Philippa Kaur

1.Strange RN, Scott PR. Plant disease: a threat to global food security. Annu Rev Phytopathol. 2005;43:83–116.CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
2.Savary S, Willocquet L, Pethybridge SJ, Esker P, McRoberts N, Nelson A. The global burden of pathogens and pests on major food crops. Nat Ecol Evol. 2019;3:430–9.PubMed 
Article 
PubMed Central 

Google Scholar 
3.Savary S, Bregaglio S, Willocquet L, Gustafson D, Mason D’Croz D, Sparks A, et al. Crop health and its global impacts on the components of food security. Food Secur. 2017;9:311–27.Article 

Google Scholar 
4.Garrett KA, Alcalá-Briseño RI, Andersen KF, Buddenhagen CE, Choudhury RA, Fulton JC, et al. Network analysis: a systems framework to address grand challenges in plant pathology. Annu Rev Phytopathol. 2018;56:559–80.CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
5.Pautasso M, Xu X, Jeger MJ, Harwood TD, Moslonka-Lefebvre M, Pellis L. Disease spread in small-size directed trade networks: the role of hierarchical categories. J Appl Ecol. 2010;47:1300–9.Article 

Google Scholar 
6.Bryant JM, Grogono DM, Rodriguez-Rincon D, Everall I, Brown KP, Moreno P, et al. Emergence and spread of a human-transmissible multidrug-resistant nontuberculous mycobacterium. Science. 2016;354:751–7.CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
7.Yang C, Zhang X, Fan H, Li Y, Hu Q, Yang R, et al. Genetic diversity, virulence factors and farm-to-table spread pattern of Vibrio parahaemolyticus food-associated isolates. Food Microbiol. 2019;84:103270.PubMed 
Article 
PubMed Central 

Google Scholar 
8.Dallman TJ, Byrne L, Ashton PM, Cowley LA, Perry NT, Adak G, et al. Whole-genome sequencing for national surveillance of Shiga toxin-producing Escherichia coli O157. Clin Infect Dis. 2015;61:305–12.PubMed 
PubMed Central 
Article 

Google Scholar 
9.Kwong JC, Mercoulia K, Tomita T, Easton M, Li HY, Bulach DM, et al. Prospective whole-genome sequencing enhances national surveillance of Listeria monocytogenes. J Clin Microbiol. 2016;54:333–42.CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
10.Mather AE, Reid SW, Maskell DJ, Parkhill J, Fookes MC, Harris SR, et al. Distinguishable epidemics of multidrug-resistant Salmonella Typhimurium DT104 in different hosts. Science. 2013;341:1514–7.CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
11.Richards VP, Velsko IM, Alam T, Zadoks RN, Manning SD, Pavinski Bitar PD, et al. Population gene introgression and high genome plasticity for the zoonotic pathogen Streptococcus agalactiae. Mol Biol Evol. 2019;36:2572–90.CAS 
PubMed Central 
Article 

Google Scholar 
12.Mellor KC, Petrovska L, Thomson NR, Harris K, Reid SWJ, Mather AE. Antimicrobial resistance diversity suggestive of distinct Salmonella Typhimurium sources or selective pressures in food-production animals. Front Microbiol. 2019;10:708.PubMed 
PubMed Central 
Article 

Google Scholar 
13.Monteil CL, Yahara K, Studholme DJ, Mageiros L, Méric G, Swingle B, et al. Population-genomic insights into emergence, crop adaptation and dissemination of Pseudomonas syringae pathogens. Micro Genom. 2016;2:e000089.
Google Scholar 
14.Perez-Quintero AL, Ortiz-Castro M, Lang JM, Rieux A, Wu G, Liu S, et al. Genomic acquisitions in emerging populations of Xanthomonas vasicola pv. vasculorum infecting corn in the United States and Argentina. Phytopathology. 2020;110:1161–73.CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
15.McCann HC, Li L, Liu Y, Li D, Pan H, Zhong C, et al. Origin and evolution of the kiwifruit canker pandemic. Genome Biol Evol. 2017;9:932–44.CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
16.Quibod IL, Atieza-Grande G, Oreiro EG, Palmos D, Nguyen MH, Coronejo ST, et al. The Green Revolution shaped the population structure of the rice pathogen Xanthomonas oryzae pv. oryzae. ISME J. 2020;14:492–505.CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
17.Straub C, Colombi E, McCann H. Population genomics of bacterial plant pathogens. Phytopathology. 2021. https://doi.org/10.1094/PHYTO-09-20-0412-RVW.18.Vinatzer BA, Monteil CL, Clarke CR. Harnessing population genomics to understand how bacterial pathogens emerge, adapt to crop hosts, and disseminate. Ann Rev Phytopathol. 2014;52:19–43.CAS 
Article 

Google Scholar 
19.Weisberg AJ, Davis EW, Tabima JF, Belcher MS, Miller M, Kuo C, et al. Unexpected conservation and global transmission of agrobacterial virulence plasmids. Science. 2020;368:eaba5256.CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
20.Jones JB, Lacy GH, Bouzar H, Stall RE, Schaad NW. Reclassification of the xanthomonads associated with bacterial spot disease of tomato and pepper. Syst Appl Microbiol. 2004;27:755–62.CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
21.Potnis N, Timilsina S, Strayer A, Shantharaj D, Barak JD, Paret ML, et al. Bacterial spot of tomato and pepper: diverse Xanthomonas species with a wide variety of virulence factors posing a worldwide challenge. Mol Plant Pathol. 2015;16:907–20.PubMed 
PubMed Central 
Article 

Google Scholar 
22.VanSickle J, Weldon R. The economic impact of bacterial leaf spot on the tomato industry. Tomato Inst Proc. 2009:30–31 https://plantpath.ifas.ufl.edu/rsol/RalstoniaPublications_PDF/Tomato_Institute_Proceedings_09.pdf.23.Horvath DM, Stall RE, Jones JB, Pauly MH, Vallad GE, Dahlbeck D, et al. Transgenic resistance confers effective field level control of bacterial spot disease in tomato. PLOS One. 2012;7:e42036.CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
24.Kunwar S, Iriarte F, Fan Q, Evaristo da Silva E, Ritchie L, Nguyen NS, et al. Transgenic expression of EFR and Bs2 genes for field management of bacterial wilt and bacterial spot of tomato. Phytopathology. 2018;108:1402–11.CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
25.Jones JB, Bouzar H, Somodi GC, Stall RE, Pernezny K, El-Morsy G, et al. Evidence for the preemptive nature of tomato race 3 of Xanthomonas campestris pv. vesicatoria in Florida. Phytopathology. 1998;88:33–38.CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
26.Timilsina S, Jibrin MO, Potnis N, Minsavage GV, Kebede M, Schwartz A, et al. Multilocus sequence analysis of xanthomonads causing bacterial spot of tomato and pepper plants reveals strains generated by recombination among species and recent global spread of Xanthomonas gardneri. Appl Environ Microbiol. 2015;81:1520–9.PubMed 
PubMed Central 
Article 
CAS 

Google Scholar 
27.United States Department of Agriculture. National Agricultural Statistics Service. Washington, DC: United States Department of Agriculture; 2019.28.Klein-Gordon JM, Xing Y, Garrett KA, Abrahamian P, Paret ML, Minsavage GV, et al. Assessing changes and associations in the Xanthomonas perforans population across Florida commercial tomato fields via a state-wide survey. Phytopathology. 2021;111:1029–1041.29.Vallad GE, Timilsina S, Adkison H, Potnis N, Minsavage G, Jones J, et al. A recent survey of xanthomonads causing bacterial spot of tomato in Florida provides insights into management strategies. Tomato Inst Proc. 2013:25–27 https://swfrec.ifas.ufl.edu/docs/pdf/veghort/tomato-institute/proceedings/ti13_proceedings.pdf.30.Timilsina S, Pereira-Martin JA, Minsavage GV, Iruegas-Bocardo F, Abrahamian P, Potnis N, et al. Multiple recombination events drive the current genetic structure of Xanthomonas perforans in Florida. Front Microbiol. 2019;10:448.PubMed 
PubMed Central 
Article 

Google Scholar 
31.Burlakoti R, Hsu C, Chen J, Wang J. Population dynamics of Xanthomonads associated with bacterial spot of tomato and pepper during twenty-seven years across Taiwan. Plant Dis. 2018;102:1348–56.CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
32.Araújo ER, Costa JR, Ferreira MASV, Quezada-Duval AM. Widespread distribution of Xanthomonas perforans and limited presence of X. gardneri in Brazil. Plant Pathol. 2017;66:159–68.Article 
CAS 

Google Scholar 
33.Jones JB, Pohronezny KL, Stall RE, Jones JP. Survival of Xanthomonas campestris pv. vesicatoria in Florida on tomato crop residue, weeds, seeds, and volunteer tomato plants. Phytopathology. 1986;76:430–4.Article 

Google Scholar 
34.Sijam K, Chang CJ, Gitaitis RD. An agar medium for the isolation and identification of Xanthomonas campestris pv. vesicatoria from seed. Phytopathology. 1991;81:831–4.Article 

Google Scholar 
35.Abrahamian P, Timilsina S, Minsavage GV, Potnis N, Jones JB, Goss EM, et al. Molecular epidemiology of Xanthomonas perforans outbreaks in tomato plants from transplant to field as determined by single-nucleotide polymorphism analysis. Appl Environ Microbiol. 2019;85:e01220–01219.CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
36.Abrahamian P, Sharma A, Jones J, Vallad GE. Dynamics and spread of bacterial spot epidemics in tomato transplants grown for field production. Plant Dis. 2021 in press.37.Baym M, Kryazhimskiy S, Lieberman TD, Chung H, Desai MM, Kishony R. Inexpensive multiplexed library preparation for megabase-sized genomes. PLOS One. 2015;10:e0128036.PubMed 
PubMed Central 
Article 
CAS 

Google Scholar 
38.Tudor-Nelson SM, Minsavage GV, Stall RE, Jones JB. Bacteriocin-like substances from tomato race 3 strains of Xanthomonas campestris pv. vesicatoria. Bacteriology. 2003;93:1415–21.CAS 

Google Scholar 
39.Schwartz A, Potnis N, Timilsina S, Wilson M, Patane J, Martins J, et al. Phylogenomics of Xanthomonas field strains infecting pepper and tomato reveals diversity in effector repertoires and identifies determinants of host specificity. Front Microbiol. 2015;6:535.PubMed 
PubMed Central 
Article 

Google Scholar 
40.Nurk S, Bankevich A, Antipov D, Gurevich AA, Korobeynikov A, Lapidus A, et al. Assembling single-cell genomes and mini-metagenomes from chimeric MDA products. J Comput Biol. 2013;20:714–37.CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
41.Langmead B, Salzberg SL. Fast gapped-read alignment with Bowtie 2. Nat Methods. 2012;9:357–9.CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
42.Li H, Handsaker B, Wysoker A, Fennell T, Ruan J, Homer N, et al. The sequence alignment/map format and SAMtools. Bioinform. 2009;25:2078–9.Article 
CAS 

Google Scholar 
43.Walker BJ, Abeel T, Shea T, Priest M, Abouelliel A, Sakthikumar S, et al. Pilon: an integrated tool for comprehensive microbial variant detection and genome assembly improvement. PLOS One. 2014;9:e112963.PubMed 
PubMed Central 
Article 
CAS 

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

Google Scholar 
45.Chen IA, Chu K, Palaniappan K, Pillay M, Ratner A, Huang J, et al. IMG/M v.5.0: an integrated data management and comparative analysis system for microbial genomes and microbiomes. Nucleic Acids Res. 2019;47:D666–d677. D1CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
46.Seemann T. Prokka: rapid prokaryotic genome annotation. Bioinformatics. 2014;30:2068–9.CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
47.Katoh K, Standley DM. MAFFT multiple sequence alignment software version 7: improvements in performance and usability. Mol Biol Evol. 2013;30:772–80.CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
48.Rice P, Longden I, Bleasby A. EMBOSS: the European molecular biology open software suite. Trends Genet. 2000;16:276–7.CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
49.Darriba D, Posada D, Kozlov AM, Stamatakis A, Morel B, Flouri T. ModelTest-NG: a new and scalable tool for the selection of DNA and protein evolutionary models. Mol Biol Evol. 2019;37:291–4.PubMed Central 
Article 
CAS 

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

Google Scholar 
51.Stamatakis A, Hoover P, Rougemont J. A rapid bootstrap algorithm for the RAxML web servers. Syst Biol. 2008;57:758–71.PubMed 
Article 
PubMed Central 

Google Scholar 
52.Didelot X, Wilson DJ. ClonalFrameML: efficient inference of recombination in whole bacterial genomes. PLOS Comput Biol. 2015;11:e1004041.PubMed 
PubMed Central 
Article 
CAS 

Google Scholar 
53.Tonkin-Hill G, Lees JA, Bentley SD, Frost SDW, Corander J. RhierBAPS: an R implementation of the population clustering algorithm hierBAPS. Wellcome Open Res. 2018;3:93.PubMed 
PubMed Central 
Article 

Google Scholar 
54.Cheng L, Connor TR, Sirén J, Aanensen DM, Corander J. Hierarchical and spatially explicit clustering of DNA sequences with BAPS software. Mol Biol Evol. 2013;30:1224–8.CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
55.Letunic I, Bork P. Interactive Tree Of Life (iTOL): an online tool for phylogenetic tree display and annotation. Bioinformatics. 2007;23:127–8.CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
56.Csardi G, Nepusz T. The igraph software package for complex network research. 2006; InterJ., Complex Systems:1695.57.R Core Team. R: a language and environment for statistical computing. Vienna, Austria: R Foundation for Statistical Computing; 2020.58.Li H, Durbin R. Fast and accurate short read alignment with Burrows-Wheeler transform. Bioinformatics. 2009;25:1754–60.CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
59.Canteros BI, Minsavage GV, Jones JB, Stall RE. Diversity of plasmids in Xanthomonas campestris pv. vesicatoria. Phytopathology. 1995;85:1482–6.Article 

Google Scholar 
60.Li H. Aligning sequence reads, clone sequences and assembly contigs with BWA-MEM. arXiv. 2013. https://arxiv.org/abs/1303.3997.61.Broad Institute: Picard. http://broadinstitute.github.io/picard/ 2019.62.Garrison E, Marth G. Haplotype-based variant detection from short-read sequencing. arXiv. 2012. https://arxiv.org/abs/1207.3907.63.Garrison, E, Kronenberg, ZN, Dawson, ET, Pedersen, BS, Prins, P. Vcflib and tools for processing the VCF variant call format. BioRxiv. 2021.64.Li H. Tabix: fast retrieval of sequence features from generic TAB-delimited files. Bioinformatics. 2011;27:718–9.PubMed 
PubMed Central 
Article 
CAS 

Google Scholar 
65.Danecek P, Auton A, Abecasis G, Albers CA, Banks E, DePristo MA, et al. The variant call format and VCFtools. Bioinformatics. 2011;27:2156–8.CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
66.Cingolani P, Platts A, Wang LL, Coon M, Nguyen T, Wang L, et al. A program for annotating and predicting the effects of single nucleotide polymorphisms, SnpEff. Fly. 2012;6:80–92.CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
67.R Core Team. A language and environment for statistical computing. Vienna, Austria: R Foundation for Statistical Computing; 2019.68.RStudio Team. RStudio: Integrated Development for R. Boston, MA: RStudio Inc.; 2016.69.Knaus B, Grünwald NJ. vcfR: a package to manipulate and visualize variant call format data in R. Mol Ecol Res. 2017;17:44–53.CAS 
Article 

Google Scholar 
70.Jombart T. adegenet: a R package for the multivariate analysis of genetic markers. Bioinform. 2008;24:1403–5.CAS 
Article 

Google Scholar 
71.Kamvar ZN, Tabima JF, Grünwald NJ. Poppr: an R package for genetic analysis of populations with clonal, partially clonal, and/or sexual reproduction. PeerJ. 2014;2:e281.PubMed 
PubMed Central 
Article 

Google Scholar 
72.Grünwald NJ, Kamvar ZN, Everhart SE. Population genetics and genomics in R: Discriminant analysis of principal components (DAPC). 2020. https://grunwaldlab.github.io/Population_Genetics_in_R/DAPC.html.73.Wickham H. ggplot2: elegant graphics for data analysis. New York: Springer-Verlag; 2016.74.Tabima JF, Knaus B, Grünwald NJ. Population genetics and genomics in R: GBS analysis. 2020. https://grunwaldlab.github.io/Population_Genetics_in_R/gbs_analysis.html.75.Dray S, Dufour A. The ade4 package: implementing the duality diagram for ecologists. J Stat Softw. 2007;22:1–20.Article 

Google Scholar 
76.Kamvar ZN, Everhart SE, Grünwald NJ. Population genetics and genomics in R: AMOVA. 2020. https://grunwaldlab.github.io/Population_Genetics_in_R/AMOVA.html.77.Rozas J, Ferrer-Mata A, Sanchez-DelBarrio JC, Guirao-Rico S, Librado P, Ramos-Onsins SE, et al. DnaSP 6: DNA sequence polymorphism analysis of large data sets. Mol Biol Evol. 2017;34:3299–302.CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
78.Lischer HE, Excoffier L. PGDSpider: an automated data conversion tool for connecting population genetics and genomics programs. Bioinformatics. 2012;28:298–9.CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
79.Excoffier L, Lischer HEL. Arlequin suite ver 3.5: a new series of programs to perform population genetics analyses under Linux and Windows. Mol Ecol Resour. 2010;10:564–7.PubMed 
Article 
PubMed Central 

Google Scholar 
80.Newberry EA, Bhandari R, Minsavage GV, Timilsina S, Jibrin MO, Kemble J, et al. Independent evolution with the gene flux originating from multiple Xanthomonas species explains genomic heterogeneity in Xanthomonas perforans. Appl Environ Microbiol. 2019;85:e00885–19.81.Jibrin MO, Potnis N, Timilsina S, Minsavage GV, Vallad GE, Roberts PD, et al. Genomic inference of recombination-mediated evolution in Xanthomonas euvesicatoria and X. perforans. Appl Environ Microbiol. 2018; 84:e00136–18. More

Field samplingWe collected samples once per week over four weeks at the Antarctic sea ice edge, in McMurdo Sound, Antarctica (December 28, 2014 “GOS-927”; January 6 “GOS-930”, 15 “GOS-933”, and 22 “GOS-935”, 2015; as previously described in [27]). Sea water (150–250 l) was pumped sequentially through three filters of decreasing size (3.0, 0.8, and 0.1 μm, 293 mm Supor filters). Separate filter sets were acquired for metagenomic, metatranscriptomic, and metaproteomic analyses, over the course of ∼3 h, each week (36 filters in total). Filters for nucleic acid analyses were preserved with a sucrose-based buffer (20 mM EDTA, 400 mM NaCl, 0.75 M sucrose, 50 mM Tris-HCl, pH 8.0) with RNAlater (Life Technologies, Inc.). Filters for protein analysis were preserved in the same sucrose-based buffer but without RNAlater. Filters were flash frozen in liquid nitrogen in the field and subsequently stored at −80 °C until processed in the laboratory.Metagenomic and metatranscriptomic sequencingWe used metagenomics and metatranscriptomics to obtain reference databases of potential proteins for metaproteomics. We additionally used a database assembled from a similarly processed metatranscriptomic incubation experiment [28], conducted with source water from the January 15, 2015 time point (these samples were collected on a 0.2 μm Sterivex filter and processed as previously described).For samples from the GOS-927, GOS-930, GOS-933, and GOS-935 filters, RNA was purified from a DNA and RNA mixture [29]. In total, 2 µg of the DNA and RNA mixture was treated with 1 µl of DNase (2 U/µl; Turbo DNase, TURBO DNase, Thermo Fisher Scientific), followed by processing with an RNA Clean and Concentrator kit (Zymo Research). An Agilent TapeStation 2200 was used to observe and verify the quality of RNA. In total, 200 ng of total RNA was used as input for rRNA removal using Ribo-Zero (Illumina) with a mixture of plant, bacterial, and human/mouse/rat Removal Solution in a ratio of 2:1:1. An Agilent TapeStation 2200 was used to subsequently observe and verify the quality of rRNA removal from total RNA. rRNA-deplete total RNA was used for cDNA synthesis with the Ovation RNA-Seq System V2 (TECAN, Redwood City, USA). DNA was extracted for metagenomics from the field samples (GOS-927, GOS-930, GOS-933, and GOS-935) according to [29]. RNase digestion was performed with 10 µl of RNase A (20 mg/ml) and 6.8 μl of RNase T1 (1000 U/µl), which were added to 2 µg of genomic DNA and RNA mixture in a total volume of 100 µl, followed by 1 h incubation at 37 °C and subsequent ethanol precipitation in −20 °C overnight.Samples of double stranded cDNA and DNA were fragmented using a Covaries E210 system with the target size of 400 bp. In total, 100 ng of fragmented cDNA or DNA was used as input into the Ovation Ultralow System V2 (TECAN, Redwood City, USA), following the manufacturer’s protocol. Ampure XP beads (Beckman Coulter) were used for final library purification. Library quality was analyzed on a 2200 TapeStation System with Agilent High Sensitivity DNA 1000 ScreenTape System (Agilent Technologies, Santa Clara, CA, USA). Twelve DNA and 18 cDNA libraries were combined into two pools with concentration 4.93 and 4.85 ng/µl, respectively. Resulting library pools were subjected to one lane of 150 bp paired-end HiSeq 4000 sequencing (Illumina). Prior to sequencing, each library was spiked with 1% PhiX (Illumina) control library. Each lane of sequencing resulted in between 106,000 and 111,000 Mbp total and 6900–12,000 Mbp and 4800–6900 Mbp for individual DNA or cDNA libraries, respectively.Metagenomic and metatranscriptomic bioinformaticsMetagenomic and metatranscriptomic data were annotated with the same pipelines. Briefly, adapter and primer sequences were filtered out from the paired reads, and then reads were quality trimmed to Phred33. rRNA reads were identified and removed with riboPicker [30]. We then assembled reads into transcript contigs using CLC Assembly Cell, and then we used FragGeneScan to predict open reading frames (ORFs) [31]. ORFs were functionally annotated using Hidden Markov models and blastp against PhyloDB [32]. Annotations which had low mapping coverage were filtered out (less than 50 reads total over all samples), as were proteins with no blastp hits and no known domains. For each ORF, we assigned a taxonomic affiliation based on Lineage Probability Index taxonomy [32, 33]. Taxa were assigned using two different reference databases: NCBI nt and PhyloDB [32]. Unless otherwise specified, we used taxonomic assignments from PhyloDB, because of the good representation of diverse marine microbial taxa.ORFs were clustered by sequence similarity using Markov clustering (MCL) [34]. Sequences were assigned MCL clusters by first running blastp for all sequences against each other, where the query was the same as the database. The MCL algorithm was subsequently used with the input as the matrix of E-values from the blastp output, with default parameters for the MCL clustering. MCL clusters were then assigned consensus annotations based on KEGG, KO, KOG, KOG class, Pfam, TIGRFAM, EC, GO, annotation enrichment [28, 32, 35,36,37,38,39]. Proteins were assigned to coarse-grained protein pools (ribosomal and photosynthetic proteins) based on these annotations. For assignment, we used a greedy approach, such that a protein was assigned a coarse-grained pool if at least one of these annotation descriptions matched our search strings (we also manually examined the coarse grains to ensure there were no peptides that mapped to multiple coarse-grained pools). For photosynthetic proteins, we included light harvesting proteins, chlorophyll a-b binding proteins, photosystems, plastocyanin, and flavodoxin. For ribosomal proteins, we just included the term “ribosom*” (where the * represents a wildcard character), and excluded proteins responsible for ribosomal synthesis.Sample preparation and LC-MS/MSWe extracted proteins from the samples by first performing a buffer exchange from the sucrose-buffer to an SDS-based extraction buffer, after which proteins were extracted from each filter individually (as previously described) [27]. After extraction and acetone-based precipitation, we prepared samples for liquid chromatography tandem mass spectrometry (LC-MS/MS). Precipitated protein was first resuspended in urea (100 µl, 8 M), after which we measured the protein concentration in each sample (Pierce BCA Protein Assay Kit). We then reduced, alkylated, and enzymatically digested the proteins: first with 10 µl of 0.5 M dithiothreitol for reduction (incubated at 60 °C for 30 min), then with 20 µl of 0.7 M iodoacetamide (in the dark for 30 min), diluted with ammonium bicarbonate (50 mM), and finally digested with trypsin (1:50 trypsin:sample protein). Samples were then acidified and desalted using C-18 columns (described in detail in ref. [40]).To characterize each metaproteomic sample, we employed one-dimensional liquid chromatography coupled to the mass spectrometer (VelosPRO Orbitrap, Thermo Fisher Scientific, San Jose, California, USA; detailed in [40]). For each injection, protein concentrations were equivalent across sample weeks, but different across filter sizes. We had higher amounts of protein on the largest filter size (3.0 μm) and less on the smaller filters, so we performed three replicate injections per 3.0 µm filter sample, and two replicate filter injections for 0.8 and 0.1 µm filters. We used a non-linear LC gradient totaling 125 min. For separation, peptides eluted through a 75 µm by 30 cm column (New Objective, Woburn, MA), which was self-packed with 4 µm, 90 A, Proteo C18 material (Phenomenex, Torrance, CA), and the LC separation was conducted with a Dionex Ultimate 3000 UHPLC (Thermo Scientific, San Jose, CA).LC-MS/MS bioinformatics—database searching, configuration, and quantificationMetaproteomics requires a database of potential protein sequences to match observed mass spectra with known peptides. Because we had sample-specific metagenome and metatranscriptome sequencing for each metaproteomic sample, we assessed various database configurations, including those that we predict would be suboptimal, to examine potential options for future metaproteomics researchers. We used five different configurations, described below. In each case, we appended a database of common contaminants (Global Proteome Machine Organization common Repository of Adventitious Proteins). We evaluated the performance of different database configurations based on the number of peptides identified (using a peptide false discovery rate of 1%).In order to make these databases (Table 1), we performed three separate assemblies on (1) the metagenomic reads (from samples GOS-927, GOS-930, GOS-933, and GOS-935), (2) metatranscriptomic reads (from samples GOS-927, GOS-930, GOS-933, and GOS-935), and (3) metatranscriptomic reads from a concurrent metatranscriptomic experiment, started at the location where GOS-933 was taken [28]. Database configurations were created by subsetting from these assemblies. The first configuration was “one-sample database”, constructed to represent the scenario where only one sample was used for metagenomic and metatranscriptomic sequencing (we chose the first sampling week). Specifically, this was done by subsetting and including ORFs from the metagenomic and metatranscriptomic assemblies if reads from this time point were present in that sample (reads mapped as in [28]), and then removing redundant protein sequences (P. Wilmarth, fasta utilities). The second configuration was the “sample-specific database”, where each metaproteomic sample had one corresponding database (prepared from both metagenome and metatranscriptome sequencing completed at the same sampling site), also done by subsetting ORFs from the metagenomic and metatranscriptomic assemblies as described above. The third configuration was pooling databases across size fractions—such that all metagenomic and metatranscriptomic sequences across the same filter sizes (e.g., 3.0 µm) were combined. ORFs were subsetted from the metagenomic and metatranscriptomic assemblies as above. The fourth and fifth configurations are from the concurrent metatranscriptomic experiment [28]. The fourth configuration (“metatranscriptome experiment (T0)”) was the metatranscriptome of the in situ microbial community (i.e., at the beginning of the experiment). This database was created by subsetting from the “metatranscriptome experiment (all)” assembly. Finally, the fifth configuration was the metatranscriptome of all experimental treatments pooled together (two iron levels, three temperatures; “metatranscriptome experiment (all)”). The overlap between databases (potential tryptic peptides) in different samples is presented graphically in Supplementary Figs. S1–S3.Table 1 Characteristics of the five different database configurations we used for metaproteomic database searches.Full size tableAfter matching mass spectra with peptide sequences for each database configuration (MSGF + with OpenMS, with a 1% false discovery rate at the peptide level; [41, 42]), we used MS1 ion intensities to quantify peptides. Specifically, we used the FeatureFinderIdentification approach, which cross-maps identified peptides from one mass spectrometry experiment to unidentified features in another experiment—increasing the number of peptide quantifications [43]. This approach requires a set of experiments to be grouped together (i.e., which samples should use this cross-mapping?). We grouped samples based on their filter sizes (including those samples that are replicate injections). First, mass spectrometry runs within each group were aligned using MapAlignerIdentification [44], and then FeatureFinderIdentification was used for obtaining peptide quantities.After peptides have been identified and quantified, we mapped them to proteins or MCL clusters of proteins, which have corresponding functional annotations (KEGG, KO, KOG, Pfams, TIGRFAM; [28, 32, 35,36,37,38,39]). Functional annotations were used in three separate analyses. (1) Exploring the overall functional changes in microbial community metabolism, we mapped peptides to MCL clusters—groups of proteins with similar sequences. These clusters have consensus annotations based on the annotations of proteins found within the clusters (described in detail in [28]). For this section, we only used peptides that uniquely map to MCL clusters. (2) We restricted the second analysis to two protein groups: ribosomal and photosynthetic proteins. For this analysis, we mapped peptides to one of these protein groups if at least one annotation mapped to the protein group (via string matching with keywords). This approach is “greedy” because does not exclude peptides if they also correspond with other functional groupings, but this is necessary because of the difficulties in comparing various annotation formats. (3) The last analysis for functional annotations was for targeted proteins, and we only mapped functions to peptides where the peptides uniquely identify a specific protein (e.g., plastocyanin).Code for the database setup and configuration, database searching, and peptide quantification is open source (https://github.com/bertrand-lab/ross-sea-meta-omics).LC-MS/MS bioinformatics—normalizationNormalization is an important aspect of metaproteomics: it influences all inferred peptide abundances. Typically, the abundance of a peptide is normalized by the sum of all identified peptide abundances. We use the term normalization factor for the inferred sum of peptide abundances. Note that the apparent abundance of observed peptides is dependent on the database chosen. In theory, if fewer peptides are observed because of a poorly matching database, this will decrease the normalization factor, and those peptides that are observed will appear to increase in abundance. It is not known how much this influences peptide quantification in metaproteomics.For each database configuration, we separately calculated normalization factors. We then correlated the sum of observed peptide abundances with each other. To get a database-independent normalization factor, we used the sum of total ion current (TIC) for each mass spectrometry experiment (using pyopenms; [45]), and also examined the correlation with database-dependent normalization factors. If normalization factors are highly correlated with each other, that would indicate database choice does not impact peptide quantification. Using TIC for normalization may have drawbacks, particularly if there are differences in contamination, or amounts of non-peptide ions across samples.Defining proteomic mass fractionProtein abundance can be calculated in two ways: (1) the number of copies of a protein (independent of a proteins’ mass), or (2) the total mass of the protein copies (the sum of peptides). We refer to the latter as a proteomic mass fraction. For example, to calculate a diatom-specific, ribosomal mass fraction, we sum all peptide abundances that are diatom- and ribosome-specific, and divide by the sum of peptide abundances that are diatom-specific. Note that this is slightly different to other methods, like the normalized spectral abundance factor, which normalizes for total protein mass (via protein length; [46]).Combining estimates across filter sizesOrganisms should separate according to their sizes when using sequential filtration with decreasing filter pore sizes. In practise, however, organisms can break because of pressure during filtration, and protein is typically present for large phytoplankton on the smallest filter size and vice versa. We used a simple method for combining observations across filter sizes, weighted by the number of observations per filter. We begin with the abundance of a given peptide, which was only considered present if it was observed across all injections of the same sample. We calculated the sum of observed peptide intensities (i.e., the normalization factor), and divided all peptide abundances by this normalization factor. Normalized peptide abundances are then averaged across replicate injections. If we are estimating the ribosomal mass fraction of the diatom proteome, we first normalize the diatom-specific peptide intensities as a proportion of diatom biomass (i.e., divide all diatom-specific peptides by the sum of all diatom-specific peptides). We then summed all diatom-normalized peptides intensities that are unique to both diatoms and ribosomal proteins, which would give us the ribosomal proportion of the diatom proteome. Yet, we typically would obtain multiple estimates of, for example, ribosomal mass fraction of diatoms, on different filters. We combined the three values by multiplying each by a coefficient that represents a weight for each observation (specific to a filter size). These coefficients sum to one, and are calculated by summing the total number of peptides observed at a time point for a filter, and dividing by the total number of peptides observed across filters (but within each time point). For example, if we observed 100 peptides that are diatom- and ribosome-specific, and 90 of these peptides were on the 3.0 µm filter and only ten were on the 0.8 μm filter, we would multiply the 3.0 µm filter estimate by 0.9 and the 0.8 µm filter by 0.1. This method uses all available information about proteome composition across different filter sizes (similar to [47]).When we estimate the proteomic mass fraction of a given protein pool, we do not need to adjust for the total protein on each filter. This is because this measurement is independent of total protein. However, for merging estimates of total relative abundance of different organisms across filters, we needed to additionally weight the abundance estimate by the amount of protein on each filter. Therefore, in addition to the weighting scheme described above, we multiplied taxon abundance estimates by the total protein on each filter divided by the total protein across filters on a given day.LC-MS/MS simulationWe used simulations of metaproteomes and LC-MS/MS to (1) quantify biases associated with inferring coarse-grained proteomes from metaproteomes, and (2) to mitigate these biases in our inferences. Specifically, we asked the question: how does sequence diversity impact quantification of coarse-grained proteomes from metaproteomes? Consider a three organism microbial community. If two organisms are extremely similar, there will be very few peptides that can uniquely map to those organisms, resulting in underestimated abundance. The third organism would also be underestimated, but to a lesser degree, unless it had a completely unique set of peptides. A similar outcome is anticipated with differences in sequence diversity across protein groups, such that highly conserved protein groups will be underestimated.Our mass spectrometry simulations offer a unique perspective on this issue: we know the “true” metaproteome, and we can compare this with an “inferred” metaproteome. We simulated variable numbers of taxonomic groups, each with different protein pools of variable sequence diversity. From this simulated metaproteome, we then simulated LC-MS/MS-like sampling of peptides. Complete details of the mass spectrometry simulation are available in [48] and the Supplementary materials. The only difference between this model and that presented in [48] is here we include dynamic exclusion. The ultimate outcomes from these simulations were (1) identifying which circumstances lead to biased inferences about proteomic composition, and (2) determining the underpinnings of these biases.Cofragmentation bias scores for peptidesWe recently developed a computational model (“cobia”) that predicts a peptides’ risk for interference by sample complexity (more specifically, by cofragmentation of multiple peptides; [48]). This study showed that coarse-grained taxonomic and functional groupings are more robust to bias, and that this model can also be used to estimate bias. We ran cobia with the sample-specific databases, which produces a “cofragmentation score”—a measure of risk of being subject to cofragmentation bias. Specifically, the retention time prediction method used was RTPredict [49] with an “OLIGO” kernel for the support vector machine. The parameters for the model were: 0.008333 (maximum injection time); 3 (precursor selection window); 1.44 (ion peak width); and 5 (degree of sparse sampling). Code for running this analysis, as well as the corresponding input parameter file, is found at https://github.com/bertrand-lab/ross-sea-meta-omics.Description of previously published datasets analyzedWe leveraged several previously published datasets to compare our metaproteomic results. Specifically, we used proteomic data of phytoplankton cultures of Phaeocystis antarctica and Thalassiosira pseudonana [27, 50], and of cultures of Escherichia coli under 22 different culture conditions [51]. Coarse-grained proteomic estimates were also compared with previously published targeted metaproteomic data [27]. More

1.Lalli, C. & Parsons, T. R. Biological Oceanography: An Introduction (Elsevier, 1997).
Google Scholar 
2.Mackas, D. L., Denman, K. L. & Abbott, M. R. Plankton patchiness: Biology in the physical vernacular. Bull. Mar. Sci. 37, 652–674 (1985).
Google Scholar 
3.Kjerfve, B. & Magill, K. E. Geographic and hydrodynamic characteristics of shallow coastal lagoons. Mar. Geol. 88, 187–199 (1989).ADS 
Article 

Google Scholar 
4.Kjerfve, B. Chapter 1, Coastal lagoons. In Elsevier Oceanography Series Vol. 60 (ed. Kjerfve, B.) 1–8 (Elsevier, 1994).
Google Scholar 
5.McManus, M. A. & Woodson, C. B. Plankton distribution and ocean dispersal. J. Exp. Biol. 215, 1008–1016 (2012).PubMed 
Article 
PubMed Central 

Google Scholar 
6.Carr, M.-E. Estimation of potential productivity in Eastern Boundary Currents using remote sensing. Deep Sea Res. Part II 49, 59–80 (2001).ADS 
Article 

Google Scholar 
7.Chavez, F. P. & Messié, M. A comparison of Eastern Boundary Upwelling Ecosystems. Prog. Oceanogr. 83, 80–96 (2009).ADS 
Article 

Google Scholar 
8.Schubert, H. & Telesh, I. Estuaries and coastal lagoons. In Biological Oceanography of the Baltic Sea (eds Snoeijs-Leijonmalm, P. et al.) 483–509 (Springer Netherlands, 2017). https://doi.org/10.1007/978-94-007-0668-2_13.Chapter 

Google Scholar 
9.Pecqueur, D. et al. Dynamics of microbial planktonic food web components during a river flash flood in a Mediterranean coastal lagoon. Hydrobiologia 673, 13–27 (2011).CAS 
Article 

Google Scholar 
10.Deininger, A. et al. Simulated terrestrial runoff triggered a phytoplankton succession and changed seston stoichiometry in coastal lagoon mesocosms. Mar. Environ. Res. 119, 40–50 (2016).CAS 
PubMed 
Article 

Google Scholar 
11.Newton, A. & Mudge, S. M. Temperature and salinity regimes in a shallow, mesotidal lagoon, the Ria Formosa, Portugal. Estuar. Coast. Shelf Sci. 57, 73–85 (2003).ADS 
CAS 
Article 

Google Scholar 
12.Huang, J., Gao, J. & Hörmann, G. Hydrodynamic-phytoplankton model for short-term forecasts of phytoplankton in Lake Taihu, China. Limnologica 42, 7–18 (2012).CAS 
Article 

Google Scholar 
13.Pérez-Ruzafa, A. et al. Connectivity between coastal lagoons and sea: Asymmetrical effects on assemblages’ and populations’ structure. Estuar. Coast. Shelf Sci. 216, 171–186 (2019).ADS 
Article 

Google Scholar 
14.Dube, A., Jayaraman, G. & Rani, R. Modelling the effects of variable salinity on the temporal distribution of plankton in shallow coastal lagoons. J. Hydro-environ. Res. 4, 199–209 (2010).Article 

Google Scholar 
15.Pulina, S., Satta, C. T., Padedda, B. M., Sechi, N. & Lugliè, A. Seasonal variations of phytoplankton size structure in relation to environmental variables in three Mediterranean shallow coastal lagoons. Estuar. Coast. Shelf Sci. 212, 95–104 (2018).ADS 
Article 

Google Scholar 
16.Millet, B. & Cecchi, P. Wind-induced hydrodynamic control of the phytoplankton biomass in a lagoon ecosystem. Limnol. Oceanogr. 37, 140–146 (1992).ADS 
Article 

Google Scholar 
17.Souchu, P. et al. Influence of shellfish farming activities on the biogeochemical composition of the water column in Thau lagoon. Mar. Ecol. Prog. Ser. 218, 141–152 (2001).ADS 
CAS 
Article 

Google Scholar 
18.Paphitis, D. & Collins, M. B. Sediment resuspension events within the (microtidal) coastal waters of Thermaikos Gulf, northern Greece. Cont. Shelf Res. 25, 2350–2365 (2005).ADS 
Article 

Google Scholar 
19.Trombetta, T. et al. Water temperature drives phytoplankton blooms in coastal waters. PLoS One 14, e0214933 (2019).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
20.Faust, K. & Raes, J. Microbial interactions: From networks to models. Nat. Rev. Microbiol. 10, 538–550 (2012).CAS 
PubMed 
Article 

Google Scholar 
21.Fuhrman, J. A., Cram, J. A. & Needham, D. M. Marine microbial community dynamics and their ecological interpretation. Nat. Rev. Microbiol. 13, 133–146 (2015).CAS 
PubMed 
Article 

Google Scholar 
22.Trombetta, T., Vidussi, F., Roques, C., Scotti, M. & Mostajir, B. Marine microbial food web networks during phytoplankton bloom and non-bloom periods: Warming favors smaller organism interactions and intensifies trophic cascade. Front. Microbiol. 11, 502336 (2020).PubMed 
PubMed Central 
Article 

Google Scholar 
23.Needham, D. M. & Fuhrman, J. A. Pronounced daily succession of phytoplankton, archaea and bacteria following a spring bloom. Nat. Microbiol. 1, 16005 (2016).CAS 
PubMed 
Article 

Google Scholar 
24.Hemraj, D. A., Hossain, A., Ye, Q., Qin, J. G. & Leterme, S. C. Anthropogenic shift of planktonic food web structure in a coastal lagoon by freshwater flow regulation. Sci. Rep. 7, 44441 (2017).ADS 
CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
25.Bec, B., Husseini Ratrema, J., Collos, Y., Souchu, P. & Vaquer, A. Phytoplankton seasonal dynamics in a Mediterranean coastal lagoon: Emphasis on the picoeukaryote community. J. Plankton Res. 27, 881–894 (2005).CAS 
Article 

Google Scholar 
26.Collos, Y. et al. Oligotrophication and emergence of picocyanobacteria and a toxic dinoflagellate in Thau lagoon, southern France. J. Sea Res. 61, 68–75 (2009).ADS 
Article 

Google Scholar 
27.Collos, Y. et al. Pheopigment dynamics, zooplankton grazing rates and the autumnal ammonium peak in a Mediterranean lagoon. Hydrobiologia 550(1), 83–93 (2005).CAS 
Article 

Google Scholar 
28.Gangnery, A. et al. Growth model of the Pacific oyster, Crassostrea gigas, cultured in Thau Lagoon (Méditerranée, France). Aquaculture 215, 267–290 (2003).Article 

Google Scholar 
29.Pernet, F. et al. Marine diatoms sustain growth of bivalves in a Mediterranean lagoon. J. Sea Res. 68, 20–32 (2012).ADS 
CAS 
Article 

Google Scholar 
30.Rose, J. M. & Caron, D. A. Does low temperature constrain the growth rates of heterotrophic protists? Evidence and implications for algal blooms in cold waters. Limnol. Oceanogr. 52, 886–895 (2007).ADS 
Article 

Google Scholar 
31.Kordas, R. L., Harley, C. D. G. & O’Connor, M. I. Community ecology in a warming world: The influence of temperature on interspecific interactions in marine systems. J. Exp. Mar. Biol. Ecol. 400, 218–226 (2011).Article 

Google Scholar 
32.Jones, K. J. & Gowen, R. J. Influence of stratification and irradiance regime on summer phytoplankton composition in coastal and shelf seas of the British Isles. Estuar. Coast. Shelf Sci. 30, 557–567 (1990).ADS 
Article 

Google Scholar 
33.Bosak, S., Godrijan, J. & Šilović, T. Dynamics of the marine planktonic diatom family Chaetocerotaceae in a Mediterranean coastal zone. Estuar. Coast. Shelf Sci. 180, 69–81 (2016).ADS 
Article 

Google Scholar 
34.Wagner, C. & Adrian, R. Consequences of changes in thermal regime for plankton diversity and trait composition in a polymictic lake: A matter of temporal scale. Freshw. Biol. 56, 1949–1961 (2011).Article 

Google Scholar 
35.Rynearson, T. A., Flickinger, S. A. & Fontaine, D. N. Metabarcoding reveals temporal patterns of community composition and realized thermal niches of Thalassiosira spp. (Bacillariophyceae) from the Narragansett Bay long-term plankton time series. Biology 9, 19 (2020).CAS 
PubMed Central 
Article 

Google Scholar 
36.Delaney, M. P. Effects of temperature and turbulence on the predator–prey interactions between a heterotrophic flagellate and a marine bacterium. Microb. Ecol. 45, 218–225 (2003).CAS 
PubMed 
Article 

Google Scholar 
37.Peter, K. H. & Sommer, U. Phytoplankton cell size: Intra- and interspecific effects of warming and grazing. PLoS One 7, e49632 (2012).ADS 
CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
38.van Donk, E. & Kilham, S. S. Temperature effects on silicon- and phosphorus-limited growth and competitive interactions among three diatoms. J. Phycol. 26, 40–50 (1990).Article 

Google Scholar 
39.Stelzer, C.-P. Population growth in planktonic rotifers. Does temperature shift the competitive advantage for different species? In Rotifera VIII: A Comparative Approach (eds Wurdak, E. et al.) 349–353 (Springer Netherlands, 1998).Chapter 

Google Scholar 
40.Arandia-Gorostidi, N., Weber, P. K., Alonso-Sáez, L., Morán, X. A. G. & Mayali, X. Elevated temperature increases carbon and nitrogen fluxes between phytoplankton and heterotrophic bacteria through physical attachment. ISME 11, 641–650 (2017).CAS 
Article 

Google Scholar 
41.Peacock, E. E., Olson, R. J. & Sosik, H. M. Parasitic infection of the diatom Guinardia delicatula, a recurrent and ecologically important phenomenon on the New England Shelf. Mar. Ecol. Prog. Sci. 503, 1–10 (2014).ADS 
Article 

Google Scholar 
42.Käse, L. et al. Host-parasitoid associations in marine planktonic time series: Can metabarcoding help reveal them?. PLoS One 16, e0244817 (2021).PubMed 
PubMed Central 
Article 
CAS 

Google Scholar 
43.Caron, D. A., Dennett, M. R., Lonsdale, D. J., Moran, D. M. & Shalapyonok, L. Microzooplankton herbivory in the Ross Sea, Antarctica. Deep Sea Res. Part II 47, 3249–3272 (2000).ADS 
Article 

Google Scholar 
44.Vidussi, F. et al. Effects of experimental warming and increased ultraviolet B radiation on the Mediterranean plankton food web. Limnol. Oceanogr. 56, 206–218 (2011).ADS 
CAS 
Article 

Google Scholar 
45.Balas, L. & Özhan, E. Three-dimensional modelling of stratified coastal waters. Estuar. Coast. Shelf Sci. 54, 75–87 (2002).ADS 
Article 

Google Scholar 
46.Dalu, T., Richoux, N. B. & Froneman, P. W. Distribution of benthic diatom communities in a permanently open temperate estuary in relation to physico-chemical variables. S. Afr. J. Bot. 107, 31–38 (2016).Article 

Google Scholar 
47.Sommer, U., Peter, K. H., Genitsaris, S. & Moustaka-Gouni, M. Do marine phytoplankton follow Bergmann’s rule sensu lato?. Biol. Rev. 92, 1011–1026 (2017).PubMed 
Article 
PubMed Central 

Google Scholar 
48.Kirk, J. T. O. Light and Photosynthesis in Aquatic Ecosystems (Cambridge University Press, 1994).Book 

Google Scholar 
49.Litchman, E., de TezanosPinto, P., Klausmeier, C. A., Thomas, M. K. & Yoshiyama, K. Linking traits to species diversity and community structure in phytoplankton. In Fifty years after the “Homage to Santa Rosalia”: Old and new paradigms on biodiversity in aquatic ecosystems (eds Naselli-Flores, L. & Rossetti, G.) 15–28 (Springer Netherlands, 2010).Chapter 

Google Scholar 
50.Unrein, F., Gasol, J. M., Not, F., Forn, I. & Massana, R. Mixotrophic haptophytes are key bacterial grazers in oligotrophic coastal waters. ISME 8, 164–176 (2014).CAS 
Article 

Google Scholar 
51.Polimene, L. et al. Modelling a light-driven phytoplankton succession. J. Plankton Res. 36, 214–229 (2014).CAS 
Article 

Google Scholar 
52.Hatzaki, M. et al. Seasonal aspects of an objective climatology of anticyclones affecting the Mediterranean. J. Clim. 27, 9272–9289 (2014).ADS 
Article 

Google Scholar 
53.Mostajir, B. et al. Experimental test of the effect of ultraviolet-B radiation in a planktonic community. Limnol. Oceanogr. 44, 586–596 (1999).ADS 
Article 

Google Scholar 
54.Lacuna, D. G. & Uye, S.-I. Influence of mid-ultraviolet (UVB) radiation on the physiology of the marine planktonic copepod Acartia omorii and the potential role of photoreactivation. J. Plankton Res. 23, 143–156 (2001).Article 

Google Scholar 
55.Halac, S. et al. An in situ enclosure experiment to test the solar UVB impact on plankton in a high-altitude mountain lake. I. Lack of effect on phytoplankton species composition and growth. J. Plankton Res. 19, 1671–1686 (1997).CAS 
Article 

Google Scholar 
56.Souchu, P. et al. Patterns in nutrient limitation and chlorophyll a along an anthropogenic eutrophication gradient in French Mediterranean coastal lagoons. Can. J. Fish. Aquat. Sci. 67, 743–753 (2010).CAS 
Article 

Google Scholar 
57.Litchman, E. & Klausmeier, C. A. Trait-based community ecology of phytoplankton. Annu. Rev. Ecol. Evol. Syst. 39, 615–639 (2008).Article 

Google Scholar 
58.Reid, P. C., Lancelot, C., Gieskes, W. W. C., Hagmeier, E. & Weichart, G. Phytoplankton of the North Sea and its dynamics: A review. Neth. J. Sea Res. 26, 295–331 (1990).Article 

Google Scholar 
59.Derolez, V. et al. Two decades of oligotrophication: Evidence for a phytoplankton community shift in the coastal lagoon of Thau (Mediterranean Sea, France). Estuar. Coast. Shelf Sci. 241, 106810 (2020).Article 

Google Scholar 
60.Yool, A., Martin, A. P., Fernández, C. & Clark, D. R. The significance of nitrification for oceanic new production. Nature 447, 999–1002 (2007).ADS 
CAS 
PubMed 
Article 

Google Scholar 
61.Constantin, S., Constantinescu, Ș & Doxaran, D. Long-term analysis of turbidity patterns in Danube Delta coastal area based on MODIS satellite data. J. Mar. Syst. 170, 10–21 (2017).Article 

Google Scholar 
62.de Jorge, V. N. & van Beusekom, J. E. E. Wind- and tide-induced resuspension of sediment and microphytobenthos from tidal flats in the Ems estuary. Limnol. Oceanogr. 40, 776–778 (1995).ADS 
Article 

Google Scholar 
63.Ubertini, M. et al. Spatial variability of benthic-pelagic coupling in an estuary ecosystem: Consequences for microphytobenthos resuspension phenomenon. PLoS One 7, e44155 (2012).ADS 
CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
64.Madoni, P. Benthic ciliates in Adriatic Sea lagoons. Eur. J. Protistol. 42, 165–173 (2006).PubMed 
Article 

Google Scholar 
65.Cruz, J. et al. Plankton community and copepod production in a temperate coastal lagoon: What is changing in a short temporal scale?. J. Sea Res. 157, 101858 (2020).Article 

Google Scholar 
66.Audouin, J. Hydrologie de l’étang de Thau. Rev. Trav. Inst. Pêches Marit. 26, 5–104 (1962).
Google Scholar 
67.Byun, D. S., Wang, X. H. & Holloway, P. E. Tidal characteristic adjustment due to dyke and seawall construction in the Mokpo Coastal Zone, Korea. Estuar. Coast. Shelf Sci. 59, 185–196 (2004).ADS 
Article 

Google Scholar 
68.Stefanidou, N., Genitsaris, S., Lopez-Bautista, J., Sommer, U. & Moustaka-Gouni, M. Unicellular eukaryotic community response to temperature and salinity variation in mesocosm experiments. Front. Microbiol. 9, 2444 (2018).PubMed 
PubMed Central 
Article 

Google Scholar 
69.Xu, N. et al. Effects of temperature, salinity and irradiance on the growth of the harmful dinoflagellate Prorocentrum donghaiense Lu. Harmful Algae 9, 13–17 (2010).Article 

Google Scholar 
70.Greenwald, G. M. & Hurlbert, S. H. Microcosm analysis of salinity effects on coastal lagoon plankton assemblages. In Saline Lakes V (ed. Hurlbert, S. H.) 307–335 (Springer Netherlands, 1993).Chapter 

Google Scholar 
71.Fiandrino, A., Giraud, A., Robin, S. & Pinatel, C. Validation d’une méthode d’estimation des volumes d’eau échangés entre la mer et les lagunes et définition d’indicateurs hydrodynamiques associés (2012).72.Mostajir, B., Mas, S., Parin, D. & Vidussi, F. High-Frequency physical, biogeochemical and meteorological data of Coastal Mediterranean Thau Lagoon Observatory. SEANOE (2018).73.Données Publiques de Météo-France—Accueil. https://donneespubliques.meteofrance.fr/.74.Kraberg, A., Baumann, M. & Dürselen, C.-D. Coastal phytoplankton: Photo guide for Northern European seas (Univerza v Ljubljani, 2010).
Google Scholar 
75.Bérard-Therriault, L., Poulin, M. & Bossé, L. Guide d’identification du phytoplancton marin de l’estuaire et du golfe du Saint-Laurent incluant également certains protozoaires Canadian Special Publication of Fisheries and Aquatic Sciences No. 128 (NRC Research Press, 1999).
Google Scholar  More

1.Clarke, A. R., Powell, K. S., Weldon, C. W. & Taylor, P. W. The ecology of Bactrocera tryoni (Diptera: Tephritidae): What do we know to assist pest management?. Ann. Appl. Biol. 158, 26–54. https://doi.org/10.1111/j.1744-7348.2010.00448.x (2011).Article 

Google Scholar 
2.Jessup, A. J. et al. in Area-Wide Control of Insect Pests: From Research to Field Implementation (eds M. J. B. Vreysen, A. S. Robinson, & J. Hendrichs) 685–697 (Springer Netherlands, 2007).3.HIA. Australian Horticulture Statistics Handbook 2019/20. Horticulture Innovation Australia Limited (2020).4.Fanson, B. G., Sundaralingam, S., Jiang, L., Dominiak, B. C. & D’Arcy, G. A review of 16 years of quality control parameters at a mass-rearing facility producing Queensland fruit fly, Bactrocera tryoni. Entomol. Exp. Appl. 151, 152–159. https://doi.org/10.1111/eea.12180 (2014)5.Knipling, E. F. Possibilities of Insect Control or Eradication Through the Use of Sexually Sterile Males. J. Econ. Entomol. 48, 459–462. https://doi.org/10.1093/jee/48.4.459 (1955).Article 

Google Scholar 
6.Shelly, T. & McInnis, D. Sterile Insect Technique and Control of Tephritid Fruit Flies: Do Species With Complex Courtship Require Higher Overflooding Ratios?. Ann. Entomol. Soc. Am. 109, 1–11. https://doi.org/10.1093/aesa/sav101 (2015).CAS 
Article 

Google Scholar 
7.Hendrichs, J. & Robinson, A. in Encyclopedia of Insects (Second Edition) (eds Vincent H. Resh & Ring T. Cardé) 953–957 (Academic Press, 2009).8.Dominiak, B., Clifford, C. S. & Nielsen, S. G. Queensland fruit fly (Bactrocera tryoni Froggatt) attraction to and chemical analysis of male annihilation blocks using three concentratios of cuelure at Dubbo, NSW, Australia. Plant Prot. Q. 24, 157–160 (2009).9.Dominiak, B. C., Ekman, J. & Broughton, S. Mass trapping and other management option for mediterranean fruit fly and Queensland fruit fly in Australia. Gen. Appl. Entomol. 44, 1–8 (2016).
Google Scholar 
10.Khan, M. A. M. et al. Semiochemical mediated enhancement of males to complement sterile insect technique in management of the tephritid pest Bactrocera tryoni (Froggatt). Sci. Rep. 7, 13366. https://doi.org/10.1038/s41598-017-13843-w (2017).ADS 
CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
11.Vargas, R. I., Shelly, T. E., Leblanc, L. & Piñero, J. C. in Vitamins & Hormones Vol. 83 (ed Gerald Litwack) Ch. 23, 575–595 (Academic Press, 2010).12.Benelli, G. et al. Sexual communication and related behaviours in Tephritidae: current knowledge and potential applications for Integrated Pest Management. J. Pest. Sci. 87, 385–405. https://doi.org/10.1007/s10340-014-0577-3 (2014).Article 

Google Scholar 
13.Shelly, T. E. Consumption of methyl eugenol by male Bactrocera dorsalis (Diptera: Tephritidae): low incidence of repeat feeding. Florida Entomologist 77, 201–208 (1994).CAS 
Article 

Google Scholar 
14.Shelly, T. E. Trapping male oriental fruit flies (Diptera: Tephritidae): does feeding on a natural source of methyl eugenol reduce capture probability?. Florida Entomologist 83, 109–111 (2000).Article 

Google Scholar 
15.Tan, K. H., & Toong, Y. C. Floral synomone of a wild orchid, Bulbophyllum cheiri, lures Bactrocera fruit flies for pollination. J. Chem. Ecol. 28, 1161–1172 (2002).16.Shelly, T. E. Evaluation of a Genetic Sexing Strain of the Oriental Fruit Fly as a Candidate for Simultaneous Application of Male Annihilation and Sterile Insect Techniques (Diptera: Tephritidae). J. Econ. Entomol. 113, 1913–1921. https://doi.org/10.1093/jee/toaa099 (2020).CAS 
Article 
PubMed 

Google Scholar 
17.Joseph, R. M. & Carlson, J. R. Drosophila Chemoreceptors: A Molecular Interface Between the Chemical World and the Brain. Trends in genetics : TIG 31, 683–695. https://doi.org/10.1016/j.tig.2015.09.005 (2015).CAS 
Article 
PubMed 

Google Scholar 
18.Venthur, H., & Zhou, J.-J. Odorant receptors and odorant-binding proteins as insect pest control targets: A comparative analysis. Front. Physiol. 9, 1. https://doi.org/10.3389/fphys.2018.01163 (2018).19.Leal, W. S. Odorant reception in insects: roles of receptors, binding proteins, and degrading enzymes. Annu Rev. Entomol. 58, 373–391. https://doi.org/10.1146/annurev-ento-120811-153635 (2013).CAS 
Article 
PubMed 

Google Scholar 
20.Zheng, W. et al. Identification and Expression Profile Analysis of Odorant Binding Proteins in the Oriental Fruit Fly Bactrocera dorsalis. Int. J. Mol. Sci. 14, 14936 (2013).Article 

Google Scholar 
21.Siciliano, P. et al. Identification of pheromone components and their binding affinity to the odorant binding protein CcapOBP83a-2 of the Mediterranean fruit fly, Ceratitis capitata. Insect Biochem. Mol. Biol. 48, 51–62. https://doi.org/10.1016/j.ibmb.2014.02.005 (2014).CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
22.Siciliano, P. et al. Sniffing Out Chemosensory Genes from the Mediterranean Fruit Fly, Ceratitis capitata. Plos One 9, e85523https://doi.org/10.1371/journal.pone.0085523 (2014)23.Campanini, E. B. & de Brito, R. A. Molecular evolution of Odorant-binding proteins gene family in two closely related Anastrepha fruit flies. BMC Evol. Biol. 16, 198–198. https://doi.org/10.1186/s12862-016-0775-0 (2016).CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
24.Park, K., et al. Expression patterns of two putative odorant-binding proteins in the olfactory organs of Drosophila have different implications for their functions. Vol. 300 (2000).25.Shanbhag, S. R. et al. Expression mosaic of odorant-binding proteins in Drosophila olfactory organs. Microsc. Res. Tech. 55, 297–306. https://doi.org/10.1002/jemt.1179 (2001).CAS 
Article 
PubMed 

Google Scholar 
26.Wu, Z. et al. Discovery of chemosensory genes in the oriental fruit fly, Bactrocera dorsalis. PloS One 10, e0129794-e0129794.https://doi.org/10.1371/journal.pone.0129794 (2015)27.Liu, Z., Smagghe, G., Lei, Z. & Wang, J.-J. Identification of male- and female-specific olfaction genes in antennae of the Oriental Fruit Fly (Bactrocera dorsalis). PLoS ONE 11, e0147783–e0147783. https://doi.org/10.1371/journal.pone.0147783 (2016).CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
28.Zhang, J. et al. Identification and expression profiles of novel odorant binding proteins and functional analysis of OBP99a in Bactrocera dorsalis. Arch. Insect Biochem. Physiol. 98, e21452. https://doi.org/10.1002/arch.21452 (2018).ADS 
CAS 
Article 
PubMed 

Google Scholar 
29.Cheng, J. et al. Identification and expression analysis of chemosensory genes in the citrus fruit fly Bactrocera (Tetradacus) minax. PeerJ Preprints 6, e27297v1. https://doi.org/10.7287/peerj.preprints.27297v1 (2018).30.Kumaran, N., Prentis, P. J., Mangalam, K. P., Schutze, M. K. & Clarke, A. R. Sexual selection in true fruit flies (Diptera: Tephritidae): transcriptome and experimental evidences for phytochemicals increasing male competitive ability. Mol. Ecol. 23, 4645–4657. https://doi.org/10.1111/mec.12880 (2014).CAS 
Article 
PubMed 

Google Scholar 
31.Liu, H. et al. BdorOBP2 plays an indispensable role in the perception of methyl eugenol by mature males of Bactrocera dorsalis (Hendel). Sci. Rep. 7, 15894. https://doi.org/10.1038/s41598-017-15893-6 (2017).ADS 
CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
32.Kumaran, N. et al. Plant-Mediated Female Transcriptomic Changes Post-Mating in a Tephritid Fruit Fly, Bactrocera tryoni. Genome biology and evolution 10, 94–107.https://doi.org/10.1093/gbe/evx257 (2017)33.Idrees, A. et al. Protein baits, volatile compounds and irradiation influence the expression profiles of odorant-binding protein genes in Bactrocera dorsalis (Diptera: Tephritidae). Appl. Ecol. Environ. Res. 15, 1883–1899 (2017).Article 

Google Scholar 
34.Pavlidi, N. et al. Transcriptomic responses of the olive fruit fly Bactrocera oleae and its symbiont Candidatus Erwinia dacicola to olive feeding. Sci. Rep. 7, 42633. https://doi.org/10.1038/srep42633 (2017).ADS 
CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
35.Arya, G. H. et al. The genetic basis for variation in olfactory behavior in Drosophila melanogaster. Chem Senses 40, 233–243. https://doi.org/10.1093/chemse/bjv001 (2015).CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
36.Akter, H., Adnan, S., Morelli, R., Rempoulakis, P. & Taylor, P. W. Suppression of cuelure attraction in male Queensland fruit flies provided raspberry ketone supplements as immature adults. PLoS ONE 12, e0184086. https://doi.org/10.1371/journal.pone.0184086 (2017).CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
37.Gilchrist, A. S. et al. The draft genome of the pest tephritid fruit fly Bactrocera tryoni: resources for the genomic analysis of hybridising species. BMC Genomics 15, 1153. https://doi.org/10.1186/1471-2164-15-1153 (2014).CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
38.Fleischer, J. & Krieger, J. Insect pheromone receptors—Key elements in sensing intraspecific chemical signals. Front. Cell. Neurosci. 12. https://doi.org/10.3389/fncel.2018.00425 (2018).39.Liu, Z. et al. An antennae-specific odorant-binding protein is involved in Bactrocera dorsalis Olfaction. Front. Ecol. Evol. 8.https://doi.org/10.3389/fevo.2020.00063 (2020).40.Tan, K. H. Recaptures of feral Bactrocera dorsalis and B. umbrosa (Diptera: Tephritidae) males after feeding on methyl eugenol. Bull. Entomol. Res. 110, 15–21, https://doi.org/10.1017/S0007485319000208 (2019).41.Liu, L. et al. Contribution of Drosophila DEG/ENaC genes to salt taste. Neuron 39, 133–146. https://doi.org/10.1016/s0896-6273(03)00394-5 (2003).CAS 
Article 
PubMed 

Google Scholar 
42.Swarup, S., Huang, W., Mackay, T. F. C. & Anholt, R. R. H. Analysis of natural variation reveals neurogenetic networks for Drosophila olfactory behavior. Proc Natl Acad Sci USA 110, 1017–1022. https://doi.org/10.1073/pnas.1220168110 (2013).ADS 
Article 
PubMed 

Google Scholar 
43.Vijayan, V., Thistle, R., Liu, T., Starostina, E. & Pikielny, C. W. Drosophila pheromone-sensing neurons expressing the ppk25 Ion channel subunit stimulate male courtship and female receptivity. PLoS Genet. 10, e1004238. https://doi.org/10.1371/journal.pgen.1004238 (2014).CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
44.Thistle, R., Cameron, P., Ghorayshi, A., Dennison, L. & Scott, K. Contact chemoreceptors mediate male-male repulsion and male-female attraction during Drosophila courtship. Cell 149, 1140–1151. https://doi.org/10.1016/j.cell.2012.03.045 (2012).CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
45.Khan, M. A. M. et al. Raspberry ketone accelerates sexual maturation and improves mating performance of sterile male Queensland fruit fly, Bactrocera tryoni (Froggatt). Pest Manag. Sci. 75, 1942–1950. https://doi.org/10.1002/ps.5307 (2019).CAS 
Article 
PubMed 

Google Scholar 
46.Weldon, C. W., Perez-Staples, D. & Taylor, P. W. Feeding on yeast hydrolysate enhances attraction to cue-lure in Queensland fruit flies, Bactrocera tryoni. Entomol. Experim. et Applicata 129, 200–209. https://doi.org/10.1111/j.1570-7458.2008.00768.x (2008)47.Najar-Rodriguez, A. J., Galizia, C. G., Stierle, J. & Dorn, S. Behavioral and neurophysiological responses of an insect to changing ratios of constituents in host plant-derived volatile mixtures. J. Exp. Biol. 213, 3388 (2010).CAS 
Article 

Google Scholar 
48.Bertschy, C., Turlings, T. C., Bellotti, A. C. & Dorn, S. Chemically-mediated attraction of three parasitoid species to mealybug-infested cassava leaves. Florida Entomologist 80, 383–395 (1997).Article 

Google Scholar 
49.Butler, D. G., Cullis, B. R., Gilmour, A. R. & Gogel, B. J. (Queensland Department of Primary Industries and Fisheries, Australia, 2009).50.Bolger, A. M., Lohse, M. & Usadel, B. Trimmomatic: a flexible trimmer for Illumina sequence data. Bioinformatics 30, 2114–2120. https://doi.org/10.1093/bioinformatics/btu170 (2014).CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
51.Zhao, Q.-Y. et al. Optimizing de novo transcriptome assembly from short-read RNA-Seq data: a comparative study. BMC Bioinf. 12, S2. https://doi.org/10.1186/1471-2105-12-S14-S2 (2011).CAS 
Article 

Google Scholar 
52.Fu, L., Niu, B., Zhu, Z., Wu, S. & Li, W. CD-HIT: accelerated for clustering the next-generation sequencing data. Bioinformatics 28, 3150–3152. https://doi.org/10.1093/bioinformatics/bts565 (2012).CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
53.Simão, F. A., Waterhouse, R. M., Ioannidis, P., Kriventseva, E. V. & Zdobnov, E. M. BUSCO: assessing genome assembly and annotation completeness with single-copy orthologs. Bioinformatics 31, 3210–3212. https://doi.org/10.1093/bioinformatics/btv351 (2015).CAS 
Article 
PubMed 

Google Scholar 
54.Conesa, A. et al. Blast2GO: a universal tool for annotation, visualization and analysis in functional genomics research. Bioinformatics 21, 3674–3676. https://doi.org/10.1093/bioinformatics/bti610 (2005).CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
55.Li, H. & Durbin, R. Fast and accurate short read alignment with Burrows-Wheeler transform. Bioinformatics 25, 1754–1760. https://doi.org/10.1093/bioinformatics/btp324 (2009).CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
56.Patro, R., Duggal, G., Love, M. I., Irizarry, R. A. & Kingsford, C. Salmon provides fast and bias-aware quantification of transcript expression. Nat. Methods 14, 417–419. https://doi.org/10.1038/nmeth.4197 (2017).CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
57.Risso, D., Ngai, J., Speed, T. P. & Dudoit, S. Normalization of RNA-seq data using factor analysis of control genes or samples. Nat. Biotechnol. 32, 896–902. https://doi.org/10.1038/nbt.2931 (2014).CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
58.Al-Shahrour, F., Díaz-Uriarte, R. & Dopazo, J. FatiGO: a web tool for finding significant associations of Gene Ontology terms with groups of genes. Bioinformatics 20, 578–580. https://doi.org/10.1093/bioinformatics/btg455 (2004).CAS 
Article 
PubMed 

Google Scholar 
59.Ritchie, M. E. et al. limma powers differential expression analyses for RNA-sequencing and microarray studies. Nucl. Acids Res. 43, e47–e47. https://doi.org/10.1093/nar/gkv007 (2015).CAS 
Article 
PubMed 
PubMed Central 

Google Scholar  More

Data collectionData on the birds’ reproductive success and the number of humans present at nest sites were collected as part of a long-term, ongoing monitoring project in Hungary, in which we investigate the impacts of urbanization on populations of great tits. The great tit is an insectivorous passerine bird that is widespread across the Western Palearctic, occupies both urban and forest habitats, readily accepts nestboxes, and shares many important ecological traits with other tit or chickadee species also occurring in urban habitats27. These traits make this species an ideal model organism for studying the effects of the anthropause on wildlife in different environments.Study sitesWe monitored breeding great tit populations and also collected human presence data in two urban areas and at one forest study site. In one of the urban sites, Veszprém (47°05′17.29″N, 17°54′29.66″E; human population: c. 56,000; the monitoring scheme started in 2013), the nestboxes were placed in public green spaces (public parks, university campuses, a bus station, and a cemetery) that are surrounded by built-up areas and roads, and experience frequent anthropogenic disturbance. At the other urban size, Budapest (47°30′27.4″N, 19°01′03.4″E; the capital city of Hungary, human population: c. 1.75 million; the monitoring scheme started in 2019), the nestboxes were placed in two public urban parks, located c. 400 m from each other in the city core area and separated by high-traffic roads. The parks are freely accessible to residents and are heavily embedded within the urban matrix. At both urban sites, most of the nestboxes are distributed along paths or walking trails. Even though the two cities greatly differ in their size and human population, our urban study plots in both cities have similar general characteristics: these are surrounded by built-up areas, are at a similar distance (c. 3–4 km) from the nearest forested areas (for Veszprém, this is the forest at Vilma-puszta: 47°05′06.7″N, 17°51′51.4″E; for Budapest, this is the forest at Normafa: 47°30′27.7″N 18°57′51.1″E), and nests also experienced a similar level of human disturbance in the pre-COVID reference period (Fig. 1b). The forest site, Szentgál (47°06′39.75″N, 17°41′17.94″E; the monitoring scheme started in 2013), is a mature woodland, dominated by beech (Fagus sylvatica) and hornbeam (Carpinus betulus), located 3 km away from the nearest human habitation (Szentgál, human population: c. 2.800), c. 20 km and 110 km away from Veszprém and Budapest, respectively. There are no paved roads in the forest, and the area is relatively free from human disturbance although it experiences occasional hunting and logging activity.Human presence around nestsTo quantify human presence at our study sites for 2020 and the reference years we counted the number of humans (motorized vehicles excluded) during each nest check, for 30 s, in the proximity of the nestboxes (for similar approach see Corsini et al. 2019). The number of humans was recorded within a 50-m radius of the nestboxes between 2013 and 2018 (Veszprém, Szentgál), and within a 15-m radius distance in 2019–2020 (all sites). We changed the counting distance in 2019 due to methodological reasons following28. However, to be able to compare the human presence data of 2020 in Veszprém and Szentgál to that recorded in earlier years, in 2020 we performed the counts with both the 15-m and the 50-m radius distances at these two sites. Thus, for 2020 in Veszprém, we have human presence data both for the 50-m and the 15-m radius areas that were used in the forest-city and the between-cities comparisons, respectively (see below). For each year and study site, we used human presence data only from seasonally first broods (defined below), and only from nests where there were either already eggs or nestlings in the nest, resulting in 9.4 ± 3.6 (mean ± SD) observations per brood which is a reliable indicator of human presence28.Birds’ reproductive successWe monitored nestboxes each year at least twice a week from mid-March to early June to record laying and hatching dates, clutch size, hatching success, and the number of nestlings in active great tit nests. We ringed nestlings at day 14–16 post-hatch (i.e. a few days before fledging; hatching day of the first chick = day 1) with a numbered metal ring and also recorded their body mass (to the nearest 0.1 g), tarsus length (to the nearest 0.1 mm and following Svensson’s ‘alternative’ method29) and wing length (from the bend of the wing to the longest primary; to nearest 1 mm). Shortly after the expected date of fledging we carefully examined the nest material to identify and count the number of chicks that died after ringing (due to e.g. starvation, predation) that we included in the calculation of nestling survival (detailed below). The aim of this is to get a more accurate estimate for the number of offspring that could indeed fledge from the nest. The number of broods (nestlings) that suffered partial or complete mortality between ringing and fledging were: n = 6 (13) in Budapest (2019–2020), n = 70 (152) in Veszprém (2013–2020), and n = 25 (83) in the Szentgál forest.From these data we determined clutch size (the maximum number of eggs observed in a brood), hatching success (the proportion of chicks hatched / eggs laid), the number of fledglings, and nestling survival (the proportion of fledged young / hatched chicks). The number of fledglings (i.e. the number of young fledged successfully) was calculated as the number of chicks ringed minus the number of chicks found dead in the nest after the ringing. We involved only seasonally first breeding attempts (as this period overlapped with the lockdown period; detailed at the Statistical analyses), and defined first broods as follows. In our study system breeding great tits are captured on their nests and receive a unique combination of colour rings. Active nests are also routinely equipped with a small, concealed video camera enabling us to reliably identify over 80% of breeding individuals each year30. Thus, relying on this setup, we considered a clutch as a first breeding attempt of a pair if it was initiated before the date of the first egg laid in the earliest second clutch at that site by an individually identifiable (i.e. colour-ringed) female that successfully raised her first clutch (i.e. fledged at least one young) in that year.Air pollution and meteorological conditionsTo describe the levels of traffic-related air pollution (nitrogen dioxide [NO2], nitrogen oxides [NOX] and ozone [O3]) and the meteorological conditions (temperature and precipitation) at the two urban study sites (Veszprém and Budapest), we used data provided by the Hungarian Air Quality Monitoring Network and the Hungarian Meteorological Service, respectively. To better understand which aspect of the anthropause might have affected great tits’ breeding success we thus assessed if the lockdown affected air pollution levels differently at the two urban study sites (compared to 2019), or if weather conditions showed different fluctuations between 2019 and 2020 at the two cities. For more details on the statistical analyses and results, see ESM: Sect. 1.Statistical analysesThe duration of the official restrictions on human mobility (lockdown) spanned between 28 March–4 May in Veszprém (calendar date: 88–125; 01 January = 1) and 28 March–18 May (88–139) in Budapest. During this period people were allowed to leave their homes e.g. to run essential errands including individual sport and recreational activities in public green spaces, although with keeping at least 1.5 m from each other (social distancing). Very importantly, from the point of view of our study, the period of movement restrictions had almost completely overlapped with the seasonally first breeding attempts (from egg-laying to fledging) of great tits at both urban sites. The date of laying the 1st egg (calendar date, mean ± SD) in Veszprém was 94.2 ± 6.4, while in Budapest 97 ± 7.8; the date of chick ringing and measuring in Veszprém was 128 ± 5.3, while in Budapest: 133 ± 9.1. Thus, we decided not to exclude any first broods based on the date in order to maximize our sample size. Similarly, the period from which we involved human presence data was also strongly overlapped with the duration of the movement restrictions in both cities. Therefore, in Veszprém, the calendar dates of the first and the last human count at each nest were 87–108 (median: 100) and 121–142 (median: 132), respectively, while in Budapest 87–128 (median: 98) and 118–155 (median: 128).Human presence around nestsIn accordance with our first objective (forest-city comparisons), we explored if the lockdown in 2020 caused any changes in human disturbance around the great tit nests. To do so, we compared the number of humans (50-m radius of the nests) between 2020 and the 2013–2018 reference period, separately for the forest (Szentgál) and urban (Veszprém) study sites. Note that in 2019, we did not collect data on human presence within a 50-m radius at Veszprém and Szentgál (see above: Data collection), therefore 2019 was not included in the reference period of this analysis. We, however, also compared human presence in Veszprém between 2019 and 2020 using the 15-m radius data which indicates a change that is consistent with the differences found using the 50-m radius data (detailed below).First, we built generalized linear mixed-effects (GLM, lme4 R package) models with Poisson error distribution with the number of humans as the response variable, including year as a fixed factor and nestbox ID as random factor to control for non-independence of the data. Next, we extracted the mean values (least-squares means; package emmeans31) and associated standard errors for each year as estimated by the model. We computed the mean of these yearly mean estimates for the 2013–2018 reference period (i.e. calculated a single overall mean describing the whole reference period) and compared this long-term mean to the mean estimate of 2020 by calculating the linear contrast between them (with the ‘contrast’ function of the emmeans package), and expressed linear contrasts as 2020 minus the reference period.For our second objective (between-cities comparisons), we compared the changes in human disturbance around the nestboxes at the two urban study sites, Veszprém and Budapest, using the number of humans recorded within the 15-m radius of the active nests in 2019 and 2020. We analysed the data from Budapest and Veszprém separately and built generalized linear mixed-effects models with Poisson error distribution with the number of humans (15-m radius of the nests) as the response variable, including year as a fixed factor and nestbox ID as random factor to control for non-independence.Birds’ reproductive successWe used data from 2019 (reference; for justification see below in this section) and 2020 (lockdown). First, we constructed separate linear models to analyse each component of reproductive success (response variables), and for the forest-city and the between-cities comparisons. We used linear models (LM) for clutch size and the number of fledglings, generalized linear models (GLM, with quasi-binomial error distribution) for hatching success and nestling survival, and linear-mixed effects models (LME) for nestling body size traits (body mass, tarsus length, and wing length). Models on nestling body size traits contained nestlings’ age at ringing as a confounding variable (three-level factor: 14, 15, or 16 d of age) and brood ID as a random factor to control for the non-independence of chicks raised in the same brood. Finally, these models always contained a habitat (Veszprém or Szentgál) × year (2019 or 2020) interaction term for forest-city comparisons and a city (Budapest or Veszprém) × year (2019 or 2020) interaction term for between-cities comparisons. We checked assumptions of residuals’ normality and homogeneity of variance by inspecting the residuals plots which were respected for all models.Next, to test the prediction for our first objective (forest-city comparisons), we extracted the mean values (least-squares means) and associated standard errors of each response variable for each habitat × year combination as estimated by the linear model’s interaction. Then, from these estimates, we calculated habitat contrasts, i.e. the mean forest-city difference (forest minus urban) for each year (i.e. for 2019 and 2020), and compared the mean habitat contrast for the 2019 reference year to the mean habitat contrast of 2020; for similar approach see14,32,33.For our second objective (between-cities comparisons), we followed the same procedure as for the forest-city comparisons (detailed above) except that here we compared the differences between cities (Budapest minus Veszprém) in 2020 and 2019. These full models (i.e. for the forest-city and between-cities comparisons) are presented in Table S1–S2 (ESM: Sect. 2).In our study, we chose 2019 as a reference year for multiple reasons. First, because this was temporally the closest year without a lockdown. Second, because for Budapest we have monitoring data only from 2019 to 2020, using 2019 and 2020 in all analyses makes the results more comparable. Finally, although we have monitoring data from a total of eight years (2013–2020) for Szentgál (forest site) and Veszprém (urban site), for the forest-city comparisons we did not include years before 2019 in the reference because we noticed a negative trend in birds’ reproductive success throughout the study years (Fig. S3). This trend was especially apparent in the forest population, and may have reduced the forest-city difference by the end of the study period. Indeed, 2019 and 2020 were amongst the poorest years and resulted in a very similar reproductive success between both years within both habitats (Fig. S3). Because such temporal trend may have confounded the comparisons of 2020 with earlier years, to take account for its effect, and to further justify our approach of using 2019 as the reference year, we conducted additional analyses on the birds’ reproductive success by comparing both 2019 and 2020 (separately) to the 2013–2018 long-term reference period. We predict that if 2019 and 2020 are similarly affected by the decreasing trend in reproductive success than then the differences between the long-term reference period and 2019 and 2020, respectively, should be similar. For the details of these long-term forest-city comparisons see ESM: Sect. 3 and Table S3).Finally, we did not conduct the forest-city comparisons (first objective) between the forest site (Szentgál) and the other urban site (Budapest) for two reasons. First, because unlike to the Szentgál vs. Veszprém setup, we did not have an appropriate forest (control) location which is close to Budapest. Second, because conducting comparisons between the long-term data and 2019 and 2020, respectively (see: ESM Sect. 3) was not possible for Budapest because we do not have similar long-term data for the latter site.Clutches that failed before reaching the incubation stage (due to predation or desertion; i.e. final clutch size was uncertain), suffered complete mortality due to weather (e.g. nestbox fall from the tree due to strong wind), and cases when complete or partial clutch or brood loss may have occurred due to the monitoring process (e.g. when a nestbox was dropped or when complete brood failure occurred soon after capturing a parent on the nest) were excluded from all analyses. In the analyses investigating the number of fledglings, fledging success, and nestling body size traits we involved nests only where at least one nestling hatched, and excluded broods that were involved in a food-supplementation experiment (as treatment group) during the nestling rearing period in 201714. We used the R 4.0.5 software environment for statistical analysis and creating figures34.Ethical statementAll procedures were in accordance with Hungarian laws, and adhered to the ASAB/ABS guidelines for the use of animals in behavioural research and teaching. Permit to the use of animals in this study was provided by the National Scientific Ethical Committee on Animal Experimentation (permit number: PE-06/KTF/997–8/2018, FPH061/1329–5/2018, PE-06/KTF/06,543–7/2020 and FPH061/3036–4/2020). Permits to study protected species and access to protected areas were provided by the Middle Transdanubian Inspectorate for Environmental Protection, Natural Protection and Water Management (permit numbers: 31559/2011, 24,861/2014 and VE-09Z/03,454–8/2018, for working in Veszprém and Szentgál) and the Environment Protection and Nature Conservation Department of the Pest County Bureau of the Hungarian Government and the Mayor’s Office of Budapest (permit numbers: PE-06/KTF/997–8/2018, FPH061/1329–5/2018, PE-06/KTF/06,543–7/2020 and FPH061/3036–4/2020, for working in Budapest). More

ScopeWe compared disposable face masks that were used once with face masks that were sterilized and used five more times (six times in total). Sterilisation and PFE test data of the Aura 1862+ (3M, Saint Paul, Minnesota, USA) face mask indicate that this type of face mask shows good performance after multiple sterilisation cycles10,11,12. In a previous pilot study, the company CSA Services (Utrecht, the Netherlands), a sterilization facility for cleaning, disinfection and sterilization of medical instruments, was rebuild to process FFP2 face masks. In total, 18,166 single use FFP2 masks were sterilised after use in a medical autoclave. As the majority (n = 7993) were Aura 1862+ (3M, Saint Paul, Minnesota, USA), this particular type of face mask was chosen for the LCA.The total weight of the face masks and packaging together during end-of-life consists of incineration for the face masks (97%) and landfill for the carton box packaging of new face masks (3%). There is no recycling potential used in our model since the materials coming from the operating room and its packaging is commonly disposed as medical waste. In the Netherlands, no energy recovery takes place at the incineration of regulated medical waste. Therefore, no co-function was applicable for the end-of-life scenario.Recycling is often a multi-functional process that produces two or more goods. To deal with the multi-functionality in the background processes, the cut-off approach was applied to exclude the allocation of the greenhouse gas emissions to additional goods. This means that potential rest materials such as energy gained during incineration are cut-off and that the greenhouse gas emissions are fully allocated to the waste treatment processes itself.In the LCA, the ‘functional unit’ defines the primary function that is fulfilled by the investigational products and indicates how much of this function is considered18. In this study, we pragmatically chose as a definition for the protection of 100 health care workers against airborne viruses, using one FFP2 certified face mask, each during one working shift of an average of 2 h in a hospital in the Netherlands.Table 1 shows the differences between the two scenarios:

1.

100 masks including packaging, transported from production to the hospital, used and disposed.

2.

100 times use of reprocessed masks. We calculated that 27.1 masks are being produced and transported from production to the hospital. The 27.1 are being reprocessed five times, taking into account that 20% of the batch cannot be reprocessed. Therefore 80% of the batch could be used for reprocessing after each step resulting in: 27.1 (new) + 21.7 (repro 1) + 17.3 (repro 2) + 13.9 (repro 3) + 11.1 (repro 4) + 8.9 (repro 5) = 100 times of use. For each time of reprocessing the batch is transported from the hospital to the (hospital) Central Sterilization Services Department (CSSD) and disposed after five times of reprocessing.

Table 1 Comparison between reference flow 1 and 2.Full size tableCombining the functional unit with the two alternative scenarios results in the reference flows for the protection of 100 health care workers against airborne viruses, either using a face mask one single time (100 virgin masks produced for the 1st scenario), or reusing a face mask for five additional times (27.1 virgin masks produced for the 2nd scenario). For both reference flows, only FFP2 certified face masks are considered. For the calculations each mask is used for a single two hours working shift in an average hospital in the Netherlands.Life cycle inventory (LCI) analysisThe inventory data includes all phases from production (including material production and part production), transport, sterilisation to end-of-life of the life cycle of the single use and reprocessed face masks. We disassembled one face mask to obtain the weight of each individual component on a precision scale (Fit Evolve, Bangosa Digital, Groningen, the Netherlands) with a calibrated inaccuracy of 1.5%. Component information and materials were obtained from the data fact sheet provided by the manufacturer. We conducted a separate validation experiment to establish the material composition in the filtering fabric (Supplement file).This LCA with the Aura 3M masks was based on steam sterilization by means of a hospital autoclave and therefore part of this study. Therefore, face masks were placed in a sterilization bag that contained up to five masks. A total of 1000 masks were placed into an autoclave (Getinge, GSS6713H-E, Sweden) per cycle. After sterilization, the masks were transported to the hospital. Masks were reprocessed for a maximum of five times before final disposal10,11.The assessment of climate change impact is done following as closely as possible the internationally accepted Life Cycle Assessment (LCA) method following the ISO 14040 and 14044 standards19,20. The LCA examines all the phases of the product’s life cycle from raw material extraction to production, packaging, transport, use and reprocessing until final disposal19. The LCA was modelled using SimaPro 9.1.0.7 (PRé Sustainability, Amersfoort, The Netherlands). The background life cycle inventory data were retrieved from the ecoinvent database (Ecoinvent version 3.6, Zürich, Switzerland)21.To make a valid comparison between the disposable and reprocessing face masks, the system boundaries should be equal in both scenarios. The system boundaries in this study consisted of the production, the use and the disposal and waste treatment of the masks. For the reprocessed face masks, the lifecycle is extended due to the sterilisation process (Fig. 1). Therefore, the additional PPE’s and materials needed to safely process the masks (e.q. masks, gloves and protective sheets) are included in the production phase. The production of machinery for the manufacturing of the face masks and the autoclave were not included in this study.Figure 1System boundary overview of new and reprocessed face masks including waste treatment by incineration.Full size imageThe production facility for the face masks is located in Shanghai, China22,23. Further distribution took place from Bracknell, UK to Neuss, Germany and the final destination was set in Rotterdam, the Netherlands.The packaging materials were disposed in the hospital where the face masks are used primarily. After first use, face masks were transported to the sterilisation department. All masks were manually checked before reprocessing by personnel wearing PPE. Of all used Aura 1862+ facemasks that entered the CSA, approximately 10% was discarded. To remain conservative, the LCA was conducted based on a 20% rejection rate as a result of face masks which could not be reused anymore due to deformities, lipstick, and broken elastic bands.A full overview of the life cycle inventory table for the two scenarios and details on model assumptions are added in the Supplemental file (Supplemental file, Part B).Life cycle impact assessmentThe carbon footprint (kg CO2 eq) was chosen as the primary unit in the impact category. ReCiPe was applied at midpoint level and used to translate greenhouse gas emissions into climate change impact16.Uncertainty analysisThe final LCA model contains several uncertainties based on assumptions and measurement inaccuracies24. The included uncertainties were based on weighted components of the masks as well as the packaging which were measured with 1.5% inaccuracy of the precision scale apparatus. A Monte Carlo sampling25 was conducted for both alternatives (disposable and reprocessing) where input parameters for the LCA were sampled randomly from their respective statistical distributions in for 10,000 ‘runs’. Because input parameters between scenarios were partly overlapping, we compared these two scenarios directly using a discernibility analysis. This technique, establishes which scenario is beneficial for each of 10,000 Monte Carlo runs. We report the percentage of instances where the reprocessing scenario has a lower carbon footprint than the disposable scenario.Sensitivity analysisA sensitivity analysis was conducted to check the sensitivity of the outcome measures to variation in the input parameters. To determine which parameters are interesting to investigate, three aspects were considered: the variations in number of face masks per sterilization cycle (autoclave capacity), rejection rate (number of losses per cycle) and transport distance to the CSSD. Finally, we included the relative contribution of these variations. The following three parameter variations were chosen for the sensitivity analysis:

1.

Rejection percentage. The rejection rate was defined based on experiences from the participating sterilisation department and studies that show that sterilisation of the face masks up to 5 times is possible. Masks were re-used for 5 times, approximately 10% was discarded during the total life cycle. Out of this experience and to remain conservative, the total rejection rate was set on 20%. Therefore it is interesting to investigate whether variation in PFE testing outcomes or differences in user protocols influence the outcomes. This should indicate if masks from higher or lower quality can also be suitable candidates for reprocessing.

2.

Autoclave capacity, which largely depends on the loading of the autoclave. To mimic different loads of the autoclave, it is interesting to know the influence of sterilizing fewer masks per run on the model.

3.

Transport. As it is likely that many hospitals have a Central Sterilisation Services Department (CSSD) it is interesting to know the effect of having zero transportation. Moreover, in case hospitals are not willing to change the routing in their CSSD it is interesting to observe how outcomes are influenced if transportation is set on the maximal realistic value of 200 km.

The parameters have been varied with 250 and 500 face masks per sterilisation batch. A rate varying with 10% and 30% of the face masks being rejected due to quality reasons and variation in transport kilometres of 0–200 km.There is a small difference between the baselines of the sensitivity, LCIA and contribution analyses because all these are performed using separate Monte-Carlo simulations. The output of the different simulations may show minor differences due to statistical distribution.Cost price comparisonA cost analysis was made to give insight in costing from a procurement perspective. The cost analysis is conducted with five face masks that were steam sterilized per batch in a permeable laminate bag, Halyard type CLFP150X300WI-S20 and includes the expenses of energy, depreciation, water consumption, cost of personnel, overhead and compared to the prices for a new disposable 3M Aura face mask during the first and second Corona waves. Five pieces per bag were chosen in order to have enough space between the masks to sterilise each mask properly. The cost analysis is based on actual sterilization as well as associated costs compared to the prices of new disposable face masks. The costs were then related to the functional unit of protecting 100 health care workers by calculating the difference in the amount of Euros per 100 face masks. More

1.von Humboldt, A. Ansichten der Natur mit Wissenschaftlichen Erlauterungen (J.G. Cotta, 1808).2.Perrigo, A., Hoorn, C. & Antonelli, A. Why mountains matter for biodiversity. J. Biogeogr. 47, 315–325 (2020).Article 

Google Scholar 
3.Badgley, C. et al. Biodiversity and topographic complexity: modern and geohistorical perspectives. Trends Ecol. Evol. 32, 211–226 (2017).PubMed 
PubMed Central 
Article 

Google Scholar 
4.Rahbek, C. et al. Building mountain biodiversity: geological and evolutionary processes. Science 365, 1114–1119 (2019).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
5.Steinbauer, M. J. et al. Topography-driven isolation, speciation and a global increase of endemism with elevation. Glob. Ecol. Biogeogr. 25, 1097–1107 (2016).Article 

Google Scholar 
6.Fjeldså, J., Bowie, R. C. K. & Rahbek, C. The role of mountain ranges in the diversification of birds. Annu. Rev. Ecol. Evol. Syst. 43, 249–265 (2012).Article 

Google Scholar 
7.Hughes, C. & Eastwood, R. Island radiation on a continental scale: exceptional rates of plant diversification after uplift of the Andes. Proc. Natl Acad. Sci. USA 103, 10334–10339 (2006).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
8.Antonelli, A. et al. Geological and climatic influences on mountain biodiversity. Nat. Geosci. 11, 718–725 (2018).CAS 
Article 

Google Scholar 
9.Quintero, I. & Jetz, W. Global elevational diversity and diversification of birds. Nature 555, 246–250 (2018).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
10.Gillooly, J. F., Allen, A. P., West, G. B. & Brown, J. H. The rate of DNA evolution: effects of body size and temperature on the molecular clock. Proc. Natl Acad. Sci. USA 102, 140–145 (2005).CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
11.Martin, A. P. & Palumbi, S. R. Body size, metabolic rate, generation time, and the molecular clock. Proc. Natl Acad. Sci. USA 90, 4087–4091 (1993).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
12.Rohde, K. Latitudinal gradients in species diversity: the search for the primary cause. Oikos 65, 514–527 (1992).Article 

Google Scholar 
13.Allen, A. P., Gillooly, J. F., Savage, V. M. & Brown, J. H. Kinetic effects of temperature on rates of genetic divergence and speciation. Proc. Natl Acad. Sci. USA 103, 9130–9135 (2006).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
14.Rabosky, D. L. et al. An inverse latitudinal gradient in speciation rate for marine fishes. Nature 559, 392–395 (2018).CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
15.Igea, J. & Tanentzap, A. J. Angiosperm speciation cools down in the tropics. Ecol. Lett. 23, 692–700 (2020).PubMed 
PubMed Central 
Article 

Google Scholar 
16.Schluter, D. Speciation, ecological opportunity, and latitude (American Society of Naturalists address). Am. Nat. 187, 1–18 (2016).PubMed 
Article 
PubMed Central 

Google Scholar 
17.Anderson, K. J. & Jetz, W. The broad-scale ecology of energy expenditure of endotherms. Ecol. Lett. 8, 310–318 (2005).Article 

Google Scholar 
18.Clarke, A. & Gaston, K. J. Climate, energy and diversity. Proc. R. Soc. B 273, 2257–2266 (2006).PubMed 
PubMed Central 
Article 

Google Scholar 
19.Dowle, E. J., Morgan-Richards, M. & Trewick, S. A. Molecular evolution and the latitudinal biodiversity gradient. Heredity 110, 501–510 (2013).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
20.Brown, J. H. Why are there so many species in the tropics? J. Biogeogr. 41, 8–22 (2014).PubMed 
Article 

Google Scholar 
21.Stevens, G. C. The latitudinal gradient in geographical range: how so many species coexist in the tropics. Am. Nat. 133, 240–256 (1989).Article 

Google Scholar 
22.Boucher-Lalonde, V. & Currie, D. J. Spatial autocorrelation can generate stronger correlations between range size and climatic niches than the biological signal — a demonstration using bird and mammal range maps. PLoS One 11, e0166243 (2016).PubMed 
PubMed Central 
Article 
CAS 

Google Scholar 
23.Cutter, A. D. & Gray, J. C. Ephemeral ecological speciation and the latitudinal biodiversity gradient. Evolution 70, 2171–2185 (2016).PubMed 
Article 
PubMed Central 

Google Scholar 
24.Morales‐Barbero, J., Martinez, P. A., Ferrer‐Castán, D. & Olalla‐Tárraga, M. Á. Quaternary refugia are associated with higher speciation rates in mammalian faunas of the Western Palaearctic. Ecography 41, 607–621 (2018).Article 

Google Scholar 
25.Xing, Y. & Ree, R. H. Uplift-driven diversification in the Hengduan Mountains, a temperate biodiversity hotspot. Proc. Natl Acad. Sci. USA 114, E3444–E3451 (2017).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
26.Lagomarsino, L. P., Condamine, F. L., Antonelli, A., Mulch, A. & Davis, C. C. The abiotic and biotic drivers of rapid diversification in Andean bellflowers (Campanulaceae). New Phytol. 210, 1430–1442 (2016).PubMed 
PubMed Central 
Article 

Google Scholar 
27.Testo, W. L., Sessa, E. & Barrington, D. S. The rise of the Andes promoted rapid diversification in Neotropical Phlegmariurus (Lycopodiaceae). New Phytol. 222, 604–613 (2019).PubMed 
Article 
PubMed Central 

Google Scholar 
28.Dowsett, H. et al. The PRISM4 (mid-Piacenzian) paleoenvironmental reconstruction. Climate 12, 1519–1538 (2016).
Google Scholar 
29.Hartley, A. J. Andean uplift and climate change. J. Geol. Soc. 160, 7–10 (2003).Article 

Google Scholar 
30.Aron, P. G. & Poulsen, C. J. in Mountains, Climate and Biodiversity (eds Hoorn, C., Perrugi, A. & Antonelli, A.) Ch. 8 (2018).31.Hewitt, G. The genetic legacy of the Quaternary ice ages. Nature 405, 907–913 (2000).CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
32.Wallis, G. P., Waters, J. M., Upton, P. & Craw, D. Transverse Alpine speciation driven by glaciation. Trends Ecol. Evol. 31, 916–926 (2016).PubMed 
Article 
PubMed Central 

Google Scholar 
33.Luebert, F. & Muller, L. A. H. Effects of mountain formation and uplift on biological diversity. Front. Genet. 6, 54 (2015).34.Huang, S., Meijers, M. J. M., Eyres, A., Mulch, A. & Fritz, S. A. Unravelling the history of biodiversity in mountain ranges through integrating geology and biogeography. J. Biogeogr. 46, 1777–1791 (2019).Article 

Google Scholar 
35.Whittaker, R. J., Triantis, K. A. & Ladle, R. J. A general dynamic theory of oceanic island biogeography. J. Biogeogr. 35, 977–994 (2008).Article 

Google Scholar 
36.Li, Y. et al. Climate and topography explain range sizes of terrestrial vertebrates. Nat. Clim. Change 6, 498–502 (2016).Article 

Google Scholar 
37.Kisel, Y. & Barraclough, T. G. Speciation has a spatial scale that depends on levels of gene flow. Am. Nat. 175, 316–334 (2010).PubMed 
Article 

Google Scholar 
38.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 
39.Rowley, D. B. & Garzione, C. N. Stable isotope-based paleoaltimetry. Annu. Rev. Earth Planet. Sci. 35, 463–508 (2007).CAS 
Article 

Google Scholar 
40.Mulch, A. Stable isotope paleoaltimetry and the evolution of landscapes and life. Earth Planet. Sci. Lett. 433, 180–191 (2016).CAS 
Article 

Google Scholar 
41.Kuhn, T. S., Mooers, A. Ø. & Thomas, G. H. A simple polytomy resolver for dated phylogenies. Methods Ecol. Evol. 2, 427–436 (2011).Article 

Google Scholar 
42.Rolland, J., Condamine, F. L., Jiguet, F. & Morlon, H. Faster speciation and reduced extinction in the tropics contribute to the mammalian latitudinal diversity gradient. PLoS Biol. 12, e1001775 (2014).43.Meredith, R. W. et al. Impacts of the Cretaceous Terrestrial Revolution and KPg Extinction on mammal diversification. Science 334, 521–524 (2011).CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
44.Britton, T., Anderson, C. L., Jacquet, D., Lundqvist, S. & Bremer, K. Estimating divergence times in large phylogenetic trees. Syst. Biol. 56, 741–752 (2007).PubMed 
Article 
PubMed Central 

Google Scholar 
45.Drummond, A. J. & Rambaut, A. BEAST: Bayesian evolutionary analysis by sampling trees. BMC Evol. Biol. 7, 214 (2007).PubMed 
PubMed Central 
Article 
CAS 

Google Scholar 
46.Schliep, K. P. phangorn: phylogenetic analysis in R. Bioinformatics 27, 592–593 (2011).CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
47.Jetz, W., Thomas, G. H., Joy, J. B., Hartmann, K. & Mooers, A. O. The global diversity of birds in space and time. Nature 491, 444–448 (2012).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
48.Redding, D. W. & Mooers, A. Ø. Incorporating evolutionary measures into conservation prioritization. Conserv. Biol. 20, 1670–1678 (2006).PubMed 
Article 
PubMed Central 

Google Scholar 
49.Rabosky, D. L. Automatic detection of key innovations, rate shifts, and diversity-dependence on phylogenetic trees. PLoS One 9, e89543 (2014).PubMed 
PubMed Central 
Article 
CAS 

Google Scholar 
50.Moore, B. R., Höhna, S., May, M. R., Rannala, B. & Huelsenbeck, J. P. Critically evaluating the theory and performance of Bayesian analysis of macroevolutionary mixtures. Proc. Natl Acad. Sci. USA 113, 9569–9574 (2016).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
51.Meyer, A. L. S., Román-Palacios, C. & Wiens, J. J. BAMM gives misleading rate estimates in simulated and empirical datasets. Evolution 72, 2257–2266 (2018).PubMed 
Article 
PubMed Central 

Google Scholar 
52.Rabosky, D. L., Mitchell, J. S. & Chang, J. Is BAMM flawed? Theoretical and practical concerns in the analysis of multi-rate diversification models. Syst. Biol. 66, 477–498 (2017).PubMed 
PubMed Central 
Article 

Google Scholar 
53.Mitchell, J. S., Etienne, R. S. & Rabosky, D. L. Inferring diversification rate variation from phylogenies with fossils. Syst. Biol. 68, 1–18 (2019).PubMed 
Article 
PubMed Central 

Google Scholar 
54.Title, P. O. & Rabosky, D. L. Tip rates, phylogenies and diversification: what are we estimating, and how good are the estimates? Methods Ecol. Evol. 10, 821–834 (2019).Article 

Google Scholar 
55.Louca, S. & Pennell, M. W. Extant timetrees are consistent with a myriad of diversification histories. Nature 580, 502–505 (2020).CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
56.Amante, C. & Eakins, B. W. ETOPO1 Arc-minute Global Relief Model: Procedures, Data Sources and Analysis. NOAA Technical Memorandum NESDIS NGDC-24 (NOAA, 2009).57.Hijmans, R. J., Cameron, S. E., Parra, J. L., Jones, P. G. & Jarvis, A. Very high resolution interpolated climate surfaces for global land areas. Int. J. Climatol. 25, 1965–1978 (2005).Article 

Google Scholar 
58.Brown, J. L., Hill, D. J., Dolan, A. M., Carnaval, A. C. & Haywood, A. M. PaleoClim, high spatial resolution paleoclimate surfaces for global land areas. Sci. Data 5, 180254 (2018).PubMed 
PubMed Central 
Article 

Google Scholar 
59.Lefcheck, J. S. piecewiseSEM: Piecewise structural equation modelling in R for ecology, evolution, and systematics. Methods Ecol. Evol. 7, 573–579 (2016).Article 

Google Scholar 
60.Bivand, R. & Piras, G. Comparing implementations of estimation methods for spatial econometrics. J. Stat. Softw. 63, v063i18 (2015).
Google Scholar  More

1.Foley, J. A. et al. Global consequences of land use. Science 309, 570–574 (2005).CAS 

Google Scholar 
2.Carpenter, S. R. et al. Science for managing ecosystem services: beyond the Millennium Ecosystem Assessment. Proc. Natl Acad. Sci. USA 106, 1305–1312 (2009).CAS 

Google Scholar 
3.Nelson, E. et al. Modeling multiple ecosystem services, biodiversity conservation, commodity production, and tradeoffs at landscape scales. Front. Ecol. Environ. 7, 4–11 (2009).
Google Scholar 
4.Maes, J. et al. An indicator framework for assessing ecosystem services in support of the EU Biodiversity Strategy to 2020. Ecosyst. Serv. 17, 14–23 (2016).
Google Scholar 
5.Maes, J. et al. Mapping ecosystem services for policy support and decision making in the European Union. Ecosyst. Serv. 1, 31–39 (2012).
Google Scholar 
6.Millennium Ecosystem Assessment. Ecosystems and Human Well-Being: Synthesis (Island Press, 2005).7.Summary for Policymakers. In Global Assessment Report on Biodiversity and Ecosystem Services (IPBES, 2019).8.Martínez-Harms, M. J. & Balvanera, P. Methods for mapping ecosystem service supply: a review. Int. J. Biodivers. Sci. Ecosyst. Serv. Manage. 8, 17–25 (2012).
Google Scholar 
9.Hauck, J. et al. ‘Maps have an air of authority’: potential benefits and challenges of ecosystem service maps at different levels of decision making. Ecosyst. Serv. 4, 25–32 (2013).
Google Scholar 
10.Balvanera, P. et al. Conserving biodiversity and ecosystem services. Science 291, 2047 (2001).CAS 

Google Scholar 
11.Dick, J., Maes, J., Smith, R. I., Paracchini, M. L. & Zulian, G. Cross-scale analysis of ecosystem services identified and assessed at local and European level. Ecol. Indic. 38, 20–30 (2014).
Google Scholar 
12.UK National Ecosystem Assessment. The UK National Ecosystem Assessment Technical Report. (UNEP-WCMC, 2011); http://uknea.unep-wcmc.org/13.Orsi, F., Ciolli, M., Primmer, E., Varumo, L. & Geneletti, D. Mapping hotspots and bundles of forest ecosystem services across the European Union. Land Use Policy 99, 104840 (2020).
Google Scholar 
14.Holland, R. A. et al. The influence of temporal variation on relationships between ecosystem services. Biodivers. Conserv. 20, 3285–3294 (2011).
Google Scholar 
15.Renard, D., Rhemtull, J. M. & Bennett, E. M. Historical dynamics in ecosystem service bundles. Proc. Natl Acad. Sci. USA 112, 13411–13416 (2015).CAS 

Google Scholar 
16.Rukundo, E. et al. Spatio-temporal dynamics of critical ecosystem services in response to agricultural expansion in Rwanda, East Africa. Ecol. Indic. 89, 696–705 (2018).
Google Scholar 
17.Stürck, J., Schulp, C. J. E. & Verburg, P. H. Spatio-temporal dynamics of regulating ecosystem services in Europe—the role of past and future land use change. Appl. Geogr. 63, 121–135 (2015).
Google Scholar 
18.Rau, A. L. et al. Temporal patterns in ecosystem services research: a review and three recommendations. Ambio 49, 1377–1393 (2020).
Google Scholar 
19.Sutherland, I. J., Bennett, E. M. & Gergel, S. E. Recovery trends for multiple ecosystem services reveal non-linear responses and long-term tradeoffs from temperate forest harvesting. For. Ecol. Manage. 374, 61–70 (2016).
Google Scholar 
20.Hansen, M. C., Stehman, S. V. & Potapov, P. V. Quantification of global gross forest cover loss. Proc. Natl Acad. Sci. USA 107, 8650–8655 (2010).CAS 

Google Scholar 
21.Vanhanen, H. et al. Making Boreal Forests Work for People and Nature (IUFRO, 2012).22.Pan, Y. et al. A large and persistent carbon sink in the world’s forests. Science 333, 988–993 (2011).CAS 

Google Scholar 
23.Moen, J. et al. Eye on the Taiga: removing global policy impediments to safeguard the boreal forest. Conserv. Lett. 7, 408–418 (2014).
Google Scholar 
24.Global Forest Industry (Swedish Forest Industries, 2019); https://www.forestindustries.se/forest-industry/statistics/global-forest-industry/25.Saastamoinen, O., Kangas, K. & Aho, H. The picking of wild berries in Finland in 1997 and 1998. Scand. J. For. Res. 15, 645–650 (2000).
Google Scholar 
26.Gamfeldt, L. et al. Higher levels of multiple ecosystem services are found in forests with more tree species. Nat. Commun. 4, 1340 (2013).
Google Scholar 
27.Hou, Y., Li, B., Müller, F., Fu, Q. & Chen, W. A conservation decision-making framework based on ecosystem service hotspot and interaction analyses on multiple scales. Sci. Total Environ. 643, 277–291 (2018).CAS 

Google Scholar 
28.Blumstein, M. & Thompson, J. R. Land-use impacts on the quantity and configuration of ecosystem service provisioning in Massachusetts, USA. J. Appl. Ecol. 52, 1009–1019 (2015).
Google Scholar 
29.Fernandez-Campo, M., Rodríguez-Morales, B., Dramstad, W. E., Fjellstad, W. & Diaz-Varela, E. R. Ecosystem services mapping for detection of bundles, synergies and trade-offs: examples from two Norwegian municipalities. Ecosyst. Serv. 28, 283–297 (2017).
Google Scholar 
30.Gissi, E., Fraschetti, S. & Micheli, F. Incorporating change in marine spatial planning: a review. Environ. Sci. Policy 92, 191–200 (2019).
Google Scholar 
31.Maxwell, S. M., Gjerde, K. M., Conners, M. G. & Crowder, L. B. Mobile protected areas for biodiversity on the high seas. Science 367, 252–254 (2020).CAS 

Google Scholar 
32.Willcock, S. et al. Do ecosystem service maps and models meet stakeholders’ needs? A preliminary survey across sub-Saharan Africa. Ecosyst. Serv. 18, 110–117 (2016).
Google Scholar 
33.Jonsson, M., Bengtsson, J., Gamfeldt, L., Moen, J. & Snäll, T. Levels of forest ecosystem services depend on specific mixtures of commercial tree species. Nat. Plants 5, 141–147 (2019).
Google Scholar 
34.Pohjanmies, T. et al. Impacts of forestry on boreal forests: an ecosystem services perspective. Ambio 46, 743–755 (2017).
Google Scholar 
35.Miina, J., Hotanen, J.-P. & Salo, K. Modelling the abundance and temporal variation in the production of bilberry (Vaccinium myrtillus L.) in Finnish mineral soil forests. Silva Fenn. 43, 577–593 (2009).
Google Scholar 
36.Hertel, A. G. et al. Berry production drives bottom–up effects on body mass and reproductive success in an omnivore. Oikos 127, 197–207 (2018).
Google Scholar 
37.Thiffault, E. Boreal forests and soils. Dev. Soil Sci. 36, 59–82 (2019).
Google Scholar 
38.Jonsson, M., Bengtsson, J., Moen, J., Gamfeldt, L. & Snäll, T. Stand age and climate influence forest ecosystem service delivery and multifunctionality. Environ. Res. Lett. 15, 0940a8 (2020).
Google Scholar 
39.Stokland, J. N. Volume increment and carbon dynamics in boreal forest when extending the rotation length towards biologically old stands. For. Ecol. Manage. 488, 119017 (2021).
Google Scholar 
40.Harmon, M. E., Ferrell, W. K. & Franklin, J. F. Effects on carbon storage of conversion of old-growth forests to young forests. Science 247, 699–702 (1990).CAS 

Google Scholar 
41.Mazziotta, A. et al. Applying a framework for landscape planning under climate change for the conservation of biodiversity in the Finnish boreal forest. Glob. Change Biol. 21, 637–651 (2015).
Google Scholar 
42.Triviño, M. et al. Optimizing management to enhance multifunctionality in a boreal forest landscape. J. Appl. Ecol. 54, 61–70 (2017).
Google Scholar 
43.Qiu, J. & Turner, M. G. Spatial interactions among ecosystem services in an urbanizing agricultural watershed. Proc. Natl Acad. Sci. USA 110, 12149–12154 (2013).CAS 

Google Scholar 
44.Felipe-Lucia, M. R. et al. Multiple forest attributes underpin the supply of multiple ecosystem services. Nat. Commun. 9, 4839 (2018).
Google Scholar 
45.Eggers, J., Räty, M., Öhman, K. & Snäll, T. How well do stakeholder-defined forest management scenarios balance economic and ecological forest values? Forests 11, 86 (2020).
Google Scholar 
46.Eyvindson, K., Repo, A. & Mönkkönen, M. Mitigating forest biodiversity and ecosystem service losses in the era of bio-based economy. For. Policy Econ. 92, 119–127 (2018).
Google Scholar 
47.Rusch, A., Bommarco, R., Jonsson, M., Smith, H. G. & Ekbom, B. Flow and stability of natural pest control services depend on complexity and crop rotation at the landscape scale. J. Appl. Ecol. 50, 345–354 (2013).
Google Scholar 
48.Schipanski, M. E. et al. A framework for evaluating ecosystem services provided by cover crops in agroecosystems. Agric. Syst. 125, 12–22 (2014).
Google Scholar 
49.Hufnagel, J., Reckling, M. & Ewert, F. Diverse approaches to crop diversification in agricultural research. A review. Agron. Sustain. Dev. 40, 14 (2020).
Google Scholar 
50.Guerry, A. D. et al. Modeling benefits from nature: using ecosystem services to inform coastal and marine spatial planning. Int. J. Biodivers. Sci. Ecosyst. Serv. Manage. 8, 107–121 (2012).
Google Scholar 
51.Wikström, P. et al. The Heureka Forestry Decision Support System: An Overview. Math. Comput. For Nat.-Resour. Sci. 3, 87–94 (2011).
Google Scholar 
52.Forest Statistics (Swedish University of Agricultural Sciences, 2020).53.Eriksson, A., Snäll, T. & Harrison, P. J. Analys av miljöförhållanden ‐ SKA 15. Report 11 (Swedish Forest Agency, 2015).54.Axelsson, A.-L. et al. in National Forest Inventories—Pathways for Common Reporting (eds Tomppo, E. et al.) 541–553 (Springer, 2010).55.Marklund, L. G. Biomass Functions for Pine, Spruce and Birch in Sweden (1988).56.Petersson, H. & Ståhl, G. Functions for below-ground biomass of Pinus sylvestris, Picea abies, Betula pendula and Betula pubescens in Sweden. Scand. J. For. Res. 21, 24–83 (2006).
Google Scholar 
57.Miina, J., Pukkala, T. & Kurttila, M. Optimal multi-product management of stands producing timber and wild berries. Eur. J. For. Res. 135, 781–794 (2016).
Google Scholar 
58.Schröter, M. & Remme, R. P. Spatial prioritisation for conserving ecosystem services: comparing hotspots with heuristic optimisation. Landsc. Ecol. 31, 431–450 (2016).
Google Scholar 
59.Wu, J., Feng, Z., Gao, Y. & Peng, J. Hotspot and relationship identification in multiple landscape services: a case study on an area with intensive human activities. Ecol. Indic. 29, 529–537 (2013).CAS 

Google Scholar 
60.Akaike, H. A new look at the statistical model identification. IEEE Trans. Automat. Contr. 19, 716–723 (1974).
Google Scholar 
61.R: A Language and Environment for Statistical Computing (R Development Core Team, 2014); https://www.R-project.org/ More