Philippa Kaur

Leng G, Hall J. Crop yield sensitivity of global major agricultural countries to droughts and the projected changes in the future. Sci Total Environ. 2019;654:811–21.CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Hueso S, García C, Hernández T. Severe drought conditions modify the microbial community structure, size and activity in amended and unamended soils. Soil Biol Biochem. 2012;50:167–73.CAS 
Article 

Google Scholar 
Alster CJ, German DP, Lu Y, Allison SD. Microbial enzymatic responses to drought and to nitrogen addition in a southern California grassland. Soil Biol Biochem. 2013;64:68–79.CAS 
Article 

Google Scholar 
Bouskill NJ, Lim HC, Borglin S, Salve R, Wood TE, Silver WL, et al. Pre-exposure to drought increases the resistance of tropical forest soil bacterial communities to extended drought. ISME J. 2013;7:384–94.CAS 
PubMed 
Article 

Google Scholar 
Acosta-Martinez V, Cotton J, Gardner T, Moore-Kucera J, Zak J, Wester D, et al. Predominant bacterial and fungal assemblages in agricultural soils during a record drought/heat wave and linkages to enzyme activities of biogeochemical cycling. Appl Soil Ecol. 2014;84:69–82.Article 

Google Scholar 
O’Connell CS, Ruan L, Silver WL. Drought drives rapid shifts in tropical rainforest soil biogeochemistry and greenhouse gas emissions. Nat Commun. 2018;9:1348.PubMed 
PubMed Central 
Article 
CAS 

Google Scholar 
Schimel JP. Life in dry soils: Effects of drought on soil microbial communities and processes. Annu Rev Ecol Evol Syst. 2018;49:409–32.Article 

Google Scholar 
Naylor D, Colemann-Derr D. Drought stress and root-associated bacterial communities. Front Plant Sci. 2018;8:2223.PubMed 
PubMed Central 
Article 

Google Scholar 
de Vries FT, Griffiths RI, Knight CG, Nicolitch O, Williams A. Harnessing rhizosphere microbiomes for drought-resilient crop production. Science. 2020;368:270–4.PubMed 
Article 
CAS 

Google Scholar 
Smith SE, Read D. Mycorrhizal symbiosis. 3rd ed. London: Academic Press; 2008. p. 145–90.Kakouridis A, Hagen JA, Kan MP, Mambelli S, Feldman LJ, Herman DJ, et al. Routes to roots: direct evidence of water transport by arbuscular mycorrhizal fungi to host plants. New Phytol. 2022; https://doi.org/10.1111/nph.18281.Rillig MC, Mummey DL. Mycorrhizas and soil structure. N Phytol. 2006;171:41–53.CAS 
Article 

Google Scholar 
Gong M, You X, Zhang Q. Effects of Glomus intraradices on the growth and reactive oxygen metabolism of foxtail millet under drought. Ann Microbiol. 2015;65:595–602.CAS 
Article 

Google Scholar 
Ruiz-Lozano JM. Arbuscular mycorrhizal symbiosis and alleviation of osmotic stress. N Perspect Mol Stud Mycorrhiza. 2003;13:309–17.Article 

Google Scholar 
Morte A, Lovisolo C, Schubert A. Effect of drought stress on growth and water relations of the mycorrhizal association Helianthemum almeriense–Terfezia claveryi. Mycorrhiza. 2000;10:115–9.CAS 
Article 

Google Scholar 
Birhane E, Sterck F, Fetene M, Bongers F, Kuyper T. Arbuscular mycorrhizal fungi enhance photosynthesis, water use efficiency, and growth of frankincense seedlings under pulsed water availability conditions. Oecologia. 2012;169:895–904.PubMed 
PubMed Central 
Article 

Google Scholar 
Duan X, Neuman DS, Reiber JM, Green CD, Saxton AM, Augé RM. Mycorrhizal influence on hydraulic and hormonal factors implicated in the control of stomatal conductance during drought. J Exp Bot. 1996;47:1541–50.CAS 
Article 

Google Scholar 
Emmett BD, Levesque-Tremblay V, Harrison MJ. Conserved and reproducible bacterial communities associate with extraradical hyphae of arbuscular mycorrhizal fungi. ISME J. 2021;15:2276–88.CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Toljander JF, Artursson V, Paul LR, Jansson JK, Finlay RD. Attachment of different soil bacteria to arbuscular mycorrhizal fungal extraradical hyphae is determined by hyphal vitality and fungal species. FEMS Microbiol Lett. 2006;254:34–40.CAS 
PubMed 
Article 

Google Scholar 
Svenningsen NB, Watts-Williams SJ, Joner EJ, Battini F, Efthymiou A, Cruz-Paredes C, et al. Suppression of the activity of arbuscular mycorrhizal fungi by the soil microbiota. ISME J. 2018;12:1296–307.CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Cruz-Paredes C, Svenningsen NB, Nybroe O, Kjøller R, Frøslev TG, Jakobsen I. Suppression of arbuscular mycorrhizal fungal activity in a diverse collection of non-cultivated soils. FEMS Microbiol Ecol. 2019;95:fiz020.CAS 
PubMed 
Article 

Google Scholar 
Nuccio EE, Hodge A, Pett-Ridge J, Herman DJ, Weber PK, Firestone MK. An arbuscular mycorrhizal fungus significantly modifies the soil bacterial community and nitrogen cycling during litter decomposition. Environ Microbiol. 2013;15:1870–81.CAS 
PubMed 
Article 

Google Scholar 
Verbruggen E, Jansa J, Hammer EC, Rillig MC. Do arbuscular mycorrhizal fungi stabilize litter-derived carbon in soil? J Ecol. 2016;104:261–9.CAS 
Article 

Google Scholar 
Kaiser C, Kilburn MR, Clode PL, Fuchslueger L, Koranda M, Cliff JP, et al. Exploring the transfer of recent plant photosynthates to soil microbes: mycorrhizal pathway vs direct root exudation. N Phytol. 2015;205:1537–51.CAS 
Article 

Google Scholar 
Zhang L, Shi N, Fan J, Wang F, George TS, Feng G. Arbuscular mycorrhizal fungi stimulate organic phosphate mobilization associated with changing bacterial community structure under field conditions. Environ Microbiol. 2018a;20:2639–51.CAS 
PubMed 
Article 

Google Scholar 
Hodge A, Campbell CD, Fitter AH. An arbuscular mycorrhizal fungus accelerates decomposition and acquires nitrogen directly from organic matter. Nature. 2001;413:297–9.CAS 
PubMed 
Article 

Google Scholar 
Hestrin R, Hammer EC, Mueller CW, Lehmann J. Synergies between mycorrhizal fungi and soil microbial communities increase plant nitrogen acquisition. Commun Biol. 2019;2:233.PubMed 
PubMed Central 
Article 
CAS 

Google Scholar 
Medina A, Probanza A, Gutierrez Mañero FJ, Azcón R. Interactions of arbuscular-mycorrhizal fungi and Bacillus strains and their effects on plant growth, microbial rhizosphere activity (thymidine and leucine incorporation) and fungal biomass (ergosterol and chitin). Appl Soil Ecol. 2003;22:15–28.Article 

Google Scholar 
Drigo B, Pijl AS, Duyts H, Kielak AM, Gamper HA, Houtekamer MJ, et al. Shifting carbon flow from roots into associated microbial communities in response to elevated atmospheric CO2. Proc Natl Acad Sci USA. 2010;107:10938–42.CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Jakobsen I, Rosenthal L. Carbon flow into soil and external hyphae from roots of mycorrhizal cucumber plants. N Phytol. 1990;115:77–83.Article 

Google Scholar 
Zhou J, Chai X, Zhang L, George TS, Wang F, Feng G. Different arbuscular mycorrhizal fungi cocolonizing on a single plant root system recruit distinct microbiomes. mSystems. 2020;5:e00929–0.CAS 
PubMed 
PubMed Central 

Google Scholar 
See CR, Keller AB, Hobbie SE, Kennedy PG, Weber PK, Pett-Ridge J. Hyphae move matter and microbes to mineral microsites: Integrating the hyphosphere into conceptual models of soil organic matter stabilization. Glob Change Biol. 2022;28:2527–40.CAS 
Article 

Google Scholar 
Carini P, Marsden P, Leff J, Morgan E, Strickland M, Fierer N. Relic DNA is abundant in soil and obscures estimates of soil microbial diversity. Nat Microbiol. 2017;2:16242.CAS 
Article 

Google Scholar 
Lennon JT, Muscarella ME, Placella MA, Lehmkuhl BK. How, when, and where relic DNA affects microbial diversity. mBio. 2018;9:e00637–18.PubMed 
PubMed Central 

Google Scholar 
Hungate BA, Mau RL, Schwartz E, Caporaso JG, Dijkstra P, van Gestel N, et al. Quantitative microbial ecology through stable isotope probing. Appl Environ Microbiol. 2015;81:7570–81.CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Neufeld JD, Vohra J, Dumont MG, Lueders T, Manefield M, Friedrich MW, et al. DNA stable-isotope probing. Nat Protoc. 2007;2:860–6.CAS 
PubMed 
Article 

Google Scholar 
Koch BJ, McHugh TA, Hayer M, Schwartz E, Blazewicz SJ, Dijkstra P, et al. Estimating taxon-specific population dynamics in diverse microbial communities. Ecosphere. 2018;9:e02090–15.Article 

Google Scholar 
Blazewicz SJ, Hungate BA, Koch BJ, Nuccio EE, Morrissey E, Brodie EL, et al. Taxon-specific microbial growth and mortality patterns reveal distinct temporal population responses to rewetting in a California grassland soil. ISME J. 2020;14:1520–32.PubMed 
PubMed Central 
Article 

Google Scholar 
Kilronomos JN. Host-specificity and functional diversity among arbuscular mycorrhizal fungi. In: Proceedings of the 8th International Symposium on Microbial Ecology. Bell CR, Brylinski M, Johnson-Green P, editors. Halifax: Atlantic Canada Society from Microbial Ecology; 2000. p. 845–51.Ray P, Guo Y, Chi MH, Krom N, Saha MC, Craven KD. Serendipita bescii promotes winter wheat growth and modulates the host root transcriptome under phosphorus and nitrogen starvation. Environ Microbiol. 2021;23:1876–88.CAS 
PubMed 
Article 

Google Scholar 
Lee MR, Hawkes CV. Widespread co-occurrence of Sebacinales and arbuscular mycorrhizal fungi in switchgrass roots and soils has limited dependence on soil carbon or nutrients. Plants People Planet. 2021;3:614–26.Article 

Google Scholar 
Ruiz-Lozano JM, Azcon R, Gomez M. Effects of arbuscular-mycorrhizal glomus species on drought tolerance: physiological and nutritional plant responses. Appl Environ Microbiol. 1995;61:456–60.CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
He F, Sheng M, Tang M. Effects of Rhizophagus irregularis on photosynthesis and antioxidative enzymatic system in Robinia pseudoacacia L. under drought stress. Front Plant Sci. 2017;8:183.PubMed 
PubMed Central 

Google Scholar 
Ghimire SR, Craven KD. Enhancement of switchgrass (Panicum virgatum L.) biomass production under drought conditions by the ectomycorrhizal fungus Sebacina vermifera. Appl Environ Microbiol. 2011;77:7063–7.CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Tisserant E, Malbreil M, Kuo A, Kohler A, Symeonidi A, Balestrini R, et al. Genome of an arbuscular mycorrhizal fungus provides insight into the oldest plant symbiosis. Proc Natl Acad Sci USA. 2013;110:20117–22.CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Kamel L, Keller-Pearson M, Roux C, Ané JM. Biology and evolution of arbuscular mycorrhizal symbiosis in the light of genomics. N Phytol. 2017;213:531–6.CAS 
Article 

Google Scholar 
Bukovská P, Bonkowski M, Konvalinková T, Beskid O, Hujslová M, Püschel D, et al. Utilization of organic nitrogen by arbuscular mycorrhizal fungi-is there a specific role for protists and ammonia oxidizers? Mycorrhiza. 2018;28:269–83.PubMed 
Article 
CAS 

Google Scholar 
Zhang L, Feng G, Declerck S. Signal beyond nutrient, fructose, exuded by an arbuscular mycorrhizal fungus triggers phytate mineralization by a phosphate solubilizing bacterium. ISME J. 2018;12:2339.CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Zuccaro A, Lahrmann U, Güldener U, Langen G, Pfiffi S, Biedenkopf D, et al. Endophytic life strategies decoded by genome and transcriptome analyses of the mutualistic root symbiont Piriformospora indica. PLoS Pathog. 2011;7:e1002290.CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Ray P, Chi MH, Guo Y, Chen C, Adam C, Kuo A, et al. Genome sequence of the plant growth promoting fungus Serendipita vermifera subsp. bescii: The first native strain from North America. Phytobiomes J. 2018;2:62–3.Article 

Google Scholar 
Dias T, Pimentel V, Cogo AJD, Costa R, Bertolazi AA, Miranda C, et al. The free-living stage growth conditions of the endophytic fungus Serendipita indica may regulate its potential as plant growth promoting microbe. Front Microbiol. 2020;11:562238.PubMed 
PubMed Central 
Article 

Google Scholar 
Moffatt HH. Soil Survey of Caddo County, Oklahoma. Washington, D.C.: United States Department of 836 Agriculture Soil Conservation Service; 1973.Sher Y, Baker NR, Herman NR, Fossum C, Hale L, Zhang XX, et al. Microbial extracellular polysaccharide production and aggregate stability controlled by Switchgrass (Panicum virgatum) root biomass and soil water potential. Soil Biol Biochem. 2020;143:107742.CAS 
Article 

Google Scholar 
Seki K. SWRC fit—a nonlinear fitting program with a water retention curve for soils having unimodal and bimodal pore structure. Hydrol Earth Syst Sci Discuss. 2007;4:407–37.
Google Scholar 
Ray P, Ishiga T, Decker SR, Turner GB, Craven KD. A novel delivery system for the root symbiotic fungus, Sebacina vermifera, and consequent biomass enhancement of low lignin COMT switchgrass lines. BioEnerg Res. 2015;8:922–33.CAS 
Article 

Google Scholar 
Blazewicz SJ, Schwartz E, Firestone MK. Growth and death of bacteria and fungi underlie rainfall-induced carbon dioxide pulses from seasonally dried soil. Ecology. 2014;95:1162–72.PubMed 
Article 

Google Scholar 
Nuccio EE, Blazewicz SJ, Lafler M, Campbell AN, Kakouridis A, Kimbrel JA, et al. HT-SIP: a semi-automated Stable Isotope Probing pipeline identifies interactions in the hyphosphere of arbuscular mycorrhizal fungi. bioRxiv. 2022; https://biorxiv.org/cgi/content/short/2022.07.01.498377v1.Buckley DH, Huangyutitham V, Hsu SF, Nelson TA. Stable isotope probing with 15N achieved by disentangling the effects of genome G+C content and isotope enrichment on DNA density. Appl Environ Microbiol. 2007;73:3189–95.CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Parada AE, Needham DM, Fuhrman JA. Every base matters: assessing small subunit rRNA primers for marine microbiomes with mock communities, time series and global field samples. Environ Microbiol. 2016;18:1403–14.CAS 
PubMed 
Article 

Google Scholar 
Apprill A, McNally S, Parsons R, Weber L. Minor revision to V4 region SSU rRNA 806R gene primer greatly increases detection of SAR11 bacterioplankton. Aquat Micro Ecol. 2015;75:129–37.Article 

Google Scholar 
Badri A, Stefani FOP, Lachance G, Roy-Arcand L, Beaudet D, Vialle A, et al. Molecular diagnostic toolkit for Rhizophagus irregularis isolate DAOM-197198 using quantitative PCR assay targeting the mitochondrial genome. Mycorrhiza. 2016;26:721–33.CAS 
PubMed 
Article 

Google Scholar 
Gamper HA, Young JP, Jones DL, Hodge A. Real-time PCR and microscopy: are the two methods measuring the same unit of arbuscular mycorrhizal fungal abundance? Fungal Genet Biol. 2008;45:581–96.CAS 
PubMed 
Article 

Google Scholar 
Tellenbach C, Grünig CR, Sieber TN. Suitability of quantitative real-time PCR to estimate the biomass of fungal root endophytes. Appl Environ Microbiol. 2010;76:5764–72.CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Schildkraut CL, Marmur J, Doty P. Determination of the base composition of deoxyribonucleic acid from its buoyant density in CsCl. J Mol Biol. 1962;4:430–43.CAS 
PubMed 
Article 

Google Scholar 
Martin-Laurent F, Phillipot L, Hallet S, Chaussod R, Germon JC, Soulas G, et al. DNA extraction from soils: old bias for new microbial diversity analysis methods. Appl Environ Microbiol. 2001;67:2354–9.CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Louca S, Doebeli M, Parfrey LW. Correcting for 16S rRNA gene copy numbers in microbiome surveys remains an unsolved problem. Microbiome. 2018;6:41.PubMed 
PubMed Central 
Article 

Google Scholar 
Kanagawa T. Bias and artifacts in multitemplate polymerase chain reactions (PCR). J Biosci Bioeng. 2003;96:317–23.CAS 
PubMed 
Article 

Google Scholar 
Kozarewa I, Ning Z, Quail MA, Sanders MJ, Berriman M, Turner DJ. Amplification-free Illumina sequencing-library preparation facilitates improved mapping and assembly of (G+C)-biased genomes. Nat Methods. 2009;6:291–5.CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Manzoni S, Taylor P, Richter A, Porporato A, Ågren GI. Environmental and stoichiometric controls on microbial carbon-use efficiency in soils. N Phytol. 2012;196:79–91.CAS 
Article 

Google Scholar 
Geyer KM, Dijkstra P, Sinsabaugh R, Frey SD. Clarifying the interpretation of carbon use efficiency in soil through methods comparison. Soil Biol Biochem. 2019;128:79–88.CAS 
Article 

Google Scholar 
R Core Team. R: a language and environment for statistical computing. Vienna: R Foundation for Statistical Computing; 2019.Oksanen J, Blanchet FG, Kindt R, Legendre P, Minchin PR, OHara RB, et al. vegan: Community Ecology Package R package version 2.3-0. 2015. http://CRAN.R-project.org/package=vegan.Lozupone C, Lladser ME, Knights D, Stombaugh J, Knight R. UniFrac: an effective distance metric for microbial community comparison. ISME J. 2011;5:169–72.PubMed 
Article 

Google Scholar 
Harris RF. Effect of water potential on microbial growth and activity. In: Water Potential Relations in Soil Microbiology. Parr JF, Gardner WR, Elliott LF, editors. Madison, WI: Am Soc Agron; 1981. p. 23–95.Wagg C, Dudenhöffer JH, Widmer F, van der Heijden MGA. Linking diversity, synchrony and stability in soil microbial communities. Funct Ecol. 2018;32:1280–92.Article 

Google Scholar 
Malik AA, Martiny JBH, Brodie EL, Martiny AC, Treseder KK, Allison SD. Defining trait-based microbial strategies with consequences for soil carbon cycling under climate change. ISME J. 2020;14:1–9.CAS 
PubMed 
Article 

Google Scholar 
Tiemann LK, Billings SA. Changes in variability of soil moisture alter microbial community C and N resource use. Soil Biol Biochem. 2011;43:1837–47.CAS 
Article 

Google Scholar 
Domeignoz-Horta LA, Pold G, Liu XJA, Frey SD, Melillo JM, DeAngelis KM. Microbial diversity drives carbon use efficiency in a model soil. Nat Commun. 2020;11:3684.CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Gefen O, Balaban NQ. The importance of being persistent: heterogeneity of bacterial populations under antibiotic stress. FEMS Microbiol Rev. 2009;33:704–17.CAS 
PubMed 
Article 

Google Scholar 
Fridman O, Goldberg O, Ronin I, Shoresh N, Balaban NQ. Optimization of lag time underlies antibiotic tolerance in evolved bacterial populations. Nature. 2014;513:418–21.CAS 
PubMed 
Article 

Google Scholar 
Bouskill NJ, Wood TE, Baran R, Hao Z, Ye Z, Bowen BP, et al. Belowground response to drought in a tropical forest soil. II. Change in microbial function impacts carbon composition. Front Microbiol. 2016;7:323.PubMed 
PubMed Central 

Google Scholar 
Sutcliffe IC. A phylum level perspective on bacterial cell envelope architecture. Trends Microbiol. 2010;18:464–70.CAS 
PubMed 
Article 

Google Scholar 
Tocheva EI, Ortega DR, Jensen GJ. Sporulation, bacterial cell envelopes and the origin of life. Nat Rev Microbiol. 2016;14:535–42.PubMed 
PubMed Central 
Article 

Google Scholar 
Xu L, Naylor D, Dong Z, Simmons T, Pierroz G, Hixson KK, et al. Drought delays development of the sorghum root microbiome and enriches for monoderm bacteria. Proc Natl Acad Sci USA. 2018;115:E4284–93.CAS 
PubMed 
PubMed Central 

Google Scholar 
Santos-Medellín C, Liechty Z, Edwards J, Nguyen B, Huang B, Weimer BC, et al. Prolonged drought imparts lasting compositional changes to the rice root microbiome. Nat Plants. 2021;7:1065–77.PubMed 
Article 
CAS 

Google Scholar 
Otoguro M, Yamamura H, Quintana ET The Family Streptosporangiaceae. In: The Prokaryotes. Rosenberg E, DeLong EF, Lory S, Stackebrandt E, Thompson F, editors. Berlin, Heidelberg: Springer; 2104. p. 1011–45.Barnard RL, Osborne CA, Firestone MK. Responses of soil bacterial and fungal communities to extreme desiccation and rewetting. ISME J. 2013;7:2229–41.CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Cruz AF, Ishii T. Arbuscular mycorrhizal fungal spores host bacteria that affect nutrient biodynamics and biocontrol of soil-borne plant pathogens. Biol Open. 2012;1:52–7.PubMed 
Article 

Google Scholar 
Rillig MC, Lutgen ER, Ramsey PW, Klironomos JN, Gannon JE. Microbiota accompanying different arbuscular mycorrhizal fungal isolates influence soil aggregation. Pedobiologia. 2005;49:251–9.Article 

Google Scholar 
Jiang F, Zhang L, Zhou J, George TS, Feng G. Arbuscular mycorrhizal fungi enhance mineralisation of organic phosphorus by carrying bacteria along their extraradical hyphae. N Phytol. 2021;230:304–15.CAS 
Article 

Google Scholar 
Hernandez DJ, David AS, Menges ES, Searcy CA, Afkhami ME. Environmental stress destabilizes microbial networks. ISME J. 2021;15:1722–34.PubMed 
PubMed Central 
Article 

Google Scholar 
Leigh J, Fitter AH, Hodge A. Growth and symbiotic effectiveness of an arbuscular mycorrhizal fungus in organic matter in competition with soil bacteria. FEMS Microbiol Ecol. 2011;76:428–38.CAS 
PubMed 
Article 

Google Scholar 
Leifheit EF, Verbruggen E, Rillig MC. Arbuscular mycorrhizal fungi reduce decomposition of woody plant litter while increasing soil aggregation. Soil Biol Biochem. 2015;81:323–8.CAS 
Article 

Google Scholar 
Bronstein JL. Conditional outcomes in mutualistic interactions. Trends Ecol Evol. 1994;9:214–7.CAS 
PubMed 
Article 

Google Scholar 
Toljander JF, Lindahl BD, Paul LR, Elfstrand M, Finlay RD. Influence of arbuscular mycorrhizal mycelial exudates on soil bacterial growth and community structure. FEMS Microbiol Ecol. 2007;61:295–304.CAS 
PubMed 
Article 

Google Scholar 
Zhang L, Zhou J, George TS, Limpens E, Feng G. Arbuscular mycorrhizal fungi conducting the hyphosphere bacterial orchestra. Trends Plant Sci. 2022;27:402–11.CAS 
PubMed 
Article 

Google Scholar 
Zhalnina K, Louie KB, Hao Z, Mansoori N, Nunes da Rocha U, Shi S, et al. Dynamic root exudate chemistry and microbial substrate preferences drive patterns in rhizosphere microbial community assembly. Nat Microbiol. 2018;3:470–80.CAS 
PubMed 
Article 

Google Scholar 
Chaparro JM, Badri DV, Bakker MG, Sugiyama A, Manter DK, Vivanco JM. Root exudation of phytochemicals in Arabidopsis follows specific patterns that are developmentally programmed and correlate with soil microbial functions. PLoS ONE. 2013;8:e55731.CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Shi S, Richardson AE, O’Callaghan M, DeAngelis KM, Jones EE, Stewart A, et al. Effects of selected root exudate components on soil bacterial communities. FEMS Microbiol Ecol. 2011;77:600–10.CAS 
PubMed 
Article 

Google Scholar  More

Alexander MA, Eischeid JK (2001) Climate variability in regions of amphibian declines. Conserv Biol 15:930–942Article 

Google Scholar 
Bates D, Mächler M, Bolker BM, Walker SC (2015) Fitting linear mixed-effects models using lme4. J Stat Softw 67:1–48Article 

Google Scholar 
Baur B (1986) Patterns of dispersion, density and dispersal in alpine populations of the land snail Arianta arbustorum (L.) (Helicidae). Holarct Ecol 9:117–125
Google Scholar 
Beier P, Majka DR, Spencer WD (2008) Forks in the road: choices in procedures for designing wildland linkages. Conserv Biol 22:836–851PubMed 
Article 

Google Scholar 
Berlow EL, Knapp R, Ostoja SM, Williams RJ, McKenny H, Matchett JR et al. (2013) A network extension of species occupancy models in a patchy environment applied to the Yosemite toad (Anaxyrus canorus). PLoS ONE 8:e72200CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Bingaman JW (1968) Pathways: a story of trails and men. End-Kian Publishing Company, Lodi, CABozzuto C, Biebach I, Muff S, Ives AR, Keller LF (2019) Inbreeding reduces long-term growth of Alpine ibex populations. Nat Ecol Evol 3:1359–1364PubMed 
Article 

Google Scholar 
Bradford D, Gordon M (1994) Acidic deposition as an unlikely cause for amphibian population declines in the Sierra Nevada, California. Biol Conserv 69:155–161Article 

Google Scholar 
Brattstrom BH (1962) Thermal control of aggregation behavior in tadpoles. Herpetologica 18:38–46
Google Scholar 
Breiman L (2001) Random forests. Mach Learn 45:5–32Article 

Google Scholar 
Brown C, Hayes MP, Green GA, Macfarlane DC, Lind AJ (2015) Yosemite toad conservation assessment. USDA Forest Service report. Sonora, CABrown C, Olsen AR (2013) Bioregional monitoring design and occupancy estimation for two Sierra Nevadan amphibian taxa. Freshw Sci 32:675–691Article 

Google Scholar 
Cal Fire (2022) Fire perimeters. FRAP Mapp. https://frap.fire.ca.gov/mapping/gis-data/Catchen JM, Amores A, Hohenlohe P, Cresko W, Postlethwait JH (2011) Stacks: building and genotyping loci de novo from short-read sequences. G3 Genes Genomes Genet 1:171–182CAS 

Google Scholar 
Catchen J, Hohenlohe PA, Bassham S, Amores A, Cresko WA (2013) Stacks: an analysis tool set for population genomics. Mol Ecol 22:3124–3140PubMed 
PubMed Central 
Article 

Google Scholar 
Chetkiewicz C-LB, St. Clair CC, Boyce MS (2006) Corridors for conservation: integrating pattern and process. Annu Rev Ecol Evol Syst 37:317–342Article 

Google Scholar 
Corn PS (2003) Amphibian breeding and climate change importance of snow in the mountains. Conserv Biol 17:622–625Article 

Google Scholar 
Csárdi G, Nepusz T (2006) The igraph software package for complex network research. Int J Complex Syst 1695:1–9
Google Scholar 
Davidson C (2004) Declining downwind: amphibian population declines in California and historical pesticide use. Ecol Appl 14:1892–1902Article 

Google Scholar 
Dileo MF, Siu JC, Rhodes MK, Lõpez-Villalobos A, Redwine A, Ksiazek K et al. (2014) The gravity of pollination: integrating at-site features into spatial analysis of contemporary pollen movement. Mol Ecol 23:3973–3982PubMed 
Article 

Google Scholar 
Dodge C, Cheng T, Vredenburg V (2012) Exploring the evidence of a historical chytrid epidemic in the Yosemite toad by PCR analysis of museum specimensDouglas DH (1994) Least-cost path in GIS using an accumulated cost surface and slopelines. Cartographica 31:37–51Article 

Google Scholar 
Dozier J, Frew J (2009) Computational provenance in hydrologic science: a snow mapping example. Philos Trans R Soc A Math Phys Eng Sci 367:1021–1033Article 

Google Scholar 
Dozier J, Painter TH, Rittger K, Frew JE (2008) Time-space continuity of daily maps of fractional snow cover and albedo from MODIS. Adv Water Resour 31:1515–1526Article 

Google Scholar 
Drost C, Fellers G (1994) Decline of frog species in the Yosemite section of the Sierra Nevada. National Park Service report. Davis, CADrost C, Fellers G (1996) Collapse of a regional frog fauna in the Yosemite area of the California Sierra Nevada, USA. Conserv Biol 10:414–425Article 

Google Scholar 
Dyer RJ, Nason JD (2004) Population graphs: the graph theoretic shape of genetic structure. Mol Ecol 13:1713–1727PubMed 
Article 

Google Scholar 
Dyer RJ, Nason JD, Garrick RC (2010) Landscape modelling of gene flow: improved power using conditional genetic distance derived from the topology of population networks. Mol Ecol 19:3746–3759PubMed 
Article 

Google Scholar 
Epps CW, Wehausen JD, Bleich VC, Torres SG, Brashares JS (2007) Optimizing dispersal and corridor models using landscape genetics. J Appl Ecol 44:714–724Article 

Google Scholar 
van Etten J (2017) R Package gdistance: distances and routes on geographical grids. J Stat Softw 76:1–21
Google Scholar 
Evans J, Oakleaf J, Cushman S, Theobald D (2014) An ArcGIS toolbox for surface gradient and geomorphometric modelingExcoffier L, Smouse PE, Quattro JM (1992) Analysis of molecular variance inferred from metric distances among DNA haplotypes: application to human mitochondrial DNA restriction data. Genetics 131:479–491CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Fitzpatrick MC, Keller SR (2015) Ecological genomics meets community-level modelling of biodiversity: mapping the genomic landscape of current and future environmental adaptation. Ecol Lett 18:1–16PubMed 
Article 

Google Scholar 
Flint LE, Flint AL, Thorne JH, Boynton R (2013) Fine-scale hydrologic modeling for regional landscape applications: the California Basin Characterization Model development and performance. Ecol Process 2:1–21Article 

Google Scholar 
Gaggiotti OE (2003) Genetic threats to population persistence. Ann Zool Fennici 40:155–168
Google Scholar 
Garroway CJ, Bowman J, Carr D, Wilson PJ (2008) Applications of graph theory to landscape genetics. Evol Appl 1:620–630PubMed 
PubMed Central 
Article 

Google Scholar 
Gotelli NJ (1991) Metapopulation models: the rescue effect, the propagule rain, and the core-satellite hypothesis. Am Nat 138:768–776Article 

Google Scholar 
Grasso RL, Coleman RM, Davidson C (2010) Palatability and antipredator response of Yosemite toads (Anaxyrus canorus) to nonnative brook trout (Salvelinus fontinalis) in the Sierra Nevada Mountains of California. Copeia 2010:457–462Article 

Google Scholar 
Graves TA, Beier P, Royle JA (2013) Current approaches using genetic distances produce poor estimates of landscape resistance to interindividual dispersal. Mol Ecol 22:3888–3903PubMed 
Article 

Google Scholar 
Gregorutti B, Michel B, Saint-Pierre P (2017) Correlation and variable importance in random forests. Stat Comput 27:659–678Article 

Google Scholar 
Grinnell J, Storer TI (1924) Animal life in the Yosemite: an account of the mammals, birds, reptiles, and amphibians in a cross-section of the Sierra Nevada. University of California Press, Berkeley, CAHall DK, Riggs GA, Salomonson VV, Digirolamo NE, Bayr KJ (2002) MODIS snow-cover products. Remote Sens Environ 83:181–194Article 

Google Scholar 
Hansson L (1991) Dispersal and connectivity in metapopulations. Biol J Linn Soc 42:89–103Article 

Google Scholar 
Heenkenda MK, Joyce KE, Maier SW, de Bruin S (2015) Quantifying mangrove chlorophyll from high spatial resolution imagery. ISPRS J Photogramm Remote Sens 108:234–244Article 

Google Scholar 
Hether TD, Hoffman EA (2012) Machine learning identifies specific habitats associated with genetic connectivity in Hyla squirella. J Evol Biol 25:1039–1052CAS 
PubMed 
Article 

Google Scholar 
Houborg R, McCabe MF (2018) A hybrid training approach for leaf area index estimation via Cubist and random forests machine-learning. ISPRS J Photogramm Remote Sens 135:173–188Article 

Google Scholar 
Huber N, Bateman P, Wahrhaftig C (2003) Geologic map of Yosemite National Park and Vicinity, California: a digital database. Menlo Park, CAIPCC (2014) Climate change 2014: synthesis report. Contribution of working groups I, II and III to the fifth assessment report of the Intergovernmental Panel on Climate Change. Geneva, SwitzerlandJennings M, Hayes M (1994) Amphibian and reptile species of special concern in California. California Department of Fish & Game report. Rancho Cordova, CAKarlstrom EL (1962) The toad genus Bufo in the Sierra Nevada of California: ecological and systematic relationships. Unviersity Calif Publ Zool 62:1–104
Google Scholar 
Keeler-Wolf T, Reyes ET, Menke JM, Johnson DN, Karavidas. DL (2012) Yosemite National Park vegetation classification and mapping project report. National Park Service report. Fort Collins, COKittlein MJ, Mora MS, Mapelli FJ, Austrich A, Gaggiotti OE (2022) Deep learning and satellite imagery predict genetic diversity and differentiation. Methods Ecol Evol 13:711–721Article 

Google Scholar 
Kleinberg JM (1999) Authoritative sources in a hyperlinked environment. J ACM 46:604–632Article 

Google Scholar 
Knapp RA, Fellers GM, Kleeman PM, Miller DAW, Vredenburg VT, Rosenblum EB et al. (2016) Large-scale recovery of an endangered amphibian despite ongoing exposure to multiple stressors. Proc Natl Acad Sci 113:11889–11894CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Knapp RA, Matthews KR (2000) Non-native fish introductions and the decline of the mountain yellow-legged frog from within protected areas. Conserv Biol 14:428–438Article 

Google Scholar 
Kuhn M (2008) Building predictive models in R using the caret package. J Stat Softw 28:1–26Article 

Google Scholar 
Lee SR, Ostoja SM, Maier PA, Matchett JR, McKenny HC, Brooks ML et al. Distribution and spatio-temporal variation of Yosemite toad populations in Sierra Nevada national parks (in preparation)Liang CT (2010) Habitat modeling and movements of the Yosemite toad (Anaxyrus (=Bufo) canorus) in the Sierra Nevada, California. Ph.D. Dissertation. University of California, DavisLiang CT, Grasso RL, Nelson-Paul JJ, Vincent KE, Lind AJ (2017) Fine-scale habitat characteristics related to occupancy of the Yosemite toad, Anaxyrus canorus. Copeia 105:120–127Article 

Google Scholar 
Liang CT, Stohlgren TJ (2011) Habitat suitability of patch types: a case study of the Yosemite toad. Front Earth Sci 5:217–228CAS 
Article 

Google Scholar 
Lindauer AL, Maier PA, Voyles J (2020) Daily fluctuating temperatures decrease growth and reproduction rate of a lethal amphibian fungal pathogen in culture. BMC Ecol 20:1–9Article 
CAS 

Google Scholar 
Lindauer AL, Voyles J (2019) Out of the frying pan, into the fire? Yosemite toad (Anaxyrus canorus) susceptibility to Batrachochytrium dendrobatidis after development under drying conditions. Herpetol Conserv Biol 14:185–198
Google Scholar 
Littlefield CE, Krosby M, Michalak JL, Lawler JJ (2019) Connectivity for species on the move: supporting climate-driven range shifts. Front Ecol Environ 17:270–278Article 

Google Scholar 
Lowe WH, Allendorf FW (2010) What can genetics tell us about population connectivity? Mol Ecol 19:3038–3051PubMed 
Article 

Google Scholar 
Maher SP, Morelli TL, Hershey M, Flint AL, Flint LE, Moritz C et al. (2017) Erosion of refugia in the Sierra Nevada meadows network with climate change. Ecosphere 8:1–17Article 

Google Scholar 
Maier PA (2018) Evolutionary past, present, and future of the Yosemite toad (Anaxyrus canorus): a total evidence approach to delineating conservation units. Ph.D. Dissertation. University of California RiversideMaier PA, Vandergast AG, Ostoja SM, Aguilar A, Bohonak AJ (2019) Pleistocene glacial cycles drove lineage diversification and fusion in the Yosemite toad (Anaxyrus canorus). Evolution 73:2476–2496PubMed 
Article 

Google Scholar 
Maier PA, Vandergast AG, Ostoja SM, Aguilar A, Bohonak AJ (2022) Gene pool boundaries for the Yosemite toad (Anaxyrus canorus) reveal asymmetrical migration within meadow neighborhoods. Front Conserv Sci 3:1–14Article 

Google Scholar 
Manel S, Holderegger R (2013) Ten years of landscape genetics. Trends Ecol Evol 28:614–621PubMed 
Article 

Google Scholar 
Manel S, Schwartz MK, Luikart G, Taberlet P (2003) Landscape genetics: combining landscape ecology and population genetics. Trends Ecol Evol 18:189–197Article 

Google Scholar 
Martin DL (2008) Decline, movement and habitat utilization of the Yosemite toad (Bufo canorus): an endangered anuran endemic to the Sierra Nevada of California. Ph.D. Dissertation. University of California, Santa BarbaraMasek JG, Vermote EF, Saleous NE, Wolfe R, Hall FG, Huemmrich KF et al. (2006) A landsat surface reflectance dataset, 1990-2000. IEEE Geosci Remote Sens Lett 3:68–72Article 

Google Scholar 
Matchett JR, Stark PB, Ostoja SM, Knapp RA, McKenny HC, Brooks ML et al. (2015) Detecting the influence of rare stressors on rare species in Yosemite National Park using a novel stratified permutation test. Sci Rep 5:1–12Article 
CAS 

Google Scholar 
Mathieu J, Barot S, Blouin M, Caro G, Decaëns T, Dubs F et al. (2010) Habitat quality, conspecific density, and habitat pre-use affect the dispersal behaviour of two earthworm species, Aporrectodea icterica and Dendrobaena veneta, in a mesocosm experiment. Soil Biol Biochem 42:203–209CAS 
Article 

Google Scholar 
Matthysen E (2005) Density-dependent dispersal in birds and mammals. Ecography 28:403–416Article 

Google Scholar 
McRae B (2006) Isolation by resistance. Evolution 60:1551–1561PubMed 
Article 

Google Scholar 
McRae BH, Beier P (2007) Circuit theory predicts gene flow in plant and animal populations. Proc Natl Acad Sci USA 104:19885–19890CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Meyer H, Pebesma E (2021) Predicting into unknown space? estimating the area of applicability of spatial prediction models. Methods Ecol Evol 12:1620–1633Article 

Google Scholar 
Morelli TL, Maher SP, Lim MCW, Kastely C, Eastman LM, Flint LE et al. (2017) Climate change refugia and habitat connectivity promote species persistence. Clim Chang Responses 4:8Article 

Google Scholar 
Morton M (1981) Seasonal changes in total body lipid and liver weight in the Yosemite toad. Copeia 1981:234–238Article 

Google Scholar 
Morton M, Pereyra M (2010) Habitat use by Yosemite toads: life history traits and implications for conservation. Herpetol Conserv Biol 5:388–394
Google Scholar 
Mullally D (1953) Observations on the ecology of the toad Bufo canorus. Copeia 1953:182–183Article 

Google Scholar 
Mullally D, Cunningham J (1956) Aspects of the thermal ecology of the Yosemite toad. Herpetologica 12:57–67
Google Scholar 
Murphy MA, Dezzani R, Pilliod D, Storfer A (2010a) Landscape genetics of high mountain frog metapopulations. Mol Ecol 19:3634–3649PubMed 
Article 

Google Scholar 
Murphy MA, Evans JS, Storfer A (2010b) Quantifying Bufo boreas connectivity in Yellowstone National Park with landscape genetics. Ecology 91:252–261PubMed 
Article 

Google Scholar 
National Park Service (2022) National park service visitor use statisticsNei M, Chesser RK (1983) Estimation of fixation indices and gene diversities. Ann Hum Genet 47:253–259CAS 
PubMed 
Article 

Google Scholar 
Nunney L, Campbell KA (1993) Assessing minimum viable population size: demography meets population genetics. Trends Ecol Evol 8:234–239CAS 
PubMed 
Article 

Google Scholar 
Painter TH, Rittger K, McKenzie C, Slaughter P, Davis RE, Dozier J (2009) Retrieval of subpixel snow covered area, grain size, and albedo from MODIS. Remote Sens Environ 113:868–879Article 

Google Scholar 
Parmesan C (2006) Ecological and evolutionary responses to recent climate change. Annu Rev Ecol Evol Syst 37:637–669Article 

Google Scholar 
Peterman WE (2018) Surfaces using genetic algorithms ResistanceGA: an R package for the optimization of resistance. Methods Ecol Evol 9:1638–1647Article 

Google Scholar 
Peterman WE, Pope NS (2021) The use and misuse of regression models in landscape genetic analyses. Mol Ecol 30:37–47PubMed 
Article 

Google Scholar 
Peterson MA (1997) Host plant phenology and butterfly dispersal: causes and consequences of uphill movement. Ecology 78:167–180Article 

Google Scholar 
Pflüger FJ, Balkenhol N (2014) A plea for simultaneously considering matrix quality and local environmental conditions when analysing landscape impacts on effective dispersal. Mol Ecol 23:2146–56PubMed 
Article 

Google Scholar 
Pless E, Saarman NP, Powell JR, Caccone A, Amatulli G (2021) A machine-learning approach to map landscape connectivity in Aedes aegypti with genetic and environmental data. Proc Natl Acad Sci USA 118:1–8Article 
CAS 

Google Scholar 
Pounds J (2001) Climate and amphibian declines. Nature 410:639–640CAS 
PubMed 
Article 

Google Scholar 
Pounds JA, Bustamante MR, Coloma LA, Consuegra JA, Fogden MPL, Foster PN et al. (2006) Widespread amphibian extinctions from epidemic disease driven by global warming. Nature 439:161–167CAS 
PubMed 
Article 

Google Scholar 
Quinlan JR (1992) Learning with continuous classes. Aust Jt Conf Artif Intell 92:343–348
Google Scholar 
Quinlan JR (1993) Combining instance-based and model-based learning. Mach Learn Proc 1993 93:236–243Article 

Google Scholar 
Rabus B, Eineder M, Roth A, Bamler R (2003) The shuttle radar topography mission—a new class of digital elevation models acquired by spaceborne radar. ISPRS J Photogramm Remote Sens 57:241–262Article 

Google Scholar 
Ratliff RD (1985) Meadows in the Sierra Nevada of California: state of knowledge. U.S. Forest Service report. Berkeley, CAReich KD, Berg N, Walton DB, Schwartz M, Sun F, Huang X et al. (2018) Climate change in the Sierra Nevada: California’s water future. UCLA Center for Climate Science report. Los Angeles, CAReynolds SJ, Christian KA (2009) Environmental moisture availability and body fluid osmolality in introduced toads. J Herpetol 43:326–331Article 

Google Scholar 
Riahi K, Rao S, Krey V, Cho C, Chirkov V, Fischer G et al. (2011) RCP 8.5—a scenario of comparatively high greenhouse gas emissions. Clim Change 109:33–57CAS 
Article 

Google Scholar 
Roche LM, Allen-Diaz B, Eastburn DJ, Tate KW (2012a) Cattle grazing and Yosemite toad (Bufo canorus, Camp) breeding habitat in Sierra Nevada meadows. Rangel Ecol Manag 65:56–65Article 

Google Scholar 
Roche LM, Latimer AM, Eastburn DJ, Tate KW (2012b) Cattle grazing and conservation of a meadow-dependent amphibian species in the Sierra Nevada. PLoS ONE 7:e35734CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Sacchei I, Kuussaari M, Kankare M, Vikman P, Fortelius W, Hanski I (1998) Inbreeding and extinction in a butterfly metapopulation. Nature 392:491–494Article 
CAS 

Google Scholar 
Sadinski W (2004) Amphibian declines: causes. U.S. Geological Survey report. La Crosse, WisconsinSadinski W, Gallant AL, Cleaver JE (2020) Climate’s cascading effects on disease, predation, and hatching success in Anaxyrus canorus, the threatened Yosemite toad. Glob Ecol Conserv 23:e01173Article 

Google Scholar 
Sawyer SC, Epps CW, Brashares JS (2011) Placing linkages among fragmented habitats: do least-cost models reflect how animals use landscapes? J Appl Ecol 48:668–678Article 

Google Scholar 
Schlaepfer DR, Braschler B, Rusterholz HP, Baur B (2018) Genetic effects of anthropogenic habitat fragmentation on remnant animal and plant populations: a meta-analysis. Ecosphere 9 e02488Schmidt G, Jenkerson C, Masek J, Vermote E, Gao F (2013) Landsat ecosystem disturbance adaptive processing system (LEDAPS) algorithm description. U.S. Geological Survey report. Reston, VAShaffer H, Fellers G, Magee A, Voss S (2000) The genetics of amphibian declines: population substructure and molecular differentiation in the Yosemite toad, Bufo canorus (Anura, Bufonidae) based on single-strand conformation polymorphism analysis (SSCP) and mitochondrial DNA sequence data. Mol Ecol 9:245–257CAS 
PubMed 
Article 

Google Scholar 
Sherman CK (1980) A comparison of the natural history and mating system of two anurans: Yosemite toads (Bufo canorus) and Black toads (Bufo exsul). Ph.D. Dissertation. University of MichiganSherman CK, Morton ML (1984) The toad that stays on its toes. Nat Hist 93:72–78
Google Scholar 
Sherman CK, Morton ML (1993) Population declines of Yosemite toads in the eastern Sierra Nevada of California. J Herpetol 27:186–198Article 

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 
Article 

Google Scholar 
Smith JB, Tirpak DA (1988) The potential effects of global climate change on the United States: draft: report to Congress. U.S. Environmental Protection Agency, Office of Policy, Planning and Evaluation, Office of Research and DevelSork VL, Davis FW, Westfall R, Flint A, Ikegami M, Wang H et al. (2010) Gene movement and genetic association with regional climate gradients in California valley oak (Quercus lobata Née) in the face of climate change. Mol Ecol 19:3806–3823PubMed 
Article 

Google Scholar 
Spear SF, Balkenhol N, Fortin M-J, McRae BH, Scribner K (2010) Use of resistance surfaces for landscape genetic studies: considerations for parameterization and analysis. Mol Ecol 19:3576–3591PubMed 
Article 

Google Scholar 
Spear SF, Peterson CR, Matocq MD, Storfer A (2005) Landscape genetics of the blotched tiger salamander (Ambystoma tigrinum melanostictum). Mol Ecol 14:2553–2564CAS 
PubMed 
Article 

Google Scholar 
Spielman D, Brook BW, Briscoe DA, Frankham R (2004) Does inbreeding and loss of genetic diversity decrease disease resistance? Conserv Genet 5:439–448Article 

Google Scholar 
Stewart IT (2009) Changes in snowpack and snowmelt runoff for key mountain regions. Hydrol Process 23:78–94Article 

Google Scholar 
Storfer A, Murphy M, Evans J, Goldberg C, Robinson S, Spear S et al. (2007) Putting the ‘landscape’ in landscape genetics. Heredity 98:128–142CAS 
PubMed 
Article 

Google Scholar 
van Strien M (2013) Advances in landscape genetic methods and theory: lessons leart from insects in agricultural landscapes. Ph.D. Dissertation. ETH Zürichvan Strien MJ, Keller D, Holderegger R (2012) A new analytical approach to landscape genetic modelling: least-cost transect analysis and linear mixed models. Mol Ecol 21:4010–23Article 

Google Scholar 
Strobl C, Boulesteix AL, Zeileis A, Hothorn T (2007) Bias in random forest variable importance measures: illustrations, sources and a solution. BMC Bioinform 8 25Sundqvist L, Keenan K, Zackrisson M, Prodöhl P, Kleinhans D (2016) Directional genetic differentiation and relative migration. Ecol Evol 6:3461–3475PubMed 
PubMed Central 
Article 

Google Scholar 
Sylvester EVA, Beiko RG, Bentzen P, Paterson I, Horne JB, Watson B et al. (2018) Environmental extremes drive population structure at the northern range limit of Atlantic salmon in North America. Mol Ecol 27:4026–4040PubMed 
Article 

Google Scholar 
Toloşi L, Lengauer T (2011) Classification with correlated features: Unreliability of feature ranking and solutions. Bioinformatics 27:1986–1994PubMed 
Article 
CAS 

Google Scholar 
Travis JMJ, Murrell DJ, Dytham C (1999) The evolution of density–dependent dispersal. Proc R Soc Lond Ser B Biol Sci 266:1837–1842Article 

Google Scholar 
Trexler KA (1975) The Tioga road: a history, 1883-1961. Yosemite Natural History Association, El Portal, CAU.S. Fish & Wildlife Service (2014) Endangered and threatened wildlife and plants; endangered status for the Sierra Nevada yellow-legged frog and the northern distinct population segment of the mountain yellow-legged frog, and threatened status for the Yosemite toad: final rule. Fed Regist 79:1–56. https://www.federalregister.gov/documents/2014/04/29/2014-09488/endangered-and-threatened-wildlife-andplants-endangered-species-status-for-sierra-nevadaVandergast AG, Bohonak AJ, Hathaway SA, Boys J, Fisher RN (2008) Are hotspots of evolutionary potential adequately protected in southern California? Biol Conserv 141:1648–1664Article 

Google Scholar 
Viers JH, Purdy SE, Peek RA, Fryjoff-Hung A, Santos NR, Katz JV et al (2013) Montane meadows in the Sierra Nevada: changing hydroclimatic conditions and concepts for vulnerability assessment. Centre for Watershed Sciences report. Davis, CAVredenburg VT, Knapp RA, Tunstall TS, Briggs CJ (2010) Dynamics of an emerging disease drive large-scale amphibian population extinctions. Proc Natl Acad Sci USA 107:9689–9694CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Wang IJ (2012) Environmental and topographic variables shape genetic structure and effective population sizes in the endangered Yosemite toad. Divers Distrib 18:1033–1041Article 

Google Scholar 
Weir BS (1996) Genetic data analysis II: methods for discrete population genetic data. Sinauer Associates, Inc., Sunderland, MAWhitlock MC, Ingvarsson PK, Hatfield T (2000) Local drift load and the heterosis of interconnected populations. Heredity 84:452–457PubMed 
Article 

Google Scholar 
Wood SH (1975) Holocene stratigraphy and chronology of mountain meadows, Sierra Nevada, California. Ph.D. Dissertation. California Institute of TechnologyWright S (1931) Evolution in Mendelian populations. Genetics 16:97–159CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Zeller KA, McGarigal K, Whiteley AR (2012) Estimating landscape resistance to movement: a review. Landsc Ecol 27:777–797Article 

Google Scholar  More

Tomasello, M. Why we cooperate (MIT Press, 2009).Book 

Google Scholar 
Turchin, P. The puzzle of human ultrasociality: How did large-scale complex societies evolve? In Cultural Evolution, Strüngmann Forum Report Vol. 12 (eds Richerson, P. J. & Christiansen, M. H.) 61–73 (MIT Press, 2013).
Google Scholar 
Kramer, K. L. How there got to be so many of us: The evolutionary story of population growth and a life history of cooperation. J. Anthropol. Res. 75, 472–497 (2019).Article 

Google Scholar 
Wrangham, R. W. The Goodness Paradox: The Strange Relationship Between Virtue and Violence in Human Evolution (Alfred A. Knopf, 2019).
Google Scholar 
Fruth, B. & Hohmann, G. Food sharing across borders. Hum. Nat. 29, 91–103 (2018).PubMed 
PubMed Central 
Article 

Google Scholar 
Garfield, Z. H., Hubbard, R. L. & Hagen, E. H. Evolutionary models of leadership. Hum. Nat. 30, 23–58 (2019).PubMed 
Article 

Google Scholar 
Rodrigues, J. & Hewig, J. Let´ s call it altruism! A psychological perspective and hierarchical framework of altruism and prosocial behavior. Preprint at https://psyarxiv.com/pj7eu/ (2021).Davies, A. Food sharing. In Routledge Handbook of Sustainable and Regenerative Food Systems (eds Duncan, J. et al.) 204–217 (Routledge, 2020).Chapter 

Google Scholar 
Ember, C. R., Skoggard, I., Ringen, E. J. & Farrer, M. Our better nature: Does resource stress predict beyond-household sharing?. Evol. Hum. Behav. 39, 380–391 (2018).Article 

Google Scholar 
Crittenden, A. N. & Schnorr, S. L. Current views on hunter-gatherer nutrition and the evolution of the human diet. Am. J. Phys. Anthropol. 162, 84–109 (2017).PubMed 
Article 

Google Scholar 
Ferguson, M. et al. Traditional food availability and consumption in remote Aboriginal communities in the Northern Territory, Australia. Aust. NZ. J. Publ. Heal. 41, 294–298 (2017).Article 

Google Scholar 
Poulain, J. P. The Sociology of Food: Eating and the Place of Food in Society (Bloomsbury Publishing, 2017).
Google Scholar 
Ready, E. & Power, E. A. Why wage earners hunt: food sharing, social structure, and influence in an Arctic mixed economy. Curr. Anthropol. 59, 74–97 (2018).Article 

Google Scholar 
Gould, R. A. To have and have not: The ecology of sharing among hunter-gatherers. In Resource Managers: North American and Australian Hunter-Gatherers (eds Williams, N. M. & Hunn, E. S.) 69–91 (Routledge, 2019).Chapter 

Google Scholar 
Allen-Arave, W., Gurven, M. & Hill, K. Reciprocal altruism, rather than kin selection, maintains nepotistic food transfers on an Ache reservation. Evol. Hum. Behav. 29, 305–318 (2008).Article 

Google Scholar 
Crittenden, A. N. & Zes, D. A. Food sharing among hadza hunter-gatherer children. PLoS One 10, e0131996. https://doi.org/10.1371/journal.pone.0131996 (2015).CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
Rochat, P. et al. Fairness in distributive justice by 3-and 5-year-olds across seven cultures. J. Cross. Cult. Psychol. 40, 416–442 (2009).Article 

Google Scholar 
Cashdan, E. A. Coping with risk: Reciprocity among the Basarwa of Northern Botswana. Man 20, 454 (1985).Article 

Google Scholar 
Fehr, E., Glätzle-Rützler, D. & Sutter, M. The development of egalitarianism, altruism, spite and parochialism in childhood and adolescence. Eur. Econ. Rev. 64, 369–383 (2013).Article 

Google Scholar 
Almås, I., Cappelen, A. W., Sørensen, E. Ø. & Tungodden, B. Fairness and the development of inequality acceptance. Science 328, 1176–1178 (2010).ADS 
PubMed 
Article 
CAS 

Google Scholar 
Malti, T. et al. “Who is worthy of my generosity?” Recipient characteristics and the development of children’s sharing. Int. J. Behav. Dev. 40, 31–40 (2016).Article 

Google Scholar 
Olson, K. R. & Spelke, E. S. Foundations of cooperation in young children. Cognition 108, 222–231 (2008).PubMed 
PubMed Central 
Article 

Google Scholar 
Renno, M. P. & Shutts, K. Children’s social category-based giving and its correlates: expectations and preferences. Dev. Psychol. 51, 533 (2015).PubMed 
Article 

Google Scholar 
Samek, A. et al. The development of social comparisons and sharing behavior across 12 countries. J. Exp. Child Psychol. 192, 104778. https://doi.org/10.1016/j.jecp.2019.104778 (2020).Article 
PubMed 

Google Scholar 
Henrich, J., Heine, S. J. & Norenzayan, A. Most people are not WEIRD. Nature 466, 29–29 (2010).ADS 
CAS 
PubMed 
Article 

Google Scholar 
House, B. R. et al. Ontogeny of prosocial behavior across diverse societies. P. Natl. Acad. Sci. USA 110, 14586–14591 (2013).ADS 
CAS 
Article 

Google Scholar 
Schäfer, M., Haun, D. B. & Tomasello, M. Fair is not fair everywhere. Psychol. Sci. 26, 1252–1260 (2015).PubMed 
Article 

Google Scholar 
Callaghan, T. & Corbit, J. Early prosocial development across cultures. Curr. Opin. Psychol. 20, 102–106 (2018).PubMed 
Article 

Google Scholar 
Rodriguez, L. M., Martí-Vilar, M., Esparza Reig, J. & Mesurado, B. Empathy as a predictor of prosocial behavior and the perceived seriousness of delinquent acts: A cross-cultural comparison of Argentina and Spain. Ethics Behav. 31, 91–101 (2021).Article 

Google Scholar 
Fehr, E., Bernhard, H. & Rockenbach, B. Egalitarianism in young children. Nature 454, 1079–1083 (2008).ADS 
CAS 
PubMed 
Article 

Google Scholar 
Henrich, J. & Muthukrishna, M. The origins and psychology of human cooperation. Ann. Rev. Psychol. 72, 207–240 (2021).Article 

Google Scholar 
Thomas, M. G. et al. Kinship underlies costly cooperation in Mosuo villages. Roy. Soc. Open Sci. 5(2), 171535. https://doi.org/10.1098/rsos.171535 (2018).ADS 
Article 

Google Scholar 
O’Gorman, R., Sheldon, K. M. & Wilson, D. S. For the good of the group? Exploring group-level evolutionary adaptations using multilevel selection theory. Group. Dyn. Theor. Res. 12, 17 (2008).Article 

Google Scholar 
Boyd, R. & Richerson, P. J. Culture and the evolution of human cooperation. Philos. Trans. R. Soc. B 364, 3281–3288 (2009).Article 

Google Scholar 
Handley, C. & Mathew, S. Human large-scale cooperation as a product of competition between cultural groups. Nat. Commun. 11, 1–9 (2020).Article 
CAS 

Google Scholar 
Gintis, H., van Schaik, C. & Boehm, C. Zoon politikon: The evolutionary origins of human socio-political systems. Behav. Process. 161, 17–30 (2019).Article 

Google Scholar 
Markovits, H., Benenson, J. F. & Kramer, D. L. Children and adolescents’ internal models of food-sharing behavior include complex evaluations of contextual factors. Child Dev. 74, 1697–1708 (2003).PubMed 
Article 

Google Scholar 
Kaplan, H., Gurven, M., Hill, K. & Hurtado, A. M. The natural history of human food sharing and cooperation: a review and a new multi-individual approach to the negotiation of norms. Moral Sentim. Mater. Interests Found. Coop. Econ. Life 6, 75–113 (2005).
Google Scholar 
Crittenden, A. N. To share or not to share? Social processes of learning to share food among Hadza hunter-gatherer children. In Social Learning and Innovation in Contemporary Hunter-Gatherers (eds Hewlett, B. S. & Terashima, H.) 61–70 (Springer, 2016).Chapter 

Google Scholar 
Barragan, R. C., Brooks, R. & Meltzoff, A. N. Altruistic food sharing behavior by human infants after a hunger manipulation. Sci. Rep. 10, 1–9 (2020).Article 
CAS 

Google Scholar 
Singh, M., Wrangham, R. & Glowacki, L. Self-interest and the design of rules. Hum. Nat. 28, 457–480 (2017).PubMed 
Article 

Google Scholar 
Richerson, P. J., Gavrilets, S. & de Waal, F. B. Modern theories of human evolution foreshadowed by Darwin’s Descent of Man. Science 372, eaba3776. https://doi.org/10.1126/science.aba3776 (2021).CAS 
Article 
PubMed 

Google Scholar 
Jordan, F. M. et al. Cultural evolution of the structure of human groups. In Cultural Evolution: Society, Technology, Language, and Religion (eds Richerson, P. J. & Christiansen, M. H.) 87–116 (MIT Press, 2013).Chapter 

Google Scholar 
Henrich, J. & Broesch, J. On the nature of cultural transmission networks: Evidence from Fijian villages for adaptive learning biases. Philos. T. Roy. Soc. B 366, 1139–1148 (2011).Article 

Google Scholar 
Hawley, P. H. The ontogenesis of social dominance: A strategy-based evolutionary perspective. Dev. Rev. 19, 97–132 (1999).Article 

Google Scholar 
Hawley, P. H., Little, T. D. & Card, N. A. The allure of a mean friend: Relationship quality and processes of aggressive adolescents with prosocial skills. Int. J. Behav. Dev. 31, 170–180 (2007).Article 

Google Scholar 
Marlowe, F. The Hadza: Hunter-Gatherers of Tanzania Vol. 3 (University of California Press, 2010).
Google Scholar 
Jones, N. B. Demography and Evolutionary Ecology of Hadza Hunter-Gatherers Vol. 71 (Cambridge University Press, 2016).
Google Scholar 
Butovskaya, M. L. Aggression and conflict resolution among the nomadic Hadza of Tanzania as compared with their pastoralist neighbors. In War, Peace, and Human Nature: the Convergence of Evolutionary and Cultural Views (ed. Fry, D. P.) 278–296 (Oxford University Press, 2013).Chapter 

Google Scholar 
Apicella, C. L., Marlowe, F. W., Fowler, J. H. & Christakis, N. A. Social networks and cooperation in hunter-gatherers. Nature 481, 497–501 (2012).ADS 
CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Sands, B., Maddieson, J. & Ladefoged, P. The phonetic structures of Hadza. Stud. Afr. Linguist. 25, 171–204 (1996).Article 

Google Scholar 
Butovskaya, M. et al. Approach to resource management and physical strength predict differences in helping: evidence from two small-scale societies. Front. Psychol. 11, 373 (2020).PubMed 
PubMed Central 
Article 

Google Scholar 
Mous, M. A Grammar of Iraqw (University of Leiden, 1992).
Google Scholar 
Rekdal, O. B. The invention by tradition: Creativity and change among the Iraqw of northern Tanzania. PhD thesis, Department of Social Anthropology, University of Bergen, Bergen (1999).Snyder, K. A. The Iraqw of Tanzania: Negotiating Rural Development (Routledge, 2018).Book 

Google Scholar 
Butovskaya, M., Burkova, V. & Mabulla, A. Sex differences in 2D: 4D ratio, aggression and conflict resolution in African children and adolescents: a cross-cultural study. J. Aggress. Confl. Peace Res. 2, 17–31 (2010).Article 

Google Scholar 
Butovskaya, M. L., Burkova, V. N. & Karelin, D. V. The Wameru of Tanzania: Historical origin and their role in the process of National Integration. Soc. Evol. Hist. 15, 141–163 (2016).
Google Scholar 
Lerner, G. The Creation of Patriarchy Vol. 1 (Oxford University Press, 1986).
Google Scholar 
Maruo, S. Differentiation of subsistence farming patterns among the Haya banana growers in northwestern Tanzania. Afr. Study Monog. 23, 147–175 (2002).
Google Scholar 
Ishengoma, J. M. African oral traditions: Riddles among the Haya of Northwestern Tanzania. Int. Rev. Educ. 51, 139–153 (2005).Article 

Google Scholar 
Stevens, L. Religious change in a Haya village, Tanzania. J. Relig. Afr. 21, 2–25 (1991).Article 

Google Scholar 
Kradin, N. N. The transformation of pastoralism in Buryatia: the Aginsky Steppe example. Inner Asia 6, 95–109 (2004).Article 

Google Scholar 
Rostovtseva, V. V., Weissing, F. J., Mezentseva, A. A. & Butovskaya, M. L. Sex differences in cooperativeness—an experiment with Buryats in Southern Siberia. PLoS One 15, e0239129. https://doi.org/10.1371/journal.pone.0239129 (2020).CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
Krader, L. Buryat religion and society. Southwest. J. Anthropol. 10, 322–351 (1954).Article 

Google Scholar 
Hooper, P. L. Quantitative description of the pastoral economy of Western Tuvan nomads. New Res. Tuva 4, 19–27 (2020).
Google Scholar 
Lindquist, G. Loyalty and command: Shamans, lamas, and spirits in a Siberian ritual. Soc. Anal. 52, 111–126 (2008).
Google Scholar 
Walters, P. Religion in Tuva: Restoration or innovation?. Relig. State Soc. 29, 23–38 (2001).Article 

Google Scholar 
Dyrtyk-ool, A. O., & Orgezhik, C. M. Kollektsiya vostochnih tuvintsev-olenevodov v natsionalnom muzee Respubliki Tyva: istoriya komplektovaniya i obshaya harakteristika [Collection of the Eastern Tuvans – deer herders in the National Museum of the Republic of Tuva: Background and general description of the acquisition]. Scientific notes of the museum-reserve “Tomskaya Pisanitsa”. 3, 4–9 (2016).Alexandrov, V. A., Vlasova, I. V. & Polischuk, N. S. The Russians (Nauka, 1997).
Google Scholar 
Fehr, E. & Schmidt, K. M. A theory of fairness, competition, and cooperation. Q. J. Econ. 114, 817–868 (1999).MATH 
Article 

Google Scholar 
Charness, G. & Rabin, M. Understanding social preferences with simple tests. J. Q. Econ. 117, 817–869 (2002).MATH 
Article 

Google Scholar  More

Today, awareness of our perilous position has grown immensely, even if our ability to do something about it has not. Analyses suggest that human activities have already pushed planetary processes past stable boundaries through destruction of biodiversity, ocean acidification, and land-use change associated with agriculture, among other effects (see Steffen, W. et al., Science 347, 1259855; 2015). Over the past few decades, estimates find that human resource extraction has reduced the total outstanding capital of the world’s base of natural resources by some 40%. What is apparently our most pressing challenge — planetary warming — is just one of many challenges linked to our inability to limit the scale of our human activities and impacts.
Your institute does not have access to this article More

IPCC. Assessment Report 6 Climate Change 2021: The Physical Science Basis. (2021).Angilletta, M. J. Thermal adaptation: a theoretical and empirical synthesis. Oxford University Press (Elsevier, 2009).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 

Google Scholar 
Hampe, A. & Petit, R. J. Conserving biodiversity under climate change: the rear edge matters. Ecol. Lett. 8, 461–467 (2005).PubMed 

Google Scholar 
Parmesan, C. Ecological and evolutionary responses to recent climate change. Annu. Rev. Ecol. Evol. Syst. 37, 637–669 (2006).
Google Scholar 
Ma, C. S., Ma, G. & Pincebourde, S. Survive a warming climate: insect responses to extreme high temperatures. Annu. Rev. Entomol. 66, 163–184 (2021).CAS 
PubMed 

Google Scholar 
Hoffmann, A. A., Sørensen, J. G. & Loeschcke, V. Adaptation of Drosophila to temperature extremes: Bringing together quantitative and molecular approaches. J. Therm. Biol. 28, 175–216 (2003).
Google Scholar 
Oostra, V., Saastamoinen, M., Zwaan, B. J. & Wheat, C. W. Strong phenotypic plasticity limits potential for evolutionary responses to climate change. Nat. Commun. 9, 1005 (2018).Štětina, T., Koštál, V. & Korbelová, J. The role of inducible Hsp70, and other heat shock proteins, in adaptive complex of cold tolerance of the fruit fly (Drosophila melanogaster). PLoS One 10, 1–22 (2015).
Google Scholar 
Overgaard, J., Sørensen, J. G., Petersen, S. O., Loeschcke, V. & Holmstrup, M. Changes in membrane lipid composition following rapid cold hardening in Drosophila melanogaster. J. Insect Physiol. 51, 1173–1182 (2005).CAS 
PubMed 

Google Scholar 
Laland, K. N. et al. The extended evolutionary synthesis: Its structure, assumptions and predictions. Proc. R. Soc. B Biol. Sci. 282, 20151019 (2015).Sánchez-Bayo, F. & Wyckhuys, K. A. G. Worldwide decline of the entomofauna: A review of its drivers. Biol. Conserv. 232, 8–27 (2019).
Google Scholar 
Deutsch, C. A. et al. Increase in crop losses to insect pests in a warming climate. Science 361, 916–919 (2018).ADS 
CAS 
PubMed 

Google Scholar 
Sgrò, C. M., Terblanche, J. S. & Hoffmann, A. A. What can plasticity contribute to insect responses to climate change? Annu. Rev. Entomol. 61, 433–451 (2016).PubMed 

Google Scholar 
Sørensen, J. G., Kristensen, T. N. & Overgaard, J. Evolutionary and ecological patterns of thermal acclimation capacity in Drosophila: is it important for keeping up with climate change? Curr. Opin. Insect Sci. 17, 98–104 (2016).PubMed 

Google Scholar 
Gunderson, A. R. & Stillman, J. H. Plasticity in thermal tolerance has limited potential to buffer ectotherms from global warming. Proc. R. Soc. B Biol. Sci. 282, 20150401 (2015).Rohr, J. R. et al. The complex drivers of thermal acclimation and breadth in ectotherms. Ecol. Lett. 21, 1425–1439 (2018).PubMed 

Google Scholar 
Gunderson, A. R., Dillon, M. E. & Stillman, J. H. Estimating the benefits of plasticity in ectotherm heat tolerance under natural thermal variability. Funct. Ecol. 31, 1529–1539 (2017).
Google Scholar 
Barley, J. M. et al. Limited plasticity in thermally tolerant ectotherm populations: Evidence for a trade-off. Proc. R. Soc. B Biol. Sci. 288, 20210765 (2021).
Google Scholar 
Morley, S. A., Peck, L. S., Sunday, J. M., Heiser, S. & Bates, A. E. Physiological acclimation and persistence of ectothermic species under extreme heat events. Glob. Ecol. Biogeogr. 28, 1018–1037 (2019).
Google Scholar 
Kellermann, V. & van Heerwaarden, B. Terrestrial insects and climate change: adaptive responses in key traits. Physiol. Entomol. 44, 99–115 (2019).
Google Scholar 
Seebacher, F., White, C. R. & Franklin, C. E. Physiological plasticity increases resilience of ectothermic animals to climate change. Nat. Clim. Chang. 5, 61–66 (2015).ADS 

Google Scholar 
Pincebourde, S. & Woods, H. A. There is plenty of room at the bottom: microclimates drive insect vulnerability to climate change. Curr. Opin. Insect Sci. 41, 63–70 (2020).PubMed 

Google Scholar 
van Heerwaarden, B. & Kellermann, V. Does plasticity trade off with basal heat tolerance? Trends Ecol. Evol. 35, 874–885 (2020).PubMed 

Google Scholar 
Stevenson, R. D. The relative importance of behavioral and physiological adjustments controlling body temperature in terrestrial ectotherms. Am. Nat. 126, 362–386 (1985).
Google Scholar 
Donelson, J. M., Salinas, S., Munday, P. L. & Shama, L. N. S. Transgenerational plasticity and climate change experiments: Where do we go from here? Glob. Chang. Biol. 24, 13–34 (2018).ADS 
PubMed 

Google Scholar 
Kristensen, T. N. et al. Costs and benefits of cold acclimation in field-released Drosophila. Proc. Natl Acad. Sci. 105, 216–221 (2008).ADS 
CAS 
PubMed 

Google Scholar 
Bozinovic, F., Calosi, P. & Spicer, J. I. Physiological correlates of geographic range in animals. Annu. Rev. Ecol. Evol. Syst. 42, 155–179 (2011).
Google Scholar 
Chown, S. L., Gaston, K. J. & Robinson, D. Macrophysiology: large-scale patterns in physiological traits and their ecological implications. Funct. Ecol. 18, 159–167 (2004).
Google Scholar 
Overgaard, J., Hoffmann, A. A. & Kristensen, T. N. Assessing population and environmental effects on thermal resistance in Drosophila melanogaster using ecologically relevant assays. J. Therm. Biol. 36, 409–416 (2011).
Google Scholar 
Sgrò, C. M. et al. A comprehensive assessment of geographic variation in heat tolerance and hardening capacity in populations of Drosophila melanogaster from Eastern Australia. J. Evol. Biol. 23, 2484–2493 (2010).PubMed 

Google Scholar 
Kingsolver, J. G. & Huey, R. B. Size, temperature, and fitness: three rules. Evol. Ecol. Res. 10, 251–268 (2008).
Google Scholar 
Brown, J., Gillooly, J. F., Allen, A. P., Savage, V. M. & West, G. Toward a metabolic theory of ecology. Ecology 85, 1771–1789 (2004).
Google Scholar 
Stillwell, R. C., Blanckenhorn, W. U., Teder, T., Davidowitz, G. & Fox, C. W. Sex differences in phenotypic plasticity affect variation in sexual size dimorphism in insects: from physiology to evolution. Annu. Rev. Entomol. 55, 227 (2010).CAS 
PubMed 
PubMed Central 

Google Scholar 
Tarka, M., Guenther, A., Niemelä, P. T., Nakagawa, S. & Noble, D. W. A. Sex differences in life history, behavior, and physiology along a slow-fast continuum: a meta-analysis. Behav. Ecol. Sociobiol. 72, 1–13 (2018).
Google Scholar 
Pottier, P., Burke, S., Drobniak, S. M., Lagisz, M. & Nakagawa, S. Sexual (in)equality? A meta-analysis of sex differences in thermal acclimation capacity across ectotherms. Funct. Ecol. 35, 2663–2678 (2021).
Google Scholar 
Bowler, K. & Terblanche, J. S. Insect thermal tolerance: what is the role of ontogeny, ageing and senescence? Biol. Rev. 83, 339–355 (2008).PubMed 

Google Scholar 
Fawcett, T. W. & Frankenhuis, W. E. Adaptive explanations for sensitive windows in development. Front. Zool. 12, 1–14 (2015).
Google Scholar 
English, S. & Barreaux, A. M. The evolution of sensitive periods in development: insights from insects. Curr. Opin. Behav. Sci. 36, 71–78 (2020).
Google Scholar 
Overgaard, J., Kristensen, T. N. & Sørensen, J. G. Validity of thermal ramping assays used to assess thermal tolerance in arthropods. PLoS One 7, e32758 (2012).ADS 
CAS 
PubMed 
PubMed Central 

Google Scholar 
Bak, C. W. et al. Comparison of static and dynamic assays when quantifying thermal plasticity of drosophilids. Insects 11, 1–11 (2020).
Google Scholar 
Rodrigues, Y. K. & Beldade, P. Thermal plasticity in insects’ response to climate change and to multifactorial environments. Front. Ecol. Evol. 8, 271 (2020).
Google Scholar 
Terblanche, J. S. & Hoffmann, A. Validating measurements of acclimation for climate change adaptation. Curr. Opin. Insect Sci. 41, 7–16 (2020).PubMed 

Google Scholar 
Loeschcke, V. & Hoffmann, A. A. The detrimental acclimation hypothesis. Trends Ecol. Evol. 17, 407–408 (2002).
Google Scholar 
Cossins, A. R. & Bowler, K. Temperature Biology of Animals. (Chapman and Hall, 1987).Pintor, A. F. V., Schwarzkopf, L. & Krockenberger, A. K. Extensive acclimation in ectotherms conceals interspecific variation in thermal tolerance limits. PLoS One 11, e0150408 (2016).PubMed 
PubMed Central 

Google Scholar 
Rezende, E. L., Tejedo, M. & Santos, M. Estimating the adaptive potential of critical thermal limits: methodological problems and evolutionary implications. Funct. Ecol. 25, 111–121 (2011).
Google Scholar 
Allen, J. L., Chown, S. L., Janion-Scheepers, C. & Clusella-Trullas, S. Interactions between rates of temperature change and acclimation affect latitudinal patterns of warming tolerance. Conserv. Physiol. 4, cow053 (2016).Lutterschmidt, W. I. & Hutchison, V. H. The critical thermal maximum: History and critique. Can. J. Zool. 75, 1561–1574 (1997).
Google Scholar 
Terblanche, J. S. et al. Phenotypic plasticity and geographic variation in thermal tolerance and water loss of the tsetse Glossina pallidipes (Diptera: Glossinidae): Implications for distribution modelling. Am. J. Trop. Med. Hyg. 74, 786–794 (2006).PubMed 

Google Scholar 
Koricheva, J., Gurevitch, J. & Mengersen, K. Handbook of meta-analysis in ecology and evolution. Handbook of Meta-analysis in Ecology and Evolution (Princeton University Press, 2013).Suggitt, A. J. et al. Habitat microclimates drive fine-scale variation in extreme temperatures. Oikos 120, 1–8 (2011).
Google Scholar 
Oyen, K. J. & Dillon, M. E. Critical thermal limits of bumblebees (Bombus impatiens) are marked by stereotypical behaviors and are unchanged by acclimation, age or feeding status. J. Exp. Biol. 221, jeb165589 (2018).Bennett, J. M. et al. The evolution of critical thermal limits of life on Earth. Nat. Commun. 12, 1–9 (2021).
Google Scholar 
Bowler, K. Heat death in poikilotherms: Is there a common cause? J. Therm. Biol. 76, 77–79 (2018).PubMed 

Google Scholar 
MacMillan, H. A. & Sinclair, B. J. Mechanisms underlying insect chill-coma. J. Insect Physiol. 57, 12–20 (2011).CAS 
PubMed 

Google Scholar 
Hoffmann, A. A., Chown, S. L. & Clusella-Trullas, S. Upper thermal limits in terrestrial ectotherms: How constrained are they? Funct. Ecol. 27, 934–949 (2013).
Google Scholar 
Sandblom, E. et al. Physiological constraints to climate warming in fish follow principles of plastic floors and concrete ceilings. Nat. Commun. 7, 1–8 (2016).
Google Scholar 
Maclean, H. J. et al. Evolution and plasticity of thermal performance: An analysis of variation in thermal tolerance and fitness in 22 Drosophila species. Philos. Trans. R. Soc. B Biol. Sci. 374, 20180548 (2019).Addo-Bediako, A., Chown, S. L. & Gaston, K. J. Thermal tolerance, climatic variability and latitude. Proc. R. Soc. B Biol. Sci. 267, 739–745 (2000).CAS 

Google Scholar 
Sales, K. et al. Experimental heatwaves compromise sperm function and cause transgenerational damage in a model insect. Nat. Commun. 9, 1–11 (2018).ADS 
CAS 

Google Scholar 
Walsh, B. S. et al. Integrated approaches to studying male and female thermal fertility limits. Trends Ecol. Evol. 34, 492–493 (2019).PubMed 

Google Scholar 
Moher, D. et al. Preferred reporting items for systematic reviews and meta-analyses: The PRISMA statement. PLoS Med. 6, e1000097 (2009).Hadley, N. F. Water relations of terrestrial arthropods. (Academic Press, 1994).Hinchliff, C. E. et al. Synthesis of phylogeny and taxonomy into a comprehensive tree of life. PNAS. 112, 12764–12769 (2015).ADS 
CAS 
PubMed 
PubMed Central 

Google Scholar 
Viechtbauer, W. Conducting meta-analyses in R with the metafor. J. Stat. Softw. 36, 1–48 (2010).
Google Scholar 
Barton, K. MuMIn: Multi-Model Inference. (2020).Nakagawa, S. et al. Methods for testing publication bias in ecological and evolutionary meta-analyses. Methods Ecol. Evol. 13, 4–21 (2022).
Google Scholar 
Macartney, E. L., Crean, A. J., Nakagawa, S. & Bonduriansky, R. Effects of nutrient limitation on sperm and seminal fluid: a systematic review and meta-analysis. Biol. Rev. 94, 1722–1739 (2019).PubMed 

Google Scholar  More

Study area and soil propertiesA field experiment was established in May 2012 at Shenyang Agro-Ecological Station (41°31′N, 123°22′E) of the Institute of Applied Ecology, Chinese Academy of Sciences, Northeast China. This region has a warm-temperate continental monsoon climate. The mean annual air temperature and annual precipitation are 7.5 °C and 680 mm, respectively. The soil is classified as Luvisol (FAO classification). The soil properties of the topsoil layer (0–20 cm) at the start of the experiment are as follows: SOC = 9.0 g kg−1, available NH4+–N = 1.18 mg kg−1; available NO3−–N = 9.04 mg kg−1; Olsen-P = 38.50 mg kg−1, available K = 97.90 mg kg−1, bulk density = 1.25 g cm−3, and pH = 5.8. The determination method of soil was shown in “Soil analysis” section.Field experimentThree treatments were established in this experiment: (1) mineral fertilizers (NPK); (2) pig manure incorporation at a local conventional AM application rate of 15 Mg ha−1 yr−1 (NPKM, 126 kg N ha−1 on dry weight); and (3) NPKM plus DMPP (3,4-Dimethylpyrazole phosphate) incorporation at a rate of 0.5% of applied urea (2.39 kg ha−1, 220 kg N/the N content of urea (0.46) × 0.5%) (NPKI + M). The treatments were applied following a randomized design across three replicate field plots (4 m × 5 m). Plots of different treatments remained unchanged in the same locations for 4 years. Each year, the composted pig manure (213 g C kg−1 and 22 g N kg−1 based on dry weight on average, characteristics of pig manure was listed in Table S1) was broadcasted evenly onto the plots a few days before maize planting, and ploughed to a depth of 20 cm by machine (TG4, Huaxing, China). For the respective treatments, urea (220 kg N ha−1 yr−1), calcium superphosphate (110 kg P2O5 ha−1 yr−1), and potassium chloride (110 kg K2O ha−1 yr−1) were applied on the same day as maize (Zea mays L.) was planted. The urea and inhibitor were fully mixed before application.Maize (cultivar was Fuyou #9) was planted on 3rd May 2012, 3rd May 2013, 6th May 2014, and 10th May 2015, at a spacing of 37 cm and 60 cm between rows. No irrigation was applied throughout the experimental period. Maize was harvested on 13th September 2012, 29th September 2013, 29th September 2014, and 29th September 2015, respectively. At harvest, maize yield and aboveground biomass yield were measured by harvesting all plants (20 m2) in each plot. The straw and grain were removed after each harvest and the soil with about 5 cm maize stem was ploughed to a depth of approximately 20 cm in April each year.Each cropping cycle, therefore, consisted of periods of maize (from May to September) and fallow (from October to April) of the following year.The precipitation and air temperature data were acquired from the meteorological station of the Shenyang Agro-Ecological Station. The precipitation during the 2012/2013, 2013/2014, 2014/2015, and 2015/2016 periods were 911.9 mm, 621.7 mm, 485.7 mm, and 585.3 mm, respectively (Fig. 1). 72.3%, 75.5%, 66.5%, and 73.0% of these annual precipitations occurred during maize-growing period, respectively. The mean annual air temperatures in these years were 7.7 °C (− 21.2 to 27.5 °C), 8.1 °C (− 22.7 to 28.3 °C), 9.5 °C (− 21.7 to 28.2 °C) and 9.3 °C (− 17.1 to 27.0 °C), respectively. The soil temperature at a depth of 5 cm varied between − 14 and 35 °C during the four-year period (Fig. 2b). The change trend of soil surface temperature was the same as that of soil temperature at 5 cm depth (Fig. 2a). The mean soil WFPS (0–15 cm) varied between 15 and 73% (Fig. 2c).Figure 1Precipitation and daily mean air temperature during four annual cycles from May 2012 to April 2016 in the experimental field.Full size imageFigure 2Seasonal variations in soil temperature (at soil surface and 5 cm soil depth) and WFPS% at 0–15 cm depth from May 2012 to April 2016.Full size imageGas sampling and analysisThe gas was sampled between 3rd May 2012 and 14th April 2016 using a static closed chamber system as described by Dong et al.16. Briefly, a stainless-steel chamber base (56 cm length × 28 cm width) was inserted into the soil of each plot to a depth of approximately 10 cm, with its long edge perpendicular to the rows of maize. The top chamber (56 cm length × 28 cm width × 20 cm height) was also made of stainless steel. Gas samples were obtained using a syringe 0, 20, and 40 min after the chambers had been closed between 9:00 am and 11:00 am on each sampling day. Gas samples were collected every 2‒6 days and every 7‒15 days during the growing seasons and non-growing seasons, respectively. The first gas sampling time was on day 1, day 3, day 1, and day 3 after maize planting each year. The N2O concentrations in gas samples were quantified using a gas chromatograph (Agilent 7890A, Shanghai, China) with an electron capture detector.Soil analysisThe soil temperature and volumetric water content (SVWC) were measured at depth of 0–15 cm using a bent stem thermometer and a time-domain reflectometry (Zhongtian Devices Co. Ltd, China), respectively. SVWC was converted to soil water-filled pore space (WFPS) using the following equation:$${text{WFPS}} = {text{SVWC}}/(1{-}{text{BD}}/{text{particle}},{text{density}}),$$
(1)
where BD is soil bulk density (g cm−3). Particle density was assumed to be 2.65 g cm−3.Soil samples from the 0–20 cm layer were collected in each plot in April 2012 (before sowing) and October 2015 (maize harvest) using a 5 cm diameter stainless steel soil sampler. The five soil samples collected from different locations in each plot were mixed thoroughly. Visible roots were removed by hand and the samples were air-dried and sieved using a 0.15 mm sieve. SOC was then quantified using an elemental analyzer (Vario EL III, Elementar, Germany). Soil available NH4+–N and NO3−–N were extracted with 2 M KCl and measured colorimetrically using a continuous flow injection analyzer (Futura, Alliance, France)17. Soil Olsen-P was extracted with NaHCO3 and colorimetrically measured using a spectrophotometer (Lambda 2, PerkinElmer, USA). Soil available K was extracted by 1 M CH3COONH4 and analyzed with a flame photometer (FP640, Jingmi, China). Soil pH was determined with deionized water (1:2.5) and analyzed using a pH meter (PHS-3C, LeiCi, China) with a glass electrode.DNA extraction and real-time quantitative PCRThe soil samples for measuring the abundance of nitrification and denitrification functional genes were collected on May 20, 2015. Soil DNA was extracted with the soil DNA extracted kits (EZNA soil DNA Kit; Omega Bio-Tek Inc., U.S.A.). The copy numbers of nitrification and denitrification functional genes were determined by q-PCR with the Roche LightCyler® 96 (Roche, Switzerland). Additional details about the primers and amplification procedure can be found in Dong et al.16.Data analysisThe N2O flux (μg N2O–N m−2 h−1) is calculated based on the increase of N2O concentration per unit chamber area for a specific time interval18 as follows:$${text{F}} = 273/left( {273 + {text{T}}} right) times {text{M}}/22.4 times {text{H}} times {text{dc}}/{text{dt}} times 1000$$
(2)
where F (μg N2O–N m−2 h−1) is the N2O flux, T (◦C) is the air temperature in the chamber, M (g N2O–N mol−1) is the molecular weight of N2O–N, 22.4 (L mol−1) is the molecular volume of the gas at 101.325 kPa and 273 K, H (m) is the chamber height, dc/dt (ppb h−1) is the rate of change in the N2O concentration in the chamber.Cumulative N2O emissions were calculated as follows:$${text{Cumulative}},{text{emission}} = mathop sum limits_{{{text{i}} = 1}}^{{text{n}}} frac{{({text{F}}_{{text{i}}} + {text{F}}_{i + 1} )}}{2} times ({text{t}}_{{{text{i}} + 1}} – {text{t}}_{{text{i}}} ) times 24$$
(3)
where F is the N2O emission flux (μg N2O–N m−2 h−1), i is the ith measurement, (ti+1 − ti) is the number of days between two adjacent measurements, and n is the total number of the measurements. Annual N2O emissions were calculated between the fertilization dates of each successive year.The SOC stock (Mg ha−1) in the topsoil was calculated as:$${text{C}}_{{{text{stock}}}} = {text{SOC}} times {text{BD}} times {text{D}} times 10,$$
(4)
where BD is soil bulk density (g cm−3), D is the depth of the topsoil (0.2 m).The topsoil SOC sequestration rate (SOCSR) (Mg ha−1 yr−1) was estimated using the following equation:$${text{SOCSR}} = left( {{text{C}}_{{{text{stock2015}}}} – {text{C}}_{{{text{stock2012}}}} } right) times {text{t}}^{ – 1} ,$$
(5)
where Cstock2015 and Cstock2012 are the SOC stocks in 2015 and 2012, respectively, and t is the duration of the experiment (years).Statistical analyses were performed using SPSS 13.0 (SPSS, Chicago, USA). The differences in cumulative N2O emissions and maize yields within a year, and other factors among treatments were assessed using one-way Analysis of Variance (ANOVA) with least significant difference post-hoc tests and a 95% confidence limit. The effects of different treatments, years, and their interactions on N2O emission, maize yield and aboveground biomass were examined using one-way repeated measures ANOVA. Pearson correlation analysis was used to analyze the relationships between cumulative N2O emissions and precipitation (N = 12 (three data each year, four years)), as well as N2O flux and soil available nitrogen content.
Statements of research involving plantsIt is stated that the current research on the plants comply with the relevant institutional, national, and international guidelines and legislation. It is also stated that the appropriate permissions have been taken wherever necessary, for collection of plant or seed specimens. It is also stated that the authors comply with the ‘IUCN Policy Statement on Research Involving Species at Risk of Extinction’ and the ‘Convention on the Trade in Endangered Species of Wild Fauna and Flora’. More

Phylogenetic relationships inside the family Palaemonidae remain unresolved, despite being frequently discussed in recent publications9,10. Nevertheless, the last published study5 presented the main lineages of the family as well supported. Among those, the studied Pon-I group of predominantly free-living taxa is basal-positioned to the remaining genera of the former subfamily Pontoniinae, usually more specialised and associated with a wide range of hosts. The basal separation of the symbiotic genera led some authors to consider the assemblage, following Bruce22, to be a primitive group, or descendants of such7,23. Additionally, Gan et al.8 suggested that the taxa of the Pon-I group might be direct descendants of the ancestors of the former subfamily Pontoniinae, sharing the main plesiomorphies appearing frequently in former palaemonine taxa, e.g., the genera Brachycarpus, Leptocarpus, Macrobrachium, or Palaemon. The median process on the fourth thoracic sternite can be considered a plesiomorphic feature; indeed, it is a common symplesiomorphy of all Pon-I taxa, including Ischnopontonia and Anapontonia, for which the process was formerly reported as missing24 (its presence was confirmed in present examined specimens). In addition to that, the mandibular palp occurring in the genera Exoclimenella, Eupontonia, Palaemonella, and Vir25, or the presence of two arthrobranchs on the third maxilliped in Exoclimenella26, can also be considered plesiomorphic features.The Pon-I group’s internal relations have been unclear until now due to lower generic and species coverage in previous studies4,5,8. The present analysis based on a six-marker molecular dataset allows a deeper insight into the phylogenetic relationships of the study group involving all 11 currently recognised genera, and represented by 52 species, i.e. about 60% of the overall known species diversity of the group. The results provide a strong support for the monophyly and/or taxonomic validity of the current genera Exoclimenella, Anapontonia, Ischnopontonia, and suggest the monophyly of genera Harpilius and Philarius. Moreover, the results reveal non-monophyly of the most speciose genera Palaemonella and Cuapetes, as well as the species-poor Eupontonia. The genus Palaemonella was found to be paraphyletic owing to the nested species of the genera Eupontonia and Vir, which all share a common synapomorphy, the presence of the mandibular palp (mentioned above). Such conclusion was expressed also in the study of Chow et al.5.The present phylogenetic analysis confirmed that the genus Cuapetes is not monophyletic, as found to a lesser extent, in a few previous molecular studies 4,5,23. In this study, the genus Cuapetes was recovered in four separate genetic lineages. The type species C. nilandensis is nested in the Clade 1 along with C. johnsoni and C. seychellensis. This phylogenetic finding is in line with the study of Marin and Sinelnikov27, who indicated morphological differences between two of the above-mentioned species and most of the remaining species of the genus (respective of the present Clade 5, also covering C. grandis, the type species of the ex-genus Kemponia), and questioned the validity of the two latter generic names. The further genetic lineage is shown by the position of C. americanus nested in the eastern Pacific—Atlantic branch of the genus Palaemonella (Clade 3). This result is also supported by recent phylogenetic studies suggesting the different systematic positions of this species4,5,10. Due to the lack of the mandibular palp, the species had been properly, but evidently incorrectly, assigned to the genus Cuapetes. The fourth genetic lineage is shown by a separate position of C. darwiniensis in the Clade 4 as the sister species of Madangella altirostris.The remaining majority of the Cuapetes species (Clade 5) are heterogeneous due to comprising also representatives of the genus Periclimenella. Ďuriš and Bruce26 hypothesised, based on morphological traits (mainly the unique shape of the first pereiopod chelae and the distinctly asymmetrical and specific second pereiopods), that the genera Exoclimenella and Periclimenella are closely related. Nevertheless, the present study revealed Periclimenella as a part of the genus Cuapetes. This result was previously supported in the molecular study by Horká et al.4 and weakly supported by Kou et al.23.Fossil records of palaemonid shrimps are rare due to their aquatic habit and poorly calcified exoskeletons. Only a few palaemonid representatives are known compared to many extant taxa; the oldest fossil records contain only genera from the previous subfamily Palaemoninae from the Lower Cretaceous (middle Albian, 100 Myr)28. For this reason, we used the known mutation rate of mitochondrial gene (16S rRNA) for dating rather than fossil records.The present inferred phylogeny and ancestral analysis indicate multiple formations of primary symbioses within the clades dominated by free-living relatives, as shown by previous molecular analyses4,5. Our results revealed eight independent lineages within the Pon-I group that evolved from free-living ancestors (Fig. 3). Free-living palaemonids (Exoclimenella, Palaemonella, Cuapetes; Fig. 2) are characterised by an elongate body shape with a dentate rostrum, slender, long, a/symmetrical chelipeds and slender ambulatory pereiopods with simple dactyli. Their carapace might bear the full complement of teeth (i.e., supraorbital, antennal, hepatic, epigastric)25. Primary symbiotic forms do not fundamentally differ morphologically from free-living ancestors. Their adaptations to the host affiliation have mainly manifested by changes in body shape, colouration, and the reduction of carapace ornamentation. Their hosts belong to different invertebrate phyla, including Cnidaria (mainly Scleractinia and Antipatharia22) and Echinodermata (Crinoidea29) in ectosymbiotic forms, but also to spoon worms (Echiura), burrowing Crustacea (alpheid shrimps), and/or gobiid fishes15, in inquilinistic forms.While scleractinian corals were hypothesised as the primary hosts of palaemonid shrimp commensalism7, our results revealed the antipatharian association as possibly the earlier one among the Pon-I shrimps. That association was established via a single speciation act at approximately 43 Myr (Eocene), specifically with the ancestor of the recent Cuapetes nilandensis (Clade 1). Except a small body size, this species does not show specific morphological adaptations to antipatharian association. The possibly oldest lineage associated with the scleractinian corals forms a common multigeneric composition of Anapontonia, Ischnopontonia, Harpilius and Philarius (Clade 4), which was established at approximately 38.2 Myr (Eocene). The genera share some homoplasic adaptations with ectosymbioses, such as strongly hooked dactyli of the ambulatory pereiopods adapted to climbing on coral colonies. An extremely compressed body and similar tail fan structure of the genera Ischnopontonia (Fig. 1H) and Anapontonia (Fig. 1D) are adaptations to life in narrow spaces amongst corallites of the oculinid coral Galaxea24,30; the intercorallite channels might be temporarily fully covered by tentacles of exposed polyps. This lifestyle was thus termed ‘semi-endosymbiosis’ by Horká et al.4, as potential evolutionary precursors of the true endosymbioses. In contrast, the genera Philarius and Harpilius have depressed bodies and associate exclusively as regular ectosymbionts with scleractinian corals, mainly of the genera Acropora and Pocillopora22.A further multispecies symbiotic lineage is represented by the genus Vir (Clade 3), whose origin is dated to approximately 21.1 Myr (Miocene). All species of this genus live in associations mainly with the acroporid, pocilloporid and euphylliid genera of scleractinian corals31,32. The adaptation to their symbiotic lifestyle is expressed in the loss of the hepatic tooth, partial or full reduction of ambulatory propodal spines, and cryptic colouration, including transparency of the body and appendages31,33 (Fig. 1J). Subsequent scleractinian-associated lineages are represented by separate species that appeared in the Miocene (21.9–10.1 Myr), namely: Eupontonia oahu, Cuapetes amymone, and C. kororensis, which live in association with Pocillopora, Acropora, and Heliofungia, and show only minor adaptations to their symbiotic habits, e.g. loss of the hepatic tooth, dense distal setae on the walking propodi, or extremely slender chelae and a specific cryptic colouration, respectively22,34,35.A single crinoid-associated species, Palaemonella pottsi (Clade 3), represents the only case of the switch from a free-living lifestyle to the association with echinoderms in the present study group; it originated at approximately 10.4 Myr (Miocene). Retaining the body shape typical for Palaemonella12, the species also does not show any noticeable morphological adaptation to such a host; its affiliation with the symbiotic life is, however, clearly observed in the deep-red to black cryptic colouration36.In Palaemonella aliska (Fig. 1E) and Eupontonia nudirostris (Clade 3), a pair of sister-positioned species in the present analyses (Figs. 2, 3), the ability to co-habit with burrowing animals (e.g., alpheids, gobiid fish, or echiurids) had developed. Their type of symbiosis, inquilinism, formed at approximately 14.8 Myr (Miocene). The reduction of the rostrum length, depressed body, stout main chelae in both, and full lack of the epigastric and hepatic teeth in the latter species15,25, were evidently due to that mode of life. Inquilinism is best known in the family Alpheidae, in which multiple genera associate with a variety of burrowing animals37. In the family Palaemonidae, inquilinism developed only in the Pon-I group, including Palaemonella shirakawai (not analysed here)14.As evident from the present and previously published reports4,5,7,8,10, the life history of the Pon-I group was largely shaped by coevolution with coral reefs. The coral reefs were deeply impacted by the K–T mass extinction at the end of the Cretaceous, which was one of the most destructive events in the Phanerozoic38. However, coral reefs recovered and became increasingly abundant in the Eocene39. This also matches the time of either the origin of host associations, or a wider species radiation of the Pon-I group. The first fossil records of the main coral hosts of the present shrimps are dated after the K-T extinction during the Paleogene (e.g., Euphyllia 66.0–61.6 Myr, Acropora 59.2–56.0 Myr, Galaxea and Pocillopora 56–33.9 Myr40).The biogeographic history suggested by S-DIVA analysis points to some dispersal and vicariant events shaping the current pattern of the Pon-I group’s distribution. This reconstruction (Fig. 4) estimates the present-day IWP region within the former Paleo-Tethys Ocean as the most likely ancestral area of the present study group, which originated ~ 91.6 Myr (Late to Early Cretaceous). The present shrimp group had radiated across the entire IWP region and subsequently expanded into the Atlantic Ocean. We assume that the spread of the group took place in the following sequence of events: (1) dispersal of Palaemonella spp. from the IWP into the eastern Pacific in the Paleocene (∼ 55.2 Myr; P. asymmetrica and P. holmesi); (2) dispersal into the western Atlantic (2 spp., complex of “Cuapetes” americanus) via the eastern Pacific and vicariance event separating the IWP at Eocene (∼ 46.2 Myr). It was the time after the formation of the Eastern Pacific Barrier (EPB), which was considered the largest extension of the open ocean (ca. 5000 km), that separated the IWP area from the eastern Pacific17; (3) the another vicariance event, separating the western Atlantic populations from those of the eastern Pacific in the Oligocene (∼ 30.9 Myr), i.e., before the closure of the Isthmus of Panama, followed by a dispersion of P. atlantica into the eastern Atlantic in the Miocene (∼ 21.6 Myr). The exact time of the formation of the Isthmus of Panama, which separated the Atlantic from the eastern Pacific and remained isolated from the central Pacific by the EPB, still remains questionable. Bacon et al.18 assume that the initial land bridge formed at approximately 23 Myr, and the final closure of the Isthmus of Panama formed between 10 and 6 Myr. Montes et al.19 presupposed the earlier formation of the barrier at ∼ 14 Myr, whereas O’Dea et al.20 concluded that the potential gene flow continued between the Pacific and Atlantic subpopulations of marine organisms until at least ∼ 2.8 Myr.The eastern Pacific Cuapetes canariensis closely related to IWP Cuapetes spp., has been recently described by Fransen et al.41, from the Canary Islands. This could indicate alternative dispersal pathways into the Atlantic, as suggested by recent studies17,42. The Tethys seaway allowed natural dispersion between the Atlantic and Indian Oceans across the region of the Mediterranean Sea. The closure of this interoceanic seaway at approximately 14 Myr (18–12 Myr) was caused by intense tectonic activity in the Near East17. Since the closure of that seaway, remaining possible dispersal to the Atlantic has been limited to the warm-water corridor around the southern tip of Africa, however curtailed by the cold Benguela Current upwelling from the Late Pliocene43. More

Data sources and pre-processingEach of the predictor variables used in our analysis (Table 1), as well as the dependent variable (fire hotspots) underwent pre-processing to transform the data into a format suitable to be passed to our CNN model for prediction. Here we briefly outline these processes and describe the method of generating a training and validation data set for model development. For further details about each predictor variable pre-processing, see Horton et al. (2021).Table 1 Model input data sources, citation, original resolution, and date ranges.Full size tableFire hotspotsWe used both Moderate Resolution Imaging Spectroradiometer (MODIS) and Visible Infrared Imaging Radiometer Suite (VIIRS) fire hotspot data as the dependent variable for use in our model development. As fire hotspots do not give precise locations, but rather indicate that a fire hotspot occurred within a grid cell of the size of the dataset (MODIS 1 km, VIIRS 375 m), we represented each fire hotspot as a 500 m buffered area around the centre point of each grid square identified. We used all fire hotspot occurrences with a confidence rating >50%.LandcoverWe use a collection of historic land cover maps generated by the Ministry of Forestry Indonesia from 1996 to 2016 at 2–3 year intervals38. Before use, we re-designated the land cover map classifications to reduce the number from 25 to just 8 (supplementary Table S2), which are ‘Primary and secondary dry forest’, ‘Swamp forest, ‘Swamp scrubland’, ‘Scrubland, Transition, and bare land’, ‘Riceland’, ‘Plantation’, ‘Settlements’, ‘water, and Cloud’.In addition to these 8 land cover classifications, we also derived a forest clearance index, which identifies areas cleared of forest and assigns an index value that is large negative (−10) immediately after clearing and degrades back towards 0 as time since clearing increases yearly. Areas that are re-forested are assigned large positive values (10) that degrade towards 0 yearly as time since afforestation increase25.Vegetation indicesAll vegetation indices were taken as pre-fire season 3-month averages from May to July. In addition to the original MODIS ET, PET, NDVI, and EVI products, we also included ‘normalised’ variables, whereby each vegetation index was expressed as the ratio of the same index taken at a reference site. The reference site was an area of dense primary forest outside of the EMRP area.Proximity to anthropogenic factorsThe distance to roads and settlement rasters were derived from OpenStreetMap data as the Euclidean distance to nearest feature in 250 m resolution. The same was done for all water bodies, which were then classified by hand into either canals or rivers. These features are taken as those shown in 2015 for all years, and therefore may misrepresent earlier years. However, the majority of canal development in the region took place between 1996 and 1998 and so should not differ dramatically from this date onwards.Oceanic Niño Index (ONI)We use a single value for the entire study area taken as the three-month average for the early fire season each year (July–September).Number of cloud daysUsing the state_1km band in the daily MODIS terra product (MOD09GA version 6), which classifies each pixel as either ‘no cloud’, ‘cloud’, ‘mixed’, or ‘unknown’, we counted the number of ‘cloud’ or ‘mixed’ designations for each pixel for the pre-fire season period May–July.Cross year normalisationAll predictor variables are normalised to be represented between 0 and 1 as the range between the minimum and maximum values for each variable that occur across all years, such that:$${V}_{{{{{{rm{norm}}}}}}}=frac{V-{V}_{{min }}}{{V}_{{max }}-{V}_{{min }}}$$where ({V}_{{{{{{rm{norm}}}}}}}) is the normalised version of the predictor variable (V), ({V}_{{max }}) is the maximum value within the training dataset across all years (2002–2019), and ({V}_{{min }}) is the minimum value within the training dataset across all years.Training and validation dataset assemblyOnce pre-processed, all predictor variable rasters were resampled to the same dimensions (with a resolution of 0.002 degrees in the WGS84 co-ordinate system) and stacked yearly, so that each year (2002–2019) comprised of a 31 feature maps input as a raster stack, with each feature map representing a different predictor variable. Each yearly stack was then split into tiles matching the input dimensions of the CNN model. Our final model was built to take an input size of 32 × 32 pixels (raster cells). Therefore, each yearly raster stack was split into many 32 × 32 × 31 raster stack tiles that span the defined study area. These were then converted to 3D arrays holding the values of all predictor variables for each raster stack tile.The same process was repeated for the yearly fire hotspot rasters used as the dependent variable in building our model. Each year was split into 32 × 32 × 1 tiles across the study area, and then converted to 3D arrays, each of which pairs with one predictor variable array.The 3D predictor variable arrays (dimensions: 32 × 32 × 31) were then stacked into one large 4D array containing all these individual tiles across all years (dimensions: W × 32 × 32 × 31, where W is a large value). The same was done with the 3D dependent variable arrays (dimension: 32 × 32 × 1), preserving the order so that each element in this large 4D array (dimensions: W × 32 × 32 × 1) matches with its counterpart in the predictor variable array.The order of this large 4D training data array was then randomised along the first dimension to avoid bias in passing to the CNN training algorithm, but the randomised re-ordering was repeated with the dependent variable array so as to preserve the elementwise pairing for cross-validation.Model development and applicationFire prediction requires the combination of spatial and temporal indicators to generate a probabilistic output for each location within a given study area. There is a need to preserve a certain level of proximity information, as the location of variables in relation to one another may have a substantial impact on the results. For example, a patch of secondary forest that is immediately adjacent to an area recently deforested may have a significantly higher probability of fire occurrence than an area surrounded entirely by primary forest.CNNs retain spatial features by employing a moving window of reference, known as a kernel, over the input image that captures these proximity relationships within the model structure. For this reason, CNNs are often used for image classification problems, and is an ideal model configuration for the problem of fire prediction across an area. Therefore, we have developed a CNN binary classification model using the Keras API package39 that builds on the TensorFlow machine learning platform40.Model structureCNN models typically apply a combination of kernel layers and dense layers that perform a series of transformations on the multi-channel input to either reduce it down to a single value, or to output an image the same width and height as the input with a single channel. These classification models can either assign a single value (binary classifier), or return one of many possible classifications.Kernels act on a subsection of the input stack (31 feature maps), assigning weights according to each cell’s position within the subsection to transform and combine the values into a new format to pass forward. As the kernel is applied to all subsections of the input stack, it transforms them to the new format, and builds a reconstituted image with dimensions that usually differ from the input. A dense layer will do the same operation, but acting only on a single grid cell of the input stack, acting at the same location upon all input feature maps within the stack at a time—using all values at that location (i.e., the 1 × 1 subsection) and transforming them according to assigned weights to pass forward a new set of channels to a single grid cell on the output stack. Each layer, either kernel or dense, may expand or contract the number of channels it passes forward. A kernel layer may also change the width and height dimensions of the subsection it passes forwards.We require an output that corresponds to a map of fire-occurrences; therefore our model needs to perform a series of transforms that preserve the width and height of the input, but reduce it to a single channel. The single channel in the output then represents the probability of each cell being classified as fire or not-fire (0–1).Our CNN model is comprised of 5 kernel layers (K1–K5 in Fig. 5), each acts on a 3 × 3 subsection and preserves width and height, passing forwards a transformed 3 × 3 section. Kernel K1 takes an input of 31 channels (predictor variables) but passes forward 128 channels to form the transformation T1 (Fig. 6). Kernels K2–K4 take inputs of 128 channels and pass forward 128 channels (T2–T4). Kernel K5 takes an input of 128 channels but passes forward 1 channel—the output. After each kernel applies its weights, there is an activation function applied before the values are passed on, which modify the answer to fit the necessary criteria to be a valid input to the next process. Kernels K1–K4 have a rectified linear (relu) activation function, which returns the input value if positive, and 0 if negative. Kernel K5 has a sigmoid activation function, that transforms the input values to between 0 and 1 such that negative values are transformed to 0.5.Fig. 6: Model structural diagram.Model structural diagram showing the input, 3 × 3 kernel layers (K1–K5), each transformation passed forwards (T1–T4) and the output, with all dimensions labelled.Full size imageModel training and validationWe used a stochastic gradient descent optimising function called Adam41 combined with a binary cross-entropy loss function to train the model against our fire-hotspot dataset iterated over 20 epochs. We split the data 70/30, using 70% as training data and 30% as validation data, recording accuracy, precision, and recall as the performance metrics, as well as the loss function itself.After model training, we applied the model to each yearly raster stack and compared the output against the fire-hotspot data for further model validation. Before validating the model outputs, we applied a simple 3 × 3 moving average window as a smoothing function to reduce the edge effects of tiling that are a by-product of having to split the study area into smaller tiles (32 × 32) for passing to the model. For this yearly validation, we again used the metrics accuracy, precision, and recall, such that:$${{{{{rm{Accuracy}}}}}}=100({{{{{rm{TP}}}}}}+{{{{{rm{TN}}}}}})/({{{{{rm{TP}}}}}}+{{{{{rm{TN}}}}}}+{{{{{rm{FP}}}}}}+{{{{{rm{FN}}}}}})$$$${{{{{rm{Precision}}}}}}=100({{{{{rm{TP}}}}}})/({{{{{rm{TP}}}}}}+{{{{{rm{FP}}}}}})$$$${{{{{rm{Recall}}}}}}=100({{{{{rm{TP}}}}}})/({{{{{rm{TP}}}}}}+{{{{{rm{FN}}}}}})$$where TP is true positive, TN is true negative, FP is false positive, and FN is false negative. These comparisons were made on a raster cell to raster cell basis after designating a 500 m buffer around each fire hotspot observation (MODIS and VIIRS data) and converting the buffers to a raster image of the same resolution and extent as the model prediction.ScenariosAfter validating the model performance, we built future scenarios to investigate the impact on fire occurrence of managing key anthropogenic features of the landscape: canals and land cover (Table 2).Table 2 Future scenario types and descriptions.Full size tableStudies have shown that unmanaged areas of heavily degraded or cleared swamp-forest are most susceptible to fires16,17,25,26,33,42. Therefore, we have built scenarios that investigate the possible impact of managing these areas by altering the model inputs to re-assign the land-cover designations ‘Swamp shrubland’ and ‘Scrubland’, as well as other land designation alterations. The first such restoration scenario investigates the impact of reforesting these areas by re-assigning the designations to ‘Swamp forest’. The second such scenario investigates the impact of converting these unmanaged areas to plantations by re-assigning the designations to ‘Plantation’. We also built two further land cover scenarios to investigate the impact of continued deforestation in the region by re-assigning the ‘Swamp forest’ designation to ‘Swamp shrubland’ and ‘Plantation’.We then built a scenario to investigate the impact of canal blocking on fire occurrence, modifying the proximity to canals model input by reducing the number of canals included in our proximity analysis to just two major canals, one that runs north-south, and one that runs west-east (Fig. 1). These canals could not practically be blocked due to their size and importance as navigation conduits.The final scenario simulates the combined impact of both re-foresting unmanaged degraded and cleared forest areas and the blocking of canals simultaneously.To evaluate the impact of each scenario on fire occurrences, we calculated the ratio of model predictions >0.5 probability (i.e., that a fire would occur in that raster cell) for each year for each scenario against the same year for the baseline scenario.Model use as a predictive toolTo evaluate the model’s potential to predict future fire distribution across the wider ex-Mega Rice Project area, we trained a second version of the model following the same methodology outlined above, but included only data from 2002 to 2018 in the training and test data passed to the model fitting algorithm. We then applied the model to the predictor variables corresponding to 2019 and compared model outputs to the observations of fire-occurrences by again looking at the metrics accuracy, precision, and recall. We also present a visual comparison of the outputs from the full model (2019 included in training data), the predictive model (2019 not included), and the observation data (MODIS and VIIRS hotspots). More