Philippa Kaur

Du, E. et al. Global patterns of terrestrial nitrogen and phosphorus limitation. Nat. Geosci. 13, 221–226 (2020).ADS 
CAS 

Google Scholar 
Schulze, E. Ueber einige stickstoffhaltige Bestandtheile der Keimlinge von Vicia sativa. Z. Phys. Chem. 17, 193–216 (1893).
Google Scholar 
Wishart, D. S. et al. HMDB 4.0: the human metabolome database for 2018. Nucleic Acids Res. 46, D608–D617 (2018).CAS 
PubMed 
PubMed Central 

Google Scholar 
Kato, T., Yamagata, M. & Tsukahara, S. Guanidine compounds in fruit trees and their seasonal variations in citrus (Citrus unshiu Marc.). J. Jpn. Soc. Hortic. Sci. 55, 169–173 (1986).CAS 

Google Scholar 
Gund, P. Guanidine, trimethylenemethane, and “Y-delocalization.” Can acyclic compounds have “aromatic” stability? J. Chem. Educ. 49, 100 (1972).CAS 

Google Scholar 
Güthner, T., Mertschenk, B. & Schulz, B. In Ullmann’s Fine Chemicals vol. 2, 657–672 (Wiley-VCH, 2014).Strecker, A. Untersuchungen über die chemischen Beziehungen zwischen Guanin, Xanthin, Theobromin, Caffeïn und Kreatinin. Justus Liebigs Ann. Chem. 118, 151–177 (1861).
Google Scholar 
Iwanoff, N. N. & Awetissowa, A. N. The fermentative conversion of guanidine in urea. Biochem. Z. 231, 67–78 (1931).
Google Scholar 
Lenkeit, F., Eckert, I., Hartig, J. S. & Weinberg, Z. Discovery and characterization of a fourth class of guanidine riboswitches. Nucleic Acids Res. 48, 12889–12899 (2020).CAS 
PubMed 
PubMed Central 

Google Scholar 
Salvail, H., Balaji, A., Yu, D., Roth, A. & Breaker, R. R. Biochemical validation of a fourth guanidine riboswitch class in bacteria. Biochemistry 59, 4654–4662 (2020).CAS 
PubMed 

Google Scholar 
Nelson, J. W., Atilho, R. M., Sherlock, M. E., Stockbridge, R. B. & Breaker, R. R. Metabolism of free guanidine in bacteria is regulated by a widespread riboswitch class. Mol. Cell 65, 220–230 (2017).CAS 
PubMed 

Google Scholar 
Sherlock, M. E. & Breaker, R. R. Biochemical validation of a third guanidine riboswitch class in bacteria. Biochemistry 56, 359–363 (2016).
Google Scholar 
Sherlock, M. E., Malkowski, S. N. & Breaker, R. R. Biochemical validation of a second guanidine riboswitch class in bacteria. Biochemistry 56, 352–358 (2016).
Google Scholar 
Kermani, A. A., Macdonald, C. B., Gundepudi, R. & Stockbridge, R. B. Guanidinium export is the primal function of SMR family transporters. Proc. Natl Acad. Sci. USA 115, 3060–3065 (2018).CAS 
PubMed 
PubMed Central 

Google Scholar 
Sinn, M., Hauth, F., Lenkeit, F., Weinberg, Z. & Hartig, J. S. Widespread bacterial utilization of guanidine as nitrogen source. Mol. Microbiol. 116, 200–210 (2021).CAS 
PubMed 

Google Scholar 
Schneider, N. O. et al. Solving the conundrum: widespread proteins annotated for urea metabolism in bacteria are carboxyguanidine deiminases mediating nitrogen assimilation from guanidine. Biochemistry 59, 3258–3270 (2020).CAS 
PubMed 

Google Scholar 
Zhao, J., Zhu, L., Fan, C., Wu, Y. & Xiang, S. Structure and function of urea amidolyase. Biosci. Rep. 38, BSR20171617 (2018).CAS 
PubMed 
PubMed Central 

Google Scholar 
Mobley, H. L., Island, M. D. & Hausinger, R. P. Molecular biology of microbial ureases. Microbiol. Rev. 59, 451–480 (1995).CAS 
PubMed 
PubMed Central 

Google Scholar 
Mazzei, L., Musiani, F. & Ciurli, S. The structure-based reaction mechanism of urease, a nickel dependent enzyme: tale of a long debate. J. Biol. Inorg. Chem. 25, 829–845 (2020).CAS 
PubMed 
PubMed Central 

Google Scholar 
Uribe, E. et al. Functional analysis of the Mn2+ requirement in the catalysis of ureohydrolases arginase and agmatinase – a historical perspective. J. Inorg. Biochem. 202, 110812 (2020).CAS 
PubMed 

Google Scholar 
Perozich, J., Hempel, J. & Morris, S. M. Jr Roles of conserved residues in the arginase family. Biochim. Biophys. Acta 1382, 23–37 (1998).CAS 
PubMed 

Google Scholar 
Sekowska, A., Danchin, A. & Risler, J. L. Phylogeny of related functions: the case of polyamine biosynthetic enzymes. Microbiology 146, 1815–1828 (2000).CAS 
PubMed 

Google Scholar 
Sekula, B. The neighboring subunit is engaged to stabilize the substrate in the active site of plant arginases. Front. Plant Sci. 11, 987 (2020).PubMed 
PubMed Central 

Google Scholar 
Quintero, M. J., Muro-Pastor, A. M., Herrero, A. & Flores, E. Arginine catabolism in the cyanobacterium Synechocystis sp. strain PCC 6803 involves the urea cycle and arginase pathway. J. Bacteriol. 182, 1008–1015 (2000).CAS 
PubMed 
PubMed Central 

Google Scholar 
Lacasse, M. J., Summers, K. L., Khorasani-Motlagh, M., George, G. N. & Zamble, D. B. Bimodal nickel-binding site on Escherichia coli [NiFe]-hydrogenase metallochaperone HypA. Inorg. Chem. 58, 13604–13618 (2019).CAS 
PubMed 
PubMed Central 

Google Scholar 
Hoffmann, D., Gutekunst, K., Klissenbauer, M., Schulz-Friedrich, R. & Appel, J. Mutagenesis of hydrogenase accessory genes of Synechocystis sp. PCC 6803. FEBS J. 273, 4516–4527 (2006).CAS 
PubMed 

Google Scholar 
Dowling, D. P., Di Costanzo, L., Gennadios, H. A. & Christianson, D. W. Evolution of the arginase fold and functional diversity. Cell. Mol. Life Sci. 65, 2039–2055 (2008).CAS 
PubMed 
PubMed Central 

Google Scholar 
Dutta, A., Mazumder, M., Alam, M., Gourinath, S. & Sau, A. K. Metal-induced change in catalytic loop positioning in Helicobacter pylori arginase alters catalytic function. Biochem. J. 476, 3595–3614 (2019).CAS 
PubMed 

Google Scholar 
Di Costanzo, L. et al. Crystal structure of human arginase I at 1.29-Å resolution and exploration of inhibition in the immune response. Proc. Natl Acad. Sci. USA 102, 13058–13063 (2005).ADS 
PubMed 
PubMed Central 

Google Scholar 
Suzek, B. E. et al. UniRef clusters: a comprehensive and scalable alternative for improving sequence similarity searches. Bioinformatics 31, 926–932 (2014).PubMed 
PubMed Central 

Google Scholar 
Alfano, M. & Cavazza, C. Structure, function, and biosynthesis of nickel-dependent enzymes. Protein Sci. 29, 1071–1089 (2020).CAS 
PubMed 
PubMed Central 

Google Scholar 
Wang, B. et al. A guanidine-degrading enzyme controls genomic stability of ethylene-producing cyanobacteria. Nat. Commun. 12, 5150 (2021).ADS 
PubMed 
PubMed Central 

Google Scholar 
McGee, D. J. et al. Purification and characterization of Helicobacter pylori arginase, RocF: unique features among the arginase superfamily. Eur. J. Biochem. 271, 1952–1962 (2004).CAS 
PubMed 

Google Scholar 
Arakawa, N., Igarashi, M., Kazuoka, T., Oikawa, T. & Soda, K. d-Arginase of Arthrobacter sp. KUJ 8602: characterization and its identity with Zn2+-guanidinobutyrase. J. Biochem. 133, 33–42 (2003).CAS 
PubMed 

Google Scholar 
Saragadam, T., Kumar, S. & Punekar, N. S. Characterization of 4-guanidinobutyrase from Aspergillus niger. Microbiology 165, 396–410 (2019).CAS 
PubMed 

Google Scholar 
Viator, R. J., Rest, R. F., Hildebrandt, E. & McGee, D. J. Characterization of Bacillus anthracis arginase: effects of pH, temperature, and cell viability on metal preference. BMC Biochem. 9, 15 (2008).PubMed 
PubMed Central 

Google Scholar 
D’Antonio, E. L., Hai, Y. & Christianson, D. W. Structure and function of non-native metal clusters in human arginase I. Biochemistry 51, 8399–8409 (2012).PubMed 

Google Scholar 
Andresen, E., Peiter, E. & Küpper, H. Trace metal metabolism in plants. J. Exp. Bot. 69, 909–954 (2018).CAS 
PubMed 

Google Scholar 
Eisenhut, M. Manganese homeostasis in cyanobacteria. Plants 9, 18 (2019).PubMed Central 

Google Scholar 
Burnat, M. & Flores, E. Inactivation of agmatinase expressed in vegetative cells alters arginine catabolism and prevents diazotrophic growth in the heterocyst-forming cyanobacterium Anabaena. MicrobiologyOpen 3, 777–792 (2014).CAS 
PubMed 
PubMed Central 

Google Scholar 
Callahan, B. P., Yuan, Y. & Wolfenden, R. The burden borne by urease. J. Am. Chem. Soc. 127, 10828–10829 (2005).CAS 
PubMed 

Google Scholar 
Lewis, C. A. Jr & Wolfenden, R. The nonenzymatic decomposition of guanidines and amidines. J. Am. Chem. Soc. 136, 130–136 (2014).CAS 
PubMed 

Google Scholar 
Grobben, Y. et al. Structural insights into human Arginase-1 pH dependence and its inhibition by the small molecule inhibitor CB-1158. J. Struct. Biol. X 4, 100014 (2020).CAS 
PubMed 

Google Scholar 
Mills, L. A., McCormick, A. J. & Lea-Smith, D. J. Current knowledge and recent advances in understanding metabolism of the model cyanobacterium Synechocystis sp. PCC 6803. Biosci. Rep. 40, BSR20193325 (2020).CAS 
PubMed 
PubMed Central 

Google Scholar 
Giner-Lamia, J. et al. Identification of the direct regulon of NtcA during early acclimation to nitrogen starvation in the cyanobacterium Synechocystis sp PCC 6803. Nucleic Acids Res. 45, 11800–11820 (2017).CAS 
PubMed 
PubMed Central 

Google Scholar 
Martinez, S. & Hausinger, R. P. Biochemical and spectroscopic characterization of the non-heme Fe(II)- and 2-oxoglutarate-dependent ethylene-forming enzyme from Pseudomonas syringae pv. phaseolicola PK2. Biochemistry 55, 5989–5999 (2016).CAS 
PubMed 

Google Scholar 
Copeland, R. A. et al. An iron(IV)-oxo intermediate initiating l-arginine oxidation but not ethylene production by the 2-oxoglutarate-dependent oxygenase, ethylene-forming enzyme. J. Am. Chem. Soc. 143, 2293–2303 (2021).CAS 
PubMed 
PubMed Central 

Google Scholar 
Rippka, R., Deruelles, J., Waterbury, J. B., Herdman, M. & Stanier, R. Y. Generic assignments, strain histories and properties of pure cultures of cyanobacteria. Microbiology 111, 1–61 (1979).
Google Scholar 
Geyer, J. W. & Dabich, D. Rapid method for determination of arginase activity in tissue homogenates. Anal. Biochem. 39, 412–417 (1971).CAS 
PubMed 

Google Scholar 
van Anken, H. C. & Schiphorst, M. E. A kinetic determination of ammonia in plasma. Clin. Chim. Acta 56, 151–157 (1974).PubMed 

Google Scholar 
Kabsch, W. XDS. Acta Crystallogr. D 66, 125–132 (2010).CAS 
PubMed 
PubMed Central 

Google Scholar 
McCoy, A. J. et al. Phaser crystallographic software. J. Appl. Crystallogr. 40, 658–674 (2007).CAS 
PubMed 
PubMed Central 

Google Scholar 
Lamzin, V. S. P. A., Wilson, K. S. In International Tables for Crystallography Vol. F (eds Arnold, E. et al.) 525–528 (Kluwer, 2012).Emsley, P., Lohkamp, B., Scott, W. G. & Cowtan, K. Features and development of Coot. Acta Crystallogr. D 66, 486–501 (2010).CAS 
PubMed 
PubMed Central 

Google Scholar 
Adams, P. D. et al. The Phenix software for automated determination of macromolecular structures. Methods 55, 94–106 (2011).CAS 
PubMed 
PubMed Central 

Google Scholar 
Williams, C. J. et al. MolProbity: more and better reference data for improved all-atom structure validation. Protein Sci. 27, 293–315 (2018).CAS 
PubMed 

Google Scholar 
Wang, J., Wang, W., Kollman, P. A. & Case, D. A. Automatic atom type and bond type perception in molecular mechanical calculations. J. Mol. Graph. Model. 25, 247–260 (2006).ADS 
PubMed 

Google Scholar 
Maier, J. A. et al. ff14SB: improving the accuracy of protein side chain and backbone parameters from ff99SB. J. Chem. Theory Comput. 11, 3696–3713 (2015).CAS 
PubMed 
PubMed Central 

Google Scholar 
Wang, J., Wolf, R. M., Caldwell, J. W., Kollman, P. A. & Case, D. A. Development and testing of a general amber force field. J. Comput. Chem. 25, 1157–1174 (2004).CAS 
PubMed 

Google Scholar 
Trott, O. & Olson, A. J. AutoDock Vina: improving the speed and accuracy of docking with a new scoring function, efficient optimization, and multithreading. J. Comput. Chem. 31, 455–461 (2010).CAS 
PubMed 
PubMed Central 

Google Scholar 
Ashkenazy, H. et al. ConSurf 2016: an improved methodology to estimate and visualize evolutionary conservation in macromolecules. Nucleic Acids Res. 44, W344–W350 (2016).CAS 
PubMed 
PubMed Central 

Google Scholar 
Lemoine, F. et al. Renewing Felsenstein’s phylogenetic bootstrap in the era of big data. Nature 556, 452–456 (2018).ADS 
CAS 
PubMed 
PubMed Central 

Google Scholar 
Lemoine, F. et al. NGPhylogeny.fr: new generation phylogenetic services for non-specialists. Nucleic Acids Res. 47, W260–W265 (2019).CAS 
PubMed 
PubMed Central 

Google Scholar 
Letunic, I. & Bork, P. Interactive Tree Of Life (iTOL) v4: recent updates and new developments. Nucleic Acids Res. 47, W256–W259 (2019).CAS 
PubMed 
PubMed Central 

Google Scholar 
Crooks, G. E., Hon, G., Chandonia, J. M. & Brenner, S. E. WebLogo: a sequence logo generator. Genome Res. 14, 1188–1190 (2004).CAS 
PubMed 
PubMed Central 

Google Scholar  More

Detection and validation of high-quality jumbo phage binsDue to the large size of jumbo bacteriophage genomes, it is likely that they are present in multiple distinct contigs in metagenomic datasets and therefore require binning to recover high-quality metagenome-assembled genomes (MAGs) [28]. This has been shown for large DNA viruses that infect eukaryotes, where several recent studies have successfully employed binning approaches to recover viral MAGs [2, 3, 30]. Here, we used the same 1545 high-quality metagenomic assemblies [31] used in a recent study to recover giant viruses of eukaryotes [3], but we modified the bioinformatic pipeline to identify bins of jumbo bacteriophages. These metagenomes were compiled by Parks et al. [31] and included available metagenomes on the NCBI’s Short Read Archive by December 31, 2015 (see Parks et al. [31]). This dataset includes a wide variety of marine metagenomes (n = 469) including many non-Tara metagenomes (n = 165). We focused our benchmarking and distribution analyses on Tara data [29] because of the well-curated metadata and size fractions in this dataset. We first binned the contigs from these assemblies with MetaBat2 [32], which groups contigs together based on similar nucleotide composition and coverage profiles, and focused on bins of at least 200 kilobases in total length. We subsequently identified bins composed of bacteriophage contigs through analysis with VirSorter2 [33], VIBRANT [34], and CheckV [35] (see Methods for details).The occurrence of multiple copies of highly conserved marker genes is typically used to assess the level of contamination present in metagenome-derived genomes of bacteria and archaea [36]. Because bacteriophage lack these marker genes [37], we developed alternative strategies to assess possible contamination in our jumbo phage bins. Firstly, we refined the set of bins by retaining those with no more than 5 contigs that were each at least 5 kilobases in length to reduce the possibility that spurious contigs were put together. Secondly, we assessed the possibility that two strains of smaller phages with similar nucleotide composition may be binned together by aligning the contigs in a bin to each other. Bins that had contigs with high sequence similarity across the majority of their lengths were discarded (Supplementary Fig. 1). Thirdly, we discarded bins if their contigs exhibited aberrant co-abundance profiles in different metagenomes (see Supplementary Methods). To generate these co-abundance profiles, we mapped reads from 225 marine metagenomes provided by Tara Oceans [29] onto the bins. Coverage variation between contigs was benchmarked based on read-mapping results from artificially-fragmented reference genomes present in the samples (See Methods for details). Only bins with coverage variation below our empirically-derived threshold were retained. Using this stringent filtering, we identified 85 bins belonging to jumbo bacteriophages. These bins ranged in length from 202 kbp to 498 kbp, and 31 consisted of a single contig, while 54 consisted of 2–5 contigs (Supplementary Fig. 2).To assess global diversity patterns of jumbo bacteriophages, we combined these jumbo phage bins together with a compiled database of previously-identified jumbo phages that included all complete jumbo Caudovirales genomes on RefSeq (downloaded July 5th, 2020), the INPHARED database [14], a recent survey of cultivated jumbo phages [6], the Al-Shayeb et al. study [4], and marine jumbo phage contigs from metagenomic surveys of GOV 2.0 [26] (60 jumbo phages), ALOHA 2.0 [38] (8 jumbo phages), and one megaphage MAG recovered from datasets of the English Channel [39]. Ultimately, we arrived at a set of 244 jumbo phages, including the 85 bins, that were present in at least one Tara Oceans sample (min. 20% genome covered, see Methods) or deriving from a marine dataset (i.e. ALOHA, GOV 2.0) which we analyzed further in this study and refer to as marine jumbo phages. Statistics on genomic features can be found in Supplementary Dataset 1.Marine jumbo phages belong to distinct groups with diverse infection strategiesBecause bacteriophages lack high-resolution, universal marker genes for classification, such as 16S rRNA in bacteria, phages are often grouped by gene content [40, 41]. Here, we generated a bipartite network that included the 85 bins of jumbo phages with a dataset of available Caudovirales complete genomes in RefSeq (3012 genomes; downloaded July 5th, 2020) and the full set of reference jumbo phages described above. To construct the bipartite network, we compared proteins encoded in all the phage genomes to the VOG database, and each genome was linked to VOG hits that were present (Fig. 1, Supplementary Dataset 2, see Methods for details). To identify groups of phage genomes with similar VOG profiles, we employed a spinglass community detection algorithm [42] to generate genome clusters. Similar methods have recently been used to analyze evolutionary relationships in other dsDNA viruses [41]. The marine jumbo phages of this study clustered into five groups that included both jumbo and non-jumbo phage genomes (Fig. 2a). We refer to these five clusters as Phage Genome Clusters (PGCs): PGC_A, PGC_B, PGC_C, PGC_D, and PGC_E. These PGCs included cultured phages and metagenome-derived jumbo phages found in various environments (i.e. aquatic, engineered) and isolated on a diversity of hosts (i.e. Firmicutes, Proteobacteria, Bacteroidetes) (Fig. 2b, c). Of the marine jumbo phages, 135 belonged to PGC_A, 11 to PGC_B, 90 to PGC_C, 7 to PGC_D, and 1 to PGC_E (Fig. 1b). In addition to this network-based analysis, we also examined phylogenies of the major capsid protein (MCP) and the terminase large subunit (TerL) encoded by the marine jumbo phages and the same reference phage set examined in the network (Fig. 1c, d). With the exception of PGC_A, the marine jumbo phages that belong to the same PGC appeared more closely related to each other than those belonging to different clusters. The polyphyletic placement of jumbo phage PGCs in these marker gene phylogenies is consistent with the view that genome gigantism evolved multiple times, independently within the Caudovirales [6].Fig. 1: Bipartite network and marker gene analyses of jumbo phages.a Network with marine jumbos and references as nodes and edges based on shared VOGs. Marine jumbo phage nodes are colored by PGC as detected with spinglass community detection analysis, other nodes are in gray. Edges and VOG nodes have been omitted to more clearly represent the pattern of phage clustering. Node size corresponds to the natural log of genome length in kilobases. b Barchart of the number of members in each PGC. PGCs with marine jumbo phages are denoted with a star and the number of marine jumbo phages in that PGC. Proportion of marine jumbo phages in that PGC is colored. Phylogenies of TerL (c) and MCP (d) proteins with references and bins. Inner ring and branches are colored by the 5 PGCs that marine jumbo phages belong to. Navy blue circles in the outer ring denote marine jumbo phages.Full size imageFig. 2: Statistics of the Phage Genomes Clusters (PGCs).a Boxplot of genome length in each network cluster (x-axis is PGC number). Star denotes PGC with a marine jumbo phage and the color matches the PGC letters of Fig. 1. b Stacked barplot of the metagenome environment from which each phage derives from in each PGC (x-axis). Reference (yellow) are cultured phages, in black are the bins of jumbo phages from this study. c Stacked barplot of the host phylum of the RefSeq cultured phages in each cluster; metagenomic phages are in gray.Full size imageWe then compared functional content encoded by the marine jumbo phages in the PGCs to identify functional differences that distinguish these groups. PGC_E was excluded from this analysis because this genome cluster contained only a single jumbo phage. Collectively, most genes of the marine jumbo phages could not be assigned a function (mean: 86.60%, std dev: 7.01%; Supplementary Dataset 3), which is common with environmental viruses [43, 44]. Genes with known functions primarily belonged to functional categories related to viral replication machinery, such as information processing and virion structure (Fig. 3a), and these genes drove the variation between the genome clusters of marine jumbo phages (Fig. 3b). A recent comparative genomic analysis of cultivated jumbo phages was able to identify three types of jumbo phages that are defined by different infection strategies and host interactions (referred to as Groups 1–3) [6]. We cross-referenced our PGCs and found that PGCs B, C, and D of this study corresponded to Groups 1, 2, and 3, respectively, suggesting that these genome clusters contain phages with distinct infection and replication strategies. PGC_A corresponded to multiple groups, indicating that this genome cluster contains a particularly broad diversity of phages.Fig. 3: Functional predictions of PGCs.a Functional categories for genes encoded by jumbo phages averaged by PGC. b Heatmap of proportion of genomes in each PGC that contain the listed genes. Listed genes were selected based on containing a known function and having a variance between the PGCs above 0.2. Dendrogram was generated based on hierarchical clustering in pheatmap.Full size imageThe second largest phage cluster with marine jumbo phages, PGC_B, consists of 238 phages (11 (4.6%) marine jumbo phages, including 10 bins generated here), and included cultured phages of Group 1, which is typified by Pseudomonas aeruginosa phage PhiKZ. Supporting this correspondence with Group 1, all marine jumbo phages of PGC_B encoded the same distinct replication and transcription machinery, including a divergent family B DNA polymerase and a multi-subunit RNA polymerase (Fig. 3b, Supplementary Dataset 3). These marine jumbo phages also encoded a PhiKZ internal head protein, and they uniquely encoded shell and tubulin homologs which has recently been found in PhiKZ phages to assist in the formation of a nucleus-like compartment during infection that protects the replicating phage from host defenses [18, 19]. Although we could not confidently predict hosts for the 11 metagenomic marine jumbo phages in this PGC_B (Supplementary Dataset 1), the cultured phages of this genome cluster infect pathogenic bacteria belonging to the phyla Proteobacteria (178 phages) and Firmicutes (6 phages) (Fig. 2c), implicating a potential host range for marine jumbo phages in PGC_B.The next largest phage genome cluster, PGC_C, comprised of 156 phages total (90 marine jumbo phages (57.7%); 4 bins generated from this study) and included reference jumbo phages in Group 2 (31, 19.9%) which are typified by Alphaproteobacteria and Cyanobacteria phages. Likewise, the host range of other cultured phages in PGC_C support the Group 2 correspondence, either infecting Cyanobacteria (139 phages) or Proteobacteria (4 phages) (Fig. 2c). Furthermore, all 3 marine metagenomic phages in PGC_C for which hosts could be predicted were matched to Cyanobacteria hosts (Supplementary Dataset 1). Functional annotations of PGC_C marine jumbo phages revealed nearly all encode a family B DNA polymerase (97.8% of phages) and the photosystem II D2 protein (PF00124, VOG04549) characteristic of cyanophages (90% of phages) (Fig. 3b). This PGC included the reference Prochlorococcus phage P-TIM68 (NC_028955.1), which encodes components of both photosystem I and II as a mechanism to enhance cyclic electron flow during infection [45]. This suggests that an enhanced complement of genes used to manipulate host physiology during infection may be a driver of large genome sizes in this group. Additionally, most of the PGC_C marine jumbo phages encoded lipopolysaccharide biosynthesis proteins (76%), which have been found in cyanophage genomes that may induce a “pseudolysogeny” state, when infected host cells are dormant, by changing the surface of the host cell and preventing additional phage infections [6] (Supplementary Dataset 3). Taken together, most marine jumbo phages of PGC_C likely follow host interactions of jumbo cyanophages, such as potentially manipulating host metabolism by encoding their own photosynthetic genes and potentially inducing a pseudolysogenic state.Finally, phages of PGC_D totaled at 47 phages, of which 7 were marine jumbo phages generated in this study (14.9%). This group included Group 3 jumbo phages (15, 31.9%), which is primarily distinguished by encoding a T7-type DNA polymerase but is not typified by a particular phage type or host range. Supporting this grouping, all marine jumbo phages in this study encoded a T7 DNA polymerase which belongs to family A DNA polymerases (Fig. 3b, Supplementary Dataset 3). Most of the other genes distinctively encoded by the marine jumbo phages in this group included structural genes related to T7 (T7 baseplate, T7 capsid proteins), a eukaryotic DNA topoisomerase I catalytic core (PF01028), and DNA structural modification genes (MmcB-like DNA repair protein, DNA gyrase B). Hosts of metagenomic marine jumbo phages in PGC_D could not be predicted (Supplementary Dataset 1); however, cultured Group 3 jumbo phages in PGC_D all infect Proteobacteria, primarily Enterobacteria and other pathogens.The largest of the phage genome clusters, PGC_A, contained 469 phages, including 135 marine jumbo phages (63 bins from this study). This genome cluster contained the largest jumbo phages, such as Bacillus phage G (498 kb) and the marine megaphage Mar_Mega_1 (656 kb) recently recovered from the English Channel [39]. Unlike other PGCs, PGC_A contained mostly metagenomic phages (401, 85%, Fig. 2b, c). Considering PGC_A contains the largest jumbo phages (Figs. 1b, 2a), the vast genetic diversity in this PGC might explain why few genes were found to distinguish this group. Of the genes unique to PGC_A, only one was present in at least half of the phages (51.9%), which was a Bacterial DNA polymerase III alpha NTPase domain (PF07733). The host ranges of cultured phages from this PGC further reflect the large diversity of this group and included a variety of phyla and genera that can perform complex metabolisms or lifestyles, such as the nitrogen-fixing Cyanobacteria of the Nodularia genus isolated from the Baltic Sea (accessions NC_048756.1 and NC_048757.1) and the Bacteroidetes bacteria Rhodothermus isolated from a hot spring in Iceland (NC_004735.1) [46]. Because this group contains an abundance of metagenome-derived genomes that encode mostly proteins with no VOG annotation (Supplementary Dataset 2), it is possible that it includes several distinct lineages that could not be distinguished using the community detection algorithm of the bipartite network analysis.Relative abundance of jumbo bacteriophages across size fractionsTo explore the distribution of the marine jumbo phages in the ocean, we first examined the size fractions in which the jumbo phages were most prevalent. To remove redundancy for the purposes of read mapping, we examined the 244 jumbo phages at the population-level ( >80% genes shared with >95% average nucleotide identity [24]), corresponding to 142 populations (11 unique to this study, corresponding to 47 bins). We then mapped Tara Oceans metagenomes onto the 142 jumbo phage populations, and 102 of these populations could be detected [min. 20% of genome covered], resulting in 74 populations in PGC_A, 2 in PGC_B, 22 in PGC_C, 3 in PGC_D, and 1 in PGC_E. Out of the 225 Tara Oceans metagenomes examined, 213 (94.6%) contained at least one jumbo phage population (median: 7, Supplementary Dataset 4). Jumbo phages were more frequently detected in samples below 0.22 µm ( More

Cardinale, B. J., Palmer, M. A. & Collins, S. L. Species diversity enhances ecosystem functioning through interspecific facilitation. Nature 415, 426–429 (2002).ADS 
CAS 
PubMed 

Google Scholar 
Gagic, V. et al. Functional identity and diversity of animals predict ecosystem functioning better than species-based indices. Proc. R. Soc. Lond. B Biol. Sci. 282, 20142620 (2015).
Google Scholar 
Oliver, T. H. et al. Biodiversity and resilience of ecosystem functions. Trends Ecol. Evol. 30, 673–684 (2015).PubMed 

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

Google Scholar 
Wardle, D. A. Do experiments exploring plant diversity–ecosystem functioning relationships inform how biodiversity loss impacts natural ecosystems?. J. Veg. Sci. 27, 646–653 (2016).
Google Scholar 
Cadotte, M. W., Carscadden, K. & Mirotchnick, N. Beyond species: Functional diversity and the maintenance of ecological processes and services. J. Appl. Ecol. 48, 1079–1087 (2011).
Google Scholar 
Petchey, O. L. & Gaston, K. J. Extinction and the loss of functional diversity. Proc. R. Soc. Lond. B Biol. Sci. 269, 1721–1727 (2002).
Google Scholar 
Rosenfeld, J. S. Functional redundancy in ecology and conservation. Oikos 98, 156–162 (2002).
Google Scholar 
Fonseca, C. R. & Ganade, G. Species functional redundancy, random extinctions and the stability of ecosystems. J. Ecol. 89, 118–125 (2001).
Google Scholar 
Mouillot, D. et al. Functional over-redundancy and high functional vulnerability in global fish faunas on tropical reefs. PNAS 111, 13757–13762 (2014).ADS 
CAS 
PubMed 
PubMed Central 

Google Scholar 
Violle, C. et al. Let the concept of trait be functional!. Oikos 116, 882–892 (2007).
Google Scholar 
Laughlin, D. C. Applying trait-based models to achieve functional targets for theory-driven ecological restoration. Ecol. Lett. 17, 771–784 (2014).PubMed 

Google Scholar 
Laughlin, D. C., Strahan, R. T., Huffman, D. W. & Sánchez Meador, A. J. Using trait-based ecology to restore resilient ecosystems: Historical conditions and the future of montane forests in western North America. Restor. Ecol. 25, S135–S146 (2017).
Google Scholar 
Petchey, O. L. & Gaston, K. J. Functional diversity (FD), species richness and community composition. Ecol. Lett. 5, 402–411 (2002).
Google Scholar 
Carmona, C. P., de Bello, F., Mason, N. W. H. & Lepš, J. Traits without borders: Integrating functional diversity across scales. Trends Ecol. Evol. 31, 382–394 (2016).PubMed 

Google Scholar 
Jain, M. et al. The importance of rare species: A trait-based assessment of rare species contributions to functional diversity and possible ecosystem function in tall-grass prairies. Ecol. Evol. 4(104), 112 (2014).
Google Scholar 
Mouillot, D. et al. Rare species support vulnerable functions in high-diversity ecosystems. PLoS Biol. 11, e1001569 (2013).CAS 
PubMed 
PubMed Central 

Google Scholar 
Leitão, R. P. et al. Rare species contribute disproportionately to the functional structure of species assemblages. Proc. R. Soc. B 283, 20160084 (2016).PubMed 
PubMed Central 

Google Scholar 
IUCN/SSC. Guidelines for Reintroductions and Other Conservation Translocations. (IUCN Species Survival Commission, 2013).Bakker, E. S. & Svenning, J.-C. Trophic rewilding: Impact on ecosystems under global change. Philos. Trans. R. Soc. B Biol. Sci. 373, 20170432 (2018).
Google Scholar 
Garrido, P. et al. Experimental rewilding enhances grassland functional composition and pollinator habitat use. J. Appl. Ecol. 56, 946–955 (2019).
Google Scholar 
Svenning, J.-C. et al. Science for a wilder Anthropocene: Synthesis and future directions for trophic rewilding research. Proc. Natl. Acad. Sci. 113, 898–906 (2016).ADS 
CAS 
PubMed 

Google Scholar 
Estes, J. A. et al. Trophic downgrading of planet earth. Science 333, 301–306 (2011).ADS 
CAS 
PubMed 
PubMed Central 

Google Scholar 
Ritchie, E. G. et al. Ecosystem restoration with teeth: What role for predators?. Trends Ecol. Evol. 27, 265–271 (2012).PubMed 

Google Scholar 
Chauvenet, A. L. M., Canessa, S. & Ewen, J. G. Setting objectives and defining the success of reintroductions. In Reintroduction of Fish and Wildlife Populations 105–121 (University of California Press, 2016).Ewen, J. G., Soorae, P. S. & Canessa, S. Reintroduction objectives, decisions and outcomes: Global perspectives from the herpetofauna. Anim. Conserv. 17, 74–81 (2014).
Google Scholar 
Kleiman, D. G., Price, M. R. S. & Beck, B. B. Criteria for reintroductions. In Creative Conservation: Interactive Management of Wild and Captive Animals (eds. Olney, P. J. S., Mace, G. M. & Feistner, A. T. C.) 287–303 (Springer Netherlands, 1994). https://doi.org/10.1007/978-94-011-0721-1_14.Hunter, M. L. & Hutchinson, A. The virtues and shortcomings of parochialism: Conserving species that are locally rare, but globally common. Conserv. Biol. 8, 1163–1165 (1994).
Google Scholar 
Brichieri-Colombi, T. A. & Moehrenschlager, A. Alignment of threat, effort, and perceived success in North American conservation translocations. Conserv. Biol. 30, 1159–1172 (2016).PubMed 

Google Scholar 
Thévenin, C., Mouchet, M., Robert, A., Kerbiriou, C. & Sarrazin, F. Reintroductions of birds and mammals involve evolutionarily distinct species at the regional scale. PNAS https://doi.org/10.1073/pnas.1714599115 (2018).Article 
PubMed 
PubMed Central 

Google Scholar 
Seddon, P. J., Soorae, P. S. & Launay, F. Taxonomic bias in reintroduction projects. Anim. Conserv. 8, 51–58 (2005).
Google Scholar 
Thévenin, C., Morin, A., Kerbiriou, C., Sarrazin, F. & Robert, A. Heterogeneity in the allocation of reintroduction efforts among terrestrial mammals in Europe. Biol. Conserv. 241, 108346 (2020).
Google Scholar 
Devictor, V. et al. Spatial mismatch and congruence between taxonomic, phylogenetic and functional diversity: The need for integrative conservation strategies in a changing world. Ecol. Lett. 13, 1030–1040 (2010).PubMed 

Google Scholar 
Crees, J. J., Turvey, S. T., Freeman, R. & Carbone, C. Mammalian tolerance to humans is predicted by body mass: Evidence from long-term archives. Ecology 100, e02783 (2019).PubMed 

Google Scholar 
Sandom, C., Faurby, S., Sandel, B. & Svenning, J.-C. Global late Quaternary megafauna extinctions linked to humans, not climate change. Proc. R. Soc. B Biol. Sci. 281, 20133254 (2014).
Google Scholar 
Wilman, H. et al. EltonTraits 1.0: Species-level foraging attributes of the world’s birds and mammals. Ecology 95, 2027–2027 (2014).
Google Scholar 
Dı́az, S. & Cabido, M. Vive la différence: Plant functional diversity matters to ecosystem processes. Trends Ecol. Evol. 16, 646–655 (2001).
Google Scholar 
Mlambo, M. C. Not all traits are ‘functional’: Insights from taxonomy and biodiversity-ecosystem functioning research. Biodivers. Conserv. 23, 781–790 (2014).
Google Scholar 
van der Plas, F. et al. Plant traits alone are poor predictors of ecosystem properties and long-term ecosystem functioning. Nat. Ecol. Evol. 4, 1602–1611 (2020).PubMed 

Google Scholar 
Lavorel, S. & Garnier, E. Predicting changes in community composition and ecosystem functioning from plant traits: Revisiting the Holy Grail. Funct. Ecol. 16, 545–556 (2002).
Google Scholar 
Luck, G. W., Lavorel, S., McIntyre, S. & Lumb, K. Improving the application of vertebrate trait-based frameworks to the study of ecosystem services. J. Anim. Ecol. 81, 1065–1076 (2012).PubMed 

Google Scholar 
Mouchet, M. et al. Towards a consensus for calculating dendrogram-based functional diversity indices. Oikos 117, 794–800 (2008).
Google Scholar 
Podani, J. & Schmera, D. On dendrogram-based measures of functional diversity. Oikos 115, 179–185 (2006).
Google Scholar 
Maire, E., Grenouillet, G., Brosse, S. & Villéger, S. How many dimensions are needed to accurately assess functional diversity? A pragmatic approach for assessing the quality of functional spaces. Glob. Ecol. Biogeogr. 24, 728–740 (2015).
Google Scholar 
Villéger, S., Maire, E. & Leprieur, F. On the risks of using dendrograms to measure functional diversity and multidimensional spaces to measure phylogenetic diversity: A comment on Sobral et al. (2016). Ecol. Lett. 20, 554–557 (2017).PubMed 

Google Scholar 
Tsirogiannis, C. & Sandel, B. PhyloMeasures: A package for computing phylogenetic biodiversity measures and their statistical moments. Ecography 39, 709–714 (2016).
Google Scholar 
Isaac, N. J., Turvey, S. T., Collen, B., Waterman, C. & Baillie, J. E. Mammals on the EDGE: Conservation priorities based on threat and phylogeny. PLoS ONE 2, e296 (2007).ADS 
PubMed 
PubMed Central 

Google Scholar 
Paradis, E., Claude, J. & Strimmer, K. APE: Analyses of phylogenetics and evolution in R language. Bioinformatics 20, 289–290 (2004).CAS 

Google Scholar 
Hidasi-Neto, J., Loyola, R. & Cianciaruso, M. V. Global and local evolutionary and ecological distinctiveness of terrestrial mammals: Identifying priorities across scales. Divers. Distrib. 21, 548–559 (2015).
Google Scholar 
Dovrat, G., Meron, E., Shachak, M., Golodets, C. & Osem, Y. The relative contributions of functional diversity and functional identity to ecosystem function in water-limited environments. J. Veg. Sci. 30, 427–437 (2019).
Google Scholar 
Funk, J. L. et al. Revisiting the Holy Grail: Using plant functional traits to understand ecological processes. Biol. Rev. 92, 1156–1173 (2017).PubMed 

Google Scholar 
Kuebbing, S. E. & Bradford, M. A. The potential for mass ratio and trait divergence effects to explain idiosyncratic impacts of non-native invasive plants on carbon mineralization of decomposing leaf litter. Funct. Ecol. 33, 1156–1171 (2019).
Google Scholar 
Devictor, V. et al. Defining and measuring ecological specialization. J. Appl. Ecol. 47, 15–25 (2010).
Google Scholar 
Byers, J. E. et al. Using ecosystem engineers to restore ecological systems. Trends Ecol. Evol. 21, 493–500 (2006).PubMed 

Google Scholar 
Jones, C. G., Lawton, J. H. & Shachak, M. Organisms as ecosystem engineers. In Ecosystem Management: Selected Readings (eds. Samson, F. B. & Knopf, F. L.) 130–147 (Springer, 1996). https://doi.org/10.1007/978-1-4612-4018-1_14.Macdonald, D. W. et al. Reintroducing the beaver (Castor fiber) to Scotland: A protocol for identifying and assessing suitable release sites. Anim. Conserv. 3, 125–133 (2000).
Google Scholar 
Wilmers, C. C., Crabtree, R. L., Smith, D. W., Murphy, K. M. & Getz, W. M. Trophic facilitation by introduced top predators: Grey wolf subsidies to scavengers in Yellowstone National Park. J. Anim. Ecol. 72, 909–916 (2003).
Google Scholar 
Dupont, H., Mihoub, J.-B., Bobbé, S. & Sarrazin, F. Modelling carcass disposal practices: Implications for the management of an ecological service provided by vultures. J. Appl. Ecol. 49, 404–411 (2012).
Google Scholar 
Moleon, M. et al. Humans and scavengers: The evolution of interactions and ecosystem services. Bioscience 64, 394–403 (2014).
Google Scholar 
Legras, G., Loiseau, N., Gaertner, J.-C., Poggiale, J.-C. & Gaertner-Mazouni, N. Assessing functional diversity: The influence of the number of the functional traits. Theor. Ecol. 13, 117–126 (2020).
Google Scholar 
Petchey, O. L. & Gaston, K. J. Functional diversity: Back to basics and looking forward. Ecol. Lett. 9, 741–758 (2006).PubMed 

Google Scholar 
Díaz, S. et al. The global spectrum of plant form and function. Nature 529, 167–171 (2016).ADS 
PubMed 

Google Scholar 
Lundgren, E. J. et al. Introduced herbivores restore Late Pleistocene ecological functions. Proc. Natl. Acad. Sci. 117, 7871–7878 (2020).CAS 
PubMed 
PubMed Central 

Google Scholar 
Malhi, Y. et al. Megafauna and ecosystem function from the Pleistocene to the Anthropocene. PNAS 113, 838–846 (2016).ADS 
CAS 
PubMed 
PubMed Central 

Google Scholar 
Osborne, P. E. & Seddon, P. J. Selecting suitable habitats for reintroductions: Variation, change and the role of species distribution modelling. Reintrod. Biol. Integr. Sci. Manag. 1, 73–104 (2012).
Google Scholar 
Lipsey, M. K., Child, M. F., Seddon, P. J., Armstrong, D. P. & Maloney, R. F. Combining the fields of reintroduction biology and restoration ecology. Conserv. Biol. 21, 1387–1390 (2007).PubMed 

Google Scholar 
Perino, A. et al. Rewilding complex ecosystems. Science 364, eaav5570 (2019).CAS 
PubMed 

Google Scholar 
Loiseau, N. et al. Global distribution and conservation status of ecologically rare mammal and bird species. Nat. Commun. 11, 5071 (2020).ADS 
CAS 
PubMed 
PubMed Central 

Google Scholar 
Cadotte, M. W. & Tucker, C. M. Difficult decisions: Strategies for conservation prioritization when taxonomic, phylogenetic and functional diversity are not spatially congruent. Biol. Conserv. 225, 128–133 (2018).
Google Scholar 
Sarrazin, F. & Barbault, R. Reintroduction: Challenges and lessons for basic ecology. Trends Ecol. Evol. (Amst.) 11, 474–478 (1996).CAS 

Google Scholar  More

Hey, J. On the arbitrary identification of real species. In Speciation and Patterns of Diversity (eds Butlin, R. K. et al.) 15–28 (Cambridge University Press, 2009).
Google Scholar 
Arbogast, B. S., Edwards, S. V., Wakeley, J., Beerli, P. & Slowinski, J. B. Estimating divergence times from molecular data on phylogenetic and population genetic timescales. Annu. Rev. Ecol. Syst. 33, 707–740 (2002).
Google Scholar 
Nielsen, R. & Wakeley, J. Distinguishing migration from isolation: A Markov chain Monte Carlo approach. Genetics 158, 885–896 (2001).CAS 
PubMed 
PubMed Central 

Google Scholar 
Wakeley, J. The effects of subdivision on the genetic divergence of populations and species. Evolution 54, 1092–1101 (2000).CAS 
PubMed 

Google Scholar 
Hey, J. & Nielsen, R. Multilocus methods for estimating population sizes, migration rates and divergence time, with applications to the divergence of Drosophila pseudoobscura and D. persimilis. Genetics 167, 747–760 (2004).CAS 
PubMed 
PubMed Central 

Google Scholar 
Hey, J. Isolation with migration models for more than two populations. Mol. Biol. Evol. 27, 905–920 (2010).CAS 
PubMed 

Google Scholar 
Mailund, T. et al. A new isolation with migration model along complete genomes infers very different divergence processes among closely related great ape species. PLoS Genet. 8, e1003125 (2012).PubMed 
PubMed Central 

Google Scholar 
Igea, J., Aymerich, P., Bannikova, A. A., Gosálbez, J. & Castresana, J. Multilocus species trees and species delimitation in a temporal context: Application to the water shrews of the genus Neomys. BMC Evol. Biol. 15, 209 (2015).PubMed 
PubMed Central 

Google Scholar 
Sánchez-Gracia, A. & Castresana, J. Impact of deep coalescence on the reliability of species tree inference from different types of DNA markers in mammals. PLoS One 7, e30239 (2012).ADS 
PubMed 
PubMed Central 

Google Scholar 
Degnan, J. H. & Rosenberg, N. A. Gene tree discordance, phylogenetic inference and the multispecies coalescent. Trends Ecol. Evol. 24, 332–340 (2009).PubMed 

Google Scholar 
Edwards, S. V. & Beerli, P. Perspective: Gene divergence, population divergence, and the variance in coalescence time in phylogeographic studies. Evolution 54, 1839–1854 (2000).CAS 
PubMed 

Google Scholar 
Peterson, B. K., Weber, J. N., Kay, E. H., Fisher, H. S. & Hoekstra, H. E. Double digest RADseq: An inexpensive method for de novo SNP discovery and genotyping in model and non-model species. PLoS One 7, e37135 (2012).ADS 
CAS 
PubMed 
PubMed Central 

Google Scholar 
Andrews, K. R., Good, J. M., Miller, M. R., Luikart, G. & Hohenlohe, P. A. Harnessing the power of RADseq for ecological and evolutionary genomics. Nat. Rev. Genet. 17, 81–92 (2016).CAS 
PubMed 
PubMed Central 

Google Scholar 
Escoda, L., Fernández-González, A. & Castresana, J. Quantitative analysis of connectivity in populations of a semi-aquatic mammal using kinship categories and network assortativity. Mol. Ecol. Resour. 19, 310–326 (2019).PubMed 

Google Scholar 
Bininda-Emonds, O. R. P. Fast genes and slow clades: Comparative rates of molecular evolution in mammals. Evol. Bioinform. 3, 59 (2007).CAS 

Google Scholar 
Welch, J. J., Bininda-Emonds, O. R. P. & Bromham, L. Correlates of substitution rate variation in mammalian protein-coding sequences. BMC Evol. Biol. 8, 53 (2008).PubMed 
PubMed Central 

Google Scholar 
Matassi, G., Sharp, P. M. & Gautier, C. Chromosomal location effects on gene sequence evolution in mammals. Curr. Biol. 9, 786–791 (1999).CAS 
PubMed 

Google Scholar 
Lercher, M. J., Chamary, J. V. & Hurst, L. D. Genomic regionality in rates of evolution is not explained by clustering of genes of comparable expression profile. Genome Res. 14, 1002–1013 (2004).CAS 
PubMed 
PubMed Central 

Google Scholar 
Castresana, J. Genes on human chromosome 19 show extreme divergence from the mouse orthologs and a high GC content. Nucleic Acids Res. 30, 1751–1756 (2002).CAS 
PubMed 
PubMed Central 

Google Scholar 
Benton, M. J., Donoghue, P. C. J. & Asher, R. J. Calibrating and constraining molecular clocks. In The Timetree of Life (eds Hedges, S. B. & Kumar, S.) 35–86 (Oxford University Press, 2009).Bouckaert, R. et al. BEAST 2.5: An advanced software platform for Bayesian evolutionary analysis. PLoS Comput. Biol. 15, e1006650 (2019).CAS 
PubMed 
PubMed Central 

Google Scholar 
Musser, G. G. & Carleton, M. D. Superfamily Muroidea. In Mammal Species of the World. A Taxonomic and Geographic Reference (eds Wilson, D. E. & Reeder, D. M.) 894–1531 (Johns Hopkins University Press, 2005).Pardiñas, U. F. J. et al. Family Cricetidae (True Hamsters, Voles, Lemmings and New World Rats and Mice). In Handbook of the Mammals of the World. Volume 7. Rodents II (eds Wilson, D. E. et al.) 204-279 (Lynx Edicions, 2017).Chevret, P. et al. Genetic structure, ecological versatility, and skull shape differentiation in Arvicola water voles (Rodentia, Cricetidae). J. Zoolog. Syst. Evol. Res. 58, 1323–1334 (2020).
Google Scholar 
Kryštufek, B. et al. Fossorial morphotype does not make a species in water voles. Mammalia 79, 293–303 (2015).
Google Scholar 
Centeno-Cuadros, A., Delibes, M. & Godoy, J. A. Dating the divergence between Southern and European water voles using molecular coalescent-based methods. J. Zool. 279, 404–409 (2009).
Google Scholar 
Castiglia, R. et al. The Italian peninsula hosts a divergent mtDNA lineage of the water vole, Arvicola amphibius s.l., including fossorial and aquatic ecotypes. Hystrix 27, 99–103 (2016).
Google Scholar 
Mahmoudi, A. et al. Evolutionary history of water voles revisited: Confronting a new phylogenetic model from molecular data with the fossil record. Mammalia 84, 171–184 (2020).
Google Scholar 
Cassola, F. Arvicola scherman, Montane Water Vole. The IUCN Red List of Threatened Species e.T136766A115519839 (2016).Somoano, A., Miñarro, M. & Ventura, J. Reproductive potential of a vole pest (Arvicola scherman) in Spanish apple orchards. Spanish J. Agric. Res. 14, e1008 (2016).
Google Scholar 
Somoano, A., Ventura, J. & Miñarro, M. Continuous breeding of fossorial water voles in northwestern Spain: Potential impact on apple orchards. Folia Zool. 66, 37–49 (2017).
Google Scholar 
Ventura, J. & Gosálbez, J. Taxonomic review of Arvicola terrestris (Linnaeus, 1758) (Rodentia, Arvicolidae) in the Iberian Peninsula. Bonn Zool. Beitr. 40, 227–242 (1989).
Google Scholar 
Ventura, J. & Sans-Fuentes, M. A. Geographic variation and divergence in nonmetric cranial traits of Arvicola (Mammalia, Rodentia) in southwestern Europe. Z. Säugetierkunde 62, 99–107 (1997).
Google Scholar 
Gómez, A. & Lunt, D. H. Refugia within refugia: Patterns of phylogeographic concordance in the Iberian Peninsula. In Phylogeography of Southern European Refugia (eds S. Weiss & N. Ferrand) 155–188 (Springer, 2007).Batsaikhan, N. et al. Arvicola amphibius, European Water Vole. The IUCN Red List of Threatened Species e.T2149A197271401 (2016).Cuenca-Bescós, G., Agustí, J., Lira, J., Rubio, M. M. & Rofes, J. A new species of water vole from the early Pleistocene of Southern Europe. Acta Palaeontol. Pol. 55, 565–580 (2010).
Google Scholar 
Cubo, J., Ventura, J. & Casinos, A. A heterochronic interpretation of the origin of digging adaptations in the northern water vole, Arvicola terrestris (Rodentia: Arvicolidae). Biol. J. Linn. Soc. 87, 381–391 (2006).
Google Scholar 
Catchen, J. M., Hohenlohe, P. A., Bassham, S., Amores, A. & Cresko, W. A. Stacks: An analysis tool set for population genomics. Mol. Ecol. 22, 3124–3140 (2013).PubMed 
PubMed Central 

Google Scholar 
Pritchard, J. K., Stephens, M. & Donnelly, P. Inference of population structure using multilocus genotype data. Genetics 155, 945–959 (2000).CAS 
PubMed 
PubMed Central 

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

Google Scholar 
Yates, A. D. et al. Ensembl 2020. Nucleic Acids Res. 48, D682–D688 (2020).CAS 

Google Scholar 
Aghová, T. et al. Fossils know it best: Using a new set of fossil calibrations to improve the temporal phylogenetic framework of murid rodents (Rodentia: Muridae). Mol. Phylogenet. Evol. 128, 98–111 (2018).PubMed 

Google Scholar 
Hey, J. et al. Phylogeny estimation by integration over isolation with migration models. Mol. Biol. Evol. 35, 2805–2818 (2018).CAS 
PubMed 
PubMed Central 

Google Scholar 
Phifer-Rixey, M., Harr, B. & Hey, J. Further resolution of the house mouse (Mus musculus) phylogeny by integration over isolation-with-migration histories. BMC Evol. Biol. 20, 120 (2020).PubMed 
PubMed Central 

Google Scholar 
Hey, J. The divergence of chimpanzee species and subspecies as revealed in multipopulation isolation-with-migration analyses. Mol. Biol. Evol. 27, 921–933 (2010).CAS 
PubMed 

Google Scholar 
Kumar, S. & Subramanian, S. Mutation rates in mammalian genomes. Proc. Natl. Acad. Sci. U.S.A. 99, 803–808 (2002).ADS 
CAS 
PubMed 
PubMed Central 

Google Scholar 
Uchimura, A. et al. Germline mutation rates and the long-term phenotypic effects of mutation accumulation in wild-type laboratory mice and mutator mice. Genome Res. 25, 1125–1134 (2015).CAS 
PubMed 
PubMed Central 

Google Scholar 
Milholland, B. et al. Differences between germline and somatic mutation rates in humans and mice. Nat. Commun. 8, 15183 (2017).ADS 
CAS 
PubMed 
PubMed Central 

Google Scholar 
Wright, B. R. et al. A demonstration of conservation genomics for threatened species management. Mol. Ecol. Resour. 20, 1526–1541 (2020).PubMed 

Google Scholar 
Escoda, L. & Castresana, J. The genome of the Pyrenean desman and the effects of bottlenecks and inbreeding on the genomic landscape of an endangered species. Evol. Appl. 14, 1898–1913 (2021).PubMed 
PubMed Central 

Google Scholar 
Arnold, B., Corbett-Detig, R. B., Hartl, D. & Bomblies, K. RADseq underestimates diversity and introduces genealogical biases due to nonrandom haplotype sampling. Mol. Ecol. 22, 3179–3190 (2013).CAS 
PubMed 

Google Scholar 
Cariou, M., Duret, L. & Charlat, S. How and how much does RAD-seq bias genetic diversity estimates?. BMC Evol. Biol. 16, 240 (2016).PubMed 
PubMed Central 

Google Scholar 
Campbell, C. R. et al. Pedigree-based and phylogenetic methods support surprising patterns of mutation rate and spectrum in the gray mouse lemur. Heredity 127, 233–244 (2021).CAS 
PubMed 

Google Scholar 
Scornavacca, C. et al. Orthomam v10: Scaling-up orthologous coding sequence and exon alignments with more than one hundred mammalian genomes. Mol. Biol. Evol. 36, 861–862 (2019).CAS 
PubMed 
PubMed Central 

Google Scholar 
Willis, S. C., Hollenbeck, C. M., Puritz, J. B., Gold, J. R. & Portnoy, D. S. Haplotyping RAD loci: An efficient method to filter paralogs and account for physical linkage. Mol. Ecol. Resour. 17, 955–965 (2017).CAS 
PubMed 

Google Scholar 
O’Leary, S. J., Puritz, J. B., Willis, S. C., Hollenbeck, C. M. & Portnoy, D. S. These aren’t the loci you’e looking for: Principles of effective SNP filtering for molecular ecologists. Mol. Ecol. 27, 3193–3206 (2018).
Google Scholar 
Dahl-Jensen, D. et al. Eemian interglacial reconstructed from a Greenland folded ice core. Nature 493, 489–494 (2013).ADS 
CAS 

Google Scholar 
Clark, P. U. et al. The last glacial maximum. Science 325, 710–714 (2009).ADS 
CAS 
PubMed 
PubMed Central 

Google Scholar 
Pinho, C. & Hey, J. Divergence with gene flow: Models and data. Annu. Rev. Ecol. Evol. Syst. 41, 215–230 (2010).
Google Scholar 
Balmori-de la Puente, A. et al. Size increase without genetic divergence in the Eurasian water shrew Neomys fodiens. Sci. Rep. 9, 17375 (2019).ADS 
PubMed 
PubMed Central 

Google Scholar 
Langmead, B. & Salzberg, S. L. Fast gapped-read alignment with Bowtie 2. Nat. Methods 9, 357–359 (2012).CAS 
PubMed 
PubMed Central 

Google Scholar 
Foll, M. & Gaggiotti, O. A genome-scan method to identify selected loci appropriate for both dominant and codominant markers: A Bayesian perspective. Genetics 180, 977–993 (2008).PubMed 
PubMed Central 

Google Scholar 
Freedman, A. H. et al. Genome sequencing highlights the dynamic early history of dogs. PLoS Genet. 10, e1004016 (2014).PubMed 
PubMed Central 

Google Scholar 
Felsenstein, J. PHYLIP-phylogeny inference package (version 3.4). Cladistics 5, 164–166 (1989).
Google Scholar 
Zheng, X. et al. A high-performance computing toolset for relatedness and principal component analysis of SNP data. Bioinformatics 28, 3326–3328 (2012).CAS 
PubMed 
PubMed Central 

Google Scholar 
Evanno, G., Regnaut, S. & Goudet, J. Detecting the number of clusters of individuals using the software STRUCTURE: A simulation study. Mol. Ecol. 14, 2611–2620 (2005).CAS 
PubMed 

Google Scholar 
Jakobsson, M. & Rosenberg, N. A. CLUMPP: A cluster matching and permutation program for dealing with label switching and multimodality in analysis of population structure. Bioinformatics 23, 1801–1806 (2007).CAS 
PubMed 

Google Scholar 
Goudet, J. HIERFSTAT, a package for R to compute and test hierarchical F-statistics. Mol. Ecol. Notes 5, 184–186 (2005).
Google Scholar 
Katoh, K. & Standley, D. M. MAFFT multiple sequence alignment software version 7: Improvements in performance and usability. Mol. Biol. Evol. 30, 772–780 (2013).CAS 
PubMed 
PubMed Central 

Google Scholar 
Castresana, J. Selection of conserved blocks from multiple alignments for their use in phylogenetic analysis. Mol. Biol. Evol. 17, 540–552 (2000).CAS 
PubMed 

Google Scholar 
Stamatakis, A. RAxML version 8: A tool for phylogenetic analysis and post-analysis of large phylogenies. Bioinformatics 30, 1312–1313 (2014).CAS 
PubMed 
PubMed Central 

Google Scholar 
Brown, R. P. & Yang, Z. Rate variation and estimation of divergence times using strict and relaxed clocks. BMC Evol. Biol. 11, 271 (2011).PubMed 
PubMed Central 

Google Scholar 
Rambaut, A., Drummond, A. J., Xie, D., Baele, G. & Suchard, M. A. Posterior Summarization in Bayesian Phylogenetics Using Tracer 1.7. Syst. Biol. 67, 901–904 (2018).CAS 
PubMed 
PubMed Central 

Google Scholar 
Hey, J. & Wang, K. The effect of undetected recombination on genealogy sampling and inference under an isolation-with-migration model. Mol. Ecol. Resour. 18, 489 (2019).
Google Scholar 
QGIS_Development_Team. QGIS Geographic Information System. Open Source Geospatial Foundation Project. http://qgis.osgeo.org (2021).IUCN. Arvicola scherman. The IUCN Red List of Threatened Species. Version 6.2. https://www.iucnredlist.org. Downloaded on 04 September 2019. (2019).IUCN. Arvicola amphibius. The IUCN Red List of Threatened Species. Version 6.2. https://www.iucnredlist.org. Downloaded on 10 July 2019. (2019). More

Following the work in36, we construct an epidemiological model which tracks the disease dynamics and population of two species of hosts following the introduction of a pathogen. The native host (hereafter simply referred to as “type 1”) is vulnerable to the disease, but due to being well adapted to the native habitat has high fecundity when uninfected. The invasive host (hereafter referred to as “type 2”), has coevolved defenses to the pathogen that increase both its tolerance of and resistance to the disease, but is not inherently as well-adapted to the habitat in the absence of infection (i.e., its intrinsic rate of growth in the new habitat is lower than that of the native).Our initial conditions correspond to a population of uninfected type 1 hosts with a small number of both uninfected and infected type 2 hosts, representing an invasion by a novel competitor carrying a novel pathogen into the type 1 population. We consider a vector-borne pathogen, and make the simplifying assumption that there is an already abundant competent vector species in the habitat. (For this initial formulation, we considered a scenario of mosquito-borne infections in birds, such as avian malaria37 or West Nile virus38, to motivate concrete choices.)The model couples two biological dynamics: the daily vector-borne spread of the disease among hosts, and a yearly host breeding cycle. We simulate in discrete time-steps that represent days using an SIR model taking into account the interactions between the disease, the two species of host, and the vectors. The model also includes a passive death rate for hosts of vectors, which increases for hosts while infected. While the vectors are assumed to breed daily, the hosts reproduce as part of an assumed annual breeding season, every (t_c) time-steps (typically equal to 365). These dynamics were informed by considering an annually breeding bird population in a tropical environment, however, they are not meant to reflect the realism of any one biological system. They are chosen here merely to allow a clean interpretation of modeled scenarios. Future models should explore the impact of greater variety in the dynamics of possible vector and host reproductive patterns.Epidemiological modelThe model tracks eight variables corresponding to combinations of host species and vectors with their infection status. Hosts may be of type 1 or 2, and are either susceptible to the disease ((S_1, S_2)), currently infected ((I_1, I_2)), or recovered ((R_1, R_2)). We assume that recovery is complete and recovered individuals suffer no residual effects from their infection aside from a lifelong immunity to becoming reinfected. (We later set the recovery rate for host type 1 to 0, so (R_1 = 0) at all times, but leave it defined for the sake of generality.) For simplicity, we model using only one stage of infection in which individuals are both infectious and symptomatic. The model also tracks the status of the vector population, which may either be susceptible ((S_v)) or infected ((I_v)). We assume that vectors do not recover from the disease, but also suffer no negative effects from being infected, acting only as carriers.For convenience of notation, we denote the total number of hosts$$begin{aligned} H = S_1 + I_1 + R_1 + S_2 + I_2 + R_2 end{aligned}$$and the relative frequencies of infection within their respective population$$begin{aligned} F_1 = frac{I_1}{H}, F_2 = frac{I_2}{H},F_v = frac{I_v}{S_v+I_v} end{aligned}$$which allows some equations to be written more compactly. Table 1 shows a summary of these variables.Table 1 Variables.Full size tableThe model also has several constant parameters that affect the dynamics. (beta _j) determines the probability that hosts of type j become infected when bitten by a single infected vector. We typically set (beta _1 > beta _2), making type 2 hosts less likely to become infected.Likewise, (delta _j) determines the probability that a vector becomes infected when biting an infected host of type j.(b_j) determines the bite rate for vectors on host type j. We assume that each vector bites the same number of hosts per day, so each vector’s probability of becoming infected depends only on the frequency of infection among hosts, while each host will be bitten more if there are more vectors.(gamma _j) determines the proportion of infected hosts of type j that recover from the disease each day. We typically set (gamma _1 = 0 < gamma _2), meaning infected hosts of type 1 do not recover, while infected type 2 recover after an average of (1/gamma _2) days.(mu _{j-}) determines the daily death rate for uninfected hosts of type j and (mu _{j+}) determines the death rate for infected host of type j. We typically set (mu _{1-} = mu _{2-}< mu _{2+} < mu _{1+}), meaning uninfected hosts have the same death rate regardless of type, infected type 2 have a higher death rate than uninfected hosts, and infected type 1 have the highest. (Both susceptible and recovered hosts are considered to be uninfected.) Table 2 shows a summary of parameters related to the SIR dynamics.Equation 1 shows continuous ordinary differential equations approximating the dynamics. Note that the actual model instantiates these in discrete time-steps using the forward Euler method with (h = 1).$$ begin{aligned}&frac{dS_1}{dt} = - S_1 beta _1 b_1 I_v /H - S_1 mu _{1-} \&frac{dI_1}{dt} = S_1 beta _1 b_1 I_v /H - gamma _1 I_1 - I_1 mu _{1+} \&frac{dR_1}{dt} = I_1 gamma _1 - R_1 mu _{1-} \&frac{dS_2}{dt} = -S_2 beta _2 b_2 I_v /H - S_2 mu _{2-} \&frac{dI_2}{dt} = S_2 beta _2 b_2 I_v /H - I_2 gamma _2 - I_2 mu _{2+} \&frac{dR_2}{dt} = I_2 gamma _2 - R_2 mu _{2-}\&frac{dS_v}{dt} = alpha _v H -S_v delta _1 b_1 F_1 -S_v delta _2 b_2 F_2 -S_v mu _v\&frac{dI_v}{dt} = S_v delta _1 b_1 F_1 + S_v delta _2 b_2 F_2 - I_v mu _v\ end{aligned} $$ (1) Table 2 Parameters for SIR dynamics.Full size tableFollowing a standard SIR model, susceptible hosts can become infected, and infected hosts become recovered, but each equation also contains a negative term corresponding to deaths. Thus, the total population of hosts is strictly decreasing in this time-frame. We assume that the vectors breed on a much shorter timescale than hosts, so we include a term for their births here, while host births are implemented by a yearly breeding event. We assume no vertical disease transmission, so all new vectors begin in the susceptible category. We assume that the daily birthrate for each vector increases with access to hosts, and decreases with competition among other vectors for hosts and breeding sites, so we set it equal to (frac{alpha _v H}{S_v + I_v}), where (alpha _v) is a constant scaling factor. Since the birthrate for each vector contains the total number of vectors in its denominator, the total number of vector births in the population will simply be (alpha _v H).A population with a larger number of hosts will be able to sustain a larger number of vectors. For a population with a constant number of hosts, the equilibrium vector population will be proportional to the number hosts: aH where (a = frac{alpha _v}{mu _v}) is the equilibrium vector density (number of vectors per host). For instance if (a = 2), then in equilibrium there will be twice as many vectors as hosts. Given a fixed number of hosts, the population of vectors will asymptotically approach the equilibrium value. In practice the total number of hosts is constantly changing, so the population of vectors will chase after this moving equilibrium, though for our standard parameters (alpha _v) and (mu _v) are sufficiently large such that this will occur on a short timescale, and the population of vectors remains close to the current equilibrium value.Breeding eventTable 3 shows a summary of parameters related to the breeding event. Every (t_c) days (typically 365), a breeding event occurs according to the following process.Table 3 Parameters for breeding event.Full size tableLet$$begin{aligned}&Delta S_1 = t_c alpha _{1-}(S_1+R_1)+t_calpha _{1+} I_1 \&Delta S_2 = t_c alpha _{2-}(S_2+R_2)+t_calpha _{2+} I_2 \ end{aligned}$$be the number of new host offspring of each type born this generation. In order to maintain consistency of temporal units among the parameters, each birthrate parameter is multiplied by (t_c). Let H be the current total number of hosts. Let$$begin{aligned} c = {left{ begin{array}{ll} 0 &{} hbox {if } H ge kappa \ 1 &{} hbox {if } H + Delta S_1 + Delta S_2 le kappa \ frac{kappa -H}{Delta S_1 + Delta S_2} &{} hbox {otherwise} \ end{array}right. } end{aligned}$$be the proportion of offspring that survive to adulthood. (None, if the population is already above carrying capacity. All, if the difference between the reproducing population size and the carrying capacity exceeds the new births. If the population is approaching carrying capacity, juvenile mortality scales proportionally so that the population will hit carrying capacity but not exceed it.)Then$$begin{aligned}&S_1 + c Delta S_1 rightarrow S_1 \&S_2 + c Delta S_2 rightarrow S_2 \ end{aligned}$$We assume there is no vertical disease transmission, so all new hosts begin in the susceptible category. We assume that the host population is iteroparous, such that the new offspring and the existing adult population both carry over to the next generation. If the new population would exceed the carrying capacity, we assume the limited space or supplies reduces the number of successful offspring so that the population exactly reaches the carry capacity by reduction in juvenile survival rather than population-wide competition that could also reduce the adult population.The carrying capacity is therefore what drives the interspecific host competition. Because births of both species are summed and then normalized by the total number of births, the higher the birthrate of one host, the larger a fraction of the available space it will capture during the breeding event. Similarly, the lower the death-rate of a host, the less space it frees up for the next breeding event. Even if one host species would be able to sustain a stable population on its own, the presence of a more fit competitor can lead to the extinction of the less fit type by driving its effective birth rate down.Immune-reproductive trade-offs and boundary conditionsWe assume that host type 1 is evolutionarily stable in the absence of the disease; an uninfected monoculture population below the carrying capacity will have at least as many births as deaths each cycle. In a continuous version of this model where births and deaths happened simultaneously, this might be defined by (alpha _{1-} ge mu _{1-}) . However in our model, the population spends many days decreasing due to deaths before the next breeding event occurs. The population exponentially decays throughout the cycle, and then jumps up during the breeding event. The number of new host births is proportional to the number of hosts at the start of the breeding event, which will be the lowest value of any other time during the cycle. Thus, the birth rate needs to be high enough that the surviving hosts can compensate despite their diminished numbers. Taking this into account, we get the condition$$begin{aligned}&alpha _{1-} ge frac{1-(1- mu _{1-})^{t_c}}{(1-mu _{1-})^{t_c}} \ end{aligned}$$Which is a higher bound on (alpha _{1-}) than the simpler one above, but will be close to it if (mu _{1-}) and (t_c) are small.To implement the scenario in which type 2 has increased resistance and tolerance to the disease at the expense of overall fecundity, we implement the following boundary conditions:$$begin{aligned}&beta _1 > beta _2 \&0 = gamma _1< gamma _2 \&mu _{1-} = mu _{2-}< mu _{2+} < mu _{1+} \&alpha _{1-} > alpha _{2-} > alpha _{2+} > alpha _{1+} end{aligned}$$Type 2 hosts are less likely to contract the disease, and are able to recover from it, while type 1 lack the immunological strength to eradicate it completely. Additionally, while both types of host are weakened by the disease, type 2 suffer fewer negative effects. However, this stronger immune response comes at the cost of reducing their birth rate when compared to healthy type 1 hosts.Due to the heterogeneous population, there is ambiguity in defining (R_0) for the disease. The two types of host have different transmission rates and durations of infection, and will therefore be responsible for different amounts of disease spread. To resolve this, we define several related values. Let (R_0^j) be the (R_0) of the disease in a homogeneous population of type j hosts: the average number of hosts infected (indirectly, through vectors) from a single infected host in a population consisting entirely of type j hosts.$$begin{aligned}&R_0^1 = frac{delta _1 beta _1 a b_1^2}{mu _v mu _{1+}} \&R_0^2 = frac{delta _2 beta _2 a b_2^2}{mu _v (mu _{2+}+gamma _2)} end{aligned}$$We simplify the equation for (R_0^1) since (gamma _1 = 0). We define w to be the frequency of host type 1: (w := (S_1 + I_1)/H). Then (R_0) for the vectors is$$begin{aligned} R_0^v = R_0^1 w + R_0^2 (1-w) end{aligned}$$which will also be the effective (R_0) of the disease for the hosts in the mixed population.For simplicity of results, we restrict to the case where type 1 is more infectious overall than type 2, in particular (R_0^1 > R_0^2). This allows us to avoid edge cases in simulation outcomes which are beyond the scope of this paper. We intend to lift this restriction and study these outcomes in future work.NoteAlthough usual epidemiological model formulations can rely on the value 1 as the boundary condition for (R_0) to determine the epidemic potential of an outbreak, in this case we are calculating effective (R_0) in a dynamic host population, such that the decrease in disease spread due to saturation from recovered hosts and already infected hosts increases the actual thresholds. More accurate criteria require a technical and somewhat cumbersome analysis, which we leave for a future paper. More

Holmgren, M. et al. Extreme climatic events shape arid and semiarid ecosystems. Front. Ecol. Environ. 4, 87–95 (2006).
Google Scholar 
Ummenhofer, C. C. & Meehl, G. A. Extreme weather and climate events with ecological relevance: a review. Philos. Trans. R. Soc. B-Biol. Sci. 372, 20160135. https://doi.org/10.1098/rstb.2016.0135 (2017).Chesson, P. et al. Resource pulses, species interactions, and diversity maintenance in arid and semi-arid environments. Oecologia 141, 236–253 (2004).ADS 
PubMed 

Google Scholar 
McCluney, K. E. et al. Shifting species interactions in terrestrial dryland ecosystems under altered water availability and climate change. Biol. Rev. 87, 563–582 (2012).PubMed 

Google Scholar 
Reyer, C. P. O. et al. A plant’s perspective of extremes: Terrestrial plant responses to changing climatic variability. Glob. Change Biol. 19, 75–89 (2013).ADS 

Google Scholar 
Scheffer, M., Carpenter, S., Foley, J. A., Folke, C. & Walker, B. Catastrophic shifts in ecosystems. Nature 413, 591–596 (2001).ADS 
CAS 
PubMed 

Google Scholar 
Schwinning, S. & Sala, O. E. Hierarchy of responses to resource pulses in and and semi-arid ecosystems. Oecologia 141, 211–220 (2004).ADS 
PubMed 

Google Scholar 
Borer, E. T., Seabloom, E. W. & Tilman, D. Plant diversity controls arthropod biomass and temporal stability. Ecol. Lett. 15, 1457–1464 (2012).PubMed 

Google Scholar 
Kwok, A. B. C., Wardle, G. M., Greenville, A. C. & Dickman, C. R. Long-term patterns of invertebrate abundance and relationships to environmental factors in arid Australia. Austral Ecol. 41, 480–491 (2016).
Google Scholar 
Prugh, L. R. et al. Ecological winners and losers of extreme drought in California. Nat. Climate Change 8, 819–824 (2018).ADS 

Google Scholar 
Deguines, N., Brashares, J. S. & Prugh, L. R. Precipitation alters interactions in a grassland ecological community. J. Anim. Ecol. 86, 262–272 (2017).PubMed 

Google Scholar 
Ripple, W. J. et al. What is a trophic cascade?. Trends Ecol. Evol. 31, 842–849 (2016).PubMed 

Google Scholar 
Greenville, A. C., Wardle, G. M. & Dickman, C. R. Extreme climatic events drive mammal irruptions: regression analysis of 100-year trends in desert rainfall and temperature. Ecol. Evol. 2, 2645–2658 (2012).PubMed 
PubMed Central 

Google Scholar 
Molyneux, J., Pavey, C. R., James, A. I. & Carthew, S. M. Persistence of ground-dwelling invertebrates in desert grasslands during a period of low rainfall—Part 2. J. Arid. Environ. 157, 39–47 (2018).ADS 

Google Scholar 
Seymour, C. L., Simmons, R. E., Joseph, G. S. & Slingsby, J. A. On bird functional diversity: Species richness and functional differentiation show contrasting responses to rainfall and vegetation structure in an arid landscape. Ecosystems 18, 971–984 (2015).
Google Scholar 
Prather, C. M. et al. Invertebrates, ecosystem services and climate change. Biol. Rev. 88, 327–348 (2013).PubMed 

Google Scholar 
Del Toro, I., Ribbons, R. R. & Pelini, S. L. The little things that run the world revisited: a review of ant-mediated ecosystem services and disservices (Hymenoptera: Formicidae). Myrmecol. News 17, 133–146 (2012).
Google Scholar 
Gerlach, J., Samways, M. & Pryke, J. Terrestrial invertebrates as bioindicators: an overview of available taxonomic groups. J. Insect Conserv. 17, 831–850 (2013).
Google Scholar 
Doblas-Miranda, E., Sanchez-Pinero, F. & Gonzalez-Megias, A. Different microhabitats affect soil macroinvertebrate assemblages in a Mediterranean arid ecosystem. Appl. Soil Ecol. 41, 329–335 (2009).
Google Scholar 
Hadley, N. F. & Szarek, S. R. Productivity of desert ecosystems. Bioscience 31, 747–753 (1981).
Google Scholar 
Barnett, K. L. & Facey, S. L. Grasslands, invertebrates, and precipitation: A review of the effects of climate change. Front. Plant Sci. 7, 1196 (2016).PubMed 
PubMed Central 

Google Scholar 
Zhu, H. et al. Effects of altered precipitation on insect community composition and structure in a meadow steppe. Ecol. Entomol. 39, 453–461 (2014).
Google Scholar 
Palmer, C. M. Chronological changes in terrestrial insect assemblages in the arid zone of Australia. Environ. Entomol. 39, 1775–1787 (2010).PubMed 

Google Scholar 
Liu, R. T., Zhu, F. & Steinberger, Y. Ground-active arthropod responses to rainfall-induced dune microhabitats in a desertified steppe ecosystem, China. J. Arid Land 8, 632–646 (2016).
Google Scholar 
Mendelsohn, J., Jarvis, A., Roberts, C. & Robertson, T. Atlas of Namibia: A portrait of the land and its people. 3rd edn, (Sunbird Publishers, 2009).Theron, L. Temporal and spatial composition of arboreal insects along the Omaruru river, Namibia Magister scientiae thesis, University of the Free State Bloemfontein, (2010).Wagner, T. C., Richter, J., Joubert, D. F. & Fischer, C. A dominance shift in arid savanna: An herbaceous legume outcompetes local C4 grasses. Ecol. Evol. 8, 6779–6787 (2018).PubMed 
PubMed Central 

Google Scholar 
Wagner, T. C., Hane, S., Joubert, D. F. & Fischer, C. Herbaceous legume encroachment reduces grass productivity and density in arid rangelands. PLoS ONE 11, e0166743; https://doi.org/10.1371/journal.pone.0166743 (2016).Picker, M., Griffiths, C. & Weaving, A. Field Guide to Insects of Southern Africa. (Struik Nature, 2004).Scholtz, C. H. & Holm, E. Insects of Southern Africa. 2nd edn, (Protea Book House, 2008).Blaum, N., Seymour, C., Rossmanith, E., Schwager, M. & Jeltsch, F. Changes in arthropod diversity along a land use driven gradient of shrub cover in savanna rangelands: identification of suitable indicators. Biodivers. Conserv. 18, 1187–1199 (2009).
Google Scholar 
Franca, L. F., Figueiredo-Paixao, V. H., Duarte-Silva, T. A. & dos Santos, K. B. The effects of rainfall and arthropod abundance on breeding season of insectivorous birds, in a semi-arid neotropical environment. Zoologia-Curitiba. https://doi.org/10.3897/zoologia.37.e37716 (2020).Wagner, T. C., Uiseb, K. & Fischer, C. Rolling pits of Hartmann’s mountain zebra (Zebra equus hartmannae) increase vegetation diversity and landscape heterogeneity in the Pre-Namib. Ecol. Evol. 11, 13036–13051 (2021).PubMed 
PubMed Central 

Google Scholar 
Dormann, C. F. et al. Collinearity: A review of methods to deal with it and a simulation study evaluating their performance. Ecography 36, 27–46 (2013).
Google Scholar 
R Core Team. R: A language and environment for statistical computing (R Foundation for Statistical Computing, Vienna, Austria, 2019).Oksanen, J., et al. vegan: Community Ecology Package. R package version 2.5-7. (2020).Legendre, P. & Gallagher, E. D. Ecologically meaningful transformations for ordination of species data. Oecologia 129, 271–280 (2001).ADS 
PubMed 
PubMed Central 

Google Scholar 
Anderson, M. J. & Walsh, D. C. I. PERMANOVA, ANOSIM, and the Mantel test in the face of heterogeneous dispersions: What null hypothesis are you testing?. Ecol. Monogr. 83, 557–574 (2013).
Google Scholar 
Anderson, M. J. in Wiley StatsRef: Statistics Reference Online (eds N. Balakrishnan et al.) (2017).Stopher, K. V., Bento, A. I., Clutton-Brock, T. H., Pemberton, J. M. & Kruuk, L. E. B. Multiple pathways mediate the effects of climate change on maternal reproductive traits in a red deer population. Ecology 95, 3124–3138 (2014).
Google Scholar 
Bolker, B. M. et al. Generalized linear mixed models: A practical guide for ecology and evolution. Trends Ecol. Evol. 24, 127–135 (2009).PubMed 

Google Scholar 
Bates, D., Mächler, M., Bolker, B. & Walker, S. Fitting linear mixed-effects models using lme4. J. Stat. Softw. 67, 1–48 (2015).
Google Scholar 
Pinheiro, J. C. & Bates, D. M. Mixed-Effects Models in S and S-PLUS. (Springer Verlag, 2000).Zhang, D. rsq: R-Squared and related measures. R package version 2.2. (2021).Barnes, A. D. et al. Direct and cascading impacts of tropical land-use change on multi-trophic biodiversity. Nat. Ecol. Evol. 1, 1511–1519 (2017).PubMed 

Google Scholar 
Henschel, J. R. Long-term population dynamics of Namib desert Tenebrionid beetles reveal complex relationships to pulse-reserve conditions. Insects 12, 804. https://doi.org/10.3390/insects12090804 (2021).Cloudsley-Thompson, J. L. The adaptational diversity of desert biota. Environ. Conserv. 20, 227–231 (1993).
Google Scholar 
Sømme, L. in Invertebrates in Hot and Cold Arid Environments 135–157 (Springer, 1995).Suttle, K. B., Thomsen, M. A. & Power, M. E. Species interactions reverse grassland responses to changing climate. Science 315, 640–642 (2007).ADS 
CAS 
PubMed 

Google Scholar 
Henschel, J., Klintenberg, P., Roberts, C. & Seely, M. Long-term ecological research from an arid, variable, drought-prone environment. Sécheresse 18, 342–347 (2007).
Google Scholar 
Cloudsley-Thompson, J. L. Adaptations of arthropoda to arid environments. Annu. Rev. Entomol. 20, 261–283 (1975).CAS 
PubMed 

Google Scholar 
Schuldt, A. et al. Belowground top-down and aboveground bottom-up effects structure multitrophic community relationships in a biodiverse forest. Sci. Rep. 7 (2017).Vidal, M. C. & Murphy, S. M. Bottom-up vs. top-down effects on terrestrial insect herbivores: a meta-analysis. Ecol. Lett. 21, 138–150 (2018).Báez, S., Collins, S. L., Lightfoot, D. & Koontz, T. L. Bottom-up regulation of plant community structure in an aridland ecosystem. Ecology 87, 2746–2754 (2006).PubMed 

Google Scholar 
Gibb, H. et al. Testing top-down and bottom-up effects on arid zone beetle assemblages following mammal reintroduction. Austral Ecol. 43, 288–300 (2018).
Google Scholar 
Coll, M. & Guershon, M. Omnivory in terrestrial arthropods: Mixing plant and prey diets. Annu. Rev. Entomol. 47, 267–297 (2002).CAS 
PubMed 

Google Scholar 
Karolyi, F., Hansal, T., Krenn, H. W. & Colville, J. F. Comparative morphology of the mouthparts of the megadiverse South African monkey beetles (Scarabaeidae: Hopliini): feeding adaptations and guild structure. PeerJ 4, e1597; https://doi.org/10.7717/peerj.1597 (2016).Greenslade, P. Survival of Collembola in arid environments: Observations in South Australia and the Sudan. J. Arid. Environ. 4, 219–228 (1981).ADS 

Google Scholar 
Fattorini, S. Effects of fire on tenebrionid communities of a Pinus pinea plantation: A case study in a Mediterranean site. Biodivers. Conserv. 19, 1237–1250 (2009).
Google Scholar 
Sanders, N. J., Moss, J. & Wagner, D. Patterns of ant species richness along elevational gradients in an arid ecosystem. Glob. Ecol. Biogeogr. 12, 93–102 (2003).
Google Scholar  More

Bhatt, S. et al. The global distribution and burden of dengue. Nature 496, 504–509 (2013).ADS 
CAS 
PubMed 
PubMed Central 

Google Scholar 
Gratz, N. G. Critical review of the vector status of Aedes albopictus. Med. Vet. Entomol. 18, 215–227 (2004).CAS 
PubMed 

Google Scholar 
Weaver, S. C., Charlier, C., Nasilakis, N. & Lecuit, M. Zika, chikungunya, and other emerging vector-borne viral diseases. Annu. Rev. Med. 69, 1–14 (2018).
Google Scholar 
Wilder-Smith, A. et al. Epidemic arboviral diseases: priorities for research and public health. Lancet Infect. Dis. 17, e101–e106 (2017).PubMed 

Google Scholar 
Felicetti, T., Manfroni, G., Cecchetti, V. & Cannalire, R. Broad-spectrum flavivirus inhibitors: a medicinal chemistry point of view. Chem. Med. Chem. 15, 2391–2419 (2020).CAS 
PubMed 

Google Scholar 
da Silveira, L. T. C., Bernardo, T. & Santos, M. Systemic review of dengue vaccine efficacy. BMC Inf. Dis. 19, 750 (2019).
Google Scholar 
Katzelnich, L. C. et al. Zika virus infection enhances future risk of severe dengue disease. Science 369, 1123–1128 (2020).ADS 

Google Scholar 
Rezza, G. & Weaver, S. C. Chikungunya as a paradigm for emerging viral diseases: evaluating disease impact and hurdles to vaccine development. PLoS Negl. Trop. Dis. 13, e0006919 (2019).PubMed 
PubMed Central 

Google Scholar 
Reiter, P. & Gubler, D. J. Surveillance and control of urban dengue vectors. In Dengue and dengue hemorrhagic fever (eds Gubler, D. J. & Kuno, G.) 425–462 (CAB International, 1997).
Google Scholar 
Moyes, C. L. et al. Contemporary status of insecticide resistance in the major Aedes vectors of arboviruses infecting humans. PLoS Negl. Trop. Dis. 11, e0005625 (2017).PubMed 
PubMed Central 

Google Scholar 
Bowman, L. R., Donegan, S. & McCall, P. J. Is dengue vector control deficient in effectiveness or evidence?: systematic review and meta-analysis. PLoS Negl. Trop. Dis. 10, e0004551 (2016).PubMed 
PubMed Central 

Google Scholar 
Erlanger, T. E., Keiser, J. & Utzinger, J. Effect of dengue vector control interventions on entomological parameters in developing countries: a systematic review and meta-analysis. Med. Vet. Entomol. 22, 203–221 (2008).CAS 
PubMed 

Google Scholar 
Banerjee, S., Aditya, G. & Saha, G. K. Household disposables as breeding habitats of dengue vectors: linking wastes and public health. Waste Manag. 33, 233–239 (2013).PubMed 

Google Scholar 
Chadee, D. D., Doon, R. & Severson, D. W. Surveillance of dengue fever cases using a novel Aedes aegypti population sampling method in Trinidad, West Indies: the cardinal points approach. Acta Trop. 104, 1–7 (2007).PubMed 

Google Scholar 
Barrera, R., Acevedo, V. & Amador, M. Role of abandoned and vacant houses on Aedes aegypti productivity. J. Med. Entomol. 104, 145–150 (2020).
Google Scholar 
Chadee, D. D. & Rahaman, A. Use of water drums by humans and Aedes aegypti in Trinidad. J. Vector Ecol. 25, 28–35 (2000).CAS 
PubMed 

Google Scholar 
Padmanabha, H., Soto, E., Mosquera, M., Lord, C. C. & Lounibos, L. P. Ecological links between water storage behaviors and Aedes aegypti production: implications for dengue vector control in variable climates. EcoHealth 7, 78–90 (2010).CAS 
PubMed 

Google Scholar 
Colton, Y. M., Chadee, D. D. & Severson, D. W. Natural skip oviposition of the mosquito Aedes aegypti indicated by codominant genetic markers. Med. Vet. Entomol. 17, 195–204 (2003).CAS 
PubMed 

Google Scholar 
Davis, T. J., Kaufman, P. E., Hogsette, J. A. & Kline, D. I. The effects of larval habitat quality on Aedes albopictus skip oviposition. J. Am. Mosq. Control Assoc. 31, 321–328 (2015).PubMed 

Google Scholar 
David, M. R., Lourenco-de-Oliveira, R. & de Freitas, R. M. Container productivity, daily survival rates and dispersal of Aedes aegypti mosquitoes in a high income dengue epidemic neighbourhood of Rio de Janeiro: presumed influence of differential urban structure on mosquito biology. Mem. Inst. Oswaldo Cruz 104, 927–932 (2009).PubMed 

Google Scholar 
Focks, D. A. & Chadee, D. D. Pupal survey: an epidemiologically significant surveillance method for Aedes aegypti: an example using data from Trinidad. Am. J. Trop. Med. Hyg. 56, 159–167 (1997).CAS 
PubMed 

Google Scholar 
Morrison, A. C. et al. Temporal and geographic patterns of Aedes aegypti (Diptera: Culicidae) production in Iquitos, Peru. J. Med. Entomol. 41, 1123–1142 (2004).PubMed 

Google Scholar 
Chadee, D. D. Oviposition strategies adopted by gravid Aedes aegypti (L.) (Diptera: Culicidae) as detected by ovitraps in Trinidad, West Indies (2002–2006). Acta Trop. 111, 279–283 (2009).CAS 
PubMed 

Google Scholar 
Chadee, D. D. Seasonal incidence and horizontal distribution patterns of oviposition by Aedes aegypti in an urban environment in Trinidad, West Indies. J. Am. Mosq. Control Asso. 8, 281–284 (1992).CAS 

Google Scholar 
Fay, R. W. & Eliason, D. A. A preferred oviposition site as a surveillance method for Aedes aegypti. Mosq. News 26, 531–535 (1966).
Google Scholar 
Johnson, B. J., Ritchie, S. A. & Fonseca, D. M. The state of the art of lethal oviposition trap-based mass interventions for arboviral control. Insects 8, 5 (2017).PubMed Central 

Google Scholar 
Eiras, A. E., Buhagiar, T. S. & Ritchie, S. A. Development of the gravid Aedes trap for the capture of adult female container-exploiting mosquitoes (Diptera: Culicidae). J. Med. Entomol. 51, 200–209 (2014).PubMed 

Google Scholar 
Mackay, A. J., Amador, M. & Barrera, R. An improvied autocidal gravid ovitrap for the control and surveillance of Aedes aegypti. Parasit. Vectors 6, 225 (2013).CAS 
PubMed 
PubMed Central 

Google Scholar 
Olson, K. E. & Blair, C. D. Arbovirus-mosquito interactions: RNAi pathway. Curr. Opin. Virol. 15, 119–126 (2015).CAS 
PubMed 
PubMed Central 

Google Scholar 
Hapairai, L. K. et al. Lure-and-kill yeast interfering RNA larvicides targeting neural genes in the human disease vector mosquito Aedes aegypti. Sci. Rep. 7, 13223 (2017).ADS 
PubMed 
PubMed Central 

Google Scholar 
Mysore, K. et al. Yeast interfering RNA larvicides targeting neural genes induce high rates of Anopheles larval mortality. Malaria J. 16, 461 (2017).
Google Scholar 
Mysore, K. et al. Characterization of a broad-based mosquito yeast interfering RNA larvicide with a conserved target site in mosquito semaphorin-1a genes. Parasit. Vectors 12, 256 (2019).PubMed 
PubMed Central 

Google Scholar 
Mysore, K. et al. Characterization of a yeast interfering RNA larvicide with a target site conserved in the synaptotagmin gene of multiple disease vector mosquitoes. PLoS Negl. Trop. Dis 13, e0007422 (2019).CAS 
PubMed 
PubMed Central 

Google Scholar 
Hapairai, L. K. et al. Evaluation of large volume yeast interfering RNA lure-and-kill ovitraps for attraction and control of Aedes mosquitoes. Med. Vet. Entomol. 35, 361–370 (2021).CAS 
PubMed 

Google Scholar 
Zeileis, A. & Hothorn, T. Diagnostic checking in regression relationships. R News 2, 7–10 (2002).
Google Scholar 
Braks, M. A. H., Honorio, N. A., Lourenco-de-Oliveira, R., Juliano, S. A. & Lounibos, L. P. Convergent habitat segregation of Aedes aegypti and Aedes albopictus (Diptera: Culicidae) in southeastern Brazil and Florida. J. Med. Entomol. 40, 785–794 (2003).PubMed 

Google Scholar 
Kumari, R., Kumar, K. & Chauhan, L. S. First dengue virus detection in Aedes albopictus from Delhi, India: Its breeding ecology and role in dengue transmission. Trop. Med. Int. Health 16, 949–954 (2012).
Google Scholar 
Apostol, B. L., Black, W. C. IV., Reiter, P. & Miller, B. R. Use of randomly amplified polymorphic DNA amplified by polymerase chain reaction markers to estimate the number of Aedes aegypti families at oviposition sites in San Juan, Puerto Rico. Am. J. Trop. Med. Hyg. 51, 89–97 (1994).CAS 
PubMed 

Google Scholar 
Corbet, P. S. & Chadee, D. D. An improved method for detecting substrate preferences shown by mosquitoes that exhibit ‘skip oviposition’. Physiol. Entomol. 18, 114–118 (1993).
Google Scholar 
Reinbold-Wasson, D. D. & Reiskind, M. H. Comparative skip-oviposition behavior among container breeding Aedes spp. mosquitoes (Diptera: Culicidae). J. Med. Entomol. https://doi.org/10.1093/jme/tjab084 (2021).Article 
PubMed 

Google Scholar 
Barrera, R. Spatial stability of adult Aedes aegypti populations. Am. J. Trop. Med. Hyg. 85, 1087–1092 (2011).PubMed 
PubMed Central 

Google Scholar 
Barrera, R., Amador, M., Ruiz-Valcarcel, J. & Acevedo, V. Factors modulating captures of gravid Aedes aegypti females. J. Am. Mosq. Control Assoc. 36, 66–73 (2020).PubMed 
PubMed Central 

Google Scholar 
Moura, M. B. C. M. et al. Spatio-temporal dynamics of Aedes aegypti and Aedes albopictus oviposition in an urban area of northeastern Brazil. Trop. Med. Int. Health 25, 1510–1521 (2020).PubMed 

Google Scholar 
Crawford, J. E. et al. Efficient production of male Wolbachia-infected Aedes aegypti mosquitoes enables large-scale suppression of wild populations. Nat. Biotechnol. 38, 482–492 (2020).CAS 

Google Scholar 
Lau, K. W. et al. Vertical distribution of Aedes mosquitoes in multiple story buildings in Selangor and Kuala Lumpur, Malaysia. Trop. Biomed. 30, 36–45 (2013).CAS 
PubMed 

Google Scholar 
Perich, M. J. et al. Field evaluation of a lethal ovitrap against dengue vectors in Brazil. Med. Vet. Entomol. 17, 205–210 (2003).CAS 
PubMed 

Google Scholar 
Serpa, L. L. N. et al. Study of the the distribution and abundance of the eggs of Aedes aegypti and Aedes albopictus according to the habitat and meteorlogical variables, municipality of Sao Sebastiao, Sao Paulo state, Brazil. Parasit. Vectors 6, 321 (2014).
Google Scholar 
Sithiprasasna, R. et al. Field evaluation of a lethal ovitrap for the control of Aedes aegypti (Diptera: Culicidae) in Thailand. J. Med. Entomol. 40, 455–462 (2003).PubMed 

Google Scholar 
Barrera, R., Amador, M., Munoz, J. & Acevedo, V. Integrated vector control of Aedes aegypti mosquitoes around target houses. Parasit. Vectors 11, 88 (2018).PubMed 
PubMed Central 

Google Scholar 
Naranjo, D. P. et al. Vector control programs in Saint Johns County, Florida and Guayas, Ecuador: Successes and barriers to integrated vector management. BMC Public Health 14, 674 (2014).PubMed 
PubMed Central 

Google Scholar 
Regis, L. N. et al. Sustained reduction of the dengue vector population resulting from an integrated control strategy applied in two Brazilian cities. PLoS ONE 8, e67682 (2013).ADS 
CAS 
PubMed 
PubMed Central 

Google Scholar 
Stewart, A. T. M. et al. Community acceptance of yeast interfering RNA larvicide technology for control of Aedes mosquitoes in Trinidad. PLoS ONE 15, e0237675 (2020).CAS 
PubMed 
PubMed Central 

Google Scholar 
Winter, N. et al. Assessment of Trinidad community stakeholder perspectives on the use of yeast interfering RNA-baited ovitraps for biorational control of Aedes mosquitoes. PLoS ONE 16, e0252997 (2021).CAS 
PubMed 
PubMed Central 

Google Scholar 
Chadee, D. D. & Corbet, P. S. Seasonal incidence and diel patterns of oviposition in the field of the mosquito, Aedes aegypti (L.) (Diptera: Culicidae) in Trinidad, West Indies: a preliminary study. Ann. Trop. Med. Parasitol. 81, 151–161 (1987).CAS 
PubMed 

Google Scholar 
Edman, J. D. et al. Aedes aegypti (Diptera: Culicdae) movement influenced by availability of oviposition sites. J. Med. Entomol. 35, 578–583 (1998).CAS 
PubMed 

Google Scholar 
Reiter, P., Amador, M. A., Anderson, R. A. & Clark, G. G. Short report: dispersal of Aedes aegypti in an urban area after blood feeding as demonstrated by rubidium-marked eggs. Am. J. Trop. Med. Hyg. 52, 177–179 (1995).CAS 
PubMed 

Google Scholar 
Mysore, K. et al. Preparation and use of a yeast shRNA delivery system for gene silencing in mosquito larvae. Methods Mol. Biol. 1858, 213–231 (2019).CAS 
PubMed 
PubMed Central 

Google Scholar 
Chadee, D. D., Fat, F. H. & Persad, R. C. First record of Aedes albopictus from Trinidad, West Indies. J. Am. Mosq. Control Assoc. 19, 438–439 (2003).PubMed 

Google Scholar 
Clemons, A., Mori, A., Haugan, M., Severson, D. W. & Duman-Scheel, M. Culturing and egg collection of Aedes aegypti. Cold Spring Harb. Protoc. https://doi.org/10.1101/pdb.prot5507 (2010).Article 
PubMed 
PubMed Central 

Google Scholar 
Stan Developmental Team. Stan Modeling Language Users Guide and Reference Manual, v.2.22.1 https://mc-stan.org (2020). More

Büyükdeveci, M. E., Balcázar, J. L., Demirkale, İ & Dikel, S. Effects of garlic-supplemented diet on growth performance and intestinal microbiota of rainbow trout (Oncorhynchus mykiss). Aquaculture 486, 170–174 (2018).
Google Scholar 
Maklakov, A. A. et al. Sex-specific fitness effects of nutrient intake on reproduction and lifespan. Curr. Biol. 18, 1062–1066 (2008).CAS 
PubMed 

Google Scholar 
Totsch, S. K. et al. Effects of a Standard American Diet and an anti-inflammatory diet in male and female mice. Eur. J. Pain 22, 1203–1213 (2018).CAS 
PubMed 

Google Scholar 
Green, D. A. & Millar, J. S. Changes in gut dimensions and capacity of Peromyscus maniculatus relative to diet quality and energy needs. Can. J. Zool. 65, 2159–2162 (1987).
Google Scholar 
Jones, V. A. et al. Crohn’s disease: Maintenance of remission by diet. Lancet 2, 177–180 (1985).CAS 
PubMed 

Google Scholar 
Hirai, T. Ontogenetic change in the diet of the pond frog, Rana nigromaculata. Ecol. Res. 17, 639–644 (2002).
Google Scholar 
Qin, J. et al. A human gut microbial gene catalogue established by metagenomic sequencing. Nature 464, 59–65 (2010).CAS 
PubMed 
PubMed Central 

Google Scholar 
Sender, R., Fuchs, S. & Milo, R. Are we really vastly outnumbered? Revisiting the ratio of bacterial to host cells in humans. Cell 164, 337–340 (2016).CAS 
PubMed 

Google Scholar 
Reikvam, D. H. et al. Depletion of murine intestinal microbiota: effects on gut mucosa and epithelial gene expression. PLoS ONE 6, e17996 (2011).ADS 
CAS 
PubMed 
PubMed Central 

Google Scholar 
Sommer, F. & Bäckhed, F. The gut microbiota-masters of host development and physiology. Nat. Rev. Microbiol. 11, 227–238 (2013).CAS 
PubMed 

Google Scholar 
Ley, R. E. et al. Evolution of mammals and their gut microbes. Science 320, 1647–1651 (2008).ADS 
CAS 
PubMed 
PubMed Central 

Google Scholar 
Muegge, B. D. et al. Diet drives convergence in gut microbiome functions across mammalian phylogeny and within humans. Science 332, 970–974 (2011).ADS 
CAS 
PubMed 
PubMed Central 

Google Scholar 
Youngblut, N. D. et al. Host diet and evolutionary history explain different aspects of gut microbiome diversity among vertebrate clades. Nat. Commun. 10, 2200 (2019).ADS 
PubMed 
PubMed Central 

Google Scholar 
Ley, R. E., Turnbaugh, P. J., Klein, S. & Gordon, J. I. Microbial ecology: Human gut microbes associated with obesity. Nature 444, 1022–1023 (2006).ADS 
CAS 
PubMed 

Google Scholar 
Zhu, Y. et al. Beef, chicken, and soy proteins in diets induce different gut microbiota and metabolites in rats. Front. Microbiol. 8, 1395 (2017).Zimmer, J. et al. A vegan or vegetarian diet substantially alters the human colonic faecal microbiota. Eur. J. Clin. Nutr. 66, 53–60 (2012).CAS 
PubMed 

Google Scholar 
McKenney, E. A., Rodrigo, A. & Yoder, A. D. Patterns of gut bacterial colonization in three primate species. PLoS ONE 10, e0124618 (2015).PubMed 
PubMed Central 

Google Scholar 
Bergmann, G. T. Microbial community composition along the digestive tract in forage- and grain-fed bison. BMC Vet. Res. 13, 253 (2017).PubMed 
PubMed Central 

Google Scholar 
Phillips, C. D. et al. Microbiome structural and functional interactions across host dietary niche space. Integr. Comp. Biol. 57, 743–755 (2017).CAS 
PubMed 

Google Scholar 
Song, S. J. et al. Comparative analyses of vertebrate gut microbiomes reveal convergence between birds and bats. mBio 11, e02901–19 (2020).Bodawatta, K. H., Sam, K., Jønsson, K. A. & Poulsen, M. Comparative analyses of the digestive tract microbiota of New Guinean passerine birds. Front. Microbiol. 9, 1830 (2018).Capunitan, D. C., Johnson, O., Terrill, R. S. & Hird, S. M. Evolutionary signal in the gut microbiomes of 74 bird species from Equatorial Guinea. Mol. Ecol. 29, 829–847 (2020).CAS 
PubMed 

Google Scholar 
Hird, S. M., Sánchez, C., Carstens, B. C. & Brumfield, R. T. Comparative gut microbiota of 59 neotropical bird species. Front. Microbiol. 6, 1403 (2015).PubMed 
PubMed Central 

Google Scholar 
Waite, D. W. & Taylor, M. W. Characterizing the avian gut microbiota: membership, driving influences, and potential function. Front. Microbiol 5, 223 (2014).PubMed 
PubMed Central 

Google Scholar 
Loo, W. T., Dudaniec, R. Y., Kleindorfer, S. & Cavanaugh, C. M. An inter-island comparison of Darwin’s finches reveals the impact of habitat, host phylogeny, and island on the gut microbiome. PLoS ONE 14, e0226432 (2019).CAS 
PubMed 
PubMed Central 

Google Scholar 
Loo, W. T., García-Loor, J., Dudaniec, R. Y., Kleindorfer, S. & Cavanaugh, C. M. Host phylogeny, diet, and habitat differentiate the gut microbiomes of Darwin’s finches on Santa Cruz Island. Sci. Rep. 9, 1–12 (2019).
Google Scholar 
Murray, M. H. et al. Gut microbiome shifts with urbanization and potentially facilitates a zoonotic pathogen in a wading bird. PLoS ONE 15, e0220926 (2020).CAS 
PubMed 
PubMed Central 

Google Scholar 
Orłowski, G. & Karg, J. Diet of nestling Barn Swallows Hirundo rustica in rural areas of Poland. Cent. Eur. J. Biol. 6, 1023–1035 (2011).
Google Scholar 
Wiesenborn, W. D. & Heydon, S. L. Diets of breeding southwestern willow flycatchers in different habitats. Wilson J. Ornithol. 119, 547–557 (2007).
Google Scholar 
Moreby, S. J. An aid to the identification of arthropod fragments in the faeces of gamebird chicks (Galliformes). Ibis 130, 519–526 (1988).
Google Scholar 
Zeale, M. R. K., Butlin, R. K., Barker, G. L. A., Lees, D. C. & Jones, G. Taxon-specific PCR for DNA barcoding arthropod prey in bat faeces. Mol. Ecol. Resour. 11, 236–244 (2011).CAS 
PubMed 

Google Scholar 
Bolnick, D. I. et al. Individuals’ diet diversity influences gut microbial diversity in two freshwater fish (threespine stickleback and Eurasian perch). Ecol. Lett. 17, 979–987 (2014).PubMed 
PubMed Central 

Google Scholar 
Bolnick, D. I. et al. Individual diet has sex-dependent effects on vertebrate gut microbiota. Nat. Commun. 5, 4500 (2014).ADS 
CAS 
PubMed 

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

Google Scholar 
Deagle, B. E., Jarman, S. N., Coissac, E., Pompanon, F. & Taberlet, P. DNA metabarcoding and the cytochrome c oxidase subunit I marker: Not a perfect match. Biol. Lett. 10, 20140562 (2014).Elbrecht, V. et al. Testing the potential of a ribosomal 16S marker for DNA metabarcoding of insects. PeerJ 4, e1966 (2016).PubMed 
PubMed Central 

Google Scholar 
Elbrecht, V. & Leese, F. Can DNA-based ecosystem assessments quantify species abundance? Testing primer bias and biomass—Sequence relationships with an innovative metabarcoding protocol. PLoS ONE 10, e0130324 (2015).PubMed 
PubMed Central 

Google Scholar 
Piñol, J., San Andrés, V., Clare, E. L., Mir, G. & Symondson, W. O. C. A pragmatic approach to the analysis of diets of generalist predators: The use of next-generation sequencing with no blocking probes. Mol. Ecol. Resour. 14, 18–26 (2014).PubMed 

Google Scholar 
Góngora, E., Elliott, K. H. & Whyte, L. Gut microbiome is affected by inter-sexual and inter-seasonal variation in diet for thick-billed murres (Uria lomvia). Sci. Rep. 11, 1200 (2021).PubMed 
PubMed Central 

Google Scholar 
Teyssier, A. et al. Diet contributes to urban-induced alterations in gut microbiota: Experimental evidence from a wild passerine. Proc. R. Soc. B 287, 20192182 (2020).PubMed 
PubMed Central 

Google Scholar 
Kreisinger, J. et al. Temporal stability and the effect of transgenerational transfer on fecal microbiota structure in a long distance migratory bird. Front. Microbiol. 8, 50 (2017).PubMed 
PubMed Central 

Google Scholar 
Petrželková, A. et al. Brood parasitism and quasi-parasitism in the European barn swallow (Hirundo rustica rustica). Behav. Ecol. Sociobiol. 69, 1405–1414 (2015).
Google Scholar 
Kreisinger, J. et al. Fecal microbiota associated with phytohaemagglutinin-induced immune response in nestlings of a passerine bird. Ecol. Evol. 8, 9793–9802 (2018).PubMed 
PubMed Central 

Google Scholar 
Klindworth, A. et al. Evaluation of general 16S ribosomal RNA gene PCR primers for classical and next-generation sequencing-based diversity studies. Nucleic Acids Res. 41, e1 (2013).CAS 
PubMed 
PubMed Central 

Google Scholar 
Elbrecht, V. & Leese, F. Validation and development of COI metabarcoding primers for freshwater macroinvertebrate bioassessment. Front. Environ. Sci. 5, 11 (2017).Jiang, H., Lei, R., Ding, S.-W. & Zhu, S. Skewer: a fast and accurate adapter trimmer for next-generation sequencing paired-end reads. BMC Bioinform. 15, 182 (2014).
Google Scholar 
R Core Team. R: A language and environment for statistical computing. R Foundation for Statistical Computing, Vienna, Austria. https://www.R-project.org/ (2018).Callahan, B. J. et al. DADA2: High-resolution sample inference from Illumina amplicon data. Nat Meth 13, 581–583 (2016).CAS 

Google Scholar 
Pafčo, B. et al. Metabarcoding analysis of strongylid nematode diversity in two sympatric primate species. Sci. Rep. 8, 5933 (2018).ADS 
PubMed 
PubMed Central 

Google Scholar 
Wang, Q., Garrity, G. M., Tiedje, J. M. & Cole, J. R. Naive Bayesian classifier for rapid assignment of rRNA sequences into the new bacterial taxonomy. Appl. Environ. Microbiol. 73, 5261–5267 (2007).ADS 
CAS 
PubMed 
PubMed Central 

Google Scholar 
McMurdie, P. J. & Holmes, S. phyloseq: An R package for reproducible interactive analysis and graphics of microbiome census data. PLoS ONE 8, e61217 (2013).ADS 
CAS 
PubMed 
PubMed Central 

Google Scholar 
Edgar, R. C., Haas, B. J., Clemente, J. C., Quince, C. & Knight, R. UCHIME improves sensitivity and speed of chimera detection. Bioinformatics 27, 2194–2200 (2011).CAS 
PubMed 
PubMed Central 

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

Google Scholar 
Wright, E. S. RNAconTest: Comparing tools for noncoding RNA multiple sequence alignment based on structural consistency. RNA 26, 531–540 (2020).CAS 
PubMed 
PubMed Central 

Google Scholar 
Price, M. N., Dehal, P. S. & Arkin, A. P. FastTree: Computing large minimum evolution trees with profiles instead of a distance matrix. Mol. Biol. Evol. 26, 1641–1650 (2009).CAS 
PubMed 
PubMed Central 

Google Scholar 
Douglas, G. M. et al. PICRUSt2 for prediction of metagenome functions. Nat. Biotechnol. 38, 685–688 (2020).CAS 
PubMed 
PubMed Central 

Google Scholar 
Caspi, R. et al. The MetaCyc database of metabolic pathways and enzymes—A 2019 update. Nucleic Acids Res. 48, D445–D453 (2020).CAS 
PubMed 

Google Scholar 
Ondov, B. D., Bergman, N. H. & Phillippy, A. M. Interactive metagenomic visualization in a Web browser. BMC Bioinform. 12, 385 (2011).
Google Scholar 
Stoffel, M. A., Nakagawa, S. & Schielzeth, H. rptR: Repeatability estimation and variance decomposition by generalized linear mixed-effects models. Methods Ecol. Evol. 8, 1639–1644 (2017).
Google Scholar 
Schielzeth, H. Simple means to improve the interpretability of regression coefficients. Methods Ecol. Evol. 1, 103–113 (2010).
Google Scholar 
Legendre, P. & Anderson, M. J. Distance-based redundancy analysis: Testing multispecies responses in multifactorial ecological experiments. Ecol. Monogr. 69, 1–24 (1999).
Google Scholar 
Oksanen, J. et al. vegan: Community Ecology Package. R package version 2.5-2. 2018. (2018).Love, M. I., Huber, W. & Anders, S. Moderated estimation of fold change and dispersion for RNA-seq data with DESeq2. Genome Biol. 15, 550 (2014).Benjamini, Y. & Hochberg, Y. Controlling the false discovery rate: A practical and powerful approach to multiple testing. J. R. Stat. Soc. Ser. B (Methodol.) 57, 289–300 (1995).MathSciNet 
MATH 

Google Scholar 
Hui, F. K. C. boral–Bayesian ordination and regression analysis of multivariate abundance data in R. Methods Ecol. Evol. 7, 744–750 (2016).
Google Scholar 
Aivelo, T. & Norberg, A. Parasite-microbiota interactions potentially affect intestinal communities in wild mammals. J. Anim. Ecol. 87, 438–447 (2018).PubMed 

Google Scholar 
Caviedes-Vidal, E. et al. The digestive adaptation of flying vertebrates: High intestinal paracellular absorption compensates for smaller guts. Proc. Natl. Acad. Sci. U.S.A. 104, 19132–19137 (2007).ADS 
CAS 
PubMed 
PubMed Central 

Google Scholar 
McWhorter, T. J., Caviedes-Vidal, E. & Karasov, W. H. The integration of digestion and osmoregulation in the avian gut. Biol. Rev. Camb. Philos. Soc. 84, 533–565 (2009).PubMed 

Google Scholar 
Grigolo, C. P. et al. Diet heterogeneity and antioxidant defence in Barn Swallow Hirundo rustica nestlings. Avocetta 43, 1 (2019).
Google Scholar 
Law, A. A. et al. Diet and prey selection of barn swallows (Hirundo rustica) at Vancouver International Airport. Canadian Field-Naturalist 131, 26 (2017).
Google Scholar 
McClenaghan, B., Nol, E. & Kerr, K. C. R. DNA metabarcoding reveals the broad and flexible diet of a declining aerial insectivore. Auk 136, uky003 (2019).Turner, A. K. The use of time and energy by aerial feeding birds (University of Stirling, 1981).
Google Scholar 
Bryant, D. M. & Turner, A. K. Central place foraging by swallows (Hirundinidae): The question of load size. Anim. Behav. 30, 845–856 (1982).
Google Scholar 
Møller, A. P. Advantages and disadvantages of coloniality in the swallow, Hirundo rustica. Anim. Behav. 35, 819–832 (1987).
Google Scholar 
Brodmann, P. A. & Reyer, H.-U. Nestling provisioning in water pipits (Anthus spinoletta): Do parents go for specific nutrients or profitable prey?. Oecologia 120, 506–514 (1999).ADS 
PubMed 

Google Scholar 
Herlugson, C. J. Food of adult and nestling Western and Mountain bluebirds. Murrelet 63, 59–65 (1982).
Google Scholar 
Batt, B. D. J., Anderson, M. G. & Afton, A. D. Ecology and management of breeding waterfowl (Univ of Minnesota Press, 1992).
Google Scholar 
Douglas, D. J. T., Evans, D. M. & Redpath, S. M. Selection of foraging habitat and nestling diet by Meadow Pipits Anthus pratensis breeding on intensively grazed moorland. Bird Study 55, 290–296 (2008).
Google Scholar 
Waugh, D. R. Predation strategies in aerial feeding birds (University of Stirling, 1978).
Google Scholar 
Kropáčková, L. et al. Co-diversification of gastrointestinal microbiota and phylogeny in passerines is not explained by ecological divergence. Mol. Ecol. 26, 5292–5304 (2017).PubMed 

Google Scholar 
Kohl, K. D. et al. Physiological and microbial adjustments to diet quality permit facultative herbivory in an omnivorous lizard. J. Exp. Biol. 219, 1903–1912 (2016).PubMed 

Google Scholar 
Baxter, N. T. et al. Intra- and interindividual variations mask interspecies variation in the microbiota of sympatric Peromyscus populations. Appl. Environ. Microbiol. 81, 396–404 (2015).ADS 
PubMed 

Google Scholar 
Holmes, I. A., Monagan, I. V. Jr., Rabosky, D. L. & Davis Rabosky, A. R. Metabolically similar cohorts of bacteria exhibit strong cooccurrence patterns with diet items and eukaryotic microbes in lizard guts. Ecol. Evol. 9, 12471–12481 (2019).PubMed 
PubMed Central 

Google Scholar 
Li, H. et al. Diet diversity is associated with beta but not alpha diversity of pika gut microbiota. Front. Microbiol. 7, 1169 (2016).Li, H. et al. Diet simplification selects for high gut microbial diversity and strong fermenting ability in high-altitude pikas. Appl. Microbiol. Biotechnol. 102, 6739–6751 (2018).CAS 
PubMed 

Google Scholar 
Ambrosini, R. et al. Cloacal microbiomes and ecology of individual barn swallows. FEMS Microbiol. Ecol. 95, fiz061 (2019).Kreisinger, J., Čížková, D., Kropáčková, L. & Albrecht, T. Cloacal microbiome structure in a long-distance migratory bird assessed using deep 16sRNA pyrosequencing. PLoS ONE 10, e0137401 (2015).PubMed 
PubMed Central 

Google Scholar 
Noguera, J. C., Aira, M., Pérez-Losada, M., Domínguez, J. & Velando, A. Glucocorticoids modulate gastrointestinal microbiome in a wild bird. R. Soc. Open Sci. 5, 171743 (2018).Shehzad, W. et al. Carnivore diet analysis based on next-generation sequencing: Application to the leopard cat (Prionailurus bengalensis) in Pakistan. Mol. Ecol. 21, 1951–1965 (2012).CAS 
PubMed 

Google Scholar 
Vestheim, H. & Jarman, S. N. Blocking primers to enhance PCR amplification of rare sequences in mixed samples—A case study on prey DNA in Antarctic krill stomachs. Front. Zool. 5, 12 (2008).PubMed 
PubMed Central 

Google Scholar  More