Philippa Kaur

Uehlinger, U., Robinson, C. T., Hieber, M. & Zah, R. The physico-chemical habitat template for periphyton in alpine glacial streams under a changing climate. in Global Change and River Ecosystems—Implications for Structure, Function and EcosystemServices (eds. Stevenson, R. J. & Sabater, S.) 107–121 (Springer Netherlands, 2010).Battin, T. J., Wille, A., Psenner, R. & Richter, A. Large-scale environmental controls on microbial biofilms in high-alpine streams. Biogeosciences 1, 159–171 (2004).ADS 
CAS 
Article 

Google Scholar 
Kuhn, M. The nutrient cycle through snow and ice, a review. Aquat. Sci. 63, 150–167 (2001).CAS 
Article 

Google Scholar 
Milner, A. M. et al. Glacier shrinkage driving global changes in downstream systems. Proc. Natl Acad. Sci. U. S. A. 114, 9770–9778 (2017).ADS 
CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
Tockner, K., Malard, F., Uehlinger, U. & Ward, J. V. Nutrients and organic matter in a glacial river-floodplain system (Val Roseg, Switzerland). Limnol. Oceanogr. 47, 266–277 (2002).ADS 
CAS 
Article 

Google Scholar 
Boix Canadell, M. et al. Regimes of primary production and their drivers in Alpine streams. Freshw. Biol. 66, 1449–1463 (2021).Article 

Google Scholar 
Bernhardt, E. S. et al. Control points in ecosystems: moving beyond the hot spot hot moment concept. Ecosystems 20, 665–682 (2017).Article 

Google Scholar 
Huss, M. & Hock, R. Global-scale hydrological response to future glacier mass loss. Nat. Clim. Chang. 8, 135–140 (2018).ADS 
Article 

Google Scholar 
Battin, T. J., Besemer, K., Bengtsson, M. M., Romani, A. M. & Packmann, A. I. The ecology and biogeochemistry of stream biofilms. Nat. Rev. Microbiol. 14, 251–263 (2016).CAS 
Article 
PubMed 

Google Scholar 
Roncoroni, M., Brandani, J., Battin, T. I. & Lane, S. N. Ecosystem engineers: biofilms and the ontogeny of glacier floodplain ecosystems. WIREs Water 6, e1390 (2019).Article 

Google Scholar 
Hoyle, J. T., Kilroy, C., Hicks, D. M. & Brown, L. The influence of sediment mobility and channel geomorphology on periphyton abundance. Freshw. Biol. 62, 258–273 (2017).Article 

Google Scholar 
Canadell, M. B. et al. Regimes of primary production and their drivers in Alpine streams. Freshwater Biol. https://doi.org/10.1111/fwb.13730 (2021).Fell, S. C., Carrivick, J. L. & Brown, L. E. The multitrophic effects of climate change and glacier retreat in mountain rivers. Bioscience 67, 897–911 (2017).Article 
PubMed 
PubMed Central 

Google Scholar 
Cole, J. J. Interactions between bacteria and algae in aquatic ecosystems. Annu. Rev. Ecol. Syst. 13, 291–314 (1982).Article 

Google Scholar 
Seymour, J. R., Amin, S. A., Raina, J.-B. & Stocker, R. Zooming in on the phycosphere: the ecological interface for phytoplankton–bacteria relationships. Nat. Microbiol. 2, 17065 (2017).Amin, S. A. et al. Interaction and signalling between a cosmopolitan phytoplankton and associated bacteria. Nature 522, 98–101 (2015).ADS 
CAS 
Article 
PubMed 

Google Scholar 
Christie-Oleza, J. A., Sousoni, D., Lloyd, M., Armengaud, J. & Scanlan, D. J. Nutrient recycling facilitates long-term stability of marine microbial phototroph–heterotroph interactions. Nat. Microbiol. 2, 1–10 (2017).Article 
CAS 

Google Scholar 
Haack, T. K. & McFeters, G. A. Nutritional relationships among microorganisms in an epilithic biofilm community. Microb. Ecol. 8, 115–126 (1982).CAS 
Article 
PubMed 

Google Scholar 
Kaplan, L. A. & Bott, T. L. Diel fluctuations in bacterial activity on streambed substrata during vernal algal blooms: effects of temperature, water chemistry, and habitat. Limnol. Oceanogr. 34, 718–733 (1989).ADS 
CAS 
Article 

Google Scholar 
Vincent, W. F., Downes, M. T., Castenholz, R. W. & Howard-Williams, C. Community structure and pigment organisation of cyanobacteria-dominated microbial mats in Antarctica. Eur. J. Phycol. 28, 213–221 (1993).Article 

Google Scholar 
Tolotti, M. et al. Alpine headwaters emerging from glaciers and rock glaciers host different bacterial communities: Ecological implications for the future. Sci. Total Environ. 717, 137101 (2020).ADS 
CAS 
Article 
PubMed 

Google Scholar 
Besemer, K., Singer, G., Hödl, I. & Battin, T. J. Bacterial community composition of stream biofilms in spatially variable-flow environments. Appl. Environ. Microbiol. 75, 7189–7195 (2009).ADS 
CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
Risse‐Buhl, U. et al. Near streambed flow shapes microbial guilds within and across trophic levels in fluvial biofilms. Limnol. Oceanogr. 65, 2261–2277 (2020).ADS 
Article 
CAS 

Google Scholar 
Palmer, M. A., Swan, C. M., Nelson, K., Silver, P. & Alvestad, R. Streambed landscapes: evidence that stream invertebrates respond to the type and spatial arrangement of patches. Landsc. Ecol. 15, 563–576 (2000).Article 

Google Scholar 
Battin, T. J. et al. Microbial landscapes: new paths to biofilm research. Nat. Rev. Microbiol. 5, 76–81 (2007).CAS 
Article 
PubMed 

Google Scholar 
Dzubakova, K. et al. Environmental heterogeneity promotes spatial resilience of phototrophic biofilms in streambeds. Biol. Lett. 14, 20180432 (2018).Sloan, W. T. et al. Quantifying the roles of immigration and chance in shaping prokaryote community structure. Environ. Microbiol. 8, 732–740 (2006).Article 
PubMed 

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

Google Scholar 
Chaudhari, N. M., Overholt, W. A. & Figueroa-Gonzalez, P. A. The economical lifestyle of CPR bacteria in groundwater allows little preference for environmental drivers. bioRxiv. 16, 1–8 (2021).Vigneron, A. et al. Ultra‐small and abundant: candidate phyla radiation bacteria are potential catalysts of carbon transformation in a thermokarst lake ecosystem. Limnol. Oceanogr. Lett. 5, 212–220 (2020).Article 

Google Scholar 
Liu, Y. et al. Expanded diversity of Asgard archaea and their relationships with eukaryotes. Nature 593, 553–557 (2021).ADS 
CAS 
Article 
PubMed 

Google Scholar 
Cai, M. et al. Ecological features and global distribution of Asgard archaea. Sci. Total Environ. 758, 143581 (2021).ADS 
CAS 
Article 
PubMed 

Google Scholar 
Niedrist, G. H. & Füreder, L. When the going gets tough, the tough get going: The enigma of survival strategies in harsh glacial stream environments. Freshw. Biol. 63, 1260–1272 (2018).Article 

Google Scholar 
Payne, A. T. et al. Widespread cryptic viral infections in lotic biofilms. Biofilms 2, 100016 (2020).Article 

Google Scholar 
Anesio, A. M., Mindl, B., Laybourn-Parry, J., Hodson, A. J. & Sattler, B. Viral dynamics in cryoconite holes on a high Arctic glacier (Svalbard). J. Geophys. Res. 112, (2007).Bellas, C. M., Schroeder, D. C., Edwards, A., Barker, G. & Anesio, A. M. Flexible genes establish widespread bacteriophage pan-genomes in cryoconite hole ecosystems. Nat. Commun. 11, 4403 (2020).ADS 
CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
Liu, Q. et al. Light stimulates anoxic and oligotrophic growth of glacial Flavobacterium strains that produce zeaxanthin. ISME J. 15, 1844–1857 (2021).CAS 
Article 
PubMed 

Google Scholar 
Sánchez Barranco, V. et al. Trophic position, elemental ratios and nitrogen transfer in a planktonic host-parasite-consumer food chain including a fungal parasite. Oecologia 194, 541–554 (2020).ADS 
Article 
PubMed 
PubMed Central 

Google Scholar 
Klawonn, I. et al. Characterizing the ‘fungal shunt’: Parasitic fungi on diatoms affect carbon flow and bacterial communities in aquatic microbial food webs. Proc. Natl. Acad. Sci. U. S. A. 118, (2021).Chróst, R. J. Microbial Enzymes in Aquatic Environments. (Springer-Verlag, 1991).Sinsabaugh, R. L., Hill, B. H. & Follstad Shah, J. J. Ecoenzymatic stoichiometry of microbial organic nutrient acquisition in soil and sediment. Nature 462, 795–798 (2009).ADS 
CAS 
Article 
PubMed 

Google Scholar 
Stoecker, D. K. & Lavrentyev, P. J. Mixotrophic plankton in the polar seas: a pan-arctic review. Front. Mar. Sci. 5, 292 (2018).Waibel, A., Peter, H. & Sommaruga, R. Importance of mixotrophic flagellates during the ice-free season in lakes located along an elevational gradient. Aquat. Sci. 81, 45 (2019).Article 
CAS 
PubMed 
PubMed Central 

Google Scholar 
Avcı, B., Krüger, K., Fuchs, B. M., Teeling, H. & Amann, R. I. Polysaccharide niche partitioning of distinct Polaribacter clades during North Sea spring algal blooms. ISME J. 14, 1369–1383 (2020).Article 
CAS 
PubMed 
PubMed Central 

Google Scholar 
Sichert, A. et al. Verrucomicrobia use hundreds of enzymes to digest the algal polysaccharide fucoidan. Nat. Microbiol 5, 1026–1039 (2020).CAS 
Article 
PubMed 

Google Scholar 
Zhou, J., Lyu, Y., Richlen, M., Anderson, D. M. & Cai, Z. Quorum sensing is a language of chemical signals and plays an ecological role in algal–bacterial interactions. CRC Crit. Rev. Plant Sci. 35, 81–105 (2016).Article 
CAS 
PubMed 
PubMed Central 

Google Scholar 
Croft, M. T., Lawrence, A. D., Raux-Deery, E., Warren, M. J. & Smith, A. G. Algae acquire vitamin B12 through a symbiotic relationship with bacteria. Nature 438, 90–93 (2005).ADS 
CAS 
Article 
PubMed 

Google Scholar 
Grossman, A. Nutrient Acquisition: The Generation of Bioactive Vitamin B12 by Microalgae. Curr. Biol.: CB vol. 26, R319–R321 (2016).CAS 
Article 

Google Scholar 
Segev, E. et al. Dynamic metabolic exchange governs a marine algal-bacterial interaction. Elife 5, e17473 (2016).Hood, E., Battin, T. J., Fellman, J., O’Neel, S. & Spencer, R. G. M. Storage and release of organic carbon from glaciers and ice sheets. Nat. Geosci. 8, 91–96 (2015).ADS 
CAS 
Article 

Google Scholar 
Fellman, J. B. et al. Evidence for the assimilation of ancient glacier organic carbon in a proglacial stream food web. Limnol. Oceanogr. 60, 1118–1128 (2015).ADS 
Article 

Google Scholar 
Singer, G. A. et al. Biogeochemically diverse organic matter in Alpine glaciers and its downstream fate. Nat. Geosci. 5, 710–714 (2012).ADS 
CAS 
Article 

Google Scholar 
Boix Canadell, M., Escoffier, N., Ulseth, A. J., Lane, S. N. & Battin, T. J. Alpine glacier shrinkage drives shift in dissolved organic carbon export from quasi‐chemostasis to transport limitation. Geophys. Res. Lett. 46, 8872–8881 (2019).ADS 
CAS 
Article 

Google Scholar 
Anesio, A. M., Lutz, S., Chrismas, N. A. M. & Benning, L. G. The microbiome of glaciers and ice sheets. NPJ Biofilms Microbiomes 3, 10 (2017).Article 
PubMed 
PubMed Central 

Google Scholar 
Tranter, M., Mills, R. & Raiswell, R. Chemical weathering reactions in Alpine glacial meltwaters. in International symposium on water-rock interaction. 687–690 (1989).Tranter, M., Brown, G., Raiswell, R., Sharp, M. & Gurnell, A. A conceptual model of solute acquisition by Alpine glacial meltwaters. J. Glaciol. 39, 573–581 (1993).ADS 
CAS 
Article 

Google Scholar 
St Pierre, K. A. et al. Proglacial freshwaters are significant and previously unrecognized sinks of atmospheric CO2. Proc. Natl Acad. Sci. U. S. A. 116, 17690–17695 (2019).ADS 
Article 
CAS 

Google Scholar 
Dunham, E. C., Dore, J. E., Skidmore, M. L., Roden, E. E. & Boyd, E. S. Lithogenic hydrogen supports microbial primary production in subglacial and proglacial environments. Proc. Natl. Acad. Sci. U. S. A. 118, (2021).Hernández, M. et al. Reconstructing genomes of carbon monoxide oxidisers in volcanic deposits including members of the Class Ktedonobacteria. Microorganisms 8, 1880 (2020).Quick, A. M. et al. Nitrous oxide from streams and rivers: a review of primary biogeochemical pathways and environmental variables. Earth-Sci. Rev. 191, 224–262 (2019).ADS 
CAS 
Article 

Google Scholar 
Kuypers, M. M. M., Marchant, H. K. & Kartal, B. The microbial nitrogen-cycling network. Nat. Rev. Microbiol. 16, 263–276 (2018).CAS 
Article 
PubMed 

Google Scholar 
Ren, Z., Gao, H., Elser, J. J. & Zhao, Q. Microbial functional genes elucidate environmental drivers of biofilm metabolism in glacier-fed streams. Sci. Rep. 7, 12668 (2017).ADS 
Article 
CAS 
PubMed 
PubMed Central 

Google Scholar 
Gooseff, M. N., McKnight, D. M., Runkel, R. L. & Duff, J. H. Denitrification and hydrologic transient storage in a glacial meltwater stream, McMurdo Dry Valleys, Antarctica. Limnol. Oceanogr. 49, 1884–1895 (2004).ADS 
CAS 
Article 

Google Scholar 
Varin, T., Lovejoy, C., Jungblut, A. D., Vincent, W. F. & Corbeil, J. Metagenomic profiling of Arctic microbial mat communities as nutrient scavenging and recycling systems. Limnol. Oceanogr. 55, 1901–1911 (2010).ADS 
CAS 
Article 

Google Scholar 
Kohler, T. J. et al. Patterns and drivers of extracellular enzyme activity in New Zealand glacier-fed streams. Front. Microbiol. 11, 591465 (2020).Article 
PubMed 
PubMed Central 

Google Scholar 
Alves, R. J. E. et al. Ammonia oxidation by the Arctic terrestrial thaumarchaeote candidatus nitrosocosmicus arcticus is stimulated by increasing temperatures. Front. Microbiol. 10, 1571 (2019).Article 
PubMed 
PubMed Central 

Google Scholar 
Könneke, M. et al. Ammonia-oxidizing archaea use the most energy-efficient aerobic pathway for CO2 fixation. Proc. Natl Acad. Sci. U. S. A. 111, 8239–8244 (2014).ADS 
Article 
CAS 
PubMed 
PubMed Central 

Google Scholar 
Battin, T. J., Kaplan, L. A., Denis Newbold, J. & Hansen, C. M. E. Contributions of microbial biofilms to ecosystem processes in stream mesocosms. Nature 426, 439–442 (2003).ADS 
CAS 
Article 
PubMed 

Google Scholar 
Cockell, C. S. et al. Influence of ice and snow covers on the UV exposure of terrestrial microbial communities: dosimetric studies. J. Photochem. Photobiol. B 68, 23–32 (2002).CAS 
Article 
PubMed 

Google Scholar 
Sommaruga, R. The role of solar UV radiation in the ecology of alpine lakes. J. Photochem. Photobiol. B 62, 35–42 (2001).CAS 
Article 
PubMed 

Google Scholar 
Margesin, R. & Collins, T. Microbial ecology of the cryosphere (glacial and permafrost habitats): current knowledge. Appl. Microbiol. Biotechnol. 103, 2537–2549 (2019).CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
De Maayer, P., Anderson, D., Cary, C. & Cowan, D. A. Some like it cold: understanding the survival strategies of psychrophiles. EMBO Rep. 15, 508–517 (2014).Article 
CAS 
PubMed 
PubMed Central 

Google Scholar 
Tribelli, P. M. & López, N. I. Reporting key features in cold-adapted bacteria. Life 8, 8 (2018).Varin, T., Lovejoy, C., Jungblut, A. D., Vincent, W. F. & Corbeil, J. Metagenomic analysis of stress genes in microbial mat communities from Antarctica and the High Arctic. Appl. Environ. Microbiol. 78, 549–559 (2012).ADS 
Article 
PubMed 
PubMed Central 

Google Scholar 
Alonso-Sáez, L. et al. Winter bloom of a rare betaproteobacterium in the Arctic Ocean. Front. Microbiol. 5, 425 (2014).Article 
PubMed 
PubMed Central 

Google Scholar 
Hornung, C. et al. The Janthinobacterium sp. HH01 genome encodes a homologue of the V. cholerae CqsA and L. pneumophila LqsA autoinducer synthases. PLoS One 8, e55045 (2013).ADS 
CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
Maillot, N. J., Honoré, F. A., Byrne, D., Méjean, V. & Genest, O. Cold adaptation in the environmental bacterium Shewanella oneidensis is controlled by a J-domain co-chaperone protein network. Commun. Biol. 2, 323 (2019).Article 
CAS 
PubMed 
PubMed Central 

Google Scholar 
Konings, W. N., Albers, S.-V., Koning, S. & Driessen, A. J. M. The cell membrane plays a crucial role in survival of bacteria and archaea in extreme environments. Antonie Van. Leeuwenhoek 81, 61–72 (2002).CAS 
Article 
PubMed 

Google Scholar 
Methé, B. A. et al. The psychrophilic lifestyle as revealed by the genome sequence of Colwellia psychrerythraea 34H through genomic and proteomic analyses. Proc. Natl Acad. Sci. U. S. A. 102, 10913–10918 (2005).ADS 
Article 
CAS 
PubMed 
PubMed Central 

Google Scholar 
Ayala-del-Río, H. L. et al. The genome sequence of Psychrobacter arcticus 273-4, a psychroactive Siberian permafrost bacterium, reveals mechanisms for adaptation to low-temperature growth. Appl. Environ. Microbiol. 76, 2304–2312 (2010).ADS 
Article 
CAS 
PubMed 
PubMed Central 

Google Scholar 
Mykytczuk, N. C. S. et al. Bacterial growth at −15 °C; molecular insights from the permafrost bacterium Planococcus halocryophilus Or1. ISME J. 7, 1211–1226 (2013).CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
Ting, L. et al. Cold adaptation in the marine bacterium, Sphingopyxis alaskensis, assessed using quantitative proteomics. Environ. Microbiol 12, 2658–2676 (2010).CAS 
PubMed 

Google Scholar 
Tribelli, P. M. et al. Novel essential role of ethanol oxidation genes at low temperature revealed by transcriptome analysis in the Antarctic bacterium Pseudomonas extremaustralis. PLoS One 10, e0145353 (2015).Article 
CAS 
PubMed 
PubMed Central 

Google Scholar 
Blagojevic, D. P., Grubor-Lajsic, G. N. & Spasic, M. B. Cold defence responses: the role of oxidative stress. Front. Biosci. 3, 416–427 (2011).Article 

Google Scholar 
Busi, S. B. et al. Optimised biomolecular extraction for metagenomic analysis of microbial biofilms from high-mountain streams. PeerJ 8, e9973 (2020).Article 
PubMed 
PubMed Central 

Google Scholar 
Fodelianakis, S. et al. Microdiversity characterizes prevalent phylogenetic clades in the glacier-fed stream microbiome. ISME J. https://doi.org/10.1038/s41396-021-01106-6 (2021).Stoeck, T. et al. Multiple marker parallel tag environmental DNA sequencing reveals a highly complex eukaryotic community in marine anoxic water. Mol. Ecol. 19, 21–31 (2010).CAS 
Article 
PubMed 

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

Google Scholar 
Bolyen, E. et al. Reproducible, interactive, scalable and extensible microbiome data science using QIIME 2. Nat. Biotechnol. 37, 852–857 (2019).CAS 
Article 
PubMed 
PubMed Central 

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

Google Scholar 
Dixon, P. VEGAN, a package of R functions for community ecology. J. Vegetation Sci. 14, 927–930 (2003).Article 

Google Scholar 
Foster, Z. S. L., Sharpton, T. J. & Grünwald, N. J. Metacoder: an R package for visualization and manipulation of community taxonomic diversity data. PLoS Comput. Biol. 13, e1005404 (2017).ADS 
Article 
CAS 
PubMed 
PubMed Central 

Google Scholar 
Burns, A. R. et al. Contribution of neutral processes to the assembly of gut microbial communities in the zebrafish over host development. ISME J. 10, 655–664 (2016).CAS 
Article 
PubMed 

Google Scholar 
Gautreau, I. E7805 NEBNext® UltraTM II FS DNA Library Prep Kit for Illumina® Protocol for use with Inputs ≤ 100 ng. https://www.protocols.io/view/e7805-nebnext-ultra-ii-fs-dna-library-prep-kit-for-k8tczwn (2020).Narayanasamy, S. et al. IMP: a pipeline for reproducible reference-independent integrated metagenomic and metatranscriptomic analyses. Genome Biol. 17, 260 (2016).Article 
PubMed 
PubMed Central 

Google Scholar 
Heintz-Buschart, A. et al. Integrated multi-omics of the human gut microbiome in a case study of familial type 1 diabetes. Nat. Microbiol 2, 16180 (2016).CAS 
Article 
PubMed 

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).CAS 
Article 
PubMed 

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 
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).CAS 
Article 
PubMed 

Google Scholar 
Hickl, O., Queirós, P., Wilmes, P., May, P. & Heintz-Buschart, A. binny: an automated binning algorithm to recover high-quality genomes from complex metagenomic datasets. bioRxiv 2021.12.22.473795 https://doi.org/10.1101/2021.12.22.473795. (2021).Sieber, C. M. K. et al. Recovery of genomes from metagenomes via a dereplication, aggregation and scoring strategy. Nat. Microbiol 3, 836–843 (2018).CAS 
Article 
PubMed 
PubMed Central 

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).CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
Chaumeil, P.-A., Mussig, A. J., Hugenholtz, P. & Parks, D. H. GTDB-Tk: a toolkit to classify genomes with the Genome Taxonomy Database. Bioinformatics. https://doi.org/10.1093/bioinformatics/btz848 (2019).Kieft, K., Zhou, Z. & Anantharaman, K. VIBRANT: automated recovery, annotation and curation of microbial viruses, and evaluation of viral community function from genomic sequences. Microbiome 8, 90 (2020).CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
Zablocki, O., Jang, H. B., Bolduc, B. & Sullivan, M. B. vConTACT 2: A tool to automate genome-based prokaryotic viral taxonomy. in Plant and Animal Genome XXVII Conference (January 12-16, 2019) (PAG, 2019).Nayfach, S. et al. CheckV assesses the quality and completeness of metagenome-assembled viral genomes. Nat. Biotechnol. 39, 578–585 (2021).CAS 
Article 
PubMed 

Google Scholar 
Krinos, A. I., Hu, S. K., Cohen, N. R. & Alexander, H. EUKulele: Taxonomic annotation of the unsung eukaryotic microbes. arXiv [q-bio.PE] (2020).Queirós, P., Delogu, F., Hickl, O., May, P. & Wilmes, P. Mantis: flexible and consensus-driven genome annotation. bioRxiv (2020).Zhou, Z. et al. METABOLIC: High-throughput profiling of microbial genomes for functional traits, biogeochemistry, and community-scale metabolic networks. bioRxiv 761643. https://doi.org/10.1101/761643 (2020).McDaniel, E. A., Anantharaman, K. & McMahon, K. D. metabolisHMM: Phylogenomic analysis for exploration of microbial phylogenies and metabolic pathways. bioRxiv 2019.12.20.884627. https://doi.org/10.1101/2019.12.20.884627 (2019).Eren, A. M. et al. Anvi’o: an advanced analysis and visualization platform for ‘omics data. PeerJ 3, e1319 (2015).Article 
PubMed 
PubMed Central 

Google Scholar 
Weissman, J. L., Hou, S. & Fuhrman, J. A. Estimating maximal microbial growth rates from cultures, metagenomes, and single cells via codon usage patterns. Proc. Natl. Acad. Sci. U. S. A. 118, (2021).Chen, S., Zhou, Y., Chen, Y. & Gu, J. fastp: an ultra-fast all-in-one FASTQ preprocessor. Bioinformatics 34, i884–i890 (2018).Article 
CAS 
PubMed 
PubMed Central 

Google Scholar 
Bushnell, B. BBMap: A fast, accurate, splice-aware aligner. https://www.osti.gov/biblio/1241166 (2014).Wood, D. E., Lu, J. & Langmead, B. Improved metagenomic analysis with Kraken 2. Genome Biol. 20, 257 (2019).CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
West, P. T., Probst, A. J., Grigoriev, I. V., Thomas, B. C. & Banfield, J. F. Genome-reconstruction for eukaryotes from complex natural microbial communities. Genome Res. 28, 569–580 (2018).CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
Alneberg, J. et al. CONCOCT: Clustering cONtigs on COverage and ComposiTion. arXiv [q-bio.GN] (2013).Levy Karin, E., Mirdita, M. & Söding, J. MetaEuk-sensitive, high-throughput gene discovery, and annotation for large-scale eukaryotic metagenomics. Microbiome 8, 48 (2020).CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
Huerta-Cepas, J. et al. eggNOG 5.0: a hierarchical, functionally and phylogenetically annotated orthology resource based on 5090 organisms and 2502 viruses. Nucleic Acids Res 47, D309–D314 (2019).CAS 
Article 
PubMed 

Google Scholar 
Seppey, M., Manni, M. & Zdobnov, E. M. BUSCO: assessing genome assembly and annotation completeness. Methods Mol. Biol. 1962, 227–245 (2019).CAS 
Article 
PubMed 

Google Scholar 
Saary, P., Mitchell, A. L. & Finn, R. D. Estimating the quality of eukaryotic genomes recovered from metagenomic analysis with EukCC. Genome Biol. 21, 244 (2020).Article 
PubMed 
PubMed Central 

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

Google Scholar 
Inferring Correlation Networks from Genomic Survey Data. https://journals.plos.org/ploscompbiol/article?id=10.1371/journal.pcbi.1002687.Kurtz, Z. D. et al. Sparse and compositionally robust inference of microbial ecological networks. PLoS Comput. Biol. 11, e1004226 (2015).Article 
CAS 
PubMed 
PubMed Central 

Google Scholar 
Csardi, G. & Nepusz, T. The igraph software package for complex network research. InterJournal Complex. Syst. 1695, 1–9 (2006).
Google Scholar 
Dormann, C. F., Frund, J., Bluthgen, N. & Gruber, B. Indices, graphs and null models: analyzing bipartite ecological networks. Open Ecol. J. 2, 7–24 (2009).Article 

Google Scholar 
Pruitt, K. D., Tatusova, T. & Maglott, D. R. NCBI reference sequences (RefSeq): a curated non-redundant sequence database of genomes, transcripts and proteins. Nucleic Acids Res 35, D61–D65 (2007).CAS 
Article 
PubMed 

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

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).ADS 
Article 
CAS 
PubMed 
PubMed Central 

Google Scholar 
Lee, M. D. GToTree: a user-friendly workflow for phylogenomics. Bioinformatics 35, 4162–4164 (2019).CAS 
Article 
PubMed 
PubMed Central 

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

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

Google Scholar 
Capella-Gutiérrez, S., Silla-Martínez, J. M. & Gabaldón, T. trimAl: a tool for automated alignment trimming in large-scale phylogenetic analyses. Bioinformatics 25, 1972–1973 (2009).Article 
CAS 
PubMed 
PubMed Central 

Google Scholar 
Tange, O. GNU Parallel 2018. (Lulu.com, 2018).Team, R. C. & Others. R: A language and environment for statistical computing. (2013).Kahle, D. & Wickham, H. Ggmap: spatial visualization with ggplot2. R. J. 5, 144 (2013).Article 

Google Scholar 
Graham, E. D., Heidelberg, J. F. & Tully, B. J. Potential for primary productivity in a globally-distributed bacterial phototroph. ISME J. 12, 1861–1866 (2018).CAS 
Article 
PubMed 
PubMed Central 

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, 1–21 (2014).kevinblighe/EnhancedVolcano: Publication-ready volcano plots with enhanced colouring and labeling. https://github.com/kevinblighe/EnhancedVolcano.Wickham, H. ggplot2: ggplot2. Wiley Interdiscip. Rev. Comput. Stat. 3, 180–185 (2011).MATH 
Article 

Google Scholar 
Bah, T. Inkscape: guide to a vector drawing program. (Prentice Hall Press, 2007).Varrette, S., Bouvry, P., Cartiaux, H. & Georgatos, F. Management of an academic HPC cluster: The UL experience. In 2014 International Conference on High Performance Computing Simulation (HPCS) 959–967 (2014). More

Blagodatskaya E, Kuzyakov Y. Active microorganisms in soil: critical review of estimation criteria and approaches. Soil Biol Biochem. 2013;67:192–211.CAS 
Article 

Google Scholar 
Jones SE, Lennon JT. Dormancy contributes to the maintenance of microbial diversity. Proc Natl Acad Sci USA. 2010;107:5881.CAS 
Article 

Google Scholar 
Lennon JT, Jones SE. Microbial seed banks: the ecological and evolutionary implications of dormancy. Nat Rev Microbiol. 2011;9:119–30.CAS 
Article 

Google Scholar 
Couradeau E, Sasse J, Goudeau D, Nath N, Hazen TC, Bowen BP, et al. Probing the active fraction of soil microbiomes using BONCAT-FACS. Nat Commun. 2019;10:2770.Article 

Google Scholar 
Carini P, Marsden PJ, Leff JW, Morgan EE, Strickland MS, Fierer N. Relic DNA is abundant in soil and obscures estimates of soil microbial diversity. Nat Microbiol. 2016;2:16242.Article 

Google Scholar 
Sorensen JW, Shade A. Dormancy dynamics and dispersal contribute to soil microbiome resilience. Philos Trans R Soc B Biolog Sci. 2020;375:20190255.CAS 
Article 

Google Scholar 
Raina J-B, Fernandez V, Lambert B, Stocker R, Seymour JR. The role of microbial motility and chemotaxis in symbiosis. Nat Rev Microbiol. 2019;17:284–94.CAS 
Article 

Google Scholar 
Bahram M, Hildebrand F, Forslund SK, Anderson JL, Soudzilovskaia NA, Bodegom PM, et al. Structure and function of the global topsoil microbiome. Nature. 2018;560:233–7.CAS 
Article 

Google Scholar 
Lauber Christian L, Hamady M, Knight R, Fierer N. Pyrosequencing-based assessment of soil pH as a predictor of soil bacterial community structure at the continental scale. Appl Environ Microbiol. 2009;75:5111–20.CAS 
Article 

Google Scholar 
Lennon JT, Aanderud ZT, Lehmkuhl BK, Schoolmaster DR Jr. Mapping the niche space of soil microorganisms using taxonomy and traits. Ecology. 2012;93:1867–79.Article 

Google Scholar 
Albright MBN, Martiny JBH. Dispersal alters bacterial diversity and composition in a natural community. ISME J. 2018;12:296–9.Article 

Google Scholar 
Barberan A, Ladau J, Leff JW, Pollard KS, Menninger HL, Dunn RR, et al. Continental-scale distributions of dust-associated bacteria and fungi. Proc Natl Acad Sci USA. 2015;112:5756–61.CAS 
Article 

Google Scholar 
Meyer KM, Memiaghe H, Korte L, Kenfack D, Alonso A, Bohannan BJM. Why do microbes exhibit weak biogeographic patterns? ISME J. 2018;12:1404–13.Article 

Google Scholar 
Eisenlord SD, Zak DR, Upchurch RA. Dispersal limitation and the assembly of soil Actinobacteria communities in a long-term chronosequence. Ecol Evol. 2012;2:538–49.Article 

Google Scholar 
Glassman SI, Lubetkin KC, Chung JA, Bruns TD. The theory of island biogeography applies to ectomycorrhizal fungi in subalpine tree “islands” at a fine scale. Ecosphere. 2017;8:e01677.Article 

Google Scholar 
Whitaker Rachel J, Grogan Dennis W, Taylor John W. Geographic barriers isolate endemic populations of hyperthermophilic archaea. Science. 2003;301:976–8.CAS 
Article 

Google Scholar 
Amor DR, Ratzke C, Gore J. Transient invaders can induce shifts between alternative stable states of microbial communities. Sci Adv. 2020;6:eaay8676.CAS 
Article 

Google Scholar 
Zhang C, Derrien M, Levenez F, Brazeilles R, Ballal SA, Kim J, et al. Ecological robustness of the gut microbiota in response to ingestion of transient food-borne microbes. ISME J. 2016;10:2235–45.Article 

Google Scholar 
Fox JE, Gulledge J, Engelhaupt E, Burow ME, McLachlan JA. Pesticides reduce symbiotic efficiency of nitrogen-fixing rhizobia and host plants. Proc Natl Acad Sci USA. 2007;104:10282.CAS 
Article 

Google Scholar 
Ramirez KS, Craine JM, Fierer N. Consistent effects of nitrogen amendments on soil microbial communities and processes across biomes. Glob Change Biol. 2012;18:1918–27.Article 

Google Scholar 
Leff JW, Jones SE, Prober SM, Barberán A, Borer ET, Firn JL, et al. Consistent responses of soil microbial communities to elevated nutrient inputs in grasslands across the globe. Proc Natl Acad Sci USA. 2015;112:10967.CAS 
Article 

Google Scholar 
Chambers CA, Smith SE, Smith FA. Effects of ammonium and nitrate ions on mycorrhizal infection, nodulation and growth of Trifolium subterraneum. New Phytol. 1980;85:47–62.CAS 
Article 

Google Scholar 
van Diepen LTA, Lilleskov EA, Pregitzer KS, Miller RM. Simulated nitrogen deposition causes a decline of intra- and extraradical abundance of arbuscular mycorrhizal fungi and changes in microbial community structure in northern hardwood forests. Ecosystems. 2010;13:683–95.Article 

Google Scholar 
Young IM, Ritz K. Tillage, habitat space and function of soil microbes. Soil Tillage Res. 2000;53:201–13.Article 

Google Scholar 
Kabir Z. Tillage or no-tillage: impact on mycorrhizae. Can J Plant Sci. 2005;85:23–29.Article 

Google Scholar 
Bell T, Tylianakis JM. Microbes in the anthropocene: spillover of agriculturally selected bacteria and their impact on natural ecosystems. Proc R Soc B Biol Sci. 2016;283;20160896.Article 

Google Scholar 
Dang K, Gong X, Zhao G, Wang H, Ivanistau A, Feng B, Intercropping alters the soil microbial diversity and community to facilitate nitrogen assimilation: a potential mechanism for increasing proso millet grain yield. Front Microbiol. 2020;11;601054.Peay KG, Garbelotto M, Bruns TD. Evidence of dispersal limitation in soil microorganisms: Isolation reduces species richness on mycorrhizal tree islands. Ecology. 2010;91:3631–40.Article 

Google Scholar 
Mummey DL, Rillig MC. Spatial characterization of arbuscular mycorrhizal fungal molecular diversity at the submetre scale in a temperate grassland. FEMS Microbiol Ecol. 2008;64:260–70.CAS 
Article 

Google Scholar 
Hiscox J, Savoury M, Müller CT, Lindahl BD, Rogers HJ, Boddy L. Priority effects during fungal community establishment in beech wood. ISME J. 2015;9:2246–60.Article 

Google Scholar 
Song Z, Kennedy PG, Liew FJ, Schilling JS. Fungal endophytes as priority colonizers initiating wood decomposition. Funct Ecol. 2017;31:407–18.Article 

Google Scholar 
Schmidt SK, Nemergut DR, Darcy JL, Lynch R. Do bacterial and fungal communities assemble differently during primary succession? Mol Ecol. 2014;23:254–8.CAS 
Article 

Google Scholar 
Reche I, D’Orta G, Mladenov N, Winget DM, Suttle CA. Deposition rates of viruses and bacteria above the atmospheric boundary layer. ISME J. 2018;12:1154–62.CAS 
Article 

Google Scholar 
Castaño C, Bonet JA, Oliva J, Farré G, Martínez de Aragón J, Parladé J, et al. Rainfall homogenizes while fruiting increases diversity of spore deposition in Mediterranean conditions. Fungal Ecol. 2019;41:279–88.Article 

Google Scholar 
Garbelotto M, Smith T, Schweigkofler W. Variation in rates of spore deposition of Fusarium circinatum, the causal agent of pine pitch canker, over a 12-month-period at two locations in Northern California. Phytopathology®. 2007;98:137–43.Article 

Google Scholar 
Bowers RM, Clements N, Emerson JB, Wiedinmyer C, Hannigan MP, Fierer N. Seasonal variability in bacterial and fungal diversity of the near-surface atmosphere. Environ Sci Technol. 2013;47:12097–106.CAS 
Article 

Google Scholar 
Trexler RV, Bell TH. Testing sustained soil-to-soil contact as an approach for limiting the abiotic influence of source soils during experimental microbiome transfer. FEMS Microbiol Lett. 2019;366:fnz228.CAS 
Article 

Google Scholar 
King, WL, Kaminsky LM, Gannett M, Thompson GL, Kao-Kniffin J, Bell TH. Soil salinization accelerates microbiome stabilization in iterative selections for plant performance. New Phytol. 2021. https://doi.org/10.1111/nph.17774. Advance online publication.Parada AE, Needham DM, Fuhrman JA. Every base matters: assessing small subunit rRNA primers for marine microbiomes with mock communities, time series and global field samples. Environ Microbiol. 2016;18:1403–14.CAS 
Article 

Google Scholar 
Apprill A, McNally S, Parsons RJ, Weber LK. Minor revision to V4 region SSU rRNA 806R gene primer greatly increases detection of SAR11 bacterioplankton. Aquat Microb Ecol. 2015;75:129–37.Article 

Google Scholar 
Gardes M, Bruns TD. ITS primers with enhanced specificity for basidiomycetes – application to the identification of mycorrhizae and rusts. Mol Ecol. 1993;2:113–8.CAS 
Article 

Google Scholar 
Martin KJ, Rygiewicz PT. Fungal-specific PCR primers developed for analysis of the ITS region of environmental DNA extracts. BMC Microbiol. 2005;5:28.Article 

Google Scholar 
Bolyen E, Rideout JR, Dillon MR, Bokulich NA, Abnet CC, Al-Ghalith GA, et al. Reproducible, interactive, scalable and extensible microbiome data science using QIIME 2. Nat Biotechnol. 2019;37:852–7.CAS 
Article 

Google Scholar 
Callahan BJ, McMurdie PJ, Rosen MJ, Han AW, Johnson AJA, Holmes SP. DADA2: high-resolution sample inference from Illumina amplicon data. Nat Methods. 2016;13:581–3.CAS 
Article 

Google Scholar 
Quast C, Pruesse E, Yilmaz P, Gerken J, Schweer T, Yarza P, et al. The SILVA ribosomal RNA gene database project: improved data processing and web-based tools. Nucleic Acids Res. 2013;41:D590–D596.CAS 
Article 

Google Scholar 
Kõljalg U, Larsson K-H, Abarenkov K, Nilsson RH, Alexander IJ, Eberhardt U, et al. UNITE: a database providing web-based methods for the molecular identification of ectomycorrhizal fungi. New Phytol. 2005;166:1063–8.Article 

Google Scholar 
R Core Team, R: a language and environment for statistical computing. R Foundation for Statistical Computing; 2012;Vienna;AustriaMcMurdie PJ, Holmes S. phyloseq: an R package for reproducible interactive analysis and graphics of microbiome census data. PLoS ONE. 2013;8:e61217.CAS 
Article 

Google Scholar 
Oksanen J, Blanchet FG, Kindt R, Legendre P, Minchin P, O’Hara B, et al. Vegan: community ecology package. R Package Version 2.2-1. 2015;2:1–2. https://cran.r-project.org/web/packages/vegan/index.html.Ogle, DH, Wheeler P, Dinno A. FSA: fisheries stock analysis; 2020. https://cran.r-project.org/web/packages/FSA/index.htmlLahti L, Shetty S. Tools for microbiome analysis in R. Microbiome package. Bioconductor; 2017. https://microbiome.github.io/tutorials/Bell T. Experimental tests of the bacterial distance–decay relationship. ISME J. 2010;4:1357–65.Article 

Google Scholar 
Boynton PJ, Peterson CN, Pringle A. Superior dispersal ability can lead to persistent ecological dominance throughout succession. Appl Environ Microbiol. 2019;85:e02421–18.CAS 
Article 

Google Scholar 
Nemergut DR, Schmidt SK, Fukami T, O’Neill SP, Bilinski TM, Stanish LF, et al. Patterns and processes of microbial community assembly. Microbiol Mol Biol Rev. 2013;77:342–56.Article 

Google Scholar 
Dickie A, N IA, Reich PB. Ectomycorrhizal fungal communities at forest edges. J Ecol. 2005;93:244–55.Article 

Google Scholar 
Vannette, RL, McMunn MS, Hall GW, Mueller TG, Munkres I, Perry D. Fungi are more dispersal limited than bacteria among flowers. https://www.biorxiv.org/content/10.1101/2020.05.19.104968v2. 2021.Zhang G, Wei G, Wei F, Chen Z, He M, Jiao S, et al., Dispersal limitation plays stronger role in the community assembly of fungi relative to bacteria in rhizosphere across the arable area of medicinal plant. Front Microbiol. 2021;12;713523.Svoboda P, Lindström ES, Ahmed Osman O, Langenheder S. Dispersal timing determines the importance of priority effects in bacterial communities. ISME J. 2018;12:644–6.Article 

Google Scholar 
Fukami T, Dickie IA, Paula Wilkie J, Paulus BC, Park D, Roberts A, et al. Assembly history dictates ecosystem functioning: evidence from wood decomposer communities. Ecol Lett. 2010;13:675–84.Article 

Google Scholar 
Rousk J, Bååth E, Brookes PC, Lauber CL, Lozupone C, Caporaso JG, et al. Soil bacterial and fungal communities across a pH gradient in an arable soil. ISME J. 2010;4:1340–51.Article 

Google Scholar 
Hartmann M, Frey B, Mayer J, Mäder P, Widmer F. Distinct soil microbial diversity under long-term organic and conventional farming. ISME J. 2015;9:1177–94.Article 

Google Scholar 
Lori M, Symnaczik S, Mäder P, De Deyn G, Gattinger A. Organic farming enhances soil microbial abundance and activity—a meta-analysis and meta-regression. PLoS ONE. 2017;12:e0180442.Article 

Google Scholar 
Blundell R, Schmidt JE, Igwe A, Cheung AL, Vannette RL, Gaudin ACM, et al. Organic management promotes natural pest control through altered plant resistance to insects. Nat Plants. 2020;6:483–91.CAS 
Article 

Google Scholar 
Riedo, J, Wettstein FE, Rösch A, Herzog C, Banerjee S, Büchi L, et al. Widespread occurrence of pesticides in organically managed agricultural soils—the ghost of a conventional agricultural past? Environ Sci Technol. 2021;55;2919–2928.Lacerda-Júnior GV, Noronha MF, Cabral L, Delforno TP, de Sousa STP, Fernandes-Júnior PI, et al., Land use and seasonal effects on the soil microbiome of a Brazilian dry forest. Front Microbiol. 2019;10;648.Article 

Google Scholar  More

Lister, B. C. & Garcia, A. Climate-driven declines in arthropod abundance restructure a rainforest food web. Proc. Natl Acad. Sci. USA 115, E10397–E10406 (2018).CAS 
Article 

Google Scholar 
Soroye, P., Newbold, T. & Kerr, J. Climate change contributes to widespread declines among bumble bees across continents. Science 367, 685–688 (2020).CAS 
Article 

Google Scholar 
Outhwaite, C. L., Gregory, R. D., Chandler, R. E., Collen, B. & Isaac, N. J. B. Complex long-term biodiversity change among invertebrates, bryophytes and lichens. Nat. Ecol. Evol. 4, 384–392 (2020).Article 

Google Scholar 
Crossley, M. S. et al. No net insect abundance and diversity declines across US long term ecological research sites. Nat. Ecol. Evol. 4, 1368–1376 (2020).Article 

Google Scholar 
Janzen, D. H. & Hallwachs, W. To us insectometers, it is clear that insect decline in our Costa Rican tropics is real, so let’s be kind to the survivors. Proc. Natl Acad. Sci. USA 118, e2002546117 (2021).CAS 
Article 

Google Scholar 
Newbold, T. et al. Global effects of land use on local terrestrial biodiversity. Nature 520, 45–50 (2015).CAS 
Article 

Google Scholar 
Spooner, F. E. B., Pearson, R. G. & Freeman, R. Rapid warming is associated with population decline among terrestrial birds and mammals globally. Glob. Change Biol. 24, 4521–4531 (2018).Article 

Google Scholar 
Powney, G. D. et al. Widespread losses of pollinating insects in Britain. Nat. Commun. 10, 1018 (2019).Article 

Google Scholar 
van Klink, R. et al. Meta-analysis reveals declines in terrestrial but increases in freshwater insect abundances. Science 368, 417–420 (2020).Article 

Google Scholar 
Yang, L. H. & Gratton, C. Insects as drivers of ecosystem processes. Curr. Opin. Insect Sci. 2, 26–32 (2014).Article 

Google Scholar 
Dainese, M. et al. A global synthesis reveals biodiversity-mediated benefits for crop production. Sci. Adv. 5, eaax0121 (2019).Article 

Google Scholar 
Halsch, C. A. et al. Insects and recent climate change. Proc. Natl. Acad. Sci. USA 118, e2002543117 (2021).CAS 
Article 

Google Scholar 
Wagner, D. L., Fox, R., Salcido, D. M. & Dyer, L. A. A window to the world of global insect declines: moth biodiversity trends are complex and heterogeneous. Proc. Natl Acad. Sci. USA 118, e2002549117 (2021).CAS 
Article 

Google Scholar 
Maxwell, S. L., Fuller, R. A., Brooks, T. M. & Watson, J. E. M. Biodiversity: the ravages of guns, nets and bulldozers. Nature 536, 143–145 (2016).CAS 
Article 

Google Scholar 
Kerr, J. T. et al. Climate change impacts on bumblebees converge across continents. Science 349, 177–180 (2015).CAS 
Article 

Google Scholar 
Uhler, J. et al. Relationship of insect biomass and richness with land use along a climate gradient. Nat. Commun. 12, 5946 (2021).CAS 
Article 

Google Scholar 
Oliver, T. H. & Morecroft, M. D. Interactions between climate change and land use change on biodiversity: attribution problems, risks, and opportunities. Wiley Interdiscip. Rev. Clim. Change 5, 317–335 (2014).Article 

Google Scholar 
Williams, J. J. & Newbold, T. Local climatic changes affect biodiversity responses to land use: a review. Divers. Distrib. 26, 76–92 (2020).Article 

Google Scholar 
González del Pliego, P. et al. Thermally buffered microhabitats recovery in tropical secondary forests following land abandonment. Biol. Conserv. 201, 385–395 (2016).Article 

Google Scholar 
Senior, R. A., Hill, J. K., González del Pliego, P., Goode, L. K. & Edwards, D. P. A pantropical analysis of the impacts of forest degradation and conversion on local temperature. Ecol. Evol. 7, 7897–7908 (2017).Article 

Google Scholar 
Peters, M. K. et al. Climate–land-use interactions shape tropical mountain biodiversity and ecosystem functions. Nature 568, 88–92 (2019).CAS 
Article 

Google Scholar 
Mantyka-Pringle, C. S., Martin, T. G. & Rhodes, J. R. Interactions between climate and habitat loss effects on biodiversity: a systematic review and meta-analysis. Glob. Change Biol. 18, 1239–1252 (2012).Article 

Google Scholar 
Northrup, J. M., Rivers, J. W., Yang, Z. & Betts, M. G. Synergistic effects of climate and land-use change influence broad-scale avian population declines. Glob. Change Biol. 25, 1561–1575 (2019).Article 

Google Scholar 
Deutsch, C. A. et al. Impacts of climate warming on terrestrial ectotherms across latitude. Proc. Natl Acad. Sci. USA 105, 6668–6672 (2008).CAS 
Article 

Google Scholar 
Newbold, T., Oppenheimer, P., Etard, A. & Williams, J. J. Tropical and Mediterranean biodiversity is disproportionately sensitive to land-use and climate change. Nat. Ecol. Evol. 4, 1630–1638 (2020).Article 

Google Scholar 
Perez, T. M., Stroud, J. T. & Feeley, K. J. Thermal trouble in the tropics. Science 351, 1392–1393 (2016).CAS 
Article 

Google Scholar 
Betts, M. G., Phalan, B., Frey, S. J. K., Rousseau, J. S. & Yang, Z. Old-growth forests buffer climate-sensitive bird populations from warming. Divers. Distrib. 24, 439–447 (2018).Article 

Google Scholar 
Hendershot, J. N. et al. Intensive farming drives long-term shifts in avian community composition. Nature 579, 393–396 (2020).CAS 
Article 

Google Scholar 
Tscharntke, T. et al. Landscape moderation of biodiversity patterns and processes – eight hypotheses. Biol. Rev. 87, 661–685 (2012).Article 

Google Scholar 
Suggitt, A. J. et al. Extinction risk from climate change is reduced by microclimatic buffering. Nat. Clim. Change 8, 713–717 (2018).Article 

Google Scholar 
Hudson, L. N. et al. The database of the PREDICTS (Projecting Responses of Ecological Diversity in Changing Terrestrial Systems) project. Ecol. Evol. 7, 145–188 (2017).Article 

Google Scholar 
Harris, I., Osborn, T. J., Jones, P. & Lister, D. Version 4 of the CRU TS monthly high-resolution gridded multivariate climate dataset. Sci. Data 7, 109 (2020).Article 

Google Scholar 
Johansson, F., Orizaola, G. & Nilsson-Örtman, V. Temperate insects with narrow seasonal activity periods can be as vulnerable to climate change as tropical insect species. Sci. Rep. 10, 8822 (2020).CAS 
Article 

Google Scholar 
Hoskins, A. J. et al. Downscaling land-use data to provide global 30′′ estimates of five land-use classes. Ecol. Evol. 6, 3040–3055 (2016).Article 

Google Scholar 
Grab, H. et al. Agriculturally dominated landscapes reduce bee phylogenetic diversity and pollination services. Science 363, 282–284 (2019).CAS 
Article 

Google Scholar 
Rusch, A. et al. Agricultural landscape simplification reduces natural pest control: a quantitative synthesis. Agric. Ecosyst. Environ. 221, 198–204 (2016).Article 

Google Scholar 
Oliver, T. H. et al. Declining resilience of ecosystem functions under biodiversity loss. Nat. Commun. 6, 10122 (2015).Article 

Google Scholar 
Sunday, J. M. et al. Thermal-safety margins and the necessity of thermoregulatory behavior across latitude and elevation. Proc. Natl Acad. Sci. USA. 111, 5610–5615 (2014).CAS 
Article 

Google Scholar 
Balmford, A. Extinction filters and current resilience: the significance of past selection pressures for conservation biology. Trends Ecol. Evol. 11, 193–196 (1996).CAS 
Article 

Google Scholar 
Garibaldi, L. A. et al. Stability of pollination services decreases with isolation from natural areas despite honey bee visits. Ecol. Lett. 14, 1062–1072 (2011).Article 

Google Scholar 
Carvalheiro, L. G., Seymour, C. L., Veldtman, R. & Nicolson, S. W. Pollination services decline with distance from natural habitat even in biodiversity-rich areas. J. Appl. Ecol. 47, 810–820 (2010).Article 

Google Scholar 
Dainese, M., Luna, D. I., Sitzia, T. & Marini, L. Testing scale-dependent effects of seminatural habitats on farmland biodiversity. Ecol. Appl. 25, 1681–1690 (2015).Article 

Google Scholar 
Fourcade, Y. et al. Habitat amount and distribution modify community dynamics under climate change. Ecol. Lett. 24, 950–957 (2021).Article 

Google Scholar 
Alexander, L. V. et al. Global observed changes in daily climate extremes of temperature and precipitation. J. Geophys. Res. 111, D05109 (2006).
Google Scholar 
Seibold, S. et al. Arthropod decline in grasslands and forests is associated with landscape-level drivers. Nature 574, 671–674 (2019).CAS 
Article 

Google Scholar 
De Palma, A. et al. Dimensions of biodiversity loss: spatial mismatch in land-use impacts on species, functional and phylogenetic diversity of European bees. Divers. Distrib. 23, 1435–1446 (2017).Article 

Google Scholar 
Collen, B., Ram, M., Zamin, T. & McRae, L. The tropical biodiversity data gap: addressing disparity in global monitoring. Trop. Conserv. Sci. 1, 75–88 (2008).Article 

Google Scholar 
Menéndez, R. How are insects responding to global warming? Tijdschr. Entomol. 150, 355 (2007).
Google Scholar 
Bale, J. S. et al. Herbivory in global climate change research: direct effects of rising temperature on insect herbivores. Glob. Change Biol. 8, 1–16 (2002).Article 

Google Scholar 
Hudson, L. N. et al. The 2016 release of the PREDICTS database. Natural History Museum Data Portal, https://doi.org/10.5519/0066354 (2016).Hudson, L. N. et al. The PREDICTS database: a global database of how local terrestrial biodiversity responds to human impacts. Ecol. Evol. 4, 4701–4735 (2014).Article 

Google Scholar 
De Palma, A. et al. Annual changes in the Biodiversity Intactness Index in tropical and subtropical forest biomes, 2001–2012. Sci. Rep. 11, 20249 (2021).Article 

Google Scholar 
Chao, A., Chazdon, R. L., Colwell, R. K. & Shen, T.-J. A new statistical approach for assessing similarity of species composition with incidence and abundance data. Ecol. Lett. 8, 148–159 (2005).Article 

Google Scholar 
Ndakidemi, B., Mtei, K. & Ndakidemi, P. A. Impacts of synthetic and botanical pesticides on beneficial insects. Agric. Sci. 07, 364–372 (2016).CAS 

Google Scholar 
Wang, X., Hua, F., Wang, L., Wilcove, D. S. & Yu, D. W. The biodiversity benefit of native forests and mixed‐species plantations over monoculture plantations. Divers. Distrib. 25, 1721–1735 (2019).Article 

Google Scholar 
Weedon, G. P. et al. The WFDEI meteorological forcing data set: WATCH Forcing Data methodology applied to ERA-Interim reanalysis data. Water Resour. Res. 50, 7505–7514 (2014).Article 

Google Scholar 
Warszawski, L. et al. The inter-sectoral impact model intercomparison project (ISI-MIP): project framework. Proc. Natl Acad. Sci. USA 111, 3228–3232 (2014).CAS 
Article 

Google Scholar 
van Vuuren, D. P. et al. The representative concentration pathways: an overview. Clim. Change 109, 5–31 (2011).Article 

Google Scholar 
New, M., Hulme, M. & Jones, P. Representing twentieth-century space–time climate variability. Part I: development of a 1961–90 mean monthly terrestrial climatology. J. Clim. 12, 829–856 (1999).Article 

Google Scholar 
Hijmans, R. J. Raster: Geographic data analysis and modelling. R package version 2.8-42018, https://CRAN.R-project.org/package=raster (2018).Rigby, R. A., Stasinopoulos, D. M. & Akantziliotou, C. A framework for modelling overdispersed count data, including the Poisson-shifted generalized inverse Gaussian distribution. Comput. Stat. Data Anal. 53, 381–393 (2008).MathSciNet 
Article 

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

Google Scholar  More

High Ambition Coalition for Nature and People. 50 Countries Announce Bold Commitment to Protect at Least 30% of the World’s Land and Ocean by 2030 (Campaign for Nature, 2021).Waldron A. et al. Protecting 30% of the Planet for Nature: Costs, Benefits and Economic Implications (Campaign for Nature, 2020).Geldmann, J., Manica, A., Burgess, N. D., Coad, L. & Balmford, A. A global-level assessment of the effectiveness of protected areas at resisting anthropogenic pressures. Proc. Natl Acad. Sci. USA 116, 23209–23223 (2019).CAS 
Article 

Google Scholar 
Nelson, A. & Chomitz, K. M. Protected Area Effectiveness in Reducing Tropical Deforestation (The World Bank, 2009).Scharlemann, J. P. W. et al. Securing tropical forest carbon: the contribution of protected areas to REDD. Oryx 44, 352–357 (2010).Article 

Google Scholar 
Feng, Y. et al. Assessing the effectiveness of global protected areas based on the difference in differences model. Ecol. Indic. 130, 108078 (2021).Article 

Google Scholar 
Laurance, W. F. et al. The fate of Amazonian forest fragments: A 32-year investigation. Biol. Conserv. 144, 56–67 (2011).Article 

Google Scholar 
Laurance, W. F. et al. Averting biodiversity collapse in tropical forest protected areas. Nature 489, 290–294 (2012).CAS 
Article 

Google Scholar 
Terraube, J., Van doninck, J., Helle, P. & Cabeza, M. Assessing the effectiveness of a national protected area network for carnivore conservation. Nat. Commun. 11, 2957 (2020).CAS 
Article 

Google Scholar 
Barnes, M. D. et al. Wildlife population trends in protected areas predicted by national socio-economic metrics and body size. Nat. Commun. 7, 12747 (2016).CAS 
Article 

Google Scholar 
Amano, T. et al. Successful conservation of global waterbird populations depends on effective governance. Nature 553, 199–202 (2018).CAS 
Article 

Google Scholar 
Kleijn, D., Cherkaoui, I., Goedhart, P. W., van der Hout, J. & Lammertsma, D. Waterbirds increase more rapidly in Ramsar-designated wetlands than in unprotected wetlands. J. Appl. Ecol. 51, 289–298 (2014).Article 

Google Scholar 
Reyes-Arriagada, R. et al. Population trends of a mixed-species colony of Humboldt and Magellanic Penguins in Southern Chile after establishing a protected area. Avian Conserv. Ecol. 8, 13 (2013).
Google Scholar 
Bukart, K. Motion 101 passes at IUCN, calls for protecting 50% of Earth’s lands and seas. One Earth https://www.oneearth.org/motion-101-passes-at-iucn-calls-for-protecting-50-of-earths-lands-and-seas/ (2021).Protected Planet Report 2020 (UNEP-WCMC and IUCN, 2021).Global Assessment Report on Biodiversity and Ecosystem Services of the Intergovernmental Science-Policy Platform on Biodiversity and Ecosystem Services (IPBES Secretariat, 2019).Barnes, M. D., Glew, L., Wyborn, C. & Craigie, I. D. Prevent perverse outcomes from global protected area policy. Nat, Ecol. Evol. 2, 759–762 (2018).Article 

Google Scholar 
Pressey, R. L., Cabeza, M., Watts, M. E., Cowling, R. M. & Wilson, K. A. Conservation planning in a changing world. Trends Ecol. Evol. 22, 583–592 (2007).Article 

Google Scholar 
Geldmann, J. et al. Effectiveness of terrestrial protected areas in reducing habitat loss and population declines. Biol. Conserv. 161, 230–238 (2013).Article 

Google Scholar 
Rodrigues, A. S. L. & Cazalis, V. The multifaceted challenge of evaluating protected area effectiveness. Nat. Commun. 11, 5147 (2020).CAS 
Article 

Google Scholar 
Redford, K. H. The empty forest. BioScience 42, 412–422 (1992).Article 

Google Scholar 
Ferraro, P. J. Counterfactual thinking and impact evaluation in environmental policy. N. Direct. Eval. 2009, 75–84 (2009).Article 

Google Scholar 
Adams, V. M., Barnes, M. & Pressey, R. L. Shortfalls in conservation evidence: moving from ecological effects of interventions to policy evaluation. One Earth 1, 62–75 (2019).Article 

Google Scholar 
Wauchope, H. S. et al. Evaluating impact using time-series data. Trends Ecol. Evol. 36, 196–205 (2021).Article 

Google Scholar 
Kingsford, R. T., Roshier, D. A. & Porter, J. L. Australian waterbirds time and space travellers in dynamic desert landscapes. Mar. Freshw. Res. 61, 875–884 (2010).CAS 
Article 

Google Scholar 
The Ramsar Convention Secretariat. Managing Ramsar Sites. ramsar.org https://www.ramsar.org/sites-countries/managing-ramsar-sites (2014).European Commission. The Birds Directive. https://ec.europa.eu/environment/nature/legislation/birdsdirective/index_en.htm (accessed 3 April 2022).Zhang, W., Sheldon, B. C., Grenyer, R. & Gaston, K. J. Habitat change and biased sampling influence estimation of diversity trends. Curr. Biol. 31, 3656–3662.e3 (2021).CAS 
Article 

Google Scholar 
Bruner, A. G., Gullison, R. E., Rice, R. E. & da Fonseca, G. A. B. Effectiveness of parks in protecting tropical biodiversity. Science 291, 125–128 (2001).CAS 
Article 

Google Scholar 
Carranza, T., Balmford, A., Kapos, V. & Manica, A. Protected area effectiveness in reducing conversion in a rapidly vanishing ecosystem: the Brazilian Cerrado. Conserv. Lett. 7, 216–223 (2014).Article 

Google Scholar 
Rabinowitz, D. In The Biological Aspects of Rare Plant Conservation (ed. Synge, H.) 205–217 (John Wiley & Sons, 1981).Daskalova, G. N., Myers-Smith, I. H. & Godlee, J. L. Rare and common vertebrates span a wide spectrum of population trends. Nat. Commun. 11, 4394 (2020).CAS 
Article 

Google Scholar 
Hettiarachchi, M., Morrison, T. H. & McAlpine, C. Forty-three years of Ramsar and urban wetlands. Glob. Environ. Change 32, 57–66 (2015).Article 

Google Scholar 
Munishi, P., Chuwa, J., Kilungu, H., Moe, S. & Temu, R. Management effectiveness and conservation initiatives in the Kilombero Valley Flood Plains Ramsar Site, Tanzania. Tanzania J. For. Nat. Conserv. 81, 1–10 (2012).
Google Scholar 
Fahrig, L. Why do several small patches hold more species than few large patches? Glob. Ecol. Biogeogr. 29, 615–628 (2020).Article 

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

Google Scholar 
Mascia, M. B. & Pailler, S. Protected area downgrading, downsizing, and degazettement (PADDD) and its conservation implications. Conserv. Lett. 4, 9–20 (2011).Article 

Google Scholar 
Di Marco, M. et al. Changing trends and persisting biases in three decades of conservation science. Glob. Ecol. Conserv. 10, 32–42 (2017).Article 

Google Scholar 
Wetlands International. Asian Waterbird Census. https://south-asia.wetlands.org/our-approach/healthy-wetland-nature/asian-waterbird-census/ (accessed 3 April 2022).Gill, D. A. et al. Capacity shortfalls hinder the performance of marine protected areas globally. Nature 543, 665–669 (2017).CAS 
Article 

Google Scholar 
Geldmann, J. et al. A global analysis of management capacity and ecological outcomes in terrestrial protected areas. Conserv. Lett. 11, e12434 (2018).Article 

Google Scholar 
Kingsford, R. T., Bino, G. & Porter, J. L. Continental impacts of water development on waterbirds, contrasting two Australian river basins: global implications for sustainable water use. Glob. Change Biol. 23, 4958–4969 (2017).Article 

Google Scholar 
Jia, Q., Wang, X., Zhang, Y., Cao, L. & Fox, A. D. Drivers of waterbird communities and their declines on Yangtze River floodplain lakes. Biol. Conserv. 218, 240–246 (2018).Article 

Google Scholar 
Lehikoinen, A., Rintala, J., Lammi, E. & Pöysä, H. Habitat-specific population trajectories in boreal waterbirds: alarming trends and bioindicators for wetlands. Animal Conserv. 19, 88–95 (2016).Article 

Google Scholar 
Boyd, C. et al. Spatial scale and the conservation of threatened species. Conserv. Lett. 1, 37–43 (2008).Article 

Google Scholar 
Schleicher, J. et al. Protecting half of the planet could directly affect over one billion people. Nat. Sustain. 2, 1094–1096 (2019).Article 

Google Scholar 
Wauchope, H. et al. Quantifying the impact of protected areas on near-global waterbird population trends, a pre-analysis plan. Preprint at https://doi.org/10.7287/peerj.preprints.27741v2 (2019).Nosek, B. A., Ebersole, C. R., DeHaven, A. C. & Mellor, D. T. The preregistration revolution. Proc. Natl Acad. Sci. USA 115, 2600–2606 (2018).CAS 
Article 

Google Scholar 
R Core Team. R: A Language and Environment for Statistical Computing (R Foundation for Statistical Computing, 2020).QGIS Geographic Information System (QGIS, 2021).Hadley Wickham. ggplot2: Elegant Graphics for Data Analysis (Springer-Verlag, 2016).Venables, W. N. & Ripley, B. D. Modern Applied Statistics with S (Springer, 2002).The World Database on Protected Areas (WDPA)/The Global Database on Protected Areas Management Effectiveness (GD-PAME) www.protectedplanet.net (UNEP-WCMC and IUCN, 2019).Global Self-consistent, Hierarchical, High-resolution Geography Database (GSHHG) (NOAA, 2017).Coetzer, K. L., Witkowski, E. T. F. & Erasmus, B. F. N. Reviewing Biosphere Reserves globally: effective conservation action or bureaucratic label? Biol. Rev. 89, 82–104 (2014).Article 

Google Scholar 
Ament, J. M. & Cumming, G. S. Scale dependency in effectiveness, isolation, and social-ecological spillover of protected areas. Conserv. Biol. 30, 846–855 (2016).Article 

Google Scholar 
Naimi, B., Hamm, N. A. S., Groen, T. A., Skidmore, A. K. & Toxopeus, A. G. Where is positional uncertainty a problem for species distribution modelling? Ecography 37, 191–203 (2014).Article 

Google Scholar 
Salmerón Gómez, R., García, Pérez, J., López Martín, M. D. M. & García, C. G. Collinearity diagnostic applied in ridge estimation through the variance inflation factor. J. Appl. Stat. 43, 1831–1849 (2016).MathSciNet 
Article 

Google Scholar 
Gu, X. S. & Rosenbaum, P. R. Comparison of multivariate matching methods: structures, distances, and algorithms. J. Comput. Graph. Stat. 2, 405–420 (1993).
Google Scholar 
Stuart, E. A. Matching methods for causal inference: a review and a look forward. Stat. Sci. 25, 1–21 (2010).MathSciNet 
Article 

Google Scholar 
King, G. & Nielsen, R. Why propensity scores should not be used for matching. Pol. Anal. 27, 435–454 (2019).Article 

Google Scholar 
Rosenbaum, P. R. DOS: design of observational studies. https://cran.r-project.org/web/packages/DOS/index.html (2018).Linden, A. A matching framework to improve causal inference in interrupted time-series analysis. J. Eval. Clin. Pract. 24, 408–415 (2018).Article 

Google Scholar 
Simmons, B. I., Hoeppke, C. & Sutherland, W. J. Beware greedy algorithms. J. Anim. Ecol. 88, 804–807 (2019).Article 

Google Scholar 
Austin, P. C. Balance diagnostics for comparing the distribution of baseline covariates between treatment groups in propensity-score matched samples. Stat. Med. 28, 3083–3107 (2009).MathSciNet 
Article 

Google Scholar 
Rubin, D. B. Using propensity scores to help design observational studies: application to the tobacco litigation. Health Serv. Outcomes Res. Methodol. 2, 169–188 (2001).Article 

Google Scholar 
Fox, J. & Weisberg, S. An R Companion to Applied Regression (Sage, 2019).Hartig, F. DHARMa: residual diagnostics for hierarchical (multi-level/mixed) regression models. https://cran.r-project.org/web/packages/DHARMa/index.html (2021).Zeileis, A. & Hothorn, T. Diagnostic checking in regression relationships. R News 2, 7–10 (2002).
Google Scholar 
Bates, D., Maechler, M., Bolker, B. & Walker, S. Fitting linear mixed-effects models using lme4. J. Stat. Softw. 67, 48 (2015).Article 

Google Scholar 
Christensen, R. Ordinal–regression models for ordinal data. https://cran.r-project.org/web/packages/ordinal/index.html (2019).Lüdecke, D. ggeffects: tidy data frames of marginal effects from regression models. J. Op. Source Softw. 3, 772 (2018).Article 

Google Scholar 
McKay, M. D., Beckman, R. J. & Conover, W. J. Comparison of three methods for selecting values of input variables in the analysis of output from a computer code. Technometrics 21, 239–245 (1979).MathSciNet 

Google Scholar 
Carnell, R. lhs: latin hypercube samples. https://cran.r-project.org/web/packages/lhs/index.html (2020).Harris, I., Jones, P. D., Osborn, T. J. & Lister, D. H. Updated high-resolution grids of monthly climatic observations—the CRU TS3.10 Dataset. Int. J. Climatol. 34, 623–642 (2014).Article 

Google Scholar 
Lu, C. & Tian, H. Global nitrogen and phosphorus fertilizer use for agriculture production in the past half century: Shifted hot spots and nutrient imbalance. Earth Syst. Sci. Data 9, 181–192 (2017).Article 

Google Scholar 
Joppa, L. N. & Pfaff, A. High and far: biases in the location of protected areas. PLoS ONE 4, e8273 (2009).Article 

Google Scholar 
Hurtt, G. C. et al. Harmonization of land-use scenarios for the period 1500-2100: 600 years of global gridded annual land-use transitions, wood harvest, and resulting secondary lands. Clim. Change 109, 117–161 (2011).Article 

Google Scholar 
Lloyd, C. T., Sorichetta, A. & Tatem, A. J. High resolution global gridded data for use in population studies. Sci. Data 4, 17001 (2017).Article 

Google Scholar 
Pekel, J.-F., Cottam, A., Gorelick, N. & Belward, A. S. High-resolution mapping of global surface water and its long-term changes. Nature 540, 418–422 (2016).CAS 
Article 

Google Scholar 
Sandvik, B. World Borders Dataset. Thematic Mapping http://thematicmapping.org/downloads/world_borders.php (2009).BirdLife International. Species Distribution Data Download http://www.birdlife.org/datazone/info/spcdownload (accessed 25 February 2020).Wilman, H. et al. EltonTraits 1.0: species-level foraging attributes of the world’s birds and mammals. Ecology 95, 2027 (2014).Article 

Google Scholar 
WWF International. Management Effectiveness Tracking Tool https://wwfeu.awsassets.panda.org/downloads/mett2_final_version_july_2007.pdf (2007). More

Azam, F. & Malfatti, F. Microbial structuring of marine ecosystems. Nat. Rev. Microbiol. 5, 782–791 (2007).CAS 
Article 

Google Scholar 
Blackburn, N., Fenchel, T. & Mitchell, J. Microscale nutrient patches in planktonic habitats shown by chemotactic bacteria. Science 282, 2254–2256 (1998).CAS 
Article 

Google Scholar 
Stocker, R. Marine microbes see a sea of gradients. Science 338, 628 (2012).CAS 
Article 

Google Scholar 
Levin, S. A. The problem of pattern and scale in ecology. Ecology 73, 1943–1967 (1992).Article 

Google Scholar 
Azam, F. Microbial control of oceanic carbon flux: the plot thickens. Science 280, 694–696 (1998).CAS 
Article 

Google Scholar 
Strom, S. L. Microbial ecology of ocean biogeochemistry: a community perspective. Science 320, 1043–1045 (2008).CAS 
Article 

Google Scholar 
Sarmento, H. & Gasol, J. M. Use of phytoplankton-derived dissolved organic carbon by different types of bacterioplankton. Env. Microbiol. 14, 2348–2360 (2012).CAS 
Article 

Google Scholar 
Grossart, H.-P., Riemann, L. & Azam, F. Bacterial motility in the sea and its ecological implications. Aquat. Microb. Ecol. 25, 247–258 (2001).Article 

Google Scholar 
Brumley, D. R. et al. Bacteria push the limits of chemotactic precision to navigate dynamic chemical gradients. Proc. Natl Acad. Sci. USA 116, 10792–10797 (2019).CAS 
Article 

Google Scholar 
Fenchel, T. Eppur si muove: many water column bacteria are motile. Aquat. Microb. Ecol. 24, 197–201 (2001).Article 

Google Scholar 
Son, K., Menolascina, F. & Stocker, R. Speed-dependent chemotactic precision in marine bacteria. Proc. Natl Acad. Sci. USA 113, 8624–8629 (2016).CAS 
Article 

Google Scholar 
Fenchel, T. Microbial behavior in a heterogeneous world. Science 296, 1068–1071 (2002).CAS 
Article 

Google Scholar 
Kiørboe, T. & Jackson, G. A. Marine snow, organic solute plumes, and optimal chemosensory behavior of bacteria. Limnol. Oceanogr. 46, 1309–1318 (2001).Article 

Google Scholar 
Lambert, B. S., Fernandez, V. I. & Stocker, R. Motility drives bacterial encounter with particles responsible for carbon export throughout the ocean. Limnol. Oceanogr. Lett. 4, 113–118 (2019).Article 

Google Scholar 
Wadhams, G. H. & Armitage, J. P. Making sense of it all: bacterial chemotaxis. Nat. Rev. Mol. Cell. Biol. 5, 1024–1037 (2004).CAS 
Article 

Google Scholar 
Stocker, R., Seymour, J. R., Samadani, A., Hunt, D. E. & Polz, M. F. Rapid chemotactic response enables marine bacteria to exploit ephemeral microscale nutrient patches. Proc. Natl Acad. Sci. USA 105, 4209–4214 (2008).CAS 
Article 

Google Scholar 
Raina, J.-B., Fernandez, V., Lambert, B., Stocker, R. & Seymour, J. R. The role of microbial motility and chemotaxis in symbiosis. Nat. Rev. Microbiol. 17, 284–294 (2019).CAS 
Article 

Google Scholar 
Seymour, J. R., Amin, S. A., Raina, J.-B. & Stocker, R. Zooming in on the phycosphere: the ecological interface for phytoplankton–bacteria relationships. Nat. Microbiol. 2, 17065 (2017).CAS 
Article 

Google Scholar 
Bell, W. & Mitchell, R. Chemotactic and growth responses of marine bacteria to algal extracellular products. Biol. Bull. 143, 265–277 (1972).Article 

Google Scholar 
Smriga, S., Fernandez, V. I., Mitchell, J. G. & Stocker, R. Chemotaxis toward phytoplankton drives organic matter partitioning among marine bacteria. Proc. Natl Acad. Sci. USA 113, 1576–1581 (2016).CAS 
Article 

Google Scholar 
Amin, S. A. et al. Interaction and signalling between a cosmopolitan phytoplankton and associated bacteria. Nature 522, 98–101 (2015).CAS 
Article 

Google Scholar 
Lambert, B. S. et al. A microfluidics-based in situ chemotaxis assay to study the behaviour of aquatic microbial communities. Nat. Microbiol. 2, 1344–1349 (2017).CAS 
Article 

Google Scholar 
Larsen, M. H., Blackburn, N., Larsen, J. L. & Olsen, J. E. Influences of temperature, salinity and starvation on the motility and chemotactic response of Vibrio anguillarum. Microbiology 150, 1283–1290 (2004).CAS 
Article 

Google Scholar 
Rinke, C. et al. Validation of picogram- and femtogram-input DNA libraries for microscale metagenomics. PeerJ 4, e2486 (2016).Article 

Google Scholar 
Becker, J. et al. Closely related phytoplankton species produce similar suites of dissolved organic matter. Front. Microbiol. 5, 111 (2014).Article 

Google Scholar 
Vraspir, J. M. & Butler, A. Chemistry of marine ligands and siderophores. Annu. Rev. Mar. Sci. 1, 43–63 (2009).Article 

Google Scholar 
Tagliabue, A. et al. The integral role of iron in ocean biogeochemistry. Nature 543, 51–59 (2017).CAS 
Article 

Google Scholar 
Hopkinson, B. M. & Morel, F. M. M. The role of siderophores in iron acquisition by photosynthetic marine microorganisms. BioMetals 22, 659–669 (2009).CAS 
Article 

Google Scholar 
Amin, S. A. et al. Photolysis of iron–siderophore chelates promotes bacterial–algal mutualism. Proc. Natl Acad. Sci. USA 106, 17071–17076 (2009).CAS 
Article 

Google Scholar 
Croft, M. T., Lawrence, A. D., Raux-Deery, E., Warren, M. J. & Smith, A. G. Algae acquire vitamin B12 through a symbiotic relationship with bacteria. Nature 438, 90–93 (2005).CAS 
Article 

Google Scholar 
Helliwell, K. E. The roles of B vitamins in phytoplankton nutrition: new perspectives and prospects. New Phytol. 216, 62–68 (2017).CAS 
Article 

Google Scholar 
Berg, G. Plant–microbe interactions promoting plant growth and health: perspectives for controlled use of microorganisms in agriculture. Appl. Microbiol. Biotechnol. 84, 11–18 (2009).CAS 
Article 

Google Scholar 
Christie, P. J., Whitaker, N. & González-Rivera, C. Mechanism and structure of the bacterial type IV secretion systems. Biochim. Biophys. Acta 1843, 1578–1591 (2014).CAS 
Article 

Google Scholar 
Preston, G. M. Metropolitan microbes: type III secretion in multihost symbionts. Cell Host Microbe 2, 291–294 (2007).CAS 
Article 

Google Scholar 
Deakin, W. J. & Broughton, W. J. Symbiotic use of pathogenic strategies: rhizobial protein secretion systems. Nat. Rev. Microbiol. 7, 312–320 (2009).CAS 
Article 

Google Scholar 
Luo, H. & Moran, M. A. Evolutionary ecology of the marine Roseobacter clade. Microbiol. Mol. Biol. Rev. 78, 573–587 (2014).Article 

Google Scholar 
Rolland, J. L., Stien, D., Sanchez-Ferandin, S. & Lami, R. Quorum sensing and quorum quenching in the phycosphere of phytoplankton: a case of chemical interactions in ecology. J. Chem. Ecol. 42, 1201–1211 (2016).CAS 
Article 

Google Scholar 
Fei, C. et al. Quorum sensing regulates ‘swim-or-stick’ lifestyle in the phycosphere. Environ. Microbiol. 22, 4761–4778 (2020).CAS 
Article 

Google Scholar 
Landa, M., Burns, A. S., Roth, S. J. & Moran, M. A. Bacterial transcriptome remodeling during sequential co-culture with a marine dinoflagellate and diatom. ISME J. 11, 2677 (2017).CAS 
Article 

Google Scholar 
Rinke, C. et al. A phylogenomic and ecological analysis of the globally abundant Marine Group II archaea (Ca. Poseidoniales ord. nov.). ISME J. 13, 663–675 (2019).CAS 
Article 

Google Scholar 
Fenchel, T. & Blackburn, N. Motile chemosensory behaviour of phagotrophic protists: mechanisms for and efficiency in congregating at food patches. Protist 150, 325–336 (1999).CAS 
Article 

Google Scholar 
Hughes, D. J. et al. Impact of nitrogen availability upon the electron requirement for carbon fixation in Australian coastal phytoplankton communities. 63, 1891–1910 (2018).Sumner, L. W. et al. Proposed minimum reporting standards for chemical analysis. Metabolomics 3, 211–221 (2007).CAS 
Article 

Google Scholar 
Chong, J., Wishart, D. S. & Xia, J. Using MetaboAnalyst 4.0 for comprehensive and integrative metabolomics data analysis. Curr. Protoc. Bioinformatics 68, e86 (2019).Article 

Google Scholar 
Xia, J. & Wishart, D. S. Web-based inference of biological patterns, functions and pathways from metabolomic data using MetaboAnalyst. Nat. Protoc. 6, 743–760 (2011).CAS 
Article 

Google Scholar 
Lambert, B. S. & Raina, J.-B. Fabrication and deployment of the in situ chemotaxis assay (ISCA). protocols.io https://doi.org/10.17504/protocols.io.kztcx6n (2019).Ritchie, R. J. Consistent sets of spectrophotometric chlorophyll equations for acetone, methanol and ethanol solvents. Photosynth. Res. 89, 27–41 (2006).CAS 
Article 

Google Scholar 
Marie, D., Partensky, F., Jacquet, S. & Vaulot, D. Enumeration and cell cycle analysis of natural populations of marine picoplankton by flow cytometry using the nucleic acid stain SYBR Green I. Appl. Environ. Microbiol. 63, 186–193 (1997).CAS 
Article 

Google Scholar 
Bramucci, A. R. et al. Microvolume DNA extraction methods for microscale amplicon and metagenomic studies. ISME Commun. 1, 79 (2021).Article 

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

Google Scholar 
Li, H. Aligning sequence reads, clone sequences and assembly contigs with BWA-MEM. Preprint at https://doi.org/10.48550/arXiv.1303.3997 (2013).Boyd, J. A., Woodcroft, B. J. & Tyson, G. W. GraftM: a tool for scalable, phylogenetically informed classification of genes within metagenomes. Nucleic Acids Res. 46, e59 (2018).Article 

Google Scholar 
Kuever, J., Rainey, F. A. & Widdel, F. In Bergey’s Manual of Systematics of Archaea and Bacteria https://doi.org/10.1002/9781118960608.obm00084 (2015).Bianchi, D., Weber, T. S., Kiko, R. & Deutsch, C. Global niche of marine anaerobic metabolisms expanded by particle microenvironments. Nat. Geosci. 11, 263–268 (2018).CAS 
Article 

Google Scholar 
Liu, X. et al. Wide distribution of anaerobic ammonium-oxidizing bacteria in the water column of the South China Sea: implications for their survival strategies. Divers. Distrib. 27, 1893–19003 (2021).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).CAS 
Article 

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

Google Scholar 
Kanehisa, M. & Goto, S. KEGG: Kyoto Encyclopedia of Genes and Genomes. Nucleic Acids Res. 28, 27–30 (2000).CAS 
Article 

Google Scholar 
Buchfink, B., Xie, C. & Huson, D. H. Fast and sensitive protein alignment using DIAMOND. Nat. Methods 12, 59 (2014).Article 

Google Scholar 
Paulson, J. N., Stine, O. C., Bravo, H. C. & Pop, M. Differential abundance analysis for microbial marker-gene surveys. Nat. Methods 10, 1200 (2013).CAS 
Article 

Google Scholar 
McMurdie, P. J. & Holmes, S. Waste not, want not: why rarefying microbiome data is inadmissible. PLOS Comput. Biol. 10, e1003531 (2014).Article 

Google Scholar 
Berges, J. A., Franklin, D. J. & Harrison, P. J. Evolution of an artificial seawater medium: improvements in enriched seawater, artificial water over the last two decades. J. Phycol. 37, 1138–1145 (2001).Article 

Google Scholar 
Lane, D. In Nucleic Acid Techniques in Bacterial Systematics (eds Stackebrandt, E. & Goodfellow, M.) 115–175 (1991).Callahan, B. J. et al. DADA2: high-resolution sample inference from Illumina amplicon data. Nature Methods 13, 581–583 (2016).CAS 
Article 

Google Scholar 
Quast, C. et al. The SILVA ribosomal RNA gene database project: improved data processing and web-based tools. Nucleic Acids Res. 41, D590–D596 (2012).Article 

Google Scholar 
Oksanen, J. et al. Package ‘Vegan’ Community Ecology Package Version 2 (2013).Durham, B. P. et al. Sulfonate-based networks between eukaryotic phytoplankton and heterotrophic bacteria in the surface ocean. Nat. Microbiol. 4, 1706–1715 (2019).CAS 
Article 

Google Scholar 
Durham, B. P. et al. Recognition cascade and metabolite transfer in a marine bacteria–phytoplankton model system. Environ. Microbiol. 19, 3500–3513 (2017).CAS 
Article 

Google Scholar 
Durham, B. P. et al. Cryptic carbon and sulfur cycling between surface ocean plankton. Proc. Natl Acad. Sci. USA 112, 453–457 (2015).CAS 
Article 

Google Scholar 
Landa, M. et al. Sulfur metabolites that facilitate oceanic phytoplankton–bacteria carbon flux. ISME J. 13, 2536–2550 (2019).CAS 
Article 

Google Scholar  More

Boyce DG, Lewis MR, Worm B. Global phytoplankton decline over the past century. Nature. 2010;466:591–6.CAS 
Article 

Google Scholar 
Bonachela JA, Klausmeier CA, Edwards KF, Litchman E, Levin SA. The role of phytoplankton diversity in the emergent oceanic stoichiometry. J Plankton Res. 2016;38:1021–35.CAS 
Article 

Google Scholar 
Falkowski PG. The role of phytoplankton photosynthesis in global biogeochemical cycles. Photosynth Res. 1994;39:235–58.CAS 
Article 

Google Scholar 
Longhurst A. Seasonal cycles of pelagic production and consumption. Prog Oceanogr. 1995;36:77–167.Article 

Google Scholar 
Li WKW, Glen Harrison W, Head EJH. Coherent assembly of phytoplankton communities in diverse temperate ocean ecosystems. Proc R Soc B Biol Sci. 2006;273:1953–60.Article 

Google Scholar 
Bolaños LM, Karp-Boss L, Choi CJ, Worden AZ, Graff JR, Haëntjens N, et al. Small phytoplankton dominate western North Atlantic biomass. ISME J. 2020;14:1–12.Article 

Google Scholar 
Buttigieg PL, Fadeev E, Bienhold C, Hehemann L, Offre P, Boetius A. Marine microbes in 4D—using time series observation to assess the dynamics of the ocean microbiome and its links to ocean health. Curr Opin Microbiol. 2018;43:169–85.Article 

Google Scholar 
Hirata T, Aiken J, Hardman-Mountford N, Smyth TJ, Barlow RG. An absorption model to determine phytoplankton size classes from satellite ocean colour. Remote Sens Environ. 2008;112:3153–9.Article 

Google Scholar 
Li WKW, Dickie PM. Monitoring phytoplankton, bacterioplankton, and virioplankton in a coastal inlet (Bedford Basin) by flow cytometry. Cytometry. 2001;44:236–46.CAS 
Article 

Google Scholar 
Karl DM, Lukas R. The Hawaii Ocean Time-series (HOT) program: background, rationale and field implementation. Deep Sea Res Part II Top Stud Oceanogr. 1996;43:129–56.CAS 
Article 

Google Scholar 
Steinberg DK, Carlson CA, Bates NR, Johnson RJ, Michaels AF, Knap AH. Overview of the US JGOFS Bermuda Atlantic Time-series Study (BATS): a decade-scale look at ocean biology and biogeochemistry. Deep Sea Res Part II Top Stud Oceanogr. 2001;48:1405–47.CAS 
Article 

Google Scholar 
Harris R. The L4 time-series: the first 20 years. J Plankton Res. 2010;32:577–83.Article 

Google Scholar 
Hunter-Cevera KR, Neubert MG, Olson RJ, Solow AR, Shalapyonok A, Sosik HM. Physiological and ecological drivers of early spring blooms of a coastal phytoplankter. Science. 2016;354:326–9.CAS 
Article 

Google Scholar 
Shi Q, Wallace D. A 3-year time series of volatile organic iodocarbons in Bedford Basin, Nova Scotia: a northwestern Atlantic fjord. Ocean Sci. 2018;14:1385–403.CAS 
Article 

Google Scholar 
Crawford A, Shore J, Shan S. Measurement of tidal currents using an autonomous underwater vehicle. IEEE J Ocean Eng 2021;1–13.Kerrigan EA, Kienast M, Thomas H, Wallace DWR. Using oxygen isotopes to establish freshwater sources in Bedford Basin, Nova Scotia, a Northwestern Atlantic fjord. Estuar Coast Shelf Sci. 2017;199:96–104.CAS 
Article 

Google Scholar 
Shan S, Sheng J. Examination of circulation, flushing time and dispersion in Halifax Harbour of Nova Scotia. Water Qual Res J. 2012;47:353–74.CAS 
Article 

Google Scholar 
Clayton S, Dutkiewicz S, Jahn O, Follows MJ. Dispersal, eddies, and the diversity of marine phytoplankton. Limnol Oceanogr Fluids Environ. 2013;3:182–97.Article 

Google Scholar 
Barton AD, Dutkiewicz S, Flierl G, Bragg J, Follows MJ. Patterns of diversity in marine phytoplankton. Science. 2010;327:1509–11.CAS 
Article 

Google Scholar 
Dutkiewicz S, Cermeno P, Jahn O, Follows MJ, Hickman AE, Taniguchi DAA, et al. Dimensions of marine phytoplankton diversity. Biogeosciences. 2020;17:609–34.Article 

Google Scholar 
Righetti D, Vogt M, Gruber N, Psomas A, Zimmermann NE. Global pattern of phytoplankton diversity driven by temperature and environmental variability. Sci Adv. 2019;5:eaau6253.Article 

Google Scholar 
Li WKW. Annual average abundance of heterotrophic bacteria and Synechococcus in surface ocean waters. Limnol Oceanogr. 1998;43:1746–53.Article 

Google Scholar 
DFO Canada. AZMP Bulletin PMZA. 2006. DFO.Cullen JJ, Doolittle WF, Levin SA, Li WKW. Patterns and prediction in microbial oceanography. Oceanography. 2007;20:34–46.Article 

Google Scholar 
El‐Swais H, Dunn KA, Bielawski JP, Li WKW, Walsh DA. Seasonal assemblages and short-lived blooms in coastal north-west Atlantic Ocean bacterioplankton. Environ Microbiol. 2015;17:3642–61.Article 

Google Scholar 
Georges AA, El-Swais H, Craig SE, Li WK, Walsh DA. Metaproteomic analysis of a winter to spring succession in coastal northwest Atlantic Ocean microbial plankton. ISME J. 2014;8:1301–13.CAS 
Article 

Google Scholar 
Conover SAM. Nitrogen utilization during spring blooms of marine phytoplankton in Bedford Basin, Nova Scotia, Canada. Mar Biol. 1975;32:247–61.CAS 
Article 

Google Scholar 
Lehman PW. Comparison of chlorophyll a and carotenoid pigments as predictors of phytoplankton biomass. Mar Biol. 1981;65:237–44.CAS 
Article 

Google Scholar 
Siddig AAH, Ellison AM, Ochs A, Villar-Leeman C, Lau MK. How do ecologists select and use indicator species to monitor ecological change? Insights from 14 years of publication in Ecological Indicators. Ecol Indic. 2016;60:223–30.Article 

Google Scholar 
Zorz J, Willis C, Comeau AM, Langille MGI, Johnson CL, Li WKW, et al. Drivers of regional bacterial community structure and diversity in the Northwest Atlantic Ocean. Front Microbiol 2019;10.Comeau AM, Douglas GM, Langille MGI. Microbiome Helper: a custom and streamlined workflow for microbiome research. mSystems. 2017;2:e00127–16.CAS 
Article 

Google Scholar 
Comeau AM, Li WKW, Tremblay J-É, Carmack EC, Lovejoy C. Arctic Ocean microbial community structure before and after the 2007 record sea ice minimum. PLOS ONE. 2011;6:e27492.CAS 
Article 

Google Scholar 
Parada AE, Needham DM, Fuhrman JA. Every base matters: assessing small subunit rRNA primers for marine microbiomes with mock communities, time series and global field samples. Environ Microbiol. 2016;18:1403–14.CAS 
Article 

Google Scholar 
Walters W, Hyde ER, Berg-Lyons D, Ackermann G, Humphrey G, Parada A, et al. Improved bacterial 16S rRNA gene (V4 and V4-5) and fungal Internal Transcribed Spacer marker gene primers for microbial community surveys. mSystems. 2015;1:e00009–15.Article 

Google Scholar 
Willis C, Desai D, LaRoche J. Influence of 16S rRNA variable region on perceived diversity of marine microbial communities of the Northern North Atlantic. FEMS Microbiol Lett. 2019;366:1–9.Article 

Google Scholar 
Bolyen E, Rideout JR, Dillon MR, Bokulich NA, Abnet CC, Al-Ghalith GA, et al. Reproducible, interactive, scalable and extensible microbiome data science using QIIME 2. Nat Biotechnol. 2019;37:852–7.CAS 
Article 

Google Scholar 
Decelle J, Romac S, Stern RF, Bendif EM, Zingone A, Audic S, et al. PhytoREF: a reference database of the plastidial 16S rRNA gene of photosynthetic eukaryotes with curated taxonomy. Mol Ecol Resour. 2015;15:1435–45.CAS 
Article 

Google Scholar 
Quast C, Pruesse E, Yilmaz P, Gerken J, Schweer T, Yarza P, et al. The SILVA ribosomal RNA gene database project: improved data processing and web-based tools. Nucleic Acids Res. 2013;41:D590–96.CAS 
Article 

Google Scholar 
NCBI Resource Coordinators. Database resources of the National Center for Biotechnology Information. Nucleic Acids Res. 2018;46:D8–13.Article 

Google Scholar 
R Core Team. R: A language and environment for statistical computing. 2020. R Foundation for Statistical Computing, Vienna, Austria. https://www.R-project.org/RStudio Team. RStudio: Integrated Development for R. 2020. RStudio, Inc., Boston, MA. http://www.rstudio.com/.Altschul SF, Gish W, Miller W, Myers EW, Lipman DJ. Basic local alignment search tool. J Mol Biol. 1990;215:403–10.CAS 
Article 

Google Scholar 
Johnson M, Zaretskaya I, Raytselis Y, Merezhuk Y, McGinnis S, Madden TL. NCBI BLAST: a better web interface. Nucleic Acids Res. 2008;36:W5–9.CAS 
Article 

Google Scholar 
Cáceres MD, Legendre P. Associations between species and groups of sites: indices and statistical inference. Ecology. 2009;90:3566–74.Article 

Google Scholar 
Edgar RC. MUSCLE: multiple sequence alignment with high accuracy and high throughput. Nucleic Acids Res. 2004;32:1792–7.CAS 
Article 

Google Scholar 
Kumar S, Stecher G, Tamura K. MEGA7: Molecular Evolutionary Genetics Analysis version 7.0 for bigger datasets. Mol Biol Evol. 2016;33:1870–4.CAS 
Article 

Google Scholar 
Oksanen J, Blanchet F, Friendly M, Kindt R, Legendre P, McGlinn D, et al. vegan: community ecology package. R package version. 2019;2:5–6. https://CRAN.R-project.org/package=vegan.
Google Scholar 
Wickham H. ggplot2: Elegant graphics for data analysis. 2016. Springer-Verlag, New York.Sohm JA, Ahlgren NA, Thomson ZJ, Williams C, Moffett JW, Saito MA, et al. Co-occurring Synechococcus ecotypes occupy four major oceanic regimes defined by temperature, macronutrients and iron. ISME J. 2016;10:333–45.CAS 
Article 

Google Scholar 
Ahlgren NA, Rocap G. Diversity and distribution of marine Synechococcus: multiple gene phylogenies for consensus classification and development of qPCR assays for sensitive measurement of clades in the ocean. Front Microbiol. 2012;3:1–24.Article 

Google Scholar 
Sunagawa S, Coelho LP, Chaffron S, Kultima JR, Labadie K, Salazar G, et al. Structure and function of the global ocean microbiome. Science. 2015;348:1261359.Article 

Google Scholar 
Logares R, Sunagawa S, Salazar G, Cornejo‐Castillo FM, Ferrera I, Sarmento H, et al. Metagenomic 16S rDNA Illumina tags are a powerful alternative to amplicon sequencing to explore diversity and structure of microbial communities. Environ Microbiol. 2014;16:2659–71.CAS 
Article 

Google Scholar 
Faust K, Raes J. CoNet app: inference of biological association networks using Cytoscape. F1000Research. 2016;5:1519.Article 

Google Scholar 
Cline MS, Smoot M, Cerami E, Kuchinsky A, Landys N, Workman C, et al. Integration of biological networks and gene expression data using Cytoscape. Nat Protoc. 2007;2:2366–82.CAS 
Article 

Google Scholar 
Fuhrman JA, Cram JA, Needham DM. Marine microbial community dynamics and their ecological interpretation. Nat Rev Microbiol. 2015;13:133–46.CAS 
Article 

Google Scholar 
Cram JA, Chow C-ET, Sachdeva R, Needham DM, Parada AE, Steele JA, et al. Seasonal and interannual variability of the marine bacterioplankton community throughout the water column over ten years. ISME J. 2015;9:563–80.Article 

Google Scholar 
Schoemann V, Becquevort S, Stefels J, Rousseau V, Lancelot C. Phaeocystis blooms in the global ocean and their controlling mechanisms: a review. J Sea Res. 2005;53:43–66.CAS 
Article 

Google Scholar 
Li W, Dickie P, Spry J. Plankton monitoring programme in the Bedford Basin, 1991-1997. 1998. Canadian Data Report of Fisheries and Aquatic Sciences 1036. Ocean Sciences Division, Maritimes Region, Fisheries and Oceans Canada.Bork P, Bowler C, Vargas C, de, Gorsky G, Karsenti E, Wincker P. Tara Oceans studies plankton at planetary scale. Science. 2015;348:873–873.CAS 
Article 

Google Scholar 
Callahan BJ, McMurdie PJ, Holmes SP. Exact sequence variants should replace operational taxonomic units in marker-gene data analysis. ISME J. 2017;11:2639–43.Article 

Google Scholar 
McLachlan JL, Seguel MR, Fritz L. Tetreutreptia pomquetensis gen. et sp. nov. (Euglenophyceae): a quadriflagellate, phototrophic marine Euglenoid. J Phycol. 1994;30:538–44.Article 

Google Scholar 
Edlund MB, Stoermer EF. Resting spores of the freshwater diatoms Acanthoceras and Urosolenia. J Paleolimnol. 1993;9:55–61.Article 

Google Scholar 
Tomas CR. Marine Phytoplankton: a guide to naked flagellates and coccolithophorids. 2012. Academic Press.Haas S, Robicheau BM, Rakshit S, Tolman J, Algar CK, LaRoche J, et al. Physical mixing in coastal waters controls and decouples nitrification via biomass dilution. Proc Natl Acad Sci. 2021;118:e2004877118.CAS 
Article 

Google Scholar 
Falkowski PG, Katz ME, Knoll AH, Quigg A, Raven JA, Schofield O, et al. The evolution of modern eukaryotic phytoplankton. Science. 2004;305:354–60.CAS 
Article 

Google Scholar 
Needham DM, Sachdeva R, Fuhrman JA. Ecological dynamics and co-occurrence among marine phytoplankton, bacteria and myoviruses shows microdiversity matters. ISME J. 2017;11:1614–29.Article 

Google Scholar 
Choi CJ, Bachy C, Jaeger GS, Poirier C, Sudek L, Sarma VVSS, et al. Newly discovered deep-branching marine plastid lineages are numerically rare but globally distributed. Curr Biol. 2017;27:R15–16.CAS 
Article 

Google Scholar 
Yoo YD, Seong KA, Kim HS, Jeong HJ, Yoon EY, Park J, et al. Feeding and grazing impact by the bloom-forming euglenophyte Eutreptiella eupharyngea on marine eubacteria and cyanobacteria. Harmful Algae. 2018;73:98–109.Article 

Google Scholar 
Dasilva CR, Li WKW, Lovejoy C. Phylogenetic diversity of eukaryotic marine microbial plankton on the Scotian Shelf Northwestern Atlantic Ocean. J Plankton Res. 2014;36:344–63.CAS 
Article 

Google Scholar 
Bolaños LM, Choi CJ, Worden AZ, Baetge N, Carlson CA, Giovannoni SJ. Seasonality of the microbial community composition in the North Atlantic. Front Mar Sci. 2021;8:23.Article 

Google Scholar 
Monier A, Worden AZ, Richards TA. Phylogenetic diversity and biogeography of the Mamiellophyceae lineage of eukaryotic phytoplankton across the oceans. Environ Microbiol Rep. 2016;8:461–9.CAS 
Article 

Google Scholar 
Irion S, Christaki U, Berthelot H, L’Helguen S, Jardillier L. Small phytoplankton contribute greatly to CO2-fixation after the diatom bloom in the Southern Ocean. ISME J. 2021;15:2509–22.CAS 
Article 

Google Scholar 
Choi CJ, Jimenez V, Needham D, Poirier C, Bachy C, Alexander H, et al. Seasonal and geographical transitions in eukaryotic phytoplankton community structure in the Atlantic and Pacific Oceans. Front Microbiol. 2020;11:2187.
Google Scholar 
Leblanc K, Quéguiner B, Diaz F, Cornet V, Michel-Rodriguez M, Durrieu de Madron X, et al. Nanoplanktonic diatoms are globally overlooked but play a role in spring blooms and carbon export. Nat Commun. 2018;9:953.Article 

Google Scholar 
Lundholm N, Hasle GR. Fragilariopsis (Bacillariophyceae) of the Northern Hemisphere – morphology, taxonomy, phylogeny and distribution, with a description of F. pacifica sp. nov. Phycologia. 2010;49:438–60.Article 

Google Scholar 
Martínez-pérez C, Mohr W, Löscher CR, Dekaezemacker J, Littmann S, Yilmaz P, et al. The small unicellular diazotrophic symbiont, UCYN-A, is a key player in the marine nitrogen cycle. Nat Microbiol. 2016;1:16163.Article 

Google Scholar 
Zehr JP, Shilova IN, Farnelid HM, Muñoz-Marín M, del C, Turk-Kubo KA. Unusual marine unicellular symbiosis with the nitrogen-fixing cyanobacterium UCYN-A. Nat Microbiol. 2016;2:1–11.
Google Scholar 
Worden AZ, Janouskovec J, McRose D, Engman A, Welsh RM, Malfatti S, et al. Global distribution of a wild alga revealed by targeted metagenomics. Curr Biol. 2012;22:R675–77.CAS 
Article 

Google Scholar 
Altenburger A, Blossom HE, Garcia-Cuetos L, Jakobsen HH, Carstensen J, Lundholm N, et al. Dimorphism in cryptophytes—The case of Teleaulax amphioxeia/Plagioselmis prolonga and its ecological implications. Sci Adv. 2020;6:eabb1611.CAS 
Article 

Google Scholar 
Kling JD, Lee MD, Fu F, Phan MD, Wang X, Qu P, et al. Transient exposure to novel high temperatures reshapes coastal phytoplankton communities. ISME J. 2020;14:413–24.CAS 
Article 

Google Scholar 
Chassot E, Bonhommeau S, Dulvy NK, Mélin F, Watson R, Gascuel D, et al. Global marine primary production constrains fisheries catches. Ecol Lett. 2010;13:495–505.Article 

Google Scholar 
Gentry RR, Froehlich HE, Grimm D, Kareiva P, Parke M, Rust M, et al. Mapping the global potential for marine aquaculture. Nat Ecol Evol. 2017;1:1317–24.Article 

Google Scholar 
Benway HM, Lorenzoni L, White AE, Fiedler B, Levine NM, Nicholson DP, et al. Ocean time series observations of changing marine ecosystems: an era of integration, synthesis, and societal applications. Front Mar Sci. 2019;6:393.Article 

Google Scholar 
Rigosi A, Fleenor W, Rueda F. State-of-the-art and recent progress in phytoplankton succession modelling. Environ Rev. 2010;18:423–40.Article 

Google Scholar 
Daniels CJ, Poulton AJ, Esposito M, Paulsen ML, Bellerby R, St John M, et al. Phytoplankton dynamics in contrasting early stage North Atlantic spring blooms: composition, succession, and potential drivers. Biogeosciences. 2015;12:2395–409.Article 

Google Scholar 
Masuda Y, Yamanaka Y, Hirata T, Nakano H. Competition and community assemblage dynamics within a phytoplankton functional group: Simulation using an eddy-resolving model to disentangle deterministic and random effects. Ecol Model. 2017;343:1–14.Article 

Google Scholar 
Percopo I, Siano R, Cerino F, Sarno D, Zingone A. Phytoplankton diversity during the spring bloom in the northwestern Mediterranean Sea. Botanica Marina. 2011;54:243–67.Article 

Google Scholar 
Sun J, Liu D. Geometric models for calculating cell biovolume and surface area for phytoplankton. J Plankton Res. 2003;25:1331–46.Article 

Google Scholar 
Agawin N, Duarte C, Agustí S, Vaqué D. Effect of N:P ratios on response of Mediterranean picophytoplankton to experimental nutrient inputs. Aquat Microb Ecol. 2004;34:57–67.Article 

Google Scholar 
Bertilsson S, Berglund O, Karl DM, Chisholm SW. Elemental composition of marine Prochlorococcus and Synechococcus: Implications for the ecological stoichiometry of the sea. Limnology Oceanogr. 2003;48:1721–31.CAS 
Article 

Google Scholar 
Tomas CR. Identifying Marine Phytoplankton. 1997. Elsevier.Harrison PJ, Zingone A, Mickelson MJ, Lehtinen S, Ramaiah N, Kraberg AC, et al. Cell volumes of marine phytoplankton from globally distributed coastal data sets. Estuarine, Coastal Shelf Sci. 2015;162:130–42.CAS 
Article 

Google Scholar 
Guillou L, Chrétiennot-Dinet M-J, Medlin LK, Claustre H, Goër SL, Vaulot D. Bolidomonas: a new genus with two species belonging to a new algal class, the Bolidophyceae (Heterokonta). J Phycol. 1999;35:368–81.Article 

Google Scholar  More

Stearns, S. C. The Evolution of Life Histories (Oxford University, 1992).
Google Scholar 
Roff, D. A., Mostowy, S. & Fairbairn, D. J. The evolution of trade-offs: Testing predictions on response to selection and environmental variation. Evolution (N. Y.). 56, 84–95 (2002).
Google Scholar 
Lack, D. The Significance of Clutch-size. Ibis (Lond. 1859). 89, 302–352 (1947).Article 

Google Scholar 
Drent, R. H. & Daan, S. The prudent parent: Energetic adjustments in avian breeding. Adrea 68, 225–263 (1980).
Google Scholar 
Harshman, L. G. & Zera, A. J. The cost of reproduction: The devil in the details. Trends Ecol. Evol. 22, 80–86 (2007).Article 

Google Scholar 
Dijkstra, C., Daan, S. & Tinbergen, J. M. Family planning in the Kestrel (Falco Tinnunculus): The ultimate control of covariation of laying date and clutch size. Behaviour 114, 83–116 (1990).Article 

Google Scholar 
Cox, R. M. et al. Experimental evidence for physiological costs underlying the trade-off between reproduction and survival. Funct. Ecol. 24, 1262–1269 (2010).Article 

Google Scholar 
Marshall, K. E. & Sinclair, B. J. Repeated stress exposure results in a survival-reproduction trade-off in Drosophila melanogaster. Proc. R. Soc. B Biol. Sci. 277, 963–969 (2010).Article 

Google Scholar 
Rivalan, P. et al. Trade-off between current reproductive effort and delay to next reproduction in the leatherback sea turtle. Oecologia 145, 564–574 (2005).ADS 
Article 

Google Scholar 
Perrins, C. M. Population Fluctuations and Clutch-Size in the Great Tit, Parus major. J. Anim. Ecol. 34, 601 (1965).Article 

Google Scholar 
Walker, R. S., Gurven, M., Burger, O. & Hamilton, M. J. The trade-off between number and size of offspring in humans and other primates. Proc. R. Soc. B Biol. Sci. 275, 827–833 (2008).Article 

Google Scholar 
Williams, G. C. Natural selection, the costs of reproduction, and a refinement of Lack’s principle. Am. Nat. 100, 687–690 (1966).Article 

Google Scholar 
Charnov, E. L. & Krebs, J. R. On clutch-size and fitness. Ibis (Lond. 1859). 116, 217–219 (1974).Article 

Google Scholar 
Ricklefs, R. E. On the evolution of reproductive strategies in birds: Reproductive effort. Am. Nat. 111, 453–478 (1977).Article 

Google Scholar 
Martin, T. E. Food as a limit on breeding birds: A life-history perspective. Annu. Rev. Ecol. Syst. 18, 435–487 (1987).Article 

Google Scholar 
Santangeli, A., Hakkarainen, H., Laaksonen, T. & Korpimäki, E. Home range size is determined by habitat composition but feeding rate by food availability in male Tengmalm’s owls. Anim. Behav. 83, 1115–1123 (2012).Article 

Google Scholar 
Kouba, M., Bartoš, L., Sindelář, J. & St’astny, K. Alloparental care and adoption in Tengmalm’s Owl (Aegolius funereus). J. Ornithol. 158, 185–191 (2017).Article 

Google Scholar 
Redpath, S. M. Habitat fragmentation and the individual: Tawny owls Strix aluco in woodland patches. J. Anim. Ecol. 64, 652 (1995).Article 

Google Scholar 
Bruun, M. & Smith, H. G. Landscape composition affects habitat use and foraging flight distances in breeding European starlings. Biol. Conserv. 114, 179–187 (2003).Article 

Google Scholar 
Frey-Roos, F., Brodmann, P. A. & Reyer, H. U. Relationships between food resources, foraging patterns, and reproductive success in the water pipit, Anthus sp. Spinoletta. Behav. Ecol. 6, 287–295 (1995).Article 

Google Scholar 
Saïd, S. et al. What shapes intra-specific variation in home range size? A case study of female roe deer. Oikos 118, 1299–1306 (2009).Article 

Google Scholar 
Van Beest, F. M., Rivrud, I. M., Loe, L. E., Milner, J. M. & Mysterud, A. What determines variation in home range size across spatiotemporal scales in a large browsing herbivore?. J. Anim. Ecol. 80, 771–785 (2011).Article 

Google Scholar 
Hakkarainen, H., Koivunen, V. & Korpimäki, E. Reproductive success and parental effort of Tengmalm’s owls: Effects of spatial and temporal variation in habitat quality. Ecoscience 4, 35–42 (1997).Article 

Google Scholar 
Kittle, A. M. et al. Wolves adapt territory size, not pack size to local habitat quality. J. Anim. Ecol. 84, 1177–1186 (2015).Article 

Google Scholar 
Trembley, I., Thomas, D., Blondel, J., Perret, P. & Lambrechts, M. M. The effect of habitat quality on foraging patterns, provisioning rate and nestling growth in Corsican Blue Tits Parus caeruleus. Ibis (Lond. 1859). 147, 17–24 (2004).Article 

Google Scholar 
Turcotte, Y. & Desrochers, A. Landscape-dependent response to predation risk by forest birds. Oikos 100, 614–618 (2003).Article 

Google Scholar 
Hinsley, S. A., Rothery, P. & Bellamy, P. E. Influence of woodland area on breeding success in great tits parus major and blue tits Parus caeruleus. J. Avian Biol. 30, 271 (1999).Article 

Google Scholar 
Hinam, H. L. & Clair, C. C. S. High levels of habitat loss and fragmentation limit reproductive success by reducing home range size and provisioning rates of Northern saw-whet owls. Biol. Conserv. 141, 524–535 (2008).Article 

Google Scholar 
Daan, S., Deerenberg, C. & Dijkstra, C. Increased daily work precipitates natural death in the Kestrel. J. Anim. Ecol. 65, 539 (1996).Article 

Google Scholar 
Slagsvold, T., Sandvik, J., Rofstad, G., Lorentsen, O. & Husby, M. On the adaptive value of intraclutch egg-size variation in birds. Auk 101, 685–697 (1984).Article 

Google Scholar 
Tripet, F., Richner, H. & Tripet, F. Host responses to ectoparasites: Food compensation by parent blue tits. Oikos 78, 557 (1997).Article 

Google Scholar 
Budden, A. E. & Beissinger, S. R. Resource allocation varies with parental sex and brood size in the asynchronously hatching green-rumped parrotlet (Forpus passerinus). Behav. Ecol. Sociobiol. 63, 637–647 (2009).Article 

Google Scholar 
Bókony, V. et al. Stress response and the value of reproduction: Are birds prudent parents?. Am. Nat. 173, 589–598 (2009).Article 

Google Scholar 
McGinley, M. A., Temme, D. H. & Geber, M. A. Parental investment in offspring in variable environments: Theoretical and empirical considerations. Am. Nat. 130, 370–398 (1987).Article 

Google Scholar 
Ghalambor, C. K. & Martin, T. E. Fecundity-survival trade-offs and parental risk-taking in birds. Science (80-.). 292, 494–497 (2001).ADS 
CAS 
Article 

Google Scholar 
Caro, S. M., Griffin, A. S., Hinde, C. A. & West, S. A. Unpredictable environments lead to the evolution of parental neglect in birds. Nat. Commun. 7, 1–10 (2016).Article 

Google Scholar 
Roulin, A. Barn Owls: Evolution and Ecology (Cambridge University Press, 2020).
Google Scholar 
Romano, A., Séchaud, R. & Roulin, A. Global biogeographical patterns in the diet of a cosmopolitan avian predator. J. Biogeogr. 47, 1467–1481 (2020).Article 

Google Scholar 
Arlettaz, R., Krähenbühl, M., Almasi, B., Roulin, A. & Schaub, M. Wildflower areas within revitalized agricultural matrices boost small mammal populations but not breeding Barn Owls. J. Ornithol. 151, 553–564 (2010).Article 

Google Scholar 
Hindmarch, S., Elliott, J. E., Mccann, S. & Levesque, P. Habitat use by barn owls across a rural to urban gradient and an assessment of stressors including, habitat loss, rodenticide exposure and road mortality. Landsc. Urban Plan. 164, 132–143 (2017).Article 

Google Scholar 
Castañeda, X. A., Huysman, A. E. & Johnson, M. D. Barn Owls select uncultivated habitats for hunting in a winegrape growing region of California. Ornithol. Appl. 123, 1–15 (2021).
Google Scholar 
Séchaud, R. et al. Behaviour-specific habitat selection patterns of breeding barn owls. Mov. Ecol. 9, 18 (2021).Article 

Google Scholar 
Roulin, A., Ducrest, A.-L. & Dijkstra, C. Effect of brood size manipulations on parents and offspring in the barn owl Tyto alba. Ardea 87, 91–100 (1999).
Google Scholar 
Béziers, P. & Roulin, A. Double brooding and offspring desertion in the barn owl Tyto alba. J. Avian Biol. 47, 235–244 (2016).Article 

Google Scholar 
Laaksonen, T., Hakkarainen, H. & Korpimäki, E. Lifetime reproduction of a forest-dwelling owl increases with age and area of forests. Proc. R. Soc. B Biol. Sci. 271, 10058 (2004).Article 

Google Scholar 
Bryant, D. M. Energy expenditure and body mass changes as measures of reproductive costs in birds. Funct. Ecol. 2, 23 (1988).Article 

Google Scholar 
Merilä, J. & Wiggins, D. A. Mass loss in breeding blue tits: The role of energetic stress. J. Anim. Ecol. 66, 452 (1997).Article 

Google Scholar 
Frey, C., Sonnay, C., Dreiss, A. & Roulin, A. Habitat, breeding performance, diet and individual age in Swiss Barn Owls (Tyto alba). J. Ornithol. 152, 279–290 (2010).Article 

Google Scholar 
Aschwanden, J., Holzgang, O. & Jenni, L. Importance of ecological compensation areas for small mammals in intensively farmed areas. Wildlife Biol. 13, 150–158 (2007).Article 

Google Scholar 
Roulin, A. Tyto alba Barn Owl. BWP Updat. 4, 115–138 (2002).
Google Scholar 
Calabrese, J. M., Fleming, C. H. & Gurarie, E. ctmm: An R package for analyzing animal relocation data as a continuous-time stochastic process. Methods Ecol. Evol. 7, 1124–1132 (2016).Article 

Google Scholar 
Fleming, C. H. et al. Rigorous home range estimation with movement data: A new autocorrelated kernel density estimator. Ecology 96, 1182–1188 (2015).CAS 
Article 

Google Scholar 
Dixon, P. VEGAN, a package of R functions for community ecology. J. Veg. Sci. 14, 927–930 (2003).Article 

Google Scholar 
Taylor, I. Barn Owls: Predator-Prey Relationships and Conservation (Cambridge University Press, 1994).
Google Scholar 
van den Brink, V., Dreiss, A. N. & Roulin, A. Melanin-based coloration predicts natal dispersal in the barn owl, Tyto alba. Anim. Behav. 84, 805–812 (2012).Article 

Google Scholar 
Dreiss, A. N. & Roulin, A. Divorce in the barn owl: Securing a compatible or better mate entails the cost of re-pairing with a less ornamented female mate. J. Evol. Biol. 27, 1114–1124 (2014).CAS 
Article 

Google Scholar 
Garriga, J., Palmer, J. R. B., Oltra, A. & Bartumeus, F. Expectation-maximization binary clustering for behavioural annotation. PLoS One 11, e0151984 (2016).Article 

Google Scholar 
San-Jose, L. M. et al. Differential fitness effects of moonlight on plumage colour morphs in barn owls. Nat. Ecol. Evol. 3, 1331–1340 (2019).Article 

Google Scholar 
Bracis, C., Bildstein, K. L. & Mueller, T. Revisitation analysis uncovers spatio-temporal patterns in animal movement data. Ecography (Cop.) 41, 1801–1811 (2018).Article 

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

Google Scholar 
Lüdecke, D. Data Visualization for Statistics in Social Science [R package sjPlot version 2.8.9]. (2021).Rutz, C. & Bijlsma, R. G. Food-limitation in a generalist predator. Proc. R. Soc. B Biol. Sci. 273, 2069–2076 (2006).Article 

Google Scholar 
Altmann, S. A. The impact of locomotor energetics on mammalian foraging. J. Zool. 211, 215–225 (1987).Article 

Google Scholar 
Evens, R. et al. Proximity of breeding and foraging areas affects foraging effort of a crepuscular, insectivorous bird. Sci. Rep. 8, 1–11 (2018).
Google Scholar 
Pfeiffer, T. & Meyburg, B. U. GPS tracking of Red Kites (Milvus milvus) reveals fledgling number is negatively correlated with home range size. J. Ornithol. 156, 963–975 (2015).Article 

Google Scholar 
Romano, A. et al. Nestling sex and plumage color predict food allocation by barn swallow parents. Behav. Ecol. 27, 1198–1205 (2016).Article 

Google Scholar 
Bryant, D. M. & Tatner, P. Hatching asynchrony, sibling competition and siblicide in nestling birds: Studies of swiftlets and bee-eaters. Anim. Behav. 39, 657–671 (1990).Article 

Google Scholar 
Mock, D. W. & Parker, G. A. Advantages and disadvantages of egret and heron brood reduction. Evolution (N. Y.). 40, 459–470 (1986).
Google Scholar 
Stenning, M. J. Hatching asynchrony, brood reduction and other rapidly reproducing hypotheses. Trends Ecol. Evol. 11, 243–246 (1996).CAS 
Article 

Google Scholar 
Roulin, A., Colliard, C., Russier, F., Fleury, M. & Grandjean, V. Sib-sib communication and the risk of prey theft in the barn owl Tyto alba. J. Avian Biol. 39, 593–598 (2008).Article 

Google Scholar 
Korpimaki, E. Costs of reproduction and success of manipulated broods under varying food conditions in Tengmalm’s owl. J. Anim. Ecol. 57, 1879 (1988).
Google Scholar 
Tolonen, P. & Korpimäki, E. Do kestrels adjust their parental effort to current or future benefit in a temporally varying environment?. Écoscience 3, 165–172 (1996).Article 

Google Scholar 
Harrison, F., Barta, Z., Cuthill, I. & Székely, T. How is sexual conflict over parental care resolved? A meta-analysis. J. Evol. Biol. 22, 1800–1812 (2009).CAS 
Article 

Google Scholar 
Osorno, J. L. & Székely, T. Sexual conflict and parental care in magnificent frigatebirds: Full compensation by deserted females. Anim. Behav. 68, 337–342 (2004).Article 

Google Scholar 
Paredes, R., Jones, I. L. & Boness, D. J. Parental roles of male and female thick-billed murres and razorbills at the Gannet Islands, Labrador. Behaviour 143, 451–481 (2006).Article 

Google Scholar 
Kleijn, D. et al. Mixed biodiversity benefits of agri-environment schemes in five European countries. Ecol. Lett. 9, 243–254 (2006).CAS 
Article 

Google Scholar 
Zingg, S., Ritschard, E., Arlettaz, R. & Humbert, J. Y. Increasing the proportion and quality of land under agri-environment schemes promotes birds and butterflies at the landscape scale. Biol. Conserv. 231, 39–48 (2019).Article 

Google Scholar  More

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In this episode:00:46 What COP26 promises will do for climateAt COP26 countries made a host of promises and commitments to tackle global warming. Now, a new analysis suggests these pledges could limit warming to below 2˚C – if countries stick to them.BBC News: Climate change: COP26 promises will hold warming under 2C03:48 Efficiency boost for energy storage solutionStoring excess energy is a key obstacle preventing wider adoption of renewable power. One potential solution has been to store this energy as heat before converting it back into electricity, but to date this process has been inefficient. Last week, a team reported the development of a new type of ‘photothermovoltaic’ that increases the efficiency of converting stored heat back into electricity, potentially making the process economically viable.Science: ‘Thermal batteries’ could efficiently store wind and solar power in a renewable grid07:56 Leeches’ lunches help ecologists count wildlifeBlood ingested by leeches may be a way to track wildlife, suggests new research. Using DNA from the blood, researchers were able to detect 86 different species in China’s Ailaoshan Nature Reserve. Their results also suggest that biodiversity was highest in the high-altitude interior of the reserve, suggesting that human activity had pushed wildlife away from other areas.ScienceNews: Leeches expose wildlife’s whereabouts and may aid conservation efforts11:05 How communication evolved in underground cave fishResearch has revealed that Mexican tetra fish are very chatty, and capable of making six distinct sounds. They also showed that fish populations living in underground caves in north-eastern Mexico have distinct accents.New Scientist: Blind Mexican cave fish are developing cave-specific accents14:36 Declassified data hints at interstellar meteorite strikeIn 2014 a meteorite hit the Earth’s atmosphere that may have come from far outside the solar system, making it the first interstellar object to be detected. However, as some of the data needed to confirm this was classified by the US Government, the study was never published. Now the United States Space Command have confirmed the researchers’ findings, although the work has yet to be peer reviewed.LiveScience: An interstellar object exploded over Earth in 2014, declassified government data revealVice: Secret Government Info Confirms First Known Interstellar Object on Earth, Scientists SaySubscribe to Nature Briefing, an unmissable daily round-up of science news, opinion and analysis free in your inbox every weekday.Never miss an episode: Subscribe to the Nature Podcast on Apple Podcasts, Google Podcasts, Spotify or your favourite podcast app. Head here for the Nature Podcast RSS feed. More