Philippa Kaur

1.Ottesen, E. A. et al. Pattern and synchrony of gene expression among sympatric marine microbial populations. Proc. Natl Acad. Sci. USA 110, E488–E497 (2013).CAS 
PubMed 
PubMed Central 

Google Scholar 
2.Muñoz-Marín, M. D. C. et al. The transcriptional cycle is suited to daytime N2 fixation in the unicellular cyanobacterium “Candidatus Atelocyanobacterium thalassa” (UCYN-A). mBio 10, e02495-18 (2019).PubMed 
PubMed Central 

Google Scholar 
3.Vislova, A., Sosa, O. A., Eppley, J. M., Romano, A. E. & DeLong, E. F. Diel oscillation of microbial gene transcripts declines with depth in oligotrophic ocean waters. Front. Microbiol. 10, 2191 (2019).PubMed 
PubMed Central 

Google Scholar 
4.Harke, M. J. et al. Periodic and coordinated gene expression between a diazotroph and its diatom host. ISME J. 13, 118–131 (2019).CAS 
PubMed 

Google Scholar 
5.Hernández Limón, M. D. et al. Transcriptional patterns of Emiliania huxleyi in the North Pacific Subtropical Gyre reveal the daily rhythms of its metabolic potential.Environ. Microbiol. 22, 381–396 (2020).PubMed 

Google Scholar 
6.Becker, K. W. et al. Daily changes in phytoplankton lipidomes reveal mechanisms of energy storage in the open ocean. Nat. Commun. 9, 5179 (2018).PubMed 
PubMed Central 

Google Scholar 
7.Frischkorn, K. R., Haley, S. T. & Dyhrman, S. T. Coordinated gene expression between Trichodesmium and its microbiome over day–night cycles in the North Pacific Subtropical Gyre. ISME J. 12, 997–1007 (2018).PubMed 
PubMed Central 

Google Scholar 
8.Ottesen, E. A. et al. Ocean microbes. Multispecies diel transcriptional oscillations in open ocean heterotrophic bacterial assemblages. Science 345, 207–212 (2014).CAS 
PubMed 

Google Scholar 
9.Wilson, S. T. et al. Coordinated regulation of growth, activity and transcription in natural populations of the unicellular nitrogen-fixing cyanobacterium Crocosphaera. Nat. Microbiol. 2, 17118 (2017).CAS 
PubMed 

Google Scholar 
10.Saito, M. A. et al. Iron conservation by reduction of metalloenzyme inventories in the marine diazotroph Crocosphaera watsonii. Proc. Natl Acad. Sci. USA 108, 2184–2189 (2011).CAS 
PubMed 
PubMed Central 

Google Scholar 
11.Strenkert, D. et al. Multiomics resolution of molecular events during a day in the life of Chlamydomonas. Proc. Natl Acad. Sci. USA 116, 2374–2383 (2019).CAS 
PubMed 
PubMed Central 

Google Scholar 
12.Boysen, A. K. et al. Particulate metabolites and transcripts reflect diel oscillations of microbial activity in the surface ocean. mSystems 6, e00896-20 (2021).CAS 
PubMed 
PubMed Central 

Google Scholar 
13.White, A. E., Barone, B., Letelier, R. M. & Karl, D. M. Productivity diagnosed from the diel cycle of particulate carbon in the North Pacific Subtropical Gyre: optically derived productivity. Geophys. Res. Lett. 44, 3752–3760 (2017).CAS 

Google Scholar 
14.DeLong, E. F. et al. Community genomics among stratified microbial assemblages in the ocean’s interior. Science 311, 496–503 (2006).CAS 
PubMed 

Google Scholar 
15.Sunagawa, S. et al. Ocean plankton. Structure and function of the global ocean microbiome. Science 348, 1261359 (2015).PubMed 

Google Scholar 
16.Coles, V. J. et al. Ocean biogeochemistry modeled with emergent trait-based genomics. Science 358, 1149–1154 (2017).CAS 
PubMed 

Google Scholar 
17.Walbauer, J. R., Rodrigue, S., Coleman, M. L. & Chisholm, S. W. Transcriptome and proteome dynamics of a light–dark synchronized bacterial cell cycle.PLoS ONE 7, e43432 (2012).
Google Scholar 
18.Steiner, P. A. et al. Highly variable mRNA half-life time within marine bacterial taxa and functional genes. Environ. Microbiol. 21, 3873–3884 (2019).CAS 
PubMed 
PubMed Central 

Google Scholar 
19.Moran, M. A. et al. Sizing up metatranscriptomics. ISME J. 7, 237–243 (2013).CAS 
PubMed 

Google Scholar 
20.Tamames, J., Cobo-Simón, M. & Puente-Sánchez, F. Assessing the performance of different approaches for functional and taxonomic annotation of metagenomes. BMC Genomics 20, 960 (2019).21.DiTullio, G. R. & Laws, E. A. Diel periodicity of nitrogen and carbon assimilation in five species of marine phytoplankton: accuracy of methodology for predicting N-assimilation rates and N/C composition ratios. Mar. Ecol. Prog. Ser. 32, 123–132 (1986).CAS 

Google Scholar 
22.Granum, E., Kirkvold, S. & Myklestad, S. M. Cellular and extracellular production of carbohydrates and amino acids by the marine diatom Skeletonema costatum: diel variations and effects of N depletion. Mar. Ecol. Prog. Ser. 242, 83–94 (2002).CAS 

Google Scholar 
23.Lacour, T., Sciandra, A., Talec, A., Mayzaud, P. & Bernard, O. Diel variations of carbohydrates and neutral lipids in nitrogen-sufficient and nitrogen-starved cyclostat cultures of Isochrysis sp. J. Phycol. 48, 966–975 (2012).PubMed 

Google Scholar 
24.Follett, C. L., Dutkiewicz, S., Karl, D. M., Inomura, K. & Follows, M. J. Seasonal resource conditions favor a summertime increase in North Pacific diatom–diazotroph associations. ISME J. 12, 1543–1557 (2018).CAS 
PubMed 
PubMed Central 

Google Scholar 
25.Chen, W.-N. U. et al. Diel rhythmicity of lipid-body formation in a coral-Symbiodinium endosymbiosis. Coral Reefs 31, 521–534 (2012).
Google Scholar 
26.Zhou, X. & Mopper, K. Photochemical production of low-molecular-weight carbonyl compounds in seawater and surface microlayer and their air-sea exchange. Mar. Chem. 56, 201–213 (1997).CAS 

Google Scholar 
27.Durham, B. P. et al. Sulfonate-based networks between eukaryotic phytoplankton and heterotrophic bacteria in the surface ocean.Nat. Microbiol. 4, 1706–1715 (2019).CAS 
PubMed 

Google Scholar 
28.Lambert, S. et al. Rhythmicity of coastal marine picoeukaryotes, bacteria and archaea despite irregular environmental perturbations. ISME J. 13, 388–401 (2019).PubMed 

Google Scholar 
29.Kolody, B. C. et al. Diel transcriptional response of a California Current plankton microbiome to light, low iron, and enduring viral infection. ISME J. 13, 2817–2833 (2019).CAS 
PubMed 
PubMed Central 

Google Scholar 
30.Aylward, F. O. et al. Microbial community transcriptional networks are conserved in three domains at ocean basin scales. Proc. Natl Acad. Sci. USA 112, 5443–5448 (2015).CAS 
PubMed 
PubMed Central 

Google Scholar 
31.Rusch, D. B. et al. The Sorcerer II Global Ocean Sampling expedition: northwest Atlantic through eastern tropical Pacific. PLoS Biol. 5, e77 (2007).PubMed 
PubMed Central 

Google Scholar 
32.Bork, P. et al. Tara Oceans studies plankton at planetary scale. Science 348, 873 (2015).CAS 
PubMed 

Google Scholar 
33.Delmont, T. O. et al. Nitrogen-fixing populations of Planctomycetes and Proteobacteria are abundant in surface ocean metagenomes. Nat. Microbiol. 3, 804–813 (2018).CAS 
PubMed 
PubMed Central 

Google Scholar 
34.Fuhrman, J. A. et al. Annually reoccurring bacterial communities are predictable from ocean conditions. Proc. Natl Acad. Sci. USA 103, 13104–13109 (2006).CAS 
PubMed 
PubMed Central 

Google Scholar 
35.Morris, R. M. et al. Temporal and spatial response of bacterioplankton lineages to annual convective overturn at the Bermuda Atlantic Time‐series Study site. Limnol. Oceanogr. 50, 1687–1696 (2005).CAS 

Google Scholar 
36.Mende, D. R. et al. Environmental drivers of a microbial genomic transition zone in the ocean’s interior. Nat. Microbiol. 2, 1367–1373 (2017).CAS 
PubMed 

Google Scholar 
37.Keeling, P. J. et al. The Marine Microbial Eukaryote Transcriptome Sequencing Project (MMETSP): illuminating the functional diversity of eukaryotic life in the oceans through transcriptome sequencing. PLoS Biol. 12, e1001889 (2014).PubMed 
PubMed Central 

Google Scholar 
38.Kanehisa, M., Sato, Y., Kawashima, M., Furumichi, M. & Tanabe, M. KEGG as a reference resource for gene and protein annotation. Nucleic Acids Res. 44, D457–D462 (2016).CAS 

Google Scholar 
39.Thaben, P. F. & Westermark, P. O. Detecting rhythms in time series with RAIN. J. Biol. Rhythms 29, 391–400 (2014).PubMed 
PubMed Central 

Google Scholar 
40.Cuhel, R. L., Ortner, P. B. & Lean, D. R. S. Night synthesis of protein by algae. Limnol. Oceanogr. 29, 731–744 (1984).CAS 

Google Scholar 
41.Coesel, S. N. et al. Diel transcriptional oscillations of light-sensitive regulatory elements in open-ocean eukaryotic plankton communities. Proc. Natl Acad. Sci. USA 118, e2011038118 (2021).CAS 
PubMed 
PubMed Central 

Google Scholar 
42.Bolay, P., Muro-Pastor, M. I., Florencio, F. J. & Klähn, S. The distinctive regulation of cyanobacterial glutamine synthetase. Life (Basel) 8, 52 (2018).CAS 

Google Scholar 
43.Karl, D. M., Church, M. J., Dore, J. E., Letelier, R. M. & Mahaffey, C. Predictable and efficient carbon sequestration in the North Pacific Ocean supported by symbiotic nitrogen fixation. Proc. Natl Acad. Sci. USA 109, 1842–1849 (2012).CAS 
PubMed 
PubMed Central 

Google Scholar 
44.Berman, T. & Bronk, D. A. Dissolved organic nitrogen: a dynamic participant in aquatic ecosystems. Aquat. Microb. Ecol. 31, 279–305 (2003).
Google Scholar 
45.Lee, C. & Bada, J. L. Amino acids in equatorial Pacific Ocean water. Earth Planet. Sci. Lett. 26, 61–68 (1975).CAS 

Google Scholar 
46.Bada, J. L. & Lee, C. Decomposition and alteration of organic compounds dissolved in seawater. Mar. Chem. 5, 523–534 (1977).CAS 

Google Scholar 
47.Poretsky, R. S., Sun, S., Mou, X. & Moran, M. A. Transporter genes expressed by coastal bacterioplankton in response to dissolved organic carbon. Environ. Microbiol. 12, 616–627 (2010).CAS 
PubMed 
PubMed Central 

Google Scholar 
48.Berthelot, H. et al. NanoSIMS single cell analyses reveal the contrasting nitrogen sources for small phytoplankton. ISME J. 13, 651–662 (2019).CAS 
PubMed 

Google Scholar 
49.Moore, L. R., Post, A. F., Rocap, G. & Chisholm, S. W. Utilization of different nitrogen sources by the marine cyanobacteria Prochlorococcus and Synechococcus. Limnol. Oceanogr. 47, 989–996 (2002).CAS 

Google Scholar 
50.Hu, S. K., Connell, P. E., Mesrop, L. Y. & Caron, D. A. A hard day’s night: diel shifts in microbial eukaryotic activity in the North Pacific Subtropical Gyre. Front. Mar. Sci. https://doi.org/10.3389/fmars.2018.00351 (2018).51.Hannides, C. C. S., Popp, B. N., Choy, C. A. & Drazen, J. C. Midwater zooplankton and suspended particle dynamics in the North Pacific Subtropical Gyre: a stable isotope perspective. Limnol. Oceanogr. 58, 1931–1946 (2013).CAS 

Google Scholar 
52.Becker, K. W. et al. Combined pigment and metatranscriptomic analysis reveals highly synchronized diel patterns of phenotypic light response across domains in the open oligotrophic ocean.ISME J. 15, 520–533 (2021).CAS 
PubMed 

Google Scholar 
53.Mruwat, N. et al. A single-cell polony method reveals low levels of infected Prochlorococcus in oligotrophic waters despite high cyanophage abundances. ISME J. 15, 41–54 (2021).CAS 
PubMed 

Google Scholar 
54.Chesson, P. L. & Warner, R. R. Environmental variability promotes coexistence in lottery competitive systems. Am. Nat. 117, 923–943 (1981).
Google Scholar 
55.Shmida, A. & Ellner, S. Coexistence of plant species with similar niches. Vegetatio 58, 29–55 (1984).
Google Scholar 
56.Ellner, S. P., Snyder, R. E. & Adler, P. B. How to quantify the temporal storage effect using simulations instead of math. Ecol. Lett. 19, 1333–1342 (2016).PubMed 

Google Scholar 
57.Adler, P. B., Fajardo, A., Kleinhesselink, A. R. & Kraft, N. J. B. Trait-based tests of coexistence mechanisms. Ecol. Lett. 16, 1294–1306 (2013).PubMed 

Google Scholar 
58.Adler, P. B., HilleRisLambers, J., Kyriakidis, P. C., Guan, Q. & Levine, J. M. Climate variability has a stabilizing effect on the coexistence of prairie grasses. Proc. Natl Acad. Sci. USA 103, 12793–12798 (2006).CAS 
PubMed 
PubMed Central 

Google Scholar 
59.Cáceres, C. E. Temporal variation, dormancy, and coexistence: a field test of the storage effect. Proc. Natl Acad. Sci. USA 94, 9171–9175 (1997).PubMed 
PubMed Central 

Google Scholar 
60.Padisák, J. Identification of relevant time-scales in non-equilibrium community dynamics: conclusions from phytoplankton surveys. N. Z. J. Ecol. 18, 169–176 (1994).
Google Scholar 
61.Anderies, J. M. & Beisner, B. E. Fluctuating environments and phytoplankton community structure: a stochastic model. Am. Nat.155, 556–569 (2000).PubMed 

Google Scholar 
62.Wagg, C. et al. Functional trait dissimilarity drives both species complementarity and competitive disparity. Funct. Ecol. 31, 2320–2329 (2017).
Google Scholar 
63.Bligh, E.G. & Dyer, W. J. A rapid method of total lipid extraction and purification. Can. J. Biochem. Physiol. 37, 911–917 (1959).CAS 
PubMed 

Google Scholar 
64.Boysen, A. K., Heal, K. R., Carlson, L. T. & Ingalls, A. E. Best-matched internal standard normalization in liquid chromatography–mass spectrometry metabolomics applied to environmental samples. Anal. Chem. 90, 1363–1369 (2018).CAS 
PubMed 

Google Scholar 
65.MacLean, B. et al. Skyline: an open source document editor for creating and analyzing targeted proteomics experiments. Bioinformatics 26, 966–968 (2010).CAS 
PubMed 
PubMed Central 

Google Scholar 
66.Fountoulakis, M. & Lahm, H. W. Hydrolysis and amino acid composition analysis of proteins. J. Chromatogr. A 826, 109–134 (1998).CAS 
PubMed 

Google Scholar 
67.Popendorf, K. J., Fredricks, H. F. & Van Mooy, B. A. S. Molecular ion-independent quantification of polar glycerolipid classes in marine plankton using triple quadrupole MS. Lipids 48, 185–195 (2013).CAS 
PubMed 

Google Scholar 
68.Collins, J. R., Edwards, B. R., Fredricks, H. F. & Van Mooy, B. A. S. LOBSTAHS: an adduct-based lipidomics strategy for discovery and identification of oxidative stress biomarkers. Anal. Chem. 88, 7154–7162 (2016).CAS 
PubMed 

Google Scholar 
69.Hummel, J. et al. Ultra performance liquid chromatography and high resolution mass spectrometry for the analysis of plant lipids. Front. Plant Sci. 2, 54 (2011).CAS 
PubMed 
PubMed Central 

Google Scholar 
70.Smith, C. A., Want, E. J., O’Maille, G., Abagyan, R. & Siuzdak, G. XCMS: processing mass spectrometry data for metabolite profiling using nonlinear peak alignment, matching, and identification. Anal. Chem. 78, 779–787 (2006).CAS 
PubMed 

Google Scholar 
71.Kuhl, C., Tautenhahn, R., Böttcher, C., Larson, T. R. & Neumann, S. CAMERA: an integrated strategy for compound spectra extraction and annotation of liquid chromatography/mass spectrometry data sets. Anal. Chem. 84, 283–289 (2012).CAS 
PubMed 

Google Scholar 
72.Biller, S. J. et al. Prochlorococcus extracellular vesicles: molecular composition and adsorption to diverse microbes.Environ. Microbiol. https://doi.org/10.1111/1462-2920.15834 (2021).Article 
PubMed 

Google Scholar 
73.Aylward, F. O. et al. Diel cycling and long-term persistence of viruses in the ocean’s euphotic zone. Proc. Natl Acad. Sci. USA 114, 11446–11451 (2017).CAS 
PubMed 
PubMed Central 

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

Google Scholar 
75.Masella, A. P., Bartram, A. K., Truszkowski, J. M., Brown, D. G. & Neufeld, J. D. PANDAseq: paired-end assembler for illumina sequences. BMC Bioinformatics 13, 31 (2012).CAS 
PubMed 
PubMed Central 

Google Scholar 
76.Joshi, N. & Fass, J. Sickle: A sliding-window, adaptive, quality-based trimming tool for FastQ files. Version 1.33. GitHub https://github.com/najoshi/sickle (2015).77.Kopylova, E., Noé, L. & Touzet, H. SortMeRNA: fast and accurate filtering of ribosomal RNAs in metatranscriptomic data. Bioinformatics 28, 3211–3217 (2012).CAS 

Google Scholar 
78.Kiełbasa, S. M., Wan, R., Sato, K., Horton, P. & Frith, M. C. Adaptive seeds tame genomic sequence comparison. Genome Res. 21, 487–493 (2011).PubMed 
PubMed Central 

Google Scholar 
79.Li, H. & Durbin, R. Fast and accurate long-read alignment with Burrows–Wheeler transform. Bioinformatics 26, 589–595 (2010).PubMed 
PubMed Central 

Google Scholar 
80.Alexander, H. et al. Functional group-specific traits drive phytoplankton dynamics in the oligotrophic ocean. Proc. Natl Acad. Sci. USA 112, E5972–E5979 (2015).CAS 
PubMed 
PubMed Central 

Google Scholar 
81.Anders, S., Pyl, P. T. & Huber, W. HTSeq—a Python framework to work with high-throughput sequencing data. Bioinformatics 31, 166–169 (2015).CAS 

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

Google Scholar 
83.Meinicke, P. UProC: tools for ultra-fast protein domain classification. Bioinformatics 31, 1382–1388 (2015).CAS 
PubMed 

Google Scholar 
84.Mende, D. R., Boeuf, D. & DeLong, E. F. Persistent core populations shape the microbiome throughout the water column in the North Pacific Subtropical Gyre. Front. Microbiol. 10, 2273 (2019).PubMed 
PubMed Central 

Google Scholar 
85.White, A. E. et al. Phenology of particle size distributions and primary productivity in the North Pacific subtropical gyre (Station ALOHA). J. Geophys. Res. Oceans 120, 7381–7399 (2015).PubMed 
PubMed Central 

Google Scholar 
86.Borchers, H. W. pracma: Practical numerical math functions. R package version 2 https://cran.r-project.org/web/packages/pracma/index.html (2019).87.Maechler, M., Rousseeuw, P., Struyf, A., Hubert, M. & Hornik, K. cluster: Cluster analysis basics and extensions. R package version 1.56 (2012).88.Wehrens, R. & Buydens, L. M. C. Self- and super-organizing maps in R: the Kohonen package. J. Stat. Softw. 21, 1–19 (2007).
Google Scholar 
89.Hennig, C. fpc: Flexible procedures for clustering. R package version 2.2-9 (2010).90.Muratore, D. Code for complex marine microbial communities partition metabolism of scarce resources over the diel cycle. Zenodo https://doi.org/10.5281/zenodo.3817416 (2020). More

1.Holechek, J. L., Pieper, R. D. & Herbel, C. H. Range management: Principles and practices 6th edn. (Pearson Education, Inc., 2011).
Google Scholar 
2.Dombroski, J. L. D., Praxedes, S. C., de Freitas, R. M. O. & Pontes, F. M. Water relations of Caatinga trees in the dry season. S. Afr. J. Bot. 77, 430–434 (2011).
Google Scholar 
3.Santos, M. G. et al. Caatinga, the Brazilian dry tropical forest: can it tolerate climate changes?. Theor. Experim. Plant Physiol. 26, 83–99 (2014).
Google Scholar 
4.Mendes, K. et al. Croton blanchetianus modulates its morphophysiological responses to tolerate drought in a tropical dry forest. Funct. Plant Biol. 10, 1–13 (2017).
Google Scholar 
5.Smith, W. K. & Nobel, P. S. Influences of seasonal changes in leaf morphology on water-use efficiency for three desert broad leaf shrubs. Ecology 58, 1033–1043 (1977).
Google Scholar 
6.Kyparissis, A. & Manetas, Y. Seasonal leaf dimorphism in a semi-deciduous Mediterranean shrub-ecophysiological comparisons between winter and summer leaves. Acta Oecol.-Oecol. Plantarum 14, 23–32 (1993).
Google Scholar 
7.Kloeppel, B. D., Abrams, M. D. & Kubiske, M. E. Seasonal ecophysiology and leaf morphology of four successional Pennsylvania barrens species in open versus understory environments. Can. J. For. Res. 23(2), 181–189 (1993).
Google Scholar 
8.Coley, P. D. Effects of plant growth rate and leaf lifetime on the amount and type of anti-herbivore defense. Oecologia 74, 531–536 (1988).ADS 
CAS 
PubMed 

Google Scholar 
9.Reich, P., Walters, M. & Ellsworth, D. From tropics to tundra: global convergence in plant functioning. Proc. Natl. Acad. Sci. USA 94, 13730–13734 (1997).ADS 
CAS 
PubMed 
PubMed Central 

Google Scholar 
10.Pompelli, M. F. et al. Allometric models for non-destructive leaf area estimation of the Jatropha curcas. Biomass Bioenerg. 36, 77–85 (2012).
Google Scholar 
11.Duan, B., Yang, Y., Lu, Y., Korpelainen, H. & Berninger, F. C. L. Interactions between drought stress, ABA and genotypes in Picea asperata. J. Exp. Bot. 58, 3025–3036 (2007).CAS 
PubMed 

Google Scholar 
12.Kwon, M. Y. & Woo, S. Y. Plants’ responses to drought and shade environments. Afr. J. Biotech. 15, 29–31 (2016).CAS 

Google Scholar 
13.Santos, J. C., Leal, I. R., Almeida-Cortez, J. S., Fernandes, G. W. & Tabarelli, M. Caatinga: the scientific negligence experienced by a dry tropical forest. Tropical Conservation Science 4, 276–286 (2011).
Google Scholar 
14.Almazroui, M., Islanm, M. N., Saeed, F., Alkhalaf, A. K. & Dambul, R. Assessing the robustness and uncertainties of projected changes in temperature and precipitation in AR5 Global Climate Models over the Arabian Peninsula. Atmos. Res. 194, 202–213 (2017).
Google Scholar 
15.Angulo-Brown, F., Sánchez-Salas, N., Barranco-Jiménez, M. A. & Rosales, M. A. Possible future scenarios for atmospheric concentration of greenhouse gases: A simplified thermodynamic approach. Renewable Energy 34, 2344–2352 (2009).CAS 

Google Scholar 
16.Glotfelty, T. & Zhang, Y. Impact of future climate policy scenarios on air quality and aerosol-cloud interactions using an advanced version of CESM/CAM5: Part II. Future trend analysis and impacts of projected anthropogenic emissions. Atmos. Environ. 152, 531–552 (2017).ADS 
CAS 

Google Scholar 
17.O’Neill, B. C. et al. IPCC reasons for concern regarding climate change risks. Nat. Clim. Change 7, 28–37 (2017).ADS 

Google Scholar 
18.Hulshof et al. Plant Functional Trait Variation in Tropical Dry Forests: A Review and Synthesis in Tropical Dry Forests in the Americas (ed. Sánchez-Azofeifa, A. et al.) 129–140 (2014).19.Mendes, K. R. et al. Seasonal variation in net ecosystem CO2 exchange of a Brazilian seasonally dry tropical forest. Sci. Rep. 10, 9454 (2020).ADS 
CAS 
PubMed 
PubMed Central 

Google Scholar 
20.Poulter, B. et al. Contribuition of semi-arid ecosystems to interannual variability of the global carbon cycle. Nature 509, 600–604 (2014).ADS 
CAS 
PubMed 

Google Scholar 
21.Campos, S. et al. Closure and partitioning of the energy balance in a preserved area of a Brazilian seasonally dry tropical forest. Agric. For. Meteorol. 471, 398–412 (2019).ADS 

Google Scholar 
22.Zappi, D. et al. Growing knowledge: An overview of seed plant diversity in Brazil. Rodriguésia 66, 1085–1113 (2015).
Google Scholar 
23.Pompelli, M. F., Pompelli, G. M., Cabrini, E. C., Alves, C. J. L. & Ventrella, M. C. Leaf anatomy, ultrastructure and plasticity of Coffea arabica L. in response to light and nitrogen availability. Biotemas 25, 13–28 (2012).
Google Scholar 
24.Rossatto, D. R. & Kolb, R. M. (2010) Gochnatia polymorpha (Less) Cabrera (Asteraceae) changes in leaf structure due to differences in light and edaphic conditions. Acta Bot. Bras. 24, 605–612 (2010).
Google Scholar 
25.Liu, Y. et al. Does greater specific leaf area plasticity help plants to maintain a high performance when shaded?. Ann. Bot. 118, 1329–1336 (2016).PubMed 
PubMed Central 

Google Scholar 
26.Pompelli, M. F., Martins, S. C., Celin, E. F., Ventrella, M. C. & Da Matta, F. M. What is the influence of ordinary epidermal cells and stomata on the leaf plasticity of coffee plants grown under full-sun and shady conditions?. Braz. J. Biol. 70, 1083–1088 (2010).CAS 
PubMed 

Google Scholar 
27.Björkman, O. Responses to different quantum flux densities. In Encyclopaedia of Plant Physiology (eds Lange, O. L. et al.) (Springer, Berlin, 1981).
Google Scholar 
28.Robakowski, P., Wyka, T., Samardakiewicz, S. & Kierzkowski, D. Growth, photosynthesis, and needle structure of silver fir (Abies alba Mill) seedlings under different canopies. For. Ecol. Manag. 201, 211–227 (2004).
Google Scholar 
29.Sam, O., Jeréz, E., Dell’Amico, J. & Ruiz-Sanchez, M. C. Water stress induced changes in anatomy of tomato leaf epidermes. Biol. Plant. 43, 275–277 (2000).
Google Scholar 
30.Shao, H. B., Chu, L.-Y., Jaleel, C. A. & Zhao, D. Water-deficit stress-induced anatomical changes in higher plants. C.R. Biol. 331, 215–225 (2008).PubMed 

Google Scholar 
31.Chartzoulakis, K., Patakas, A., Kofidis, G., Bosabalidis, A. & Nastou, A. Water stress affects leaf anatomy, gas exchange, water relations and growth of two avocado cultivars. Sci. Hortic. 95, 39–50 (2002).CAS 

Google Scholar 
32.Ennajeh, M., Vadel, A. M., Cochard, H. & Khemira, H. Comparative impacts of water stress on the leaf anatomy of a drought-resistant and a drought-sensitive olive cultivar. J. Hortic. Sci. Biotechnol. 85, 289–294 (2010).
Google Scholar 
33.Oguchi, R., Hikosaka, K. & Hirose, T. Does the photosynthetic light-acclimation need change in leaf anatomy?. Plant Cell Environ. 26, 505–512 (2003).
Google Scholar 
34.Johnson, D., Meinzer, F., Woodruff, D. & McCulloh, K. Leaf xylem embolism, detected acoustically and by cryo-SEM, corresponds to decreases in leaf hydraulic conductance in four evergreen species. Plant Cell Environ. 32, 828–836 (2009).PubMed 

Google Scholar 
35.Tyree, M. & Sperry, J. B. Vulnerability of xylem to cavitation and embolism. Annu. Rev. Plant Biol. 40, 19–36 (1989).
Google Scholar 
36.McKown, A., Cochard, H. & Sack, L. Decoding leaf hydraulics with a spatially explicit model: principles of venation architecture and implications for its evolution. Am. Nat. 175, 447–460 (2010).PubMed 

Google Scholar 
37.Nardini, A., Pedà, G. & Rocca, N. Trade-offs between leaf hydraulic capacity and drought vulnerability: Morpho-anatomical bases, carbon costs and ecological consequences. New Phytol. 196, 788–798 (2012).PubMed 

Google Scholar 
38.Nunes, A. et al. Plants used to feed ruminants in semi-arid Brazil: A study of nutritional composition guided by local ecological knowledge. J. Arid Environ. 135, 96–103 (2016).ADS 

Google Scholar 
39.Santos, A. C. J. & Melo, J. I. M. Flora vascular de uma área de caatinga no estado da Paraíba – Nordeste do Brasil. Revista Caatinga 23, 32–40 (2010).
Google Scholar 
40.Flexas, J. et al. Mesophyll conductance to CO2 and Rubisco as targets for improving intrinsic water use efficiency in C3 plants. Plant Cell Environ. 39, 965–982 (2016).CAS 
PubMed 

Google Scholar 
41.Flexas, J. & Medrano, H. Drought-inhibition of photosynthesis in C3 plants: stomatal and non-stomatal limitations revisited. Ann. Bot. 89, 183–189 (2002).CAS 
PubMed 
PubMed Central 

Google Scholar 
42.He, W. & Zhang, X. Responses of an evergreen shrub Sabina vulgaris to soil water and nutrient shortages in the semi-arid Mu Us Sandland in China. J. Arid Environ. 53, 307–316 (2003).ADS 

Google Scholar 
43.Pinho-Pessoa, A. C. B. et al. Interannual variation in temperature and rainfall can modulate the physiological and photoprotective mechanisms of a native semiarid plant species. Indian J. Sci. Technol. 11, 1–17 (2018).CAS 

Google Scholar 
44.Reddy, T., Reddy, V. & Anbumozhi, V. Physiological responses of groundnut (Arachis hypogea L.) to drought stress and its amelioration: A critical review. Plant Growth Regul. 41, 75–88 (2003).CAS 

Google Scholar 
45.Thakur, P. & Sood, R. Drought tolerance of multipurpose agroforestry tree species during first and second summer droughts after transplanting. Indian J. Plant Physiol. 10, 32–40 (2005).
Google Scholar 
46.Leigh, A., Sevanto, S., Close, J. D. & Nicotra, A. B. The influence of leaf size and shape on leaf thermal dynamics: Does theory hold up under natural conditions?. Plant, Cell Environ. 40, 237–248 (2016).
Google Scholar 
47.Markesteijn, L., Poorter, L. & Bongers, F. Light-dependent leaf trait variation in 43 tropical dry forest tree species. Am. J. Bot. 94, 515–525 (2007).PubMed 

Google Scholar 
48.Gotsch, S., Powers, J. & Lerdau, M. Leaf traits and water relations of 12 evergreen species in Costa Rican wet and dry forests: patterns of intra-specific variation across forests and seasons. Plant Ecol. 211, 133–146 (2010).
Google Scholar 
49.Popma, J. & Bongers, F. The effect of canopy gaps on growth and morphology of seedlings of rain forest species. Oecologia 75, 625–632 (1988).ADS 
CAS 
PubMed 

Google Scholar 
50.Evans, J. R. & Poorter, H. Photosynthetic acclimation of plants to growth irradiance: the relative importance of specific leaf area and nitrogen partitioning in maximizing carbon gain. Plant Cell Environ. 24, 755–767 (2001).CAS 

Google Scholar 
51.Pompelli, M. F. et al. Mesophyll thickness and sclerophylly among Calotropis procera morphotypes reveal water-saved adaptation to environments. J Arid Land. 11, 795–810 (2019).
Google Scholar 
52.Leigh, A., Sevanto, S., Close, J. & D & Nicotra A. B.,. The influence of leaf size and shape on leaf thermal dynamics: Does theory hold up under natural conditions?. Plant Cell Environ. 40, 237–248 (2016).PubMed 

Google Scholar 
53.Gil-Pelegrín, E., Saz, M. A., Cuadrat, J. M., Peguero-Pina, J. J. & Sancho-Knapik, D. Oaks Under Mediterranean-Type Climates: Functional Response to Summer Aridity. In Oaks Physiological Ecology Exploring the Functional Diversity of Genus Quercus L (eds Gil-Pelegrín, E. et al.) 137–193 (Springer, London, 2017).
Google Scholar 
54.Chazdon, R. L. & Kaufmann, S. Plasticity of leaf anatomy of two rain forest shrubs in relation to photosynthetic light acclimation. Funct. Ecol. 7, 385–394 (1993).
Google Scholar 
55.Smith, W., Vogelmann, T., De Lucia, E., Bell, D. & Shepherd, K. Leaf form and photosynthesis: Do leaf structure and orientation interact to regulate internal light and carbon dioxide?. Bioscience 47, 785–793 (1997).
Google Scholar 
56.Boanares, D., Isaias, R. R. M. S., Sousa, H. C. & Kozovits, A. R. Strategies of leaf water uptake based on anatomical traits. Plant Biol. 20, 848–856 (2018).CAS 
PubMed 

Google Scholar 
57.Fah, N. A. Plant Anatomy. 2nd ed, Oxford,USA, Butterworth Heinemann (1990).58.Holbrook, N.M. Water Balance of Plants. In: Taiz L, Zeiger E eds. Plant Physiology, 5th ed. Sunderland, Sinauer Associates Inc (2010).59.Glover, B. Differentiation in plant epidermal cells. J. Exp. Bot. 51, 497–505 (2000).CAS 
PubMed 

Google Scholar 
60.Vogelman, T., Nishio, J. & Smith, W. Leaves and light capture: light propagation and gradients of carbon fixation within leaves. Trends Plant Sci. 1, 65–70 (1996).
Google Scholar 
61.Fini, A. M. et al. Mesophyll conductance plays a central role in leaf functioning of Oleaceae species exposed to contrasting sunlight irradiance. Physiol. Plant. 157, 54–68 (2016).CAS 
PubMed 

Google Scholar 
62.Oguchi, R., Hikosaka, K. & Hirose, T. Leaf anatomy as a constraint for photosynthetic acclimation: Differential responses in leaf anatomy to increasing growth irradiance among three deciduous trees. Plant, Cell Environ. 28, 916–927 (2005).
Google Scholar 
63.Pollastrini, M. et al. Interaction and competition processes among tree species in young experimental mixed forests, assessed with chlorophyll fluorescence and leaf morphology. Plant Biol. 16, 323–331 (2014).CAS 
PubMed 

Google Scholar 
64.Sevillano, I., Short, I., Grant, J. & O’Reilly, C. Effects of light availability on morphology, growth and biomass allocation of Fagus sylvatica and Quercus robur seedlings. For. Ecol. Manag. 374, 11–19 (2016).
Google Scholar 
65.Nguyen, H. T., Radacsi, P., Gosztola, B. & Nemeth, E. Effects of temperature and light intensity on morphological and phytochemical characters and antioxidant potential of wormwood (Artemisia absinthium L.). Biochem. Syst. Ecol. 79, 1–7 (2018).CAS 

Google Scholar 
66.Boardman, N. K. Comparative photosynthesis of sun and shade plants. Ann. Rev. Plant Physiol. 28, 355–377 (1977).CAS 

Google Scholar 
67.Bejaoui, F. et al. Changes in chloroplast lipid contents and chloroplast ultrastructure in Sulla carnosa and Sulla coronaria leaves under salt stress. J. Plant Physiol. 198, 32–38 (2016).CAS 
PubMed 

Google Scholar 
68.Van Rensburg, L., Krüger, G. H. J. & Krüger, H. Proline accumulation as drought-tolerance selection criterion: Its relationship to membrane integrity and chloroplast ultrastructure in Nicotiana tabacum L. J. Plant Physiol. 141, 188–194 (1993).
Google Scholar 
69.Westoby, M. & Wright, I. The leaf size – twig size spectrum and its relationship to other important spectra of variation among species. Oecologia 135, 621–628 (2003).ADS 
PubMed 

Google Scholar 
70.Scoffoni, C. et al. Leaf vein xylem conduit diameter influences susceptibility to embolism and hydraulic decline. New Phytol. 213, 1076–1092 (2017).CAS 
PubMed 

Google Scholar 
71.Sack, L. & Scoffoni, C. Leaf venation: Structure, function, development, evolution, ecology andapplications in the past, present and future. New Phytol. 198, 983–1000 (2013).PubMed 

Google Scholar 
72.Brodribb, T., Holbrook, N., Edwards, E. & Gutierrez, M. Relations between stomatal closure, leaf turgor and xylem vulnerability in eight tropical dry forest trees. Plant Cell Environ. 26, 443–450 (2003).
Google Scholar 
73.Scoffoni, C. et al. Light-induced plasticity in leaf hydraulics, venation, anatomy, and gas exchange in ecologically diverse Hawaiian lobeliads. New Phytol. 207, 43–58 (2015).CAS 
PubMed 

Google Scholar 
74.Mendes, K. R. & Marenco, R. A. Leaf traits and gas exchange in saplings of native tree species in the Central Amazon. Scientia Agricola 67, 624–632 (2010).
Google Scholar 
75.Puglielli, G., Varone, L., Gratani, L. & Catoni, R. Specific leaf area variations drive acclimation of Cistus salvifolius in different light environments. Photosynthetica 55, 31–40 (2017).CAS 

Google Scholar 
76.O’Brien, T., Feder, N. & McCully, M. Polychromatic staining of plant cell walls by toluidine blue. Protoplasma 59, 368–373 (1965).
Google Scholar 
77.Karnovsky, M. J. A formaldehyde-glutaraldehyde fixative of high osmolality for use in electron microscopy. J. Cell Biol. 27, 137–138 (1965).
Google Scholar 
78.Spurr, A. R. A low viscosity epoxy resin embedding medium for electron microscopy. J. Ultrastruct. Res. 26, 31–43 (1969).CAS 
PubMed 

Google Scholar 
79.Reynolds, E. S. The use of load citrate at a high pH as an electron-opaque stain in electron microscopy. J. Cell Biol. 17, 208–212 (1963).CAS 
PubMed 
PubMed Central 

Google Scholar 
80.Hardoon, D. R., Szedmak, S. & Shawe-Taylor, J. Canonical correlation analysis: an overview with application to learning methods. Neural Comput. 16, 2639–2664 (2004).PubMed 
MATH 

Google Scholar  More

EDITORIAL
19 January 2022

Biodiversity faces its make-or-break year, and research will be key

A new action plan to halt biodiversity loss needs scientific specialists to work with those who study how governments function.

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Targeted measures can help to stop extinctions, including of Père David’s deer (Elaphurus davidianus), but conserving biodiversity will also require combating climate change, cutting pollution and enhancing sustainable food systems.Credit: Staffan Widstrand/Wild Wonders of China/Nature Picture Library

Biodiversity is being lost at a rate not seen since the last mass extinction. But the United Nations decade-old plan to slow down and eventually stop the decline of species and ecosystems by 2020 has failed. Most of the plan’s 20 targets — known as the Aichi Biodiversity Targets — have not been met.The Aichi targets are part of an international agreement called the UN Convention on Biological Diversity, and member states are now finalizing replacements for them. Currently referred to as the post-2020 global biodiversity framework (GBF), the new targets are expected to be agreed this summer at the second part of the convention’s Conference of the Parties (COP15) in Kunming, China. The meeting was due to be held in May, but is likely to be delayed by a few months. Finalizing the framework will be down to government representatives working with the world’s leading biodiversity specialists. But input from social-science researchers, especially those who study how organizations and governments work, would improve its chances of success.A draft of the GBF was published last July. It aims to slow down the rate of biodiversity loss by 2030. And by 2050, biodiversity will be “valued, conserved, restored and wisely used, maintaining ecosystem services, sustaining a healthy planet and delivering benefits essential for all people”. The plan comprises 4 broad goals and 21 associated targets. The headline targets include conserving 30% of land and sea areas by 2030, and reducing government subsidies that harm biodiversity by US$500 billion per year. Overall, the goals and targets are designed to tackle each of the main contributors to biodiversity loss, which include agriculture and food systems, climate change, invasive species, pollution and unsustainable production and consumption.
Fewer than 20 extinctions a year: does the world need a single target for biodiversity?
The biodiversity convention’s science advisory body is reviewing the GBF and helping governments to decide how the targets are to be monitored. But researchers and policymakers have been writing biodiversity action plans since the 1990s, and most of these strategies have failed to make a lasting impact on two of the three key demands: that global biodiversity be conserved and that natural resources be used sustainably.Some of these failures are to do with governance, which is why it is important to involve not just researchers in the biological sciences, but also people who study organizations and how governments work. This knowledge, when allied to conservation science, will help policymakers to obtain a fuller picture of both the science gaps and the organizational challenges in implementing biodiversity plans.The GBF is a comprehensive plan. But success will require systemic change across public policy. That is both a strength and a weakness. If systemic change can be implemented, it will lead to real change. But if it cannot, there’s no plan B. This has led some researchers to argue that one target or number should be prioritized, and defined in a way that is clear to the public and to policymakers. It would be biodiversity’s equivalent of the 2 °C climate target. The researchers’ “rallying point for policy action and agreements” is to keep species extinction to well below 20 per year across all major groups (M. D. A. Rounsevell et al. Science 368, 1193–1195; 2020). Such focus does yield results. A study published in Conservation Letters found a high probability that targeted action has prevented 21–32 bird and 7–16 mammal extinctions since 1993 (F. C. Bolam et al. Conserv. Lett. 14, e12762; 2021). Extinction rates would have been around three to four times greater without conservation action, the researchers found.But not all agree that just one target should be given priority. A group of more than 50 biodiversity researchers from 23 countries point out in a policy report this week (see go.nature.com/3fv8oiv) that data on species are distributed unequally: 10, mostly high-income, countries account for 82% of records.
The United Nations must get its new biodiversity targets right
The researchers also modelled how different scenarios would affect the GBF’s 21 targets. They found that achieving the targets would require action in all of the target areas — not just a few. Focusing strongly on just one or two targets — such as expanding protected areas — will have, at best, a modest impact on achieving the UN convention’s goals and targets.The difficulty in getting governments to adopt such an integrated approach is that they (as well as non-governmental organizations and businesses) tend to tackle sustainability challenges piecemeal. Actions from last November’s climate COP in Glasgow, UK, will be implemented separately from those decided at the biodiversity COP because, in most countries, different government departments deal with climate change and biodiversity.The science advisers for the biodiversity convention will meet in Geneva, Switzerland, in March to finalize their advice. They are not advocating reform of how governments organize themselves to implement policies in sustainable development — partly (and rightly) because this is generally beyond their fields of expertise. But it’s not too late to consult those with the relevant knowledge.In the past, the UN has commissioned social scientists, for example in the UN Intellectual History Project, a series of 17 studies summarizing the experience of UN agencies spanning gender equality, diplomacy, development, trade and official statistics. However, this work, which ended in 2010, did not assess what has and hasn’t worked in science and environmental policy. Unless these perspectives are incorporated into biodiversity-research advice, any future plans risk going the way of their predecessors.

Nature 601, 298 (2022)
doi: https://doi.org/10.1038/d41586-022-00110-w

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All methods in this study were approved by the Institutional Animal Care and Use Committee at Texas Tech University (protocol 18032-12). All procedures were performed in accordance with relevant guidelines in the manuscript and the ARRIVE (Animal Research: Reporting of In Vivo Experiments) guidelines (https://arriveguidelines.org/).Experimental design for testing effects of temperature and humidity on Pd infection severity on Perimyotis subflavus
We randomly assigned bats to seven environmental chambers (Caron, Model 7000-33-1, Marietta, Ohio, USA) in a blocked experimental design, controlling temperature and humidity in each chamber (Fig. 1). In each environmental chamber, we divided bats into two cages (23 × 38 × 50 cm) constructed from mesh fabric (Part FMLF, Seattle Fabrics, Inc., Seattle, Washington, USA), PVC pipe, and plastic sheeting. We stratified random assignment to ensure even distribution of initial body mass and sex across microclimate treatments. In addition to the seven treatments with fixed temperature and humidity conditions, we had two treatments that allowed bats to freely move among temperature or humidity conditions (Fig. 1). One group of bats (n = 14) was free to move among three chambers with a common temperature (8 °C) but different humidity (water vapor pressure deficit (VPD) = 0.05 kPa, 0.10 kPa, or 0.15 kPa, corresponding to 95, 90, and 85% relative humidity (RH))36. A second group of bats (n = 14) was free to move among three chambers with a common VPD condition (0.10 kPa, medium humidity) but different temperatures (5, 8, or 11 °C) (Fig. 1). Because our research questions were focused on comparing the effect of temperature and humidity conditions on disease severity, we did not include sham-inoculated control animals in the experiment. We made this decision to reduce the total number of animals used in the experiment and to maximize replication to test the effects of temperature and humidity on disease.Figure 1Schematic of the experimental design and sample sizes with 7 environmental chambers with fixed temperature and humidity conditions and two sets of connected chambers allowing bats to behaviorally select temperature (left) or humidity conditions (bottom) for the infection trial on tri-colored bats (Perimyotis subflavus). Water loss conditions were based on water vapor pressure deficit (VPD) levels set to 0.05 kPA to produce low potential evaporative water loss (pEWL) for high humidity, 0.10 kPa for medium pEWL and humidity, or 0.15 kPA for high pEWL and low humidity. Numbers are sample sizes of bats assigned to separate cages within each chamber. Bats in the low temperature and high humidity chamber were combined into a single cage after a camera failed at the start of the experiment (top right).Full size imageWe inoculated each bat by spreading 20 µL of Pd solution (5 × 105 conidia µL−1) evenly across both wings, following established protocols8,9,32,37; treatments were conducted blind without knowledge of which bat was being assigned to what group and bats were inoculated in no particular order to reduce the confounding influence on the order of treatment. We used a Pd strain collected by Karen J. Vanderwolf at Trent University from naturally infected Myotis lucifugus. We cultured Pd on Sabouraud Dextrose Agar with chloramphenicol and gentamicin (SabDex) (Part L96359, Fisher Scientific, Houston, Texas, USA) and incubated subcultured plates at 10 °C for 60 days to allow the formation of conidia. We then harvested conidia by flooding plates with phosphate buffered saline solution containing 0.5% Tween20 (PBST). Conidia were resuspended in PBST, enumerated, and diluted to the inoculum concentration8.Microclimate treatment conditionsWe used three temperatures 5, 8, or 11 °C to represent a range of roosting temperatures of P. subflavus in natural hibernacula24,29. We set humidity in environmental chambers to achieve specific levels of water vapor pressure deficit (VPD) between the surface of the bat and the environment because relative humidity varies by temperature36. Higher VPD corresponds to drier air resulting in higher potential evaporative water loss (pEWL). We used three levels of VPD: 0.05, 0.10, or 0.15 kPa corresponding to low pEWL (high humidity), medium pEWL (medium humidity), and high pEWL (low humidity) levels (Fig. 1). We verified the ambient temperature and relative humidity in each chamber at 10-min intervals (Hobo Model U23-001, Onset Computer Corporation, Bourne, Massachussetts, USA). For bats in the connected chambers that could behaviorally select their temperature and humidity conditions, we quantified the number of days bats spent in each condition38.Animal handling and data collectionWe used 98 (42 females, 56 males) tricolored bats collected on 10 December 2018 from culverts in Mississippi and transported directly to Texas Tech University39. We took morphometric measurements (body mass ± 0.1 g, forearm length ± 0.1 mm) and used quantitative magnetic resonance (QMR; Echo-MRI-B, Echo Medical Systems, Houston, Texas, USA) to determine pre-hibernation fat at the start of the experiment39,40. As an indicator of pre-hibernation stress, we collected a fur sample from the dorsal intrascapular region to quantify fur cortisol concentration with a commercial ELISA kit, following the manufacturer’s protocol (Arbor Assays, Michigan, USA) (see Supplemental Methods). Fur is moulted once per year in the late summer period41 and therefore fur cortisol reflects the level of circulating cortisol during the period of fur growth prior to hibernation. We attached a uniquely marked, modified datalogger42 (DS1925L iButton, Maxim Integrated, San Jose, California, USA) to the back of each bat using ostomy cement to record skin temperature39. Prior to inoculation, we swabbed bats with a sterile polyester swab (Fisherbrand synthetic tipped applicators 23-400-116) five times on forearm and five times on muzzle to determine if any bats were naturally infected with Pd at time of collection. Swabs were stored in RNAlater at  − 20 °C until testing using quantitative polymerase chain reaction (qPCR) at Northern Arizona University43.During the experiment, we provided ad libitum drinking water in each cage but did not provide food. We secured a motion-activated infrared camera (Model HT5940T, Speco Technologies, New York, New York, USA) above each cage to monitor bats throughout the experiment. Because one camera failed at the start of the experiment, we combined bats in that treatment chamber into a single cage (Fig. 1) and replicated this disturbance among all chambers. We monitored bats without disturbance by reviewing video recordings daily. Three bats died of unknown cause before the end of the experiment and were removed from analyses.After 83 days of hibernation, we terminated the experiment and bats were removed from cages and processed to determine body condition using QMR39. We took respirometry measurements on a subset of animals38, and swabbed for Pd as described above. We photographed the left ventral wing using ultraviolet (UV) transillumination (368-nm wavelength and 2-s exposure) to detect and measure florescence associated with Pd infection37,44. For histology, we removed the wing section from the fifth digit and the body and rolled wing tissue around dental wax dowels and 10% neutral buffered formalin. We collected a 90–110 µL blood sample in lithium-heparin-treated capillary tubes for immediate analysis of blood chemistry with a handheld analyzer (i-STAT1 Vet Scan, Abaxis, Union City, California, USA). Using an EC8+ cartridge, we measured sodium, potassium, chloride, anion gap, glucose, BUN (urea nitrogen), hematocrit, hemoglobin, pH, pCO2, TCO2, HCO3, and base excess (Table S1). We quantified arousals from torpor as reported by McGuire et al.39. All bats were handled and euthanized under Animal Care and Use Committee permit 18032-12 at Texas Tech University.Infection and disease metricsWe used several metrics to determine pathogen and disease presence and severity37: presence and amount of the pathogen, Pd, on a bat were determined by qPCR43, and presence of the disease, WNS, was determined via detection of orange-yellow florescence under UV light characteristic of Pd infection44 and histological presence of characteristic lesions and pustules with fungal hyphae45,46. Three types of cutaneous infection were described histologically, including characteristic cupping erosions with fungal hyphae, neutrophilic pustules with fungal hyphae, and fungal hyphae in the stratum corneum with dermal necrosis. Any bats with any of these three conditions noted were scored as WNS positive by histology. Presence and quantity of DNA of Pd was tested by qPCR at Northern Arizona University. All samples were run in duplicate and considered positive if at least one run was positive below a cycle threshold (Ct) of 40 and quantified using a quantification curve from serial dilutions (nanograms of Pd using the equation load = 10((22.049-Ct value)/3.34789), r2 = 0.986)47. Load values were averaged across multiple runs and then converted to attograms by multiplying loads in nanograms by 109.Statistical analysesWe used three different response variables (Pd prevalence, Pd loads, and WNS prevalence by histology) to determine whether infection status varied by microclimate treatment conditions. Low sample sizes of positive infection status by UV detection (n = 4) precluded use in statistical analyses (Table 1). We used generalized linear models with binomial distribution for analyses of Pd prevalence and WNS prevalence and a linear mixed effects model with Gaussian errors for Pd loads. Although the experiment was designed with replication at the cage level to account for cage effects, we were unable to include cage as a random effect because of the low numbers of bats that had signs of Pd or WNS infection. We analyzed whether infection status (i.e., Pd prevalence, Pd load, or WNS prevalence) varied by sex and cortisol separately from an a priori candidate model set (Table 2) to cope efficiently with small sample sizes. We first asked whether infection response varied by sex to determine if bats could be pooled in subsequent analyses. We analyzed separately whether infection response varied by pre-hibernation cortisol at the start of the experiment on the subset of animals for which we had cortisol measurements (n = 83). We then used an information-theoretic approach comparing a candidate set of models with Akaike Information Criterion (AIC)48 using initial fat mass as an individual covariate and temperature and humidity treatment conditions as categorical treatment groups to assess the effect of microclimate on infection response (Table 2). Bats behaviorally selecting their temperature and humidity conditions were assigned to a temperature or humidity treatment level if a bat spent  > 89% of captive days at that condition or was otherwise placed in an ‘inconstant condition’ treatment group. For WNS prevalence, we used the bias reduction method implemented in package brglm49 to deal with complete separation present in the data (in some treatments all bats were scored as negative for WNS) (Table 1; Fig. 2).Table 1 Signs of Pd infection or WNS disease for tri-colored bats (Perimyotis subflavus) exposed to different temperature and humidity regimes.Full size tableTable 2 Model selection results for model comparisons of humidity and temperature and pre-hibernation fat mass on Pd prevalence, Pd load, and WNS prevalence.Full size tableFigure 2Signs of Pseudogymnoascus destructans (Pd) infection or white-nose syndrome (WNS) disease for tri-colored bats (Perimyotis subflavus) exposed to different temperature and humidity regimes. (A) Fraction of bats with Pd detected by qPCR; (B) Fraction of bats with signs of WNS disease by histology, and (C) Mean quantity of Pd on bats at the end of the experiment. There was no statistical support for differences between temperature or humidity treatments for any response metrics. Points are estimated means and vertical lines show binomial standard error for prevalence and standard errors for Pd load.Full size imageBecause this was the first captive hibernation experiment with P. subflavus, we investigated the effects of temperature and humidity on the hibernation physiology of the species38,39 and how physiological markers (e.g., blood chemistry) may be associated with disease. To determine if physiological indicators were related to infection status at the end of the experiment, we compared total number of torpor arousal bouts during the experiment and 13 different blood chemistry metrics from blood samples taken at the end of the experiment and used t-test comparisons (at α = 0.05) for each metric between Pd/WNS positive and negative bats. We designated bats as Pd/WNS positive if a bat tested positive for either Pd or WNS by qPCR, UV, or histology. We used Program R version 3.6.2 to conduct all analyses.Experimental design for testing effects of temperature and humidity on Pd growth on substratesWe used five environmental chambers (CARON, Model 7000-33-1, Marietta, Ohio, USA) to test for the effects of temperature and humidity on fungal growth on natural and artificial substrates (Fig. S1). Our experimental design comprised a reduced temperature series and humidity gradient than what we used for the experiment on bats. In the humidity gradient, temperature was held constant at 8 °C, with 85%, 90%, and 95% RH representing our low, medium, and high humidity treatments, respectively. In the temperature series, vapor pressure deficit (VPD) was held constant across the low (5 °C), medium (8 °C), and high (11 °C) temperatures (VPD = nominally 0.01 kPa, range (0.105–0.107). The chamber set to 8 °C and 90% humidity (VPD = 0.107 kPa) was common to both series.Media plate inoculation and fungal growth measurementWe constructed modified plate lids to prevent contamination while allowing humidity to equilibrate across the plate lid. We drilled 14 equidistant holes (5.5 mm diameter) into each plate lid and hot glued a piece of circular filter paper to the top of the lid. Lids were then disinfected thoroughly with a hydrogen peroxide wipe before being placed in a disinfected, sealed storage container.We prepared Pd inoculum as described above for the infection trial on bats. We inoculated 30 SabDex plates with 100 µL of inoculum at a concentration of 20 conidia µL−1 by serial dilution with a starting concentration of 2.0 × 104 conidia µL−1 diluted four times by a factor of 10. We used sterile, individually wrapped 1-µL plastic inoculation loops to spread the inoculum evenly across the surface of the plates, added the modified plate lids, and immediately transferred plates into environmental chambers. We included six replicate plates in each of the five microclimate conditions.We took weekly digital photographs (Nikon, Model 26524, Tokyo, Japan) of each plate for the 5-week duration of the experiment (Fig. 3A). Our camera was mounted on a tripod to ensure consistent placement of plates relative to the camera. Each photo included a ruler, which was used to calibrate measurements made in ImageJ (Version 2.0.0-rc-69/1.52p, National Institutes of Health, Bethesda, Maryland, USA). One observer made all measurements for consistency. We used the freehand selection tool to trace the boundary of each fungal colony using a drawing tablet (Wacom, Model CTL-490, Kazo, Saitama, Japan). From these selections, we obtained the total surface area growth as the sum of all area selection (in cm2).Figure 3Examples demonstrate the process of measuring and estimating fungal growth of Pseudogymnoascus destructans (Pd) on media plates in temperature and humidity treatment conditions. (A) Examples of fungal growth on media plates measured at days 7, 14, 21, 28, and 34 from two of the treatment conditions (11 °C, 92% RH and 5 °C, 88% RH). (B) Examples of estimating maximum growth rate and latency variables from fungal growth measurements in panel A. We fit a sigmoidal curve to describe fungal growth (thick solid black line) to estimate the inflection point of the curve (vertical solid line). We calculated the slope (solid red line) at the inflection point of the curve to estimate maximum growth rate, and the days until total growth area reached 2.5 cm2 (dashed red lines) as an estimate of latency.Full size imageWe modelled the growth of Pd on each plate as a sigmoidal curve (Fig. 3B), which we fit using the SSlogis and nls functions in Program R v. 3.6.350. The model fitting function provides an estimate of the inflection point of the curve, and we calculated the slope at the inflection point to estimate the maximum growth rate. We also estimated the latency to rapid fungal growth on the plates by determining the date at which the total area of fungus on the plate reached 2.5 cm2 as an arbitrary threshold.We also quantified growth of individual colonies. To avoid biasing growth rate estimates, we excluded colonies that intercepted another colony by choosing independent colonies at the final time point and tracking them backwards through time. If there were fewer than 10 independent colonies at the final time point, we added additional unimpeded colonies with each earlier time point until the total number of colonies reached 10. We modelled growth of individual colonies following the same procedure as for total area of growth on the plate, with an arbitrary threshold of 0.05 cm2 for latency calculations. We used linear mixed models to test for the effects of temperature and humidity on maximum growth rate or latency, including plate as a random factor to account for measuring multiple colonies per plate.Rock inoculation and fungal growth measurementTo evaluate fungal growth and persistence on a natural substrate, we inoculated pieces of sandstone flagstone. We etched a 4 × 6 sampling grid, composed of 5 × 5 cm squares, onto the surface of each sandstone rock (Texas Rock and Flagstone, Lubbock, Texas, USA), where each square served as a sampling unit (Fig. S2). Each row represented a time series for a single replicate, while each column was composed of replicates for the respective time point. Rocks were then autoclaved at 121 °C for 40 min and stored individually in a disinfected, sealed container until inoculation. At the time of inoculation, we evenly spread 200 µL of inoculum (2.5 × 104 conidia µL−1) across each sampling square and immediately transferred the rock to an environmental chamber.We measured fungal growth at days 0, 14, 28, and 56. We used a sterile cotton swab to collect fungal DNA from each sampling square. Swabs were moistened with RNAlater and rolled horizontally, vertically, and diagonally across the surface of the sampling square to ensure contact with the total surface area. One researcher collected all swabs to maximize consistency among swabs collected throughout the experiment. Swabs were placed in RNAlater and stored at − 20 °C until shipped to Northern Arizona University for qPCR analysis43. We quantified fungal loads for each swab sample from qPCR using the quantification curve provided above and normalized fungal loads to the value at day zero for each rock respectively. We then used linear models to test for effects of temperature and humidity on changes in fungal load (log transformed) over time.To evaluate viability of Pd, we swabbed the entire inoculated surface of each rock at the end of the experiment and vortexed the swabs in RNAlater for one minute to release fungal DNA from the swab. We then applied 100 µL of RNAlater fungal solution from each rock to a respective SabDex media plate, using a sterile inoculation loop. After 2 weeks of incubation at 11 °C and 92% RH, we visually assessed plates for presence of fungal growth to determine viability of Pd collected from rocks at the end of the growth experiment. More

1.Díaz, S., Cabido, M. & Casanoves, F. Functional implications of trait-environment linkages in plant communities. Ecolog. Assem. Rules Perspect. Adv. Retreat. 26, 338–362 (1999).
Google Scholar 
2.Ordoñez, J. C. et al. A global study of relationships between leaf traits, climate and soil measures of nutrient fertility. Glob. Ecol. Biogeogr. 18(2), 137–149. https://doi.org/10.1111/j.1466-8238.2008.00441.x (2009).Article 

Google Scholar 
3.Westoby, M., Falster, D. S., Moles, A. T., Vesk, P. A. & Wright, I. J. Plant ecological strategies: some leading dimensions of variation between species. Annu. Rev. Ecol. Syst. 33(1), 125–159 (2002).
Google Scholar 
4.Brown, A. M. et al. The fourth-corner solution–using predictive models to understand how species traits interact with the environment. Methods Ecol. Evol. 5(4), 344–352. https://doi.org/10.1111/2041-210X.12163 (2014).Article 

Google Scholar 
5.Jamil, T., Ozinga, W. A., Kleyer, M. & ter Braak, C. J. F. Selecting traits that explain species–environment relationships: a generalized linear mixed model approach. J. Veg. Sci. 24(6), 988–1000 (2013).
Google Scholar 
6.Pollock, L. J., Morris, W. K. & Vesk, P. A. The role of functional traits in species distributions revealed through a hierarchical model. Ecography 35(8), 716–725 (2012).
Google Scholar 
7.Elith, J. & Leathwick, J. R. Species distribution models: ecological explanation and prediction across space and time. Annu. Rev. Ecol. Evol. Syst. 40, 677–697 (2009).
Google Scholar 
8.Moeslund, J. E., Arge, L., Bøcher, P. K., Dalgaard, T. & Svenning, J.-C. Topography as a driver of local terrestrial vascular plant diversity patterns. Nord. J. Bot. 31(2), 129–144. https://doi.org/10.1111/j.1756-1051.2013.00082.x (2013).Article 

Google Scholar 
9.Burnett, B. N., Meyer, G. A. & McFadden, L. D. Aspect-related microclimatic influences on slope forms and processes, Northeastern Arizona. J. Geophys. Res. Earth Surf. 113(3), 129. https://doi.org/10.1029/2007JF000789 (2008).Article 

Google Scholar 
10.Hais, M., Chytrý, M. & Horsák, M. Exposure-related forest-steppe: a diverse landscape type determined by topography and climate. J. Arid Environ. 135, 75–84. https://doi.org/10.1016/j.jaridenv.2016.08.011 (2016).ADS 
Article 

Google Scholar 
11.Holden, Z. A. & Jolly, W. M. Modeling topographic influences on fuel moisture and fire danger in complex terrain to improve wildland fire management decision support. Forest Ecol. Manag. 262(12), 2133–2141. https://doi.org/10.1016/j.foreco.2011.08.002 (2011).Article 

Google Scholar 
12.Dyer, J. M. Assessing topographic patterns in moisture use and stress using a water balance approach. Landscape Ecol. 24(3), 391–403. https://doi.org/10.1007/s10980-008-9316-6 (2009).Article 

Google Scholar 
13.Lan, G., Hu, Y., Cao, M. & Zhu, H. Topography related spatial distribution of dominant tree species in a tropical seasonal rain forest in China. Forest Ecol. Manag. 262(8), 1507–1513. https://doi.org/10.1016/j.foreco.2011.06.052 (2011).Article 

Google Scholar 
14.Punchi-Manage, R. et al. Effects of topography on structuring local species assemblages in a Sri Lankan mixed dipterocarp forest. J. Ecol. 101(1), 149–160. https://doi.org/10.1111/1365-2745.12017 (2013).Article 

Google Scholar 
15.Rubino, D. L. & McCarthy, B. C. Evaluation of coarse woody debris and forest vegetation across topographic gradients in a southern Ohio forest. Forest Ecol. Manag. 183(1), 221–238. https://doi.org/10.1016/S0378-1127(03)00108-7 (2003).Article 

Google Scholar 
16.Sefidi, K., Esfandiary Darabad, F. & Azaryan, M. Effect of topography on tree species composition and volume of coarse woody debris in an Oriental beech (Fagus orientalis Lipsky) old growth forests, northern Iran. IForest-Biogeosciences and Forestry 9(4), 658 (2016).
Google Scholar 
17.Liu, J., Yunhong, T. & Slik, J. F. Topography related habitat associations of tree species traits, composition and diversity in a Chinese tropical forest. Forest Ecol. Manag. 330, 75–81 (2014).
Google Scholar 
18.Díaz, S. et al. The global spectrum of plant form and function. Nature 529(7585), 167 (2016).ADS 
PubMed 

Google Scholar 
19.Westoby, M. A leaf-height-seed (LHS) plant ecology strategy scheme. Plant Soil 199(2), 213–227 (1998).CAS 

Google Scholar 
20.King, D. A. The adaptive significance of tree height. Am. Nat. 135(6), 809–828 (1990).
Google Scholar 
21.Koch, G. W., Sillett, S. C., Jennings, G. M. & Davis, S. D. The limits to tree height. Nature 428(6985), 851–854 (2004).ADS 
CAS 
PubMed 

Google Scholar 
22.Mäkelä, A. Implications of the pipe model theory on dry matter partitioning and height growth in trees. J. Theor. Biol. 123(1), 103–120 (1986).ADS 

Google Scholar 
23.King, D. Tree dimensions: maximizing the rate of height growth in dense stands. Oecologia 51(3), 351–356 (1981).ADS 
PubMed 

Google Scholar 
24.Hoch, G., Popp, M. & Körner, C. Altitudinal increase of mobile carbon pools in Pinus cembra suggests sink limitation of growth at the Swiss treeline. Oikos 98(3), 361–374. https://doi.org/10.1034/j.1600-0706.2002.980301.x (2002).CAS 
Article 

Google Scholar 
25.Körner, C. A re-assessment of high elevation treeline positions and their explanation. Oecologia 115(4), 445–459 (1998).ADS 
PubMed 

Google Scholar 
26.Hoch, G. & Körner, C. Growth and carbon relations of tree line forming conifers at constant vs. variable low temperatures. J. Ecol. 97(1), 57–66. https://doi.org/10.1111/j.1365-2745.2008.01447.x (2009).Article 

Google Scholar 
27.Hoch, G. & Körner, C. Global patterns of mobile carbon stores in trees at the high-elevation tree line. Glob. Ecol. Biogeogr. 21(8), 861–871. https://doi.org/10.1111/j.1466-8238.2011.00731.x (2012).Article 

Google Scholar 
28.Shi, P., Körner, C. & Hoch, G. A test of the growth-limitation theory for alpine tree line formation in evergreen and deciduous taxa of the eastern Himalayas. Funct. Ecol. 22(2), 213–220. https://doi.org/10.1111/j.1365-2435.2007.01370.x (2008).Article 

Google Scholar 
29.Nagelmüller, S., Hiltbrunner, E. & Körner, C. Low temperature limits for root growth in alpine species are set by cell differentiation. AoB Plants https://doi.org/10.1093/aobpla/plx054 (2017).Article 
PubMed 
PubMed Central 

Google Scholar 
30.Hendrickson, L., Ball, M. C., Wood, J. T., Chow, W. S. & Furbank, R. T. Low temperature effects on photosynthesis and growth of grapevine. Plant Cell Environ. 27(7), 795–809. https://doi.org/10.1111/j.1365-3040.2004.01184.x (2004).CAS 
Article 

Google Scholar 
31.Körner, C. & Hoch, G. A test of treeline theory on a montane permafrost island. Arct. Antarct. Alp. Res. 38(1), 113–119 (2006).
Google Scholar 
32.Muller-Landau, H. C. The tolerance–fecundity trade-off and the maintenance of diversity in seed size. Proc. Natl. Acad. Sci. 107(9), 4242–4247 (2010).ADS 
PubMed 
PubMed Central 

Google Scholar 
33.Lloret, F., Casanovas, C. & Peñuelas, J. Seedling survival of Mediterranean shrubland species in relation to root: shoot ratio, seed size and water and nitrogen use. Funct. Ecol. 13(2), 210–216. https://doi.org/10.1046/j.1365-2435.1999.00309.x (1999).Article 

Google Scholar 
34.Quero, J. L., Villar, R., Marañón, T., Zamora, R. & Poorter, L. Seed-mass effects in four Mediterranean Quercus species (Fagaceae) growing in contrasting light environments. Am. J. Bot. 94(11), 1795–1803. https://doi.org/10.3732/ajb.94.11.1795 (2007).Article 
PubMed 

Google Scholar 
35.Hallett, L. M., Standish, R. J. & Hobbs, R. J. Seed mass and summer drought survival in a Mediterranean-climate ecosystem. Plant Ecol. 212(9), 1479. https://doi.org/10.1007/s11258-011-9922-2 (2011).Article 

Google Scholar 
36.McFadden, I. R. et al. Disentangling the functional trait correlates of spatial aggregation in tropical forest trees. Ecology 100(3), e02591. https://doi.org/10.1002/ecy.2591 (2019).Article 
PubMed 

Google Scholar 
37.Moles, A. T. & Westoby, M. Seedling survival and seed size: a synthesis of the literature. J. Ecol. 92(3), 372–383. https://doi.org/10.1111/j.0022-0477.2004.00884.x (2004).Article 

Google Scholar 
38.Shipley, B. et al. Predicting habitat affinities of plant species using commonly measured functional traits. J. Veg. Sci. 28(5), 1082–1095. https://doi.org/10.1111/jvs.12554 (2017).Article 

Google Scholar 
39.Willson, C. J. & Jackson, R. B. Xylem cavitation caused by drought and freezing stress in four co-occurring Juniperus species. Physiol. Plant. 127(3), 374–382 (2006).CAS 

Google Scholar 
40.Peguero-Pina, J. J. et al. Hydraulic traits are associated with the distribution range of two closely related Mediterranean firs, Abies alba Mill. and Abies pinsapo Boiss. Tree Physiol. 31(10), 1067–1075 (2011).PubMed 

Google Scholar 
41.Tyree, M. & Sperry, J. Vulnerability of xylem to cavitation and embolism. Ann. Rev. Plant Biol 40, 19–36 (1989).
Google Scholar 
42.Wubbels, J. (2010). Tree Species Distribution in Relation to Stem Hydraulic Traits and Soil Moisture in a Mixed Hardwood Forest in Central Pennsylvania.43.Perez-Harguindeguy, N. et al. Corrigendum to: new handbook for standardised measurement of plant functional traits worldwide. Aust. J. Bot. 64(8), 715–716 (2016).
Google Scholar 
44.Oliveira, R. S. et al. Embolism resistance drives the distribution of Amazonian rainforest tree species along hydro-topographic gradients. New Phytol. 221(3), 1457–1465 (2019).PubMed 

Google Scholar 
45.Ahrens, C. W., Rymer, P. D. & Tissue, D. T. Intra-specific trait variation remains hidden in the environment. New Phytol. 2, 1183–1185 (2021).
Google Scholar 
46.Siefert, A. et al. A global meta-analysis of the relative extent of intraspecific trait variation in plant communities. Ecol. Lett. 18(12), 1406–1419 (2015).PubMed 

Google Scholar 
47.Benito Garzón, M., Alía, R., Robson, T. M. & Zavala, M. A. Intra-specific variability and plasticity influence potential tree species distributions under climate change. Glob. Ecol. Biogeogr. 20(5), 766–778 (2011).
Google Scholar 
48.Henn, J. J. et al. Intraspecific trait variation and phenotypic plasticity mediate alpine plant species response to climate change. Front. Plant Sci. 9, 1548 (2018).PubMed 
PubMed Central 

Google Scholar 
49.Zhang, B. et al. Species responses to changing precipitation depend on trait plasticity rather than trait means and intraspecific variation. Funct. Ecol. 34(12), 2622–2633 (2020).
Google Scholar 
50.Xu, H., Wang, H., Prentice, I. C., Harrison, S. P. & Wright, I. J. Coordination of plant hydraulic and photosynthetic traits: confronting optimality theory with field measurements. New Phytol. 2, 90387 (2021).
Google Scholar 
51.Yang, Y. et al. Quantifying leaf-trait covariation and its controls across climates and biomes. New Phytol. 221(1), 155–168 (2019).CAS 
PubMed 

Google Scholar 
52.Li, X., Lu, H., Yu, L. & Yang, K. Comparison of the spatial characteristics of four remotely sensed leaf area index products over China: Direct validation and relative uncertainties. Remote Sens. 10(1), 148 (2018).ADS 

Google Scholar 
53.Peel, M. C., Finlayson, B. L. & McMahon, T. A. Updated world map of the Köppen-Geiger climate classification. Sci. Rep. 3, 1069 (2007).
Google Scholar 
54.Gittleman, J. L. & Kot, M. Adaptation: statistics and a null model for estimating phylogenetic effects. Syst. Zool. 39(3), 227–241 (1990).
Google Scholar 
55.Reich, P. B., Wright, I. J. & Lusk, C. H. Predicting leaf physiology from simple plant and climate attributes: a global GLOPNET analysis. Ecol. Appl. 17(7), 1982–1988 (2007).PubMed 

Google Scholar 
56.Leishman, M. R., Wright, I. J., Moles, A. T. & Westoby, M. The evolutionary ecology of seed size. Seeds Ecol. Regener. Plant Commun. 2, 31–57 (2000).
Google Scholar 
57.Kattge, J. et al. TRY plant trait database–enhanced coverage and open access. Glob. Change Biol. 26(1), 119–188 (2020).ADS 

Google Scholar 
58.Wang, H. et al. The China plant trait database: toward a comprehensive regional compilation of functional traits for land plants. Ecology 99(2), 1039 (2018).
Google Scholar 
59.Knapp, B. O., Wang, G. G., Clark, S. L., Pile, L. S. & Schlarbaum, S. E. Leaf physiology and morphology of Castanea dentata (Marsh.) Borkh., Castanea mollissima Blume, and three backcross breeding generations planted in the southern Appalachians, USA. New Forests 45(2), 283–293 (2014).
Google Scholar 
60.Chen, L. et al. Seed dispersal and seedling recruitment of trees at different successional stages in a temperate forest in northeastern China. J. Plant Ecol. 7(4), 337–346 (2014).
Google Scholar 
61.Marchi, S., Tognetti, R., Minnocci, A., Borghi, M. & Sebastiani, L. Variation in mesophyll anatomy and photosynthetic capacity during leaf development in a deciduous mesophyte fruit tree (Prunus persica) and an evergreen Sclerophyllous Mediterranean shrub (Olea europaea). Trees 22(4), 559 (2008).CAS 

Google Scholar 
62.Gelman, A. Scaling regression inputs by dividing by two standard deviations. Stat. Med. 27(15), 2865–2873 (2008).MathSciNet 
PubMed 

Google Scholar 
63.Miller, J. E. D., Damschen, E. I. & Ives, A. R. Functional traits and community composition: a comparison among community-weighted means, weighted correlations, and multilevel models. Methods Ecol. Evol. 10(3), 415–425. https://doi.org/10.1111/2041-210X.13119 (2019).Article 

Google Scholar 
64.Chung, Y., Rabe-Hesketh, S., Dorie, V., Gelman, A. & Liu, J. A nondegenerate penalized likelihood estimator for variance parameters in multilevel models. Psychometrika 78(4), 685–709 (2013).MathSciNet 
PubMed 
MATH 

Google Scholar 
65.Boyd, K., Costa, V. S., Davis, J., & Page, C. D. (2012). Unachievable region in precision-recall space and its effect on empirical evaluation. in Proceedings of the International Conference on Machine Learning. International Conference on Machine Learning, 2012, 349. NIH Public Access.66.Sofaer, H. R., Hoeting, J. A. & Jarnevich, C. S. The area under the precision-recall curve as a performance metric for rare binary events. Methods Ecol. Evol. 10(4), 565–577 (2019).
Google Scholar 
67.Grau, J., Grosse, I. & Keilwagen, J. PRROC: computing and visualizing precision-recall and receiver operating characteristic curves in R. Bioinformatics 31(15), 2595–2597 (2015).CAS 
PubMed 
PubMed Central 

Google Scholar 
68.Keilwagen, J., Grosse, I. & Grau, J. Area under precision-recall curves for weighted and unweighted data. PloS One 9(3), e92209 (2014).ADS 
PubMed 
PubMed Central 

Google Scholar 
69.Saito, T. & Rehmsmeier, M. The precision-recall plot is more informative than the ROC plot when evaluating binary classifiers on imbalanced datasets. PloS One 10(3), e0118432 (2015).PubMed 
PubMed Central 

Google Scholar 
70.R Core Team (2019). R: A language and environment for statistical computing. R Foundation for Statistical Computing, Vienna, Austria. URL https://www.R-project.org/.71.Schmitt, S. et al. Topography consistently drives intra-and inter-specific leaf trait variation within tree species complexes in a Neotropical forest. Oikos 129(10), 1521–1530 (2020).
Google Scholar  More

1.Cummings, M. E., Rosenthal, G. G. & Ryan, M. J. A private ultraviolet channel in visual communication. Proc. R. Soc. B-Biol. Sci. 270, 897–904. https://doi.org/10.1098/rspb.2003.2334 (2003).Article 

Google Scholar 
2.Tedore, C. & Nilsson, D. E. Avian UV vision enhances leaf surface contrasts in forest environments. Nat. Commun. https://doi.org/10.1038/s41467-018-08142-5 (2019).Article 
PubMed 
PubMed Central 

Google Scholar 
3.Qi, Y. D., Bai, S. J. & Heisler, G. M. Changes in ultraviolet-B and visible optical properties and absorbing pigment concentrations in pecan leaves during a growing season. Agric. For. Meteorol. 120, 229–240. https://doi.org/10.1016/j.agrformet.2003.08.018 (2003).ADS 
Article 

Google Scholar 
4.Mollon, J. D. “Tho’ she kneel’d in that place where they grew…” The uses and origins of primate colour vision. J. Exp. Biol. 146, 21–38. https://doi.org/10.1242/jeb.146.1.21 (1989).ADS 
CAS 
Article 
PubMed 

Google Scholar 
5.Osorio, D. & Vorobyev, M. Photoreceptor sectral sensitivities in terrestrial animals: Adaptations for luminance and colour vision. Proc. R. Soc. B Biol. Sci. 272, 1745–1752. https://doi.org/10.1098/rspb.2005.3156 (2005).CAS 
Article 

Google Scholar 
6.Osorio, D. & Vorobyev, M. A review of the evolution of animal colour vision and visual communication signals. Vis. Res. 48, 2042–2051. https://doi.org/10.1016/j.visres.2008.06.018 (2008).CAS 
Article 
PubMed 

Google Scholar 
7.Bowmaker, J. K. & Dartnall, H. J. A. Visual pigments of rods and cones in a human retina. J. Physiol. Lond. 298, 501–511. https://doi.org/10.1113/jphysiol.1980.sp013097 (1980).CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
8.Bowmaker, J. K. & Hunt, D. M. Evolution of vertebrate visual pigments. Curr. Biol. 16, R484–R489. https://doi.org/10.1016/j.cub.2006.06.016 (2006).CAS 
Article 
PubMed 

Google Scholar 
9.van der Kooi, C. J., Stavenga, D. G., Arikawa, K., Belušič, G. & Kelber, A. Evolution of insect color vision: From spectral sensitivity to visual ecology. Annu. Rev. Entomol. 66, 435–461 (2021).Article 

Google Scholar 
10.Briscoe, A. D. & Chittka, L. The evolution of color vision in insects. Annu. Rev. Entomol. 46, 471–510. https://doi.org/10.1146/annurev.ento.46.1.471 (2001).CAS 
Article 
PubMed 

Google Scholar 
11.Ogawa, Y., Kinoshita, M., Stavenga, D. G. & Arikawa, K. Sex-specific retinal pigmentation results in sexually dimorphic long-wavelength-sensitive photoreceptors in the eastern pale clouded yellow butterfly, Colias erate. J. Exp. Biol. 216, 1916–1923. https://doi.org/10.1242/jeb.083485 (2013).CAS 
Article 
PubMed 

Google Scholar 
12.Kelber, A. Ovipositing butterflies use a red receptor to see green. J. Exp. Biol. 202, 2619–2630 (1999).Article 

Google Scholar 
13.Osorio, D. & Vorobyev, M. Colour vision as an adaptation to frugivory in primates. Proc. R. Soc. B Biol. Sci. 263, 593–599. https://doi.org/10.1098/rspb.1996.0089 (1996).ADS 
CAS 
Article 

Google Scholar 
14.Zaccardi, G., Kelber, A., Sison-Mangus, M. P. & Briscoe, A. D. Color discrimination in the red range with only one long-wavelength sensitive opsin. J. Exp. Biol. 209, 1944–1955. https://doi.org/10.1242/jeb.02207 (2006).Article 
PubMed 

Google Scholar 
15.Wakakuwa, M., Stavenga, D. G., Kurasawa, M. & Arikawa, K. A unique visual pigment expressed in green, red and deep-red receptors in the eye of the small white butterfly, Pieris rapae crucivora. J. Exp. Biol. 207, 2803–2810. https://doi.org/10.1242/jeb.01078 (2004).CAS 
Article 
PubMed 

Google Scholar 
16.Satoh, A. et al. Red-shift of spectral sensitivity due to screening pigment migration in the eyes of a moth, Adoxophyes orana. Zool. Lett. https://doi.org/10.1186/s40851-017-0075-6 (2017).Article 

Google Scholar 
17.Pirih, P. et al. The giant butterfly-moth Paysandisia archon has spectrally rich apposition eyes with unique light-dependent photoreceptor dynamics. J. Comp. Physiol. A Neuroethol. Sens. Neural Behav. Physiol. 204, 639–651. https://doi.org/10.1007/s00359-018-1267-z (2018).CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
18.Cronin, T. W., Jarvilehto, M., Weckstrom, M. & Lall, A. B. Tuning of photoreceptor spectral sensitivity in fireflies (Coleoptera: Lampyridae). J. Comp. Physiol. A Sens. Neural Behav. Physiol. 186, 1–12. https://doi.org/10.1007/s003590050001 (2000).CAS 
Article 

Google Scholar 
19.Lall, A. B. et al. Vision in click beetles (Coleoptera: Elateridae): pigments and spectral correspondence between visual sensitivity and species bioluminescence emission. J. Comp. Physiol. A Neuroethol. Sens. Neural Behav. Physiol. 196, 629–638. https://doi.org/10.1007/s00359-010-0549-x (2010).Article 
PubMed 

Google Scholar 
20.Frentiu, F. D. et al. Adaptive evolution of color vision as seen through the eyes of butterflies. Proc. Natl. Acad. Sci. U.S.A. 104, 8634–8640. https://doi.org/10.1073/pnas.0701447104 (2007).ADS 
CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
21.Liénard, M. A. et al. The evolution of red color vision is linked to coordinated rhodopsin tuning in lycaenid butterflies. Proc. Natl. Acad. Sci. U. S. A. https://doi.org/10.1073/pnas.2008986118 (2021).Article 
PubMed 
PubMed Central 

Google Scholar 
22.Saito, T. et al. Spectral tuning mediated by helix III in butterfly long wavelength-sensitive visual opsins revealed by heterologous action spectroscopy. Zool. Lett. https://doi.org/10.1186/s40851-019-0150-2 (2019).Article 

Google Scholar 
23.Enright, J. M. et al. Cyp27c1 red-shifts the spectral sensitivity of photoreceptors by converting vitamin A1 into A2. Curr. Biol. 25, 3048–3057. https://doi.org/10.1016/j.cub.2015.10.018 (2015).CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
24.Martin, M., Le Galliard, J. F., Meylan, S. & Loew, E. R. The importance of ultraviolet and near-infrared sensitivity for visual discrimination in two species of lacertid lizards. J. Exp. Biol. 218, 458–465. https://doi.org/10.1242/jeb.115923 (2015).Article 
PubMed 

Google Scholar 
25.Ala-Laurila, P., Donner, K. & Koskelainen, A. Thermal activation and photoactivation of visual pigments. Biophys. J. 86, 3653–3662. https://doi.org/10.1529/biophysj.103.035626 (2004).CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
26.Ala-Laurila, P., Pahlberg, J., Koskelainen, A. & Donner, K. On the relation between the photoactivation energy and the absorbance spectrum of visual pigments. Vis. Res. 44, 2153–2158. https://doi.org/10.1016/j.visres.2004.03.031 (2004).Article 
PubMed 

Google Scholar 
27.Barlow, H. B. Purkinje shift and retinal noise. Nature 179, 255–256. https://doi.org/10.1038/179255b0 (1957).ADS 
CAS 
Article 
PubMed 

Google Scholar 
28.Koskelainen, A., Ala-Laurila, P., Fyhrquist, N. & Donner, K. Measurement of thermal contribution to photoreceptor sensitivity. Nature 403, 220–223. https://doi.org/10.1038/35003242 (2000).ADS 
CAS 
Article 
PubMed 

Google Scholar 
29.Luo, D. G., Yue, W. W. S., Ala-Laurila, P. & Yau, K. W. Activation of visual pigments by light and heat. Science 332, 1307–1312. https://doi.org/10.1126/science.1200172 (2011).ADS 
CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
30.Rieke, F. & Baylor, D. A. Origin and functional impact of dark noise in retinal cones. Neuron 26, 181–186. https://doi.org/10.1016/s0896-6273(00)81148-4 (2000).CAS 
Article 
PubMed 

Google Scholar 
31.Cronin, T. W., Johnsen, S., Marshall, N. J. & Warrant, E. J. Visual pigments and photoreceptors. In Visual Ecology, pp. 37–65: Princeton University Press.32.Kelber, A., Yovanovich, C. & Olsson, P. Thresholds and noise limitations of colour vision in dim light. Philos. Trans. R. Soc. B-Biol. Sci. https://doi.org/10.1098/rstb.2016.0065 (2017).Article 

Google Scholar 
33.Kemp, D. J. et al. An integrative framework for the appraisal of coloration in nature. Am. Nat. 185, 705–724. https://doi.org/10.1086/681021 (2015).Article 
PubMed 

Google Scholar 
34.Hawkeswood, T. Observations on some Buprestidae (Coleoptera) from the Blue Mountains, N.S.W.. Aust. Zool. 19, 257–275 (1978).
Google Scholar 
35.Hawkeswood, T. Observations on two sympatric species of Buprestidae (Coleoptera) from sand dunes on the north coast of New South Wales. Victorian Naturalist 98, 146–151 (1981).
Google Scholar 
36.Hawkeswood, T. Observations on some jewel beetles (Coleoptera Buprestidae) from the Armidale district, North-eastern New South Wales. Vic. Nat. 98, 152–155 (1981).
Google Scholar 
37.Poland, T. M., Chen, Y. G., Koch, J. & Pureswaran, D. Review of the emerald ash borer (Coleoptera: Buprestidae), life history, mating behaviours, host plant selection, and host resistance. Can. Entomol. 147, 252–262. https://doi.org/10.4039/tce.2015.4 (2015).Article 

Google Scholar 
38.Bellamy, C. L., Williams, G., Hasenpusch, J. & Sundholm, A. A summary of the published data on host plants and morphology of immature stages of Australian jewel beetles (Coleoptera: Buprestidae), with additional new records. Insecta Mundi, 1–172 (2013).39.Domingue, M. J. et al. Field observations of visual attraction of three European oak buprestid beetles toward conspecific and heterospecific models. Entomol. Exp. Appl. 140, 112–121. https://doi.org/10.1111/j.1570-7458.2011.01139.x (2011).Article 

Google Scholar 
40.Domingue, M. J. et al. Differences in spectral selectivity between stages of visually guided mating approaches in a buprestid beetle. J. Exp. Biol. 219, 2837–2843 (2016).PubMed 

Google Scholar 
41.Pureswaran, D. S. & Poland, T. M. Effects of visual silhouette, leaf size and host species on feeding preference by adult emerald ash borer, Agrilus planipennis Fairmaire (Coleoptera: Buprestidae). Great Lakes Entomol. 42, 4 (2018).
Google Scholar 
42.Crook, D. J. et al. Laboratory and field response of the emerald ash borer (Coleoptera: Buprestidae), to selected regions of the electromagnetic spectrum. J. Econ. Entomol. 102, 2160–2169 (2009).Article 

Google Scholar 
43.Lord, N. P. et al. A cure for the blues: Opsin duplication and subfunctionalization for short-wavelength sensitivity in jewel beetles (Coleoptera: Buprestidae). BMC Evol. Biol. 16, 107 (2016).Article 

Google Scholar 
44.Meglič, A., Ilić, M., Quero, C., Arikawa, K. & Belušič, G. Two chiral types of randomly rotated ommatidia are distributed across the retina of the flathead oak borer Coraebus undatus (Coleoptera: Buprestidae). J. Exp. Biol. 223, jeb225920. https://doi.org/10.1242/jeb.225920 (2020).Article 
PubMed 

Google Scholar 
45.Chen, Y. G. & Poland, T. M. Biotic and abiotic factors affect green ash volatile production and emerald Ash borer adult feeding preference. Environ. Entomol. 38, 1756–1764. https://doi.org/10.1603/022.038.0629 (2009).CAS 
Article 
PubMed 

Google Scholar 
46.Govardovskii, V. I., Fyhrquist, N., Reuter, T., Kuzmin, D. G. & Donner, K. In search of the visual pigment template. Vis. Neurosci. 17, 509–528. https://doi.org/10.1017/s0952523800174036 (2000).CAS 
Article 
PubMed 

Google Scholar 
47.Dartnall, H. J. A. Visual pigment. Trans. Zool. Soc. Lond. 33, 147–152. https://doi.org/10.1111/j.1096-3642.1976.tb00047.x (1976).Article 

Google Scholar 
48.Arikawa, K., Scholten, D. G. W., Kinoshita, M. & Stavenga, D. G. Tuning of photoreceptor spectral sensitivities by red and yellow pigments in the butterfly Papilio xuthus. Zool. Sci. 16, 17–24. https://doi.org/10.2108/zsj.16.17 (1999).Article 

Google Scholar 
49.Das, D., Wilkie, S. E., Hunt, D. M. & Bowmaker, J. K. Visual pigments and oil droplets in the retina of a passerine bird, the canary Serinus canaria: microspectrophotometry and opsin sequences. Vision. Res. 39, 2801–2815. https://doi.org/10.1016/s0042-6989(99)00023-1 (1999).CAS 
Article 
PubMed 

Google Scholar 
50.Sison-Mangus, M. P., Bernard, G. D., Lampel, J. & Briscoe, A. D. Beauty in the eye of the beholder: The two blue opsins of lycaenid butterflies and the opsin gene-driven evolution of sexually dimorphic eyes. J. Exp. Biol. 209, 3079–3090. https://doi.org/10.1242/jeb.02360 (2006).CAS 
Article 
PubMed 

Google Scholar 
51.Bernard, G. D. Red-absorbing visual pigment of butterflies. Science 203, 1125. https://doi.org/10.1126/science.203.4385.1125 (1979).ADS 
CAS 
Article 
PubMed 

Google Scholar 
52.Martínez-Harms, J. et al. Evidence of red sensitive photoreceptors in Pygopleurus israelitus (Glaphyridae: Coleoptera) and its implications for beetle pollination in the southeast Mediterranean. J. Comp. Physiol. A Neuroethol. Sens. Neural Behav. Physiol. 198, 451–463. https://doi.org/10.1007/s00359-012-0722-5 (2012).CAS 
Article 
PubMed 

Google Scholar 
53.Stavenga, D. G. & Arikawa, K. Photoreceptor spectral sensitivities of the Small White butterfly Pieris rapae crucivora interpreted with optical modeling. J. Comp. Physiol. A Neuroethol. Sens. Neural Behav. Physiol. 197, 373–385. https://doi.org/10.1007/s00359-010-0622-5 (2011).Article 
PubMed 
PubMed Central 

Google Scholar 
54.Vorobyev, M., Osorio, D., Bennett, A. T. D., Marshall, N. J. & Cuthill, I. C. Tetrachromacy, oil droplets and bird plumage colours. J. Comp. Physiol. A Neuroethol. Sens. Neural Behav. Physiol. 183, 621–633. https://doi.org/10.1007/s003590050286 (1998).CAS 
Article 

Google Scholar 
55.Vorobyev, M. & Osorio, D. Receptor noise as a determinant of colour thresholds. Proc. R. Soc. Lond. Ser. B Biol. Sci. 265, 351–358. https://doi.org/10.1098/rspb.1998.0302 (1998).CAS 
Article 

Google Scholar 
56.Vorobyev, M., Brandt, R., Peitsch, D., Laughlin, S. B. & Menzel, R. Colour thresholds and receptor noise: Behaviour and physiology compared. Vis. Res. 41, 639–653. https://doi.org/10.1016/s0042-6989(00)00288-1 (2001).CAS 
Article 
PubMed 

Google Scholar 
57.Maia, R., Gruson, H., Endler, J. A. & White, T. E. pavo 2: new tools for the spectral and spatial analysis of colour in R. Methods in Ecology and Evolution 10, 1097–1107 (2019).58.Matsushita, A., Awata, H., Wakakuwa, M., Takemura, S. Y. & Arikawa, K. Rhabdom evolution in butterflies: insights from the uniquely tiered and heterogeneous ommatidia of the Glacial Apollo butterfly, Parnassius glacialis. Proc. R. Soc. B Biol. Sci. 279, 3482–3490. https://doi.org/10.1098/rspb.2012.0475 (2012).Article 

Google Scholar 
59.McCulloch, K. J. et al. Sexual dimorphism and retinal mosaic diversification following the evolution of a violet receptor in butterflies. Mol. Biol. Evol. 34, 2271–2284. https://doi.org/10.1093/molbev/msx163 (2017).CAS 
Article 
PubMed 

Google Scholar 
60.Bates, D., Mächler, M., Bolker, B. M. & Walker, S. C. Fitting linear mixed-effects models using lme4. J. Stat. Softw. 67, 1–48. https://doi.org/10.18637/jss.v067.i01 (2015).Article 

Google Scholar 
61.R: A Language and environment for statistical computing (R Foundation for Statistical Computing, 2018).62.van der Kooi, C. J., Elzenga, J. T. M., Staal, M. & Stavenga, D. G. How to colour a flower: On the optical principles of flower coloration. Proc. R. Soc. B Biol. Sci. 283, 20160429. https://doi.org/10.1098/rspb.2016.0429 (2016).CAS 
Article 

Google Scholar 
63.Horler, D. N. H., Dockray, M. & Barber, J. The red edge of plant leaf reflectance. Int. J. Remote Sens. 4, 273–288. https://doi.org/10.1080/01431168308948546 (1983).Article 

Google Scholar 
64.Silberglied, R. E. Communication in the Ultraviolet. Annu. Rev. Ecol. Syst. 10, 373–398. https://doi.org/10.1146/annurev.es.10.110179.002105 (1979).Article 

Google Scholar 
65.Lind, O. Colour vision and background adaptation in a passerine bird, the zebra finch (Taeniopygia guttata). R. Soc. Open Sci. 3, 160383. https://doi.org/10.1098/rsos.160383 (2016).ADS 
Article 
PubMed 
PubMed Central 

Google Scholar 
66.Santiago, C. et al. Does conspicuousness scale linearly with colour distance? A test using reef fish. Proc. R. Soc. B Biol. Sci. 287, 20201456. https://doi.org/10.1098/rspb.2020.1456 (2020).Article 

Google Scholar 
67.Giurfa, M., Vorobyev, M., Brandt, R., Posner, B. & Menzel, R. Discrimination of coloured stimuli by honeybees: Alternative use of achromatic and chromatic signals. J. Comp. Physiol. A. 180, 235–243. https://doi.org/10.1007/s003590050044 (1997).Article 

Google Scholar 
68.Garcia, J. E., Spaethe, J. & Dyer, A. G. The path to colour discrimination is S-shaped: Behaviour determines the interpretation of colour models. J. Comp. Physiol. A Neuroethol. Sens. Neural Behav. Physiol. 203, 983–997. https://doi.org/10.1007/s00359-017-1208-2 (2017).Article 
PubMed 

Google Scholar 
69.Hart, N. S., Bailes, H. J., Vorobyev, M., Marshall, N. J. & Collin, S. P. Visual ecology of the Australian lungfish (Neoceratodus forsteri). BMC Ecol. 8, 21. https://doi.org/10.1186/1472-6785-8-21 (2008).Article 
PubMed 
PubMed Central 

Google Scholar 
70.Vorobyev, M. Coloured oil droplets enhance colour discrimination. Proc. R. Soc. B Biol. Sci. 270, 1255–1261. https://doi.org/10.1098/rspb.2003.2381 (2003).Article 

Google Scholar 
71.Carleton, K. L. et al. Visual sensitivities tuned by heterochronic shifts in opsin gene expression. Bmc Biol. https://doi.org/10.1186/1741-7007-6-22 (2008).Article 
PubMed 
PubMed Central 

Google Scholar 
72.Seki, T. & Vogt, K. Evolutionary aspects of the diversity of visual pigment chromophores in the class Insecta. Comp. Biochem. Physiol. B Biochem. Mol. Biol. 119, 53–64. https://doi.org/10.1016/s0305-0491(97)00322-2 (1998).Article 

Google Scholar 
73.Stavenga, D. G., Smits, R. P. & Hoenders, B. J. Simple exponential functions describing the absorbance bands of visual pigment spectra. Vis. Res. 33, 1011–1017. https://doi.org/10.1016/0042-6989(93)90237-q (1993).CAS 
Article 
PubMed 

Google Scholar 
74.Kinoshita, M. & Arikawa, K. Color and polarization vision in foraging Papilio. J. Comp. Physiol. A Neuroethol. Sens. Neural Behav. Physiol. 200, 513–526. https://doi.org/10.1007/s00359-014-0903-5 (2014).Article 
PubMed 

Google Scholar 
75.Vorobyev, M. & Menzel, R. Flower advertisement for insects: Bees, a case study. In Adaptive Mechanisms in the Ecology of Vision (eds S. N. Archer et al.) 537–553 (Springer Netherlands, 1999).76.Bernard, G. D. & Remington, C. L. Color vision in Lycaena butterflies: Spectral tuning of receptor arrays in relation to behavioral ecology. Proc. Natl. Acad. Sci. U.S.A. 88, 2783–2787. https://doi.org/10.1073/pnas.88.7.2783 (1991).ADS 
CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
77.McCulloch, K. J., Osorio, D. & Briscoe, A. D. Sexual dimorphism in the compound eye of Heliconius erato: A nymphalid butterfly with at least five spectral classes of photoreceptor. J. Exp. Biol. 219, 2377–2387. https://doi.org/10.1242/jeb.136523 (2016).Article 
PubMed 

Google Scholar  More

1.Hughes TP, Kerry J, Álvarez-Noriega M, Álvarez-Romero J, Anderson K, Baird A, et al. Global warming and recurrent mass bleaching of corals. Nature. 2017;543:373–7.CAS 

Google Scholar 
2.Hoegh-Guldberg O. Climate change, coral bleaching and the future of the world’s coral reefs. Mar Freshw Res. 1999;50:839–66.
Google Scholar 
3.Patten NL, Harrison PL, Mitchell JG. Prevalence of virus-like particles within a staghorn scleractinian coral (Acropora muricata) from the Great Barrier Reef. Coral Reefs. 2008;27:569–80.
Google Scholar 
4.Leruste A, Bouvier T, Bettarel Y. Enumerating viruses in coral mucus. Appl Environ Microbiol. 2012;78:6377–9.CAS 
PubMed 
PubMed Central 

Google Scholar 
5.Nguyen-kim H, Bouvier T, Bouvier C, Bui VN, Le-lan H, Bettarel Y. Viral and bacterial epibionts in thermally-stressed corals. J Mar Sci Eng. 2015;3:1272–86.
Google Scholar 
6.Vega Thurber R, Payet JP, Thurber AR, Correa AMS. Virus–host interactions and their roles in coral reef health and disease. Nat Rev Microbiol. 2017;15:205–16.
Google Scholar 
7.Sweet M, Bythell J. The role of viruses in coral health and disease. J Invertebr Pathol. 2017;147:136–44.PubMed 

Google Scholar 
8.Wilson WH, Francis I, Ryan K, Davy SK. Temperature induction of viruses in symbiotic dinoflagellates. Aquat Micro Ecol. 2001;25:99–102.
Google Scholar 
9.Lohr J, Munn CB, Wilson WH. Characterization of a latent virus-like infection of symbiotic zooxanthellae. Appl Environ Microbiol. 2007;73:2976–81.CAS 
PubMed 
PubMed Central 

Google Scholar 
10.Lawrence SA, Wilson WH, Davy JE, Davy SK. Latent virus-like infections are present in a diverse range of Symbiodinium spp. (Dinophyta). J Phycol. 2014;50:984–97.PubMed 

Google Scholar 
11.Messyasz A, Rosales SM, Mueller RS, Sawyer T, Correa AMS, Thurber AR, et al. Coral bleaching phenotypes associated with differential abundances of nucleocytoplasmic large DNA viruses. Front Mar Sci. 2020;7:555474.
Google Scholar 
12.Marhaver KL, Edwards RA, Rohwer F. Viral communities associated with healthy and bleaching corals. Environ Microbiol. 2008;10:2277–86.CAS 
PubMed 
PubMed Central 

Google Scholar 
13.Correa AMS, Ainsworth TD, Rosales SM, Thurber AR, Butler CR, Vega Thurber RL. Viral outbreak in corals associated with an in situ bleaching event: atypical herpes-like viruses and a new megavirus infecting Symbiodinium. Front Microbiol. 2016;7:127.PubMed 
PubMed Central 

Google Scholar 
14.Bettarel Y, Thuy NT, Huy TQ, Hoang PK, Bouvier T. Observation of virus-like particles in thin sections of the bleaching scleractinian coral Acropora cytherea. J Mar Biol Assoc U K. 2013;93:909–12.
Google Scholar 
15.Lesser MP, Bythell JC, Gates RD, Johnstone RW, Hoegh-Guldberg O. Are infectious diseases really killing corals? Alternative interpretations of the experimental and ecological data. J Exp Mar Biol Ecol. 2007;346:36–44.
Google Scholar 
16.Soffer N, Brandt ME, Correa AMS, Smith TB, Thurber RV. Potential role of viruses in white plague coral disease. ISME J. 2014;8:271–83.CAS 
PubMed 

Google Scholar 
17.Lawrence SA, Davy JE, Aeby GS, Wilson WH, Davy SK. Quantification of virus-like particles suggests viral infection in corals affected by Porites tissue loss. Coral Reefs. 2014;33:687–91.
Google Scholar 
18.Lawrence SA, Davy JE, Wilson WH, Hoegh-Guldberg O, Davy SK. Porites white patch syndrome: associated viruses and disease physiology. Coral Reefs. 2015;34:249–57.
Google Scholar 
19.Pollock FJ, M. Wood-Charlson E, Van Oppen MJH, Bourne DG, Willis BL, Weynberg KD. Abundance and morphology of virus-like particles associated with the coral Acropora hyacinthus differ between healthy and white syndrome-infected states. Mar Ecol Prog Ser. 2014;510:39–43.
Google Scholar 
20.Vega Thurber RL, Correa AMS. Viruses of reef-building scleractinian corals. J Exp Mar Biol Ecol. 2011;408:102–13.
Google Scholar 
21.Weynberg KD, Voolstra CR, Neave MJ, Buerger P, Van Oppen MJH. From cholera to corals: viruses as drivers of virulence in a major coral bacterial pathogen. Sci Rep. 2015;5:17889.CAS 
PubMed 
PubMed Central 

Google Scholar 
22.Quistad SD, Grasis JA, Barr JJ, Rohwer FL. Viruses and the origin of microbiome selection and immunity. ISME J. 2017;11:835–40.CAS 
PubMed 

Google Scholar 
23.Oppen MJHV, Leong J, Gates RD. Coral-virus interactions: a double-edged sword? SYMBIOSIS. 2009;47:1–8.
Google Scholar 
24.Correa AMS, Howard-Varona C, Coy SR, Buchan A, Sullvian MB, Weitz JS. The virus-microbe infection continuum: revisiting the viral rules of life. Nat Rev Microbiol. 2021;19:501–13.CAS 
PubMed 

Google Scholar 
25.Correa AMS, Welsh RM, Vega Thurber RL. Unique nucleocytoplasmic dsDNA and +ssRNA viruses are associated with the dinoflagellate endosymbionts of corals. ISME J. 2013;7:13–27.CAS 
PubMed 

Google Scholar 
26.Lawrence SA, Floge SA, Davy JE, Davy SK, Wilson WH. Exploratory analysis of Symbiodinium transcriptomes reveals potential latent infection by large dsDNA viruses. Environ Microbiol. 2017;19:3909–19.CAS 
PubMed 

Google Scholar 
27.Levin RA, Voolstra CR, Weynberg KD, Van Oppen M. Evidence for a role of viruses in the thermal sensitivity of coral photosymbionts. ISME J. 2017;11:808–12.CAS 
PubMed 

Google Scholar 
28.Knowles B, Bonachela JA, Behrenfeld MJ, Bondoc KG, Cael BB, Carlson CA, et al. Temperate infection in a virus–host system previously known for virulent dynamics. Nat Commun. 2020;11:1–13.
Google Scholar 
29.Montalvo-Proaño J, Buerger P, Weynberg KD, Van Oppen MJH. A PCR-based assay targeting the major capsid protein gene of a dinorna-like ssRNA virus that infects coral photosymbionts. Front Microbiol. 2017;8:1665.PubMed 
PubMed Central 

Google Scholar 
30.Tomaru Y, Katanozaka N, Nishida K, Shirai Y, Tarutani K, Yamaguchi M, et al. Isolation and characterization of two distinct types of HcRNAV, a single-stranded RNA virus infecting the bivalve-killing microalga Heterocapsa circularisquama. Aquat Micro Ecol. 2004;34:207–18.
Google Scholar 
31.Miller JL, Chen S, Nagasaki K, Roseman A, Wepf R, Sewell T, et al. Three-dimensional reconstruction of Heterocapsa circularisquama RNA virus by electron cryo-microscopy. J Gen Virol. 2011;92:1960–70.CAS 
PubMed 

Google Scholar 
32.Shi M, Lin XD, Tian J-H, Chen L-J, Chen X, Li C-IU, et al. Redefining the invertebrate RNA virosphere. Nature. 2016;540:539–43.CAS 
PubMed 

Google Scholar 
33.Domingo E, Sheldon J, Perales C. Viral quasispecies evolution. Microbiol Mol Biol Rev. 2012;76:159–216.CAS 
PubMed 
PubMed Central 

Google Scholar 
34.Sanjuán R, Domingo-Calap P. Mechanisms of viral mutation. Cell Mol Life Sci. 2016;73:4433–48.PubMed 
PubMed Central 

Google Scholar 
35.Vlok M, Lang AS, Suttle CA. Marine RNA virus quasispecies are distributed throughout the oceans. mSphere. 2019;4:1–18.
Google Scholar 
36.Domingo E, Martinez-salas E, Sobrino F, de la Torre JC, Portela A, Ortin J, et al. The quasispecies (extremely heterogeneous) nature of viral RNA genome populations: biological relevance—a review. Gene. 1985;40:1–8.CAS 
PubMed 

Google Scholar 
37.Vignuzzi M, Stone JK, Arnold JJ, Cameron CE, Andino R. Quasispecies diversity determines pathogenesis through cooperative interactions in a viral population. Nature. 2006;439:344–8.CAS 
PubMed 

Google Scholar 
38.Holland J, Spindler K, Horodyski F, Grabau E, Nichol S, VandePol S. Rapid evolution of RNA genomes. Science. 1982;215:1577–85.CAS 
PubMed 

Google Scholar 
39.Gélin P, Postaire B, Fauvelot C, Magalon H. Molecular phylogenetics and evolution reevaluating species number, distribution and endemism of the coral genus Pocillopora Lamarck, 1816 using species delimitation methods and microsatellites. Mol Phylogenet Evol. 2017;109:430–46.PubMed 

Google Scholar 
40.Pratchett MS, McCowan D, Maynard JA, Heron SF. Changes in bleaching susceptibility among corals subject to ocean warming and recurrent bleaching in Moorea, French Polynesia. PLoS ONE. 2013;8:1–10.
Google Scholar 
41.Donovan MK, Adam TC, Shantz AA, Speare KE, Munsterman KS, Rice MM, et al. Nitrogen pollution interacts with heat stress to increase coral bleaching across the seascape. Proc Natl Acad Sci USA. 2020;117:5351–7.CAS 
PubMed 
PubMed Central 

Google Scholar 
42.Siebeck UE, Marshall NJ, Kluter A, Hoegh-Guldberg O. Monitoring coral bleaching using a colour reference card. Coral Reefs. 2006;25:453–60.
Google Scholar 
43.Winters G, Holzman R, Blekhman A, Beer S, Loya Y. Photographic assessment of coral chlorophyll contents: Implications for ecophysiological studies and coral monitoring. J Exp Mar Biol Ecol. 2009;380:25–35.CAS 

Google Scholar 
44.Turnham KE, Wham DC, Sampayo E, LaJeunesse TC. Mutualistic microalgae co-diversify with reef corals that acquire symbionts during egg development. ISME J. 2021;15:3271–85.PubMed 

Google Scholar 
45.Pinzón JH, Lajeunesse TC. Species delimitation of common reef corals in the genus Pocillopora using nucleotide sequence phylogenies, population genetics and symbiosis ecology. Mol Ecol. 2011;20:311–25.PubMed 

Google Scholar 
46.Flot JF, Tillier S. The mitochondrial genome of Pocillopora (Cnidaria: Scleractinia) contains two variable regions: the putative D-loop and a novel ORF of unknown function. Gene. 2007;401:80–87.CAS 
PubMed 

Google Scholar 
47.Pinzón JH, Sampayo E, Cox E, Chauka LJ, Chen CA, Voolstra CR, et al. Blind to morphology: genetics identifies several widespread ecologically common species and few endemics among Indo-Pacific cauliflower corals (Pocillopora, Scleractinia). J Biogeogr. 2013;40:1595–608.
Google Scholar 
48.Johnston EC, Forsman ZH, Flot JF, Schmidt-Roach S, Pinzón JH, Knapp ISS, et al. A genomic glance through the fog of plasticity and diversification in Pocillopora. Sci Rep. 2017;7:5991.PubMed 
PubMed Central 

Google Scholar 
49.Wham DC, Carmichael M, LaJeunesse TC. Microsatellite loci for Symbiodinium goreaui and other Clade C Symbiodinium. Coservation Genet Resour. 2014;6:127–9.
Google Scholar 
50.Bay LK, Ulstrup KE, Nielsen HB, Jarmer H, Goffard N, Willis BL, et al. Microarray analysis reveals transcriptional plasticity in the reef building coral Acropora millepora. Mol Ecol. 2009;18:3062–75.CAS 
PubMed 

Google Scholar 
51.Veglia AJ, Vicéns RER, Grupstra CGB, Howe-Kerr LI, Correa AMS. vAMPirus: an automated, comprehensive virus amplicon sequencing analysis program. 2021: available at https://zenodo.org/record/4549851 (accessed February 17, 2021).52.Edgar RC. UNOISE2: improved error-correction for Illumina 16S and ITS amplicon sequencing. bioRxiv 2016;81257: available at https://doi.org/10.1101/081257.53.Rognes T, Flouri T, Nichols B, Quince C, Mahé F. VSEARCH: a versatile open source tool for metagenomics. PeerJ. 2016;2016:1–22.
Google Scholar 
54.Edgar RC. MUSCLE: a multiple sequence alignment method with reduced time and space complexity. BMC Bioinforma. 2004;5:1–19.
Google Scholar 
55.Buchfink B, Xie C, Huson DH. Fast and sensitive protein alignment using DIAMOND. Nat Methods. 2014;12:59–60.PubMed 

Google Scholar 
56.Darriba D, Posada D, Kozlov AM, Stamatakis A, Morel B, Flouri T. ModelTest-NG: a new and scalable tool for the selection of DNA and protein evolutionary models. Mol Biol Evol. 2020;37:291–4.CAS 
PubMed 

Google Scholar 
57.Oksanen J, Blanchet G, Friendly M, Kindt R, Legendre P, McGlinn D, et al. vegan: Community Ecology Package. R package version 2.4-6. R Package Version 25-6 2019.58.Anderson MJ, Walsh DCI. PERMANOVA, ANOSIM, and the Mantel test in the face of heterogeneous dispersions: what null hypothesis are you testing? Ecol Monogr. 2013;83:557–74.
Google Scholar 
59.Bates DM, Maechler M, Bolker B, Walker S. lme4: Mixed-effects modeling with R. R Package Version 11-7 HttpCRANR-Proj 2014.60.Love MI, Huber W, Anders S. Moderated estimation of fold change and dispersion for RNA-seq data with DESeq2. Genome Biol. 2014;15:550–550.PubMed 
PubMed Central 

Google Scholar 
61.Bankevich A, Nurk S, Antipov D, Gurevich AA, Dvorkin M, Kulikov AS, et al. SPAdes: a new genome assembly algorithm and its applications to single-cell sequencing. J Comput Biol. 2012;19:455–77.CAS 
PubMed 
PubMed Central 

Google Scholar 
62.Goodacre N, Aljanahi A, Nandakumar S, Mikailov M, Khan AS. A reference viral database (RVDB) to enhance bioinformatics analysis of high-throughput sequencing for novel virus detection. mSphere. 2018;3:e00069–18.PubMed 
PubMed Central 

Google Scholar 
63.Rädecker N, Pogoreutz C, Gegner HM, Cárdenas A, Roth F. Heat stress destabilizes symbiotic nutrient cycling in corals. Proc Natl Acad Sci USA. 2021;118:e2022653118.PubMed 
PubMed Central 

Google Scholar 
64.Biebricher CK, Eigen M. What Is a Quasispecies? In: Domingo E (ed). Quasispecies: concept and implications for virology. 2006. Springer Berlin Heidelberg, Berlin, Heidelberg, pp 1–31.65.Gelbart M, Harari S, Ben-ari Y, Kustin T, Wolf D, Mandelboim M, et al. Drivers of within-host genetic diversity in acute infections of viruses. PLoS Pathog. 2020;16:e1009029.CAS 
PubMed 
PubMed Central 

Google Scholar 
66.Breitbart M, Bonnain C, Malki K, Sawaya NA. Phage puppet masters of the marine microbial realm. Nat Microbiol. 2018;3:754–66.CAS 
PubMed 

Google Scholar 
67.Mann NH, Cook A, Millard A, Bailey S, Clokie M. Bacterial photosynthesis genes in a virus. Nature. 2003;424:741.CAS 
PubMed 

Google Scholar 
68.Marquez LM, Redman RS, Rodriguez RJ, Roossinck MJ. A virus in a fungus in a plant: three-way symbiosis required for thermal tolerance. Science. 2007;315:513–5.CAS 
PubMed 

Google Scholar 
69.Holmes EC. The RNA virus quasispecies: fact or fiction? J Mol Biol. 2010;400:271–3.CAS 
PubMed 

Google Scholar 
70.Pybus OG, Rambaut A, Belshaw R, Freckleton RP, Drummond AJ, Holmes EC. Phylogenetic evidence for deleterious mutation load in RNA viruses and its contribution to viral evolution. Mol Biol Evol. 2007;24:845–52.CAS 
PubMed 

Google Scholar 
71.Holmes EC. Patterns of intra- and interhost nonsynonymous variation reveal strong purifying selection in dengue virus. J Virol. 2003;77:11296–8.CAS 
PubMed 
PubMed Central 

Google Scholar 
72.Edwards CTT, Holmes EC, Pybus OG, Wilson DJ, Viscidi RP, Abrams EJ, et al. Evolution of the human immunodeficiency virus envelope gene is dominated by purifying selection. Genetics. 2006;174:1441–53.CAS 
PubMed 
PubMed Central 

Google Scholar 
73.Roux S, Hawley AK, Beltran MT, Scofield M, Schwientek P, Stepanauskas R, et al. Ecology and evolution of viruses infecting uncultivated SUP05 bacteria as revealed by single-cell- and meta-genomics. eLIFE. 2014;3:e03125.PubMed 
PubMed Central 

Google Scholar 
74.Labonté JM, Swan BK, Poulos B, Luo H, Koren S, Hallam SJ, et al. Single-cell genomics-based analysis of virus-host interactions in marine surface bacterioplankton. ISME J. 2015;9:2386–99.PubMed 
PubMed Central 

Google Scholar 
75.Munson-mcgee JH, Peng S, Dewerff S, Stepanauskas R, Whitaker RJ, Weitz JS, et al. A virus or more in (nearly) every cell: ubiquitous networks of virus–host interactions in extreme environments. ISME J. 2018;12:1706–14.CAS 
PubMed 
PubMed Central 

Google Scholar 
76.Díaz-Muñoz SL. Viral coinfection is shaped by host ecology and virus-virus interactions across diverse microbial taxa and environments. Virus Evol. 2017;3:1–14.
Google Scholar 
77.Wang L, Wu S, Liu T, Sun J, Chi S, Liu C, et al. Endogenous viral elements in algal genomes. Acta Oceano Sin. 2014;33:102–7.CAS 

Google Scholar 
78.Moniruzzaman M, Weinheimer AR, Martinez-Gutierrez CA, Aylward FO. Widespread endogenization of giant viruses shapes genomes of green algae. Nature. 2020;588:141–5.CAS 
PubMed 

Google Scholar 
79.Koonin EV, Dolja VV, Krupovic M, Varsani A, Wolf YI, Yutin N, et al. Global organization and proposed megataxonomy of the virus world. Microbiol Mol Biol Rev. 2020;84:e00061–19.CAS 
PubMed 
PubMed Central 

Google Scholar 
80.Holmes EC. The evolution of endogenous viral elements. Cell Host Microbe. 2011;10:368–77.CAS 
PubMed 
PubMed Central 

Google Scholar 
81.Ripp S, Miller RV. The role of pseudolysogeny in bacteriophage-host interactions in a natural freshwater environment. Microbiology. 1997;143:2065–70.CAS 
PubMed 

Google Scholar 
82.Onodera S, Olkkonen VM, Gottlieb P, Strassman J, Qiao XY, Bamford DH, et al. Construction of a transducing virus from double-stranded RNA bacteriophage phi6: establishment of carrier states in host cells. J Virol. 1992;66:190–6.CAS 
PubMed 
PubMed Central 

Google Scholar 
83.de la Higuera I, Kasun GW, Torrance EL, Pratt AA, Maluenda A, Colombet J, et al. Unveiling crucivirus diversity by mining metagenomic data. mBio. 2020;11:e01410–20.PubMed 
PubMed Central 

Google Scholar 
84.Ku C, Sheyn U, Sebé-Pedrós A, Ben-Dor S, Schatz D, Tanay A, et al. A single-cell view on alga-virus interactions reveals sequential transcriptional programs and infection states. Sci Adv. 2020;6:eaba4137.85.Deng L, Ignacio-espinoza JC, Gregory AC, Poulos BT, Weitz JS, Hugenholtz P, et al. Viral tagging reveals discrete populations in Synechococcus viral genome sequence space. Nature. 2014;513:242–6.CAS 

Google Scholar 
86.Jonge PAD, Costa AR, Franklin L, Brouns SJJ, Jonge PAD, Meijenfeldt FABV, et al. Adsorption sequencing as a rapid method to link environmental bacteriophages to hosts. ISCIENCE. 2020;23:101439.PubMed 
PubMed Central 

Google Scholar 
87.Jiang SC, Paul JH. Seasonal and diel abundance of viruses and occurrence of lysogeny/bacteriocinogeny in the marine environment. Mar Ecol Prog Ser. 1994;104:163–72.
Google Scholar 
88.Brum JR, Hurwitz BL, Schofield O, Ducklow HW, Sullivan MB. Seasonal time bombs: dominant temperate viruses affect Southern Ocean microbial dynamics. ISME J. 2016;10:437–49.CAS 
PubMed 

Google Scholar 
89.Winter C, Bouvier T, Weinbauer MG, Thingstad TF. Trade-offs between competition and defense specialists among unicellular planktonic organisms: the “killing the winner” hypothesis revisited. Microbiol Mol Biol Rev. 2010;74:42–57.CAS 
PubMed 
PubMed Central 

Google Scholar 
90.Thingstad TF, Våge S, Storesund JE, Sandaa R, Giske J. A theoretical analysis of how strain-specific viruses can control microbial species diversity. PNAS. 2014;111:7813–8.CAS 
PubMed 
PubMed Central 

Google Scholar 
91.Thingstad TF. Elements of a theory for the mechanisms controlling abundance, diversity, and biogeochemical role of lytic bacterial viruses in aquatic systems. Limnol Oceanogr. 2000;45:1320–8.
Google Scholar 
92.Tomaru Y, Hata N, Masuda T, Tsuji M, Igata K, Masuda Y, et al. Ecological dynamics of the bivalve-killing dinoflagellate Heterocapsa circularisquama and its infectious viruses in different locations of western Japan. Environ Microbiol. 2007;9:1376–83.PubMed 

Google Scholar 
93.Sadeghi M, Tomaru Y, Ahola T. RNA viruses in aquatic unicellular eukaryotes. Viruses 2021.94.Randall RE, Griffin DE. Within host RNA virus persistence: mechanisms and consequences. Curr Opin Virol. 2017;23:35–42.CAS 
PubMed 
PubMed Central 

Google Scholar 
95.Roossinck MJ. Lifestyles of plant viruses. Philos Trans R Soc B Biol Sci. 2010;365:1899–905.
Google Scholar 
96.Honjo MN, Emura N, Kawagoe T, Sugisaka J, Kamitani M, Nagano AJ, et al. Seasonality of interactions between a plant virus and its host during persistent infection in a natural environment. ISME J. 2020;14:506–18.CAS 
PubMed 

Google Scholar 
97.Kim Y, Kim YJ, Paek K-H. Temperature-specific vsiRNA confers RNAi-mediated viral resistance at elevated temperature in Capsicum annuum. J Exp Bot. 2020;72:1432–48.
Google Scholar 
98.Jones RAC. Chapter three – future scenarios for plant virus pathogens as climate change progresses. In: Kielian M, Maramorosch K, Mettenleiter TC (eds).2016. Academic Press, pp 87–147.99.Brüwer JD, Agrawal S, Liew YJ, Aranda M, Voolstra CR. Association of coral algal symbionts with a diverse viral community responsive to heat shock. BMC Microbiol. 2017;17:1–11.
Google Scholar 
100.Cevallos RC, Sarnow P. Temperature protects insect cells from infection by cricket paralysis virus. J Virol. 2010;84:1652–5.CAS 
PubMed 

Google Scholar 
101.Edgar RS, Lielausis I. Temperature-sensitive mutants of bacteriophage T4D: their isolation and genetic characterization. Genetics. 1964;49:649–62.CAS 
PubMed 
PubMed Central 

Google Scholar 
102.Vega Thurber RL, Barott KL, Hall D, Liu H, Rodriguez-Mueller B, Desnues C, et al. Metagenomic analysis indicates that stressors induce production of herpes-like viruses in the coral Porites compressa. Proc Natl Acad Sci USA. 2008;105:18413–8.CAS 
PubMed 
PubMed Central 

Google Scholar 
103.Seifert M, van Nies P, Papini FS, Arnold JJ, Poranen MM, Cameron CE, et al. Temperature controlled high-throughput magnetic tweezers show striking difference in activation energies of replicating viral RNA-dependent RNA polymerases. Nucleic Acids Res. 2020;48:5591–602.CAS 
PubMed 
PubMed Central 

Google Scholar 
104.Wooldridge SA. Differential thermal bleaching susceptibilities amongst coral taxa: re-posing the role of the host. Coral Reefs. 2014;33:15–27.
Google Scholar 
105.Hédouin L, Rouzé H, Berthe C, Perez-Rosales G, Martinez E, Chancerelle Y, et al. Contrasting patterns of mortality in Polynesian coral reefs following the third global coral bleaching event in 2016. Coral Reefs. 2020;39:939–52.
Google Scholar 
106.Tonk L, Sampayo EM, Weeks S, Magno-Canto M, Hoegh-Guldberg O. Host-Specific interactions with environmental factors shape the distribution of Symbiodinium across the Great Barrier Reef. PLoS ONE. 2013;8:e68533–e68533.CAS 
PubMed 
PubMed Central 

Google Scholar 
107.Serrano X, Baums IB, O’Reilly K, Smith TB, Jones RJ, Shearer TL, et al. Geographic differences in vertical connectivity in the Caribbean coral Montastraea cavernosa despite high levels of horizontal connectivity at shallow depths. Mol Ecol. 2014;23:4226–40.CAS 
PubMed 

Google Scholar  More

1.McKenney EA, Koelle K, Dunn RR, Yoder AD. The ecosystem services of animal microbiomes. Mol Ecol. 2018;27:2164–72.CAS 
PubMed 

Google Scholar 
2.Miller ET, Svanbäck R, Bohannan BJM. Microbiomes as metacommunities: understanding host-associated microbes through metacommunity ecology. Trends Ecol Evol. 2018;33:926–35.PubMed 

Google Scholar 
3.Johnson AJ, Vangay P, Al-Ghalith GA, Hillmann BM, Ward TL, Shields-Cutler RR, et al. Daily sampling reveals personalized diet-microbiome associations in humans. Cell Host Microbe. 2019;25:789–802.CAS 
PubMed 

Google Scholar 
4.Björk JR, Dasari M, Grieneisen L, Archie EA. Primate microbiomes over time: Longitudinal answers to standing questions in microbiome research. Am J Primatol. 2019;81:1–23.
Google Scholar 
5.Sun B, Wang X, Bernstein S, Huffman MA, Xia DP, Gu Z, et al. Marked variation between winter and spring gut microbiota in free-ranging Tibetan Macaques (Macaca thibetana). Sci Rep. 2016;6:1–8.
Google Scholar 
6.Wu Q, Wang X, Ding Y, Hu Y, Nie Y, Wei W, et al. Seasonal variation in nutrient utilization shapes gut microbiome structure and function in wild giant pandas. Proc R Soc B Biol Sci. 2017;284:20170955.7.Maurice CF, Knowles SCL, Ladau J, Pollard KS, Fenton A, Pedersen AB, et al. Marked seasonal variation in the wild mouse gut microbiota. ISME J. 2015;9:2423–34.CAS 
PubMed 
PubMed Central 

Google Scholar 
8.Amato KR, Leigh SR, Kent A, Mackie RI, Yeoman CJ, Stumpf RM, et al. The gut microbiota appears to compensate for seasonal diet variation in the wild black howler monkey (Alouatta pigra). Microb Ecol. 2014;69:434–43.PubMed 

Google Scholar 
9.Orkin JD, Campos FA, Myers MS, Cheves Hernandez SE, Guadamuz A, Melin AD. Seasonality of the gut microbiota of free-ranging white-faced capuchins in a tropical dry forest. ISME J. 2019;13:183–96. 22.CAS 
PubMed 

Google Scholar 
10.Springer A, Fichtel C, Al-Ghalith GA, Koch F, Amato KR, Clayton JB, et al. Patterns of seasonality and group membership characterize the gut microbiota in a longitudinal study of wild Verreaux’s sifakas (Propithecus verreauxi). Ecol Evol. 2017;7:5732–45.PubMed 
PubMed Central 

Google Scholar 
11.Jagsi R, Jiang J, Momoh AO, Alderman A, Giordano SH, Buchholz TA, et al. Seasonal cycling in the gut microbiome of the Hadza Hunter-Gatherers of Tanzania. Science. 2017;357:802–6.
Google Scholar 
12.Hicks AL, Lee KJ, Couto-Rodriguez M, Patel J, Sinha R, Guo C, et al. Gut microbiomes of wild great apes fluctuate seasonally in response to diet. Nat Commun. 2018;9:1786.PubMed 
PubMed Central 

Google Scholar 
13.Baniel A, Amato KR, Beehner JC, Bergman TJ, Mercer A, Perlman RF, et al. Seasonal shifts in the gut microbiome indicate plastic responses to diet in wild geladas. Microbiome. 2021;9:1–20.
Google Scholar 
14.Ren T, Grieneisen LE, Alberts SC, Archie EA, Wu M. Development, diet and dynamism: longitudinal and cross-sectional predictors of gut microbial communities in wild baboons. Environ Microbiol. 2016;18:1312–25.PubMed 

Google Scholar 
15.Větrovský T, Baldrian P. The variability of the 16S rRNA gene in bacterial genomes and its consequences for bacterial community analyses. PLoS One. 2013;8:e57923.PubMed 
PubMed Central 

Google Scholar 
16.De Vrieze J, Pinto AJ, Sloan WT, Ijaz UZ. The active microbial community more accurately reflects the anaerobic digestion process: 16S rRNA (gene) sequencing as a predictive tool. Microbiome. 2018;6:63.PubMed 
PubMed Central 

Google Scholar 
17.Kappeler PM, Fichtel C. A 15-year perspective on the social organization and life history of sifaka in Kirindy Forest. In: Long-term field studies of primates. Springer Berlin Heidelberg; 2012. p. 101–21.18.Peckre LR, Defolie C, Kappeler PM, Fichtel C. Potential self-medication using millipede secretions in red-fronted lemurs: combining anointment and ingestion for a joint action against gastrointestinal parasites? Primates. 2018;59:483–94.PubMed 

Google Scholar 
19.Ostner J. Social thermoregulation in redfronted lemurs (Eulemur fulvus rufus). Folia Primatol. 2002;73:175–80.
Google Scholar 
20.Amoroso CR, Kappeler PM, Fichtel C, Nunn CL. Water availability impacts habitat use by red-fronted lemurs (Eulemur rufifrons): An experimental and observational study. Int J Primatol. 2020;41:61–80.
Google Scholar 
21.Koch F, Ganzhorn JU, Rothman JM, Chapman CA, Fichtel C. Sex and seasonal differences in diet and nutrient intake in Verreaux’s sifakas (Propithecus verreauxi). Am J Primatol. 2017;79:1–10.CAS 
PubMed 

Google Scholar 
22.Clough D. Gastro-intestinal parasites of red-fronted lemurs in Kirindy Forest, western Madagascar. J Parasitol. 2010;96:245–51.PubMed 

Google Scholar 
23.Gogarten JF, Calvignac-Spencer S, Nunn CL, Ulrich M, Saiepour N, Nielsen HV, et al. Metabarcoding of eukaryotic parasite communities describes diverse parasite assemblages spanning the primate phylogeny. Mol Ecol Resour. 2020;20:204–15.PubMed 

Google Scholar 
24.Lai GC, Tan TG, Pavelka N. The mammalian mycobiome: A complex system in a dynamic relationship with the host. Wiley Interdiscip Rev Syst Biol Med. 2019;11:e1438.PubMed 

Google Scholar 
25.Mann AE, Mazel F, Lemay MA, Morien E, Billy V, Kowalewski M, et al. Biodiversity of protists and nematodes in the wild nonhuman primate gut. ISME J. 2020;14:609–22.CAS 
PubMed 

Google Scholar 
26.Grieneisen LE, Livermore J, Alberts S, Tung J, Archie EA. Group living and male dispersal predict the core gut microbiome in wild baboons. Integr Comp Biol. 2017;57:770–85.PubMed 
PubMed Central 

Google Scholar 
27.Klindworth A, Pruesse E, Schweer T, Peplies J, Quast C, Horn M, et al. Evaluation of general 16S ribosomal RNA gene PCR primers for classical and next-generation sequencing-based diversity studies. Nucleic Acids Res. 2013;41:1–11.
Google Scholar 
28.Toju H, Tanabe AS, Yamamoto S, Sato H. High-coverage ITS primers for the DNA-based identification of ascomycetes and basidiomycetes in environmental samples. PLoS One. 2012;7:e40863.29.Porat I, Vishnivetskaya TA, Mosher JJ, Brandt CC, Yang ZK, Brooks SC, et al. Characterization of archaeal community in contaminated and uncontaminated surface stream sediments. Microb Ecol. 2010;60:784–95.PubMed 
PubMed Central 

Google Scholar 
30.Gantner S, Andersson AF, Alonso-Sáez L, Bertilsson S. Novel primers for 16S rRNA-based archaeal community analyses in environmental samples. J Microbiol Methods. 2011;84:12–8.CAS 
PubMed 

Google Scholar 
31.Stoeck T, Bass D, Nebel M, Christen R, Jones MDM, Breiner H-W, et al. Multiple marker parallel tag environmental DNA sequencing reveals a highly complex eukaryotic community in marine anoxic water. Mol Ecol. 2010;19:21–31.CAS 
PubMed 

Google Scholar 
32.Bahram M, Anslan S, Hildebrand F, Bork P, Tedersoo L. Newly designed 16S rRNA metabarcoding primers amplify diverse and novel archaeal taxa from the environment. Environ Microbiol Rep. 2019;11:487–94.PubMed 

Google Scholar 
33.Berkelmann D, Schneider D, Hennings N, Meryandini A, Daniel R. Soil bacterial community structures in relation to different oil palm management practices. Sci Data. 2020;7:1–7.
Google Scholar 
34.Chen S, Zhou Y, Chen Y, Gu J. Fastp: An ultra-fast all-in-one FASTQ preprocessor. Bioinformatics. 2018;34:i884–90.PubMed 
PubMed Central 

Google Scholar 
35.Zhang J, Kobert K, Flouri T, Stamatakis A. PEAR: A fast and accurate Illumina paired-end read mergeR. Bioinformatics. 2014;30:614–20.CAS 
PubMed 

Google Scholar 
36.Martin M. Cutadapt removes adapter sequences from high-throughput sequencing reads. EMBnet J. 2011;17:10–2.
Google Scholar 
37.Rognes T, Flouri T, Nichols B, Quince C, Mahé F. VSEARCH: A versatile open source tool for metagenomics. PeerJ. 2016;2016:1–22.
Google Scholar 
38.Edgar RC UNOISE2: improved error-correction for Illumina 16S and ITS amplicon sequencing. bioRxiv. 2016;081257.39.Edgar RC UCHIME2: improved chimera prediction for amplicon sequencing. bioRxiv. 2016;074252.40.Altschul SF, Gish W, Miller W, Myers EW, Lipman DJ. Basic local alignment search tool. J Mol Biol. 1990;215:403–10.CAS 

Google Scholar 
41.Quast C, Pruesse E, Yilmaz P, Gerken J, Schweer T, Yarza P, et al. The SILVA ribosomal RNA gene database project: Improved data processing and web-based tools. Nucleic Acids Res. 2013;41:590–6.
Google Scholar 
42.Guillou L, Bachar D, Audic S, Bass D, Berney C, Bittner L, et al. The protist ribosomal reference database (PR2): A catalog of unicellular eukaryote small sub-unit rRNA sequences with curated taxonomy. Nucleic Acids Res. 2013;41:597–604.
Google Scholar 
43.Nilsson RH, Larsson KH, Taylor AFS, Bengtsson-Palme J, Jeppesen TS, Schigel D, et al. The UNITE database for molecular identification of fungi: Handling dark taxa and parallel taxonomic classifications. Nucleic Acids Res. 2019;47:D259–64.CAS 

Google Scholar 
44.Yarza P, Yilmaz P, Pruesse E, Glöckner FO, Ludwig W, Schleifer KH, et al. Uniting the classification of cultured and uncultured bacteria and archaea using 16S rRNA gene sequences. Nat Rev Microbiol. 2014;12:635–45.CAS 
PubMed 

Google Scholar 
45.Louca S, Parfrey LW, Doebeli M. Decoupling function and taxonomy in the global ocean microbiome. Science. 2016;353:1272–7.CAS 
PubMed 

Google Scholar 
46.Team Rc. R: a language and environment for statistical computing. Vienna, Austria: R Foundation for Statistical Computing; 2020. Available from: https://www.r-project.org.47.Team Rs. RStudio: Integrated Development for R. Boston, MA: RStudio; 2019.48.Andersen KS, Kirkegaard RH, Albertsen M ampvis2: an R package to analyse and visualise 16S rRNA amplicon data. bioRxiv. 2018;10–1.49.Paradis E, Claude J, Strimmer K. APE: Analyses of phylogenetics and evolution in R language. Bioinformatics. 2004;20:289–90.CAS 
PubMed 

Google Scholar 
50.Wickham H, Rstudio. stringr: Simple, consistent wrappers for common string operations. 2019. Available from: https://cran.r-project.org/package=stringr.51.Wickham H. Reshaping data with the reshape package. J Stat Softw. 2007;21:1–20.
Google Scholar 
52.Dowle M, Srinivasan A, Gorecki J, Chirico M, Stetsenko P, Short T, et al. data.table: Extension of “data.frame”. 2019. Available from: https://cran.r-project.org/package=data.table.53.Wickham H, Averick M, Bryan J, Chang W, McGowan L, François R, et al. Welcome to the Tidyverse. J Open Source Softw. 2019;4:1686.
Google Scholar 
54.Wickham H ggplot2: Elegant graphics for data analysis. New York: Spinger-Verlag New York; 2016.55.Chen L, Reeve J, Zhang L, Huang S, Wang X, Chen J. GMPR: A robust normalization method for zero-inflated count data with application to microbiome sequencing data. PeerJ. 2018;2018:1–20.
Google Scholar 
56.Katoh K, Standley DM. MAFFT multiple sequence alignment software version 7: Improvements in performance and usability. Mol Biol Evol. 2013;30:772–80.CAS 
PubMed 
PubMed Central 

Google Scholar 
57.Price MN, Dehal PS, Arkin AP. FastTree 2—Approximately maximum-likelihood trees for large alignments. PLoS One. 2010;5:e9490.58.Rambaut A. FigTree—tree figure drawing tool. Institute of Evolutionary Biology, University of Edinburg; 2020. Available from: http://tree.bio.ed.ac.uk/software/figtree.59.Kembel SW, Cowan PD, Helmus MR, Cornwell WK, Morlon H, Ackerly DD, et al. Picante: R tools for integrating phylogenies and ecology. Bioinformatics. 2010;26:1463–4.CAS 
PubMed 

Google Scholar 
60.Marzano V, Mancinelli L, Bracaglia G, Del Chierico F, Vernocchi P, Di Girolamo F, et al. “Omic” investigations of protozoa and worms for a deeper understanding of the human gut “parasitome.”. PLoS Negl Trop Dis. 2017;11:e0005916.PubMed 
PubMed Central 

Google Scholar 
61.Mandal S, Van Treuren W, White RA, Eggesbø M, Knight R, Peddada SD. Analysis of composition of microbiomes: a novel method for studying microbial composition. Microb Ecol Heal Dis. 2015;26:27663.62.Hothorn T, Hornik K exactRankTests: exact distributions for rank and permutation tests. 2019. Available from: https://cran.r-project.org/package=exactRankTests.63.Pinheiro J, Bates D, DebRoy S, Sarkar D, Core Team R nlme: Linear and nonlinear mixed effects models. 2020. Available from: https://cran.r-project.org/package=nlme.64.van den Boogaart G, Tolosana R compositions: an R package for compositional data analysis. 2020. Available from: http://www.stat.boogaart.de/compositions/.65.Wickham H, Francois R, Core Team R, Rstudio, Jylänki J, Jorgensen M readr: Read rectangular text data. 2018. Available from: http://readr.tidyverse.org.66.Oksanen J, Blanchet G, Friendly M, Kindt R, Legendre P, McGlinn D, et al. vegan: Community ecology package. 2019. Available from: https://cran.r-project.org/package=vegan.67.Lozupone C, Lladser ME, Knights D, Stombaugh J, Knight R. UniFrac: An effective distance metric for microbial community comparison. ISME J. 2011;5:169–72.PubMed 

Google Scholar 
68.Bates D, Mächler M, Bolker BM, Walker SC. Fitting linear mixed-effects models using lme4. J Stat Softw. 2015;67:1–48.
Google Scholar 
69.Reitmeier S, Hitch TCA, Treichel N, Fikas N, Hausmann B, Ramer-Tait AE, et al. Handling of spurious sequences affects the outcome of high-throughput 16S rRNA gene amplicon profiling. ISME COMMUN. 2021;1:31.
Google Scholar 
70.Sweeny AR, Lemon H, Ibrahim A, Nussey DH, Free A, McNally L. A mixed model approach for estimating drivers of microbiota community composition and differential taxonomic abundance (preprint). bioRxiv. 2020; https://doi.org/10.1101/2020.11.24.395715.71.Barr DJ, Levy R, Scheepers C, Tily HJ. Random effects structure for confirmatory hypothesis testing: Keep it maximal. J Mem Lang. 2013;68:255–78.
Google Scholar 
72.Matuschek H, Kliegl R, Vasishth S, Baayen H, Bates D. Balancing type I error and power in linear mixed models. J Mem Lang. 2017;94:305–15.
Google Scholar 
73.Fox J, Weisberg S. An R companion to applied regression. 3rd ed. Thousand Oaks, California: Sage Publishing; 2019.74.Bolker BM. Ecological models and data in R. New Jersey, USA: Princeton University Press; 2008.75.Adams DC, Anthony CD. Using randomization techniques to analyse behavioural data. Anim Behav. 1996;51:733–8.
Google Scholar 
76.Baayen RH. Analyzing linguistic data. Cambridge: Cambridge University Press; 2008.77.Bartón K MuMIn: Multi-Model Inference. 2020. Available from: https://cran.r-project.org/package=MuMIn.78.Lüdecke D. sjPlot: Data Visualization for Statistics in Social Science. 2021. Available from: https://cran.r-project.org/package=sjPlot.79.Edgar RC. MUSCLE: Multiple sequence alignment with high accuracy and high throughput. Nucleic Acids Res. 2004;32:1792–7.CAS 
PubMed 
PubMed Central 

Google Scholar 
80.Kumar S, Stecher G, Li M, Knyaz C, Tamura K. MEGA X: Molecular evolutionary genetics analysis across computing platforms. Mol Biol Evol. 2018;35:1547–9.CAS 
PubMed 
PubMed Central 

Google Scholar 
81.NCBI Resource Coordinators. Database resources of the National Center for Biotechnology Information. Nucleic Acids Res. 2018;46:D8–13.
Google Scholar 
82.Nilsson RH, Anslan S, Bahram M, Wurzbacher C, Baldrian P, Tedersoo L. Mycobiome diversity: high-throughput sequencing and identification of fungi. Nat Rev Microbiol. 2019;17:95–109. 15.CAS 
PubMed 
PubMed Central 

Google Scholar 
83.Koskinen K, Pausan MR, Perras AK, Beck M, Bang C, Mora M, et al. First insights into the diverse human archaeome: specific detection of archaea in the gastrointestinal tract, lung, and nose and on skin. MBio. 2017;8:1–17.
Google Scholar 
84.Raymann K, Moeller AH, Goodman AL, Ochman H. Unexplored archaeal diversity in the great ape gut microbiome. mSphere. 2017;2:1–12.
Google Scholar 
85.Ley RE. Prevotella in the gut: choose carefully. Nat Rev Gastroenterol Hepatol. 2016;13:69–70.CAS 
PubMed 

Google Scholar 
86.Manara S, Asnicar F, Beghini F, Bazzani D, Cumbo F, Zolfo M, et al. Microbial genomes from non-human primate gut metagenomes expand the primate-associated bacterial tree of life with over 1000 novel species. Genome Biol. 2019;20:299.CAS 
PubMed 
PubMed Central 

Google Scholar 
87.Umanets A, de Winter I, IJdema F, Ramiro-Garcia J, van Hooft P, Heitkönig IMA, et al. Occupancy strongly influences faecal microbial composition of wild lemurs. FEMS Microbiol Ecol. 2018;94:1–13.
Google Scholar 
88.Greene LK, Clayton JB, Rothman RS, Semel BP, Semel MA, Gillespie TR, et al. Local habitat, not phylogenetic relatedness, predicts gut microbiota better within folivorous than frugivorous lemur lineages. Biol Lett. 2019;15:5–11.
Google Scholar 
89.Daniel H, Gholami AM, Berry D, Desmarchelier C, Hahne H, Loh G, et al. High-fat diet alters gut microbiota physiology in mice. ISME J. 2014;8:295–308.CAS 
PubMed 

Google Scholar 
90.Hiippala K, Kainulainen V, Kalliomäki M, Arkkila P, Satokari R. Mucosal prevalence and interactions with the epithelium indicate commensalism of Sutterella spp. Front Microbiol. 2016;7:1–13.
Google Scholar 
91.Heintz-Buschart A, Wilmes P. Human gut microbiome: Function matters. Trends Microbiol. 2018;26:563–74.CAS 
PubMed 

Google Scholar 
92.Ricaboni D, Mailhe M, Cadoret F, Vitton V, Fournier PE, Raoult D. ‘Colidextribacter massiliensis’ gen. nov., sp. nov., isolated from human right colon. New Microbes New Infect. 2017;17:27–9.CAS 
PubMed 

Google Scholar 
93.Qin P, Zou Y, Dai Y, Luo G, Zhang X, Xiao L. Characterization a novel butyric acid-producing bacterium Collinsella aerofaciens subsp. shenzhenensis subsp. nov. Microorganisms. 2019;7:78.94.Sizova MV, Muller PA, Stancyk D, Panikov NS, Mandalakis M, Hazen A, et al. Oribacterium parvum sp. nov. and Oribacterium asaccharolyticum sp. nov., obligately anaerobic bacteria from the human oral cavity, and emended description of the genus Oribacterium. Int J Syst Evol Microbiol. 2014;64:2642–9.CAS 
PubMed 
PubMed Central 

Google Scholar 
95.Amato KR, Garber PA. Nutrition and foraging strategies of the black howler monkey (Alouatta pigra) in Palenque National Park, Mexico. Am J Primatol. 2014;76:774–87.CAS 
PubMed 

Google Scholar 
96.White TCR. The significance of unripe seeds and animal tissues in the protein nutrition of herbivores. Biol Rev. 2011;86:217–24.PubMed 

Google Scholar 
97.Ortmann S, Bradley BJ, Stolter C, Ganzhorn JU. Estimating the quality and composition of wild animal diets—a critical survey of methods. In: Hohmann G, Robbins M, Boesch C, editors. Feeding ecology in apes and other primates ecological, physical, and behavioral aspects. Cambridge: Cambridge University Press; 2006. p. 395–418.98.Hippe H, Hagelstein A, Kramer I, Swiderski J, Stackebrandt E. Phylogenetic analysis of Formivibrio citricus, Propionivibrio dicarboxylicus, Anaerobiospirillum thomasii, Succinirnonas amylolytica and Succinivibrio dextrinosolvens and proposal of Succinivibrionaceae fam. nov. Int J Syst Evol Microbiol. 1999;49:779–82.
Google Scholar 
99.Privé F, Kaderbhai NN, Girdwood S, Worgan HJ, Pinloche E, Scollan ND, et al. Identification and characterization of three novel lipases belonging to families II and V from Anaerovibrio lipolyticus 5ST. PLoS One. 2013;8:e69076.100.Flaiz M, Baur T, Brahner S, Poehlein A, Daniel R, Bengelsdorf FR. Caproicibacter fermentans gen. nov., sp. nov., a new caproate-producing bacterium and emended description of the genus Caproiciproducens. Int J Syst Evol Microbiol. 2020;70:4269–79.CAS 
PubMed 

Google Scholar 
101.Watanabe Y, Nagai F, Morotomi M. Characterization of Phascolarctobacterium succinatutens sp. Nov., an asaccharolytic, succinate-utilizing bacterium isolated from human feces. Appl Environ Microbiol. 2012;78:511–8.PubMed 
PubMed Central 

Google Scholar 
102.Clough D, Heistermann M, Kappeler PM. Host intrinsic determinants and potential consequences of parasite infection in free-ranging red-fronted lemurs (Eulemur fulvus rufus). Am J Phys Anthropol. 2010;142:441–52.PubMed 

Google Scholar 
103.Sarkar A, Harty S, Johnson KVA, Moeller AH, Archie EA, Schell LD, et al. Microbial transmission in animal social networks and the social microbiome. Nat Ecol Evol. 2020;4:1020–35.PubMed 

Google Scholar 
104.van Vliet DM, Lin Y, Bale NJ, Koenen M, Villanueva L, Stams AJM, et al. Pontiella desulfatans gen. nov., sp. nov., and Pontiella sulfatireligans sp. nov., two marine anaerobes of the Pontiellaceae fam. nov. producing sulfated glycosaminoglycan-like exopolymers. Microorganisms. 2020;8:920. 18.PubMed Central 

Google Scholar  More