More stories

  • in

    Metagenome-assembled genome extraction and analysis from microbiomes using KBase

    Hug, L. A. et al. A new view of the tree of life. Nat. Microbiol. 1, 16048 (2016).Article 
    PubMed 
    CAS 

    Google Scholar 
    Spang, A. et al. Complex archaea that bridge the gap between prokaryotes and eukaryotes. Nature 521, 173–179 (2015).Article 
    PubMed 
    PubMed Central 
    CAS 

    Google Scholar 
    Tyson, G. W. et al. Community structure and metabolism through reconstruction of microbial genomes from the environment. Nature 428, 37–43 (2004).Article 
    PubMed 
    CAS 

    Google Scholar 
    Anantharaman, K. et al. Thousands of microbial genomes shed light on interconnected biogeochemical processes in an aquifer system. Nat. Commun. 7, 13219 (2016).Article 
    PubMed 
    PubMed Central 
    CAS 

    Google Scholar 
    Parks, D. H. et al. Recovery of nearly 8,000 metagenome-assembled genomes substantially expands the tree of life. Nat. Microbiol. 2, 1533–1542 (2017).Article 
    PubMed 
    CAS 

    Google Scholar 
    Tully, B. J. & Graham, E. D. & Heidelberg, J. F. The reconstruction of 2,631 draft metagenome-assembled genomes from the global oceans. Sci. Data 5, 170203 (2018).Article 
    PubMed 
    PubMed Central 
    CAS 

    Google Scholar 
    Stewart, R. D. et al. Assembly of 913 microbial genomes from metagenomic sequencing of the cow rumen. Nat. Commun. 9, 870 (2018).Article 
    PubMed 
    PubMed Central 

    Google Scholar 
    Pasolli, E. et al. Extensive unexplored human microbiome diversity revealed by over 150,000 genomes from metagenomes spanning age, geography and lifestyle. Cell 176, 649–662 (2019).Article 
    PubMed 
    PubMed Central 
    CAS 

    Google Scholar 
    Nayfach, S. et al. A genomic catalog of Earth’s microbiomes. Nat. Biotechnol. 39, 499–509, https://doi.org/10.1038/s41587-020-0718-6 (2021).Article 
    PubMed 
    CAS 

    Google Scholar 
    Gilbert, J. A., Jansson, J. K. & Knight, R. The Earth Microbiome project: successes and aspirations. BMC Biol 12, 69 (2014).Article 
    PubMed 
    PubMed Central 

    Google Scholar 
    Saheb Kashaf, S., Almeida, A., Segre, J. A. & Finn, R. D. Recovering prokaryotic genomes from host-associated, short-read shotgun metagenomic sequencing data. Nat. Protoc. 16, 2520–2541 (2021).Article 
    PubMed 
    CAS 

    Google Scholar 
    Chong, J., Liu, P., Zhou, G. & Xia, J. Using MicrobiomeAnalyst for comprehensive statistical, functional, and meta-analysis of microbiome data. Nat. Protoc. 15, 799–821 (2020).Article 
    PubMed 
    CAS 

    Google Scholar 
    Arkin, A. P. et al. KBase: The United States Department of Energy Systems Biology Knowledgebase. Nat. Biotechnol. 36, 566–569 (2018).Article 
    PubMed 
    PubMed Central 
    CAS 

    Google Scholar 
    Sayers, E. W. et al. Database resources of the National Center for Biotechnology Information. Nucleic Acids Res 49, D10–D17 (2021).Article 
    PubMed 
    CAS 

    Google Scholar 
    Kluyver, T., et al. Jupyter Notebooks – a publishing format for reproducible computational workflows. In: Loizides F, Schmidt B, editors. Positioning and Power in Academic Publishing: Players, Agents and Agendas. p. 87–90 (2016).Banfield, J. Development of a Knowledgebase to Integrate, Analyze, Distribute, and Visualize Microbial Community Systems Biology Data. (2015). Report number: DOE-UCB-4918, OSTI ID: 1167269.Chen, I.-M. A. et al. IMG/M v.5.0: an integrated data management and comparative analysis system for microbial genomes and microbiomes. Nucleic Acids Res 47, D666–D677 (2019).Article 
    PubMed 
    CAS 

    Google Scholar 
    Afgan, E. et al. The Galaxy platform for accessible, reproducible and collaborative biomedical analyses: 2016 update. Nucleic Acids Res 44, W3–W10 (2016).Article 
    PubMed 
    PubMed Central 
    CAS 

    Google Scholar 
    Devisetty, U. K., Kennedy, K., Sarando, P., Merchant, N. & Lyons, E. Bringing your tools to CyVerse discovery environment using Docker. F1000Res. 5, 1442 (2016).Article 
    PubMed 
    PubMed Central 

    Google Scholar 
    Wang, L., Lu, Z., Van Buren, P. & Ware, D. SciApps: a bioinformatics workflow platform powered by XSEDE and CyVerse. in Proceedings of the Practice and Experience on Advanced Research Computing 1–5 (Association for Computing Machinery, 2018).Eren, A. M. et al. Community-led, integrated, reproducible multi-omics with anvi’o. Nat. Microbiol. 6, 3–6 (2021).Article 
    PubMed 
    PubMed Central 
    CAS 

    Google Scholar 
    Wattam, A. R. et al. Improvements to PATRIC, the all-bacterial bioinformatics database and analysis resource center. Nucleic Acids Res 45, D535–D542 (2017).Article 
    PubMed 
    CAS 

    Google Scholar 
    Mitchell, A. L. et al. MGnify: the microbiome analysis resource in 2020. Nucleic Acids Res. 48, D570–D578 (2020).PubMed 
    CAS 

    Google Scholar 
    Wu, Y.-W. et al. Ionic liquids impact the bioenergy feedstock-degrading microbiome and transcription of enzymes relevant to polysaccharide hydrolysis. mSystems 1, e00120–16 (2016).Article 
    PubMed 
    PubMed Central 

    Google Scholar 
    Rajeev, L. et al. Dynamic cyanobacterial response to hydration and dehydration in a desert biological soil crust. ISME J 7, 2178–2191 (2013).Article 
    PubMed 
    PubMed Central 
    CAS 

    Google Scholar 
    Foster, I. Globus Online: accelerating and democratizing science through cloud-based services. IEEE Internet Comput 15, 70–73 (2011).Article 

    Google Scholar 
    Nurk, S., Meleshko, D., Korobeynikov, A. & Pevzner, P. A. metaSPAdes: a new versatile metagenomic assembler. Genome Res 27, 824–834 (2017).Article 
    PubMed 
    PubMed Central 
    CAS 

    Google Scholar 
    Zhang, H. et al. dbCAN2: a meta server for automated carbohydrate-active enzyme annotation. Nucleic Acids Res 46, W95–W101 (2018).Article 
    PubMed 
    PubMed Central 
    CAS 

    Google Scholar 
    Chaumeil, P.-A., Mussig, A. J., Hugenholtz, P. & Parks, D. H. GTDB-Tk: a toolkit to classify genomes with the Genome Taxonomy Database. Bioinformatics 36, 1925–1927 (2019).PubMed Central 

    Google Scholar 
    Camacho, C. et al. BLAST+: architecture and applications. BMC Bioinforma 10, 421 (2009).Article 

    Google Scholar 
    Nordberg, H. et al. The genome portal of the Department of Energy Joint Genome Institute: 2014 updates. Nucleic Acids Res 42, D26–D31 (2014).Article 
    PubMed 
    CAS 

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

    Google Scholar 
    Martin, M. Cutadapt removes adapter sequences from high-throughput sequencing reads. EMBnet. J. 17, 10–12 (2011).Article 

    Google Scholar 
    Menzel, P., Ng, K. L. & Krogh, A. Fast and sensitive taxonomic classification for metagenomics with Kaiju. Nat. Commun. 7, 11257 (2016).Article 
    PubMed 
    PubMed Central 
    CAS 

    Google Scholar 
    Freitas, T. A. K., Li, P.-E., Scholz, M. B. & Chain, P. S. G. Accurate read-based metagenome characterization using a hierarchical suite of unique signatures. Nucleic Acids Res 43, e69 (2015).Article 
    PubMed 
    PubMed Central 

    Google Scholar 
    Wood, D. E., Lu, J. & Langmead, B. Improved metagenomic analysis with Kraken 2. Genome Biol 20, 257 (2019).Article 
    PubMed 
    PubMed Central 
    CAS 

    Google Scholar 
    Truong, D. T. et al. MetaPhlAn2 for enhanced metagenomic taxonomic profiling. Nat. Methods 12, 902–903 (2015).Article 
    PubMed 
    CAS 

    Google Scholar 
    Milanese, A. et al. Microbial abundance, activity and population genomic profiling with mOTUs2. Nat. Commun. 10, 2014 (2019).Article 

    Google Scholar 
    Youngblut, N. D. & Ley, R. E. Struo2: efficient metagenome profiling database construction for ever-expanding microbial genome datasets. Peer J 9, e12198 (2021).Article 
    PubMed 
    PubMed Central 

    Google Scholar 
    Ondov, B. D., Bergman, N. H. & Phillippy, A. M. Interactive metagenomic visualization in a Web browser. BMC Bioinform 12, 385 (2011).Article 

    Google Scholar 
    Li, D., Liu, C.-M., Luo, R., Sadakane, K. & Lam, T.-W. MEGAHIT: an ultra-fast single-node solution for large and complex metagenomics assembly via succinct de Bruijn graph. Bioinformatics 31, 1674–1676 (2015).Article 
    PubMed 
    CAS 

    Google Scholar 
    Peng, Y., Leung, H. C. M., Yiu, S. M. & Chin, F. Y. L. IDBA-UD: a de novo assembler for single-cell and metagenomic sequencing data with highly uneven depth. Bioinformatics 28, 1420–1428 (2012).Article 
    PubMed 
    CAS 

    Google Scholar 
    Orakov, A. et al. GUNC: detection of chimerism and contamination in prokaryotic genomes. Genome Biol 22, 178 (2021).Article 
    PubMed 
    PubMed Central 
    CAS 

    Google Scholar 
    Gurevich, A., Saveliev, V., Vyahhi, N. & Tesler, G. QUAST: quality assessment tool for genome assemblies. Bioinformatics 29, 1072–1075 (2013).Article 
    PubMed 
    PubMed Central 
    CAS 

    Google Scholar 
    Wu, Y.-W., Simmons, B. A. & Singer, S. W. MaxBin 2.0: an automated binning algorithm to recover genomes from multiple metagenomic datasets. Bioinformatics 32, 605–607 (2016).Article 
    PubMed 
    CAS 

    Google Scholar 
    Kang, D. D. et al. MetaBAT 2: an adaptive binning algorithm for robust and efficient genome reconstruction from metagenome assemblies. PeerJ 7, e7359 (2019).Article 
    PubMed 
    PubMed Central 

    Google Scholar 
    Alneberg, J. et al. Binning metagenomic contigs by coverage and composition. Nat. Methods 11, 1144–1146 (2014).Article 
    PubMed 
    CAS 

    Google Scholar 
    Sieber, C. M. K. et al. Recovery of genomes from metagenomes via a dereplication, aggregation and scoring strategy. Nat. Microbiol. 3, 836–843 (2018).Article 
    PubMed 
    PubMed Central 
    CAS 

    Google Scholar 
    Parks, D. H., Imelfort, M., Skennerton, C. T., Hugenholtz, P. & Tyson, G. W. CheckM: assessing the quality of microbial genomes recovered from isolates, single cells, and metagenomes. Genome Res 25, 1043–1055 (2015).Article 
    PubMed 
    PubMed Central 
    CAS 

    Google Scholar 
    Delcher, A. L., Salzberg, S. L. & Phillippy, A. M. Using MUMmer to identify similar regions in large sequence sets. Curr. Protoc. Bioinform. Chapter 10, Unit 10.3 (2003).
    Google Scholar 
    Darling, A. C. E., Mau, B., Blattner, F. R. & Perna, N. T. Mauve: multiple alignment of conserved genomic sequence with rearrangements. Genome Res 14, 1394–1403 (2004).Article 
    PubMed 
    PubMed Central 
    CAS 

    Google Scholar 
    Parks, D. H. et al. GTDB: an ongoing census of bacterial and archaeal diversity through a phylogenetically consistent, rank normalized and complete genome-based taxonomy. Nucleic Acids Res 50, D785–D794 (2022).Article 
    PubMed 
    CAS 

    Google Scholar 
    Bowers, R. M. et al. Minimum information about a single amplified genome (MISAG) and a metagenome-assembled genome (MIMAG) of bacteria and archaea. Nat. Biotechnol. 35, 725–731 (2017).Article 
    PubMed 
    PubMed Central 
    CAS 

    Google Scholar 
    Brettin, T. et al. RASTtk: a modular and extensible implementation of the RAST algorithm for building custom annotation pipelines and annotating batches of genomes. Sci. Rep. 5, 8365 (2015).Article 
    PubMed 
    PubMed Central 

    Google Scholar 
    Overbeek, R. et al. The SEED and the rapid annotation of microbial genomes using Subsystems Technology (RAST). Nucleic Acids Res 42, D206–D214 (2014).Article 
    PubMed 
    CAS 

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

    Google Scholar 
    Hyatt, D. et al. Prodigal: prokaryotic gene recognition and translation initiation site identification. BMC Bioinform 11, 119 (2010).Article 

    Google Scholar 
    Parks, D. H. et al. A standardized bacterial taxonomy based on genome phylogeny substantially revises the tree of life. Nat. Biotechnol. 36, 996–1004 (2018).Article 
    PubMed 
    CAS 

    Google Scholar 
    Rinke, C. et al. A standardized archaeal taxonomy for the Genome Taxonomy Database. Nat. Microbiol. 6, 946–959 (2021).Article 
    PubMed 
    CAS 

    Google Scholar 
    Haft, D. H. et al. RefSeq: an update on prokaryotic genome annotation and curation. Nucleic Acids Res 46, D851–D860 (2018).Article 
    PubMed 
    CAS 

    Google Scholar 
    Price, M. N., Dehal, P. S. & Arkin, A. P. FastTree 2–approximately maximum-likelihood trees for large alignments. PLoS ONE 5, e9490 (2010).Article 
    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).Article 
    PubMed 
    PubMed Central 
    CAS 

    Google Scholar 
    Galperin, M. Y., Makarova, K. S., Wolf, Y. I. & Koonin, E. V. Expanded microbial genome coverage and improved protein family annotation in the COG database. Nucleic Acids Res 43, D261–D269 (2015). (Database Issue).Article 
    PubMed 
    CAS 

    Google Scholar 
    El-Gebali, S. et al. The Pfam protein families database in 2019. Nucleic Acids Res 47, D427–D432 (2019).Article 
    PubMed 
    CAS 

    Google Scholar 
    Haft, D. H. et al. TIGRFAMs and Genome Properties in 2013. Nucleic Acids Res 41, D387–D395 (2013). (Database issue).Article 
    PubMed 
    CAS 

    Google Scholar 
    Eddy, S. R. Accelerated Profile HMM Searches. PLoS Comput. Biol. 7, e1002195 (2011).Article 
    PubMed 
    PubMed Central 
    CAS 

    Google Scholar 
    Lombard, V., Golaconda Ramulu, H., Drula, E., Coutinho, P. M. & Henrissat, B. The carbohydrate-active enzymes database (CAZy) in 2013. Nucleic Acids Res 42, D490–D495 (2014).Article 
    PubMed 
    CAS 

    Google Scholar 
    Chivian, D., Dehal, P. S., Keller, K. & Arkin, A. P. MetaMicrobesOnline: phylogenomic analysis of microbial communities. Nucleic Acids Res 41, D648–D654 (2013).Article 
    PubMed 
    CAS 

    Google Scholar 
    Karaoz, U. & Brodie, E. L. microTrait: a toolset for a trait-based representation of microbial genomes. Front. Bioinform. https://doi.org/10.3389/fbinf.2022.918853 (2022).Article 
    PubMed 
    PubMed Central 

    Google Scholar 
    Wood-Charlson, E. M. et al. The National Microbiome Data Collaborative: enabling microbiome science. Nat. Rev. Microbiol. 18, 313–314 (2020).Article 
    PubMed 
    CAS 

    Google Scholar 
    Hofmeyr, S. et al. Terabase-scale metagenome coassembly with MetaHipMer. Sci. Rep. 10, 10689 (2020).Article 
    PubMed 
    PubMed Central 
    CAS 

    Google Scholar 
    Kolmogorov, M. et al. metaFlye: scalable long-read metagenome assembly using repeat graphs. Nat. Methods 17, 1103–1110 (2020).Article 
    PubMed 
    CAS 

    Google Scholar 
    Koren, S. et al. Canu: scalable and accurate long-read assembly via adaptive k-mer weighting and repeat separation. Genome Res 27, 722–736 (2017).Article 
    PubMed 
    PubMed Central 
    CAS 

    Google Scholar 
    Bertrand, D. et al. Hybrid metagenomic assembly enables high-resolution analysis of resistance determinants and mobile elements in human microbiomes. Nat. Biotechnol. 37, 937–944 (2019).Article 
    PubMed 
    CAS 

    Google Scholar 
    Chen, L.-X. et al. Accurate and complete genomes from metagenomes. Genome Res 30, 315–333 (2020).Article 
    PubMed 
    PubMed Central 
    CAS 

    Google Scholar 
    Lui, L. M., Nielsen, T. N. & Arkin, A. P. A method for achieving complete microbial genomes and improving bins from metagenomics data. PLoS Comput Biol 17, e1008972 (2021).Article 
    PubMed 
    PubMed Central 
    CAS 

    Google Scholar 
    Miller, C. S., Baker, B. J., Thomas, B. C., Singer, S. W. & Banfield, J. F. EMIRGE: reconstruction of full-length ribosomal genes from microbial community short read sequencing data. Genome Biol 12, R44 (2011).Article 
    PubMed 
    PubMed Central 
    CAS 

    Google Scholar 
    Chivian, D. et al. Genome extraction from shotgun metagenome sequence data. KBase n/33233/628 https://doi.org/10.25982/33233.606/1831502 (2022).Article 

    Google Scholar 
    Chivian, D., et al. Moab desert crust – sample 4E. KBase n/62384/334 (2022). https://doi.org/10.25982/62384.253/1831503Jain, C., Rodriguez-R, L. M., Phillippy, A. M., Konstantinidis, K. T. & Aluru, S. High throughput ANI analysis of 90K prokaryotic genomes reveals clear species boundaries. Nat. Commun. 9, 5114 (2018).Article 
    PubMed 
    PubMed Central 

    Google Scholar 
    Matsen, F. A., Kodner, R. B. & Armbrust, E. V. pplacer: linear time maximum-likelihood and Bayesian phylogenetic placement of sequences onto a fixed reference tree. BMC Bioinform 11, 538 (2010).Article 

    Google Scholar 
    Benson, D. A. et al. GenBank. Nucleic Acids Res 46, D41–D47 (2018).Article 
    PubMed 
    CAS 

    Google Scholar 
    Ewing, B. & Green, P. Base-calling of automated sequencer traces using phred. II. Error probabilities. Genome Res. 8, 186–194 (1998).Article 
    PubMed 
    CAS 

    Google Scholar 
    Teiling, C. BaseSpace: Simplifying metagenomic analysis. 26th European Congress of Clinical Microbiology and Infectious Diseases (2016) 10.26226/morressier.56d5ba2ed462b80296c9509dReich, M. et al. The GenePattern notebook environment. Cell Syst 5, 149–151.e1 (2017).Article 
    PubMed 
    PubMed Central 
    CAS 

    Google Scholar 
    Uritskiy, G. V., DiRuggiero, J. & Taylor, J. MetaWRAP-a flexible pipeline for genome-resolved metagenomic data analysis. Microbiome 6, 158 (2018).Article 
    PubMed 
    PubMed Central 

    Google Scholar 
    Karp, P. D. et al. A comparison of microbial genome web portals. Front. Microbiol. 10, 208 (2019).Article 
    PubMed 
    PubMed Central 

    Google Scholar 
    Yue, Y. et al. Evaluating metagenomics tools for genome binning with real metagenomic datasets and CAMI datasets. BMC Bioinform 21, 334 (2020).Article 
    CAS 

    Google Scholar 
    Nelson, W. C., Tully, B. J. & Mobberley, J. M. Biases in genome reconstruction from metagenomic data. PeerJ 8, e10119 (2020).Article 
    PubMed 
    PubMed Central 

    Google Scholar 
    Olm, M. R., Brown, C. T., Brooks, B. & Banfield, J. F. dRep: a tool for fast and accurate genomic comparisons that enables improved genome recovery from metagenomes through de-replication. ISME J 11, 2864–2868 (2017).Article 
    PubMed 
    PubMed Central 
    CAS 

    Google Scholar 
    Li, L., Stoeckert, C. J. Jr & Roos, D. S. OrthoMCL: identification of ortholog groups for eukaryotic genomes. Genome Res 13, 2178–2189 (2003).Article 
    PubMed 
    PubMed Central 
    CAS 

    Google Scholar 
    Edgar, R. C. MUSCLE: multiple sequence alignment with high accuracy and high throughput. Nucleic Acids Res 32, 1792–1797 (2004).Article 
    PubMed 
    PubMed Central 
    CAS 

    Google Scholar 
    Kim, D., Paggi, J. M., Park, C., Bennett, C. & Salzberg, S. L. Graph-based genome alignment and genotyping with HISAT2 and HISAT-genotype. Nat. Biotechnol. 37, 907–915 (2019).Article 
    PubMed 
    PubMed Central 
    CAS 

    Google Scholar 
    Pertea, M. et al. StringTie enables improved reconstruction of a transcriptome from RNA-seq reads. Nat. Biotechnol. 33, 290–295 (2015).Article 
    PubMed 
    PubMed Central 
    CAS 

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

    Google Scholar 
    Kumari, S. et al. A KBase case study on genome-wide transcriptomics and plant primary metabolism in response to drought stress in sorghum. Curr. Plant Biol. 28, 100229 (2021).Article 
    CAS 

    Google Scholar 
    Seaver, S. M. D. et al. The ModelSEED biochemistry database for the integration of metabolic annotations and the reconstruction, comparison and analysis of metabolic models for plants, fungi and microbes. Nucleic Acids Res 49, D575–D588 (2021).Article 
    PubMed 
    CAS 

    Google Scholar 
    Schloss, P. D. et al. Introducing mothur: open-source, platform-independent, community-supported software for describing and comparing microbial communities. Appl. Environ. Microbiol. 75, 7537–7541 (2009).Article 
    PubMed 
    PubMed Central 
    CAS 

    Google Scholar 
    Caporaso, J. G. et al. QIIME allows analysis of high-throughput community sequencing data. Nat. Methods 7, 335–336 (2010).Article 
    PubMed 
    PubMed Central 
    CAS 

    Google Scholar  More

  • in

    Surprising effects of cascading higher order interactions

    Study siteWe conducted laboratory studies at the field site in Finca Irlanda, which is a 300-hectare organic shaded coffee farm located at 1100-m altitude, in the municipality of Tapachula, the state of Chiapas in Southern Mexico (92° 20′ 29″ W and 15° 10′ 65″ N). For the laboratory experiments, all organisms were freshly collected from Finca Irlanda or reared in the lab from insects collected from the field close by. The lab and field work was performed with a permit from the farm owner the Peters family.Ant aggression testTo examine the effect of phorid flies (P. lascinosus) on the aggressivity of ants (A. sericeasur) towards the parasitoids of the beetle larvae (A. orbigera), we conducted an ant aggression test with two treatments: with and without phorids. In the first treatment, a small coffee branch containing two leaves with scale insects (C. viridis) and 20 ant workers were both introduced into a one-liter plastic container. This was done to mimic as much as possible field conditions where the ants are tending scale insects. After waiting for at least 15 min for the ants to calm down and start tending the scale insects, one third- or fourth-instar larva of the beetle was introduced. In the second treatment, all settings were the same except for the addition of 3–4 phorid flies. Once the two treatments were established, one female parasitoid wasp (H. shuvakhinae) was released into each container. During a forty-minute trial, each time that a parasitoid wasp encountered an ant worker, the response of the ant individual was recorded. Ant responses to parasitoids were classified into two categories: (1) the ant ignores the wasp; (2) the ant attacks the wasp. All insects were used for a single replicate and then discarded. A total of four replicates were completed for both the presence and absence of phorids. For each trial, we calculated the proportion of actions (either aggressive or none) by ants when encountering the parasitoid wasp in the treatments with and without phorid flies. We used R36 to conduct a two-sample Mann–Whitney U test on the proportion of ant actions.Parasitism experiments and analysesTo examine the parasitoid wasp’s host preference and the effect of the 1st degree and the 2nd degree HOIs on the beetle’s parasitism and sex ratio, we conducted a laboratory experiment in insect tents (60 cm × 60 cm × 60 cm) with three treatments: (1) no ants (no HOIs but only the wasp and the beetle larvae), (2) ants (1st degree HOI), and (3) ants and phorids (1st and 2nd degree HOIs) (Fig. 1-B). We randomly assigned insect tents to each treatment in each trial, and the tents for each treatment were also shuffled in each trial. All beetle larvae used for these experiments were reared in the laboratory for at least two generations from freshly collected beetle adults. In each tent we placed a coffee branch with 4–6 leaves infested with approximately 100 scale insects inside a plastic container at the center of an insect tent. The set up for the three treatments of species combinations were as follows: (1) 4–5 third or fourth instar beetle larvae and a parasitoid wasp; (2) 4–5 third or fourth instar beetle larvae, a parasitoid wasp, and about 60–80 ant workers; (3) 4–5 third or fourth instar beetle larvae, a parasitoid wasp, about 60–80 ant workers and 3–4 phorid flies. Organism densities in these treatments were close to those observed in the field. To allow for acclimation, we introduced organisms into the tents in the following order: first, we introduced the coffee branch containing scales, immediately followed by the ants (in treatments 2 and 3). After the ants settled down and started tending the scale insects, we introduced the beetle larvae. Once the larvae began moving on the coffee leaves, we introduced the phorids (in treatment 3). When the three treatments were established, and the organisms exhibit normal behavior, we released one lab-reared female parasitoid wasp (H. shuvakhinae) in each tent (treatments 1, 2, and 3). We allowed the organisms to interact for 24 h. After 24 h, we collected all beetle larvae in each treatment and reared them with sufficient scale insects as food, until beetle adults emerged or parasitism symptoms appeared (parasitized larvae turned into hardened black mummies). The treatments of no HOI and 1st + 2nd degree HOI were repeated for 10 consecutive times, and the treatment of 1st degree HOI was repeated for 11 consecutive times, with new individuals of each organism. We recorded parasitism instances and beetle sexes upon emergence. To estimate the sex ratio without parasitoid influence, 78 randomly selected beetle individuals were reared on coffee leaves with scale insects without any interaction with other organisms.To analyze the effect of the parasitoid, the ant and the phorid fly on the parasitism rate and the sex ratio of the beetle, we developed a nested model, starting from$$logitleft(widehat{P}(S)right)=a+bA$$where (widehat{P}(S)) is the probability of an individual being parasitized, A is a binary variable, standing for the absence (0) and presence (1) of ants, a is the baseline probability of parasitism, and b is the magnitude of parasitism altered by ants in the logistic function. We further hypothesized that phorid attacks modify the strength of the interaction modification that ants exert upon the host-parasitoid interaction. Therefore,$$b=g+hP$$where P is another binary variable, standing for the presence (1) and absence (0) of phorids. Substituting b, we obtain the following function,$$logitleft(widehat{P}(S)right)=a+gA+hAP$$where g represents the effect of ants on the parasitism rate of A. orbigera larvae, and h represents the effect of the fly’s facilitation, via interfering with the ant’s interference on the parasitism rate of A. orbigera larvae. We used binary responses (1: survival; 0: parasitized) of all available beetle individuals across the three treatments. We performed model selection based on the Akaike Information Criterion (AIC) and likelihood ratio tests. For the latter, we started model selection by fitting the full model and preceding each step by eliminating the term that had the least significance (the greatest p-value) on the explanation of the dependent variable. The analysis was performed with the application of the bbmle package in R. By doing this, we determined the maximum likelihood estimates of survival probability of the beetle, (widehat{P}(S)), in the three treatments: (1) A = 0, AP = 0 (no HOI); (2) A = 1, AP = 0 (one HOI: ant interference) and (3) A = 1, AP = 1 (interacting HOIs: phorid interference with ant interference), and errors associated with these estimates.The same idea applies to the sex ratio of the beetle under the influence of various organisms. We developed the following equation,$$logitleft(widehat{P}(F|S)right)= r+mA+nAP$$where (widehat{P}(F|S)) is the probability of a parasitism survivor being female. A and P are both binary variables. Respectively, they represent the ant and the phorid fly, and the numeric attributes, 0 and 1, denote their absence and presence. As before, model selection and parameter estimates were conducted with AIC. By doing this, we determined (widehat{P}(F|S)), the estimate of being a female beetle given survival, for the three treatments: (1) A = 0, AP = 0 (no HOI); (2) A = 1, AP = 0 (one HOI: ant interference) and (3) A = 1, AP = 1 (interacting HOIs: phorid interference with ant interference), and errors associated with these estimates. We employed the mle2 function in the bbmle package in R to estimate the female probability (1) in the absence of HOI (the beetle and the parasitoid alone), (2) in the presence of the 1st degree HOI (the beetle, the parasitoid and the ant), and (3) in the presence of the 1st and the 2nd degree HOIs (the beetle, the parasitoid, the ant and the phorid fly).Probabilities of per capita female and per capita male survival from parasitism under the influence of ant and the phorid flyTo test whether the sex ratio of beetle survivors’ population is due to sex-differential survival probability, Bayes’ theorem was employed. Per capita female survival probability from parasitism in each treatment of the parasitism experiment was derived based on (widehat{P}(F)), (widehat{P}left(F|Sright),) and (widehat{P}(S)), and per capita male survival probability was derived based on (widehat{P}(M)), (widehat{P}left(M|Sright),) and (widehat{P}(S)). According to the Central Limit Theorem, the estimates of proportions, (widehat{P}left(S|Fright)) and (widehat{P}left(S|Mright)), are approximately normally distributed,$$widehat{P}left(S|Fright)sim Nleft(widehat{P}left(S|Fright), sqrt{frac{widehat{P}(S|F)times left(1-widehat{P}left(S|Fright)right)}{{n}^{*}}}right)$$$$widehat{P}left(S|Mright)sim Nleft(widehat{P}left(S|Mright), sqrt{frac{widehat{P}(S|M)times left(1-widehat{P}left(S|Mright)right)}{{n}^{*}}}right)$$with means (widehat{P}left(S|Fright)) and (widehat{P}(S|M)), and standard deviations (sqrt{frac{widehat{P}left(S|Fright)times (1-widehat{P}left(S|Fright))}{{n}^{*}}}) and (sqrt{frac{widehat{P}left(S|Mright)times (1-widehat{P}left(S|Mright))}{{n}^{*}}}), where (widehat{P}(S|F)) and (widehat{P}(S|M)), respectively, are the population proportions of females and males. Here we employ n*, the smallest sample size among those of the three variables in the Bayesian formulas for males and females. Since the three variables have different sample sizes, n* guarantees a conservative estimate of standard error, and thus confidence interval, of each derived probability. More

  • in

    Source apportionment of soil heavy metals with PMF model and Pb isotopes in an intermountain basin of Tianshan Mountains, China

    The plots of Igeo, PERI, and PLI of HMs in the topsoil of the tourist area of Sayram Lake (Fig. 5) reveal the degree of HM pollution and eco-risk in this study area on the one hand and, on the other hand, indicate the direction for the relevant agencies to target soil environmental protection and HM pollution prevention and control measures. In this study, the Igeo results showed that Cd was the most highly enriched HM, and Pb, Zn, Cd, and Ni were slightly enriched in a few sample sites. The unnatural accumulation of these elements is usually closely associated with human activities in the area34. Tourism is the main economic activity in the district, and published studies have reported that tourism infrastructure construction (e.g., roads, buildings, etc.) and tourism wastes (e.g., plastic bags, batteries, hotel wastewater) release Cd into the soil35. Additionally, the accumulation of Pb, Zn, Cu and Ni in soils is usually associated with traffic emissions36. The PERI showed that the study area was at low risk overall, with only point ss04 exhibiting medium risk; however, this result was caused by the abnormally high Cd concentration value (Fig. 4) at point ss04 (Cd (concentration): 1.08 mg/kg, Cd (background): 0.34 mg/kg). This anomalous concentration value has a large influence on the PERI calculated based on the measured concentration, the background value and the toxicity coefficient. Therefore, references to this point can be appropriately removed when considering eco-risk. The PLI of each sampling point was greater than 1 and less than 2, which means that the area was in a moderately contaminated state. In general, the degree of soil HM contamination in this area was low; however, due to HM toxicity, bioaccumulation, and persistence37, the HM contamination of this area still requires sustained attention.Figure 5Contamination and ecological risk indices: (a) geoaccumulation index (Igeo) of HMs; (b) ecological risk of individual HMs; (c) potential ecological risk index (PERI) of HMs; (d) pollution load index (PLI) of HMs.Full size imageCorrelation analysis is an efficient way to reveal correlations among HMs through Pearson correlation coefficients, and HMs with significant correlations may originate from the same source38. As shown in Table S5, the elemental pairs Cd-Cu (p  More

  • in

    Crop diversification and parasitic weed abundance: a global meta-analysis

    Chauhan, B. S. Grand challenges in weed management. Front. Agron. https://doi.org/10.3389/fagro.2019.00003 (2020).Article 

    Google Scholar 
    Oerke, E. C. Crop losses to pests. J. Agric. Sci. 144, 31–43 (2006).
    Google Scholar 
    Samejima, H. & Sugimoto, Y. Recent research progress in combatting root parasitic weeds. Biotechnol. Biotechnol. Equip. 32(2), 221–240 (2018).CAS 

    Google Scholar 
    Aly, R. Conventional and biotechnological approaches for control of parasitic weeds. In Vitro Cell. Dev. Biol. Plant 43(4), 304–317 (2007).
    Google Scholar 
    Fernández-Aparicio, M., Delavault, P. & Timko, M. P. Management of infection by parasitic weeds: A review. Plants 9(9), 1184 (2020).PubMed Central 

    Google Scholar 
    Rodenburg, J., Demont, M., Zwart, S. J. & Bastiaans, L. Parasitic weed incidence and related economic losses in rice in Africa. Agric. Ecosyst. Environ. 235, 306–317 (2016).
    Google Scholar 
    Weisberger, D., Nichols, V. & Liebman, M. Does diversifying crop rotations suppress weeds? A meta-analysis. PLoS One 14(7), e0219847 (2019).CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    Ejeta, G. The Striga scourge in Africa: A growing pandemic. In Integrating New Technologies for Striga Control: Towards Ending the Witch-hunt 3–16 (World Scientific, 2007). https://doi.org/10.1142/9789812771506_0001.Chapter 

    Google Scholar 
    Netting, R. M. & Stone, M. P. Agro-diversity on a farming frontier: Kofyar smallholders on the Benue plains of central Nigeria. Africa 66(1), 52–70 (1996).
    Google Scholar 
    Pimentel, D. et al. Conserving biological diversity in agricultural and forestry systems. Bioscience 42, 354–362 (1992).
    Google Scholar 
    Khoshbakht, K. & Hammer, K. How many plant species are cultivated?. Genet. Resour. Crop Evol. 55(7), 925–928. https://doi.org/10.1007/s10722-008 (2008).Article 

    Google Scholar 
    Hajjar, R., Jarvis, D. I. & Gemmill-Herren, B. The utility of crop genetic diversity in maintaining ecosystem services. Agric. Ecosyst. Environ. 123(4), 261–270 (2008).
    Google Scholar 
    He, H. M. et al. Crop diversity and pest management in sustainable agriculture. J. Integr. Agric. 18(9), 1945–1952 (2019).
    Google Scholar 
    Ofori, F. & Stern, W. R. Cereal–legume intercropping systems. Adv. Agron. 41, 41–90 (1987).
    Google Scholar 
    Tanveer, M., Anjum, S. A., Hussain, S., Cerdà, A. & Ashraf, U. Relay cropping as a sustainable approach: Problems and opportunities for sustainable crop production. Environ. Sci. Pollut. Res. 24(8), 6973–6988 (2017).
    Google Scholar 
    Hartwig, N. L. & Ammon, H. U. Cover crops and living mulches. Weed Sci. 50(6), 688–699 (2002).CAS 

    Google Scholar 
    Raseduzzaman, M. D. & Jensen, E. S. Does intercropping enhance yield stability in arable crop production? A meta-analysis. Eur. J. Agron. 91, 25–33 (2017).
    Google Scholar 
    Davis, A. S., Hill, J. D., Chase, C. A., Johanns, A. M. & Liebman, M. Increasing cropping system diversity balances productivity, profitability and environmental health. PLoS One 7(10), e47149 (2012).ADS 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    Himmelstein, J., Ares, A., Gallagher, D. & Myers, J. A meta-analysis of intercropping in Africa: Impacts on crop yield, farmer income, and integrated pest management effects. Int. J. Agric. Sustain. 15(1), 1–10 (2017).
    Google Scholar 
    Abson, D. J., Fraser, E. D. & Benton, T. G. Landscape diversity and the resilience of agricultural returns: A portfolio analysis of land-use patterns and economic returns from lowland agriculture. Agric. Food Secur. 2(1), 1–15 (2013).
    Google Scholar 
    Renard, D. & Tilman, D. National food production stabilized by crop diversity. Nature 571(7764), 257–260 (2019).ADS 
    CAS 
    PubMed 

    Google Scholar 
    Gaudin, A. C. et al. Increasing crop diversity mitigates weather variations and improves yield stability. PLoS One 10(2), e0113261 (2015).PubMed 
    PubMed Central 

    Google Scholar 
    Bowles, T. M. et al. Long-term evidence shows that crop-rotation diversification increases agricultural resilience to adverse growing conditions in North America. One Earth 2(3), 284–293 (2020).ADS 

    Google Scholar 
    Chauhan, B. S., Singh, R. G. & Mahajan, G. Ecology and management of weeds under conservation agriculture: A review. Crop Prot. 38, 57–65 (2012).
    Google Scholar 
    Nichols, V., Verhulst, N., Cox, R. & Govaerts, B. Weed dynamics and conservation agriculture principles: A review. Field Crop Res. 183, 56–68 (2015).
    Google Scholar 
    Banik, P., Midya, A., Sarkar, B. K. & Ghose, S. S. Wheat and chickpea intercropping systems in an additive series experiment: Advantages and weed smothering. Eur. J. Agron. 24(4), 325–332 (2006).
    Google Scholar 
    Workayehu, T. & Wortmann, C. S. Maize–bean intercrop weed suppression and profitability in Southern Ethiopia. Agron. J. 103(4), 1058–1063 (2011).
    Google Scholar 
    Haugaard-Nielsen, H., Ambus, P. & Jensen, E. S. Interspecific competition, N use and interference with weeds in pea barley intercropping. Field Crop Res. 70, 101–109 (2001).
    Google Scholar 
    Jensen, E. S. Intercropping of Cereals and Grain Legumes for Increased Production, Weed Control, Improved Product Quality and Prevention of N-losses in European Organic Farming Systems, Final Report on Intercrop Project (QLK5-CT-2002-02352) (Risø National Laboratory, 2006).Arlauskienė, A., Šarūnaitė, L., Kadžiulienė, Ž, Deveikytė, I. & Maikštėnienė, S. Suppression of annual weeds in pea and cereal intercrops. Agron. J. 106(5), 1765–1774 (2014).
    Google Scholar 
    Szumigalski, A. & van Acker, R. Weed suppression and crop production in annual intercrops. Weed Sci. 53(6), 813–825 (2005).CAS 

    Google Scholar 
    Stoltz, E. & Nadeau, E. Effects of intercropping on yield, weed incidence, forage quality and soil residual N in organically grown forage maize (Zea mays L.) and faba bean (Vicia faba L.). Field Crop Res. 169, 21–29 (2014).
    Google Scholar 
    Sauerborn, J., Müller-Stöver, D. & Hershenhorn, J. The role of biological control in managing parasitic weeds. Crop Prot. 26(3), 246–254 (2007).
    Google Scholar 
    Jamil, M., Rodenburg, J., Charnikhova, T. & Bouwmeester, H. J. Pre-attachment Striga hermonthica resistance of New Rice for Africa (NERICA) cultivars based on low strigolactone production. New Phytol. 192(4), 964–975. https://doi.org/10.1111/j.1469-8137.2011.03850.x (2011).Article 
    CAS 
    PubMed 

    Google Scholar 
    Yoneyama, K. et al. Nitrogen deficiency as well as phosphorus deficiency in sorghum promotes the production and exudation of 5-deoxystrigol, the host recognition signal for arbuscular mycorrhizal fungi and root parasites. Planta 227(1), 125–132. https://doi.org/10.1007/s00425-007-0600-5 (2007).Article 
    CAS 
    PubMed 

    Google Scholar 
    Sauerborn, J. Legumes used for weed control in agroecosystems in the tropics. Plant Res. Dev. 50, 74–82 (1999).
    Google Scholar 
    Ejeta, G. & Butler, L. G. Host-parasite interactions throughout the Striga life cycle, and their contributions to Striga resistance. Afr. Crop Sci. J. 1(2), 75–80. https://doi.org/10.4314/acsj.v1i2.69889 (1993).Article 

    Google Scholar 
    Carsky, R. J., Singh, L. & Ndikawa, R. Suppression of Striga hermonthica on sorghum using a cowpea intercrop. Exp. Agric. 30(3), 349–358. https://doi.org/10.1017/s0014479700024467 (1994).Article 

    Google Scholar 
    Hsiao, A. I., Worsham, A. D. & Moreland, D. E. Effects of temperature and dl-strigol on seed conditioning and germination of witchweed (Striga asiatica). Ann. Bot. 61(1), 65–72. https://doi.org/10.1093/oxfordjournals.aob.a087528 (1988).Article 
    CAS 

    Google Scholar 
    Patterson, D. T. Effects of Environment on Growth and Reproduction of Witchweed and the Ecological Range of Witchweed (Monograph Series of the Weed Science Society of America, 1990).Stewart, G. R. & Press, M. C. The physiology and biochemistry of parasitic angiosperms. Annu. Rev. Plant Biol. 41(1), 127–151. https://doi.org/10.1146/annurev.pp.41.060190.001015 (1990).Article 
    CAS 

    Google Scholar 
    Anil, L., Park, R. H. P. & Miller, F. A. Temperate intercropping of cereals for forage: A review of the potential for growth and utilization with particular reference to the UK. Grass Forage Sci. 53, 301–317 (1998).
    Google Scholar 
    Mamolos, A. & Kalburtji, K. Significance of allelopathy in crop rotation. J. Crop Prod. 4, 197–218 (2001).
    Google Scholar 
    Khan, T. D., Chung, M. I., Xuan, T. D. & Tawata, S. The exploitation of crop allelopathy in sustainable agricultural production. J. Agron. Crop Sci. 191(3), 172–184 (2005).
    Google Scholar 
    Cissoko, M., Boisnard, A., Rodenburg, J., Press, M. C. & Scholes, J. D. New Rice for Africa (NERICA) cultivars exhibit different levels of post-attachment resistance against the parasitic weeds Striga hermonthica and Striga asiatica. New Phytol. 192(4), 952–963 (2011).CAS 
    PubMed 

    Google Scholar 
    Rodenburg, J. et al. Do NERICA rice cultivars express resistance to Striga hermonthica (Del.) Benth. and Striga asiatica (L.) Kuntze under field conditions?. Field Crop Res. 170, 83–94 (2015).
    Google Scholar 
    Randrianjafizanaka, M. T., Autfray, P., Andrianaivo, A. P., Ramonta, I. R. & Rodenburg, J. Combined effects of cover crops, mulch, zero-tillage and resistant varieties on Striga asiatica (L.) Kuntze in rice-maize rotation systems. Agric. Ecosyst. Environ. 256, 23–33 (2018).
    Google Scholar 
    Rodenburg, J. et al. Genetic variation and host–parasite specificity of Striga resistance and tolerance in rice: The need for predictive breeding. New Phytol. 214(3), 1267–1280. https://doi.org/10.1111/nph.14451 (2017).Article 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    Nickrent, D. L. & Musselman, L. J. Introduction to parasitic flowering plants. Plant Health Instr. 13(6), 300–315 (2004).
    Google Scholar 
    Parker, C. Parasitic weeds: A world challenge. Weed Sci. 60(2), 269–276 (2012).CAS 

    Google Scholar 
    Shai Vaingast 2014. im2graph. Retrieved from: https://www.im2graph.co.il/free-downloads/windows-3264bit/ (2014).Google Maps 2021. https://maps.google.com [Accessed February 2021–December 2022].Kambach, S. et al. Consequences of multiple imputation of missing standard deviations and sample sizes in meta-analysis. Ecol. Evol. 10(20), 11699–11712 (2020).PubMed 
    PubMed Central 

    Google Scholar 
    Nakagawa, S. & Freckleton, R. P. Missing inaction: The dangers of ignoring missing data. Trends Ecol. Evol. 23(11), 592–596 (2008).PubMed 

    Google Scholar 
    Idris, N. R. N. & Robertson, C. The effects of imputing the missing standard deviations on the standard error of meta analysis estimates. Commun. Stat. Simul. Comput. 38(3), 513–526. https://doi.org/10.1080/03610910802556106 (2009).Article 
    MathSciNet 
    MATH 

    Google Scholar 
    van Buuren, S. & Groothuis-Oudshoorn, K. mice: Multivariate imputation by chained equations in R. J. Stat. Softw. 45, 1–67 (2011).
    Google Scholar 
    van Buuren, S. Flexible Imputation of Missing Data (CRC Press, 2018).MATH 

    Google Scholar 
    Fick, S. E. & Hijmans, R. J. WorldClim 2: New 1-km spatial resolution climate surfaces for global land areas. Int. J. Climatol. 37, 4302–4315. https://doi.org/10.1002/joc.5086 (2017).Article 

    Google Scholar 
    O’Donnell, M. S. & Ignizio, D. A. Bioclimatic predictors for supporting ecological applications in the conterminous United States. US Geol. Surv. Data Ser. 691(10), 4–9 (2012).
    Google Scholar 
    Reuter, H. I., Nelson, A. & Jarvis, A. An evaluation of void filling interpolation methods for SRTM data. Int. J. Geogr. Inf. Sci. 21(9), 983–1008 (2007).
    Google Scholar 
    CGIAR—Consortium for Spatial Information. http://srtm.csi.cgiar.org © 2004–2021. Accessed September 19, 2021, via: http://srtm.csi.cgiar.org/srtmdata/.QGIS Development Team. QGIS Geographic Information System http://qgis.osgeo.org (Open Source Geospatial Foundation Project, 2020).Kuznetsova, A., Brockhoff, P. B. & Christensen, R. H. B. lmerTest Package: Tests in linear mixed effects models. J. Stat. Softw. 82(13), 26. https://doi.org/10.18637/jss.v082.i13 (2017).Article 

    Google Scholar 
    Song, C., Peacor, S. D., Osenberg, C. W. & Bence, J. R. An assessment of statistical methods for non-independent data in ecological meta-analyses. Ecology 101(12), e03184. https://doi.org/10.1002/ecy.3184 (2020).Article 
    PubMed 

    Google Scholar 
    Del Rey, A. C. compute.es: Compute Effect Sizes. R package version 0.2-2. https://cran.r-project.org/package=compute.es (2013).R Core Team. R: A language and environment for statistical computing. http://www.R-project.org/ (R Foundation for Statistical Computing, 2020).Wickham, H., Francois, R., Henry, L. & Müller, K. dplyr: A grammar of data manipulation. R package version 0.4. 3 (2015)Bates, D., Mächler, M., Bolker, B. & Walker, S. Fitting linear mixed-effects models using lme4. J. Stat. Softw. 67(1), 48. https://doi.org/10.18637/jss.v067.i01 (2015).Article 

    Google Scholar 
    Liebman, M. & Dyck, E. Crop rotation and intercropping strategies for weed management. Ecol. Appl. 3(1), 92–122 (1993).PubMed 

    Google Scholar 
    Pumariño, L. et al. Effects of agroforestry on pest, disease and weed control: A meta-analysis. Basic Appl. Ecol. 16(7), 573–582 (2015).
    Google Scholar 
    Kuyah, S., Whitney, C. W., Jonsson, M., Sileshi, G. W., Öborn, I., Muthuri, C. W. & Luedeling, E. Agroforestry delivers a win-win solution for ecosystem services in sub-Saharan Africa. A meta-analysis (2019).Evidente, A., Fernandez-Aparicio, M., Andolfi, A., Rubiales, D. & Motta, A. Trigoxazonane, a mono-substituted trioxazonane from Trigonella foenum-graecum root exudates, inhibits Orobanche crenata seed germination. Phytochemistry 68, 2487–2492 (2007).CAS 
    PubMed 

    Google Scholar 
    Khan, Z. R. et al. Control of witchweed Striga hermonthica by intercropping with Desmodium spp., and the mechanism defined as allelopathic. J. Chem. Ecol. 28(9), 1871–1885 (2002).CAS 
    PubMed 

    Google Scholar 
    Nakagawa, S. et al. Methods for testing publication bias in ecological and evolutionary meta-analyses. Methods Ecol. Evol. 13(1), 4–21 (2022).
    Google Scholar 
    Bakker, A. et al. Beyond small, medium, or large: Points of consideration when interpreting effect sizes. Educ. Stud. Math. 102(1), 1–8 (2019).
    Google Scholar 
    Scott, D. et al. Mapping the drivers of parasitic weed abundance at a national scale: A new approach applied to Striga asiatica in the mid-west of Madagascar. Weed Res. 60(5), 323–333 (2020).
    Google Scholar 
    Scott, D. et al. Identifying existing management practices in the control of Striga asiatica within rice–maize systems in mid-west Madagascar. Ecol. Evol. 11(19), 13579–13592 (2021).PubMed 
    PubMed Central 

    Google Scholar 
    Rubiales, D. & Fernández-Aparicio, M. Innovations in parasitic weeds management in legume crops. A review. Agron. Sustain. Dev. 32(2), 433–449 (2012).CAS 

    Google Scholar 
    Bir, M. S. H. et al. Weed population dynamics under climatic change. Weed Turfgrass Sci. 3(3), 174–182 (2014).
    Google Scholar 
    Mohamed, K. I., Bolin, J. F., Musselman, L. J. & Townsend, P. A. Genetic diversity of Striga and implications for control and modelling future distributions. In Integrating New Technologies for Striga Control—Towards Ending the Witch-Hunt (eds Ejeta, G. & Gressel, J.) 71–84 (World Scientific, 2007).
    Google Scholar 
    Mandumbu, R., Mutengwa, C. S., Mabasa, S. & Mwenje, E. Predictions of the Striga scourge under new climate in southern Africa. J. Biol. Sci. 17, 192–201. https://doi.org/10.3923/jbs.2017.194.201 (2017).Article 

    Google Scholar 
    Mudereri, B. T. et al. Multi-source spatial data-based invasion risk modelling of Striga (Striga asiatica) in Zimbabwe. GIScience Remote Sens. 57(4), 553–571. https://doi.org/10.1080/15481603.2020.1744250 (2020).Article 

    Google Scholar  More

  • in

    Indication of a personality trait in dairy calves and its link to weight gain through automatically collected feeding behaviours

    Réale, D., Reader, S. M., Sol, D., McDougall, P. T. & Dingemanse, N. J. Integrating animal temperament within ecology and evolution. Biol. Rev. 82, 291–318 (2007).PubMed 

    Google Scholar 
    Kaiser, M. I. & Müller, C. What is an animal personality?. Biol. Philos. 36, 1 (2021).
    Google Scholar 
    Sih, A., Bell, A. & Johnson, J. C. Behavioral syndromes: An ecological and evolutionary overview. Trends Ecol. Evol. 19, 372–378 (2004).PubMed 

    Google Scholar 
    Gosling, S. D. From mice to men: What can we learn about personality from animal research?. Psychol. Bull. 127, 45–86 (2001).PubMed 

    Google Scholar 
    Biro, P. A. & Stamps, J. A. Are animal personality traits linked to life-history productivity?. Trends Ecol. Evol. 23, 361–368 (2008).PubMed 

    Google Scholar 
    Koolhaas, J. M. Coping style and immunity in animals: Making sense of individual variation. Brain Behav. Immun. 22, 662–667 (2008).PubMed 

    Google Scholar 
    Réale, D. et al. Personality and the emergence of the pace-of-life syndrome concept at the population level. Philos. Trans. R. Soc. B Biol. Sci. 365, 4051–4063 (2010).
    Google Scholar 
    Stamps, J. A. Growth-mortality tradeoffs and ‘personality traits’ in animals. Ecol. Lett. 10, 355–363 (2007).PubMed 

    Google Scholar 
    Finkemeier, M. A., Langbein, J. & Puppe, B. Personality research in mammalian farm animals: Concepts, measures, and relationship to welfare. Front Vet. Sci. 10(5), 355–363 (2018).
    Google Scholar 
    Murphy, E., Nordquist, R. E. & van der Staay, F. J. A review of behavioural methods to study emotion and mood in pigs. Sus. Scrofa. Appl. Anim. Behav. Sci 159, 9–28 (2014).
    Google Scholar 
    Lauber, M. C. Y., Hemsworth, P. H. & Barnett, J. L. The effects of age and experience on behavioural development in dairy calves. Appl. Anim. Behav. Sci. 99, 41–52 (2006).
    Google Scholar 
    Neave, H. W., Costa, J. H. C., Weary, D. M. & von Keyserlingk, M. A. G. Personality is associated with feeding behavior and performance in dairy calves. J. Dairy Sci. 101, 7437–7449 (2018).PubMed 

    Google Scholar 
    Foris, B., Zebunke, M., Langbein, J. & Melzer, N. Evaluating the temporal and situational consistency of personality traits in adult dairy cattle. Plos One 13, e0204619 (2018).PubMed 
    PubMed Central 

    Google Scholar 
    Dingemanse, N. J. & Dochtermann, N. A. Quantifying individual variation in behaviour: Mixed-effect modelling approaches. J. Anim. Ecol. 82, 39–54 (2013).PubMed 

    Google Scholar 
    Dingemanse, N. J., Kazem, A. J. N., Réale, D. & Wright, J. Behavioural reaction norms: Animal personality meets individual plasticity. Trends Ecol. Evol. 25, 81–89 (2010).PubMed 

    Google Scholar 
    Nakagawa, S. & Schielzeth, H. Repeatability for Gaussian and non-Gaussian data: A practical guide for biologists. Biol. Rev. https://doi.org/10.1111/j.1469-185X.2010.00141.x (2010).Article 
    PubMed 

    Google Scholar 
    Bell, A. M., Hankison, S. J. & Laskowski, K. L. The repeatability of behaviour: A meta-analysis. Anim. Behav. 77, 771–783 (2009).PubMed 
    PubMed Central 

    Google Scholar 
    Neave, H. W., Costa, J. H. C., Benetton, J. B., Weary, D. M. & von Keyserlingk, M. A. G. Individual characteristics in early life relate to variability in weaning age, feeding behavior, and weight gain of dairy calves automatically weaned based on solid feed intake. J. Dairy Sci. 102, 10250–10265 (2019).PubMed 

    Google Scholar 
    Berckmans, D. Precision livestock farming technologies for welfare management in intensive livestock systems. Rev. Sci. Tech. OIE 33, 189–196 (2014).
    Google Scholar 
    Carslake, C., Vázquez-Diosdado, J. A. & Kaler, J. Machine learning algorithms to classify and quantify multiple behaviours in dairy calves using a sensor: Moving beyond classification in precision livestock. Sensors 21, 88 (2020).ADS 
    PubMed Central 

    Google Scholar 
    Hertel, A. G., Niemelä, P. T., Dingemanse, N. J. & Mueller, T. A guide for studying among-individual behavioral variation from movement data in the wild. Mov. Ecol. 8(1), 1–18 (2020).
    Google Scholar 
    Occhiuto, F., Vázquez-Diosdado, J. A., Carslake, C. & Kaler, J. Personality and predictability in farmed calves using movement and space-use behaviours quantified by ultra-wideband sensors. R. Soc. Open Sci. 9, 212019 (2022).ADS 
    PubMed 
    PubMed Central 

    Google Scholar 
    Carslake, C., Occhiuto, F., Vázquez-Diosdado, J. A. & Kaler, J. Repeatability and predictability of calf feeding behaviors—quantifying between- and within-individual variation for precision livestock farming. Front. Vet. Sci. https://doi.org/10.3389/fvets.2022.827124 (2022).Article 
    PubMed 
    PubMed Central 

    Google Scholar 
    Tolkamp, B. J. & Kyriazakis, I. To split behaviour into bouts, log-transform the intervals. Anim. Behav. 57, 807–817 (1999).PubMed 

    Google Scholar 
    Houslay, T. M. & Wilson, A. J. Avoiding the misuse of BLUP in behavioural ecology. Behav. Ecol. 28, 948 (2017).PubMed 
    PubMed Central 

    Google Scholar 
    R Core Team. R. Preprint at (2021).Bürkner, P.-C. Advanced bayesian multilevel modeling with the R package brms. R. J. 10, 395 (2018).
    Google Scholar 
    Dancey, C. P. & Reidy, J. Statistics without maths for psychology (Pearson education, 2007).
    Google Scholar 
    von Keyserlingk, M. A. G., Brusius, L. & Weary, D. M. Competition for teats and feeding behavior by group-housed dairy calves. J. Dairy Sci. 87, 4190–4194 (2004).
    Google Scholar 
    Fraley, R. C. & Roberts, B. W. Patterns of continuity: A dynamic model for conceptualizing the stability of individual differences in psychological constructs across the life course. Psychol. Rev. 112, 60–74 (2005).PubMed 

    Google Scholar 
    Ashcroft, J., Semmler, C., Carnell, S., van Jaarsveld, C. H. M. & Wardle, J. Continuity and stability of eating behaviour traits in children. Eur. J. Clin. Nutr. 62, 985–990 (2008).PubMed 

    Google Scholar 
    Neave, H. W., Costa, J. H. C., Weary, D. M. & von Keyserlingk, M. A. G. Long-term consistency of personality traits of cattle. R. Soc. Open Sci. 7, 191849 (2020).ADS 
    PubMed 
    PubMed Central 

    Google Scholar 
    Müller, R. & von Keyserlingk, M. A. G. Consistency of flight speed and its correlation to productivity and to personality in Bos taurus beef cattle. Appl. Anim. Behav. Sci. 99, 193–204 (2006).
    Google Scholar 
    Neja, W., Sawa, A., Jankowska, M., Bogucki, M. & Krężel-Czopek, S. Effect of the temperament of dairy cows on lifetime production efficiency. Arch. Anim. Breed 58, 193–197 (2015).
    Google Scholar 
    Haskell, M. J., Simm, G. & Turner, S. P. Genetic selection for temperament traits in dairy and beef cattle. Front Genet. https://doi.org/10.3389/fgene.2014.00368 (2014).Article 
    PubMed 
    PubMed Central 

    Google Scholar 
    Whalin, L., Neave, H. W., Føske Johnsen, J., Mejdell, C. M. & Ellingsen-Dalskau, K. The influence of personality and weaning method on early feeding behavior and growth of Norwegian red calves. J. Dairy Sci. 105, 1369–1386 (2022).PubMed 

    Google Scholar 
    Dammhahn, M., Dingemanse, N. J., Niemelä, P. T. & Réale, D. Pace-of-life syndromes: A framework for the adaptive integration of behaviour, physiology and life history. Behav. Ecol. Sociobiol. 72(3), 1–8 (2018).
    Google Scholar 
    Kelly, D. N. et al. Large variability in feeding behavior among crossbred growing cattle. J. Anim. Sci. 98, 1–10 (2020).
    Google Scholar 
    Neave, H. W., Weary, D. M. & von Keyserlingk, M. A. G. Review: Individual variability in feeding behaviour of domesticated ruminants. Animal 12, S419–S430 (2018).PubMed 

    Google Scholar 
    DeVries, T. J., von Keyserlingk, M. A. G., Weary, D. M. & Beauchemin, K. A. Measuring the feeding behavior of lactating dairy cows in early to peak lactation. J. Dairy Sci. 86, 3354–3361 (2003).PubMed 

    Google Scholar 
    Kelly, D. N., Sleator, R. D., Murphy, C. P., Conroy, S. B. & Berry, D. P. Phenotypic and genetic associations between feeding behavior and carcass merit in crossbred growing cattle. J. Anim. Sci. 99, skab285 (2021).PubMed 

    Google Scholar 
    Weary, D. M., Huzzey, J. M. & von Keyserlingk, M. A. G. Board-invited review: Using behavior to predict and identify ill health in animals. J. Anim. Sci. 87, 770–777 (2009).PubMed 

    Google Scholar 
    Carter, A. J., Feeney, W. E., Marshall, H. H., Cowlishaw, G. & Heinsohn, R. Animal personality: What are behavioural ecologists measuring?. Biol. Rev. 88, 465–475 (2013).PubMed 

    Google Scholar 
    Biro, P. A. Do rapid assays predict repeatability in labile (behavioural) traits?. Anim Behav 83, 1295–1300 (2012).
    Google Scholar 
    Percie du Sert, N. et al. Reporting animal research: Explanation and elaboration for the ARRIVE guidelines 20. Plos Biol. 18, e3000411 (2020).PubMed 
    PubMed Central 

    Google Scholar  More

  • in

    Coastal upwelling generates cryptic temperature refugia

    Ackerly, D. D. et al. The geography of climate change: Implications for conservation biogeography. Divers. Distrib. 16, 476–487 (2010).
    Google Scholar 
    Lawton, J. H. Are there general laws in ecology?. Oikos 84, 177–192 (1999).
    Google Scholar 
    Simberloff, D. Community ecology: Is it time to move on?. Am. Nat. 163, 787–799 (2004).PubMed 

    Google Scholar 
    Ricklefs, R. E. Disintegration of the ecological community. Am. Nat. 172, 741–750 (2008).PubMed 

    Google Scholar 
    McGill, B. J. et al. Species abundance distributions: Moving beyond single prediction theories to integration within an ecological framework. Ecol. Lett. 10, 995–1015 (2007).PubMed 

    Google Scholar 
    Paine, R. T. The Pisaster-Tegula interaction: Prey patches, predator food preference, and intertidal community structure. Ecology 50, 950–961 (1969).
    Google Scholar 
    Dayton, P. K. Competition, disturbance, and community organization: The provision and subsequent utilization of space in a rocky intertidal community. Ecol. Monogr. 41, 351–389 (1971).
    Google Scholar 
    Hairston, N. G., Smith, F. E. & Slobodkin, L. B. Community structure, population control, and competition. Am. Nat. 94, 421–425 (1960).
    Google Scholar 
    Brose, U., Berlow, E. L. & Martinez, N. D. Scaling up keystone effects from simple to complex ecological networks. Ecol. Lett. 8, 1317–1325 (2005).
    Google Scholar 
    Stouffer, D. B. & Bascompte, J. Understanding food-web persistence from local to global scales. Ecol. Lett. 13, 154–161 (2010).PubMed 

    Google Scholar 
    Loreau, M., Mouquet, N. & Gonzalez, A. Biodiversity as spatial insurance in heterogeneous landscapes. Proc. Natl. Acad. Sci. 100, 12765–12770 (2003).ADS 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    Leibold, M. A. et al. The metacommunity concept: A framework for multi-scale community ecology. Ecol. Lett. 7, 601–613 (2004).
    Google Scholar 
    Holyoak, M., Leibold, M. A. & Holt, R. D. Metacommunities: Spatial Dynamics and Ecological Communities (University of Chicago Press, 2005).
    Google Scholar 
    Gotelli, N. J. Macroecological signals of species interactions in the Danish avifauna. Proc. Natl. Acad. Sci. USA. 107, 5030–5035 (2010).ADS 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    Gouhier, T. C., Guichard, F. & Menge, B. A. Ecological processes can synchronize marine population dynamics over continental scales. Proc. Natl. Acad. Sci. 107, 8281–8286 (2010).ADS 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    Salois, S. L., Gouhier, T. C. & Menge, B. A. The multifactorial effects of dispersal on biodiversity in environmentally forced metacommunities. Ecosphere 9, e02357 (2018).
    Google Scholar 
    Helmuth, B. et al. Beyond long-term averages: Making biological sense of a rapidly changing world. Clim. Change Responses 1, 6 (2014).
    Google Scholar 
    Pacifici, M. et al. Assessing species vulnerability to climate change. Nat. Clim. Change 5, 215 (2015).ADS 

    Google Scholar 
    Gunderson, A. R., Armstrong, E. J. & Stillman, J. H. Multiple stressors in a changing world: The need for an improved perspective on physiological responses to the dynamic marine environment. Annu. Rev. Mar. Sci. 8, 357–378 (2016).ADS 

    Google Scholar 
    Rilov, G. et al. Adaptive marine conservation planning in the face of climate change: What can we learn from physiological, ecological and genetic studies?. Glob. Ecol. Conserv. 17, e00566 (2019).
    Google Scholar 
    Hampe, A. Bioclimate envelope models: What they detect and what they hide. Glob. Ecol. Biogeogr. 13, 469–471 (2004).
    Google Scholar 
    Pearson, R. G. & Dawson, T. P. Predicting the impacts of climate change on the distribution of species: Are bioclimate envelope models useful?. Glob. Ecol. Biogeogr. 12, 361–371 (2003).
    Google Scholar 
    Gilman, S. E., Urban, M. C., Tewksbury, J., Gilchrist, G. W. & Holt, R. D. A framework for community interactions under climate change. Trends Ecol. Evol. 25, 325–331 (2010).PubMed 

    Google Scholar 
    Davis, A. J., Jenkinson, L. S., Lawton, J. H., Shorrocks, B. & Wood, S. Making mistakes when predicting shifts in species range in response to global warming. Nature 391, 783–786 (1998).ADS 
    CAS 
    PubMed 

    Google Scholar 
    Araújo, M. B. & Peterson, A. T. Uses and misuses of bioclimatic envelope modeling. Ecology 93, 1527–1539 (2012).PubMed 

    Google Scholar 
    Helmuth, B. et al. Mosaic patterns of thermal stress in the rocky intertidal zone: Implications for climate change. Ecol. Monogr. 76, 461–479 (2006).
    Google Scholar 
    Helmuth, B., Mieszkowska, N., Moore, P. & Hawkins, S. J. Living on the edge of two changing worlds: Forecasting the responses of rocky intertidal ecosystems to climate change. Annu. Rev. Ecol. Evol. Syst. 37, 373–404 (2006).
    Google Scholar 
    Vasseur, D. A. et al. Synchronous dynamics of zooplankton competitors prevail in temperate lake ecosystems. Proc. R. Soc. B Biol. Sci. 281, 20140633 (2014).
    Google Scholar 
    Dillon, M. E. et al. Life in the frequency domain: The biological impacts of changes in climate variability at multiple time scales. Integr. Comp. Biol. icw024 (2016).Kroeker, K. J. et al. Interacting environmental mosaics drive geographic variation in mussel performance and predation vulnerability. Ecol. Lett. 19, 771–779 (2016).PubMed 

    Google Scholar 
    Seabra, R., Wethey, D. S., Santos, A. M. & Lima, F. P. Understanding complex biogeographic responses to climate change. Sci. Rep. 5, (2015).Di Cecco, G. J. & Gouhier, T. C. Increased spatial and temporal autocorrelation of temperature under climate change. Sci. Rep. 8, 14850 (2018).ADS 
    PubMed 
    PubMed Central 

    Google Scholar 
    Keppel, G. et al. Refugia: identifying and understanding safe havens for biodiversity under climate change. Glob. Ecol. Biogeogr. 21, 393–404 (2012).
    Google Scholar 
    Morelli, T. L. et al. Climate change refugia and habitat connectivity promote species persistence. Clim. Change Responses 4, 8 (2017).
    Google Scholar 
    Bates, A. E. et al. Biologists ignore ocean weather at their peril. Nature 560, 299–301 (2018).ADS 
    CAS 
    PubMed 

    Google Scholar 
    Molinos, J. G. et al. Climate velocity and the future global redistribution of marine biodiversity. Nat. Clim. Change (2015).Levins, R. Some demographic and genetic consequences of environmental heterogeneity for biological control. Bull. Entomol. Soc. Am. 15, 237–240 (1969).
    Google Scholar 
    Brown, J. H. & Kodric-Brown, A. Turnover rates in insular biogeography: Effect of immigration on extinction. Ecology 58, 445–449 (1977).
    Google Scholar 
    Pulliam, H. R. Sources, sinks, and population regulation. Am. Nat. 132, 652–661 (1988).
    Google Scholar 
    Hannah, L. et al. Fine-grain modeling of species’ response to climate change: Holdouts, stepping-stones, and microrefugia. Trends Ecol. Evol. 29, 390–397 (2014).PubMed 

    Google Scholar 
    Barceló, C., Ciannelli, L. & Brodeur, R. D. Pelagic marine refugia and climatically sensitive areas in an eastern boundary current upwelling system. Glob. Change Biol. 24, 668–680 (2018).ADS 

    Google Scholar 
    Dong, Y. et al. Untangling the roles of microclimate, behaviour and physiological polymorphism in governing vulnerability of intertidal snails to heat stress. Proc. R. Soc. B Biol. Sci. 284, 20162367 (2017).
    Google Scholar 
    Smit, A. J. et al. A coastal seawater temperature dataset for biogeographical studies: large biases between in situ and remotely-sensed data sets around the Coast of South Africa. PLoS ONE 8, e81944 (2013).ADS 
    PubMed 
    PubMed Central 

    Google Scholar 
    Castro, S. L., Monzon, L. A., Wick, G. A., Lewis, R. D. & Beylkin, G. Subpixel variability and quality assessment of satellite sea surface temperature data using a novel High Resolution Multistage Spectral Interpolation (HRMSI) technique. Remote Sens. Environ. 217, 292–308 (2018).ADS 

    Google Scholar 
    Rahaghi, A. I., Lemmin, U. & Barry, D. A. Surface water temperature heterogeneity at subpixel satellite scales and its effect on the surface cooling estimates of a large lake: Airborne remote sensing results from Lake Geneva. J. Geophys. Res. Oceans 124, 635–651 (2019).ADS 

    Google Scholar 
    Pfister, C. A., Wootton, J. T. & Neufeld, C. J. The relative roles of coastal and oceanic processes in determining physical and chemical characteristics of an intensively sampled nearshore system. Limnol. Oceanogr. 52, 1767–1775 (2007).ADS 
    CAS 

    Google Scholar 
    Meneghesso, C. et al. Remotely-sensed L4 SST underestimates the thermal fingerprint of coastal upwelling. Remote Sens. Environ. 237, 111588 (2020).ADS 

    Google Scholar 
    Leichter, J. J., Helmuth, B. & Fischer, A. M. Variation beneath the surface: Quantifying complex thermal environments on coral reefs in the Caribbean, Bahamas and Florida. J. Mar. Res. 64, 563–588 (2006).
    Google Scholar 
    Castillo, K. D. & Lima, F. P. Comparison of in situ and satellite-derived (MODIS-Aqua/Terra) methods for assessing temperatures on coral reefs. Limnol. Oceanogr. Methods 8, 107–117 (2010).
    Google Scholar 
    Wyatt, A. S. J. et al. Heat accumulation on coral reefs mitigated by internal waves. Nat. Geosci. 13, 28–34 (2020).ADS 
    CAS 

    Google Scholar 
    Lourenço, C. R. et al. Upwelling areas as climate change refugia for the distribution and genetic diversity of a marine macroalga. J. Biogeogr. 43, 1595–1607 (2016).
    Google Scholar 
    Seabra, R. et al. Reduced nearshore warming associated with eastern boundary upwelling systems. Front. Mar. Sci. 6, (2019).Randall, C. J., Toth, L. T., Leichter, J. J., Maté, J. L. & Aronson, R. B. Upwelling buffers climate change impacts on coral reefs of the eastern tropical Pacific. Ecology 101, (2020).Varela, R., Lima, F. P., Seabra, R., Meneghesso, C. & Gómez-Gesteira, M. Coastal warming and wind-driven upwelling: A global analysis. Sci. Total Environ. 639, 1501–1511 (2018).ADS 
    CAS 
    PubMed 

    Google Scholar 
    Schulz, K. G., Hartley, S. & Eyre, B. Upwelling amplifies ocean acidification on the east Australian shelf: Implications for marine ecosystems. Front. Mar. Sci. 6, (2019).Connell, J. H. The influence of interspecific competition and other factors on the distribution of the barnacle Chthamalus stellatus. Ecology 42, 710–723 (1961).
    Google Scholar 
    Somero, G. N. Linking biogeography to physiology: Evolutionary and acclimatory adjustments of thermal limits. Front. Zool. 2, 1 (2005).PubMed 
    PubMed Central 

    Google Scholar 
    Sydeman, W. J. et al. Climate change and wind intensification in coastal upwelling ecosystems. Science 345, 77–80 (2014).ADS 
    CAS 
    PubMed 

    Google Scholar 
    Sweijd, N. A. & Smit, A. J. Trends in sea surface temperature and chlorophyll-a in the seven African Large Marine Ecosystems. Environ. Dev. 36, 100585 (2020).
    Google Scholar 
    Wang, D., Gouhier, T. C., Menge, B. A. & Ganguly, A. R. Intensification and spatial homogenization of coastal upwelling under climate change. Nature 518, 390–394 (2015).ADS 
    CAS 
    PubMed 

    Google Scholar 
    Lima, F. P. & Wethey, D. S. Robolimpets: measuring intertidal body temperatures using biomimetic loggers: Biomimetic loggers for intertidal temperatures. Limnol. Oceanogr. Methods 7, 347–353 (2009).
    Google Scholar 
    Judge, R., Choi, F. & Helmuth, B. Recent advances in data logging for intertidal ecology. Front. Ecol. Evol. 6, (2018).Harley, C. D. G. & Helmuth, B. S. T. Local- and regional-scale effects of wave exposure, thermal stress, and absolute versus effective shore level on patterns of intertidal zonation. Limnol. Oceanogr. 48, 1498–1508 (2003).ADS 

    Google Scholar 
    Seabra, R., Wethey, D. S., Santos, A. M., Gomes, F. & Lima, F. P. Equatorial range limits of an intertidal ectotherm are more linked to water than air temperature. Glob. Change Biol. 22, 3320–3331 (2016).ADS 

    Google Scholar 
    Lima, F. P. et al. Loss of thermal refugia near equatorial range limits. Glob. Change Biol. 22, 254–263 (2016).ADS 

    Google Scholar 
    Tapia, F. J. et al. Thermal indices of upwelling effects on inner-shelf habitats. Prog. Oceanogr. 83, 278–287 (2009).ADS 

    Google Scholar 
    Freeman, E. et al. ICOADS release 3.0: A major update to the historical marine climate record. Int. J. Climatol. 37, 2211–2232 (2017).
    Google Scholar 
    Lemos, R. T. & Pires, H. O. The upwelling regime off the West Portuguese Coast, 1941–2000. Int. J. Climatol. 24, 511–524 (2004).
    Google Scholar 
    Seabra, R., Wethey, D. S., Santos, A. M. & Lima, F. P. Side matters: Microhabitat influence on intertidal heat stress over a large geographical scale. J. Exp. Mar. Biol. Ecol. 400, 200–208 (2011).
    Google Scholar 
    Legendre, P. Species associations: The Kendall coefficient of concordance revisited. J. Agric. Biol. Environ. Stat. 10, 226–245 (2005).
    Google Scholar 
    Gouhier, T. C. & Guichard, F. Synchrony: Quantifying variability in space and time. Methods Ecol. Evol. 5, 524–533 (2014).
    Google Scholar 
    Torrence, C. & Compo, G. P. A practical guide to wavelet analysis. Bull. Am. Meteorol. Soc. 79, 61–78 (1998).ADS 

    Google Scholar 
    Cazelles, B. et al. Wavelet analysis of ecological time series. Oecologia 156, 287–304 (2008).ADS 
    PubMed 

    Google Scholar 
    Recknagel, F., Ostrovsky, I., Cao, H., Zohary, T. & Zhang, X. Ecological relationships, thresholds and time-lags determining phytoplankton community dynamics of Lake Kinneret, Israel elucidated by evolutionary computation and wavelets. Ecol. Model. 255, 70–86 (2013).CAS 

    Google Scholar 
    Mislan, K. A. S., Helmuth, B. & Wethey, D. S. Geographical variation in climatic sensitivity of intertidal mussel zonation: Biogeography of climatic sensitivity. Glob. Ecol. Biogeogr. 23, 744–756 (2014).
    Google Scholar 
    Grinsted, A., Moore, J. C. & Jevrejeva, S. Application of the cross wavelet transform and wavelet coherence to geophysical time series. Nonlinear Process. Geophys. 11, 561–566 (2004).ADS 

    Google Scholar 
    Cazelles, B. & Stone, L. Detection of imperfect population synchrony in an uncertain world. J. Anim. Ecol. 72, 953–968 (2003).
    Google Scholar 
    Keppel, G. et al. The capacity of refugia for conservation planning under climate change. Front. Ecol. Environ. 13, 106–112 (2015).
    Google Scholar 
    Vasseur, D. A. et al. Increased temperature variation poses a greater risk to species than climate warming. Proc. R. Soc. B Biol. Sci. 281, 20132612–20132612 (2014).
    Google Scholar 
    Potter, K. A., Woods, H. A. & Pincebourde, S. Microclimatic challenges in global change biology. Glob. Change Biol. 19, 2932–2939 (2013).ADS 

    Google Scholar 
    Sandel, B. et al. The influence of late quaternary climate-change velocity on species endemism. Science 334, 660–664 (2011).ADS 
    CAS 
    PubMed 

    Google Scholar 
    Pinsky, M. L., Worm, B., Fogarty, M. J., Sarmiento, J. L. & Levin, S. A. Marine taxa track local climate velocities. Science 341, 1239–1242 (2013).ADS 
    CAS 
    PubMed 

    Google Scholar 
    Araújo, M. B. & Luoto, M. The importance of biotic interactions for modelling species distributions under climate change. Glob. Ecol. Biogeogr. 16, 743–753 (2007).
    Google Scholar 
    Morelli, T. L. et al. Managing climate change refugia for climate adaptation. PLoS ONE 11, e0159909 (2016).PubMed 
    PubMed Central 

    Google Scholar 
    Stenseth, N. Ecological effects of climate fluctuations. Science 297, 1292–1296 (2002).ADS 
    CAS 
    PubMed 

    Google Scholar 
    Zellweger, F., De Frenne, P., Lenoir, J., Rocchini, D. & Coomes, D. Advances in microclimate ecology arising from remote sensing. Trends Ecol. Evol. 34, 327–341 (2019).PubMed 

    Google Scholar 
    Helmuth, B. et al. Long-term, high frequency in situ measurements of intertidal mussel bed temperatures using biomimetic sensors. Sci. Data 3, 160087 (2016).MathSciNet 
    PubMed 
    PubMed Central 

    Google Scholar 
    Wikelski, M. & Cooke, S. J. Conservation physiology. Trends Ecol. Evol. 21, 38–46 (2006).PubMed 

    Google Scholar 
    Helmuth, B. S. T. & Hofmann, G. E. Microhabitats, thermal heterogeneity, and patterns of physiological stress in the rocky intertidal zone. Biol. Bull. 201, 374–384 (2001).CAS 
    PubMed 

    Google Scholar 
    Kearney, M. Habitat, environment and niche: What are we modelling?. Oikos 115, 186–191 (2006).
    Google Scholar 
    Ashcroft, M. B. Identifying refugia from climate change. J. Biogeogr. 37, 1407–1413 (2010).
    Google Scholar 
    Maggs, C. A. et al. Evaluating signatures of glacial refugia for North Atlantic Benthic Marine Taxa. Ecology 89, S108–S122 (2008).PubMed 

    Google Scholar 
    Bennett, K. & Provan, J. What do we mean by ‘refugia’?. Quat. Sci. Rev. 27, 2449–2455 (2008).ADS 

    Google Scholar 
    Ashcroft, M. B., Chisholm, L. A. & French, K. O. Climate change at the landscape scale: predicting fine-grained spatial heterogeneity in warming and potential refugia for vegetation. Glob. Change Biol. 15, 656–667 (2009).ADS 

    Google Scholar 
    Hofmann, G. E. et al. High-frequency dynamics of ocean pH: A multi-ecosystem comparison. PLoS ONE 6, e28983 (2011).ADS 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    Bakun, A. et al. Anticipated Effects of Climate Change on Coastal Upwelling Ecosystems. Curr. Clim. Change Rep. 1, 85–93 (2015).
    Google Scholar 
    Iles, A. C. et al. Climate-driven trends and ecological implications of event-scale upwelling in the California Current System. Glob. Change Biol. 18, 783–796 (2012).ADS 

    Google Scholar 
    García-Reyes, M. et al. Under pressure: Climate change, upwelling, and eastern boundary upwelling ecosystems. Front. Mar. Sci. 2, (2015).Liebhold, A., Koenig, W. D. & Bjørnstad, O. N. Spatial synchrony in population dynamics. Annu. Rev. Ecol. Evol. Syst. 467–490 (2004).Amarasekare, P. & Nisbet, R. M. Spatial heterogeneity, source-sink dynamics, and the local coexistence of competing species. Am. Nat. 158, 572–584 (2001).CAS 
    PubMed 

    Google Scholar 
    Adler, F. R. & Nuernberger, B. Persistence in patchy irregular landscapes. Theor. Popul. Biol. 45, 41–75 (1994).MATH 

    Google Scholar 
    Rykaczewski, R. R. et al. Poleward displacement of coastal upwelling-favorable winds in the ocean’s eastern boundary currents through the 21st century. Geophys. Res. Lett. 42, 6424–6431 (2015).ADS 

    Google Scholar 
    Varela, R., Rodríguez-Díaz, L., de Castro, M. & Gómez-Gesteira, M. Influence of Canary upwelling system on coastal SST warming along the 21st century using CMIP6 GCMs. Glob. Planet. Change 208, 103692 (2022).
    Google Scholar 
    Ocean deoxygenation: everyone’s problem. Causes, impacts, consequences and solutions. (IUCN, International Union for Conservation of Nature, 2019). https://doi.org/10.2305/IUCN.CH.2019.13.en.Howard, E. M. et al. Climate-driven aerobic habitat loss in the California Current System. Sci. Adv. 6, eaay3188 (2020).ADS 
    PubMed 
    PubMed Central 

    Google Scholar 
    Iles, A. C. Toward predicting community-level effects of climate: Relative temperature scaling of metabolic and ingestion rates. Ecology 95, 2657–2668 (2014).
    Google Scholar 
    Harris, R. M. B. et al. Biological responses to the press and pulse of climate trends and extreme events. Nat. Clim. Change 8, 579 (2018).ADS 

    Google Scholar 
    Salinas, S., Irvine, S. E., Schertzing, C. L., Golden, S. Q. & Munch, S. B. Trait variation in extreme thermal environments under constant and fluctuating temperatures. Philos. Trans. R. Soc. B Biol. Sci. 374, 20180177 (2019).
    Google Scholar 
    Fischer, E. M. & Knutti, R. Anthropogenic contribution to global occurrence of heavy-precipitation and high-temperature extremes. Nat. Clim. Change 5, 560–564 (2015).ADS 

    Google Scholar 
    Buckley, L. B. & Huey, R. B. Temperature extremes: geographic patterns, recent changes, and implications for organismal vulnerabilities. Glob. Change Biol. 22, 3829–3842 (2016).ADS 

    Google Scholar  More

  • in

    Diversity of soil faunal community as influenced by crop straw combined with different synthetic fertilizers in upland purple soil

    Lavelle, P. et al. Soil invertebrates and ecosystem services. Eur. J. Soil Sci. 42, S3–S15 (2006).
    Google Scholar 
    Nielsen, U. N. et al. Response of belowground communities to short-term phosphorus addition in a phosphorus-limited woodland. Plant Soil 391, 321–331 (2015).
    Google Scholar 
    Nielsen, U. N., Ayres, E., Wall, D. H. & Bardgett, R. D. Soil biodiversity and carbon cycling: A review and synthesis of studies examining diversity function relationships. Eur. J. Soil Sci. 62, 105–116 (2011).
    Google Scholar 
    Lu, P. et al. Composition and structure of soil fauna communities and their relationships with environmental factors in copper mine waste rock after re-vegetation. Glob. Ecol. Conserv. 32, e01889 (2021).
    Google Scholar 
    Lin, D. et al. Soil fauna promote litter decomposition but do not alter the relationship between leaf economics spectrum and litter decomposability. Soil Biol. Biochem. 136, 107519 (2019).
    Google Scholar 
    Shao, Y., Zhang, W., Liu, S., Wang, X. & Fu, S. Diversity and function of soil fauna. Acta Ecol. Sin. (in Chinese) 35, 6614–6625 (2015).
    Google Scholar 
    Voronin, A. N. & Kotyak, P. A. Influence of different agricultural practices on the number of soil fauna and productivity of agricultural crops. Taurida Herald Agrar. Sci. 3, 49–56 (2019).
    Google Scholar 
    Zhu, X. & Zhu, B. Effect of different fertilization regimes on the main groups of soil fauna in cropland of purple soil. Sci. Agric. Sin. (in Chinese) 45, 911–920 (2015).
    Google Scholar 
    Islam, M. U., Guo, Z., Jiang, F. & Peng, X. Does straw return increase crop yield in the wheat-maize cropping system in China? A meta-analysis. Field Crop Res. 279, 108447 (2022).
    Google Scholar 
    Cui, H. et al. Straw return strategies to improve soil properties and crop productivity in a winter wheat-summer maize cropping system. Eur. J. Agron. 133, 126436 (2022).
    Google Scholar 
    Wang, X. et al. Changes in soil characteristics and maize yield under straw returning system in dryland farming. Field Crop Res. 218, 11–17 (2018).
    Google Scholar 
    Gai, X. et al. Contrasting impacts of long-term application of manure and crop straw on residual nitrate-N along the soil profile in the North China Plain. Sci. Total Environ. 650, 2251–2259 (2019).ADS 
    PubMed 

    Google Scholar 
    Wang, W. et al. Effects of different fertility-building practices on the meso-micro soil fauna communities in a black soil area. Chin. J. Appl. Environ. Biol. (in Chinese) 25, 1344–1351 (2019).
    Google Scholar 
    Kautz, T., López-Fando, C. & Ellmer, F. Abundance and biodiversity of soil microarthropods as influenced by different types of organic manure in a long-term field experiment in Central Spain. Appl. Soil Ecol. 33, 278–285 (2006).
    Google Scholar 
    Zhang, T. et al. Effects of straw returning on soil meso-and micro-arthropod community diversity in wheat-maize fields in North China. Chin. J. Appl. Environ. Biol. (in Chinese) 25, 70–75 (2019).
    Google Scholar 
    Yang, P., Wang, H. & Yue, J. Ecological distribution of middle-small-size soil faunas under conservation tillage and straw mulch conditions. Res. Soil Water Conserv. (in Chinese) 20, 145–150 (2013).
    Google Scholar 
    Zhu, Q., Zhu, A., Zhang, J., Zhang, H. & Zhang, C. Effect of conservation tillage on soil fauna in wheat field of Huang-huai-hai Plain. J. Agro Environ. Sci. (in Chinese) 28, 1766–1772 (2009).
    Google Scholar 
    Cao, Z. et al. Changes in the abundance and structure of a soil mite (Acari) community under long-term organic and chemical fertilizer treatments. Appl. Soil Ecol. 49, 131–138 (2011).
    Google Scholar 
    Li, Y., Xu, Z., Xu, H., Chen, Y. & Ruan, H. Review of the effect of fertilizer application on the soil fauna in soil ecosystems. J. Nanjing For. Univ. Nat. Sci. Ed. (in Chinese) 42, 179–184 (2018).
    Google Scholar 
    McGee, K. M. & Eaton, W. D. A comparison of the wet and dry season DNA-based soil invertebrate community characteristics in large patches of the bromeliad Bromelia pinguin in a primary forest in Costa Rica. Appl. Soil Ecol. 87, 99–107 (2015).
    Google Scholar 
    Zhu, B., Wang, T., You, X. & Gao, M. Nutrient release from weathering of purplish rocks in the Sichuan Basin, China. Pedosphere 18, 257–264 (2008).
    Google Scholar 
    Zhu, B. et al. Measurements of nitrate leaching from a hillslope cropland in the Central Sichuan Basin, China. Soil Sci. Soc. Am. J. 73, 1419–1426 (2009).ADS 

    Google Scholar 
    He, Y. Purple Soil of China Part (II) (Science Press, 2003).
    Google Scholar 
    Huang, R. et al. Responses of soil carbon pool and soil aggregates associated organic carbon to straw and straw-derived biochar addition in a dryland cropping mesocosm system. Agric. Ecosyst. Environ. 265, 576–586 (2018).
    Google Scholar 
    Zhu, X., Dong, Z., Kuang, F. & Zhu, B. Effects of fertilization regimes on soil faunal communities in cropland of purple soil. Acta Ecol. Sin. (in Chinese) 33, 464–474 (2013).
    Google Scholar 
    Querner, P. & Bruckner, A. Combining pitfall traps and soil samples to collect Collembola for site scale biodiversity assessments. Appl. Soil. Ecol. 45, 293–297 (2010).
    Google Scholar 
    Smith, M. A. et al. Extreme diversity of tropical parasitoid wasps exposed by iterative integration of natural history, DNA barcoding, morphology, and collections. PNAS 105, 12359–12364 (2008).ADS 
    PubMed 
    PubMed Central 

    Google Scholar 
    Müller, C. A. et al. Meiofaunal diversity in the Atlantic Forest soil: A quest for nematodes in a native reserve using eukaryotic metabarcoding analysis. For. Ecol. Manag. 453, 117591 (2019).
    Google Scholar 
    Ding, J. et al. Effects of long-term fertilization on the associated microbiota of soil collembolan. Soil Biol. Biochem. 130, 141–149 (2019).
    Google Scholar 
    Oliverio, A. M., Gan, H., Wickings, K. & Fierer, N. A DNA metabarcoding approach to characterize soil arthropod communities. Soil Biol. Biochem. 125, 37–43 (2018).
    Google Scholar 
    McGee, K. M., Porter, T. M., Wright, M. & Hajibabaei, M. Drivers of tropical soil invertebrate community composition and richness across tropical secondary forests using DNA metasystematics. Sci. Rep. 10, 18429 (2020).PubMed 
    PubMed Central 

    Google Scholar 
    Porter, T. M. et al. Variations in terrestrial arthropod DNA metabarcoding methods recovers robust beta diversity but variable richness and site indicators. Sci. Rep. 9, 18218 (2019).ADS 
    PubMed 
    PubMed Central 

    Google Scholar 
    Morise, H., Miyazaki, E., Yoshimitsu, S. & Eki, T. Profiling nematode communities in unmanaged flowerbed and agricultural field soils in Japan by DNA barcode sequencing. PLoS One 7, e51785 (2012).ADS 
    PubMed 
    PubMed Central 

    Google Scholar 
    Drummond, A. J. et al. Evaluating a multigene environmental DNA approach for biodiversity assessment. Gigascience 4, 46 (2015).PubMed 
    PubMed Central 

    Google Scholar 
    Dopheide, A. et al. Estimating the biodiversity of terrestrial invertebrates on a forested island using DNA barcodes and metabarcoding data. Ecol. Appl. 29, e01877 (2019).PubMed 

    Google Scholar 
    Watts, C. et al. DNA metabarcoding as a tool for invertebrate community monitoring: A case study comparison with conventional techniques. Austral Entomol. 58, 675–686 (2019).
    Google Scholar 
    Kvist, S. Barcoding in the dark? A critical view of the sufficiency of zoological DNA barcoding databases and a plea for broader integration of taxonomic knowledge. Mol. Phylogenet. Evol. 69, 39–45 (2013).PubMed 

    Google Scholar 
    Shao, Y. et al. Nematodes as indicators of soil recovery in tailings of a lead/zinc mine. Soil Biol. Biochem. 40, 2040–2046 (2008).
    Google Scholar 
    Neher, D. A., Wu, J., Barbercheck, M. E. & Anas, O. Ecosystem type affects interpretation of soil nematode community measures. Appl. Soil Ecol. 30, 47–64 (2005).
    Google Scholar 
    Yang, C., Ji, Y., Wang, X., Yang, C. & Yu, D. W. Testing three pipelines for 18S rDNA-based metabarcoding of soil faunal diversity. Sci. China Life Sci. 56, 73–81 (2013).ADS 
    PubMed 

    Google Scholar 
    Horton, D. J., Kershner, M. W. & Blackwood, C. B. Suitability of PCR primers for characterizing invertebrate communities from soil and leaf litter targeting metazoan 18S ribosomal or cytochrome oxidase I (COI) genes. Eur. J. Soil Biol. 80, 43–48 (2017).
    Google Scholar 
    Geisen, S., Laros, I., Vizcaino, A., Bonkowski, M. & de Groot, G. A. Not all are free-living: High-throughput DNA metabarcoding reveals a diverse community of protists parasitizing soil metazoa. Mol. Ecol. 24, 4556–4569 (2015).PubMed 

    Google Scholar 
    Clarke, L. J., Soubrier, J., Weyrich, L. S. & Cooper, A. Environmental metabarcodes for insects: In silico PCR reveals potential for taxonomic bias. Mol. Ecol. Resour. 14, 1160–1170 (2014).PubMed 

    Google Scholar 
    Kitagami, Y. & Matsuda, Y. High-throughput sequencing covers greater nematode diversity than conventional morphotyping on natural cedar forests in Yakushima Island, Japan. Eur. J. Soil Biol. 112, 103432 (2022).
    Google Scholar 
    Juliet, W. K., Lisa, B. F., Lamers, J. P. A., Till, S. & Christian, B. Soil fertility and biodiversity on organic and conventional smallholder farms in Kenya. Appl. Soil Ecol. 134, 85–97 (2019).
    Google Scholar 
    Li, Q., Zhou, D. & Chen, X. The accumulation decomposition and ecological effects of above-ground litter in terrestrial ecosystem. Acta Ecol. Sin. (in Chinese) 34, 3807–3819 (2014).
    Google Scholar 
    Tie, L. et al. Phosphorus addition reverses the negative effect of nitrogen addition on soil arthropods during litter decomposition in a subtropical forest. Sci. Total. Environ. 781, 146786 (2021).ADS 

    Google Scholar 
    Nottingham, A. T., Turner, B. L., Stott, A. W. & Tanner, E. V. J. Nitrogen and phosphorus constrain labile and stable carbon turnover in lowland tropical forest soils. Soil Biol. Biochem. 80, 26–33 (2015).
    Google Scholar 
    Xiao, Q. et al. Impact of soil thickness on productivity and nitrate leaching from sloping cropland in the upper Yangtze River Basin. Agric. Ecosyst. Environ. 311, 107266 (2021).
    Google Scholar 
    Zhu, X. & Zhu, B. Diversity and abundance of soil fauna as influenced by long-term fertilization in cropland of purple soil, China. Soil Till. Res. 146, 39–46 (2015).
    Google Scholar 
    Wei, K., Wang, J., Dong, Z., Tang, J. & Zhu, B. The combined application of organic materials and chemical fertilizer mitigates the deterioration of the trophic structure of nematode community by increasing soil N concentration. J. Soil Sci. Plant Nutr. 21, 2530–2537 (2021).
    Google Scholar 
    Kuo, S. Phosphorus. In Methods of Soil Analysis (ed. Sparks, D. L.) 869–919 (Soil Science Society of America, 1996).
    Google Scholar 
    Nelson, D. W. & Sommers, L. E. Total carbon, organic carbon and organic matter. In Methods of Soil Analysis (ed. Sparks, D. L.) 960–1010 (ASA and SSSA, 1996).
    Google Scholar 
    Lu, R. Analysis of Soil Agro-Chemistry (Chinese Agricultural Science and Technology Press, 2000).
    Google Scholar 
    Page, A. L., Miller, R. H. & Keeney, D. R. Chemical and microbiological properties. In Methods of Soil Analysis (ASA and SSSA, 1982).
    Google Scholar 
    Olsen, S. R., Cole, C. U., Watanabe, F. S. & Deen, L. A. Estimation of Available Phosphorus in Soil by Extracting with Sodium Bicarbonate (USDA Circular 939, 1954).
    Google Scholar 
    Townshend, J. L. A modification and evaluation of the apparatus for the Oostenbrink direct cottonwool filter extraction method. Nematologica 9, 106–110 (1963).
    Google Scholar 
    Geller, J., Meyer, C., Parker, M. & Hawk, H. Redesign of PCR primers for mitochondrial cytochrome c oxidase subunit I for marine invertebrates and application in all-taxa biotic surveys. Mol. Ecol. Resour. 13, 851–861 (2013).PubMed 

    Google Scholar 
    Yang, T., Song, X., Xu, X., Zhou, C. & Shi, A. A comparative analysis of spider prey spectra analyzed through the next-generation sequencing of individual and mixed DNA samples. Ecol. Evol. 11, 15444–15454 (2021).PubMed 
    PubMed Central 

    Google Scholar 
    Chen, H. & Jiang, W. Application of high-throughput sequencing in understanding human oral microbiome related with health and disease. Front. Microbiol. 5, 508 (2014).PubMed 
    PubMed Central 

    Google Scholar 
    Magoc, T. & Salzberg, S. L. FLASH: Fast length adjustment of short reads to improve genome assemblies. Bioinformatics 27, 2957–2963 (2011).PubMed 
    PubMed Central 

    Google Scholar 
    Edgar, R. C. Search and clustering orders of magnitude faster than BLAST. Bioinformatics 26, 2460–2461 (2010).PubMed 

    Google Scholar 
    Altschul, S. F. et al. Gapped BLAST and PSI-BLAST a new generation of protein database search programs. Nucleic Acids Res. 25, 3389–3402 (1997).PubMed 
    PubMed Central 

    Google Scholar 
    R Core Team. R: A language and environment for statistical computing. http://www.r-project.org (2020).Shannon, C. E. A mathematical theory of communication. Bell Syst. Tech. J. 27, 379–423 (1948).MathSciNet 
    MATH 

    Google Scholar 
    Margalef, R. Perspectives in Ecological Theory 111–119 (The University of Chicago Press, 1970).
    Google Scholar 
    Pielou, E. C. The measurement of diversity in different types of biological collections. J. Theor. Biol. 88, 131–144 (1966).ADS 

    Google Scholar 
    Zhou, Y. et al. Species richness and phylogenetic diversity of seed plants across vegetation zones of Mount Kenya, East Africa. Ecol. Evol. 8, 8930–8939 (2018).PubMed 
    PubMed Central 

    Google Scholar 
    Wang, H. et al. Nitrogen addition reduces soil bacterial richness, while phosphorus addition alters community composition in an old-growth N-rich tropical forest in southern China. Soil Biol. Biochem. 127, 22–30 (2018).
    Google Scholar 
    Yang, K. et al. Responses of soil ammonia-oxidizing bacteria and archaea diversity to N, P and NP fertilization: Relationships with soil environmental variables and plant community diversity. Soil Biol. Biochem. 145, 107795 (2020).
    Google Scholar 
    Zhang, S., Li, Q., Lü, Y., Zhang, X. & Liang, W. Contributions of soil biota to C sequestration varied with aggregate fractions under different tillage systems. Soil Biol. Biochem. 62, 147–156 (2013).
    Google Scholar  More

  • in

    Temporal and functional interrelationships between bacterioplankton communities and the development of a toxigenic Microcystis bloom in a lowland European reservoir

    Paerl, H. W. Mitigating toxic planktonic cyanobacterial blooms in aquatic ecosystems facing increasing anthropogenic and climatic pressures. Toxins. 10, 1–16 (2018).
    Google Scholar 
    Harke, M. J. et al. A review of the global ecology, genomics, and biogeography of the toxic cyanobacterium Microcystis spp. Harmful Algae 54, 4–20. https://doi.org/10.1016/j.hal.2015.12.007 (2016).Article 
    PubMed 

    Google Scholar 
    Paerl, H. W. & Barnard, M. A. Mitigating the global expansion of harmful cyanobacterial blooms: Moving targets in a human- and climatically-altered world. Harmful Algae 96, 101845. https://doi.org/10.1016/j.hal.2020.101845 (2020).Article 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    Paerl, H. W. Mitigating harmful cyanobacterial blooms in a human- and climatically-impacted world. Life. 4, 988–1012 (2014).PubMed 
    PubMed Central 

    Google Scholar 
    Burford, M. A. et al. Perspective: Advancing the research agenda for improving understanding of cyanobacteria in a future of global change. Harmful Algae 91, 101601. https://doi.org/10.1016/j.hal.2019.04.004 (2020).Article 
    CAS 
    PubMed 

    Google Scholar 
    Havens, K. E., James, R. T., East, T. L. & Smith, V. H. N: P ratios, light limitation, and cyanobacterial dominance in a subtropical lake impacted by non-point source nutrient pollution. Environ. Pollut. 122, 379–390 (2003).CAS 
    PubMed 

    Google Scholar 
    Bernard, C. Cyanobacteria and cyanotoxins. Rev. Franç. Lab. 2014, 53–68 (2014).
    Google Scholar 
    Paerl, H. W. & Otten, T. G. Harmful cyanobacterial blooms: Causes, consequences, and controls. Microb. Ecol. 65, 995–1010 (2013).CAS 
    PubMed 

    Google Scholar 
    Dolman, A. M. et al. Cyanobacteria and cyanotoxins: The influence of nitrogen versus phosphorus. PLoS ONE 7, 38575 (2012).
    Google Scholar 
    Svirčev, Z. et al. Global geographical and historical overview of cyanotoxin distribution and cyanobacterial poisonings. Arch. Toxicol. https://doi.org/10.1007/s00204-019-02524-4 (2019).Article 
    PubMed 

    Google Scholar 
    Massey, I. Y. & Yang, F. A mini review on microcystins and bacterial degradation. Toxins 12, 268 (2020).CAS 
    PubMed Central 

    Google Scholar 
    Paerl, H. W. et al. Mitigating eutrophication and toxic cyanobacterial blooms in large lakes: The evolution of a dual nutrient (N and P) reduction paradigm. Hydrobiologia 847, 4359–4375. https://doi.org/10.1007/s10750-019-04087-y (2020).Article 
    CAS 

    Google Scholar 
    Sapp, M. et al. Species-specific bacterial communities in the phycosphere of microalgae?. Microb. Ecol. 53, 683–699 (2007).PubMed 

    Google Scholar 
    Cai, H., Jiang, H., Krumholz, L. R. & Yang, Z. Bacterial community composition of size-fractioned aggregates within the phycosphere of cyanobacterial blooms in a eutrophic freshwater lake. PLoS ONE 9, 102879 (2014).ADS 

    Google Scholar 
    Grant, M. A. A., Kazamia, E., Cicuta, P. & Smith, A. G. Direct exchange of vitamin B 12 is demonstrated by modelling the growth dynamics of algal-bacterial cocultures. ISME J. Nat. Publ. Group 8, 1418–1427 (2014).CAS 

    Google Scholar 
    Shi, L., Cai, Y., Kong, F. & Yu, Y. Specific association between bacteria and buoyant Microcystis colonies compared with other bulk bacterial communities in the eutrophic Lake Taihu, China. Environ. Microbiol. Rep. 4, 669–678 (2012).CAS 
    PubMed 

    Google Scholar 
    Brunberg, A. K. Contribution of bacteria in the mucilage of Microcystis spp (Cyanobacteria) to benthic and pelagic bacterial production in a hypereutrophic lake. FEMS Microbiol. Ecol. 29, 13–22 (1999).CAS 

    Google Scholar 
    Shao, K. et al. The responses of the taxa composition of particle-attached bacterial community to the decomposition of Microcystis blooms. Sci. Total. Environ. 488–489, 236–242. https://doi.org/10.1016/j.scitotenv.2014.04.101 (2014).Article 
    ADS 
    CAS 
    PubMed 

    Google Scholar 
    Jankowiak, J. G. & Gobler, C. J. The composition and function of microbiomes within microcystis colonies are significantly different than native bacterial assemblages in two North American lakes. Front. Microbiol. 11, 1–26 (2020).
    Google Scholar 
    Bauer, A. & Forchhammer, K. Bacterial predation on cyanobacteria. Microb. Physiol. 99, 108 (2021).
    Google Scholar 
    Ndlela, L. L., Oberholster, P. J., Van Wyk, J. H. & Cheng, P. H. Bacteria as biological control agents of freshwater cyanobacteria: Is it feasible beyond the laboratory?. Appl. Microbiol. Biotechnol. 102, 9911–9923 (2018).CAS 
    PubMed 

    Google Scholar 
    Yang, C. et al. Distinct network interactions in particle-associated and free-living bacterial communities during a Microcystis aeruginosa bloom in a plateau lake. Front. Microbiol. 8, 1–15 (2017).
    Google Scholar 
    Xu, H. et al. Contrasting network features between free-living and particle-attached bacterial communities in Taihu Lake. Microb. Ecol. 76, 303–313 (2018).PubMed 

    Google Scholar 
    Liu, M. et al. Community dynamics of free-living and particle-attached bacteria following a reservoir Microcystis bloom. Sci. Total Environ. 660, 501–511. https://doi.org/10.1016/j.scitotenv.2018.12.414 (2019).Article 
    ADS 
    CAS 
    PubMed 

    Google Scholar 
    Parveen, B. et al. Bacterial communities associated with Microcystis colonies differ from free-living communities living in the same ecosystem. Environ. Microbiol. Rep. 5, 716–724 (2013).CAS 
    PubMed 

    Google Scholar 
    Louati, I. et al. Structural diversity of bacterial communities associated with bloom-forming freshwater cyanobacteria differs according to the cyanobacterial genus. PLoS ONE 10, 0140614 (2015).
    Google Scholar 
    Zwirglmaier, K., Keiz, K., Engel, M., Geist, J. & Raeder, U. Seasonal and spatial patterns of microbial diversity along a trophic gradient in the interconnected lakes of the Osterseen Lake District, Bavaria. Front. Microbiol. 6, 1–18 (2015).
    Google Scholar 
    Scherer, P. I. et al. Temporal dynamics of the microbial community composition with a focus on toxic cyanobacteria and toxin presence during harmful algal blooms in two South German lakes. Front. Microbiol. 8, 1–17 (2017).
    Google Scholar 
    Kokocinski, M., Dziga, D., Antosiak, A. & Soininen, J. Are bacterio- and phytoplankton community compositions related in lakes differing in their cyanobacteria contribution and physico-chemical properties?. Genes 12, 855 (2021).CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    Dziga, D. et al. Correlation between specific groups of heterotrophic bacteria and microcystin biodegradation in freshwater bodies of central Europe. FEMS Microbiol. Ecol. https://doi.org/10.1111/j.1574-6941.1999.tb00594.x (2019).Article 
    PubMed 

    Google Scholar 
    Jurczak, T. et al. Elimination of microcystins by water treatment processes: Examples from Sulejow Reservoir, Poland. Water Res. 39, 2394–2406 (2005).CAS 
    PubMed 

    Google Scholar 
    Mankiewicz-Boczek, J. et al. Detection and monitoring toxigenicity of cyanobacteria by application of molecular methods. Environ Toxicol. 21, 380–387 (2006).ADS 
    CAS 
    PubMed 

    Google Scholar 
    Rajaniemi-Wacklin, P. et al. Correspondence between phylogeny and morphology of Snowella spp. and Woronichinia naegeliana, cyanobacteria commonly occurring in lakes. J. Phycol. 42, 226–232 (2006).
    Google Scholar 
    DrobacBacković, D. et al. Cyanobacteria, cyanotoxins, and their histopathological effects on fish tissues in Fehérvárcsurgó reservoir Hungary. Environ. Monit. Assess. https://doi.org/10.1007/s10661-021-09324-3 (2021).Article 

    Google Scholar 
    Kallscheuer, N. et al. Analysis of bacterial communities in a municipal duck pond during a phytoplankton bloom and isolation of Anatilimnocola aggregata gen. nov., sp. Nov., Lacipirellula limnantheis sp. Nov. and Urbifossiella limnaea gen. nov. sp. nov. belonging to the phylum. Environ. Microbiol. 23, 1379–1396 (2021).CAS 
    PubMed 

    Google Scholar 
    Davis, T. W. et al. Effects of nitrogenous compounds and phosphorus on the growth of toxic and non-toxic strains of Microcystis during cyanobacterial blooms. Aquat. Microb. Ecol. 61, 149–162 (2010).
    Google Scholar 
    Gobler, C. J., Davis, T. W., Coyne, K. J. & Boyer, G. L. Interactive influences of nutrient loading, zooplankton grazing, and microcystin synthetase gene expression on cyanobacterial bloom dynamics in a eutrophic New York lake. Harmful Algae 6, 119–133 (2007).CAS 

    Google Scholar 
    Mankiewicz-Boczek, J. et al. Cyanophages infection of microcystis bloom in lowland dam reservoir of Sulejów, Poland. Microb. Ecol. 71, 315–325 (2016).CAS 
    PubMed 

    Google Scholar 
    Davis, T. W., Berry, D. L., Boyer, G. L. & Gobler, C. J. The effects of temperature and nutrients on the growth and dynamics of toxic and non-toxic strains of Microcystis during cyanobacteria blooms. Harmful Algae 8, 715–725 (2009).CAS 

    Google Scholar 
    Yoshida, M., Yoshida, T., Takashima, Y., Hosoda, N. & Hiroishi, S. Dynamics of microcystin-producing and non-microcystin-producing Microcystis populations is correlated with nitrate concentration in a Japanese lake. FEMS Microbiol. Lett. 266, 49–53 (2007).CAS 
    PubMed 

    Google Scholar 
    Sezenna, M. L. Proteobacteria: Phylogeny, Metabolic Diversity and Ecological Effects (Nova Science Publishers, Inc., 2011).
    Google Scholar 
    Rilling, J. I., Acuña, J. J., Sadowsky, M. J. & Jorquera, M. A. Putative nitrogen-fixing bacteria associated with the rhizosphere and root endosphere of wheat plants grown in an andisol from southern Chile. Front. Microbiol. 9, 1–13 (2018).
    Google Scholar 
    Lukumbuzya, M. et al. A refined set of rRNA-targeted oligonucleotide probes for in situ detection and quantification of ammonia-oxidizing bacteria. Water Res. 186, 116375 (2020).
    Google Scholar 
    Prosser, J. I., Head, I. M. & Stein, L. Y. The family Nitrosomonadaceae. In The Prokaryotes: Alphaproteobacteria and Betaproteobacteria (eds Rosenberg, E. et al.) 901–918 (Springer, 2014). https://doi.org/10.1007/978-3-642-30197-1_372.Chapter 

    Google Scholar 
    Jia, L., Jiang, B., Huang, F. & Hu, X. Nitrogen removal mechanism and microbial community changes of bioaugmentation subsurface wastewater infiltration system. Bioresour. Technol. 294, 122140. https://doi.org/10.1016/j.biortech.2019.122140 (2019).Article 
    CAS 
    PubMed 

    Google Scholar 
    Daft, M. J. & Stewart, W. D. P. Bacterial pathogens of freshwater blue-green algae. New Phytol. 70, 819–829 (1971).
    Google Scholar 
    Chun, S. J. et al. Network analysis reveals succession of Microcystis genotypes accompanying distinctive microbial modules with recurrent patterns. Water Res. 170, 115326. https://doi.org/10.1016/j.watres.2019.115326 (2020).Article 
    CAS 
    PubMed 

    Google Scholar 
    Parulekar, N. N. et al. Characterization of bacterial community associated with phytoplankton bloom in a eutrophic lake in South Norway using 16S rRNA gene amplicon sequence analysis. PLoS ONE 12, 1–22 (2017).
    Google Scholar 
    Guedes, I. A. et al. Close link between harmful cyanobacterial dominance and associated bacterioplankton in a tropical eutrophic reservoir. Front. Microbiol. 9, 424 (2018).PubMed 
    PubMed Central 

    Google Scholar 
    Allgaier, M. & Grossart, H. P. Seasonal dynamics and phylogenetic diversity of free-living and particle-associated bacterial communities in four lakes in northeastern Germany. Aquat. Microb. Ecol. 45, 115–128 (2006).
    Google Scholar 
    Chen, S. et al. Disentangling the drivers of Microcystis decomposition: Metabolic profile and co-occurrence of bacterial community. Sci. Total Environ. 739, 140062. https://doi.org/10.1016/j.scitotenv.2020.140062 (2020).Article 
    ADS 
    CAS 
    PubMed 

    Google Scholar 
    Leflaive, J. & Ten-Hage, L. Algal and cyanobacterial secondary metabolites in freshwaters: A comparison of allelopathic compounds and toxins. Freshw. Biol. 52, 199–214 (2007).CAS 

    Google Scholar 
    Song, H. et al. Biological and chemical factors driving the temporal distribution of cyanobacteria and heterotrophic bacteria in a eutrophic lake (West Lake, China). Appl. Microbiol. Biotechnol. 101, 1685–1696. https://doi.org/10.1007/s00253-016-7968-8 (2017).Article 
    CAS 
    PubMed 

    Google Scholar 
    Bagatini, I. L. et al. Host-specificity and dynamics in bacterial communities associated with bloom-forming freshwater phytoplankton. PLoS ONE 9, 85957 (2014).ADS 

    Google Scholar 
    Kohler, E. et al. Biodegradation of microcystins during gravity-driven membrane (GDM) ultrafiltration. PLoS ONE 9, 111794 (2014).ADS 

    Google Scholar 
    Wu, X. et al. Culturing of “unculturable” subsurface microbes: Natural organic carbon source fuels the growth of diverse and distinct bacteria from groundwater. Front. Microbiol. 11, 1–10 (2020).CAS 

    Google Scholar 
    Morotomi, M., Nagai, F. & Watanabe, Y. Parasutterella secunda sp. no., isolated from human faeces and proposal of Sutterellaceae fam. nov. in the order Burkholderiales. Int. J. Syst. Evol. Microbiol. 61, 637–643 (2011).CAS 
    PubMed 

    Google Scholar 
    Kiedrzyńska, E. et al. Point sources of nutrient pollution in the lowland river catchment in the context of the baltic Sea eutrophication. Ecol. Eng. 70, 337–348 (2014).
    Google Scholar 
    Hwang, W. M., Ko, Y., Kim, J. H. & Kang, K. Ahniella affigens gen Nov, sp. nov., a gammaproteobacterium isolated from sandy soil near a stream. Int. J. Syst. Evol. Microbiol. 68, 2478–2484 (2018).CAS 
    PubMed 

    Google Scholar 
    Qian, H. et al. Spatial variability of cyanobacteria and heterotrophic bacteria in Lake Taihu (China). Bull. Environ. Contam. Toxicol. 99, 380–384 (2017).CAS 
    PubMed 

    Google Scholar 
    Humbert, J. F. et al. Comparison of the structure and composition of bacterial communities from temperate and tropical freshwater ecosystems. Environ. Microbiol. 11, 2339–2350 (2009).CAS 
    PubMed 

    Google Scholar 
    Newton, R. J., Jones, S. E., Eiler, A., McMahon, K. D. & Bertilsson, S. A guide to the natural history of freshwater lake Bacteria. Microbiol. Mol. Biol. Rev. 1, 1–10 (2011).
    Google Scholar 
    Parveen, B., Mary, I., Vellet, A., Ravet, V. & Debroas, D. Temporal dynamics and phylogenetic diversity of free-living and particle-associated Verrucomicrobia communities in relation to environmental variables in a mesotrophic lake. FEMS Microbiol. Ecol. 83, 189–201 (2013).CAS 
    PubMed 

    Google Scholar 
    Henson, M. W., Lanclos, V. C., Faircloth, B. C. & Thrash, J. C. Cultivation and genomics of the first freshwater SAR11 (LD12) isolate. ISME J. 12, 1846–1860 (2018).CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    Yang, C. et al. The characteristics and algicidal mechanisms of cyanobactericidal bacteria, a review. World J. Microbiol. Biotechnol. 36, 1–10. https://doi.org/10.1007/s11274-020-02965-5 (2020).Article 

    Google Scholar 
    Izydorczyk, K. et al. Influence of abiotic and biotic factors on microcystin content in Microcystis aeruginosa cells in a eutrophic temperate reservoir. J. Plankton Res. 30, 393–400 (2008).CAS 

    Google Scholar 
    Mankiewicz-Boczek, J. et al. Bacteria homologus to Aeromonas capable of microcystin degradation. Open Life Sci. 10, 106–116 (2015).CAS 

    Google Scholar 
    Jaskulska, A., Font Nájera, A., Czarny, P., Serwecińska, L. & Mankiewicz-boczek, J. Daily dynamic of transcripts abundance of Ma-LMM01-like cyanophages in two lowland European reservoirs. Ecohydrol. Hydrobiol. 21, 543–548 (2021).
    Google Scholar 
    Gągała, I. et al. Role of environmental factors and toxic genotypes in the regulation of microcystins-producing cyanobacterial blooms. Microb. Ecol. 67, 465–479 (2014).PubMed 

    Google Scholar 
    Klindworth, A. et al. Evaluation of general 16S ribosomal RNA gene PCR primers for classical and next-generation sequencing-based diversity studies. Nucleic Acids Res. 41, 1–11 (2013).
    Google Scholar 
    Illumina. 16S Metagenomic Sequencing Library Preparation. (2013). http://support.illumina.com/content/dam/illumina-support/documents/documentation/chemistry_documentation/16s/16s-metagenomic-library-prep-guide-15044223-b.pdf.Frangeul, L. et al. Highly plastic genome of Microcystis aeruginosa PCC 7806, a ubiquitous toxic freshwater cyanobacterium. BMC Genomics 9, 1–20 (2008).
    Google Scholar 
    Hammer, Ø., Harper, D. A. T. & Ryan, P. D. Past: Paleontological statistics software package for education and data analysis even a cursory glance at the recent paleontological literature should convince anyone tha. Palaeontol. Electron. 4, 1–9 (2001).
    Google Scholar 
    Suzuki, M. T., Taylor, L. T. & DeLong, E. F. Quantitative analysis of small-subunit rRNA genes in mixed microbial populations via 5’-nuclease assays. Appl. Environ. Microbiol. 66, 4605–4614. https://doi.org/10.1128/AEM.66.11.4605-4614.2000 (2000).ADS 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    Neilan B. A et al. rRNA sequences and evolutionary relationships among toxic and nontoxic cyanobacteria of the genus Microcystis Int J Syst Bacteriol 47(3), 693–697, https://doi.org/10.1099/00207713-47-3-693 (1997).
    Google Scholar  More