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    Oil palm cultivation can be expanded while sparing biodiversity in India

    1.Vijay, V., Pimm, S. L., Jenkins, C. N. & Smith, S. J. The impacts of oil palm on recent deforestation and biodiversity loss. PLoS One 11, pe0159668 (2016).Article 

    Google Scholar 
    2.Rulli, M. C. et al. Interdependencies and telecoupling of oil palm expansion at the expense of Indonesian rainforest. Renew. Sustain. Energy Rev. 105, 499–512 (2019).Article 

    Google Scholar 
    3.Davis, K. F. et al. Tropical forest loss enhanced by large-scale land acquisitions. Nat. Geosci. 13, 482–488 (2020).ADS 
    CAS 
    Article 

    Google Scholar 
    4.Strona, G. et al. Small room for compromise between oil palm cultivation and primate conservation in Africa. Proc. Natl Acad. Sci. USA 115, 8811–8816 (2018).CAS 
    Article 

    Google Scholar 
    5.United States Department of Agriculture, Foreign Agricultural Service. Data retrieved from: https://apps.fas.usda.gov/psdonline/app/index.html#/app/advQuery (2020).6.Sagar, H. S. et al. India in the oil palm era: describing India’s dependence on palm oil, recommendations for sustainable production, and opportunities to become an influential consumer. Trop. Conserv. Sci. 12, 1940082919838918 (2019).Article 

    Google Scholar 
    7.Jadhav, R. Exclusive: India urges boycott of Malaysian palm oil after diplomatic row—sources. Reuters (13 January 2020).8.Srinivasan, U. Oil palm should not be expanded in Arunachal Pradesh. Arunachal Times (October 2016).9.Ministry of Agriculture and Farmers’ Welfare. National Mission on Oilseeds and Oil Palm; https://nmoop.gov.in (Government of India, 2020).10.Bose, P. Oil palm plantations vs shifting cultivation for indigenous peoples: analyzing Mizoram’s New Land Use Policy. Land Use Policy 81, 115–123 (2019).Article 

    Google Scholar 
    11.Dhar, A. Enter oil palm in northeast India: centre, Patanjali, Godrej bet big. The Citizen (16 September 2020).12.Raman, T. R. S. R. Is oil palm expansion good for Mizoram? The Frontier Despatch 3, 6–7 (2016).
    Google Scholar 
    13.Khandekar, N. Expanding oil palm plantations in the northeast could extract a long-term cost. The Wire (4 August 2020).14.Mandal, J. & Raman, T. R. S. R. Shifting agriculture supports more tropical forest birds than oil palm or teak plantations in Mizoram, northeast India. The Condor 118, 345–359 (2016).Article 

    Google Scholar 
    15.Nandi, J. Oil palm push on the northeast may impact biodiversity, water table, say experts. Hindustan Times 10, 51 (2020).
    Google Scholar 
    16.Myers, N., Mittermeier, R. A., Mittermeier, C. G., Da Fonseca, G. A. B. & Kent, J. Biodiversity hotspots for conservation priorities. Nature 403, 853–858 (2000).ADS 
    CAS 
    Article 

    Google Scholar 
    17.Global Agro-Ecological Zones, GAEZ v.3.0 (Food and Agriculture Organization, 2016); https://gaez.fao.org/pages/data-viewer18.Corley, R. H. V. How much palm oil do we need? Environ. Sci. Policy 12, 134–139 (2009).CAS 
    Article 

    Google Scholar 
    19.Meijaard, E. et al. The environmental impacts of palm oil in context. Nat. Plants 6, 1418–1426 (2020).Article 

    Google Scholar 
    20.West, P. C. et al. Leverage points for improving global food security and the environment. Science 18, 325–328 (2014).ADS 
    Article 

    Google Scholar 
    21.Fan, Y., Li, H. & Miguez-Macho, G. Global patterns of groundwater table depth. Science 339, 940–943 (2013).22.Shaktivadivel, R. The Agricultural Groundwater Revolution: Opportunities and Threats to Development (CAB International, 2007).
    Google Scholar 
    23.Lee, J. S. H., Miteva, D. A., Carlson, K. M., Heilmayr, R. & Saif, O. Does oil palm certification create trade-offs between environment and development in Indonesia? Env. Res. Lett. 15, 124064 (2020).Article 

    Google Scholar 
    24.Sankar, K. N. M. Oil palm finds favour with East Godavari farmers. The Hindu (25 January 2017).25.Curry, G. N. & Koczberski, G. Finding common ground: relational concepts of land tenure and economy in the oil palm frontier of Papua New Guinea. Geogr. J. 175, 98–111 (2009).Article 

    Google Scholar 
    26.DeVos, R., Kohne, M. & Roth, D. We’ll turn your water in Coca Cola: the atomising practices of oil palm development in Indonesia. J. Agrar. Change 1, 385–405 (2018).Article 

    Google Scholar 
    27.IPCC. Climate Change: Synthesis Report (eds Core Writing Team, Pachauri, R. K. & Meyer, L. A.) (IPCC, 2014).28.IPCC. IPCC Special Reports on Emissions Scenarios: Summary for Policymakers (IPCC, 2000).29.Schwalm, C. R., Glendon, S. & Duffy, P. B. RCP8. 5 tracks cumulative CO2 emissions. Proc. Natl Acad. Sci. USA 18, 19656–19657 (2020).ADS 
    Article 

    Google Scholar 
    30.Copernicus Land Monitoring Service (European Environment Agency, 2020).31.Hoffman, M., Koenig, K., Bunting, G., Cosntanza, J. & Willams, K. J. Biodiversity Hotspots v.2016.1 (2016); https://doi.org/10.5281/zenodo.326180632.IUCN World Database on Protected Areas, online April 2017 (UNEP-WCMC, 2016); www.protectedplanet.net33.QGIS Development Team. QGIS Geographic Information System (Open Source Geospatial Foundation, 2021); http://qgis.osgeo.org34.R Core Team. R: A Language and Environment for Statistical Computing (R Foundation for Statistical Computing, 2021). More

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    Observed increasing water constraint on vegetation growth over the last three decades

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

    Google Scholar 
    2.Ciais, P. et al. Europe-wide reduction in primary productivity caused by the heat and drought in 2003. Nature 437, 529 (2005).CAS 
    PubMed 
    Article 
    ADS 

    Google Scholar 
    3.Porporato, A., D’odorico, P., Laio, F., Ridolfi, L. & Rodriguez-Iturbe, I. Ecohydrology of water-controlled ecosystems. Adv. Water Resour. 25, 1335–1348 (2002).Article 
    ADS 

    Google Scholar 
    4.Huang, K. et al. Enhanced peak growth of global vegetation and its key mechanisms. Nat. Ecol. Evolution 2, 1897 (2018).Article 

    Google Scholar 
    5.Huang, J., Yu, H., Guan, X., Wang, G. & Guo, R. Accelerated dryland expansion under climate change. Nat. Clim. Change 6, 166 (2016).Article 
    ADS 

    Google Scholar 
    6.Yuan, W. et al. Increased atmospheric vapor pressure deficit reduces global vegetation growth. Sci. Adv. 5, eaax1396 (2019).CAS 
    PubMed 
    PubMed Central 
    Article 
    ADS 

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

    Google Scholar 
    8.Sherwood, S. & Fu, Q. A drier future? Science 343, 737–739 (2014).CAS 
    PubMed 
    Article 
    ADS 

    Google Scholar 
    9.Lucht, W. et al. Climatic control of the high-latitude vegetation greening trend and Pinatubo effect. Science 296, 1687–1689 (2002).CAS 
    PubMed 
    Article 
    ADS 
    PubMed Central 

    Google Scholar 
    10.Zhu, Z. et al. Greening of the Earth and its drivers. Nat. Clim. change 6, 791–795 (2016).CAS 
    Article 
    ADS 

    Google Scholar 
    11.Fensholt, R. et al. Greenness in semi-arid areas across the globe 1981–2007—an Earth Observing Satellite based analysis of trends and drivers. Remote Sens. Environ. 121, 144–158 (2012).Article 
    ADS 

    Google Scholar 
    12.Fernández-Martínez, M. et al. Global trends in carbon sinks and their relationships with CO2 and temperature. Nat. Clim. change 9, 73 (2019).Article 
    ADS 
    CAS 

    Google Scholar 
    13.Dannenberg, M. P., Wise, E. K. & Smith, W. K. Reduced tree growth in the semiarid United States due to asymmetric responses to intensifying precipitation extremes. Sci. Adv. 5, eaaw0667 (2019).PubMed 
    PubMed Central 
    Article 
    ADS 

    Google Scholar 
    14.Piao, S. et al. Evidence for a weakening relationship between interannual temperature variability and northern vegetation activity. Nat. Commun. 5, 5018 (2014).CAS 
    PubMed 
    Article 
    ADS 
    PubMed Central 

    Google Scholar 
    15.Wild, M. et al. From dimming to brightening: decadal changes in solar radiation at Earth’s surface. Science 308, 847–850 (2005).CAS 
    PubMed 
    Article 
    ADS 
    PubMed Central 

    Google Scholar 
    16.Beer, C. et al. Terrestrial gross carbon dioxide uptake: global distribution and covariation with climate. Science 329, 834–838 (2010).CAS 
    PubMed 
    Article 
    ADS 
    PubMed Central 

    Google Scholar 
    17.Forzieri, G., Alkama, R., Miralles, D. G. & Cescatti, A. Satellites reveal contrasting responses of regional climate to the widespread greening of Earth. Science 356, 1180–1184 (2017).CAS 
    PubMed 
    Article 
    PubMed Central 

    Google Scholar 
    18.Vicente-Serrano, S. M. et al. Response of vegetation to drought time-scales across global land biomes. Proc. Natl Acad. Sci. USA 110, 52–57 (2013).CAS 
    PubMed 
    Article 
    ADS 
    PubMed Central 

    Google Scholar 
    19.Anderegg, W. R. et al. Hydraulic diversity of forests regulates ecosystem resilience during drought. Nature 561, 538 (2018).CAS 
    PubMed 
    Article 
    ADS 
    PubMed Central 

    Google Scholar 
    20.Keenan, T. F. et al. Increase in forest water-use efficiency as atmospheric carbon dioxide concentrations rise. Nature 499, 324 (2013).CAS 
    PubMed 
    Article 
    ADS 
    PubMed Central 

    Google Scholar 
    21.Jiao, W., Wang, L. & McCabe, M. F. Multi-sensor remote sensing for drought characterization: current status, opportunities and a roadmap for the future. Remote Sens. Environ. 256, 112313 (2021).Article 
    ADS 

    Google Scholar 
    22.Lobell, D. B. et al. Greater sensitivity to drought accompanies maize yield increase in the US Midwest. Science 344, 516–519 (2014).CAS 
    PubMed 
    Article 
    ADS 
    PubMed Central 

    Google Scholar 
    23.Saleska, S. R., Didan, K., Huete, A. R. & Da Rocha, H. R. Amazon forests green-up during 2005 drought. Science 318, 612–612 (2007).CAS 
    PubMed 
    Article 
    ADS 
    PubMed Central 

    Google Scholar 
    24.Chen, T., Werf, G., Jeu, R., Wang, G. & Dolman, A. A global analysis of the impact of drought on net primary productivity. Hydrol. Earth Syst. Sci. 17, 3885 (2013).Article 
    ADS 

    Google Scholar 
    25.Nemani, R. R. et al. Climate-driven increases in global terrestrial net primary production from 1982 to 1999. Science 300, 1560–1563 (2003).CAS 
    PubMed 
    Article 
    ADS 
    PubMed Central 

    Google Scholar 
    26.Kreuzwieser, J. & Rennenberg, H. Molecular and physiological responses of trees to waterlogging stress. Plant, Cell Environ. 37, 2245–2259 (2014).CAS 

    Google Scholar 
    27.Buermann, W. et al. Widespread seasonal compensation effects of spring warming on northern plant productivity. Nature 562, 110 (2018).CAS 
    PubMed 
    Article 
    ADS 
    PubMed Central 

    Google Scholar 
    28.Zhao, M. & Running, S. W. Drought-induced reduction in global terrestrial net primary production from 2000 through 2009. Science 329, 940–943 (2010).CAS 
    PubMed 
    Article 
    ADS 
    PubMed Central 

    Google Scholar 
    29.Anderegg, W. R. et al. Pervasive drought legacies in forest ecosystems and their implications for carbon cycle models. Science 349, 528–532 (2015).CAS 
    PubMed 
    Article 
    ADS 
    PubMed Central 

    Google Scholar 
    30.Field, C. B., Barros, V., Stocker, T. F. & Dahe, Q. Managing the Risks of Extreme Events and Disasters to Advance Climate Change Adaptation: Special Report of the Intergovernmental Panel on Climate Change. (Cambridge University Press, 2012).31.Dai, A. Increasing drought under global warming in observations and models. Nat. Clim. Change 3, 52 (2013).Article 
    ADS 

    Google Scholar 
    32.Trenberth, K. E. et al. Global warming and changes in drought. Nat. Clim. Change 4, 17 (2013).Article 
    ADS 

    Google Scholar 
    33.Cook, B. I., Ault, T. R. & Smerdon, J. E. Unprecedented 21st century drought risk in the American Southwest and Central Plains. Sci. Adv. 1, e1400082 (2015).PubMed 
    PubMed Central 
    Article 
    ADS 

    Google Scholar 
    34.Milly, P. C. & Dunne, K. A. Potential evapotranspiration and continental drying. Nat. Clim. Change 6, 946 (2016).Article 
    ADS 

    Google Scholar 
    35.Xu, C. et al. Increasing impacts of extreme droughts on vegetation productivity under climate change. Nat. Clim. Change 9, 948–953 (2019).CAS 
    Article 
    ADS 

    Google Scholar 
    36.Konapala, G., Mishra, A. K., Wada, Y. & Mann, M. E. Climate change will affect global water availability through compounding changes in seasonal precipitation and evaporation. Nat. Commun. 11, 1–10 (2020).Article 
    CAS 

    Google Scholar 
    37.Peñuelas, J. et al. Shifting from a fertilization-dominated to a warming-dominated period. Nat. Ecol. Evolution 1, 1438–1445 (2017).Article 

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

    Google Scholar 
    39.Vicente-Serrano, S. M., Beguería, S. & López-Moreno, J. I. A multiscalar drought index sensitive to global warming: the standardized precipitation evapotranspiration index. J. Clim. 23, 1696–1718 (2010).Article 
    ADS 

    Google Scholar 
    40.Doughty, C. E. et al. Drought impact on forest carbon dynamics and fluxes in Amazonia. Nature 519, 78–82 (2015).CAS 
    PubMed 
    Article 
    ADS 
    PubMed Central 

    Google Scholar 
    41.Schwalm, C. R. et al. Global patterns of drought recovery. Nature 548, 202 (2017).CAS 
    PubMed 
    Article 
    ADS 
    PubMed Central 

    Google Scholar 
    42.Peters, W. et al. Increased water-use efficiency and reduced CO2 uptake by plants during droughts at a continental scale. Nat. Geosci. 11, 744 (2018).CAS 
    PubMed 
    PubMed Central 
    Article 
    ADS 

    Google Scholar 
    43.Hugelius, G. et al. Large stocks of peatland carbon and nitrogen are vulnerable to permafrost thaw. Proc. Natl Acad. Sci. USA 117, 20438–20446 (2020).CAS 
    PubMed 
    Article 
    ADS 
    PubMed Central 

    Google Scholar 
    44.Minasny, B. et al. Digital mapping of peatlands–A critical review. Earth-Sci. Rev. 196, 102870 (2019).CAS 
    Article 

    Google Scholar 
    45.Cronk, J. K. & Fennessy, M. S. Wetland Plants: Biology and Ecology. (CRC press, 2016).46.Zohaib, M. & Choi, M. Satellite-based global-scale irrigation water use and its contemporary trends. Sci. Total Environ. 714, 136719 (2020).47.Smith, W. K. et al. Large divergence of satellite and Earth system model estimates of global terrestrial CO2 fertilization. Nat. Clim. Change 6, 306 (2016).Article 
    ADS 
    CAS 

    Google Scholar 
    48.Abel, C. et al. The human–environment nexus and vegetation–rainfall sensitivity in tropical drylands. Nat. Sustain. 4, 25–32 (2020).49.Lu, X., Wang, L. & McCabe, M. F. Elevated CO2 as a driver of global dryland greening. Sci. Rep. 6, 20716 (2016).CAS 
    PubMed 
    PubMed Central 
    Article 
    ADS 

    Google Scholar 
    50.Oliveira, P. J., Davin, E. L., Levis, S. & Seneviratne, S. I. Vegetation‐mediated impacts of trends in global radiation on land hydrology: a global sensitivity study. Glob. Change Biol. 17, 3453–3467 (2011).Article 
    ADS 

    Google Scholar 
    51.Grömping, U. Relative importance for linear regression in R: the package relaimpo. J. Stat. Softw. 17, 1–27 (2006).Article 

    Google Scholar 
    52.Moesinger, L. et al. The global long-term microwave vegetation optical depth climate archive (VODCA). Earth Syst. Sci. Data 12, 177–196 (2020).Article 
    ADS 

    Google Scholar 
    53.Li, X. & Xiao, J. A global, 0.05-degree product of solar-induced chlorophyll fluorescence derived from OCO-2, MODIS, and reanalysis data. Remote Sens. 11, 517 (2019).Article 
    ADS 

    Google Scholar 
    54.Palmer, W. C. Meteorological Drought. Vol. 30 (Citeseer, 1965).55.Abatzoglou, J. T., Dobrowski, S. Z., Parks, S. A. & Hegewisch, K. C. TerraClimate, a high-resolution global dataset of monthly climate and climatic water balance from 1958–2015. Sci. Data 5, 170191 (2018).PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    56.Trabucco, A. & Zomer, R. J. Global aridity index (global-aridity) and global potential evapo-transpiration (global-PET) geospatial database. CGIAR Consortium for Spatial Information (2009).57.Zomer, R. J., Trabucco, A., Bossio, D. A. & Verchot, L. V. Climate change mitigation: a spatial analysis of global land suitability for clean development mechanism afforestation and reforestation. Agriculture, Ecosyst. Environ. 126, 67–80 (2008).Article 

    Google Scholar 
    58.Gruber, A., Scanlon, T., Schalie, R. V. D., Wagner, W. & Dorigo, W. Evolution of the ESA CCI soil moisture climate data records and their underlying merging methodology. Earth Syst. Sci. Data 11, 717–739 (2019).Article 
    ADS 

    Google Scholar 
    59.Dorigo, W. et al. ESA CCI Soil moisture for improved Earth system understanding: State-of-the art and future directions. Remote Sens. Environ. 203, 185–215 (2017).Article 
    ADS 

    Google Scholar 
    60.Wagner, W. et al. Fusion of active and passive microwave observations to create an essential climate variable data record on soil moisture. ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences (ISPRS Annals) 7, 315–321 (2012).61.Harris, I., Jones, P., Osborn, T. & Lister, D. Updated high‐resolution grids of monthly climatic observations–the CRU TS3. 10 Dataset. Int. J. Climatol. 34, 623–642 (2014).Article 

    Google Scholar 
    62.Sheffield, J., Goteti, G. & Wood, E. F. Development of a 50-year high-resolution global dataset of meteorological forcings for land surface modeling. J. Clim. 19, 3088–3111 (2006).Article 
    ADS 

    Google Scholar 
    63.Tian, F. et al. Remote sensing of vegetation dynamics in drylands: Evaluating vegetation optical depth (VOD) using AVHRR NDVI and in situ green biomass data over West African Sahel. Remote Sens. Environ. 177, 265–276 (2016).Article 
    ADS 

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

    Google Scholar 
    65.Frank, D. et al. Effects of climate extremes on the terrestrial carbon cycle: concepts, processes and potential future impacts. Glob. Change Biol. 21, 2861–2880 (2015).Article 
    ADS 

    Google Scholar 
    66.Goetz, S. J., Bunn, A. G., Fiske, G. J. & Houghton, R. A. Satellite-observed photosynthetic trends across boreal North America associated with climate and fire disturbance. Proc. Natl Acad. Sci. USA 102, 13521–13525 (2005).CAS 
    PubMed 
    Article 
    ADS 
    PubMed Central 

    Google Scholar 
    67.Wu, D. et al. Time‐lag effects of global vegetation responses to climate change. Glob. Change Biol. 21, 3520–3531 (2015).Article 
    ADS 

    Google Scholar 
    68.Tei, S. & Sugimoto, A. Time lag and negative responses of forest greenness and tree growth to warming over circumboreal forests. Glob. Change Biol. 24, 4225–4237 (2018).Article 
    ADS 

    Google Scholar 
    69.Wen, Y. et al. Cumulative effects of climatic factors on terrestrial vegetation growth. J. Geophys. Res.: Biogeosciences 124, 789–806 (2019).Article 
    ADS 

    Google Scholar 
    70.McKee, T. B., Doesken, N. J. & Kleist, J. in Proceedings of the 8th Conference on Applied Climatology. 179-183 (American Meteorological Society Boston, MA).71.Jiao, W., Tian, C., Chang, Q., Novick, K. A. & Wang, L. A new multi-sensor integrated index for drought monitoring. Agric. For. Meteorol. 268, 74–85 (2019).Article 
    ADS 

    Google Scholar  More

  • in

    A subterranean adaptive radiation of amphipods in Europe

    1.Alfaro, M. E. et al. Nine exceptional radiations plus high turnover explain species diversity in jawed vertebrates. PNAS 106, 134–14 (2009).Article 

    Google Scholar 
    2.Seehausen, O. Process and pattern in cichlid radiations – inferences for understanding unusually high rates of evolutionary diversification. N. Phytol. 207, 304–312 (2015).Article 

    Google Scholar 
    3.Tank, D. C. et al. Nested radiations and the pulse of angiosperm diversification: Increased diversification rates often follow whole genome duplications. N. Phytol. 207, 454–467 (2015).Article 

    Google Scholar 
    4.Neubauer, T. A., Harzhauser, M., Georgopoulou, E., Kroh, A. & Mandic, O. Tectonics, climate, and the rise and demise of continental aquatic species richness hotspots. PNAS 112, 11478–11483 (2015).ADS 
    CAS 
    PubMed 
    Article 
    PubMed Central 

    Google Scholar 
    5.Schluter, D. The Ecology of Adaptive Radiation (Oxford University Press, 2000).6.Schenk, J. J., Rowe, K. C. & Steppan, S. J. Ecological opportunity and incumbency in the diversification of repeated continental colonizations by muroid rodents. Syst. Biol. 62, 837–864 (2013).PubMed 
    Article 
    PubMed Central 

    Google Scholar 
    7.Jenkins, N. C., Pimm, S. L. & Joppa, L. N. Global vertebrate diversity and conservation. PNAS 110, E2602–E2610 (2013).ADS 
    CAS 
    PubMed 
    Article 
    PubMed Central 

    Google Scholar 
    8.Kier, G. et al. A global assessment of endemism and species richness across island and mainland regions. PNAS 106, 9322–9327 (2009).ADS 
    CAS 
    PubMed 
    Article 
    PubMed Central 

    Google Scholar 
    9.Reyjol, Y. et al. Patterns in species richness and endemism of European freshwater fish. Glob. Ecol. Biogeogr. 16, 65–75 (2007).Article 

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

    Google Scholar 
    11.Albrecht, C., Trajanovski, S., Kuhn, K., Streit, B. & Wilke, T. Rapid evolution of an ancient lake species flock: freshwater limpets (Gastropoda: Ancylidae) in the Balkan lake Ohrid. Org. Divers. Evol. 6, 294–307 (2006).Article 

    Google Scholar 
    12.Vonlanthen, P. et al. Eutrophication causes speciation reversal in whitefish adaptive radiations. Nature 482, 357–362 (2012).ADS 
    CAS 
    PubMed 
    Article 
    PubMed Central 

    Google Scholar 
    13.Renema, W. et al. Hopping hotspots: Global shifts in marine biodiversity. Science 321, 654–657 (2008).ADS 
    CAS 
    PubMed 
    Article 
    PubMed Central 

    Google Scholar 
    14.Gargani, J. & Rigollet, C. Mediterranean Sea level variations during the Messinian salinity crisis. Geophys. Res. Lett. 34, 1–5 (2007).Article 

    Google Scholar 
    15.Culver, D. C. et al. The mid-latitude biodiversity ridge in terrestrial cave fauna. Ecography 29, 120–128 (2006).Article 

    Google Scholar 
    16.Zagmajster, M. et al. Geographic variation in range size and beta diversity of groundwater crustaceans: Insights from habitats with low thermal seasonality. Glob. Ecol. Biogeogr. 23, 1135–1145 (2014).Article 

    Google Scholar 
    17.Trontelj, P., Blejec, A. & Fišer, C. Ecomorphological convergence of cave communities. Evolution 66, 3852–3865 (2012).PubMed 
    Article 
    PubMed Central 

    Google Scholar 
    18.Trontelj, P., Borko, Š. & Delić, T. Testing the uniqueness of deep terrestrial life. Sci. Rep. 9, 15188 (2019).ADS 
    PubMed 
    PubMed Central 
    Article 
    CAS 

    Google Scholar 
    19.Morvan, C. et al. Timetree of Aselloidea reveals species diversification dynamics in groundwater. Syst. Biol. 62, 512–522 (2013).CAS 
    PubMed 
    Article 
    PubMed Central 

    Google Scholar 
    20.Eme, D. et al. Do cryptic species matter in macroecology? Sequencing European groundwater crustaceans yields smaller ranges but does not challenge biodiversity determinants. Ecography 41, 424–436 (2017).Article 

    Google Scholar 
    21.Lukić, M., Delić, T., Pavlek, M., Deharveng, L. & Zagmajster, M. Distribution pattern and radiation of the European subterranean genus Verhoeffiella (Collembola, Entomobryidae). Zool. Scr. 49, 86–100 (2019).Article 

    Google Scholar 
    22.Väinölä, R. et al. Global diversity of amphipods (Amphipoda; Crustacea) in freshwater. Hydrobiologia 595, 241–255 (2008).Article 

    Google Scholar 
    23.Fišer, C., Delić, T., Luštik, R., Zagmajster, M. & Altermatt, F. Niches within a niche: ecological differentiation of subterranean amphipods across Europe’s interstitial waters. Ecography 42, 1212–1223 (2019).Article 

    Google Scholar 
    24.Stroud, J. T. & Losos, J. B. Ecological opportunity and adaptive radiation. Annu. Rev. Ecol. Evol. Syst. 47, 507–532 (2016).Article 

    Google Scholar 
    25.McInerney, C. E. et al. The ancient Britons: groundwater fauna survived extreme climate change over tens of millions of years across NW Europe. Mol. Ecol. 23, 1153–1166 (2014).PubMed 
    Article 
    PubMed Central 

    Google Scholar 
    26.Revell, L. J. phytools: An R package for phylogenetic comparative biology (and other things). Methods Ecol. Evol. 3, 217–223 (2012).Article 

    Google Scholar 
    27.Culver, D. C. & Pipan, T. The Biology of Caves and Other Subterranean Habitats 2nd edn (OUP, 2019).28.Kralj-Fišer, S. et al. The interplay between habitat use, morphology and locomotion in subterranean crustaceans of the genus. Niphargus. Zool. 139, 125742 (2020).Article 

    Google Scholar 
    29.Delić, T., Trontelj, P., Rendoš, M. & Fišer, C. The importance of naming cryptic species and the conservation of endemic subterranean amphipods. Sci. Rep. 7, 1–12 (2017).Article 
    CAS 

    Google Scholar 
    30.Harmon, L. J., Schulte, J. A., Losos, J. B. & Larson, A. Tempo and mode of evolutionary radiation in iguanian lizards. Science 301, 961–964 (2003).ADS 
    CAS 
    PubMed 
    Article 
    PubMed Central 

    Google Scholar 
    31.Murrell, D. J. A global envelope test to detect non‐random bursts of trait evolution. Methods Ecol. Evol. 9, 1739–1748 (2018).Article 

    Google Scholar 
    32.Freckleton, R. P. & Harvey, P. H. Detecting non-Brownian trait evolution in adaptive radiations. PLoS Biol. 4, e373 (2006).PubMed 
    PubMed Central 
    Article 
    CAS 

    Google Scholar 
    33.Clavel, J., Escarguel, G. & Merceron, G. mvMORPH: an R package for fitting multivariate evolutionary models to morphometric data. Methods Ecol. Evol. 6, 1311–1319 (2015).Article 

    Google Scholar 
    34.Kováč, M. et al. The central paratethyspalaeoceanography: a water circulation model based on microfossilproxies, climate, and changes of depositional environment. Acta Geol. Slov. 9, 75–114 (2017).
    Google Scholar 
    35.Kováč, M. et al. Towards better correlation of the Central Paratethys regional time scale with the standard geological time scale of the Miocene Epoch. Geol. Carpath. 69, 283–300 (2018).ADS 
    Article 

    Google Scholar 
    36.Mahler, D. L., Ingram, T., Revell, L. J. & Losos, J. B. Exceptional convergence on the macroevolutionary landscape in island lizard radiations. Science 341, 6143 (2013).Article 
    CAS 

    Google Scholar 
    37.Ingram, T. & Mahler, D. SURFACE: detecting convergent evolution from comparative data by fitting Ornstein‐Uhlenbeck models with stepwise Akaike Information Criterion. Methods Ecol. Evol. 4, 416–425 (2013).Article 

    Google Scholar 
    38.Hansen, T. F. Stabilizing selection and the comparative analysis of adaptation. Evolution 51, 1341 (1997).Article 

    Google Scholar 
    39.Popov, S. V., Rögl, F. & Rozanov, A. Y. Lithological-Paleogeographic Maps of Paratethys: 10 Maps Late Eocene to Pliocene (Schweizerbart’sche Verlagsbuchhandlung, 2004).40.Barrier, E., Vrielynck, B., Brouillet, J. F. & Brunet, M. F. Paleotectonic Reconstruction of the Central Tethyan Realm. Tectonono-Sedimentary-Palinspastic Maps from Late Permian to Pliocene (CCGM/CGMW, 2018).41.Handy, M. R., Ustaszewski, K. & Kissling, E. Reconstructing the Alps–Carpathians–Dinarides as a key to understanding switches in subduction polarity, slab gaps and surface motion. Int. J. Earth. Sci. 104, 1–26 (2015).CAS 
    Article 

    Google Scholar 
    42.Esmaeili-Rineh, S., Sari, A., Delić, T., Moškrič, A. & Fišer, C. Molecular phylogeny of the subterranean genus Niphargus (Crustacea: Amphipoda) in the Middle East: a comparison with European Niphargids. Zool. J. Linn. Soc. 175, 812–826 (2015).Article 

    Google Scholar 
    43.Pimm, S. L. et al. The biodiversity of species and their rates of extinction, distribution, and protection. Science 344, 6187 (2014).Article 
    CAS 

    Google Scholar 
    44.Hou, Z. & Sket, B. A review of Gammaridae (Crustacea: Amphipoda): the family extent, its evolutionary history, and taxonomic redefinition of genera. Zool. J. Linn. Soc. 176, 323–348 (2016).Article 

    Google Scholar 
    45.Corrigan, L. J., Horton, T., Fotherby, H., White, T. A. & Hoelzel, A. R. Adaptive evolution of deep‐sea amphipods from the superfamily lysiassanoidea in the North Atlantic. Evol. Biol. 41, 154–165 (2014).Article 

    Google Scholar 
    46.Clarke, A. & Johnston, I. A. Evolution and adaptive radiation of Antarctic fishes. Trends Ecol. Evol. 11, 212–218 (1996).CAS 
    PubMed 
    Article 
    PubMed Central 

    Google Scholar 
    47.Macdonald, K. S. 3rd, Yampolsky, L. & Duffy, J. E. Molecular and morphological evolution of the amphipod radiation of Lake Baikal. Mol. Phylogenet. Evol. 35, 323–343 (2005).CAS 
    PubMed 
    Article 
    PubMed Central 

    Google Scholar 
    48.Elmer, K. R. et al. Parallel evolution of Nicaraguan crater lake cichlid fishes via non-parallel routes. Nat. Commun. 5, 5168 (2014).ADS 
    CAS 
    PubMed 
    Article 
    PubMed Central 

    Google Scholar 
    49.Esquerré, D. & Keogh, J. S. Parallel selective pressures drive convergent diversification of phenotypes in pythons and boas. Ecol. Lett. 19, 800–809 (2016).PubMed 
    Article 
    PubMed Central 

    Google Scholar 
    50.von Saltzwedel, H., Scheu, S. & Schaefer, I. Founder events and pre-glacial divergences shape the genetic structure of European Collembola species. BMC Evol. Biol. 16, 148 (2016).Article 
    CAS 

    Google Scholar 
    51.Mammola, S. et al. Scientists’ warning on the conservation of subterranean ecosystems. BioScience 69, 641–650 (2019).Article 

    Google Scholar 
    52.Lefébure, T., Douady, C. J., Malard, F. & Gibert, J. Testing dispersal and cryptic diversity in a widely distributed groundwater amphipod (Niphargus rhenorhodanensis). Mol. Phylogenet. Evol. 42, 676–686 (2007).PubMed 
    Article 
    CAS 
    PubMed Central 

    Google Scholar 
    53.Zagmajster, M., Turjak, M., & Sket, B. Database on subterranean biodiversity of the Dinarides and neighboring regions – SubBioDatabase. In 21st International Conference on Subterranean Biology, 2–7 September, 2012, Košice, Slovakia, Abstract book (ed. Kováč, Ĺ., et al.) 116–117 https://doi.org/10.13140/2.1.4518.0487 (Pavol Jozef Šafárik University, Košice, 2012).54.Clark, K., Karsch-Mizrachi, I., Lipman, D. J., Ostell, J. & Sayers, E. W. GenBank. Nucleic Acids Res. 44, D67–D72 (2016).CAS 
    PubMed 
    Article 
    PubMed Central 

    Google Scholar 
    55.Fišer, C. et al. Translating Niphargus barcodes from Switzerland into taxonomy with a description of two new species (Amphipoda, Niphargidae). ZooKeys 760, 113–141 (2018).Article 

    Google Scholar 
    56.Jurado-Rivera, J. A. et al. Molecular systematics of Haploginglymus, a genus of subterranean amphipods endemic to the Iberian Peninsula (Amphipoda: Niphargidae). Contrib. Zool. 86, 239–260 (2017).Article 

    Google Scholar 
    57.Copilaş-Ciocianu, D., Borko, Š. & Fišer, C. The late blooming amphipods: global change promoted post-Jurassic ecological radiation despite Palaeozoic origin. Mol. Phylogenet. Evol. 143, 106664 (2020).PubMed 
    Article 
    PubMed Central 

    Google Scholar 
    58.Horton, T. et al. World Register of Marine Species. https://www.marinespecies.org. Accessed 6 Mar 2020 (2020).59.Fišer, C., Trontelj, P., Luštrik, R. & Sket, B. Toward a unified taxonomy of Niphargus (Crustacea: Amphipoda): a review of morphological variability. Zootaxa 2061, 1–22 (2009).Article 

    Google Scholar 
    60.Katoh, K. & Standley, D. M. MAFFT multiple sequence alignment software version 7: improvements in performance and usability. Mol. Biol. Evol. 30, 772–780 (2013).CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    61.Talavera, G. & Castresana, J. Improvement of phylogenies after removing divergent and ambiguously aligned blocks from protein sequence alignments. Syst. Biol. 56, 564–577 (2007).CAS 
    PubMed 
    Article 
    PubMed Central 

    Google Scholar 
    62.Guindon, S. et al. New algorithms and methods to estimate maximum-likelihood phylogenies: assessing the performance of PhyML 3.0. Syst. Biol. 59, 307–321 (2010).CAS 
    PubMed 
    Article 
    PubMed Central 

    Google Scholar 
    63.Lanfear, R., Calcott, B., Ho, S. Y. & Guindon, S. PartitionFinder: combined selection of partitioning schemes and substitution models for phylogenetic analyses. Mol. Biol. Evol. 29, 1695–1701 (2012).64.Ronquist, F. et al. MrBayes 3.2: efficient Bayesian phylogenetic inference and model choice across a large model space. Syst. Biol. 61, 539–542 (2012).PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    65.Nguyen, L. T., Schmidt, H. A., von Haeseler, A. & Minh, B. Q. IQ-TREE: A fast and effective stochastic algorithm for estimating maximum likelihood phylogenies. Mol. Biol. Evol. 32, 268–274 (2015).CAS 
    PubMed 
    Article 
    PubMed Central 

    Google Scholar 
    66.Rambaut, A., Drummond, A. J., Xie, D., Baele, G. & Suchard, M. A. Tracer v1.7. http://tree.bio.ed.ac.uk/software/tracer/ (2018).67.Minh, B. Q., Nguyen, M. A. T. & von Haeseler, A. Ultrafast approximation for phylogenetic bootstrap. Mol. Biol. Evol. 30, 1188–1195 (2013).CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    68.Bouckaert, R. et al. BEAST 2: a software platform for Bayesian evolutionary analysis. PLoS Comput. Biol. 10, e1003537 (2014).PubMed 
    PubMed Central 
    Article 
    CAS 

    Google Scholar 
    69.Jażdżewskii, K. & Kupryjanowicz, J. One more fossil Niphargid (Malacostraca: Amphipoda) from Baltic Amber. J. Crustac. Biol. 30, 413–416 (2010).Article 

    Google Scholar 
    70.Brikiatis, L. Late Mesozic North Atlantic land bridges. Earth-Sci. Rev. 159, 47–57 (2016).ADS 
    Article 

    Google Scholar 
    71.Allegrucci, G., Trucchi, E. & Sbordoni, V. Tempo and mode of species diversification in Dolichopoda cave crickets (Orthoptera, Rhaphidophoridae). Mol. Phylogenet. Evol. 60, 108–121 (2011).PubMed 
    Article 
    PubMed Central 

    Google Scholar 
    72.R Core Team. R: A Language and Environment for Statistical Computing. https://www.R-project.org/ (R Foundation for Statistical Computing, 2018).73.Pybus, O. G. & Harvey, P. H. Testing macro-evolutionary models using incomplete molecular phylogenies. Proc. R. Soc. B. 267, 2267–2272 (2000).CAS 
    PubMed 
    Article 
    PubMed Central 

    Google Scholar 
    74.Etienne, R. S. & Haegeman, B. A conceptual and statistical framework for adaptive radiations with a key role for diversity dependence. Am. Nat. 180, E75–E89 (2012).PubMed 
    Article 
    PubMed Central 

    Google Scholar 
    75.Bollback, J. P. SIMMAP: Stochastic character mapping of discrete traits on phylogenies. BMC Bioinformatics 7, 88 (2006).PubMed 
    PubMed Central 
    Article 
    CAS 

    Google Scholar 
    76.Pagel, M., Meade, A. & Barker, D. Bayesian estimation of ancestral character states on phylogenies. Syst. Biol. 53, 673–684 (2004).PubMed 
    Article 
    PubMed Central 

    Google Scholar 
    77.QGIS.org. QGIS Geographic Information System. http://www.qgis.org (QGIS Association, 2021).78.Esri & U.S. National Park Service. “Physical” [basemap]. Scale Not Given. “World Physical Map”. https://www.arcgis.com/home/item.html?id=c4ec722a1cd34cf0a23904aadf8923a0. Accessed 12 Dec 2019 (2019).79.Borko, Š., Trontelj, P., Seehausen, O., Moškrič, A., Fišer, C. Supplementary data and code: a subterranean adaptive radiation of amphipods in Europe [Data set]. Zenodo https://doi.org/10.5281/zenodo.4779097 (2021). More

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    Life history and nesting ecology of a Japanese tube-nesting spider wasp Dipogon sperconsus (Hymenoptera: Pompilidae)

    Nesting recordsDipogon nests were created singly per cane, because there were no examples in which wasps of two species emerged from the same cane in the study site. Thus, we designate “utilized canes” as “nests”.In the four years, in pine forests in Takarazuka, Hyogo, Japan, we collected a total of 419 nests with 1033 cells from which species of Dipogon emerged (Fig. 1; Table 1; Supplementary Table S1). The numbers of nests and cells and the average and SD of the number of cells per nest for each species are shown in Table 1. Other wasps, bees, and parasitic wasps and flies also emerged from our trap nests (Supplementary Table S2), but we did not consider their nesting in the following analyses. Among 1033 cells, D. sperconsus emerged from 623 cells, D. inconspersus from 26 cells, and D. bifasciatus (Geoffroy) from 4 cells, while rearing failure occurred in 380 cells (Table 1), the owners of which we designate as “unknown Dipogon spp.” Based on the total cells of Dipogon, the proportion of cells constructed by D. sperconsus was 60.3% (623/1033*100), that of D. inconspersus was 2.5% (26/1033*100), and that of D. bifasciatus was 0.39% (4/1033*100). Based on the cells of the identified species, the proportion of cells constructed by D. sperconsus was 95.4% (623/(623 + 26 + 4)*100), that of D. inconspersus was 4.0% (26/(623 + 26 + 4)*100), and that of D. bifasciatus was 0.6% (4/(623 + 26 + 4)*100). From these proportions, we can estimate the number of cells constructed by the three species of Dipogon in the total 1033 Dipogon cells as ca. 985.5 cells (1033*0.954) by D. sperconsus, ca. 41.3 cells (1033*0.04) by D inconspersus, and ca. 6.2 cells (1033*0.006) by D. bifasciatus.Figure 1The study site in Kirihata, Takarazuka City, Hyogo Pref., Japan, and trap nests. (a) An old pine forest in which trap nests were installed. (b) A set of trap nests (cane bundle), 15 mixed-size bamboo canes bound vertically with vinyl-covered wires like a screen, attached to a tree trunk approximately 1.5 m above the ground. (c) A nest of D. sperconsus; this cane was installed in Shibutani, Takarazuka, Hyoto Pref. about 1 km southeast of the present study site on 29 July 2007 and was withdrawn on 6 August 2007. (d) A nest (6–5-5–1) of D. sperconsus; this cane was installed in Kirihata, Takarazuka, Hyoto Pref. about 500 m west-southwest of the present study site on 25 August 2010 and was withdrawn on 27 August 2010 (prey spider, Agelena limbata Thorell). (e) A nest of D. sperconsus; this cane was installed in Najio, Nishinomiya, Hyoto Pref. about 10 km southwest of the present study site on 15 July 2007 and was withdrawn on 25 July 2007. The minimum grid in the background graph paper of (c)–(e) is 1 mm. All photos taken by Y. Nishimoto.Full size imageTable 1 The numbers of the collected nests and brood cells, and the mean number of cells per nest in three species of Dipogon (Deuteragenia).Full size tableBecause multiple cells were often constructed in a single nest, the number of nests was much smaller than the number of constructed cells. Among the 419 nests, 221 nests belonged to D. sperconsus, 7 nests belonged to D. inconspersus, and a single nest belonged to D. bifasciatus, but the remaining 190 nests could not be identified because of rearing failure (Table 1). The proportions of the nests in the three Dipogon species were calculated as follows: 96.5% (221/(221 + 7 + 1)*100) in D. sperconsus, 3.1% (7/(221 + 7 + 1)*100) in D inconspersus, and 0.4% (1/(221 + 7 + 1)*100) in D. bifasciatus. Thus, the estimated number of nests in each species was ca. 404.3 (419*0.965) in D. sperconsus, ca. 13.0 (419*0.031) in D inconspersus, and ca. 1.7 (419*0.004) in D. bifasciatus.Next, we considered whether the cane bundles were used randomly. Based on the yearly frequency distributions of nests (Supplementary Tables S3–S6), we developed a null hypothesis assuming the nests are randomly distributed over bundles, where a negative binomial distribution is expected (Supplementary Tables S7–S8). Our yearly data indicate that the null hypothesis was rejected and that nests were more or less aggregated in a few bundles (Supplementary Figure S1; test statistics, Supplementary Table S8). This aggregation tendency (e.g., no nests in some bundles) may imply that some selected sites for bundles are not appropriate for D. sperconsus, for some unknown behavioral reasons. Further studies are needed to verify the habitat use of this species.Yearly frequency distributions of the number of cells show that the range of cells constructed by D. sperconsus and unknown D. spp. combined were 1–10 cells, and the median was 2 cells (Supplementary Table S3–S6, Supplementary Figure S2). Most of the nests included 1–3 cells, and five or more cells were very rare. Most of the nests with many cells (e.g., 7–10 cells) were likely to be constructed by a single wasp because these wasps avoid interactions with other spider wasps. The average number of D. sperconsus cells per nest was 2.82 for four years, varying from 2.21 (2014) to 3.16 (2016) (Table 1), and the yearly differences were significant (Kruskal–Wallis test, (chi ^{2} = 7.70), df = 3, p = 0.05). In contrast, the average number of cells per nest of D. sayi sayi was slightly greater than that of D. sperconsus: 3.2 (1–6, SD = 1.47, n = 41) in the first generation and 4.7 (1–13, SD = 2.52, n = 107) in the second generation in Wisconsin, USA8; and 6.2 (1961), 4.0 (1962) and 3.0 (1963) in the summer generation and 7.5 (1961) and 3.2 (1962) in the overwintering generation in Northwestern Ontario9.Life history of Dipogon sperconsusDevelopmental periodThe developmental period of reared wasps was estimated in the summer and overwintering generations separately (Table 2, Supplementary Figure S3, Supplementary Tables S9–S12). In the summer generation, both females and males developed from egg to adult over approximately three weeks (23.1 days for females and 21.6 days for males; Table 2). There was no significant difference between sexes (t-test, after adjustment by Bonferroni method: p  > 0.05). In the overwintering generation, approximately eight months were required from egg to adult (246 days for females and 247 days for males). There was also no significant difference between sexes (t-test, after adjustment by Bonferroni method: p  > 0.05). In females, all developmental periods were significantly longer in the overwintering generation than in the summer generation (t-test, after adjustment by Bonferroni method: p  0.05 for egg and larval periods; p  0.1). Among the 40 coelotid female spiders, the sex ratio of wasp eggs was even: 20 female wasp eggs and 20 males. However, the female spiders on which female wasp eggs were laid were significantly greater in cephalothorax width than those on which male eggs were laid (t = 3.98, p  More

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    Evidence for competition and cannibalism in wormlions

    1.Schoener, T. W. Field experiments on interspecific competition. Am. Nat. 122, 240–285 (1983).Article 

    Google Scholar 
    2.Keddy, P. A. Competition 2nd edn. (Kluwer, 2001).Book 

    Google Scholar 
    3.Kotler, B. P. & Brown, J. S. Environmental heterogeneity and the coexistence of desert rodents. Annu. Rev. Ecol. Syst. 19, 281–307 (1988).Article 

    Google Scholar 
    4.Kronfeld-Schor, N. & Dayan, T. Partitioning of time as an ecological resource. Annu. Rev. Ecol. Evol. Syst. 34, 153–181 (2003).Article 

    Google Scholar 
    5.Connell, J. H. On the prevalence and relative importance of interspecific competition: evidence from field experiments. Am. Nat. 122, 661–696 (1983).Article 

    Google Scholar 
    6.Adler, P. B. et al. Competition and coexistence in plant communities: intraspecific competition is stronger than interspecific competition. Ecol. Lett. 21, 1319–1329 (2018).PubMed 
    Article 
    PubMed Central 

    Google Scholar 
    7.Morris, D. W. Toward an ecological synthesis: a case for habitat selection. Oecologia 136, 1–13 (2003).ADS 
    PubMed 
    Article 
    PubMed Central 

    Google Scholar 
    8.Barkae, E. D., Abramsky, Z. & Ovadia, O. Can models of density-dependent habitat selection be applied for trap-building predators?. Popul. Ecol. 56, 175–184 (2014).Article 

    Google Scholar 
    9.Halliday, W. D. & Blouin-Demers, G. Red flour beetles balance thermoregulation and food acquisition via density-dependent habitat selection. J. Zool. 294, 198–205 (2014).Article 

    Google Scholar 
    10.Tregenza, T. Building on the ideal free distribution. Adv. Ecol. Res. 26, 253–307 (1995).Article 

    Google Scholar 
    11.Kingsolver, J. G. & Pfennig, D. W. Individual-level selection as a cause of Cope’s rule of phyletic size increase. Evolution 58, 1608–1612 (2004).PubMed 
    Article 
    PubMed Central 

    Google Scholar 
    12.Alatalo, R. V. & Moreno, J. Body size, interspecific interactions, and use of foraging sites in tits (Paridae). Ecology 68, 1773–1777 (1987).PubMed 
    Article 
    PubMed Central 

    Google Scholar 
    13.Honěk, A. Intraspecific variation in body size and fecundity in insects: a general relationship. Oikos 66, 483–492 (1993).Article 

    Google Scholar 
    14.Sokolovska, N., Rowe, L. & Johansson, F. Fitness and body size in mature odonates. Ecol. Entomol. 25, 239–248 (2000).Article 

    Google Scholar 
    15.Werner, E. E. & Anholt, B. R. Ecological consequences of the trade-off between growth and mortality rates mediated by foraging activity. Am. Nat. 142, 242–272 (1993).CAS 
    PubMed 
    Article 
    PubMed Central 

    Google Scholar 
    16.Blanckenhorn, W. U. The evolution of body size: What keeps organisms small?. Q. Rev. Biol. 75, 385–407 (2000).CAS 
    PubMed 
    Article 
    PubMed Central 

    Google Scholar 
    17.Gotthard, K. Increased risk of predation as a cost of high growth rate: an experimental test in a butterfly. J. Anim. Ecol. 69, 896–902 (2000).PubMed 
    Article 
    PubMed Central 

    Google Scholar 
    18.Van Buskirk, J. Competition, cannibalism, and size class dominance in a dragonfly. Oikos 65, 455–464 (1992).Article 

    Google Scholar 
    19.Fincke, O. M. Larval behaviour of a giant damselfly: Territoriality or size-dependent dominance?. Anim. Behav. 51, 77–87 (1996).Article 

    Google Scholar 
    20.Hopper, K. R., Crowley, P. H. & Kielman, D. Density dependence, hatching synchrony, and within-cohort cannibalism in young dragonfly larvae. Ecology 77, 191–200 (1996).Article 

    Google Scholar 
    21.Eitam, A., Blaustein, L. & Mangel, M. Density and intercohort priority effects on larval Salamandra salamandra in temporary pools. Oecologia 146, 36–42 (2005).ADS 
    PubMed 
    Article 
    PubMed Central 

    Google Scholar 
    22.Barkae, E. D., Scharf, I. & Ovadia, O. A stranger is tastier than a neighbor: cannibalism in Mediterranean and desert antlion populations. Behav. Ecol. 28, 69–76 (2017).Article 

    Google Scholar 
    23.Alford, R. A. & Wilbur, H. M. Priority effects in experimental pond communities: competition between Bufo and Rana. Ecology 66, 1097–1105 (1985).Article 

    Google Scholar 
    24.Dayton, G. H. & Fitzgerald, L. A. Priority effects and desert anuran communities. Can. J. Zool. 83, 1112–1116 (2005).Article 

    Google Scholar 
    25.Louette, G. & De Meester, L. Predation and priority effects in experimental zooplankton communities. Oikos 116, 419–426 (2007).Article 

    Google Scholar 
    26.Geange, S. W. & Stier, A. C. Order of arrival affects competition in two reef fishes. Ecology 90, 2868–2878 (2009).PubMed 
    Article 
    PubMed Central 

    Google Scholar 
    27.Huey, R. B. & Pianka, E. R. Ecological consequences of foraging mode. Ecology 62, 991–999 (1981).Article 

    Google Scholar 
    28.Shine, R. & Li-Xin, S. Arboreal ambush site selection by pit-vipers Gloydius shedaoensis. Anim. Behav. 63, 565–576 (2002).Article 

    Google Scholar 
    29.Clark, R. W. Feeding experience modifies the assessment of ambush sites by the timber rattlesnake, a sit-and-wait predator. Ethology 110, 471–483 (2004).Article 

    Google Scholar 
    30.Tsairi, H. & Bouskila, A. Ambush site selection of a desert snake (Echis coloratus) at an oasis. Herpetologica 60, 13–23 (2004).Article 

    Google Scholar 
    31.Scharf, I., Lubin, Y. & Ovadia, O. Foraging decisions and behavioural flexibility in trap-building predators: a review. Biol. Rev. 86, 626–639 (2011).PubMed 
    Article 

    Google Scholar 
    32.Blamires, S. J. Biomechanical costs and benefits of sit-and-wait foraging traps. Isr. J. Ecol. Evol. 66, 5–14 (2020).Article 

    Google Scholar 
    33.Simberloff, D. et al. Holes in the doughnut theory: the dispersion of ant-lions. Brenesia 14, 13–46 (1978).
    Google Scholar 
    34.Farji-Brener, A. G., Carvajal, D., Gei, M. G., Olano, J. & Sanchez, J. D. Direct and indirect effect of soil structure on the density of an antlion larva in a tropical dry forest. Ecol. Entomol. 33, 183–188 (2008).Article 

    Google Scholar 
    35.Lucas, J. R. Metabolic rates and pit-construction costs of two antlion species. J. Anim. Ecol. 54, 295–309 (1985).Article 

    Google Scholar 
    36.Tanaka, K. Energetic cost of web construction and its effect on web relocation in the web-building spider Agelena limbata. Oecologia 81, 459–464 (1989).ADS 
    PubMed 
    Article 
    PubMed Central 

    Google Scholar 
    37.Lubin, Y., Ellner, S. & Kotzman, M. Web relocation and habitat selection in desert widow spider. Ecology 74, 1915–1928 (1993).Article 

    Google Scholar 
    38.Loria, R., Scharf, I., Subach, A. & Ovadia, O. The interplay between foraging mode, habitat structure, and predator presence in antlions. Behav. Ecol. Sociobiol. 62, 1185–1192 (2008).Article 

    Google Scholar 
    39.Griffiths, D. Interference competition in ant-lion (Macroleon quinquemaculatus) larvae. Ecol. Entomol. 17, 219–226 (1992).Article 

    Google Scholar 
    40.Heiling, A. M. & Herberstein, M. E. The importance of being larger: intraspecific competition for prime web sites in orb-web spiders (Araneae, Araneidae). Behaviour 136, 669–677 (1999).Article 

    Google Scholar 
    41.Rayor, L. S. & Uetz, G. W. Trade-offs in foraging success and predation risk with spatial position in colonial spiders. Behav. Ecol. Sociobiol. 27, 77–85 (1990).Article 

    Google Scholar 
    42.Wilson, D. S. Prey capture and competition in the ant lion. Biotropica 6, 187–193 (1974).Article 

    Google Scholar 
    43.Rao, D. Experimental evidence for the amelioration of shadow competition in an orb-web spider through the ‘ricochet’ effect. Ethology 115, 691–697 (2009).Article 

    Google Scholar 
    44.Scharf, I. Factors that can affect the spatial positioning of large and small individuals in clusters of sit-and-wait predators. Am. Nat. 195, 649–663 (2020).PubMed 
    Article 
    PubMed Central 

    Google Scholar 
    45.Matsura, T. & Takano, H. Pit-relocation of antlion larvae in relation to their density. Res. Popul. Ecol. 31, 225–234 (1989).Article 

    Google Scholar 
    46.Griffiths, D. Intraspecific competition in larvae of the ant-lion Morter sp. and interspecific interactions with Macroleon quinquemaculatus. Ecol. Entomol. 16, 193–201 (1991).Article 

    Google Scholar 
    47.Wise, D. H. Cannibalism, food limitation, intraspecific competition, and the regulation of spider populations. Annu. Rev. Entomol. 51, 441–465 (2006).CAS 
    PubMed 
    Article 
    PubMed Central 

    Google Scholar 
    48.Klokočovnik, V., Veler, E. & Devetak, D. Antlions in interaction: confrontation of two competitors in limited space. Isr. J. Ecol. Evol. 66, 73–81 (2020).Article 

    Google Scholar 
    49.Buddle, C. M., Walker, S. E. & Rypstra, A. L. Cannibalism and density-dependent mortality in the wolf spider Pardosa milvina (Araneae: Lycosidae). Can. J. Zool. 81, 1293–1297 (2003).Article 

    Google Scholar 
    50.Ovadia, O., Scharf, I., Barkae, E. D., Levi, T. & Alcalay, Y. Asymmetrical intra-guild predation and niche differentiation in two pit-building antlions. Isr. J. Ecol. Evol. 66, 82–90 (2020).Article 

    Google Scholar 
    51.Devetak, D. Wormlion Vermileo vermileo (L.) (Diptera: Vermileonidae) in Slovenia and Croatia. Ann. Ser. Hist. Nat. 18, 283–286 (2008).
    Google Scholar 
    52.Dor, R., Rosenstein, S. & Scharf, I. Foraging behaviour of a neglected pit-building predator: the wormlion. Anim. Behav. 93, 69–76 (2014).Article 

    Google Scholar 
    53.Miler, K., Yahya, B. E. & Czarnoleski, M. Substrate moisture, particle size and temperature preferences of trap-building larvae of sympatric antlions and wormlions from the rainforest of Borneo. Ecol. Entomol. 44, 488–493 (2019).Article 

    Google Scholar 
    54.Miler, K., Yahya, B. E. & Czarnoleski, M. Different predation efficiencies of trap-building larvae of sympatric antlions and wormlions from the rainforest of Borneo. Ecol. Entomol. 43, 255–262 (2018).Article 

    Google Scholar 
    55.Franks, N. R., Worley, A., Falkenberg, M., Sendova-Franks, A. B. & Christensen, K. Digging the optimum pit: antlions, spirals and spontaneous stratification. Proc. R. Soc. B 286, 20190365 (2019).PubMed 
    Article 
    PubMed Central 

    Google Scholar 
    56.Scharf, I., Daniel, A., MacMillan, H. A. & Katz, N. The effect of fasting and body reserves on cold tolerance in 2 pit-building insect predators. Curr. Zool. 63, 287–294 (2017).PubMed 
    PubMed Central 

    Google Scholar 
    57.Devetak, D. Substrate particle size-preference of wormlion Vermileo vermileo (Diptera: Vermileonidae) larvae and their interaction with antlions. Eur. J. Entomol. 105, 631–635 (2008).Article 

    Google Scholar 
    58.Adar, S., Dor, R. & Scharf, I. Habitat choice and complex decision making in a trap-building predator. Behav. Ecol. 27, 1491–1498 (2016).Article 

    Google Scholar 
    59.Scharf, I. et al. The contribution of shelter from rain to the success of pit-building predators in urban habitats. Anim. Behav. 142, 139–145 (2018).Article 

    Google Scholar 
    60.Katz, N., Pruitt, J. N. & Scharf, I. The complex effect of illumination, temperature, and thermal acclimation on habitat choice and foraging behavior of a pit-building wormlion. Behav. Ecol. Sociobiol. 71, 137 (2017).Article 

    Google Scholar 
    61.Bar-Ziv, M. A., Bega, D., Subach, A. & Scharf, I. Wormlions prefer both fine and deep sand but only deep sand leads to better performance. Curr. Zool. 65, 393–400 (2019).PubMed 
    Article 
    PubMed Central 

    Google Scholar 
    62.Abramoff, M. D., Magalhaes, P. J. & Ram, S. J. Image processing with ImageJ. Biophoton. Int. 11, 36–42 (2004).
    Google Scholar 
    63.Ovadia, O. & Abramsky, Z. Density-dependent habitat selection: evaluation of the isodar method. Oikos 73, 86–94 (1995).Article 

    Google Scholar 
    64.Jensen, W. E. & Cully, J. F. Density-dependent habitat selection by brown-headed cowbirds (Molothrus ater) in tallgrass prairie. Oecologia 142, 136–149 (2005).ADS 
    PubMed 
    Article 
    PubMed Central 

    Google Scholar 
    65.Whitham, T. G. The theory of habitat selection: examined and extended using Pemphigus aphids. Am. Nat. 115, 449–466 (1980).Article 

    Google Scholar 
    66.van Beest, F. M. et al. Increasing density leads to generalization in both coarse-grained habitat selection and fine-grained resource selection in a large mammal. J. Anim. Ecol. 83, 147–156 (2014).PubMed 
    Article 
    PubMed Central 

    Google Scholar 
    67.Mathis, A. Territoriality in a terrestrial salamander: the influence of resource quality and body size. Behaviour 112, 162–175 (1990).Article 

    Google Scholar 
    68.Croy, M. I. & Hughes, R. N. Effects of food supply, hunger, danger and competition on choice of foraging location by the fifteen-spined stickleback, Spinachia spinachia L. Anim. Behav. 42, 131–139 (1991).Article 

    Google Scholar 
    69.Davey, A. J. H., Hawkins, S. J., Turner, G. F. & Doncaster, C. P. Size-dependent microhabitat use and intraspecific competition in Cottus gobio. J. Fish Biol. 67, 428–443 (2005).Article 

    Google Scholar 
    70.Abrahams, M. V. Patch choice under perceptual constraints: a cause for departures from an ideal free distribution. Behav. Ecol. Sociobiol. 19, 409–415 (1986).Article 

    Google Scholar 
    71.Sutherland, W. J., Townsend, C. R. & Patmore, J. M. A test of the ideal free distribution with unequal competitors. Behav. Ecol. Sociobiol. 23, 51–53 (1988).Article 

    Google Scholar 
    72.McClure, M. S. Spatial distribution of pit-making ant lion larvae (Neuroptera: Myrmeleontidae): density effects. Biotropica 8, 179–183 (1976).Article 

    Google Scholar 
    73.Rayor, L. S. & Uetz, G. W. Age-related sequential web building in the colonial spider Metepeira incrassata (Araneidae): an adaptive spacing strategy. Anim. Behav. 59, 1251–1259 (2000).CAS 
    PubMed 
    Article 
    PubMed Central 

    Google Scholar 
    74.Yip, E. C., Levy, T. & Lubin, Y. Bad neighbors: hunger and dominance drive spacing and position in an orb-weaving spider colony. Behav. Ecol. Sociobiol. 71, 128 (2017).Article 

    Google Scholar 
    75.Murcia, C. Edge effects in fragmented forests: implications for conservation. Trends Ecol. Evol. 10, 58–62 (1995).CAS 
    PubMed 
    Article 
    PubMed Central 

    Google Scholar 
    76.Minias, P., Janiszewski, T. & Lesner, B. Center-periphery gradients of chick survival in the colonies of Whiskered Terns Chlidonias hybrida may be explained by the variation in the maternal effects of egg size. Acta Ornithol. 48, 179–186 (2013).Article 

    Google Scholar 
    77.Geange, S. W. & Stier, A. C. Priority effects and habitat complexity affect the strength of competition. Oecologia 163, 111–118 (2010).ADS 
    PubMed 
    Article 
    PubMed Central 

    Google Scholar 
    78.Hallander, H. Prey, cannibalism and microhabitat selection in the wolf spiders Pardosa chelata OF Müller and P. pullata Clerck. Oikos 21, 337–340 (1970).Article 

    Google Scholar 
    79.Skevington, J. H. & Dang, P. T. Exploring the diversity of flies (Diptera). Biodiversity 3, 3–27 (2002).Article 

    Google Scholar 
    80.Scharf, I., Silberklang, A., Avidov, B. & Subach, A. Do pit-building predators prefer or avoid barriers? Wormlions’ preference for walls depends on light conditions. Sci. Rep. 10, 10928 (2020).ADS 
    CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar  More

  • in

    Russian forest sequesters substantially more carbon than previously reported

    Russia has been reporting almost no changes in forested area, growing stock volume (GSV) and biomass to the United Nations Framework Convention on Climate Change (UNFCCC)1 and the Food and Agriculture Organization of the United Nations (FAO) Forest Resources Assessment (FRA)2 since the collapse of the USSR and the decline in the Soviet Forest Inventory and Planning (FIP) system. According to the State Forest Register (SFR)3, which is the main repository of forest information, and national reporting to the FAO FRA2, the GSV and the above ground biomass (AGB) increased by 1.1% and 0.6% (Table S1), respectively, during 1990–2015, yet studies using remote sensing (RS) indicate increased vegetation productivity4, tree cover (annual rate + 0.417% over 1982–2016)5, increased AGB (+ 329 Tg C yr−1 over 2000–20076), total biomass (annual rate + 0.44% or + 153 Tg C yr−1 over 1990–20077), and forest ecosystem carbon pools (ca + 470 Tg C yr−1 over 2001–20198). This inconsistency in estimates can be explained by an information gap that appeared when Russia decided to move from the FIP to another system for the collection of forest information at the national scale – the National Forest Inventory (NFI).The FIP involves revisiting every forest stand (on the ground for managed forests or using RS techniques for remote non-commercial forests) on a 10–15-year interval, with the measurement of forest parameters combined with the formulation of forest management directives. After the collapse of the USSR, the inventory within the FIP system slowed down substantially. For example, more than 50% of the forest area was surveyed by the FIP more than 25 years ago9. For these reasons, the reliability of information on forests in Russia has deteriorated since 1988, which is the year when FIP-based reporting10 involved the largest inventory efforts in recent decades. According to this report10, the total GSV of Russian forests was 81.7 × 109 m3 (without shrubland, bias corrected11). This value is used here as a reference to quantify biomass stock changes in Russia with respect to the current decade.In contrast, NFI is a state-of-the-art inventory system based on a statistical sampling method. It was initiated in 2007 and the first cycle was completed in 2020. The NFI data processing is ongoing, but the first official press release12 suggests that Russian forest accumulated 102 × 109 m3 over its lifespan until 2014. Once finalized, the NFI will be verified before adoption as the official source of information to the SFR and national reporting. The NFI has received some criticism13 because of the relatively sparse sampling employed and the stratification method used, which is partially based on outdated FIP data.In Russia, the long intervals between consecutive surveys and the difficulty in accessing very remote regions in a timely manner by an inventory system make satellite RS an essential tool for capturing forest dynamics and providing a comprehensive, wall-to-wall perspective on biomass distribution. However, observations from current RS sensors are not suited for producing accurate biomass estimates unless the estimation method is calibrated with a dense network of measurements from ground surveys14. Here we calibrated models relating two global RS biomass data products (GlobBiomass GSV15 and CCI Biomass GSV16) and additional RS data layers (forest cover mask9, the Copernicus Global Land Cover CGLS‐LC100 product17) with ca 10,000 ground plots (see Material and Methods) to reduce nuances in the individual input maps due to imperfections in the RS data and approximations in the retrieval procedure18,19. The combination of these two sources of information, i.e., ground measurements and RS, utilizes the advantages of both sources in terms of: (i) highly accurate ground measurements and (ii) the spatially comprehensive coverage of RS products and methods. The amount of ground plots currently available may be insufficient for providing an accurate estimate of GSV for the country when used alone, but they are the key to obtaining unbiased estimates when used to calibrate RS datasets20. The map merging procedure was preferred over a plot-aided direct estimation of GSV or AGB from the RS data because of the usually poor association between biomass measured at inventory plots and remote sensing observables21. In addition, models relating biomass and remote sensing observables that are trained with spatially inhomogeneous datasets (Figure S1) tend to be biased in regions not represented by the dataset of the reference biomass measurements.We estimate the total GSV of Russia for the year 2014 for the official forested area (713.1 × 106 ha) to be 111 ± 1.3 × 109 m3, which is 39% higher than the 79.9 × 109 m3 (excluding shrubland) figure reported in the SFR3 for the same year. An additional 7.1 × 109 m3 or 9% were found due to the larger forested area (+ 45.7 106 ha) recognized by RS9, following the expansion of forests to the north22, to higher elevations, in abandoned arable land23, as well as the inclusion of parks, gardens and other trees outside of forest, which were not counted as forest in the SFR. Based on cross-validation, our estimate at the regional level (81 regions of Russia – Table S2, Figure S2) is unbiased. The standard error varied from 0.6 to 17.6% depending on the region. The median error was 1.6%, while the area weighted error was 1.2%. The predicted GSV (Fig. 1) with associated uncertainties is available here (https://doi.org/10.5281/zenodo.3981198) as a GeoTiff at a spatial resolution of 3.2 arc sec. (ca 0.5 ha).Figure 1Predicted mean forest growing stock volume (m3 ha-1) for the year ca 2014 (Generated by Esri ArcGIS Desktop v.10.7, URL: https://desktop.arcgis.com/en/arcmap/).Full size imageHoughton et al.24 estimated forest biomass based on RS and FIP data in Russia for the year 2000. Average forest biomass density varied between 80.6 and 88.2 Mg ha-1 depending on which forest mask was used. Our estimate for the year 2014 of 107 Mg ha-1 (using the conversion factor of GSV to AGB from24 0.6859) is 21–33% higher than the one by Houghton et al., but this is consistent with expected biomass increases over time, i.e., 14 years after the Houghton et al. estimate.Assuming an unchanged total forest area (721.7 × 106 ha) in 1988 and 2014, we conclude that Russian forests have accumulated 1,163 × 106 m3 yr-1 or 407 Tg C yr-1 in live biomass of trees on average over 26 years. This gives an average GSV change rate of + 1.61 m3 ha-1 yr-1 or + 0.56 t C ha-1 yr-1. The sequestration rate obtained, however, should be treated with caution because different methods have been applied in 1988 and 2014 (see “Caveats and Limitations” section). To provide some context for the magnitude of these numbers, one can compare the Russian forest gain to the net GSV losses in tropical forests over the period 1990–2015 according to FAO FRA25 (-1,033 × 106 m3 yr-1 in the regions with a negative trend: South and Central America, South and Southeast Asia, and Africa). A similar divergence in the carbon sink between Tropical and Boreal forest was recognized by Tagesson et al.26.In terms of carbon stock change, our estimates are substantially higher than those reported by Pan et al.7 for 1990–2007 (+ 153 Tg C yr-1) based on FIP data. The biomass carbon estimates by Liu et al.6 are instead in line with our results. There is an increase in the annual rate of AGB in Russia of + 329 Tg C yr−1 (annual variation from 214 to 400 Tg C yr−1) over 2000–20076. Interestingly, another boreal country – Canada – has demonstrated neutral or negative trends (from 0 to -14 Tg C yr−1) for the same time span using the same estimation method6.We can observe different spatial patterns in the change in the GSV density between 1988 (FIP10, bias corrected11) and 2014 (our estimate), which can be explained by climate change, CO2 fertilisation and changes in disturbance regimes (Fig. 2). The average linear trend in the annual temperature increase during 1976–2014 in Russia is + 0.45 °C per 10 years27. The temperature increase is statistically significant in every region except for western Siberia (Fig. 2–3). Significantly increased temperature extremes and an increase in the number of days without precipitation is observed in the south of European Russia, Baikal, Kamchatka, and Chukotka27 (Fig. 2–1). Some regions in the south of the European part of Russia are colored in dark blue, but they, as a rule, have a small share of forested area, which is often linked to water bodies and, therefore, suffers less from increased drought (Fig. 2–1). Central and eastern Siberia suffer from an increase in disturbances, which offsets the climate stimulation effect (Fig. 2–4). The forested area in the Nenets region (Fig. 2–2) is 4 times larger in 2014 based on the RS forest mask compared to the SFR in 1988 (where forest was accounted for up until a certain latitude at that time), where the increase in area resulted in a decrease in the average GSV.Figure 2Change in growing stock volume (m3 ha-1) from 1988 to 2014 (average over administrative regions) (Generated by Esri ArcGIS Desktop v.10.7, URL: https://desktop.arcgis.com/en/arcmap/). These changes can be categorized into: 1—significant increase in air temperature and drought; 2—substantially increased forest area, which lowers the average GSV density; 3—least (not significant) temperature increase; 4—increase of disturbances: wildfire and harvest (southern part), which offsets the climate stimulation effect.Full size imageFocusing specifically on national reporting of managed forest to the UNFCCC, 72% of forested area in Russia is considered to be managed1. We multiplied the GSV density by the managed forest area for each administrative region (Table S3). The difference in GSV estimation (between ours and the one from the SFR report) is 23.6 × 109 m3 (Table S3) or 33% higher. From the GSV of managed forests in 2014 and based on the same area in 1988, we can estimate the sequestration rate of live biomass of managed forests as 354 Tg C yr-1 , which is considerably higher than the figure of 230 Tg C yr-1 in the current report1.This proof of concept demonstrates the relevance of complementing recent NFI data with remote sensing map products. Our study demonstrates that the already considerable value of forest inventory data can be further enhanced in a forest resources mapping scenario. In addition, we seek to promote greater access to these data by opening up their access to the larger scientific community. Through the integration of RS estimates of GSV and forest inventory data from Russia, we confirm that carbon stocks increased substantially during the last few decades in contrast to the figures provided in official national reporting. Russian forests play an even more important global role in carbon sequestration than previously thought, where the increase in growing stock is of the same magnitude as the net losses in tropical forests over the same time period. More

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    Ecological factors influence balancing selection on leaf chemical profiles of a wildflower

    1.Falconer, D. S. & Mackay, T. F. C. Introduction to Quantitative Genetics (Longman, 1996).2.Lande, R. & Arnold, S. J. The measurement of selection on correlated characters. Evolution 37, 1210–1226 (1983).Article 

    Google Scholar 
    3.Kingsolver, J. G., Diamond, S. E., Siepielski, A. M. & Carlson, S. M. Synthetic analyses of phenotypic selection in natural populations: lessons, limitations and future directions. Evol. Ecol. 26, 1101–1118 (2012).Article 

    Google Scholar 
    4.Barrett, R. D. H. & Schluter, D. Adaptation from standing genetic variation. Trends Ecol. Evol. 23, 38–44 (2008).PubMed 
    Article 
    PubMed Central 

    Google Scholar 
    5.Kulbaba, M. W., Sheth, S. N., Pain, R. E., Eckhart, V. M. & Shaw, R. G. Additive genetic variance for lifetime fitness and the capacity for adaptation in an annual plant. Evolution 73, 1746–1758 (2019).PubMed 
    Article 
    PubMed Central 

    Google Scholar 
    6.Lande, R. & Shannon, S. The role of genetic variation in adaptation and population persistence in a changing environment. Evolution 50, 434–437 (1996).PubMed 
    Article 
    PubMed Central 

    Google Scholar 
    7.Etterson, J. R. & Shaw, R. G. Constraint to adaptive evolution in response to global warming. Science 294, 151–154 (2001).CAS 
    PubMed 
    Article 
    PubMed Central 

    Google Scholar 
    8.Anderson, J. T., Inouye, D. W., McKinney, A. M., Colautti, R. I. & Mitchell-Olds, T. Phenotypic plasticity and adaptive evolution contribute to advancing flowering phenology in response to climate change. Proc. R. Soc. B 279, 3843–3852 (2012).PubMed 
    Article 
    PubMed Central 

    Google Scholar 
    9.Steffen, W., Crutzen, P. J. & McNeil, J. R. The Anthropocene: are humans now overwhelming the great forces of nature? Ambio 36, 614–621 (2007).CAS 
    PubMed 
    Article 
    PubMed Central 

    Google Scholar 
    10.Zhang, X.-S. & Hill, W. G. Genetic variability under mutation selection balance. Trends Ecol. Evol. 20, 468–470 (2005).PubMed 
    Article 
    PubMed Central 

    Google Scholar 
    11.McGuigan, K., Aguirre, J. D. & Blows, M. W. Simultaneous estimation of additive and mutational genetic variance in an outbred population of Drosophila serrata. Genetics 201, 1239–1251 (2015).PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    12.Huang, W. et al. Spontaneous mutations and the origin and maintenance of quantitative genetic variation. eLife 5, e14625 (2016).PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    13.Mitchell-Olds, T., Willis, J. H. & Goldstein, D. B. Which evolutionary processes influence natural genetic variation for phenotypic traits? Nat. Rev. Genet. 8, 845–856 (2007).CAS 
    PubMed 
    Article 
    PubMed Central 

    Google Scholar 
    14.Charlesworth, B. Causes of natural variation in fitness: evidence from studies of Drosophila populations. Proc. Natl Acad. Sci. USA 112, 1662–1669 (2015).CAS 
    PubMed 
    Article 
    PubMed Central 

    Google Scholar 
    15.Subramaniam, B. & Rausher, M. D. Balancing selection on a floral polymorphism. Evolution 54, 691–695 (2000).CAS 
    PubMed 
    Article 
    PubMed Central 

    Google Scholar 
    16.Charlesworth, D. Balancing selection and its effects on sequences in nearby genome regions. PLoS Genet. 2, e64 (2006).PubMed 
    PubMed Central 
    Article 
    CAS 

    Google Scholar 
    17.Hedrick, P. W. & Thomson, G. Evidence for balancing selection at HLA. Genetics 104, 449–456 (1983).CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    18.Troth, A., Puzey, J. R., Kim, R. S., Willis, J. H. & Kelly, J. K. Selective trade-offs maintain alleles underpinning complex trait variation in plants. Science 361, 475–478 (2018).CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    19.Delph, L. F. & Kelly, J. K. On the importance of balancing selection in plants. N. Phytol. 201, 45–56 (2014).Article 

    Google Scholar 
    20.Anderson, J. T., Wagner, M. R., Rushworth, C. A., Prasad, K. V. S. K. & Mitchell-Olds, T. The evolution of quantitative traits in complex environments. Heredity 112, 4–12 (2014).CAS 
    PubMed 
    Article 
    PubMed Central 

    Google Scholar 
    21.Anderson, J. T. & Wadgymar, S. M. Climate change disrupts local adaptation and favours upslope migration. Ecol. Lett. 23, 181–192 (2020).PubMed 
    Article 
    PubMed Central 

    Google Scholar 
    22.Agrawal, A. A. & Fishbein, M. Plant defense syndromes. Ecology 87, S132–S149 (2006).PubMed 
    Article 
    PubMed Central 

    Google Scholar 
    23.Carmona, D., Lajeunesse, M. J. & Johnson, M. T. Plant traits that predict resistance to herbivores. Funct. Ecol. 25, 358–367 (2011).Article 

    Google Scholar 
    24.DeLucia, E. H., Nabity, P. D., Zavala, J. A. & Berenbaum, M. R. Climate change: resetting plant–insect interactions. Plant Physiol. 160, 1677–1685 (2012).CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    25.Mithöfer, A. & Boland, W. Plant defense against herbivores: chemical aspects. Annu. Rev. Plant Biol. 63, 431–450 (2012).PubMed 
    Article 
    CAS 
    PubMed Central 

    Google Scholar 
    26.Prasad, K. V. S. K. et al. A gain-of-function polymorphism controlling complex traits and fitness in nature. Science 337, 1081–1084 (2012).CAS 
    PubMed 
    Article 
    PubMed Central 

    Google Scholar 
    27.Bergelson, J., Dwyer, G. & Emerson, J. J. Models and data on plant–enemy coevolution. Annu. Rev. Genet. 35, 469–499 (2001).CAS 
    PubMed 
    Article 
    PubMed Central 

    Google Scholar 
    28.Hodgins, K. A. & Barrett, S. C. H. Female reproductive success and the evolution of mating-type frequencies in tristylous populations. N. Phytol. 171, 569–580 (2006).Article 

    Google Scholar 
    29.Trotter, M. V. & Spencer, H. G. Complex dynamics occur in a single-locus, multiallelic model of general frequency-dependent selection. Theor. Popul. Biol. 76, 292–298 (2009).PubMed 
    Article 
    PubMed Central 

    Google Scholar 
    30.Tuinstra, M. R., Ejeta, G. & Goldsbrough, P. B. Heterogeneous inbred family (HIF) analysis: a method for developing near-isogenic loci that differ at quantitative traits. Theor. Appl. Genet. 95, 1005–1011 (1997).CAS 
    Article 

    Google Scholar 
    31.Salehin, M. et al. Auxin-sensitive Aux/IAA proteins mediate drought tolerance in Arabidopsis by regulating glucosinolate levels. Nat. Commun. 10, 4021 (2019).PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    32.Hossain, M. S. et al. Glucosinolate degradation products, isothiocyanates, nitriles, and thiocyanates, induce stomatal closure accompanied by peroxidase-mediated reactive oxygen species production in Arabidopsis thaliana. Biosci. Biotechnol. Biochem. 77, 977–983 (2013).CAS 
    PubMed 
    Article 
    PubMed Central 

    Google Scholar 
    33.Mitchell-Olds, T. & Schmitt, J. Genetic mechanisms and evolutionary significance of natural variation in Arabidopsis. Nature 441, 947–952 (2006).CAS 
    PubMed 
    Article 
    PubMed Central 

    Google Scholar 
    34.Wang, B. et al. Ancient polymorphisms contribute to genome-wide variation by long-term balancing selection and divergent sorting in Boechera stricta. Genome Biol. 20, 126 (2019).PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    35.Bloom, T. C., Baskin, J. M. & Baskin, C. C. Ecological life history of the facultative woodland biennial Arabis laevigata variety laevigata (Brassicaceae): seed dispersal. J. Torrey Bot. Soc. 129, 21–28 (2002).Article 

    Google Scholar 
    36.Song, B.-H. et al. Multilocus patterns of nucleotide diversity, population structure, and linkage disequilibrium in Boechera stricta, a wild relative of Arabidopsis. Genetics 181, 1021–1033 (2009).CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    37.Mackay, T., Stone, E. & Ayroles, J. The genetics of quantitative traits: challenges and prospects. Nat. Rev. Genet. 10, 565–577 (2009).CAS 
    PubMed 
    Article 
    PubMed Central 

    Google Scholar 
    38.Hedrick, P. W. Genetic polymorphism in heterogeneous environments: a decade later. Annu. Rev. Ecol. Syst. 17, 535–566 (1986).Article 

    Google Scholar 
    39.Hedrick, P. W. Antagonistic pleiotropy and genetic polymorphism: a perspective. Heredity 82, 126–133 (1999).Article 

    Google Scholar 
    40.Turelli, M. & Barton, N. H. Polygenic variation maintained by balancing selection: pleiotropy, sex-dependent allelic effects and G × E interactions. Genetics 166, 1053–1079 (2004).PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    41.Gillespie, J. H. & Langley, C. H. A general model to account for enzyme variation in natural populations. Genetics 76, 837–848 (1974).CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    42.Anderson, J. T., Willis, J. H. & Mitchell-Olds, T. Evolutionary genetics of plant adaptation. Trends Genet. 27, 258–266 (2011).CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    43.Anderson, J. T., Lee, C.-R., Rushworth, C. A., Colautti, R. I. & Mitchell-Olds, T. Genetic trade-offs and conditional neutrality contribute to local adaptation. Mol. Ecol. 22, 699–708 (2013).PubMed 
    Article 
    PubMed Central 

    Google Scholar 
    44.Oakley, C. G., Ågren, J., Atchison, R. A. & Schemske, D. W. QTL mapping of freezing tolerance: links to fitness and adaptive trade-offs. Mol. Ecol. 23, 4304–4315 (2014).PubMed 
    Article 
    PubMed Central 

    Google Scholar 
    45.Price, N. et al. Combining population genomics and fitness QTLs to identify the genetics of local adaptation in Arabidopsis thaliana. Proc. Natl Acad. Sci. USA 115, 5028–5033 (2018).CAS 
    PubMed 
    Article 
    PubMed Central 

    Google Scholar 
    46.Kettunen, J. et al. Genome-wide association study identifies multiple loci influencing human serum metabolite levels. Nat. Genet. 44, 269–276 (2012).CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    47.Abuelsoud, W., Hirschmann, F. & Papenbrock, J. in Drought Stress in Plants Vol. 1 (eds Hossain, M. A. et al.) 227–248 (Springer, 2016).48.Nguyen, D., Rieu, I., Mariani, C. & van Dam, N. M. How plants handle multiple stresses: hormonal interactions underlying responses to abiotic stress and insect herbivory. Plant Mol. Biol. 91, 727–740 (2016).CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    49.Shani, E. M. et al. Plant stress tolerance requires auxin-sensitive Aux/IAA transcriptional repressors. Curr. Biol. 27, 437–444 (2017).CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    50.Hopkins, R. J., van Dam, N. M. & van Loon, J. J. A. Role of glucosinolates in insect–plant relationships and multitrophic interactions. Annu. Rev. Entomol. 54, 57–83 (2009).CAS 
    PubMed 
    Article 
    PubMed Central 

    Google Scholar 
    51.Burow, M., Müller, R., Gershenzon, J. & Wittstock, U. Altered glucosinolate hydrolysis in genetically engineered Arabidopsis thaliana and its influence on the larval development of Spodoptera littoralis. J. Chem. Ecol. 32, 2333–2349 (2006).CAS 
    PubMed 
    Article 
    PubMed Central 

    Google Scholar 
    52.Wagner, M. R. & Mitchell-Olds, T. Plasticity of plant defense and its evolutionary implications in wild populations of Boechera stricta. Evolution 72, 1034–1049 (2018).CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    53.Purcell, S. et al. PLINK: a tool set for whole-genome association and population-based linkage analyses. Am. J. Hum. Genet. 81, 559–575 (2007).CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    54.Pagès, H., Aboyoun, P., Gentleman, R. & DebRoy, S. Biostrings: Efficient manipulation of biological strings. R package version 2.56.0 (2020).55.Wang et al. Correction to: Ancient polymorphisms contribute to genome-wide variation by long-term balancing selection and divergent sorting in Boechera stricta. Genome Biol. 20, 16 (2019).Article 

    Google Scholar 
    56.Carley, L. et al. Data to accompany: Ecological factors influence balancing selection on leaf chemical profiles of a wildflower. Dryad Data https://doi.org/10.5061/dryad.7h44j0zsr (2021).57.Atkinson, N. J., Lilley, C. J. & Urwin, P. E. Identification of genes involved in the response of Arabidopsis to simultaneous biotic and abiotic stresses. Plant Physiol. 162, 2028–2041 (2013).CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    58.Sharma, A. et al. Comprehensive analysis of plant rapid alkalization factor (RALF) genes. Plant Physiol. Biochem. 106, 82–90 (2016).CAS 
    PubMed 
    Article 
    PubMed Central 

    Google Scholar 
    59.Dutilleul, C., Jourdain, A., Bourguignon, J. & Hugouvieux, V. The Arabidopsis putative selenium-binding protein family: expression study and characterization of SBP1 as a potential new player in cadmium detoxification processes. Plant Physiol. 147, 239–251 (2008).CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    60.Jiang, S.-C. et al. Crucial roles of the pentatricopeptide repeat protein SOAR1 in Arabidopsis response to drought, salt and cold stresses. Plant Mol. Biol. 88, 369–385 (2015).CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    61.Wen, J., Vanek-Krebitz, M., Hoffmann-Sommergruber, K., Scheiner, O. & Breitender, H. The potential of Betv1 homologues, a nuclear multigene family, as phylogenetic markers in flowering plants. Mol. Phylogenet. Evol. 8, 317–333 (1997).CAS 
    PubMed 
    Article 
    PubMed Central 

    Google Scholar 
    62.Koo, A. J., Fulda, M., Browse, J. & Ohlrogge, J. B. Identification of a plastid acyl‐acyl carrier protein synthetase in Arabidopsis and its role in the activation and elongation of exogenous fatty acids. Plant J. 44, 620–632 (2005).CAS 
    PubMed 
    Article 
    PubMed Central 

    Google Scholar 
    63.Henrissat, B. et al. Conserved catalytic machinery and the prediction of a common fold for several families of glycosyl hydrolases. Proc. Natl Acad. Sci. USA 92, 7090–7094 (1995).CAS 
    PubMed 
    Article 
    PubMed Central 

    Google Scholar  More

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    Longevity and germination of Juniperus communis L. pollen after storage

    A uniform response of the pollen grains towards storage conditions was registered in all five shrubs investigated with a conspicuous decline in germination percentage and pollen tube length after storage. Pollen tube growth reacted more sensitively to storage than germination. The most profound reductions in pollen viability traits were observed in samples stored at + 4 °C. The germination percentage of freshly collected pollen of individual shrubs ranged between 67.3 and 88.6%, whereas that in stored pollen was between 18.0 and 39.6%. In relative terms, storage represented a 49.3–73.2% decline in germination (Fig. 1). The same tendency was also observed in pollen tube growth, when freshly collected pollen possessed 248.0–367.3 µm long pollen tubes, and pollen stored at + 4 °C was characterised by 93.9–218.5 µm long pollen tubes. The corresponding decline reached 32.5–68.7%.Figure 1Graphical illustrations of variation in pollen germination percentage (a) and pollen tube length (b) of individual shrubs revealed in fresh pollen and in pollen under storage. Different letters refer to the statistical significance of the differences between tested individuals and storage variants, resulting from Duncan’s pairwise tests.Full size imageContrary to storage at + 4 °C, pollen stored at − 20 °C had an increased germination by 0.3% in shrub no. 1 and 0.6% in shrub no. 5 as compared with fresh pollen. A more conspicuous increase in pollen germinability was registered in individual no. 4, exhibiting 70.0% germination in fresh pollen and 93.6% in pollen stored at − 20 °C. In the remaining two shrubs (no. 2, 3), only a negligible decline in pollen germination was recorded. The deviation from freshly collected pollen varied within 0.5–16.8%. In general, the germination characteristics of pollen stored at − 20 °C were comparable with those of the fresh pollen and varied between 67.6 and 93.6%. As a second viability trait, pollen tube growth deviated more profoundly from that of fresh pollen than germination. On average, the pollen tube length of pollen stored at − 20 °C ranged from 163.0 to 286.6 µm, which represents a 11.4–45.7% decline compared to fresh pollen (Figs. 1, S1). ANOVA and Duncan`s grouping confirmed the highly significant differences between tested shrubs in both pollen germination percentage (P  More