. 6, 34087 (2016).CAS 
PubMed 
PubMed Central 

Google Scholar 
Merrick, M. J. & Koprowski, J. L. Should we consider individual behavior differences in applied wildlife conservation studies? Biol. Conserv. 209, 34–44 (2017).
Google Scholar 
Kressler, M. M., Gerlam, A., Spence-Jones, H. & Webster, M. M. Passive traps and sampling bias: Social effects and personality affect trap entry by sticklebacks. Ethology 127, 446–452 (2021).
Google Scholar 
Blumstein, D. T. In Social Behaviour (eds. Szekely, T., Moore, A. J., Komdeur, J.) 520–534 (Cambridge University Press, 2010).Berger-Tal, O. et al. A systematic survey of the integration of animal behavior into conservation. Conserv. Biol. 30, 744–753 (2016).PubMed 

Google Scholar 
Mucientes, G. R., Queiroz, N., Sousa, L. L., Tarroso, P. & Sims, D. W. Sexual segregation of pelagic sharks and the potential threat from fisheries. Biol. Lett. 5, 156–159 (2009).PubMed 
PubMed Central 

Google Scholar 
Mourier, J., Vercelloni, J. & Planes, S. Evidence of social communities in a spatially structured network of a free-ranging shark species. Anim. Behav. 83, 389–401 (2012).
Google Scholar 
Perryman, R. J. Y. et al. Social preferences and network structure in a population of reef manta rays. Behav. Ecol. Sociobiol. 73, 114 (2019).
Google Scholar 
He, P., Maldonado-Chaparro, A. A. & Farine, D. R. The role of habitat configuration in shaping social structure: a gap in studies of animal social complexity. Behav. Ecol. Sociobiol. 73, 9 (2019).
Google Scholar 
Mourier, J., Lédée, E. J. I. & Jacoby, D. M. P. A multilayer perspective for inferring spatial and social functioning in animal movement networks. bioRxiv https://www.biorxiv.org/content/10.1101/749085v1.full (2019).Snijders, L., Blumstein, D. T., Stanley, C. R. & Franks, D. W. Animal social network theory can help wildlife conservation. Trends Ecol. Evol. 32, 567–577 (2017). This review paper outlines how understanding of direct and indirect relationships between animals can be profitably applied by wildlife managers and conservationists.Beyer, K., Gozlan, R. E. & Copp, G. H. Social network properties within a fish assemblage invaded by non-native sunbleak Leucaspius delineatus. Ecol. Modell. 221, 2118–2122 (2010).
Google Scholar 
Hasenjager, M. J., Leadbeater, E. & Hoppitt, W. Detecting and quantifying social transmission using network-based diffusion analysis. J. Anim. Ecol. 90, 8–26 (2021).PubMed 

Google Scholar 
Fritzsche McKay, A. & Hoye, B. J. Are migratory animals superspreaders of infection? Integr. Comp. Biol. 56, 260–267 (2016).PubMed 

Google Scholar 
Albery, G. F., Kirkpatrick, L., Firth, J. A. & Bansal, S. Unifying spatial and social network analysis in disease ecology. J. Anim. Ecol. 90, 45–61 (2021).PubMed 

Google Scholar 
Salvanes, A. & Braithwaite, V. The need to understand the behaviour of fish reared for mariculture or restocking. ICES J. Mar. Sci. 63, 345–354 (2006).
Google Scholar 
Andrew, J. E., Holm, J., Kadri, S. & Huntingford, F. A. The effect of competition on the feeding efficiency and feed handling behaviour in gilthead sea bream (Sparus aurata L.) held in tanks. Aquaculture 232, 317–331 (2004).
Google Scholar 
Muñoz, L., Aspillaga, E., Palmer, M., Saraiva, J. L. & Arechavala-Lopez, P. Acoustic telemetry: a tool to monitor fish swimming behavior in sea-cage aquaculture. Front. Mar. Sci. 7, 645 (2020).
Google Scholar 
Macaulay, G., Bui, S., Oppedal, F. & Dempster, T. Challenges and benefits of applying fish behaviour to improve production and welfare in industrial aquaculture. Rev. Aquac. 13, 934–948 (2021).
Google Scholar 
Jacoby, D. M. P. et al. Social network analysis reveals the subtle impacts of tourist provisioning on the social behavior of a generalist marine apex predator. Front. Mar. Sci. 8, 1202 (2021).
Google Scholar 
Shizuka, D. & Johnson, A. E. How demographic processes shape animal social networks. Behav. Ecol. 31, 1–11 (2020).
Google Scholar 
Guerra, A. S., Kao, A. B., McCauley, D. J. & Berdahl, A. M. Fisheries-induced selection against schooling behaviour in marine fishes. Proc. R. Soc. B Biol. Sci. 287, 20201752 (2020).
Google Scholar 
Frisch, A. Sex-change and gonadal steroids in sequentially-hermaphroditic teleost fish. Rev. Fish. Biol. Fish. 14, 481–499 (2004).
Google Scholar 
Webber, Q. M. R. & Vander Wal, E. An evolutionary framework outlining the integration of individual social and spatial ecology. J. Anim. Ecol. 87, 113–127 (2018).PubMed 

Google Scholar 
Staveley, T. A. B. et al. Sea surface temperature dictates movement and habitat connectivity of Atlantic cod in a coastal fjord system. Ecol. Evol. 9, 9076–9086 (2019).PubMed 
PubMed Central 

Google Scholar 
Sosa, S., Jacoby, D. M. P., Lihoreau, M. & Sueur, C. Animal social networks: towards an integrative framework embedding social interactions, space and time. Methods Ecol. Evol. 12, 4–9 (2021).
Google Scholar 
Albery, G. F. et al. Multiple spatial behaviours govern social network positions in a wild ungulate. Ecol. Lett. 24, 676–686 (2021).PubMed 

Google Scholar 
Ellis, S. et al. Mortality risk and social network position in resident killer whales: sex differences and the importance of resource abundance. Proc. R. Soc. B Biol. Sci. 284, 20171313 (2017).
Google Scholar 
Ellis, S., Snyder-Mackler, N., Ruiz-Lambides, A., Platt, M. L. & Brent, L. J. N. Deconstructing sociality: the types of social connections that predict longevity in a group-living primate. Proc. R. Soc. B Biol. Sci. 286, 20191991 (2019).
Google Scholar 
Kohn, G. M. Friends give benefits: autumn social familiarity preferences predict reproductive output. Anim. Behav. 132, 201–208 (2017).
Google Scholar 
Villegas-Ríos, D., Freitas, C., Moland, E., Thorbjørnsen, S. H. & Olsen, E. M. Inferring individual fate from aquatic acoustic telemetry data. Methods Ecol. Evol. 11, 1186–1198 (2020).
Google Scholar 
Mourier, J., Bass, N. C., Guttridge, T. L., Day, J. & Brown, C. Does detection range matter for inferring social networks in a benthic shark using acoustic telemetry? R. Soc. open Sci. 4, 170485 (2017).PubMed 
PubMed Central 

Google Scholar 
Vanovac, S., Howard, D., Monk, C. T., Arlinghaus, R. & Giabbanelli, P. J. Network analysis of intra- and interspecific freshwater fish interactions using year-around tracking. J. R. Soc. Interface 18, 20210445 (2021).PubMed 

Google Scholar 
Dahl, K. A., Patterson, W. F. & Snyder, R. A. Experimental assessment of lionfish removals to mitigate reef fish community shifts on northern Gulf of Mexico artificial reefs. Mar. Ecol. Prog. Ser. 558, 207–221 (2016).
Google Scholar 
Fitzpatrick, J. L. et al. Female-mediated causes and consequences of status change in a social fish. Proc. R. Soc. B Biol. Sci. 275, 929–936 (2008).CAS 

Google Scholar 
Mourier, J., Brown, C. & Planes, S. Learning and robustness to catch-and-release fishing in a shark social network. Biol. Lett. 13, 20160824 (2017).PubMed 
PubMed Central 

Google Scholar 
Rutledge, L. Y. et al. Protection from harvesting restores the natural social structure of eastern wolf packs. Biol. Conserv. 143, 332–339 (2010).
Google Scholar 
Jacoby, D. M. P. et al. Synergistic patterns of threat and the challenges facing global anguillid eel conservation. Glob. Ecol. Conserv 4, 321–333 (2015).
Google Scholar 
Geffroy, B., Bru, N., Dossou-Gbété, S., Tentelier, C. & Bardonnet, A. The link between social network density and rank-order consistency of aggressiveness in juvenile eels. Behav. Ecol. Sociobiol. 68, 1073–1083 (2014).
Google Scholar  More

Lombard, M. et al. South African and Lesotho Stone Age sequence updated. S. Afr. Archaeol. Bull. 67, 120–144 (2012).
Google Scholar 
Kandel, A. W. et al. Increasing behavioral flexibility? An integrative macro-scale approach to understanding the Middle Stone Age of southern Africa. J. Archaeol. Method Theory 23, 623–628 (2015).
Google Scholar 
Porraz, G. et al. Experimentation preceding innovation in a MIS5 Pre-Still Bay layer from Diepkloof Rock Shelter (South Africa): emerging technologies and symbols. Preprint at EcoEvoRxiv https://ecoevorxiv.org/ch53r/ (2020).Texier, P. J. et al. A Howiesons Poort tradition of engraving ostrich eggshell containers dated to 60,000 years ago at Diepkloof Rock Shelter, South Africa. Proc. Natl Acad. Sci. USA 107, 6180–6185 (2010).CAS 
PubMed 
PubMed Central 

Google Scholar 
Henshilwood, C. S. et al. Klipdrift Shelter, southern Cape, South Africa: preliminary report on the Howiesons Poort layers. J. Archaeol. Sci. 45, 284–303 (2014).
Google Scholar 
Powell, A., Shennan, S. & Thomas, M. G. Late Pleistocene demography and the appearance of modern human behavior. Science 324, 1298–1301 (2009).CAS 
PubMed 

Google Scholar 
Marean, C. W. The transition to foraging for dense and predictable resources and its impact on the evolution of modern humans. Phil. Trans. R. Soc. B 371, 20150239 (2016).PubMed 
PubMed Central 

Google Scholar 
Mackay, A., Stewart, B. A. & Chase, B. M. Coalescence and fragmentation in the late Pleistocene archaeology of southernmost Africa. J. Hum. Evol. 72, 26–51 (2014).PubMed 

Google Scholar 
Wilkins, J. et al. Innovative Homo sapiens behaviours 105,000 years ago in a wetter Kalahari. Nature 592, 248–252 (2021).CAS 
PubMed 

Google Scholar 
Dewar, G. & Stewart, B. A. Preliminary results of excavations at Spitzkloof Rockshelter, Richtersveld, South Africa. Quat. Int. 270, 30–39 (2012).
Google Scholar 
Cowling, R. M. & Pierce, S. Namaqualand: A Succulent Desert (Fernwood Press, 1999).Hijmans, R. J., Cameron, S. E., Parra, J. L., Jones, P. G. & Jarvis, A. Very high resolution interpolated climate surfaces for global land areas. Int. J. Climatol. 25, 1965–1978 (2005).
Google Scholar 
Mucina, L. et al. in The Vegetation of South Africa, Lesotho and Swaziland (eds Mucina, L. & Rutherford, M. C.) 221–299 (SANBI, 2006).Rebelo, A. G., Boucher, C., Helme, N., Mucina, L. & Rutherford, M. C. in The Vegetation of South Africa, Lesotho and Swaziland (eds Mucina, L. & Rutherford, M. C.) 53–219 (SANBI, 2006).Marean, C. W. et al. in Fynbos: Ecology, Evolution, and Conservation of a Megadiverse Region (eds Allsopp, N. et al.) 164–199 (Oxford Univ. Press, 2014).Carr, A. S., Chase, B. M. & Mackay, A. in Africa from MIS 6-2: Population Dynamics and Paleoenvironments (eds Jones, S. & Stewart, B. A.) 23–47 (Springer, 2016).Chase, B. M. & Meadows, M. E. Late Quaternary dynamics of southern Africa’s winter rainfall zone. Earth Sci. Rev. 84, 103–138 (2007).
Google Scholar 
Steele, T. E. et al. Varsche Rivier 003: a Middle and Later Stone Age site with Still Bay and Howiesons Poort assemblages in southern Namaqualand, South Africa. Paleoanthropology 2016, 100–163 (2016).
Google Scholar 
Sharp, W. D. et al. 230Th/U burial dating of ostrich eggshell. Quat. Sci. Rev. 219, 263–276 (2019).
Google Scholar 
Chase, B. M. et al. South African speleothems reveal influence of high- and low-latitude forcing over the last 113.5 kyr. Geology 49, 1353–1357 (2021).CAS 

Google Scholar 
Chase, B. M. et al. Influence of tropical easterlies in southern Africa’s winter rainfall zone during the Holocene. Quat. Sci. Rev. 107, 138–148 (2015).
Google Scholar 
Manning, J. Namaqualand (Briza, 2008).Skinner, J. D. & Chimimba, C. T. The Mammals of the Southern African Subregion 3rd edn (Cambridge Univ. Press, 2005).Skead, C. J. Historical Mammal Incidence in the Cape Province Vol 1: The Western and Northern Cape (Cape Town Department of Nature and Environmental Conservation, 1980).Churcher, C. S. Distribution and history of the Cape zebra (Equus capensis) in the Quaternary of Africa. Trans. R. Soc. S. Afr. 61, 89–95 (2006).
Google Scholar 
Spratt, R. M. & Lisiecki, L. E. A Late Pleistocene sea level stack. Climate 12, 1079–1092 (2016).
Google Scholar 
De Wet, W. Bathymetry of the South African Continental Shelf. MSc thesis, Univ. Cape Town (2013).Jerardino, A. & Marean, C. W. Shellfish gathering, marine paleoecology and modern human behavior: perspectives from Cave PP13B, Pinnacle Point, South Africa. J. Hum. Evol. 59, 412–424 (2010).PubMed 

Google Scholar 
Marean, C. W. Pinnacle Point Cave 13B (Western Cape Province, South Africa) in context: the Cape Floral kingdom, shellfish, and modern human origins. J. Hum. Evol. 59, 425–443 (2010).PubMed 

Google Scholar 
Kandel, A. W. Modification of ostrich eggs by carnivores and its bearing on the interpretation of archaeological and paleontological find. J. Archaeol. Sci. 31, 377–391 (2004).
Google Scholar 
Steele, T. E. & Klein, R. G. The Middle and Later Stone Age faunal remains from Diepkloof Rock Shelter, Western Cape, South Africa. J. Archaeol. Sci. 40, 3453–3462 (2013).
Google Scholar 
Klein, R. G. et al. The Ysterfontein 1 Middle Stone Age site, South Africa, and early human exploitation of coastal resources. Proc. Natl Acad. Sci. USA 101, 5708–5715 (2004).CAS 
PubMed 
PubMed Central 

Google Scholar 
Vogelsang, R. et al. New excavations of Middle Stone Age deposits at Apollo 11 Rockshelter, Namibia: stratigraphy, archaeology, chronology and past environments. J. Afr. Archaeol. 8, 185–218 (2010).Marean, C. W. et al. Early human use of marine resources and pigment in South Africa during the Middle Pleistocene. Nature 449, 905–908 (2007).CAS 
PubMed 

Google Scholar 
Schmidt, I. et al. New investigations at the Middle Stone Age site of Pockenbank Rockshelter, Namibia. Antiquity 90, e2 (2016).
Google Scholar 
Vogelsang, R. Middle Stone Age Fundstellen in Südwest-Namibia, Africa (Heinrich-Barth-Institut, 1998).Plug, I. Aquatic animals and their associates from the Middle Stone Age levels at Sibudu. South. Afr. Humanit. 18, 289–299 (2006).
Google Scholar 
Wurz, S. Technological trends in the Middle Stone Age of South Africa between MIS 7 and MIS 3. Curr. Anthropol. 54, S305–S319 (2013).
Google Scholar 
Volman, T. P. The Middle Stone Age in the Southern Cape. PhD thesis, Univ. Chicago (1981).Schmidt, P. & Mackay, A. Why was silcrete heat-treated in the Middle Stone Age? An early transformative technology in the context of raw material use at Mertenhof Rock Shelter, South Africa. PloS ONE 11, e0149243 (2016).PubMed 
PubMed Central 

Google Scholar 
Porraz, G. et al. Technological successions in the Middle Stone Age sequence of Diepkloof Rock Shelter, Western Cape, South Africa. J. Archaeol. Sci. 40, 3376–3400 (2013).
Google Scholar 
Schmid, V., Conard, N. J., Parkington, J., Texier, P. J. & Porraz, G. The ‘MSA 1’ of Elands Bay Cave (South Africa) in the context of the southern African early MSA technologies. South. Afr. Humanit. 29, 153–201 (2016).
Google Scholar 
Evans, U. Hollow Rock Shelter, a Middle Stone Age site in the Cederberg. South. Afr. Field Archaeol. 3, 63–73 (1994).
Google Scholar 
Mackay, A., Jacobs, Z. & Steele, T. E. Pleistocene archaeology and chronology of Putslaagte 8 (PL8) rockshelter, Western Cape, South Africa. J. Afr. Archaeol. 13, 71–98 (2015).
Google Scholar 
Thompson, J. C. et al. Ecological risk, demography and technological complexity in the Late Pleistocene of northern Malawi: implications for geographical patterning in the Middle Stone Age. J. Quat. Sci. 33, 261–284 (2018).
Google Scholar 
Vaesen, K. & Houkes, W. Is human culture cumulative? Curr. Anthropol. 62, 218–238 (2021).
Google Scholar 
Boyd, R., Richerson, P. J. & Henrich, J. The cultural niche: why social learning is essential for human adaptation. Proc. Natl Acad. Sci. USA 108, 10918–10925 (2011).CAS 
PubMed 
PubMed Central 

Google Scholar 
Sterelny, K. From hominins to humans: how sapiens became behaviourally modern. Phil. Trans. R. Soc. B 366, 809–822 (2011).PubMed 
PubMed Central 

Google Scholar 
Gärdenfors, P. & Högberg, A. The archaeology of teaching and the evolution of Homo docens. Curr. Anthropol. 58, 188–208 (2017).
Google Scholar 
Marwick, B. Pleistocene exchange networks as evidence for the evolution of language. Camb. Archaeol. J. 13, 67–81 (2003).
Google Scholar 
Blegen, N. The earliest long-distance obsidian transport: evidence from the ∼200 ka Middle Stone Age Sibilo School Road Site, Baringo, Kenya. J. Hum. Evol. 103, 1–19 (2017).PubMed 

Google Scholar 
McBrearty, S. & Brooks, A. S. The revolution that wasn’t: a new interpretation of the origin of modern human behavior. J. Hum. Evol. 39, 453–563 (2000).CAS 
PubMed 

Google Scholar 
Klein, R. G. Archeology and the evolution of human behavior. Evol. Anthropol. 9, 17–36 (2000).
Google Scholar 
Wynn, T. & Coolidge, F. L. Archeological insights into hominin cognitive evolution. Evol. Anthropol. 25, 200–213 (2016).PubMed 

Google Scholar 
Derex, M. & Mesoudi, A. Cumulative cultural evolution within evolving population structures. Trends Cogn. Sci. 24, 654–667 (2020).PubMed 

Google Scholar 
Sterelny, K. Adaptable individuals and innovative lineages. Phil. Trans. R. Soc. B 371, 20150196 (2016).PubMed 
PubMed Central 

Google Scholar 
Henshilwood, C. S. & Marean, C. W. The origin of modern human behavior: critique of the models and their test implications. Curr. Anthropol. 44, 627–651 (2003).PubMed 

Google Scholar 
Stoops, G., Marcelino, V. & Mees, F. (eds) Interpretation of Micromorphological Features of Soils and Regoliths (Elsevier, 2010).Stoops, G. Guidelines for Analysis and Description of Soil and Regolith Thin Sections (Soil Science Society of America, 2003).McPherron, S. P. Additional statistical and graphical methods for analyzing site formation processes using artifact orientations. PLoS ONE 13, e0190195 (2018).PubMed 
PubMed Central 

Google Scholar 
Thomsen, K. J., Murray, A. S., Jain, M. & Bøtter-Jensen, L. Laboratory fading rates of various luminescence signals from feldspar-rich sediment extracts. Radiat. Meas. 43, 1474–1486 (2008).CAS 

Google Scholar 
Armitage, S. J. & Bailey, R. M. The measured dependence of laboratory beta dose rates on sample grain size. Radiat. Meas. 39, 123–127 (2005).CAS 

Google Scholar 
Huntley, D. J. & Lamothe, M. Ubiquity of anomalous fading in K-feldspars and the measurement and correction for it in optical dating. Can. J. Earth Sci. 38, 1093–1106 (2001).CAS 

Google Scholar 
Jaffey, A. H., Flynn, K. F., Glendenin, L. E. & Essling, A. M. Precision measurement of half-lives and specific activities of 235U and 238U. Phys. Rev. C 4, 1889–1906 (1971).
Google Scholar 
Cheng, H. et al. Improvements in 230Th dating, 230Th and 234U half-life values, and U–Th isotopic measurements by multi-collector inductively coupled plasma mass spectrometry. Earth Planet. Sci. Lett. 371–372, 82–91 (2013).
Google Scholar 
Holden, N. E. Total half-lives for selected nuclides. Pure Appl. Chem. 62, 941–958 (1990).CAS 

Google Scholar 
Ludwig, K. R. Isoplot/Ex Version 3.75: A Geochronological Toolkit for Microsoft Excel (Berkeley Geochronology Center Special Publication, 2010).Collins, B. & Steele, T. E. An often overlooked resource: ostrich (Struthio spp.) eggshell in the archaeological record. J. Archaeol. Sci. Rep. 13, 121–131 (2017).
Google Scholar 
Schmidt, P. How reliable is the visual identification of heat treatment on silcrete? A quantitative verification with a new method. Archaeol. Anthropol. Sci. 11, 713–726 (2017).
Google Scholar 
Roberts, D. L. Age, Genesis and Significance of South African Coastal Belt Silcretes (Council for Geoscience, South Africa, 2003).Schmidt, P. et al. A previously undescribed organic residue sheds light on heat treatment in the Middle Stone Age. J. Hum. Evol. 85, 22–34 (2015).PubMed 

Google Scholar 
Trabucco, A. & Zomer, R. Global Aridity Index and Potential Evapotranspiration (ET0) Climate Database v2 (figshare, 2019); https://doi.org/10.6084/m9.figshare.7504448.v3World Atlas of Desertification 2nd edn (UNEP, 1997).Mucina, L. & Rutherford, M. C. The Vegetation of South Africa, Lesotho and Swaziland (South African National Biodiversity Institute, 2006).Cordova, C. E. C3 Poaceae and Restionaceae phytoliths as potential proxies for reconstructing winter rainfall in South Africa. Quat. Int. 287, 121–140 (2013).
Google Scholar 
Esteban, I. et al. Modern soil phytolith assemblages used as proxies for paleoscape reconstruction on the south coast of South Africa. Quat. Int. 434, 160–179 (2017).
Google Scholar 
Laskar, J. et al. A long-term numerical solution for the insolation quantities of the Earth. Astron. Astrophys. 428, 261–285 (2004).
Google Scholar 
Chase, B. M. et al. Orbital controls on Namib Desert hydroclimate over the past 50,000 years. Geology 47, 867–871 (2019).
Google Scholar 
Farmer, E. C., deMenocal, P. B. & Marchitto, T. M. Holocene and deglacial ocean temperature variability in the Benguela upwelling region: implications for low‐latitude atmospheric circulation. Paleoceanography 20, PA2018 (2005).
Google Scholar 
Pichevin, L., Cremer, M., Giraudeau, J. & Bertrand, P. A 190 kyr record of lithogenic grain size on the Namibian slope: forging a tight link between past wind‐strength and coastal upwelling dynamics. Mar. Geol. 218, 81–96 (2005).
Google Scholar 
Little, M. G. et al. Trade wind forcing of upwelling, seasonality, and Heinrich events as a response to sub‐Milankovitch climate variability. Paleoceanography 12, 568–576 (2005).
Google Scholar 
Stuut, J.-B. et al. A 300‐kyr record of aridity and wind strength in southwestern Africa: inferences from grain‐size distributions of sediments on Walvis Ridge, SE Atlantic. Mar. Geol. 180, 221–233 (2002).
Google Scholar 
Kandel, A. W. & Conard, N. J. Production sequences of ostrich eggshell beads and settlement dynamics in the Geelbek Dunes of the Western Cape, South Africa. J. Archaeol. Sci. 32, 1711–1721 (2005).
Google Scholar  More

Trenberth, K. E. & Jones, P. D. in Climate Change 2007: The Physical Science Basis (eds Solomon, S. et al.) 235–335 (Cambridge Univ. Press, 2007).Linderholm, H. W. Growing season changes in the last century. Agr. For. Meteorol. 137, 1–14 (2006).
Google Scholar 
Yang, B. et al. New perspective on spring vegetation phenology and global climate change based on Tibetan Plateau tree-ring data. Proc. Natl Acad. Sci. USA 114, 6966–6971 (2017).CAS 
PubMed 
PubMed Central 

Google Scholar 
Shen, M., Tang, Y., Chen, J. & Yang, W. Specification of thermal growing season in temperate China from 1960 to 2009. Clim. Change 114, 783–798 (2012).
Google Scholar 
Zhou, B., Zhai, P., Chen, Y. & Yu, R. Projected changes of thermal growing season over Northern Eurasia in a 1.5 °C and 2 °C warming world. Environ. Res. Lett. 13, 35004 (2018).
Google Scholar 
Barichivich, J., Briffa, K. R., Osborn, T. J., Melvin, T. M. & Caesar, J. Thermal growing season and timing of biospheric carbon uptake across the Northern Hemisphere. Glob. Biogeochem. Cycles 26, B4015 (2012).
Google Scholar 
Buitenwerf, R., Rose, L. & Higgins, S. I. Three decades of multi-dimensional change in global leaf phenology. Nat. Clim. Change 5, 364–368 (2015).
Google Scholar 
Gonsamo, A., Chen, J. M. & Ooi, Y. W. Peak season plant activity shift towards spring is reflected by increasing carbon uptake by extratropical ecosystems. Glob. Change Biol. 24, 2117–2128 (2018).
Google Scholar 
Menzel, A. et al. European phenological response to climate change matches the warming pattern. Glob. Change Biol. 12, 1969–1976 (2006).
Google Scholar 
Montgomery, R. A., Rice, K. E., Stefanski, A., Rich, R. L. & Reich, P. B. Phenological responses of temperate and boreal trees to warming depend on ambient spring temperatures, leaf habit, and geographic range. Proc. Natl Acad. Sci. USA 117, 10397–10405 (2020).CAS 
PubMed 
PubMed Central 

Google Scholar 
Piao, S. et al. Leaf onset in the northern hemisphere triggered by daytime temperature. Nat. Commun. 6, 6911 (2015).CAS 
PubMed 

Google Scholar 
Barichivich, J. et al. Large-scale variations in the vegetation growing season and annual cycle of atmospheric CO2 at high northern latitudes from 1950 to 2011. Glob. Change Biol. 19, 3167–3183 (2013).
Google Scholar 
Peñuelas, J., Rutishauser, T. & Filella, I. Phenology feedbacks on climate change. Science 324, 887–888 (2009).PubMed 

Google Scholar 
Piao, S. et al. Weakening temperature control on the interannual variations of spring carbon uptake across northern lands. Nat. Clim. Change 7, 359–363 (2017).CAS 

Google Scholar 
Richardson, A. D. et al. Influence of spring and autumn phenological transitions on forest ecosystem productivity. Philos. Trans. R. Soc. B 365, 3227–3246 (2010).
Google Scholar 
Bonan, G. B. Forests and climate change: forcings, feedbacks, and the climate benefits of forests. Science 320, 1444–1449 (2008).CAS 
PubMed 

Google Scholar 
Piao, S. et al. Plant phenology and global climate change: current progresses and challenges. Glob. Change Biol. 25, 1922–1940 (2019).
Google Scholar 
Fu, Y. H. et al. Declining global warming effects on the phenology of spring leaf unfolding. Nature 526, 104–107 (2015).CAS 
PubMed 

Google Scholar 
Park, T. et al. Changes in timing of seasonal peak photosynthetic activity in northern ecosystems. Glob. Change Biol. 25, 2382–2395 (2019).
Google Scholar 
Xu, C., Liu, H., Williams, A. P., Yin, Y. & Wu, X. Trends toward an earlier peak of the growing season in Northern Hemisphere mid-latitudes. Glob. Change Biol. 22, 2852–2860 (2016).
Google Scholar 
Wang, X. et al. No trends in spring and autumn phenology during the global warming hiatus. Nat. Commun. 10, 2389 (2019).PubMed 
PubMed Central 

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, B3018 (2007).
Google Scholar 
Buermann, W., Bikash, P. R., Jung, M., Burn, D. H. & Reichstein, M. Earlier springs decrease peak summer productivity in North American boreal forests. Environ. Res. Lett. 8, 24027 (2013).
Google Scholar 
Buermann, W. et al. Widespread seasonal compensation effects of spring warming on northern plant productivity. Nature 562, 110–114 (2018).CAS 
PubMed 

Google Scholar 
Lian, X. et al. Summer soil drying exacerbated by earlier spring greening of northern vegetation. Sci. Adv. 6, eaax0255 (2020).PubMed 
PubMed Central 

Google Scholar 
Piao, S. et al. Net carbon dioxide losses of northern ecosystems in response to autumn warming. Nature 451, 49–52 (2008).CAS 
PubMed 

Google Scholar 
Wang, H. et al. Alpine grassland plants grow earlier and faster but biomass remains unchanged over 35 years of climate change. Ecol. Lett. 23, 701–710 (2020).PubMed 
PubMed Central 

Google Scholar 
Pan, Y. et al. A large and persistent carbon sink in the world’s forests. Science 333, 988–993 (2011).CAS 
PubMed 

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).
Google Scholar 
Huang, J. et al. Photoperiod and temperature as dominant environmental drivers triggering secondary growth resumption in Northern Hemisphere conifers. Proc. Natl Acad. Sci. USA 117, 20645–20652 (2020).CAS 
PubMed 
PubMed Central 

Google Scholar 
Li, X. et al. Critical minimum temperature limits xylogenesis and maintains treelines on the southeastern Tibetan Plateau. Sci. Bull. 62, 804–812 (2017).
Google Scholar 
Rossi, S. et al. Critical temperatures for xylogenesis in conifers of cold climates. Glob. Ecol. Biogeogr. 17, 696–707 (2008).
Google Scholar 
Lenz, A., Vitasse, Y., Hoch, G. & Körner, C. Growth and carbon relations of temperate deciduous tree species at their upper elevation range limit. J. Ecol. 102, 1537–1548 (2014).
Google Scholar 
Zeng, Q., Rossi, S., Yang, B., Qin, C. & Li, G. Environmental drivers for cambial reactivation of Qilian junipers (Juniperus przewalskii) in a semi-arid region of northwestern China. Atmosphere 11, 232 (2020).
Google Scholar 
Ren, P. et al. Growth rate rather than growing season length determines wood biomass in dry environments. Agr. For. Meteorol. 271, 46–53 (2019).
Google Scholar 
Sanginés De Cárcer, P. et al. Vapor-pressure deficit and extreme climatic variables limit tree growth. Glob. Change Biol. 24, 1108–1122 (2017).
Google Scholar 
Zhang, J. et al. Drought limits wood production of Juniperus przewalskii even as growing seasons lengthens in a cold and arid environment. Catena 196, 104936 (2021).
Google Scholar 
Huang, J., Deslauriers, A. & Rossi, S. Xylem formation can be modeled statistically as a function of primary growth and cambium activity. New Phytol. 203, 831–841 (2014).CAS 
PubMed 

Google Scholar 
Rossi, S., Morin, H. & Deslauriers, A. Causes and correlations in cambium phenology: towards an integrated framework of xylogenesis. J. Exp. Bot. 63, 2117–2126 (2012).CAS 
PubMed 

Google Scholar 
Rossi, S., Girard, M. J. & Morin, H. Lengthening of the duration of xylogenesis engenders disproportionate increases in xylem production. Glob. Change Biol. 20, 2261–2271 (2014).
Google Scholar 
Cuny, H. E. et al. Woody biomass production lags stem-girth increase by over one month in coniferous forests. Nat. Plants 1, 15160 (2015).CAS 
PubMed 

Google Scholar 
Pasho, E., Camarero, J. J. & Vicente-Serrano, S. M. Climatic impacts and drought control of radial growth and seasonal wood formation in Pinus halepensis. Trees 26, 1875–1886 (2012).
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).CAS 

Google Scholar 
Chen, L. et al. Leaf senescence exhibits stronger climatic responses during warm than during cold autumns. Nat. Clim. Change 10, 777–780 (2020).CAS 

Google Scholar 
Körner, C. Paradigm shift in plant growth control. Curr. Opin. Plant Biol. 25, 107–114 (2015).PubMed 

Google Scholar 
Muller, B. et al. Water deficits uncouple growth from photosynthesis, increase C content, and modify the relationships between C and growth in sink organs. J. Exp. Bot. 62, 1715–1729 (2011).CAS 
PubMed 

Google Scholar 
Charney, N. D. et al. Observed forest sensitivity to climate implies large changes in 21st century North American forest growth. Ecol. Lett. 19, 1119–1128 (2016).PubMed 

Google Scholar 
Liu, Q. et al. Extension of the growing season increases vegetation exposure to frost. Nat. Commun. 9, 426 (2018).PubMed 
PubMed Central 

Google Scholar 
Deslauriers, A. & Morin, H. Intra-annual tracheid production in balsam fir stems and the effect of meteorological variables. Trees 19, 402–408 (2005).
Google Scholar 
Piao, S. et al. Characteristics, drivers and feedbacks of global greening. Nat. Rev. Earth Environ. 1, 14–27 (2020).
Google Scholar 
Huang, M. et al. Air temperature optima of vegetation productivity across global biomes. Nat. Ecol. Evol. 3, 772–779 (2019).PubMed 
PubMed Central 

Google Scholar 
Keenan, T. F. & Riley, W. J. Greening of the land surface in the world’s cold regions consistent with recent warming. Nat. Clim. Change 8, 825–828 (2018).CAS 

Google Scholar 
Camarero, J. J., Olano, J. M. & Parras, A. Plastic bimodal xylogenesis in conifers from continental Mediterranean climates. New Phytol. 185, 471–480 (2010).PubMed 

Google Scholar 
Fu, Y. H. et al. Unexpected role of winter precipitation in determining heat requirement for spring vegetation green-up at northern middle and high latitudes. Glob. Change Biol. 20, 3743–3755 (2014).
Google Scholar 
Wu, X. et al. Uneven winter snow influence on tree growth across temperate China. Glob. Change Biol. 25, 144–154 (2018).
Google Scholar 
Wang, X. et al. Disentangling the mechanisms behind winter snow impact on vegetation activity in northern ecosystems. Glob. Change Biol. 24, 1651–1662 (2018).
Google Scholar 
Adams, H. D. et al. Experimental drought and heat can delay phenological development and reduce foliar and shoot growth in semiarid trees. Glob. Change Biol. 21, 4210–4220 (2015).
Google Scholar 
He, W., Liu, H., Qi, Y., Liu, F. & Zhu, X. Patterns in nonstructural carbohydrate contents at the tree organ level in response to drought duration. Glob. Change Biol. 26, 3627–3638 (2020).
Google Scholar 
Williams, A. P. et al. Temperature as a potent driver of regional forest drought stress and tree mortality. Nat. Clim. Change 3, 292–297 (2012).
Google Scholar 
Vitasse, Y. et al. Contrasting resistance and resilience to extreme drought and late spring frost in five major European tree species. Glob. Change Biol. 25, 3781–3792 (2019).
Google Scholar 
Zhao, S. et al. The International Tree-Ring Data Bank (ITRDB) revisited: data availability and global ecological representativity. J. Biogeogr. 46, 355–368 (2019).
Google Scholar 
Babst, F., Poulter, B., Bodesheim, P., Mahecha, M. D. & Frank, D. C. Improved tree-ring archives will support earth-system science. Nat. Ecol. Evol. 1, 8 (2017).PubMed 

Google Scholar 
Elmore, A. J., Guinn, S. M., Minsley, B. J. & Richardson, A. D. Landscape controls on the timing of spring, autumn, and growing season length in mid-Atlantic forests. Glob. Change Biol. 18, 656–674 (2012).
Google Scholar 
Kannenberg, S. A. et al. Drought legacies are dependent on water table depth, wood anatomy and drought timing across the eastern US. Ecol. Lett. 22, 119–127 (2018).PubMed 

Google Scholar 
Rossi, S., Deslauriers, A., Anfodillo, T. & Carraro, V. Evidence of threshold temperatures for xylogenesis in conifers at high altitudes. Oecologia 152, 1–12 (2007).PubMed 

Google Scholar 
Gao, S. et al. Dynamic responses of tree-ring growth to multiple dimensions of drought. Glob. Change Biol. 24, 5380–5390 (2018).
Google Scholar 
Peltier, D. M. P. & Ogle, K. Tree growth sensitivity to climate is temporally variable. Ecol. Lett. 23, 1561–1572 (2020).PubMed 

Google Scholar 
Wilmking, M. et al. Global assessment of relationships between climate and tree growth. Glob. Change Biol. 26, 3212–3220 (2020).
Google Scholar 
Seftigen, K., Frank, D. C., Björklund, J., Babst, F. & Poulter, B. The climatic drivers of normalized difference vegetation index and tree-ring-based estimates of forest productivity are spatially coherent but temporally decoupled in Northern Hemispheric forests. Glob. Ecol. Biogeogr. 27, 1352–1365 (2018).
Google Scholar 
Bunn, A. G. A dendrochronology program library in R (dplR). Dendrochronologia 26, 115–124 (2008).
Google Scholar 
R Core Team. R: A Language and Environment for Statistical Computing (R Foundation for Statistical Computing, 2019); https://www.R-project.org/Sheffield, J., Goteti, G. & Wood, E. F. Development of a 50-year high-resolution global dataset of meteorological forcings for land surface modeling. J. Clim. 19, 3088–3111 (2006).
Google Scholar 
Frich, P. L. et al. Observed coherent changes in climatic extremes during the second half of the twentieth century. Clim. Res. 19, 193–212 (2002).
Google Scholar 
Selyaninov, G. T. About climate agricultural estimation (in Russian). Proc. Agric. Meteorol. 20, 165–177 (1928).
Google Scholar 
Streiner, D. L. Finding our way: an introduction to path analysis. Can. J. Psychiatry 50, 115–122 (2005).PubMed 

Google Scholar 
Fox, J., Nie, Z. & Byrnes, J. sem: Structural equation models. R package version 3.1-9 https://CRAN.R-project.org/package=sem (2017).Iturbide, M. et al. An update of IPCC climate reference regions for subcontinental analysis of climate model data: definition and aggregated datasets. Earth Syst. Sci. Data 12, 2959–2970 (2020).
Google Scholar 
Bagozzi, R. P. & Yi, Y. Specification, evaluation, and interpretation of structural equation models. J. Acad. Mark. Sci. 40, 8–34 (2012).
Google Scholar  More

Global map of forest cover change and its validationWe use a high-resolution map of global forest cover change15 (annual intervals at 30 m spatial resolution; version 1.7) to quantify forest loss across the tropics. The GFC dataset maps where and when forests were converted (naturally and anthropogenically) from 2001 to 2019. Trees are defined as all vegetation taller than 5 m in height, and forests are defined with a tree canopy threshold of at least 30%. The definition of forests includes plantations and tree crops such as oil palm. Forest loss is the mortality or removal of all tree cover within a pixel.Our study uses the v.1.7 product that spans the period 2001–2019 for the analysis. Different methods are used for detecting forest cover loss in two periods (2001–2010 and 2011–2019). This change in detection method as well as in satellite data (Landsat 7 and Landsat 8) might result in inconsistencies of data during the two periods. Therefore, we perform an independent assessment of the v.1.7 product throughout the study period (2001–2019) using stratified random-sample reference data. We randomly sample 18,000 pixels, a much larger sample population than the assessment of the original product (v.1.0, 628 pixels across the tropics; ref. 15), and visually interpret forest loss using Landsat imagery. Specifically, we randomly select 50 path/row locations (World Reference System II) of Landsat imagery in each tropical continent (150 path/row locations in total in the three tropical continents; Supplementary Fig. 6). For each path/row location, we randomly select 20 loss pixels and 10 non-loss pixels in each period of 2001–2005, 2006–2010, 2011–2014 and 2015–2019, with total sampling pixels of ~18,000 ((20 loss + 10 non-loss) × 50 path/row locations × 4 periods × 3 continents). Our study compares the increase in forest carbon loss from the start (~2001) to the end (~2019) of the study period. Thus, we divided the whole 19 yr study period into four subperiods (2001–2005, 2006–2010, 2011–2014 and 2015–2019), with the first five years considered as the start period and the last five years considered as the end period for the comparison. Some path/row locations do not have 20 loss pixels in a specific period; for example, there is no loss detected in some locations of the Sahara. Therefore, we sample 11,198 loss pixels and 6,000 non-loss pixels (Supplementary Data). Finally, we download time-series Landsat imagery covering 1999–2020 to visually interpret these pixels as reference data.Following the suggestion of Global Forest Watch19 and per best practice guidance of ref. 18, we use a stratified random-sample approach for area estimation, which is independent of the method and satellite changes in the GFC data. The sampling reference data (Supplementary Data) are used to estimate loss area:$${it{p}}_{hi} = w_hfrac{{mathop {sum}limits_{j = 1}^{150} {n_{hij} times A_{hi}} }}{{mathop {sum}limits_{j = 1}^{150} {n_{hj} times A_i} }}$$
(1)
where phi is the stratified random-sample estimated area for GFC map class h that is classified as reference class i; wh is the proportion of the total area GFC map class h; nhij is the number of pixels in GFC map class h that is classified as reference class i in jth Landsat path/row location; nhj is the total number of pixels in GFC map class h in jth Landsat path/row location; Ahj is the total area in GFC map class h in jth Landsat path/row location; and Aj is the total land area of jth Landsat path/row location.We then calculate the error matrix, which includes overall accuracy (OA), user’s accuracy (UA) and producer’s accuracy (PA), as follows:$${mathrm{OA}} = mathop {sum}limits_{h = 1}^H {p_{hh}}$$
(2)
$${mathrm{UA}}_h = frac{{p_{hh}}}{{p_h}}$$
(3)
$${mathrm{PA}}_j = frac{{p_{jj}}}{{p_j}}$$
(4)
where UAh is the UA for stratum h; ph and pj are the total area in stratum h and j, respectively; and PAj is the PA in stratum j.Using the preceding equations, we estimate OA, UA and PA in the four periods (Supplementary Table 2). In general, OAs are >99%, UAs are >88% and PAs are >72% in each period. The stratified random-sample approach for area estimation is considered the most robust method to investigate loss trends in GFC product and can avoid inconsistencies of the dataset due to changes in detection model and satellite sensors19. Our results show that the forest loss from the stratified random-sample approach is similar to GFC mapped loss, both of which show a consistent increase during the four periods of 2001–2019 (Fig. 1a), confirming the increasing forest carbon loss across the tropics during the early twenty-first century.Forest carbon stocksWe estimate forest (aboveground and belowground) biomass carbon losses by co-locating GFC loss data with corresponding biomass data. Forest biomass maps are not universally reliable, owing to uncertainties and some degree of bias. We use four biomass maps to quantify forest carbon stocks, which helps reduce the uncertainties and bias44. The four maps were developed by refs. 9,48,49,20 and are hereafter referred to as ‘Baccini’, ‘Saatchi’, ‘Avitabile’ and ‘Zarin’ maps, respectively. The Baccini map, derived from Moderate Resolution Imaging Spectroradiometer (MODIS) data, presents aboveground live woody biomass (AGB) across the tropics at 500 m spatial resolution. The Saatchi map, also derived from MODIS data, presents total forest carbon stocks at 1 km spatial resolution across the tropics. The Avitabile map, integrated from the Baccini and Saatchi maps, shows AGB at 1 km resolution across the tropics. The Zarin map, derived from Landsat data, presents AGB across the globe at 30 m resolution.Belowground root biomass (BGB) data are sparse because measurements of BGB are time consuming, laborious and technically challenging50. Thus, we calculate BGB (in Mg ha−1 biomass) from AGB maps (Baccini, Avitabile and Zarin) using an empirical model at the pixel level51:$${mathrm{BGB}} = 0.489 times {mathrm{AGB}}^{0.89}$$
(5)
Total forest biomass is calculated as the sum of AGB and BGB. Finally, total forest carbon stocks (MgC ha−1) in live woody forest are estimated as 50% of total biomass20,50. The Saatchi map provides total forest carbon stocks rather than AGB. The total forest carbon stocks are calculated from AGB using the same method mentioned in the preceding50. Thus, we estimate AGB and BGB from total forest carbon stocks in the Saatchi map using the preceding method to separate aboveground and belowground parts of forest carbon stocks.There are inconsistencies in the MODIS-derived biomass maps (Baccini, Saatchi and Avitabile) and Landsat-derived GFC data41, which may underestimate forest carbon loss by the three coarse forest biomass maps (Supplementary Fig. 7). The Zarin map is derived from Landsat data and considers tree cover using GFC data, and tree loss can be co-located with the corresponding biomass20, indicating the consistencies of the Zarin map and GFC. Therefore, to correct the three biomass maps with coarse resolution and reduce the inconsistencies, we resample the Zarin map from 30 m to 500 m (the resolution of the Baccini map) and 1 km (the resolutions of the Avitabile and Baccini maps) and calculate forest carbon loss using the two resampled biomass maps. We then estimate the ratios of forest carbon loss derived from the 30 m biomass map to the forest carbon loss derived from resampled biomass maps in each GFC tile (10° × 10°). The ratios are then used as a scale factor to correct the three biomass maps (Baccini, Saatchi and Avitabile). For the resampling, forest carbon density at 500 m or 1 km is averaged from all 30 m pixels in the corresponding locations.Soil organic carbon stocksDeforestation not only causes forest biomass carbon loss, but also results in loss of SOC10. We calculate SOC loss at 0–30 cm soil depth as:$${mathrm{SOC}}_{{{{mathrm{loss}}}}} = {mathrm{OCS}} times theta$$
(6)
where SOCloss is SOC loss resulting from forest loss measured in MgC ha−1, OCS is SOC stocks at 0–30 cm depth measured in MgC ha−1 and θ is the SOC loss rate.We obtain SOC stocks at 0–30 cm depth from SoilGrids (version 2.0), created by the International Soil Reference and Information Centre52. SOC stocks are calculated using a calibrated quantile random forest model at a spatial resolution of 250 m. We further resample the data from 250 m to 30 m using the nearest-neighbour method to match the scale of forest cover loss data.SOC loss rate is affected predominantly by land-use types following forest loss and tree species53. The loss rate data are compiled from a previous meta-analysis, which summarizes the rate of SOC loss resulting from forest loss over the tropics50. SOC losses resulting from primary and secondary forest loss differ (Supplementary Table 3). We use a map of primary humid tropical forests in 2001 developed by ref. 54 to classify primary and secondary forests. The land covers following forest loss are determined according to the driver of forest loss (see Drivers of forest carbon loss). The spatial resolution of the SOC data is coarse compared with GFC data, which may result in inconsistencies in the calculation. However, as SOC loss resulting from forest loss accounts for a small proportion of total forest carbon loss (8%), we ignore the potential inconsistencies.Drivers of forest carbon lossWe determine drivers of tree-cover loss using the dataset generated by ref. 27. This dataset shows the dominant driver of tree-cover loss at each 10 km grid cell for 2001–2019. There are five categories of drivers of tree-cover loss: commodity-driven deforestation, defined as permanent and/or long-term clearing of trees to other land uses (for example, commodity croplands), shifting agriculture, forestry, wildfire and urbanization. Commodity-driven deforestation, shifting agriculture and forestry dominate tropical forest loss27. Thus, wildfire and urbanization are combined and categorized as ‘others’. In tropical Africa, spatial patterns of commodity-driven deforestation are almost similar to that of shifting agriculture, as pointed out by ref. 27, resulting in large uncertainties in separating commodity agriculture from shifting agriculture. Since commodity-driven deforestation is usually for large-scale agricultural plantations, we treat commodity-driven deforestation as loss for large-scale agriculture. Because shifting agriculture is usually smallholder and/or patchy farming systems, we term shifting agriculture as small-scale agriculture, which may include commodity agriculture with similar spatial patterns to shifting agriculture in some regions such as tropical Africa. The driver data are created using decision-tree models trained by ~5,000 high-resolution Google Earth imagery cells, showing overall accuracy of 89 ± 3% from a separate validation of more than 1,500 randomly selected cells. We resample the data from 10 km resolution to 30 m using the nearest-neighbour method to match the scale of forest cover loss data.Interpretation of post-forest-loss land covers in 2020We collect cloudless and very-high-resolution satellite imagery in 2020 from Planet to determine the fate of the agriculture-driven forest loss during 2001–2019. Planet provides two products, RapidEye (at a spatial resolution of 5 m) and Doves (at a spatial resolution of 3 m; 4-band PlanetScope Scene). We randomly sample 500 pixels that show forest loss owing to agriculture expansion during 2001–2005 and 500 pixels that show forest loss owing to agriculture expansion during 2015–2019, then check cloudless satellite imagery in 2020 to visually interpret the land cover of each sampled pixel in 2020. We classify three types of land cover: agricultural land, forest/shrubland and others (Supplementary Fig. 4). Rubber and oil-palm plantations are classified as agricultural lands.Uncertainty and methods for analysisWe use committed emissions of forest carbon, even though some of this carbon will be lost only in later years or transited to other carbon pools or stored as wood products12. Forest carbon loss is defined as gross carbon loss due to forest removal (as indicated by GFC product), including (aboveground and belowground) forest biomass carbon and SOC losses. We calculate only the gross loss of forest carbon stocks while gain of carbon via reforestation and afforestation is not considered.We first estimate reference sample-based forest loss area using sample data:$${mathrm{AS}}_j = {mathrm{AM}}_jmathop {sum}limits_{h = 1}^H {p_{hj}}$$
(7)
where ASj and AMj are reference sample-based forest loss areas and mapped forest loss areas in stratum j, respectively.To estimate forest carbon loss (aboveground, belowground and soil carbon) using sample-based forest loss area, we apply a ‘stratify and multiply’ approach21,22 by assigning mean forest carbon density for each stratum. Aboveground and belowground forest carbon losses are estimated using four biomass maps (Baccini, Saatchi, Avitabile and Zarin), and we report ensemble mean ± s.d. from the four maps as our best estimate.Mountain forest carbon loss at different elevations is calculated by overlaying mountain polygon from the Global Mountain Biodiversity Assessment inventory55 (version 1.2) and the 30 m ASTER Global Digital Elevation Model56 (version 3).Although our validation shows that the GFC (v.1.7) product could accurately map forest loss, we cannot reduce the omission and commission errors. In addition, the accuracy of the disturbance year is 75.2%, with 96.7% of the disturbance occurring within one year before or after the estimated disturbance year in GFC product15. Therefore, we calculate 3 yr moving averages of annual forest and related carbon losses for time-series analysis, following the suggestion of the Global Forest Watch19 and refs. 28,38. We use a non-parametric Theil–Sen estimator regression method53 to detect trends in time-series results and test the significance of the trend by Mann–Kendall test57.Reporting SummaryFurther information on research design is available in the Nature Research Reporting Summary linked to this article. More