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    Long term crop rotation effect on subsequent soybean yield explained by soil and root-associated microbiomes and soil health indicators

    1.Hartman, K. et al. Cropping practices manipulate abundance patterns of root and soil microbiome members paving the way to smart farming. Microbiome 6, 14 (2018).Article 

    Google Scholar 
    2.Berzsenyi, Z., Győrffy, B. & Lap, D. Effect of crop rotation and fertilisation on maize and wheat yields and yield stability in a long-term experiment. Eur. J. Agron. 13, 225–244. https://doi.org/10.1016/S1161-0301(00)00076-9 (2000).Article 

    Google Scholar 
    3.Körschens, M. The importance of long-term field experiments for soil science and environmental research: a review. Plant Soil Environ. 52, 1–8 (2006).
    Google Scholar 
    4.Zuber, S. M., Behnke, G., Nafziger, E. & Villamil, M. B. Crop rotation and tillage effects on soil physical and chemical properties in Illinois. Agron. J. 107, 971–978. https://doi.org/10.2134/agronj14.0465 (2015).CAS 
    Article 

    Google Scholar 
    5.Stott, D. E. Recommended Soil Health Indicators and Associated Laboratory Procedures. National Soil Health Specialist, Soil Health Division, U.S. Department of Agriculture (USDA), Natural Resources Conservation Service (NRCS), Washington, D.C. (2019).6.Barrios, E. Soil biota, ecosystem services and land productivity. Ecol. Econ. 64, 269–285. https://doi.org/10.1016/j.ecolecon.2007.03.004 (2007).Article 

    Google Scholar 
    7.Garbeva, P., van Veen, J. A. & van Elsas, J. D. Microbial diversity in soil: selection of microbial populations by plant and soil type and implications for disease suppressiveness. Annu. Rev. Phytopathol. 42, 243–270. https://doi.org/10.1146/annurev.phyto.42.012604.135455 (2004).CAS 
    Article 
    PubMed 

    Google Scholar 
    8.Hatfield, J., Prueger, J. & Kustas, W. Spatial and temporal variation of energy and carbon fluxes in central Iowa. Agron. J. 99, 285–296. https://doi.org/10.2134/agronj2005.0116S (2007).CAS 
    Article 

    Google Scholar 
    9.Lehman, R. M. et al. Understanding and enhancing soil biological health: the solution for reversing soil degradation. Sustainability 7, 988–1027. https://doi.org/10.3390/su7010988 (2015).Article 

    Google Scholar 
    10.Weil, R. R., Islam, K. R., Stine, M. A., Gruver, J. B. & Samson-Liebig, S. E. Estimating active carbon for soil quality assessment: a simplified method for laboratory and field use. Am. J. Alter. Agric. 18, 3–17. https://doi.org/10.1079/AJAA200228 (2003).Article 

    Google Scholar 
    11.Idowu, O. et al. Farmer-oriented assessment of soil quality using field, laboratory, and VNIR spectroscopy methods. Int. J. Plant-Soil Relatsh. 307, 243–253. https://doi.org/10.1007/s11104-007-9521-0 (2008).CAS 
    Article 

    Google Scholar 
    12.Gunapala, N. & Scow, K. M. Dynamics of soil microbial biomass and activity in conventional and organic farming systems. Soil Biol. Biochem. 30, 805–816. https://doi.org/10.1016/S0038-0717(97)00162-4 (1998).CAS 
    Article 

    Google Scholar 
    13.Plaza-Bonilla, D., Álvaro-Fuentes, J. & Cantero-Martínez, C. Identifying soil organic carbon fractions sensitive to agricultural management practices. Soil Tillage Res. 139, 19–22. https://doi.org/10.1016/j.still.2014.01.006 (2014).Article 

    Google Scholar 
    14.Mirsky, S., Lanyon, L. & Needelman, B. Evaluating soil management using particulate and chemically labile soil organic matter fractions. Soil Sci. Soc. Am. J. 72, 180–185. https://doi.org/10.2136/sssaj2005.0279 (2008).ADS 
    CAS 
    Article 

    Google Scholar 
    15.Wright, S. F. & Upadhyaya, A. Extraction of an abundant and unusual protein from soil and comparison with hyphal protein of arbuscular mycorrhizal fungi. Soil Sci. 161, 575–586 (1996).ADS 
    CAS 
    Article 

    Google Scholar 
    16.Jan, M. T., Roberts, P., Tonheim, S. K. & Jones, D. L. Protein breakdown represents a major bottleneck in nitrogen cycling in grassland soils. Soil Biol. Biochem. 41, 2272–2282. https://doi.org/10.1016/j.soilbio.2009.08.013 (2009).CAS 
    Article 

    Google Scholar 
    17.Nannipieri, P. & Eldor, P. The chemical and functional characterization of soil N and its biotic components. Soil Biol. Biochem. 41, 2357–2369. https://doi.org/10.1016/j.soilbio.2009.07.013 (2009).CAS 
    Article 

    Google Scholar 
    18.Weintraub, M. N. & Schimel, J. P. Seasonal protein dynamics in Alaskan arctic tundra soils. Soil Biol. Biochem. 37, 1469–1475. https://doi.org/10.1016/j.soilbio.2005.01.005 (2005).CAS 
    Article 

    Google Scholar 
    19.Ros, G. H., Temminghoff, E. J. M. & Hoffland, E. Nitrogen mineralization: a review and meta-analysis of the predictive value of soil tests. Eur. J. Soil Sci. 62, 162–173. https://doi.org/10.1111/j.1365-2389.2010.01318.x (2011).CAS 
    Article 

    Google Scholar 
    20.Ros, G. H., Hanegraaf, M. C., Hoffland, E. & van Riemsdijk, W. H. Predicting soil N mineralization: relevance of organic matter fractions and soil properties. Soil Biol. Biochem. 43, 1714–1722. https://doi.org/10.1016/j.soilbio.2011.04.017 (2011).CAS 
    Article 

    Google Scholar 
    21.Chang, E.-H., Chung, R.-S. & Tsai, Y.-H. Effect of different application rates of organic fertilizer on soil enzyme activity and microbial population. Soil Sci. Plant Nutr. 53, 132–140. https://doi.org/10.1111/j.1747-0765.2007.00122.x (2007).ADS 
    CAS 
    Article 

    Google Scholar 
    22.Liebig, M., Carpenter-Boggs, L., Johnson, J. M. F., Wright, S. & Barbour, N. Cropping system effects on soil biological characteristics in the Great Plains. Renew. Agric. Food Syst. 21, 36–48. https://doi.org/10.1079/RAF2005124 (2006).Article 

    Google Scholar 
    23.Wright, S. & Upadhyaya, A. Comparison of N-linked oligosaccharides of glomalin from arbuscular mycorrhizal fungi and soils by capillary electrophoresis. Soil Biol. Biochem. 30, 1853–1857 (1998).CAS 
    Article 

    Google Scholar 
    24.Wright, S. F. & Upadhyaya, A. A survey of soils for aggregate stability and glomalin, a glycoprotein produced by hyphae of arbuscular mycorrhizal fungi. Plant Soil 198, 97–107 (1998).CAS 
    Article 

    Google Scholar 
    25.Lovelock, C. E., Wright, S. F., Clark, D. A. & Ruess, R. W. Soil stocks of glomalin produced by arbuscular mycorrhizal fungi across a tropical rain forest landscape. J. Ecol. 92, 278–287. https://doi.org/10.1111/j.0022-0477.2004.00855.x (2004).CAS 
    Article 

    Google Scholar 
    26.Emran, M., Gispert, M. & Pardini, G. Patterns of soil organic carbon, glomalin and structural stability in abandoned Mediterranean terraced lands. Eur. J. Soil Sci. 63, 637–649. https://doi.org/10.1111/j.1365-2389.2012.01493.x (2012).CAS 
    Article 

    Google Scholar 
    27.Nichols, K. A. & Millar, J. Glomalin and soil aggregation under six management systems in the Northern Great Plains, USA. Open J. Soil Sci. 03(08), 5. https://doi.org/10.4236/ojss.2013.38043 (2013).CAS 
    Article 

    Google Scholar 
    28.Rillig, M., Ramsey, P., Morris, S. & Paul, E. Glomalin, an arbuscular-mycorrhizal fungal soil protein, responds to land-use change. Int. J. Plant-Soil Relatsh. 253, 293–299. https://doi.org/10.1023/A:1024807820579 (2003).CAS 
    Article 

    Google Scholar 
    29.Klose, S. & Tabatabai, M. A. Response of phosphomonoesterases in soils to chloroform fumigation. J. Plant Nutr. Soil Sci. 165, 429–434. https://doi.org/10.1002/1522-2624(200208)165:4%3c429::AID-JPLN429%3e3.0.CO;2-S (2002).CAS 
    Article 

    Google Scholar 
    30.Wang, X.-C. & Lu, Q. Beta-glucosidase activity in paddy soils of the Taihu Lake Region, China. Pedosphere 16, 118–124. https://doi.org/10.1016/S1002-0160(06)60033-7 (2006).CAS 
    Article 

    Google Scholar 
    31.Wilson, D. B. Microbial diversity of cellulose hydrolysis. Curr. Opin. Microbiol. 14, 259–263. https://doi.org/10.1016/j.mib.2011.04.004 (2011).CAS 
    Article 
    PubMed 

    Google Scholar 
    32.Shewale, J. G. β-Glucosidase: Its role in cellulase synthesis and hydrolysis of cellulose. Int. J. Biochem. 14, 435–443. https://doi.org/10.1016/0020-711X(82)90109-4 (1982).CAS 
    Article 
    PubMed 

    Google Scholar 
    33.Acosta-Martínez, V., Reicher, Z., Bischoff, M. & Turco, R. F. The role of tree leaf mulch and nitrogen fertilizer on turfgrass soil quality. Biol. Fertil. Soils 29, 55–61. https://doi.org/10.1007/s003740050524 (1999).Article 

    Google Scholar 
    34.Krogh, K. et al. Characterization and kinetic analysis of a thermostable GH3 β-glucosidase from Penicillium brasilianum. Appl. Microbiol. Biotechnol. 86, 143–154. https://doi.org/10.1007/s00253-009-2181-7 (2010).CAS 
    Article 
    PubMed 

    Google Scholar 
    35.Chen, M. et al. Isolation and characterization of a β-glucosidase from Penicillium decumbens and improving hydrolysis of corncob residue by using it as cellulase supplementation. Enzyme Microb. Technol. 46, 444–449. https://doi.org/10.1016/j.enzmictec.2010.01.008 (2010).CAS 
    Article 
    PubMed 

    Google Scholar 
    36.Günata, Z. & Vallier, M.-J. Production of a highly glucose-tolerant extracellular β-glucosidase by three Aspergillus strains. Biotechnol. Lett. 21, 219–223. https://doi.org/10.1023/A:1005407710806 (1999).Article 

    Google Scholar 
    37.Riou, C., Salmon, J.-M., Vallier, M.-J., Gunata, Z. & Barre, P. Purification, characterization, and substrate specificity of a novel highly glucose-tolerant beta -glucosidase from Aspergillus oryzae. Appl. Environ. Microbiol. 64, 3607 (1998).CAS 
    Article 

    Google Scholar 
    38.Tsukada, T., Igarashi, K., Yoshida, M. & Samejima, M. Molecular cloning and characterization of two intracellular β-glucosidases belonging to glycoside hydrolase family 1 from the basidiomycete Phanerochaete chrysosporium. Appl. Microbiol. Biotechnol. 73, 807–814. https://doi.org/10.1007/s00253-006-0526-z (2006).CAS 
    Article 
    PubMed 

    Google Scholar 
    39.Yang, S., Wang, L., Yan, Q., Jiang, Z. & Li, L. Hydrolysis of soybean isoflavone glycosides by a thermostable β-glucosidase from Paecilomyces thermophila. Food Chem. 115, 1247–1252. https://doi.org/10.1016/j.foodchem.2009.01.038 (2009).CAS 
    Article 

    Google Scholar 
    40.Arévalo Villena, M., Úbeda Iranzo, J. F., Gundllapalli, S. B., Cordero Otero, R. R. & Briones Pérez, A. I. Characterization of an exocellular β-glucosidase from Debaryomyces pseudopolymorphus. Enzyme Microb. Technol. 39, 229–234. https://doi.org/10.1016/j.enzmictec.2005.10.018 (2006).CAS 
    Article 

    Google Scholar 
    41.Amouri, B. & Gargouri, A. Characterization of a novel β-glucosidase from a Stachybotrys strain. Biochem. Eng. J. 32, 191–197. https://doi.org/10.1016/j.bej.2006.09.022 (2006).CAS 
    Article 

    Google Scholar 
    42.Okamoto, K., Sugita, Y., Nishikori, N., Nitta, Y. & Yanase, H. Characterization of two acidic β-glucosidases and ethanol fermentation in the brown rot fungus Fomitopsis palustris. Enzyme Microb. Technol. 48, 359–364. https://doi.org/10.1016/j.enzmictec.2010.12.012 (2011).CAS 
    Article 
    PubMed 

    Google Scholar 
    43.Singhania, R. R., Patel, A. K., Sukumaran, R. K., Larroche, C. & Pandey, A. Role and significance of beta-glucosidases in the hydrolysis of cellulose for bioethanol production. Biores. Technol. 127, 500–507. https://doi.org/10.1016/j.biortech.2012.09.012 (2013).CAS 
    Article 

    Google Scholar 
    44.Okamoto, K., Nakano, H., Yatake, T., Kiso, T. & Kitahata, S. Purification and some properties of a β-glucosidase from Flavobacterium johnsonae. Biosci. Biotechnol. Biochem. 64, 333–340. https://doi.org/10.1271/bbb.64.333 (2000).CAS 
    Article 
    PubMed 

    Google Scholar 
    45.Spano, G. et al. A β-glucosidase gene isolated from wine Lactobacillus plantarum is regulated by abiotic stresses. J. Appl. Microbiol. 98, 855–861. https://doi.org/10.1111/j.1365-2672.2004.02521.x (2005).CAS 
    Article 
    PubMed 

    Google Scholar 
    46.Mendes, R. et al. Deciphering the rhizosphere microbiome for disease-suppressive bacteria. Science 332, 1097–1100. https://doi.org/10.1126/science.1203980 (2011).ADS 
    CAS 
    Article 
    PubMed 

    Google Scholar 
    47.Crookston, R., Kurle, J., Copeland, P. J., Ford, J. H. & Lueschen, W. E. Rotational cropping sequence affects yield of corn and soybean. Agron. J. 83, 108–113 (1991).Article 

    Google Scholar 
    48.Meese, B. G., Carter, P. R., Oplinger, E. S. & Pendleton, J. W. Corn/soybean rotation effect as influenced by tillage, nitrogen, and hybrid/cultivar. J. Prod. Agric. 4, 74–80 (1991).Article 

    Google Scholar 
    49.Kelley, K. W., Long, J. H. & Todd, T. C. Long-term crop rotations affect soybean yield, seed weight, and soil chemical properties. Field Crop Res. 83, 41–50. https://doi.org/10.1016/S0378-4290(03)00055-8 (2003).Article 

    Google Scholar 
    50.Mourtzinis, S. et al. Corn and soybean yield response to tillage, rotation, and nematicide seed treatment. Crop Sci. 57, 1704–1712. https://doi.org/10.2135/cropsci2016.09.0792 (2017).Article 

    Google Scholar 
    51.Farmaha, B. S. et al. Rotation impact on on-farm yield and input-use efficiency in high-yield irrigated maize-soybean systems. Agron. J. 108, 2313–2321. https://doi.org/10.2134/agronj2016.01.0046 (2016).Article 

    Google Scholar 
    52.Crookston, R. K. & Kurle, J. E. Corn residue effect on the yield of corn and soybean grown in rotation. Agron. J. 81, 229–232. https://doi.org/10.2134/agronj1989.00021962008100020018x (1989).Article 

    Google Scholar 
    53.Whiting, K. R. & Crookston, R. K. Host-specific pathogens do not account for the corn-soybean rotation effect. Crop Sci. 33, 539–543. https://doi.org/10.2135/cropsci1993.0011183X003300030024x (1993).Article 

    Google Scholar 
    54.Copeland, P. J., Allmaras, R. R., Crookston, R. K. & Nelson, W. W. Corn-soybean rotation effects on soil water depletion. Agron. J. 85, 203–210. https://doi.org/10.2134/agronj1993.00021962008500020008x (1993).Article 

    Google Scholar 
    55.Li, J. et al. Soil-plant indices help explain legume response to crop rotation in a semiarid environment. Front. Plant Sci. https://doi.org/10.3389/fpls.2018.01488 (2018).Article 
    PubMed 
    PubMed Central 

    Google Scholar 
    56.Nickel, S. E., Crookston, R. K. & Russelle, M. P. Root growth and distribution are affected by corn-soybean cropping sequence. Agron. J. 87, 895–902. https://doi.org/10.2134/agronj1995.00021962008700050020x (1995).Article 

    Google Scholar 
    57.Bennett, A. J., Bending, G. D., Chandler, D., Hilton, S. & Mills, P. Meeting the demand for crop production: the challenge of yield decline in crops grown in short rotations. Biol. Rev. 87, 52–71. https://doi.org/10.1111/j.1469-185X.2011.00184.x (2012).Article 
    PubMed 

    Google Scholar 
    58.Johnson, N., Copeland, P. J., Crookston, R. & Pfleger, F. L. Mycorrhizae: possible explanation for yield decline with continuous corn and soybean. Agron. J. 84, 387–390 (1992).Article 

    Google Scholar 
    59.Chen, S., Porter, P. M., Reese, C. D. & Stienstra, W. C. Crop sequence effects on soybean cyst nematode and soybean and corn yields this research was supported by Minnesota soybean producers check-off funding through Minnesota research and promotion council and Minnesota agric. exp. stn.. Crop Sci. 41, 1843–1849. https://doi.org/10.2135/cropsci2001.1843 (2001).Article 

    Google Scholar 
    60.Grabau, Z. J. & Chen, S. Determining the role of plant-parasitic nematodes in the corn-soybean crop rotation yield effect using nematicide application: II. Soybean. Agron. J. 108, 1168–1179. https://doi.org/10.2134/agronj2015.0432 (2016).CAS 
    Article 

    Google Scholar 
    61.Hoss, M., Behnke, G., Davis, A., Nafziger, E. & Villamil, M. B. Short corn rotations do not improve soil quality. Compared with corn monocultures. Agron. J. 110, 1274–1288. https://doi.org/10.2134/agronj2017.11.0633 (2018).CAS 
    Article 

    Google Scholar 
    62.Plaza, C., Courtier-Murias, D., Fernández, J. M., Polo, A. & Simpson, A. J. Physical, chemical, and biochemical mechanisms of soil organic matter stabilization under conservation tillage systems: a central role for microbes and microbial by-products in C sequestration. Soil Biol. Biochem. 57, 124–134. https://doi.org/10.1016/j.soilbio.2012.07.026 (2013).CAS 
    Article 

    Google Scholar 
    63.Tardy, V. et al. Shifts in microbial diversity through land use intensity as drivers of carbon mineralization in soil. Soil Biol. Biochem. 90, 204–213. https://doi.org/10.1016/j.soilbio.2015.08.010 (2015).CAS 
    Article 

    Google Scholar 
    64.Tiedje, J. M., Asuming-Brempong, S., Nüsslein, K., Marsh, T. L. & Flynn, S. J. Opening the black box of soil microbial diversity. Appl. Soil. Ecol. 13, 109–122. https://doi.org/10.1016/S0929-1393(99)00026-8 (1999).Article 

    Google Scholar 
    65.Hussain, S., Ghaffar, A. & Aslam, M. Biological-control of macrophomina-phaseolina charcoal rot of sunflower and mung bean. J. Phytopathol. 130, 157–160. https://doi.org/10.1111/j.1439-0434.1990.tb01163.x (1990).Article 

    Google Scholar 
    66.Khan, A. N. et al. Molecular identification and genetic characterization of Macrophomina phaseolina strains causing pathogenicity on sunflower and chickpea. Front. Microbiol. 8, 1309. https://doi.org/10.3389/fmicb.2017.01309 (2017).Article 
    PubMed 
    PubMed Central 

    Google Scholar 
    67.Ramezani, M. et al. Soybean charcoal rot disease fungus Macrophomina phaseolina in Mississippi produces the phytotoxin (-)-botryodiplodin but no detectable phaseolinone. J. Nat. Prod. 70, 128–129. https://doi.org/10.1021/np060480t (2007).CAS 
    Article 
    PubMed 

    Google Scholar 
    68.Smith, L.J., Datnoff, L.E., Pernezny, K. & Schlub, R.L. Phylogenetic and pathogenic characterization of Corynespora cassiicola isolates. In II International Symposium on Tomato Diseases 808, 51–56 (2007)69.Deon, M. et al. Characterization of a cassiicolin-encoding gene from Corynespora cassiicola, pathogen of rubber tree (Hevea brasiliensis). Plant Sci. 185–186, 227–237. https://doi.org/10.1016/j.plantsci.2011.10.017 (2012).CAS 
    Article 
    PubMed 

    Google Scholar 
    70.Videira, S. I. R. et al. Mycosphaerellaceae: chaos or clarity?. Stud. Mycol. 87, 257–421 (2017).CAS 
    Article 

    Google Scholar 
    71.Wijayawardene, N. N. et al. Outline of ascomycota: 2017. Fungal Divers. 88, 167–263. https://doi.org/10.1007/s13225-018-0394-8 (2018).Article 

    Google Scholar 
    72.Olofsson, J., Ericson, L., Torp, M., Stark, S. & Baxter, R. Carbon balance of Arctic tundra under increased snow cover mediated by a plant pathogen. Nat. Clim. Change 1, 220–223. https://doi.org/10.1038/Nclimate1142 (2011).ADS 
    CAS 
    Article 

    Google Scholar 
    73.Wells, L. D. & McManus, P. S. A photographic diagnostic guide for identification of the principal cranberry fruit rot pathogens. Plant Health Prog. https://doi.org/10.1094/php-2013-0729-01-dg (2013).Article 

    Google Scholar 
    74.Yeager, C. M. et al. Polysaccharide degradation capability of actinomycetales soil isolates from a semiarid grassland of the Colorado Plateau. Appl. Environ. Microbiol. https://doi.org/10.1128/aem.03020-16 (2017).Article 
    PubMed 
    PubMed Central 

    Google Scholar 
    75.Loria, R., Bukhalid, R. A., Fry, B. A. & King, R. R. Plant pathogenicity in the genus Streptomyces. Plant Dis. 81, 836–846. https://doi.org/10.1094/Pdis.1997.81.8.836 (1997).Article 
    PubMed 

    Google Scholar 
    76.Li, Y., Liu, J., Diaz-Cruz, G., Cheng, Z. & Bignell, D. R. D. Virulence mechanisms of plant-pathogenic Streptomyces species: an updated review. Microbiology 165, 1025–1040. https://doi.org/10.1099/mic.0.000818 (2019).CAS 
    Article 
    PubMed 

    Google Scholar 
    77.Abdalla, M. H. Solubilization of rock phosphates by rhizobium and bradyrhizobium. Folia Microbiol. 39, 53–56. https://doi.org/10.1007/Bf02814530 (1994).CAS 
    Article 

    Google Scholar 
    78.Bargaz, A., Lyamlouli, K., Chtouki, M., Zeroual, Y. & Dhiba, D. Soil microbial resources for improving fertilizers efficiency in an integrated plant nutrient management system. Front Microbiol. https://doi.org/10.3389/fmicb.2018.01606 (2018).Article 
    PubMed 
    PubMed Central 

    Google Scholar 
    79.Moebius-Clune, B. N. Comprehensive Assessment of Soil Health: The Cornell Framework Manual (Cornell University, 2016).
    Google Scholar 
    80.Deng, S. P. & Tabatabai, M. A. Cellulase activity of soils. Soil Biol. Biochem. 26, 1347–1354. https://doi.org/10.1016/0038-0717(94)90216-X (1994).CAS 
    Article 

    Google Scholar 
    81.Riesenfeld, C. S., Goodman, R. M. & Handelsman, J. Uncultured soil bacteria are a reservoir of new antibiotic resistance genes. Environ. Microbiol. 6, 981–989 (2004).CAS 
    Article 

    Google Scholar 
    82.Gohl, D. et al. Systematic improvement of amplicon marker gene methods for increased accuracy in microbiome studies. Nat. Biotechnol. 34, 942–949. https://doi.org/10.1038/nbt.3601 (2016).CAS 
    Article 
    PubMed 

    Google Scholar 
    83.R: A Language and Environment for Statistical Computing. R Foundation for Statistical Computing (Vienna, Austria, 2020).84.Bates, D., Machler, M., Bolker, B. M. & Walker, S. C. Fitting linear mixed-effects models using lme4. J. Stat. Softw. 67, 1–48 (2015).Article 

    Google Scholar 
    85.Bolyen, E. et al. Qiime 2: reproducible, interactive, scalable, and extensible microbiome data science. Report No. 2167–9843, (PeerJ Preprints, 2018).86.Callahan, B. J. et al. DADA2: high-resolution sample inference from Illumina amplicon data. Nat. Methods 13, 581–583. https://doi.org/10.1038/nmeth.3869 (2016).CAS 
    Article 
    PubMed 
    PubMed Central 

    Google Scholar 
    87.Mandal, S. et al. Analysis of composition of microbiomes: a novel method for studying microbial composition. Microb. Ecol. Health Dis. https://doi.org/10.3402/mehd.v26.27663 (2015).Article 
    PubMed 
    PubMed Central 

    Google Scholar 
    88.Morton, J. T. et al. Balance trees reveal microbial niche differentiation. mSystems https://doi.org/10.1128/mSystems.00162-16 (2017).Article 
    PubMed 
    PubMed Central 

    Google Scholar 
    89.Nilsson, R. H. et al. The UNITE database for molecular identification of fungi: handling dark taxa and parallel taxonomic classifications. Nucleic Acids Res. 47, D259–D264. https://doi.org/10.1093/nar/gky1022 (2019).CAS 
    Article 
    PubMed 

    Google Scholar 
    90.Quast, C. et al. The SILVA ribosomal RNA gene database project: improved data processing and web-based tools. Nucleic Acids Res. 41, D590-596. https://doi.org/10.1093/nar/gks1219 (2013).CAS 
    Article 
    PubMed 

    Google Scholar 
    91.Pedregosa, F. et al. Scikit-learn: machine learning in python. J. Mach. Learn. Res. 12, 2825–2830 (2011).MathSciNet 
    MATH 

    Google Scholar 
    92.Kruskal, W. H. & Wallis, W. A. Use of ranks in one-criterion variance analysis. J. Am. Stat. Assoc. 47, 583–621. https://doi.org/10.1080/01621459.1952.10483441 (1952).Article 
    MATH 

    Google Scholar  More

  • in

    The Asian plethodontid salamander preserves historical genetic imprints of recent northern expansion

    1.Hewitt, G. M. Quaternary phylogeography: the roots of hybrid zones. Genetica 139, 617–638 (2011).PubMed 
    Article 

    Google Scholar 
    2.Gillespie, R. G. & Roderick, G. K. Evolution: geology and climate drive diversification. Nature 509, 297–298 (2014).ADS 
    CAS 
    PubMed 
    Article 

    Google Scholar 
    3.Hewitt, G. M. The genetic legacy of the quaternary ice ages. Nature 405, 907–913 (2000).ADS 
    CAS 
    PubMed 
    Article 

    Google Scholar 
    4.Gómez, A. & Lunt, D. H. Refugia within refugia: patterns of phylogeographic concordance in the Iberian Peninsula. In Phylogeography of Southern European Refugia (eds Weiss, S. & Ferrand, N.) 155–188 (Springer, 2007).
    Google Scholar 
    5.Abellán, P. & Svenning, J. C. Refugia within refugia—patterns in endemism and genetic divergence are linked to Late Quaternary climate stability in the Iberian Peninsula. Biol. J. Linn. Soc. 113, 13–28 (2014).Article 

    Google Scholar 
    6.Avise, J. C. Phylogeography: The History and Formation of Species (Harvard University Press, 2000).
    Google Scholar 
    7.Juan, C., Emerson, B. C., Oromí, P. & Hewitt, G. M. Colonization and diversification: towards a phylogeographic synthesis for the Canary Islands. Trends Ecol. Evol. 15, 104–109 (2000).CAS 
    PubMed 
    Article 

    Google Scholar 
    8.Chung, M. Y., López-Pujol, J. & Chung, M. G. The role of the Baekdudaegan (Korean Peninsula) as a major glacial refugium for plant species: a priority for conservation. Biol. Conserv. 206, 236–248 (2017).Article 

    Google Scholar 
    9.Chung, M. Y. et al. The Korean baekdudaegan mountains: a glacial refugium and a biodiversity hotspot that needs to be conserved. Front. Genet. 9, 489 (2018).PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    10.AmphibiaWeb. https://amphibiaweb.org/. (Accessed: 15th July 2020).11.Borzée, A. & Min, M.-S. Disentangling the impacts of speciation, sympatry and the island effect on the morphology of seven Hynobius sp. salamanders. Animals 11, 187 (2021).PubMed 
    Article 

    Google Scholar 
    12.Baek, H.-J., Lee, M.-Y., Lee, H. & Min, M.-S. Mitochondrial DNA data unveil highly divergent populations within the Genus Hynobius (Caudata: Hynobiidae) in South Korea. Mol. Cells 31, 105–112 (2011).CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    13.Suk, H. Y. et al. Phylogenetic structure and ancestry of Korean clawed salamander, Onychodactylus koreanus (Caudata: Hynobiidae). Mitochondrial DNA Part A DNA Mapp. Seq. Anal. 29, 650–658 (2018).CAS 
    Article 

    Google Scholar 
    14.Min, M.-S. et al. Discovery of the first Asian plethodontid salamander. Nature 435, 87–90 (2005).ADS 
    CAS 
    PubMed 
    Article 

    Google Scholar 
    15.Wiens, J. J., Engstrom, T. N. & Chippindale, P. T. Rapid diversification, incomplete isolation, and the “speciation clock” in North American salamanders (genus Plethodon): testing the hybrid swarm hypothesis of rapid radiation. Evolution 60, 2585–2603 (2006).CAS 
    PubMed 

    Google Scholar 
    16.Kozak, K. H., Mendyk, R. W. & Wiens, J. J. Can parallel diversification occur in sympatry? Repeated patterns of body-size evolution in coexisting clades of North American salamanders. Evolution 63, 1769–1784 (2009).PubMed 
    Article 

    Google Scholar 
    17.Zhang, P. & Wake, D. B. Higher-level salamander relationships and divergence dates inferred from complete mitochondrial genomes. Mol. Phylogenet. Evol. 53, 492–508 (2009).CAS 
    PubMed 
    Article 

    Google Scholar 
    18.Vieites, D. R., Román, S. N., Wake, M. H. & Wake, D. B. A multigenic perspective on phylogenetic relationships in the largest family of salamanders, the Plethodontidae. Mol. Phylogenet. Evol. 59, 623–635 (2011).PubMed 
    Article 

    Google Scholar 
    19.Shen, X. X. et al. Enlarged multilocus data set provides surprisingly younger time of origin for the Plethodontidae, the largest family of salamanders. Syst. Biol. 65, 66–81 (2016).PubMed 
    Article 

    Google Scholar 
    20.Wake, D. B. Persistent plethodontid themes: species, phylogenies, and biogeography. Herpetologica 73, 242–251 (2017).Article 

    Google Scholar 
    21.Wake, D. B. The enigmatic history of the European, Asian and American plethodontid salamanders. Amphib-reptil 34, 323–336 (2013).Article 

    Google Scholar 
    22.Vieites, D. R., Min, M.-S. & Wake, D. B. Rapid diversification and dispersal during periods of global warming by plethodontid salamanders. Proc. Natl. Acad. Sci. U.S.A. 104, 19903–19907 (2007).ADS 
    CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    23.IUCN SSC Amphibian Specialist Group. Karsenia koreana. The IUCN Red List of Threatened Species 2019. (2019). https://www.iucnredlist.org/species/61903/110101886. (Accessed: 15th July 2020).24.Borzée, A. et al. Climate change-based models predict range shifts in the distribution of the only Asian plethodontid salamander: Karsenia koreana. Sci. Rep. 9, 1–9 (2019).Article 
    CAS 

    Google Scholar 
    25.Sessions, S. K. et al. Cytogenetic analysis of the Asian Plethodontid salamander, Karsenia koreana: evidence for karyotypic conservation, chromosome repatterning, and genome size evolution. Chromosom. Res. 16, 563–574 (2008).CAS 
    Article 

    Google Scholar 
    26.Buckley, D., Wake, M. H. & Wake, D. B. Comparative skull osteology of Karsenia koreana (Amphibia, Caudata, Plethodontidae). J. Morphol. 271, 533–558 (2010).PubMed 
    Article 

    Google Scholar 
    27.Sever, D. M., Pinsoneault, A. D., Mackenzie, B. W., Siegel, D. S. & Staub, N. L. A description of the skin glands and cloacal morphology of the plethodontid salamander Karsenia koreana. Copeia 104, 816–823 (2016).Article 

    Google Scholar 
    28.Moon, K. Y. & Park, D. Report of Karsenia koreana eggs oviposited within a semi-natural terrarium constructed at natural habitat. Korean J. Herpetol. 7, 1–5 (2016).
    Google Scholar 
    29.Song, J.-Y. et al. Life history of a unique Asian plethodontid salamander, Karsenia koreana. Zool. Sci. 34, 122–128 (2017).Article 

    Google Scholar 
    30.Jung, J.-H., Lee, E.-J., Lee, W.-S. & Park, C.-D. Habitat suitability models of Korean crevice salamander (Karsenia koreana) at forested area in Daejeon metropolitan city, Republic of Korea. J. For. Res. 24, 349–355 (2019).CAS 
    Article 

    Google Scholar 
    31.Rozen, S. & Skaletsky, H. Primer3 on the WWW for general users and for biologist programmers. In Bioinformatics Methods and Protocols: Methods in Molecular Biology Vol. 132 (eds Misener, S. & Krawetz, S. A.) 365–386 (Humana Press, 2000).
    Google Scholar 
    32.Su, X. Z., Wu, Y., Sifri, C. D. & Wellems, T. E. Reduced extension temperatures required for PCR amplification of extremely A+T-rich DNA. Nucleic Acids Res. 24, 1574–1575 (1996).CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    33.Smit, A. F. A., Hubley, R. & Green, P. RepeatModeler Open-1.0. 2008–2015. (2014). http://www.repeatmasker.org.34.Stieneke, D. L. & Eujayl, I. L. Imperfect SSR Finder Version 1.0. United States Department of Agriculture. (2019). https://data.nal.usda.gov/dataset/imperfect-ssr-finder.35.Thompson, J. D., Higgins, D. G. & Gibson, T. J. CLUSTAL W: improving the sensitivity of progressive multiple sequence alignment through sequence weighting, position-specific gap penalties and weight matrix choice. Nucleic Acids Res. 22, 4673–4680 (1994).CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    36.Kumar, S., Stecher, G., Li, M., Knyaz, C. & Tamura, K. MEGA X: molecular evolutionary genetics analysis across computing platforms. Mol. Biol. Evol. 35, 1547–1549 (2018).CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    37.Librado, P. & Rozas, J. DnaSP v5: a software for comprehensive analysis of DNA polymorphism data. Bioinformatics 25, 1451–1452 (2009).CAS 
    Article 

    Google Scholar 
    38.Nei, M. Molecular Evolutionary Genetics (Columbia University Press, 1987).Book 

    Google Scholar 
    39.Tajima, F. Evolutionary relationship of DNA sequences in finite populations. Genetics 105, 437–460 (1983).CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    40.Excoffier, L., Smouse, P. & Quattro, J. Analysis of molecular variance inferred from metric distances among DNA haplotypes: application to human mitochondrial DNA restriction data. Genetics 131, 479–491 (1992).CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    41.Excoffier, L. & Lischer, H. E. L. Arlequin suite ver 3.5: a new series of programs to perform population genetics analyses under Linux and Windows. Mol. Ecol. Resour. 10, 564–567 (2010).PubMed 
    Article 

    Google Scholar 
    42.Bandelt, H.-J., Forster, P. & Röhl, A. Median-joining networks for inferring intraspecific phylogenies. Mol. Biol. Evol. 16, 37–48 (1999).CAS 
    PubMed 
    Article 

    Google Scholar 
    43.Wake, D. B. Taxonomy of salamanders of the family Plethodontidae (Amphibia: Caudata). Zootaxa 3484, 75–82 (2012).Article 

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

    Google Scholar 
    45.Lanfear, R., Frandsen, P. B., Wright, A. M., Senfeld, T. & Calcott, B. PartitionFinder 2: new methods for selecting partitioned models of evolution for molecular and morphological phylogenetic analyses. Mol. Biol. Evol. 34, 772–773 (2017).CAS 
    PubMed 

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

    Google Scholar 
    47.Miller, M. A., Pfeiffer, W. & Schwartz, T. Creating the CIPRES Science Gateway for inference of large phylogenetic trees. In Proceedings of the Gateway Computing Environment Workshop (GCE) 1–8 (2010).48.Ronquist, F. & Huelsenbeck, J. P. Bayesian phylogenetic inference under mixed models. Bioinformatics 19, 1572–1574 (2003).CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    49.Tavaré, S. Some probabilistic and statistical problems in the analysis of DNA sequences. In Some Mathematical Questions in Biology: DNA Sequence Analysis (ed. Miura, R. M.) 57–86 (American Mathematical Society, 1986).
    Google Scholar 
    50.Rambaut, A., Drummond, A. J., Xie, D., Baele, G. & Suchard, M. A. Posterior summarization in Bayesian phylogenetics using Tracer 1.7. Syst. Biol. 67, 901–904 (2018).CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    51.Bouckaert, R. et al. BEAST 2.5: an advanced software platform for Bayesian evolutionary analysis. PLoS Comput. Biol. 15, e1006650 (2019).CAS 
    PubMed 
    PubMed Central 
    Article 

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

    Google Scholar 
    53.Skilling, J. Nested sampling for general Bayesian computation. Bayesian Anal. 1, 833–860 (2006).MathSciNet 
    MATH 
    Article 

    Google Scholar 
    54.Russel, P. M., Brewer, B. J., Klaere, S. & Bouckaert, R. Model selection and parameter inference in phylogenetics using nested sampling. Syst. Biol. 68, 219–233 (2018).Article 

    Google Scholar 
    55.Heled, J. & Drummond, A. J. Bayesian inference of population size history from multiple loci. BMC Evol. Biol. 8, 289 (2008).PubMed 
    PubMed Central 
    Article 
    CAS 

    Google Scholar 
    56.Hasegawa, M., Kishino, H. & Yano, T. Dating of the human-ape splitting by a molecular clock of mitochondrial DNA. J. Mol. Evol. 22, 160–174 (1985).CAS 
    PubMed 
    Article 

    Google Scholar 
    57.R Core Team. R: A language and environment for statistical computing. R Foundation for Statistical Computing, Vienna, Austria. (2013). http://www.r-project.org/.58.Tajima, F. The effect of change in population size on DNA polymorphism. Genetics 123, 597–601 (1989).CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    59.Fu, Y.-X. Statistical tests of neutrality of mutations against population growth, hitchhiking and background selection. Genetics 147, 915–9125 (1997).CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    60.van Oosterhout, C., Hutchinson, B., Wills, D. & Shipley, P. MICRO-CHECKER: software for identifying and correcting genotyping errors in microsatellite data. Mol. Ecol. Notes 4, 535–538 (2004).Article 
    CAS 

    Google Scholar 
    61.Rousset, F. Genepop’007: a complete reimplementation of the Genepop software for Windows and Linux. Mol. Ecol. Resour. 8, 103–106 (2008).PubMed 
    Article 

    Google Scholar 
    62.Peakall, R. O. D. & Smouse, P. E. GENALEX 6: genetic analysis in Excel. Population genetic software for teaching and research. Mol. Ecol. Notes 6, 288–295 (2006).Article 

    Google Scholar 
    63.Cornuet, J. M. & Luikart, G. Description and power analysis of two tests for detecting recent population bottlenecks from allele frequency data. Genetics 144, 2001–2014 (1997).Article 

    Google Scholar 
    64.Garza, J. C. & Williamson, E. G. Detection of reduction in population size using data from microsatellite loci. Mol. Ecol. 10, 305–318 (2001).CAS 
    PubMed 
    Article 

    Google Scholar 
    65.Harley, E. H. AGARst: a programme for calculating allele frequencies, Gst and Rst from microsatellite data, version 2. University of Cape Town, Cape Town, South Africa. (2001).66.Wright, S. The interpretation of population structure by F-statistics with special regard to systems of mating. Evolution 19, 395–420 (1965).Article 

    Google Scholar 
    67.Slatkin, M. A measure of population subdivision based on microsatellite allele frequencies. Genetics 139, 457–462 (1995).CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    68.Goudet, J. Fstat (ver. 2.9.4), a program to estimate and test population genetics parameters. (2003). https://www2.unil.ch/popgen/softwares/fstat.htm69.Rousset, F. Genetic differentiation and estimation of gene flow from F-statistics under isolation by distance. Genetics 145, 1219–1228 (1997).CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    70.Manni, F., Guérard, E. & Heyer, E. Geographic patterns of (genetic, morphologic, linguistic) variation: how barriers can be detected by “Monmonier’s algorithm”. Hum. Biol. 76, 173–190 (2004).71.Monmonier, M. Maximum-difference barriers: an alternative numerical regionalization method. Geogr. Anal. 3, 245–261 (1973).
    Google Scholar 
    72.Nei, M., Tajima, F. & Tateno, Y. Accuracy of estimated phylogenetic trees from molecular data. J. Mol. Evol. 19, 153–170 (1983).ADS 
    CAS 
    PubMed 
    Article 

    Google Scholar 
    73.Dieringer, D. & Schlötterer, C. Microsatellite analyser (MSA): a platform independent analysis tool for large microsatellite data sets. Mol. Ecol. Notes 3, 167–169 (2003).CAS 
    Article 

    Google Scholar 
    74.Kitada, S., Nakamichi, R. & Kishino, H. The empirical Bayes estimators of fine-scale population structure in high gene flow species. Mol. Ecol. Resour. 17, 1210–1222 (2017).CAS 
    PubMed 
    Article 

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

    Google Scholar 
    76.Earl, D. A. & Vonholdt, B. M. STRUCTURE HARVESTER: a website and program for visualizing STRUCTURE output and implementing the Evanno method. Conserv. Genet. Resour. 4, 359–361 (2012).Article 

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

    Google Scholar 
    78.Cornuet, J.-M. et al. DIYABC v2.0: a software to make approximate Bayesian computation inferences about population history using single nucleotide polymorphism, DNA sequence and microsatellite data. Bioinformatics 30, 1187–1189 (2014).CAS 
    PubMed 
    Article 

    Google Scholar 
    79.Penny, D. Relativity for molecular clocks. Nature 436, 183–184 (2005).ADS 
    CAS 
    PubMed 
    Article 
    PubMed Central 

    Google Scholar 
    80.Dakin, E. E. & Avise, J. C. Microsatellite null alleles in parentage analysis. Heredity 93, 504–509 (2004).CAS 
    PubMed 
    Article 

    Google Scholar 
    81.Oromi, N. et al. Genetic structure of lake and stream populations in a Pyrenean amphibian (Calotriton asper) reveals evolutionary significant units associated with paedomorphosis. J. Zool. Syst. Evol. Res. 57, 418–430 (2019).Article 

    Google Scholar 
    82.Janes, J. K. et al. The K = 2 conundrum. Mol. Ecol. 26, 3594–3602 (2017).PubMed 
    Article 

    Google Scholar 
    83.Chiari, Y. et al. Phylogeography of Sardinian cave salamanders (genus Hydromantes) is mainly determined by geomorphology. PLoS ONE 7, e32332 (2012).ADS 
    CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    84.Cimmaruta, R., Lucente, D. & Nascetti, G. Persistence, isolation and diversification of a naturally fragmented species in local refugia: the case of Hydromantes strinatii. PLoS ONE 10, e0131298 (2015).PubMed 
    PubMed Central 
    Article 
    CAS 

    Google Scholar 
    85.Kuchta, S. R., Haughey, M., Wynn, A. H., Jacobs, J. F. & Highton, R. Ancient river systems and phylogeographical structure in the spring salamander, Gyrinophilus porphyriticus. J. Biogeogr. 43, 639–652 (2016).Article 

    Google Scholar 
    86.Pan, T. et al. Long-term sky islands generate highly divergent lineages of a narrowly distributed stream salamander (Pachyhynobius shangchengensis) in mid-latitude mountains of East Asia. BMC Evol. Biol. 19, 1–15 (2019).PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    87.Suk, H. Y. et al. Genetic and phylogenetic structure of Hynobius quelpaertensis, an endangered endemic salamander species on the Korean Peninsula. Genes Genom. 42, 165–178 (2020).CAS 
    Article 

    Google Scholar 
    88.Cameron, A. C., Anderson, J. J. & Page, R. B. Assessment of intra and interregional genetic variation in the Eastern Red-backed Salamander, Plethodon cinereus, via analysis of novel microsatellite markers. PLoS ONE 12, e0186866 (2017).PubMed 
    PubMed Central 
    Article 
    CAS 

    Google Scholar 
    89.Yoshikawa, N. & Nagata, N. Eighteen SSR Markers for the Japanese clawed salamander, Onychodactylus japonicus, and cross-amplification in its congeners. Curr. Herpetol. 36, 153–158 (2017).90.Moritz, C. Applications of mitochondrial DNA analysis in conservation: a critical review. Mol. Ecol. 3, 401–411 (1994).CAS 
    Article 

    Google Scholar 
    91.Estoup, A., Jarne, P. & Cornuet, J. M. Homoplasy and mutation model at microsatellite loci and their consequences for population genetics analysis. Mol. Ecol. 11, 1591–1604 (2002).CAS 
    PubMed 
    Article 

    Google Scholar 
    92.Toews, D. P. L. & Brelsford, A. The biogeography of mitochondrial and nuclear discordance in animals. Mol. Ecol. 21, 3907–3930 (2012).CAS 
    PubMed 
    Article 

    Google Scholar 
    93.Karl, S. A., Toonen, R. J., Grant, W. S. & Bowen, B. W. Common misconceptions in molecular ecology: echoes of the modern synthesis. Mol. Ecol. 21, 4171–4189 (2012).CAS 
    PubMed 
    Article 

    Google Scholar 
    94.Cook, B. D., Bunn, S. E. & Hughes, J. M. Molecular genetic and stable isotope signatures reveal complementary patterns of population connectivity in the regionally vulnerable southern pygmy perch (Nannoperca australis). Biol. Conserv. 138, 60–72 (2007).Article 

    Google Scholar 
    95.Hewitt, G. M. Some genetic consequences of ice ages, and their role in divergence and speciation. Biol. J. Linn. Soc. 58, 247–276 (1996).Article 

    Google Scholar 
    96.Avise, J. C., Walker, D. & Johns, G. C. Speciation durations and Pleistocene effects on vertebrate phylogeography. Proc. R. Soc. Lond. Ser. B Biol. Sci. 265, 1707–1712 (1998).CAS 
    Article 

    Google Scholar 
    97.Alexandrino, J., Froufe, E., Arntzen, J. W. & Ferrand, N. Genetic subdivision, glacial refugia and postglacial recolonization in the golden-striped salamander, Chioglossa lusitanica (Amphibia: Urodela). Mol. Ecol. 9, 771–781 (2000).CAS 
    PubMed 
    Article 

    Google Scholar 
    98.Alexandrino, J., Arntzen, J. W. & Ferrand, N. Nested clade analysis and the genetic evidence for population expansion in the phylogeography of the golden-striped salamander, Chioglossa lusitanica (Amphibia: Urodela). Heredity 88, 66–74 (2002).CAS 
    PubMed 
    Article 

    Google Scholar 
    99.Rovito, S. M. Lineage divergence and speciation in the Web-toed Salamanders (Plethodontidae: Hydromantes) of the Sierra Nevada, California . Mol. Ecol. 19, 4554–4571 (2010).PubMed 
    Article 

    Google Scholar 
    100.Shafer, A. B. A., Cullingham, C. I., Côté, S. D. & Coltman, D. W. Of glaciers and refugia: a decade of study sheds new light on the phylogeography of northwestern North America. Mol. Ecol. 19, 4589–4621 (2010).PubMed 
    Article 

    Google Scholar 
    101.Zhang, R.-Z. Geological events and mammalian distribution in China. Acta Zool. Sin. 48, 141–153 (2002).
    Google Scholar 
    102.Matsui, M., Tominaga, A., Liu, W. Z. & Tanaka-Ueno, T. Reduced genetic variation in the Japanese giant salamander, Andrias japonicus (Amphibia: Caudata). Mol. Phylogenet. Evol. 49, 318–326 (2008).CAS 
    PubMed 
    Article 

    Google Scholar 
    103.Matsui, M. et al. Phylogenetic relationships of two Salamandrella species as revealed by mitochondrial DNA and allozyme variation (Amphibia: Caudata: Hynobiidae). Mol. Phylogenet. Evol. 48, 84–93 (2008).CAS 
    PubMed 
    Article 

    Google Scholar 
    104.Malyarchuk, B., Derenko, M. & Denisova, G. Phylogeny and genetic history of the Siberian salamander (Salamandrella keyserlingii, Dybowski, 1870) inferred from complete mitochondrial genomes. Mol. Phylogenet. Evol. 67, 348–357 (2013).CAS 
    PubMed 
    Article 

    Google Scholar 
    105.Honda, A. et al. Late Pleistocene climate change and population dynamics of Japanese Myodes voles inferred from mitochondrial cytochrome b sequences. J. Mammal. 100, 1156–1168 (2019).PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    106.Moritz, C. Defining “evolutionarily significant units” for conservation. Trends Ecol. Evol. 9, 373–375 (1994).CAS 
    PubMed 
    Article 

    Google Scholar  More

  • in

    Reproductive success of the parasitic mite (Varroa destructor) is lower in honeybee colonies that target infested cells with recapping

    1.Cremer, S., Armitage, S. A. O. & Schmid-Hempel, P. Social immunity. Curr. Biol. 17, R693–R702 (2007).CAS 
    Article 

    Google Scholar 
    2.Page, P. et al. Social apoptosis in honey bee superorganisms. Sci. Rep. 6, 1–6 (2016).Article 

    Google Scholar 
    3.Winston, M. L. The Biology of the Honey Bee. (Harvard University Press, 1991).4.Beye, M. et al. Exceptionally high levels of recombination across the honey bee genome. Genome Res. 16, 1339–1344 (2006).CAS 
    Article 

    Google Scholar 
    5.Kent, C. F., Minaei, S., Harpur, B. A. & Zayed, A. Recombination is associated with the evolution of genome structure and worker behavior in honey bees. Proc. Natl. Acad. Sci. 109, 18012–18017 (2012).ADS 
    CAS 
    Article 

    Google Scholar 
    6.Rosenkranz, P., Aumeier, P. & Ziegelmann, B. Biology and control of Varroa destructor. J. Invertebr. Pathol. 103, S96–S119 (2010).Article 

    Google Scholar 
    7.Traynor, K. S. et al. Varroa destructor: a complex Parasite, Crippling Honey Bees Worldwide. Trends Parasitol. 36, 592–606 (2020).CAS 
    Article 

    Google Scholar 
    8.Neumann, P. & Carreck, N. L. Honey bee colony losses. J. Apic. Res. 49, 1–6 (2010).Article 

    Google Scholar 
    9.Thompson, C. E., Biesmeijer, J. C., Allnutt, T. R., Pietravalle, S. & Budge, G. E. Parasite Pressures on Feral Honey Bees (Apis mellifera sp.). PLoS ONE 9, (2014).10.Camazine, S. Differential Reproduction of the Mite, Varroa jacobsoni (Mesostigmata: Varroidae), on Africanized and European Honey Bees (Hymenoptera: Apidae). Ann. Entomol. Soc. Am. 79, 801–803 (1986).Article 

    Google Scholar 
    11.Corrêa-Marques, M.-H. & De Jong, D. Uncapping of worker bee brood, a component of the hygienic behavior of Africanized honey bees against the mite Varroa jacobsoni Oudemans. Apidologie 29, 283–289 (1998).Article 

    Google Scholar 
    12.Allsopp, M. H. Analysis of Varroa destructor infestation of southern African honeybee populations. (University of Pretoria, 2007).13.Locke, B., Le Conte, Y., Crauser, D. & Fries, I. Host adaptations reduce the reproductive success of Varroa destructor in two distinct European honey bee populations. Ecol. Evol. 2, 1144–1150 (2012).Article 

    Google Scholar 
    14.Oddie, M. A. Y., Dahle, B. & Neumann, P. Norwegian honey bees surviving Varroa destructor mite infestations by means of natural selection. PeerJ 5, e3956 (2017).Article 

    Google Scholar 
    15.Locke, B. Natural Varroa mite-surviving Apis mellifera honeybee populations. Apidologie 47, 467–482 (2016).Article 

    Google Scholar 
    16.Villegas, A. J. & Villa, J. D. Uncapping of pupal cells by European bees in the United States as responses to Varroa destructor and Galleria mellonella. J. Apic. Res. 45, 203–206 (2006).Article 

    Google Scholar 
    17.Spivak, M. & Gilliam, M. Facultative expression of hygienic behaviour of honey bees in relation to disease resistance. J. Apic. Res. 32, 147–157 (1993).Article 

    Google Scholar 
    18.Le Conte, Y., Arnold, G. & Desenfant, P. Influence of brood temperature and hygrometry variations on the development of the honey bee Ectoparasite Varroa jacobsoni (Mesostigmata: Varroidae). Environ. Entomol. 19, 1780–1785 (1990).Article 

    Google Scholar 
    19.Kraus, B. & Velthuis, H. H. W. High humidity in the honey bee (Apis mellifera L.) Brood nest limits reproduction of the parasitic mite varroa jacobsoni oud. Naturwissenschaften 84, 217–218 (1997).ADS 
    CAS 
    Article 

    Google Scholar 
    20.Harris, J. W., Danka, R. G. & Villa, J. D. Changes in infestation, cell cap condition, and reproductive status of varroa destructor (Mesostigmata: Varroidae) in brood exposed to honey bees with varroa sensitive hygiene. Ann. Entomol. Soc. Am. 105, 512–518 (2012).Article 

    Google Scholar 
    21.Oddie, M. A. Y. et al. Rapid parallel evolution overcomes global honey bee parasite. Sci. Rep. 8, 1–9 (2018).ADS 
    CAS 
    Article 

    Google Scholar 
    22.Martin, S. J. et al. Varroa destructor reproduction and cell re-capping in mite-resistant Apis mellifera populations. Apidologie 51, 369–381 (2020).CAS 
    Article 

    Google Scholar 
    23.Martin, S. J. Ontogenesis of the mite Varroa jacobsoni Oud. in worker brood of the honeybee Apis mellifera L under natural conditions. Exp. Appl. Acarol. 18, 87–100 (1994).Article 

    Google Scholar 
    24.Donzé, G., Herrmann, M., Bachofen, B. & Guerin, P. R. M. Effect of mating frequency and brood cell infestation rate on the reproductive success of the honeybee parasite Varroa jacobsoni. Ecol. Entomol. 21, 17–26 (1996).Article 

    Google Scholar 
    25.Harris, J. W., Danka, R. G. & Villa, J. D. Honey bees (Hymenoptera: Apidae) with the Trait of varroa sensitive hygiene remove brood with all reproductive stages of varroa mites (Mesostigmata: Varroidae). Ann. Entomol. Soc. Am. 103, 146–152 (2010).Article 

    Google Scholar 
    26.Harris, J. W. & Harbo, J. R. Low sperm counts and reduced fecundity of mites in colonies of honey bees (Hymenoptera: Apidae) resistant to varroa jacobsoni (mesostigmata: Varroidae). J. Econ. Entomol. 92, 83–90 (1999).Article 

    Google Scholar 
    27.Peck, D. T. & Seeley, T. D. Mite bombs or robber lures? The roles of drifting and robbing in Varroa destructor transmission from collapsing honey bee colonies to their neighbors. PLoS ONE 14, (2019).28.Arathi, H. S., Ho, G. & Spivak, M. Inefficient task partitioning among nonhygienic honeybees, Apis mellifera L, and implications for disease transmission. Anim. Behav. 72, 431–438 (2006).Article 

    Google Scholar 
    29.Kirrane, M. J. et al. Asynchronous Development of Honey Bee Host and Varroa destructor (Mesostigmata: Varroidae) Influences Reproductive Potential of Mites. J. Econ. Entomol. 104, 1146–1152 (2011).Article 

    Google Scholar 
    30.Locke, B. & Fries, I. Characteristics of honey bee colonies (Apis mellifera) in Sweden surviving Varroa destructor infestation. Apidologie 42, 533–542 (2011).Article 

    Google Scholar 
    31.Chantawannakul, P., Ramsey, S., vanEngelsdorp, D., Khongphinitbunjong, K. & Phokasem, P. Tropilaelaps mite: an emerging threat to European honey bee. Curr. Opin. Insect Sci. 26, 69–75 (2018).Article 

    Google Scholar 
    32.Bates, D., Maechler, M., Bolker, B. & Walker, S. Fitting linear mixed-effects models using lme4. J. Stat. Softw. 67, 1–48 (2015).Article 

    Google Scholar 
    33.R Core team. R: A language and environment for statistical computing. (R Foundation for Statistical Computing, 2019), https://www.R-project.org. More

  • in

    Predicting range shifts of three endangered endemic plants of the Khorassan-Kopet Dagh floristic province under global change

    1.Ferrarini, A., Dai, J., Bai, Y. & Alatalo, J. M. Redefining the climate niche of plant species: A novel approach for realistic predictions of species distribution under climate change. Sci. Total Environ. 671, 1086–1093 (2019).ADS 
    CAS 
    Article 

    Google Scholar 
    2.Ferrarini, A., Alsafran, M. H. S. A., Dai, J. & Alatalo, J. M. Improving niche projections of plant species under climate change: Silene acaulis on the British Isles as a case study. Clim. Dyn. 52, 1413–1423 (2019).Article 

    Google Scholar 
    3.Walther, G.-R. et al. Ecological responses to recent climate change. Nature 416, 389–395 (2002).ADS 
    CAS 
    Article 

    Google Scholar 
    4.Thuiller, W., Lavorel, S., Araujo, M. B., Sykes, M. T. & Prentice, I. C. Climate change threats to plant diversity in Europe. Proc. Natl. Acad. Sci. 102, 8245–8250 (2005).ADS 
    CAS 
    PubMed 
    Article 

    Google Scholar 
    5.Mousavi Kouhi, S. M. & Erfanian, M. B. Predicting the present and future distribution of medusahead and barbed goatgrass in Iran. Ecopersia 8, 41–46 (2020).
    Google Scholar 
    6.Alavi, S. J., Ahmadi, K., Hosseini, S. M., Tabari, M. & Nouri, Z. The response of English yew (Taxus baccata L.) to climate change in the Caspian Hyrcanian Mixed Forest ecoregion. Reg. Environ. Change 19, 1495–1506 (2019).Article 

    Google Scholar 
    7.Huntley, B., Berry, P. M., Cramer, W. & McDonald, A. P. Special paper: Modelling present and potential future ranges of some European higher plants using climate response surfaces. J. Biogeogr. 22, 967 (1995).Article 

    Google Scholar 
    8.Pearson, R. G. & Dawson, T. P. Predicting the impacts of climate change on the distribution of species: Are bioclimate envelope models useful?: Evaluating bioclimate envelope models. Glob. Ecol. Biogeogr. 12, 361–371 (2003).Article 

    Google Scholar 
    9.Hällfors, M. H. et al. Assessing the need and potential of assisted migration using species distribution models. Biol. Conserv. 196, 60–68 (2016).Article 

    Google Scholar 
    10.Kamakhina, G. L. Kopetdagh-Khorassan Flora: Regional Features of Central Kopetdagh. In Biogeography and Ecology of Turkmenistan (eds. Fet, V. & Atamuradov, K. I.) Vol. 72 129–148 (Springer Netherlands, 1994).11.Memariani, F., Zarrinpour, V. & Akhani, H. A review of plant diversity, vegetation, and phytogeography of the Khorassan-Kopet Dagh floristic province in the Irano-Turanian region (northeastern Iran–southern Turkmenistan). Phytotaxa 249, 8 (2016).Article 

    Google Scholar 
    12.Fet, V. Biogeographic Position of the Khorassan-Kopetdagh. In Biogeography and Ecology of Turkmenistan (eds. Fet, V. & Atamuradov, K. I.) Vol. 72 197–204 (Springer Netherlands, 1994).13.Memariani, F. Khorassan-Kopet Dagh mountains. In Plant Biogeography and Vegetation of High Mountains of Central and South-West Asia (ed. Noroozi, J.) (Springer, 2020). https://datadryad.org/stash/dataset/doi:10.5061/dryad.4sb638314.Behroozian, M., Ejtehadi, H., Peterson, A. T., Memariani, F. & Mesdaghi, M. Climate change influences on the potential distribution of Dianthus polylepis Bien. ex Boiss. (Caryophyllaceae), an endemic species in the Irano-Turanian region. PLoS ONE 15, e0237527 (2020).CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    15.Erfanian, M. B. et al. Data from: Plant community responses to environmentally-friendly piste management in northeast Iran. Dryad Dataset. https://datadryad.org/stash/dataset/doi:10.5061/dryad.4sb6383 (2019).16.Jamzad, Z. Flora of Iran vol. 76 Lamiaceae. (Research Institute of Forests & Rangelands, 2012).17.Sagharyan, M., Ganjeali, A. & Cheniany, M. Investigating the effect of antioxidant compounds and various concentrations of BAP and NAA on the improvement of in vitro stem and root formation of Nepeta binaloudensis Jamzad. NBR 6, 198–205 (2019).Article 

    Google Scholar 
    18.Nadjafi, F., Koocheki, A., Moghaddam, P. R. & Rastgoo, M. Ethnopharmacology of Nepeta binaludensis Jamzad a highly threatened medicinal plant of Iran. J. Med. Plants 8, 29–35 (2009).
    Google Scholar 
    19.Nadjafi, F., Koocheki, A., Honermeier, B. & Asili, J. Autecology, ethnomedicinal and phytochemical studies of Nepeta binaludensis Jamzad a highly endangered medicinal plant of Iran. J. Essent. Oil Bear. Plants 12, 97–110 (2009).CAS 
    Article 

    Google Scholar 
    20.Memariani, F., Akhani, H. & Joharchi, M. R. Endemic plants of Khorassan-Kopet Dagh floristic province in Irano-Turanian region: Diversity, distribution patterns and conservation status. Phytotaxa 249, 31 (2016).Article 

    Google Scholar 
    21.Salmaki, Y. & Joharchi, M. R. Phlomoides binaludensis (Phlomideae, Lamioideae, Lamiaceae), a new species from northeastern Iran. Phytotaxa 172, 265 (2014).Article 

    Google Scholar 
    22.Pahlevani, A. H., Liede-Schumann, S. & Akhani, H. Seed and capsule morphology of Iranian perennial species of Euphorbia (Euphorbiaceae) and its phylogenetic application: Perennial Species of Euphorbia in Iran. Bot. J. Linn. Soc. 177, 335–377 (2015).Article 

    Google Scholar 
    23.Olson, D. M. et al. Terrestrial ecoregions of the world: A new map of life on earth. Bioscience 51, 933 (2001).Article 

    Google Scholar 
    24.Djamali, M. et al. Application of the global bioclimatic classification to Iran: Implications for understanding the modern vegetation and biogeography. Ecol. Mediterr. 37, 91–114 (2011).Article 

    Google Scholar 
    25.Farashi, A., Shariati, M. & Hosseini, M. Identifying biodiversity hotspots for threatened mammal species in Iran. Mamm. Biol. 87, 71–88 (2017).Article 

    Google Scholar 
    26.Hosseinzadeh, M. S., Fois, M., Zangi, B. & Kazemi, S. M. Predicting past, current and future habitat suitability and geographic distribution of the Iranian endemic species Microgecko latifi (Sauria: Gekkonidae). J. Arid Environ. 183, 104283 (2020).ADS 
    Article 

    Google Scholar 
    27.Noroozi, J. et al. Endemic diversity and distribution of the Iranian vascular flora across phytogeographical regions, biodiversity hotspots and areas of endemism. Sci. Rep. 9, 12991 (2019).ADS 
    PubMed 
    PubMed Central 
    Article 
    CAS 

    Google Scholar 
    28.Erfanian, M. B., Ejtehadi, H., Vaezi, J. & Moazzeni, H. Plant community responses to multiple disturbances in an arid region of northeast Iran. Land Degrad. Dev. 30, 1554–1563 (2019).Article 

    Google Scholar 
    29.Erfanian, M. B. et al. Plant community responses to environmentally friendly piste management in northeast Iran. Ecol. Evol. 9, 8193–8200 (2019).PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    30.Memariani, F. et al. Plant diversity of the Khorassan-Kopet Dagh Floristic Province (Irano-Turanian Region). (Magnolia Press, 2016)31.Memariani, F., Joharchi, M. R., Ejtehadi, H. & Emadzade, K. A contribution to the flora and vegetation of Binalood mountain range, NE Iran: Floristic and chorological studies in Fereizi region. Ferdowsi Univ. Int. J. Biol. Sci. J. Cell Mol. Res. 1, 1–17 (2009).
    Google Scholar 
    32.Memariani, F. & Joharchi, M. R. Iris ferdowsii (Iridaceae), a new species of section Regelia from northeast of Iran. Phytotaxa 291, 192 (2017).Article 

    Google Scholar 
    33.Thuiller, W., Georges, D., Engler, R. & Breiner, F. biomod2: Ensemble Platform for Species Distribution Modeling. R Package. https://cran.r-project.org/package=biomod2 (2019).34.R Core Team. R: A Language and Environment for Statistical Computing. (R Foundation for Statistical Computing, 2020).35.Aiello-Lammens, M. E., Boria, R. A., Radosavljevic, A., Vilela, B. & Anderson, R. P. spThin: An R package for spatial thinning of species occurrence records for use in ecological niche models. Ecography 38, 541–545 (2015).Article 

    Google Scholar 
    36.Fick, S. E. & Hijmans, R. J. WorldClim 2: New 1-km spatial resolution climate surfaces for global land areas. Int. J. Climatol. 37, 4302–4315 (2017).Article 

    Google Scholar 
    37.Ahmadi, M., Dadashi Roudbari, A. A., Akbari Azirani, T. & Karami, J. The performance of the HadGEM2-ES model in the evaluation of seasonal temperature anomaly of Iran under RCP scenarios. J. Earth Space Phys. 45, 625–644 (2019).
    Google Scholar 
    38.Dray, S. & Dufour, A.-B. The ade4 Package: Implementing the duality diagram for ecologists. J. Stat. Softw. 22, 1–20 (2007).Article 

    Google Scholar 
    39.Guisan, A., Thuiller, W. & Zimmermann, N. E. Habitat Suitability and Distribution Models: With Applications in R. (Cambridge University Press, 2017).40.Naimi, B., Hamm, N. A. S., Groen, T. A., Skidmore, A. K. & Toxopeus, A. G. Where is positional uncertainty a problem for species distribution modelling. Ecography 37, 191–203 (2014).Article 

    Google Scholar 
    41.Menard, S. W. Applied Logistic Regression Analysis (Sage Publications, Thousand Oaks, 2002).Book 

    Google Scholar 
    42.Landis, J. R. & Koch, G. G. The measurement of observer agreement for categorical data. Biometrics 33, 159 (1977).CAS 
    MATH 
    Article 

    Google Scholar 
    43.Araujo, M. & New, M. Ensemble forecasting of species distributions. Trends Ecol. Evol. 22, 42–47 (2007).PubMed 
    Article 

    Google Scholar 
    44.Breiner, F. T., Guisan, A., Bergamini, A. & Nobis, M. P. Overcoming limitations of modelling rare species by using ensembles of small models. Methods Ecol. Evol. 6, 1210–1218 (2015).Article 

    Google Scholar 
    45.Kaky, E., Nolan, V., Alatawi, A. & Gilbert, F. A comparison between Ensemble and MaxEnt species distribution modelling approaches for conservation: A case study with Egyptian medicinal plants. Ecol. Inform. 60, 101150 (2020).Article 

    Google Scholar 
    46.Hao, T., Elith, J., Lahoz-Monfort, J. J. & Guillera-Arroita, G. Testing whether ensemble modelling is advantageous for maximising predictive performance of species distribution models. Ecography 43, 549–558 (2020).Article 

    Google Scholar 
    47.Abdelaal, M., Fois, M., Fenu, G. & Bacchetta, G. Using MaxEnt modeling to predict the potential distribution of the endemic plant Rosa arabica Crép, Egypt. Ecol. Inform. 50, 68–75 (2019).Article 

    Google Scholar 
    48.Thuiller, W. et al. Endemic species and ecosystem sensitivity to climate change in Namibia. Glob. Change Biol. 12, 759–776 (2006).ADS 
    Article 

    Google Scholar 
    49.Chitale, V. S., Behera, M. D. & Roy, P. S. Future of endemic flora of biodiversity hotspots in India. PLoS ONE 9, e115264 (2014).ADS 
    PubMed 
    PubMed Central 
    Article 
    CAS 

    Google Scholar 
    50.Fois, M., Bacchetta, G., Cogoni, D. & Fenu, G. Current and future effectiveness of the Natura 2000 network for protecting plant species in Sardinia: A nice and complex strategy in its raw state?. J. Environ. Plan. Manag. 61, 332–347 (2018).Article 

    Google Scholar 
    51.Mamet, S. D., Brown, C. D., Trant, A. J. & Laroque, C. P. Shifting global Larix distributions: Northern expansion and southern retraction as species respond to changing climate. J. Biogeogr. 46, 30–44 (2019).Article 

    Google Scholar 
    52.Thuiller, W., Lavorel, S. & Araújo, M. B. Niche properties and geographical extent as predictors of species sensitivity to climate change: Predicting species sensitivity to climate change. Glob. Ecol. Biogeogr. 14, 347–357 (2005).Article 

    Google Scholar 
    53.Hosseini, S. S., Ejtehadi, H. & Memariani, F. The first report Nepeta binaloudensis Jamzad in Hezar masjed mountains of Khorasan Razavi province. In Proceedings of the 9th National Congress and 7th International Congrees of Bilogy of Iran (2016).54.Dullinger, S. et al. Extinction debt of high-mountain plants under twenty-first-century climate change. Nat. Clim. Change 2, 619–622 (2012).ADS 
    Article 

    Google Scholar 
    55.Wiens, J. J. Climate-related local extinctions are already widespread among plant and animal species. PLoS Biol. 14, e2001104 (2016).PubMed 
    PubMed Central 
    Article 
    CAS 

    Google Scholar 
    56.Casazza, G. et al. Climate change hastens the urgency of conservation for range-restricted plant species in the central-northern Mediterranean region. Biol. Conserv. 179, 129–138 (2014).Article 

    Google Scholar 
    57.Zhang, M.-G. et al. Major declines of woody plant species ranges under climate change in Yunnan, China. Divers. Distrib. 20, 405–415 (2014).CAS 
    Article 

    Google Scholar 
    58.Sanjerehei, M. M. & Rundel, P. W. The impact of climate change on habitat suitability for Artemisia sieberi and Artemisia aucheri (Asteraceae)—A modeling approach. Pol. J. Ecol. 65, 97–109 (2017).Article 

    Google Scholar 
    59.Abolmaali, S.M.-R., Tarkesh, M. & Bashari, H. MaxEnt modeling for predicting suitable habitats and identifying the effects of climate change on a threatened species, Daphne mucronata, in central Iran. Ecol. Inform. 43, 116–123 (2018).Article 

    Google Scholar 
    60.Di Musciano, M. et al. Dispersal ability of threatened species affects future distributions. Plant Ecol. 221, 265–281 (2020).Article 

    Google Scholar 
    61.Fois, M., Cuena-Lombraña, A., Fenu, G., Cogoni, D. & Bacchetta, G. The reliability of conservation status assessments at regional level: Past, present and future perspectives on Gentiana lutea L. ssp. lutea in Sardinia. J. Nat. Conserv. 33, 1–9 (2016).Article 

    Google Scholar  More

  • in

    Evidence for magnesium–phosphorus synergism and co-limitation of grain yield in wheat agriculture

    1.Elser, J. J. et al. Global analysis of nitrogen and phosphorus limitation of primary producers in freshwater, marine and terrestrial ecosystems. Ecol. Lett. 10, 1135–1142 (2007).Article 

    Google Scholar 
    2.Mengel, K. & Kirkby, E. A. Principles of Plant Nutrition (Kluwer Academic Publishers, 2001).Book 

    Google Scholar 
    3.Reich, M., Aghajanzadeh, T. & De Kok, L. J. Physiological basis of plant nutrient use efficiency—Concepts, opportunities and challenges for its improvement. In Nutrient Use Efficiency in Plants: Concepts and Approaches (eds Hawkesford, M. J. et al.) (Springer, 2014).
    Google Scholar 
    4.Agren, G. I. Ideal nutrient productivities and nutrient proportions in plant growth. Plant Cell Environ. 11, 613–620 (1988).Article 

    Google Scholar 
    5.Weih, M., Hamner, K. & Pourazari, F. Analyzing plant nutrient uptake and utilization efficiencies: Comparison between crops and approaches. Plant Soil 430, 7–21 (2018).CAS 
    Article 

    Google Scholar 
    6.Sterner, R. W. & Elser, J. J. Ecological stoichiometry: The biology of elements from molecules to the biosphere (2002).7.Reich, P. B. et al. Evidence of a general 2/3-power law of scaling leaf nitrogen to phosphorus among major plant groups and biomes. Proc. R. Soc. B Biol. Sci. 277, 877–883 (2010).CAS 
    Article 

    Google Scholar 
    8.Hutchinson, G. E. Population studies—Animal ecology and demography—Concluding remarks. Cold Spring Harbor. Symp. Quant. Biol. 22, 415–427 (1957).Article 

    Google Scholar 
    9.Agren, G. I. & Weih, M. Multi-dimensional plant element stoichiometry-looking beyond carbon, nitrogen, and phosphorus. Front. Plant Sci. 11, 23 (2020).Article 

    Google Scholar 
    10.Niklas, K. J. Plant allometry, leaf nitrogen and phosphorus stoichiometry, and interspecific trends in annual growth rates. Ann. Bot. 97, 155–163 (2006).CAS 
    Article 

    Google Scholar 
    11.Hou, E. et al. Global meta-analysis shows pervasive phosphorus limitation of aboveground plant production in natural terrestrial ecosystems. Nat. Commun. 11, 637 (2020).ADS 
    CAS 
    Article 

    Google Scholar 
    12.Ryan, P. R. et al. Early vigour improves phosphate uptake in wheat. J. Exp. Bot. 66, 7089–7100 (2015).CAS 
    Article 

    Google Scholar 
    13.Wiel, CCMvd., Linden, CGvd & Scholten, O. E. Improving phosphorus use efficiency in agriculture: Opportunities for breeding. Euphytica 207, 1–22 (2016).Article 

    Google Scholar 
    14.Bilal, H. M., Aziz, T., Maqsood, M. A., Farooq, M. & Yan, G. Categorization of wheat genotypes for phosphorus efficiency. PLoS ONE 13, e0205471 (2018).Article 

    Google Scholar 
    15.Wang, Z. et al. Magnesium fertilization improves crop yield in most production systems: A meta-analysis. Front. Plant Sci. 10, 1727 (2020).Article 

    Google Scholar 
    16.Hauer-Jakli, M. & Traenkner, M. Critical leaf magnesium thresholds and the impact of magnesium on plant growth and photo-oxidative defense: a systematic review and meta-analysis from 70 years of research. Front. Plant Sci. 10, 766 (2019).Article 

    Google Scholar 
    17.Chawade, A. et al. A transnational and holistic breeding approach is needed for sustainable wheat production in the Baltic Sea region. Physiol. Plant. 164, 442–451 (2018).CAS 
    Article 

    Google Scholar 
    18.Weih, M., Pourazari, F. & Vico, G. Nutrient stoichiometry in winter wheat: Element concentration pattern reflects developmental stage and weather. Sci. Rep. 6, 35958–35958 (2016).ADS 
    CAS 
    Article 

    Google Scholar 
    19.Hamner, K., Weih, M., Eriksson, J. & Kirchmann, H. Influence of nitrogen supply on macro- and micronutrient accumulation during growth of winter wheat. Field Crop Res. 213, 118–129 (2017).Article 

    Google Scholar 
    20.Jia, X., Liu, P. & Lynch, J. P. Greater lateral root branching density in maize improves phosphorus acquisition from low phosphorus soil. J. Exp. Bot. 69, 4961–4970 (2018).CAS 
    Article 

    Google Scholar 
    21.Kumar, A. et al. Root trait plasticity and plant nutrient acquisition in phosphorus limited soil. J. Plant Nutr. Soil Sci. 182, 945–952 (2019).CAS 
    Article 

    Google Scholar 
    22.Lynch, J. P. Root phenes for enhanced soil exploration and phosphorus acquisition: Tools for future crops. Plant Physiol. 156, 1041–1049 (2011).CAS 
    Article 

    Google Scholar 
    23.Lynch, J. P. Steep, cheap and deep: An ideotype to optimize water and N acquisition by maize root systems. Ann. Bot. 112, 347–357 (2013).CAS 
    Article 

    Google Scholar 
    24.Lambers, H., Shane, M., Cramer, M., Pearse, S. & Veneklaas, E. Root structure and functioning for efficient acquisition of phosphorus: Matching morphological and physiological traits. Ann. Bot. 98, 693–713 (2006).Article 

    Google Scholar 
    25.Trachsel, S., Kaeppler, S. M., Brown, K. M. & Lynch, J. P. Maize root growth angles become steeper under low N conditions. Field Crop Res 140, 18–31 (2013).Article 

    Google Scholar 
    26.Jobbagy, E. G. & Jackson, R. B. The distribution of soil nutrients with depth: Global patterns and the imprint of plants. Biogeochemistry 53, 51–77 (2001).CAS 
    Article 

    Google Scholar 
    27.Sun, B. R., Gao, Y. Z. & Lynch, J. P. Large crown root number improves topsoil foraging and phosphorus acquisition. Plant Physiol. 177, 90–104 (2018).CAS 
    Article 

    Google Scholar 
    28.Weih, M., Asplund, L. & Bergkvist, G. Assessment of nutrient use in annual and perennial crops: A functional concept for analyzing nitrogen use efficiency. Plant Soil 339, 513–520 (2011).CAS 
    Article 

    Google Scholar 
    29.Malhi, S. S., Johnston, A. M., Schoenau, J. J., Wang, Z. H. & Vera, C. L. Seasonal biomass accumulation and nutrient uptake of wheat, barley and oat on a Black Chernozern soil in Saskatchewan. Can. J. Plant Sci. 86, 1005–1014 (2006).Article 

    Google Scholar 
    30.Maeoka, R. E. et al. Changes in the phenotype of winter wheat varieties released between 1920 and 2016 in response to in-furrow fertilizer: Biomass allocation, yield, and grain protein concentration. Front. Plant Sci. 10, 1786 (2020).Article 

    Google Scholar 
    31.Pourazari, F., Vico, G., Ehsanzadeh, P. & Weih, M. Contrasting growth pattern and nitrogen economy in ancient and modern wheat varieties. Can. J. Plant Sci. 95, 851–860 (2015).Article 

    Google Scholar 
    32.Rietra, R. P. J. J., Heinen, M., Dimkpa, C. O. & Bindraban, P. S. Effects of nutrient antagonism and synergism on yield and fertilizer use efficiency. Commun. Soil Sci. Plant Anal. 48, 1895–1920 (2017).CAS 
    Article 

    Google Scholar 
    33.Pedro, A., Savin, R. & Slafer, G. A. Crop productivity as related to single-plant traits at key phenological stages in durum wheat. Field Crop Res. 138, 42–51 (2012).Article 

    Google Scholar 
    34.Cakmak, I. & Yazici, A. M. Magnesium: A forgotten element in crop production. Better Crops Plant Food 94, 23–25 (2010).
    Google Scholar 
    35.Lancashire, P. D. et al. A uniform decimal code for growth-stages of crops and weeds. Ann. Appl. Biol. 119, 561–601 (1991).Article 

    Google Scholar 
    36.Trachsel, S., Kaeppler, S. M., Brown, K. M. & Lynch, J. P. Shovelomics: High throughput phenotyping of maize (Zea mays L.) root architecture in the field. Plant Soil 341, 75–87 (2011).CAS 
    Article 

    Google Scholar 
    37.Colombi, T. & Walter, A. Root responses of triticale and soybean to soil compaction in the field are reproducible under controlled conditions. Funct. Plant Biol. 43, 114–128 (2016).Article 

    Google Scholar  More

  • in

    Sight of parasitoid wasps accelerates sexual behavior and upregulates a micropeptide gene in Drosophila

    We asked whether the mating of male and female fruit flies would be affected by the presence of parasitoid wasps. We placed a pair of D. melanogaster flies in a small Petri dish, either with or without parasitoid wasps (Fig. 1a). In an initial experiment we used the wasp Leptopilina boulardi, which specializes on D. melanogaster and on closely related fly species14.Fig. 1: Exposure of Drosophila to wasps accelerates sexual behavior.a Courtship arena containing a male and virgin female fly with (left) and without (right) two wasps, one male and one female. b Copulation latency of D. melanogaster. p  More

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    China’s wildlife protection: add annual reviews and oversight

    Now that China has finally updated its List of Wildlife under Special State Protection, a more nimble and responsive approach is needed to aid conservation. The list should be reviewed every year, as well as subjected to the planned five-yearly updates. Species can quickly become endangered in times of rapid development.The latest additions are the first in more than 30 years (see go.nature.com/2q7sfga). During that time, China has changed profoundly, but the list of protected species has not kept pace. This lag has been disastrous for some animals that were not given the protection they needed.At least 33 species became extinct in China and many more are critically endangered (Y. Xie & W. Sung Integr. Zool. 2, 26–35; 2007; Z. Jiang et al. Biodivers. Sci. 24, 500–551; 2016).An independent government committee should be created to oversee amendments. When making decisions, it could refer to appendices of the Convention on International Trade in Endangered Species of Wild Fauna and Flora (CITES) and the ‘red lists’ of threatened species curated by the Chinese Academy of Sciences and the International Union for Conservation of Nature (IUCN). These steps would build on the more forceful approach to managing wildlife that China has taken since the start of the COVID-19 pandemic. More

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    Monsoon forced evolution of savanna and the spread of agro-pastoralism in peninsular India

    1.Whyte, R. O. Grassland and Fodder Resources of India Revised. (Indian Council of Agricultural Research, 1964).
    Google Scholar 
    2.Misra, R. The vegetation of the Indian Savannas. In Tropical Savannas (ed. Bourliere, F.) 151–166 (Elsevier, 1983).3.Behrensmeyer, A. K. et al. The structure and rate of late Miocene expansion of C4 plants: evidence from lateral variation in stable isotopes in paleosols of the Siwalik Group, northern Pakistan. GSA Bull. 119, 1486–1505 (2007).CAS 
    Article 

    Google Scholar 
    4.Champion, H. G. & Seth, S. K. A Revised Survey of the Forest Types of India (Government of India Press, 1968).
    Google Scholar 
    5.Mani, M. S. The Flora. In Ecology and Biogeography in India (ed. Mani, M. S.) 159–177 (Dr. W. Junk b.v. Publishers, 1974).6.Ratnam, J., Tomlinson, K. W., Rasquinha, D. N. & Sankaran, M. Savannahs of Asia: antiquity, biogeography, and an uncertain future. Philos. Trans. R. Soc. B 371, 20150305 (2016).Article 
    CAS 

    Google Scholar 
    7.Blasco, F. The transition from open forest to Savanna in continental Southeast Asia. In Tropical Savannas (ed. Bourliere, F.) 167–182 (Elsevier, 1983).8.Puri, G. S., Meher Homji, V. M., Gupta, R. K. & Puri, S. Forest Ecology. Phytogeography and Conservation Vol. 1 (Oxford & IBH Publishing, 1983).
    Google Scholar 
    9.Fuller, D. Q. & Korisettar, R. The vegetational context of early agriculture in South India. Man Environ. 29, 7–27 (2004).
    Google Scholar 
    10.Fuller, D. Q. Finding plant domestication in the Indian subcontinent. Curr. Anthropol. 52, S347–S362 (2011).Article 

    Google Scholar 
    11.Lehmann, C. E. R., Archibald, S. A., Hoffmann, W. A. & Bond, W. J. Deciphering the distribution of the savanna biome. New Phytol. 191, 197–209 (2011).PubMed 
    Article 
    PubMed Central 

    Google Scholar 
    12.Staver, A. C., Archibald, S. & Levin, S. A. Tree-cover in sub-Saharan Africa: rainfall and fire constrain forest and savanna as alternative stable states. Ecology 92, 1063–1072 (2011).PubMed 
    Article 
    PubMed Central 

    Google Scholar 
    13.Bond, W. J. What limits trees in C4 grasslands and savannas?. Annu. Rev. Ecol. Evol. Syst. 39, 641–659 (2008).Article 

    Google Scholar 
    14.Hirota, M., Holmgren, M., Van Nes, E. & Scheffer, M. Global resilience of tropical forest and savanna to critical transitions. Science 334, 232–235 (2011).ADS 
    CAS 
    PubMed 
    Article 
    PubMed Central 

    Google Scholar 
    15.Staver, A. C., Archibald, S. & Levin, S. A. The global extent and determinants of savanna and forest as alternative biome states. Science 334, 230–232 (2011).ADS 
    CAS 
    PubMed 
    MATH 
    Article 
    PubMed Central 

    Google Scholar 
    16.Mayle, F. E. & Power, M. J. Impact of a drier early–mid-Holocene climate upon Amazonian forests. Philos. Trans. R. Soc. Lond. B Biol. Sci. 363, 1829–1838 (2008).PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    17.Ngomanda, A. et al. Western equatorial African forest-savanna mosaics: a legacy of late Holocene climatic change?. Clim. Past 5, 647–659 (2009).Article 

    Google Scholar 
    18.Metwally, A. A., Scott, L., Neumann, F. H., Bamford, M. K. & Oberhänsli, H. Holocene palynology and palaeoenvironments in the Savanna Biome at Tswaing Crater, central South Africa. Palaeogeogr. Palaeoclimatol. Palaeoecol. 402, 125–135 (2014).Article 

    Google Scholar 
    19.Kuper, R. & Kröpelin, S. Climate-controlled Holocene occupation in the Sahara: motor of Africa’s evolution. Science 313, 803–807 (2006).ADS 
    CAS 
    PubMed 
    Article 
    PubMed Central 

    Google Scholar 
    20.Mayewski, P. A. et al. Holocene climate variability. Quat. Res. 62, 243–255 (2004).Article 

    Google Scholar 
    21.Wanner, H. et al. Mid- to late Holocene climate change: an overview. Quat. Sci. Rev. 27, 1791–1828 (2008).ADS 
    Article 

    Google Scholar 
    22.Kathayat, G. et al. The Indian monsoon variability and civilization changes in the Indian subcontinent. Sci. Adv. 3, e1701296 (2017).ADS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    23.Shinde, V. The origin and development of the Chalcolithic in Central India. Indo-Pac. Prehist. Assoc. Bull. 19, 125–136 (2000).
    Google Scholar 
    24.Fuller, D. Q. Agricultural origins and frontiers in South Asia: a working synthesis. J. World Prehist. 20, 1–86 (2006).Article 

    Google Scholar 
    25.Fuller, D. Q., Boivin, N. & Korisettar, R. Dating the Neolithic of South India: new radiometric evidence for key economic, social and ritual transformations. Antiquity 81, 755–778 (2007).Article 

    Google Scholar 
    26.Johansen, P. G. Landscape, monumental architecture, and ritual: a reconsideration of the South Indian ashmounds. J. Anthropol. Archaeol. 23, 309–330 (2004).Article 

    Google Scholar 
    27.Fuller, D. Q. Asia, South: Neolithic cultures. In Encyclopedia of Archaeology (ed. Pearsall, D.) 756–768 (Springer, 2008).
    Google Scholar 
    28.Asouti, E. & Fuller, D. Q. Trees and Woodlands of South India: Archaeological Perspectives (Left Coast Press, 2008).
    Google Scholar 
    29.Singh, G., Joshi, R. D., Chopra, S. K. & Singh, A. B. Late quaternary history of vegetation and climate of the Rajasthan desert, India. Philos. Trans. R. Soc. Lond. B Biol. Sci. 267, 467–501 (1974).ADS 
    Article 

    Google Scholar 
    30.Singh, I. B. Quaternary palaeoenvironments of the Ganga plain and anthropogenic activity. Man Environ. 30, 1–35 (2005).
    Google Scholar 
    31.Clarkson, C. et al. The oldest and longest enduring microlithic sequence in India: 35 000 years of modern human occupation and change at the Jwalapuram locality 9 rockshelter. Antiquity 83, 326–348 (2009).Article 

    Google Scholar 
    32.Riedel, N. et al. Modern pollen vegetation relationships in a dry deciduous monsoon forest: a case study from Lonar Crater Lake, central India. Quat. Int. 371 (2015).33.Sarkar, S. et al. Monsoon source shifts during the drying mid-Holocene: biomarker isotope based evidence from the core ‘monsoon zone’ (CMZ) of India. Quat. Sci. Rev. 123, 144–157 (2015).ADS 
    Article 

    Google Scholar 
    34.Chakraborty, A., Joshi, P. K., Ghosh, A. & Areendran, G. Assessing biome boundary shifts under climate change scenarios in India. Ecol. Indic. 34, 536–547 (2013).Article 

    Google Scholar 
    35.Rasquinha, D. N. & Sankaran, M. Modelling biome shifts in the Indian subcontinent under scenarios of future climate change. Curr. Sci. 111, 147–156 (2016).Article 

    Google Scholar 
    36.Berkelhammer, M. et al. An abrupt shift in the Indian monsoon 4000 years ago in Climates, Landscapes, and Civilizations (eds. Giosan, L. et al.) 75–88 (American Geophysical Union, 2013).37.Fleitmann, D. et al. Holocene ITCZ and Indian monsoon dynamics recorded in stalagmites from Oman and Yemen (Socotra). Quat. Sci. Rev. 26, 170–188 (2007).ADS 
    Article 

    Google Scholar 
    38.Sinha, A. et al. A global context for megadroughts in monsoon Asia during the past millennium. Quat. Sci. Rev. 30, 47–62 (2011).ADS 
    Article 

    Google Scholar 
    39.Berkelhammer, M. et al. Persistent multidecadal power of the Indian Summer Monsoon. Earth Planet. Sci. Lett. 290, 166–172 (2010).ADS 
    CAS 
    Article 

    Google Scholar 
    40.Laskar, A. H., Yadava, M. G., Ramesh, R., Polyak, V. J. & Asmerom, Y. A 4 kyr stalagmite oxygen isotopic record of the past Indian Summer Monsoon in the Andaman Islands. Geochem. Geophys. Geosyst. 14, 3555–3566 (2013).ADS 
    CAS 
    Article 

    Google Scholar 
    41.Thamban, M., Kawahata, H. & Rao, V. P. Indian summer monsoon variability during the Holocene as recorded in sediments of the Arabian Sea: timing and implications. J. Oceanogr. 63, 1009–1020 (2007).Article 

    Google Scholar 
    42.Ponton, C. et al. Holocene aridification of India. Geophys. Res. Lett. 39, L03704 (2012).ADS 
    Article 

    Google Scholar 
    43.Deblauwe, V. et al. Remotely sensed temperature and precipitation data improve species distribution modelling in the tropics. Glob. Ecol. Biogeogr. 25, 443–454 (2016).Article 

    Google Scholar 
    44.Gaussen, H. et al. International Map of the Vegetation at Scale 1:1.000.000 (French Institute of Pondycherry, 1964).
    Google Scholar 
    45.ESRI Inc. ArcGIS Pro (ESRI Inc., 2019).
    Google Scholar 
    46.Saha, K. Tropical Circulation Systems and Monsoons (Springer, 2010).Book 

    Google Scholar 
    47.Goswami, B. N. South Asian monsoon. In Intraseasonal Variability in the Atmosphere–Ocean Climate System (eds. Lau, W. K. M. & Waliser, D. E.) 19–61 (Springer, 2005).48.Dabadghao, P. M. & Shankarnarayan, K. A. The Grass Cover of India (Indian Council of Agricultural Research, 1973).
    Google Scholar 
    49.Prasad, S. & Enzel, Y. Holocene paleoclimates of India. Quat. Res. 66, 442–453 (2006).Article 

    Google Scholar 
    50.Fleitmann, D. et al. Palaeoclimatic interpretation of high-resolution oxygen isotope profiles derived from annually laminated speleothems from Southern Oman. Quat. Sci. Rev. 23, 935–945 (2004).ADS 
    Article 

    Google Scholar 
    51.Kale, V. S. Fluvio–sedimentary response of the monsoon-fed Indian rivers to Late Pleistocene–Holocene changes in monsoon strength: reconstruction based on existing 14C dates. Quat. Sci. Rev. 26, 1610–1620 (2007).ADS 
    MathSciNet 
    Article 

    Google Scholar 
    52.Prasad, S. et al. Prolonged monsoon droughts and links to Indo-Pacific warm pool: a Holocene record from Lonar Lake, central India. Earth Planet. Sci. Lett. 391, 171–182 (2014).ADS 
    CAS 
    Article 

    Google Scholar 
    53.Dixit, Y., Hodell, D. A. & Petrie, C. A. Abrupt weakening of the summer monsoon in northwest India ∼ 4100 yr ago. Geology https://doi.org/10.1130/G35236.1 (2014).Article 

    Google Scholar 
    54.Laskar, J. et al. A long-term numerical solution for the insolation quantities of the Earth. Astron. Astrophys. 428, 261–285 (2004).ADS 
    Article 

    Google Scholar 
    55.Marzin, C. & Braconnot, P. Variations of Indian and African monsoons induced by insolation changes at 6 and 9.5 kyr BP. Clim. Dyn. 33, 215–231 (2009).Article 

    Google Scholar 
    56.Bush, R. T. & McInerney, F. A. Leaf wax n-alkane distributions in and across modern plants: implications for paleoecology and chemotaxonomy. Geochim. Cosmochim. Acta 117, 161–179 (2013).ADS 
    CAS 
    Article 

    Google Scholar 
    57.Murphy, C. & Fuller, D. Q. The agriculture of early India. In Oxford Research Encyclopedia of Environmental Science (ed. Shugart, H.) (Oxford University Press, 2017).
    Google Scholar 
    58.Kumaran, N. K. P. et al. Vegetation response and landscape dynamics of Indian Summer Monsoon variations during Holocene: an eco-geomorphological appraisal of tropical evergreen forest subfossil logs. PLoS ONE 9, e93596 (2014).ADS 
    PubMed 
    PubMed Central 
    Article 
    CAS 

    Google Scholar 
    59.Singh, G., Wasson, R. J. & Agrawal, D. P. Vegetational and seasonal climatic changes since the last full glacial in the Thar Desert, northwestern India. Rev. Palaeobot. Palynol. 64, 351–358 (1990).Article 

    Google Scholar 
    60.Cole, M. M. The Savannas, Biogeography and Geobotany (Academic Press, 1986).61.Sankaran, M. et al. Determinants of woody cover in African savannas. Nature 438, 846–849 (2005).ADS 
    CAS 
    PubMed 
    Article 
    PubMed Central 

    Google Scholar 
    62.Kodandapani, N., Cochrane, M. A. & Sukumar, R. A comparative analysis of spatial, temporal, and ecological characteristics of forest fires in seasonally dry tropical ecosystems in the Western Ghats, India. For. Ecol. Manag. 256, 607–617 (2008).Article 

    Google Scholar 
    63.Hegde, V., Chandran, M. D. S. & Gadgil, M. Variation in bark thickness in a tropical forest community of Western Ghats in India. Funct. Ecol. 12, 313–318 (1998).Article 

    Google Scholar 
    64.Stott, P. A., Goldammer, J. G. & Werner, W. L. The role of fire in the tropical lowland deciduous forests of Asia. In Fire in the Tropical Biota. Ecosystem Processes and Global Challenges (ed. Goldammer, J. G.) 32–44 (Springer, 1990).65.Murphy, C. & Fuller, D. Q. Seed coat thinning during horsegram (Macrotyloma uniflorum) domestication documented through synchrotron tomography of archaeological seeds. Sci. Rep. 7, 5369 (2017).ADS 
    PubMed 
    PubMed Central 
    Article 
    CAS 

    Google Scholar 
    66.Kingwell-Banham, E. & Fuller, D. Q. Shifting cultivators in South Asia: expansion, marginalisation and specialisation over the long term. Quat. Int. 249, 84–95 (2012).Article 

    Google Scholar 
    67.Kajale, M. Excavation at Inamgaon (Deccan College Postgraduate and Research Institute, 1988).
    Google Scholar 
    68.Shirvalkar, P. & Prasad, E. The archaeology of the Late Holocene on the Deccan Plateau (The Deccan Chalcolithic). In A Companion to South Asia in the Past (eds. Schug, G. R. & Walimbe, S. R.) 240-254 (John Wiley & Sons, 2016).69.Roberts, P. et al. Local diversity in settlement, demography and subsistence across the southern Indian Neolithic-Iron Age transition: site growth and abandonment at Sanganakallu-Kupgal. Archaeol. Anthropol. Sci. 8, 575–599 (2016).Article 

    Google Scholar 
    70.Nayar, T. S. Pollen Flora of Maharashtra State, India (Today & Tomorrow Printers and Publishers, 1990).
    Google Scholar 
    71.APSA Members. The Australasian Pollen and Spore Atlas V1.0 (Australian National University, 2007).
    Google Scholar 
    72.Tinner, W. & Hu, F. S. Size parameters, size-class distribution and area-number relationship of microscopic charcoal: relevance for fire reconstruction. Holocene 13, 499–505 (2003).ADS 
    Article 

    Google Scholar 
    73.Conedera, M. et al. Reconstructing past fire regimes: methods, applications, and relevance to fire management and conservation. Quat. Sci. Rev. 28, 555–576 (2009).ADS 
    Article 

    Google Scholar 
    74.Higuera, P., Peters, M., Brubaker, L. & Gavin, D. Understanding the origin and analysis of sediment-charcoal records with a simulation model. Quat. Sci. Rev. 26, 1790–1809 (2007).ADS 
    Article 

    Google Scholar 
    75.McDermott, F. Palaeo-climate reconstruction from stable isotope variations in speleothems: a review. Quat. Sci. Rev. 23, 901–918 (2004).ADS 
    Article 

    Google Scholar 
    76.Baldini, J., McDermott, F. & Fairchild, I. Spatial variability in cave drip water hydrochemistry: implications for stalagmite paleoclimate records. Chem. Geol. 235, 390–404 (2006).ADS 
    CAS 
    Article 

    Google Scholar 
    77.Allchin, B. & Allchin, F. R. The Rise of Civilization in India and Pakistan (Cambridge University Press, 1982).
    Google Scholar 
    78.Shinde, V. S. New light on the origin, settlement system and decline of the Jorwe culture in the Deccan India. South Asian Stud. 5, 59–72 (1989).Article 

    Google Scholar 
    79.Shinde, V. S. Settlement pattern of the Savalda culture—the first farming community of Maharashtra. Bull. Deccan Coll. Res. Inst. 49–50, 417–426 (1990).
    Google Scholar 
    80.Paddayya, K. Investigations Into the Neolithic Culture of the Shorapur Doab, South India Vol. 3 (Brill, 1973).
    Google Scholar  More