Ecology

Download PDF

I study the Chinese giant salamander (Andrias davidianus), which is native to the Yangtze River Basin of central China. This particular species is critically endangered in the wild owing to habitat loss and overcatching — a particular problem is their use in traditional Chinese medicine. My research focuses on the salamander’s conservation biology and evolutionary ecology.In this photo, I am releasing a Chinese giant salamander at the Golden Whip River in Zhangjiajie National Forest Park on an early morning in September 2021. My team and I caught the salamander the night before, to measure its size and collect tissue samples for genetic analyses.My interest in aquatic animals started as a child. I grew up in a rural village in Hunan province, and I remember spending most of my childhood playing and fishing near my home. Because of this, I knew where each fish species lived in nearby rivers and lakes, and it sparked my interest in river ecology.I’m employed as an associate professor at Jishou University, where I lead a team dedicated to researching this species of salamander. Wild salamanders are quiet, nocturnal animals that live in remote areas. This makes studying them challenging. My team tried many creative ways to track down the animals, including walking along riverbanks with torches and photographing salamanders under water — but these techniques didn’t work as well as we needed them to. We eventually found that the best way to trap wild salamanders is to use small live fish and chicken livers as bait. The research is challenging, but we’ve learnt to be patient and celebrate every small success we have.Studying Chinese giant salamanders has also taught me an important life lesson: adapt to thrive. When food is abundant, the salamanders grow rapidly; when food is scarce, they can go up to 11 months without feeding. In my personal life and work, I have experienced successes and failures, and taking on that lesson has been useful.

Nature 603, 194 (2022)
doi: https://doi.org/10.1038/d41586-022-00564-y

Related Articles

Close-up with a parasite that can blind

Handling snakes for science

Broaden your scientific audience with video animation

Managing up: how to communicate effectively with your PhD adviser

Subjects

Careers

Conservation biology

Ecology

Latest on:

Careers

Smaller science company? Tailor your CV for a manager, not HR
Career Column 25 FEB 22

Female scientists in Africa are changing the face of their continent
Editorial 22 FEB 22

African scientists engage with the public to tackle local challenges
Career Feature 15 FEB 22

Ecology

How colonialism fed the flames of Australia’s catastrophic wildfires
Research Highlight 24 FEB 22

Apply Singapore Index on Cities’ Biodiversity at scale
Correspondence 22 FEB 22

Marching in the streets for climate-crisis action
Career Q&A 22 FEB 22

Jobs

POST-DOC POSITION IN ELECTROPHYSIOLOGY OF FUNGAL NETWORKS

VU University Amsterdam
Amsterdam, Netherlands

Director, Division of Receipt and Referral Center for Scientific Review National Institutes of Health (NIH) Department of Health and Human Services (DHHS)

National Institutes of Health (NIH)
Bethesda, MD, United States

Postdoctoral Fellow

NIH National Heart, Lung, and Blood Institute (NHLBI)
Bethesda, MD, United States

Two postdoctorial researchers in structural biology, with focus on artificial intelligence

University of Gothenburg (GU)
Uppsala, Sweden More

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

Castelle CJ, Banfield JF. Major new microbial groups expand diversity and alter our understanding of the tree of life. Cell. 2018;172:1181–97.CAS 
PubMed 

Google Scholar 
Hug LA, Baker BJ, Anantharaman K, Brown CT, Probst AJ, Castelle CJ, et al. A new view of the tree of life. Nat Microbiol. 2016;1:16048.CAS 
PubMed 
PubMed Central 

Google Scholar 
Parks DH, Rinke C, Chuvochina M, Chaumeil PA, Woodcroft BJ, Evans PN, et al. Recovery of nearly 8,000 metagenome-assembled genomes substantially expands the tree of life. Nat Microbiol. 2017;2:1533–42.CAS 
PubMed 

Google Scholar 
Imachi H, Nobu MK, Nakahara N, Morono Y, Ogawara M, Takaki Y. et al. Isolation of an archaeon at the prokaryote-eukaryote interface. Nature. 2020;577:519–25.CAS 
PubMed 
PubMed Central 

Google Scholar 
Spang A, Saw JH, Jorgensen SL, Zaremba-Niedzwiedzka K, Martijn J, Lind AE. et al. Complex archaea that bridge the gap between prokaryotes and eukaryotes. Nature. 2015;521:173–9.CAS 
PubMed 
PubMed Central 

Google Scholar 
Liu Y, Makarova KS, Huang W-C, Wolf YI, Nikolskaya AN, Zhang X. et al. Expanded diversity of Asgard archaea and their relationships with eukaryotes. Nature. 2021;593:553–7.CAS 
PubMed 

Google Scholar 
Huber H, Stetter KO Thermoplasmatales. In: M Dworkin, S Falkow, E Rosenberg, KH Schleifer, E Stackebrandt (eds). The Prokaryotes, 3rd edn. (Springer, New York, 2006), pp 101–12.Inagaki F, Suzuki M, Takai K, Oida H, Sakamoto T, Aoki K, et al. Microbial communities associated with geological horizons in coastal subseafloor sediments from the Sea of Okhotsk. Appl Environ Microbiol. 2003;69:7224–35.CAS 
PubMed 
PubMed Central 

Google Scholar 
Vetriani C, Jannasch HW, MacGregor AJ, Stahl DA, Reysenbach AR. Population structure and phylogenetic characterization of marine benthic archaea in deep-sea sediments. Appl Environ Microbiol. 1999;65:4375–84.CAS 
PubMed 
PubMed Central 

Google Scholar 
Orsi WD, Vuillemin A, Rodriguez P, Coskun OK, Gomez-Saez GV, Lavik G, et al. Metabolic activity analyses demonstrate that Lokiarchaeon exhibits homoacetogenesis in sulfidic marine sediments. Nat Microbiol. 2019;5:248–55.PubMed 

Google Scholar 
Yu T, Wu W, Liang W, Lever MA, Hinrichs KU, Wang F. Growth of sedimentary Bathyarchaeota on lignin as an energy source. Proc Natl Acad Sci USA. 2018;115:6022–7.CAS 
PubMed 
PubMed Central 

Google Scholar 
Yin X, Cai M, Liu Y, Zhou G, Richter-Heitmann T, Aromokeye DA, et al. Subgroup level differences of physiological activities in marine Lokiarchaeota. ISME J. 2020;15:848–61.PubMed 
PubMed Central 

Google Scholar 
Lloyd KG, Schreiber L, Petersen DG, Kjeldsen KU, Lever MA, Steen AD. et al. Predominant archaea in marine sediments degrade detrital proteins. Nature. 2013;496:215–8.CAS 
PubMed 

Google Scholar 
Lin X, Handley KM, Gilbert JA, Kostka JE. Metabolic potential of fatty acid oxidation and anaerobic respiration by abundant members of Thaumarchaeota and Thermoplasmata in deep anoxic peat. ISME J. 2015;9:2740–4.CAS 
PubMed 
PubMed Central 

Google Scholar 
He Y, Li M, Perumal V, Feng X, Fang J, Xie J, et al. Genomic and enzymatic evidence for acetogenesis among multiple lineages of the archaeal phylum Bathyarchaeota widespread in marine sediments. Nat Microbiol. 2016;1:16035.CAS 
PubMed 

Google Scholar 
Zhou Z, Liu Y, Lloyd KG, Pan J, Yang Y, Gu J-D, et al. Genomic and transcriptomic insights into the ecology and metabolism of benthic archaeal cosmopolitan, Thermoprofundales (MBG-D archaea). ISME J. 2019;13:885–901.CAS 
PubMed 

Google Scholar 
Lazar CS, Baker BJ, Seitz K, Hyde AS, Dick GJ, Hinrichs KU, et al. Genomic evidence for distinct carbon substrate preferences and ecological niches of Bathyarchaeota in estuarine sediments. Environ Microbiol. 2016;18:1200–11.CAS 
PubMed 

Google Scholar 
Cai M, Liu Y, Yin X, Zhou Z, Friedrich MW, Richter-Heitmann T, et al. Diverse Asgard archaea including the novel phylum Gerdarchaeota participate in organic matter degradation. Sci China Life Sci. 2020;63:886–97.CAS 
PubMed 

Google Scholar 
Spang A, Stairs CW, Dombrowski N, Eme L, Lombard J, Caceres EF, et al. Proposal of the reverse flow model for the origin of the eukaryotic cell based on comparative analyses of Asgard archaeal metabolism. Nat Microbiol. 2019;4:1138–48.CAS 
PubMed 

Google Scholar 
Baker BJ, Appler KE, Gong X. New microbial biodiversity in marine sediments. Ann Rev Mar Sci. 2020;13:161–75.PubMed 

Google Scholar 
Gorke B, Stulke J. Carbon catabolite repression in bacteria: many ways to make the most out of nutrients. Nat Rev Microbiol. 2008;6:613–24.PubMed 

Google Scholar 
Siliakus MF, van der Oost J, Kengen SWM. Adaptations of archaeal and bacterial membranes to variations in temperature, pH and pressure. Extremophiles. 2017;21:651–70.CAS 
PubMed 
PubMed Central 

Google Scholar 
Takano Y, Chikaraishi Y, Ogawa NO, Nomaki H, Morono Y, Inagaki F. et al. Sedimentary membrane lipids recycled by deep-sea benthic archaea. Nat Geosci. 2010;3:858–61.CAS 

Google Scholar 
Li M, Baker BJ, Anantharaman K, Jain S, Breier JA, Dick GJ. Genomic and transcriptomic evidence for scavenging of diverse organic compounds by widespread deep-sea archaea. Nat Commun. 2015;6:8933.CAS 
PubMed 

Google Scholar 
Dekas AE, Parada AE, Mayali X, Fuhrman JA, Wollard J, Weber PK, et al. Characterizing chemoautotrophy and heterotrophy in marine Archaea and Bacteria with single-cell multi-isotope NanoSIP. Front Microbiol. 2019;10:2682.PubMed 
PubMed Central 

Google Scholar 
Vuillemin A, Wankel SD, Coskun ÖK, Magritsch T, Vargas S, Estes ER. et al. Archaea dominate oxic subseafloor communities over multimillion-year time scales. Sci Adv. 2019;5:eaaw4108PubMed 
PubMed Central 

Google Scholar 
Könneke M, Bernhard AE, de la Torre JR, Walker CB, Waterbury JB, Stahl DA. Isolation of an autotrophic ammonia-oxidizing marine archaeon. Nature. 2005;437:543–6.PubMed 

Google Scholar 
Qin W, Amin SA, Martens-Habbena W, Walker CB, Urakawa H, Devol AH, et al. Marine ammonia-oxidizing archaeal isolates display obligate mixotrophy and wide ecotypic variation. Proc Natl Acad Sci USA. 2014;111:12504–9.CAS 
PubMed 
PubMed Central 

Google Scholar 
Aoyagi T, Hanada S, Itoh H, Sato Y, Ogata A, Friedrich MW, et al. Ultra-high-sensitivity stable-isotope probing of rRNA by high-throughput sequencing of isopycnic centrifugation gradients. Environ Microbiol Rep. 2015;7:282–7.CAS 
PubMed 

Google Scholar 
Yin X, Wu W, Maeke M, Richter-Heitmann T, Kulkarni AC, Oni OE, et al. CO2 conversion to methane and biomass in obligate methylotrophic methanogens in marine sediments. ISME J. 2019;13:2107–19.CAS 
PubMed 
PubMed Central 

Google Scholar 
Oni O, Miyatake T, Kasten S, Richter-Heitmann T, Fischer D, Wagenknecht L, et al. Distinct microbial populations are tightly linked to the profile of dissolved iron in the methanic sediments of the Helgoland mud area, North Sea. Front Microbiol. 2015;6:365.PubMed 
PubMed Central 

Google Scholar 
Bohrmann G, Aromokeye AD, Bihler V, Dehning K, Dohrmann I, Gentz T, et al. R/V METEOR Cruise Report M134, Emissions of free gas from cross-shelf troughs of South Georgia: distribution, quantification, and sources for methane ebullition sites in sub-Antarctic waters, Port Stanley (Falkland Islands) – Punta Arenas (Chile). Ber aus dem MARUM und dem Fachbereich Geowissenschaften der Univät Brem. 2017;317:1–220.
Google Scholar 
Yin X, Kulkarni AC, Friedrich MW DNA and RNA stable isotope probing of methylotrophic methanogenic archaea. In: Dumont M, Hernández García M (eds), Stable Isotope Probing, Methods in Molecular Biology, (Humana Press New York, 2019) pp 189–206.Danovaro R, Dell¹Anno A, Fabiano M. Bioavailability of organic matter in the sediments of the Porcupine Abyssal Plain, northeastern Atlantic. Mar Ecol Prog Ser. 2001;220:25–32.CAS 

Google Scholar 
Yang T, Jiang S-Y, Yang J-H, Lu G, Wu N-Y, Liu J, et al. Dissolved inorganic carbon (DIC) and its carbon isotopic composition in sediment pore waters from the Shenhu area, northern South China Sea. J Oceanogr. 2008;64:303–10.CAS 

Google Scholar 
Lueders T, Manefield M, Friedrich MW. Enhanced sensitivity of DNA- and rRNA-based stable isotope probing by fractionation and quantitative analysis of isopycnic centrifugation gradients. Environ Microbiol. 2003;6:73–8.
Google Scholar 
Ovreas L, Forney L, Daae FL, Torsvik V. Distribution of bacterioplankton in meromictic Lake Saelenvannet, as determined by denaturing gradient gel electrophoresis of PCR-amplified gene fragments coding for 16S rRNA. Appl Environ Microbiol. 1997;63:3367–73.CAS 
PubMed 
PubMed Central 

Google Scholar 
Takai K, Horikoshi K. Rapid detection and quantification of members of the archaeal community by quantitative PCR using fluorogenic probes. Appl Environ Microbiol. 2000;66:5066–72.CAS 
PubMed 
PubMed Central 

Google Scholar 
Aromokeye DA, Richter-Heitmann T, Oni OE, Kulkarni A, Yin X, Kasten S, et al. Temperature controls crystalline iron oxide utilization by microbial communities in methanic ferruginous marine sediment incubations. Front Microbiol. 2018;9:2574.PubMed 
PubMed Central 

Google Scholar 
Edgar RC. UPARSE: highly accurate OTU sequences from microbial amplicon reads. Nat Methods. 2013;10:996–8.CAS 

Google Scholar 
Quast C, Pruesse E, Yilmaz P, Gerken J, Schweer T, Yarza P, et al. The SILVA ribosomal RNA gene database project: improved data processing and web-based tools. Nucleic Acids Res. 2013;41:D590–6.CAS 
PubMed 

Google Scholar 
Wegener G, Kellermann MY, Elvert M. Tracking activity and function of microorganisms by stable isotope probing of membrane lipids. Curr Opin Biotechnol. 2016;41:43–52.CAS 
PubMed 

Google Scholar 
Boschker HTS, Nold SC, Wellsbury P, Bos D, de Graaf W, Pel R. et al. Direct linking of microbial populations to specific biogeochemical processes by 13C-labelling of biomarkers. Nature. 1998;392:801–5.CAS 

Google Scholar 
Sturt HF, Summons RE, Smith K, Elvert M, Hinrichs KU. Intact polar membrane lipids in prokaryotes and sediments deciphered by high-performance liquid chromatography/electrospray ionization multistage mass spectrometry-new biomarkers for biogeochemistry and microbial ecology. Rapid Commun Mass Spectrom. 2004;18:617–28.CAS 
PubMed 

Google Scholar 
Liu XL, Lipp JS, Simpson JH, Lin YS, Summons RE, Hinrichs KU. Mono- and dihydroxyl glycerol dibiphytanyl glycerol tetraethers in marine sediments: Identification of both core and intact polar lipid forms. Geochim Cosmochim Acta. 2012;89:102–15.CAS 

Google Scholar 
Ertefai TF, Heuer VB, Prieto-Mollar X, Vogt C, Sylva SP, Seewald J, et al. The biogeochemistry of sorbed methane in marine sediments. Geochim Cosmochim Acta. 2010;74:6033–48.CAS 

Google Scholar 
Baker BJ, De Anda V, Seitz KW, Dombrowski N, Santoro AE, Lloyd KG. Diversity, ecology and evolution of Archaea. Nat Microbiol. 2020;5:887–900.CAS 
PubMed 

Google Scholar 
Hu W, Pan J, Wang B, Guo J, Li M, Xu M. Metagenomic insights into the metabolism and evolution of a new Thermoplasmata order (Candidatus Gimiplasmatales). Environ Microbiol. 2020;23:3695–709.PubMed 

Google Scholar 
Spang A, Stairs CW, Dombrowski N, Eme L, Lombard J, Caceres EF, et al. Proposal of the reverse flow model for the origin of the eukaryotic cell based on comparative analyses of Asgard archaeal metabolism. Nat Microbiol. 2019;4:1138–48.CAS 
PubMed 

Google Scholar 
Almagro Armenteros JJ, Tsirigos KD, Sonderby CK, Petersen TN, Winther O, Brunak S, et al. SignalP 5.0 improves signal peptide predictions using deep neural networks. Nat Biotechnol. 2019;37:420–3.CAS 
PubMed 

Google Scholar 
Uritskiy GV, DiRuggiero J, Taylor J. MetaWRAP-a flexible pipeline for genome-resolved metagenomic data analysis. Microbiome. 2018;6:158PubMed 
PubMed Central 

Google Scholar 
Li D, Luo R, Liu CM, Leung CM, Ting HF, Sadakane K. et al. MEGAHIT v1.0: A fast and scalable metagenome assembler driven by advanced methodologies and community practices. Methods. 2016;102:3–11.CAS 
PubMed 

Google Scholar 
Wu Y-W, Simmons BA, Singer SW. MaxBin 2.0: an automated binning algorithm to recover genomes from multiple metagenomic datasets. Bioinformatics. 2015;32:605–7.PubMed 

Google Scholar 
Alneberg J, Bjarnason BS, de Bruijn I, Schirmer M, Quick J, Ijaz UZ, et al. Binning metagenomic contigs by coverage and composition. Nat Methods. 2014;11:1144–6.CAS 
PubMed 

Google Scholar 
Kang DD, Li F, Kirton E, Thomas A, Egan R, An H. et al. MetaBAT 2: an adaptive binning algorithm for robust and efficient genome reconstruction from metagenome assemblies. PeerJ. 2019;7:e7359–e.PubMed 
PubMed Central 

Google Scholar 
Li H, Durbin R. Fast and accurate short read alignment with Burrows-Wheeler transform. Bioinformatics. 2009;25:1754–60.CAS 
PubMed 
PubMed Central 

Google Scholar 
Nurk S, Meleshko D, Korobeynikov A, Pevzner PA. metaSPAdes: a new versatile metagenomic assembler. Genome Res. 2017;27:824–34.CAS 
PubMed 
PubMed Central 

Google Scholar 
Parks DH, Imelfort M, Skennerton CT, Hugenholtz P, Tyson GW. CheckM: assessing the quality of microbial genomes recovered from isolates, single cells, and metagenomes. Genome Res. 2015;25:1043–55.CAS 
PubMed 
PubMed Central 

Google Scholar 
Chaumeil PA, Mussig AJ, Hugenholtz P, Parks DH. GTDB-Tk: a toolkit to classify genomes with the Genome Taxonomy Database. J Bioinform. 2019;36:1925–7.
Google Scholar 
Hyatt D, Chen GL, LoCascio PF, Land ML, Larimer FW, Hauser LJ. Prodigal: prokaryotic gene recognition and translation initiation site identification. BMC Bioinforma. 2010;11:119.
Google Scholar 
Kanehisa M, Sato Y, Morishima K. BlastKOALA and GhostKOALA: KEGG tools for functional characterization of genome and metagenome sequences. J Mol Biol. 2016;428:726–31.CAS 
PubMed 

Google Scholar 
Huerta-Cepas J, Forslund K, Coelho LP, Szklarczyk D, Jensen LJ, von Mering C, et al. Fast genome-wide functional annotation through orthology assignment by eggNOG-mapper. Mol Biol Evol. 2017;34:2115–22.CAS 
PubMed 
PubMed Central 

Google Scholar 
Jones P, Binns D, Chang H-Y, Fraser M, Li W, McAnulla C. et al. InterProScan 5: genome-scale protein function classification. Bioinformatics. 2014;30:1236–40.CAS 
PubMed 
PubMed Central 

Google Scholar 
Pruesse E, Peplies J, Glöckner FO. SINA: accurate high-throughput multiple sequence alignment of ribosomal RNA genes. Bioinformatics. 2012;28:1823–9.CAS 
PubMed 
PubMed Central 

Google Scholar 
Ludwig W, Strunk O, Westram R, Richter L, Meier H, Yadhukumar, et al. ARB: a software environment for sequence data. Nucleic Acids Res. 2004;32:1363–71.CAS 
PubMed 
PubMed Central 

Google Scholar 
Stamatakis A. RAxML version 8: a tool for phylogenetic analysis and post-analysis of large phylogenies. Bioinformatics. 2014;30:1312–3.CAS 
PubMed 
PubMed Central 

Google Scholar 
Letunic I, Bork P. Interactive Tree Of Life (iTOL): an online tool for phylogenetic tree display and annotation. Bioinformatics. 2006;23:127–8.PubMed 

Google Scholar 
Zhou Z, Pan J, Wang F, Gu JD, Li M. Bathyarchaeota: globally distributed metabolic generalists in anoxic environments. FEMS Microbiol Rev. 2018;42:639–55.CAS 
PubMed 

Google Scholar 
Lee MD. GToTree: a user-friendly workflow for phylogenomics. Bioinformatics. 2019;35:4162–4.CAS 
PubMed 
PubMed Central 

Google Scholar 
Eren AM, Esen OC, Quince C, Vineis JH, Morrison HG, Sogin ML. et al. Anvi’o: an advanced analysis and visualization platform for ‘omics data. PeerJ. 2015;3:e1319PubMed 
PubMed Central 

Google Scholar 
Nguyen LT, Schmidt HA, von Haeseler A, Minh BQ. IQ-TREE: a fast and effective stochastic algorithm for estimating maximum-likelihood phylogenies. Mol Biol Evol. 2015;32:268–74.CAS 

Google Scholar 
Manefield M, Whiteley AS, Ostle N, Ineson P, Bailey MJ. Technical considerations for RNA- based stable isotope probing an approach to associating microbial diversity with microbial community function. Rapid Commun Mass Spectrom. 2002;16:2179–83.CAS 
PubMed 

Google Scholar 
Lazar CS, Baker BJ, Seitz KW, Teske AP. Genomic reconstruction of multiple lineages of uncultured benthic archaea suggests distinct biogeochemical roles and ecological niches. ISME J. 2017;11:1118–29.CAS 
PubMed 
PubMed Central 

Google Scholar 
Villanueva L, Damste JS, Schouten S. A re-evaluation of the archaeal membrane lipid biosynthetic pathway. Nat Rev Microbiol. 2014;12:438–48.CAS 
PubMed 

Google Scholar 
Konstantinidis KT, Rossello-Mora R, Amann R. Uncultivated microbes in need of their own taxonomy. ISME J. 2017;11:2399–406.PubMed 
PubMed Central 

Google Scholar 
Hedges JI, Keil RG. Sedimentary organic matter preservation: an assessment and speculative synthesis. Mar Chem. 1995;49:81–115.CAS 

Google Scholar 
Arndt S, Jørgensen BB, LaRowe DE, Middelburg JJ, Pancost RD, Regnier P. Quantifying the degradation of organic matter in marine sediments: a review and synthesis. Earth-Sci Rev. 2013;123:53–86.CAS 

Google Scholar 
LaRowe DE, Arndt S, Bradley JA, Estes ER, Hoarfrost A, Lang SQ, et al. The fate of organic carbon in marine sediments – New insights from recent data and analysis. Earth-Sci Rev. 2020;204:103146.CAS 

Google Scholar 
Zhu Q-Z, Elvert M, Meador TB, Becker KW, Heuer VB, Hinrichs KU. Stable carbon isotopic compositions of archaeal lipids constrain terrestrial, planktonic, and benthic sources in marine sediments. Geochim Cosmochim Acta. 2021;307:319–37.CAS 

Google Scholar 
Jain S, Caforio A, Driessen AJ. Biosynthesis of archaeal membrane ether lipids. Front Microbiol. 2014;5:641.PubMed 
PubMed Central 

Google Scholar 
Yang S, Lv Y, Liu X, Wang Y, Fan Q, Yang Z, et al. Genomic and enzymatic evidence of acetogenesis by anaerobic methanotrophic archaea. Nat Commun. 2020;11:3941.CAS 
PubMed 
PubMed Central 

Google Scholar 
Zinke LA, Evans PN, Santos-Medellín C, Schroeder AL, Parks DH, Varner RK, et al. Evidence for non-methanogenic metabolisms in globally distributed archaeal clades basal to the Methanomassiliicoccales. Environ Microbiol. 2021;23:340–57.CAS 
PubMed 

Google Scholar 
Bhatnagar L, Jain MK, Aubert JP, Zeikus JG. Comparison of assimilatory organic nitrogen, sulfur, and carbon sources for growth of methanobacterium species. Appl Environ Microbiol. 1984;48:785–90.CAS 
PubMed 
PubMed Central 

Google Scholar 
Maupin-Furlow JA. Proteolytic systems of archaea: slicing, dicing, and mincing in the extreme. Emerg Top Life Sci. 2018;2:561–80.CAS 
PubMed 
PubMed Central 

Google Scholar 
Pohlschroder M, Pfeiffer F, Schulze S, Abdul Halim MF. Archaeal cell surface biogenesis. FEMS Microbiol Rev. 2018;42:694–717.CAS 
PubMed 
PubMed Central 

Google Scholar 
Yancey P, Clark M, Hand S, Bowlus R, Somero G. Living with water stress: evolution of osmolyte systems. Science. 1982;217:1214–22.CAS 
PubMed 

Google Scholar 
Orsi WD, Smith JM, Liu S, Liu Z, Sakamoto CM, Wilken S, et al. Diverse, uncultivated bacteria and archaea underlying the cycling of dissolved protein in the ocean. ISME J. 2016;10:2158–73.CAS 
PubMed 
PubMed Central 

Google Scholar 
Oni OE, Schmidt F, Miyatake T, Kasten S, Witt M, Hinrichs KU, et al. Microbial communities and organic matter composition in surface and subsurface sediments of the Helgoland Mud Area, North Sea. Front Microbiol. 2015;6:1290.PubMed 
PubMed Central 

Google Scholar 
Pelikan C, Wasmund K, Glombitza C, Hausmann B, Herbold CW, Flieder M, et al. Anaerobic bacterial degradation of protein and lipid macromolecules in subarctic marine sediment. ISME J. 2021;15:833–47.CAS 
PubMed 

Google Scholar 
Orsi WD, Schink B, Buckel W, Martin WF. Physiological limits to life in anoxic subseafloor sediment. FEMS Microbiol Rev. 2020;44:219–31.CAS 
PubMed 
PubMed Central 

Google Scholar 
Heijnen JJ, Van, Dijken JP. In search of a thermodynamic description of biomass yields for the chemotrophic growth of microorganisms. Biotechnol Bioeng. 1992;39:833–58.CAS 
PubMed 

Google Scholar 
Braun S, Mhatre SS, Jaussi M, Røy H, Kjeldsen KU, Pearce C, et al. Microbial turnover times in the deep seabed studied by amino acid racemization modelling. Sci Rep. 2017;7:5680.PubMed 
PubMed Central 

Google Scholar  More

Konduri, V. S., Vandal, T. J., Ganguly, S. & Ganguly, A. R. Data science for weather impacts on crop yield. Front. Sustain. Food Syst. 4, 52. https://doi.org/10.3389/fsufs.2020.00052 (2020).Article 

Google Scholar 
Xu, X., Gao, P., Zhu, X., Guo, W., Ding, J., Li, C., … & Wu, X. Design of an integrated climatic assessment indicator (ICAI) for wheat production: A case study in Jiangsu Province, China. Eco. Ind., 101, 943953. https://doi.org/10.1016/J.ECOLIND.2019.01.059 (2019).Moeinizade, S., Hu, G., Wang, L. & Schnable, P. S. Optimizing selection and mating in genomic selection with a look-ahead approach: An operations research framework. G3: Genes Genomes Genet. 9, 2123–2133. https://doi.org/10.1534/g3.118.200842 (2019).Article 

Google Scholar 
Basso, B. & Liu, L. Chapter four: Seasonal crop yield forecast: Methods, applications, and accuracies. In Advances in Agronomy Vol. 154 (ed. Sparks, D. L.) 201–255. https://doi.org/10.1016/bs.agron.2018.11.002 (2019)Zarindast, A., & Wood, J. A Data-Driven Personalized Lighting Recommender System. Front. in Big Data, 4. (2021).Shahhosseini, M., Hu, G., Khaki, S., & Archontoulis, S. V. Corn yield prediction with ensemble CNN-DNN. Front. Plant Sci., 12. (2021).Haghighat, A. K., Ravichandra-Mouli, V., Chakraborty, P., Esfandiari, Y., Arabi, S., & Sharma, A. Applications of deep learning in intelligent transportation systems. J. Big Data Anal. Transp. 2, 115–145. https://doi.org/10.1007/s42421-020-00020-1 (2020).Zarindast, A., Sharma, A., & Wood, J. Application of text mining in smart lighting literature-an analysis of existing literature and a research agenda. Int. J. Info. Mgmt. Data Insights, 1(2), 100032. (2021).Chlingaryan, A., Sukkarieh, S. & Whelan, B. Machine learning approaches for crop yield prediction and nitrogen status estimation in precision agriculture: A review. Comput. Electron. Agric. 151, 61–69. https://doi.org/10.1016/j.compag.2018.05.012 (2018).Article 

Google Scholar 
Zarindast, A., Poddar, S., & Sharma, A. A Data-Driven Method for Congestion Identification and Classification. J. Trans. Eng., Part A: Sys., 148(4), 04022012. (2022)Shahhosseini, M., Martinez-Feria, R. A., Hu, G. & Archontoulis, S. V. Maize yield and nitrate loss prediction with machine learning algorithms. Environ. Res. Lett. 14, 124026. https://doi.org/10.1088/1748-9326/ab5268 (2019).Article 
ADS 

Google Scholar 
Khaki, S. & Wang, L. Crop yield prediction using deep neural networks. Front. Plant Sci.https://doi.org/10.3389/fpls.2019.00621 (2019).Article 
PubMed 
PubMed Central 

Google Scholar 
Feng, P., Wang, B., Li Liu, D., Waters, C., Xiao, D., Shi, L., & Yu, Q. Dynamic wheat yield forecasts are improved by a hybrid approach using a biophysical model and machine learning technique. Agric. For. Meteorol. 285–286, 107922. https://doi.org/10.1016/j.agrformet.2020.107922 (2020).Article 
ADS 

Google Scholar 
Kang, Y., Ozdogan, M., Zhu, X., Ye, Z., Hain, C., & Anderson, M. Comparative assessment of environmental variables and machine learning algorithms for maize yield prediction in the US Midwest. Environ. Res. Lett. 15, 064005. https://doi.org/10.1088/1748-9326/ab7df9 (2020).Article 
ADS 

Google Scholar 
Van Klompenburg, T., Kassahun, A. & Catal, C. Crop yield prediction using machine learning: A systematic literature review. Comput. Electron. Agric. 177, 105709. https://doi.org/10.1016/j.compag.2020.105709 (2020).Article 

Google Scholar 
Khaki, S., Safaei, N., Pham, H. & Wang, L. WheatNet: A Lightweight Convolutional Neural Network for High-throughput Image-based Wheat Head Detection and Counting. arXiv:2103.09408 (2021).Hassan, M. A., Khalil, A., Kaseb, S. & Kassem, M. A. Exploring the potential of tree-based ensemble methods in solar radiation modeling. Appl. Energy 203, 897–916. https://doi.org/10.1016/j.apenergy.2017.06.104 (2017).Article 

Google Scholar 
Mishra, S. & Santra, D. M. A. G. H. Applications of machine learning techniques in agricultural crop production: A review paper. Indian J. Sci. Technol. 9, 1–14. https://doi.org/10.17485/ijst/2016/v9i38/95032 (2016).Article 

Google Scholar 
Kamilaris, A. & Prenafeta-Boldú, F. Deep learning in agriculture: A survey. Comput. Electron. Agric.https://doi.org/10.1016/j.compag.2018.02.016 (2018).Article 

Google Scholar 
Liakos, K. G., Busato, P., Moshou, D., Pearson, S. & Bochtis, D. Machine learning in agriculture: A review. Sensors 18, 2674. https://doi.org/10.3390/s18082674 (2018).Article 
PubMed Central 
ADS 

Google Scholar 
Wang, Y., Zhang, Z., Feng, L., Du, Q. & Runge, T. Combining multi-source data and machine learning approaches to predict winter wheat yield in the conterminous United States. Remote Sens. 12, 1232. https://doi.org/10.3390/rs12081232 (2020).Article 
ADS 

Google Scholar 
Cao, J., Zhang, Z., Luo, Y., Zhang, L., Zhang, J., Li, Z., & Tao, F. Wheat yield predictions at a county and field scale with deep learning, machine learning, and google earth engine. Eur. J. Agron. 123, 126204. https://doi.org/10.1016/j.eja.2020.126204 (2021).Article 

Google Scholar 
FAO. FAOSTAT (2021).Zhao, G., Webber, H., Hoffmann, H., Wolf, J., Siebert, S., & Ewert, F. The implication of irrigation in climate change impact assessment: a European‐wide study. Glob. Chang. Biol. 21, 4031–4048. https://doi.org/10.1111/gcb.13008 (2015).Article 
PubMed 
ADS 

Google Scholar 
EUROSTAT. Glossary: Nomenclature of territorial units for statistics (NUTS) (2019).COPERNICUS. CORINE Land Cover (2006).Webber, H., Lischeid, G., Sommer, M., Finger, R., Nendel, C., Gaiser, T., & Ewert, F. No perfect storm for crop yield failure in Germany. Environ. Res. Lett. 15, 104012 (2020).Article 
ADS 

Google Scholar 
EUROSTAT. NUTS – Nomenclature of territorial units for statistics – Eurostat (2019).Ämter, S. Regionaldatenbank Deutschland (2020).DWD. Wetter und Klima – Deutscher Wetterdienst (2020).Shook, J., Gangopadhyay, T., Wu, L., Ganapathysubramanian, B., Sarkar, S., & Singh, A. K. Crop yield prediction integrating genotype and weather variables using deep learning. PLOS ONE 16, e0252402. https://doi.org/10.1371/journal.pone.0252402 (2021).CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
Nevavuori, P., Narra, N., Linna, P. & Lipping, T. Crop yield prediction using multitemporal UAV data and spatio-temporal deep learning models. Remote Sens. 12, 4000. https://doi.org/10.3390/rs12234000 (2020).Article 
ADS 

Google Scholar 
Breiman, L. Random forests. Mach. Learn. 45, 5–32. https://doi.org/10.1023/A:1010933404324 (2001).Article 
MATH 

Google Scholar 
Cover, T. & Hart, P. Nearest neighbor pattern classification. IEEE Trans. Inf. Theory 13, 21–27. https://doi.org/10.1109/TIT.1967.1053964 (1967).Article 
MATH 

Google Scholar 
Hu, L.-Y., Huang, M.-W., Ke, S.-W. & Tsai, C.-F. The distance function effect on k-nearest neighbor classification for medical datasets. SpringerPlus 5, 1304. https://doi.org/10.1186/s40064-016-2941-7 (2016).CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
James, G., Witten, D., Hastie, T. & Tibshirani, R. An Introduction to Statistical Learning: With Applications in R (Springer, 2013).Book 

Google Scholar 
Hoerl, A. E. & Kennard, R. W. Ridge regression: Biased estimation for nonorthogonal problems. Technometrics 12, 55–67. https://doi.org/10.1080/00401706.1970.10488634 (1970).Article 
MATH 

Google Scholar 
Breiman, L., Friedman, J., Stone, C. J. & Olshen, R. Classification and Regression Trees 1st edn. (Chapman and Hall/CRC Press, 1984).MATH 

Google Scholar 
Cortes, C. & Vapnik, V. Support-vector networks. Mach. Learn. 20, 273–297. https://doi.org/10.1007/BF00994018 (1995).Article 
MATH 

Google Scholar 
Chen, T., & Guestrin, C. Xgboost: A scalable tree boosting system. In Proceedings of the 22nd acm sigkdd international conference on knowledge discovery and data mining (pp. 785–794) (2016).Zhang, L., Zhang, Z., Luo, Y., Cao, J. & Tao, F. Combining optical, fluorescence, thermal satellite, and environmental data to predict county-level maize yield in China using machine learning approaches. Remote Sens. 12, 21. https://doi.org/10.3390/rs12010021 (2020).Article 
ADS 

Google Scholar 
Nigam, A., Garg, S., Agrawal, A. & Agrawal, P. Crop Yield Prediction Using Machine Learning Algorithms. 2019 Fifth International Conference on Image Information Processing (ICIIP) https://doi.org/10.1109/ICIIP47207.2019.8985951 (2019).LeCun, Y., Bengio, Y. & Hinton, G. Deep learning. Nature 521, 436–444. https://doi.org/10.1038/nature14539 (2015).CAS 
Article 
ADS 

Google Scholar 
Khaki, S., Wang, L. & Archontoulis, S. V. A CNN-RNN framework for crop yield prediction. Front. Plant Sci. https://doi.org/10.3389/fpls.2019.01750 (2020).Article 
PubMed 
PubMed Central 

Google Scholar 
Khaki, S., Pham, H., & Wang, L. Simultaneous corn and soybean yield prediction from remote sensing data using deep transfer learning. Sci. Rep. 11(1), 1–14. (2021).Article 

Google Scholar 
Khaki, S., Khalilzadeh, Z. & Wang, L. Predicting yield performance of parents in plant breeding: A neural collaborative filtering approach. PLoS ONE 15, e0233382. https://doi.org/10.1371/journal.pone.0233382 (2020).CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
Lamorski, K., Pachepsky, Y., Sławiński, C. & Walczak, R. T. Using support vector machines to develop pedotransfer functions for water retention of soils in Poland. Soil Sci. Soc. Am. J. 72, 1243–1247. https://doi.org/10.2136/sssaj2007.0280N (2008).CAS 
Article 
ADS 

Google Scholar 
Merdun, H., Çınar, C., Meral, R. & Apan, M. Comparison of artificial neural network and regression pedotransfer functions for prediction of soil water retention and saturated hydraulic conductivity. Soil Tillage Res. 1–2, 108–116. https://doi.org/10.1016/j.still.2005.08.011 (2006).Article 

Google Scholar 
Landeras, G., Ortiz-Barredo, A. & López, J. J. Comparison of artificial neural network models and empirical and semi-empirical equations for daily reference evapotranspiration estimation in the Basque Country (Northern Spain). Agric. Water Manag. 95, 553–565. https://doi.org/10.1016/j.agwat.2007.12.011 (2008).Article 

Google Scholar 
Yamaç, S. S. & Todorovic, M. Estimation of daily potato crop evapotranspiration using three different machine learning algorithms and four scenarios of available meteorological data. Agric. Water Manag. 228, 105875. https://doi.org/10.1016/j.agwat.2019.105875 (2020).Article 

Google Scholar 
Obsie, E. Y., Qu, H. & Drummond, F. Wild blueberry yield prediction using a combination of computer simulation and machine learning algorithms. Comput. Electron. Agric. 178, 105778. https://doi.org/10.1016/j.compag.2020.105778 (2020).Article 

Google Scholar 
Shapley, L. S. A Value for n-Person Games (Princeton University Press, 1953).MATH 

Google Scholar 
Vogel, E., Donat, M. G., Alexander, L. V., Meinshausen, M., Ray, D. K., Karoly, D., … & Frieler, K. The effects of climate extremes on global agricultural yields. Environ. Res. Lett. 14, 054010. https://doi.org/10.1088/1748-9326/ab154b (2019).Article 
ADS 

Google Scholar 
Stokes, V. J., Morecroft, M. D. & Morison, J. I. L. Boundary layer conductance for contrasting leaf shapes in a deciduous broadleaved forest canopy. Agric. For. Meteorol. 139, 40–54. https://doi.org/10.1016/j.agrformet.2006.05.011 (2006).Article 
ADS 

Google Scholar 
Chen, X. & Chen, S. China feels the heat: Negative impacts of high temperatures on China’s rice sector. Aust. J. Agric. Resour. Econ. 62, 576–588. https://doi.org/10.1111/1467-8489.12267 (2018).Article 

Google Scholar 
Tao, F., Xiao, D., Zhang, S., Zhang, Z. & Rötter, R. P. Wheat yield benefited from increases in minimum temperature in the Huang–Huai–Hai Plain of China in the past three decades. Agric. For. Meteorol. 239, 1–14. https://doi.org/10.1016/j.agrformet.2017.02.033 (2017).Article 
ADS 

Google Scholar 
Zheng, C., Zhang, J., Chen, J., Chen, C., Tian, Y., Deng, A., … & Zhang, W. Nighttime warming increases winter-sown wheat yield across major Chinese cropping regions. Field Crops Res. 214, 202–210. https://doi.org/10.1016/j.fcr.2017.09.014 (2017).Article 
ADS 

Google Scholar 
Gibson, L. R. & Paulsen, G. M. Yield components of wheat grown under high temperature stress during reproductive growth. Crop Sci. 39, 1841–1846. https://doi.org/10.2135/cropsci1999.3961841x (1999).Article 

Google Scholar 
Lobell, D. B., Sibley, A. & Ivan Ortiz-Monasterio, J. Extreme heat effects on wheat senescence in India. Nat. Clim. Chang. 2, 186–189. https://doi.org/10.1038/nclimate1356 (2012).Article 
ADS 

Google Scholar 
Tashiro, T. & Wardlaw, I. A comparison of the effect of high temperature on grain development in wheat and rice. Ann. Bot. 64, 59–65. https://doi.org/10.1093/oxfordjournals.aob.a087808 (1989).Article 

Google Scholar 
Wollenweber, B., Porter, J. R. & Schellberg, J. Lack of interaction between extreme high-temperature events at vegetative and reproductive growth stages in wheat. J. Agron. Crop Sci. 189, 142–150. https://doi.org/10.1046/j.1439-037X.2003.00025.x (2003).Article 

Google Scholar 
Mäkinen, H., Kaseva, J., Trnka, M., Balek, J., Kersebaum, K. C., Nendel, C., … & Kahiluoto, H. Sensitivity of European wheat to extreme weather. Field Crops Res., 222, 209–217. https://doi.org/10.1016/j.fcr.2017.11.008 (2018).Article 

Google Scholar 
Farooq, M., Bramley, H., Palta, J. A. & Siddique, K. H. Heat stress in wheat during reproductive and grain-filling phases. Crit. Rev. Plant Sci. 30, 491–507. https://doi.org/10.1080/07352689.2011.615687 (2011).Article 

Google Scholar 
Peichl, M., Thober, S., Meyer, V. & Samaniego, L. The effect of soil moisture anomalies on maize yield in Germany. Nat. Hazards Earth Syst. Sci. 18, 889–906. https://doi.org/10.5194/nhess-18-889-2018 (2018).Article 
ADS 

Google Scholar 
Rezaei, E. E. et al. Quantifying the response of wheat yields to heat stress: The role of the experimental setup. Field Crops Res., 217, 93–103. https://doi.org/10.1016/j.fcr.2017.12.015 (2018).Article 

Google Scholar 
Cannell, R. Q., Belford, R. K., Gales, K., Dennis, C. W. & Prew, R. D. Effects of waterlogging at different stages of development on the growth and yield of winter wheat. J. Sci. Food Agric. 31, 117–132. https://doi.org/10.1002/jsfa.2740310203 (1980).Article 

Google Scholar 
Gömann, H. Wetterextreme: mögliche Folgen für die Landwirtschaft in Deutschland (2018).Kumar, I. E., Venkatasubramanian, S., Scheidegger, C., & Friedler, S. Problems with Shapley-value-based explanations as feature importance measures. In International Conference on Machine Learning (pp. 5491–5500). PMLR. (2020).Rudin, C. Stop explaining black box machine learning models for high stakes decisions and use interpretable models instead. Nature Machine Intelligence, 1(5), 206–215. (2019). More

Tay, D. Vegetable hybrid seed production. in Seeds: Trade, Production and Technology. 18–139. (2002).Piquerez, S. J., Harvey, S. E., Beynon, J. L. & Ntoukakis, V. Improving crop disease resistance: Lessons from research on Arabidopsis and tomato. Front. Plant Sci. 5, 671. https://doi.org/10.3389/fpls.2014.00671 (2014).Article 
PubMed 
PubMed Central 

Google Scholar 
Mason, A. S. & Batley, J. Creating new interspecific hybrid and polyploid crops. Trends Biotechnol. 33, 436–441. https://doi.org/10.1016/j.tibtech.2015.06.004 (2015).CAS 
Article 
PubMed 

Google Scholar 
Broussard, M. A., Mas, F., Howlett, B., Pattemore, D. & Tylianakis, J. M. Possible mechanisms of pollination failure in hybrid carrot seed and implications for industry in a changing climate. PLoS ONE 12, 180215.  https://doi.org/10.1371/journal.pone.0180215 (2017).CAS 
Article 

Google Scholar 
Wilcock, C. & Neiland, R. Pollination failure in plants: why it happens and when it matters. Trends Plant Sci. 7, 270–277. https://doi.org/10.1016/S1360-1385(02)02258-6 (2002).CAS 
Article 
PubMed 

Google Scholar 
Fijen, T. P., Scheper, J. A., Vogel, C., van Ruijven, J. & Kleijn, D. Insect pollination is the weakest link in the production of a hybrid seed crop. Agric. Ecosyst. Environ. 290, 106743. https://doi.org/10.1016/j.agee.2019.106743 (2020).CAS 
Article 

Google Scholar 
Batra, S. W. Male-fertile potato flowers are selectively buzz-pollinated only by Bombus terricola Kirby in upstate New York. J. Kans. Entomol. Soc. 1, 252–254 (1993).
Google Scholar 
Evans, L., Goodwin, R., Walker, M. & Howlett, B. Honey bee (Apis mellifera) distribution and behaviour on hybrid radish (Raphanus sativus L.) crops. N.Z. Plant Prot. 64, 32–36. https://doi.org/10.30843/nzpp.2011.64.5952 (2011).Article 

Google Scholar 
Estravis Barcala, M. C., Palottini, F. & Farina, W. M. Honey bee and native solitary bee foraging behavior in a crop with dimorphic parental lines. PLoS ONE 14, e0223865. https://doi.org/10.1371/journal.pone.0223865 (2019).CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
Nye, W. P., Shasha’a, N., Campbell, W. & Hamson, A. Insect pollination and seed set of onions (Allium cepa L.). Utah State Univ. Agric. Exp. Station Res. Rep. 6, 1 (1973).
Google Scholar 
Zulkarnain, Z., Eliyanti, E. & Swari, E. I. Pollen viability and stigma receptivity in Swainsona formosa (G. Don) J. Thompson (Fabaceae), an ornamental legume native to Australia. Ornam. Hortic. 25, 158–167. https://doi.org/10.14295/oh.v25i2.2011 (2019).Article 

Google Scholar 
Ne’eman, G., Jürgens, A., Newstrom-Lloyd, L., Potts, S. G. & Dafni, A. A framework for comparing pollinator performance: Effectiveness and efficiency. Biol. Rev. 85, 435–451. https://doi.org/10.1111/j.1469-185X.2009.00108.x (2010).Article 
PubMed 

Google Scholar 
Bione, N. C. P., Pagliarini, M. S. & Toledo, J. F. F. D. Meiotic behavior of several Brazilian soybean varieties. Genet. Mol. 23, 623–631. https://doi.org/10.1590/S1415-47572000000300022 (2000).Article 

Google Scholar 
Levin, D. A. The exploitation of pollinators by species and hybrids of Phlox. Evolution 1, 367–377 (1970).Article 

Google Scholar 
Smith-Huerta, N. L. & Vasek, F. C. Pollen longevity and stigma pre-emption in Clarkia. Am. J. Bot. 71, 1183–1191 (1984).Article 

Google Scholar 
Ashman, T. L. & Arceo-Gómez, G. Toward a predictive understanding of the fitness costs of heterospecific pollen receipt and its importance in co-flowering communities. Am. J. Bot. 100, 1061–1070. https://doi.org/10.3732/ajb.1200496 (2013).Article 
PubMed 

Google Scholar 
Stanghellini, M., Schultheis, J. & Ambrose, J. Pollen mobilization in selected Cucurbitaceae and the putative effects of pollinator abundance on pollen depletion rates. J. Am. Soc. Hortic. Sci. 127, 729–736. https://doi.org/10.21273/Jashs.127.5.729 (2002).Article 

Google Scholar 
Jahed, K. R. & Hirst, P. M. Pollen tube growth and fruit set in apple. HortScience 52, 1054–1059. https://doi.org/10.21273/Hortsci11511-16 (2017).Article 

Google Scholar 
Erdtman, G. Pollen Morphology and Plant Taxonomy: Angiosperms. Vol. 1. (Brill Archive, 1986).Weber, R. W. Pollen identification. Ann. Allergy Asthma Immunol. 80, 141–148. https://doi.org/10.1016/S1081-1206(10)62947-X (1998).CAS 
Article 
PubMed 

Google Scholar 
Castro López, A. J. et al. Seedless watermelons: From the microscope to the table through the greenhouse. High Sch. Students Agric. Scii. Res.. 3. 27–32 (2013).
Google Scholar 
Laws, H. M. Pollen-grain morphology of polyploid Oenotheras. J. Hered. 56, 18–21 (1965).Article 

Google Scholar 
Shoemaker, J. S. Pollen development in the apple, with special reference to chromosome behavior. Bot. Gaz. 81, 148–172 (1926).Article 

Google Scholar 
Hao, L., Ma, H., da Silva, J. A. T. & Yu, X. Pollen morphology of herbaceous peonies with different ploidy levels. J. Am. Soc. Hortic. Sci. 141, 275–284. https://doi.org/10.21273/Jashs.141.3.275 (2016).CAS 
Article 

Google Scholar 
Jacob, Y. & Pierret, V. Pollen size and ploidy level in the genus Rosa. XIX International Symposium on Improvement of Ornamental Plants, Vol. 508. 289–292. (1998).Karlsdóttir, L., Hallsdóttir, M., Thórsson, A. T. & Anamthawat-Jónsson, K. Characteristics of pollen from natural triploid Betula hybrids. Grana 47, 52–59. https://doi.org/10.1080/00173130801927498 (2008).Article 

Google Scholar 
Wrońska-Pilarek, D., Danielewicz, W., Bocianowski, J., Maliński, T. & Janyszek, M. Comparative pollen morphological analysis and its systematic implications on three European Oak (Quercus L., Fagaceae) species and their spontaneous hybrids. PLoS ONE 11, e0161762. https://doi.org/10.1371/journal.pone.0161762 (2016).CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
Martin, C., Viruel, M., Lora, J. & Hormaza, J. I. Polyploidy in fruit tree crops of the genus Annona (Annonaceae). Front. Plant Sci. 10, 99. https://doi.org/10.3389/fpls.2019.00099 (2019).Article 
PubMed 
PubMed Central 

Google Scholar 
Sedgley, M. & Scholefield, P. B. Stigma secretion in the watermelon before and after pollination. Bot. Gaz. 141, 428–434 (1980).Article 

Google Scholar 
Sedgley, M. Anatomy of the unpollinated and pollinated watermelon stigma. J. Cell Sci. 54, 341–355.  https://doi.org/10.1242/jcs.54.1.341 (1982).Article 

Google Scholar 
Sedgley, M. & Blesing, M. A. Foreign pollination of the stigma of watermelon (Citrullus lanatus [Thunb.] Matsum and Nakai). Bot. Gaz. 143, 210–215 (1982).Article 

Google Scholar 
Hiscock, S. J. & Allen, A. M. Diverse cell signalling pathways regulate pollen-stigma interactions: The search for consensus. New Phytol. 179, 286–317. https://doi.org/10.1111/j.1469-8137.2008.02457.x (2008).CAS 
Article 
PubMed 

Google Scholar 
Swanson, R., Edlund, A. F. & Preuss, D. Species specificity in pollen-pistil interactions. Annu. Rev. Genet. 38, 793–818. https://doi.org/10.1146/annurev.genet.38.072902.092356 (2004).CAS 
Article 
PubMed 

Google Scholar 
Edlund, A. F., Swanson, R. & Preuss, D. Pollen and stigma structure and function: the role of diversity in pollination. Plant Cell 16, S84–S97. https://doi.org/10.1105/tpc.015800 (2004).CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
Wehner, T. Cucurbit Breeding. https://cucurbitbreeding.wordpress.ncsu.edu/watermelon-breeding/seedless-watermelon-breeding/ (2011).Maynard, D. N. & Elmstrom, G.W. Triploid watermelon production practices and varieties. Acta Hort. 318, 169–173 (1992).Article 

Google Scholar 
Tupý, J. Callose formation in pollen tubes and incompatibility. Biol. Plant. 1, 192–198. https://doi.org/10.1007/BF02928684 (1959).Article 

Google Scholar 
Distefano, G. et al. Pollen tube behavior in different mandarin hybrids. J. Am. Soc. Hortic. Sci. 134, 583–588. https://doi.org/10.21273/Jashs.134.6.583 (2009).Article 

Google Scholar 
Glišić, I. et al. Examination of self-compatibility in promising plum (Prunus domestica L.) genotypes developed at the Fruit Research Institute. Čačak. Sci. Hortic. 224, 156–162. https://doi.org/10.1016/j.scienta.2017.06.006 (2017).Article 

Google Scholar 
Arndt, G. C., Rueda, J., Kidane-Mariam, H. & Peloquin, S. Pollen fertility in relation to open pollinated true seed production in potatoes. Am. Potato. J 67, 499–505. https://doi.org/10.1007/Bf03045112 (1990).Article 

Google Scholar 
Jing, S., Kryger, P., Markussen, B. & Boelt, B. Pollination and plant reproductive success of two ploidy levels in red clover (Trifolium pratense L.). Front. Plant Sci. 1, 1580.  https://doi.org/10.3389/fpls.2021.720069 (2021).Article 

Google Scholar 
Suárez-Mariño, A., Arceo-Gómez, G., Sosenski, P. & Parra-Tabla, V. Patterns and effects of heterospecific pollen transfer between an invasive and two native plant species: The importance of pollen arrival time to the stigma. Am. J. Bot. 106, 1308–1315. https://doi.org/10.1002/ajb2.1361 (2019).Article 
PubMed 

Google Scholar 
FAO. FAOSTAT. Food and Agriculture Organization of the United Nations, Rome, Italy. http://www.fao.org/faostat/en/#data (2017).Stanghellini, M., Ambrose, J. & Schultheis, J. Seed production in watermelon: A comparison between two commercially available pollinators. HortScience 33, 28–30.  https://doi.org/10.21273/Hortsci.33.1.28 (1998).Article 

Google Scholar 
Delaplane, K. S. A. M. D.F. Crop Pollination by Bees. (CABI Publishing, 2005).Wijesinghe, S., Evans, L., Kirkland, L. & Rader, R. A global review of watermelon pollination biology and ecology: The increasing importance of seedless cultivars. Sci. Hortic. 271, 109493. https://doi.org/10.1016/j.scienta.2020.109493 (2020).CAS 
Article 

Google Scholar 
AgMRC. Watermelon. https://www.agmrc.org/commodities-products/vegetables/watermelon (2018).Bomfim, I. G. A., Bezerra, A. D. D. M., Nunes, A. C., Freitas, B. M. & Aragão, F. A. S. D. Pollination requirements of seeded and seedless mini watermelon varieties cultivated under protected environment. Pesqui. Agropecu. Bras. 50, 44–53. https://doi.org/10.1590/s0100-204×2015000100005 (2015).Article 

Google Scholar 
Maynard, D. N. & Elmstrom, G. W. Triploid watermelon production practices and varieties. II International Symposium on Specialty and Exotic Vegetable Crops, Vol. 318. 169–178.Jones, G. D. Pollen analyses for pollination research, acetolysis. J. Pollinat. Ecol. 13, 203–217. https://doi.org/10.26786/1920-7603(2014)19 (2014).Article 

Google Scholar 
Kurtz, E. B. Jr. Pollen morphology of the Cactaceae. Grana 4, 367–372.  https://doi.org/10.1080/00173136309429110 (1963).Article 

Google Scholar 
Halbritter, H. et al. Illustrated Pollen Terminology. 97–127. (Springer, 2018).Punt, W., Hoen, P., Blackmore, S., Nilsson, S. & Le Thomas, A. Glossary of pollen and spore terminology. Rev. Palaeobot. Palynol. 143, 1–81.  https://doi.org/10.1016/j.revpalbo.2006.06.008 (2007).Article 

Google Scholar 
Kaya, Y., Mesut Pınar, S., Emre Erez, M., Fidan, M. & Riding, J. B. Identification of Onopordum pollen using the extreme learning machine, a type of artificial neural network. Palynology 38, 129–137. https://doi.org/10.1080/09500340.2013.868173 (2014).Article 

Google Scholar 
Pruesapan, K. & Van Der Ham, R. Pollen morphology of Trichosanthes (Cucurbitaceae). Grana 44, 75–90. https://doi.org/10.1080/00173130510010512 (2005).Article 

Google Scholar 
Sedgley, M. & Buttrose, M. Some effects of light intensity, daylength and temperature on flowering and pollen tube growth in the watermelon (Citrullus lanatus). Ann. Bot. 42, 609–616. https://doi.org/10.1093/oxfordjournals.aob.a085495 (1978).Article 

Google Scholar 
Martin, F. W. Staining and observing pollen tubes in the style by means of fluorescence. Stain Technol. 34, 125–128. https://doi.org/10.3109/10520295909114663 (1959).CAS 
Article 
PubMed 

Google Scholar 
Godini, A. Counting pollen grains of some almond cultivars by means of an haemocytometer. Riv. Studi Ital. 1, 173–178 (1981).
Google Scholar 
Howlett, B., Evans, L., Pattemore, D. & Nelson, W. Stigmatic pollen delivery by flies and bees: Methods comparing multiple species within a pollinator community. Basic Appl. Ecol. 19, 19–25. https://doi.org/10.1016/j.baae.2016.12.002 (2017).Article 

Google Scholar 
Winfree, R., Williams, N. M., Dushoff, J. & Kremen, C. Native bees provide insurance against ongoing honey bee losses. Ecol. Lett. 10, 1105–1113. https://doi.org/10.1111/j.1461-0248.2007.01110.x (2007).Article 
PubMed 

Google Scholar 
Abdelgadir, H., Johnson, S. & Van Staden, J. Pollen viability, pollen germination and pollen tube growth in the biofuel seed crop Jatropha curcas (Euphorbiaceae). S. Afr. J. Bot. 79, 132–139. https://doi.org/10.1016/j.sajb.2011.10.005 (2012).Article 

Google Scholar 
R Core Team. R: A Language and Environment for Statistical Computing. https://www.R-project.org/ (R Foundation for Statistical Computing, 2020).Pinheiro, J., Bates, D., Deb Roy, S. & Sarkar; D. R Core Team. nlme: Linear and nonlinear mixed effects models. R package version 3.1-117. http://CRAN.R-project.org/package=nlme (2014).Lenth, R., Singmann, H., Love, J., Buerkner, P. & Herve, M. emmeans: Estimated marginal means, aka least-squares means. R package. https://CRAN.R-project.org/package=emmeans (2018).Bolker, B. M. et al. Generalized linear mixed models: a practical guide for ecology and evolution. Trends Ecol. Evol 24, 127–135. https://doi.org/10.1016/j.tree.2008.10.008 (2009).Article 
PubMed 

Google Scholar 
Hartig, F. DHARMa: Residual diagnostics for hierarchical (multi-level/mixed) regression models. R package v. 0.2. 0. (Regensburg: University of Regensburg, 2018). More

Marqués, M. J., Martínez-Conde, E., Rovira, J. V. & Ordóñez, S. Heavy metals pollution of aquatic ecosystems in the vicinity of a recently closed underground lead-zinc mine (Basque Country, Spain). Environ. Geol. 40, 1125–1137 (2001).
Google Scholar 
Bud, I., Duma, S., Denuţ, I. & Taşcu, I. Water pollution due to mining activity. Causes and consequences Wasserverunreinigung aufgrund von Bergbauaktivitäten. Ursachen und Konsequenzen. BHM Berg- Hüttenmännische Monatsh. 152, 326–328 (2007).CAS 

Google Scholar 
Ugya, Y. Assessment of ambient air quality resulting from anthropogenic emissions. Am. J. Prev. Med. Public Health https://doi.org/10.5455/ajpmph.20171030080402 (2017).Article 

Google Scholar 
Dore, E. Environment and society: Long-term trends in Latin American mining. Environ. Hist. Camb. 6, 1–29 (2000).
Google Scholar 
Zhou, Q. et al. Total concentrations and sources of heavy metal pollution in global river and lake water bodies from 1972 to 2017. Glob. Ecol. Conserv. 22, 925 (2020).
Google Scholar 
Graesser, J., Aide, T. M., Grau, H. R. & Ramankutty, N. Cropland/pastureland dynamics and the slowdown of deforestation in Latin America. Environ. Res. Lett. 10, 034017 (2015).ADS 

Google Scholar 
Ramírez, A., Pringle, C. M. & Wantzen, K. M. Tropical stream conservation. Trop. Stream Ecol. https://doi.org/10.1016/B978-012088449-0.50012-1 (2008).Article 

Google Scholar 
Uriarte, M., Yackulic, C. B., Lim, Y. & Arce-Nazario, J. A. Influence of land use on water quality in a tropical landscape: A multi-scale analysis. Landsc. Ecol. 26, 1151–1164 (2011).PubMed 
PubMed Central 

Google Scholar 
White, M. & Barquera, S. Mexico adopts food warning labels, why now?. Health Syst. Reform 6, e1752063 (2020).PubMed 

Google Scholar 
Koleff, P. et al. Biodiversity in Mexico: State of knowledge. in Global Biodiversity. 285–337. https://doi.org/10.1201/9780429433634-8. (Apple Academic Press, 2018).Armendáriz-Villegas, E. J. et al. Metal mining and natural protected areas in Mexico: Geographic overlaps and environmental implications. Environ. Sci. Policy 48, 9–19 (2015).
Google Scholar 
Montoya-Lopera, P. et al. New geological, geochronological and geochemical characterization of the San Dimas mineral system: Evidence for a telescoped Eocene-Oligocene Ag/Au deposit in the Sierra Madre Occidental, Mexico. Ore Geol. Rev. 118, 103195 (2020).
Google Scholar 
LeuraVicencio, A. K., CarrizalesYañez, L. & RazoSoto, I. Mercury pollution assessment of mining wastes and soils from former silver amalgamation area in North-Central Mexico. Rev. Int. Contam. Ambient. 33, 655–669 (2017).
Google Scholar 
Veiga, M. M. Introducing New Technologies for Abatement of Global Mercury Pollution in Latin America. United Nations Industrial Development Organization (UNIDO), University of British Columbia (UBC), Center of Mineral Technology (CETEM) (UNIDO, UBC, CETEM, 1997).Camacho, A. et al. Mercury mining in Mexico: I. Community engagement to improve health outcomes from artisanal mining. Ann. Glob. Health 82, 149 (2016).PubMed 

Google Scholar 
IUCN. Benefits Beyond Boundaries: Proceedings of the Vth IUCN World Parks Congress : Durban, South Africa. 8–17 September 2003. (Iucn, 2005).González, S. O., Almeida, C. A., Calderón, M., Mallea, M. A. & González, P. Assessment of the water self-purification capacity on a river affected by organic pollution: Application of chemometrics in spatial and temporal variations. Environ. Sci. Pollut. Res. 21, 10583–10593 (2014).
Google Scholar 
Rico-Sánchez, A. E. et al. Biological diversity in protected areas: Not yet known but already threatened. Glob. Ecol. Conserv. 22, e01006 (2020).
Google Scholar 
Harvey, C. A. et al. Integrating agricultural landscapes with biodiversity conservation in the Mesoamerican hotspot. Conserv. Biol. 22, 8–15 (2008).PubMed 

Google Scholar 
Messerli, B., Grosjean, M. & Vuille, M. Water availability, protected areas, and natural resources in the Andean desert altiplano. Mt. Res. Dev. 17, 229–238 (1997).
Google Scholar 
Servicio Geológico Mexicano. Conoce GeoInfoMex en 3D. https://www.gob.mx/sgm/articulos/conoce-el-sistema-de-consulta-de-informacion-geocientifica-geoinfomex?idiom=es. Accessed 18 Feb 2021. (2019).Resh, V. H. Which group is best? Attributes of different biological assemblages used in freshwater biomonitoring programs. Environ. Monit. Assess. 138, 131–138 (2008).PubMed 

Google Scholar 
Ruiz-Picos, R. A., Sedeño-Díaz, J. E. & López-López, E. Calibrating and validating the biomonitoring working party (BMWP) index for the bioassessment of water quality in neotropical streams. in Water Quality (InTech, 2017).Oertel, N. & Salánki, J. Biomonitoring and bioindicators in aquatic ecosystems. in Modern Trends in Applied Aquatic Ecology. 219–246. https://doi.org/10.1007/978-1-4615-0221-0_10. (Springer, 2011).Goodyear, K. L. & McNeill, S. Bioaccumulation of heavy metals by aquatic macro-invertebrates of different feeding guilds: A review. Sci. Total Environ. 229, 1–19 (1999).ADS 
CAS 

Google Scholar 
Clements, W. H. Small-scale experiments support causal relationships between metal contamination and macroinvertebrate community responses. Ecol. Appl. 14, 954–967 (2004).
Google Scholar 
Michailova, P., Warchałowska-Śliwa, E., Szarek-Gwiazda, E. & Kownacki, A. Does biodiversity of macroinvertebrates and genome response of Chironomidae larvae (Diptera) reflect heavy metal pollution in a small pond?. Environ. Monit. Assess. 184, 1–14 (2012).CAS 
PubMed 

Google Scholar 
Wright, I. A. & Ryan, M. M. Impact of mining and industrial pollution on stream macroinvertebrates: Importance of taxonomic resolution, water geochemistry and EPT indices for impact detection. Hydrobiologia 772, 103–115 (2016).CAS 

Google Scholar 
Wright, I. A. & Burgin, S. Comparison of sewage and coal-mine wastes on stream macroinvertebrates within an otherwise clean upland catchment, Southeastern Australia. Water Air Soil Pollut. 204, 227–241 (2009).ADS 
CAS 

Google Scholar 
Batty, L. C. The potential importance of mine sites for biodiversity. Mine Water Environ. 24, 101–103 (2005).
Google Scholar 
Dolédec, S. & Chessel, D. Co-inertia analysis: An alternative method for studying species–environment relationships. Freshw. Biol. 31, 277–294 (1994).
Google Scholar 
Thioulouse, J. et al. Multivariate Analysis of Ecological Data with ade4. Multivariate Analysis of Ecological Data with ade4. https://doi.org/10.1007/978-1-4939-8850-1. (Springer, 2018).Dodds, W. K., Clements, W. H., Gido, K., Hilderbrand, R. H. & King, R. S. Thresholds, breakpoints, and nonlinearity in freshwaters as related to management. J. N. Am. Benthol. Soc. 29, 988–997 (2010).
Google Scholar 
Sundermann, A., Gerhardt, M., Kappes, H. & Haase, P. Stressor prioritisation in riverine ecosystems: Which environmental factors shape benthic invertebrate assemblage metrics?. Ecol. Indic. 27, 83–96 (2013).
Google Scholar 
Gutiérrez-Yurrita, P. J., García-Serrano, L. A. & Plata, M. R. Is ecotourism a viable option to generate wealth in brittle environments? A reflection on the case of the Sierra Gorda Biosphere Reserve, México. WIT Trans. Ecol. Environ. 161, 141–151 (2012).
Google Scholar 
Vinson, M. R. Long-term dynamics of an invertebrate assemblage downstream from a large dam. Ecol. Appl. 11, 711–730 (2001).
Google Scholar 
Torres-Olvera, M. J., Durán-Rodríguez, O. Y., Torres-García, U., Pineda-López, R. & Ramírez-Herrejón, J. P. Validation of an index of biological integrity based on aquatic macroinvertebrates assemblages in two subtropical basins of central Mexico. Lat. Am. J. Aquat. Res. 46, 945–960 (2018).
Google Scholar 
Carabias Lillo, J., Provencio, E., de la Maza Elvira, J. & Ruiz Corzo, M. Programa de Manejo Reserva de la Biosfera Sierra Gorda. (México, Instituto Nacional de Ecologıa, SEMARNAT, 1999).Macedo, D. R. et al. The relative influence of catchment and site variables on fish and macroinvertebrate richness in cerrado biome streams. Landsc. Ecol. 29, 1001–1016 (2014).
Google Scholar 
Dutra, S. L. & Callisto, M. Macroinvertebrates as tadpole food: Importance and body size relationships. Rev. Bras. Zool. 22, 923–927 (2005).
Google Scholar 
Wang, Z. et al. River-groundwater interaction affected species composition and diversity perpendicular to a regulated river in an arid riparian zone. Glob. Ecol. Conserv. 27, e01595 (2021).
Google Scholar 
López-López, E., Sedeño-Díaz, J. E., Mendoza-Martínez, E., Gómez-Ruiz, A. & Ramírez, E. M. Water quality and macroinvertebrate community in dryland streams: The case of the Tehuacán-Cuicatlán Biosphere Reserve (México) facing climate change. Water (Switzerland) 11, 1376 (2019).
Google Scholar 
O’Connor, N. A. The effects of habitat complexity on the macroinvertebrates colonising wood substrates in a lowland stream. Oecologia 85, 504–512 (1991).ADS 
PubMed 

Google Scholar 
Milner, A. M. & Gloyne-Phillips, I. T. The role of riparian vegetation and woody debris in the development of macroinvertebrate assemblages in streams. River Res. Appl. 21, 403–420 (2005).
Google Scholar 
Vannote, R. L., Minshall, G. W., Cummins, K. W., Sedell, J. R. & Cushing, C. E. The river continuum concept. Can. J. Fish. Aquat. Sci. 37, 130–137 (1980).
Google Scholar 
Malmqvist, B. & Hoffsten, P.-O. Influence of drainage from old mine deposits on benthic macroinvertebrate communities in central Swedish streams. Water Res. 33, 2415–2423 (1999).CAS 

Google Scholar 
Jost, L. Independence of alpha and beta diversities. Ecology 91, 1969–1974 (2010).PubMed 

Google Scholar 
Cottenie, K. Integrating environmental and spatial processes in ecological community dynamics. Ecol. Lett. 8, 1175–1182 (2005).PubMed 

Google Scholar 
Jerves-Cobo, R. et al. Biological impact assessment of sewage outfalls in the urbanized area of the Cuenca River basin (Ecuador) in two different seasons. Limnologica 71, 8–28 (2018).CAS 

Google Scholar 
US Environmental Protection Agency. National Recommended Water Quality Criteria-Aquatic Life Criteria Table. Arsenic. (US Environmental Protection Agency, 1995).DeNicola, D. M. & Lellock, A. J. Nutrient limitation of algal periphyton in streams along an acid mine drainage gradient. J. Phycol. 51, 739–749 (2015).CAS 
PubMed 

Google Scholar 
Younos, T. & Schreiber, M. The Handbook of Environmental Chemistry 68. Tamim Younos, Madeline Schreiber, Katarina Kosič Ficco-Karst Water Environment-Springer International Publishing (2019).pdf. (Springer, 2019).Robles, I. et al. Characterization and remediation of soils and sediments polluted with Mercury: Occurrence, transformations, environmental considerations and San Joaquin’s Sierra Gorda case. in Environmental Risk Assessment of Soil Contamination. https://doi.org/10.5772/57284. (InTech, 2014).Hernández-Silva, G. et al. Presencia Del Hg total En Una Relación Suelo-Planta-Atmósfera Al Sur De La Sierra Gorda De Querétaro, México. TIP Rev. Espec. Ciencias Químico-Biol. 15, 5–15 (2012).
Google Scholar 
Campos, E. M. P. & Muñoz, A. J. H. Minas y mineros: Presencia de metales en sedimentos y restos humanos al sur de la sierra gorda de Querétaro en México. Chungara 45, 161–176 (2013).
Google Scholar 
Carrillo-Martínez, M. & Suter-Cargneluti, M. Tectónica de los alrededores de Zimapán, Hidalgo y Querétaro, Libro Guía de la excursión geológica a la región de Zimapán y áreas circundantes, estados de Hidalgo y Querétaro, Hidalgo, México. in VI Convención Geológica Nacional México, DF, Society Geológica Mexico. 1–20. (1982).Allan, J. D. Stream ecology: Structure and function of running waters. Stream Ecol. Struct. Funct. Run. Waters https://doi.org/10.2307/2261644 (2007).Article 

Google Scholar 
Trang, N. T. T., Shrestha, S., Shrestha, M., Datta, A. & Kawasaki, A. Evaluating the impacts of climate and land-use change on the hydrology and nutrient yield in a transboundary river basin: A case study in the 3S River Basin (Sekong, Sesan, and Srepok). Sci. Total Environ. 576, 586–598 (2017).ADS 
CAS 
PubMed 

Google Scholar 
Khatri, N. & Tyagi, S. Influences of natural and anthropogenic factors on surface and groundwater quality in rural and urban areas. Front. Life Sci. 8, 23–39 (2015).CAS 

Google Scholar 
Simões, N. R. et al. Impact of reservoirs on zooplankton diversity and implications for the conservation of natural aquatic environments. Hydrobiologia 758, 3–17 (2015).
Google Scholar 
Dallas, H. F. & Rivers-Moore, N. A. Critical thermal maxima of aquatic macroinvertebrates: Towards identifying bioindicators of thermal alteration. Hydrobiologia 679, 61–76 (2012).
Google Scholar 
Struijs, J., De Zwart, D., Posthuma, L., Leuven, R. S. & Huijbregts, M. A. Field sensitivity distribution of macroinvertebrates for phosphorus in inland waters. Integr. Environ. Assess. Manag. 7, 280–286 (2011).CAS 
PubMed 

Google Scholar 
Molina, C. I. et al. Transfer of mercury and methylmercury along macroinvertebrate food chains in a floodplain lake of the Beni River, Bolivian Amazonia. Sci. Total Environ. 408, 3382–3391 (2010).ADS 
CAS 
PubMed 

Google Scholar 
Corkum, L. D. Patterns of benthic invertebrate assemblages in rivers of northwestern North America. Freshw. Biol. 21, 191–205 (1989).
Google Scholar 
Dalu, T. et al. Assessing drivers of benthic macroinvertebrate community structure in African highland streams: An exploration using multivariate analysis. Sci. Total Environ. 601–602, 1340–1348 (2017).ADS 
PubMed 

Google Scholar 
Eriksen, T. E. et al. A global perspective on the application of riverine macroinvertebrates as biological indicators in Africa, South-Central America, Mexico and Southern Asia. Ecol. Indic. 126, 107609 (2021).
Google Scholar 
Mercado-Garcia, D. et al. Assessing the freshwater quality of a large-scale mining watershed: The need for integrated approaches. Water 11, 1797 (2019).CAS 

Google Scholar 
Gerhardt, A., Janssens De Bisthoven, L. & Soares, A. M. V. M. Effects of acid mine drainage and acidity on the activity of Choroterpes picteti (Ephemeroptera: Leptophlebiidae). Arch. Environ. Contam. Toxicol. 48, 450–458 (2005).CAS 
PubMed 

Google Scholar 
Qu, X., Wu, N., Tang, T., Cai, Q. & Park, Y.-S. Effects of heavy metals on benthic macroinvertebrate communities in high mountain streams. Ann. Limnol. Int. J. Limnol. 46, 291–302 (2010).
Google Scholar 
Soucek, D. J., Denson, B. C., Schmidt, T. S., Cherry, D. S. & Zipper, C. E. Impaired Acroneuria sp. (Plecoptera, Perlidae) populations associated with aluminum contamination in neutral pH surface waters. Arch. Environ. Contam. Toxicol. 42, 416–422 (2002).CAS 
PubMed 

Google Scholar 
Ankley, G. T. Evaluation of metal/acid-volatile sulfide relationships in the prediction of metal bioaccumulation by benthic macroinvertebrates. Environ. Toxicol. Chem. 15, 2138–2146 (1996).CAS 

Google Scholar 
Croteau, M. N., Luoma, S. N. & Stewart, A. R. Trophic transfer of metals along freshwater food webs: Evidence of cadmium biomagnification in nature. Limnol. Oceanogr. 50, 1511–1519 (2005).ADS 
CAS 

Google Scholar 
Specht, W. L., Cherry, D. S., Lechleitner, R. A. & Cairns, J. Structural, functional, and recovery responses of stream invertebrates to fly ash effluent. Can. J. Fish. Aquat. Sci. 41, 884–896 (1984).CAS 

Google Scholar 
Corbi, J. J., Froehlich, C. G., Strixino, S. T. & Dos Santos, A. Bioaccumulation of metals in aquatic insects of streams located in areas with sugar cane cultivation. Quim. Nova 33, 644–648 (2010).CAS 

Google Scholar 
Poff, N. L., Bledsoe, B. P. & Cuhaciyan, C. O. Hydrologic variation with land use across the contiguous United States: Geomorphic and ecological consequences for stream ecosystems. Geomorphology 79, 264–285 (2006).ADS 

Google Scholar 
Chang, F. H., Lawrence, J. E., Rios-Touma, B. & Resh, V. H. Tolerance values of benthic macroinvertebrates for stream biomonitoring: Assessment of assumptions underlying scoring systems worldwide. Environ. Monit. Assess. 186, 2135–2149 (2014).CAS 
PubMed 

Google Scholar 
Brittain, J. E. Life History Strategies in Ephemeroptera and Plecoptera. in Mayflies and Stoneflies: Life Histories and Biology. 1–12. https://doi.org/10.1007/978-94-009-2397-3_1 (Springer Netherlands, 1990).Bispo, P. C., Oliveira, L. G., Bini, L. M. & Sousa, K. G. Ephemeroptera, Plecoptera and Trichoptera assemblages from riffles in mountain streams of central Brazil: Environmental factors influencing the distribution and abundance of immatures. Braz. J. Biol. 66, 611–622 (2006).CAS 
PubMed 

Google Scholar 
Jacobsen, D. Tropical high-altitude streams. in Tropical Stream Ecology. 219–256. https://doi.org/10.1016/B978-012088449-0.50010-8 (Elsevier, 2008).Jacobsen, D., Rostgaard, S. & Vasconez, J. J. Are macroinvertebrates in high altitude streams affected by oxygen deficiency?. Freshw. Biol. 48, 2025–2032 (2003).
Google Scholar 
Courtney, L. A. & Clements, W. H. Assessing the influence of water and substratum quality on benthic macroinvertebrate communities in a metal-polluted stream: An experimental approach. Freshw. Biol. 47, 1766–1778 (2002).CAS 

Google Scholar 
Buss, D. F. & Salles, F. F. Using Baetidae species as biological indicators of environmental degradation in a Brazilian river basin. Environ. Monit. Assess. 130, 365–372 (2007).CAS 
PubMed 

Google Scholar 
Ristau, K., Faupel, M. & Traunspurger, W. The effects of nutrient enrichment on a freshwater meiofaunal assemblage. Freshw. Biol. 57, 824–834 (2012).CAS 

Google Scholar 
Cornelis, R. & Nordberg, M. General chemistry, sampling, analytical methods, and speciation. in Handbook on the Toxicology of Metals. 11–38. https://doi.org/10.1016/B978-012369413-3/50057-4 (Elsevier, 2007).Santore, R. C., Di Toro, D. M., Paquin, P. R., Allen, H. E. & Meyer, J. S. Biotic ligand model of the acute toxicity of metals. 2. Application to acute copper toxicity in freshwater fish and Daphnia. Environ. Toxicol. Chem. 20, 2397–2402 (2001).CAS 
PubMed 

Google Scholar 
Kozlova, T., Wood, C. M. & McGeer, J. C. The effect of water chemistry on the acute toxicity of nickel to the cladoceran Daphnia pulex and the development of a biotic ligand model. Aquat. Toxicol. 91, 221–228 (2009).CAS 
PubMed 

Google Scholar 
Valdez, R., Guzmán-Aranda, J. C., Abarca, F. J., Tarango-Arámbula, L. A. & Sánchez, F. C. Wildlife conservation and management in Mexico. Wildl. Soc. Bull. 34, 270–282 (2006).
Google Scholar 
INEGI. Por Actividad Económica. https://www.inegi.org.mx/temas/pib/. Accessed 6 Jan 2021. (2020).García, E. Modificaciones Al Sistema de Classificación Climática de Koppen. (Institute of Geography, UNAM, 1988).HACH. User Manual—HACH DR 3900. in 1–148 (2013).APHA. Standard Methods for the Examination of Water and Wastewater. (Association, American Public Health, 2005).NMX-AA-051-SCFI-2001. Análisis de agua—Determinación de metales por absorción atómica en aguas naturales, potables, residuales y residuales tratadas. Norma Mex. 1–47 (2001).Helsel, D. R. Less than obvious: Statistical treatment of data below the detection limit. Environ. Sci. Technol. 24, 1766–1774 (1990).ADS 
CAS 

Google Scholar 
Barbour, M. T., Stribling, J. B. & Verdonschot, P. F. M. The multihabitat approach of USEPA’s rapid bioassessment protocols: benthic macroinvertebrates. Limnetica 25, 839–850 (2006).
Google Scholar 
USEPA. National Rivers and Streams Assessment 2018/19: Field Operations Manual—Wadeable. Vol. EPA-841-B-. 169. (2017).Michaud, J. P. & Wierenga, M. Estimating Discharge and Stream Flows (Ecology Publication, 2005).
Google Scholar 
Hering, D., Moog, O., Sandin, L. & Verdonschot, P. F. M. Overview and application of the AQEM assessment system. Hydrobiologia 516, 1–20 (2004).
Google Scholar 
Merrit, R. & Cummins, K. W. An Introduction to the Aquatic Insects of North America 3rd edn. (Kendall Hunt, 1996).
Google Scholar 
Thorp, J. H. & Covich, A. P. Ecology and Classification of North American Freshwater Invertebrates (Academic Press, 2009).
Google Scholar 
Bueno-Soria, J. Guía de Identificación Ilustrada de Losgéneros de Larvas de Insectos del Orden Trichoptera de México (Universidad Nacional Autónoma de México, 2010).
Google Scholar 
Springer, M., Ramírez, A. & Hanson, P. Macroinvertebrados de agua dulce I. Rev. Biol. Trop. 58, 198 (2010).
Google Scholar 
Hamada, N., Thorp, J. H. & Rogers, D. C. Thorp and Covich’s Freshwater Invertebrates (Elsevier, 2018).
Google Scholar 
Jost, L. et al. Partitioning diversity for conservation analyses. Divers. Distrib. 16, 65–76 (2010).
Google Scholar 
Lavit, C., Escoufier, Y. & Sabatier, R. The ACT (STATIS method) J q G G Fl { q K q *. Comput. Stat. Data Anal. 18, 97–119 (1994).MATH 

Google Scholar  More