Ecology

Molecular Phylogeny of Paramecium bursaria and Identification of its EndosymbiontsThe SSU and ITS rDNA of the nuclear ribosomal operon were sequenced to infer the genetic variability of the investigated strains. The SSU and ITS rDNA sequences were aligned according to their secondary structure (examples are presented for the strain SAG 27.96; Fig. 1 and Supplementary Fig. 1). Additional sequences acquired from GenBank were incorporated into a dataset, which included all syngens also from references known for P. bursaria. The phylogenetic analyses revealed five highly supported lineages among the P. bursaria strains, which corresponded to their syngen assignment. As demonstrated in Fig. 2, all investigated strains belonging to the syngens R1, R2 and R5 originated from Europe, whereas the others of the syngens R3-R4 showed a worldwide distribution. The three known green algal endosymbionts, i.e., Chlorella variabilis (Cvar), Chlorella vulgaris (Cvul) and Micractinium conductrix (Mcon) showed no or only little affiliation to specific syngens.Figure 1ITS‐1 (A) and ITS-2 (B) secondary structures of Paramecium protobursaria, SAG 27.96 (syngen R1).Full size imageFigure 2Molecular phylogeny of the Paramecium bursaria species complex based on SSU and ITS rDNA sequence comparisons. The phylogenetic tree shown was inferred using the maximum likelihood method based on the datasets (2197 aligned positions of 19 taxa) using the computer program PAUP 4.0a169. For the analyses, the best model was calculated by PAUP 4.0a169. The setting of the best model was given as follows: TVM + I (base frequencies: A 0.2983, C 0.1840, G 0.2271, T 0.2906; rate matrix A–C 2.6501, A–G 8.6851, A–U 5.3270, C–G 0.91732, C–U 8.6851, G–U 1.0000) with the proportion of invariable sites (I = 0.9544). The branches in bold are highly supported in all bootstrap analyses (bootstrap values  > 50% calculated with PAUP using the maximum likelihood, neighbour—joining, and maximum parsimony). The clades are named after the syngens (color‐coded) proposed by Greczek‐Stachura et al.10 and Bomford9 in brackets. The accession numbers are given after the strain numbers. The endosymbiotic green algae identified are highlighted (Mcon—Micractinium conductrix, Cvar—Chlorella variabilis and Cvul—Chlorella vulgaris) after the origin of the P. bursaria strains. The reference strain of each syngen is marked with an asterisk. The strains used for morphological comparisons are marked with a green dot next to the strain number.Full size imageSynapomorphies of the Paramecium bursaria SyngensAs demonstrated in Fig. 2, the subdivision of the P. bursaria strains into syngens is supported by the phylogenetic analyses of the SSU and ITS rDNA sequences. To figure out if these splits were also supported by characteristic molecular signatures, we studied the secondary structures of both SSU and ITS of all available sequences. We discovered 30, respectively 23 variable positions among the SSU and ITS sequences (numbers of these positions in the respective alignments are given in Fig. 3). All syngens showed characteristic patterns among the SSU and ITS. Only the syngens R1 and R2 could not be distinguished using the SSU only, however, in combination with the ITS, each syngen is characterized by unique synapomorphies as highlighted in yellow (Fig. 3). In addition, few variable base positions within syngens (marked in blue in Fig. 3) have been recognized in the ITS regions. For comparison with literature data, we also analyzed all available sequences of the mitochondrial COI gene to find synapomorphies for the five syngens. Within this gene, only 18 variable positions at the amino acid level could be discovered of which 13 are diagnostic for the five syngens (Fig. 3).Figure 3Variable base positions among the SSU, ITS rRNA, and COI sequences of the five syngens among the Paramecium bursaria species complex. The unique synapomorphies are highlighted in yellow, variable positions marked in blue.Full size imageThe synapomorphies discovered above were used to get insights into the geographical distribution of each P. bursaria syngen. Despite the complete SSU and ITS rDNA sequences included in the phylogeny presented in Fig. 2, records of the partial SSU or ITS rDNA sequences are available in GenBank (BLASTn search; 100% identity;13). Considering the metadata of our investigated strains and of the entries in GenBank (Supplementary Table 1), we constructed three haplotype networks using the Templeton-Crandall-Sing (TCS) approach. The SSU haplotype network (Fig. 4) containing 84 records showed that the syngens R1, R2 and R5 were only found in Europe, whereas the other three syngens have been discovered around the world. A similar distribution pattern occurred when using the ITS (101 entries in GenBank). Records of syngens R1 and R5 have only been found in Europe, whereas all other syngens were distributed around the world. The 132 COI records found in GenBank by the BLASTn search were used for the haplotype network, which also showed the similar pattern (Fig. 4).Figure 4TCS haplotype networks of the five syngens inferred from SSU, ITS rRNA, and COI sequences of the Paramecium bursaria species complex. This network was inferred using the algorithm described by Clement et al.40,41. Sequence nodes corresponding to samples collected from different geographical regions.Full size imageCiliate TaxonomyConsidering all our findings, P. bursaria is morphologically highly variable, and obviously represents a cryptic species complex (Figs. 5, 6; Supplementary Table 2). The known five syngens most likely represent biological species according to Mayr14 and can be attributed to the cryptic species described by Greczek-Stachura et al.11. As mentioned above, the assignments of these cryptic species by Greczek-Stachura et al.11 have not been validly described according to the ICZN. In addition, the naming using a mixture of Latin prefix and Greek suffix is also not appropriate (the epithet bursa derived from the Greek word byrsa). Therefore, we describe the five syngens as new species as follows. The general morphological features of these species are summarized in Table 1.Figure 5Ventral views of Paramecium bursaria morphotypes in vivo: P. protobursaria (syngen R1), i.e., strains SAG 2645 (A) and PB-25 (B); P. deuterobursaria (syngen R2), i.e., strains CCAP 1660/36 (C) and CCAP 1660/34 (D); P. tritobursaria (syngen R3), i.e., strains CCAP 1660/28 (E), CCAP 1660/26 (F) and CCAP 1660/31 (G); P. tetratobursaria (syngen R4), i.e., strains CCAP 1660/25 (H) and CCAP 1660/33 (I); P. pentobursaria (syngen R5), i.e., strain CCAP 1660/30 (J). Scale bar 20 µm.Full size imageFigure 6Morphological details of the Paramecium bursaria species complex from specimens of strains PB-25 (A), CCAP 1660/30 (B), SAG 2645 (C, F, G, I, L–N), CCAP 1660/36 (D), CCAP 1660/26 (E, H), CCAP 1660/30 (J, O), CCAP 1660/16 (K) in vivo (A–F, H–O) and after silver nitrate staining (G). Adoral membranelles (A, B), endosymbiotic algae Micractinium conductrix (C), caudal and somatic cilia (D), arrows denote excretory pores of the contractile vacuoles: extruded extrusomes are shown and caudal cilia (E), ventral views showing the preoral suture and the oral opening (F), the ciliary pattern (G), arrows denote excretory pores of the contractile vacuoles (H), trichocysts and symbiotic algae underneath the pellicula (I, J), cell size variations (K), radial collecting channels (white arrows) and excretory pores (black arrows) of contractile vacuoles (L), macro- and micronucleus (M), cytopyge and characteristic rectangular pellicular pattern (N), pattern of the pellicula (O). AS anterior suture, CC caudal cilia, CP cytopyge (cell after), CV contractile vacuole, EP excretory pore of a contractile vacuole, EX extrusomes, M1–M3 membranelles 1–3, MA macronucleus, MI micronucleus, OO oral opening, S symbiotic algae, SC somatic cilia, SK somatic kineties, UM undulating membrane. Scale bars 10 µm (A, I), 20 µm (B, D–H, J, L–O), 50 µm (K).Full size imageTable 1 Main morphometric and morphological characteristics of the Paramecium bursaria syngens (min and max values).Full size table
Paramecium protobursaria sp. nov.Synonym: Paramecium primabursaria nom. inval.Description: The strains SAG 27.96 and PB-25 belong to syngen R1 according to Greczek-Stachura et al.10,11 and differ from other syngens by their SSU and ITS rDNA sequences (MT231333). From morphology, the cells are ellipsoidal to broadly ellipsoidal and dorso-ventrally flattened in vivo. The cells measure 70–164 × 44–65 µm; the single macronucleus is located around mid-cell and measures 25–38 × 11–22 µm; the adjacent single compact micronucleus measures 11–20 × 5–8 µm; the usually two (rarely one) contractile vacuoles, one in the anterior and one in the posterior cell portion have radial collecting channels and 1–3 excretory pores each; the number of ciliary rows/20 µm is 14–22; the length of the caudal cilia is 9–19 µm; the numerous trichocysts located in the cell cortex are 4–6 µm in length. The symbiotic algae belong to M. conductrix; the larger algae measure 4–7 × 4–7 µm; the smaller algal cells measure 2–5 × 2–5 µm.Geographic distribution: The investigated strains of syngen R1 were found in Europe: Göttingen, Germany; Lake Mondsee, Austria. In addition, this species has been reported from different places in Europe, Asia and North America (see details in Supplementary Table 1).Reference material: Strain SAG 27.96 and the clonal strain SAG 2645 derived from SAG 27.96 are available at the Culture Collection of Algae (SAG), University of Göttingen, Germany.Holotype: Two slides (one holotype, one paratype) with protargol-impregnated specimens from the clonal culture SAG 2645, which derived from the reference material SAG 27.96, isolated from the pond of the Old Botanical Garden of the University of Göttingen (Germany), have been deposited in the Oberösterreichisches Landesmuseum at Linz (LI, Austria).Zoobank Registration LSID: AFD967ED-BC2A-43FD-847E-5DF588BB025C.
Paramecium deuterobursaria sp. nov.Synonym: Paramecium bibursaria nom. inval.Description: The strains CCAP 1660/34 and CCAP 1660/36 belong to syngen R2 according to Greczek-Stachura et al.10,11 and differ from other syngens by their SSU and ITS rDNA sequences (OK318487). From morphology, the cells are ellipsoidal to broadly ellipsoidal and dorso-ventrally flattened in vivo. The cells measure 81–167 × 35–83 µm; the single macronucleus is located around mid-cell and measures 24–46 × 10–32 µm; the adjacent single compact micronucleus measures 10–18 × 5–9 µm, no micronucleus seen in live cells of strain CCAP 1660/34; the usually two (rarely one or three) contractile vacuoles, one in the anterior and one in the posterior cell portion have radial collecting channels and 1–3 excretory pores each; the number of ciliary rows/20 µm is 13–22; the length of the caudal cilia is 11–20 µm; the numerous trichocysts located in the cell cortex are 4–6 µm in length. The symbiotic algae belong to M. conductrix; the larger algae measure 5–7 × 4–7 µm; the smaller algal cells measure 3–5 × 2–5 µm.Geographic distribution: The investigated strains of syngen R2 were found in Europe: Zurich, Switzerland; Lake Piburg, Austria. In addition, this species has been reported from different places in Europe, Asia and Australia (see details in Supplementary Table 1).Reference material: Strain CCAP 1660/36 is available at the Culture Collection of Algae and Protozoa (CCAP) at the Scottish Association for Marine Science, Oban, Scotland.Holotype: Two slides (one holotype, one paratype) with protargol-impregnated specimens from the reference material CCAP 1660/36, isolated from Lake Piburg (Tyrol, Austria), have been deposited in the Oberösterreichisches Landesmuseum at Linz (LI, Austria).Zoobank Registration LSID: D1C20BE6-9A15-4A3D-A7E5-DFC31FF04679.
Paramecium tritobursaria sp. nov.Synonym: Paramecium tribursaria nom. inval.Description: The strains CCAP 1660/26, CCAP 1660/28 and CCAP 1660/31 belong to syngen R3 according to Greczek-Stachura et al.10,11 and differ from other syngens by their SSU and ITS rDNA sequences (MT231339). From morphology, the cells are ellipsoidal to broadly ellipsoidal and dorso-ventrally flattened in vivo. The cells measure 80–153 × 49–73 µm; the single macronucleus is located around mid-cell and measures 21–53 × 12–31 µm; the adjacent single compact micronucleus measures 9–17 × 3–6 µm; no micronucleus seen in live cells of strain CCAP 1660/28; the usually two (rarely one or three) contractile vacuoles, one in the anterior and one in the posterior cell portion have radial collecting channels and 1–3 excretory pores each; the number of ciliary rows/20 µm is 12–20; the length of the caudal cilia is 8–19 µm; the numerous trichocysts located in the cell cortex are 4–6 µm in length. The symbiotic algae belong to C. variabilis; the larger algae measure 4–7 × 3–6 µm; the smaller algal cells measure 3–5 × 2–4 µm.Geographic distribution: The investigated strains of syngen R3 were found in Europe and Asia: Lake Piburg, Austria; Tokyo, Japan; Khabarovsk region, Amur River, Russia. In addition, this species has been reported from different places in Europe, Asia, North and South America as well as in Australia (see details in Supplementary Table 1).Reference material: Strain CCAP 1660/26 is available at the Culture Collection of Algae and Protozoa (CCAP) at the Scottish Association for Marine Science, Oban, Scotland.Holotype: Two slides (one holotype, one paratype) with protargol-impregnated specimens from the reference material CCAP 1660/26, isolated from Japan, have been deposited in the Oberösterreichisches Landesmuseum at Linz (LI, Austria).Zoobank Registration LSID: CC0FBA7E-9E3A-4C37-B424-C9BFF2018EC0.
Paramecium tetratobursaria sp. nov.Synonym: Paramecium tetrabursaria nom. inval.Description: The strains CCAP 1660/25 and CCAP 1660/33 belong to syngen R4 according to Greczek-Stachura et al.10,11 and differ from other syngens by their SSU and ITS rDNA sequences (MT231347). From morphology, the cells are ellipsoidal to broadly ellipsoidal and dorso-ventrally flattened in vivo. The cells measure 65–179 × 37–79 µm; the single macronucleus is located around mid-cell and measures 18–53 × 10–29 µm; the adjacent single compact micronucleus measures 8–18 × 4–10 µm; the usually two (rarely one or three) contractile vacuoles, one in the anterior and one in the posterior cell portion have radial collecting channels and 1–3 excretory pores each; the number of ciliary rows/20 µm is 14–19; the length of the caudal cilia is 12–20 µm; the numerous trichocysts located in the cell cortex are 4–7 µm in length. The symbiotic algae belong to C. variabilis (CCAP 1660/25) and M. conductrix (CCAP 1660/33); the larger algae measure 3–6 × 3–6 µm; the smaller algal cells measure 2–5 × 1–4 µm.Geographic distribution: The investigated strains of syngen R4 are found in North- and South America: Burlington, North Carolina, USA; San Pedro de la Paz, Laguna Grande, Chile. In addition, this species has been reported from Europe (see details in Supplementary Table 1).Reference material: Strain CCAP 1660/25 is available at the Culture Collection of Algae and Protozoa (CCAP) at the Scottish Association for Marine Science, Oban, Scotland.Holotype: Two slides (one holotype, one paratype) with protargol-impregnated specimens from the reference material CCAP 1660/25, isolated from a pond in Burlington (North Carolina, USA), have been deposited in the Oberösterreichisches Landesmuseum at Linz (LI, Austria).Zoobank Registration LSID: 78BA9923-07A9-4918-AD7C-9E5E15CC9CDB.
Paramecium pentobursaria sp. nov.Synonym: Paramecium pentabursaria nom. inval.Description: The strain CCAP 1660/30 belongs to syngen R5 according to Greczek-Stachura et al.10,11 and differs from other syngens by their SSU and ITS rDNA sequences (MT231348). From morphology, the cells are ellipsoidal to broadly ellipsoidal and dorso-ventrally flattened in vivo. The cells measure 161–194 × 76–99 µm; the single macronucleus is located around mid-cell and measures 24–47 × 19–31 µm; the adjacent single compact micronucleus measures 13–20 × 4–9 µm; the usually two (rarely one or three) contractile vacuoles, one in the anterior and one in the posterior cell portion have radial collecting channels and 1–4 excretory pores each; the number of ciliary rows/20 µm is 13–19; the length of the caudal cilia is 14–25 µm; the numerous trichocysts located in the cell cortex are 5–7 µm in length. The symbiotic algae belong to C. variabilis; the larger algae measure 5–6 × 5–6 µm; the smaller algal cells measure 4–5 × 3–4 µm.Geographic distribution: The investigated strain of Syngen R5 was found in Europe: Astrakhan Nature Reserve, Russia.Reference material: Strain CCAP 1660/30 is available at the Culture Collection of Algae and Protozoa (CCAP) at the Scottish Association for Marine Science, Oban, Scotland.Holotype: Two slides (one holotype, one paratype) with protargol-impregnated specimens from the reference material CCAP 1660/30, isolated from Astrakhan Nature Reserve (Russia), have been deposited in the Oberösterreichisches Landesmuseum at Linz (LI, Austria).Zoobank Registration LSID: 6629FA71-E00F-48C6-83AB-61C0CA4823B6.Syngen Affiliation related to Ciliate Morphology, Endosymbionts and Geographic DistributionPearson-correlations of morphometric, syngen-specific and endosymbiont datasets of the P. bursaria strains revealed four significant positive correlations (p  r  > 0.75) between ciliate cell length (BLEN) and width (BWID), BWID and macronucleus width (MACWID), as well as length and width of large symbiotic algae (LSALEN and LSAWID; Fig. 7).Figure 7Pearson-correlations of morphometric, symbiont and syngen data of Paramecium strains under study. Colored dots indicate the strength of correlation, and the size of dots represent p-values. Bold squares highlight significant correlations, with − 0.75  > r  > 0.75 and p  1, accounting for 73.1% variation in total (Supplementary Table 3). Principal component axis 1 (PC1) appears to be most negatively weighted by syngen (SYN) and width of the macronucleus (MACWID), separating CCAP 1660/30 and CCAP 1660/33 from the other strains. Principal component axis 2 (PC2) is primarily positively influenced by symbiotic algae characteristics (LSALEN, LSAWID, small symbiotic algal length (SSALEN) and width (SSAWID)) and, ciliate cell length (BLEN) and width (BWID; Supplementary Table 4), partitioning strain PB-25, CCAP 1660/26 and CCAP 1660/36 from CCAP 1660/31 and SAG 27.96 (Fig. 8).Figure 8PCA of morphometric data of Paramecium bursaria strains. Only the top eight contributing variables are shown.Full size imageThe redundancy analysis (RDA; Fig. 9) revealed a large difference between morphometric features and the tested set of explanatory variables (i.e., algal species (ALSPEC), LSAWID, SSALEN, SYN and GEO) as only 26.9% of the total variation could be explained.Figure 9Ordination diagram for redundancy analysis (RDA) of morphometric data and shown syngen (SYN), geographic region (GEO), and algal features (ALSPEC, LSAWID and SSALEN) as explanatory features.Full size image More

Gill, J. A. et al. Proc. R. Soc. B 281, 20132161 (2014).Article 

Google Scholar 
Tomotani, B. M. et al. Glob. Chang. Biol. 24, 823–835 (2018).Article 

Google Scholar 
Teplitsky, C., Mills, J. A., Alho, J. S., Yarrall, J. W. & Merilä, J. Proc. Natl Acad. Sci. USA 105, 13492–13496 (2008).Article 
CAS 

Google Scholar 
Gardner, J. L., Peters, A., Kearney, M. R., Joseph, L. & Heinsohn, R. Trends Ecol. Evol. 26, 285–291 (2011).Article 

Google Scholar 
Youngflesh, C., Saracco, J. F., Siegel, R. B. & Tingley, M. W. Nat. Ecol. Evol. https://doi.org/10.1038/s41559-022-01893-x (2022).Article 

Google Scholar 
Hughes, E. C. et al. Ecol. Lett. 25, 598–610 (2022).Article 

Google Scholar 
Tobias, J. A. et al. Ecol. Lett. 25, 581–597 (2022).Article 

Google Scholar 
Shine, R. Q. Rev. Biol. 64, 419–461 (1989).Article 
CAS 

Google Scholar 
Dubiner, S. & Meiri, S. Glob. Ecol. Biogeogr. 31, 791–801 (2022).Article 

Google Scholar 
Jirinec, V. et al. Sci. Adv. 7, eabk1743 (2021).Article 

Google Scholar 
Weeks, B. C. et al. Ecol. Lett. 23, 316–325 (2020).Article 

Google Scholar 
Parmesan, C. Annu. Rev. Ecol. Evol. Syst. 37, 637–669 (2006).Article 

Google Scholar 
Bonamour, S., Chevin, L. M., Charmantier, A. & Teplitsky, C. Phil. Trans. R. Soc. Lond. B 374, 20180178 (2019).Article 

Google Scholar 
Bellard, C., Bertelsmeier, C., Leadley, P., Thuiller, W. & Courchamp, F. Ecol. Lett. 15, 365–377 (2012).Article 

Google Scholar 
Hickling, R., Roy, D. B., Hill, J. K., Fox, R. & Thomas, C. D. Glob. Change Biol. 12, 450–455 (2006).Article 

Google Scholar 
Forero-Medina, G., Joppa, L. & Pimm, S. L. Conserv. Biol. 25, 163–171 (2011).Article 

Google Scholar 
Peters, R. H. The Ecological Implications of Body Size (Cambridge Univ. Press, 1983).Berg, M. P. & Ellers, J. Evol. Ecol. 24, 617–629 (2010).Article 

Google Scholar  More

Callaghan, T. V. et al. Multiple effects of changes in arctic snow cover. Ambio 40, 32–45 (2011).
Google Scholar 
Cooper, E. J. Warmer shorter winters disrupt arctic terrestrial ecosystems. Annu. Rev. Ecol. Evol. Syst. 45, 271 (2014).
Google Scholar 
IPCC. IPCC, 2013: Climate Change 2013: The Physical Science Basis. Contribution of Working Group I to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change [Stocker, T.F., D. Qin, G.-K. Plattner, M. Tignor, S.K. Allen, J. Boschung, A. Nauels, Y. Xia, V. Bex and P.M. Midgley (eds.)]. Cambridge University Press, Cambridge, United Kingdom and New York, NY, USA, 1535 pp. https://doi.org/10.1017/CBO9781107415324. (Cambridge University Press, 2013).Seastedt, T. R. The role of microarthropods in decomposition and mineralization processes. Annu. Rev. Entomol. 29, 25–46 (1984).
Google Scholar 
Osler, G. H. & Sommerkorn, M. Toward a complete soil C and N cycle: Incorporating the soil fauna. Ecology 88, 1611–1621 (2007).PubMed 

Google Scholar 
Coulson, S. J. et al. The terrestrial and freshwater invertebrate biodiversity of the archipelagoes of the Barents Sea, Svalbard, Franz Josef Land and Novaya Zemlya. Soil Biol. Biochem. 68, 440–470 (2014).CAS 

Google Scholar 
Hodkinson, I. D. Terrestrial and freshwater invertebrates. In Arctic Biodiversity Assessment (ed. Barry, T.) 246–274 (Arctic Council, 2013).
Google Scholar 
Strathdee, A. T. & Bale, J. S. Life on the edge: Insect ecology in arctic environments. Annu. Rev. Entomol. 43, 85–106 (1998).CAS 
PubMed 

Google Scholar 
Templer, P. H. et al. Impact of a reduced winter snowpack on litter arthropod abundance and diversity in a northern hardwood forest ecosystem. Biol. Fertil. Soils 48, 413–424 (2012).
Google Scholar 
Bokhorst, S., Metcalfe, D. B. & Wardle, D. A. Reduction in snow depth negatively affects decomposers but impact on decomposition rates is substrate dependent. Soil Biol. Biochem. 62, 157–164 (2013).CAS 

Google Scholar 
Slatyer, R. A., Nash, M. A. & Hoffmann, A. A. Measuring the effects of reduced snow cover on Australia’s alpine arthropods. Austral Ecol. 42, 844–857 (2017).
Google Scholar 
Bokhorst, S. et al. Extreme winter warming events more negatively impact small rather than large soil fauna: Shift in community composition explained by traits not taxa. Glob. Change Biol. 18, 1152–1162 (2012).ADS 

Google Scholar 
Sulkava, P. & Huhta, V. Effects of hard frost and freeze-thaw cycles on decomposer communities and N mineralisation in boreal forest soil. Appl. Soil Ecol. 22, 225–239 (2003).
Google Scholar 
Konestabo, H. S., Michelsen, A. & Holmstrup, M. Responses of springtail and mite populations to prolonged periods of soil freeze-thaw cycles in a sub-arctic ecosystem. Appl. Soil Ecol. 36, 136–146 (2007).
Google Scholar 
Coulson, S. J., Leinaas, H. P., Ims, R. A. & Søvik, G. Experimental manipulation of the winter surface ice layer: The effects on a high arctic soil microarthropod community. Ecography 23, 299–306 (2000).
Google Scholar 
Dollery, R., Hodkinson, I. D. & Jonsdottir, I. S. Impact of warming and timing of snow melt on soil microarthropod assemblages associated with Dryas-dominated plant communities on Svalbard. Ecography 29, 111–119 (2006).
Google Scholar 
Ávila-Jimenez, M. L., Coulson, S. J., Solhoy, T. & Sjoblom, A. Overwintering of terrestrial Arctic arthropods: The fauna of Svalbard now and in the future. Polar Res. 29, 127–137 (2010).
Google Scholar 
Makkonen, M. et al. Traits explain the responses of a sub-arctic Collembola community to climate manipulation. Soil Biol. Biochem. 43, 377–384 (2011).CAS 

Google Scholar 
Lindo, Z. Warming favours small-bodied organisms through enhanced reproduction and compositional shifts in belowground systems. Soil Biol. Biochem. 91, 271–278 (2015).CAS 

Google Scholar 
Hågvar, S. A review of Fennoscandian arthropods living on and in snow. Eur. J. Entomol. 107, 281–298 (2010).
Google Scholar 
Hao, C., Chen, T.-W., Wu, Y., Chang, L. & Wu, D. Snow microhabitats provide food resources for winter-active Collembola. Soil Biol. Biochem. 143, 107731 (2020).CAS 

Google Scholar 
Christenson, L. et al. Winter climate change influences on soil faunal distribution and abundance: Implications for decomposition in the Northern Forest. Northeast. Nat. 24, B209–B234 (2017).
Google Scholar 
Convey, P. et al. Survival of rapidly fluctuating natural low winter temperatures by High Arctic soil invertebrates. J. Therm. Biol. 54, 111–117 (2015).PubMed 

Google Scholar 
Krab, E. J., Monteux, S., Weedon, J. T. & Dorrepaal, E. Plant expansion drives bacteria and collembola communities under winter climate change in frost-affected tundra. Soil Biol. Biochem. 138, 107569 (2019).CAS 

Google Scholar 
Sörensen, J. G. & Holmstrup, M. Cryoprotective dehydration is widespread in Arctic springtails. J. Insect Physiol. 57, 1147–1153 (2011).PubMed 

Google Scholar 
Convey, P., Coulson, S. J., Worland, M. R. & Sjöblom, A. The importance of understanding annual and shorter-term temperature patterns and variation in the surface levels of polar soils for terrestrial biota. Polar Biol. 41, 1587–1605 (2018).
Google Scholar 
Birkemoe, T. & Leinaas, H. P. Reproductive biology of the arctic collembolan Hypogastrura tullbergi. Ecography 22, 31–39 (1999).
Google Scholar 
Birkemoe, T. & Leinaas, H. P. Effects of temperature on the development of an arctic Collembola (Hypogastrura tullbergi). Funct. Ecol. 14, 693–700 (2001).
Google Scholar 
Kankaanpää, T. et al. Spatiotemporal snowmelt patterns within a high Arctic landscape, with implications for flora and fauna. Arct. Antarct. Alp. Res. 50, e1415624 (2018).
Google Scholar 
Cooper, E. J., Dullinger, S. & Semenchuk, P. Late snowmelt delays plant development and results in lower reproductive success in the high arctic. Plant Sci. 180, 157–167 (2011).CAS 
PubMed 

Google Scholar 
Krab, E. J. et al. Winter warming effects on tundra shrub performance are species-specific and dependent on spring conditions. J. Ecol. 106, 599–612 (2018).CAS 

Google Scholar 
Wheeler, H. C., Hoye, T. T., Schmidt, N. M., Svenning, J.-C. & Forchhammer, M. C. Phenological mismatch with abiotic conditions-implications for flowering in Arctic plants. Ecology 96, 775–787 (2015).PubMed 

Google Scholar 
Wheeler, J. A. et al. The snow and the willows: Earlier spring snowmelt reduces performance in the low-lying alpine shrub Salix herbacea. J. Ecol. 104, 1041–1050 (2016).CAS 

Google Scholar 
Pollierer, M. M., Langel, R., Körner, C., Maraun, M. & Scheu, S. The underestimated importance of belowground carbon input for forest soil animal food webs. Ecol. Lett. 10, 729–736 (2007).PubMed 

Google Scholar 
Coulson, S. J., Hodkinson, I. D. & Webb, N. R. Microscale distribution patterns in high Arctic soil microarthropod communities: The influence of plant species within the vegetation mosaic. Ecography 26, 801–809 (2003).
Google Scholar 
Hodkinson, I. D. et al. Global change and Arctic ecosystems: Conclusions and predictions from experiments with terrestrial invertebrates on Spitsbergen. Arct. Alp. Res. 30, 306–313 (1998).
Google Scholar 
Førland, E. J., Benestad, R., Hanssen-Bauer, I., Haugen, J. E. & Skaugen, T. E. Temperature and precipitation development at Svalbard 1900–2100. Adv. Meteorol. 2011, 893790 (2011).
Google Scholar 
Alatalo, J. M., Jagerbrand, A. K. & Cuchta, P. Collembola at three alpine subarctic sites resistant to twenty years of experimental warming. Sci. Rep. 5, 18161 (2015).ADS 
CAS 
PubMed 
PubMed Central 

Google Scholar 
Coulson, S. J. et al. Effects of experimental temperature elevation on high-arctic soil microarthropod populations. Polar Biol. 16, 147–153 (1996).
Google Scholar 
Decker, K. L. M., Wang, D., Waite, C. & Scherbatskoy, T. Snow removal and ambient air temperature effects on forest soil temperatures in Northern Vermont. Soil Sci. Soc. Am. J. 67, 1234–1242 (2003).ADS 
CAS 

Google Scholar 
van Pelt, W. J. J. et al. Multidecadal climate and seasonal snow conditions in Svalbard. J. Geophys. Res. Earth Surf. 121, 2100–2117 (2016).ADS 

Google Scholar 
Semenchuk, P. R. et al. Deeper snow alters soil nutrient availability and leaf nutrient status in high Arctic tundra. Biogeochemistry 124, 81–94 (2015).
Google Scholar 
Sjursen, H., Michelsen, A. & Jonasson, S. Effects of long-term soil warming and fertilisation on microarthropod abundances in three sub-arctic ecosystems. Appl. Soil Ecol. 30, 148–161 (2005).
Google Scholar 
Meehan, M. L. et al. Response of soil fauna to simulated global change factors depends on ambient climate conditions. Pedobiologia 83, 150672 (2020).
Google Scholar 
Harte, J., Rawa, A. & Price, V. Effects of manipulated soil microclimate on mesofaunal biomass and diversity. Soil Biol. Biochem. 28, 313–322 (1996).CAS 

Google Scholar 
Siepel, H. Life history tactics of soil microarthropods. Biol. Fertil. Soils 18, 263–278 (1994).
Google Scholar 
Chernova, N. M., Potapov, M. B., Savenkova, Y. Y. & Bokova, A. I. Ecological significance of parthenogenesis in Collembola. Zool. Zhurnal 88, 1455–1470 (2009).
Google Scholar 
Birkemoe, T. & Somme, L. Population dynamics of two collembolan species in an Arctic tundra. Pedobiologia 42, 131–145 (1998).
Google Scholar 
Bokhorst, S. et al. Contrasting responses of springtails and mites to elevation and vegetation type in the sub-Arctic. Pedobiologia 67, 57–64 (2018).
Google Scholar 
Widenfalk, L. A., Malmstrom, A., Berg, M. P. & Bengtsson, J. Small-scale Collembola community composition in a pine forest soil—Overdispersion in functional traits indicates the importance of species interactions. Soil Biol. Biochem. 103, 52–62 (2016).CAS 

Google Scholar 
Morgner, E. The importance of winter in annual ecosystem respiration in the High Arctic: Effects of snow depth in two vegetation types. Polar Res. 29, 474–474 (2010).
Google Scholar 
Green, K. & Slatyer, R. Arthropod community composition along snowmelt gradients in snowbeds in the Snowy Mountains of south-eastern Australia. Austral Ecol. 45, 144–157 (2020).
Google Scholar 
Ayres, E. et al. Experimentally increased snow accumulation alters soil moisture and animal community structure in a polar desert. Polar Biol. 33, 897–907 (2010).
Google Scholar 
Semenchuk, P. R., Elberling, B. & Cooper, E. J. Snow cover and extreme winter warming events control flower abundance of some, but not all species in high arctic Svalbard. Ecol. Evol. 3, 2586–2599 (2013).PubMed 
PubMed Central 

Google Scholar 
Morsdorf, M. A. et al. Deepened winter snow significantly influences the availability and forms of nitrogen taken up by plants in High Arctic tundra. Soil Biol. Biochem. 135, 222–234 (2019).CAS 

Google Scholar 
Cooper, E. J., Little, C. J., Pilsbacher, A. K. & Morsdorf, M. A. Disappearing green: Shrubs decline and bryophytes increase with nine years of increased snow accumulation in the High Arctic. J. Veg. Sci. 30, 857–867 (2019).
Google Scholar 
Mundra, S. et al. Ectomycorrhizal and saprotrophic fungi respond differently to long-term experimentally increased snow depth in the High Arctic. Microbiologyopen 5, 856–869 (2016).CAS 
PubMed 
PubMed Central 

Google Scholar 
Schneider, K. & Maraun, M. Feeding preferences among dark pigmented fungal taxa (“Dematiacea”) indicate limited trophic niche differentiation of oribatid mites (Oribatida, Acari). Pedobiologia 49, 61–67 (2005).
Google Scholar 
Krab, E. J., Berg, M. P., Aerts, R., van Logtestijn, R. S. P. & Cornelissen, J. H. C. Vascular plant litter input in subarctic peat bogs changes Collembola diets and decomposition patterns. Soil Biol. Biochem. 63, 106–115 (2013).CAS 

Google Scholar 
Jucevica, E. & Melecis, V. Global warming affect Collembola community: A long-term study. Pedobiologia 50, 177–184 (2006).
Google Scholar 
Krab, E. J., Oorsprong, H., Berg, M. P. & Cornelissen, J. H. C. Turning northern peatlands upside down: Disentangling microclimate and substrate quality effects on vertical distribution of Collembola. Funct. Ecol. 24, 1362–1369 (2010).
Google Scholar 
Zettel, J. Alpine Collembola—Adaptations and strategies for survival in harsh environments. Zool. Anal. Complex Syst. 102, 73–89 (2000).
Google Scholar 
Block, W. Terrestrial arthropods and low-temperature. Cryobiology 18, 436–444 (1981).CAS 
PubMed 

Google Scholar 
Semenchuk, P. R., Christiansen, C. T., Grogan, P., Elberling, B. & Cooper, E. J. Long-term experimentally deepened snow decreases growing-season respiration in a low- and high-arctic tundra ecosystem. J. Geophys. Res. Biogeosci. 121, 1236–1248 (2016).
Google Scholar 
Semenchuk, P. R. et al. Soil organic carbon depletion and degradation in surface soil after long-term non-growing season warming in High Arctic Svalbard. Sci. Total Environ. 646, 158–167 (2019).ADS 
CAS 
PubMed 

Google Scholar 
Gillespie, M. A. K. et al. Status and trends of terrestrial arthropod abundance and diversity in the North Atlantic region of the Arctic. Ambio 49, 718–731 (2020).PubMed 

Google Scholar 
Andriuzzi, W. S., Adams, B. J., Barrett, J. E., Virginia, R. A. & Wall, D. H. Observed trends of soil fauna in the Antarctic Dry Valleys: Early signs of shifts predicted under climate change. Ecology 99, 312–321 (2018).CAS 
PubMed 

Google Scholar 
Staub, B. & Delaloye, R. Using near-surface ground temperature data to derive snow insulation and melt indices for mountain permafrost applications. Permafr. Periglac. Process. 28, 237–248 (2017).
Google Scholar 
Rendos, M. et al. Organic carbon content and temperature as substantial factors affecting diversity and vertical distribution of Collembola on forested scree slopes. Eur. J. Soil Biol. 75, 180–187 (2016).
Google Scholar 
Fjellberg, A. The Collembola of the Norwegian Arctic Islands (Norsk Polarinstitutt, 1994).
Google Scholar 
R Core Team. R: A Language and Environment for Statistical Computing. https://www.R-project.org/. (R Foundation for Statistical Computing, 2020). Accessed 06 June 2020. More

Dehling, D. M., Jordano, P., Schaefer, H. M., Böhning-Gaese, K. & Schleuning, M. Morphology predicts species’ functional roles and their degree of specialization in plant–frugivore interactions. Proc. R. Soc. B 283, 20152444 (2016).PubMed 
PubMed Central 

Google Scholar 
Grant, P. R. Inheritance of size and shape in a population of Darwin’s finches, Geospiza conirostris. Proc. R. Soc. Lond. B 220, 219–236 (1983).
Google Scholar 
Des Roches, S. et al. The ecological importance of intraspecific variation. Nat. Ecol. Evol. 2, 57–64 (2018).PubMed 

Google Scholar 
Bergmann, C. Über die verhältnisse der wärmeökonomie der thiere zu ihrer grösse. Gött. Stud. 3, 595–708 (1847).
Google Scholar 
Allen, J. A. The influence of physical conditions in the genesis of species. Radic. Rev. 1, 108–140 (1877).
Google Scholar 
Altshuler, D. L. & Dudley, R. The physiology and biomechanics of avian flight at high altitude. Integr. Comp. Biol. 46, 62–71 (2006).PubMed 

Google Scholar 
Teplitsky, C. & Millien, V. Climate warming and Bergmann’s rule through time: is there any evidence? Evol. Appl. 7, 156–168 (2014).PubMed 

Google Scholar 
Gardner, J. L., Peters, A., Kearney, M. R., Joseph, L. & Heinsohn, R. Declining body size: a third universal response to warming? Trends Ecol. Evol. 26, 285–291 (2011).PubMed 

Google Scholar 
Yom-Tov, Y., Yom-Tov, S., Wright, J., Thorne, C. J. R. & Du Feu, R. Recent changes in body weight and wing length among some British passerine birds. Oikos 112, 91–101 (2006).
Google Scholar 
Van Buskirk, J., Mulvihill, R. S. & Leberman, R. C. Declining body sizes in North American birds associated with climate change. Oikos 119, 1047–1055 (2010).
Google Scholar 
Weeks, B. C. et al. Shared morphological consequences of global warming in North American migratory birds. Ecol. Lett. 23, 316–325 (2020).PubMed 

Google Scholar 
Rosenberg, K. V. et al. Decline of the North American avifauna. Science 366, 120–124 (2019).CAS 
PubMed 

Google Scholar 
DeSante, D. F., Saracco, J. F., O’Grady, D. R., Burton, K. M. & Walker, B. L. Methodological considerations of the Monitoring Avian Productivity and Survivorship (MAPS) program. Stud. Avian Biol. 29, 28–45 (2004).West, G. B., Brown, J. H. & Enquist, B. J. A general model for the origin of allometric scaling laws in biology. Science 276, 122–126 (1997).CAS 
PubMed 

Google Scholar 
Jirinec, V. et al. Morphological consequences of climate change for resident birds in intact Amazonian rainforest. Sci. Adv. 7, eabk1743 (2021).PubMed 
PubMed Central 

Google Scholar 
Dubiner, S. & Meiri, S. Widespread recent changes in morphology of Old World birds, global warming the immediate suspect. Glob. Ecol. Biogeogr. 31, 791–801 (2022).
Google Scholar 
Ballinger, M. A. & Nachman, M. W. The contribution of genetic and environmental effects to Bergmann’s rule and Allen’s rule in house mice. Am. Nat. https://doi.org/10.1086/719028 (2022).Andrew, S. C., Hurley, L. L., Mariette, M. M. & Griffith, S. C. Higher temperatures during development reduce body size in the zebra finch in the laboratory and in the wild. J. Evol. Biol. 30, 2156–2164 (2017).CAS 
PubMed 

Google Scholar 
Siepielski, A. M. et al. No evidence that warmer temperatures are associated with selection for smaller body sizes. Proc. R. Soc. B 286, 20191332 (2019).PubMed 
PubMed Central 

Google Scholar 
Salewski, V., Siebenrock, K.-H., Hochachka, W. M., Woog, F. & Fiedler, W. Morphological change to birds over 120 years is not explained by thermal adaptation to climate change. PLoS ONE 9, e101927 (2014).PubMed 
PubMed Central 

Google Scholar 
Riddell, E. A., Iknayan, K. J., Wolf, B. O., Sinervo, B. & Beissinger, S. R. Cooling requirements fueled the collapse of a desert bird community from climate change. Proc. Natl Acad. Sci. USA 116, 21609–21615 (2019).CAS 
PubMed 
PubMed Central 

Google Scholar 
Pecl, G. T. et al. Biodiversity redistribution under climate change: impacts on ecosystems and human well-being. Science 355, eaai9214 (2017).PubMed 

Google Scholar 
Futuyma, D. J. Evolutionary constraint and ecological consequences. Evolution 64, 1865–1884 (2010).PubMed 

Google Scholar 
Murren, C. J. et al. Constraints on the evolution of phenotypic plasticity: limits and costs of phenotype and plasticity. Heredity 115, 293–301 (2015).CAS 
PubMed 
PubMed Central 

Google Scholar 
Rollinson, C. R. et al. Working across space and time: nonstationarity in ecological research and application. Front. Ecol. Environ. 19, 66–72 (2021).
Google Scholar 
Riemer, K., Guralnick, R. P. & White, E. P. No general relationship between mass and temperature in endothermic species. eLife 7, e27166 (2018).PubMed 
PubMed Central 

Google Scholar 
Ryding, S., Klaassen, M., Tattersall, G. J., Gardner, J. L. & Symonds, M. R. Shape-shifting: changing animal morphologies as a response to climatic warming. Trends Ecol. Evol. 36, 1036–1048 (2021).PubMed 

Google Scholar 
Baldwin, M. W., Winkler, H., Organ, C. L. & Helm, B. Wing pointedness associated with migratory distance in common-garden and comparative studies of stonechats (Saxicola torquata). J. Evol. Biol. 23, 1050–1063 (2010).CAS 
PubMed 

Google Scholar 
Förschler, M. I. & Bairlein, F. Morphological shifts of the external flight apparatus across the range of a passerine (Northern Wheatear) with diverging migratory behaviour. PLoS ONE 6, e18732 (2011).PubMed 
PubMed Central 

Google Scholar 
Macpherson, M. P., Jahn, A. E. & Mason, N. A. Morphology of migration: associations between wing shape, bill morphology and migration in kingbirds (Tyrannus). Biol. J. Linn. Soc. 135, 71–83 (2022).
Google Scholar 
Newton, I. The Migration Ecology of Birds (Elsevier, 2010).Clegg, S. M., Kelly, J. F., Kimura, M. & Smith, T. B. Combining genetic markers and stable isotopes to reveal population connectivity and migration patterns in a neotropical migrant, Wilson’s warbler (Wilsonia pusilla). Mol. Ecol. 12, 819–830 (2003).CAS 
PubMed 

Google Scholar 
Bell, C. P. Leap-frog migration in the fox sparrow: minimizing the cost of spring migration. Condor 99, 470–477 (1997).
Google Scholar 
Billerman, S., Keeney, B., Rodewald, P. & Schulenberg, T. (eds) Birds of the World (Cornell Laboratory of Ornithology, 2020).Desrochers, A. Morphological response of songbirds to 100 years of landscape change in North America. Ecology 91, 1577–1582 (2010).CAS 
PubMed 

Google Scholar 
Swaddle, J. P. & Lockwood, R. Morphological adaptations to predation risk in passerines. J. Avian Biol. 29, 172–176 (1998).
Google Scholar 
Chown, S. L. & Klok, C. J. Altitudinal body size clines: latitudinal effects associated with changing seasonality. Ecography 26, 445–455 (2003).
Google Scholar 
Hsiung, A. C., Boyle, W. A., Cooper, R. J. & Chandler, R. B. Altitudinal migration: ecological drivers, knowledge gaps, and conservation implications: animal altitudinal migration review. Biol. Rev. 93, 2049–2070 (2018).PubMed 

Google Scholar 
Barras, A. G., Liechti, F. & Arlettaz, R. Seasonal and daily movement patterns of an alpine passerine suggest high flexibility in relation to environmental conditions. J. Avian Biol. 52, jav.02860 (2021).
Google Scholar 
Spence, A. R. & Tingley, M. W. Body size and environment influence both intraspecific and interspecific variation in daily torpor use across hummingbirds. Funct. Ecol. 35, 870–883 (2021).CAS 

Google Scholar 
Moreau, R. E. Variation in the western Zosteropidae (Aves). Bull. Br. Mus. Nat. Hist. Zool. 4, 311–433 (1957).
Google Scholar 
Hamilton, T. H. The adaptive significances of intraspecific trends of variation in wing length and body size among bird species. Evolution 15, 180–194 (1961).
Google Scholar 
Hodkinson, I. D. Terrestrial insects along elevation gradients: species and community responses to altitude. Biol. Rev. 80, 489–513 (2005).PubMed 

Google Scholar 
Feinsinger, P., Colwell, R. K., Terborgh, J. & Chaplin, S. B. Elevation and the morphology, flight energetics, and foraging ecology of tropical hummingbirds. Am. Nat. 113, 481–497 (1979).
Google Scholar 
Aldrich, J. W. Ecogeographical Variation in Size and Proportions of Song Sparrows (Melospiza melodia) (American Ornithological Society, 1984).Sun, Y. et al. The role of climate factors in geographic variation in body mass and wing length in a passerine bird. Avian Res. 8, 1 (2017).Des Roches, S., Pendleton, L. H., Shapiro, B. & Palkovacs, E. P. Conserving intraspecific variation for nature’s contributions to people. Nat. Ecol. Evol. 5, 574–582 (2021).PubMed 

Google Scholar 
McKechnie, A. E. & Wolf, B. O. Climate change increases the likelihood of catastrophic avian mortality events during extreme heat waves. Biol. Lett. 6, 253–256 (2010).PubMed 

Google Scholar 
Conradie, S. R., Woodborne, S. M., Cunningham, S. J. & McKechnie, A. E. Chronic, sublethal effects of high temperatures will cause severe declines in southern African arid-zone birds during the 21st century. Proc. Natl Acad. Sci. USA 116, 14065–14070 (2019).CAS 
PubMed 
PubMed Central 

Google Scholar 
Radchuk, V. et al. Adaptive responses of animals to climate change are most likely insufficient. Nat. Commun. 10, 3109 (2019).PubMed 
PubMed Central 

Google Scholar 
Riddell, E. A. et al. Exposure to climate change drives stability or collapse of desert mammal and bird communities. Science 371, 633–636 (2021).CAS 
PubMed 

Google Scholar 
Tingley, M. W., Monahan, W. B., Beissinger, S. R. & Moritz, C. Birds track their Grinnellian niche through a century of climate change. Proc. Natl Acad. Sci. USA 106, 19637–19643 (2009).CAS 
PubMed 
PubMed Central 

Google Scholar 
Youngflesh, C. et al. Migratory strategy drives species-level variation in bird sensitivity to vegetation green-up. Nat. Ecol. Evol. 5, 987–994 (2021).PubMed 

Google Scholar 
Blueweiss, L. et al. Relationships between body size and some life history parameters. Oecologia 37, 257–272 (1978).CAS 
PubMed 

Google Scholar 
Kleiber, M. Body size and metabolic rate. Physiol. Rev. 27, 511–541 (1947).CAS 
PubMed 

Google Scholar 
Yodzis, P. & Innes, S. Body size and consumer-resource dynamics. Am. Nat. 139, 1151–1175 (1992).
Google Scholar 
Prum, R. O. Interspecific social dominance mimicry in birds: social mimicry in birds. Zool. J. Linn. Soc. 172, 910–941 (2014).
Google Scholar 
Pyle, P. Identification Guide to North American Birds: A Compendium of Information on Identifying, Ageing, and Sexing ‘Near-Passerines’ and Passerines in the Hand (Slate Creek Press, 1997).Leys, C., Ley, C., Klein, O., Bernard, P. & Licata, L. Detecting outliers: do not use standard deviation around the mean, use absolute deviation around the median. J. Exp. Soc. Psychol. 49, 764–766 (2013).
Google Scholar 
Danielson, J. J. & Gesch, D. B. Global Multi-Resolution Terrain Elevation Data 2010 (GMTED2010) (US Geological Survey, 2011).Thornton, M. M. et al. Daymet: Daily Surface Weather Data on a 1-km Grid for North America, Version 4 (ORNL Distributed Active Archive Center, 2020).Greenewalt, C. H. The flight of birds: the significant dimensions, their departure from the requirements for dimensional similarity, and the effect on flight aerodynamics of that departure. Trans. Am. Philos. Soc. 65, 1–67 (1975).
Google Scholar 
Longo, G. & Montévil, M. Perspectives on Organisms: Biological Time, Symmetries, and Singularities (Springer, 2014).Harvey, P. H. in Scaling in Biology (eds Brown, J. H. & West, G. B.) 253–265 (Oxford Univ. Press, 2000).Orme, D. et al. The caper package: comparative analysis of phylogenetics and evolution in R. R package version 5 (2013).R Core Team. R: A Language and Environment for Statistical Computing (R Foundation for Statistical Computing, 2021).Jetz, W., Thomas, G. H., Joy, J. B., Hartmann, K. & Mooers, A. O. The global diversity of birds in space and time. Nature 491, 444–448 (2012).CAS 
PubMed 

Google Scholar 
Nudds, R. L., Kaiser, G. W. & Dyke, G. J. Scaling of avian primary feather length. PLoS ONE 6, e15665 (2011).CAS 
PubMed 
PubMed Central 

Google Scholar 
Nudds, R. Wing-bone length allometry in birds. J. Avian Biol. 38, 515–519 (2007).
Google Scholar 
Anderson, S. C., Branch, T. A., Cooper, A. B. & Dulvy, N. K. Black-swan events in animal populations. Proc. Natl Acad. Sci. USA 114, 3252–3257 (2017).CAS 
PubMed 
PubMed Central 

Google Scholar 
Stan Modeling Language Users Guide and Reference Manual, Version 2.18.0 (Stan Development Team, 2018); http://mc-stan.orgCarpenter, B. et al. Stan: a probabilistic programming language. J. Stat. Softw. 76, 1–32 (2017).Youngflesh, C. MCMCvis: tools to visualize, manipulate, and summarize MCMC output. J. Open Source Softw. 3, 640 (2018).
Google Scholar 
Wickham, H. et al. Welcome to the Tidyverse. J. Open Source Softw. 4, 1686 (2019).
Google Scholar 
Gabry, J., Simpson, D., Vehtari, A., Betancourt, M. & Gelman, A. Visualization in Bayesian workflow. J. R. Stat. Soc. A 182, 389–402 (2019).
Google Scholar 
McElreath, R. Statistical Rethinking: A Bayesian Course with Examples in R and Stan (Chapman and Hall/CRC, 2018).Data Zone (BirdLife International, 2019); http://datazone.birdlife.org/species/requestdisCramp, S. & Brooks, D. Handbook of the Birds of Europe, the Middle East and North Africa. The Birds of the Western Palearctic, Vol. VI. Warblers (Oxford Univ. Press, 1992).Che-Castaldo, J., Che-Castaldo, C. & Neel, M. C. Predictability of demographic rates based on phylogeny and biological similarity. Conserv. Biol. 32, 1290–1300 (2018).PubMed 

Google Scholar 
Villemereuil, P., de, Wells, J. A., Edwards, R. D. & Blomberg, S. P. Bayesian models for comparative analysis integrating phylogenetic uncertainty. BMC Evol. Biol. 12, 102 (2012).PubMed 
PubMed Central 

Google Scholar 
Revell, L. J. phytools: an R package for phylogenetic comparative biology (and other things). Methods Ecol. Evol. 3, 217–223 (2012).
Google Scholar 
Pagel, M. Inferring the historical patterns of biological evolution. Nature 401, 877–884 (1999).CAS 
PubMed 

Google Scholar 
Hendry, A. P. & Kinnison, M. T. Perspective: the pace of modern life: measuring rates of contemporary microevolution. Evolution 53, 1637–1653 (1999).PubMed 

Google Scholar 
Gingerich, P. Rates of evolution: effects of time and temporal scaling. Science 222, 159–162 (1983).CAS 
PubMed 

Google Scholar 
Bird, J. P. et al. Generation lengths of the world’s birds and their implications for extinction risk. Conserv. Biol. 34, 1252–1261 (2020).Gingerich, P. D. Rates of evolution. Annu. Rev. Ecol. Evol. Syst. 40, 657–675 (2009).
Google Scholar 
Bürger, R. & Lynch, M. Evolution and extinction in a changing environment: a quantitative-genetic analysis. Evolution 49, 151–163 (1995).PubMed 

Google Scholar 
Hendry, A. P., Farrugia, T. J. & Kinnison, M. T. Human influences on rates of phenotypic change in wild animal populations. Mol. Ecol. 17, 20–29 (2008).PubMed 

Google Scholar  More

Ault, T. R. On the essentials of drought in a changing climate. Science 368, 256–260 (2020).CAS 

Google Scholar 
Rodell, M. et al. Emerging trends in global freshwater availability. Nature 557, 651–659 (2018).CAS 

Google Scholar 
Green, J. K. et al. Large influence of soil moisture on long-term terrestrial carbon uptake. Nature 565, 476–479 (2019).CAS 

Google Scholar 
Reichstein, M. et al. Climate extremes and the carbon cycle. Nature 500, 287–295 (2013).CAS 

Google Scholar 
Goulden, M. L. & Bales, R. C. California forest die-off linked to multi-year deep soil drying in 2012–2015 drought. Nat. Geosci. 12, 632–637 (2019).CAS 

Google Scholar 
Brodribb, T. J., Powers, J., Cochard, H. & Choat, B. Hanging by a thread? Forests and drought. Science 368, 261–266 (2020).CAS 

Google Scholar 
Seneviratne, S. I. et al. Investigating soil moisture–climate interactions in a changing climate: a review. Earth Sci. Rev. 99, 125–161 (2010).CAS 

Google Scholar 
Short Gianotti, D. J., Rigden, A. J., Salvucci, G. D. & Entekhabi, D. Satellite and station observations demonstrate water availability’s effect on continental-scale evaporative and photosynthetic land surface dynamics. Water Resour. Res. 55, 540–554 (2019).
Google Scholar 
Anderegg, W. R. L., Trugman, A. T., Bowling, D. R., Salvucci, G. & Tuttle, S. E. Plant functional traits and climate influence drought intensification and land–atmosphere feedbacks. Proc. Natl Acad. Sci. USA 116, 14071–14076 (2019).CAS 

Google Scholar 
Jung, M. et al. Recent decline in the global land evapotranspiration trend due to limited moisture supply. Nature 467, 951–954 (2010).CAS 

Google Scholar 
Novick, K. A. et al. The increasing importance of atmospheric demand for ecosystem water and carbon fluxes. Nat. Clim. Chang. 6, 1023–1027 (2016).CAS 

Google Scholar 
Liu, Y., Kumar, M., Katul, G. G., Feng, X. & Konings, A. G. Plant hydraulics accentuates the effect of atmospheric moisture stress on transpiration. Nat. Clim. Chang. 10, 691–695 (2020).CAS 

Google Scholar 
Gupta, A., Rico-Medina, A. & Caño-Delgado, A. I. The physiology of plant responses to drought. Science 368, 266–269 (2020).CAS 

Google Scholar 
Wolf, S. et al. Warm spring reduced carbon cycle impact of the 2012 US summer drought. Proc. Natl Acad. Sci. USA 113, 5880–5885 (2016).CAS 

Google Scholar 
Teuling, A. J. et al. Evapotranspiration amplifies European summer drought. Geophys. Res. Lett. 40, 2071–2075 (2013).
Google Scholar 
Mastrotheodoros, T. et al. More green and less blue water in the Alps during warmer summers. Nat. Clim. Chang. 10, 155–161 (2020).
Google Scholar 
Peterson, T. J., Saft, M., Peel, M. C. & John, A. Watersheds may not recover from drought. Science 372, 745–749 (2021).CAS 

Google Scholar 
Helbig, M. et al. Increasing contribution of peatlands to boreal evapotranspiration in a warming climate. Nat. Clim. Chang. 10, 555–560 (2020).CAS 

Google Scholar 
Massmann, A., Gentine, P. & Lin, C. When does vapor pressure deficit drive or reduce evapotranspiration? J. Adv. Model. Earth Syst. 11, 3305–3320 (2019).
Google Scholar 
Grossiord, C. et al. Plant responses to rising vapor pressure deficit. New Phytol. 226, 1550–1566 (2020).
Google Scholar 
Orth, R. & Destouni, G. Drought reduces blue-water fluxes more strongly than green-water fluxes in Europe. Nat. Commun. 9, 3602 (2018).
Google Scholar 
Pendergrass, A. G. et al. Flash droughts present a new challenge for subseasonal-to-seasonal prediction. Nat. Clim. Chang. 10, 191–199 (2020).
Google Scholar 
Chu, H., Baldocchi, D. D., John, R., Wolf, S. & Reichstein, M. Fluxes all of the time? A primer on the temporal representativeness of FLUXNET. J. Geophys. Res. Biogeosci. 122, 289–307 (2017).
Google Scholar 
Ukkola, A. M. et al. Land surface models systematically overestimate the intensity, duration and magnitude of seasonal-scale evaporative droughts. Environ. Res. Lett. 11, 104012 (2016).
Google Scholar 
Trugman, A. T., Medvigy, D., Mankin, J. S. & Anderegg, W. R. L. Soil moisture stress as a major driver of carbon cycle uncertainty. Geophys. Res. Lett. 45, 6495–6503 (2018).
Google Scholar 
De Kauwe, M. G. et al. Do land surface models need to include differential plant species responses to drought? Examining model predictions across a mesic–xeric gradient in Europe. Biogeosciences 12, 7503–7518 (2015).
Google Scholar 
Dong, J., Lei, F. & Crow, W. T. Land transpiration–evaporation partitioning errors responsible for modeled summertime warm bias in the central United States. Nat. Commun. 13, 336 (2022).CAS 

Google Scholar 
Kennedy, D. et al. Implementing plant hydraulics in the Community Land Model, version 5. J. Adv. Model. Earth Syst. 11, 485–513 (2019).
Google Scholar 
Novick, K. A. et al. Confronting the water potential information gap. Nat. Geosci. 15, 158–164 (2022).CAS 

Google Scholar 
Liu, Y., Holtzman, N. M. & Konings, A. G. Global ecosystem-scale plant hydraulic traits retrieved using model–data fusion. Hydrol. Earth Syst. Sci. 25, 2399–2417 (2021).CAS 

Google Scholar 
Lin, Y.-S. et al. Optimal stomatal behaviour around the world. Nat. Clim. Chang. 5, 459–464 (2015).CAS 

Google Scholar 
Fan, Y., Miguez-Macho, G., Jobbágy, E. G., Jackson, R. B. & Otero-Casal, C. Hydrologic regulation of plant rooting depth. Proc. Natl Acad. Sci. USA 114, 10572–10577 (2017).CAS 

Google Scholar 
Anderegg, W. R. L. et al. Hydraulic diversity of forests regulates ecosystem resilience during drought. Nature 561, 538–541 (2018).CAS 

Google Scholar 
Lehmann, P., Merlin, O., Gentine, P. & Or, D. Soil texture effects on surface resistance to bare-soil evaporation. Geophys. Res. Lett. 45, 10398–10405 (2018).
Google Scholar 
Fatichi, S. et al. Soil structure is an important omission in Earth System Models. Nat. Commun. 11, 522 (2020).CAS 

Google Scholar 
McCormick, E. L. et al. Widespread woody plant use of water stored in bedrock. Nature 597, 225–229 (2021).CAS 

Google Scholar 
Baldocchi, D., Ma, S. & Verfaillie, J. On the inter- and intra-annual variability of ecosystem evapotranspiration and water use efficiency of an oak savanna and annual grassland subjected to booms and busts in rainfall. Glob. Chang. Biol. 27, 359–375 (2021).CAS 

Google Scholar 
Condon, L. E., Atchley, A. L. & Maxwell, R. M. Evapotranspiration depletes groundwater under warming over the contiguous United States. Nat. Commun. 11, 873 (2020).CAS 

Google Scholar 
Maxwell, R. M. & Condon, L. E. Connections between groundwater flow and transpiration partitioning. Science 353, 377–380 (2016).CAS 

Google Scholar 
Humphrey, V. et al. Sensitivity of atmospheric CO2 growth rate to observed changes in terrestrial water storage. Nature 560, 628–631 (2018).CAS 

Google Scholar 
Mankin, J. S., Seager, R., Smerdon, J. E., Cook, B. I. & Williams, A. P. Mid-latitude freshwater availability reduced by projected vegetation responses to climate change. Nat. Geosci. 12, 983–988 (2019).CAS 

Google Scholar 
Strassburg, B. B. N. et al. Global priority areas for ecosystem restoration. Nature 586, 724–729 (2020).CAS 

Google Scholar 
Zhao, M. et al. Ecological restoration impact on total terrestrial water storage. Nat. Sustain. 4, 56–62 (2021).
Google Scholar 
Feng, X. et al. Revegetation in China’s Loess Plateau is approaching sustainable water resource limits. Nat. Clim. Chang. 6, 1019–1022 (2016).
Google Scholar 
Guan, K. et al. Photosynthetic seasonality of global tropical forests constrained by hydroclimate. Nat. Geosci. 8, 284–289 (2015).CAS 

Google Scholar 
Fan, Y., Li, H. & Miguez-Macho, G. Global patterns of groundwater table depth. Science 339, 940–943 (2013).CAS 

Google Scholar 
Zhao, M., Geruo, A., Velicogna, I. & Kimball, J. S. A global gridded dataset of GRACE drought severity index for 2002–14: comparison with PDSI and SPEI and a case study of the Australia Millennium Drought. J. Hydrometeorol. 18, 2117–2129 (2017).
Google Scholar 
Watkins, M. M., Wiese, D. N., Yuan, D.-N., Boening, C. & Landerer, F. W. Improved methods for observing Earth’s time variable mass distribution with GRACE using spherical cap mascons. J. Geophys. Res. Solid Earth 120, 2648–2671 (2015).
Google Scholar 
Wiese, D. N., Landerer, F. W. & Watkins, M. M. Quantifying and reducing leakage errors in the JPL RL05M GRACE mascon solution. Water Resour. Res. 52, 7490–7502 (2016).
Google Scholar 
Adler, R. F. et al. The Version-2 Global Precipitation Climatology Project (GPCP) monthly precipitation analysis (1979–present). J. Hydrometeorol. 4, 1147–1167 (2003).
Google Scholar 
Sun, Q. et al. A review of global precipitation data sets: data sources, estimation, and intercomparisons. Rev. Geophys. 56, 79–107 (2018).
Google Scholar 
Gebremichael, M. et al. Error uncertainty analysis of GPCP monthly rainfall products: a data-based simulation study. J. Appl. Meteorol. 42, 1837–1848 (2003).
Google Scholar 
Rodell, M. et al. Basin scale estimates of evapotranspiration using GRACE and other observations. Geophys. Res. Lett. 31, L20504 (2004).
Google Scholar 
Major River Basins of the World (Global Runoff Data Centre, 2020).Pascolini-Campbell, M. A., Reager, J. T. & Fisher, J. B. GRACE-based mass conservation as a validation target for basin-scale evapotranspiration in the contiguous United States. Water Resour. Res. 56, e2019WR026594 (2020).
Google Scholar 
Fekete, B. M., Vörösmarty, C. J. & Grabs, W. High-resolution fields of global runoff combining observed river discharge and simulated water balances. Global Biogeochem. Cycles 16, 15-1–15-10 (2002).
Google Scholar 
Breiman, L. Random forests. Mach. Learn. 45, 5–32 (2001).
Google Scholar 
Myneni, R., Knyazikhin, Y. & Park, T (ed. NASA EOSDIS Land Processes DAAC) (2021).Hersbach, H. et al. The ERA5 global reanalysis. Q. J. R. Meteorol. Soc. 146, 1999–2049 (2020).
Google Scholar 
Sulla-Menashe, D. & Friedl, M. A. User Guide to Collection 6 MODIS Land Cover (MCD12Q1 and MCD12C1) Product (US Geological Survey, 2018).Zhao, M., Aa, G., Liu, Y. & Konings, A. Evapotranspiration frequently increases during droughts. Zenodo https://doi.org/10.5281/zenodo.6842054 (2022). More

Steen AD, Carini ACP, Lloyd KG, Thrash JC, Deangelis KM, Fierer N. High proportions of bacteria and archaea across most biomes remain uncultured. ISME J. 2019;13:3126–30.PubMed 
PubMed Central 

Google Scholar 
Lloyd KG, Steen AD, Ladau J, Yin J. Phylogenetically novel uncultured microbial cells dominate earth microbiomes. mSystems. 2018;3:e00055–18.CAS 
PubMed 
PubMed Central 

Google Scholar 
Marcy Y, Ouverney C, Bik EM, Lo T, Ivanova N, Garcia H, et al. Dissecting biological “dark matter” with single-cell genetic analysis of rare and uncultivated TM7 microbes from the human mouth. Proc Natl Acad Sci USA. 2007;104:11889–94.CAS 
PubMed 
PubMed Central 

Google Scholar 
Pascoal F, Costa R, Magalhães C. The microbial rare biosphere: current concepts, methods and ecological principles. FEMS Microbiol Ecol. 2021;97:fiaa227.CAS 
PubMed 

Google Scholar 
Sogin ML, Morrison HG, Huber JA, Welch DM, Huse SM, Neal PR, et al. Microbial diversity in the deep sea and the underexplored “rare biosphere”. Proc Natl Acad Sci USA. 2006;103:12115–20.CAS 
PubMed 
PubMed Central 

Google Scholar 
Gareev KG, Grouzdev DS, Kharitonskii PV, Kosterov A, Koziaeva VV, Sergienko ES, et al. Magnetotactic bacteria and magnetosomes: basic properties and applications. Magnetochemistry. 2021;7:86.CAS 

Google Scholar 
Lefevre CT, Bazylinski DA. Ecology, diversity, and evolution of magnetotactic bacteria. Microbiol Mol Biol Rev. 2013;77:497–526.CAS 
PubMed 
PubMed Central 

Google Scholar 
Lin W, Pan Y, Bazylinsky DA. Diversity and ecology of and biomineralization by magnetotactic bacteria. Environ Microbiol Rep. 2017;9:345–56.CAS 
PubMed 

Google Scholar 
Uebe R, Schüler D. Magnetosome biogenesis in magnetotactic bacteria. Nat Rev Microbiol. 2016;14:621–37.CAS 
PubMed 

Google Scholar 
Lefèvre CT, Frankel RB, Bazylinski DA. Magnetotaxis in prokaryotes. eLS. 2011. https://onlinelibrary.wiley.com/action/showCitFormats?doi=10.1002%2F9780470015902.a0000397.pub2https://onlinelibrary.wiley.com/action/showCitFormats?doi=10.1002%2F9780470015902.a0000397.pub2.Goswami P, He K, Li J, Pan Y, Roberts AP, Lin W. Magnetotactic bacteria and magnetofossils: ecology, evolution and environmental implications. npj Biofilms Microbiomes. 2022;8:43.PubMed 
PubMed Central 

Google Scholar 
Flies CB, Jonkers HM, De Beer D, Bosselmann K, Böttcher ME, Schüler D. Diversity and vertical distribution of magnetotactic bacteria along chemical gradients in freshwater microcosms. FEMS Microbiol Ecol. 2005;52:185–95.CAS 
PubMed 

Google Scholar 
Wolfe RS, Thauer RK, Pfennig N. A’capillary racetrack’ method for isolation of magnetotactic bacteria. FEMS Microbiol Ecol. 1987;45:31–5.
Google Scholar 
Jogler C, Lin W, Meyerdierks A, Kube M, Katzmann E, Flies C, et al. Toward cloning of the magnetotactic metagenome: identification of magnetosome island gene clusters in uncultivated magnetotactic bacteria from different aquatic sediments. Appl Environ Microbiol. 2009;75:3972–9.CAS 
PubMed 
PubMed Central 

Google Scholar 
Lin W, Zhang W, Paterson GA, Zhu Q, Zhao X. Expanding magnetic organelle biogenesis in the domain Bacteria. Microbiome. 2020;8:152.CAS 
PubMed 
PubMed Central 

Google Scholar 
Geissinger O, Herlemann DPR, Mo E, Maier UG, Brune A. The ultramicrobacterium “Elusimicrobium minutum” gen. nov., sp. nov., the first cultivated representative of the Termite Group 1 phylum. Appl Environ Microbiol. 2009;75:2831–40.CAS 
PubMed 
PubMed Central 

Google Scholar 
Wakako I-O, Brune A. Cospeciation of termite gut flagellates and their bacterial endosymbionts: Trichonympha species and ‘Candidatus Endomicrobium trichonymphae’. Mol Ecol. 2009;18:332–42.
Google Scholar 
Zheng H, Dietrich C, Radek R, Brune A. Endomicrobium proavitum, the first isolate of Endomicrobia class. nov. (phylum Elusimicrobia) – an ultramicrobacterium with an unusual cell cycle that fixes nitrogen with a Group IV nitrogenase. Environ Ecol Stat. 2016;18:191–204.CAS 

Google Scholar 
Méheust R, Castelle CJ, Carnevali PBM, Chen L, Amano Y, Hug LA, et al. Groundwater Elusimicrobia are metabolically diverse compared to gut microbiome Elusimicrobia and some have a novel nitrogenase paralog. ISME J. 2020;14:2907–22.PubMed 
PubMed Central 

Google Scholar 
Lin H, Ascher DB, Myung Y, Lamborg CH, Hallam SJ, Gionfriddo CM, et al. Mercury methylation by metabolically versatile and cosmopolitan marine bacteria. ISME J. 2021;15:1810–25.CAS 
PubMed 
PubMed Central 

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

Google Scholar 
Zhang L, Gong X, Wang L, Guo K, Cao S, Zhou Y. Science of the total environment metagenomic insights into the effect of thermal hydrolysis pre-treatment on microbial community of an anaerobic digestion system. Sci Total Environ. 2021;791:148096.CAS 
PubMed 

Google Scholar 
Woodcroft BJ, Singleton CM, Boyd JA, Evans PN, Emerson JB, Zayed AAF, et al. Genome-centric view of carbon processing in thawing permafrost. Nature. 2018;560:49–54.CAS 
PubMed 

Google Scholar 
Uzun M, Alekseeva L, Krutkina M, Koziaeva V, Grouzdev D. Unravelling the diversity of magnetotactic bacteria through analysis of open genomic databases. Sci Data. 2020;7:252.PubMed 
PubMed Central 

Google Scholar 
Tully BJ, Wheat CG, Glazer BT, Huber JA. A dynamic microbial community with high functional redundancy inhabits the cold, oxic subseafloor aquifer. ISME J. 2018;12:1–16.CAS 
PubMed 

Google Scholar 
Kirillova NP, Sileva TM, Ul’yanova TY, Rozov SY, Il’yashenko MA, Makarov MI. Digital soil map of Chashnikovo training and experimental soil ecological center, Moscow State University. Mosc Univ Soil Sci Bull. 2015;70:58–65.
Google Scholar 
Koziaeva VV, Alekseeva LM, Uzun MM, Leão P, Sukhacheva MV, Patutina EO, et al. Biodiversity of magnetotactic bacteria in the freshwater lake Beloe Bordukovskoe, Russia. Microbiology. 2020;89:348–58.CAS 

Google Scholar 
Edgar RC. Search and clustering orders of magnitude faster than BLAST. Bioinformatics. 2010;26:2460–1.CAS 
PubMed 

Google Scholar 
Edgar RC, Flyvbjerg H. Error filtering, pair assembly and error correction for next-generation sequencing reads. Bioinformatics. 2015;31:3476–82.CAS 
PubMed 

Google Scholar 
Edgar RC. UNOISE2: improved error-correction for Illumina 16S and ITS amplicon sequencing. bioRxiv 2016. https://doi.org/10.1101/081257.Pruesse E, Peplies J, Glöckner FO. SINA: Accurate high-throughput multiple sequence alignment of ribosomal RNA genes. Bioinformatics. 2012;28:1823–9.CAS 
PubMed 
PubMed Central 

Google Scholar 
Fierer N, Jackson JA, Vilgalys R, Jackson RB. Assessment of soil microbial community structure by use of taxon-specific quantitative PCR assays. Appl Environ Microbiol. 2005;71:4117–20.CAS 
PubMed 
PubMed Central 

Google Scholar 
Bolger AM, Lohse M, Usadel B. Trimmomatic: a flexible trimmer for Illumina sequence data. Bioinformatics. 2014;30:2114–20.CAS 
PubMed 
PubMed Central 

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

Google Scholar 
Wu YW, Simmons BA, Singer SW. MaxBin 2.0: an automated binning algorithm to recover genomes from multiple metagenomic datasets. Bioinformatics. 2016;32:605–7.CAS 
PubMed 

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

Google Scholar 
Lin HH, Liao YC. Accurate binning of metagenomic contigs via automated clustering sequences using information of genomic signatures and marker genes. Sci Rep. 2016;6:12–9.
Google Scholar 
Sieber CMK, Probst AJ, Sharrar A, Thomas BC, Hess M, Tringe SG, et al. Recovery of genomes from metagenomes via a dereplication, aggregation and scoring strategy. Nat Microbiol. 2018;3:836–43.CAS 
PubMed 
PubMed Central 

Google Scholar 
Gurevich A, Saveliev V, Vyahhi N, Tesler G. QUAST: quality assessment tool for genome assemblies. Bioinformatics. 2013;29:1072–5.CAS 
PubMed 
PubMed Central 

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

Google Scholar 
Chaumeil P, Mussig AJ, Parks DH, Hugenholtz P. GTDB-Tk: a toolkit to classify genomes with the Genome Taxonomy Database. Bioinformatics. 2019;36:1925–7.PubMed Central 

Google Scholar 
Tatusova T, Dicuccio M, Badretdin A, Chetvernin V, Nawrocki EP, Zaslavsky L, et al. NCBI prokaryotic genome annotation pipeline. Nucleic Acids Res. 2016;44:6614–24.CAS 
PubMed 
PubMed Central 

Google Scholar 
Ji R, Zhang W, Pan Y, Lin W. MagCluster: a tool for identification, annotation, and visualization of magnetosome gene clusters. Microbiol Resour Announc. 2022;11:e01031–21.CAS 
PubMed Central 

Google Scholar 
Wu S, Zhu Z, Fu L, Niu B, Li W. WebMGA: a customizable web server for fast metagenomic sequence analysis. BMC Genomics. 2011;12:444.PubMed 
PubMed Central 

Google Scholar 
Kanehisa M, Sato Y. KEGG Mapper for inferring cellular functions from protein sequences. Protein Sci. 2020;29:28–35.CAS 
PubMed 

Google Scholar 
Shaffer M, Borton MA, McGivern BB, Zayed AA, La Rosa SL. 0003 3527 8101, Solden LM, et al. DRAM for distilling microbial metabolism to automate the curation of microbiome function. Nucleic Acids Res. 2020;48:8883–900.CAS 
PubMed 
PubMed Central 

Google Scholar 
Jain C, Rodriguez-R LM, Phillippy AM, Konstantinidis KT, Aluru S. High throughput ANI analysis of 90K prokaryotic genomes reveals clear species boundaries. Nat Commun. 2018;9:5114.PubMed 
PubMed Central 

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

Google Scholar 
Kalyaanamoorthy S, Minh BQ, Wong TKF, Haeseler AVon, Jermiin LS. ModelFinder: fast model selection for accurate phylogenetic estimates. Nat Methods. 2017;14:587–9.CAS 
PubMed 
PubMed Central 

Google Scholar 
Hoang DT, Chernomor O, Von Haeseler A, Minh BQ, Vinh LS. UFBoot2: improving the ultrafast bootstrap approximation. Mol Biol Evol. 2018;35:518–22.CAS 
PubMed 

Google Scholar 
Letunic I, Bork P. Interactive tree of life (iTOL) v5: an online tool for phylogenetic tree display and annotation. Nucleic Acids Res. 2021;49:W293–6.CAS 
PubMed 
PubMed Central 

Google Scholar 
Coleman GA, Davín AA, Mahendrarajah TA, Szánthó LL, Spang A, Hugenholtz P, et al. A rooted phylogeny resolves early bacterial evolution. Science. 2021;372:eabe0511.CAS 
PubMed 

Google Scholar 
Parks DH. https://github.com/dparks1134/CompareM.Dombrowski N, Lee JH, Williams TA, Offre P, Spang A. Genomic diversity, lifestyles and evolutionary origins of DPANN archaea. FEMS Microbiol Lett. 2019;366:fnz008.CAS 
PubMed Central 

Google Scholar 
Lin W, Zhang W, Zhao X, Roberts AP, Paterson GA, Bazylinski DA, et al. Genomic expansion of magnetotactic bacteria reveals an early common origin of magnetotaxis with lineage-specific evolution. ISME J. 2018;12:1508–19.CAS 
PubMed 
PubMed Central 

Google Scholar 
Urakawa H, Garcia JC, Nielsen JL, Le VQ, Kozlowski JA, Stein LY, et al. Nitrosospira lacus sp. nov., a psychrotolerant, ammonia-oxidizing bacterium from sandy lake sediment. Int J Syst Evol Microbiol. 2015;65:242–50.CAS 
PubMed 

Google Scholar 
Kalyuzhnaya MG, De Marco P, Bowerman S, Pacheco CC, Lara JC, Lidstrom ME, et al. Methyloversatilis universalis gen. nov., sp. nov., a novel taxon within the Betaproteobacteria represented by three methylotrophic isolates. Int J Syst Evol Microbiol. 2006;56:2517–22.CAS 
PubMed 

Google Scholar 
Bazylinski DA, Frankel RB, Konhauser KO. Modes of biomineralization of magnetite by microbes. Geomicrobiol J. 2007;24:465–75.CAS 

Google Scholar 
Uzun M, Koziaeva V, Dziuba M, Leão P, Krutkina M, Grouzdev D. Detection of interphylum transfers of the magnetosome gene cluster in magnetotactic bacteria. Front Microbiol. 2022;13:945734.PubMed 
PubMed Central 

Google Scholar 
Parks DH, Chuvochina M, Waite DW, Rinke C, Skarshewski A, Chaumeil PA, et al. A standardized bacterial taxonomy based on genome phylogeny substantially revises the tree of life. Nat Biotechnol. 2018;36:996.CAS 
PubMed 

Google Scholar 
Murphy CL, Biggerstaff J, Eichhorn A, Ewing E, Shahan R, Soriano D, et al. Genomic characterization of three novel Desulfobacterota classes expand the metabolic and phylogenetic diversity of the phylum. Environ Microbiol. 2021;23:4326–43.CAS 
PubMed 

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

Google Scholar 
Denise R, Abby SS, Rocha EPC. Diversification of the type IV filament superfamily into machines for adhesion, protein secretion, DNA uptake, and motility. PLoS Biol. 2019;17:e3000390.PubMed 
PubMed Central 

Google Scholar 
Hennell James R, Deme JC, Kjӕr A, Alcock F, Silale A, Lauber F, et al. Structure and mechanism of the proton-driven motor that powers type 9 secretion and gliding motility. Nat Microbiol. 2021;6:221–33.CAS 
PubMed 

Google Scholar 
Nolan LM, Whitchurch CB, Barquist L, Katrib M, Boinett CJ, Mayho M, et al. A global genomic approach uncovers novel components for twitching motility-mediated biofilm expansion in Pseudomonas aeruginosa. Micro Genomics. 2018;4:e000229.
Google Scholar 
Uzun M, Koziaeva V, Dziuba M, Alekseeva L, Grouzdev D. Mam protein trees. 2022. https://doi.org/10.6084/m9.figshare.c.6045158.v1.Arnoux P, Siponen MI, Lefèvre CT, Ginet N, Pignol D. Structure and evolution of the magnetochrome domains: no longer alone. Front Microbiol. 2014;5:117.PubMed 
PubMed Central 

Google Scholar 
Katzmann E, Scheffel A, Gruska M, Plitzko JM, Schüler D. Loss of the actin-like protein MamK has pleiotropic effects on magnetosome formation and chain assembly in Magnetospirillum gryphiswaldense. Mol Microbiol. 2010;77:208–24.CAS 
PubMed 

Google Scholar 
Wagner-Döbler I, Bennasar A, Vancanneyt M, Strömpl C, Brümmer I, Eichner C, et al. Microcosm enrichment of biphenyl-degrading microbial communities from soils and sediments. Appl Environ Microbiol. 1998;64:3014–22.PubMed 
PubMed Central 

Google Scholar 
Ibekwe AM, Papiernik SK, Gan J, Yates SR, Crowley DE, Yang CH. Microcosm enrichment of 1,3-dichloropropene-degrading soil microbial communities in a compost-amended soil. J Appl Microbiol. 2001;91:668–76.CAS 
PubMed 

Google Scholar 
Yakimov MM, Denaro R, Genovese M, Cappello S, D’Auria G, Chernikova TN, et al. Natural microbial diversity in superficial sediments of Milazzo Harbor (Sicily) and community successions during microcosm enrichment with various hydrocarbons. Environ Microbiol. 2005;7:1426–41.CAS 
PubMed 

Google Scholar 
Tringe SG, Von Mering C, Kobayashi A, Salamov AA, Chen K, Chang HW, et al. Comparative metagenomics of microbial communities. Science. 2005;308:554–7.CAS 
PubMed 

Google Scholar 
Lefèvre CT, Trubitsyn D, Abreu F, Kolinko S, Jogler C, de Almeida LGP, et al. Comparative genomic analysis of magnetotactic bacteria from the Deltaproteobacteria provides new insights into magnetite and greigite magnetosome genes required for magnetotaxis. Environ Microbiol. 2013;15:2712–35.PubMed 

Google Scholar 
Wadhwa N, Berg HC. Bacterial motility: machinery and mechanisms. Nat Rev Microbiol. 2022;20:161–73.CAS 
PubMed 

Google Scholar 
Zhu K, Pan H, Li J, Yu-Zhang K, Zhang SD, Zhang WY, et al. Isolation and characterization of a marine magnetotactic spirillum axenic culture QH-2 from an intertidal zone of the China Sea. Res Microbiol. 2010;161:276–83.CAS 
PubMed 

Google Scholar 
Kaimer C, Zusman DR. Regulation of cell reversal frequency in Myxococcus xanthus requires the balanced activity of CheY-like domains in FrzE and FrzZ. Mol Microbiol. 2016;100:379–95.CAS 
PubMed 

Google Scholar 
Kühn MJ, Talà L, Inclan YF, Patino R, Pierrat X, Vos I, et al. Mechanotaxis directs Pseudomonas aeruginosa twitching motility. Proc Natl Acad Sci USA. 2021;118:e2101759118.PubMed 
PubMed Central 

Google Scholar  More

De Grave, S., & Fransen, C. H. J. M. Carideorum Catalogus: The Recent Species of the Dendrobranchiate, Stenopodidean, Procarididean and Caridean Shrimps (Crustacea: Decapoda). Zool. Meded. 85, (2011).Garassino, A. The macruran decapod crustaceans of the Lower Cretaceous (Lower Barremian) of Las Hoyas (Cuenca, Spain). Atti Soc. it. Sci. nat. Museo civ. Stor. nat. Milano 137, 101–126 (1997).Bravi, S., Coppa, M. G., Garassino, A., & Patricelli, R. Palaemon vesolensis n. sp. (Crustacea, Decapoda) from the Plattenkalk of Vesole Mount (Salerno, Southern Italy). Atti Soc. it. Sci. nat. Museo civ. Stor. nat. Milano 140, 141–169 (1999).Colleary, C. et al. Chemical, experimental, and morphological evidence for diagenetically altered melanin in exceptionally preserved fossils. Proc. Natl. Acad. Sci. U.S.A. 11241, 12592–12597 (2015).Article 
ADS 

Google Scholar 
Vinther, J., Briggs, D. E., Clarke, J., Mayr, G. & Prum, R. O. Structural coloration in a fossil feather. Biol. Lett. 6, 128–131 (2010).Article 
PubMed 

Google Scholar 
McNamara, M. E. et al. Fossilised biophotonic nanostructures reveal the original colors of 47 million-year-old moths. PLoS Biol. 9, e1001200 (2011).Article 
CAS 
PubMed 
PubMed Central 

Google Scholar 
Rietschel, S. Taphonomic biasing in the Messel Fauna and Flora. Cour. Forsch. Inst. Senckenberg 107, 169–182 (1988).
Google Scholar 
Wolf, H. W. Schätze im Schiefer (Westermann, 1991).Rabenstein, R. Messel 2000 – Das Weltnaturerbe Deutschlands (eds Forschungsinstitut Senckenberg) (2000).Gruber, G., & Micklich, N. Messel – Treasures of the Eocene (Hessisches Landesmuseum Darmstadt, 2007).Wedmann, S. Annotated taxon-list of the invertebrate animals from the Eocene fossil site Grube Messel near Darmstadt Germany. Cour. Forsch. Inst. Senckenberg 255, 103–110 (2005).
Google Scholar 
Schaal, S. F. K. & Rabenstein, R. D. Tagebau Messel in Linien und Zahlen. Senckenberg Nat. Forsch. Mus. 142, 376–377 (2012).
Google Scholar 
Moshayedi, M., Lenz, O. K., Wilde, V. & Hinderer, M. The recolonisation of volcanically disturbed Eocene habitats of Central Europe: the maar lakes of Messel and Offenthal (SW Germany) compared. Paleobiodivers. Paleoenviron. 100, 951–973 (2020).Article 

Google Scholar 
Schulz, R., Harms, F.-J. & Felder, M. Die Forschungsbohrung Messel 2001: Ein Beitrag zur Entschlüsselung der Genese einer Ölschieferlagerstätte. Z. angew. Geol. 2002, 9–17 (2002).
Google Scholar 
Felder, M. & Harms, F. J. Lithologie und genetische Interpretation der vulkano-sedimentären Ablagerungen aus der Grube Messel anhand der Forschungsbohrung Messel 2001 und weiterer Bohrungen (Eozän, Messel-Formation, Sprendlinger Horst, Südhessen). Cour. Forsch. Inst. Senckenberg 252, 151–203 (2004).
Google Scholar 
Büchel, G. N., & Schaal, S. F. K. The formation of the Messel maar in Messel: An Ancient Greenhouse Ecosystem (eds. Smith, K. T., Schaal, S. F. K. & Habersetzer, J.) 62–103 (Schweizerbart, 2018).Der, G. K. Messeler Ölschiefer – ein Algenlaminit. Cour. Forsch. Inst. Senckenberg 131, 1–143 (1990).
Google Scholar 
Lenz, O. K., Wilde, V. & Riegel, W. Recolonization of a Middle Eocene volcanic site: quantitative palynology of the initial phase of the maar lake of Messel (Germany). Rev. Palaeobot. Palynol. 145, 217–242 (2007).Article 

Google Scholar 
Bauersachs, T., Schouten, S. & Schwark, L. Characterization of the sedimentary organic matter preserved in Messel oil shale by bulk geochemistry and stable isotopes. Palaeogeogr. Palaeoclimatol. Palaeoecol. 410, 390–400 (2014).Article 

Google Scholar 
Mertz, D. F. & Renne, P. R. A numerical age for the Messel fossil deposit (UNESCO world natural heritage site) from 40Ar/39Ar dating. Cour. Forsch. Inst. Senckenberg 255, 67–75 (2005).
Google Scholar 
Lenz, O. K., Wilde, V., Mertz, D. F. & Riegel, W. New palynology-based astronomical and revised 40Ar/39Ar ages for the Eocene maar lake of Messel (Germany). Int. J. Earth Sci. 104, 873–889 (2015).Article 
CAS 

Google Scholar 
Lenz, O. K. & Wilde, V. Changes in Eocene plant diversity and composition of vegetation: The lacustrine archive of Messel (Germany). Paleobiology 44, 709–735 (2018).Article 

Google Scholar 
Lenz, O. K., Wilde, V, Riegel, W., & Harms, F-J. A 600 k.y. record of El Niño–Southern Oscillation (ENSO): evidence for persisting teleconnections during the Middle Eocene greenhouse climate of Central Europe. Geology 38, 627–630 (2010).Lenz, O. K., Wilde, V, & Riegel, W. Paleoclimate – Learning from the past for the future in Messel: An Ancient Greenhouse Ecosystem (eds. Smith, K. T., Schaal, S. F. K. & Habersetzer, J.) 16–23 (Schweizerbart, 2018).Grein, M., Utescher, T., Wilde, V. & Roth-Nebelsick, A. Reconstruction of the middle Eocene climate of Messel using palaeobotanical data. Neues Jb. Geol. Paläontol. Abh. 260, 305–318 (2011).Article 

Google Scholar 
Tütken, T. Isotope compositions (C, O, Sr, Nd) of vertebrate fossils from the Middle Eocene oil shale of Messel, Germany: Implications for their taphonomy and palaeoenvironment. Palaeogeogr. Palaeoclimatol. Palaeoecol. 416, 92–109 (2014).Article 

Google Scholar 
Wilde, V. The fossil flora of Messel in Messel: An Ancient Greenhouse Ecosystem (eds. Smith, K. T., Schaal, S. F. K. & Habersetzer, J.) 42–61 (Schweizerbart, 2018).Smith, K. T., Schaal, S. F. K. & Habersetzer, J. (eds.) Messel: An Ancient Greenhouse Ecosystem. (Schweizerbart, 2018).Wedmann, S., Hörnschemeyer, T., Engel, M. S., Zetter, R. & Grímsson, F. The last meal of an Eocene pollen-feeding fly. Curr. Biol. 31, 2020–2026 (2021).Article 
CAS 
PubMed 

Google Scholar 
Wedmann, S. Jewels in the oil shale – insects and other invertebrates in Messel: An Ancient Greenhouse Ecosystem (eds. Smith, K. T., Schaal, S. F. K. & Habersetzer, J.) 62–103 (Schweizerbart, 2018).Franzen J. L. Odd-toed ungulates – Early horses and tapiromorphs in Messel: An Ancient Greenhouse Ecosystem (eds. Smith, K. T., Schaal, S. F. K. & Habersetzer, J.) 292–301 (Schweizerbart, 2018).Franzen, J. L., Aurich, C. & Habersetzer, J. Description of a well preserved fetus of the European Eocene Equoid Eurohippus messelensis. PLoS ONE 10, e0137985 (2015).Article 
PubMed 
PubMed Central 

Google Scholar 
Franzen J. L., & Gingerich, P. D. Primates – Rareties in Messel in Messel: An Ancient Greenhouse Ecosystem (eds. Smith, K. T., Schaal, S. F. K. & Habersetzer, J.) 240–247 (Schweizerbart, 2018).Franzen, J. L. et al. Complete primate skeleton from the middle Eocene of Messel in Germany: Morphology and paleobiology. PLoS ONE 4(5), e5723 (2009).Article 
ADS 
PubMed 
PubMed Central 

Google Scholar 
Houša, V. Bechleja inopinata n. g., n. sp., nový ráček z českých třetihor (Decapoda, Palaemonidae). Ústřed. Ústavu Geol. Sborník 23, 365–377 (1957).Glaessner, M. F. Decapoda. In Part R Arthropoda 4(2) Treatise on Invertebrate Paleontology (ed Moore, R. C.) (The University of Kansas Press and The Geological Society of America, 1969).De Grave, S., Cai, Y. & Anker, A. Global diversity of shrimps (Crustacea: Decapoda: Caridea) in freshwater. Hydrobiologia 595, 287–293 (2008).Article 

Google Scholar 
Garassino, A. & Bravi, S. Palaemon antonellae new species (Crustacea, Decapoda, Caridea) from the Lower Cretaceous “Platydolomite” of profeti (Caserta, Italy). J. Paleontol. 77, 589–592 (2003).Article 

Google Scholar 
Schweitzer, C., Karasawa, H., Schweigert, G., Feldmann, R. & Garassino, A. Systematic list of fossil decapod crustacean species. Crustac. Monogr. 10, 1–222 (2010).
Google Scholar 
Plotnick, R. E. Taphonomy of a modern shrimp: implications for the arthropod fossil record. Palaios 1, 286–293 (1986).Article 
ADS 

Google Scholar 
Klompmaker, A. A., Portell, R. W. & Frick, M. G. Comparative experimental taphonomy of eight marine arthropods indicates distinct differences in preservation potential. Palaeontology 60, 773–794 (2017).Article 

Google Scholar 
Vannier, J., Schoenemann, B., Gillot, T., Charbonnier, S. & Clarkson, E. Exceptional preservation of eye structure in arthropod visual predators from the Middle Jurassic. Nat. Commun. 7, 1–9 (2016).Article 

Google Scholar 
Jauvion, C., Audo, D., Charbonnier, S. & Vannier, J. Virtual dissection and lifestyle of a 165-million-year-old female polychelidan lobster. Arthropod Struct. Dev. 45, 122–132 (2016).Article 
PubMed 

Google Scholar 
Pazinato, P. G., Jauvion, C., Schweigert, G., Haug, J. T. & Haug, C. After 100 years: a detailed view of an eumalacostracan crustacean from the Upper Jurassic Solnhofen Lagerstätte with raptorial appendages unique to Euarthropoda. Lethaia 54, 55–72 (2021).Article 

Google Scholar 
Briggs, D. E. G. & Kear, A. J. Decay and mineralization of shrimps. Palaios 9, 431–456 (1994).Article 
ADS 

Google Scholar 
Wuttke, M. Conservation-dissolution-transformation. On the behaviour of biogenic materials during fossilization In Messel: an insight into the history of life and of the earth (eds. Schaal, S. & Ziegler, W.) 263–275 (Claredon, 1992).Thompson, J. R. Comments on phylogeny of section Caridea (Decapoda Natantia) and the phylogenetic importance of the Oplophoridea. Proc. Symp. Crustacea Part 1, 314–326 (1967).
Google Scholar 
Ashelby, C. W., De Grave, S. & Johnson, M. L. Preliminary observations on the mandibles of palaemonoid shrimp (Crustacea: Decapoda: Caridea: Palaemonoidea). PeerJ 3, e846 (2015).Article 
PubMed 
PubMed Central 

Google Scholar 
Felgenhauer, B. E., & Abele, L. G. Phylogenetic relationships among shrimp-like decapods. In Crustacean Phylogeny (ed Schram, F. R.) 291–311 (A. A. Balkema, 1983).Wowor, D., Cai, Y., & Ng, P. K. L. Crustacea: Decapoda, Caridea. In Freshwater Invertebrates of the Malaysian Region (eds Yule, C. M. & Y. H. Sen, Y. H.) 337–357 (Academy of Sciences Malaysia, 2004).Rodd, F. H., & Reznick, D. N. Life History Evolution in Guppies: III. The Impact of Prawn Predation on Guppy Life Histories. Oikos 62, 13–19 (1991).Felgenhauer, B. E. & Abele, L. G. Feeding structures of two atyid shrimps, with comments on Caridean phylogeny. J. Crustac. Biol. 5, 397–419 (1985).Article 

Google Scholar 
de Mazancourt, V., Marquet, G., & Keith, P. The “Pinocchio-shrimp effect”: First evidence of variation in rostrum length with the environment in Caridina H. Milne-Edwards, 1837 (Decapoda: Caridea: Atyidae). J. Crustac. Biol. 37, 249–257 (2017).Zimmermann, G. et al. Geometric morphometrics of carapace of Macrobrachium australe (Crustacea: Palaemonidae) from Reunion Island. Acta Zool. 93, 492–500 (2012).Article 

Google Scholar 
Bauer, R. T. Amphidromy in shrimps: a life cycle between rivers and the sea. Lat. Am. J. Aquat. Res. 41, 633–650 (2013).Article 

Google Scholar 
Jalihal, D. R., Sankolli, K. N. & Shenoy, S. Evolution of larval developmental patterns and the process of freshwaterization in the prawn genus Macrobrachium Bate, 1868 (Decapoda, Palaemonidae). Crustaceana 65, 365–376 (1993).Article 

Google Scholar 
Grande, L. Paleontology of the Green River Formation, with a review of the fish fauna. Bull. Geol. Surv. Wyoming 63, 1–333 (1984).
Google Scholar 
Grande, L. The Lost World of Fossil Lake: snapshots from deep time (University of Chicago Press, 2013).Micklich, N. Peculiarities of the Messel fish fauna and their palaeoecological implications: A case study. Palaeobiodivers. Palaeoenviron. 92, 585–629 (2012).Article 

Google Scholar 
Micklich, N. Actinopterygians—the fishes of the Messel lake. in Messel: An Ancient Greenhouse Ecosystem (eds. Smith, K. T., Schaal, S. F. K. & Habersetzer, J.) 104–111 (Schweizerbart, 2018).Christodoulou, M., Anastasiadou, C., Jugovic, J., & Tzomos, T. Freshwater Shrimps (Atyidae, Palaemonidae, Typhlocarididae) in the Broader Mediterranean Region: Distribution, Life Strategies, Threats, Conservation Challenges and Taxonomic Issues. In A Global Overview of the Conservation of Freshwater Decapod Crustaceans (eds Kawai, T. & Cumberlidge, N.) 199–236 (Springer, 2016).Anger, K. Neotropical Macrobrachium (Caridea: Palaemonidae): On the biology, origin, and radiation of freshwater-invading shrimp. J. Crustac. Biol. 33, 151–183 (2013).Article 

Google Scholar  More

LaBarbera, M. The evolution and ecology of body size. In Patterns and Processes in the History of Life (eds Raup, D. M. & Jablonski, D.) 69–98 (Springer, 1986).
Google Scholar 
Peters, R. H. & Peters, R. H. The Ecological Implications of Body Size Vol. 2 (Cambridge University Press, 1986).
Google Scholar 
Klingenberg, C. P. & Spence, J. On the role of body size for life-history evolution. Ecol. Entomol. 22(1), 55–68 (1997).
Google Scholar 
Blanckenhorn, W. U. The evolution of body size: What keeps organisms small?. Q. Rev. Biol. 75(4), 385–407 (2000).CAS 
PubMed 

Google Scholar 
Sibly, R. M. & Brown, J. H. Effects of body size and lifestyle on evolution of mammal life histories. Proc. Natl. Acad. Sci. USA 104(45), 17707–17712 (2007).ADS 
CAS 
PubMed 
PubMed Central 

Google Scholar 
Schmidt-Nielsen, K. Scaling in biology: The consequences of size. J. Exp. Zool. 194(1), 287–307 (1975).CAS 
PubMed 

Google Scholar 
Calder, W. A. Size, Function, and Life History (Courier Corporation, 1996).
Google Scholar 
Gould, S. J. Allometry and size in ontogeny and phylogeny. Biol. Rev. 41(4), 587–638 (1966).CAS 
PubMed 

Google Scholar 
Gillooly, J. F., Brown, J. H., West, G. B., Savage, V. M. & Charnov, E. L. Effects of size and temperature on metabolic rate. Science 293(5538), 2248–2251 (2001).ADS 
CAS 
PubMed 

Google Scholar 
Gearty, W. & Payne, J. L. Physiological constraints on body size distributions in Crocodyliformes. Evolution 74(2), 245–255 (2020).PubMed 

Google Scholar 
Maurer, B. A., Brown, J. H. & Rusler, R. D. The micro and macro in body size evolution. Evolution 46(4), 939–953 (1992).PubMed 

Google Scholar 
Hone, D. W. & Benton, M. J. The evolution of large size: How does Cope’s Rule work?. Trends Ecol. Evol. 20(1), 4–6 (2005).PubMed 

Google Scholar 
Reeve, J. P. & Fairbairn, D. J. Predicting the evolution of sexual size dimorphism. J. Evol. Biol. 14(2), 244–254 (2001).
Google Scholar 
Blanckenhorn, W. U. Behavioral causes and consequences of sexual size dimorphism. Ethology 111(11), 977–1016 (2005).
Google Scholar 
Wu, H., Jiang, T., Huang, X. & Feng, J. Patterns of sexual size dimorphism in horseshoe bats: Testing Rensch’s rule and potential causes. Sci. Rep. 8(1), 1–13 (2018).ADS 

Google Scholar 
Cox, R. M., Butler, M. A. & John-Alder, H. B. The evolution of sexual size dimorphism in reptiles. In Sex, Size and Gender Roles: Evolutionary Studies of Sexual Size Dimorphism (eds Fairbairn, D. J. et al.) 38–49 (Oxford University Press, 2007).
Google Scholar 
Stillwell, R. C., Blanckenhorn, W. U., Teder, T., Davidowitz, G. & Fox, C. W. Sex differences in phenotypic plasticity affect variation in sexual size dimorphism in insects: From physiology to evolution. Annu. Rev. Entomol. 55, 227–245 (2010).CAS 
PubMed 
PubMed Central 

Google Scholar 
Rensch, B. Die Abhängigkeit der relativen Sexualdifferenz von der Körpergrösse. Bonn. Zool. Beitr. 1, 58–69 (1950).
Google Scholar 
Rensch, B. Evolution Above the Species Level (Columbia University Press, 1960).
Google Scholar 
Shine, R. Ecological causes for the evolution of sexual dimorphism: A review of the evidence. Q. Rev. Biol. 64(4), 419–461 (1989).CAS 
PubMed 

Google Scholar 
Portik, D. M., Blackburn, D. C. & McGuire, J. A. Macroevolutionary patterns of sexual size dimorphism among African tree frogs (Family: Hyperoliidae). J. Hered. 111(4), 379–391 (2020).PubMed 

Google Scholar 
Ceballos, C. P., Adams, D. C., Iverson, J. B. & Valenzuela, N. Phylogenetic patterns of sexual size dimorphism in turtles and their implications for Rensch’s rule. Evol. Biol. 40(2), 194–208 (2013).
Google Scholar 
Amado, T. F., Martinez, P. A., Pincheira-Donoso, D. & Olalla-Tárraga, M. Á. Body size distributions of anurans are explained by diversification rates and the environment. Glob. Ecol. Biogeogr. 30(1), 154–164 (2021).
Google Scholar 
Starostová, Z., Kubička, L. & Kratochvíl, L. Macroevolutionary pattern of sexual size dimorphism in geckos corresponds to intraspecific temperature-induced variation. J. Evol. Biol. 23(4), 670–677 (2010).PubMed 

Google Scholar 
Herczeg, G., Gonda, A. & Merilä, J. Rensch’s rule inverted–female-driven gigantism in nine-spined stickleback Pungitius pungitius. J. Anim. Ecol. 79(3), 581–588 (2010).PubMed 

Google Scholar 
Liao, W. B. & Chen, W. Inverse Rensch’s rule in a frog with female-biased sexual size dimorphism. Naturwissenschaften 99(5), 427–431 (2012).ADS 
CAS 
PubMed 

Google Scholar 
Cooper, M. I. Sexual size dimorphism and the rejection of Rensch’s rule in Diplopoda (Arthropoda). J. Entomol. Zool. Stud. 6(1), 1582–1587 (2018).
Google Scholar 
Cheng, R. C. & Kuntner, M. Phylogeny suggests nondirectional and isometric evolution of sexual size dimorphism in argiopine spiders. Evolution 68(10), 2861–2872 (2014).PubMed 

Google Scholar 
Webb, T. J. & Freckleton, R. P. Only half right: Species with female-biased sexual size dimorphism consistently break Rensch’s rule. PLoS ONE 2(9), e897 (2007).ADS 
PubMed 
PubMed Central 

Google Scholar 
Gaston, K. J., Chown, S. L. & Evans, K. L. Ecogeographical rules: Elements of a synthesis. J. Biogeogr. 35(3), 483–500 (2008).
Google Scholar 
Olalla-Tárraga, M. Á. & Rodríguez, M. Á. Energy and interspecific body size patterns of amphibian faunas in Europe and North America: Anurans follow Bergmann’s rule, urodeles its converse. Glob. Ecol. Biogeogr. 16(5), 606–617 (2007).
Google Scholar 
Olalla-Tárraga, M. Á., Diniz-Filho, J. A. F., Bastos, R. P. & Rodríguez, M. Á. Geographic body size gradients in tropical regions: Water deficit and anuran body size in the Brazilian Cerrado. Ecography 32(4), 581–590 (2009).
Google Scholar 
Gouveia, S. F. & Correia, I. Geographical clines of body size in terrestrial amphibians: Water conservation hypothesis revisited. J. Biogeogr. 43(10), 2075–2084 (2016).
Google Scholar 
Pincheira-Donoso, D., Meiri, S., Jara, M., Olalla-Tárraga, M. Á. & Hodgson, D. J. Global patterns of body size evolution are driven by precipitation in legless amphibians. Ecography 42(10), 1682–1690 (2019).
Google Scholar 
Nevo, E. Adaptive color polymorphism in cricket frogs. Evolution 27(3), 353–367 (1973).PubMed 

Google Scholar 
Ashton, K. G. Do amphibians follow Bergmann’s rule?. Can. J. Zool. 80(4), 708–716 (2002).MathSciNet 

Google Scholar 
Bergmann, C. Ueber die Verhältnisse der Wärmeökonomie der Thiere zu ihrer Grösse. Göttinger Studien. 1, 595–708 (1847).
Google Scholar 
Olalla-Tárraga, M. Á., Rodríguez, M. Á. & Hawkins, B. A. Broad-scale patterns of body size in squamate reptiles of Europe and North America. J. Biogeogr. 33(5), 781–793 (2006).
Google Scholar 
Trullas, S. C., van Wyk, J. H. & Spotila, J. R. Thermal melanism in ectotherms. J. Therm. Biol. 32(5), 235–245 (2007).
Google Scholar 
Rodríguez, M. Á., López-Sañudo, I. L. & Hawkins, B. A. The geographic distribution of mammal body size in Europe. Glob. Ecol. Biogeogr. 15(2), 173–181 (2006).
Google Scholar 
Olalla-Tárraga, M. Á., Diniz-Filho, J. A. F., Bastos, R. P. & Rodriguez, M. A. Geographic body size gradients in tropical regions: Water deficit and anuran body size in the Brazilian Cerrado. Ecography 32(4), 581–590 (2009).
Google Scholar 
Womack, M. C. & Bell, R. C. Two-hundred million years of anuran body-size evolution in relation to geography, ecology and life history. J. Evol. Biol. 33(10), 1417–1432 (2020).PubMed 

Google Scholar 
Frost, D. R. Amphibian Species of the World: An online reference, version 6. http://research.amnh.org/herpetology/amphibia/index.php. Accessed 12 July 2021 (2021).
Acevedo, A. A., Armesto, O. & Palma, R. E. Two new species of Pristimantis (Anura: Craugastoridae) with notes on the distribution of the genus in northeastern Colombia. Zootaxa 4750(4), 499–523 (2020).
Google Scholar 
Heinicke, M. P., Duellman, W. E. & Hedges, S. B. Major Caribbean and Central American frog faunas originated by ancient oceanic dispersal. Proc. Natl. Acad. Sci. USA 104(24), 10092–10097 (2007).ADS 
CAS 
PubMed 
PubMed Central 

Google Scholar 
Pinto-Sánchez, N. R. et al. The great American biotic interchange in frogs: Multiple and early colonization of Central America by the South American genus Pristimantis (Anura: Craugastoridae). Mol. Phylogenet. Evol. 62(3), 954–972 (2012).PubMed 

Google Scholar 
Zumel, D., Buckley, D. & Ron, S. R. The Pristimantis trachyblepharis species group, a clade of miniaturized frogs: Description of four new species and insights into the evolution of body size in the genus. Zool. J. Linn. Soc. zlab044 (2021).Pincheira-Donoso, D. et al. The multiple origins of sexual size dimorphism in global amphibians. Glob. Ecol. Biogeogr. 30(2), 443–458 (2021).
Google Scholar 
Woolbright, L. L. Sexual selection and size dimorphism in anuran amphibia. Am. Nat. 121(1), 110–119 (1983).
Google Scholar 
Nali, R. C., Zamudio, K. R., Haddad, C. F. & Prado, C. P. Size-dependent selective mechanisms on males and females and the evolution of sexual size dimorphism in frogs. Am. Nat. 184(6), 727–740 (2014).PubMed 

Google Scholar 
Hill, R. et al. Herpetological husbandry observations on the captive reproduction of gaige’s rain frog Pristimantis gaigeae (Dunn 1931). Herpetol. Rev. 41(4), 465 (2010).
Google Scholar 
Rojas-Rivera, A., Cortés-Bedoya, S., Gutiérrez-Cárdenas, P. D. A. & Castellanos, J. M. Pristimantis achatinus (Cachabi robber frog). Parental care and clutch size. Herpetol. Rev. 42, 588–589 (2011).
Google Scholar 
Granados-Pérez, Y. & Ramirez-Pinilla, M. P. Reproductive phenology of three species of Pristimantis in an Andean cloud forest. Revista Acad. Colomb. Ci. Exact. 44(173), 1083–1098 (2020).
Google Scholar 
Levy, D. L. & Heald, R. Biological scaling problems and solutions in amphibians. Cold Spring Harb. Perspect. Biol. 8(1), a019166 (2016).PubMed Central 

Google Scholar 
O’Donnell, M. S. & Ignizio, D. A. Bioclimatic predictors for supporting ecological applications in the conterminous United States. US Geol. Survey Data Series. 691(10), 4–9 (2012).
Google Scholar 
Valenzuela-Sánchez, A., Cunningham, A. A. & Soto-Azat, C. Geographic body size variation in ectotherms: Effects of seasonality on an anuran from the southern temperate forest. Front. Zool. 12(1), 1–10 (2015).
Google Scholar 
Parsons, J. J. The northern Andean environment. Mt. Res. Dev. 2(3), 253–264 (1982).
Google Scholar 
Navas, C. A., Carvajalino-Fernández, J. M., Saboyá-Acosta, L. P., Rueda-Solano, L. A. & Carvajalino-Fernández, M. A. The body temperature of active amphibians along a tropical elevation gradient: Patterns of mean and variance and inference from environmental data. Funct. Ecol. 27(5), 1145–1154 (2013).
Google Scholar 
Swemmer, A. M., Knapp, A. K. & Snyman, H. A. Intra-seasonal precipitation patterns and above-ground productivity in three perennial grasslands. J. Ecol. 95(4), 780–788 (2007).
Google Scholar 
Losos, J. B. Lizards in an Evolutionary Tree: Ecology and Adaptive Radiation of Anoles (Univ. of California Press, 2011).
Google Scholar 
Pincheira-Donoso, D. & Hunt, J. Fecundity selection theory: Concepts and evidence. Biol. Rev. 92(1), 341–356 (2017).PubMed 

Google Scholar 
Morrison, C. & Hero, J. M. Geographic variation in life-history characteristics of amphibians: A review. J. Anim. Ecol. 72(2), 270–279 (2003).
Google Scholar 
Morrow, C. B., Ernest, S. M. & Kerkhoff, A. J. Macroevolution of dimensionless life-history metrics in tetrapods. Proc. Royal Soc. B. 288, 20210200 (2021).
Google Scholar 
Revell, L. J., Harmon, L. J. & Collar, D. C. Phylogenetic signal, evolutionary process, and rate. Syst. Biol. 57(4), 591–601 (2008).PubMed 

Google Scholar 
Kamilar, J. M. & Cooper, N. Phylogenetic signal in primate behaviour, ecology and life history. Philos. Trans. R. Soc. Lond. B Biol. Sci. 368(1618), 20120341 (2013).PubMed 
PubMed Central 

Google Scholar 
Meyer, A. L. & Wiens, J. J. Estimating diversification rates for higher taxa: BAMM can give problematic estimates of rates and rate shifts. Evolution 72(1), 39–53 (2018).PubMed 

Google Scholar 
Rabosky, D. L. et al. Rates of speciation and morphological evolution are correlated across the largest vertebrate radiation. Nat. Commun. 4(1), 1–8 (2013).
Google Scholar 
Mendoza, A. M., Ospina, O. E., Cárdenas-Henao, H. & García-R, J. C. A likelihood inference of historical biogeography in the world’s most diverse terrestrial vertebrate genus: Diversification of direct-developing frogs (Craugastoridae: Pristimantis) across the Neotropics. Mol. Phylogenet. Evol. 85, 50–58 (2015).PubMed 

Google Scholar 
Baker, J., Meade, A., Pagel, M. & Venditti, C. Adaptive evolution toward larger size in mammals. Proc. Natl. Acad. Sci. USA 112(16), 5093–5098 (2015).ADS 
CAS 
PubMed 
PubMed Central 

Google Scholar 
Hariharan, I. K., Wake, D. B. & Wake, M. H. Indeterminate growth: Could it represent the ancestral condition?. Cold Spring Harb. Perspect. Biol. 8(2), a019174 (2016).PubMed Central 

Google Scholar 
Amado, T. F., Bidau, C. J. & Olalla-Tárraga, M. Á. Geographic variation of body size in New World anurans: Energy and water in a balance. Ecography 42(3), 456–466 (2019).
Google Scholar 
Watters, J. L., Cummings, S. T., Flanagan, R. L. & Siler, C. D. Review of morphometric measurements used in anuran species descriptions and recommendations for a standardized approach. Zootaxa 4072, 477–495 (2016).PubMed 

Google Scholar 
Lovich, J. E. & Gibbons, J. W. A review of techniques for quantifying sexual size dimorphism. Growth Dev. Aging. 56, 269–269 (1992).CAS 
PubMed 

Google Scholar 
Lanfear, R., Calcott, B., Ho, S. Y. & Guindon, S. PartitionFinder: Combined selection of partitioning schemes and substitution models for phylogenetic analyses. Mol. Biol. Evol. 29(6), 1695–1701 (2012).CAS 
PubMed 

Google Scholar 
Drummond, A. J. & Rambaut, A. BEAST: Bayesian evolutionary analysis by sampling trees. BMC Evol. Biol. 7(1), 1–8 (2007).
Google Scholar 
Drummond, A. J., Ho, S. Y., Phillips, M. J. & Rambaut, A. Relaxed phylogenetics and dating with confidence. PLoS Biol. 4(5), e88 (2006).PubMed 
PubMed Central 

Google Scholar 
Rambaut, A. FigTree, A Graphical Viewer of Phylogenetic Trees. (2007)Olalla-Tárraga, M. A., Bini, L. M., Diniz-Filho, J. A. & Rodríguez, M. Á. Cross-species and assemblage-based approaches to Bergmann’s rule and the biogeography of body size in Plethodon salamanders of eastern North America. Ecography 33(2), 362–368 (2010).
Google Scholar 
QGIS.org. QGIS Geographic Information System. QGIS Association. http://www.qgis.org. Accessed 10 July 2021 (2022).Wei, T. et al. Package ‘corrplot’. Statistician. 56(316), e24 (2017).
Google Scholar 
James, F. C. Geographic size variation in birds and its relationship to climate. Ecology 51(3), 365–390 (1970).
Google Scholar 
Hawkins, B. A. & Felizola Diniz-Filho, J. A. Beyond Rapoport’s rule: Evaluating range size patterns of New World birds in a two-dimensional framework. Glob. Ecol. Biogeogr. 15(5), 461–469 (2006).
Google Scholar 
Eager, C. standardize: Tools for standardizing variables for regression in R. R package version 0.21 (2017).Meireles, J. E., O’Meara, B. & Cavender-Bares, J. Linking leaf spectra to the plant tree of life. In Remote Sensing of Plant Biodiversity (eds Cavender-Bares, J. et al.) 155–172 (Springer, 2010).
Google Scholar 
Pagel, M. Inferring the historical patterns of biological evolution. Nature 401(6756), 877–884 (1999).ADS 
CAS 
PubMed 

Google Scholar 
Pagel, M. The maximum likelihood approach to reconstructing ancestral character states of discrete characters on phylogenies. Syst. Biol. 48(3), 612–622 (1999).
Google Scholar 
Revell, L. J. Phytools: An R package for phylogenetic comparative biology (and other things). Methods Ecol. Evol. 3(2), 217–223 (2012).
Google Scholar 
Revell, L. J. Two new graphical methods for mapping trait evolution on phylogenies. Methods Ecol. Evol. 4(8), 754–759 (2013).
Google Scholar 
Rabosky, D. L. et al. BAMM tools: An R package for the analysis of evolutionary dynamics on phylogenetic trees. Methods Ecol. Evol. 5(7), 701–707 (2014).
Google Scholar 
Rabosky, D. L. Automatic detection of key innovations, rate shifts, and diversity-dependence on phylogenetic trees. PLoS ONE 9(2), e89543 (2014).ADS 
PubMed 
PubMed Central 

Google Scholar 
Rabosky, D. L., Mitchell, J. S. & Chang, J. Is BAMM flawed? Theoretical and practical concerns in the analysis of multi-rate diversification models. Syst. Biol. 66(4), 477–498 (2017).PubMed 
PubMed Central 

Google Scholar 
Plummer, M., Best, N., Cowles, K. & Vines, K. CODA: Convergence diagnosis and output analysis for MCMC. R News. 6(1), 7–11 (2006).
Google Scholar 
R Core Team. R: A Language and Environment for Statistical Computing (R Foundation for Statistical Computing, Vienna, 2021). https://www.R-project.org. Accessed 1 June 2021 (2021).Fairbairn, D. J. Allometry for sexual size dimorphism: Pattern and process in the coevolution of body size in males and females. Annu. Rev. Ecol. Evol. Syst. 28(1), 659–687 (1997).
Google Scholar 
Fairbairn, D. J. Allometry for sexual size dimorphism: Testing two hypotheses for Rensch’s rule in the water strider Aquarius remigis. Am. Nat. 166(S4), S69–S84 (2005).PubMed 

Google Scholar 
Visser, A. G., Beevers, L. & Patidar, S. Complexity in hydroecological modelling: A comparison of stepwise selection and information theory. River Res. Appl. 34(8), 1045–1056 (2018).
Google Scholar 
Calcagno, V. & de Mazancourt, C. glmulti: An R package for easy automated model selection with (generalized) linear models. J. Stat. Softw. 34(12), 1–29 (2010).
Google Scholar 
Callaghan, S., Guilyardi, E., Steenman-Clark, L. & Morgan, M. The METAFOR project. in Earth System Modelling-Volume 1 (Springer, 2013).Garamszegi, L. Z. & Mundry, R. Multimodel-inference in comparative analyses. In Modern Phylogenetic Comparative Methods and THEIR Application in Evolutionary Biology (ed. Garamszegi, L. Z.) 305–331 (Springer Berlin, 2014).
Google Scholar  More