Ecology

1.Hewitt, G. The genetic legacy of the Quaternary ice ages. Nature 405, 907–913. https://doi.org/10.1038/35016000 (2000).ADS 
Article 
PubMed 
CAS 

Google Scholar 
2.Hewitt, G. Genetic consequences of climatic oscillations in the Quaternary. Philos. Trans. R. Soc. Lond. 359, 183–195. https://doi.org/10.1098/rstb.2003.1388 (2004).Article 
CAS 

Google Scholar 
3.Ehlers, J. & Gibbard, P. Quaternary Glaciations-Extent and Chronology: Part I: Europe Vol. 2 (Elsevier, New York, 2004).
Google Scholar 
4.Call, A. et al. Genetic structure and post-glacial expansion of Cornus florida L. (Cornaceae): Integrative evidence from phylogeography, population demographic history, and species distribution modeling. J. Syst. Evol. 54, 136–151. https://doi.org/10.1111/jse.12171 (2016).Article 

Google Scholar 
5.Jackson, S. et al. Vegetation and environment in eastern North America during the Last Glacial Maximum. Quatern. Sci. Rev. 19, 489–508. https://doi.org/10.1016/S0277-3791(99)00093-1 (2000).ADS 
Article 

Google Scholar 
6.Nadeau, S. et al. Contrasting patterns of genetic diversity across the ranges of Pinus monticola and P. strobus: A comparison between eastern and western North American postglacial colonization histories. Am. J. Bot. 102, 1342–1355. https://doi.org/10.3732/ajb.1500160 (2015).Article 
PubMed 
CAS 

Google Scholar 
7.Beaulieu, J. & Simon, J. Genetic structure and variability in Pinus strobus in Quebec. Can. J. For. Res. 24, 1726–1733. https://doi.org/10.1139/x94-223 (1994).Article 

Google Scholar 
8.Provan, J. & Bennett, K. Phylogeographic insights into cryptic glacial refugia. Trends Ecol. Evol. 23, 564–571. https://doi.org/10.1016/j.tree.2008.06.010 (2008).Article 
PubMed 

Google Scholar 
9.Soltis, D., Morris, A., McLachlan, J., Manos, P. & Soltis, P. Comparative phylogeography of unglaciated eastern North America. Mol. Ecol. 15, 4261–4293. https://doi.org/10.1111/j.1365-294X.2006.03061.x (2006).Article 
PubMed 

Google Scholar 
10.Mee, J. & Moore, J. The ecological and evolutionary implications of microrefugia. J. Biogeogr. 41, 837–841. https://doi.org/10.1111/jbi.12254 (2014).Article 

Google Scholar 
11.Hoban, S. et al. Range-wide distribution of genetic diversity in the North American tree Juglans cinerea: A product of range shifts, not ecological marginality or recent population decline. Mol. Ecol. 19, 4876–4891. https://doi.org/10.1111/j.1365-294X.2010.04834.x (2010).Article 
PubMed 

Google Scholar 
12.Hampe, A. & Petit, R. Conserving biodiversity under climate change: The rear edge matters. Ecol. Lett. 8, 461–467. https://doi.org/10.1111/j.1461-0248.2005.00739.x (2005).Article 
PubMed 

Google Scholar 
13.Excoffier, L., Foll, M. & Petit, R. Genetic consequences of range expansions. Annu. Rev. Ecol. Evol. Syst. 40, 481–501. https://doi.org/10.1146/annurev.ecolsys.39.110707.173414 (2009).Article 

Google Scholar 
14.McLachlan, J., Clark, J. & Manos, P. Molecular indicators of tree migration capacity under rapid climate change. Ecology 86, 2088–2098. https://doi.org/10.1890/04-1036 (2005).Article 

Google Scholar 
15.Bemmels, J. & Dick, C. Genomic evidence of a widespread southern distribution during the Last Glacial Maximum for two eastern North American hickory species. J. Biogeogr. 45, 1739–1750. https://doi.org/10.1111/jbi.13358 (2018).Article 

Google Scholar 
16.Jaramillo-Correa, J., Beaulieu, J., Khasa, D. & Bousquet, J. Inferring the past from the present phylogeographic structure of North American forest trees: Seeing the forest for the genes. Can. J. For. Res. 39, 286–307. https://doi.org/10.1139/X08-181 (2009).Article 

Google Scholar 
17.Eckert, C., Samis, K. & Lougheed, S. Genetic variation across species’ geographical ranges: The central–marginal hypothesis and beyond. Mol. Ecol. 17, 1170–1188. https://doi.org/10.1111/j.1365-294X.2007.03659.x (2008).Article 
PubMed 
CAS 

Google Scholar 
18.Foll, M. & Gaggiotti, O. Identifying the environmental factors that determine the genetic structure of populations. Genetics 174, 875–891. https://doi.org/10.1534/genetics.106.059451 (2006).Article 
PubMed 
PubMed Central 
CAS 

Google Scholar 
19.Loveless, M. & Hamrick, J. Ecological determinants of genetic structure in plant populations. Ann. Rev. Ecol. Syst. 15, 65–95. https://doi.org/10.1146/annurev.es.15.110184.000433 (1984).Article 

Google Scholar 
20.Roberts, D., Werner, D., Wadl, P. & Trigiano, R. Inheritance and allelism of morphological traits in eastern redbud (Cercis canadensis L.). Hortic. Res. 2, 1–11 (2015).Article 

Google Scholar 
21.Couvillon, G. Cercis canadensis L. seed size influences germination rate, seedling dry matter, and seedling leaf area. HortScience 37, 206–207 (2002).Article 

Google Scholar 
22.Li, S. et al. Methods for breaking the dormancy of eastern redbud (Cercis canadensis) seeds. Seed Sci. Technol. 41, 27–35 (2013).Article 

Google Scholar 
23.Cheong, E. & Pooler, M. Micropropagation of Chinese redbud (Cercis yunnanensis) through axillary bud breaking and induction of adventitious shoots from leaf pieces. In Vitro Cell. Dev. Biol. Plant 39, 455–458 (2003).Article 

Google Scholar 
24.Pooler, M., Jacobs, K. & Kramer, M. Differential resistance to Botryosphaeria ribis among Cercis taxa. Plant Dis. 86, 880–882. https://doi.org/10.1094/PDIS.2002.86.8.880 (2002).Article 
PubMed 
CAS 

Google Scholar 
25.Trigiano, R., Beaty, R. & Graham, E. Somatic embryogenesis from immature embryos of redbud (Cercis canadensis). Plant Cell Rep. 7, 148–150. https://doi.org/10.1007/BF00270127 (1988).Article 
PubMed 
CAS 

Google Scholar 
26.Wadl, P., Trigiano, R., Werner, D., Pooler, M. & Rinehart, T. Simple sequence repeat markers from Cercis canadensis show wide cross-species transfer and use in genetic studies. J. Am. Soc. Hortic. Sci. 137, 189–201. https://doi.org/10.21273/JASHS.137.3.189 (2012).Article 

Google Scholar 
27.Ony, M. et al. Habitat fragmentation influences genetic diversity and differentiation: Fine-scale population structure of Cercis canadensis (eastern redbud). Ecol. Evol. 10, 3655–3670. https://doi.org/10.1002/ece3.6141 (2020).Article 
PubMed 
PubMed Central 

Google Scholar 
28.Amos, W. et al. Automated binning of microsatellite alleles: Problems and solutions. Mol. Ecol. Resour. 7, 10–14. https://doi.org/10.1111/j.1471-8286.2006.01560.x (2007).Article 
CAS 

Google Scholar 
29.R: A language and environment for statistical computing (R Foundation for Statistical Computing, Vienna, Austria, 2019).30.Kamvar, Z., Tabima, J. & Grünwald, N. Poppr: An R package for genetic analysis of populations with clonal, partially clonal, and/or sexual reproduction. PeerJ 2, e281. https://doi.org/10.7717/peerj.281 (2014).Article 
PubMed 
PubMed Central 

Google Scholar 
31.Kamvar, Z., Brooks, J. & Grünwald, N. Novel R tools for analysis of genome-wide population genetic data with emphasis on clonality. Front. Genet. 6, 208. https://doi.org/10.3389/fgene.2015.00208 (2015).Article 
PubMed 
PubMed Central 
CAS 

Google Scholar 
32.Tsui, C. et al. Population structure and migration pattern of a conifer pathogen, Grosmannia clavigera, as influenced by its symbiont, the mountain pine beetle. Mol. Ecol. 21, 71–86. https://doi.org/10.1111/j.1365-294X.2011.05366.x (2012).Article 
PubMed 

Google Scholar 
33.Nei, M. Estimation of average heterozygosity and genetic distance from a small number of individuals. Genetics 89, 583–590 (1978).Article 
CAS 

Google Scholar 
34.Shannon, C. E. A mathematical theory of communication. Bell System Tech. J. 27, 379–423 (1948).MathSciNet 
Article 

Google Scholar 
35.Goudet, J. Hierfstat, a package for R to compute and test hierarchical F-statistics. Mol. Ecol. Notes 5, 184–186. https://doi.org/10.1111/j.1471-8286.2004.00828.x (2005).Article 

Google Scholar 
36.Hurlbert, S. The nonconcept of species diversity: A critique and alternative parameters. Ecology 52, 577–586. https://doi.org/10.2307/1934145 (1971).Article 

Google Scholar 
37.El Mousadik, A. & Petit, R. High level of genetic differentiation for allelic richness among populations of the Argan tree [Argania spinosa (L.) Skeels] endemic to Morocco. Theor. Appl. Genet. 92, 832–839. https://doi.org/10.1007/BF00221895 (1996).Article 
PubMed 

Google Scholar 
38.Bird, C., Karl, S., Smouse, P. & Toonen, R. In Phylogeography and Population Genetics in Crustacea Vol. 19 (eds Held Christoph, Koenemann Stefan, & Schubart Christoph) pp. 31–55 (Boca Raton, FL: CRC Press, 2011).39.Meirmans, P. & Hedrick, P. Assessing population structure: FST and related measures. Mol. Ecol. Resour. 11, 5–18. https://doi.org/10.1111/j.1755-0998.2010.02927.x (2011).Article 
PubMed 

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

Google Scholar 
41.Earl, D. & Bridgett, V. STRUCTURE HARVESTER: A website and program for visualizing STRUCTURE output and implementing the Evanno method. Conserv. Genet. Resour. 4, 359–361. https://doi.org/10.1007/s12686-011-9548-7 (2012).Article 

Google Scholar 
42.Evanno, G., Regnaut, S. & Goudet, J. Detecting the number of clusters of individuals using the software STRUCTURE: A simulation study. Mol. Ecol. 14, 2611–2620. https://doi.org/10.1111/j.1365-294X.2005.02553.x (2005).Article 
CAS 

Google Scholar 
43.Francis, R. Pophelper: An R package and web app to analyse and visualize population structure. Mol. Ecol. Resour. 17, 27–32. https://doi.org/10.1111/1755-0998.12509 (2017).Article 
PubMed 
CAS 

Google Scholar 
44.Becker, R. & Wilks, A. MAPS: An R Package to Drae Geographical Maps (Version package 3.3.0, 2018).45.Lemon, J. Plotrix: An R Package for Various Plotting Functions (Version R package 3.8–1, 2006).46.Bruvo, R., Michiels, N., D’souza, T. & Schulenburg, H. A simple method for the calculation of microsatellite genotype distances irrespective of ploidy level. Mol. Ecol. 13, 2101–2106. https://doi.org/10.1111/j.1365-294X.2004.02209.x (2004).Article 
PubMed 
CAS 

Google Scholar 
47.Grünwald, N., Everhart, S., Knaus, B. & Kamvar, Z. Best practices for population genetic analyses. Phytopathology 107, 1000–1010. https://doi.org/10.1094/PHYTO-12-16-0425-RVW (2017).Article 
PubMed 

Google Scholar 
48.Jombart, T. & Ahmed, I. adegenet 1.3–1: New tools for the analysis of genome-wide SNP data. Bioinformatics 27, 3070–3072. https://doi.org/10.1093/bioinformatics/btr521 (2011).Article 
PubMed 
PubMed Central 
CAS 

Google Scholar 
49.Jombart, T., Devillard, S. & Balloux, F. Discriminant analysis of principal components: A new method for the analysis of genetically structured populations. BMC Genet. 11, 9. https://doi.org/10.1186/1471-2156-11-94 (2010).Article 

Google Scholar 
50.Cullingham, C., Cooke, J. & Coltman, D. Effects of introgression on the genetic population structure of two ecologically and economically important conifer species: Lodgepole pine (Pinus contorta var. latifolia) and jack pine (Pinus banksiana). Genome 56, 577–585. https://doi.org/10.1139/gen-2013-0071 (2013).Article 
PubMed 
CAS 

Google Scholar 
51.Diniz-Filho, J. et al. Mantel test in population genetics. Genet. Mol. Biol. 36, 475–485. https://doi.org/10.1590/S1415-47572013000400002 (2013).Article 
PubMed 
PubMed Central 

Google Scholar 
52.Mantel, N. The detection of disease clustering and a generalized regression approach. Can. Res. 27, 209–220 (1967).CAS 

Google Scholar 
53.Vegan: Community ecology package v. R package version 2.5–3 (R package version 2.5–3). (2018).54.Excoffier, L., Smouse, P. & Quattro, J. Analysis of molecular variance inferred from metric distances among DNA haplotypes: Application to human mitochondrial DNA restriction data. Genetics 131, 479–491 (1992).Article 
CAS 

Google Scholar 
55.Cornuet, J., Ravigné, V. & Estoup, A. Inference on population history and model checking using DNA sequence and microsatellite data with the software DIYABC (v1.0). BMC Bioinform. 11, 401–411. https://doi.org/10.1186/1471-2105-11-401 (2010).Article 
CAS 

Google Scholar 
56.Cornuet, J. et al. DIYABC v2.0: A software to make approximate Bayesian computation inferences about population history using single nucleotide polymorphism, DNA sequence and microsatellite data. Bioinformatics 30, 1187–1189. https://doi.org/10.1093/bioinformatics/btt763 (2014).Article 
PubMed 
CAS 

Google Scholar 
57.Dickson, J. In Silvics of North America Vol. 2 (eds Burns, R. & Honkala, B.) 266–269 (United States Department of Agriculture-Forest Service, 1990).58.Thomson, A., Dick, C. & Dayanandan, S. A similar phylogeographical structure among sympatric North American birches (Betula) is better explained by introgression than by shared biogeographical history. J. Biogeogr. 42, 339–350. https://doi.org/10.1111/jbi.12394 (2015).Article 

Google Scholar 
59.Petit, R. et al. Glacial refugia: Hotspots but not melting pots of genetic diversity. Science 300, 1563–1565 (2003).ADS 
Article 
CAS 

Google Scholar 
60.David, R. & Hamann, A. Glacial refugia and modern genetic diversity of 22 western North American tree species. Proc. R. Soc. B Biol. Sci. 282, 20142903. https://doi.org/10.1098/rspb.2014.2903 (2015).Article 

Google Scholar 
61.Lumibao, C., Hoban, S. & McLachlan, J. Ice ages leave genetic diversity ‘hotspots’ in Europe but not in Eastern North America. Ecol. Lett. 20, 1459–1468. https://doi.org/10.1111/ele.12853 (2017).Article 
PubMed 

Google Scholar 
62.Bialozyt, R., Ziegenhagen, B. & Petit, R. Contrasting effects of long distance seed dispersal on genetic diversity during range expansion. J. Evol. Biol. 19, 12–20. https://doi.org/10.1111/j.1420-9101.2005.00995.x (2006).Article 
PubMed 
CAS 

Google Scholar 
63.Petit, R. Early insights into the genetic consequences of range expansions. Heredity 106, 203–204. https://doi.org/10.1038/hdy.2010.60 (2011).Article 
PubMed 
CAS 

Google Scholar 
64.Dubreuil, M. et al. Genetic effects of chronic habitat fragmentation revisited: Strong genetic structure in a temperate tree, Taxus baccata (Taxaceae), with great dispersal capability. Am. J. Bot. 97, 303–310. https://doi.org/10.3732/ajb.0900148 (2010).Article 
PubMed 

Google Scholar 
65.Hamrick, J., Godt, M. & Sherman-Broyles, S. In Population Genetics of Forest Trees Vol. 42 (eds Adams, W., Strauss, S., Copes, D. & Griffin, A) 95–124 (Springer, Dordrecht, 1992).66.Hamrick, J. & Godt, M. Effects of life history traits on genetic diversity in plant species. Philos. Trans. R. Soc. Lond. Ser. B Biol. Sci. 351, 1291–1298 (1996).ADS 
Article 

Google Scholar 
67.Spaulding, H. & Rieske, L. The aftermath of an invasion: Structure and composition of central appalachian hemlock forests following establishment of the hemlock woolly adelgid, Aelges tsugae. Biol. Invasions 12, 3135–3143. https://doi.org/10.1007/s10530-010-9704-0 (2010).Article 

Google Scholar 
68.Hadziabdic, D. et al. Analysis of genetic diversity in flowering dogwood natural stands using microsatellites: The effects of dogwood anthracnose. Genetica 138, 1047–1057. https://doi.org/10.1007/s10709-010-9490-8 (2010).Article 
PubMed 
CAS 

Google Scholar 
69.Marquardt, P., Echt, C., Epperson, B. & Pubanz, D. Genetic structure, diversity, and inbreeding of eastern white pine under different management conditions. Can. J. For. Res. 37, 2652–2662 (2007).Article 
CAS 

Google Scholar 
70.Potter, K. et al. Widespread inbreeding and unexpected geographic patterns of genetic variation in eastern hemlock (Tsuga canadensis), an imperiled North American conifer. Conserv. Genet. 13, 475–498. https://doi.org/10.1007/s10592-011-0301-2 (2012).Article 

Google Scholar 
71.Thammina, C., Kidwell-Slak, D., Lura, S. & Pooler, M. SSR markers reveal the genetic diversity of asian Cercis taxa at the US National Arboretum. HortScience 52, 498–502. https://doi.org/10.21273/hortsci11441-16 (2017).Article 

Google Scholar 
72.Chang, C., Bongarten, B. & Hamrick, J. Genetic structure of natural populations of black locust (Robinia pseudoacacia L.) at Coweeta, North Carolina. J. Plant Res. 111, 17–24. https://doi.org/10.1007/BF02507146.pdf (1998).Article 

Google Scholar 
73.Marquardt, P. & Epperson, B. Spatial and population genetic structure of microsatellites in white pine. Mol. Ecol. 13, 3305–3315. https://doi.org/10.1111/j.1365-294X.2004.02341.x (2004).Article 
PubMed 
CAS 

Google Scholar 
74.Victory, E., Glaubitz, J., Rhodes-Jr, O. & Woeste, K. Genetic homogeneity in Juglans nigra (Juglandaceae) at nuclear microsatellites. Am. J. Bot. 93, 118–126. https://doi.org/10.3732/ajb.93.1.118 (2006).Article 
CAS 

Google Scholar 
75.Hadziabdic, D. et al. Genetic diversity of flowering dogwood in the Great Smoky Mountains National Park. Tree Genet. Genomes 8, 855–871. https://doi.org/10.1007/s11295-012-0471-1 (2012).Article 

Google Scholar 
76.Nybom, H. Comparison of different nuclear DNA markers for estimating intraspecific genetic diversity in plants. Mol. Ecol. 13, 1143–1155. https://doi.org/10.1111/j.1365-294X.2004.02141.x (2004).Article 
PubMed 
CAS 

Google Scholar 
77.Donselman, H. Variation in native populations of eastern redbud (Cercis canadensis L.) as influenced by geographic location [USA]. In Proceedings, of the Florida State Horticulture Society Vol. 89. 370–373 (1976).78.Dirr, M. Manual of Woody Landscape Plants: Their Identification, Ornamental Characteristics, Culture, Propagation and Uses (Stipes Publishing Co, Champaign, 1990).
Google Scholar 
79.Fritsch, P., Schiller, A. & Larson, K. Taxonomic implications of morphological variation in Cercis canadensis (Fabaceae) from Mexico and adjacent parts of Texas. Syst. Bot. 34, 510–520. https://doi.org/10.1600/036364409789271254 (2009).Article 

Google Scholar 
80.Nevo, E. et al. Drought and light anatomical adaptive leaf strategies in three woody species caused by microclimatic selection at evolution canyon, Israel. Israel J. Plant Sci. 48, 33–46 (2000).
Google Scholar 
81.Fritsch, P. et al. Leaf adaptations and species boundaries in North American Cercis: Implications for the evolution of dry floras. Am. J. Bot. 105, 1577–1594. https://doi.org/10.1002/ajb2.1155 (2018).Article 
PubMed 

Google Scholar 
82.Raulston, J. Redbud. Am. Nurseryman 171, 39–51 (1990).
Google Scholar 
83.Robertson, K. Cercis: The redbuds. Arnoldia 36, 37–49 (1976).
Google Scholar 
84.Davis, C., Fritsch, P., Li, J. & Donoghue, M. Phylogeny and biogeography of Cercis (Fabaceae): Evidence from nuclear ribosomal ITS and chloroplast ndhF sequence data. Syst. Bot. 27, 289–302. https://doi.org/10.1043/0363-6445-27.2.289 (2002).Article 

Google Scholar 
85.Hopkins, M. In Rhodora Vol. 44 (eds M Fernald, C Eatherby, L Griscom, & S Marris) 193–211 (New England Botanical Club, Inc., 1942).86.Griffin, J., Ranney, T. & Pharr, D. Heat and drought influence photosynthesis, water relations, and soluble carbohydrates of two ecotypes of redbud (Cercis canadensis). J. Am. Soc. Hortic. Sci. 129, 497–502. https://doi.org/10.21273/JASHS.129.4.0497 (2004).Article 
CAS 

Google Scholar 
87.Fritsch, P. & Cruz, B. Phylogeny of Cercis based on DNA sequences of nuclear ITS and four plastid regions: Implications for transatlantic historical biogeography. Mol. Phylogenet. Evol. 62, 816–825. https://doi.org/10.1016/j.ympev.2011.11.016 (2012).Article 
PubMed 

Google Scholar 
88.Chung, M., Chung, M., Oh, G. & Epperson, B. Spatial genetic structure in a Neolitsea sericea population (Lauraceae). Heredity 85, 490–497. https://doi.org/10.1046/j.1365-2540.2000.00781.x (2000).Article 
PubMed 

Google Scholar 
89.Dean, D. et al. Analysis of genetic diversity and population structure for the native tree Viburnum rufidulum occurring in Kentucky and Tennessee. J. Am. Soc. Hortic. Sci. 140, 523–531. https://doi.org/10.21273/JASHS.140.6.523 (2015).Article 
CAS 

Google Scholar 
90.Hagler, J., Mueller, S., Teuber, L., Machtley, S. & Van-Deynze, A. Foraging range of honey bees, Apis mellifera, in alfalfa seed production fields. J. Insect Sci. 11, 144. https://doi.org/10.1673/031.011.14401 (2011).Article 
PubMed 
PubMed Central 

Google Scholar 
91.Pasquet, R. et al. Long-distance pollesn flow assessment through evaluation of pollinator foraging range suggests transgene escape distances. Proc. Natl. Acad. Sci. 105, 13456–13461 (2008).ADS 
Article 

Google Scholar 
92.Hayden, W. Redbud seedpods hold surprises. Bull. Virginia Native Plant Soc. 32, 1–6 (2013).
Google Scholar 
93.Schnabel, A., Laushman, R. & Hamrick, J. Comparative genetic structure of two co-occurring tree species, Maclura pomifera (Moraceae) and Gleditsia triacanthos (Leguminosae). Heredity 67, 357–364. https://doi.org/10.1038/hdy.1991.99 (1991).Article 

Google Scholar 
94.Nakanishi, A., Tomaru, N., Yoshimaru, H., Manabe, T. & Yamamoto, S. Effects of seed- and pollen-mediated gene dispersal on genetic structure among Quercus salicina saplings. Heredity 102, 182–189. https://doi.org/10.1038/hdy.2008.101 (2008).Article 
PubMed 
CAS 

Google Scholar 
95.Vekemans, X. & Hardy, O. New insights from fine-scale spatial genetic structure analyses in plant populations. Mol. Ecol. 13, 921–935. https://doi.org/10.1046/j.1365-294X.2004.02076.x (2004).Article 
PubMed 
CAS 

Google Scholar 
96.Gonzales, E., Hamrick, J., Smouse, P., Trapnell, D. & Peakall, R. The impact of landscape disturbance on spatial genetic structure in the Guanacaste tree, Enterolobium cyclocarpum (Fabaceae). J. Hered. 101, 133–143. https://doi.org/10.1093/jhered/esp101 (2009).Article 
PubMed 
CAS 

Google Scholar 
97.Post, D. Change in nutrient content of foods stored by eastern woodrats (Neotoma floridana). J. Mammal. 73, 835–839 (1992).Article 

Google Scholar 
98.Surrency, D. & Owsley, C. (ed. Natural Resources Conservation Service United States Department of Agriculture) 146 (United States Department of Agriculture, Natural Resources Conservation Service, 2001).99.Wakeland, B. & Swihart, R. Ratings of white-tailed deer preferences for woody browse in Indiana. Proceedings of the Indiana Academy of Science 118, 96–101 (2009).
Google Scholar 
100.Wright, V., Fleming, E. & Post, D. Survival of Rhyzopertha dominica (Coleoptera, Bostrichidae) on fruits and seeds collected from woodrat nests in Kansas. J. Kansas Entomol. Soc. 63, 344–347 (1990).
Google Scholar 
101.Sullivan, J. (ed. Forest Service U.S. Department of Agriculture, Rocky Mountain Research Station) (U.S. Department of Agriculture, Forest Service, Rocky Mountain Research Station. Fire Sciences Laboratory, 1994).102.Weir, B. & Ott, J. Genetic data analysis II. Trends Genet. 13, 379 (1997).Article 

Google Scholar 
103.Magni, C., Ducousso, A., Caron, H., Petit, R. & Kremer, A. Chloroplast DNA variation of Quercus rubra L. in North America and comparison with other Fagaceae. Mol. Ecol. 14, 513–524. https://doi.org/10.1111/j.1365-294X.2005.02400.x (2005).Article 
PubMed 
CAS 

Google Scholar 
104.Peterson, B. & Graves, W. Chloroplast phylogeography of Dirca palustris L. indicates populations near the glacial boundary at the Last Glacial Maximum in eastern North America. Journal of Biogeography 43, 314–327, doi:https://doi.org/10.1111/jbi.12621 (2016).105.Shaw, J. & Small, R. Chloroplast DNA phylogeny and phylogeography of the North American plums (Prunus subgenus Prunus section Prunocerasus, Rosaceae). Am. J. Bot. 92, 2011–2030. https://doi.org/10.3732/ajb.92.12.2011 (2005).Article 
PubMed 
CAS 

Google Scholar 
106.Rowe, K., Heske, E., Brown, P. & Paige, K. Surviving the ice: Northern refugia and postglacial colonization. Proc. Natl. Acad. Sci. 101, 10355–10359 (2004).ADS 
Article 
CAS 

Google Scholar 
107.Graignic, N., Tremblay, F. & Bergeron, Y. Influence of northern limit range on genetic diversity and structure in a widespread North American tree, sugar maple (Acer saccharum Marshall). Ecol. Evol. 8, 2766–2780. https://doi.org/10.1002/ece3.3906 (2018).Article 
PubMed 
PubMed Central 

Google Scholar 
108.Bemmels, J., Knowles, L. & Dick, C. Genomic evidence of survival near ice sheet margins for some, but not all, North American trees. Proc. Natl. Acad. Sci. 116, 8431–8436. https://doi.org/10.7302/Z2JS9NNG (2019).Article 
PubMed 
PubMed Central 
CAS 

Google Scholar 
109.Jia, H. & Steven, R. Fossil leaves and fruits of Cercis L. (Leguminosae) from the Eocene of western North America. International Journal of Plant Sciences 175, 601–612, doi:https://doi.org/10.1086/675693 (2014).110.Kraemer, M. & Favi, F. Emergence phenology of Osmia lignaria subsp lignaria (Hymenoptera: Megachilidae), its parasitoid Chrysura kyrae (Hymenoptera: Chrysididae), and bloom of Cercis canadensis. Environ. Entomol. 39, 351–358. https://doi.org/10.1603/en09242 (2010).Article 
PubMed 
CAS 

Google Scholar 
111.USDA. Census of horticultural specialties. Volume 3 AC-12-SS-3, Washington, DC (2014). More

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Netting seabirds is great fun. And it’s crucial for science and conservation.In this photo, taken in July, I’m heading out to capture birds on Inishtrahull, Ireland’s northernmost island. Lying about 10 kilometres northeast of the mainland, the island is home to thousands of seabirds during the summer nesting season, including storm petrels (Hydrobates pelagicus), Manx shearwaters (Puffinus puffinus) and fulmars (Fulmarus glacialis). The fulmars are experiencing a population crash, which I’m investigating.Migratory birds are protected here, but we need to know where they go when they leave their nests. I attach an identification band and a light-level geolocator — a sensor that helps to estimate location from day length — to every bird I catch. A few birds get GPS monitors, but we dole those out carefully, because each costs about £1,000 (US$1,368).The birds tend to nest on cliffs, and on a bad day I’ll catch just three. Some days I get as many as 12. Shearwaters are a challenge, because they nest only at night: I have to use a torch and watch my step.The birds don’t enjoy getting caught, but the stress is only temporary. The data they provide help us to understand their migration patterns. Fulmars spend almost their entire lives at sea. I’m interested in finding out how often they share waters with long-line fishers, which would be a potentially fatal scenario for the birds. That’s not the only threat: a study has found that more than half of beached North Sea fulmars have large amounts of plastic in their stomachs (see go.nature.com/3cosy8j).The lighthouse behind me is now home to the Inishtrahull Bird Observatory, a base for birdwatchers. I’m the founding chairman, but the observatory, part of a network of monitoring spots stretching 1,200 kilometres from Scotland to southern Ireland, will outlive me. It will be a centre for science and education for years to come.

Nature 599, 340 (2021)
doi: https://doi.org/10.1038/d41586-021-03055-8

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Soil-derived microbial communities were subject to diversity removal by treatments with dilution (D0  > D1  > D2), filtering (bacteria predominantly “Bonly”), and heat (spore forming “SF”), and incubated under different moisture and temperature in order to generate distinct microbial communities in a model soil matrix [6]. In a sibling study aiming to disentangle the biotic and abiotic drivers of carbon use efficiency, we observed that the microbial community characteristics, e.g. bacterial community structure, bacterial diversity, fungi presence, and enzymatic activity influenced microbial community carbon use efficiency [6]. Here, we analyzed the formed SOM after four months of growth on cellobiose, using a method commonly used to quantify thermal stability and gradual stabilization of SOM [10]. The hydrocarbon compounds released at each temperature for each sample during the pyrolytic phase of Rock-Eval® was used to calculate the Bray–Curtis-based chemical dissimilarity of the soil samples as a proxy for soil C composition, and the and the Rock-Eval® thermal stability index (R-index) was calculated as a proxy for C persistence, as previously [10]. Bacterial or fungal diversity did not drive SOM composition. However, the resultant NMDS and analysis of similarity (ANOSIM) (R = 0.198, P  D2); selection of spore-forming microorganisms (SF); fungal exclusion (“Bonly”); inoculated into a model soil and grown on cellobiose as sole carbon source for 120 days under two temperatures (15 oC and 25 oC) and two moistures (30% and 60% WHC) in a full factorial design. Non-metric multidimensional scaling of Bray–Curtis distance from the pyrolyzed fraction of SOM based on Rock-Eval® analysis. Red contour lines represent the SOM thermal-stability R-index with higher numbers indicating more thermal-stable SOM. Significant explanatory variables (P  More

Alexander JS, Zhang C, Shi K, Riordan P (2016) A granular view of a snow leopard population using camera traps in Central China. Biol Conserv 197:27–31
Google Scholar 
Aryal A, Brunton D, Ji W, Karmacharya D, McCarthy T, Bencini R et al. (2014) Multipronged strategy including genetic analysis for assessing conservation options for the snow leopard in the central Himalaya. J Mammal 95:871–881
Google Scholar 
Atzeni L, Cushman SA, Bai D, Wang J, Chen P, Shi K et al. (2020) Meta-replication, sampling bias, and multi-scale model selection: a case study on snow leopard (Panthera uncia) in western China. Ecol Evol 10:7686–7712PubMed 
PubMed Central 

Google Scholar 
Bai D-F, Chen P-J, Atzeni L, Cering L, Li Q, Shi K (2018) Assessment of habitat suitability of the snow leopard (Panthera uncia) in Qomolangma National Nature Reserve based on MaxEnt modeling. Zool Res 39:373–386PubMed 
PubMed Central 

Google Scholar 
Balkenhol N, Cushman SA, Storfer AT, Waits LP, eds (2016) Landscape genetics: concepts, methods, applications, 1st ed. John Wiley and Sons Ltd. Oxford, UKBauman D, Vleminckx J, Hardy OJ, Drouet T (2018c) Testing and interpreting the shared space-environment fraction in variation partitioning analyses of ecological data. Oikos 128:274–285
Google Scholar 
Bauman D, Drouet T, Dray S, Vleminckx J (2018b) Disentangling good from bad practices in the selection of spatial or phylogenetic eigenvectors. Ecography 41:1638–1649
Google Scholar 
Bauman D, Drouet T, Fortin M, Dray S (2018a) Optimizing the choice of a spatial weighting matrix in eigenvector-based methods. Ecology 99:2159–2166PubMed 

Google Scholar 
Benone NL, Soares BE, Lobato CMC, Seabra LB, Bauman D, Montag LF de A (2020) How modified landscapes filter rare species and modulate the regional pool of ecological traits? HydrobiologiaBlair C, Weigel DE, Lazik M, Keeley AT, Walker FM, Landguth E et al. (2012) A simulation-based evaluation of methods for inferring linear barriers to gene flow. Mol Ecol Resour 12:822–833PubMed 

Google Scholar 
Blanchet FG, Legendre P, Borcard D (2008) Forward selection of explanatory variables. Ecology 89:2623–2632PubMed 

Google Scholar 
Bothwell HM, Cushman SA, Woolbright SA, Hersch-Green EI, Evans LM, Whitham TG et al. (2017) Conserving threatened riparian ecosystems in the American West: Precipitation gradients and river networks drive genetic connectivity and diversity in a foundation riparian tree (Populus angustifolia). Mol Ecol 26:5114–5132PubMed 

Google Scholar 
Breyne P, Mergeay J, Casaer J (2014) Roe deer population structure in a highly fragmented landscape. Eur J Wildl Res 60:909–917
Google Scholar 
Bruggeman DJ, Wiegand T, Fernández N (2010) The relative effects of habitat loss and fragmentation on population genetic variation in the red-cockaded woodpecker (Picoides borealis). Mol Ecol 19:3679–3691PubMed 

Google Scholar 
Burgess SM, Garrick RC (2020) Regional replication of landscape genetics analyses of the Mississippi slimy salamander, Plethodon mississippi. Landsc Ecol 35:337–351
Google Scholar 
Castillo JA, Epps CW, Davis AR, Cushman SA (2014) Landscape effects on gene flow for a climate-sensitive montane species, the American pika. Mol Ecol 23:843–856PubMed 

Google Scholar 
Chambers SM (1995) Spatial structure, genetic variation, and the neighborhood adjustment to effective population size. Conserv Biol 9:1312–1315PubMed 

Google Scholar 
Charlesworth B (2009) Effective population size and patterns of molecular evolution and variation. Nat Rev Genet 10:195–205CAS 
PubMed 

Google Scholar 
Chybicki IJ, Burczyk J (2009) Simultaneous estimation of null alleles and inbreeding coefficients. J Heredity 100:106–113CAS 

Google Scholar 
Cushman SA, Landguth EL (2010) Scale dependent inference in landscape genetics. Landsc Ecol 25:967–979
Google Scholar 
Cushman SA, Shirk A, Landguth EL (2012) Separating the effects of habitat area, fragmentation and matrix resistance on genetic differentiation in complex landscapes. Landsc Ecol 27:369–380
Google Scholar 
Cushman SA, Shirk AJ, Landguth EL (2013) Landscape genetics and limiting factors. Conserv Genet 14:263–274
Google Scholar 
Cushman SA, McRae BH, McGarigal K (2015) Basics of landscape ecology: an introduction to landscapes and population processes for landscape geneticists. In: Balkhenol N, Cushman S, Storfer A, Waits L (Eds) Landscape genetics: concepts, methods, applications. Wiley, Ofxord, UK, p 11–34
Google Scholar 
Cushman SA, McKelvey KS, Hayden J, Schwartz MK (2006) Gene flow in complex landscapes: testing multiple hypotheses with causal modeling. Am Naturalist 168:486–499
Google Scholar 
Dalongeville A, Andrello M, Mouillot D, Lobreaux S, Fortin M, Lasram F et al. (2018) Geographic isolation and larval dispersal shape seascape genetic patterns differently according to spatial scale. Evol Appl 11:1437–1447CAS 
PubMed 
PubMed Central 

Google Scholar 
Dharmarajan G, Beasley JC, Fike JA, Rhodes OE (2014) Effects of landscape, demographic and behavioral factors on kin structure: testing ecological predictions in a mesopredator with high dispersal capability. Anim Conserv 17:225–234
Google Scholar 
Do C, Waples RS, Peel D, Macbeth GM, Tillett BJ, Ovenden JR (2014) NeEstimator v2: re-implementation of software for the estimation of contemporary effective population size (Ne) from genetic data. Mol Ecol Resour 14:209–214CAS 
PubMed 

Google Scholar 
Dray S, Dufour A (2007) The ade4 package: implementing the duality diagram for ecologists. J Stat Soft 22:1–20
Google Scholar 
Dray S, Legendre P, Peres-Neto PR (2006) Spatial modelling: a comprehensive framework for principal coordinate analysis of neighbour matrices (PCNM). Ecol Model 196:483–493
Google Scholar 
Dray S, Bauman D, Blanchet G, Borcard D, Clappe S, Guenard G, et al. (2020) adespatial: multivariate multiscale spatial analysis. R package version 0.3-8. https://CRAN.R-project.org/package=adespatialEvans JS (2020) spatialEco. R package version 1.3-1, https://github.com/jeffreyevans/spatialEcoForester BR, Jones MR, Joost S, Landguth EL, Lasky JR (2016) Detecting spatial genetic signatures of local adaptation in heterogeneous landscapes. Mol Ecol 25:104–120CAS 
PubMed 

Google Scholar 
François O, Durand E (2010) Spatially explicit Bayesian clustering models in population genetics. Mol Ecol Resour 10:773–784PubMed 

Google Scholar 
Frankham R (1996) Relationship of genetic variation to population size in wildlife. Conserv Biol 10:1500–1508
Google Scholar 
Frankham R (2005) Genetics and extinction. Biol Conserv 126:131–140
Google Scholar 
Galpern P, Peres-Neto PR, Polfus J, Manseau M (2014) MEMGENE: Spatial pattern detection in genetic distance data. Methods Ecol Evolution 5:1116–1120
Google Scholar 
Galpern P, Manseau M, Hettinga P, Smith K, Wilson P (2012) Allelematch: an R package for identifying unique multilocus genotypes where genotyping error and missing data may be present. Mol Ecol Resour 12:771–778PubMed 

Google Scholar 
Guerrero J, Byrne AW, Lavery J, Presho E, Kelly G, Courcier EA et al. (2018) The population and landscape genetics of the European badger (Meles meles) in Ireland. Ecol Evol 8:10233–10246PubMed 
PubMed Central 

Google Scholar 
Guillot G, Leblois R, Coulon A, Frantz AC (2009) Statistical methods in spatial genetics. Mol Ecol 18:4734–4756PubMed 

Google Scholar 
Hearn AJ, Cushman SA, Goossens B, Ross J, Macdonald EA, Hunter LTB et al. (2019) Predicting connectivity, population size and genetic diversity of Sunda clouded leopards across Sabah, Borneo. Landsc Ecol 34:275–290
Google Scholar 
Hein C, Moniem HEA, Wagner HH (2021) Can we compare effect size of spatial genetic structure between studies and species using moran eigenvector maps? Frontiers. Ecol Evol 9:612718
Google Scholar 
Jackson DA (1993) Stopping rules in principal components analysis: a comparison of heuristical and statistical approaches. Ecology 74:2204–2214
Google Scholar 
Jackson ND, Fahrig L (2016) Habitat amount, not habitat configuration, best predicts population genetic structure in fragmented landscapes. Landsc Ecol 31:951–968
Google Scholar 
Janecka J, Jackson R, Yuquang Z et al. (2008) Population monitoring of snow leopards using noninvasive collection of scat samples: a pilot study. Anim Conserv 11:401–411
Google Scholar 
Janecka JE, Janecka JE, Yu-Guang Z, Di-Qiang L, Munkhtsog B, Bayaraa M et al. (2017) Range-wide snow leopard phylogeography supports three subspecies. J Hered 108:597–607PubMed 

Google Scholar 
Johansson Ö, Rauset G, Samelius G, McCarthy T, Andrén H, Tumursukh L et al. (2016) Land sharing is essential for snow leopard conservation. Biol Conserv 203:1–7
Google Scholar 
Johansson Ö, Koehler G, Rauset G, Samelius G, Andrén H, Mishra C et al. (2018) Sex-specific seasonal variation in puma and snow leopard home range utilization. Ecosphere 9(8):e02371. https://doi.org/10.1002/ecs2.2371.Article 

Google Scholar 
Johansson Ö, Ausilio G, Low M, Lkhagvajav P, Weckworth B, Sharma K (2021) The timing of breeding and independence for snow leopard females and their cubs. Mamm Biol 101:173–180
Google Scholar 
Jombart T (2008b) adegenet: a R package for the multivariate analysis of genetic markers. Bioinformatics 24:1403–1405CAS 
PubMed 

Google Scholar 
Jombart T, Pontier D, Dufour AB (2009) Genetic markers in the playground of multivariate analysis. Heredity 102:330–341CAS 
PubMed 

Google Scholar 
Jombart T, Devillard S, Dufour A-B, Pontier D (2008a) Revealing cryptic spatial patterns in genetic variability by a new multivariate method. Heredity 101:hdy200834
Google Scholar 
Jombart T (2017) An introduction to adegenet 2.1.0. https://github.com/thibautjombart/adegenet/wiki/TutorialsKarmacharya DB, Thapa K, Shrestha R, Dhakal M, Janecka JE (2011) Noninvasive genetic population survey of snow leopards (Panthera uncia) in Kangchenjunga conservation area, Shey Phoksundo National Park and surrounding buffer zones of Nepal. BMC Research Notes 4:516PubMed 
PubMed Central 

Google Scholar 
Kaszta Ż, Cushman SA, Hearn AJ, Burnham D, Macdonald EA, Goossens B et al. (2019) Integrating Sunda clouded leopard (Neofelis diardi) conservation into development and restoration planning in Sabah (Borneo). Biol Conserv 235:63–76
Google Scholar 
Kaszta Ż, Cushman SA, Htun S, Naing H, Burnham D, Macdonald DW (2020) Simulating the impact of Belt and Road initiative and other major developments in Myanmar on an ambassador felid, the clouded leopard, Neofelis nebulosa. Landsc Ecol 35:727–746
Google Scholar 
Kindt R, Coe R (2005) Tree diversity analysis: a manual and software for common statistical methods for ecological and biodiversity studies. World Agroforestry Centre (ICRAF), Nairobi (Kenya). http://www.worldagroforestry.org/output/tree-diversity-analysisKorablev MP, Poyarkov AD, Karnaukhov AS, Zvychaynaya EYU, Kuksin AN, Malykh SV et al. (1776) Large-scale and fine-grain population structure and genetic diversity of snow leopards (Panthera uncia Schreber, 1776) from the northern and western parts of the range with an emphasis on the Russian population. Conserv Genet 22:397–410
Google Scholar 
Kuhn A, Bauman D, Darras H, Aron S (2017) Sex-biased dispersal creates spatial genetic structure in a parthenogenetic ant with a dependent-lineage reproductive system. Heredity 119:207–213CAS 
PubMed 
PubMed Central 

Google Scholar 
Landguth E, Cushman S, Schwz M, Mckelvey K, Mury M, Luikart G (2010) Quantifying the lag time to detect barriers in landscape genetics. Mol Ecol 19:4179–4191CAS 
PubMed 

Google Scholar 
Landguth EL, Cushman SA (2010) cdpop: A spatially explicit cost distance population genetics program. Mol Ecol Resour 10:156–161CAS 
PubMed 

Google Scholar 
Landguth EL, Fedy BC, Oyler-Mccance SJ, Garey AL, Emel SL, Mumma M et al. (2012) Effects of sample size, number of markers, and allelic richness on the detection of spatial genetic pattern. Mol Ecol Resour 12:276–284
Google Scholar 
Legendre P, Oksanen J, ter Braak CJF (2011) Testing the significance of canonical axes in redundancy analysis. Methods Ecol Evol 2:269–277
Google Scholar 
Legendre P, Fortin M-J, Borcard D (2015) Should the Mantel test be used in spatial analysis? Methods Ecol Evol 6:1239–1247
Google Scholar 
Li J, Weckworth BV, McCarthy TM, Liang X, Liu Y, Xing R et al. (2020) Defining priorities for global snow leopard conservation landscapes. Biol Conserv 241:108387
Google Scholar 
Macdonald EA, Cushman SA, Landguth EL, Hearn AJ, Malhi Y, Macdonald DW (2018) Simulating impacts of rapid forest loss on population size, connectivity and genetic diversity of Sunda clouded leopards (Neofelis diardi) in Borneo. Plos One 13:e0196974PubMed 
PubMed Central 

Google Scholar 
Manel S, Poncet BN, Legendre P, Gugerli F, Holderegger R (2010) Common factors drive adaptive genetic variation at different spatial scales in Arabis alpina. Mol Ecol 19:3824–35PubMed 

Google Scholar 
Mateo-Sánchez MC, Balkenhol N, Cushman S, Pérez T, Domínguez A, Saura S (2015) A comparative framework to infer landscape effects on population genetic structure: are habitat suitability models effective in explaining gene flow? Landscape Ecol 30:1405–1420
Google Scholar 
McCarthy TM, Fuller TK, Munkhtsog B (2005) Movements and activities of snow leopards in Southwestern Mongolia. Biol Conserv 124:527–537
Google Scholar 
McCarthy T, Mallon D, Jackson R, Zahler P, McCarthy K (2017) Panthera uncia. The IUCN red list of threatened species 2017: e.T22732A50664030. https://doi.org/10.2305/IUCN.UK.2017-2.RLTS.T22732A50664030.en. Accessed 29 June 2019Miquel C, Bellemain E, Poillot C, Bessiere J, Durand A, Taberlet P (2006) Quality indexes to assess the reliability of genotypes in studies using noninvasive sampling and multiple-tube approach. Mol Ecol Notes 6:985–988
Google Scholar 
Neel MC, McKelvey K, Ryman N, Lloyd MW, Bull RS, Allendorf FW et al. (2013) Estimation of effective population size in continuously distributed populations: there goes the neighborhood. Heredity 111:189–199CAS 
PubMed 
PubMed Central 

Google Scholar 
Oksanen J, Blanchet FG, Friendly M, Kindt R, Legendre P, McGlinn D, et al. (2019) vegan: community ecology package. R package version 2.5-6. https://CRAN.R-project.org/package=vegancitatOyler-McCance SJ, Fedy BC, Landguth EL (2013) Sample design effects in landscape genetics. Conserv Genet 14:275–285
Google Scholar 
Paradis E (2010) pegas: an R package for population genetics with an integrated–modular approach. Bioinformatics 26:419–420CAS 
PubMed 

Google Scholar 
Patterson N, Price AL, Reich D (2006) Population structure and eigenanalysis. Plos Genet 2:e190PubMed 
PubMed Central 

Google Scholar 
Peakall R, Smouse PE (2006) GENALEX 6: genetic analysis in Excel. Population genetic software for teaching and research. Mol Ecol Notes 6:288–295
Google Scholar 
Peakall R, Smouse PE (2012) GenAlEx 6.5: genetic analysis in Excel. Population genetic software for teaching and research-an update. Bioinformatics 28:2537–2539CAS 
PubMed 
PubMed Central 

Google Scholar 
Pearson K (1901) On lines and planes of closest fit to systems of points in space. Philos Mag 2:559–572
Google Scholar 
Peres-Neto PR, Legendre P, Dray S, Borcard D (2006) Variation partitioning of species data matrices: estimation and comparison of fractions. Ecology 87:2614–2625PubMed 

Google Scholar 
Phillips SJ, Anderson RP, Schapire RE (2006) Maximum entropy modeling of species geographic distributions. Ecol Model 190:231–259
Google Scholar 
Riordan P, Cushman SA, Mallon D, Shi K, Hughes J (2016) Predicting global population connectivity and targeting conservation action for snow leopard across its range. Ecography 39:419–426
Google Scholar 
Rousset F (2008) genepop’007: a complete re-implementation of the genepop software for Windows and Linux. Mol Ecol Resour 8:103–106PubMed 

Google Scholar 
Ruiz-Gonzalez A, Cushman SA, Madeira MJ, Randi E, Gómez-Moliner BJ (2015) Isolation by distance, resistance and/or clusters? Lessons learned from a forest-dwelling carnivore inhabiting a heterogeneous landscape. Mol Ecol 24:5110–5129PubMed 

Google Scholar 
Savary P, Foltête J, Moal H, Vuidel G, Garnier S (2021) Analysing landscape effects on dispersal networks and gene flow with genetic graphs. Mol Ecol Resour 21:1167–1185PubMed 

Google Scholar 
Schwartz MK, McKelvey KS (2008) Why sampling scheme matters: the effect of sampling scheme on landscape genetic results. Conserv Genet 10:441
Google Scholar 
Shirk AJ, Cushman SA (2011) sGD: software for estimating spatially explicit indices of genetic diversity. Mol Ecol Resour 11:922–934CAS 
PubMed 

Google Scholar 
Shirk AJ, Cushman SA (2014) Spatially-explicit estimation of Wright’s neighborhood size in continuous populations. Front Ecol Evolution 2:62
Google Scholar 
Shirk AJ, Landguth EL, Cushman SA (2020) The effect of gene flow from unsampled demes in landscape genetic analysis. Mol Ecol Resour 21:394–403PubMed 

Google Scholar 
Shirk AJ, Wallin DO, Cushman SA, Rice CG, Warheit KI (2010) Inferring landscape effects on gene flow: a new model selection framework. Mol Ecol 19:3603–3619CAS 
PubMed 

Google Scholar 
Shirk AJ, Landguth EL, Cushman SA (2017a) A comparison of regression methods for model selection in individual-based landscape genetic analysis. Mol Ecol Resour 18:55–67Shirk AJ, Landguth EL, Cushman SA (2017b) A comparison of individual-based genetic distance metrics for landscape genetics. Mol Ecol Resour 17:1308–1317Short-Bull RA, Cushman S, Mace R, Chilton T, Kendall K, Landguth E et al. (2011) Why replication is important in landscape genetics: American black bear in the Rocky Mountains. Mol Ecol 20:1092–1107
Google Scholar 
Shrestha B, Kindlmann P (2020) Implications of landscape genetics and connectivity of snow leopard in the Nepalese Himalayas for its conservation. Sci Rep 10:19853CAS 
PubMed 
PubMed Central 

Google Scholar 
Stekhoven DJ (2013) missForest: nonparametric missing value imputation using random forest. R package version 1.4Storfer A, Murphy MA, Spear SF, Holderegger R, Waits LP (2010) Landscape genetics: where are we now? Mol Ecol 19:3496–3514PubMed 

Google Scholar 
Wagner HH, Fortin M-J (2012) A conceptual framework for the spatial analysis of landscape genetic data. Conserv Genet 14:253–261
Google Scholar 
Wagner HH, Fortin MJ (2016) Basics of spatial data analysis: linking landscape and genetic data for landscape genetic studies. In: Balkenhol N, Cushman SA, Storfer AT, Waits LP, eds. Landscape genetics: concepts, methods, applications, 1st ed. John Wiley and Sons Ltd. Oxford, UK. pp. 77–98Wahlund S (1928) Composition of populations and correlation appearances viewed in relation to the studies of inheritance. Hereditas 11:65–106
Google Scholar 
Wan HY, Cushman SA, Ganey JL (2019) Improving habitat and connectivity model predictions with multi-scale resource selection functions from two geographic areas. Landsc Ecol 34:503–519
Google Scholar 
Wasserman TN, Cushman SA, Schwartz MK, Wallin DO (2010) Spatial scaling and multi-model inference in landscape genetics: Martes americana in northern Idaho. Landsc Ecol 25:1601–1612
Google Scholar 
Weckworth B (2021) Snow leopard (Panthera uncia) genetics: the knowledge gaps, needs, and implications for conservation. J Indian I Sci 101:279–290
Google Scholar 
Wollenberg AL, van den (1977) Redundancy analysis. An alternative for canonical correlation analysis. Psychometrika 42:207–219
Google Scholar 
Wright S (1943) Isolation by distance. Genetics 28:114–138CAS 
PubMed 
PubMed Central 

Google Scholar 
Wright S (1946) Isolation by distance under diverse systems of mating. Genetics 31:39–59CAS 
PubMed 
PubMed Central 

Google Scholar 
Wu S, Qinye Y, Du Z (2003) Delineation of eco-geographic regional system of China. J Geogr Sci 13:309
Google Scholar 
Zeller KA, Jennings MK, Vickers TW, Ernest HB, Cushman SA, Boyce WM (2018) Are all data types and connectivity models created equal? Validating common connectivity approaches with dispersal data. Divers Distrib 24:868–879
Google Scholar 
Zhang Y, Hacker C, Zhang Y, Xue Y, Wu L, Dai Y et al. (2019) An analysis of genetic structure of snow leopard populations in Sanjiang-Yuan and Qilianshan National Parks. Acta Theriologica Sin 39:442–449
Google Scholar  More

1.Bever JD, Mangan SA, Alexander HM. Maintenance of plant species diversity by pathogens. Annu Rev Ecol Evol Syst. 2015;46:305–25.
Google Scholar 
2.Peay KG. The mutualistic niche: mycorrhizal symbiosis and community dynamics. Annu Rev Ecol Evol Syst. 2016;47:143–64.
Google Scholar 
3.Tedersoo L, Bahram M, Zobel M. How mycorrhizal associations drive plant population and community biology. Science. 2020;367:eaba1223.CAS 
PubMed 

Google Scholar 
4.Bever JD, Dickie IA, Facelli E, Facelli JM, Klironomos J, Moora M, et al. Rooting theories of plant community ecology in microbial interactions. Trends Ecol Evol. 2010;25:468–78.PubMed 
PubMed Central 

Google Scholar 
5.Bever JD, Platt TG, Morton ER. Microbial population and community dynamics on plant roots and their feedbacks on plant communities. Annu Rev Microbiol. 2012;66:265–83.CAS 
PubMed 
PubMed Central 

Google Scholar 
6.Ke PJ, Miki T. Incorporating the soil environment and microbial community into plant competition theory. Front Microbiol. 2015;6:1066.PubMed 
PubMed Central 

Google Scholar 
7.Mangan SA, Schnitzer SA, Herre EA, Mack KM, Valencia MC, Sanchez EI, et al. Negative plant-soil feedback predicts tree-species relative abundance in a tropical forest. Nature. 2010;466:752–5.CAS 
PubMed 

Google Scholar 
8.Bennett JA, Maherali H, Reinhart KO, Lekberg Y, Hart MM, Klironomos J. Plant-soil feedbacks and mycorrhizal type influence temperate forest population dynamics. Science. 2017;355:181–4.CAS 
PubMed 

Google Scholar 
9.Teste FP, Kardol P, Turner BL, Wardle DA, Zemunik G, Renton M, et al. Plant-soil feedback and the maintenance of diversity in Mediterranean-climate shrublands. Science. 2017;355:173–6.CAS 
PubMed 

Google Scholar 
10.Semchenko M, Leff JW, Lozano YM, Saar S, Davison J, Wilkinson A, et al. Fungal diversity regulates plant-soil feedbacks in temperate grassland. Sci Adv. 2018;4:eaau4578.CAS 
PubMed 
PubMed Central 

Google Scholar 
11.Chen L, Swenson NG, Ji N, Mi X, Ren H, Guo L, et al. Differential soil fungus accumulation and density dependence of trees in a subtropical forest. Science. 2019;366:124–8.CAS 
PubMed 

Google Scholar 
12.LaManna JA, Walton ML, Turner BL, Myers JA. Negative density dependence is stronger in resource-rich environments and diversifies communities when stronger for common but not rare species. Ecol Lett. 2016;19:657–67.PubMed 

Google Scholar 
13.Eppinga MB, Baudena M, Johnson DJ, Jiang J, Mack KM, Strand AE, et al. Frequency-dependent feedback constrains plant community coexistence. Nat Ecol Evol. 2018;2:1403–7.PubMed 

Google Scholar 
14.Brundrett MC. Coevolution of roots and mycorrhizas of land plants. New Phytol. 2002;154:275–304.PubMed 

Google Scholar 
15.van der Linde S, Suz LM, Orme CDL, Cox F, Andreae H, Asi E, et al. Environment and host as large-scale controls of ectomycorrhizal fungi. Nature. 2018;558:243–8.PubMed 

Google Scholar 
16.Schroeder JW, Martin JT, Angulo DF, Razo IAD, Barbosa JM, Perea R, et al. Host plant phylogeny and abundance predict root‐associated fungal community composition and diversity of mutualists and pathogens. J Ecol. 2019;107:1557–66.
Google Scholar 
17.Jiang J, Karen CA, Mara B, Maarten BE, James AE, James DB. Pathogens and mutualists as joint drivers of host species coexistence and turnover: implications for plant competition and succession. Am Nat. 2020;195:591–602.
Google Scholar 
18.Schroeder JW, Dobson A, Mangan SA, Petticord DF, Herre EA. Mutualist and pathogen traits interact to affect plant community structure in a spatially explicit model. Nat Commun. 2020;11:2204.CAS 
PubMed 
PubMed Central 

Google Scholar 
19.Gilbert GS, Webb CO. Phylogenetic signal in plant pathogen‐host range. Proc Natl Acad Sci USA. 2007;104:4979–83.CAS 
PubMed 
PubMed Central 

Google Scholar 
20.Liu X, Liang M, Etienne RS, Wang Y, Staehelin C, Yu S. Experimental evidence for a phylogenetic Janzen‐Connell effect in a subtropical forest. Ecol Lett. 2012;15:111–8.PubMed 

Google Scholar 
21.Liang M, Liu X, Etienne RS, Huang F, Wang Y, Yu S. Arbuscular mycorrhizal fungi counteract the Janzen‐Connell effect of soil pathogens. Ecology. 2015;96:562–74.PubMed 

Google Scholar 
22.Benítez MS, Hersh MH, Vilgalys R, Clark JS. Pathogen regulation of plant diversity via effective specialization. Trends Ecol Evol. 2013;28:705–11.PubMed 

Google Scholar 
23.Klironomos J, Zobel M, Tibbett M. Forces that structure plant communities: quantifying the importance of the mycorrhizal symbiosis. New Phytol. 2011;189:366–70.PubMed 

Google Scholar 
24.van der Heijden MGA, Bardgett RD, van Straalen NM. The unseen majority: soil microbes as drivers of plant diversity and productivity in terrestrial ecosystems. Eco Lett. 2008;11:296–310.
Google Scholar 
25.Wiegand T, Moloney KA. Rings, circles, and null‐models for point pattern analysis in ecology. Oikos. 2004;104:209–29.
Google Scholar 
26.Perry GL, Miller BP, Enright NJ. A comparison of methods for the statistical analysis of spatial point patterns in plant ecology. Plant Ecol. 2006;187:59–82.
Google Scholar 
27.Law R, Illian J, Burslem DF, Gratzer G, Gunatilleke CV, Gunatilleke IA. Ecological information from spatial patterns of plants: insights from point process theory. J Ecol. 2009;97:616–28.
Google Scholar 
28.Liang M, Liu X, Parker IM, Johnson D, Zheng Y, Luo S, et al. Soil microbes drive phylogenetic diversity-productivity relationships in a subtropical forest. Sci Adv. 2019;5:eaax5088.CAS 
PubMed 
PubMed Central 

Google Scholar 
29.Chen Y, Jia P, Cadotte MW, Wang P, Liu X, Qi Y, et al. Rare and phylogenetically distinct plant species exhibit less diverse root-associated pathogen communities. J Ecol. 2019;107:1226–37.
Google Scholar 
30.Peters HA. Neighbour‐regulated mortality: the influence of positive and negative density dependence on tree populations in species‐rich tropical forests. Ecol Lett. 2003;6:757–65.
Google Scholar 
31.Cutler DR, Edwards TC Jr, Beard KH, Cutler A, Hess KT, Gibson J, et al. Random forests for classification in ecology. Ecology. 2007;88:2783–92.PubMed 

Google Scholar 
32.Kattge J, Diaz S, Lavorel S, Prentice IC, Leadley P, Bönisch G, et al. TRY – a global database of plant traits. Glob Chang Biol. 2011;17:2905–35.PubMed Central 

Google Scholar 
33.Davey ML, Heegaard E, Halvorsen R, Ohlson M, Kauserud H. Seasonal trends in the biomass and structure of bryophyte-associated fungal communities explored by 454 pyrosequencing. New Phytol. 2012;195:844–56.CAS 
PubMed 

Google Scholar 
34.Nguyen NH, Song Z, Bates ST, Branco S, Tedersoo L, Menke J, et al. FUNGuild: an open annotation tool for parsing fungal community datasets by ecological guild. Fungal Ecol. 2016;20:241–8.
Google Scholar 
35.Leff JW, Bardgett RD, Wilkinson A, Jackson BG, Pritchard WJ, Jonathan R, et al. Predicting the structure of soil communities from plant community taxonomy, phylogeny, and traits. ISME J. 2018;12:1794–805.PubMed 
PubMed Central 

Google Scholar 
36.Wang Z, Jiang Y, Deane DC, He F, Shu W, Liu Y. Effects of host phylogeny, habitat and spatial proximity on host specificity and diversity of pathogenic and mycorrhizal fungi in a subtropical forest. New Phytol. 2019;223:462–74.PubMed 

Google Scholar 
37.Zhao Z, Li X, Liu MF, Merckx VS, Saunders RM, Zhang D. Specificity of assemblage, not fungal partner species, explains mycorrhizal partnerships of mycoheterotrophic Burmannia plants. ISME J. 2021;15:1614–27.CAS 
PubMed 
PubMed Central 

Google Scholar 
38.Peay KG, Baraloto C, Fine PV. Strong coupling of plant and fungal community structure across western Amazonian rainforests. ISME J. 2013;7:1852–61.CAS 
PubMed 
PubMed Central 

Google Scholar 
39.Barberán A, McGuire KL, Wolf JA, Jones FA, Wright SJ, Turner BL, et al. Relating belowground microbial composition to the taxonomic, phylogenetic, and functional trait distributions of trees in a tropical forest. Ecol Lett. 2015;18:1397–405.PubMed 

Google Scholar 
40.LaManna JA, Belote RT, Burkle LA, Catano CP, Myers JA. Negative density dependence mediates biodiversity-productivity relationships across scales. Nat Ecol Evol. 2017;1:1107–15.PubMed 

Google Scholar 
41.Peh KS, Lewis SL, Lloyd J. Mechanisms of monodominance in diverse tropical tree‐dominated systems. J Ecol. 2011;99:891–8.
Google Scholar 
42.Johnson DJ, Clay K, Phillips RP. Mycorrhizal associations and the spatial structure of an old-growth forest community. Oecologia. 2018;186:195–204.PubMed 

Google Scholar 
43.Waud M, Busschaert P, Lievens B, Jacquemyn H. Specificity and localised distribution of mycorrhizal fungi in the soil may contribute to co-existence of orchid species. Fungal Ecol. 2016;20:155–65.
Google Scholar 
44.Põlme S, Bahram M, Jacquemyn H, Kennedy P, Kohout P, Moora M, et al. Host preference and network properties in biotrophic plant–fungal associations. New Phytol. 2018;217:1230–9.PubMed 

Google Scholar 
45.Simard SW, Beiler KJ, Bingham MA, Deslippe JR, Philip LJ, Teste FP. Mycorrhizal networks: mechanisms, ecology and modelling. Fungal Biol Rev. 2012;26:39–60.
Google Scholar 
46.Bever JD, Westover KM, Antonovics J. Incorporating the soil community into plant population dynamics: the utility of the feedback approach. J Ecol. 1997;85:561–73.
Google Scholar 
47.Bardgett RD, Wardle DA. Aboveground-belowground linkages: biotic interactions, ecosystem processes, and global change. New York: Oxford University Press; 2010.48.Kandlikar GS, Johnson CA, Yan X, Kraft NJ, Levine JM. Winning and losing with microbes: how microbially mediated fitness differences influence plant diversity. Ecol Lett. 2019;22:1178–91.PubMed 

Google Scholar 
49.Swenson NG, Iida Y, Howe R, Wolf A, Umaña MN, Petprakob K, et al. Tree co-occurrence and transcriptomic response to drought. Nat Commun. 2017;8:1996.PubMed 
PubMed Central 

Google Scholar 
50.Řezáčová V, Gryndler M, Bukovská P, Šmilauer P, Jansa J. Molecular community analysis of arbuscular mycorrhizal fungi—contributions of PCR primer and host plant selectivity to the detected community profiles. Pedobiologia. 2016;59:179–87.
Google Scholar 
51.Hart MM, Reader RJ, Klironomos JN. Plant coexistence mediated by arbuscular mycorrhizal fungi. Trends Ecol Evol. 2003;18:418–23.
Google Scholar 
52.Taylor DL, Walters WA, Lennon NJ, Bochicchio J, Krohn A, Caporaso JG, et al. Accurate estimation of fungal diversity and abundance through improved lineage-specific primers optimized for Illumina amplicon sequencing. Appl Environ Microbiol. 2016;82:7217–26.CAS 
PubMed 
PubMed Central 

Google Scholar 
53.Lekberg Y, Vasar M, Bullington LS, Sepp SK, Antunes PM, Bunn R, et al. More bang for the buck? Can arbuscular mycorrhizal fungal communities be characterized adequately alongside other fungi using general fungal primers? New Phytol. 2018;220:971–6.PubMed 

Google Scholar 
54.Egan CP, Rummel A, Kokkoris V, Klironomos J, Lekberg Y, Hart MM. Using mock communities of arbuscular mycorrhizal fungi to evaluate fidelity associated with Illumina sequencing. Fungal Ecol. 2018;33:52–64.
Google Scholar  More

1.Fuhrman JA. Marine viruses and their biogeochemical and ecological effects. Nature. 1999;399:541–8.CAS 
PubMed 

Google Scholar 
2.Suttle CA. Viruses in the sea. Nature. 2005;437:356–61.CAS 
PubMed 

Google Scholar 
3.Breitbart M. Marine viruses: truth or dare. Ann Rev Mar Sci. 2012;4:425–48.PubMed 

Google Scholar 
4.Brum JR, Sullivan MB. Rising to the challenge: accelerated pace of discovery transforms marine virology. Nat Rev Microbiol. 2015;13:147–59.5.Breitbart M, Thompson L, Suttle C, Sullivan M. Exploring the vast diversity of marine viruses. Oceanography. 2007;20:135–9.
Google Scholar 
6.Puxty RJ, Millard AD, Evans DJ, Scanlan DJ. Shedding new light on viral photosynthesis. Photosynth Res. 2015;126:71–97.7.Sharon I, Tzahor S, Williamson S, Shmoish M, Man-Aharonovich D, Rusch DB, et al. Viral photosynthetic reaction center genes and transcripts in the marine environment. ISME J. 2007;1:492–501.CAS 
PubMed 

Google Scholar 
8.Roux S, Hawley AK, Torres Beltran M, 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:e03125.9.Trubl G, Jang H Bin, Roux S, Emerson JB, Solonenko N, Vik DR, et al. Soil viruses are underexplored players in ecosystem carbon processing. mSystems. 2018;3:e00076–18.10.Roux S, Brum JR, Dutilh BE, Sunagawa S, Duhaime MB, Loy A, et al. Ecogenomics and potential biogeochemical impacts of globally abundant ocean viruses. Nature. 2016;537:689–93.CAS 
PubMed 

Google Scholar 
11.Hurwitz BL, Brum JR, Sullivan MB. Depth-stratified functional and taxonomic niche specialization in the ‘core’ and ‘flexible’ Pacific Ocean Virome. ISME J. 2015;9:472–84.CAS 
PubMed 

Google Scholar 
12.Gazitúa MC, Vik DR, Roux S, Gregory AC, Bolduc B, Widner B, et al. Potential virus-mediated nitrogen cycling in oxygen-depleted oceanic waters. ISME J. 2021;15:981–98.PubMed 

Google Scholar 
13.Brum JR, Ignacio-Espinoza JC, Roux S, Doulcier G, Acinas SG, Alberti A, et al. Ocean plankton. Patterns and ecological drivers of ocean viral communities. Science. 2015;348:1261498.PubMed 

Google Scholar 
14.Cassman N, Prieto-Davó A, Walsh K, Silva GGZ, Angly F, Akhter S, et al. Oxygen minimum zones harbour novel viral communities with low diversity. Environ Microbiol. 2012;14:3043–65.CAS 
PubMed 

Google Scholar 
15.Vik D, Gazitúa MC, Sun CL, Zayed AA, Aldunate M, Mulholland MR, et al. Genome-resolved viral ecology in a marine oxygen minimum zone. Environ Microbiol. 2021;23:2858–74.16.Mara P, Vik D, Pachiadaki MG, Suter EA, Poulos B, Taylor GT, et al. Viral elements and their potential influence on microbial processes along the permanently stratified Cariaco Basin redoxcline. ISME J. 2020;14:3079–92.CAS 
PubMed 
PubMed Central 

Google Scholar 
17.Tiano L, Garcia-Robledo E, Dalsgaard T, Devol AH, Ward BB, Ulloa O, et al. Oxygen distribution and aerobic respiration in the north and south eastern tropical Pacific oxygen minimum zones. Deep Res Part I Oceanogr Res Pap. 2014;94:173–83.CAS 

Google Scholar 
18.Schmidtko S, Stramma L, Visbeck M. Decline in global oceanic oxygen content during the past five decades. Nature. 2017;542:335–9.CAS 
PubMed 

Google Scholar 
19.Paulmier A, Ruiz-Pino D. Oxygen minimum zones (OMZs) in the modern ocean. Prog Oceanogr. 2009;80:113–28.
Google Scholar 
20.Wright JJ, Konwar KM, Hallam SJ. Microbial ecology of expanding oxygen minimum zones. Nat Rev Microbiol. 2012;10:381–94.CAS 
PubMed 

Google Scholar 
21.Bertagnolli AD, Stewart FJ. Microbial niches in marine oxygen minimum zones. Nat Rev Microbiol. 2018;1:723–9.22.Codispoti LA, Friedrich GE, Packard TT, Glover HE, Kelly PJ, Spinrad RW, et al. High nitrite levels off northern Peru: a signal of instability in the marine denitrification rate. Science. 1986;233:1200 LP–1202.
Google Scholar 
23.Canfield DE, Stewart FJ, Thamdrup B, De Brabandere L, Dalsgaard T, Delong EF, et al. A cryptic sulfur cycle in oxygen-minimum-zone waters off the Chilean coast. Science. 2010;330:1375–8.CAS 
PubMed 

Google Scholar 
24.Hurwitz BL, Westveld AH, Brum JR, Sullivan MB. Modeling ecological drivers in marine viral communities using comparative metagenomics and network analyses. Proc Natl Acad Sci USA. 2014;111:10714 LP–10719.
Google Scholar 
25.Garcia-Robledo E, Padilla CC, Aldunate M, Stewart FJ, Ulloa O, Paulmier A, et al. Cryptic oxygen cycling in anoxic marine zones. Proc Natl Acad Sci USA. 2017;114:8319–24.CAS 
PubMed 
PubMed Central 

Google Scholar 
26.Lavin P, González B, Santibáñez JF, Scanlan DJ, Ulloa O. Novel lineages of Prochlorococcus thrive within the oxygen minimum zone of the eastern tropical South Pacific. Environ Microbiol Rep. 2010;2:728–38.CAS 
PubMed 

Google Scholar 
27.Ulloa O, Canfield DE, DeLong EF, Letelier RM, Stewart FJ. Microbial oceanography of anoxic oxygen minimum zones. Proc Natl Acad Sci USA. 2012;109:15996–6003.CAS 
PubMed 
PubMed Central 

Google Scholar 
28.Bettarel Y, Sime-Ngando T, Amblard C, Dolan J. Viral activity in two contrasting lake ecosystems. Appl Environ Microbiol. 2004;70:2941–51.CAS 
PubMed 
PubMed Central 

Google Scholar 
29.Weinbauer MG, Brettar I, Höfle MG. Lysogeny and virus-induced mortality of bacterioplankton in surface, deep, and anoxic marine waters. Limnol Oceanogr. 2003;48:1457–65.
Google Scholar 
30.Heldal M, Bratbak G. Production and decay of viruses in aquatic environments. Mar Ecol Prog Ser. 1991;72:205–12.
Google Scholar 
31.Proctor LM, Fuhrman JA. Viral mortality of marine bacteria and cyanobacteria. Nature. 1990;343:60–62.
Google Scholar 
32.Brum JR, Morris J, Décima M, Stukel M. Mortality in the oceans: causes and consequences. In Eco-DAS IX Symposium Proceedings. Association for the Sciences of Limnology and Oceanography; 2014.33.Colombet J, Sime-Ngando T. Seasonal depth-related gradients in virioplankton: lytic activity and comparison with protistan grazing potential in Lake Pavin (France). Micro Ecol. 2012;64:67–78.
Google Scholar 
34.Colombet J, Sime-Ngando T, Cauchie HM, Fonty G, Hoffmann L, Demeure G. Depth-related gradients of viral activity in Lake Pavin. Appl Environ Microbiol. 2006;72:4440–5.CAS 
PubMed 
PubMed Central 

Google Scholar 
35.Brum J, Steward G, Jiang S, Jellison R. Spatial and temporal variability of prokaryotes, viruses, and viral infections of prokaryotes in an alkaline, hypersaline lake. Aquat Micro Ecol. 2005;41:247–60.
Google Scholar 
36.Brum JR, Schenck RO, Sullivan MB. Global morphological analysis of marine viruses shows minimal regional variation and dominance of non-tailed viruses. ISME J. 2013;7:1738–51.CAS 
PubMed 
PubMed Central 

Google Scholar 
37.Kauffman KM, Hussain FA, Yang J, Arevalo P, Brown JM, Chang WK, et al. A major lineage of non-tailed dsDNA viruses as unrecognized killers of marine bacteria. Nature. 2018;554:118–22.CAS 
PubMed 

Google Scholar 
38.Székely AJ, Breitbart M. Single-stranded DNA phages: from early molecular biology tools to recent revolutions in environmental microbiology. FEMS Microbiol Lett. 2016;363:27.
Google Scholar 
39.Roux S, Enault F, Hurwitz BL, Sullivan MB. VirSorter: Mining viral signal from microbial genomic data. PeerJ. 2015;2015:e985.
Google Scholar 
40.Hurwitz BL, Sullivan MB. The Pacific Ocean Virome (POV): a marine viral metagenomic dataset and associated protein clusters for quantitative viral ecology. PLoS One. 2013;8:e57355.CAS 
PubMed 
PubMed Central 

Google Scholar 
41.Aldunate M, Henríquez-Castillo C, Ji Q, Lueders-Dumont J, Mulholland MR, Ward BB, et al. Nitrogen assimilation in picocyanobacteria inhabiting the oxygen-deficient waters of the eastern tropical North and South Pacific. Limnol Oceanogr. 2020;65:437–53.CAS 

Google Scholar 
42.Solonenko SA, Ignacio-Espinoza JC, Alberti A, Cruaud C, Hallam S, Konstantinidis K, et al. Sequencing platform and library preparation choices impact viral metagenomes. BMC Genom. 2013;14:320.CAS 

Google Scholar 
43.Duhaime MB, Deng L, Poulos BT, Sullivan MB. Towards quantitative metagenomics of wild viruses and other ultra-low concentration DNA samples: a rigorous assessment and optimization of the linker amplification method. Environ Microbiol. 2012;14:2526–37.CAS 
PubMed 
PubMed Central 

Google Scholar 
44.Ganesh S, Bristow LA, Larsen M, Sarode N, Thamdrup B, Stewart FJ. Size-fraction partitioning of community gene transcription and nitrogen metabolism in a marine oxygen minimum zone. ISME J. 2015;9:2682–96.CAS 
PubMed 
PubMed Central 

Google Scholar 
45.Allen LZ, Allen EE, Badger JH, McCrow JP, Paulsen IT, Elbourne LD, et al. Influence of nutrients and currents on the genomic composition of microbes across an upwelling mosaic. ISME J. 2012;6:1403–14.CAS 
PubMed 
PubMed Central 

Google Scholar 
46.Hurwitz BL, U’Ren JM. Viral metabolic reprogramming in marine ecosystems. Curr Opin Microbiol. 2016;31:161–8.47.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 
48.Ignacio-Espinoza JC, Sullivan MB. Phylogenomics of T4 cyanophages: lateral gene transfer in the ‘core’ and origins of host genes. Environ Microbiol. 2012;14:2113–26.CAS 
PubMed 

Google Scholar 
49.Crummett LT, Puxty RJ, Weihe C, Marston MF, Martiny JBH. The genomic content and context of auxiliary metabolic genes in marine cyanomyoviruses. Virology. 2016;499:219–29.CAS 
PubMed 

Google Scholar 
50.Sullivan MB, Lindell D, Lee JA, Thompson LR, Bielawski JP, Chisholm SW. Prevalence and evolution of core photosystem II genes in marine cyanobacterial viruses and their hosts. PLoS Biol. 2006;4:e234.PubMed 
PubMed Central 

Google Scholar 
51.Bragg JG, Chisholm SW. Modeling the fitness consequences of a cyanophage-encoded photosynthesis gene. PLoS One. 2008;3:e3550.PubMed 
PubMed Central 

Google Scholar 
52.Puxty RJ, Evans DJ, Millard AD, Scanlan DJ. Energy limitation of cyanophage development: implications for marine carbon cycling. ISME J. 2018;12:1273–86.CAS 
PubMed 
PubMed Central 

Google Scholar 
53.White AE, Foster RA, Benitez-Nelson CR, Masqué P, Verdeny E, Popp BN, et al. Nitrogen fixation in the Gulf of California and the Eastern Tropical North Pacific. Prog Oceanogr. 2013;109:1–17.
Google Scholar 
54.Jayakumar A, Chang BX, Widner B, Bernhardt P, Mulholland MR, Ward BB. Biological nitrogen fixation in the oxygen-minimum region of the eastern tropical North Pacific ocean. ISME J. 2017;11:2356–67.CAS 
PubMed 
PubMed Central 

Google Scholar 
55.Fuchsman CA, Devol AH, Saunders JK, McKay C, Rocap G. Niche partitioning of the N cycling microbial community of an offshore oxygen deficient zone. Front Microbiol. 2017;8:2384.PubMed 
PubMed Central 

Google Scholar 
56.Zhang Y, Pohlmann EL, Halbleib CM, Ludden PW, Roberts GP. Effect of P(II) and its homolog GlnK on reversible ADP-ribosylation of dinitrogenase reductase by heterologous expression of the Rhodospirillum rubrum dinitrogenase reductase ADP-ribosyl transferase-dinitrogenase reductase-activating glycohydrolase regula. J Bacteriol. 2001;183:1610–20.CAS 
PubMed 
PubMed Central 

Google Scholar 
57.Tong W-H. Distinct iron-sulfur cluster assembly complexes exist in the cytosol and mitochondria of human cells. EMBO J. 2000;19:5692–5700.CAS 
PubMed 
PubMed Central 

Google Scholar 
58.Py B, Barras F. Building Feg-S proteins: bacterial strategies. Nat Rev Microbiol. 2010;8:436–46.59.Eichhorn E, van der Ploeg JR, Kertesz MA, Leisinger T. Characterization of alpha-ketoglutarate-dependent taurine dioxygenase from Escherichia coli. J Biol Chem. 1997;272:23031–6.CAS 
PubMed 

Google Scholar 
60.Friedrich CG, Bardischewsky F, Rother D, Quentmeier A, Fischer J. Prokaryotic sulfur oxidation. Curr Opin Microbiol. 2005;8:253–9.61.Anantharaman K, Duhaime MB, Breier JA, Wendt KA, Toner BM, Dick GJ. Sulfur oxidation genes in diverse deep-sea viruses. Science. 2014;344:757 LP–760.
Google Scholar 
62.Callbeck CM, Lavik G, Ferdelman TG, Fuchs B, Gruber-Vodicka HR, Hach PF, et al. Oxygen minimum zone cryptic sulfur cycling sustained by offshore transport of key sulfur oxidizing bacteria. Nat Commun. 2018;9:1729.PubMed 
PubMed Central 

Google Scholar 
63.Carolan MT, Smith JM, Beman JM. Transcriptomic evidence for microbial sulfur cycling in the eastern tropical North Pacific oxygen minimum zone. Front Microbiol. 2015;6:334.PubMed 
PubMed Central 

Google Scholar 
64.Ganesh S, Bertagnolli AD, Bristow LA, Padilla CC, Blackwood N, Aldunate M, et al. Single cell genomic and transcriptomic evidence for the use of alternative nitrogen substrates by anammox bacteria. ISME J. 2018;1:2706–22.65.Howard-Varona C, Hargreaves KR, Abedon ST, Sullivan MB. Lysogeny in nature: mechanisms, impact and ecology of temperate phages. ISME J. 2017;11:1511–20.PubMed 
PubMed Central 

Google Scholar 
66.Lill R, Dutkiewicz R, Elsässer HP, Hausmann A, Netz DJA, Pierik AJ, et al. Mechanisms of iron-sulfur protein maturation in mitochondria, cytosol and nucleus of eukaryotes. Biochim Biophys Acta Mol Cell Res. 2006;1763:652–67.67.Fontecave M. Iron-sulfur clusters: ever-expanding roles. Nat Chem Biol. 2006;2:171–4.CAS 
PubMed 

Google Scholar 
68.Roche B, Aussel L, Ezraty B, Mandin P, Py B, Barras F. Iron/sulfur proteins biogenesis in prokaryotes: formation, regulation and diversity. Biochim Biophys Acta Bioenerg. 2013;1827:455–69.CAS 

Google Scholar 
69.Xu XM, Møller SG. Iron-sulfur clusters: Biogenesis, molecular mechanisms, and their functional significance. Antioxid Redox Signal. 2011;15:271–307.PubMed 

Google Scholar 
70.Miller HK, Auerbuch V. Bacterial iron-sulfur cluster sensors in mammalian pathogens. Metallomics. 2015;7:943–56.CAS 
PubMed 
PubMed Central 

Google Scholar 
71.Sharon I, Battchikova N, Aro E-M, Giglione C, Meinnel T, Glaser F, et al. Comparative metagenomics of microbial traits within oceanic viral communities. ISME J. 2011;5:1178–90.CAS 
PubMed 
PubMed Central 

Google Scholar 
72.Loiseau L, Ollagnier-de-Choudens S, Nachin L, Fontecave M, Barras F. Biogenesis of Fe-S cluster by the bacterial suf system. SufS and SufE form a new type of cysteine desulfurase. J Biol Chem. 2003;278:38352–9.CAS 
PubMed 

Google Scholar 
73.Outten FW, Wood MJ, Muñoz FM, Storz G. The SufE protein and the SufBCD complex enhance SufS cysteine desulfurase activity as part of a sulfur transfer pathway for Fe-S cluster assembly in Escherichia coli. J Biol Chem. 2003;278:45713–9.CAS 
PubMed 

Google Scholar 
74.Ayala-Castro C, Saini A, Outten FW. Fe-S cluster assembly pathways in bacteria. Microbiol Mol Biol Rev. 2008;72:110–25.CAS 
PubMed 
PubMed Central 

Google Scholar 
75.Shepard EM, Boyd ES, Broderick JB, Peters JW. Biosynthesis of complex iron-sulfur enzymes. Curr Opin Chem Biol. 2011;15:319–27.76.Lill R. Function and biogenesis of iron–sulphur proteins. Nature. 2009;460:831–8.CAS 
PubMed 

Google Scholar 
77.Seidler A, Jaschkowitz K, Wollenberg M. Incorporation of iron-sulphur clusters in membrane-bound proteins. Biochem Soc Trans. 2001;29:418–21.CAS 
PubMed 

Google Scholar 
78.Buchanan BB, Schürmann P, Wolosiuk RA, Jacquot J-P. The ferredoxin/thioredoxin system: from discovery to molecular structures and beyond. Photosynth Res. 2002;73:215–22.CAS 
PubMed 

Google Scholar 
79.Dubnau D, Losick R. Bistability in bacteria. Mol Microbiol. 2006;61:564–72.CAS 
PubMed 

Google Scholar 
80.Resnekov O, Driks A, Losick R. Identification and characterization of sporulation gene spoVS from Bacillus subtilis. J Bacteriol. 1995;177:5628–35.CAS 
PubMed 
PubMed Central 

Google Scholar 
81.Sonenshein AL. Bacteriophages: how bacterial spores capture and protect phage DNA. Curr Biol. 2006;16:R14–R16.82.Sullivan MB, Coleman ML, Weigele P, Rohwer F, Chisholm SW. Three Prochlorococcus cyanophage genomes: signature features and ecological interpretations. PLoS Biol. 2005;3:0790–806.CAS 

Google Scholar 
83.Fortier L-C, Sekulovic O. Importance of prophages to evolution and virulence of bacterial pathogens. Virulence. 2013;4:354–65.PubMed 
PubMed Central 

Google Scholar 
84.Mobberley J, Nathan Authement R, Segall AM, Edwards RA, Slepecky RA, Paul JH. Lysogeny and sporulation in Bacillus isolates from the Gulf of Mexico. Appl Environ Microbiol. 2010;76:829–42.CAS 
PubMed 

Google Scholar 
85.Brüssow H, Canchaya C, Hardt W-D. Phages and the evolution of bacterial pathogens: from genomic rearrangements to lysogenic conversion. Microbiol Mol Biol Rev. 2004;68:560–602.PubMed 
PubMed Central 

Google Scholar 
86.Meinhart A, Alonso JC, Strater N, Saenger W. Crystal structure of the plasmid maintenance system /: functional mechanism of toxin and inactivation by 2 2 complex formation. Proc Natl Acad Sci. 2003;100:1661–6.CAS 
PubMed 
PubMed Central 

Google Scholar 
87.Schuster CF, Bertram R. Toxin-antitoxin systems are ubiquitous and versatile modulators of prokaryotic cell fate. FEMS Microbiol Lett. 2013;340:73–85.88.Kawano M. Divergently overlapping cis -encoded antisense RNA regulating toxin-antitoxin systems from E. coli. RNA Biol. 2012;9:1520–7.CAS 
PubMed 

Google Scholar 
89.Smith MA, Bidochka MJ. Bacterial fitness and plasmid-loss: the importance of culture conditions and plasmid size. Can J Microbiol. 1998;44:351–5.CAS 
PubMed 

Google Scholar 
90.Summers DK. The kinetics of plasmid loss. Trends Biotechnol. 1991;9: 273–8.91.Persad AK, Williams ML, LeJeune JT. Rapid loss of a green fluorescent plasmid in Escherichia coli O157:H7. AIMS Microbiol. 2017;3:872–84.CAS 
PubMed 
PubMed Central 

Google Scholar 
92.Modi SR, Lee HH, Spina CS, Collins JJ. Antibiotic treatment expands the resistance reservoir and ecological network of the phage metagenome. Nature. 2013;499:219–22.CAS 
PubMed 
PubMed Central 

Google Scholar 
93.Hargreaves KR, Kropinski AM, Clokie MR. Bacteriophage behavioral ecology. Bacteriophage. 2014;4:e29866.PubMed 
PubMed Central 

Google Scholar 
94.Naught LE, Gilbert S, Imhoff R, Snook C, Beamer L, Tipton P. Allosterism and cooperativity in Pseudomonas aeruginosa GDP-mannose dehydrogenase. Biochemistry. 2002;41:9637–45.CAS 
PubMed 

Google Scholar 
95.Dong C, Flecks S, Unversucht S, Haupt C, van Pee K-H, Naismith JH. Tryptophan 7-halogenase (PrnA) structure suggests a mechanism for regioselective chlorination. Science. 2005;309:2216–9.CAS 
PubMed 
PubMed Central 

Google Scholar 
96.Fouces R, Mellado E, Diez B, Barredo JL. The tylosin biosynthetic cluster from Streptomyces fradiae: genetic organization of the left region. Microbiology. 1999;145:855–68.CAS 
PubMed 

Google Scholar 
97.Heacock-Kang Y, Zarzycki-Siek J, Sun Z, Poonsuk K, Bluhm AP, Cabanas D, et al. Novel dual regulators of Pseudomonas aeruginosa essential for productive biofilms and virulence. Mol Microbiol. 2018;109:401–14.CAS 
PubMed 
PubMed Central 

Google Scholar 
98.Kurtov D, Kinghorn JR, Unkles SE. The Aspergillus nidulans panB gene encodes ketopantoate hydroxymethyltransferase, required for biosynthesis of pantothenate and Coenzyme A. Mol Gen Genet. 1999;262:115–20.CAS 
PubMed 

Google Scholar 
99.Huisjes R, Card DJ. Methods for assessment of pantothenic acid (Vitamin B5). In: Harrington D, editor. Laboratory assessment of vitamin status. London, UK; San Diego, CA, USA; Cambridge, MA, USA; Oxford, UK : Elsevier Inc.; 2019. p. 265–299. https://doi.org/10.1038/s41396-021-01143-1.100.Leonardi R, Jackowski S. Biosynthesis of pantothenic acid and coenzyme A. EcoSal Plus. 2007;2:2.101.Begley TP, Kinsland C, Strauss E. The biosynthesis of coenzyme a in bacteria. Vitam Horm. 2001;61:157–71.CAS 
PubMed 

Google Scholar 
102.Cameron B, Guilhot C, Blanche F, Cauchois L, Rouyez MC, Rigault S, et al. Genetic and sequence analyses of a Pseudomonas denitrificans DNA fragment containing two cob genes. J Bacteriol. 1991;173:6058–65.CAS 
PubMed 
PubMed Central 

Google Scholar 
103.Doxey AC, Kurtz DA, Lynch MD, Sauder LA, Neufeld JD. Aquatic metagenomes implicate Thaumarchaeota in global cobalamin production. ISME J. 2015;9:461–71.CAS 
PubMed 

Google Scholar 
104.Heal KR, Qin W, Amin SA, Devol AH, Moffett JW, Armbrust EV, et al. Accumulation of NO2-cobalamin in nutrient-stressed ammonia-oxidizing archaea and in the oxygen deficient zone of the eastern tropical North Pacific. Environ Microbiol Rep. 2018;10:453–7.CAS 
PubMed 

Google Scholar 
105.Vik DR, Roux S, Brum JR, Bolduc B, Emerson JB, Padilla CC, et al. Putative archaeal viruses from the mesopelagic ocean. PeerJ. 2017;5:e3428.PubMed 
PubMed Central 

Google Scholar 
106.Streisinger G, Emrich J, Stahl MM. Chromosome structure in phage T4, III. Terminal redundancy and length determination. Proc Natl Acad Sci USA. 1967;57:292–5.CAS 
PubMed 
PubMed Central 

Google Scholar 
107.Mahmoudabadi G, Milo R, Phillips R. Energetic cost of building a virus. Proc Natl Acad Sci USA. 2017;114:E4324–E4333.CAS 
PubMed 
PubMed Central 

Google Scholar 
108.Brum J. 5m intervals of CTD profiles from R/V New Horizon cruise NH1315 in the Eastern Tropical North Pacific (ETNP) during June 2013. Biological and Chemical Oceanography Data Management Office (BCO-DMO). (Version 1) Version Date 2020-08-31 (2020). https://doi.org/10.26008/1912/bco-dmo.822818.1.109.Noble RT, Fuhrman JA. Use of SYBR Green I for rapid epifluorescence counts of marine viruses and bacteria. Aquat Micro Ecol. 1998;14:113–8.
Google Scholar 
110.Brum J. Estimated abundances of viruses and bacteria determined in samples collected in the Eastern Tropical North Pacific (ETNP) on R/V New Horizon cruise NH1315 during June 2013. Biological and Chemical Oceanography Data Management Office (BCO-DMO). (Version 1) Version Date 2020-09-02 (2020). https://doi.org/10.26008/1912/bco-dmo.823094.1.111.Binder B. Reconsidering the relationship between vitally induced bacterial mortality and frequency of infected cells. Aquat Micro Ecol. 1999;18:207–15.
Google Scholar 
112.Brum J. Estimated frequency of lytic viral infection from samples collected in the Eastern Tropical North Pacific oxygen minimum zone region (ETNP OMZ) on R/V New Horizon cruise NH1315 during June 2013. Biological and Chemical Oceanography Data Management Office (BCO-DMO). (Version 1) Version Date 2020-09-01 (2020). https://doi.org/10.26008/1912/bco-dmo.822914.1.113.Abramoff MD, Magalhaes PJ, Ram SJ. Image processing with ImageJ. Biophotonics Int 2004;11:36–42.114.Brum J. Morphotypes, capsid widths, and tail lengths of viruses from samples collected in the Eastern Tropical North Pacific oxygen minimum zone region (ETNP OMZ) on R/V New Horizon cruise NH1315 during June 2013. Biological and Chemical Oceanography Data Management Office (BCO-DMO). (Version 1) Version Date 2020-09-02 (2020). https://doi.org/10.26008/1912/bco-dmo.823131.1.115.John SG, Mendez CB, Deng L, Poulos B, Kauffman AKM, Kern S, et al. A simple and efficient method for concentration of ocean viruses by chemical flocculation. Environ Microbiol Rep. 2011;3:195–202.CAS 
PubMed 
PubMed Central 

Google Scholar 
116.Duhaime MB, Sullivan MB. Ocean viruses: Rigorously evaluating the metagenomic sample-to-sequence pipeline. Virology. 2012;434:181–6.CAS 
PubMed 

Google Scholar 
117.Brum J. Accession numbers of viral metagenomes from samples collected in the Eastern Tropical North Pacific oxygen minimum zone region (ETNP OMZ) on R/V New Horizon cruise NH1315 during June 2013. Biological and Chemical Oceanography Data Management Office (BCO-DMO). (Version 1) Version Date 2020-09-04 (2020) https://doi.org/10.26008/1912/bco-dmo.823295.1.118.Peng Y, Leung HCM, Yiu SM, Chin FYL. IDBA-UD: A de novo assembler for single-cell and metagenomic sequencing data with highly uneven depth. Bioinformatics. 2012;28:1420–8.CAS 
PubMed 

Google Scholar 
119.Delcher AL, Salzberg SL, Phillippy AM. Using MUMmer to identify similar regions in large sequence sets. Curr Protoc Bioinforma. 2003; Chapter 10: Unit 10.3.120.Finn RD, Bateman A, Clements J, Coggill P, Eberhardt RY, Eddy SR, et al. Pfam: the protein families database. Nucleic Acids Res. 2014;42:D222–D230.121.Eddy SR. Accelerated profile HMM searches. PLoS Comput Biol. 2011;7:1002195.
Google Scholar 
122.Team RCR. A language and environment for statistical computing. Vienna, Austria: R Foundation for Statistical Computing; 2018.123.Wickham H. ggplot2: elegant graphics for data analysis. Springer-Verlag, New York: 2016.124.Oksanen J, Blanchet FG, Kindt R, Legendre P, Minchin PR, O’Hara RB et al. vegan: Community Ecology Package. R package version 2.5-2. 2013 http://RForge.R-project.org/projects/vegan/.125.Wilkinson L. venneuler: Venn and Euler diagrams. R package version 1.1-0. 2011 https://CRAN.Rproject.org/package=venneuler.126.Harrell FE, With contributions from Charles Dupont and many others. Hmisc: Harrell Miscellaneous. R package version 4.3-0. 2019 https://CRAN.R-project.org/package=Hmisc.127.Wei T, Simko V. R package “corrplot”: Visualization of a Correlation Matrix (Version 0.84). 2017. Available from https://github.com/taiyun/corrplot.128.Emerson JB, Roux S, Brum JR, Bolduc B, Woodcroft BJ, Jang H Bin, et al. Host-linked soil viral ecology along a permafrost thaw gradient. Nat Microbiol. 2018;3:870–80.129.Sturges HA. The choice of a class interval. J Am Stat Assoc. 1926;21:65–66.130.Suzuki R, Shimodaira H. Pvclust: an R package for assessing the uncertainty in hierarchical clustering. Bioinformatics. 2006;22:1540–2.CAS 
PubMed 

Google Scholar  More

1.Field, C. B., Behrenfeld, M. J., Randerson, J. T. & Falkowski, P. Primary production of the biosphere: Integrating terrestrial and cceanic components. Science 281, 237–240 (1998).ADS 
CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
2.Falkowski, P. G., Barber, R. T. & Smetacek, V. Biogeochemical controls and feedbacks on ocean primary production. Science 281, 200–206 (1998).CAS 
PubMed 
Article 
PubMed Central 

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

Google Scholar 
4.Comte, L. & Olden, J. D. Climatic vulnerability of the world’s freshwater and marine fishes. Nat. Clim. Chang. 7, 718–722 (2017).ADS 
Article 

Google Scholar 
5.Dutkiewicz, S., Scott, J. R. & Follows, M. J. Winners and losers: ecological and biogeochemical changes in a warming ocean. Glob. Biogeochem. Cycles 27, 463–477 (2013).ADS 
CAS 
Article 

Google Scholar 
6.Sarmiento, J. L. et al. Response of ocean ecosystems to climate warming. Glob. Biogeochem. Cycles 18, GB3003 (2004).ADS 
Article 
CAS 

Google Scholar 
7.Taucher, J. & Oschlies, A. Can we predict the direction of marine primary production change under global warming? Geophys. Res. Lett. 38, 1–6 (2011).Article 
CAS 

Google Scholar 
8.Vallina, S. M., Cermeno, P., Dutkiewicz, S., Loreau, M. & Montoya, J. M. Phytoplankton functional diversity increases ecosystem productivity and stability. Ecol. Modell. 361, 184–196 (2017).Article 

Google Scholar 
9.Dutkiewicz, S. et al. Impact of ocean acidification on the structure of future phytoplankton communities. Nat. Clim. Chang. 5, 1002–1006 (2015).ADS 
CAS 
Article 

Google Scholar 
10.Laufkotter, C. et al. Drivers and uncertainties of future global marine primary production in marine ecosystem models. Biogeosciences 12, 6955–6984 (2015).ADS 
Article 

Google Scholar 
11.Behrenfeld, M. J., Boss, E., Siegel, D. A. & Shea, D. M. Carbon-based ocean productivity and phytoplankton physiology from space. Glob. Biogeochem. Cycles 19, 1–14 (2005).Article 
CAS 

Google Scholar 
12.Anderson, S. I. & Rynearson, T. A. Variability approaching the thermal limits can drive diatom community dynamics. Limnol. Oceanogr. 65, 1961–1973 (2020).ADS 
CAS 
Article 

Google Scholar 
13.Boyd, P. W. Physiology and iron modulate diverse responses of diatoms to a warming Southern Ocean. Nat. Clim. Chang. 9, 148–152 (2019).ADS 
CAS 
Article 

Google Scholar 
14.Thomas, M. K. & Litchman, E. Effects of temperature and nitrogen availability on the growth of invasive and native cyanobacteria. Hydrobiologia 763, 357–369 (2016).Article 

Google Scholar 
15.Kremer, C. T., Thomas, M. K. & Litchman, E. Temperature- and size-scaling of phytoplankton population growth rates: Reconciling the Eppley curve and the metabolic theory of ecology. Limnol. Oceanogr. 62, 1658–1670 (2017).ADS 
Article 

Google Scholar 
16.Edwards, K. F., Thomas, M. K., Klausmeier, C. A. & Litchman, E. Allometric scaling and taxonomic variation in nutrient utilization traits and maximum growth rate of phytoplankton. Limnol. Oceanogr. 57, 554–566 (2012).ADS 
Article 

Google Scholar 
17.Poloczanska, E. S. et al. Global imprint of climate change on marine life. Nat. Clim. Chang. 3, 919–925 (2013).ADS 
Article 

Google Scholar 
18.Thomas, M. K., Kremer, C. T., Klausmeier, C. A. & Litchman, E. A global pattern of thermal adaptation in marine phytoplankton. Science 338, 1085–1088 (2012).ADS 
CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
19.Righetti, D., Vogt, M., Gruber, N., Psomas, A. & Zimmermann, N. E. Global pattern of phytoplankton diversity driven by temperature and environmental variability. Sci. Adv. 5, 1–11 (2019).Article 

Google Scholar 
20.Barton, A. D., Irwin, A. J., Finkel, Z. V. & Stock, C. A. Anthropogenic climate change drives shift and shuffle in North Atlantic phytoplankton communities. Proc. Natl Acad. Sci. USA 113, 2964–2969 (2016).ADS 
CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
21.García Molinos, J. et al. Climate velocity and the future global redistribution of marine biodiversity. Nat. Clim. Chang. 6, 4–11 (2015).
Google Scholar 
22.Uitz, J., Claustre, H., Gentili, B. & Stramski, D. Phytoplankton class-specific primary production in the world’s oceans: Seasonal and interannual variability from satellite observations. Glob. Biogeochem. Cycles 24, 1–19 (2010).Article 
CAS 

Google Scholar 
23.Toseland, A. et al. The impact of temperature on marine phytoplankton resource allocation and metabolism. Nat. Clim. Chang. 3, 979–984 (2013).ADS 
CAS 
Article 

Google Scholar 
24.Boyd, P. W. & Hutchins, D. A. Understanding the responses of ocean biota to a complex matrix of cumulative anthropogenic change. Mar. Ecol. Prog. Ser. 470, 125–135 (2012).ADS 
Article 

Google Scholar 
25.Bopp, L. et al. Multiple stressors of ocean ecosystems in the 21st century: Projections with CMIP5 models. Biogeosciences 10, 6225–6245 (2013).ADS 
Article 

Google Scholar 
26.Thomas, M. K., Kremer, C. T. & Litchman, E. Environment and evolutionary history determine the global biogeography of phytoplankton temperature traits. Glob. Ecol. Biogeogr. 25, 75–86 (2016).Article 

Google Scholar 
27.Angilletta, M. J. Thermal Adaptation: A Theoretical and Empirical Synthesis (Oxford University Press, 2009).28.Eppley, R. W. Temperature and phytoplankton growth in the sea. Fish. Bull. 70, 1063–1085 (1972).
Google Scholar 
29.Bissinger, J. E., Montagnes, D. J. S., Sharples, J. & Atkinson, D. Predicting marine phytoplankton maximum growth rates from temperature: Improving on the Eppley curve using quantile regression. Limnol. Oceanogr. 53, 487–493 (2008).ADS 
Article 

Google Scholar 
30.Prowe, A. E. F., Pahlow, M., Dutkiewicz, S. & Oschlies, A. How important is diversity for capturing environmental-change responses in ecosystem models? Biogeosciences 11, 3397–3407 (2014).ADS 
Article 

Google Scholar 
31.Chen, B. & Liu, H. Relationships between phytoplankton growth and cell size in surface oceans: Interactive effects of temperature, nutrients, and grazing. Limnol. Oceanogr. 55, 965–972 (2010).ADS 
CAS 
Article 

Google Scholar 
32.Barton, S. & Yvon‐Durocher, G. Quantifying the temperature dependence of growth rate in marine phytoplankton within and across species. Limnol. Oceanogr. 64, 2081–2091 (2019).ADS 
Article 

Google Scholar 
33.Sherman, E., Moore, J. K., Primeau, F. & Tanouye, D. Temperature influence on phytoplankton community growth rates. Glob. Biogeochem. Cycles 30, 550–559 (2016).ADS 
CAS 
Article 

Google Scholar 
34.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 
Article 

Google Scholar 
35.Cermeño, P. et al. The role of nutricline depth in regulating the ocean carbon cycle. Proc. Natl Acad. Sci. USA 105, 20344–20349 (2008).ADS 
PubMed 
PubMed Central 
Article 

Google Scholar 
36.Calvo, E., Pelejero, C., Pena, L. D., Cacho, I. & Logan, G. A. Eastern Equatorial Pacific productivity and related-CO2 changes since the last glacial period. Proc. Natl Acad. Sci. USA 108, 5537–5541 (2011).ADS 
CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
37.McCabe, R. M. et al. An unprecedented coastwide toxic algal bloom linked to anomalous ocean conditions. Geophys. Res. Lett. 43, 10,366–10,376 (2016).Article 

Google Scholar 
38.Roberts, S. D., Van Ruth, P. D., Wilkinson, C., Bastianello, S. S. & Bansemer, M. S. Marine heatwave, harmful algae blooms and an extensive fish kill event during 2013 in South Australia. Front. Mar. Sci. 6, 1–20 (2019).CAS 
Article 

Google Scholar 
39.Oliver, E. C. J. et al. Longer and more frequent marine heatwaves over the past century. Nat. Commun. 9, 1–12 (2018).CAS 
Article 

Google Scholar 
40.Oliver, E. C. J. et al. Projected marine heatwaves in the 21st century and the potential for ecological impact. Front. Mar. Sci. 6, 1–12 (2019).MathSciNet 
Article 

Google Scholar 
41.Keeling, P. J. The endosymbiotic origin, diversification and fate of plastids. Philos. Trans. R. Soc. B Biol. Sci. 365, 729–748 (2010).CAS 
Article 

Google Scholar 
42.Yoon, H. S., Hackett, J. D., Pinto, G. & Bhattacharya, D. The single, ancient origin of chromist plastids. Proc. Natl Acad. Sci. USA 99, 15507–15512 (2002).ADS 
CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
43.Pinsky, M. L., Eikeset, A. M., McCauley, D. J., Payne, J. L. & Sunday, J. M. Greater vulnerability to warming of marine versus terrestrial ectotherms. Nature 569, 108–111 (2019).ADS 
CAS 
PubMed 
Article 
PubMed Central 

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

Google Scholar 
45.Jönsson, B. F. & Watson, J. R. The timescales of global surface-ocean connectivity. Nat. Commun. 7, 1–6 (2016).Article 
CAS 

Google Scholar 
46.Doblin, M. A. & van Sebille, E. Drift in ocean currents impacts intergenerational microbial exposure to temperature. Proc. Natl Acad. Sci. USA 113, 5700–5705 (2016).ADS 
CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
47.Whittaker, K. & Rynearson, T. Evidence for environmental and ecological selection in a microbe with no geographic limits to gene flow. Proc. Natl Acad. Sci. USA 114, 2651–2656 (2017).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
48.Ward, B. A., Cael, B. B., Collins, S. & Robert Young, C. Selective constraints on global plankton dispersal. Proc. Natl Acad. Sci. USA 118, 1–7 (2021).
Google Scholar 
49.Huey, R. B. & Stevenson, R. D. Integrating thermal physiology and ecology of ectotherms: A discussion of approaches. Integr. Comp. Biol. 19, 357–366 (1979).
Google Scholar 
50.Collins, M. et al. in Climate change 2013: The physical science basis. Contribution of working group I to the fifth assessment report of the Intergovernmental Panel on Climate Change (eds. Stocker, T. F. et al.) 1029–1136 (Cambridge University Press, 2013).51.Bopp, L., Aumont, O., Cadule, P., Alvain, S. & Gehlen, M. Response of diatoms distribution to global warming and potential implications: A global model study. Geophys. Res. Lett. 32, L19606 (2005).ADS 
Article 
CAS 

Google Scholar 
52.Ward, B. A. Temperature-correlated changes in phytoplankton community structure are restricted to polar waters. PLoS ONE 10, 1–15 (2015).
Google Scholar 
53.Winter, A., Henderiks, J., Beaufort, L., Rickaby, R. E. M. & Brown, C. W. Poleward expansion of the coccolithophore Emiliania huxleyi. J. Plankton Res. 36, 316–325 (2014).CAS 
Article 

Google Scholar 
54.Rivero-Calle, S., Gnanadesikan, A., Del Castillo, C. E., Balch, W. M. & Guikema, S. D. Multidecadal increase in North Atlantic coccolithophores and the potential role of rising CO2. Science 350, 1533–1537 (2015).ADS 
CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
55.Steinacher, M. et al. Projected 21st century decrease in marine productivity: a multi-model analysis. Biogeosciences Discuss. 7, 979–1005 (2010).ADS 
CAS 
Article 

Google Scholar 
56.Arrigo, K. R., van Dijken, G. L. & Strong, A. L. Environmental controls of marine productivity hot spots around Antarctica. J. Geophys. Res. Ocean. 120, 2813–2825 (2015).Article 

Google Scholar 
57.Aranguren-Gassis, M., Kremer, C. T., Klausmeier, C. A. & Litchman, E. Nitrogen limitation inhibits marine diatom adaptation to high temperatures. Ecol. Lett. 22, 1860–1869 (2019).PubMed 
Article 
PubMed Central 

Google Scholar 
58.Edwards, K. F., Thomas, M. K., Klausmeier, C. A. & Litchman, E. Phytoplankton growth and the interaction of light and temperature: A synthesis at the species and community level. Limnol. Oceanogr. 61, 1232–1244 (2016).ADS 
Article 

Google Scholar 
59.Ibarbalz, F. M. et al. Global trends in marine plankton diversity across kingdoms of life. Cell 179, 1084–1097.e21 (2019).CAS 
PubMed 
PubMed Central 
Article 

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

Google Scholar 
61.Padfield, D., Yvon-Durocher, G., Buckling, A., Jennings, S. & Yvon-Durocher, G. Rapid evolution of metabolic traits explains thermal adaptation in phytoplankton. Ecol. Lett. 19, 133–142 (2016).PubMed 
Article 
PubMed Central 

Google Scholar 
62.Baker, K. G. et al. Thermal niche evolution of functional traits in a tropical marine phototroph. J. Phycol. 54, 799–810 (2018).CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
63.O’Donnell, D. R. et al. Rapid thermal adaptation in a marine diatom reveals constraints and trade-offs. Glob. Chang. Biol. 24, 4554–4565 (2018).ADS 
PubMed 
Article 
PubMed Central 

Google Scholar 
64.Seong, K. A., Jeong, H. J., Kim, S., Kim, G. H. & Kang, J. H. Bacterivory by co-occurring red-tide algae, heterotrophic nanoflagellates, and ciliates. Mar. Ecol. Prog. Ser. 322, 85–97 (2006).ADS 
Article 

Google Scholar 
65.Arizona Software Inc. GraphClick 3.0.2. http://www.arizona-software.ch/graphclick/ (2010).66.Norberg, J. Biodiversity and ecosystem functioning: a complex adaptive systems approach. Limnol. Oceanogr. 49, 1269–1277 (2004).ADS 
Article 

Google Scholar 
67.Bolker, B. & Team, R. D. C. bbmle: Tools for general maximum likelihood estimation. https://github.com/bbolker/bbmle (2017).68.R Core Team. R: A language and environment for statistical computing. https://www.R-project.org/ (2020).69.Riahi, K. et al. RCP 8.5-A scenario of comparatively high greenhouse gas emissions. Clim. Change 109, 33–57 (2011).ADS 
CAS 
Article 

Google Scholar 
70.Koenker, R. quantreg: Quantile regression. https://cran.r-project.org/package=quantreg (2019).71.Chen, B. & Laws, E. A. Is there a difference of temperature sensitivity between marine phytoplankton and heterotrophs? Limnol. Oceanogr. 62, 806–817 (2017).ADS 
Article 

Google Scholar 
72.Sal, S., Alonso-Saez, L., Bueno, J., Garcıa, F. C. & Lopez-Urrutia, A. Thermal adaptation, phylogeny, and the unimodal size scaling of marine phytoplankton growth. Limnol. Oceanogr. 60, 1212–1221 (2015).ADS 
Article 

Google Scholar 
73.Koenker, R. Quantile Regression, https://doi.org/10.1017/CBO9780511754098 (Cambridge University Press, 2005).74.Tomas, C. R. et al. Identifying Marine Phytoplankton. (Academic Press, 1997).75.He, X. & Hu, F. Markov chain marginal bootstrap. J. Am. Stat. Assoc. 97, 783–795 (2002).MathSciNet 
MATH 
Article 

Google Scholar 
76.Rynearson, T. A. Literature compilation of thermal growth rates from four phytoplankton functional types. Biological and Chemical Oceanography Data Management Office (BCO-DMO), (2021). https://doi.org/10.26008/1912/bco-dmo.839696.177.Rynearson, T. A. Estimated thermal capacities for phytoplankton strains. Biological and Chemical Oceanography Data Management Office (BCO-DMO), https://doi.org/10.26008/1912/bco-dmo.839713.1 (2021).78.Rynearson, T. A. Estimated thermal traits for phytoplankton. Biological and Chemical Oceanography Data Management Office (BCO-DMO), https://doi.org/10.26008/1912/bco-dmo.839689.1 (2021).79.Anderson, S. I. sianderson/PFT_thermal_response: Marine Phytoplankton Functional Types Exhibit Diverse Responses to Thermal Change. zenodo. https://doi.org/10.5281/zenodo.5507532 (2021).80.Buitenhuis, E. T., Pangerc, T., Franklin, D. J., Le Quéré, C. & Malin, G. Growth rates of six coccolithophorid strains as a function of temperature. Limnol. Oceanogr. 53, 1181–1185 (2008).ADS 
Article 

Google Scholar 
81.Stawiarski, B., Buitenhuis, E. T. & Le Quéré, C. The physiological response of picophytoplankton to temperature and its model representation. Front. Mar. Sci. 3, 1–13 (2016).Article 

Google Scholar  More

Field trialsField trials were conducted in three raised beds (1 × 2 × 0.6 m) on the Penn State University campus from July to August 2020. The raised beds were separated by at least 8 m to avoid treatment cross-contamination. Faba bean (Vicia faba L.) seeds were planted at a density of 20 seeds/ m2 (50 plants per bed), and each bed was caged using a metal-framed tent. “Noseeum” nylon mesh (Outdoor Wilderness Fabric s, Inc., Caldwell, ID) was draped over the frame and the edges buried in the soil of the bed. The sides of the cages were fastened closed with zippers to allow access.InsectsAphid and predator beetle colonies were raised separately on faba bean plants in cages (BugDorm 20 cm × 40 cm × 20 cm, BioQuip Products, Inc., Rancho Dominguez, CA) in the field. Larvae and adults of predator beetles were fed with a combination of A. pisum and Rhopalosiphum padi every other day (Supplementary information Fig. S1). Trials involving plants, insects, and entomopathogenic fungi were conducted according to institutional, national, and international guidelines and legislation.Fungal inoculations (Beavueria bassiana)We released first instar aphid nymphs on each faba bean plant on the raised beds (~ 1100 aphids) by gently shaking plastic containers with groups of 20 nymphs and placing them on the plants using a paintbrush. They were allowed to grow and reproduce for fifteen days. During the night, we sprayed spore suspension of the Beauveria strain GHA (BotaniGard ®, MT, USA) at 1.4 × 106 and 1.4 × 1012 spore ha−1, low and high load respectively. Two days after inoculation, we collected adult aphids (~ 4–5 days old) from the experimental plots and measured physiological parameters (see details below). Next, we released 300 adult beetles inside each aphid–fungal inoculated cage, allowed them to feed for 2–3 days in our experimental cages, and then collected beetles for physiological measurement.Identification of critical thermal limits (CTMax and CTMin) of healthy and infected insectsTo determine critical thermal maximum for locomotion (CTMax) of healthy and infected individuals of each species, we employed a protocol modified from Ribeiro et al.25, using a hotplate with a programmable heating rate controlled by a computer interface (Sable Systems, LV, USA). The temperature was monitored by independent thermocouple channels connected to a Hobo 4-channel data logger. One thermocouple was attached to the surface of the hotplate, and the other sensor was attached inside the glass tube plugged by a cotton ball in which we placed an individual insect. This equipment was located inside an automated thermal chamber (interior dimensions: width 40.5 cm × 35 cm length × 40 cm height). We transferred an adult aphid (4-day-old) into the glass tube and exposed it to increasing temperatures at a rate of 0.3 °C min−1 until its locomotion stopped. CTMax was recorded when the insect turned upside down and could no longer return to the upright position within 5 s. The insect was returned to a faba bean leaf for recovery (n = 10 individuals per treatment).To measure the critical thermal minimum for locomotion (CTMin) of healthy and infected individuals of each species (n = 10 individuals per treatment), we used an insulated incubator where the temperature was monitored by independent thermocouple channels connected to a Hobo 4-channel data logger. The sensors were attached inside three glass tubes, each tube with an adult (3 to 4-day-old), and plugged by a cotton ball. The glass tube was exposed to decreasing temperature at a rate of 0.3 °C min−1 until its locomotion stopped. CTMin was recorded when no movement was recorded within 5 s. The insect was returned to an aphid-infested faba bean leaf for recovery. Data were only considered valid if the insect displayed normal activity 2 h after a CTMax or CTMin test.Impacts of infection on voluntary exposure aphids and predator beetles to extreme thermal zonesTo examine how voluntary exposure to ETZ was affected by fungal infection, we collected aphids and predator beetles (3 to 5 day-old) from our field plots and transferred them to a dark plastic bottle. Next, a bottle containing the insects was attached to a choice test arena following a modified protocol from Navas et al.24. This experimental arena allows insects to freely move across extreme temperatures to access food in containers located at each end of the device. To reach food, individuals had to cross an ETZ, either warm or cold. The location of each insect was recorded after 60 min, and it was classified as: exploration for individuals that left the initial black bottle, warm or cold ETZ crossings. The experiment was replicated ten times for each species and treatment condition [aphid: healthy, infected (low and high spore load); predator beetle: healthy, infected (low and high spore load)].Effects of fungal infection and thermal conditions (critical thermal limits and voluntary exposure to ETZs) on longevity of aphids and predator beetlesTo examine whether fungal infection and thermal conditions alter longevity in aphids and beetles, we isolated three individuals from each factor combination (low, high fungal load, CTMin, CTMax, behavior: crosses to ETZ cold, warm, and no cross) from previous experiments, and counted the number of days the adults survived after the exposure to the thermal condition (n = 3 factor combination).Energetic cost associated with fungal infection of aphid and predator beetles under critical thermal limits and voluntary exposure to ETIntracellular ATP content was determined in neutralized perchloric acid extracts and by a spectrophotometric coupled enzyme assay, based on modified protocol from Churchill and Storey26 content (n = 3 per treatment condition). An insect was ground to powder using a mortar and pestle cooled in liquid nitrogen, and then weighed into 1.5 mL microcentrifuge tubes (Eppendorf). Powder was dissolved with 0.1 mL ice-cold TE buffer (50 mM Tris–HCl, pH 7.5 plus 1 mM EGTA) and homogenized by sonication (15 s, three times), using a Q500 Sonicator system (QSonica, Newtown, CT, USA). An aliquot (10 µL) of the well-mixed homogenate was removed for protein determination. Cells were lysed by adding 6% (v/v) ice-cold perchloric acid, strongly vortexed for 2 min and incubated at 4 °C for 10 min. Next, the cell homogenate was centrifuged at 14,462 rpm and 4 °C for 5 min. The resulting supernatant was neutralized by adding KOH/Tris (3 M/0.1 M) and centrifuged again to discard the perchlorate salts. Extracts were kept at 4 °C for their immediate utilization. ATP content was determined spectrophotometrically by following the production of NADPH at 340 nm (ε = 6.22 mM−1 cm−1) and using CARY WinUV-Vis Spectrophotometer (Agilent, Santa Clara, CA, USA). The following reagents were used for the spectrophotometric coupled enzyme assay: 5 U Hexokinase, 10 U Glucose 6-phosphate dehydrogenase, 1 mM NADP + , 5 mM MgCl2 and 10 mM Glucose in HE buffer (100 mM Hepes-HCl plus 1 mM EGTA, pH 7.0) at 25 °C. Chemicals were purchased from Roche (Manheim, Germany) and Sigma (St Louis, MO, USA).Infection statusWe used two different protocols to confirm fungal infection: (1) placing each individual in wet towel paper inside a Ziploc bag to observe hyphal growth27. (2) For insects used in ATP measurements, we followed a modified protocol from Wraight and Ramos28 and Castrillo et al.29. Insect were washed using a serial dilution technique, vortexed for 10 s, and mounted in a drop of lactophenol blue, diluted with distilled water. We then preserved insect body parts (i.e., legs and abdomen terga) at − 80 °C for 12 months and placed in Petri dishes containing potato dextrose agar (PDA HiMedia-GM096) medium (pH 6.8), and incubated for ten days. To confirm infection by B. bassiana, we observed plates every 3 days, identified fungal growth (dense white mycelia), then randomly chose three samples, collected mycelia, and DNA was extracted using PureLink Genomic DNA Kit (Invitrogen by Thermo Fisher Scientific, Waltham, MA, USA), according to manufacturer’s protocol. Next, we used PCR essays (25 µL) contained 1 × Q5 Hot Start High-Fidelity Master Mix (New England BioLabs), following a protocol modified from Castrillo et al.29 using primers GHTqF1 (5′-TTTTCATCGAAAGGTTGTTTCTCG) and GHTq R1 (5′-CTGTGCTGGGTACTGACGTG) amplified a 96-bp region of the SCAR fragment. The PCR protocol was initial denaturation at 98 °C, followed by 30 cycles at 98 °C for 1 min, annealing at 58 °C for 1 min; and extension at 72 °C for 1 min. PCR products were visualized in a 1.0% (wt/vol) agarose gel stained with ethidium bromide.Data analysisAll data were tested for statistical test assumptions using a qqplot, Levene’s homogeneity test and the Shapiro–Wilk normality test at alpha = 0.05 significance level. For critical thermal limits (CTMax and CTMin) experiments, the data sets were non-normal and transformation did not normalize the residuals, so we used nonparametric ANOVAs (Kruskal–Wallis) followed by post-hoc nonparametric multiple comparisons. For voluntary exposure to ETZs, we used a generalized linear model with treatment (healthy, low and high spore load) with Poisson distribution, followed by comparisons within each treatment group. For healthy insects, we used a t-test to compare crosses between warm or cold ETZs; for infected insects, we conducted ANOVAS for comparisons among 23 °C, warm or cold ETZs.ATP data: Data for CTMax of A. pisum were non-normal, and transformation did not normalize the residuals, nonparametric ANOVAs (Kruskal–Wallis) were then used and followed by post-hoc nonparametric pairwise comparisons with Wilcoxon tests. ATP data sets from voluntary exposure to ETZs were analyzed following the same protocol as described previously for in crosses analysis of ETZ experiment. Longevity was analyzed using a two-way ANOVA with fungal load and thermal condition (critical temperature and behavior) as factors. Analyses were performed in the R programming environment (v. 3.4.3., CRAN project)30 and JMP-Pro version 15 (SAS Institute 2020). More